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"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
class __lowerCamelCase ( __lowercase ):
def __init__(self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
warnings.warn(
"""The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use SegformerImageProcessor instead.""" , lowerCamelCase , )
super().__init__(*lowerCamelCase , **lowerCamelCase )
| 317
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
SCREAMING_SNAKE_CASE : List[str] = False
class __lowerCamelCase ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(
image=lowerCamelCase , generator=lowerCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
_lowerCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 317
| 1
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : list[int] , snake_case_ : list[int] , snake_case_ : int ) -> bool:
"""simple docstring"""
return not any(
neighbour == 1 and colored_vertices[i] == color
for i, neighbour in enumerate(snake_case_ ) )
def __UpperCAmelCase ( snake_case_ : list[list[int]] , snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> bool:
"""simple docstring"""
if index == len(snake_case_ ):
return True
# Recursive Step
for i in range(snake_case_ ):
if valid_coloring(graph[index] , snake_case_ , snake_case_ ):
# Color current vertex
_lowerCAmelCase = i
# Validate coloring
if util_color(snake_case_ , snake_case_ , snake_case_ , index + 1 ):
return True
# Backtrack
_lowerCAmelCase = -1
return False
def __UpperCAmelCase ( snake_case_ : list[list[int]] , snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = [-1] * len(snake_case_ )
if util_color(snake_case_ , snake_case_ , snake_case_ , 0 ):
return colored_vertices
return []
| 317
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCamelCase ( __lowercase , unittest.TestCase ):
__UpperCamelCase = DiTPipeline
__UpperCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__UpperCamelCase = PipelineTesterMixin.required_optional_params - {
'latents',
'num_images_per_prompt',
'callback',
'callback_steps',
}
__UpperCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__UpperCamelCase = False
def A__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
_lowerCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=lowerCamelCase , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1_000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=lowerCamelCase , )
_lowerCAmelCase = AutoencoderKL()
_lowerCAmelCase = DDIMScheduler()
_lowerCAmelCase = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def A__ (self , lowerCamelCase , lowerCamelCase=0 ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(lowerCamelCase )
else:
_lowerCAmelCase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
_lowerCAmelCase = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = """cpu"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_lowerCAmelCase = pipe(**lowerCamelCase ).images
_lowerCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_lowerCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_lowerCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase , 1e-3 )
def A__ (self ):
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=lowerCamelCase , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def A__ (self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_lowerCAmelCase = pipe.get_label_ids(lowerCamelCase )
_lowerCAmelCase = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = load_numpy(
f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-2
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella"""]
_lowerCAmelCase = pipe.get_label_ids(lowerCamelCase )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
f"""/dit/{word}_512.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-1
| 317
| 1
|
"""simple docstring"""
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import subprocess
from packaging.version import Version, parse
from accelerate.commands.config.config_args import default_config_file, load_config_from_file
SCREAMING_SNAKE_CASE : Optional[int] = '''Run commands across TPU VMs for initial setup before running `accelerate launch`.'''
def __UpperCAmelCase ( snake_case_ : List[Any]=None ) -> Any:
"""simple docstring"""
if subparsers is not None:
_lowerCAmelCase = subparsers.add_parser("""tpu-config""" , description=_description )
else:
_lowerCAmelCase = argparse.ArgumentParser("""Accelerate tpu-config command""" , description=_description )
# Core arguments
_lowerCAmelCase = parser.add_argument_group(
"""Config Arguments""" , """Arguments that can be configured through `accelerate config`.""" )
config_args.add_argument(
"""--config_file""" , type=snake_case_ , default=snake_case_ , help="""Path to the config file to use for accelerate.""" , )
config_args.add_argument(
"""--tpu_name""" , default=snake_case_ , help="""The name of the TPU to use. If not specified, will use the TPU specified in the config file.""" , )
config_args.add_argument(
"""--tpu_zone""" , default=snake_case_ , help="""The zone of the TPU to use. If not specified, will use the zone specified in the config file.""" , )
_lowerCAmelCase = parser.add_argument_group("""TPU Arguments""" , """Arguments for options ran inside the TPU.""" )
pod_args.add_argument(
"""--use_alpha""" , action="""store_true""" , help="""Whether to use `gcloud alpha` when running the TPU training script instead of `gcloud`.""" , )
pod_args.add_argument(
"""--command_file""" , default=snake_case_ , help="""The path to the file containing the commands to run on the pod on startup.""" , )
pod_args.add_argument(
"""--command""" , action="""append""" , nargs="""+""" , help="""A command to run on the pod. Can be passed multiple times.""" , )
pod_args.add_argument(
"""--install_accelerate""" , action="""store_true""" , help="""Whether to install accelerate on the pod. Defaults to False.""" , )
pod_args.add_argument(
"""--accelerate_version""" , default="""latest""" , help="""The version of accelerate to install on the pod. If not specified, will use the latest pypi version. Specify 'dev' to install from GitHub.""" , )
pod_args.add_argument(
"""--debug""" , action="""store_true""" , help="""If set, will print the command that would be run instead of running it.""" )
if subparsers is not None:
parser.set_defaults(func=snake_case_ )
return parser
def __UpperCAmelCase ( snake_case_ : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase = None
# Get the default from the config file if it exists.
if args.config_file is not None or os.path.isfile(snake_case_ ):
_lowerCAmelCase = load_config_from_file(args.config_file )
if not args.command_file and defaults.command_file is not None and not args.command:
_lowerCAmelCase = defaults.command_file
if not args.command and defaults.commands is not None:
_lowerCAmelCase = defaults.commands
if not args.tpu_name:
_lowerCAmelCase = defaults.tpu_name
if not args.tpu_zone:
_lowerCAmelCase = defaults.tpu_zone
if args.accelerate_version == "dev":
_lowerCAmelCase = """git+https://github.com/huggingface/accelerate.git"""
elif args.accelerate_version == "latest":
_lowerCAmelCase = """accelerate -U"""
elif isinstance(parse(args.accelerate_version ) , snake_case_ ):
_lowerCAmelCase = F"""accelerate=={args.accelerate_version}"""
if not args.command_file and not args.command:
raise ValueError("""You must specify either a command file or a command to run on the pod.""" )
if args.command_file:
with open(args.command_file , """r""" ) as f:
_lowerCAmelCase = [f.read().splitlines()]
# To turn list of lists into list of strings
if isinstance(args.command[0] , snake_case_ ):
_lowerCAmelCase = [line for cmd in args.command for line in cmd]
# Default to the shared folder and install accelerate
_lowerCAmelCase = ["""cd /usr/share"""]
if args.install_accelerate:
new_cmd += [F"""pip install {args.accelerate_version}"""]
new_cmd += args.command
_lowerCAmelCase = """; """.join(snake_case_ )
# Then send it to gcloud
# Eventually try to use google-api-core to do this instead of subprocess
_lowerCAmelCase = ["""gcloud"""]
if args.use_alpha:
cmd += ["alpha"]
cmd += [
"compute",
"tpus",
"tpu-vm",
"ssh",
args.tpu_name,
"--zone",
args.tpu_zone,
"--command",
args.command,
"--worker",
"all",
]
if args.debug:
print(F"""Running {" ".join(snake_case_ )}""" )
return
subprocess.run(snake_case_ )
print("""Successfully setup pod.""" )
def __UpperCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase = tpu_command_parser()
_lowerCAmelCase = parser.parse_args()
tpu_command_launcher(snake_case_ )
| 317
|
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
return getitem, k
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return setitem, k, v
def __UpperCAmelCase ( snake_case_ : str ) -> Optional[int]:
"""simple docstring"""
return delitem, k
def __UpperCAmelCase ( snake_case_ : Optional[Any] , snake_case_ : Tuple , *snake_case_ : Tuple ) -> str:
"""simple docstring"""
try:
return fun(snake_case_ , *snake_case_ ), None
except Exception as e:
return None, e
SCREAMING_SNAKE_CASE : int = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
SCREAMING_SNAKE_CASE : List[Any] = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
SCREAMING_SNAKE_CASE : Any = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
SCREAMING_SNAKE_CASE : Union[str, Any] = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
SCREAMING_SNAKE_CASE : Optional[Any] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
SCREAMING_SNAKE_CASE : Optional[int] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
"""operations""" , (
pytest.param(_add_items , id="""add items""" ),
pytest.param(_overwrite_items , id="""overwrite items""" ),
pytest.param(_delete_items , id="""delete items""" ),
pytest.param(_access_absent_items , id="""access absent items""" ),
pytest.param(_add_with_resize_up , id="""add with resize up""" ),
pytest.param(_add_with_resize_down , id="""add with resize down""" ),
) , )
def __UpperCAmelCase ( snake_case_ : List[Any] ) -> Tuple:
"""simple docstring"""
_lowerCAmelCase = HashMap(initial_block_size=4 )
_lowerCAmelCase = {}
for _, (fun, *args) in enumerate(snake_case_ ):
_lowerCAmelCase , _lowerCAmelCase = _run_operation(snake_case_ , snake_case_ , *snake_case_ )
_lowerCAmelCase , _lowerCAmelCase = _run_operation(snake_case_ , snake_case_ , *snake_case_ )
assert my_res == py_res
assert str(snake_case_ ) == str(snake_case_ )
assert set(snake_case_ ) == set(snake_case_ )
assert len(snake_case_ ) == len(snake_case_ )
assert set(my.items() ) == set(py.items() )
def __UpperCAmelCase ( ) -> Tuple:
"""simple docstring"""
def is_public(snake_case_ : str ) -> bool:
return not name.startswith("""_""" )
_lowerCAmelCase = {name for name in dir({} ) if is_public(snake_case_ )}
_lowerCAmelCase = {name for name in dir(HashMap() ) if is_public(snake_case_ )}
assert dict_public_names > hash_public_names
| 317
| 1
|
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import jax
import jaxlib
SCREAMING_SNAKE_CASE : Union[str, Any] = get_logger()
SCREAMING_SNAKE_CASE : Optional[dict] = None
class __lowerCamelCase ( TensorFormatter[Mapping, 'jax.Array', Mapping] ):
def __init__(self , lowerCamelCase=None , lowerCamelCase=None , **lowerCamelCase ):
'''simple docstring'''
super().__init__(features=lowerCamelCase )
import jax
from jaxlib.xla_client import Device
if isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError(
f"""Expected {device} to be a `str` not {type(lowerCamelCase )}, as `jaxlib.xla_extension.Device` """
"""is not serializable neither with `pickle` nor with `dill`. Instead you can surround """
"""the device with `str()` to get its string identifier that will be internally mapped """
"""to the actual `jaxlib.xla_extension.Device`.""" )
_lowerCAmelCase = device if isinstance(lowerCamelCase , lowerCamelCase ) else str(jax.devices()[0] )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
_lowerCAmelCase = self._map_devices_to_str()
if self.device not in list(DEVICE_MAPPING.keys() ):
logger.warning(
f"""Device with string identifier {self.device} not listed among the available """
f"""devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default """
f"""device: {str(jax.devices()[0] )}.""" )
_lowerCAmelCase = str(jax.devices()[0] )
_lowerCAmelCase = jnp_array_kwargs
@staticmethod
def A__ ():
'''simple docstring'''
import jax
return {str(lowerCamelCase ): device for device in jax.devices()}
def A__ (self , lowerCamelCase ):
'''simple docstring'''
import jax
import jax.numpy as jnp
if isinstance(lowerCamelCase , lowerCamelCase ) and column:
if all(
isinstance(lowerCamelCase , jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
return jnp.stack(lowerCamelCase , axis=0 )
return column
def A__ (self , lowerCamelCase ):
'''simple docstring'''
import jax
import jax.numpy as jnp
if isinstance(lowerCamelCase , (str, bytes, type(lowerCamelCase )) ):
return value
elif isinstance(lowerCamelCase , (np.character, np.ndarray) ) and np.issubdtype(value.dtype , np.character ):
return value.tolist()
_lowerCAmelCase = {}
if isinstance(lowerCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.integer ):
# the default int precision depends on the jax config
# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
if jax.config.jax_enable_xaa:
_lowerCAmelCase = {"""dtype""": jnp.intaa}
else:
_lowerCAmelCase = {"""dtype""": jnp.intaa}
elif isinstance(lowerCamelCase , (np.number, np.ndarray) ) and np.issubdtype(value.dtype , np.floating ):
_lowerCAmelCase = {"""dtype""": jnp.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(lowerCamelCase , PIL.Image.Image ):
_lowerCAmelCase = np.asarray(lowerCamelCase )
# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
# with `pickle` nor with `dill`, so we need to use a global variable instead
global DEVICE_MAPPING
if DEVICE_MAPPING is None:
_lowerCAmelCase = self._map_devices_to_str()
with jax.default_device(DEVICE_MAPPING[self.device] ):
# calling jnp.array on a np.ndarray does copy the data
# see https://github.com/google/jax/issues/4486
return jnp.array(lowerCamelCase , **{**default_dtype, **self.jnp_array_kwargs} )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
import jax
# support for torch, tf, jax etc.
if config.TORCH_AVAILABLE and "torch" in sys.modules:
import torch
if isinstance(lowerCamelCase , torch.Tensor ):
return self._tensorize(data_struct.detach().cpu().numpy()[()] )
if hasattr(lowerCamelCase , """__array__""" ) and not isinstance(lowerCamelCase , jax.Array ):
_lowerCAmelCase = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(lowerCamelCase , np.ndarray ):
if data_struct.dtype == object: # jax arrays cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(lowerCamelCase ) for substruct in data_struct] )
elif isinstance(lowerCamelCase , (list, tuple) ):
return self._consolidate([self.recursive_tensorize(lowerCamelCase ) for substruct in data_struct] )
return self._tensorize(lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return map_nested(self._recursive_tensorize , lowerCamelCase , map_list=lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.numpy_arrow_extractor().extract_row(lowerCamelCase )
_lowerCAmelCase = self.python_features_decoder.decode_row(lowerCamelCase )
return self.recursive_tensorize(lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.numpy_arrow_extractor().extract_column(lowerCamelCase )
_lowerCAmelCase = self.python_features_decoder.decode_column(lowerCamelCase , pa_table.column_names[0] )
_lowerCAmelCase = self.recursive_tensorize(lowerCamelCase )
_lowerCAmelCase = self._consolidate(lowerCamelCase )
return column
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.numpy_arrow_extractor().extract_batch(lowerCamelCase )
_lowerCAmelCase = self.python_features_decoder.decode_batch(lowerCamelCase )
_lowerCAmelCase = self.recursive_tensorize(lowerCamelCase )
for column_name in batch:
_lowerCAmelCase = self._consolidate(batch[column_name] )
return batch
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(snake_case_ : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(snake_case_ )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
snake_case_ : int , snake_case_ : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
_lowerCAmelCase = sum(
count_of_possible_combinations_with_dp_array(target - item , snake_case_ )
for item in array )
_lowerCAmelCase = answer
return answer
_lowerCAmelCase = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(snake_case_ , snake_case_ )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
_lowerCAmelCase = [0] * (target + 1)
_lowerCAmelCase = 1
for i in range(1 , target + 1 ):
for j in range(snake_case_ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : Tuple = 3
SCREAMING_SNAKE_CASE : Any = 5
SCREAMING_SNAKE_CASE : Optional[int] = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 317
| 1
|
"""simple docstring"""
from math import asin, atan, cos, radians, sin, sqrt, tan
SCREAMING_SNAKE_CASE : str = 6_3_7_8_1_3_7.0
SCREAMING_SNAKE_CASE : Optional[Any] = 6_3_5_6_7_5_2.3_1_4_2_4_5
SCREAMING_SNAKE_CASE : Any = 6_3_7_8_1_3_7
def __UpperCAmelCase ( snake_case_ : float , snake_case_ : float , snake_case_ : float , snake_case_ : float ) -> float:
"""simple docstring"""
_lowerCAmelCase = (AXIS_A - AXIS_B) / AXIS_A
_lowerCAmelCase = atan((1 - flattening) * tan(radians(snake_case_ ) ) )
_lowerCAmelCase = atan((1 - flattening) * tan(radians(snake_case_ ) ) )
_lowerCAmelCase = radians(snake_case_ )
_lowerCAmelCase = radians(snake_case_ )
# Equation
_lowerCAmelCase = sin((phi_a - phi_a) / 2 )
_lowerCAmelCase = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
_lowerCAmelCase = sqrt(sin_sq_phi + (cos(snake_case_ ) * cos(snake_case_ ) * sin_sq_lambda) )
return 2 * RADIUS * asin(snake_case_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 317
|
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for letter in string.ascii_lowercase]
SCREAMING_SNAKE_CASE : set[int] = {ord(char) for char in VALID_CHARS}
SCREAMING_SNAKE_CASE : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def __UpperCAmelCase ( snake_case_ : list[int] , snake_case_ : tuple[int, ...] ) -> str | None:
"""simple docstring"""
_lowerCAmelCase = ""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
for keychar, cipherchar in zip(cycle(snake_case_ ) , snake_case_ ):
_lowerCAmelCase = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(snake_case_ )
return decoded
def __UpperCAmelCase ( snake_case_ : list[int] ) -> list[str]:
"""simple docstring"""
_lowerCAmelCase = []
for key in product(snake_case_ , repeat=3 ):
_lowerCAmelCase = try_key(snake_case_ , snake_case_ )
if encoded is not None:
possibles.append(snake_case_ )
return possibles
def __UpperCAmelCase ( snake_case_ : list[str] , snake_case_ : str ) -> list[str]:
"""simple docstring"""
return [possible for possible in possibles if common_word in possible.lower()]
def __UpperCAmelCase ( snake_case_ : str = "p059_cipher.txt" ) -> int:
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = Path(snake_case_ ).parent.joinpath(snake_case_ ).read_text(encoding="""utf-8""" )
_lowerCAmelCase = [int(snake_case_ ) for number in data.strip().split(""",""" )]
_lowerCAmelCase = filter_valid_chars(snake_case_ )
for common_word in COMMON_WORDS:
_lowerCAmelCase = filter_common_word(snake_case_ , snake_case_ )
if len(snake_case_ ) == 1:
break
_lowerCAmelCase = possibles[0]
return sum(ord(snake_case_ ) for char in decoded_text )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase=13 , lowerCamelCase=7 , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=99 , lowerCamelCase=64 , lowerCamelCase=5 , lowerCamelCase=4 , lowerCamelCase=37 , lowerCamelCase="gelu" , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=512 , lowerCamelCase=16 , lowerCamelCase=2 , lowerCamelCase=0.02 , lowerCamelCase=3 , lowerCamelCase=4 , lowerCamelCase=None , ):
'''simple docstring'''
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_input_mask
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = num_labels
_lowerCAmelCase = num_choices
_lowerCAmelCase = scope
_lowerCAmelCase = vocab_size - 1
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_input_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase = self.get_config()
return config, input_ids, input_mask, token_labels
def A__ (self ):
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase , initializer_range=self.initializer_range , pad_token_id=self.pad_token_id , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase = True
return config, input_ids, input_mask, token_labels
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = GPTNeoXModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase )
_lowerCAmelCase = model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = True
_lowerCAmelCase = GPTNeoXModel(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = GPTNeoXForCausalLM(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = GPTNeoXForQuestionAnswering(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase )
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 A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = GPTNeoXForSequenceClassification(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = GPTNeoXForTokenClassification(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = True
_lowerCAmelCase = GPTNeoXForCausalLM(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
# first forward pass
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , use_cache=lowerCamelCase )
_lowerCAmelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
_lowerCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
_lowerCAmelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
_lowerCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
_lowerCAmelCase = torch.cat([input_mask, next_mask] , dim=-1 )
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , output_hidden_states=lowerCamelCase )
_lowerCAmelCase = output_from_no_past["""hidden_states"""][0]
_lowerCAmelCase = model(
lowerCamelCase , attention_mask=lowerCamelCase , past_key_values=lowerCamelCase , output_hidden_states=lowerCamelCase , )["""hidden_states"""][0]
# select random slice
_lowerCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
_lowerCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
_lowerCAmelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1e-3 ) )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __lowerCamelCase ( __lowercase , __lowercase , __lowercase , unittest.TestCase ):
__UpperCamelCase = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
__UpperCamelCase = (GPTNeoXForCausalLM,) if is_torch_available() else ()
__UpperCamelCase = (
{
'feature-extraction': GPTNeoXModel,
'question-answering': GPTNeoXForQuestionAnswering,
'text-classification': GPTNeoXForSequenceClassification,
'text-generation': GPTNeoXForCausalLM,
'token-classification': GPTNeoXForTokenClassification,
'zero-shot': GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = GPTNeoXModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=lowerCamelCase , hidden_size=64 , num_attention_heads=8 )
def A__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_decoder()
_lowerCAmelCase = None
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase )
@unittest.skip(reason="""Feed forward chunking is not implemented""" )
def A__ (self ):
'''simple docstring'''
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = ids_tensor([1, 10] , config.vocab_size )
_lowerCAmelCase = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] , config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_lowerCAmelCase = GPTNeoXModel(lowerCamelCase )
original_model.to(lowerCamelCase )
original_model.eval()
_lowerCAmelCase = original_model(lowerCamelCase ).last_hidden_state
_lowerCAmelCase = original_model(lowerCamelCase ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
_lowerCAmelCase = {"""type""": scaling_type, """factor""": 10.0}
_lowerCAmelCase = GPTNeoXModel(lowerCamelCase )
scaled_model.to(lowerCamelCase )
scaled_model.eval()
_lowerCAmelCase = scaled_model(lowerCamelCase ).last_hidden_state
_lowerCAmelCase = scaled_model(lowerCamelCase ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1e-5 ) )
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
@slow
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped""" )
for checkpointing in [True, False]:
_lowerCAmelCase = GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped""" )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowerCamelCase )
_lowerCAmelCase = tokenizer("""My favorite food is""" , return_tensors="""pt""" ).to(lowerCamelCase )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
_lowerCAmelCase = """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""
_lowerCAmelCase = model.generate(**lowerCamelCase , do_sample=lowerCamelCase , max_new_tokens=20 )
_lowerCAmelCase = tokenizer.batch_decode(lowerCamelCase )[0]
self.assertEqual(lowerCamelCase , lowerCamelCase )
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 1000000 ) -> int:
"""simple docstring"""
_lowerCAmelCase = limit + 1
_lowerCAmelCase = [0] * limit
for first_term in range(1 , snake_case_ ):
for n in range(snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
_lowerCAmelCase = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
from functools import reduce
SCREAMING_SNAKE_CASE : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def __UpperCAmelCase ( snake_case_ : str = N ) -> int:
"""simple docstring"""
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda snake_case_ , snake_case_ : str(int(snake_case_ ) * int(snake_case_ ) ) , n[i : i + 13] ) )
for i in range(len(snake_case_ ) - 12 ) )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
|
"""simple docstring"""
from functools import reduce
SCREAMING_SNAKE_CASE : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def __UpperCAmelCase ( snake_case_ : str = N ) -> int:
"""simple docstring"""
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda snake_case_ , snake_case_ : str(int(snake_case_ ) * int(snake_case_ ) ) , n[i : i + 13] ) )
for i in range(len(snake_case_ ) - 12 ) )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
from __future__ import annotations
def __UpperCAmelCase ( snake_case_ : list[list[int]] ) -> int:
"""simple docstring"""
for i in range(1 , len(matrix[0] ) ):
matrix[0][i] += matrix[0][i - 1]
# preprocessing the first column
for i in range(1 , len(snake_case_ ) ):
matrix[i][0] += matrix[i - 1][0]
# updating the path cost for current position
for i in range(1 , len(snake_case_ ) ):
for j in range(1 , len(matrix[0] ) ):
matrix[i][j] += min(matrix[i - 1][j] , matrix[i][j - 1] )
return matrix[-1][-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 600851475143 ) -> int:
"""simple docstring"""
try:
_lowerCAmelCase = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
while n % i == 0:
_lowerCAmelCase = i
n //= i
i += 1
if n > 1:
_lowerCAmelCase = n
return int(snake_case_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def __UpperCAmelCase ( snake_case_ : Dict ) -> Union[str, Any]: # picklable for multiprocessing
"""simple docstring"""
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def __UpperCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
with parallel_backend("""spark""" ):
assert ParallelBackendConfig.backend_name == "spark"
_lowerCAmelCase = [1, 2, 3]
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ , snake_case_ , num_proc=2 )
with pytest.raises(snake_case_ ):
with parallel_backend("""unsupported backend""" ):
map_nested(snake_case_ , snake_case_ , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("""num_proc""" , [2, -1] )
def __UpperCAmelCase ( snake_case_ : int ) -> Optional[int]:
"""simple docstring"""
_lowerCAmelCase = [1, 2]
_lowerCAmelCase = {"""a""": 1, """b""": 2}
_lowerCAmelCase = {"""a""": [1, 2], """b""": [3, 4]}
_lowerCAmelCase = {"""a""": {"""1""": 1}, """b""": 2}
_lowerCAmelCase = {"""a""": 1, """b""": 2, """c""": 3, """d""": 4}
_lowerCAmelCase = [2, 3]
_lowerCAmelCase = {"""a""": 2, """b""": 3}
_lowerCAmelCase = {"""a""": [2, 3], """b""": [4, 5]}
_lowerCAmelCase = {"""a""": {"""1""": 2}, """b""": 3}
_lowerCAmelCase = {"""a""": 2, """b""": 3, """c""": 4, """d""": 5}
with parallel_backend("""spark""" ):
assert map_nested(snake_case_ , snake_case_ , num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ , snake_case_ , num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ , snake_case_ , num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ , snake_case_ , num_proc=snake_case_ ) == expected_map_nested_sa
assert map_nested(snake_case_ , snake_case_ , num_proc=snake_case_ ) == expected_map_nested_sa
| 317
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
SCREAMING_SNAKE_CASE : Optional[Any] = logging.getLogger(__name__)
@dataclass
class __lowerCamelCase :
__UpperCamelCase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Whether tp freeze the encoder.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class __lowerCamelCase :
__UpperCamelCase = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__UpperCamelCase = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
__UpperCamelCase = field(
default=1_024 , 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=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCamelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
__UpperCamelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCamelCase = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
__UpperCamelCase = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
__UpperCamelCase = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Source language id for translation.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Target language id for translation.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': '# num_beams to use for evaluation.'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def __UpperCAmelCase ( snake_case_ : Optional[int] , snake_case_ : Any , snake_case_ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(snake_case_ , os.path.join(snake_case_ , F"""{split}_results.json""" ) )
def __UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_args_into_dataclasses()
check_output_dir(snake_case_ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("""Training/evaluation parameters %s""" , snake_case_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_lowerCAmelCase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(snake_case_ , snake_case_ , snake_case_ ):
assert hasattr(snake_case_ , snake_case_ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(snake_case_ , snake_case_ , getattr(snake_case_ , snake_case_ ) )
_lowerCAmelCase = 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 = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=snake_case_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(snake_case_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCAmelCase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(snake_case_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(snake_case_ , snake_case_ ):
_lowerCAmelCase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCAmelCase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(snake_case_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCAmelCase = SeqaSeqDataset
# Get datasets
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCAmelCase = (
build_compute_metrics_fn(data_args.task , snake_case_ ) if training_args.predict_with_generate else None
)
_lowerCAmelCase = SeqaSeqTrainer(
model=snake_case_ , args=snake_case_ , data_args=snake_case_ , train_dataset=snake_case_ , eval_dataset=snake_case_ , data_collator=SeqaSeqDataCollator(
snake_case_ , snake_case_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=snake_case_ , tokenizer=snake_case_ , )
_lowerCAmelCase = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
_lowerCAmelCase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCAmelCase = train_result.metrics
_lowerCAmelCase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_lowerCAmelCase = trainer.evaluate(metric_key_prefix="""val""" )
_lowerCAmelCase = data_args.n_val
_lowerCAmelCase = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
_lowerCAmelCase = trainer.predict(test_dataset=snake_case_ , metric_key_prefix="""test""" )
_lowerCAmelCase = test_output.metrics
_lowerCAmelCase = data_args.n_test
if trainer.is_world_process_zero():
_lowerCAmelCase = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
if training_args.predict_with_generate:
_lowerCAmelCase = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=snake_case_ , clean_up_tokenization_spaces=snake_case_ )
_lowerCAmelCase = lmap(str.strip , snake_case_ )
write_txt_file(snake_case_ , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(snake_case_ , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def __UpperCAmelCase ( snake_case_ : Any ) -> Dict:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 317
| 1
|
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
SCREAMING_SNAKE_CASE : str = 1_6
SCREAMING_SNAKE_CASE : Dict = 3_2
def __UpperCAmelCase ( snake_case_ : Accelerator , snake_case_ : int = 16 ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = AutoTokenizer.from_pretrained("""bert-base-cased""" )
_lowerCAmelCase = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(snake_case_ : List[str] ):
# max_length=None => use the model max length (it's actually the default)
_lowerCAmelCase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=snake_case_ , max_length=snake_case_ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
_lowerCAmelCase = datasets.map(
snake_case_ , batched=snake_case_ , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowerCAmelCase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(snake_case_ : Union[str, Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
_lowerCAmelCase = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
_lowerCAmelCase = 16
elif accelerator.mixed_precision != "no":
_lowerCAmelCase = 8
else:
_lowerCAmelCase = None
return tokenizer.pad(
snake_case_ , padding="""longest""" , max_length=snake_case_ , pad_to_multiple_of=snake_case_ , return_tensors="""pt""" , )
# Instantiate dataloaders.
_lowerCAmelCase = DataLoader(
tokenized_datasets["""train"""] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
_lowerCAmelCase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=snake_case_ , collate_fn=snake_case_ , batch_size=snake_case_ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
SCREAMING_SNAKE_CASE : Union[str, Any] = mocked_dataloaders # noqa: F811
def __UpperCAmelCase ( snake_case_ : Union[str, Any] , snake_case_ : Any ) -> Union[str, Any]:
"""simple docstring"""
if os.environ.get("""TESTING_MOCKED_DATALOADERS""" , snake_case_ ) == "1":
_lowerCAmelCase = 2
# New Code #
_lowerCAmelCase = int(args.gradient_accumulation_steps )
_lowerCAmelCase = int(args.local_sgd_steps )
# Initialize accelerator
_lowerCAmelCase = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=snake_case_ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError("""LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)""" )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowerCAmelCase = config["""lr"""]
_lowerCAmelCase = int(config["""num_epochs"""] )
_lowerCAmelCase = int(config["""seed"""] )
_lowerCAmelCase = int(config["""batch_size"""] )
_lowerCAmelCase = evaluate.load("""glue""" , """mrpc""" )
set_seed(snake_case_ )
_lowerCAmelCase , _lowerCAmelCase = get_dataloaders(snake_case_ , snake_case_ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=snake_case_ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
_lowerCAmelCase = model.to(accelerator.device )
# Instantiate optimizer
_lowerCAmelCase = AdamW(params=model.parameters() , lr=snake_case_ )
# Instantiate scheduler
_lowerCAmelCase = get_linear_schedule_with_warmup(
optimizer=snake_case_ , num_warmup_steps=100 , num_training_steps=(len(snake_case_ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = accelerator.prepare(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
# Now we train the model
for epoch in range(snake_case_ ):
model.train()
with LocalSGD(
accelerator=snake_case_ , model=snake_case_ , local_sgd_steps=snake_case_ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case_ ):
_lowerCAmelCase = model(**snake_case_ )
_lowerCAmelCase = output.loss
accelerator.backward(snake_case_ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case_ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_lowerCAmelCase = model(**snake_case_ )
_lowerCAmelCase = outputs.logits.argmax(dim=-1 )
_lowerCAmelCase , _lowerCAmelCase = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=snake_case_ , references=snake_case_ , )
_lowerCAmelCase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F"""epoch {epoch}:""" , snake_case_ )
def __UpperCAmelCase ( ) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=snake_case_ , default=snake_case_ , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
# New Code #
parser.add_argument(
"""--gradient_accumulation_steps""" , type=snake_case_ , default=1 , help="""The number of minibatches to be ran before gradients are accumulated.""" , )
parser.add_argument(
"""--local_sgd_steps""" , type=snake_case_ , default=8 , help="""Number of local SGD steps or None to disable local SGD""" )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
_lowerCAmelCase = parser.parse_args()
_lowerCAmelCase = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(snake_case_ , snake_case_ )
if __name__ == "__main__":
main()
| 317
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : List[Any] = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = [
'''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FocalNetForImageClassification''',
'''FocalNetForMaskedImageModeling''',
'''FocalNetBackbone''',
'''FocalNetModel''',
'''FocalNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 317
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|
"""simple docstring"""
from dataclasses import asdict, dataclass
from typing import Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
# TODO Update this
SCREAMING_SNAKE_CASE : Tuple = {
'''facebook/esm-1b''': '''https://huggingface.co/facebook/esm-1b/resolve/main/config.json''',
# See all ESM models at https://huggingface.co/models?filter=esm
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'esm'
def __init__(self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=768 , lowerCamelCase=12 , lowerCamelCase=12 , lowerCamelCase=3_072 , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=1_026 , lowerCamelCase=0.02 , lowerCamelCase=1e-12 , lowerCamelCase="absolute" , lowerCamelCase=True , lowerCamelCase=None , lowerCamelCase=False , lowerCamelCase=False , lowerCamelCase=None , lowerCamelCase=None , **lowerCamelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase , mask_token_id=lowerCamelCase , **lowerCamelCase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = position_embedding_type
_lowerCAmelCase = use_cache
_lowerCAmelCase = emb_layer_norm_before
_lowerCAmelCase = token_dropout
_lowerCAmelCase = is_folding_model
if is_folding_model:
if esmfold_config is None:
logger.info("""No esmfold_config supplied for folding model, using default values.""" )
_lowerCAmelCase = EsmFoldConfig()
elif isinstance(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = EsmFoldConfig(**lowerCamelCase )
_lowerCAmelCase = esmfold_config
if vocab_list is None:
logger.warning("""No vocab_list supplied for folding model, assuming the ESM-2 vocabulary!""" )
_lowerCAmelCase = get_default_vocab_list()
else:
_lowerCAmelCase = vocab_list
else:
_lowerCAmelCase = None
_lowerCAmelCase = None
if self.esmfold_config is not None and getattr(self.esmfold_config , """use_esm_attn_map""" , lowerCamelCase ):
raise ValueError("""The HuggingFace port of ESMFold does not support use_esm_attn_map at this time!""" )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = super().to_dict()
if isinstance(self.esmfold_config , lowerCamelCase ):
_lowerCAmelCase = self.esmfold_config.to_dict()
return output
@dataclass
class __lowerCamelCase :
__UpperCamelCase = None
__UpperCamelCase = True
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = 0
__UpperCamelCase = True
__UpperCamelCase = False
__UpperCamelCase = 128
__UpperCamelCase = None
def A__ (self ):
'''simple docstring'''
if self.trunk is None:
_lowerCAmelCase = TrunkConfig()
elif isinstance(self.trunk , lowerCamelCase ):
_lowerCAmelCase = TrunkConfig(**self.trunk )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = asdict(self )
_lowerCAmelCase = self.trunk.to_dict()
return output
@dataclass
class __lowerCamelCase :
__UpperCamelCase = 48
__UpperCamelCase = 1_024
__UpperCamelCase = 128
__UpperCamelCase = 32
__UpperCamelCase = 32
__UpperCamelCase = 32
__UpperCamelCase = 0
__UpperCamelCase = 0
__UpperCamelCase = False
__UpperCamelCase = 4
__UpperCamelCase = 128
__UpperCamelCase = None
def A__ (self ):
'''simple docstring'''
if self.structure_module is None:
_lowerCAmelCase = StructureModuleConfig()
elif isinstance(self.structure_module , lowerCamelCase ):
_lowerCAmelCase = StructureModuleConfig(**self.structure_module )
if self.max_recycles <= 0:
raise ValueError(f"""`max_recycles` should be positive, got {self.max_recycles}.""" )
if self.sequence_state_dim % self.sequence_state_dim != 0:
raise ValueError(
"""`sequence_state_dim` should be a round multiple of `sequence_state_dim`, got"""
f""" {self.sequence_state_dim} and {self.sequence_state_dim}.""" )
if self.pairwise_state_dim % self.pairwise_state_dim != 0:
raise ValueError(
"""`pairwise_state_dim` should be a round multiple of `pairwise_state_dim`, got"""
f""" {self.pairwise_state_dim} and {self.pairwise_state_dim}.""" )
_lowerCAmelCase = self.sequence_state_dim // self.sequence_head_width
_lowerCAmelCase = self.pairwise_state_dim // self.pairwise_head_width
if self.sequence_state_dim != sequence_num_heads * self.sequence_head_width:
raise ValueError(
"""`sequence_state_dim` should be equal to `sequence_num_heads * sequence_head_width, got"""
f""" {self.sequence_state_dim} != {sequence_num_heads} * {self.sequence_head_width}.""" )
if self.pairwise_state_dim != pairwise_num_heads * self.pairwise_head_width:
raise ValueError(
"""`pairwise_state_dim` should be equal to `pairwise_num_heads * pairwise_head_width, got"""
f""" {self.pairwise_state_dim} != {pairwise_num_heads} * {self.pairwise_head_width}.""" )
if self.pairwise_state_dim % 2 != 0:
raise ValueError(f"""`pairwise_state_dim` should be even, got {self.pairwise_state_dim}.""" )
if self.dropout >= 0.4:
raise ValueError(f"""`dropout` should not be greater than 0.4, got {self.dropout}.""" )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = asdict(self )
_lowerCAmelCase = self.structure_module.to_dict()
return output
@dataclass
class __lowerCamelCase :
__UpperCamelCase = 384
__UpperCamelCase = 128
__UpperCamelCase = 16
__UpperCamelCase = 128
__UpperCamelCase = 12
__UpperCamelCase = 4
__UpperCamelCase = 8
__UpperCamelCase = 0.1
__UpperCamelCase = 8
__UpperCamelCase = 1
__UpperCamelCase = 2
__UpperCamelCase = 7
__UpperCamelCase = 10
__UpperCamelCase = 1E-8
__UpperCamelCase = 1E5
def A__ (self ):
'''simple docstring'''
return asdict(self )
def __UpperCAmelCase ( ) -> str:
"""simple docstring"""
return (
"<cls>",
"<pad>",
"<eos>",
"<unk>",
"L",
"A",
"G",
"V",
"S",
"E",
"R",
"T",
"I",
"D",
"P",
"K",
"Q",
"N",
"F",
"Y",
"M",
"H",
"W",
"C",
"X",
"B",
"U",
"Z",
"O",
".",
"-",
"<null_1>",
"<mask>",
)
| 317
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __lowerCamelCase ( unittest.TestCase ):
def __init__(self , lowerCamelCase , lowerCamelCase=7 , lowerCamelCase=3 , lowerCamelCase=18 , lowerCamelCase=30 , lowerCamelCase=400 , lowerCamelCase=True , lowerCamelCase=None , lowerCamelCase=True , lowerCamelCase=None , ):
'''simple docstring'''
_lowerCAmelCase = size if size is not None else {"""shortest_edge""": 20}
_lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = min_resolution
_lowerCAmelCase = max_resolution
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
def A__ (self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __lowerCamelCase ( __lowercase , unittest.TestCase ):
__UpperCamelCase = MobileNetVaImageProcessor if is_vision_available() else None
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = MobileNetVaImageProcessingTester(self )
@property
def A__ (self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(lowerCamelCase , """size""" ) )
self.assertTrue(hasattr(lowerCamelCase , """do_center_crop""" ) )
self.assertTrue(hasattr(lowerCamelCase , """crop_size""" ) )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 20} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} )
def A__ (self ):
'''simple docstring'''
pass
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , Image.Image )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , numpify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , np.ndarray )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , torchify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , torch.Tensor )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 317
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|
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for letter in string.ascii_lowercase]
SCREAMING_SNAKE_CASE : set[int] = {ord(char) for char in VALID_CHARS}
SCREAMING_SNAKE_CASE : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def __UpperCAmelCase ( snake_case_ : list[int] , snake_case_ : tuple[int, ...] ) -> str | None:
"""simple docstring"""
_lowerCAmelCase = ""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
for keychar, cipherchar in zip(cycle(snake_case_ ) , snake_case_ ):
_lowerCAmelCase = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(snake_case_ )
return decoded
def __UpperCAmelCase ( snake_case_ : list[int] ) -> list[str]:
"""simple docstring"""
_lowerCAmelCase = []
for key in product(snake_case_ , repeat=3 ):
_lowerCAmelCase = try_key(snake_case_ , snake_case_ )
if encoded is not None:
possibles.append(snake_case_ )
return possibles
def __UpperCAmelCase ( snake_case_ : list[str] , snake_case_ : str ) -> list[str]:
"""simple docstring"""
return [possible for possible in possibles if common_word in possible.lower()]
def __UpperCAmelCase ( snake_case_ : str = "p059_cipher.txt" ) -> int:
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = Path(snake_case_ ).parent.joinpath(snake_case_ ).read_text(encoding="""utf-8""" )
_lowerCAmelCase = [int(snake_case_ ) for number in data.strip().split(""",""" )]
_lowerCAmelCase = filter_valid_chars(snake_case_ )
for common_word in COMMON_WORDS:
_lowerCAmelCase = filter_common_word(snake_case_ , snake_case_ )
if len(snake_case_ ) == 1:
break
_lowerCAmelCase = possibles[0]
return sum(ord(snake_case_ ) for char in decoded_text )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : list ) -> list:
"""simple docstring"""
for i in range(len(snake_case_ ) - 1 , 0 , -1 ):
_lowerCAmelCase = False
for j in range(snake_case_ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
_lowerCAmelCase , _lowerCAmelCase = unsorted[j - 1], unsorted[j]
_lowerCAmelCase = True
for j in range(snake_case_ ):
if unsorted[j] > unsorted[j + 1]:
_lowerCAmelCase , _lowerCAmelCase = unsorted[j + 1], unsorted[j]
_lowerCAmelCase = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : List[Any] = input('''Enter numbers separated by a comma:\n''').strip()
SCREAMING_SNAKE_CASE : List[str] = [int(item) for item in user_input.split(''',''')]
print(F'{cocktail_shaker_sort(unsorted) = }')
| 317
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|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : str ) -> list:
"""simple docstring"""
_lowerCAmelCase = len(snake_case_ )
_lowerCAmelCase = []
for i in range(len(snake_case_ ) - pat_len + 1 ):
_lowerCAmelCase = True
for j in range(snake_case_ ):
if s[i + j] != pattern[j]:
_lowerCAmelCase = False
break
if match_found:
position.append(snake_case_ )
return position
if __name__ == "__main__":
assert naive_pattern_search('''ABCDEFG''', '''DE''') == [3]
print(naive_pattern_search('''ABAAABCDBBABCDDEBCABC''', '''ABC'''))
| 317
|
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
def __UpperCAmelCase ( snake_case_ : bool , snake_case_ : bool ) -> Tuple:
"""simple docstring"""
def run_func(snake_case_ : Union[str, Any] ):
@wraps(snake_case_ )
def run_in_eager_mode(*snake_case_ : Optional[int] , **snake_case_ : Union[str, Any] ):
return func(*snake_case_ , **snake_case_ )
@wraps(snake_case_ )
@tf.function(experimental_compile=snake_case_ )
def run_in_graph_mode(*snake_case_ : Dict , **snake_case_ : Union[str, Any] ):
return func(*snake_case_ , **snake_case_ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"""Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : int , snake_case_ : int ) -> ["tf.Tensor"]:
"""simple docstring"""
_lowerCAmelCase = random.Random()
_lowerCAmelCase = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(snake_case_ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 42
__UpperCamelCase = 42
__UpperCamelCase = "TensorFlow"
@property
def A__ (self ):
'''simple docstring'''
return tf.__version__
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_inference_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_speed(_inference )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_train_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_speed(_train )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase )
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_inference_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_memory(_inference )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase )
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_train_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_memory(_train )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
_lowerCAmelCase = (
hasattr(lowerCamelCase , """architectures""" )
and isinstance(config.architectures , lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model_cls(lowerCamelCase )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
_lowerCAmelCase = TF_MODEL_MAPPING[config.__class__](lowerCamelCase )
# encoder-decoder has vocab size saved differently
_lowerCAmelCase = config.vocab_size if hasattr(lowerCamelCase , """vocab_size""" ) else config.encoder.vocab_size
_lowerCAmelCase = random_input_ids(lowerCamelCase , lowerCamelCase , lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(lowerCamelCase , decoder_input_ids=lowerCamelCase , training=lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(lowerCamelCase , training=lowerCamelCase )
_lowerCAmelCase = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.""" )
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
_lowerCAmelCase = (
hasattr(lowerCamelCase , """architectures""" )
and isinstance(config.architectures , lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model_cls(lowerCamelCase )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
_lowerCAmelCase = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase )
# encoder-decoder has vocab size saved differently
_lowerCAmelCase = config.vocab_size if hasattr(lowerCamelCase , """vocab_size""" ) else config.encoder.vocab_size
_lowerCAmelCase = random_input_ids(lowerCamelCase , lowerCamelCase , lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
_lowerCAmelCase = model(lowerCamelCase , decoder_input_ids=lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )[0]
_lowerCAmelCase = tf.gradients(lowerCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
_lowerCAmelCase = model(lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )[0]
_lowerCAmelCase = tf.gradients(lowerCamelCase , model.trainable_variables )
return gradients
_lowerCAmelCase = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def A__ (self , lowerCamelCase ):
'''simple docstring'''
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation""" )
timeit.repeat(lowerCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
_lowerCAmelCase = timeit.repeat(
lowerCamelCase , repeat=self.args.repeat , number=10 , )
return min(lowerCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
logger.info(
"""Note that TensorFlow allocates more memory than """
"""it might need to speed up computation. """
"""The memory reported here corresponds to the memory """
"""reported by `nvidia-smi`, which can vary depending """
"""on total available memory on the GPU that is used.""" )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"""`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
""" consumption line by line.""" )
_lowerCAmelCase = start_memory_tracing("""transformers""" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"""Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
""" with `args.memory=False`""" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"""py3nvml not installed, we won't log GPU memory usage. """
"""Install py3nvml (pip install py3nvml) to log information about GPU.""" )
_lowerCAmelCase = """N/A"""
else:
logger.info(
"""Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
""" running on the same GPU.""" )
# init nvml
nvml.nvmlInit()
func()
_lowerCAmelCase = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
_lowerCAmelCase = nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase )
_lowerCAmelCase = meminfo.used
_lowerCAmelCase = Memory(lowerCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"""When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
""" TensorFlow.""" )
_lowerCAmelCase = None
else:
_lowerCAmelCase = measure_peak_memory_cpu(lowerCamelCase )
_lowerCAmelCase = Memory(lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
_lowerCAmelCase = stop_memory_tracing(lowerCamelCase )
if memory is None:
_lowerCAmelCase = summary.total
else:
_lowerCAmelCase = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
return "N/A", None
| 317
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"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
SCREAMING_SNAKE_CASE : List[str] = False
class __lowerCamelCase ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(
image=lowerCamelCase , generator=lowerCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
_lowerCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 317
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'transfo-xl'
__UpperCamelCase = ['mems']
__UpperCamelCase = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__(self , lowerCamelCase=267_735 , lowerCamelCase=[20_000, 40_000, 200_000] , lowerCamelCase=1_024 , lowerCamelCase=1_024 , lowerCamelCase=16 , lowerCamelCase=64 , lowerCamelCase=4_096 , lowerCamelCase=4 , lowerCamelCase=False , lowerCamelCase=18 , lowerCamelCase=1_600 , lowerCamelCase=1_000 , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=0 , lowerCamelCase=-1 , lowerCamelCase=True , lowerCamelCase=0.1 , lowerCamelCase=0.0 , lowerCamelCase=True , lowerCamelCase="normal" , lowerCamelCase=0.01 , lowerCamelCase=0.01 , lowerCamelCase=0.02 , lowerCamelCase=1e-5 , lowerCamelCase=0 , **lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = vocab_size
_lowerCAmelCase = []
self.cutoffs.extend(lowerCamelCase )
if proj_share_all_but_first:
_lowerCAmelCase = [False] + [True] * len(self.cutoffs )
else:
_lowerCAmelCase = [False] + [False] * len(self.cutoffs )
_lowerCAmelCase = d_model
_lowerCAmelCase = d_embed
_lowerCAmelCase = d_head
_lowerCAmelCase = d_inner
_lowerCAmelCase = div_val
_lowerCAmelCase = pre_lnorm
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = mem_len
_lowerCAmelCase = same_length
_lowerCAmelCase = attn_type
_lowerCAmelCase = clamp_len
_lowerCAmelCase = sample_softmax
_lowerCAmelCase = adaptive
_lowerCAmelCase = dropout
_lowerCAmelCase = dropatt
_lowerCAmelCase = untie_r
_lowerCAmelCase = init
_lowerCAmelCase = init_range
_lowerCAmelCase = proj_init_std
_lowerCAmelCase = init_std
_lowerCAmelCase = layer_norm_epsilon
super().__init__(eos_token_id=lowerCamelCase , **lowerCamelCase )
@property
def A__ (self ):
'''simple docstring'''
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def A__ (self , lowerCamelCase ):
'''simple docstring'''
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 317
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|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'ctrl'
__UpperCamelCase = ['past_key_values']
__UpperCamelCase = {
'max_position_embeddings': 'n_positions',
'hidden_size': 'n_embd',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__(self , lowerCamelCase=246_534 , lowerCamelCase=256 , lowerCamelCase=1_280 , lowerCamelCase=8_192 , lowerCamelCase=48 , lowerCamelCase=16 , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=1e-6 , lowerCamelCase=0.02 , lowerCamelCase=True , **lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = vocab_size
_lowerCAmelCase = n_positions
_lowerCAmelCase = n_embd
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = dff
_lowerCAmelCase = resid_pdrop
_lowerCAmelCase = embd_pdrop
_lowerCAmelCase = layer_norm_epsilon
_lowerCAmelCase = initializer_range
_lowerCAmelCase = use_cache
super().__init__(**lowerCamelCase )
| 317
|
"""simple docstring"""
import math
def __UpperCAmelCase ( snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 2
_lowerCAmelCase = int(math.sqrt(snake_case_ ) ) # Size of every segment
_lowerCAmelCase = [True] * (end + 1)
_lowerCAmelCase = []
while start <= end:
if temp[start] is True:
in_prime.append(snake_case_ )
for i in range(start * start , end + 1 , snake_case_ ):
_lowerCAmelCase = False
start += 1
prime += in_prime
_lowerCAmelCase = end + 1
_lowerCAmelCase = min(2 * end , snake_case_ )
while low <= n:
_lowerCAmelCase = [True] * (high - low + 1)
for each in in_prime:
_lowerCAmelCase = math.floor(low / each ) * each
if t < low:
t += each
for j in range(snake_case_ , high + 1 , snake_case_ ):
_lowerCAmelCase = False
for j in range(len(snake_case_ ) ):
if temp[j] is True:
prime.append(j + low )
_lowerCAmelCase = high + 1
_lowerCAmelCase = min(high + end , snake_case_ )
return prime
print(sieve(1_0**6))
| 317
| 1
|
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
SCREAMING_SNAKE_CASE : int = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
inspect_dataset(snake_case_ , snake_case_ )
_lowerCAmelCase = path + """.py"""
assert script_name in os.listdir(snake_case_ )
assert "__pycache__" not in os.listdir(snake_case_ )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def __UpperCAmelCase ( snake_case_ : List[str] , snake_case_ : int ) -> str:
"""simple docstring"""
inspect_metric(snake_case_ , snake_case_ )
_lowerCAmelCase = path + """.py"""
assert script_name in os.listdir(snake_case_ )
assert "__pycache__" not in os.listdir(snake_case_ )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : Optional[int] , snake_case_ : Dict ) -> Any:
"""simple docstring"""
_lowerCAmelCase = get_dataset_config_info(snake_case_ , config_name=snake_case_ )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : int , snake_case_ : int ) -> Union[str, Any]:
"""simple docstring"""
with pytest.raises(snake_case_ ):
get_dataset_config_info(snake_case_ , config_name=snake_case_ )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def __UpperCAmelCase ( snake_case_ : Tuple , snake_case_ : Tuple ) -> int:
"""simple docstring"""
_lowerCAmelCase = get_dataset_config_names(snake_case_ )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def __UpperCAmelCase ( snake_case_ : Optional[int] , snake_case_ : int , snake_case_ : int ) -> Any:
"""simple docstring"""
_lowerCAmelCase = get_dataset_infos(snake_case_ )
assert list(infos.keys() ) == expected_configs
_lowerCAmelCase = expected_configs[0]
assert expected_config in infos
_lowerCAmelCase = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def __UpperCAmelCase ( snake_case_ : Any , snake_case_ : str , snake_case_ : Tuple ) -> Dict:
"""simple docstring"""
_lowerCAmelCase = get_dataset_infos(snake_case_ )
assert expected_config in infos
_lowerCAmelCase = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def __UpperCAmelCase ( snake_case_ : List[str] , snake_case_ : Any , snake_case_ : int ) -> List[str]:
"""simple docstring"""
with pytest.raises(snake_case_ ):
get_dataset_split_names(snake_case_ , config_name=snake_case_ )
| 317
|
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE : Any = (7_2_0, 1_2_8_0) # Height, Width
SCREAMING_SNAKE_CASE : List[str] = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE : List[Any] = 1 / 1_0_0
SCREAMING_SNAKE_CASE : Optional[Any] = ''''''
SCREAMING_SNAKE_CASE : Dict = ''''''
SCREAMING_SNAKE_CASE : List[Any] = ''''''
SCREAMING_SNAKE_CASE : Dict = 2_5_0
def __UpperCAmelCase ( ) -> None:
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = get_dataset(snake_case_ , snake_case_ )
for index in range(snake_case_ ):
_lowerCAmelCase = random.sample(range(len(snake_case_ ) ) , 4 )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = update_image_and_anno(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , filter_scale=snake_case_ , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_lowerCAmelCase = random_chars(32 )
_lowerCAmelCase = path.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
_lowerCAmelCase = F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
cva.imwrite(F"""{file_root}.jpg""" , snake_case_ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
_lowerCAmelCase = []
for anno in new_annos:
_lowerCAmelCase = anno[3] - anno[1]
_lowerCAmelCase = anno[4] - anno[2]
_lowerCAmelCase = anno[1] + width / 2
_lowerCAmelCase = anno[2] + height / 2
_lowerCAmelCase = F"""{anno[0]} {x_center} {y_center} {width} {height}"""
annos_list.append(snake_case_ )
with open(F"""{file_root}.txt""" , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : str ) -> tuple[list, list]:
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = []
for label_file in glob.glob(os.path.join(snake_case_ , """*.txt""" ) ):
_lowerCAmelCase = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(snake_case_ ) as in_file:
_lowerCAmelCase = in_file.readlines()
_lowerCAmelCase = os.path.join(snake_case_ , F"""{label_name}.jpg""" )
_lowerCAmelCase = []
for obj_list in obj_lists:
_lowerCAmelCase = obj_list.rstrip("""\n""" ).split(""" """ )
_lowerCAmelCase = float(obj[1] ) - float(obj[3] ) / 2
_lowerCAmelCase = float(obj[2] ) - float(obj[4] ) / 2
_lowerCAmelCase = float(obj[1] ) + float(obj[3] ) / 2
_lowerCAmelCase = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(snake_case_ )
labels.append(snake_case_ )
return img_paths, labels
def __UpperCAmelCase ( snake_case_ : list , snake_case_ : list , snake_case_ : list[int] , snake_case_ : tuple[int, int] , snake_case_ : tuple[float, float] , snake_case_ : float = 0.0 , ) -> tuple[list, list, str]:
"""simple docstring"""
_lowerCAmelCase = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
_lowerCAmelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_lowerCAmelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_lowerCAmelCase = int(scale_x * output_size[1] )
_lowerCAmelCase = int(scale_y * output_size[0] )
_lowerCAmelCase = []
_lowerCAmelCase = []
for i, index in enumerate(snake_case_ ):
_lowerCAmelCase = all_img_list[index]
path_list.append(snake_case_ )
_lowerCAmelCase = all_annos[index]
_lowerCAmelCase = cva.imread(snake_case_ )
if i == 0: # top-left
_lowerCAmelCase = cva.resize(snake_case_ , (divid_point_x, divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = bbox[1] * scale_x
_lowerCAmelCase = bbox[2] * scale_y
_lowerCAmelCase = bbox[3] * scale_x
_lowerCAmelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
_lowerCAmelCase = cva.resize(snake_case_ , (output_size[1] - divid_point_x, divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = scale_x + bbox[1] * (1 - scale_x)
_lowerCAmelCase = bbox[2] * scale_y
_lowerCAmelCase = scale_x + bbox[3] * (1 - scale_x)
_lowerCAmelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
_lowerCAmelCase = cva.resize(snake_case_ , (divid_point_x, output_size[0] - divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = bbox[1] * scale_x
_lowerCAmelCase = scale_y + bbox[2] * (1 - scale_y)
_lowerCAmelCase = bbox[3] * scale_x
_lowerCAmelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
_lowerCAmelCase = cva.resize(
snake_case_ , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = scale_x + bbox[1] * (1 - scale_x)
_lowerCAmelCase = scale_y + bbox[2] * (1 - scale_y)
_lowerCAmelCase = scale_x + bbox[3] * (1 - scale_x)
_lowerCAmelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
_lowerCAmelCase = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def __UpperCAmelCase ( snake_case_ : int ) -> str:
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
_lowerCAmelCase = ascii_lowercase + digits
return "".join(random.choice(snake_case_ ) for _ in range(snake_case_ ) )
if __name__ == "__main__":
main()
print('''DONE ✅''')
| 317
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|
"""simple docstring"""
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class __lowerCamelCase ( __lowercase , __lowercase ):
@register_to_config
def __init__(self , *,
lowerCamelCase = 4 , lowerCamelCase = 768 , lowerCamelCase , lowerCamelCase , ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase = nn.Parameter(torch.zeros(lowerCamelCase ) )
# parameters for additional clip time embeddings
_lowerCAmelCase = nn.Linear(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = nn.Linear(lowerCamelCase , lowerCamelCase )
# parameters for encoder hidden states
_lowerCAmelCase = clip_extra_context_tokens
_lowerCAmelCase = nn.Linear(
lowerCamelCase , self.clip_extra_context_tokens * cross_attention_dim )
_lowerCAmelCase = nn.Linear(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = nn.LayerNorm(lowerCamelCase )
def A__ (self , *, lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if do_classifier_free_guidance:
# Add the classifier free guidance embeddings to the image embeddings
_lowerCAmelCase = image_embeddings.shape[0]
_lowerCAmelCase = self.learned_classifier_free_guidance_embeddings.unsqueeze(0 )
_lowerCAmelCase = classifier_free_guidance_embeddings.expand(
lowerCamelCase , -1 )
_lowerCAmelCase = torch.cat([classifier_free_guidance_embeddings, image_embeddings] , dim=0 )
# The image embeddings batch size and the text embeddings batch size are equal
assert image_embeddings.shape[0] == prompt_embeds.shape[0]
_lowerCAmelCase = prompt_embeds.shape[0]
# "Specifically, we modify the architecture described in Nichol et al. (2021) by projecting and
# adding CLIP embeddings to the existing timestep embedding, ...
_lowerCAmelCase = self.embedding_proj(lowerCamelCase )
_lowerCAmelCase = self.clip_image_embeddings_project_to_time_embeddings(lowerCamelCase )
_lowerCAmelCase = time_projected_image_embeddings + time_projected_prompt_embeds
# ... and by projecting CLIP embeddings into four
# extra tokens of context that are concatenated to the sequence of outputs from the GLIDE text encoder"
_lowerCAmelCase = self.clip_extra_context_tokens_proj(lowerCamelCase )
_lowerCAmelCase = clip_extra_context_tokens.reshape(lowerCamelCase , -1 , self.clip_extra_context_tokens )
_lowerCAmelCase = clip_extra_context_tokens.permute(0 , 2 , 1 )
_lowerCAmelCase = self.encoder_hidden_states_proj(lowerCamelCase )
_lowerCAmelCase = self.text_encoder_hidden_states_norm(lowerCamelCase )
_lowerCAmelCase = torch.cat([clip_extra_context_tokens, text_encoder_hidden_states] , dim=1 )
return text_encoder_hidden_states, additive_clip_time_embeddings
| 317
|
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
SCREAMING_SNAKE_CASE : Dict = abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def __UpperCAmelCase ( snake_case_ : Optional[int] ) -> List[str]:
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case_ )
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> int:
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_terminal_summary_main
_lowerCAmelCase = terminalreporter.config.getoption("""--make-reports""" )
if make_reports:
pytest_terminal_summary_main(snake_case_ , id=snake_case_ )
| 317
| 1
|
"""simple docstring"""
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 __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = tempfile.mkdtemp()
# fmt: off
_lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """want""", """##want""", """##ed""", """wa""", """un""", """runn""", """##ing""", """,""", """low""", """lowest"""]
# fmt: on
_lowerCAmelCase = 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] ) )
_lowerCAmelCase = {
"""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],
}
_lowerCAmelCase = os.path.join(self.tmpdirname , lowerCamelCase )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(lowerCamelCase , lowerCamelCase )
def A__ (self , **lowerCamelCase ):
'''simple docstring'''
return BertTokenizer.from_pretrained(self.tmpdirname , **lowerCamelCase )
def A__ (self , **lowerCamelCase ):
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase )
def A__ (self ):
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_lowerCAmelCase = [Image.fromarray(np.moveaxis(lowerCamelCase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_image_processor()
_lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
processor.save_pretrained(self.tmpdirname )
_lowerCAmelCase = 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 A__ (self ):
'''simple docstring'''
_lowerCAmelCase = VisionTextDualEncoderProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_lowerCAmelCase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
_lowerCAmelCase = self.get_image_processor(do_normalize=lowerCamelCase , padding_value=1.0 )
_lowerCAmelCase = 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 A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.get_image_processor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
_lowerCAmelCase = self.prepare_image_inputs()
_lowerCAmelCase = image_processor(lowerCamelCase , return_tensors="""np""" )
_lowerCAmelCase = 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 A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.get_image_processor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
_lowerCAmelCase = """lower newer"""
_lowerCAmelCase = processor(text=lowerCamelCase )
_lowerCAmelCase = tokenizer(lowerCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.get_image_processor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
_lowerCAmelCase = """lower newer"""
_lowerCAmelCase = self.prepare_image_inputs()
_lowerCAmelCase = 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 A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.get_image_processor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
_lowerCAmelCase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_lowerCAmelCase = processor.batch_decode(lowerCamelCase )
_lowerCAmelCase = tokenizer.batch_decode(lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.get_image_processor()
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = VisionTextDualEncoderProcessor(tokenizer=lowerCamelCase , image_processor=lowerCamelCase )
_lowerCAmelCase = """lower newer"""
_lowerCAmelCase = self.prepare_image_inputs()
_lowerCAmelCase = processor(text=lowerCamelCase , images=lowerCamelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
| 317
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"""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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
SCREAMING_SNAKE_CASE : Optional[Any] = {
'''Acehnese Arabic''': '''ace_Arab''',
'''Acehnese Latin''': '''ace_Latn''',
'''Mesopotamian Arabic''': '''acm_Arab''',
'''Ta\'izzi-Adeni Arabic''': '''acq_Arab''',
'''Tunisian Arabic''': '''aeb_Arab''',
'''Afrikaans''': '''afr_Latn''',
'''South Levantine Arabic''': '''ajp_Arab''',
'''Akan''': '''aka_Latn''',
'''Amharic''': '''amh_Ethi''',
'''North Levantine Arabic''': '''apc_Arab''',
'''Modern Standard Arabic''': '''arb_Arab''',
'''Modern Standard Arabic Romanized''': '''arb_Latn''',
'''Najdi Arabic''': '''ars_Arab''',
'''Moroccan Arabic''': '''ary_Arab''',
'''Egyptian Arabic''': '''arz_Arab''',
'''Assamese''': '''asm_Beng''',
'''Asturian''': '''ast_Latn''',
'''Awadhi''': '''awa_Deva''',
'''Central Aymara''': '''ayr_Latn''',
'''South Azerbaijani''': '''azb_Arab''',
'''North Azerbaijani''': '''azj_Latn''',
'''Bashkir''': '''bak_Cyrl''',
'''Bambara''': '''bam_Latn''',
'''Balinese''': '''ban_Latn''',
'''Belarusian''': '''bel_Cyrl''',
'''Bemba''': '''bem_Latn''',
'''Bengali''': '''ben_Beng''',
'''Bhojpuri''': '''bho_Deva''',
'''Banjar Arabic''': '''bjn_Arab''',
'''Banjar Latin''': '''bjn_Latn''',
'''Standard Tibetan''': '''bod_Tibt''',
'''Bosnian''': '''bos_Latn''',
'''Buginese''': '''bug_Latn''',
'''Bulgarian''': '''bul_Cyrl''',
'''Catalan''': '''cat_Latn''',
'''Cebuano''': '''ceb_Latn''',
'''Czech''': '''ces_Latn''',
'''Chokwe''': '''cjk_Latn''',
'''Central Kurdish''': '''ckb_Arab''',
'''Crimean Tatar''': '''crh_Latn''',
'''Welsh''': '''cym_Latn''',
'''Danish''': '''dan_Latn''',
'''German''': '''deu_Latn''',
'''Southwestern Dinka''': '''dik_Latn''',
'''Dyula''': '''dyu_Latn''',
'''Dzongkha''': '''dzo_Tibt''',
'''Greek''': '''ell_Grek''',
'''English''': '''eng_Latn''',
'''Esperanto''': '''epo_Latn''',
'''Estonian''': '''est_Latn''',
'''Basque''': '''eus_Latn''',
'''Ewe''': '''ewe_Latn''',
'''Faroese''': '''fao_Latn''',
'''Fijian''': '''fij_Latn''',
'''Finnish''': '''fin_Latn''',
'''Fon''': '''fon_Latn''',
'''French''': '''fra_Latn''',
'''Friulian''': '''fur_Latn''',
'''Nigerian Fulfulde''': '''fuv_Latn''',
'''Scottish Gaelic''': '''gla_Latn''',
'''Irish''': '''gle_Latn''',
'''Galician''': '''glg_Latn''',
'''Guarani''': '''grn_Latn''',
'''Gujarati''': '''guj_Gujr''',
'''Haitian Creole''': '''hat_Latn''',
'''Hausa''': '''hau_Latn''',
'''Hebrew''': '''heb_Hebr''',
'''Hindi''': '''hin_Deva''',
'''Chhattisgarhi''': '''hne_Deva''',
'''Croatian''': '''hrv_Latn''',
'''Hungarian''': '''hun_Latn''',
'''Armenian''': '''hye_Armn''',
'''Igbo''': '''ibo_Latn''',
'''Ilocano''': '''ilo_Latn''',
'''Indonesian''': '''ind_Latn''',
'''Icelandic''': '''isl_Latn''',
'''Italian''': '''ita_Latn''',
'''Javanese''': '''jav_Latn''',
'''Japanese''': '''jpn_Jpan''',
'''Kabyle''': '''kab_Latn''',
'''Jingpho''': '''kac_Latn''',
'''Kamba''': '''kam_Latn''',
'''Kannada''': '''kan_Knda''',
'''Kashmiri Arabic''': '''kas_Arab''',
'''Kashmiri Devanagari''': '''kas_Deva''',
'''Georgian''': '''kat_Geor''',
'''Central Kanuri Arabic''': '''knc_Arab''',
'''Central Kanuri Latin''': '''knc_Latn''',
'''Kazakh''': '''kaz_Cyrl''',
'''Kabiyè''': '''kbp_Latn''',
'''Kabuverdianu''': '''kea_Latn''',
'''Khmer''': '''khm_Khmr''',
'''Kikuyu''': '''kik_Latn''',
'''Kinyarwanda''': '''kin_Latn''',
'''Kyrgyz''': '''kir_Cyrl''',
'''Kimbundu''': '''kmb_Latn''',
'''Northern Kurdish''': '''kmr_Latn''',
'''Kikongo''': '''kon_Latn''',
'''Korean''': '''kor_Hang''',
'''Lao''': '''lao_Laoo''',
'''Ligurian''': '''lij_Latn''',
'''Limburgish''': '''lim_Latn''',
'''Lingala''': '''lin_Latn''',
'''Lithuanian''': '''lit_Latn''',
'''Lombard''': '''lmo_Latn''',
'''Latgalian''': '''ltg_Latn''',
'''Luxembourgish''': '''ltz_Latn''',
'''Luba-Kasai''': '''lua_Latn''',
'''Ganda''': '''lug_Latn''',
'''Luo''': '''luo_Latn''',
'''Mizo''': '''lus_Latn''',
'''Standard Latvian''': '''lvs_Latn''',
'''Magahi''': '''mag_Deva''',
'''Maithili''': '''mai_Deva''',
'''Malayalam''': '''mal_Mlym''',
'''Marathi''': '''mar_Deva''',
'''Minangkabau Arabic ''': '''min_Arab''',
'''Minangkabau Latin''': '''min_Latn''',
'''Macedonian''': '''mkd_Cyrl''',
'''Plateau Malagasy''': '''plt_Latn''',
'''Maltese''': '''mlt_Latn''',
'''Meitei Bengali''': '''mni_Beng''',
'''Halh Mongolian''': '''khk_Cyrl''',
'''Mossi''': '''mos_Latn''',
'''Maori''': '''mri_Latn''',
'''Burmese''': '''mya_Mymr''',
'''Dutch''': '''nld_Latn''',
'''Norwegian Nynorsk''': '''nno_Latn''',
'''Norwegian Bokmål''': '''nob_Latn''',
'''Nepali''': '''npi_Deva''',
'''Northern Sotho''': '''nso_Latn''',
'''Nuer''': '''nus_Latn''',
'''Nyanja''': '''nya_Latn''',
'''Occitan''': '''oci_Latn''',
'''West Central Oromo''': '''gaz_Latn''',
'''Odia''': '''ory_Orya''',
'''Pangasinan''': '''pag_Latn''',
'''Eastern Panjabi''': '''pan_Guru''',
'''Papiamento''': '''pap_Latn''',
'''Western Persian''': '''pes_Arab''',
'''Polish''': '''pol_Latn''',
'''Portuguese''': '''por_Latn''',
'''Dari''': '''prs_Arab''',
'''Southern Pashto''': '''pbt_Arab''',
'''Ayacucho Quechua''': '''quy_Latn''',
'''Romanian''': '''ron_Latn''',
'''Rundi''': '''run_Latn''',
'''Russian''': '''rus_Cyrl''',
'''Sango''': '''sag_Latn''',
'''Sanskrit''': '''san_Deva''',
'''Santali''': '''sat_Olck''',
'''Sicilian''': '''scn_Latn''',
'''Shan''': '''shn_Mymr''',
'''Sinhala''': '''sin_Sinh''',
'''Slovak''': '''slk_Latn''',
'''Slovenian''': '''slv_Latn''',
'''Samoan''': '''smo_Latn''',
'''Shona''': '''sna_Latn''',
'''Sindhi''': '''snd_Arab''',
'''Somali''': '''som_Latn''',
'''Southern Sotho''': '''sot_Latn''',
'''Spanish''': '''spa_Latn''',
'''Tosk Albanian''': '''als_Latn''',
'''Sardinian''': '''srd_Latn''',
'''Serbian''': '''srp_Cyrl''',
'''Swati''': '''ssw_Latn''',
'''Sundanese''': '''sun_Latn''',
'''Swedish''': '''swe_Latn''',
'''Swahili''': '''swh_Latn''',
'''Silesian''': '''szl_Latn''',
'''Tamil''': '''tam_Taml''',
'''Tatar''': '''tat_Cyrl''',
'''Telugu''': '''tel_Telu''',
'''Tajik''': '''tgk_Cyrl''',
'''Tagalog''': '''tgl_Latn''',
'''Thai''': '''tha_Thai''',
'''Tigrinya''': '''tir_Ethi''',
'''Tamasheq Latin''': '''taq_Latn''',
'''Tamasheq Tifinagh''': '''taq_Tfng''',
'''Tok Pisin''': '''tpi_Latn''',
'''Tswana''': '''tsn_Latn''',
'''Tsonga''': '''tso_Latn''',
'''Turkmen''': '''tuk_Latn''',
'''Tumbuka''': '''tum_Latn''',
'''Turkish''': '''tur_Latn''',
'''Twi''': '''twi_Latn''',
'''Central Atlas Tamazight''': '''tzm_Tfng''',
'''Uyghur''': '''uig_Arab''',
'''Ukrainian''': '''ukr_Cyrl''',
'''Umbundu''': '''umb_Latn''',
'''Urdu''': '''urd_Arab''',
'''Northern Uzbek''': '''uzn_Latn''',
'''Venetian''': '''vec_Latn''',
'''Vietnamese''': '''vie_Latn''',
'''Waray''': '''war_Latn''',
'''Wolof''': '''wol_Latn''',
'''Xhosa''': '''xho_Latn''',
'''Eastern Yiddish''': '''ydd_Hebr''',
'''Yoruba''': '''yor_Latn''',
'''Yue Chinese''': '''yue_Hant''',
'''Chinese Simplified''': '''zho_Hans''',
'''Chinese Traditional''': '''zho_Hant''',
'''Standard Malay''': '''zsm_Latn''',
'''Zulu''': '''zul_Latn''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'facebook/nllb-200-distilled-600M'
__UpperCamelCase = (
'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
)
__UpperCamelCase = 'translator'
__UpperCamelCase = AutoTokenizer
__UpperCamelCase = AutoModelForSeqaSeqLM
__UpperCamelCase = LANGUAGE_CODES
__UpperCamelCase = ['text', 'text', 'text']
__UpperCamelCase = ['text']
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if src_lang not in self.lang_to_code:
raise ValueError(f"""{src_lang} is not a supported language.""" )
if tgt_lang not in self.lang_to_code:
raise ValueError(f"""{tgt_lang} is not a supported language.""" )
_lowerCAmelCase = self.lang_to_code[src_lang]
_lowerCAmelCase = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
lowerCamelCase , return_tensors="""pt""" , src_lang=lowerCamelCase , tgt_lang=lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.model.generate(**lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=lowerCamelCase )
| 317
| 1
|
"""simple docstring"""
class __lowerCamelCase :
def __init__(self ):
'''simple docstring'''
_lowerCAmelCase = {} # Mapping from char to TrieNode
_lowerCAmelCase = False
def A__ (self , lowerCamelCase ):
'''simple docstring'''
for word in words:
self.insert(lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self
for char in word:
if char not in curr.nodes:
_lowerCAmelCase = TrieNode()
_lowerCAmelCase = curr.nodes[char]
_lowerCAmelCase = True
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self
for char in word:
if char not in curr.nodes:
return False
_lowerCAmelCase = curr.nodes[char]
return curr.is_leaf
def A__ (self , lowerCamelCase ):
'''simple docstring'''
def _delete(lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> bool:
if index == len(lowerCamelCase ):
# If word does not exist
if not curr.is_leaf:
return False
_lowerCAmelCase = False
return len(curr.nodes ) == 0
_lowerCAmelCase = word[index]
_lowerCAmelCase = curr.nodes.get(lowerCamelCase )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
_lowerCAmelCase = _delete(lowerCamelCase , lowerCamelCase , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , lowerCamelCase , 0 )
def __UpperCAmelCase ( snake_case_ : TrieNode , snake_case_ : str ) -> None:
"""simple docstring"""
if node.is_leaf:
print(snake_case_ , end=""" """ )
for key, value in node.nodes.items():
print_words(snake_case_ , word + key )
def __UpperCAmelCase ( ) -> bool:
"""simple docstring"""
_lowerCAmelCase = """banana bananas bandana band apple all beast""".split()
_lowerCAmelCase = TrieNode()
root.insert_many(snake_case_ )
# print_words(root, "")
assert all(root.find(snake_case_ ) for word in words )
assert root.find("""banana""" )
assert not root.find("""bandanas""" )
assert not root.find("""apps""" )
assert root.find("""apple""" )
assert root.find("""all""" )
root.delete("""all""" )
assert not root.find("""all""" )
root.delete("""banana""" )
assert not root.find("""banana""" )
assert root.find("""bananas""" )
return True
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : bool ) -> None:
"""simple docstring"""
print(str(snake_case_ ) , """works!""" if passes else """doesn't work :(""" )
def __UpperCAmelCase ( ) -> None:
"""simple docstring"""
assert test_trie()
def __UpperCAmelCase ( ) -> None:
"""simple docstring"""
print_results("""Testing trie functionality""" , test_trie() )
if __name__ == "__main__":
main()
| 317
|
"""simple docstring"""
from math import isqrt
def __UpperCAmelCase ( snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case_ , snake_case_ ):
_lowerCAmelCase = False
return [i for i in range(2 , snake_case_ ) if is_prime[i]]
def __UpperCAmelCase ( snake_case_ : int = 10**8 ) -> int:
"""simple docstring"""
_lowerCAmelCase = calculate_prime_numbers(max_number // 2 )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = len(snake_case_ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __UpperCAmelCase ( snake_case_ : Optional[Any] ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = SwinConfig()
_lowerCAmelCase = swin_name.split("""_""" )
_lowerCAmelCase = name_split[1]
_lowerCAmelCase = int(name_split[4] )
_lowerCAmelCase = int(name_split[3][-1] )
if model_size == "tiny":
_lowerCAmelCase = 96
_lowerCAmelCase = (2, 2, 6, 2)
_lowerCAmelCase = (3, 6, 12, 24)
elif model_size == "small":
_lowerCAmelCase = 96
_lowerCAmelCase = (2, 2, 18, 2)
_lowerCAmelCase = (3, 6, 12, 24)
elif model_size == "base":
_lowerCAmelCase = 128
_lowerCAmelCase = (2, 2, 18, 2)
_lowerCAmelCase = (4, 8, 16, 32)
else:
_lowerCAmelCase = 192
_lowerCAmelCase = (2, 2, 18, 2)
_lowerCAmelCase = (6, 12, 24, 48)
if "in22k" in swin_name:
_lowerCAmelCase = 21841
else:
_lowerCAmelCase = 1000
_lowerCAmelCase = """huggingface/label-files"""
_lowerCAmelCase = """imagenet-1k-id2label.json"""
_lowerCAmelCase = json.load(open(hf_hub_download(snake_case_ , snake_case_ , repo_type="""dataset""" ) , """r""" ) )
_lowerCAmelCase = {int(snake_case_ ): v for k, v in idalabel.items()}
_lowerCAmelCase = idalabel
_lowerCAmelCase = {v: k for k, v in idalabel.items()}
_lowerCAmelCase = img_size
_lowerCAmelCase = num_classes
_lowerCAmelCase = embed_dim
_lowerCAmelCase = depths
_lowerCAmelCase = num_heads
_lowerCAmelCase = window_size
return config
def __UpperCAmelCase ( snake_case_ : Optional[Any] ) -> Dict:
"""simple docstring"""
if "patch_embed.proj" in name:
_lowerCAmelCase = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
_lowerCAmelCase = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
_lowerCAmelCase = """encoder.""" + name
if "attn.proj" in name:
_lowerCAmelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
_lowerCAmelCase = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
_lowerCAmelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
_lowerCAmelCase = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
_lowerCAmelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
_lowerCAmelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "norm.weight":
_lowerCAmelCase = """layernorm.weight"""
if name == "norm.bias":
_lowerCAmelCase = """layernorm.bias"""
if "head" in name:
_lowerCAmelCase = name.replace("""head""" , """classifier""" )
else:
_lowerCAmelCase = """swin.""" + name
return name
def __UpperCAmelCase ( snake_case_ : List[Any] , snake_case_ : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_lowerCAmelCase = orig_state_dict.pop(snake_case_ )
if "mask" in key:
continue
elif "qkv" in key:
_lowerCAmelCase = key.split(""".""" )
_lowerCAmelCase = int(key_split[1] )
_lowerCAmelCase = int(key_split[3] )
_lowerCAmelCase = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_lowerCAmelCase = val[:dim, :]
_lowerCAmelCase = val[
dim : dim * 2, :
]
_lowerCAmelCase = val[-dim:, :]
else:
_lowerCAmelCase = val[
:dim
]
_lowerCAmelCase = val[
dim : dim * 2
]
_lowerCAmelCase = val[
-dim:
]
else:
_lowerCAmelCase = val
return orig_state_dict
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : Optional[Any] ) -> Tuple:
"""simple docstring"""
_lowerCAmelCase = timm.create_model(snake_case_ , pretrained=snake_case_ )
timm_model.eval()
_lowerCAmelCase = get_swin_config(snake_case_ )
_lowerCAmelCase = SwinForImageClassification(snake_case_ )
model.eval()
_lowerCAmelCase = convert_state_dict(timm_model.state_dict() , snake_case_ )
model.load_state_dict(snake_case_ )
_lowerCAmelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_lowerCAmelCase = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" , """-""" ) ) )
_lowerCAmelCase = Image.open(requests.get(snake_case_ , stream=snake_case_ ).raw )
_lowerCAmelCase = image_processor(images=snake_case_ , return_tensors="""pt""" )
_lowerCAmelCase = timm_model(inputs["""pixel_values"""] )
_lowerCAmelCase = model(**snake_case_ ).logits
assert torch.allclose(snake_case_ , snake_case_ , atol=1e-3 )
print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(snake_case_ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(snake_case_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swin_name''',
default='''swin_tiny_patch4_window7_224''',
type=str,
help='''Name of the Swin timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
SCREAMING_SNAKE_CASE : Dict = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 317
|
"""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 numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
__UpperCamelCase = 'CIDAS/clipseg-rd64-refined'
__UpperCamelCase = 'image_segmenter'
__UpperCamelCase = CLIPSegForImageSegmentation
__UpperCamelCase = ['image', 'text']
__UpperCamelCase = ['image']
def __init__(self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
requires_backends(self , ["""vision"""] )
super().__init__(*lowerCamelCase , **lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return self.pre_processor(text=[label] , images=[image] , padding=lowerCamelCase , return_tensors="""pt""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
with torch.no_grad():
_lowerCAmelCase = self.model(**lowerCamelCase ).logits
return logits
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = outputs.cpu().detach().numpy()
_lowerCAmelCase = 0
_lowerCAmelCase = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 317
| 1
|
"""simple docstring"""
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
SCREAMING_SNAKE_CASE : Optional[Any] = datasets.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Union[str, Any] = '''\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
booktitle={ACL},
year={2020},
url={https://arxiv.org/abs/2004.04696}
}
'''
SCREAMING_SNAKE_CASE : Optional[Any] = '''\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).
See the project\'s README at https://github.com/google-research/bleurt#readme for more information.
'''
SCREAMING_SNAKE_CASE : Dict = '''
BLEURT score.
Args:
`predictions` (list of str): prediction/candidate sentences
`references` (list of str): reference sentences
`checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.
Returns:
\'scores\': List of scores.
Examples:
>>> predictions = ["hello there", "general kenobi"]
>>> references = ["hello there", "general kenobi"]
>>> bleurt = datasets.load_metric("bleurt")
>>> results = bleurt.compute(predictions=predictions, references=references)
>>> print([round(v, 2) for v in results["scores"]])
[1.03, 1.04]
'''
SCREAMING_SNAKE_CASE : Any = {
'''bleurt-tiny-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip''',
'''bleurt-tiny-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip''',
'''bleurt-base-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip''',
'''bleurt-base-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip''',
'''bleurt-large-128''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip''',
'''bleurt-large-512''': '''https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip''',
'''BLEURT-20-D3''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip''',
'''BLEURT-20-D6''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip''',
'''BLEURT-20-D12''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip''',
'''BLEURT-20''': '''https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip''',
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __lowerCamelCase ( datasets.Metric ):
def A__ (self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""https://github.com/google-research/bleurt""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Value("""string""" , id="""sequence""" ),
} ) , codebase_urls=["""https://github.com/google-research/bleurt"""] , reference_urls=["""https://github.com/google-research/bleurt""", """https://arxiv.org/abs/2004.04696"""] , )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
if self.config_name == "default":
logger.warning(
"""Using default BLEURT-Base checkpoint for sequence maximum length 128. """
"""You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512').""" )
_lowerCAmelCase = """bleurt-base-128"""
if self.config_name.lower() in CHECKPOINT_URLS:
_lowerCAmelCase = self.config_name.lower()
elif self.config_name.upper() in CHECKPOINT_URLS:
_lowerCAmelCase = self.config_name.upper()
else:
raise KeyError(
f"""{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}""" )
# download the model checkpoint specified by self.config_name and set up the scorer
_lowerCAmelCase = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
_lowerCAmelCase = score.BleurtScorer(os.path.join(lowerCamelCase , lowerCamelCase ) )
def A__ (self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.scorer.score(references=lowerCamelCase , candidates=lowerCamelCase )
return {"scores": scores}
| 317
|
"""simple docstring"""
from __future__ import annotations
import queue
class __lowerCamelCase :
def __init__(self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = data
_lowerCAmelCase = None
_lowerCAmelCase = None
def __UpperCAmelCase ( ) -> TreeNode:
"""simple docstring"""
print("""\n********Press N to stop entering at any point of time********\n""" )
_lowerCAmelCase = input("""Enter the value of the root node: """ ).strip().lower()
_lowerCAmelCase = queue.Queue()
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = q.get()
_lowerCAmelCase = F"""Enter the left node of {node_found.data}: """
_lowerCAmelCase = input(snake_case_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
_lowerCAmelCase = left_node
q.put(snake_case_ )
_lowerCAmelCase = F"""Enter the right node of {node_found.data}: """
_lowerCAmelCase = input(snake_case_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
_lowerCAmelCase = right_node
q.put(snake_case_ )
raise
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
print(node.data , end=""",""" )
pre_order(node.left )
pre_order(node.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
in_order(node.left )
print(node.data , end=""",""" )
in_order(node.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=""",""" )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = queue.Queue()
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = queue.Queue()
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = []
while not q.empty():
_lowerCAmelCase = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(snake_case_ )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = []
_lowerCAmelCase = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=""",""" )
stack.append(snake_case_ )
_lowerCAmelCase = n.left
# end of while means current node doesn't have left child
_lowerCAmelCase = stack.pop()
# start to traverse its right child
_lowerCAmelCase = n.right
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = []
_lowerCAmelCase = node
while n or stack:
while n:
stack.append(snake_case_ )
_lowerCAmelCase = n.left
_lowerCAmelCase = stack.pop()
print(n.data , end=""",""" )
_lowerCAmelCase = n.right
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase , _lowerCAmelCase = [], []
_lowerCAmelCase = node
stacka.append(snake_case_ )
while stacka: # to find the reversed order of post order, store it in stack2
_lowerCAmelCase = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(snake_case_ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=""",""" )
def __UpperCAmelCase ( snake_case_ : str = "" , snake_case_ : int=50 , snake_case_ : Dict="*" ) -> str:
"""simple docstring"""
if not s:
return "\n" + width * char
_lowerCAmelCase , _lowerCAmelCase = divmod(width - len(snake_case_ ) - 2 , 2 )
return F"""{left * char} {s} {(left + extra) * char}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('''Binary Tree Traversals'''))
SCREAMING_SNAKE_CASE : TreeNode = build_tree()
print(prompt('''Pre Order Traversal'''))
pre_order(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal'''))
in_order(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal'''))
post_order(node)
print(prompt() + '''\n''')
print(prompt('''Level Order Traversal'''))
level_order(node)
print(prompt() + '''\n''')
print(prompt('''Actual Level Order Traversal'''))
level_order_actual(node)
print('''*''' * 5_0 + '''\n''')
print(prompt('''Pre Order Traversal - Iteration Version'''))
pre_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal - Iteration Version'''))
in_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal - Iteration Version'''))
post_order_iter(node)
print(prompt())
| 317
| 1
|
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class __lowerCamelCase ( tf.keras.layers.Layer ):
def __init__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=1 , lowerCamelCase=False , **lowerCamelCase ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = d_embed
_lowerCAmelCase = d_proj
_lowerCAmelCase = cutoffs + [vocab_size]
_lowerCAmelCase = [0] + self.cutoffs
_lowerCAmelCase = div_val
_lowerCAmelCase = self.cutoffs[0]
_lowerCAmelCase = len(self.cutoffs ) - 1
_lowerCAmelCase = self.shortlist_size + self.n_clusters
_lowerCAmelCase = keep_order
_lowerCAmelCase = []
_lowerCAmelCase = []
def A__ (self , lowerCamelCase ):
'''simple docstring'''
if self.n_clusters > 0:
_lowerCAmelCase = self.add_weight(
shape=(self.n_clusters, self.d_embed) , initializer="""zeros""" , trainable=lowerCamelCase , name="""cluster_weight""" )
_lowerCAmelCase = self.add_weight(
shape=(self.n_clusters,) , initializer="""zeros""" , trainable=lowerCamelCase , name="""cluster_bias""" )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
_lowerCAmelCase = self.add_weight(
shape=(self.d_embed, self.d_proj) , initializer="""zeros""" , trainable=lowerCamelCase , name=f"""out_projs_._{i}""" , )
self.out_projs.append(lowerCamelCase )
else:
self.out_projs.append(lowerCamelCase )
_lowerCAmelCase = self.add_weight(
shape=(self.vocab_size, self.d_embed) , initializer="""zeros""" , trainable=lowerCamelCase , name=f"""out_layers_._{i}_._weight""" , )
_lowerCAmelCase = self.add_weight(
shape=(self.vocab_size,) , initializer="""zeros""" , trainable=lowerCamelCase , name=f"""out_layers_._{i}_._bias""" , )
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
_lowerCAmelCase , _lowerCAmelCase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_lowerCAmelCase = self.d_embed // (self.div_val**i)
_lowerCAmelCase = self.add_weight(
shape=(d_emb_i, self.d_proj) , initializer="""zeros""" , trainable=lowerCamelCase , name=f"""out_projs_._{i}""" )
self.out_projs.append(lowerCamelCase )
_lowerCAmelCase = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) , initializer="""zeros""" , trainable=lowerCamelCase , name=f"""out_layers_._{i}_._weight""" , )
_lowerCAmelCase = self.add_weight(
shape=(r_idx - l_idx,) , initializer="""zeros""" , trainable=lowerCamelCase , name=f"""out_layers_._{i}_._bias""" , )
self.out_layers.append((weight, bias) )
super().build(lowerCamelCase )
@staticmethod
def A__ (lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=None ):
'''simple docstring'''
_lowerCAmelCase = x
if proj is not None:
_lowerCAmelCase = tf.einsum("""ibd,ed->ibe""" , lowerCamelCase , lowerCamelCase )
return tf.einsum("""ibd,nd->ibn""" , lowerCamelCase , lowerCamelCase ) + b
@staticmethod
def A__ (lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = shape_list(lowerCamelCase )
_lowerCAmelCase = tf.range(lp_size[0] , dtype=target.dtype )
_lowerCAmelCase = tf.stack([r, target] , 1 )
return tf.gather_nd(lowerCamelCase , lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase=True , lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase = 0
if self.n_clusters == 0:
_lowerCAmelCase = self._logit(lowerCamelCase , self.out_layers[0][0] , self.out_layers[0][1] , self.out_projs[0] )
if target is not None:
_lowerCAmelCase = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=lowerCamelCase , logits=lowerCamelCase )
_lowerCAmelCase = tf.nn.log_softmax(lowerCamelCase , axis=-1 )
else:
_lowerCAmelCase = shape_list(lowerCamelCase )
_lowerCAmelCase = []
_lowerCAmelCase = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
_lowerCAmelCase , _lowerCAmelCase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
_lowerCAmelCase = (target >= l_idx) & (target < r_idx)
_lowerCAmelCase = tf.where(lowerCamelCase )
_lowerCAmelCase = tf.boolean_mask(lowerCamelCase , lowerCamelCase ) - l_idx
if self.div_val == 1:
_lowerCAmelCase = self.out_layers[0][0][l_idx:r_idx]
_lowerCAmelCase = self.out_layers[0][1][l_idx:r_idx]
else:
_lowerCAmelCase = self.out_layers[i][0]
_lowerCAmelCase = self.out_layers[i][1]
if i == 0:
_lowerCAmelCase = tf.concat([cur_W, self.cluster_weight] , 0 )
_lowerCAmelCase = tf.concat([cur_b, self.cluster_bias] , 0 )
_lowerCAmelCase = self._logit(lowerCamelCase , lowerCamelCase , lowerCamelCase , self.out_projs[0] )
_lowerCAmelCase = tf.nn.log_softmax(lowerCamelCase )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
_lowerCAmelCase = tf.boolean_mask(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = self._gather_logprob(lowerCamelCase , lowerCamelCase )
else:
_lowerCAmelCase = self._logit(lowerCamelCase , lowerCamelCase , lowerCamelCase , self.out_projs[i] )
_lowerCAmelCase = tf.nn.log_softmax(lowerCamelCase )
_lowerCAmelCase = self.cutoffs[0] + i - 1 # No probability for the head cluster
_lowerCAmelCase = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(lowerCamelCase )
if target is not None:
_lowerCAmelCase = tf.boolean_mask(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = tf.boolean_mask(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = self._gather_logprob(lowerCamelCase , lowerCamelCase )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(lowerCamelCase , -cur_logprob , shape_list(lowerCamelCase ) )
_lowerCAmelCase = tf.concat(lowerCamelCase , axis=-1 )
if target is not None:
if return_mean:
_lowerCAmelCase = tf.reduce_mean(lowerCamelCase )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(lowerCamelCase )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(lowerCamelCase , name=self.name , aggregation="""mean""" if return_mean else """""" )
return out
| 317
|
"""simple docstring"""
from __future__ import annotations
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = text, pattern
_lowerCAmelCase , _lowerCAmelCase = len(lowerCamelCase ), len(lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def A__ (self , lowerCamelCase ):
'''simple docstring'''
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = []
for i in range(self.textLen - self.patLen + 1 ):
_lowerCAmelCase = self.mismatch_in_text(lowerCamelCase )
if mismatch_index == -1:
positions.append(lowerCamelCase )
else:
_lowerCAmelCase = self.match_in_pattern(self.text[mismatch_index] )
_lowerCAmelCase = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
SCREAMING_SNAKE_CASE : Any = '''ABAABA'''
SCREAMING_SNAKE_CASE : Optional[int] = '''AB'''
SCREAMING_SNAKE_CASE : str = BoyerMooreSearch(text, pattern)
SCREAMING_SNAKE_CASE : Tuple = bms.bad_character_heuristic()
if len(positions) == 0:
print('''No match found''')
else:
print('''Pattern found in following positions: ''')
print(positions)
| 317
| 1
|
"""simple docstring"""
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 __UpperCAmelCase ( snake_case_ : Features ) -> Optional[int]:
"""simple docstring"""
_lowerCAmelCase = np.inf
def set_batch_size(snake_case_ : FeatureType ) -> None:
nonlocal batch_size
if isinstance(snake_case_ , snake_case_ ):
_lowerCAmelCase = min(snake_case_ , config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS )
elif isinstance(snake_case_ , snake_case_ ):
_lowerCAmelCase = min(snake_case_ , config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS )
elif isinstance(snake_case_ , snake_case_ ) and feature.dtype == "binary":
_lowerCAmelCase = min(snake_case_ , config.PARQUET_ROW_GROUP_SIZE_FOR_BINARY_DATASETS )
_visit(snake_case_ , snake_case_ )
return None if batch_size is np.inf else batch_size
class __lowerCamelCase ( __lowercase ):
def __init__(self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = False , lowerCamelCase = False , lowerCamelCase = None , **lowerCamelCase , ):
'''simple docstring'''
super().__init__(
lowerCamelCase , split=lowerCamelCase , features=lowerCamelCase , cache_dir=lowerCamelCase , keep_in_memory=lowerCamelCase , streaming=lowerCamelCase , num_proc=lowerCamelCase , **lowerCamelCase , )
_lowerCAmelCase = path_or_paths if isinstance(lowerCamelCase , lowerCamelCase ) else {self.split: path_or_paths}
_lowerCAmelCase = _PACKAGED_DATASETS_MODULES["""parquet"""][1]
_lowerCAmelCase = Parquet(
cache_dir=lowerCamelCase , data_files=lowerCamelCase , features=lowerCamelCase , hash=lowerCamelCase , **lowerCamelCase , )
def A__ (self ):
'''simple docstring'''
if self.streaming:
_lowerCAmelCase = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
self.builder.download_and_prepare(
download_config=lowerCamelCase , download_mode=lowerCamelCase , verification_mode=lowerCamelCase , base_path=lowerCamelCase , num_proc=self.num_proc , )
_lowerCAmelCase = self.builder.as_dataset(
split=self.split , verification_mode=lowerCamelCase , in_memory=self.keep_in_memory )
return dataset
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase = None , **lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = dataset
_lowerCAmelCase = path_or_buf
_lowerCAmelCase = batch_size or get_writer_batch_size(dataset.features )
_lowerCAmelCase = parquet_writer_kwargs
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = 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:
_lowerCAmelCase = self._write(file_obj=lowerCamelCase , batch_size=lowerCamelCase , **self.parquet_writer_kwargs )
else:
_lowerCAmelCase = self._write(file_obj=self.path_or_buf , batch_size=lowerCamelCase , **self.parquet_writer_kwargs )
return written
def A__ (self , lowerCamelCase , lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = 0
_lowerCAmelCase = parquet_writer_kwargs.pop("""path_or_buf""" , lowerCamelCase )
_lowerCAmelCase = self.dataset.features.arrow_schema
_lowerCAmelCase = pq.ParquetWriter(lowerCamelCase , schema=lowerCamelCase , **lowerCamelCase )
for offset in logging.tqdm(
range(0 , len(self.dataset ) , lowerCamelCase ) , unit="""ba""" , disable=not logging.is_progress_bar_enabled() , desc="""Creating parquet from Arrow format""" , ):
_lowerCAmelCase = query_table(
table=self.dataset._data , key=slice(lowerCamelCase , offset + batch_size ) , indices=self.dataset._indices if self.dataset._indices is not None else None , )
writer.write_table(lowerCamelCase )
written += batch.nbytes
writer.close()
return written
| 317
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
SCREAMING_SNAKE_CASE : List[str] = False
class __lowerCamelCase ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(
image=lowerCamelCase , generator=lowerCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
_lowerCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 317
| 1
|
"""simple docstring"""
import os
import sys
SCREAMING_SNAKE_CASE : Dict = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
SCREAMING_SNAKE_CASE : Dict = [
'''torch''',
'''numpy''',
'''tokenizers''',
'''filelock''',
'''requests''',
'''tqdm''',
'''regex''',
'''sentencepiece''',
'''sacremoses''',
'''importlib_metadata''',
'''huggingface_hub''',
]
@add_start_docstrings(AutoConfig.__doc__ )
def __UpperCAmelCase ( *snake_case_ : List[str] , **snake_case_ : List[str] ) -> Optional[int]:
"""simple docstring"""
return AutoConfig.from_pretrained(*snake_case_ , **snake_case_ )
@add_start_docstrings(AutoTokenizer.__doc__ )
def __UpperCAmelCase ( *snake_case_ : Any , **snake_case_ : Optional[Any] ) -> Dict:
"""simple docstring"""
return AutoTokenizer.from_pretrained(*snake_case_ , **snake_case_ )
@add_start_docstrings(AutoModel.__doc__ )
def __UpperCAmelCase ( *snake_case_ : Optional[Any] , **snake_case_ : Dict ) -> Dict:
"""simple docstring"""
return AutoModel.from_pretrained(*snake_case_ , **snake_case_ )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def __UpperCAmelCase ( *snake_case_ : Union[str, Any] , **snake_case_ : str ) -> List[str]:
"""simple docstring"""
return AutoModelForCausalLM.from_pretrained(*snake_case_ , **snake_case_ )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def __UpperCAmelCase ( *snake_case_ : Optional[int] , **snake_case_ : List[str] ) -> List[Any]:
"""simple docstring"""
return AutoModelForMaskedLM.from_pretrained(*snake_case_ , **snake_case_ )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def __UpperCAmelCase ( *snake_case_ : Tuple , **snake_case_ : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
return AutoModelForSequenceClassification.from_pretrained(*snake_case_ , **snake_case_ )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def __UpperCAmelCase ( *snake_case_ : Dict , **snake_case_ : List[str] ) -> Optional[Any]:
"""simple docstring"""
return AutoModelForQuestionAnswering.from_pretrained(*snake_case_ , **snake_case_ )
| 317
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCamelCase ( __lowercase , unittest.TestCase ):
__UpperCamelCase = DiTPipeline
__UpperCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__UpperCamelCase = PipelineTesterMixin.required_optional_params - {
'latents',
'num_images_per_prompt',
'callback',
'callback_steps',
}
__UpperCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__UpperCamelCase = False
def A__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
_lowerCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=lowerCamelCase , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1_000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=lowerCamelCase , )
_lowerCAmelCase = AutoencoderKL()
_lowerCAmelCase = DDIMScheduler()
_lowerCAmelCase = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def A__ (self , lowerCamelCase , lowerCamelCase=0 ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(lowerCamelCase )
else:
_lowerCAmelCase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
_lowerCAmelCase = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = """cpu"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_lowerCAmelCase = pipe(**lowerCamelCase ).images
_lowerCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_lowerCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_lowerCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase , 1e-3 )
def A__ (self ):
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=lowerCamelCase , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def A__ (self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_lowerCAmelCase = pipe.get_label_ids(lowerCamelCase )
_lowerCAmelCase = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = load_numpy(
f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-2
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella"""]
_lowerCAmelCase = pipe.get_label_ids(lowerCamelCase )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
f"""/dit/{word}_512.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-1
| 317
| 1
|
"""simple docstring"""
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 __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
_lowerCAmelCase = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(lowerCamelCase )
_lowerCAmelCase = -1
_lowerCAmelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(lowerCamelCase )
_lowerCAmelCase = model.generate(lowerCamelCase , max_new_tokens=10 , do_sample=lowerCamelCase )
_lowerCAmelCase = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
_lowerCAmelCase = TextStreamer(lowerCamelCase )
model.generate(lowerCamelCase , max_new_tokens=10 , do_sample=lowerCamelCase , streamer=lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_lowerCAmelCase = cs.out[:-1]
self.assertEqual(lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
_lowerCAmelCase = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(lowerCamelCase )
_lowerCAmelCase = -1
_lowerCAmelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(lowerCamelCase )
_lowerCAmelCase = model.generate(lowerCamelCase , max_new_tokens=10 , do_sample=lowerCamelCase )
_lowerCAmelCase = tokenizer.decode(greedy_ids[0] )
_lowerCAmelCase = TextIteratorStreamer(lowerCamelCase )
_lowerCAmelCase = {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
_lowerCAmelCase = Thread(target=model.generate , kwargs=lowerCamelCase )
thread.start()
_lowerCAmelCase = """"""
for new_text in streamer:
streamer_text += new_text
self.assertEqual(lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
_lowerCAmelCase = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(lowerCamelCase )
_lowerCAmelCase = -1
_lowerCAmelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(lowerCamelCase )
_lowerCAmelCase = model.generate(lowerCamelCase , max_new_tokens=10 , do_sample=lowerCamelCase )
_lowerCAmelCase = greedy_ids[:, input_ids.shape[1] :]
_lowerCAmelCase = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
_lowerCAmelCase = TextStreamer(lowerCamelCase , skip_prompt=lowerCamelCase )
model.generate(lowerCamelCase , max_new_tokens=10 , do_sample=lowerCamelCase , streamer=lowerCamelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_lowerCAmelCase = cs.out[:-1]
self.assertEqual(lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = AutoTokenizer.from_pretrained("""distilgpt2""" )
_lowerCAmelCase = AutoModelForCausalLM.from_pretrained("""distilgpt2""" ).to(lowerCamelCase )
_lowerCAmelCase = -1
_lowerCAmelCase = torch.ones((1, 5) , device=lowerCamelCase ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
_lowerCAmelCase = 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
_lowerCAmelCase = cs.out[:-1] # Remove the final "\n"
_lowerCAmelCase = tokenizer(lowerCamelCase , return_tensors="""pt""" )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" )
_lowerCAmelCase = AutoModelForCausalLM.from_pretrained("""hf-internal-testing/tiny-random-gpt2""" ).to(lowerCamelCase )
_lowerCAmelCase = -1
_lowerCAmelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(lowerCamelCase )
_lowerCAmelCase = TextIteratorStreamer(lowerCamelCase , timeout=0.001 )
_lowerCAmelCase = {"""input_ids""": input_ids, """max_new_tokens""": 10, """do_sample""": False, """streamer""": streamer}
_lowerCAmelCase = 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 ):
_lowerCAmelCase = """"""
for new_text in streamer:
streamer_text += new_text
| 317
|
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
return getitem, k
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return setitem, k, v
def __UpperCAmelCase ( snake_case_ : str ) -> Optional[int]:
"""simple docstring"""
return delitem, k
def __UpperCAmelCase ( snake_case_ : Optional[Any] , snake_case_ : Tuple , *snake_case_ : Tuple ) -> str:
"""simple docstring"""
try:
return fun(snake_case_ , *snake_case_ ), None
except Exception as e:
return None, e
SCREAMING_SNAKE_CASE : int = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
SCREAMING_SNAKE_CASE : List[Any] = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
SCREAMING_SNAKE_CASE : Any = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
SCREAMING_SNAKE_CASE : Union[str, Any] = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
SCREAMING_SNAKE_CASE : Optional[Any] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
SCREAMING_SNAKE_CASE : Optional[int] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
"""operations""" , (
pytest.param(_add_items , id="""add items""" ),
pytest.param(_overwrite_items , id="""overwrite items""" ),
pytest.param(_delete_items , id="""delete items""" ),
pytest.param(_access_absent_items , id="""access absent items""" ),
pytest.param(_add_with_resize_up , id="""add with resize up""" ),
pytest.param(_add_with_resize_down , id="""add with resize down""" ),
) , )
def __UpperCAmelCase ( snake_case_ : List[Any] ) -> Tuple:
"""simple docstring"""
_lowerCAmelCase = HashMap(initial_block_size=4 )
_lowerCAmelCase = {}
for _, (fun, *args) in enumerate(snake_case_ ):
_lowerCAmelCase , _lowerCAmelCase = _run_operation(snake_case_ , snake_case_ , *snake_case_ )
_lowerCAmelCase , _lowerCAmelCase = _run_operation(snake_case_ , snake_case_ , *snake_case_ )
assert my_res == py_res
assert str(snake_case_ ) == str(snake_case_ )
assert set(snake_case_ ) == set(snake_case_ )
assert len(snake_case_ ) == len(snake_case_ )
assert set(my.items() ) == set(py.items() )
def __UpperCAmelCase ( ) -> Tuple:
"""simple docstring"""
def is_public(snake_case_ : str ) -> bool:
return not name.startswith("""_""" )
_lowerCAmelCase = {name for name in dir({} ) if is_public(snake_case_ )}
_lowerCAmelCase = {name for name in dir(HashMap() ) if is_public(snake_case_ )}
assert dict_public_names > hash_public_names
| 317
| 1
|
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
SCREAMING_SNAKE_CASE : Any = '''platform'''
import jax
import jax.numpy as jnp
from transformers import BlenderbotTokenizer
from transformers.models.blenderbot.modeling_flax_blenderbot import (
FlaxBlenderbotForConditionalGeneration,
FlaxBlenderbotModel,
shift_tokens_right,
)
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : Optional[int] , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=None , snake_case_ : Optional[Any]=None , snake_case_ : List[Any]=None , snake_case_ : Optional[Any]=None , snake_case_ : Tuple=None , ) -> Optional[int]:
"""simple docstring"""
if attention_mask is None:
_lowerCAmelCase = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
_lowerCAmelCase = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
_lowerCAmelCase = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
_lowerCAmelCase = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
_lowerCAmelCase = np.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": attention_mask,
}
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase=13 , lowerCamelCase=7 , lowerCamelCase=True , lowerCamelCase=False , lowerCamelCase=99 , lowerCamelCase=16 , lowerCamelCase=2 , lowerCamelCase=4 , lowerCamelCase=4 , lowerCamelCase="gelu" , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=32 , lowerCamelCase=2 , lowerCamelCase=1 , lowerCamelCase=0 , lowerCamelCase=0.02 , ):
'''simple docstring'''
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = eos_token_id
_lowerCAmelCase = pad_token_id
_lowerCAmelCase = bos_token_id
_lowerCAmelCase = initializer_range
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
_lowerCAmelCase = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
_lowerCAmelCase = shift_tokens_right(lowerCamelCase , 1 , 2 )
_lowerCAmelCase = BlenderbotConfig(
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_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowerCamelCase , )
_lowerCAmelCase = prepare_blenderbot_inputs_dict(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return config, inputs_dict
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = 20
_lowerCAmelCase = model_class_name(lowerCamelCase )
_lowerCAmelCase = model.encode(inputs_dict["""input_ids"""] )
_lowerCAmelCase , _lowerCAmelCase = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
_lowerCAmelCase = model.init_cache(decoder_input_ids.shape[0] , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="""i4""" )
_lowerCAmelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_lowerCAmelCase = model.decode(
decoder_input_ids[:, :-1] , lowerCamelCase , decoder_attention_mask=lowerCamelCase , past_key_values=lowerCamelCase , decoder_position_ids=lowerCamelCase , )
_lowerCAmelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
_lowerCAmelCase = model.decode(
decoder_input_ids[:, -1:] , lowerCamelCase , decoder_attention_mask=lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCamelCase , )
_lowerCAmelCase = model.decode(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = 20
_lowerCAmelCase = model_class_name(lowerCamelCase )
_lowerCAmelCase = model.encode(inputs_dict["""input_ids"""] )
_lowerCAmelCase , _lowerCAmelCase = (
inputs_dict["""decoder_input_ids"""],
inputs_dict["""decoder_attention_mask"""],
)
_lowerCAmelCase = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
_lowerCAmelCase = model.init_cache(decoder_input_ids.shape[0] , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
_lowerCAmelCase = model.decode(
decoder_input_ids[:, :-1] , lowerCamelCase , decoder_attention_mask=lowerCamelCase , past_key_values=lowerCamelCase , decoder_position_ids=lowerCamelCase , )
_lowerCAmelCase = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="""i4""" )
_lowerCAmelCase = model.decode(
decoder_input_ids[:, -1:] , lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCamelCase , decoder_position_ids=lowerCamelCase , )
_lowerCAmelCase = model.decode(lowerCamelCase , lowerCamelCase , decoder_attention_mask=lowerCamelCase )
_lowerCAmelCase = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=f"""Max diff is {diff}""" )
@require_flax
class __lowerCamelCase ( unittest.TestCase ):
__UpperCamelCase = 99
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
_lowerCAmelCase = input_ids.shape[0]
_lowerCAmelCase = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self._get_config_and_data()
_lowerCAmelCase = FlaxBlenderbotForConditionalGeneration(lowerCamelCase )
_lowerCAmelCase = lm_model(input_ids=lowerCamelCase )
_lowerCAmelCase = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = BlenderbotConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
_lowerCAmelCase = FlaxBlenderbotForConditionalGeneration(lowerCamelCase )
_lowerCAmelCase = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
_lowerCAmelCase = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
_lowerCAmelCase = lm_model(input_ids=lowerCamelCase , decoder_input_ids=lowerCamelCase )
_lowerCAmelCase = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["""logits"""].shape , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
_lowerCAmelCase = shift_tokens_right(lowerCamelCase , 1 , 2 )
_lowerCAmelCase = np.equal(lowerCamelCase , 1 ).astype(np.floataa ).sum()
_lowerCAmelCase = np.equal(lowerCamelCase , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(lowerCamelCase , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class __lowerCamelCase ( __lowercase , unittest.TestCase , __lowercase ):
__UpperCamelCase = True
__UpperCamelCase = (
(
FlaxBlenderbotModel,
FlaxBlenderbotForConditionalGeneration,
)
if is_flax_available()
else ()
)
__UpperCamelCase = (FlaxBlenderbotForConditionalGeneration,) if is_flax_available() else ()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = FlaxBlenderbotModelTester(self )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_lowerCAmelCase = self._prepare_for_class(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model_class(lowerCamelCase )
@jax.jit
def encode_jitted(lowerCamelCase , lowerCamelCase=None , **lowerCamelCase ):
return model.encode(input_ids=lowerCamelCase , attention_mask=lowerCamelCase )
with self.subTest("""JIT Enabled""" ):
_lowerCAmelCase = encode_jitted(**lowerCamelCase ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
_lowerCAmelCase = encode_jitted(**lowerCamelCase ).to_tuple()
self.assertEqual(len(lowerCamelCase ) , len(lowerCamelCase ) )
for jitted_output, output in zip(lowerCamelCase , lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
_lowerCAmelCase = model_class(lowerCamelCase )
_lowerCAmelCase = model.encode(inputs_dict["""input_ids"""] , inputs_dict["""attention_mask"""] )
_lowerCAmelCase = {
"""decoder_input_ids""": inputs_dict["""decoder_input_ids"""],
"""decoder_attention_mask""": inputs_dict["""decoder_attention_mask"""],
"""encoder_outputs""": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
return model.decode(
decoder_input_ids=lowerCamelCase , decoder_attention_mask=lowerCamelCase , encoder_outputs=lowerCamelCase , )
with self.subTest("""JIT Enabled""" ):
_lowerCAmelCase = decode_jitted(**lowerCamelCase ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
_lowerCAmelCase = decode_jitted(**lowerCamelCase ).to_tuple()
self.assertEqual(len(lowerCamelCase ) , len(lowerCamelCase ) )
for jitted_output, output in zip(lowerCamelCase , lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def A__ (self ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
_lowerCAmelCase = model_class_name.from_pretrained("""facebook/blenderbot-400M-distill""" )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
_lowerCAmelCase = np.ones((1, 1) ) * model.config.eos_token_id
_lowerCAmelCase = model(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
@unittest.skipUnless(jax_device != """cpu""" , """3B test too slow on CPU.""" )
@slow
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = {"""num_beams""": 1, """early_stopping""": True, """min_length""": 15, """max_length""": 25}
_lowerCAmelCase = {"""skip_special_tokens""": True, """clean_up_tokenization_spaces""": True}
_lowerCAmelCase = FlaxBlenderbotForConditionalGeneration.from_pretrained("""facebook/blenderbot-3B""" , from_pt=lowerCamelCase )
_lowerCAmelCase = BlenderbotTokenizer.from_pretrained("""facebook/blenderbot-3B""" )
_lowerCAmelCase = ["""Sam"""]
_lowerCAmelCase = tokenizer(lowerCamelCase , return_tensors="""jax""" )
_lowerCAmelCase = model.generate(**lowerCamelCase , **lowerCamelCase )
_lowerCAmelCase = """Sam is a great name. It means \"sun\" in Gaelic."""
_lowerCAmelCase = tokenizer.batch_decode(lowerCamelCase , **lowerCamelCase )
assert generated_txt[0].strip() == tgt_text
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(snake_case_ : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(snake_case_ )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
snake_case_ : int , snake_case_ : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
_lowerCAmelCase = sum(
count_of_possible_combinations_with_dp_array(target - item , snake_case_ )
for item in array )
_lowerCAmelCase = answer
return answer
_lowerCAmelCase = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(snake_case_ , snake_case_ )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
_lowerCAmelCase = [0] * (target + 1)
_lowerCAmelCase = 1
for i in range(1 , target + 1 ):
for j in range(snake_case_ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : Tuple = 3
SCREAMING_SNAKE_CASE : Any = 5
SCREAMING_SNAKE_CASE : Optional[int] = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 317
| 1
|
"""simple docstring"""
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[Any] = {'''vocab_file''': '''vocab.json'''}
SCREAMING_SNAKE_CASE : int = {
'''vocab_file''': {
'''mgp-str''': '''https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json''',
}
}
SCREAMING_SNAKE_CASE : List[str] = {'''mgp-str''': 2_7}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self , lowerCamelCase , lowerCamelCase="[GO]" , lowerCamelCase="[GO]" , lowerCamelCase="[s]" , lowerCamelCase="[GO]" , **lowerCamelCase ):
'''simple docstring'''
super().__init__(
unk_token=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , pad_token=lowerCamelCase , **lowerCamelCase , )
with open(lowerCamelCase , encoding="""utf-8""" ) as vocab_handle:
_lowerCAmelCase = json.load(lowerCamelCase )
_lowerCAmelCase = {v: k for k, v in self.vocab.items()}
@property
def A__ (self ):
'''simple docstring'''
return len(self.vocab )
def A__ (self ):
'''simple docstring'''
return dict(self.vocab , **self.added_tokens_encoder )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = []
for s in text:
char_tokens.extend(lowerCamelCase )
return char_tokens
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.vocab.get(lowerCamelCase , self.vocab.get(self.unk_token ) )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.decoder.get(lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase = None ):
'''simple docstring'''
if not os.path.isdir(lowerCamelCase ):
logger.error("""Vocabulary path ({}) should be a directory""".format(lowerCamelCase ) )
return
_lowerCAmelCase = os.path.join(
lowerCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
with open(lowerCamelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=lowerCamelCase , ensure_ascii=lowerCamelCase ) + """\n""" )
return (vocab_file,)
| 317
|
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for letter in string.ascii_lowercase]
SCREAMING_SNAKE_CASE : set[int] = {ord(char) for char in VALID_CHARS}
SCREAMING_SNAKE_CASE : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def __UpperCAmelCase ( snake_case_ : list[int] , snake_case_ : tuple[int, ...] ) -> str | None:
"""simple docstring"""
_lowerCAmelCase = ""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
for keychar, cipherchar in zip(cycle(snake_case_ ) , snake_case_ ):
_lowerCAmelCase = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(snake_case_ )
return decoded
def __UpperCAmelCase ( snake_case_ : list[int] ) -> list[str]:
"""simple docstring"""
_lowerCAmelCase = []
for key in product(snake_case_ , repeat=3 ):
_lowerCAmelCase = try_key(snake_case_ , snake_case_ )
if encoded is not None:
possibles.append(snake_case_ )
return possibles
def __UpperCAmelCase ( snake_case_ : list[str] , snake_case_ : str ) -> list[str]:
"""simple docstring"""
return [possible for possible in possibles if common_word in possible.lower()]
def __UpperCAmelCase ( snake_case_ : str = "p059_cipher.txt" ) -> int:
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = Path(snake_case_ ).parent.joinpath(snake_case_ ).read_text(encoding="""utf-8""" )
_lowerCAmelCase = [int(snake_case_ ) for number in data.strip().split(""",""" )]
_lowerCAmelCase = filter_valid_chars(snake_case_ )
for common_word in COMMON_WORDS:
_lowerCAmelCase = filter_common_word(snake_case_ , snake_case_ )
if len(snake_case_ ) == 1:
break
_lowerCAmelCase = possibles[0]
return sum(ord(snake_case_ ) for char in decoded_text )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
SCREAMING_SNAKE_CASE : Optional[int] = None
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : int = '''▁'''
SCREAMING_SNAKE_CASE : str = {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}
SCREAMING_SNAKE_CASE : List[str] = {
'''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
'''tokenizer_file''': {
'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
},
}
SCREAMING_SNAKE_CASE : Optional[int] = {
'''google/pegasus-xsum''': 5_1_2,
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = PegasusTokenizer
__UpperCamelCase = ['input_ids', 'attention_mask']
def __init__(self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase="<pad>" , lowerCamelCase="</s>" , lowerCamelCase="<unk>" , lowerCamelCase="<mask_2>" , lowerCamelCase="<mask_1>" , lowerCamelCase=None , lowerCamelCase=103 , **lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = offset
if additional_special_tokens is not None:
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise TypeError(
f"""additional_special_tokens should be of type {type(lowerCamelCase )}, but is"""
f""" {type(lowerCamelCase )}""" )
_lowerCAmelCase = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f"""<unk_{i}>""" for i in range(len(lowerCamelCase ) , self.offset - 1 )
]
if len(set(lowerCamelCase ) ) != len(lowerCamelCase ):
raise ValueError(
"""Please make sure that the provided additional_special_tokens do not contain an incorrectly"""
f""" shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.""" )
_lowerCAmelCase = additional_special_tokens_extended
else:
_lowerCAmelCase = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f"""<unk_{i}>""" for i in range(2 , self.offset )]
super().__init__(
lowerCamelCase , tokenizer_file=lowerCamelCase , pad_token=lowerCamelCase , eos_token=lowerCamelCase , unk_token=lowerCamelCase , mask_token=lowerCamelCase , mask_token_sent=lowerCamelCase , offset=lowerCamelCase , additional_special_tokens=lowerCamelCase , **lowerCamelCase , )
_lowerCAmelCase = vocab_file
_lowerCAmelCase = False if not self.vocab_file else True
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
if all_special_ids != set(range(len(self.additional_special_tokens ) + 3 ) ):
raise ValueError(
"""There should be 3 special tokens: mask_token, pad_token, and eos_token +"""
f""" {len(self.additional_special_tokens )} additional_special_tokens, but got {all_special_ids}""" )
return [1 if x in all_special_ids else 0 for x in seq]
def A__ (self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = False ):
'''simple docstring'''
if already_has_special_tokens:
return self._special_token_mask(lowerCamelCase )
elif token_ids_a is None:
return self._special_token_mask(lowerCamelCase ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def A__ (self , lowerCamelCase , lowerCamelCase=None ):
'''simple docstring'''
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def A__ (self , lowerCamelCase , lowerCamelCase = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(lowerCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowerCAmelCase = os.path.join(
lowerCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ):
copyfile(self.vocab_file , lowerCamelCase )
return (out_vocab_file,)
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 1000000 ) -> int:
"""simple docstring"""
_lowerCAmelCase = limit + 1
_lowerCAmelCase = [0] * limit
for first_term in range(1 , snake_case_ ):
for n in range(snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
_lowerCAmelCase = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
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
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
# General docstring
SCREAMING_SNAKE_CASE : int = '''RegNetConfig'''
# Base docstring
SCREAMING_SNAKE_CASE : str = '''facebook/regnet-y-040'''
SCREAMING_SNAKE_CASE : List[Any] = [1, 1_0_8_8, 7, 7]
# Image classification docstring
SCREAMING_SNAKE_CASE : Any = '''facebook/regnet-y-040'''
SCREAMING_SNAKE_CASE : Dict = '''tabby, tabby cat'''
SCREAMING_SNAKE_CASE : Any = [
'''facebook/regnet-y-040''',
# See all regnet models at https://huggingface.co/models?filter=regnet
]
class __lowerCamelCase ( nn.Module ):
def __init__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase = 3 , lowerCamelCase = 1 , lowerCamelCase = 1 , lowerCamelCase = "relu" , ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase = nn.Convad(
lowerCamelCase , lowerCamelCase , kernel_size=lowerCamelCase , stride=lowerCamelCase , padding=kernel_size // 2 , groups=lowerCamelCase , bias=lowerCamelCase , )
_lowerCAmelCase = nn.BatchNormad(lowerCamelCase )
_lowerCAmelCase = ACTaFN[activation] if activation is not None else nn.Identity()
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.convolution(lowerCamelCase )
_lowerCAmelCase = self.normalization(lowerCamelCase )
_lowerCAmelCase = self.activation(lowerCamelCase )
return hidden_state
class __lowerCamelCase ( nn.Module ):
def __init__(self , lowerCamelCase ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase = RegNetConvLayer(
config.num_channels , config.embedding_size , kernel_size=3 , stride=2 , activation=config.hidden_act )
_lowerCAmelCase = config.num_channels
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = 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.""" )
_lowerCAmelCase = self.embedder(lowerCamelCase )
return hidden_state
class __lowerCamelCase ( nn.Module ):
def __init__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase = 2 ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase = nn.Convad(lowerCamelCase , lowerCamelCase , kernel_size=1 , stride=lowerCamelCase , bias=lowerCamelCase )
_lowerCAmelCase = nn.BatchNormad(lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.convolution(lowerCamelCase )
_lowerCAmelCase = self.normalization(lowerCamelCase )
return hidden_state
class __lowerCamelCase ( nn.Module ):
def __init__(self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
_lowerCAmelCase = nn.Sequential(
nn.Convad(lowerCamelCase , lowerCamelCase , kernel_size=1 ) , nn.ReLU() , nn.Convad(lowerCamelCase , lowerCamelCase , kernel_size=1 ) , nn.Sigmoid() , )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.pooler(lowerCamelCase )
_lowerCAmelCase = self.attention(lowerCamelCase )
_lowerCAmelCase = hidden_state * attention
return hidden_state
class __lowerCamelCase ( nn.Module ):
def __init__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase = in_channels != out_channels or stride != 1
_lowerCAmelCase = max(1 , out_channels // config.groups_width )
_lowerCAmelCase = (
RegNetShortCut(lowerCamelCase , lowerCamelCase , stride=lowerCamelCase ) if should_apply_shortcut else nn.Identity()
)
_lowerCAmelCase = nn.Sequential(
RegNetConvLayer(lowerCamelCase , lowerCamelCase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(lowerCamelCase , lowerCamelCase , stride=lowerCamelCase , groups=lowerCamelCase , activation=config.hidden_act ) , RegNetConvLayer(lowerCamelCase , lowerCamelCase , kernel_size=1 , activation=lowerCamelCase ) , )
_lowerCAmelCase = ACTaFN[config.hidden_act]
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = hidden_state
_lowerCAmelCase = self.layer(lowerCamelCase )
_lowerCAmelCase = self.shortcut(lowerCamelCase )
hidden_state += residual
_lowerCAmelCase = self.activation(lowerCamelCase )
return hidden_state
class __lowerCamelCase ( nn.Module ):
def __init__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 1 ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase = in_channels != out_channels or stride != 1
_lowerCAmelCase = max(1 , out_channels // config.groups_width )
_lowerCAmelCase = (
RegNetShortCut(lowerCamelCase , lowerCamelCase , stride=lowerCamelCase ) if should_apply_shortcut else nn.Identity()
)
_lowerCAmelCase = nn.Sequential(
RegNetConvLayer(lowerCamelCase , lowerCamelCase , kernel_size=1 , activation=config.hidden_act ) , RegNetConvLayer(lowerCamelCase , lowerCamelCase , stride=lowerCamelCase , groups=lowerCamelCase , activation=config.hidden_act ) , RegNetSELayer(lowerCamelCase , reduced_channels=int(round(in_channels / 4 ) ) ) , RegNetConvLayer(lowerCamelCase , lowerCamelCase , kernel_size=1 , activation=lowerCamelCase ) , )
_lowerCAmelCase = ACTaFN[config.hidden_act]
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = hidden_state
_lowerCAmelCase = self.layer(lowerCamelCase )
_lowerCAmelCase = self.shortcut(lowerCamelCase )
hidden_state += residual
_lowerCAmelCase = self.activation(lowerCamelCase )
return hidden_state
class __lowerCamelCase ( nn.Module ):
def __init__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 2 , lowerCamelCase = 2 , ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase = RegNetXLayer if config.layer_type == """x""" else RegNetYLayer
_lowerCAmelCase = nn.Sequential(
# downsampling is done in the first layer with stride of 2
layer(
lowerCamelCase , lowerCamelCase , lowerCamelCase , stride=lowerCamelCase , ) , *[layer(lowerCamelCase , lowerCamelCase , lowerCamelCase ) for _ in range(depth - 1 )] , )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.layers(lowerCamelCase )
return hidden_state
class __lowerCamelCase ( nn.Module ):
def __init__(self , lowerCamelCase ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase = 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(
lowerCamelCase , config.embedding_size , config.hidden_sizes[0] , stride=2 if config.downsample_in_first_stage else 1 , depth=config.depths[0] , ) )
_lowerCAmelCase = zip(config.hidden_sizes , config.hidden_sizes[1:] )
for (in_channels, out_channels), depth in zip(lowerCamelCase , config.depths[1:] ):
self.stages.append(RegNetStage(lowerCamelCase , lowerCamelCase , lowerCamelCase , depth=lowerCamelCase ) )
def A__ (self , lowerCamelCase , lowerCamelCase = False , lowerCamelCase = True ):
'''simple docstring'''
_lowerCAmelCase = () if output_hidden_states else None
for stage_module in self.stages:
if output_hidden_states:
_lowerCAmelCase = hidden_states + (hidden_state,)
_lowerCAmelCase = stage_module(lowerCamelCase )
if output_hidden_states:
_lowerCAmelCase = 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=lowerCamelCase , hidden_states=lowerCamelCase )
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = RegNetConfig
__UpperCamelCase = 'regnet'
__UpperCamelCase = 'pixel_values'
__UpperCamelCase = True
def A__ (self , lowerCamelCase ):
'''simple docstring'''
if isinstance(lowerCamelCase , nn.Convad ):
nn.init.kaiming_normal_(module.weight , mode="""fan_out""" , nonlinearity="""relu""" )
elif isinstance(lowerCamelCase , (nn.BatchNormad, nn.GroupNorm) ):
nn.init.constant_(module.weight , 1 )
nn.init.constant_(module.bias , 0 )
def A__ (self , lowerCamelCase , lowerCamelCase=False ):
'''simple docstring'''
if isinstance(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = value
SCREAMING_SNAKE_CASE : 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.
'''
SCREAMING_SNAKE_CASE : 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.' , __lowercase , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class __lowerCamelCase ( __lowercase ):
def __init__(self , lowerCamelCase ):
'''simple docstring'''
super().__init__(lowerCamelCase )
_lowerCAmelCase = config
_lowerCAmelCase = RegNetEmbeddings(lowerCamelCase )
_lowerCAmelCase = RegNetEncoder(lowerCamelCase )
_lowerCAmelCase = nn.AdaptiveAvgPoolad((1, 1) )
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(lowerCamelCase )
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC , output_type=lowerCamelCase , config_class=_CONFIG_FOR_DOC , modality="""vision""" , expected_output=_EXPECTED_OUTPUT_SHAPE , )
def A__ (self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
_lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
_lowerCAmelCase = self.embedder(lowerCamelCase )
_lowerCAmelCase = self.encoder(
lowerCamelCase , output_hidden_states=lowerCamelCase , return_dict=lowerCamelCase )
_lowerCAmelCase = encoder_outputs[0]
_lowerCAmelCase = self.pooler(lowerCamelCase )
if not return_dict:
return (last_hidden_state, pooled_output) + encoder_outputs[1:]
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=lowerCamelCase , pooler_output=lowerCamelCase , hidden_states=encoder_outputs.hidden_states , )
@add_start_docstrings(
'\n RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n ImageNet.\n ' , __lowercase , )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class __lowerCamelCase ( __lowercase ):
def __init__(self , lowerCamelCase ):
'''simple docstring'''
super().__init__(lowerCamelCase )
_lowerCAmelCase = config.num_labels
_lowerCAmelCase = RegNetModel(lowerCamelCase )
# classification head
_lowerCAmelCase = 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(lowerCamelCase )
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT , output_type=lowerCamelCase , config_class=_CONFIG_FOR_DOC , expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT , )
def A__ (self , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None , ):
'''simple docstring'''
_lowerCAmelCase = return_dict if return_dict is not None else self.config.use_return_dict
_lowerCAmelCase = self.regnet(lowerCamelCase , output_hidden_states=lowerCamelCase , return_dict=lowerCamelCase )
_lowerCAmelCase = outputs.pooler_output if return_dict else outputs[1]
_lowerCAmelCase = self.classifier(lowerCamelCase )
_lowerCAmelCase = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
_lowerCAmelCase = """regression"""
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
_lowerCAmelCase = """single_label_classification"""
else:
_lowerCAmelCase = """multi_label_classification"""
if self.config.problem_type == "regression":
_lowerCAmelCase = MSELoss()
if self.num_labels == 1:
_lowerCAmelCase = loss_fct(logits.squeeze() , labels.squeeze() )
else:
_lowerCAmelCase = loss_fct(lowerCamelCase , lowerCamelCase )
elif self.config.problem_type == "single_label_classification":
_lowerCAmelCase = CrossEntropyLoss()
_lowerCAmelCase = loss_fct(logits.view(-1 , self.num_labels ) , labels.view(-1 ) )
elif self.config.problem_type == "multi_label_classification":
_lowerCAmelCase = BCEWithLogitsLoss()
_lowerCAmelCase = loss_fct(lowerCamelCase , lowerCamelCase )
if not return_dict:
_lowerCAmelCase = (logits,) + outputs[2:]
return (loss,) + output if loss is not None else output
return ImageClassifierOutputWithNoAttention(loss=lowerCamelCase , logits=lowerCamelCase , hidden_states=outputs.hidden_states )
| 317
|
"""simple docstring"""
from functools import reduce
SCREAMING_SNAKE_CASE : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def __UpperCAmelCase ( snake_case_ : str = N ) -> int:
"""simple docstring"""
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda snake_case_ , snake_case_ : str(int(snake_case_ ) * int(snake_case_ ) ) , n[i : i + 13] ) )
for i in range(len(snake_case_ ) - 12 ) )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import requests
SCREAMING_SNAKE_CASE : int = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def __UpperCAmelCase ( snake_case_ : str ) -> None:
"""simple docstring"""
_lowerCAmelCase = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["""articles"""] , 1 ):
print(F"""{i}.) {article["title"]}""" )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key='''<Your BBC News API key goes here>''')
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 600851475143 ) -> int:
"""simple docstring"""
try:
_lowerCAmelCase = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
while n % i == 0:
_lowerCAmelCase = i
n //= i
i += 1
if n > 1:
_lowerCAmelCase = n
return int(snake_case_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import os
SCREAMING_SNAKE_CASE : List[Any] = {'''I''': 1, '''V''': 5, '''X''': 1_0, '''L''': 5_0, '''C''': 1_0_0, '''D''': 5_0_0, '''M''': 1_0_0_0}
def __UpperCAmelCase ( snake_case_ : str ) -> int:
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = 0
while index < len(snake_case_ ) - 1:
_lowerCAmelCase = SYMBOLS[numerals[index]]
_lowerCAmelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def __UpperCAmelCase ( snake_case_ : int ) -> str:
"""simple docstring"""
_lowerCAmelCase = """"""
_lowerCAmelCase = num // 1000
numerals += m_count * "M"
num %= 1000
_lowerCAmelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
_lowerCAmelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def __UpperCAmelCase ( snake_case_ : str = "/p089_roman.txt" ) -> int:
"""simple docstring"""
_lowerCAmelCase = 0
with open(os.path.dirname(snake_case_ ) + roman_numerals_filename ) as filea:
_lowerCAmelCase = filea.readlines()
for line in lines:
_lowerCAmelCase = line.strip()
_lowerCAmelCase = parse_roman_numerals(snake_case_ )
_lowerCAmelCase = generate_roman_numerals(snake_case_ )
savings += len(snake_case_ ) - len(snake_case_ )
return savings
if __name__ == "__main__":
print(F'{solution() = }')
| 317
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
SCREAMING_SNAKE_CASE : Optional[Any] = logging.getLogger(__name__)
@dataclass
class __lowerCamelCase :
__UpperCamelCase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Whether tp freeze the encoder.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class __lowerCamelCase :
__UpperCamelCase = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__UpperCamelCase = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
__UpperCamelCase = field(
default=1_024 , 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=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCamelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
__UpperCamelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCamelCase = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
__UpperCamelCase = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
__UpperCamelCase = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Source language id for translation.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Target language id for translation.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': '# num_beams to use for evaluation.'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def __UpperCAmelCase ( snake_case_ : Optional[int] , snake_case_ : Any , snake_case_ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(snake_case_ , os.path.join(snake_case_ , F"""{split}_results.json""" ) )
def __UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_args_into_dataclasses()
check_output_dir(snake_case_ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("""Training/evaluation parameters %s""" , snake_case_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_lowerCAmelCase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(snake_case_ , snake_case_ , snake_case_ ):
assert hasattr(snake_case_ , snake_case_ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(snake_case_ , snake_case_ , getattr(snake_case_ , snake_case_ ) )
_lowerCAmelCase = 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 = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=snake_case_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(snake_case_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCAmelCase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(snake_case_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(snake_case_ , snake_case_ ):
_lowerCAmelCase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCAmelCase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(snake_case_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCAmelCase = SeqaSeqDataset
# Get datasets
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCAmelCase = (
build_compute_metrics_fn(data_args.task , snake_case_ ) if training_args.predict_with_generate else None
)
_lowerCAmelCase = SeqaSeqTrainer(
model=snake_case_ , args=snake_case_ , data_args=snake_case_ , train_dataset=snake_case_ , eval_dataset=snake_case_ , data_collator=SeqaSeqDataCollator(
snake_case_ , snake_case_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=snake_case_ , tokenizer=snake_case_ , )
_lowerCAmelCase = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
_lowerCAmelCase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCAmelCase = train_result.metrics
_lowerCAmelCase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_lowerCAmelCase = trainer.evaluate(metric_key_prefix="""val""" )
_lowerCAmelCase = data_args.n_val
_lowerCAmelCase = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
_lowerCAmelCase = trainer.predict(test_dataset=snake_case_ , metric_key_prefix="""test""" )
_lowerCAmelCase = test_output.metrics
_lowerCAmelCase = data_args.n_test
if trainer.is_world_process_zero():
_lowerCAmelCase = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
if training_args.predict_with_generate:
_lowerCAmelCase = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=snake_case_ , clean_up_tokenization_spaces=snake_case_ )
_lowerCAmelCase = lmap(str.strip , snake_case_ )
write_txt_file(snake_case_ , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(snake_case_ , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def __UpperCAmelCase ( snake_case_ : Any ) -> Dict:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 317
| 1
|
"""simple docstring"""
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.json"""],
["""dataset_infos.json"""],
["""full:README.md"""],
] , )
def __UpperCAmelCase ( snake_case_ : Any , snake_case_ : Optional[Any] ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = tmp_path_factory.mktemp("""dset_infos_dir""" )
if "full:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""---\ndataset_info:\n dataset_size: 42\n---""" )
if "empty:README.md" in files:
with open(dataset_infos_dir / """README.md""" , """w""" ) as f:
f.write("""""" )
# we want to support dataset_infos.json for backward compatibility
if "dataset_infos.json" in files:
with open(dataset_infos_dir / """dataset_infos.json""" , """w""" ) as f:
f.write("""{\"default\": {\"dataset_size\": 42}}""" )
_lowerCAmelCase = DatasetInfosDict.from_directory(snake_case_ )
assert dataset_infos
assert dataset_infos["default"].dataset_size == 42
@pytest.mark.parametrize(
"""dataset_info""" , [
DatasetInfo(),
DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=42 , ),
] , )
def __UpperCAmelCase ( snake_case_ : Optional[int] , snake_case_ : DatasetInfo ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = str(snake_case_ )
dataset_info.write_to_directory(snake_case_ )
_lowerCAmelCase = DatasetInfo.from_directory(snake_case_ )
assert dataset_info == reloaded
assert os.path.exists(os.path.join(snake_case_ , """dataset_info.json""" ) )
def __UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = DatasetInfo(
description="""foo""" , citation="""bar""" , homepage="""https://foo.bar""" , license="""CC0""" , features=Features({"""a""": Value("""int32""" )} ) , post_processed={} , supervised_keys=() , task_templates=[] , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train""", """num_examples""": 42}] , download_checksums={} , download_size=1337 , post_processing_size=442 , dataset_size=1234 , size_in_bytes=1337 + 442 + 1234 , )
_lowerCAmelCase = dataset_info._to_yaml_dict()
assert sorted(snake_case_ ) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML )
for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
assert key in dataset_info_yaml_dict
assert isinstance(dataset_info_yaml_dict[key] , (list, dict, int, str) )
_lowerCAmelCase = yaml.safe_dump(snake_case_ )
_lowerCAmelCase = yaml.safe_load(snake_case_ )
assert dataset_info_yaml_dict == reloaded
def __UpperCAmelCase ( ) -> Dict:
"""simple docstring"""
_lowerCAmelCase = DatasetInfo()
_lowerCAmelCase = dataset_info._to_yaml_dict()
assert dataset_info_yaml_dict == {}
@pytest.mark.parametrize(
"""dataset_infos_dict""" , [
DatasetInfosDict(),
DatasetInfosDict({"""default""": DatasetInfo()} ),
DatasetInfosDict({"""my_config_name""": DatasetInfo()} ),
DatasetInfosDict(
{
"""default""": DatasetInfo(
description="""foo""" , features=Features({"""a""": Value("""int32""" )} ) , builder_name="""builder""" , config_name="""config""" , version="""1.0.0""" , splits=[{"""name""": """train"""}] , download_size=42 , )
} ),
DatasetInfosDict(
{
"""v1""": DatasetInfo(dataset_size=42 ),
"""v2""": DatasetInfo(dataset_size=1337 ),
} ),
] , )
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : DatasetInfosDict ) -> List[Any]:
"""simple docstring"""
_lowerCAmelCase = str(snake_case_ )
dataset_infos_dict.write_to_directory(snake_case_ )
_lowerCAmelCase = DatasetInfosDict.from_directory(snake_case_ )
# the config_name of the dataset_infos_dict take over the attribute
for config_name, dataset_info in dataset_infos_dict.items():
_lowerCAmelCase = config_name
# the yaml representation doesn't include fields like description or citation
# so we just test that we can recover what we can from the yaml
_lowerCAmelCase = DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict() )
assert dataset_infos_dict == reloaded
if dataset_infos_dict:
assert os.path.exists(os.path.join(snake_case_ , """README.md""" ) )
| 317
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : List[Any] = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = [
'''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FocalNetForImageClassification''',
'''FocalNetForMaskedImageModeling''',
'''FocalNetBackbone''',
'''FocalNetModel''',
'''FocalNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 317
| 1
|
"""simple docstring"""
import unittest
from transformers import SqueezeBertConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
SqueezeBertModel,
)
class __lowerCamelCase ( __lowercase ):
def __init__(self , lowerCamelCase , lowerCamelCase=13 , lowerCamelCase=7 , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=False , lowerCamelCase=True , lowerCamelCase=99 , lowerCamelCase=32 , lowerCamelCase=5 , lowerCamelCase=4 , lowerCamelCase=64 , lowerCamelCase="gelu" , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=512 , lowerCamelCase=16 , lowerCamelCase=2 , lowerCamelCase=0.02 , lowerCamelCase=3 , lowerCamelCase=4 , lowerCamelCase=None , lowerCamelCase=2 , lowerCamelCase=2 , lowerCamelCase=2 , lowerCamelCase=2 , lowerCamelCase=4 , lowerCamelCase=1 , ):
'''simple docstring'''
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_input_mask
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = num_labels
_lowerCAmelCase = num_choices
_lowerCAmelCase = scope
_lowerCAmelCase = q_groups
_lowerCAmelCase = k_groups
_lowerCAmelCase = v_groups
_lowerCAmelCase = post_attention_groups
_lowerCAmelCase = intermediate_groups
_lowerCAmelCase = output_groups
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_input_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def A__ (self ):
'''simple docstring'''
return SqueezeBertConfig(
embedding_size=self.hidden_size , 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 , attention_probs_dropout_prob=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , q_groups=self.q_groups , k_groups=self.k_groups , v_groups=self.v_groups , post_attention_groups=self.post_attention_groups , intermediate_groups=self.intermediate_groups , output_groups=self.output_groups , )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = SqueezeBertModel(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model(lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = SqueezeBertForMaskedLM(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = SqueezeBertForQuestionAnswering(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(
lowerCamelCase , attention_mask=lowerCamelCase , start_positions=lowerCamelCase , end_positions=lowerCamelCase )
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 A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = SqueezeBertForSequenceClassification(lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = SqueezeBertForTokenClassification(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = model(lowerCamelCase , attention_mask=lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.num_choices
_lowerCAmelCase = SqueezeBertForMultipleChoice(config=lowerCamelCase )
model.to(lowerCamelCase )
model.eval()
_lowerCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase = model(
lowerCamelCase , attention_mask=lowerCamelCase , labels=lowerCamelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.prepare_config_and_inputs()
((_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase)) = config_and_inputs
_lowerCAmelCase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __lowerCamelCase ( __lowercase , __lowercase , unittest.TestCase ):
__UpperCamelCase = (
(
SqueezeBertModel,
SqueezeBertForMaskedLM,
SqueezeBertForMultipleChoice,
SqueezeBertForQuestionAnswering,
SqueezeBertForSequenceClassification,
SqueezeBertForTokenClassification,
)
if is_torch_available()
else None
)
__UpperCamelCase = (
{
'feature-extraction': SqueezeBertModel,
'fill-mask': SqueezeBertForMaskedLM,
'question-answering': SqueezeBertForQuestionAnswering,
'text-classification': SqueezeBertForSequenceClassification,
'token-classification': SqueezeBertForTokenClassification,
'zero-shot': SqueezeBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = True
__UpperCamelCase = False
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = SqueezeBertModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=lowerCamelCase , dim=37 )
def A__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_model(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_masked_lm(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_question_answering(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_sequence_classification(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_token_classification(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_squeezebert_for_multiple_choice(*lowerCamelCase )
@slow
def A__ (self ):
'''simple docstring'''
for model_name in SQUEEZEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = SqueezeBertModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
@require_sentencepiece
@require_tokenizers
@require_torch
class __lowerCamelCase ( unittest.TestCase ):
@slow
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = SqueezeBertForSequenceClassification.from_pretrained("""squeezebert/squeezebert-mnli""" )
_lowerCAmelCase = torch.tensor([[1, 29_414, 232, 328, 740, 1_140, 12_695, 69, 13, 1_588, 2]] )
_lowerCAmelCase = model(lowerCamelCase )[0]
_lowerCAmelCase = torch.Size((1, 3) )
self.assertEqual(output.shape , lowerCamelCase )
_lowerCAmelCase = torch.tensor([[0.6401, -0.0349, -0.6041]] )
self.assertTrue(torch.allclose(lowerCamelCase , lowerCamelCase , atol=1e-4 ) )
| 317
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __lowerCamelCase ( unittest.TestCase ):
def __init__(self , lowerCamelCase , lowerCamelCase=7 , lowerCamelCase=3 , lowerCamelCase=18 , lowerCamelCase=30 , lowerCamelCase=400 , lowerCamelCase=True , lowerCamelCase=None , lowerCamelCase=True , lowerCamelCase=None , ):
'''simple docstring'''
_lowerCAmelCase = size if size is not None else {"""shortest_edge""": 20}
_lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = min_resolution
_lowerCAmelCase = max_resolution
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
def A__ (self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __lowerCamelCase ( __lowercase , unittest.TestCase ):
__UpperCamelCase = MobileNetVaImageProcessor if is_vision_available() else None
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = MobileNetVaImageProcessingTester(self )
@property
def A__ (self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(lowerCamelCase , """size""" ) )
self.assertTrue(hasattr(lowerCamelCase , """do_center_crop""" ) )
self.assertTrue(hasattr(lowerCamelCase , """crop_size""" ) )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 20} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} )
def A__ (self ):
'''simple docstring'''
pass
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , Image.Image )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , numpify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , np.ndarray )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , torchify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , torch.Tensor )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 317
| 1
|
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : list ) -> list:
"""simple docstring"""
for i in range(len(snake_case_ ) - 1 , 0 , -1 ):
_lowerCAmelCase = False
for j in range(snake_case_ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
_lowerCAmelCase , _lowerCAmelCase = unsorted[j - 1], unsorted[j]
_lowerCAmelCase = True
for j in range(snake_case_ ):
if unsorted[j] > unsorted[j + 1]:
_lowerCAmelCase , _lowerCAmelCase = unsorted[j + 1], unsorted[j]
_lowerCAmelCase = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : List[Any] = input('''Enter numbers separated by a comma:\n''').strip()
SCREAMING_SNAKE_CASE : List[str] = [int(item) for item in user_input.split(''',''')]
print(F'{cocktail_shaker_sort(unsorted) = }')
| 317
| 1
|
"""simple docstring"""
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
SCREAMING_SNAKE_CASE : int = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE : List[Any] = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE : Union[str, Any] = re.compile(R'''TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
SCREAMING_SNAKE_CASE : List[str] = re.compile(R'''Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE : Any = re.compile(R'''(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)''')
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE : Union[str, Any] = [
('''pretraining''', '''MODEL_FOR_PRETRAINING_MAPPING_NAMES''', '''AutoModelForPreTraining'''),
('''feature-extraction''', '''MODEL_MAPPING_NAMES''', '''AutoModel'''),
('''audio-classification''', '''MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForAudioClassification'''),
('''text-generation''', '''MODEL_FOR_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForCausalLM'''),
('''automatic-speech-recognition''', '''MODEL_FOR_CTC_MAPPING_NAMES''', '''AutoModelForCTC'''),
('''image-classification''', '''MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForImageClassification'''),
('''image-segmentation''', '''MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES''', '''AutoModelForImageSegmentation'''),
('''fill-mask''', '''MODEL_FOR_MASKED_LM_MAPPING_NAMES''', '''AutoModelForMaskedLM'''),
('''object-detection''', '''MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES''', '''AutoModelForObjectDetection'''),
(
'''zero-shot-object-detection''',
'''MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES''',
'''AutoModelForZeroShotObjectDetection''',
),
('''question-answering''', '''MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES''', '''AutoModelForQuestionAnswering'''),
('''text2text-generation''', '''MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES''', '''AutoModelForSeq2SeqLM'''),
('''text-classification''', '''MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForSequenceClassification'''),
('''automatic-speech-recognition''', '''MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES''', '''AutoModelForSpeechSeq2Seq'''),
(
'''table-question-answering''',
'''MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES''',
'''AutoModelForTableQuestionAnswering''',
),
('''token-classification''', '''MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForTokenClassification'''),
('''multiple-choice''', '''MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES''', '''AutoModelForMultipleChoice'''),
(
'''next-sentence-prediction''',
'''MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES''',
'''AutoModelForNextSentencePrediction''',
),
(
'''audio-frame-classification''',
'''MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES''',
'''AutoModelForAudioFrameClassification''',
),
('''audio-xvector''', '''MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES''', '''AutoModelForAudioXVector'''),
(
'''document-question-answering''',
'''MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES''',
'''AutoModelForDocumentQuestionAnswering''',
),
(
'''visual-question-answering''',
'''MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES''',
'''AutoModelForVisualQuestionAnswering''',
),
('''image-to-text''', '''MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES''', '''AutoModelForVision2Seq'''),
(
'''zero-shot-image-classification''',
'''MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES''',
'''AutoModelForZeroShotImageClassification''',
),
('''depth-estimation''', '''MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES''', '''AutoModelForDepthEstimation'''),
('''video-classification''', '''MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES''', '''AutoModelForVideoClassification'''),
('''mask-generation''', '''MODEL_FOR_MASK_GENERATION_MAPPING_NAMES''', '''AutoModelForMaskGeneration'''),
]
def __UpperCAmelCase ( snake_case_ : Any ) -> str:
"""simple docstring"""
_lowerCAmelCase = re.finditer(""".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)""" , snake_case_ )
return [m.group(0 ) for m in matches]
def __UpperCAmelCase ( ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
_lowerCAmelCase = {
config.replace("""Config""" , """""" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
_lowerCAmelCase = collections.defaultdict(snake_case_ )
_lowerCAmelCase = collections.defaultdict(snake_case_ )
_lowerCAmelCase = collections.defaultdict(snake_case_ )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(snake_case_ ):
_lowerCAmelCase = None
if _re_tf_models.match(snake_case_ ) is not None:
_lowerCAmelCase = tf_models
_lowerCAmelCase = _re_tf_models.match(snake_case_ ).groups()[0]
elif _re_flax_models.match(snake_case_ ) is not None:
_lowerCAmelCase = flax_models
_lowerCAmelCase = _re_flax_models.match(snake_case_ ).groups()[0]
elif _re_pt_models.match(snake_case_ ) is not None:
_lowerCAmelCase = pt_models
_lowerCAmelCase = _re_pt_models.match(snake_case_ ).groups()[0]
if lookup_dict is not None:
while len(snake_case_ ) > 0:
if attr_name in model_prefix_to_model_type:
_lowerCAmelCase = True
break
# Try again after removing the last word in the name
_lowerCAmelCase = """""".join(camel_case_split(snake_case_ )[:-1] )
_lowerCAmelCase = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
_lowerCAmelCase = list(snake_case_ )
all_models.sort()
_lowerCAmelCase = {"""model_type""": all_models}
_lowerCAmelCase = [pt_models[t] for t in all_models]
_lowerCAmelCase = [tf_models[t] for t in all_models]
_lowerCAmelCase = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
_lowerCAmelCase = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
_lowerCAmelCase = """AutoProcessor"""
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
_lowerCAmelCase = """AutoTokenizer"""
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
_lowerCAmelCase = """AutoFeatureExtractor"""
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
_lowerCAmelCase = """AutoTokenizer"""
_lowerCAmelCase = [processors[t] for t in all_models]
return pd.DataFrame(snake_case_ )
def __UpperCAmelCase ( snake_case_ : Optional[Any] ) -> Dict:
"""simple docstring"""
_lowerCAmelCase = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
_lowerCAmelCase = [model_mapping, F"""TF_{model_mapping}""", F"""FLAX_{model_mapping}"""]
_lowerCAmelCase = [auto_class, F"""TF_{auto_class}""", F"""Flax_{auto_class}"""]
# Loop through all three frameworks
for module, cls, mapping in zip(snake_case_ , snake_case_ , snake_case_ ):
# The type of pipeline may not exist in this framework
if not hasattr(snake_case_ , snake_case_ ):
continue
# First extract all model_names
_lowerCAmelCase = []
for name in getattr(snake_case_ , snake_case_ ).values():
if isinstance(snake_case_ , snake_case_ ):
model_names.append(snake_case_ )
else:
model_names.extend(list(snake_case_ ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = get_frameworks_table()
_lowerCAmelCase = Dataset.from_pandas(snake_case_ )
_lowerCAmelCase = hf_hub_download(
"""huggingface/transformers-metadata""" , """pipeline_tags.json""" , repo_type="""dataset""" , token=snake_case_ )
_lowerCAmelCase = Dataset.from_json(snake_case_ )
_lowerCAmelCase = {
tags_dataset[i]["""model_class"""]: (tags_dataset[i]["""pipeline_tag"""], tags_dataset[i]["""auto_class"""])
for i in range(len(snake_case_ ) )
}
_lowerCAmelCase = update_pipeline_and_auto_class_table(snake_case_ )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
_lowerCAmelCase = sorted(table.keys() )
_lowerCAmelCase = pd.DataFrame(
{
"""model_class""": model_classes,
"""pipeline_tag""": [table[m][0] for m in model_classes],
"""auto_class""": [table[m][1] for m in model_classes],
} )
_lowerCAmelCase = Dataset.from_pandas(snake_case_ )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(snake_case_ , """frameworks.json""" ) )
tags_dataset.to_json(os.path.join(snake_case_ , """pipeline_tags.json""" ) )
if commit_sha is not None:
_lowerCAmelCase = (
F"""Update with commit {commit_sha}\n\nSee: """
F"""https://github.com/huggingface/transformers/commit/{commit_sha}"""
)
else:
_lowerCAmelCase = """Update"""
upload_folder(
repo_id="""huggingface/transformers-metadata""" , folder_path=snake_case_ , repo_type="""dataset""" , token=snake_case_ , commit_message=snake_case_ , )
def __UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
_lowerCAmelCase = transformers_module.pipelines.SUPPORTED_TASKS
_lowerCAmelCase = []
for key in pipeline_tasks:
if key not in in_table:
_lowerCAmelCase = pipeline_tasks[key]["""pt"""]
if isinstance(snake_case_ , (list, tuple) ):
_lowerCAmelCase = model[0]
_lowerCAmelCase = model.__name__
if model not in in_table.values():
missing.append(snake_case_ )
if len(snake_case_ ) > 0:
_lowerCAmelCase = """, """.join(snake_case_ )
raise ValueError(
"""The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside """
F"""`utils/update_metadata.py`: {msg}. Please add them!""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Tuple = argparse.ArgumentParser()
parser.add_argument('''--token''', type=str, help='''The token to use to push to the transformers-metadata dataset.''')
parser.add_argument('''--commit_sha''', type=str, help='''The sha of the commit going with this update.''')
parser.add_argument('''--check-only''', action='''store_true''', help='''Activate to just check all pipelines are present.''')
SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 317
|
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
def __UpperCAmelCase ( snake_case_ : bool , snake_case_ : bool ) -> Tuple:
"""simple docstring"""
def run_func(snake_case_ : Union[str, Any] ):
@wraps(snake_case_ )
def run_in_eager_mode(*snake_case_ : Optional[int] , **snake_case_ : Union[str, Any] ):
return func(*snake_case_ , **snake_case_ )
@wraps(snake_case_ )
@tf.function(experimental_compile=snake_case_ )
def run_in_graph_mode(*snake_case_ : Dict , **snake_case_ : Union[str, Any] ):
return func(*snake_case_ , **snake_case_ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"""Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : int , snake_case_ : int ) -> ["tf.Tensor"]:
"""simple docstring"""
_lowerCAmelCase = random.Random()
_lowerCAmelCase = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(snake_case_ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 42
__UpperCamelCase = 42
__UpperCamelCase = "TensorFlow"
@property
def A__ (self ):
'''simple docstring'''
return tf.__version__
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_inference_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_speed(_inference )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_train_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_speed(_train )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase )
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_inference_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_memory(_inference )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase )
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_train_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_memory(_train )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
_lowerCAmelCase = (
hasattr(lowerCamelCase , """architectures""" )
and isinstance(config.architectures , lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model_cls(lowerCamelCase )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
_lowerCAmelCase = TF_MODEL_MAPPING[config.__class__](lowerCamelCase )
# encoder-decoder has vocab size saved differently
_lowerCAmelCase = config.vocab_size if hasattr(lowerCamelCase , """vocab_size""" ) else config.encoder.vocab_size
_lowerCAmelCase = random_input_ids(lowerCamelCase , lowerCamelCase , lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(lowerCamelCase , decoder_input_ids=lowerCamelCase , training=lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(lowerCamelCase , training=lowerCamelCase )
_lowerCAmelCase = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.""" )
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
_lowerCAmelCase = (
hasattr(lowerCamelCase , """architectures""" )
and isinstance(config.architectures , lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model_cls(lowerCamelCase )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
_lowerCAmelCase = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase )
# encoder-decoder has vocab size saved differently
_lowerCAmelCase = config.vocab_size if hasattr(lowerCamelCase , """vocab_size""" ) else config.encoder.vocab_size
_lowerCAmelCase = random_input_ids(lowerCamelCase , lowerCamelCase , lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
_lowerCAmelCase = model(lowerCamelCase , decoder_input_ids=lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )[0]
_lowerCAmelCase = tf.gradients(lowerCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
_lowerCAmelCase = model(lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )[0]
_lowerCAmelCase = tf.gradients(lowerCamelCase , model.trainable_variables )
return gradients
_lowerCAmelCase = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def A__ (self , lowerCamelCase ):
'''simple docstring'''
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation""" )
timeit.repeat(lowerCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
_lowerCAmelCase = timeit.repeat(
lowerCamelCase , repeat=self.args.repeat , number=10 , )
return min(lowerCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
logger.info(
"""Note that TensorFlow allocates more memory than """
"""it might need to speed up computation. """
"""The memory reported here corresponds to the memory """
"""reported by `nvidia-smi`, which can vary depending """
"""on total available memory on the GPU that is used.""" )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"""`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
""" consumption line by line.""" )
_lowerCAmelCase = start_memory_tracing("""transformers""" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"""Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
""" with `args.memory=False`""" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"""py3nvml not installed, we won't log GPU memory usage. """
"""Install py3nvml (pip install py3nvml) to log information about GPU.""" )
_lowerCAmelCase = """N/A"""
else:
logger.info(
"""Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
""" running on the same GPU.""" )
# init nvml
nvml.nvmlInit()
func()
_lowerCAmelCase = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
_lowerCAmelCase = nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase )
_lowerCAmelCase = meminfo.used
_lowerCAmelCase = Memory(lowerCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"""When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
""" TensorFlow.""" )
_lowerCAmelCase = None
else:
_lowerCAmelCase = measure_peak_memory_cpu(lowerCamelCase )
_lowerCAmelCase = Memory(lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
_lowerCAmelCase = stop_memory_tracing(lowerCamelCase )
if memory is None:
_lowerCAmelCase = summary.total
else:
_lowerCAmelCase = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
return "N/A", None
| 317
| 1
|
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = ['image_processor', 'tokenizer']
__UpperCamelCase = 'BlipImageProcessor'
__UpperCamelCase = ('BertTokenizer', 'BertTokenizerFast')
def __init__(self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = False
super().__init__(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = self.image_processor
def __call__(self , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = True , lowerCamelCase = False , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = 0 , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = False , lowerCamelCase = False , lowerCamelCase = False , lowerCamelCase = False , lowerCamelCase = False , lowerCamelCase = True , lowerCamelCase = None , **lowerCamelCase , ):
'''simple docstring'''
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None:
_lowerCAmelCase = self.tokenizer
_lowerCAmelCase = self.tokenizer(
text=lowerCamelCase , add_special_tokens=lowerCamelCase , padding=lowerCamelCase , truncation=lowerCamelCase , max_length=lowerCamelCase , stride=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=lowerCamelCase , return_overflowing_tokens=lowerCamelCase , return_special_tokens_mask=lowerCamelCase , return_offsets_mapping=lowerCamelCase , return_token_type_ids=lowerCamelCase , return_length=lowerCamelCase , verbose=lowerCamelCase , return_tensors=lowerCamelCase , **lowerCamelCase , )
return text_encoding
# add pixel_values
_lowerCAmelCase = self.image_processor(lowerCamelCase , return_tensors=lowerCamelCase )
if text is not None:
_lowerCAmelCase = self.tokenizer(
text=lowerCamelCase , add_special_tokens=lowerCamelCase , padding=lowerCamelCase , truncation=lowerCamelCase , max_length=lowerCamelCase , stride=lowerCamelCase , pad_to_multiple_of=lowerCamelCase , return_attention_mask=lowerCamelCase , return_overflowing_tokens=lowerCamelCase , return_special_tokens_mask=lowerCamelCase , return_offsets_mapping=lowerCamelCase , return_token_type_ids=lowerCamelCase , return_length=lowerCamelCase , verbose=lowerCamelCase , return_tensors=lowerCamelCase , **lowerCamelCase , )
else:
_lowerCAmelCase = None
if text_encoding is not None:
encoding_image_processor.update(lowerCamelCase )
return encoding_image_processor
def A__ (self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase , **lowerCamelCase )
def A__ (self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase , **lowerCamelCase )
@property
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.tokenizer.model_input_names
_lowerCAmelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 317
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'transfo-xl'
__UpperCamelCase = ['mems']
__UpperCamelCase = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__(self , lowerCamelCase=267_735 , lowerCamelCase=[20_000, 40_000, 200_000] , lowerCamelCase=1_024 , lowerCamelCase=1_024 , lowerCamelCase=16 , lowerCamelCase=64 , lowerCamelCase=4_096 , lowerCamelCase=4 , lowerCamelCase=False , lowerCamelCase=18 , lowerCamelCase=1_600 , lowerCamelCase=1_000 , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=0 , lowerCamelCase=-1 , lowerCamelCase=True , lowerCamelCase=0.1 , lowerCamelCase=0.0 , lowerCamelCase=True , lowerCamelCase="normal" , lowerCamelCase=0.01 , lowerCamelCase=0.01 , lowerCamelCase=0.02 , lowerCamelCase=1e-5 , lowerCamelCase=0 , **lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = vocab_size
_lowerCAmelCase = []
self.cutoffs.extend(lowerCamelCase )
if proj_share_all_but_first:
_lowerCAmelCase = [False] + [True] * len(self.cutoffs )
else:
_lowerCAmelCase = [False] + [False] * len(self.cutoffs )
_lowerCAmelCase = d_model
_lowerCAmelCase = d_embed
_lowerCAmelCase = d_head
_lowerCAmelCase = d_inner
_lowerCAmelCase = div_val
_lowerCAmelCase = pre_lnorm
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = mem_len
_lowerCAmelCase = same_length
_lowerCAmelCase = attn_type
_lowerCAmelCase = clamp_len
_lowerCAmelCase = sample_softmax
_lowerCAmelCase = adaptive
_lowerCAmelCase = dropout
_lowerCAmelCase = dropatt
_lowerCAmelCase = untie_r
_lowerCAmelCase = init
_lowerCAmelCase = init_range
_lowerCAmelCase = proj_init_std
_lowerCAmelCase = init_std
_lowerCAmelCase = layer_norm_epsilon
super().__init__(eos_token_id=lowerCamelCase , **lowerCamelCase )
@property
def A__ (self ):
'''simple docstring'''
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def A__ (self , lowerCamelCase ):
'''simple docstring'''
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 317
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : List[Any] = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = [
'''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FocalNetForImageClassification''',
'''FocalNetForMaskedImageModeling''',
'''FocalNetBackbone''',
'''FocalNetModel''',
'''FocalNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 317
|
"""simple docstring"""
import math
def __UpperCAmelCase ( snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 2
_lowerCAmelCase = int(math.sqrt(snake_case_ ) ) # Size of every segment
_lowerCAmelCase = [True] * (end + 1)
_lowerCAmelCase = []
while start <= end:
if temp[start] is True:
in_prime.append(snake_case_ )
for i in range(start * start , end + 1 , snake_case_ ):
_lowerCAmelCase = False
start += 1
prime += in_prime
_lowerCAmelCase = end + 1
_lowerCAmelCase = min(2 * end , snake_case_ )
while low <= n:
_lowerCAmelCase = [True] * (high - low + 1)
for each in in_prime:
_lowerCAmelCase = math.floor(low / each ) * each
if t < low:
t += each
for j in range(snake_case_ , high + 1 , snake_case_ ):
_lowerCAmelCase = False
for j in range(len(snake_case_ ) ):
if temp[j] is True:
prime.append(j + low )
_lowerCAmelCase = high + 1
_lowerCAmelCase = min(high + end , snake_case_ )
return prime
print(sieve(1_0**6))
| 317
| 1
|
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'SpeechT5FeatureExtractor'
__UpperCamelCase = 'SpeechT5Tokenizer'
def __init__(self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
super().__init__(lowerCamelCase , lowerCamelCase )
def __call__(self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = kwargs.pop("""audio""" , lowerCamelCase )
_lowerCAmelCase = kwargs.pop("""text""" , lowerCamelCase )
_lowerCAmelCase = kwargs.pop("""text_target""" , lowerCamelCase )
_lowerCAmelCase = kwargs.pop("""audio_target""" , lowerCamelCase )
_lowerCAmelCase = kwargs.pop("""sampling_rate""" , lowerCamelCase )
if audio is not None and text is not None:
raise ValueError(
"""Cannot process both `audio` and `text` inputs. Did you mean `audio_target` or `text_target`?""" )
if audio_target is not None and text_target is not None:
raise ValueError(
"""Cannot process both `audio_target` and `text_target` inputs. Did you mean `audio` or `text`?""" )
if audio is None and audio_target is None and text is None and text_target is None:
raise ValueError(
"""You need to specify either an `audio`, `audio_target`, `text`, or `text_target` input to process.""" )
if audio is not None:
_lowerCAmelCase = self.feature_extractor(lowerCamelCase , *lowerCamelCase , sampling_rate=lowerCamelCase , **lowerCamelCase )
elif text is not None:
_lowerCAmelCase = self.tokenizer(lowerCamelCase , **lowerCamelCase )
else:
_lowerCAmelCase = None
if audio_target is not None:
_lowerCAmelCase = self.feature_extractor(audio_target=lowerCamelCase , *lowerCamelCase , sampling_rate=lowerCamelCase , **lowerCamelCase )
_lowerCAmelCase = targets["""input_values"""]
elif text_target is not None:
_lowerCAmelCase = self.tokenizer(lowerCamelCase , **lowerCamelCase )
_lowerCAmelCase = targets["""input_ids"""]
else:
_lowerCAmelCase = None
if inputs is None:
return targets
if targets is not None:
_lowerCAmelCase = labels
_lowerCAmelCase = targets.get("""attention_mask""" )
if decoder_attention_mask is not None:
_lowerCAmelCase = decoder_attention_mask
return inputs
def A__ (self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = kwargs.pop("""input_values""" , lowerCamelCase )
_lowerCAmelCase = kwargs.pop("""input_ids""" , lowerCamelCase )
_lowerCAmelCase = kwargs.pop("""labels""" , lowerCamelCase )
if input_values is not None and input_ids is not None:
raise ValueError("""Cannot process both `input_values` and `input_ids` inputs.""" )
if input_values is None and input_ids is None and labels is None:
raise ValueError(
"""You need to specify either an `input_values`, `input_ids`, or `labels` input to be padded.""" )
if input_values is not None:
_lowerCAmelCase = self.feature_extractor.pad(lowerCamelCase , *lowerCamelCase , **lowerCamelCase )
elif input_ids is not None:
_lowerCAmelCase = self.tokenizer.pad(lowerCamelCase , **lowerCamelCase )
else:
_lowerCAmelCase = None
if labels is not None:
if "input_ids" in labels or (isinstance(lowerCamelCase , lowerCamelCase ) and "input_ids" in labels[0]):
_lowerCAmelCase = self.tokenizer.pad(lowerCamelCase , **lowerCamelCase )
_lowerCAmelCase = targets["""input_ids"""]
else:
_lowerCAmelCase = self.feature_extractor.feature_size
_lowerCAmelCase = self.feature_extractor.num_mel_bins
_lowerCAmelCase = self.feature_extractor.pad(lowerCamelCase , *lowerCamelCase , **lowerCamelCase )
_lowerCAmelCase = feature_size_hack
_lowerCAmelCase = targets["""input_values"""]
else:
_lowerCAmelCase = None
if inputs is None:
return targets
if targets is not None:
_lowerCAmelCase = labels
_lowerCAmelCase = targets.get("""attention_mask""" )
if decoder_attention_mask is not None:
_lowerCAmelCase = decoder_attention_mask
return inputs
def A__ (self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase , **lowerCamelCase )
def A__ (self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase , **lowerCamelCase )
| 317
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"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE : Any = (7_2_0, 1_2_8_0) # Height, Width
SCREAMING_SNAKE_CASE : List[str] = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE : List[Any] = 1 / 1_0_0
SCREAMING_SNAKE_CASE : Optional[Any] = ''''''
SCREAMING_SNAKE_CASE : Dict = ''''''
SCREAMING_SNAKE_CASE : List[Any] = ''''''
SCREAMING_SNAKE_CASE : Dict = 2_5_0
def __UpperCAmelCase ( ) -> None:
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = get_dataset(snake_case_ , snake_case_ )
for index in range(snake_case_ ):
_lowerCAmelCase = random.sample(range(len(snake_case_ ) ) , 4 )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = update_image_and_anno(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , filter_scale=snake_case_ , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_lowerCAmelCase = random_chars(32 )
_lowerCAmelCase = path.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
_lowerCAmelCase = F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
cva.imwrite(F"""{file_root}.jpg""" , snake_case_ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
_lowerCAmelCase = []
for anno in new_annos:
_lowerCAmelCase = anno[3] - anno[1]
_lowerCAmelCase = anno[4] - anno[2]
_lowerCAmelCase = anno[1] + width / 2
_lowerCAmelCase = anno[2] + height / 2
_lowerCAmelCase = F"""{anno[0]} {x_center} {y_center} {width} {height}"""
annos_list.append(snake_case_ )
with open(F"""{file_root}.txt""" , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : str ) -> tuple[list, list]:
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = []
for label_file in glob.glob(os.path.join(snake_case_ , """*.txt""" ) ):
_lowerCAmelCase = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(snake_case_ ) as in_file:
_lowerCAmelCase = in_file.readlines()
_lowerCAmelCase = os.path.join(snake_case_ , F"""{label_name}.jpg""" )
_lowerCAmelCase = []
for obj_list in obj_lists:
_lowerCAmelCase = obj_list.rstrip("""\n""" ).split(""" """ )
_lowerCAmelCase = float(obj[1] ) - float(obj[3] ) / 2
_lowerCAmelCase = float(obj[2] ) - float(obj[4] ) / 2
_lowerCAmelCase = float(obj[1] ) + float(obj[3] ) / 2
_lowerCAmelCase = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(snake_case_ )
labels.append(snake_case_ )
return img_paths, labels
def __UpperCAmelCase ( snake_case_ : list , snake_case_ : list , snake_case_ : list[int] , snake_case_ : tuple[int, int] , snake_case_ : tuple[float, float] , snake_case_ : float = 0.0 , ) -> tuple[list, list, str]:
"""simple docstring"""
_lowerCAmelCase = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
_lowerCAmelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_lowerCAmelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_lowerCAmelCase = int(scale_x * output_size[1] )
_lowerCAmelCase = int(scale_y * output_size[0] )
_lowerCAmelCase = []
_lowerCAmelCase = []
for i, index in enumerate(snake_case_ ):
_lowerCAmelCase = all_img_list[index]
path_list.append(snake_case_ )
_lowerCAmelCase = all_annos[index]
_lowerCAmelCase = cva.imread(snake_case_ )
if i == 0: # top-left
_lowerCAmelCase = cva.resize(snake_case_ , (divid_point_x, divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = bbox[1] * scale_x
_lowerCAmelCase = bbox[2] * scale_y
_lowerCAmelCase = bbox[3] * scale_x
_lowerCAmelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
_lowerCAmelCase = cva.resize(snake_case_ , (output_size[1] - divid_point_x, divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = scale_x + bbox[1] * (1 - scale_x)
_lowerCAmelCase = bbox[2] * scale_y
_lowerCAmelCase = scale_x + bbox[3] * (1 - scale_x)
_lowerCAmelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
_lowerCAmelCase = cva.resize(snake_case_ , (divid_point_x, output_size[0] - divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = bbox[1] * scale_x
_lowerCAmelCase = scale_y + bbox[2] * (1 - scale_y)
_lowerCAmelCase = bbox[3] * scale_x
_lowerCAmelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
_lowerCAmelCase = cva.resize(
snake_case_ , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = scale_x + bbox[1] * (1 - scale_x)
_lowerCAmelCase = scale_y + bbox[2] * (1 - scale_y)
_lowerCAmelCase = scale_x + bbox[3] * (1 - scale_x)
_lowerCAmelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
_lowerCAmelCase = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def __UpperCAmelCase ( snake_case_ : int ) -> str:
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
_lowerCAmelCase = ascii_lowercase + digits
return "".join(random.choice(snake_case_ ) for _ in range(snake_case_ ) )
if __name__ == "__main__":
main()
print('''DONE ✅''')
| 317
| 1
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : str ) -> list:
"""simple docstring"""
_lowerCAmelCase = [0] * len(snake_case_ )
for i in range(1 , len(snake_case_ ) ):
# use last results for better performance - dynamic programming
_lowerCAmelCase = prefix_result[i - 1]
while j > 0 and input_string[i] != input_string[j]:
_lowerCAmelCase = prefix_result[j - 1]
if input_string[i] == input_string[j]:
j += 1
_lowerCAmelCase = j
return prefix_result
def __UpperCAmelCase ( snake_case_ : str ) -> int:
"""simple docstring"""
return max(prefix_function(snake_case_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 317
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"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
SCREAMING_SNAKE_CASE : Dict = abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def __UpperCAmelCase ( snake_case_ : Optional[int] ) -> List[str]:
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case_ )
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> int:
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_terminal_summary_main
_lowerCAmelCase = terminalreporter.config.getoption("""--make-reports""" )
if make_reports:
pytest_terminal_summary_main(snake_case_ , id=snake_case_ )
| 317
| 1
|
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
SCREAMING_SNAKE_CASE : List[Any] = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def __UpperCAmelCase ( snake_case_ : dict[int, list[int]] , snake_case_ : int , snake_case_ : list[bool] ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(snake_case_ , snake_case_ , snake_case_ )
order.append(snake_case_ )
return order
def __UpperCAmelCase ( snake_case_ : dict[int, list[int]] , snake_case_ : int , snake_case_ : list[bool] ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = True
_lowerCAmelCase = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(snake_case_ , snake_case_ , snake_case_ )
return component
def __UpperCAmelCase ( snake_case_ : dict[int, list[int]] ) -> list[list[int]]:
"""simple docstring"""
_lowerCAmelCase = len(snake_case_ ) * [False]
_lowerCAmelCase = {vert: [] for vert in range(len(snake_case_ ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(snake_case_ )
_lowerCAmelCase = []
for i, was_visited in enumerate(snake_case_ ):
if not was_visited:
order += topology_sort(snake_case_ , snake_case_ , snake_case_ )
_lowerCAmelCase = []
_lowerCAmelCase = len(snake_case_ ) * [False]
for i in range(len(snake_case_ ) ):
_lowerCAmelCase = order[len(snake_case_ ) - i - 1]
if not visited[vert]:
_lowerCAmelCase = find_components(snake_case_ , snake_case_ , snake_case_ )
components_list.append(snake_case_ )
return components_list
| 317
|
"""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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
SCREAMING_SNAKE_CASE : Optional[Any] = {
'''Acehnese Arabic''': '''ace_Arab''',
'''Acehnese Latin''': '''ace_Latn''',
'''Mesopotamian Arabic''': '''acm_Arab''',
'''Ta\'izzi-Adeni Arabic''': '''acq_Arab''',
'''Tunisian Arabic''': '''aeb_Arab''',
'''Afrikaans''': '''afr_Latn''',
'''South Levantine Arabic''': '''ajp_Arab''',
'''Akan''': '''aka_Latn''',
'''Amharic''': '''amh_Ethi''',
'''North Levantine Arabic''': '''apc_Arab''',
'''Modern Standard Arabic''': '''arb_Arab''',
'''Modern Standard Arabic Romanized''': '''arb_Latn''',
'''Najdi Arabic''': '''ars_Arab''',
'''Moroccan Arabic''': '''ary_Arab''',
'''Egyptian Arabic''': '''arz_Arab''',
'''Assamese''': '''asm_Beng''',
'''Asturian''': '''ast_Latn''',
'''Awadhi''': '''awa_Deva''',
'''Central Aymara''': '''ayr_Latn''',
'''South Azerbaijani''': '''azb_Arab''',
'''North Azerbaijani''': '''azj_Latn''',
'''Bashkir''': '''bak_Cyrl''',
'''Bambara''': '''bam_Latn''',
'''Balinese''': '''ban_Latn''',
'''Belarusian''': '''bel_Cyrl''',
'''Bemba''': '''bem_Latn''',
'''Bengali''': '''ben_Beng''',
'''Bhojpuri''': '''bho_Deva''',
'''Banjar Arabic''': '''bjn_Arab''',
'''Banjar Latin''': '''bjn_Latn''',
'''Standard Tibetan''': '''bod_Tibt''',
'''Bosnian''': '''bos_Latn''',
'''Buginese''': '''bug_Latn''',
'''Bulgarian''': '''bul_Cyrl''',
'''Catalan''': '''cat_Latn''',
'''Cebuano''': '''ceb_Latn''',
'''Czech''': '''ces_Latn''',
'''Chokwe''': '''cjk_Latn''',
'''Central Kurdish''': '''ckb_Arab''',
'''Crimean Tatar''': '''crh_Latn''',
'''Welsh''': '''cym_Latn''',
'''Danish''': '''dan_Latn''',
'''German''': '''deu_Latn''',
'''Southwestern Dinka''': '''dik_Latn''',
'''Dyula''': '''dyu_Latn''',
'''Dzongkha''': '''dzo_Tibt''',
'''Greek''': '''ell_Grek''',
'''English''': '''eng_Latn''',
'''Esperanto''': '''epo_Latn''',
'''Estonian''': '''est_Latn''',
'''Basque''': '''eus_Latn''',
'''Ewe''': '''ewe_Latn''',
'''Faroese''': '''fao_Latn''',
'''Fijian''': '''fij_Latn''',
'''Finnish''': '''fin_Latn''',
'''Fon''': '''fon_Latn''',
'''French''': '''fra_Latn''',
'''Friulian''': '''fur_Latn''',
'''Nigerian Fulfulde''': '''fuv_Latn''',
'''Scottish Gaelic''': '''gla_Latn''',
'''Irish''': '''gle_Latn''',
'''Galician''': '''glg_Latn''',
'''Guarani''': '''grn_Latn''',
'''Gujarati''': '''guj_Gujr''',
'''Haitian Creole''': '''hat_Latn''',
'''Hausa''': '''hau_Latn''',
'''Hebrew''': '''heb_Hebr''',
'''Hindi''': '''hin_Deva''',
'''Chhattisgarhi''': '''hne_Deva''',
'''Croatian''': '''hrv_Latn''',
'''Hungarian''': '''hun_Latn''',
'''Armenian''': '''hye_Armn''',
'''Igbo''': '''ibo_Latn''',
'''Ilocano''': '''ilo_Latn''',
'''Indonesian''': '''ind_Latn''',
'''Icelandic''': '''isl_Latn''',
'''Italian''': '''ita_Latn''',
'''Javanese''': '''jav_Latn''',
'''Japanese''': '''jpn_Jpan''',
'''Kabyle''': '''kab_Latn''',
'''Jingpho''': '''kac_Latn''',
'''Kamba''': '''kam_Latn''',
'''Kannada''': '''kan_Knda''',
'''Kashmiri Arabic''': '''kas_Arab''',
'''Kashmiri Devanagari''': '''kas_Deva''',
'''Georgian''': '''kat_Geor''',
'''Central Kanuri Arabic''': '''knc_Arab''',
'''Central Kanuri Latin''': '''knc_Latn''',
'''Kazakh''': '''kaz_Cyrl''',
'''Kabiyè''': '''kbp_Latn''',
'''Kabuverdianu''': '''kea_Latn''',
'''Khmer''': '''khm_Khmr''',
'''Kikuyu''': '''kik_Latn''',
'''Kinyarwanda''': '''kin_Latn''',
'''Kyrgyz''': '''kir_Cyrl''',
'''Kimbundu''': '''kmb_Latn''',
'''Northern Kurdish''': '''kmr_Latn''',
'''Kikongo''': '''kon_Latn''',
'''Korean''': '''kor_Hang''',
'''Lao''': '''lao_Laoo''',
'''Ligurian''': '''lij_Latn''',
'''Limburgish''': '''lim_Latn''',
'''Lingala''': '''lin_Latn''',
'''Lithuanian''': '''lit_Latn''',
'''Lombard''': '''lmo_Latn''',
'''Latgalian''': '''ltg_Latn''',
'''Luxembourgish''': '''ltz_Latn''',
'''Luba-Kasai''': '''lua_Latn''',
'''Ganda''': '''lug_Latn''',
'''Luo''': '''luo_Latn''',
'''Mizo''': '''lus_Latn''',
'''Standard Latvian''': '''lvs_Latn''',
'''Magahi''': '''mag_Deva''',
'''Maithili''': '''mai_Deva''',
'''Malayalam''': '''mal_Mlym''',
'''Marathi''': '''mar_Deva''',
'''Minangkabau Arabic ''': '''min_Arab''',
'''Minangkabau Latin''': '''min_Latn''',
'''Macedonian''': '''mkd_Cyrl''',
'''Plateau Malagasy''': '''plt_Latn''',
'''Maltese''': '''mlt_Latn''',
'''Meitei Bengali''': '''mni_Beng''',
'''Halh Mongolian''': '''khk_Cyrl''',
'''Mossi''': '''mos_Latn''',
'''Maori''': '''mri_Latn''',
'''Burmese''': '''mya_Mymr''',
'''Dutch''': '''nld_Latn''',
'''Norwegian Nynorsk''': '''nno_Latn''',
'''Norwegian Bokmål''': '''nob_Latn''',
'''Nepali''': '''npi_Deva''',
'''Northern Sotho''': '''nso_Latn''',
'''Nuer''': '''nus_Latn''',
'''Nyanja''': '''nya_Latn''',
'''Occitan''': '''oci_Latn''',
'''West Central Oromo''': '''gaz_Latn''',
'''Odia''': '''ory_Orya''',
'''Pangasinan''': '''pag_Latn''',
'''Eastern Panjabi''': '''pan_Guru''',
'''Papiamento''': '''pap_Latn''',
'''Western Persian''': '''pes_Arab''',
'''Polish''': '''pol_Latn''',
'''Portuguese''': '''por_Latn''',
'''Dari''': '''prs_Arab''',
'''Southern Pashto''': '''pbt_Arab''',
'''Ayacucho Quechua''': '''quy_Latn''',
'''Romanian''': '''ron_Latn''',
'''Rundi''': '''run_Latn''',
'''Russian''': '''rus_Cyrl''',
'''Sango''': '''sag_Latn''',
'''Sanskrit''': '''san_Deva''',
'''Santali''': '''sat_Olck''',
'''Sicilian''': '''scn_Latn''',
'''Shan''': '''shn_Mymr''',
'''Sinhala''': '''sin_Sinh''',
'''Slovak''': '''slk_Latn''',
'''Slovenian''': '''slv_Latn''',
'''Samoan''': '''smo_Latn''',
'''Shona''': '''sna_Latn''',
'''Sindhi''': '''snd_Arab''',
'''Somali''': '''som_Latn''',
'''Southern Sotho''': '''sot_Latn''',
'''Spanish''': '''spa_Latn''',
'''Tosk Albanian''': '''als_Latn''',
'''Sardinian''': '''srd_Latn''',
'''Serbian''': '''srp_Cyrl''',
'''Swati''': '''ssw_Latn''',
'''Sundanese''': '''sun_Latn''',
'''Swedish''': '''swe_Latn''',
'''Swahili''': '''swh_Latn''',
'''Silesian''': '''szl_Latn''',
'''Tamil''': '''tam_Taml''',
'''Tatar''': '''tat_Cyrl''',
'''Telugu''': '''tel_Telu''',
'''Tajik''': '''tgk_Cyrl''',
'''Tagalog''': '''tgl_Latn''',
'''Thai''': '''tha_Thai''',
'''Tigrinya''': '''tir_Ethi''',
'''Tamasheq Latin''': '''taq_Latn''',
'''Tamasheq Tifinagh''': '''taq_Tfng''',
'''Tok Pisin''': '''tpi_Latn''',
'''Tswana''': '''tsn_Latn''',
'''Tsonga''': '''tso_Latn''',
'''Turkmen''': '''tuk_Latn''',
'''Tumbuka''': '''tum_Latn''',
'''Turkish''': '''tur_Latn''',
'''Twi''': '''twi_Latn''',
'''Central Atlas Tamazight''': '''tzm_Tfng''',
'''Uyghur''': '''uig_Arab''',
'''Ukrainian''': '''ukr_Cyrl''',
'''Umbundu''': '''umb_Latn''',
'''Urdu''': '''urd_Arab''',
'''Northern Uzbek''': '''uzn_Latn''',
'''Venetian''': '''vec_Latn''',
'''Vietnamese''': '''vie_Latn''',
'''Waray''': '''war_Latn''',
'''Wolof''': '''wol_Latn''',
'''Xhosa''': '''xho_Latn''',
'''Eastern Yiddish''': '''ydd_Hebr''',
'''Yoruba''': '''yor_Latn''',
'''Yue Chinese''': '''yue_Hant''',
'''Chinese Simplified''': '''zho_Hans''',
'''Chinese Traditional''': '''zho_Hant''',
'''Standard Malay''': '''zsm_Latn''',
'''Zulu''': '''zul_Latn''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'facebook/nllb-200-distilled-600M'
__UpperCamelCase = (
'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
)
__UpperCamelCase = 'translator'
__UpperCamelCase = AutoTokenizer
__UpperCamelCase = AutoModelForSeqaSeqLM
__UpperCamelCase = LANGUAGE_CODES
__UpperCamelCase = ['text', 'text', 'text']
__UpperCamelCase = ['text']
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if src_lang not in self.lang_to_code:
raise ValueError(f"""{src_lang} is not a supported language.""" )
if tgt_lang not in self.lang_to_code:
raise ValueError(f"""{tgt_lang} is not a supported language.""" )
_lowerCAmelCase = self.lang_to_code[src_lang]
_lowerCAmelCase = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
lowerCamelCase , return_tensors="""pt""" , src_lang=lowerCamelCase , tgt_lang=lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.model.generate(**lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=lowerCamelCase )
| 317
| 1
|
"""simple docstring"""
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin, SchedulerOutput
@dataclass
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 42
__UpperCamelCase = 42
class __lowerCamelCase ( __lowercase , __lowercase ):
__UpperCamelCase = 1
@register_to_config
def __init__(self , lowerCamelCase = 2_000 , lowerCamelCase = 0.15 , lowerCamelCase = 0.01 , lowerCamelCase = 1348.0 , lowerCamelCase = 1e-5 , lowerCamelCase = 1 , ):
'''simple docstring'''
_lowerCAmelCase = sigma_max
# setable values
_lowerCAmelCase = None
self.set_sigmas(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase = None ):
'''simple docstring'''
return sample
def A__ (self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase = sampling_eps if sampling_eps is not None else self.config.sampling_eps
_lowerCAmelCase = torch.linspace(1 , lowerCamelCase , lowerCamelCase , device=lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase = sigma_min if sigma_min is not None else self.config.sigma_min
_lowerCAmelCase = sigma_max if sigma_max is not None else self.config.sigma_max
_lowerCAmelCase = sampling_eps if sampling_eps is not None else self.config.sampling_eps
if self.timesteps is None:
self.set_timesteps(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = sigma_min * (sigma_max / sigma_min) ** (self.timesteps / sampling_eps)
_lowerCAmelCase = torch.exp(torch.linspace(math.log(lowerCamelCase ) , math.log(lowerCamelCase ) , lowerCamelCase ) )
_lowerCAmelCase = torch.tensor([sigma_min * (sigma_max / sigma_min) ** t for t in self.timesteps] )
def A__ (self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return torch.where(
timesteps == 0 , torch.zeros_like(t.to(timesteps.device ) ) , self.discrete_sigmas[timesteps - 1].to(timesteps.device ) , )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = True , ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
_lowerCAmelCase = timestep * torch.ones(
sample.shape[0] , device=sample.device ) # torch.repeat_interleave(timestep, sample.shape[0])
_lowerCAmelCase = (timestep * (len(self.timesteps ) - 1)).long()
# mps requires indices to be in the same device, so we use cpu as is the default with cuda
_lowerCAmelCase = timesteps.to(self.discrete_sigmas.device )
_lowerCAmelCase = self.discrete_sigmas[timesteps].to(sample.device )
_lowerCAmelCase = self.get_adjacent_sigma(lowerCamelCase , lowerCamelCase ).to(sample.device )
_lowerCAmelCase = torch.zeros_like(lowerCamelCase )
_lowerCAmelCase = (sigma**2 - adjacent_sigma**2) ** 0.5
# equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
# also equation 47 shows the analog from SDE models to ancestral sampling methods
_lowerCAmelCase = diffusion.flatten()
while len(diffusion.shape ) < len(sample.shape ):
_lowerCAmelCase = diffusion.unsqueeze(-1 )
_lowerCAmelCase = drift - diffusion**2 * model_output
# equation 6: sample noise for the diffusion term of
_lowerCAmelCase = randn_tensor(
sample.shape , layout=sample.layout , generator=lowerCamelCase , device=sample.device , dtype=sample.dtype )
_lowerCAmelCase = sample - drift # subtract because `dt` is a small negative timestep
# TODO is the variable diffusion the correct scaling term for the noise?
_lowerCAmelCase = prev_sample_mean + diffusion * noise # add impact of diffusion field g
if not return_dict:
return (prev_sample, prev_sample_mean)
return SdeVeOutput(prev_sample=lowerCamelCase , prev_sample_mean=lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase = None , lowerCamelCase = True , ):
'''simple docstring'''
if self.timesteps is None:
raise ValueError(
"""`self.timesteps` is not set, you need to run 'set_timesteps' after creating the scheduler""" )
# For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
# sample noise for correction
_lowerCAmelCase = randn_tensor(sample.shape , layout=sample.layout , generator=lowerCamelCase ).to(sample.device )
# compute step size from the model_output, the noise, and the snr
_lowerCAmelCase = torch.norm(model_output.reshape(model_output.shape[0] , -1 ) , dim=-1 ).mean()
_lowerCAmelCase = torch.norm(noise.reshape(noise.shape[0] , -1 ) , dim=-1 ).mean()
_lowerCAmelCase = (self.config.snr * noise_norm / grad_norm) ** 2 * 2
_lowerCAmelCase = step_size * torch.ones(sample.shape[0] ).to(sample.device )
# self.repeat_scalar(step_size, sample.shape[0])
# compute corrected sample: model_output term and noise term
_lowerCAmelCase = step_size.flatten()
while len(step_size.shape ) < len(sample.shape ):
_lowerCAmelCase = step_size.unsqueeze(-1 )
_lowerCAmelCase = sample + step_size * model_output
_lowerCAmelCase = prev_sample_mean + ((step_size * 2) ** 0.5) * noise
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = timesteps.to(original_samples.device )
_lowerCAmelCase = self.discrete_sigmas.to(original_samples.device )[timesteps]
_lowerCAmelCase = (
noise * sigmas[:, None, None, None]
if noise is not None
else torch.randn_like(lowerCamelCase ) * sigmas[:, None, None, None]
)
_lowerCAmelCase = noise + original_samples
return noisy_samples
def __len__(self ):
'''simple docstring'''
return self.config.num_train_timesteps
| 317
|
"""simple docstring"""
from math import isqrt
def __UpperCAmelCase ( snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case_ , snake_case_ ):
_lowerCAmelCase = False
return [i for i in range(2 , snake_case_ ) if is_prime[i]]
def __UpperCAmelCase ( snake_case_ : int = 10**8 ) -> int:
"""simple docstring"""
_lowerCAmelCase = calculate_prime_numbers(max_number // 2 )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = len(snake_case_ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 600851475143 ) -> int:
"""simple docstring"""
try:
_lowerCAmelCase = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
while n % i == 0:
_lowerCAmelCase = i
n //= i
i += 1
if n > 1:
_lowerCAmelCase = n
return int(snake_case_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
|
"""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 numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
__UpperCamelCase = 'CIDAS/clipseg-rd64-refined'
__UpperCamelCase = 'image_segmenter'
__UpperCamelCase = CLIPSegForImageSegmentation
__UpperCamelCase = ['image', 'text']
__UpperCamelCase = ['image']
def __init__(self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
requires_backends(self , ["""vision"""] )
super().__init__(*lowerCamelCase , **lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return self.pre_processor(text=[label] , images=[image] , padding=lowerCamelCase , return_tensors="""pt""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
with torch.no_grad():
_lowerCAmelCase = self.model(**lowerCamelCase ).logits
return logits
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = outputs.cpu().detach().numpy()
_lowerCAmelCase = 0
_lowerCAmelCase = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 317
| 1
|
"""simple docstring"""
from math import factorial
SCREAMING_SNAKE_CASE : dict[str, int] = {str(digit): factorial(digit) for digit in range(1_0)}
def __UpperCAmelCase ( snake_case_ : int ) -> int:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ):
raise TypeError("""Parameter number must be int""" )
if number < 0:
raise ValueError("""Parameter number must be greater than or equal to 0""" )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(snake_case_ ) )
def __UpperCAmelCase ( snake_case_ : int = 60 , snake_case_ : int = 1000000 ) -> int:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not isinstance(snake_case_ , snake_case_ ):
raise TypeError("""Parameters chain_length and number_limit must be int""" )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
"""Parameters chain_length and number_limit must be greater than 0""" )
# the counter for the chains with the exact desired length
_lowerCAmelCase = 0
# the cached sizes of the previous chains
_lowerCAmelCase = {}
for start_chain_element in range(1 , snake_case_ ):
# The temporary set will contain the elements of the chain
_lowerCAmelCase = set()
_lowerCAmelCase = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
_lowerCAmelCase = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(snake_case_ )
chain_set_length += 1
_lowerCAmelCase = digit_factorial_sum(snake_case_ )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
_lowerCAmelCase = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{solution()}')
| 317
|
"""simple docstring"""
from __future__ import annotations
import queue
class __lowerCamelCase :
def __init__(self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = data
_lowerCAmelCase = None
_lowerCAmelCase = None
def __UpperCAmelCase ( ) -> TreeNode:
"""simple docstring"""
print("""\n********Press N to stop entering at any point of time********\n""" )
_lowerCAmelCase = input("""Enter the value of the root node: """ ).strip().lower()
_lowerCAmelCase = queue.Queue()
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = q.get()
_lowerCAmelCase = F"""Enter the left node of {node_found.data}: """
_lowerCAmelCase = input(snake_case_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
_lowerCAmelCase = left_node
q.put(snake_case_ )
_lowerCAmelCase = F"""Enter the right node of {node_found.data}: """
_lowerCAmelCase = input(snake_case_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
_lowerCAmelCase = right_node
q.put(snake_case_ )
raise
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
print(node.data , end=""",""" )
pre_order(node.left )
pre_order(node.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
in_order(node.left )
print(node.data , end=""",""" )
in_order(node.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=""",""" )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = queue.Queue()
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = queue.Queue()
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = []
while not q.empty():
_lowerCAmelCase = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(snake_case_ )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = []
_lowerCAmelCase = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=""",""" )
stack.append(snake_case_ )
_lowerCAmelCase = n.left
# end of while means current node doesn't have left child
_lowerCAmelCase = stack.pop()
# start to traverse its right child
_lowerCAmelCase = n.right
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = []
_lowerCAmelCase = node
while n or stack:
while n:
stack.append(snake_case_ )
_lowerCAmelCase = n.left
_lowerCAmelCase = stack.pop()
print(n.data , end=""",""" )
_lowerCAmelCase = n.right
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase , _lowerCAmelCase = [], []
_lowerCAmelCase = node
stacka.append(snake_case_ )
while stacka: # to find the reversed order of post order, store it in stack2
_lowerCAmelCase = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(snake_case_ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=""",""" )
def __UpperCAmelCase ( snake_case_ : str = "" , snake_case_ : int=50 , snake_case_ : Dict="*" ) -> str:
"""simple docstring"""
if not s:
return "\n" + width * char
_lowerCAmelCase , _lowerCAmelCase = divmod(width - len(snake_case_ ) - 2 , 2 )
return F"""{left * char} {s} {(left + extra) * char}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('''Binary Tree Traversals'''))
SCREAMING_SNAKE_CASE : TreeNode = build_tree()
print(prompt('''Pre Order Traversal'''))
pre_order(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal'''))
in_order(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal'''))
post_order(node)
print(prompt() + '''\n''')
print(prompt('''Level Order Traversal'''))
level_order(node)
print(prompt() + '''\n''')
print(prompt('''Actual Level Order Traversal'''))
level_order_actual(node)
print('''*''' * 5_0 + '''\n''')
print(prompt('''Pre Order Traversal - Iteration Version'''))
pre_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal - Iteration Version'''))
in_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal - Iteration Version'''))
post_order_iter(node)
print(prompt())
| 317
| 1
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
class __lowerCamelCase ( __lowercase ):
def __init__(self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
warnings.warn(
"""The class YolosFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use YolosImageProcessor instead.""" , lowerCamelCase , )
super().__init__(*lowerCamelCase , **lowerCamelCase )
| 317
|
"""simple docstring"""
from __future__ import annotations
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = text, pattern
_lowerCAmelCase , _lowerCAmelCase = len(lowerCamelCase ), len(lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def A__ (self , lowerCamelCase ):
'''simple docstring'''
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = []
for i in range(self.textLen - self.patLen + 1 ):
_lowerCAmelCase = self.mismatch_in_text(lowerCamelCase )
if mismatch_index == -1:
positions.append(lowerCamelCase )
else:
_lowerCAmelCase = self.match_in_pattern(self.text[mismatch_index] )
_lowerCAmelCase = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
SCREAMING_SNAKE_CASE : Any = '''ABAABA'''
SCREAMING_SNAKE_CASE : Optional[int] = '''AB'''
SCREAMING_SNAKE_CASE : str = BoyerMooreSearch(text, pattern)
SCREAMING_SNAKE_CASE : Tuple = bms.bad_character_heuristic()
if len(positions) == 0:
print('''No match found''')
else:
print('''Pattern found in following positions: ''')
print(positions)
| 317
| 1
|
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : str = ['''model.decoder.embed_positions.weights''']
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
if "emb" in name:
_lowerCAmelCase = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
_lowerCAmelCase = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
_lowerCAmelCase = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
_lowerCAmelCase = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
_lowerCAmelCase = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
_lowerCAmelCase = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
_lowerCAmelCase = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
_lowerCAmelCase = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
_lowerCAmelCase = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
_lowerCAmelCase = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
_lowerCAmelCase = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def __UpperCAmelCase ( snake_case_ : OrderedDict , snake_case_ : int ) -> Tuple[Dict, Dict]:
"""simple docstring"""
_lowerCAmelCase = list(state_dict.keys() )
_lowerCAmelCase = {}
for key in keys:
_lowerCAmelCase = state_dict.pop(snake_case_ )
_lowerCAmelCase = rename_keys(snake_case_ )
if "in_proj_weight" in key:
# split fused qkv proj
_lowerCAmelCase = val[:hidden_size, :]
_lowerCAmelCase = val[hidden_size : 2 * hidden_size, :]
_lowerCAmelCase = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
_lowerCAmelCase = val
else:
_lowerCAmelCase = val
return state_dict, enc_dec_proj_state_dict
def __UpperCAmelCase ( snake_case_ : str ) -> MusicgenDecoderConfig:
"""simple docstring"""
if checkpoint == "small":
# default config values
_lowerCAmelCase = 1024
_lowerCAmelCase = 24
_lowerCAmelCase = 16
elif checkpoint == "medium":
_lowerCAmelCase = 1536
_lowerCAmelCase = 48
_lowerCAmelCase = 24
elif checkpoint == "large":
_lowerCAmelCase = 2048
_lowerCAmelCase = 48
_lowerCAmelCase = 32
else:
raise ValueError(F"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
_lowerCAmelCase = MusicgenDecoderConfig(
hidden_size=snake_case_ , ffn_dim=hidden_size * 4 , num_hidden_layers=snake_case_ , num_attention_heads=snake_case_ , )
return config
@torch.no_grad()
def __UpperCAmelCase ( snake_case_ : List[str] , snake_case_ : int=None , snake_case_ : Tuple=None , snake_case_ : str="cpu" ) -> Any:
"""simple docstring"""
_lowerCAmelCase = MusicGen.get_pretrained(snake_case_ , device=snake_case_ )
_lowerCAmelCase = decoder_config_from_checkpoint(snake_case_ )
_lowerCAmelCase = fairseq_model.lm.state_dict()
_lowerCAmelCase , _lowerCAmelCase = rename_state_dict(
snake_case_ , hidden_size=decoder_config.hidden_size )
_lowerCAmelCase = TaEncoderModel.from_pretrained("""t5-base""" )
_lowerCAmelCase = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
_lowerCAmelCase = MusicgenForCausalLM(snake_case_ ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
_lowerCAmelCase , _lowerCAmelCase = decoder.load_state_dict(snake_case_ , strict=snake_case_ )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(snake_case_ )
if len(snake_case_ ) > 0:
raise ValueError(F"""Missing key(s) in state_dict: {missing_keys}""" )
if len(snake_case_ ) > 0:
raise ValueError(F"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
_lowerCAmelCase = MusicgenForConditionalGeneration(text_encoder=snake_case_ , audio_encoder=snake_case_ , decoder=snake_case_ )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(snake_case_ )
# check we can do a forward pass
_lowerCAmelCase = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
_lowerCAmelCase = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
_lowerCAmelCase = model(input_ids=snake_case_ , decoder_input_ids=snake_case_ ).logits
if logits.shape != (8, 1, 2048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
_lowerCAmelCase = AutoTokenizer.from_pretrained("""t5-base""" )
_lowerCAmelCase = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
_lowerCAmelCase = MusicgenProcessor(feature_extractor=snake_case_ , tokenizer=snake_case_ )
# set the appropriate bos/pad token ids
_lowerCAmelCase = 2048
_lowerCAmelCase = 2048
# set other default generation config params
_lowerCAmelCase = int(30 * audio_encoder.config.frame_rate )
_lowerCAmelCase = True
_lowerCAmelCase = 3.0
if pytorch_dump_folder is not None:
Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
logger.info(F"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(snake_case_ )
processor.save_pretrained(snake_case_ )
if repo_id:
logger.info(F"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(snake_case_ )
processor.push_to_hub(snake_case_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint''',
default='''small''',
type=str,
help='''Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.''',
)
parser.add_argument(
'''--pytorch_dump_folder''',
required=True,
default=None,
type=str,
help='''Path to the output PyTorch model directory.''',
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
parser.add_argument(
'''--device''', default='''cpu''', type=str, help='''Torch device to run the conversion, either cpu or cuda.'''
)
SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 317
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
SCREAMING_SNAKE_CASE : List[str] = False
class __lowerCamelCase ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(
image=lowerCamelCase , generator=lowerCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
_lowerCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 317
| 1
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : Optional[Any] ) -> Any:
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = set({"""(""", """[""", """{"""} )
_lowerCAmelCase = set({""")""", """]""", """}"""} )
_lowerCAmelCase = {"""{""": """}""", """[""": """]""", """(""": """)"""}
for i in range(len(snake_case_ ) ):
if s[i] in open_brackets:
stack.append(s[i] )
elif s[i] in closed_brackets and (
len(snake_case_ ) == 0 or (len(snake_case_ ) > 0 and open_to_closed[stack.pop()] != s[i])
):
return False
return len(snake_case_ ) == 0
def __UpperCAmelCase ( ) -> str:
"""simple docstring"""
_lowerCAmelCase = input("""Enter sequence of brackets: """ )
if is_balanced(snake_case_ ):
print(snake_case_ , """is balanced""" )
else:
print(snake_case_ , """is not balanced""" )
if __name__ == "__main__":
main()
| 317
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCamelCase ( __lowercase , unittest.TestCase ):
__UpperCamelCase = DiTPipeline
__UpperCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__UpperCamelCase = PipelineTesterMixin.required_optional_params - {
'latents',
'num_images_per_prompt',
'callback',
'callback_steps',
}
__UpperCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__UpperCamelCase = False
def A__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
_lowerCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=lowerCamelCase , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1_000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=lowerCamelCase , )
_lowerCAmelCase = AutoencoderKL()
_lowerCAmelCase = DDIMScheduler()
_lowerCAmelCase = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def A__ (self , lowerCamelCase , lowerCamelCase=0 ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(lowerCamelCase )
else:
_lowerCAmelCase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
_lowerCAmelCase = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = """cpu"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_lowerCAmelCase = pipe(**lowerCamelCase ).images
_lowerCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_lowerCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_lowerCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase , 1e-3 )
def A__ (self ):
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=lowerCamelCase , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def A__ (self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_lowerCAmelCase = pipe.get_label_ids(lowerCamelCase )
_lowerCAmelCase = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = load_numpy(
f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-2
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella"""]
_lowerCAmelCase = pipe.get_label_ids(lowerCamelCase )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
f"""/dit/{word}_512.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-1
| 317
| 1
|
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM
@require_tf
@require_sentencepiece
@require_tokenizers
class __lowerCamelCase ( unittest.TestCase ):
@slow
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = TFAutoModelForSeqaSeqLM.from_pretrained("""google/mt5-small""" )
_lowerCAmelCase = AutoTokenizer.from_pretrained("""google/mt5-small""" )
_lowerCAmelCase = tokenizer("""Hello there""" , return_tensors="""tf""" ).input_ids
_lowerCAmelCase = tokenizer("""Hi I am""" , return_tensors="""tf""" ).input_ids
_lowerCAmelCase = model(lowerCamelCase , labels=lowerCamelCase ).loss
_lowerCAmelCase = -tf.math.reduce_mean(lowerCamelCase ).numpy()
_lowerCAmelCase = -21.22_8168
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 2e-4 )
| 317
|
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
return getitem, k
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return setitem, k, v
def __UpperCAmelCase ( snake_case_ : str ) -> Optional[int]:
"""simple docstring"""
return delitem, k
def __UpperCAmelCase ( snake_case_ : Optional[Any] , snake_case_ : Tuple , *snake_case_ : Tuple ) -> str:
"""simple docstring"""
try:
return fun(snake_case_ , *snake_case_ ), None
except Exception as e:
return None, e
SCREAMING_SNAKE_CASE : int = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
SCREAMING_SNAKE_CASE : List[Any] = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
SCREAMING_SNAKE_CASE : Any = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
SCREAMING_SNAKE_CASE : Union[str, Any] = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
SCREAMING_SNAKE_CASE : Optional[Any] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
SCREAMING_SNAKE_CASE : Optional[int] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
"""operations""" , (
pytest.param(_add_items , id="""add items""" ),
pytest.param(_overwrite_items , id="""overwrite items""" ),
pytest.param(_delete_items , id="""delete items""" ),
pytest.param(_access_absent_items , id="""access absent items""" ),
pytest.param(_add_with_resize_up , id="""add with resize up""" ),
pytest.param(_add_with_resize_down , id="""add with resize down""" ),
) , )
def __UpperCAmelCase ( snake_case_ : List[Any] ) -> Tuple:
"""simple docstring"""
_lowerCAmelCase = HashMap(initial_block_size=4 )
_lowerCAmelCase = {}
for _, (fun, *args) in enumerate(snake_case_ ):
_lowerCAmelCase , _lowerCAmelCase = _run_operation(snake_case_ , snake_case_ , *snake_case_ )
_lowerCAmelCase , _lowerCAmelCase = _run_operation(snake_case_ , snake_case_ , *snake_case_ )
assert my_res == py_res
assert str(snake_case_ ) == str(snake_case_ )
assert set(snake_case_ ) == set(snake_case_ )
assert len(snake_case_ ) == len(snake_case_ )
assert set(my.items() ) == set(py.items() )
def __UpperCAmelCase ( ) -> Tuple:
"""simple docstring"""
def is_public(snake_case_ : str ) -> bool:
return not name.startswith("""_""" )
_lowerCAmelCase = {name for name in dir({} ) if is_public(snake_case_ )}
_lowerCAmelCase = {name for name in dir(HashMap() ) if is_public(snake_case_ )}
assert dict_public_names > hash_public_names
| 317
| 1
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int ) -> int:
"""simple docstring"""
_lowerCAmelCase = [1]
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 0, 0, 0
_lowerCAmelCase = ugly_nums[ia] * 2
_lowerCAmelCase = ugly_nums[ia] * 3
_lowerCAmelCase = ugly_nums[ia] * 5
for _ in range(1 , snake_case_ ):
_lowerCAmelCase = min(snake_case_ , snake_case_ , snake_case_ )
ugly_nums.append(snake_case_ )
if next_num == next_a:
ia += 1
_lowerCAmelCase = ugly_nums[ia] * 2
if next_num == next_a:
ia += 1
_lowerCAmelCase = ugly_nums[ia] * 3
if next_num == next_a:
ia += 1
_lowerCAmelCase = ugly_nums[ia] * 5
return ugly_nums[-1]
if __name__ == "__main__":
from doctest import testmod
testmod(verbose=True)
print(F'{ugly_numbers(2_0_0) = }')
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(snake_case_ : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(snake_case_ )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
snake_case_ : int , snake_case_ : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
_lowerCAmelCase = sum(
count_of_possible_combinations_with_dp_array(target - item , snake_case_ )
for item in array )
_lowerCAmelCase = answer
return answer
_lowerCAmelCase = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(snake_case_ , snake_case_ )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
_lowerCAmelCase = [0] * (target + 1)
_lowerCAmelCase = 1
for i in range(1 , target + 1 ):
for j in range(snake_case_ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : Tuple = 3
SCREAMING_SNAKE_CASE : Any = 5
SCREAMING_SNAKE_CASE : Optional[int] = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 317
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : int = {
'''configuration_mobilenet_v2''': [
'''MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''MobileNetV2Config''',
'''MobileNetV2OnnxConfig''',
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : List[str] = ['''MobileNetV2FeatureExtractor''']
SCREAMING_SNAKE_CASE : List[str] = ['''MobileNetV2ImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Any = [
'''MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MobileNetV2ForImageClassification''',
'''MobileNetV2ForSemanticSegmentation''',
'''MobileNetV2Model''',
'''MobileNetV2PreTrainedModel''',
'''load_tf_weights_in_mobilenet_v2''',
]
if TYPE_CHECKING:
from .configuration_mobilenet_va import (
MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileNetVaConfig,
MobileNetVaOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
from .image_processing_mobilenet_va import MobileNetVaImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilenet_va import (
MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileNetVaForImageClassification,
MobileNetVaForSemanticSegmentation,
MobileNetVaModel,
MobileNetVaPreTrainedModel,
load_tf_weights_in_mobilenet_va,
)
else:
import sys
SCREAMING_SNAKE_CASE : Dict = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 317
|
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for letter in string.ascii_lowercase]
SCREAMING_SNAKE_CASE : set[int] = {ord(char) for char in VALID_CHARS}
SCREAMING_SNAKE_CASE : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def __UpperCAmelCase ( snake_case_ : list[int] , snake_case_ : tuple[int, ...] ) -> str | None:
"""simple docstring"""
_lowerCAmelCase = ""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
for keychar, cipherchar in zip(cycle(snake_case_ ) , snake_case_ ):
_lowerCAmelCase = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(snake_case_ )
return decoded
def __UpperCAmelCase ( snake_case_ : list[int] ) -> list[str]:
"""simple docstring"""
_lowerCAmelCase = []
for key in product(snake_case_ , repeat=3 ):
_lowerCAmelCase = try_key(snake_case_ , snake_case_ )
if encoded is not None:
possibles.append(snake_case_ )
return possibles
def __UpperCAmelCase ( snake_case_ : list[str] , snake_case_ : str ) -> list[str]:
"""simple docstring"""
return [possible for possible in possibles if common_word in possible.lower()]
def __UpperCAmelCase ( snake_case_ : str = "p059_cipher.txt" ) -> int:
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = Path(snake_case_ ).parent.joinpath(snake_case_ ).read_text(encoding="""utf-8""" )
_lowerCAmelCase = [int(snake_case_ ) for number in data.strip().split(""",""" )]
_lowerCAmelCase = filter_valid_chars(snake_case_ )
for common_word in COMMON_WORDS:
_lowerCAmelCase = filter_common_word(snake_case_ , snake_case_ )
if len(snake_case_ ) == 1:
break
_lowerCAmelCase = possibles[0]
return sum(ord(snake_case_ ) for char in decoded_text )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def __UpperCAmelCase ( snake_case_ : Optional[int] , snake_case_ : Any=False ) -> str:
"""simple docstring"""
_lowerCAmelCase = OmegaConf.load(snake_case_ )
if display:
print(yaml.dump(OmegaConf.to_container(snake_case_ ) ) )
return config
def __UpperCAmelCase ( snake_case_ : Any , snake_case_ : int=None , snake_case_ : List[str]=None ) -> Tuple:
"""simple docstring"""
if conf_path is None:
_lowerCAmelCase = """./model_checkpoints/vqgan_only.yaml"""
_lowerCAmelCase = load_config(snake_case_ , display=snake_case_ )
_lowerCAmelCase = VQModel(**config.model.params )
if ckpt_path is None:
_lowerCAmelCase = """./model_checkpoints/vqgan_only.pt"""
_lowerCAmelCase = torch.load(snake_case_ , map_location=snake_case_ )
if ".ckpt" in ckpt_path:
_lowerCAmelCase = sd["""state_dict"""]
model.load_state_dict(snake_case_ , strict=snake_case_ )
model.to(snake_case_ )
del sd
return model
def __UpperCAmelCase ( snake_case_ : List[Any] , snake_case_ : Tuple ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = model.encode(snake_case_ )
print(F"""VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}""" )
_lowerCAmelCase = model.decode(snake_case_ )
return xrec
def __UpperCAmelCase ( snake_case_ : List[Any] , snake_case_ : Any=False ) -> str:
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = string.rsplit(""".""" , 1 )
if reload:
_lowerCAmelCase = importlib.import_module(snake_case_ )
importlib.reload(snake_case_ )
return getattr(importlib.import_module(snake_case_ , package=snake_case_ ) , cls )
def __UpperCAmelCase ( snake_case_ : List[str] ) -> List[Any]:
"""simple docstring"""
if "target" not in config:
raise KeyError("""Expected key `target` to instantiate.""" )
return get_obj_from_str(config["""target"""] )(**config.get("""params""" , {} ) )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : Union[str, Any] , snake_case_ : str=True , snake_case_ : Union[str, Any]=True ) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase = instantiate_from_config(snake_case_ )
if sd is not None:
model.load_state_dict(snake_case_ )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : Optional[int] , snake_case_ : Union[str, Any] , snake_case_ : Any ) -> str:
"""simple docstring"""
if ckpt:
_lowerCAmelCase = torch.load(snake_case_ , map_location="""cpu""" )
_lowerCAmelCase = pl_sd["""global_step"""]
print(F"""loaded model from global step {global_step}.""" )
else:
_lowerCAmelCase = {"""state_dict""": None}
_lowerCAmelCase = None
_lowerCAmelCase = load_model_from_config(config.model , pl_sd["""state_dict"""] , gpu=snake_case_ , eval_mode=snake_case_ )["""model"""]
return model, global_step
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 1000000 ) -> int:
"""simple docstring"""
_lowerCAmelCase = limit + 1
_lowerCAmelCase = [0] * limit
for first_term in range(1 , snake_case_ ):
for n in range(snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
_lowerCAmelCase = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'bert-generation'
def __init__(self , lowerCamelCase=50_358 , lowerCamelCase=1_024 , lowerCamelCase=24 , lowerCamelCase=16 , lowerCamelCase=4_096 , lowerCamelCase="gelu" , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=512 , lowerCamelCase=0.02 , lowerCamelCase=1e-12 , lowerCamelCase=0 , lowerCamelCase=2 , lowerCamelCase=1 , lowerCamelCase="absolute" , lowerCamelCase=True , **lowerCamelCase , ):
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase , bos_token_id=lowerCamelCase , eos_token_id=lowerCamelCase , **lowerCamelCase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = position_embedding_type
_lowerCAmelCase = use_cache
| 317
|
"""simple docstring"""
from functools import reduce
SCREAMING_SNAKE_CASE : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def __UpperCAmelCase ( snake_case_ : str = N ) -> int:
"""simple docstring"""
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda snake_case_ , snake_case_ : str(int(snake_case_ ) * int(snake_case_ ) ) , n[i : i + 13] ) )
for i in range(len(snake_case_ ) - 12 ) )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""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
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[int] = {
'''microsoft/beit-base-patch16-224-pt22k''': (
'''https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'''
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'beit'
def __init__(self , lowerCamelCase=8_192 , lowerCamelCase=768 , lowerCamelCase=12 , lowerCamelCase=12 , lowerCamelCase=3_072 , lowerCamelCase="gelu" , lowerCamelCase=0.0 , lowerCamelCase=0.0 , lowerCamelCase=0.02 , lowerCamelCase=1e-12 , lowerCamelCase=224 , lowerCamelCase=16 , lowerCamelCase=3 , lowerCamelCase=False , lowerCamelCase=False , lowerCamelCase=False , lowerCamelCase=False , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=True , lowerCamelCase=[3, 5, 7, 11] , lowerCamelCase=[1, 2, 3, 6] , lowerCamelCase=True , lowerCamelCase=0.4 , lowerCamelCase=256 , lowerCamelCase=1 , lowerCamelCase=False , lowerCamelCase=255 , **lowerCamelCase , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = initializer_range
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = image_size
_lowerCAmelCase = patch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = use_mask_token
_lowerCAmelCase = use_absolute_position_embeddings
_lowerCAmelCase = use_relative_position_bias
_lowerCAmelCase = use_shared_relative_position_bias
_lowerCAmelCase = layer_scale_init_value
_lowerCAmelCase = drop_path_rate
_lowerCAmelCase = use_mean_pooling
# decode head attributes (semantic segmentation)
_lowerCAmelCase = out_indices
_lowerCAmelCase = pool_scales
# auxiliary head attributes (semantic segmentation)
_lowerCAmelCase = use_auxiliary_head
_lowerCAmelCase = auxiliary_loss_weight
_lowerCAmelCase = auxiliary_channels
_lowerCAmelCase = auxiliary_num_convs
_lowerCAmelCase = auxiliary_concat_input
_lowerCAmelCase = semantic_loss_ignore_index
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = version.parse('1.11' )
@property
def A__ (self ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def A__ (self ):
'''simple docstring'''
return 1e-4
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 600851475143 ) -> int:
"""simple docstring"""
try:
_lowerCAmelCase = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
while n % i == 0:
_lowerCAmelCase = i
n //= i
i += 1
if n > 1:
_lowerCAmelCase = n
return int(snake_case_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE : List[str] = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Optional[Any] = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Tuple = [
'''PLBART_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''PLBartForCausalLM''',
'''PLBartForConditionalGeneration''',
'''PLBartForSequenceClassification''',
'''PLBartModel''',
'''PLBartPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 317
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
SCREAMING_SNAKE_CASE : Optional[Any] = logging.getLogger(__name__)
@dataclass
class __lowerCamelCase :
__UpperCamelCase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Whether tp freeze the encoder.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class __lowerCamelCase :
__UpperCamelCase = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__UpperCamelCase = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
__UpperCamelCase = field(
default=1_024 , 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=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCamelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
__UpperCamelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCamelCase = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
__UpperCamelCase = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
__UpperCamelCase = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Source language id for translation.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Target language id for translation.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': '# num_beams to use for evaluation.'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def __UpperCAmelCase ( snake_case_ : Optional[int] , snake_case_ : Any , snake_case_ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(snake_case_ , os.path.join(snake_case_ , F"""{split}_results.json""" ) )
def __UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_args_into_dataclasses()
check_output_dir(snake_case_ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("""Training/evaluation parameters %s""" , snake_case_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_lowerCAmelCase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(snake_case_ , snake_case_ , snake_case_ ):
assert hasattr(snake_case_ , snake_case_ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(snake_case_ , snake_case_ , getattr(snake_case_ , snake_case_ ) )
_lowerCAmelCase = 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 = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=snake_case_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(snake_case_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCAmelCase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(snake_case_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(snake_case_ , snake_case_ ):
_lowerCAmelCase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCAmelCase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(snake_case_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCAmelCase = SeqaSeqDataset
# Get datasets
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCAmelCase = (
build_compute_metrics_fn(data_args.task , snake_case_ ) if training_args.predict_with_generate else None
)
_lowerCAmelCase = SeqaSeqTrainer(
model=snake_case_ , args=snake_case_ , data_args=snake_case_ , train_dataset=snake_case_ , eval_dataset=snake_case_ , data_collator=SeqaSeqDataCollator(
snake_case_ , snake_case_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=snake_case_ , tokenizer=snake_case_ , )
_lowerCAmelCase = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
_lowerCAmelCase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCAmelCase = train_result.metrics
_lowerCAmelCase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_lowerCAmelCase = trainer.evaluate(metric_key_prefix="""val""" )
_lowerCAmelCase = data_args.n_val
_lowerCAmelCase = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
_lowerCAmelCase = trainer.predict(test_dataset=snake_case_ , metric_key_prefix="""test""" )
_lowerCAmelCase = test_output.metrics
_lowerCAmelCase = data_args.n_test
if trainer.is_world_process_zero():
_lowerCAmelCase = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
if training_args.predict_with_generate:
_lowerCAmelCase = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=snake_case_ , clean_up_tokenization_spaces=snake_case_ )
_lowerCAmelCase = lmap(str.strip , snake_case_ )
write_txt_file(snake_case_ , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(snake_case_ , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def __UpperCAmelCase ( snake_case_ : Any ) -> Dict:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 317
| 1
|
"""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
SCREAMING_SNAKE_CASE : Tuple = get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class __lowerCamelCase ( __lowercase , unittest.TestCase ):
__UpperCamelCase = XGLMTokenizer
__UpperCamelCase = XGLMTokenizerFast
__UpperCamelCase = True
__UpperCamelCase = True
def A__ (self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
_lowerCAmelCase = XGLMTokenizer(lowerCamelCase , keep_accents=lowerCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = """<pad>"""
_lowerCAmelCase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase ) , lowerCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase ) , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<s>""" )
self.assertEqual(vocab_keys[1] , """<pad>""" )
self.assertEqual(len(lowerCamelCase ) , 1_008 )
def A__ (self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 1_008 )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = XGLMTokenizer(lowerCamelCase , keep_accents=lowerCamelCase )
_lowerCAmelCase = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(lowerCamelCase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCamelCase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_lowerCAmelCase = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
lowerCamelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_lowerCAmelCase = tokenizer.convert_tokens_to_ids(lowerCamelCase )
self.assertListEqual(
lowerCamelCase , [
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]
] , )
_lowerCAmelCase = tokenizer.convert_ids_to_tokens(lowerCamelCase )
self.assertListEqual(
lowerCamelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""<unk>""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""<unk>""",
""".""",
] , )
@cached_property
def A__ (self ):
'''simple docstring'''
return XGLMTokenizer.from_pretrained("""facebook/xglm-564M""" )
def A__ (self ):
'''simple docstring'''
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(lowerCamelCase , f.name )
_lowerCAmelCase = XGLMTokenizer(f.name , keep_accents=lowerCamelCase )
_lowerCAmelCase = pickle.dumps(lowerCamelCase )
pickle.loads(lowerCamelCase )
def A__ (self ):
'''simple docstring'''
if not self.test_rust_tokenizer:
return
_lowerCAmelCase = self.get_tokenizer()
_lowerCAmelCase = self.get_rust_tokenizer()
_lowerCAmelCase = """I was born in 92000, and this is falsé."""
_lowerCAmelCase = tokenizer.tokenize(lowerCamelCase )
_lowerCAmelCase = rust_tokenizer.tokenize(lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = tokenizer.encode(lowerCamelCase , add_special_tokens=lowerCamelCase )
_lowerCAmelCase = rust_tokenizer.encode(lowerCamelCase , add_special_tokens=lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = self.get_rust_tokenizer()
_lowerCAmelCase = tokenizer.encode(lowerCamelCase )
_lowerCAmelCase = rust_tokenizer.encode(lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
@slow
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = """Hello World!"""
_lowerCAmelCase = [2, 31_227, 4_447, 35]
self.assertListEqual(lowerCamelCase , self.big_tokenizer.encode(lowerCamelCase ) )
@slow
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = (
"""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
_lowerCAmelCase = [2, 1_018, 67, 11, 1_988, 2_617, 5_631, 278, 11, 3_407, 48, 71_630, 28_085, 4, 3_234, 157, 13, 6, 5, 6, 4, 3_526, 768, 15, 659, 57, 298, 3_983, 864, 129, 21, 6, 5, 13_675, 377, 652, 7_580, 10_341, 155, 2_817, 422, 1_666, 7, 1_674, 53, 113, 202_277, 17_892, 33, 60, 87, 4, 3_234, 157, 61, 2_667, 52_376, 19, 88, 23, 735]
# fmt: on
self.assertListEqual(lowerCamelCase , self.big_tokenizer.encode(lowerCamelCase ) )
@slow
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = {
"""input_ids""": [[2, 108_825, 1_163, 15, 88_010, 473, 15_898, 157, 13_672, 1_857, 312, 8, 238_021, 1_163, 53, 13_672, 1_857, 312, 8, 53_283, 182_396, 8, 18_566, 16, 36_733, 4_101, 8, 230, 244_017, 122_553, 7, 15, 132_597, 4, 293, 12_511, 7_610, 4, 3_414, 132_597, 9, 4, 32_361, 362, 4, 734, 28_512, 32_569, 18, 4, 32_361, 26_096, 14_982, 73, 18_715, 21_433, 235_261, 15, 492, 12_427, 16, 53, 18_715, 21_433, 65_454, 15, 23_659, 563, 16, 278, 597, 2_843, 595, 7_931, 182_396, 64_186, 22, 886, 595, 132_981, 53, 25_540, 3_449, 43_982, 39_901, 5_951, 878, 330, 4, 27_694, 80_269, 312, 53, 6_517, 11_780, 611, 20_408, 5], [2, 6, 132_597, 67, 42_897, 33, 592, 8, 163_729, 25_540, 361, 136_997, 109_514, 173_230, 7, 501, 60, 102_913, 196, 5_631, 235, 63_243, 473, 6, 231_757, 74, 5_277, 7_905, 53, 3_095, 37_317, 22, 454, 183_874, 5], [2, 268, 31_298, 46_530, 6, 132_935, 43_831, 7, 597, 32, 24, 3_688, 9_865, 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=lowerCamelCase , model_name="""facebook/xglm-564M""" , padding=lowerCamelCase , )
| 317
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : List[Any] = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = [
'''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FocalNetForImageClassification''',
'''FocalNetForMaskedImageModeling''',
'''FocalNetBackbone''',
'''FocalNetModel''',
'''FocalNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 317
| 1
|
"""simple docstring"""
from collections.abc import Generator
from math import sin
def __UpperCAmelCase ( snake_case_ : bytes ) -> bytes:
"""simple docstring"""
if len(snake_case_ ) != 32:
raise ValueError("""Input must be of length 32""" )
_lowerCAmelCase = B""""""
for i in [3, 2, 1, 0]:
little_endian += string_aa[8 * i : 8 * i + 8]
return little_endian
def __UpperCAmelCase ( snake_case_ : int ) -> bytes:
"""simple docstring"""
if i < 0:
raise ValueError("""Input must be non-negative""" )
_lowerCAmelCase = format(snake_case_ , """08x""" )[-8:]
_lowerCAmelCase = B""""""
for i in [3, 2, 1, 0]:
little_endian_hex += hex_rep[2 * i : 2 * i + 2].encode("""utf-8""" )
return little_endian_hex
def __UpperCAmelCase ( snake_case_ : bytes ) -> bytes:
"""simple docstring"""
_lowerCAmelCase = B""""""
for char in message:
bit_string += format(snake_case_ , """08b""" ).encode("""utf-8""" )
_lowerCAmelCase = format(len(snake_case_ ) , """064b""" ).encode("""utf-8""" )
# Pad bit_string to a multiple of 512 chars
bit_string += b"1"
while len(snake_case_ ) % 512 != 448:
bit_string += b"0"
bit_string += to_little_endian(start_len[32:] ) + to_little_endian(start_len[:32] )
return bit_string
def __UpperCAmelCase ( snake_case_ : bytes ) -> Generator[list[int], None, None]:
"""simple docstring"""
if len(snake_case_ ) % 512 != 0:
raise ValueError("""Input must have length that's a multiple of 512""" )
for pos in range(0 , len(snake_case_ ) , 512 ):
_lowerCAmelCase = bit_string[pos : pos + 512]
_lowerCAmelCase = []
for i in range(0 , 512 , 32 ):
block_words.append(int(to_little_endian(block[i : i + 32] ) , 2 ) )
yield block_words
def __UpperCAmelCase ( snake_case_ : int ) -> int:
"""simple docstring"""
if i < 0:
raise ValueError("""Input must be non-negative""" )
_lowerCAmelCase = format(snake_case_ , """032b""" )
_lowerCAmelCase = """"""
for c in i_str:
new_str += "1" if c == "0" else "0"
return int(snake_case_ , 2 )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : int ) -> int:
"""simple docstring"""
return (a + b) % 2**32
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : int ) -> int:
"""simple docstring"""
if i < 0:
raise ValueError("""Input must be non-negative""" )
if shift < 0:
raise ValueError("""Shift must be non-negative""" )
return ((i << shift) ^ (i >> (32 - shift))) % 2**32
def __UpperCAmelCase ( snake_case_ : bytes ) -> bytes:
"""simple docstring"""
_lowerCAmelCase = preprocess(snake_case_ )
_lowerCAmelCase = [int(2**32 * abs(sin(i + 1 ) ) ) for i in range(64 )]
# Starting states
_lowerCAmelCase = 0X67_452_301
_lowerCAmelCase = 0Xef_cda_b89
_lowerCAmelCase = 0X98_bad_cfe
_lowerCAmelCase = 0X10_325_476
_lowerCAmelCase = [
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
7,
12,
17,
22,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
5,
9,
14,
20,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
4,
11,
16,
23,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
6,
10,
15,
21,
]
# Process bit string in chunks, each with 16 32-char words
for block_words in get_block_words(snake_case_ ):
_lowerCAmelCase = aa
_lowerCAmelCase = ba
_lowerCAmelCase = ca
_lowerCAmelCase = da
# Hash current chunk
for i in range(64 ):
if i <= 15:
# f = (b & c) | (not_32(b) & d) # Alternate definition for f
_lowerCAmelCase = d ^ (b & (c ^ d))
_lowerCAmelCase = i
elif i <= 31:
# f = (d & b) | (not_32(d) & c) # Alternate definition for f
_lowerCAmelCase = c ^ (d & (b ^ c))
_lowerCAmelCase = (5 * i + 1) % 16
elif i <= 47:
_lowerCAmelCase = b ^ c ^ d
_lowerCAmelCase = (3 * i + 5) % 16
else:
_lowerCAmelCase = c ^ (b | not_aa(snake_case_ ))
_lowerCAmelCase = (7 * i) % 16
_lowerCAmelCase = (f + a + added_consts[i] + block_words[g]) % 2**32
_lowerCAmelCase = d
_lowerCAmelCase = c
_lowerCAmelCase = b
_lowerCAmelCase = sum_aa(snake_case_ , left_rotate_aa(snake_case_ , shift_amounts[i] ) )
# Add hashed chunk to running total
_lowerCAmelCase = sum_aa(snake_case_ , snake_case_ )
_lowerCAmelCase = sum_aa(snake_case_ , snake_case_ )
_lowerCAmelCase = sum_aa(snake_case_ , snake_case_ )
_lowerCAmelCase = sum_aa(snake_case_ , snake_case_ )
_lowerCAmelCase = reformat_hex(snake_case_ ) + reformat_hex(snake_case_ ) + reformat_hex(snake_case_ ) + reformat_hex(snake_case_ )
return digest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 317
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __lowerCamelCase ( unittest.TestCase ):
def __init__(self , lowerCamelCase , lowerCamelCase=7 , lowerCamelCase=3 , lowerCamelCase=18 , lowerCamelCase=30 , lowerCamelCase=400 , lowerCamelCase=True , lowerCamelCase=None , lowerCamelCase=True , lowerCamelCase=None , ):
'''simple docstring'''
_lowerCAmelCase = size if size is not None else {"""shortest_edge""": 20}
_lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = min_resolution
_lowerCAmelCase = max_resolution
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
def A__ (self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __lowerCamelCase ( __lowercase , unittest.TestCase ):
__UpperCamelCase = MobileNetVaImageProcessor if is_vision_available() else None
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = MobileNetVaImageProcessingTester(self )
@property
def A__ (self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(lowerCamelCase , """size""" ) )
self.assertTrue(hasattr(lowerCamelCase , """do_center_crop""" ) )
self.assertTrue(hasattr(lowerCamelCase , """crop_size""" ) )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 20} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} )
def A__ (self ):
'''simple docstring'''
pass
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , Image.Image )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , numpify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , np.ndarray )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , torchify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , torch.Tensor )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 317
| 1
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 600851475143 ) -> int:
"""simple docstring"""
try:
_lowerCAmelCase = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
_lowerCAmelCase = 2
_lowerCAmelCase = 0
if n == 2:
return 2
while n > 2:
while n % i != 0:
i += 1
_lowerCAmelCase = i
while n % i == 0:
_lowerCAmelCase = n // i
i += 1
return int(snake_case_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : list ) -> list:
"""simple docstring"""
for i in range(len(snake_case_ ) - 1 , 0 , -1 ):
_lowerCAmelCase = False
for j in range(snake_case_ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
_lowerCAmelCase , _lowerCAmelCase = unsorted[j - 1], unsorted[j]
_lowerCAmelCase = True
for j in range(snake_case_ ):
if unsorted[j] > unsorted[j + 1]:
_lowerCAmelCase , _lowerCAmelCase = unsorted[j + 1], unsorted[j]
_lowerCAmelCase = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : List[Any] = input('''Enter numbers separated by a comma:\n''').strip()
SCREAMING_SNAKE_CASE : List[str] = [int(item) for item in user_input.split(''',''')]
print(F'{cocktail_shaker_sort(unsorted) = }')
| 317
| 1
|
"""simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
SCREAMING_SNAKE_CASE : Tuple = pytest.mark.integration
SCREAMING_SNAKE_CASE : Any = {'''comet'''}
SCREAMING_SNAKE_CASE : Union[str, Any] = importlib.util.find_spec('''fairseq''') is not None
SCREAMING_SNAKE_CASE : str = {'''code_eval'''}
SCREAMING_SNAKE_CASE : Dict = os.name == '''nt'''
SCREAMING_SNAKE_CASE : str = {'''bertscore''', '''frugalscore''', '''perplexity'''}
SCREAMING_SNAKE_CASE : Optional[int] = importlib.util.find_spec('''transformers''') is not None
def __UpperCAmelCase ( snake_case_ : List[str] ) -> int:
"""simple docstring"""
@wraps(snake_case_ )
def wrapper(self : Optional[Any] , snake_case_ : Tuple ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("""\"test requires Fairseq\"""" )
else:
test_case(self , snake_case_ )
return wrapper
def __UpperCAmelCase ( snake_case_ : List[Any] ) -> List[Any]:
"""simple docstring"""
@wraps(snake_case_ )
def wrapper(self : Any , snake_case_ : int ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("""\"test requires transformers\"""" )
else:
test_case(self , snake_case_ )
return wrapper
def __UpperCAmelCase ( snake_case_ : Optional[int] ) -> Dict:
"""simple docstring"""
@wraps(snake_case_ )
def wrapper(self : Dict , snake_case_ : Union[str, Any] ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("""\"test not supported on Windows\"""" )
else:
test_case(self , snake_case_ )
return wrapper
def __UpperCAmelCase ( ) -> Any:
"""simple docstring"""
_lowerCAmelCase = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("""./metrics/*/""" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
__lowercase , __lowercase , __lowercase )
@local
class __lowerCamelCase ( parameterized.TestCase ):
__UpperCamelCase = {}
__UpperCamelCase = None
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:load_metric is deprecated:FutureWarning""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = """[...]"""
_lowerCAmelCase = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , lowerCamelCase ) ).module_path )
_lowerCAmelCase = datasets.load.import_main_class(metric_module.__name__ , dataset=lowerCamelCase )
# check parameters
_lowerCAmelCase = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(lowerCamelCase , metric_module.__name__ ):
with self.use_local_metrics():
try:
_lowerCAmelCase = doctest.testmod(lowerCamelCase , verbose=lowerCamelCase , raise_on_error=lowerCamelCase )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = """[...]"""
_lowerCAmelCase = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("""metrics""" , lowerCamelCase ) ).module_path )
# run doctest
with self.use_local_metrics():
_lowerCAmelCase = doctest.testmod(lowerCamelCase , verbose=lowerCamelCase , raise_on_error=lowerCamelCase )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def A__ (self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCamelCase ):
yield
else:
yield
@contextmanager
def A__ (self ):
'''simple docstring'''
def load_local_metric(lowerCamelCase , *lowerCamelCase , **lowerCamelCase ):
return load_metric(os.path.join("""metrics""" , lowerCamelCase ) , *lowerCamelCase , **lowerCamelCase )
with patch("""datasets.load_metric""" ) as mock_load_metric:
_lowerCAmelCase = load_local_metric
yield
@classmethod
def A__ (cls , lowerCamelCase ):
'''simple docstring'''
def wrapper(lowerCamelCase ):
_lowerCAmelCase = contextmanager(lowerCamelCase )
_lowerCAmelCase = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("""bleurt""" )
def __UpperCAmelCase ( snake_case_ : List[Any] ) -> Any:
"""simple docstring"""
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("""sv""" , """""" , """""" ) # handle pytest cli flags
class __lowerCamelCase ( __lowercase ):
def A__ (self , lowerCamelCase ):
'''simple docstring'''
assert len(input_dict["""input_ids"""] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("""bleurt.score._create_predictor""" ) as mock_create_predictor:
_lowerCAmelCase = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("""bertscore""" )
def __UpperCAmelCase ( snake_case_ : List[Any] ) -> Any:
"""simple docstring"""
import torch
def bert_cos_score_idf(snake_case_ : Dict , snake_case_ : Optional[Any] , *snake_case_ : Optional[int] , **snake_case_ : Optional[Any] ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(snake_case_ ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("""bert_score.scorer.get_model""" ), patch(
"""bert_score.scorer.bert_cos_score_idf""" ) as mock_bert_cos_score_idf:
_lowerCAmelCase = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("""comet""" )
def __UpperCAmelCase ( snake_case_ : List[str] ) -> List[Any]:
"""simple docstring"""
def load_from_checkpoint(snake_case_ : int ):
class __lowerCamelCase :
def A__ (self , lowerCamelCase , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
assert len(lowerCamelCase ) == 2
_lowerCAmelCase = [0.19, 0.92]
return scores, sum(lowerCamelCase ) / len(lowerCamelCase )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("""comet.download_model""" ) as mock_download_model:
_lowerCAmelCase = None
with patch("""comet.load_from_checkpoint""" ) as mock_load_from_checkpoint:
_lowerCAmelCase = load_from_checkpoint
yield
def __UpperCAmelCase ( ) -> List[Any]:
"""simple docstring"""
_lowerCAmelCase = load_metric(os.path.join("""metrics""" , """seqeval""" ) )
_lowerCAmelCase = """ERROR"""
_lowerCAmelCase = F"""Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}"""
with pytest.raises(snake_case_ , match=re.escape(snake_case_ ) ):
metric.compute(predictions=[] , references=[] , scheme=snake_case_ )
| 317
|
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
def __UpperCAmelCase ( snake_case_ : bool , snake_case_ : bool ) -> Tuple:
"""simple docstring"""
def run_func(snake_case_ : Union[str, Any] ):
@wraps(snake_case_ )
def run_in_eager_mode(*snake_case_ : Optional[int] , **snake_case_ : Union[str, Any] ):
return func(*snake_case_ , **snake_case_ )
@wraps(snake_case_ )
@tf.function(experimental_compile=snake_case_ )
def run_in_graph_mode(*snake_case_ : Dict , **snake_case_ : Union[str, Any] ):
return func(*snake_case_ , **snake_case_ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"""Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : int , snake_case_ : int ) -> ["tf.Tensor"]:
"""simple docstring"""
_lowerCAmelCase = random.Random()
_lowerCAmelCase = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(snake_case_ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 42
__UpperCamelCase = 42
__UpperCamelCase = "TensorFlow"
@property
def A__ (self ):
'''simple docstring'''
return tf.__version__
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_inference_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_speed(_inference )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_train_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_speed(_train )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase )
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_inference_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_memory(_inference )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase )
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_train_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_memory(_train )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
_lowerCAmelCase = (
hasattr(lowerCamelCase , """architectures""" )
and isinstance(config.architectures , lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model_cls(lowerCamelCase )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
_lowerCAmelCase = TF_MODEL_MAPPING[config.__class__](lowerCamelCase )
# encoder-decoder has vocab size saved differently
_lowerCAmelCase = config.vocab_size if hasattr(lowerCamelCase , """vocab_size""" ) else config.encoder.vocab_size
_lowerCAmelCase = random_input_ids(lowerCamelCase , lowerCamelCase , lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(lowerCamelCase , decoder_input_ids=lowerCamelCase , training=lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(lowerCamelCase , training=lowerCamelCase )
_lowerCAmelCase = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.""" )
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
_lowerCAmelCase = (
hasattr(lowerCamelCase , """architectures""" )
and isinstance(config.architectures , lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model_cls(lowerCamelCase )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
_lowerCAmelCase = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase )
# encoder-decoder has vocab size saved differently
_lowerCAmelCase = config.vocab_size if hasattr(lowerCamelCase , """vocab_size""" ) else config.encoder.vocab_size
_lowerCAmelCase = random_input_ids(lowerCamelCase , lowerCamelCase , lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
_lowerCAmelCase = model(lowerCamelCase , decoder_input_ids=lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )[0]
_lowerCAmelCase = tf.gradients(lowerCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
_lowerCAmelCase = model(lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )[0]
_lowerCAmelCase = tf.gradients(lowerCamelCase , model.trainable_variables )
return gradients
_lowerCAmelCase = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def A__ (self , lowerCamelCase ):
'''simple docstring'''
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation""" )
timeit.repeat(lowerCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
_lowerCAmelCase = timeit.repeat(
lowerCamelCase , repeat=self.args.repeat , number=10 , )
return min(lowerCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
logger.info(
"""Note that TensorFlow allocates more memory than """
"""it might need to speed up computation. """
"""The memory reported here corresponds to the memory """
"""reported by `nvidia-smi`, which can vary depending """
"""on total available memory on the GPU that is used.""" )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"""`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
""" consumption line by line.""" )
_lowerCAmelCase = start_memory_tracing("""transformers""" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"""Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
""" with `args.memory=False`""" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"""py3nvml not installed, we won't log GPU memory usage. """
"""Install py3nvml (pip install py3nvml) to log information about GPU.""" )
_lowerCAmelCase = """N/A"""
else:
logger.info(
"""Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
""" running on the same GPU.""" )
# init nvml
nvml.nvmlInit()
func()
_lowerCAmelCase = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
_lowerCAmelCase = nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase )
_lowerCAmelCase = meminfo.used
_lowerCAmelCase = Memory(lowerCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"""When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
""" TensorFlow.""" )
_lowerCAmelCase = None
else:
_lowerCAmelCase = measure_peak_memory_cpu(lowerCamelCase )
_lowerCAmelCase = Memory(lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
_lowerCAmelCase = stop_memory_tracing(lowerCamelCase )
if memory is None:
_lowerCAmelCase = summary.total
else:
_lowerCAmelCase = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
return "N/A", None
| 317
| 1
|
"""simple docstring"""
import torch
from torch import nn
class __lowerCamelCase ( nn.Module ):
def __init__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=1 , lowerCamelCase=False ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase = n_token
_lowerCAmelCase = d_embed
_lowerCAmelCase = d_proj
_lowerCAmelCase = cutoffs + [n_token]
_lowerCAmelCase = [0] + self.cutoffs
_lowerCAmelCase = div_val
_lowerCAmelCase = self.cutoffs[0]
_lowerCAmelCase = len(self.cutoffs ) - 1
_lowerCAmelCase = self.shortlist_size + self.n_clusters
if self.n_clusters > 0:
_lowerCAmelCase = nn.Parameter(torch.zeros(self.n_clusters , self.d_embed ) )
_lowerCAmelCase = nn.Parameter(torch.zeros(self.n_clusters ) )
_lowerCAmelCase = nn.ModuleList()
_lowerCAmelCase = nn.ParameterList()
if div_val == 1:
for i in range(len(self.cutoffs ) ):
if d_proj != d_embed:
self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCamelCase , lowerCamelCase ) ) )
else:
self.out_projs.append(lowerCamelCase )
self.out_layers.append(nn.Linear(lowerCamelCase , lowerCamelCase ) )
else:
for i in range(len(self.cutoffs ) ):
_lowerCAmelCase , _lowerCAmelCase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_lowerCAmelCase = d_embed // (div_val**i)
self.out_projs.append(nn.Parameter(torch.FloatTensor(lowerCamelCase , lowerCamelCase ) ) )
self.out_layers.append(nn.Linear(lowerCamelCase , r_idx - l_idx ) )
_lowerCAmelCase = keep_order
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if proj is None:
_lowerCAmelCase = nn.functional.linear(lowerCamelCase , lowerCamelCase , bias=lowerCamelCase )
else:
# if CUDA_MAJOR <= 9 and CUDA_MINOR <= 1:
_lowerCAmelCase = nn.functional.linear(lowerCamelCase , proj.t().contiguous() )
_lowerCAmelCase = nn.functional.linear(lowerCamelCase , lowerCamelCase , bias=lowerCamelCase )
# else:
# logit = torch.einsum('bd,de,ev->bv', (hidden, proj, weight.t()))
# if bias is not None:
# logit = logit + bias
return logit
def A__ (self , lowerCamelCase , lowerCamelCase=None , lowerCamelCase=False ):
'''simple docstring'''
if labels is not None:
# Shift so that tokens < n predict n
_lowerCAmelCase = hidden[..., :-1, :].contiguous()
_lowerCAmelCase = labels[..., 1:].contiguous()
_lowerCAmelCase = hidden.view(-1 , hidden.size(-1 ) )
_lowerCAmelCase = labels.view(-1 )
if hidden.size(0 ) != labels.size(0 ):
raise RuntimeError("""Input and labels should have the same size in the batch dimension.""" )
else:
_lowerCAmelCase = hidden.view(-1 , hidden.size(-1 ) )
if self.n_clusters == 0:
_lowerCAmelCase = self._compute_logit(lowerCamelCase , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
if labels is not None:
_lowerCAmelCase = labels != -100
_lowerCAmelCase = torch.zeros_like(lowerCamelCase , dtype=hidden.dtype , device=hidden.device )
_lowerCAmelCase = (
-nn.functional.log_softmax(lowerCamelCase , dim=-1 )[mask].gather(1 , labels[mask].unsqueeze(1 ) ).squeeze(1 )
)
else:
_lowerCAmelCase = nn.functional.log_softmax(lowerCamelCase , dim=-1 )
else:
# construct weights and biases
_lowerCAmelCase , _lowerCAmelCase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
_lowerCAmelCase , _lowerCAmelCase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_lowerCAmelCase = self.out_layers[0].weight[l_idx:r_idx]
_lowerCAmelCase = self.out_layers[0].bias[l_idx:r_idx]
else:
_lowerCAmelCase = self.out_layers[i].weight
_lowerCAmelCase = self.out_layers[i].bias
if i == 0:
_lowerCAmelCase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
_lowerCAmelCase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(lowerCamelCase )
biases.append(lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = weights[0], biases[0], self.out_projs[0]
_lowerCAmelCase = self._compute_logit(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = nn.functional.log_softmax(lowerCamelCase , dim=1 )
if labels is None:
_lowerCAmelCase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
else:
_lowerCAmelCase = torch.zeros_like(lowerCamelCase , dtype=hidden.dtype , device=hidden.device )
_lowerCAmelCase = 0
_lowerCAmelCase = [0] + self.cutoffs
for i in range(len(lowerCamelCase ) - 1 ):
_lowerCAmelCase , _lowerCAmelCase = cutoff_values[i], cutoff_values[i + 1]
if labels is not None:
_lowerCAmelCase = (labels >= l_idx) & (labels < r_idx)
_lowerCAmelCase = mask_i.nonzero().squeeze()
if indices_i.numel() == 0:
continue
_lowerCAmelCase = labels.index_select(0 , lowerCamelCase ) - l_idx
_lowerCAmelCase = head_logprob.index_select(0 , lowerCamelCase )
_lowerCAmelCase = hidden.index_select(0 , lowerCamelCase )
else:
_lowerCAmelCase = hidden
if i == 0:
if labels is not None:
_lowerCAmelCase = head_logprob_i.gather(1 , target_i[:, None] ).squeeze(1 )
else:
_lowerCAmelCase = head_logprob[:, : self.cutoffs[0]]
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = weights[i], biases[i], self.out_projs[i]
_lowerCAmelCase = self._compute_logit(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = nn.functional.log_softmax(lowerCamelCase , dim=1 )
_lowerCAmelCase = self.cutoffs[0] + i - 1 # No probability for the head cluster
if labels is not None:
_lowerCAmelCase = head_logprob_i[:, cluster_prob_idx] + tail_logprob_i.gather(
1 , target_i[:, None] ).squeeze(1 )
else:
_lowerCAmelCase = head_logprob[:, cluster_prob_idx, None] + tail_logprob_i
_lowerCAmelCase = logprob_i
if labels is not None:
if (hasattr(self , """keep_order""" ) and self.keep_order) or keep_order:
out.index_copy_(0 , lowerCamelCase , -logprob_i )
else:
out[offset : offset + logprob_i.size(0 )].copy_(-logprob_i )
offset += logprob_i.size(0 )
return out
def A__ (self , lowerCamelCase ):
'''simple docstring'''
if self.n_clusters == 0:
_lowerCAmelCase = self._compute_logit(lowerCamelCase , self.out_layers[0].weight , self.out_layers[0].bias , self.out_projs[0] )
return nn.functional.log_softmax(lowerCamelCase , dim=-1 )
else:
# construct weights and biases
_lowerCAmelCase , _lowerCAmelCase = [], []
for i in range(len(self.cutoffs ) ):
if self.div_val == 1:
_lowerCAmelCase , _lowerCAmelCase = self.cutoff_ends[i], self.cutoff_ends[i + 1]
_lowerCAmelCase = self.out_layers[0].weight[l_idx:r_idx]
_lowerCAmelCase = self.out_layers[0].bias[l_idx:r_idx]
else:
_lowerCAmelCase = self.out_layers[i].weight
_lowerCAmelCase = self.out_layers[i].bias
if i == 0:
_lowerCAmelCase = torch.cat([weight_i, self.cluster_weight] , dim=0 )
_lowerCAmelCase = torch.cat([bias_i, self.cluster_bias] , dim=0 )
weights.append(lowerCamelCase )
biases.append(lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = weights[0], biases[0], self.out_projs[0]
_lowerCAmelCase = self._compute_logit(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = hidden.new_empty((head_logit.size(0 ), self.n_token) )
_lowerCAmelCase = nn.functional.log_softmax(lowerCamelCase , dim=1 )
_lowerCAmelCase = [0] + self.cutoffs
for i in range(len(lowerCamelCase ) - 1 ):
_lowerCAmelCase , _lowerCAmelCase = cutoff_values[i], cutoff_values[i + 1]
if i == 0:
_lowerCAmelCase = head_logprob[:, : self.cutoffs[0]]
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = weights[i], biases[i], self.out_projs[i]
_lowerCAmelCase = self._compute_logit(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = nn.functional.log_softmax(lowerCamelCase , dim=1 )
_lowerCAmelCase = head_logprob[:, -i] + tail_logprob_i
_lowerCAmelCase = logprob_i
return out
| 317
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'transfo-xl'
__UpperCamelCase = ['mems']
__UpperCamelCase = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__(self , lowerCamelCase=267_735 , lowerCamelCase=[20_000, 40_000, 200_000] , lowerCamelCase=1_024 , lowerCamelCase=1_024 , lowerCamelCase=16 , lowerCamelCase=64 , lowerCamelCase=4_096 , lowerCamelCase=4 , lowerCamelCase=False , lowerCamelCase=18 , lowerCamelCase=1_600 , lowerCamelCase=1_000 , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=0 , lowerCamelCase=-1 , lowerCamelCase=True , lowerCamelCase=0.1 , lowerCamelCase=0.0 , lowerCamelCase=True , lowerCamelCase="normal" , lowerCamelCase=0.01 , lowerCamelCase=0.01 , lowerCamelCase=0.02 , lowerCamelCase=1e-5 , lowerCamelCase=0 , **lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = vocab_size
_lowerCAmelCase = []
self.cutoffs.extend(lowerCamelCase )
if proj_share_all_but_first:
_lowerCAmelCase = [False] + [True] * len(self.cutoffs )
else:
_lowerCAmelCase = [False] + [False] * len(self.cutoffs )
_lowerCAmelCase = d_model
_lowerCAmelCase = d_embed
_lowerCAmelCase = d_head
_lowerCAmelCase = d_inner
_lowerCAmelCase = div_val
_lowerCAmelCase = pre_lnorm
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = mem_len
_lowerCAmelCase = same_length
_lowerCAmelCase = attn_type
_lowerCAmelCase = clamp_len
_lowerCAmelCase = sample_softmax
_lowerCAmelCase = adaptive
_lowerCAmelCase = dropout
_lowerCAmelCase = dropatt
_lowerCAmelCase = untie_r
_lowerCAmelCase = init
_lowerCAmelCase = init_range
_lowerCAmelCase = proj_init_std
_lowerCAmelCase = init_std
_lowerCAmelCase = layer_norm_epsilon
super().__init__(eos_token_id=lowerCamelCase , **lowerCamelCase )
@property
def A__ (self ):
'''simple docstring'''
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def A__ (self , lowerCamelCase ):
'''simple docstring'''
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 317
| 1
|
"""simple docstring"""
import argparse
from copy import deepcopy
import numpy as np
from datasets import ClassLabel, DatasetDict, load_dataset
from evaluate import load
from transformers import (
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
Trainer,
TrainerCallback,
TrainingArguments,
set_seed,
)
def __UpperCAmelCase ( ) -> str:
"""simple docstring"""
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--model_ckpt""" , type=snake_case_ , default="""microsoft/unixcoder-base-nine""" )
parser.add_argument("""--num_epochs""" , type=snake_case_ , default=5 )
parser.add_argument("""--batch_size""" , type=snake_case_ , default=6 )
parser.add_argument("""--gradient_accumulation_steps""" , type=snake_case_ , default=1 )
parser.add_argument("""--freeze""" , type=snake_case_ , default=snake_case_ )
parser.add_argument("""--learning_rate""" , type=snake_case_ , default=5e-4 )
parser.add_argument("""--seed""" , type=snake_case_ , default=0 )
parser.add_argument("""--lr_scheduler_type""" , type=snake_case_ , default="""cosine""" )
parser.add_argument("""--num_warmup_steps""" , type=snake_case_ , default=10 )
parser.add_argument("""--weight_decay""" , type=snake_case_ , default=0.0_1 )
parser.add_argument("""--output_dir""" , type=snake_case_ , default="""./results""" )
return parser.parse_args()
SCREAMING_SNAKE_CASE : Optional[int] = load('''accuracy''')
def __UpperCAmelCase ( snake_case_ : Optional[Any] ) -> List[Any]:
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = eval_pred
_lowerCAmelCase = np.argmax(snake_case_ , axis=1 )
return metric.compute(predictions=snake_case_ , references=snake_case_ )
class __lowerCamelCase ( __lowercase ):
def __init__(self , lowerCamelCase ):
'''simple docstring'''
super().__init__()
_lowerCAmelCase = trainer
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
if control.should_evaluate:
_lowerCAmelCase = deepcopy(lowerCamelCase )
self._trainer.evaluate(eval_dataset=self._trainer.train_dataset , metric_key_prefix="""train""" )
return control_copy
def __UpperCAmelCase ( ) -> str:
"""simple docstring"""
_lowerCAmelCase = get_args()
set_seed(args.seed )
_lowerCAmelCase = load_dataset("""codeparrot/codecomplex""" , split="""train""" )
_lowerCAmelCase = dataset.train_test_split(test_size=0.2 )
_lowerCAmelCase = train_test["""test"""].train_test_split(test_size=0.5 )
_lowerCAmelCase = DatasetDict(
{
"""train""": train_test["""train"""],
"""test""": test_validation["""train"""],
"""valid""": test_validation["""test"""],
} )
print("""Loading tokenizer and model""" )
_lowerCAmelCase = AutoTokenizer.from_pretrained(args.model_ckpt )
_lowerCAmelCase = tokenizer.eos_token
_lowerCAmelCase = AutoModelForSequenceClassification.from_pretrained(args.model_ckpt , num_labels=7 )
_lowerCAmelCase = model.config.eos_token_id
if args.freeze:
for param in model.roberta.parameters():
_lowerCAmelCase = False
_lowerCAmelCase = ClassLabel(num_classes=7 , names=list(set(train_test_validation["""train"""]["""complexity"""] ) ) )
def tokenize(snake_case_ : List[Any] ):
_lowerCAmelCase = tokenizer(example["""src"""] , truncation=snake_case_ , max_length=1024 )
_lowerCAmelCase = labels.straint(example["""complexity"""] )
return {
"input_ids": inputs["input_ids"],
"attention_mask": inputs["attention_mask"],
"label": label,
}
_lowerCAmelCase = train_test_validation.map(
snake_case_ , batched=snake_case_ , remove_columns=train_test_validation["""train"""].column_names , )
_lowerCAmelCase = DataCollatorWithPadding(tokenizer=snake_case_ )
_lowerCAmelCase = TrainingArguments(
output_dir=args.output_dir , learning_rate=args.learning_rate , lr_scheduler_type=args.lr_scheduler_type , evaluation_strategy="""epoch""" , save_strategy="""epoch""" , logging_strategy="""epoch""" , per_device_train_batch_size=args.batch_size , per_device_eval_batch_size=args.batch_size , num_train_epochs=args.num_epochs , gradient_accumulation_steps=args.gradient_accumulation_steps , weight_decay=0.0_1 , metric_for_best_model="""accuracy""" , run_name="""complexity-java""" , report_to="""wandb""" , )
_lowerCAmelCase = Trainer(
model=snake_case_ , args=snake_case_ , train_dataset=tokenized_datasets["""train"""] , eval_dataset=tokenized_datasets["""valid"""] , tokenizer=snake_case_ , data_collator=snake_case_ , compute_metrics=snake_case_ , )
print("""Training...""" )
trainer.add_callback(CustomCallback(snake_case_ ) )
trainer.train()
if __name__ == "__main__":
main()
| 317
|
"""simple docstring"""
import math
def __UpperCAmelCase ( snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 2
_lowerCAmelCase = int(math.sqrt(snake_case_ ) ) # Size of every segment
_lowerCAmelCase = [True] * (end + 1)
_lowerCAmelCase = []
while start <= end:
if temp[start] is True:
in_prime.append(snake_case_ )
for i in range(start * start , end + 1 , snake_case_ ):
_lowerCAmelCase = False
start += 1
prime += in_prime
_lowerCAmelCase = end + 1
_lowerCAmelCase = min(2 * end , snake_case_ )
while low <= n:
_lowerCAmelCase = [True] * (high - low + 1)
for each in in_prime:
_lowerCAmelCase = math.floor(low / each ) * each
if t < low:
t += each
for j in range(snake_case_ , high + 1 , snake_case_ ):
_lowerCAmelCase = False
for j in range(len(snake_case_ ) ):
if temp[j] is True:
prime.append(j + low )
_lowerCAmelCase = high + 1
_lowerCAmelCase = min(high + end , snake_case_ )
return prime
print(sieve(1_0**6))
| 317
| 1
|
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__) # pylint: disable=invalid-name
SCREAMING_SNAKE_CASE : Union[str, Any] = '''
Examples:
```py
>>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline
>>> from diffusers.utils import load_image
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
... )
>>> pipe_prior.to("cuda")
>>> prompt = "A red cartoon frog, 4k"
>>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)
>>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16
... )
>>> pipe.to("cuda")
>>> init_image = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/frog.png"
... )
>>> image = pipe(
... image=init_image,
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=100,
... strength=0.2,
... ).images
>>> image[0].save("red_frog.png")
```
'''
def __UpperCAmelCase ( snake_case_ : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : List[Any]=8 ) -> Tuple:
"""simple docstring"""
_lowerCAmelCase = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
_lowerCAmelCase = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : str=512 , snake_case_ : Any=512 ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = pil_image.resize((w, h) , resample=Image.BICUBIC , reducing_gap=1 )
_lowerCAmelCase = np.array(pil_image.convert("""RGB""" ) )
_lowerCAmelCase = arr.astype(np.floataa ) / 1_2_7.5 - 1
_lowerCAmelCase = np.transpose(snake_case_ , [2, 0, 1] )
_lowerCAmelCase = torch.from_numpy(snake_case_ ).unsqueeze(0 )
return image
class __lowerCamelCase ( __lowercase ):
def __init__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=lowerCamelCase , scheduler=lowerCamelCase , movq=lowerCamelCase , )
_lowerCAmelCase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = min(int(num_inference_steps * strength ) , lowerCamelCase )
_lowerCAmelCase = max(num_inference_steps - init_timestep , 0 )
_lowerCAmelCase = self.scheduler.timesteps[t_start:]
return timesteps, num_inference_steps - t_start
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=None ):
'''simple docstring'''
if not isinstance(lowerCamelCase , (torch.Tensor, PIL.Image.Image, list) ):
raise ValueError(
f"""`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowerCamelCase )}""" )
_lowerCAmelCase = image.to(device=lowerCamelCase , dtype=lowerCamelCase )
_lowerCAmelCase = batch_size * num_images_per_prompt
if image.shape[1] == 4:
_lowerCAmelCase = image
else:
if isinstance(lowerCamelCase , lowerCamelCase ) and len(lowerCamelCase ) != batch_size:
raise ValueError(
f"""You have passed a list of generators of length {len(lowerCamelCase )}, but requested an effective batch"""
f""" size of {batch_size}. Make sure the batch size matches the length of the generators.""" )
elif isinstance(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = [
self.movq.encode(image[i : i + 1] ).latent_dist.sample(generator[i] ) for i in range(lowerCamelCase )
]
_lowerCAmelCase = torch.cat(lowerCamelCase , dim=0 )
else:
_lowerCAmelCase = self.movq.encode(lowerCamelCase ).latent_dist.sample(lowerCamelCase )
_lowerCAmelCase = self.movq.config.scaling_factor * init_latents
_lowerCAmelCase = torch.cat([init_latents] , dim=0 )
_lowerCAmelCase = init_latents.shape
_lowerCAmelCase = randn_tensor(lowerCamelCase , generator=lowerCamelCase , device=lowerCamelCase , dtype=lowerCamelCase )
# get latents
_lowerCAmelCase = self.scheduler.add_noise(lowerCamelCase , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = init_latents
return latents
def A__ (self , lowerCamelCase=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
_lowerCAmelCase = torch.device(f"""cuda:{gpu_id}""" )
_lowerCAmelCase = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCamelCase , lowerCamelCase )
def A__ (self , lowerCamelCase=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
_lowerCAmelCase = torch.device(f"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=lowerCamelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
_lowerCAmelCase = None
for cpu_offloaded_model in [self.unet, self.movq]:
_lowerCAmelCase , _lowerCAmelCase = cpu_offload_with_hook(lowerCamelCase , lowerCamelCase , prev_module_hook=lowerCamelCase )
# We'll offload the last model manually.
_lowerCAmelCase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def A__ (self ):
'''simple docstring'''
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCamelCase , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowerCamelCase )
def __call__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 512 , lowerCamelCase = 512 , lowerCamelCase = 100 , lowerCamelCase = 4.0 , lowerCamelCase = 0.3 , lowerCamelCase = 1 , lowerCamelCase = None , lowerCamelCase = "pil" , lowerCamelCase = True , ):
'''simple docstring'''
_lowerCAmelCase = self._execution_device
_lowerCAmelCase = guidance_scale > 1.0
if isinstance(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = torch.cat(lowerCamelCase , dim=0 )
_lowerCAmelCase = image_embeds.shape[0]
if isinstance(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = torch.cat(lowerCamelCase , dim=0 )
if do_classifier_free_guidance:
_lowerCAmelCase = image_embeds.repeat_interleave(lowerCamelCase , dim=0 )
_lowerCAmelCase = negative_image_embeds.repeat_interleave(lowerCamelCase , dim=0 )
_lowerCAmelCase = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase )
if not isinstance(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = [image]
if not all(isinstance(lowerCamelCase , (PIL.Image.Image, torch.Tensor) ) for i in image ):
raise ValueError(
f"""Input is in incorrect format: {[type(lowerCamelCase ) for i in image]}. Currently, we only support PIL image and pytorch tensor""" )
_lowerCAmelCase = torch.cat([prepare_image(lowerCamelCase , lowerCamelCase , lowerCamelCase ) for i in image] , dim=0 )
_lowerCAmelCase = image.to(dtype=image_embeds.dtype , device=lowerCamelCase )
_lowerCAmelCase = self.movq.encode(lowerCamelCase )["""latents"""]
_lowerCAmelCase = latents.repeat_interleave(lowerCamelCase , dim=0 )
self.scheduler.set_timesteps(lowerCamelCase , device=lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase = self.get_timesteps(lowerCamelCase , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = timesteps[:1].repeat(batch_size * num_images_per_prompt )
_lowerCAmelCase , _lowerCAmelCase = downscale_height_and_width(lowerCamelCase , lowerCamelCase , self.movq_scale_factor )
_lowerCAmelCase = self.prepare_latents(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , image_embeds.dtype , lowerCamelCase , lowerCamelCase )
for i, t in enumerate(self.progress_bar(lowerCamelCase ) ):
# expand the latents if we are doing classifier free guidance
_lowerCAmelCase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_lowerCAmelCase = {"""image_embeds""": image_embeds}
_lowerCAmelCase = self.unet(
sample=lowerCamelCase , timestep=lowerCamelCase , encoder_hidden_states=lowerCamelCase , added_cond_kwargs=lowerCamelCase , return_dict=lowerCamelCase , )[0]
if do_classifier_free_guidance:
_lowerCAmelCase , _lowerCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
_lowerCAmelCase , _lowerCAmelCase = noise_pred.chunk(2 )
_lowerCAmelCase , _lowerCAmelCase = variance_pred.chunk(2 )
_lowerCAmelCase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
_lowerCAmelCase = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
_lowerCAmelCase , _lowerCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
_lowerCAmelCase = self.scheduler.step(
lowerCamelCase , lowerCamelCase , lowerCamelCase , generator=lowerCamelCase , )[0]
# post-processing
_lowerCAmelCase = self.movq.decode(lowerCamelCase , force_not_quantize=lowerCamelCase )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" )
if output_type in ["np", "pil"]:
_lowerCAmelCase = image * 0.5 + 0.5
_lowerCAmelCase = image.clamp(0 , 1 )
_lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_lowerCAmelCase = self.numpy_to_pil(lowerCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase )
| 317
|
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE : Any = (7_2_0, 1_2_8_0) # Height, Width
SCREAMING_SNAKE_CASE : List[str] = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE : List[Any] = 1 / 1_0_0
SCREAMING_SNAKE_CASE : Optional[Any] = ''''''
SCREAMING_SNAKE_CASE : Dict = ''''''
SCREAMING_SNAKE_CASE : List[Any] = ''''''
SCREAMING_SNAKE_CASE : Dict = 2_5_0
def __UpperCAmelCase ( ) -> None:
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = get_dataset(snake_case_ , snake_case_ )
for index in range(snake_case_ ):
_lowerCAmelCase = random.sample(range(len(snake_case_ ) ) , 4 )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = update_image_and_anno(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , filter_scale=snake_case_ , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_lowerCAmelCase = random_chars(32 )
_lowerCAmelCase = path.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
_lowerCAmelCase = F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
cva.imwrite(F"""{file_root}.jpg""" , snake_case_ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
_lowerCAmelCase = []
for anno in new_annos:
_lowerCAmelCase = anno[3] - anno[1]
_lowerCAmelCase = anno[4] - anno[2]
_lowerCAmelCase = anno[1] + width / 2
_lowerCAmelCase = anno[2] + height / 2
_lowerCAmelCase = F"""{anno[0]} {x_center} {y_center} {width} {height}"""
annos_list.append(snake_case_ )
with open(F"""{file_root}.txt""" , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : str ) -> tuple[list, list]:
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = []
for label_file in glob.glob(os.path.join(snake_case_ , """*.txt""" ) ):
_lowerCAmelCase = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(snake_case_ ) as in_file:
_lowerCAmelCase = in_file.readlines()
_lowerCAmelCase = os.path.join(snake_case_ , F"""{label_name}.jpg""" )
_lowerCAmelCase = []
for obj_list in obj_lists:
_lowerCAmelCase = obj_list.rstrip("""\n""" ).split(""" """ )
_lowerCAmelCase = float(obj[1] ) - float(obj[3] ) / 2
_lowerCAmelCase = float(obj[2] ) - float(obj[4] ) / 2
_lowerCAmelCase = float(obj[1] ) + float(obj[3] ) / 2
_lowerCAmelCase = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(snake_case_ )
labels.append(snake_case_ )
return img_paths, labels
def __UpperCAmelCase ( snake_case_ : list , snake_case_ : list , snake_case_ : list[int] , snake_case_ : tuple[int, int] , snake_case_ : tuple[float, float] , snake_case_ : float = 0.0 , ) -> tuple[list, list, str]:
"""simple docstring"""
_lowerCAmelCase = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
_lowerCAmelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_lowerCAmelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_lowerCAmelCase = int(scale_x * output_size[1] )
_lowerCAmelCase = int(scale_y * output_size[0] )
_lowerCAmelCase = []
_lowerCAmelCase = []
for i, index in enumerate(snake_case_ ):
_lowerCAmelCase = all_img_list[index]
path_list.append(snake_case_ )
_lowerCAmelCase = all_annos[index]
_lowerCAmelCase = cva.imread(snake_case_ )
if i == 0: # top-left
_lowerCAmelCase = cva.resize(snake_case_ , (divid_point_x, divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = bbox[1] * scale_x
_lowerCAmelCase = bbox[2] * scale_y
_lowerCAmelCase = bbox[3] * scale_x
_lowerCAmelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
_lowerCAmelCase = cva.resize(snake_case_ , (output_size[1] - divid_point_x, divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = scale_x + bbox[1] * (1 - scale_x)
_lowerCAmelCase = bbox[2] * scale_y
_lowerCAmelCase = scale_x + bbox[3] * (1 - scale_x)
_lowerCAmelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
_lowerCAmelCase = cva.resize(snake_case_ , (divid_point_x, output_size[0] - divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = bbox[1] * scale_x
_lowerCAmelCase = scale_y + bbox[2] * (1 - scale_y)
_lowerCAmelCase = bbox[3] * scale_x
_lowerCAmelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
_lowerCAmelCase = cva.resize(
snake_case_ , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = scale_x + bbox[1] * (1 - scale_x)
_lowerCAmelCase = scale_y + bbox[2] * (1 - scale_y)
_lowerCAmelCase = scale_x + bbox[3] * (1 - scale_x)
_lowerCAmelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
_lowerCAmelCase = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def __UpperCAmelCase ( snake_case_ : int ) -> str:
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
_lowerCAmelCase = ascii_lowercase + digits
return "".join(random.choice(snake_case_ ) for _ in range(snake_case_ ) )
if __name__ == "__main__":
main()
print('''DONE ✅''')
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|
"""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 numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
__UpperCamelCase = 'CIDAS/clipseg-rd64-refined'
__UpperCamelCase = 'image_segmenter'
__UpperCamelCase = CLIPSegForImageSegmentation
__UpperCamelCase = ['image', 'text']
__UpperCamelCase = ['image']
def __init__(self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
requires_backends(self , ["""vision"""] )
super().__init__(*lowerCamelCase , **lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return self.pre_processor(text=[label] , images=[image] , padding=lowerCamelCase , return_tensors="""pt""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
with torch.no_grad():
_lowerCAmelCase = self.model(**lowerCamelCase ).logits
return logits
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = outputs.cpu().detach().numpy()
_lowerCAmelCase = 0
_lowerCAmelCase = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 317
|
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
SCREAMING_SNAKE_CASE : Dict = abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def __UpperCAmelCase ( snake_case_ : Optional[int] ) -> List[str]:
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case_ )
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> int:
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_terminal_summary_main
_lowerCAmelCase = terminalreporter.config.getoption("""--make-reports""" )
if make_reports:
pytest_terminal_summary_main(snake_case_ , id=snake_case_ )
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| 1
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : int ) -> str:
"""simple docstring"""
return "\n".join(
F"""{number} * {i} = {number * i}""" for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_terms=1_0))
| 317
|
"""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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
SCREAMING_SNAKE_CASE : Optional[Any] = {
'''Acehnese Arabic''': '''ace_Arab''',
'''Acehnese Latin''': '''ace_Latn''',
'''Mesopotamian Arabic''': '''acm_Arab''',
'''Ta\'izzi-Adeni Arabic''': '''acq_Arab''',
'''Tunisian Arabic''': '''aeb_Arab''',
'''Afrikaans''': '''afr_Latn''',
'''South Levantine Arabic''': '''ajp_Arab''',
'''Akan''': '''aka_Latn''',
'''Amharic''': '''amh_Ethi''',
'''North Levantine Arabic''': '''apc_Arab''',
'''Modern Standard Arabic''': '''arb_Arab''',
'''Modern Standard Arabic Romanized''': '''arb_Latn''',
'''Najdi Arabic''': '''ars_Arab''',
'''Moroccan Arabic''': '''ary_Arab''',
'''Egyptian Arabic''': '''arz_Arab''',
'''Assamese''': '''asm_Beng''',
'''Asturian''': '''ast_Latn''',
'''Awadhi''': '''awa_Deva''',
'''Central Aymara''': '''ayr_Latn''',
'''South Azerbaijani''': '''azb_Arab''',
'''North Azerbaijani''': '''azj_Latn''',
'''Bashkir''': '''bak_Cyrl''',
'''Bambara''': '''bam_Latn''',
'''Balinese''': '''ban_Latn''',
'''Belarusian''': '''bel_Cyrl''',
'''Bemba''': '''bem_Latn''',
'''Bengali''': '''ben_Beng''',
'''Bhojpuri''': '''bho_Deva''',
'''Banjar Arabic''': '''bjn_Arab''',
'''Banjar Latin''': '''bjn_Latn''',
'''Standard Tibetan''': '''bod_Tibt''',
'''Bosnian''': '''bos_Latn''',
'''Buginese''': '''bug_Latn''',
'''Bulgarian''': '''bul_Cyrl''',
'''Catalan''': '''cat_Latn''',
'''Cebuano''': '''ceb_Latn''',
'''Czech''': '''ces_Latn''',
'''Chokwe''': '''cjk_Latn''',
'''Central Kurdish''': '''ckb_Arab''',
'''Crimean Tatar''': '''crh_Latn''',
'''Welsh''': '''cym_Latn''',
'''Danish''': '''dan_Latn''',
'''German''': '''deu_Latn''',
'''Southwestern Dinka''': '''dik_Latn''',
'''Dyula''': '''dyu_Latn''',
'''Dzongkha''': '''dzo_Tibt''',
'''Greek''': '''ell_Grek''',
'''English''': '''eng_Latn''',
'''Esperanto''': '''epo_Latn''',
'''Estonian''': '''est_Latn''',
'''Basque''': '''eus_Latn''',
'''Ewe''': '''ewe_Latn''',
'''Faroese''': '''fao_Latn''',
'''Fijian''': '''fij_Latn''',
'''Finnish''': '''fin_Latn''',
'''Fon''': '''fon_Latn''',
'''French''': '''fra_Latn''',
'''Friulian''': '''fur_Latn''',
'''Nigerian Fulfulde''': '''fuv_Latn''',
'''Scottish Gaelic''': '''gla_Latn''',
'''Irish''': '''gle_Latn''',
'''Galician''': '''glg_Latn''',
'''Guarani''': '''grn_Latn''',
'''Gujarati''': '''guj_Gujr''',
'''Haitian Creole''': '''hat_Latn''',
'''Hausa''': '''hau_Latn''',
'''Hebrew''': '''heb_Hebr''',
'''Hindi''': '''hin_Deva''',
'''Chhattisgarhi''': '''hne_Deva''',
'''Croatian''': '''hrv_Latn''',
'''Hungarian''': '''hun_Latn''',
'''Armenian''': '''hye_Armn''',
'''Igbo''': '''ibo_Latn''',
'''Ilocano''': '''ilo_Latn''',
'''Indonesian''': '''ind_Latn''',
'''Icelandic''': '''isl_Latn''',
'''Italian''': '''ita_Latn''',
'''Javanese''': '''jav_Latn''',
'''Japanese''': '''jpn_Jpan''',
'''Kabyle''': '''kab_Latn''',
'''Jingpho''': '''kac_Latn''',
'''Kamba''': '''kam_Latn''',
'''Kannada''': '''kan_Knda''',
'''Kashmiri Arabic''': '''kas_Arab''',
'''Kashmiri Devanagari''': '''kas_Deva''',
'''Georgian''': '''kat_Geor''',
'''Central Kanuri Arabic''': '''knc_Arab''',
'''Central Kanuri Latin''': '''knc_Latn''',
'''Kazakh''': '''kaz_Cyrl''',
'''Kabiyè''': '''kbp_Latn''',
'''Kabuverdianu''': '''kea_Latn''',
'''Khmer''': '''khm_Khmr''',
'''Kikuyu''': '''kik_Latn''',
'''Kinyarwanda''': '''kin_Latn''',
'''Kyrgyz''': '''kir_Cyrl''',
'''Kimbundu''': '''kmb_Latn''',
'''Northern Kurdish''': '''kmr_Latn''',
'''Kikongo''': '''kon_Latn''',
'''Korean''': '''kor_Hang''',
'''Lao''': '''lao_Laoo''',
'''Ligurian''': '''lij_Latn''',
'''Limburgish''': '''lim_Latn''',
'''Lingala''': '''lin_Latn''',
'''Lithuanian''': '''lit_Latn''',
'''Lombard''': '''lmo_Latn''',
'''Latgalian''': '''ltg_Latn''',
'''Luxembourgish''': '''ltz_Latn''',
'''Luba-Kasai''': '''lua_Latn''',
'''Ganda''': '''lug_Latn''',
'''Luo''': '''luo_Latn''',
'''Mizo''': '''lus_Latn''',
'''Standard Latvian''': '''lvs_Latn''',
'''Magahi''': '''mag_Deva''',
'''Maithili''': '''mai_Deva''',
'''Malayalam''': '''mal_Mlym''',
'''Marathi''': '''mar_Deva''',
'''Minangkabau Arabic ''': '''min_Arab''',
'''Minangkabau Latin''': '''min_Latn''',
'''Macedonian''': '''mkd_Cyrl''',
'''Plateau Malagasy''': '''plt_Latn''',
'''Maltese''': '''mlt_Latn''',
'''Meitei Bengali''': '''mni_Beng''',
'''Halh Mongolian''': '''khk_Cyrl''',
'''Mossi''': '''mos_Latn''',
'''Maori''': '''mri_Latn''',
'''Burmese''': '''mya_Mymr''',
'''Dutch''': '''nld_Latn''',
'''Norwegian Nynorsk''': '''nno_Latn''',
'''Norwegian Bokmål''': '''nob_Latn''',
'''Nepali''': '''npi_Deva''',
'''Northern Sotho''': '''nso_Latn''',
'''Nuer''': '''nus_Latn''',
'''Nyanja''': '''nya_Latn''',
'''Occitan''': '''oci_Latn''',
'''West Central Oromo''': '''gaz_Latn''',
'''Odia''': '''ory_Orya''',
'''Pangasinan''': '''pag_Latn''',
'''Eastern Panjabi''': '''pan_Guru''',
'''Papiamento''': '''pap_Latn''',
'''Western Persian''': '''pes_Arab''',
'''Polish''': '''pol_Latn''',
'''Portuguese''': '''por_Latn''',
'''Dari''': '''prs_Arab''',
'''Southern Pashto''': '''pbt_Arab''',
'''Ayacucho Quechua''': '''quy_Latn''',
'''Romanian''': '''ron_Latn''',
'''Rundi''': '''run_Latn''',
'''Russian''': '''rus_Cyrl''',
'''Sango''': '''sag_Latn''',
'''Sanskrit''': '''san_Deva''',
'''Santali''': '''sat_Olck''',
'''Sicilian''': '''scn_Latn''',
'''Shan''': '''shn_Mymr''',
'''Sinhala''': '''sin_Sinh''',
'''Slovak''': '''slk_Latn''',
'''Slovenian''': '''slv_Latn''',
'''Samoan''': '''smo_Latn''',
'''Shona''': '''sna_Latn''',
'''Sindhi''': '''snd_Arab''',
'''Somali''': '''som_Latn''',
'''Southern Sotho''': '''sot_Latn''',
'''Spanish''': '''spa_Latn''',
'''Tosk Albanian''': '''als_Latn''',
'''Sardinian''': '''srd_Latn''',
'''Serbian''': '''srp_Cyrl''',
'''Swati''': '''ssw_Latn''',
'''Sundanese''': '''sun_Latn''',
'''Swedish''': '''swe_Latn''',
'''Swahili''': '''swh_Latn''',
'''Silesian''': '''szl_Latn''',
'''Tamil''': '''tam_Taml''',
'''Tatar''': '''tat_Cyrl''',
'''Telugu''': '''tel_Telu''',
'''Tajik''': '''tgk_Cyrl''',
'''Tagalog''': '''tgl_Latn''',
'''Thai''': '''tha_Thai''',
'''Tigrinya''': '''tir_Ethi''',
'''Tamasheq Latin''': '''taq_Latn''',
'''Tamasheq Tifinagh''': '''taq_Tfng''',
'''Tok Pisin''': '''tpi_Latn''',
'''Tswana''': '''tsn_Latn''',
'''Tsonga''': '''tso_Latn''',
'''Turkmen''': '''tuk_Latn''',
'''Tumbuka''': '''tum_Latn''',
'''Turkish''': '''tur_Latn''',
'''Twi''': '''twi_Latn''',
'''Central Atlas Tamazight''': '''tzm_Tfng''',
'''Uyghur''': '''uig_Arab''',
'''Ukrainian''': '''ukr_Cyrl''',
'''Umbundu''': '''umb_Latn''',
'''Urdu''': '''urd_Arab''',
'''Northern Uzbek''': '''uzn_Latn''',
'''Venetian''': '''vec_Latn''',
'''Vietnamese''': '''vie_Latn''',
'''Waray''': '''war_Latn''',
'''Wolof''': '''wol_Latn''',
'''Xhosa''': '''xho_Latn''',
'''Eastern Yiddish''': '''ydd_Hebr''',
'''Yoruba''': '''yor_Latn''',
'''Yue Chinese''': '''yue_Hant''',
'''Chinese Simplified''': '''zho_Hans''',
'''Chinese Traditional''': '''zho_Hant''',
'''Standard Malay''': '''zsm_Latn''',
'''Zulu''': '''zul_Latn''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'facebook/nllb-200-distilled-600M'
__UpperCamelCase = (
'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
)
__UpperCamelCase = 'translator'
__UpperCamelCase = AutoTokenizer
__UpperCamelCase = AutoModelForSeqaSeqLM
__UpperCamelCase = LANGUAGE_CODES
__UpperCamelCase = ['text', 'text', 'text']
__UpperCamelCase = ['text']
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if src_lang not in self.lang_to_code:
raise ValueError(f"""{src_lang} is not a supported language.""" )
if tgt_lang not in self.lang_to_code:
raise ValueError(f"""{tgt_lang} is not a supported language.""" )
_lowerCAmelCase = self.lang_to_code[src_lang]
_lowerCAmelCase = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
lowerCamelCase , return_tensors="""pt""" , src_lang=lowerCamelCase , tgt_lang=lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.model.generate(**lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=lowerCamelCase )
| 317
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|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : list ) -> bool:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(snake_case_ ) == 0:
raise ValueError("""Input list must be a non empty list""" )
if len(snake_case_ ) == 1:
return True
_lowerCAmelCase = series[1] - series[0]
for index in range(len(snake_case_ ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def __UpperCAmelCase ( snake_case_ : list ) -> float:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(snake_case_ ) == 0:
raise ValueError("""Input list must be a non empty list""" )
_lowerCAmelCase = 0
for val in series:
answer += val
return answer / len(snake_case_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 317
|
"""simple docstring"""
from math import isqrt
def __UpperCAmelCase ( snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case_ , snake_case_ ):
_lowerCAmelCase = False
return [i for i in range(2 , snake_case_ ) if is_prime[i]]
def __UpperCAmelCase ( snake_case_ : int = 10**8 ) -> int:
"""simple docstring"""
_lowerCAmelCase = calculate_prime_numbers(max_number // 2 )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = len(snake_case_ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'{solution() = }')
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|
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # noqa: this is just for tests
SCREAMING_SNAKE_CASE : Optional[Any] = open # noqa: we just need to have a builtin inside this module to test it properly
| 317
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"""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 numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
__UpperCamelCase = 'CIDAS/clipseg-rd64-refined'
__UpperCamelCase = 'image_segmenter'
__UpperCamelCase = CLIPSegForImageSegmentation
__UpperCamelCase = ['image', 'text']
__UpperCamelCase = ['image']
def __init__(self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
requires_backends(self , ["""vision"""] )
super().__init__(*lowerCamelCase , **lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return self.pre_processor(text=[label] , images=[image] , padding=lowerCamelCase , return_tensors="""pt""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
with torch.no_grad():
_lowerCAmelCase = self.model(**lowerCamelCase ).logits
return logits
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = outputs.cpu().detach().numpy()
_lowerCAmelCase = 0
_lowerCAmelCase = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 317
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE : Dict = {
'''configuration_efficientformer''': [
'''EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EfficientFormerConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = ['''EfficientFormerImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : List[str] = [
'''EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''EfficientFormerForImageClassification''',
'''EfficientFormerForImageClassificationWithTeacher''',
'''EfficientFormerModel''',
'''EfficientFormerPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = [
'''TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFEfficientFormerForImageClassification''',
'''TFEfficientFormerForImageClassificationWithTeacher''',
'''TFEfficientFormerModel''',
'''TFEfficientFormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_efficientformer import EfficientFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_efficientformer import (
EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
EfficientFormerForImageClassification,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerModel,
EfficientFormerPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
TFEfficientFormerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : List[str] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 317
|
"""simple docstring"""
from __future__ import annotations
import queue
class __lowerCamelCase :
def __init__(self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = data
_lowerCAmelCase = None
_lowerCAmelCase = None
def __UpperCAmelCase ( ) -> TreeNode:
"""simple docstring"""
print("""\n********Press N to stop entering at any point of time********\n""" )
_lowerCAmelCase = input("""Enter the value of the root node: """ ).strip().lower()
_lowerCAmelCase = queue.Queue()
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = q.get()
_lowerCAmelCase = F"""Enter the left node of {node_found.data}: """
_lowerCAmelCase = input(snake_case_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
_lowerCAmelCase = left_node
q.put(snake_case_ )
_lowerCAmelCase = F"""Enter the right node of {node_found.data}: """
_lowerCAmelCase = input(snake_case_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
_lowerCAmelCase = right_node
q.put(snake_case_ )
raise
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
print(node.data , end=""",""" )
pre_order(node.left )
pre_order(node.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
in_order(node.left )
print(node.data , end=""",""" )
in_order(node.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=""",""" )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = queue.Queue()
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = queue.Queue()
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = []
while not q.empty():
_lowerCAmelCase = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(snake_case_ )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = []
_lowerCAmelCase = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=""",""" )
stack.append(snake_case_ )
_lowerCAmelCase = n.left
# end of while means current node doesn't have left child
_lowerCAmelCase = stack.pop()
# start to traverse its right child
_lowerCAmelCase = n.right
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = []
_lowerCAmelCase = node
while n or stack:
while n:
stack.append(snake_case_ )
_lowerCAmelCase = n.left
_lowerCAmelCase = stack.pop()
print(n.data , end=""",""" )
_lowerCAmelCase = n.right
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase , _lowerCAmelCase = [], []
_lowerCAmelCase = node
stacka.append(snake_case_ )
while stacka: # to find the reversed order of post order, store it in stack2
_lowerCAmelCase = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(snake_case_ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=""",""" )
def __UpperCAmelCase ( snake_case_ : str = "" , snake_case_ : int=50 , snake_case_ : Dict="*" ) -> str:
"""simple docstring"""
if not s:
return "\n" + width * char
_lowerCAmelCase , _lowerCAmelCase = divmod(width - len(snake_case_ ) - 2 , 2 )
return F"""{left * char} {s} {(left + extra) * char}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('''Binary Tree Traversals'''))
SCREAMING_SNAKE_CASE : TreeNode = build_tree()
print(prompt('''Pre Order Traversal'''))
pre_order(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal'''))
in_order(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal'''))
post_order(node)
print(prompt() + '''\n''')
print(prompt('''Level Order Traversal'''))
level_order(node)
print(prompt() + '''\n''')
print(prompt('''Actual Level Order Traversal'''))
level_order_actual(node)
print('''*''' * 5_0 + '''\n''')
print(prompt('''Pre Order Traversal - Iteration Version'''))
pre_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal - Iteration Version'''))
in_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal - Iteration Version'''))
post_order_iter(node)
print(prompt())
| 317
| 1
|
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
SCREAMING_SNAKE_CASE : str = None
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : int = {'''vocab_file''': '''sentencepiece.bpe.model''', '''tokenizer_file''': '''tokenizer.json'''}
SCREAMING_SNAKE_CASE : Tuple = {
'''vocab_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model''',
},
'''tokenizer_file''': {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/tokenizer.json''',
},
}
SCREAMING_SNAKE_CASE : Any = {
'''camembert-base''': 5_1_2,
}
SCREAMING_SNAKE_CASE : int = '''▁'''
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ['input_ids', 'attention_mask']
__UpperCamelCase = CamembertTokenizer
def __init__(self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase="<s>" , lowerCamelCase="</s>" , lowerCamelCase="</s>" , lowerCamelCase="<s>" , lowerCamelCase="<unk>" , lowerCamelCase="<pad>" , lowerCamelCase="<mask>" , lowerCamelCase=["<s>NOTUSED", "</s>NOTUSED"] , **lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = AddedToken(lowerCamelCase , lstrip=lowerCamelCase , rstrip=lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else mask_token
super().__init__(
lowerCamelCase , tokenizer_file=lowerCamelCase , bos_token=lowerCamelCase , eos_token=lowerCamelCase , sep_token=lowerCamelCase , cls_token=lowerCamelCase , unk_token=lowerCamelCase , pad_token=lowerCamelCase , mask_token=lowerCamelCase , additional_special_tokens=lowerCamelCase , **lowerCamelCase , )
_lowerCAmelCase = vocab_file
_lowerCAmelCase = False if not self.vocab_file else True
def A__ (self , lowerCamelCase , lowerCamelCase = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
_lowerCAmelCase = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def A__ (self , lowerCamelCase , lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def A__ (self , lowerCamelCase , lowerCamelCase = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(lowerCamelCase ):
logger.error(f"""Vocabulary path ({save_directory}) should be a directory""" )
return
_lowerCAmelCase = os.path.join(
lowerCamelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCamelCase ):
copyfile(self.vocab_file , lowerCamelCase )
return (out_vocab_file,)
| 317
|
"""simple docstring"""
from __future__ import annotations
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = text, pattern
_lowerCAmelCase , _lowerCAmelCase = len(lowerCamelCase ), len(lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def A__ (self , lowerCamelCase ):
'''simple docstring'''
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = []
for i in range(self.textLen - self.patLen + 1 ):
_lowerCAmelCase = self.mismatch_in_text(lowerCamelCase )
if mismatch_index == -1:
positions.append(lowerCamelCase )
else:
_lowerCAmelCase = self.match_in_pattern(self.text[mismatch_index] )
_lowerCAmelCase = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
SCREAMING_SNAKE_CASE : Any = '''ABAABA'''
SCREAMING_SNAKE_CASE : Optional[int] = '''AB'''
SCREAMING_SNAKE_CASE : str = BoyerMooreSearch(text, pattern)
SCREAMING_SNAKE_CASE : Tuple = bms.bad_character_heuristic()
if len(positions) == 0:
print('''No match found''')
else:
print('''Pattern found in following positions: ''')
print(positions)
| 317
| 1
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 1000 ) -> int:
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = 1, 1
_lowerCAmelCase = []
for i in range(1 , n + 1 ):
_lowerCAmelCase = prev_numerator + 2 * prev_denominator
_lowerCAmelCase = prev_numerator + prev_denominator
if len(str(snake_case_ ) ) > len(str(snake_case_ ) ):
result.append(snake_case_ )
_lowerCAmelCase = numerator
_lowerCAmelCase = denominator
return len(snake_case_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
SCREAMING_SNAKE_CASE : List[str] = False
class __lowerCamelCase ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(
image=lowerCamelCase , generator=lowerCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
_lowerCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 317
| 1
|
"""simple docstring"""
import logging
import os
import quant_trainer
import torch
from torch.utils.data import DataLoader
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput
SCREAMING_SNAKE_CASE : Tuple = logging.getLogger(__name__)
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 ( __lowercase ):
def __init__(self , *lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , **lowerCamelCase ):
'''simple docstring'''
super().__init__(*lowerCamelCase , **lowerCamelCase )
_lowerCAmelCase = eval_examples
_lowerCAmelCase = post_process_function
_lowerCAmelCase = quant_trainer_args
_lowerCAmelCase = 128 # default number of calibration samples
def A__ (self , lowerCamelCase=None ):
'''simple docstring'''
if calib_dataset is None and self.calib_dataset is None:
raise ValueError("""Trainer: calibration requires an calib_dataset.""" )
_lowerCAmelCase = calib_dataset if calib_dataset is not None else self.calib_dataset
_lowerCAmelCase = self._remove_unused_columns(lowerCamelCase , description="""Calibration""" )
return DataLoader(
lowerCamelCase , batch_size=self.args.eval_batch_size , collate_fn=self.data_collator , drop_last=self.args.dataloader_drop_last , num_workers=self.args.dataloader_num_workers , pin_memory=self.args.dataloader_pin_memory , shuffle=lowerCamelCase , )
def A__ (self , lowerCamelCase=None ):
'''simple docstring'''
_lowerCAmelCase = self.train_dataset if calib_dataset is None else calib_dataset
_lowerCAmelCase = self.get_calib_dataloader(lowerCamelCase )
_lowerCAmelCase = self.model
quant_trainer.configure_model(lowerCamelCase , self.quant_trainer_args , calib=lowerCamelCase )
model.eval()
quant_trainer.enable_calibration(lowerCamelCase )
logger.info("""***** Running calibration *****""" )
logger.info(f""" Num examples = {self.calib_num}""" )
logger.info(f""" Batch size = {calib_dataloader.batch_size}""" )
for step, inputs in enumerate(lowerCamelCase ):
# Prediction step
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = self.prediction_step(lowerCamelCase , lowerCamelCase , prediction_loss_only=lowerCamelCase )
if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
break
quant_trainer.finish_calibration(lowerCamelCase , self.quant_trainer_args )
_lowerCAmelCase = model
def A__ (self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase = "eval" ):
'''simple docstring'''
_lowerCAmelCase = self.eval_dataset if eval_dataset is None else eval_dataset
_lowerCAmelCase = self.get_eval_dataloader(lowerCamelCase )
_lowerCAmelCase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
_lowerCAmelCase = self.compute_metrics
_lowerCAmelCase = None
_lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_lowerCAmelCase = eval_loop(
lowerCamelCase , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowerCamelCase , )
finally:
_lowerCAmelCase = compute_metrics
if self.post_process_function is not None and self.compute_metrics is not None:
_lowerCAmelCase = self.post_process_function(lowerCamelCase , lowerCamelCase , output.predictions )
_lowerCAmelCase = 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}_""" ):
_lowerCAmelCase = metrics.pop(lowerCamelCase )
self.log(lowerCamelCase )
else:
_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() )
_lowerCAmelCase = self.callback_handler.on_evaluate(self.args , self.state , self.control , lowerCamelCase )
return metrics
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase=None , lowerCamelCase = "test" ):
'''simple docstring'''
_lowerCAmelCase = self.get_test_dataloader(lowerCamelCase )
# Temporarily disable metric computation, we will do it in the loop here.
_lowerCAmelCase = self.compute_metrics
_lowerCAmelCase = None
_lowerCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
try:
_lowerCAmelCase = eval_loop(
lowerCamelCase , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowerCamelCase , )
finally:
_lowerCAmelCase = compute_metrics
if self.post_process_function is None or self.compute_metrics is None:
return output
_lowerCAmelCase = self.post_process_function(lowerCamelCase , lowerCamelCase , output.predictions , """predict""" )
_lowerCAmelCase = 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}_""" ):
_lowerCAmelCase = metrics.pop(lowerCamelCase )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=lowerCamelCase )
def A__ (self , lowerCamelCase="./" ):
'''simple docstring'''
_lowerCAmelCase = self.eval_dataset
_lowerCAmelCase = self.get_eval_dataloader(lowerCamelCase )
_lowerCAmelCase = next(iter(lowerCamelCase ) )
# saving device - to make it consistent
_lowerCAmelCase = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
# convert to tuple
_lowerCAmelCase = tuple(v.to(lowerCamelCase ) for k, v in batch.items() )
logger.info("""Converting model to be onnx compatible""" )
from pytorch_quantization.nn import TensorQuantizer
_lowerCAmelCase = True
_lowerCAmelCase = self.model.to(lowerCamelCase )
model.eval()
model.float()
_lowerCAmelCase = model.module if hasattr(lowerCamelCase , """module""" ) else model
quant_trainer.configure_model(lowerCamelCase , self.quant_trainer_args )
_lowerCAmelCase = os.path.join(lowerCamelCase , """model.onnx""" )
logger.info(f"""exporting model to {output_model_file}""" )
_lowerCAmelCase = {0: """batch_size""", 1: """seq_len"""}
torch.onnx.export(
lowerCamelCase , lowerCamelCase , lowerCamelCase , export_params=lowerCamelCase , opset_version=13 , do_constant_folding=lowerCamelCase , input_names=["""input_ids""", """attention_mask""", """token_type_ids"""] , output_names=["""output_start_logits""", """output_end_logits"""] , dynamic_axes={
"""input_ids""": axes,
"""attention_mask""": axes,
"""token_type_ids""": axes,
"""output_start_logits""": axes,
"""output_end_logits""": axes,
} , verbose=lowerCamelCase , )
logger.info("""onnx export finished""" )
| 317
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCamelCase ( __lowercase , unittest.TestCase ):
__UpperCamelCase = DiTPipeline
__UpperCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__UpperCamelCase = PipelineTesterMixin.required_optional_params - {
'latents',
'num_images_per_prompt',
'callback',
'callback_steps',
}
__UpperCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__UpperCamelCase = False
def A__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
_lowerCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=lowerCamelCase , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1_000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=lowerCamelCase , )
_lowerCAmelCase = AutoencoderKL()
_lowerCAmelCase = DDIMScheduler()
_lowerCAmelCase = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def A__ (self , lowerCamelCase , lowerCamelCase=0 ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(lowerCamelCase )
else:
_lowerCAmelCase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
_lowerCAmelCase = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = """cpu"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_lowerCAmelCase = pipe(**lowerCamelCase ).images
_lowerCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_lowerCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_lowerCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase , 1e-3 )
def A__ (self ):
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=lowerCamelCase , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def A__ (self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_lowerCAmelCase = pipe.get_label_ids(lowerCamelCase )
_lowerCAmelCase = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = load_numpy(
f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-2
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella"""]
_lowerCAmelCase = pipe.get_label_ids(lowerCamelCase )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
f"""/dit/{word}_512.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-1
| 317
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"""simple docstring"""
from manim import *
class __lowerCamelCase ( __lowercase ):
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = Rectangle(height=0.5 , width=0.5 )
_lowerCAmelCase = Rectangle(height=0.46 , width=0.46 ).set_stroke(width=0 )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
_lowerCAmelCase = VGroup(*lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
_lowerCAmelCase = VGroup(lowerCamelCase , lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
_lowerCAmelCase = Text("""CPU""" , font_size=24 )
_lowerCAmelCase = Group(lowerCamelCase , lowerCamelCase ).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowerCamelCase )
_lowerCAmelCase = [mem.copy() for i in range(4 )]
_lowerCAmelCase = VGroup(*lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
_lowerCAmelCase = Text("""GPU""" , font_size=24 )
_lowerCAmelCase = Group(lowerCamelCase , lowerCamelCase ).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase )
gpu.move_to([-1, -1, 0] )
self.add(lowerCamelCase )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
_lowerCAmelCase = Text("""Model""" , font_size=24 )
_lowerCAmelCase = Group(lowerCamelCase , lowerCamelCase ).arrange(lowerCamelCase , buff=0.5 , aligned_edge=lowerCamelCase )
model.move_to([3, -1.0, 0] )
self.add(lowerCamelCase )
_lowerCAmelCase = []
for i, rect in enumerate(lowerCamelCase ):
rect.set_stroke(lowerCamelCase )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
_lowerCAmelCase = Rectangle(height=0.46 / 4 , width=0.46 / 3 ).set_stroke(width=0.0 ).set_fill(lowerCamelCase , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.02 , direction=lowerCamelCase )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=lowerCamelCase , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=lowerCamelCase , buff=0.0 )
self.add(lowerCamelCase )
cpu_targs.append(lowerCamelCase )
_lowerCAmelCase = [mem.copy() for i in range(6 )]
_lowerCAmelCase = VGroup(*lowerCamelCase ).arrange(lowerCamelCase , buff=0 )
_lowerCAmelCase = Text("""Loaded Checkpoint""" , font_size=24 )
_lowerCAmelCase = Group(lowerCamelCase , lowerCamelCase ).arrange(lowerCamelCase , aligned_edge=lowerCamelCase , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
_lowerCAmelCase = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_lowerCAmelCase = MarkupText(
f"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model""" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = MarkupText(
f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" , font_size=18 , )
blue_text.next_to(lowerCamelCase , DOWN * 2.4 , aligned_edge=key_text.get_left() )
_lowerCAmelCase = MarkupText(
f"""Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>.""" , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCamelCase ) , Write(lowerCamelCase ) )
self.play(Write(lowerCamelCase , run_time=1 ) , Create(lowerCamelCase , run_time=1 ) )
_lowerCAmelCase = []
_lowerCAmelCase = []
for i, rect in enumerate(lowerCamelCase ):
_lowerCAmelCase = fill.copy().set_fill(lowerCamelCase , opacity=0.7 )
target.move_to(lowerCamelCase )
first_animations.append(GrowFromCenter(lowerCamelCase , run_time=1 ) )
_lowerCAmelCase = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(lowerCamelCase , run_time=1.5 ) )
self.play(*lowerCamelCase )
self.play(*lowerCamelCase )
self.wait()
| 317
|
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
return getitem, k
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return setitem, k, v
def __UpperCAmelCase ( snake_case_ : str ) -> Optional[int]:
"""simple docstring"""
return delitem, k
def __UpperCAmelCase ( snake_case_ : Optional[Any] , snake_case_ : Tuple , *snake_case_ : Tuple ) -> str:
"""simple docstring"""
try:
return fun(snake_case_ , *snake_case_ ), None
except Exception as e:
return None, e
SCREAMING_SNAKE_CASE : int = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
SCREAMING_SNAKE_CASE : List[Any] = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
SCREAMING_SNAKE_CASE : Any = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
SCREAMING_SNAKE_CASE : Union[str, Any] = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
SCREAMING_SNAKE_CASE : Optional[Any] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
SCREAMING_SNAKE_CASE : Optional[int] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
"""operations""" , (
pytest.param(_add_items , id="""add items""" ),
pytest.param(_overwrite_items , id="""overwrite items""" ),
pytest.param(_delete_items , id="""delete items""" ),
pytest.param(_access_absent_items , id="""access absent items""" ),
pytest.param(_add_with_resize_up , id="""add with resize up""" ),
pytest.param(_add_with_resize_down , id="""add with resize down""" ),
) , )
def __UpperCAmelCase ( snake_case_ : List[Any] ) -> Tuple:
"""simple docstring"""
_lowerCAmelCase = HashMap(initial_block_size=4 )
_lowerCAmelCase = {}
for _, (fun, *args) in enumerate(snake_case_ ):
_lowerCAmelCase , _lowerCAmelCase = _run_operation(snake_case_ , snake_case_ , *snake_case_ )
_lowerCAmelCase , _lowerCAmelCase = _run_operation(snake_case_ , snake_case_ , *snake_case_ )
assert my_res == py_res
assert str(snake_case_ ) == str(snake_case_ )
assert set(snake_case_ ) == set(snake_case_ )
assert len(snake_case_ ) == len(snake_case_ )
assert set(my.items() ) == set(py.items() )
def __UpperCAmelCase ( ) -> Tuple:
"""simple docstring"""
def is_public(snake_case_ : str ) -> bool:
return not name.startswith("""_""" )
_lowerCAmelCase = {name for name in dir({} ) if is_public(snake_case_ )}
_lowerCAmelCase = {name for name in dir(HashMap() ) if is_public(snake_case_ )}
assert dict_public_names > hash_public_names
| 317
| 1
|
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = 0 # The first color of the flag.
SCREAMING_SNAKE_CASE : Optional[int] = 1 # The second color of the flag.
SCREAMING_SNAKE_CASE : List[str] = 2 # The third color of the flag.
SCREAMING_SNAKE_CASE : Optional[Any] = (red, white, blue)
def __UpperCAmelCase ( snake_case_ : list ) -> list:
"""simple docstring"""
if not sequence:
return []
if len(snake_case_ ) == 1:
return list(snake_case_ )
_lowerCAmelCase = 0
_lowerCAmelCase = len(snake_case_ ) - 1
_lowerCAmelCase = 0
while mid <= high:
if sequence[mid] == colors[0]:
_lowerCAmelCase , _lowerCAmelCase = sequence[mid], sequence[low]
low += 1
mid += 1
elif sequence[mid] == colors[1]:
mid += 1
elif sequence[mid] == colors[2]:
_lowerCAmelCase , _lowerCAmelCase = sequence[high], sequence[mid]
high -= 1
else:
_lowerCAmelCase = F"""The elements inside the sequence must contains only {colors} values"""
raise ValueError(snake_case_ )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : int = input('''Enter numbers separated by commas:\n''').strip()
SCREAMING_SNAKE_CASE : Any = [int(item.strip()) for item in user_input.split(''',''')]
print(F'{dutch_national_flag_sort(unsorted)}')
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(snake_case_ : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(snake_case_ )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
snake_case_ : int , snake_case_ : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
_lowerCAmelCase = sum(
count_of_possible_combinations_with_dp_array(target - item , snake_case_ )
for item in array )
_lowerCAmelCase = answer
return answer
_lowerCAmelCase = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(snake_case_ , snake_case_ )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
_lowerCAmelCase = [0] * (target + 1)
_lowerCAmelCase = 1
for i in range(1 , target + 1 ):
for j in range(snake_case_ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : Tuple = 3
SCREAMING_SNAKE_CASE : Any = 5
SCREAMING_SNAKE_CASE : Optional[int] = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 317
| 1
|
"""simple docstring"""
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
import tensorflow as tf
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
@add_end_docstrings(__lowercase )
class __lowerCamelCase ( __lowercase ):
def __init__(self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
super().__init__(*lowerCamelCase , **lowerCamelCase )
requires_backends(self , """vision""" )
self.check_model_type(
TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING )
def A__ (self , lowerCamelCase=None ):
'''simple docstring'''
_lowerCAmelCase = {}
if top_k is not None:
_lowerCAmelCase = top_k
return {}, {}, postprocess_params
def __call__(self , lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
return super().__call__(lowerCamelCase , **lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = load_image(lowerCamelCase )
_lowerCAmelCase = self.image_processor(images=lowerCamelCase , return_tensors=self.framework )
return model_inputs
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.model(**lowerCamelCase )
return model_outputs
def A__ (self , lowerCamelCase , lowerCamelCase=5 ):
'''simple docstring'''
if top_k > self.model.config.num_labels:
_lowerCAmelCase = self.model.config.num_labels
if self.framework == "pt":
_lowerCAmelCase = model_outputs.logits.softmax(-1 )[0]
_lowerCAmelCase , _lowerCAmelCase = probs.topk(lowerCamelCase )
elif self.framework == "tf":
_lowerCAmelCase = stable_softmax(model_outputs.logits , axis=-1 )[0]
_lowerCAmelCase = tf.math.top_k(lowerCamelCase , k=lowerCamelCase )
_lowerCAmelCase , _lowerCAmelCase = topk.values.numpy(), topk.indices.numpy()
else:
raise ValueError(f"""Unsupported framework: {self.framework}""" )
_lowerCAmelCase = scores.tolist()
_lowerCAmelCase = ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(lowerCamelCase , lowerCamelCase )]
| 317
|
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for letter in string.ascii_lowercase]
SCREAMING_SNAKE_CASE : set[int] = {ord(char) for char in VALID_CHARS}
SCREAMING_SNAKE_CASE : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def __UpperCAmelCase ( snake_case_ : list[int] , snake_case_ : tuple[int, ...] ) -> str | None:
"""simple docstring"""
_lowerCAmelCase = ""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
for keychar, cipherchar in zip(cycle(snake_case_ ) , snake_case_ ):
_lowerCAmelCase = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(snake_case_ )
return decoded
def __UpperCAmelCase ( snake_case_ : list[int] ) -> list[str]:
"""simple docstring"""
_lowerCAmelCase = []
for key in product(snake_case_ , repeat=3 ):
_lowerCAmelCase = try_key(snake_case_ , snake_case_ )
if encoded is not None:
possibles.append(snake_case_ )
return possibles
def __UpperCAmelCase ( snake_case_ : list[str] , snake_case_ : str ) -> list[str]:
"""simple docstring"""
return [possible for possible in possibles if common_word in possible.lower()]
def __UpperCAmelCase ( snake_case_ : str = "p059_cipher.txt" ) -> int:
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = Path(snake_case_ ).parent.joinpath(snake_case_ ).read_text(encoding="""utf-8""" )
_lowerCAmelCase = [int(snake_case_ ) for number in data.strip().split(""",""" )]
_lowerCAmelCase = filter_valid_chars(snake_case_ )
for common_word in COMMON_WORDS:
_lowerCAmelCase = filter_common_word(snake_case_ , snake_case_ )
if len(snake_case_ ) == 1:
break
_lowerCAmelCase = possibles[0]
return sum(ord(snake_case_ ) for char in decoded_text )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class __lowerCamelCase :
def A__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
_lowerCAmelCase = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
_lowerCAmelCase = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
_lowerCAmelCase = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
_lowerCAmelCase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
_lowerCAmelCase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def A__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
_lowerCAmelCase = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
_lowerCAmelCase = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
_lowerCAmelCase = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , class_embed_type="""timestep""" , mid_block_scale_factor=1.414 , time_embedding_act_fn="""gelu""" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
_lowerCAmelCase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0001 , beta_end=0.02 , thresholding=lowerCamelCase , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
_lowerCAmelCase = DDPMScheduler(
num_train_timesteps=1_000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
_lowerCAmelCase = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_lowerCAmelCase = inputs["""prompt"""]
_lowerCAmelCase = inputs["""generator"""]
_lowerCAmelCase = inputs["""num_inference_steps"""]
_lowerCAmelCase = inputs["""output_type"""]
if "image" in inputs:
_lowerCAmelCase = inputs["""image"""]
else:
_lowerCAmelCase = None
if "mask_image" in inputs:
_lowerCAmelCase = inputs["""mask_image"""]
else:
_lowerCAmelCase = None
if "original_image" in inputs:
_lowerCAmelCase = inputs["""original_image"""]
else:
_lowerCAmelCase = None
_lowerCAmelCase , _lowerCAmelCase = pipe.encode_prompt(lowerCamelCase )
# inputs with prompt converted to embeddings
_lowerCAmelCase = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
_lowerCAmelCase = image
if mask_image is not None:
_lowerCAmelCase = mask_image
if original_image is not None:
_lowerCAmelCase = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(lowerCamelCase , lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
_lowerCAmelCase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowerCamelCase , lowerCamelCase ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
_lowerCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_lowerCAmelCase = inputs["""generator"""]
_lowerCAmelCase = inputs["""num_inference_steps"""]
_lowerCAmelCase = inputs["""output_type"""]
# inputs with prompt converted to embeddings
_lowerCAmelCase = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
_lowerCAmelCase = image
if mask_image is not None:
_lowerCAmelCase = mask_image
if original_image is not None:
_lowerCAmelCase = original_image
_lowerCAmelCase = pipe_loaded(**lowerCamelCase )[0]
_lowerCAmelCase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_lowerCAmelCase = pipe(**lowerCamelCase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowerCamelCase )
_lowerCAmelCase = self.pipeline_class.from_pretrained(lowerCamelCase )
pipe_loaded.to(lowerCamelCase )
pipe_loaded.set_progress_bar_config(disable=lowerCamelCase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
_lowerCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_lowerCAmelCase = pipe_loaded(**lowerCamelCase )[0]
_lowerCAmelCase = np.abs(to_np(lowerCamelCase ) - to_np(lowerCamelCase ) ).max()
self.assertLess(lowerCamelCase , 1e-4 )
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 1000000 ) -> int:
"""simple docstring"""
_lowerCAmelCase = limit + 1
_lowerCAmelCase = [0] * limit
for first_term in range(1 , snake_case_ ):
for n in range(snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
_lowerCAmelCase = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
SCREAMING_SNAKE_CASE : Optional[List[str]] = None
SCREAMING_SNAKE_CASE : Tuple = '''<''' if sys.byteorder == '''little''' else '''>'''
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
SCREAMING_SNAKE_CASE : List[str] = [
np.dtype('''|b1'''),
np.dtype('''|u1'''),
np.dtype('''<u2'''),
np.dtype('''>u2'''),
np.dtype('''<i2'''),
np.dtype('''>i2'''),
np.dtype('''<u4'''),
np.dtype('''>u4'''),
np.dtype('''<i4'''),
np.dtype('''>i4'''),
np.dtype('''<f4'''),
np.dtype('''>f4'''),
np.dtype('''<f8'''),
np.dtype('''>f8'''),
]
@dataclass
class __lowerCamelCase :
__UpperCamelCase = True
__UpperCamelCase = None
# Automatically constructed
__UpperCamelCase = "PIL.Image.Image"
__UpperCamelCase = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
__UpperCamelCase = field(default='Image' , init=__lowercase , repr=__lowercase )
def __call__(self ):
'''simple docstring'''
return self.pa_type
def A__ (self , lowerCamelCase ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = np.array(lowerCamelCase )
if isinstance(lowerCamelCase , lowerCamelCase ):
return {"path": value, "bytes": None}
elif isinstance(lowerCamelCase , lowerCamelCase ):
return {"path": None, "bytes": value}
elif isinstance(lowerCamelCase , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowerCamelCase )
elif isinstance(lowerCamelCase , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowerCamelCase )
elif value.get("""path""" ) is not None and os.path.isfile(value["""path"""] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("""path""" )}
elif value.get("""bytes""" ) is not None or value.get("""path""" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("""bytes""" ), "path": value.get("""path""" )}
else:
raise ValueError(
f"""An image sample should have one of 'path' or 'bytes' but they are missing or None in {value}.""" )
def A__ (self , lowerCamelCase , lowerCamelCase=None ):
'''simple docstring'''
if not self.decode:
raise RuntimeError("""Decoding is disabled for this feature. Please use Image(decode=True) instead.""" )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support decoding images, please install 'Pillow'.""" )
if token_per_repo_id is None:
_lowerCAmelCase = {}
_lowerCAmelCase , _lowerCAmelCase = value["""path"""], value["""bytes"""]
if bytes_ is None:
if path is None:
raise ValueError(f"""An image should have one of 'path' or 'bytes' but both are None in {value}.""" )
else:
if is_local_path(lowerCamelCase ):
_lowerCAmelCase = PIL.Image.open(lowerCamelCase )
else:
_lowerCAmelCase = path.split("""::""" )[-1]
try:
_lowerCAmelCase = string_to_dict(lowerCamelCase , config.HUB_DATASETS_URL )["""repo_id"""]
_lowerCAmelCase = token_per_repo_id.get(lowerCamelCase )
except ValueError:
_lowerCAmelCase = None
with xopen(lowerCamelCase , """rb""" , use_auth_token=lowerCamelCase ) as f:
_lowerCAmelCase = BytesIO(f.read() )
_lowerCAmelCase = PIL.Image.open(bytes_ )
else:
_lowerCAmelCase = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def A__ (self ):
'''simple docstring'''
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def A__ (self , lowerCamelCase ):
'''simple docstring'''
if pa.types.is_string(storage.type ):
_lowerCAmelCase = pa.array([None] * len(lowerCamelCase ) , type=pa.binary() )
_lowerCAmelCase = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_lowerCAmelCase = pa.array([None] * len(lowerCamelCase ) , type=pa.string() )
_lowerCAmelCase = pa.StructArray.from_arrays([storage, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("""bytes""" ) >= 0:
_lowerCAmelCase = storage.field("""bytes""" )
else:
_lowerCAmelCase = pa.array([None] * len(lowerCamelCase ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
_lowerCAmelCase = storage.field("""path""" )
else:
_lowerCAmelCase = pa.array([None] * len(lowerCamelCase ) , type=pa.string() )
_lowerCAmelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
_lowerCAmelCase = pa.array(
[encode_np_array(np.array(lowerCamelCase ) )["""bytes"""] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
_lowerCAmelCase = pa.array([None] * len(lowerCamelCase ) , type=pa.string() )
_lowerCAmelCase = pa.StructArray.from_arrays(
[bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowerCamelCase , self.pa_type )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
@no_op_if_value_is_null
def path_to_bytes(lowerCamelCase ):
with xopen(lowerCamelCase , """rb""" ) as f:
_lowerCAmelCase = f.read()
return bytes_
_lowerCAmelCase = pa.array(
[
(path_to_bytes(x["""path"""] ) if x["""bytes"""] is None else x["""bytes"""]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
_lowerCAmelCase = pa.array(
[os.path.basename(lowerCamelCase ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
_lowerCAmelCase = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(lowerCamelCase , self.pa_type )
def __UpperCAmelCase ( ) -> List[str]:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
_lowerCAmelCase = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def __UpperCAmelCase ( snake_case_ : "PIL.Image.Image" ) -> bytes:
"""simple docstring"""
_lowerCAmelCase = BytesIO()
if image.format in list_image_compression_formats():
_lowerCAmelCase = image.format
else:
_lowerCAmelCase = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(snake_case_ , format=snake_case_ )
return buffer.getvalue()
def __UpperCAmelCase ( snake_case_ : "PIL.Image.Image" ) -> dict:
"""simple docstring"""
if hasattr(snake_case_ , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(snake_case_ )}
def __UpperCAmelCase ( snake_case_ : np.ndarray ) -> dict:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
_lowerCAmelCase = array.dtype
_lowerCAmelCase = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
_lowerCAmelCase = dtype.kind
_lowerCAmelCase = dtype.itemsize
_lowerCAmelCase = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
_lowerCAmelCase = np.dtype("""|u1""" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
F"""Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.""" )
if dtype is not dest_dtype:
warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
_lowerCAmelCase = dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
_lowerCAmelCase = dtype_byteorder + dtype_kind + str(snake_case_ )
_lowerCAmelCase = np.dtype(snake_case_ )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(F"""Downcasting array dtype {dtype} to {dest_dtype} to be compatible with 'Pillow'""" )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
F"""Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}""" )
_lowerCAmelCase = PIL.Image.fromarray(array.astype(snake_case_ ) )
return {"path": None, "bytes": image_to_bytes(snake_case_ )}
def __UpperCAmelCase ( snake_case_ : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
_lowerCAmelCase , _lowerCAmelCase = first_non_null_value(snake_case_ )
if isinstance(snake_case_ , snake_case_ ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(snake_case_ , np.ndarray ):
_lowerCAmelCase = no_op_if_value_is_null(snake_case_ )
return [obj_to_image_dict_func(snake_case_ ) for obj in objs]
elif isinstance(snake_case_ , PIL.Image.Image ):
_lowerCAmelCase = no_op_if_value_is_null(snake_case_ )
return [obj_to_image_dict_func(snake_case_ ) for obj in objs]
else:
return objs
else:
return objs
| 317
|
"""simple docstring"""
from functools import reduce
SCREAMING_SNAKE_CASE : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def __UpperCAmelCase ( snake_case_ : str = N ) -> int:
"""simple docstring"""
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda snake_case_ , snake_case_ : str(int(snake_case_ ) * int(snake_case_ ) ) , n[i : i + 13] ) )
for i in range(len(snake_case_ ) - 12 ) )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = ['image_processor', 'tokenizer']
__UpperCamelCase = 'AutoImageProcessor'
__UpperCamelCase = 'AutoTokenizer'
def __init__(self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
super().__init__(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = self.image_processor
def __call__(self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , **lowerCamelCase ):
'''simple docstring'''
if text is None and images is None:
raise ValueError("""You have to specify either text or images. Both cannot be none.""" )
if text is not None:
_lowerCAmelCase = self.tokenizer(lowerCamelCase , return_tensors=lowerCamelCase , **lowerCamelCase )
if images is not None:
_lowerCAmelCase = self.image_processor(lowerCamelCase , return_tensors=lowerCamelCase , **lowerCamelCase )
if text is not None and images is not None:
_lowerCAmelCase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowerCamelCase ) , tensor_type=lowerCamelCase )
def A__ (self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
return self.tokenizer.batch_decode(*lowerCamelCase , **lowerCamelCase )
def A__ (self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
return self.tokenizer.decode(*lowerCamelCase , **lowerCamelCase )
@property
def A__ (self ):
'''simple docstring'''
return ["input_ids", "attention_mask", "pixel_values"]
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 600851475143 ) -> int:
"""simple docstring"""
try:
_lowerCAmelCase = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
while n % i == 0:
_lowerCAmelCase = i
n //= i
i += 1
if n > 1:
_lowerCAmelCase = n
return int(snake_case_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import argparse
import torch
from transformers import (
SpeechTaConfig,
SpeechTaFeatureExtractor,
SpeechTaForSpeechToSpeech,
SpeechTaForSpeechToText,
SpeechTaForTextToSpeech,
SpeechTaProcessor,
SpeechTaTokenizer,
logging,
)
from transformers.tokenization_utils import AddedToken
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : List[str] = logging.get_logger('''transformers.models.speecht5''')
SCREAMING_SNAKE_CASE : List[str] = {
'''speech_encoder_prenet.layer_norm''': '''speecht5.encoder.prenet.feature_projection.layer_norm''',
'''speech_encoder_prenet.post_extract_proj''': '''speecht5.encoder.prenet.feature_projection.projection''',
'''speech_encoder_prenet.pos_conv.0''': '''speecht5.encoder.prenet.pos_conv_embed.conv''',
'''speech_encoder_prenet.mask_emb''': '''speecht5.encoder.prenet.masked_spec_embed''',
}
SCREAMING_SNAKE_CASE : Optional[Any] = {
'''text_encoder_prenet.encoder_prenet.0''': '''speecht5.encoder.prenet.embed_tokens''',
'''text_encoder_prenet.encoder_prenet.1.alpha''': '''speecht5.encoder.prenet.encode_positions.alpha''',
}
SCREAMING_SNAKE_CASE : Optional[int] = {
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.0.0''': '''speecht5.decoder.prenet.layers.0''',
'''speech_decoder_prenet.decoder_prenet.0.0.prenet.1.0''': '''speecht5.decoder.prenet.layers.1''',
'''speech_decoder_prenet.decoder_prenet.0.1''': '''speecht5.decoder.prenet.final_layer''',
'''speech_decoder_prenet.decoder_prenet.1.alpha''': '''speecht5.decoder.prenet.encode_positions.alpha''',
'''speech_decoder_prenet.spkembs_layer.0''': '''speecht5.decoder.prenet.speaker_embeds_layer''',
}
SCREAMING_SNAKE_CASE : Optional[Any] = {
'''speech_decoder_postnet.feat_out''': '''speech_decoder_postnet.feat_out''',
'''speech_decoder_postnet.prob_out''': '''speech_decoder_postnet.prob_out''',
'''speech_decoder_postnet.postnet.postnet.0.0''': '''speech_decoder_postnet.layers.0.conv''',
'''speech_decoder_postnet.postnet.postnet.0.1''': '''speech_decoder_postnet.layers.0.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.1.0''': '''speech_decoder_postnet.layers.1.conv''',
'''speech_decoder_postnet.postnet.postnet.1.1''': '''speech_decoder_postnet.layers.1.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.2.0''': '''speech_decoder_postnet.layers.2.conv''',
'''speech_decoder_postnet.postnet.postnet.2.1''': '''speech_decoder_postnet.layers.2.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.3.0''': '''speech_decoder_postnet.layers.3.conv''',
'''speech_decoder_postnet.postnet.postnet.3.1''': '''speech_decoder_postnet.layers.3.batch_norm''',
'''speech_decoder_postnet.postnet.postnet.4.0''': '''speech_decoder_postnet.layers.4.conv''',
'''speech_decoder_postnet.postnet.postnet.4.1''': '''speech_decoder_postnet.layers.4.batch_norm''',
}
SCREAMING_SNAKE_CASE : Optional[Any] = {
'''text_decoder_prenet.embed_tokens''': '''speecht5.decoder.prenet.embed_tokens''',
}
SCREAMING_SNAKE_CASE : Tuple = {
'''text_decoder_postnet.output_projection''': '''text_decoder_postnet.lm_head''',
}
SCREAMING_SNAKE_CASE : Optional[Any] = {
'''encoder.layers.*.self_attn.k_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.k_proj''',
'''encoder.layers.*.self_attn.v_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.v_proj''',
'''encoder.layers.*.self_attn.q_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.q_proj''',
'''encoder.layers.*.self_attn.out_proj''': '''speecht5.encoder.wrapped_encoder.layers.*.attention.out_proj''',
'''encoder.layers.*.self_attn_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.layer_norm''',
'''encoder.layers.*.fc1''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.intermediate_dense''',
'''encoder.layers.*.fc2''': '''speecht5.encoder.wrapped_encoder.layers.*.feed_forward.output_dense''',
'''encoder.layers.*.final_layer_norm''': '''speecht5.encoder.wrapped_encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''speecht5.encoder.wrapped_encoder.layer_norm''',
'''encoder.pos_emb.pe_k''': '''speecht5.encoder.wrapped_encoder.embed_positions.pe_k''',
}
SCREAMING_SNAKE_CASE : List[Any] = {
'''decoder.layers.*.self_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.k_proj''',
'''decoder.layers.*.self_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.v_proj''',
'''decoder.layers.*.self_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.q_proj''',
'''decoder.layers.*.self_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn.out_proj''',
'''decoder.layers.*.self_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.self_attn_layer_norm''',
'''decoder.layers.*.encoder_attn.k_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.k_proj''',
'''decoder.layers.*.encoder_attn.v_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.v_proj''',
'''decoder.layers.*.encoder_attn.q_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.q_proj''',
'''decoder.layers.*.encoder_attn.out_proj''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn.out_proj''',
'''decoder.layers.*.encoder_attn_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.encoder_attn_layer_norm''',
'''decoder.layers.*.fc1''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.intermediate_dense''',
'''decoder.layers.*.fc2''': '''speecht5.decoder.wrapped_decoder.layers.*.feed_forward.output_dense''',
'''decoder.layers.*.final_layer_norm''': '''speecht5.decoder.wrapped_decoder.layers.*.final_layer_norm''',
}
SCREAMING_SNAKE_CASE : str = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_TEXT_DECODER_PRENET,
**MAPPING_TEXT_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE : Tuple = {
**MAPPING_TEXT_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE : Dict = {
**MAPPING_SPEECH_ENCODER_PRENET,
**MAPPING_ENCODER,
**MAPPING_DECODER,
**MAPPING_SPEECH_DECODER_PRENET,
**MAPPING_SPEECH_DECODER_POSTNET,
}
SCREAMING_SNAKE_CASE : Optional[Any] = []
SCREAMING_SNAKE_CASE : Union[str, Any] = [
'''encoder.version''',
'''encoder.layers.*.norm_k.weight''',
'''encoder.layers.*.norm_k.bias''',
'''decoder.version''',
'''decoder.layers.*.norm_k.weight''',
'''decoder.layers.*.norm_k.bias''',
'''decoder.pos_emb.pe_k''',
'''speech_encoder_prenet.embed_positions._float_tensor''',
'''text_decoder_prenet.embed_positions._float_tensor''',
]
SCREAMING_SNAKE_CASE : List[Any] = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''speech_decoder_prenet.*''',
'''speech_decoder_postnet.*''',
]
SCREAMING_SNAKE_CASE : int = IGNORE_KEYS + [
'''encoder.proj''',
'''speech_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
SCREAMING_SNAKE_CASE : List[Any] = IGNORE_KEYS + [
'''encoder.proj''',
'''text_encoder_prenet.*''',
'''text_decoder_prenet.*''',
'''text_decoder_postnet.*''',
]
def __UpperCAmelCase ( snake_case_ : Union[str, Any] , snake_case_ : Optional[Any] , snake_case_ : int , snake_case_ : Any , snake_case_ : Tuple ) -> List[str]:
"""simple docstring"""
for attribute in key.split(""".""" ):
_lowerCAmelCase = getattr(snake_case_ , snake_case_ )
if weight_type is not None:
_lowerCAmelCase = getattr(snake_case_ , snake_case_ ).shape
else:
_lowerCAmelCase = 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":
_lowerCAmelCase = value
elif weight_type == "weight_g":
_lowerCAmelCase = value
elif weight_type == "weight_v":
_lowerCAmelCase = value
elif weight_type == "bias":
_lowerCAmelCase = value
elif weight_type == "running_mean":
_lowerCAmelCase = value
elif weight_type == "running_var":
_lowerCAmelCase = value
elif weight_type == "num_batches_tracked":
_lowerCAmelCase = value
else:
_lowerCAmelCase = value
logger.info(F"""{key + ("." + weight_type if weight_type is not None else "")} was initialized from {full_name}.""" )
def __UpperCAmelCase ( snake_case_ : List[str] , snake_case_ : str ) -> Any:
"""simple docstring"""
for key in ignore_keys:
if key.endswith(""".*""" ):
if name.startswith(key[:-1] ):
return True
elif ".*." in key:
_lowerCAmelCase , _lowerCAmelCase = key.split(""".*.""" )
if prefix in name and suffix in name:
return True
elif key in name:
return True
return False
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : Union[str, Any] , snake_case_ : List[Any] ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = []
if task == "s2t":
_lowerCAmelCase = hf_model.speechta.encoder.prenet.feature_encoder
_lowerCAmelCase = MAPPING_S2T
_lowerCAmelCase = IGNORE_KEYS_S2T
elif task == "t2s":
_lowerCAmelCase = None
_lowerCAmelCase = MAPPING_T2S
_lowerCAmelCase = IGNORE_KEYS_T2S
elif task == "s2s":
_lowerCAmelCase = hf_model.speechta.encoder.prenet.feature_encoder
_lowerCAmelCase = MAPPING_S2S
_lowerCAmelCase = IGNORE_KEYS_S2S
else:
raise ValueError(F"""Unsupported task: {task}""" )
for name, value in fairseq_dict.items():
if should_ignore(snake_case_ , snake_case_ ):
logger.info(F"""{name} was ignored""" )
continue
_lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , hf_model.config.feat_extract_norm == """group""" , )
_lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
# mapped_key = "speecht5." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if "*" in key:
_lowerCAmelCase , _lowerCAmelCase = key.split(""".*.""" )
if prefix in name and suffix in name:
_lowerCAmelCase = suffix
# if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if key in name:
_lowerCAmelCase = True
if "*" in mapped_key:
_lowerCAmelCase = name.split(snake_case_ )[0].split(""".""" )[-2]
_lowerCAmelCase = mapped_key.replace("""*""" , snake_case_ )
if "weight_g" in name:
_lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase = """weight_v"""
elif "bias" in name:
_lowerCAmelCase = """bias"""
elif "weight" in name:
_lowerCAmelCase = """weight"""
elif "running_mean" in name:
_lowerCAmelCase = """running_mean"""
elif "running_var" in name:
_lowerCAmelCase = """running_var"""
elif "num_batches_tracked" in name:
_lowerCAmelCase = """num_batches_tracked"""
else:
_lowerCAmelCase = None
set_recursively(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
continue
if not is_used:
unused_weights.append(snake_case_ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : Union[str, Any] , snake_case_ : Tuple , snake_case_ : List[str] , snake_case_ : Dict ) -> int:
"""simple docstring"""
_lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
_lowerCAmelCase = name.split(""".""" )
_lowerCAmelCase = int(items[0] )
_lowerCAmelCase = 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.""" )
_lowerCAmelCase = 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.""" )
_lowerCAmelCase = 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.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
_lowerCAmelCase = 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.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
_lowerCAmelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(snake_case_ )
@torch.no_grad()
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : Union[str, Any] , snake_case_ : Optional[Any] , snake_case_ : str=None , snake_case_ : Union[str, Any]=None , snake_case_ : str=None , ) -> Union[str, Any]:
"""simple docstring"""
if config_path is not None:
_lowerCAmelCase = SpeechTaConfig.from_pretrained(snake_case_ )
else:
_lowerCAmelCase = SpeechTaConfig()
if task == "s2t":
_lowerCAmelCase = config.max_text_positions
_lowerCAmelCase = SpeechTaForSpeechToText(snake_case_ )
elif task == "t2s":
_lowerCAmelCase = 1876
_lowerCAmelCase = 600
_lowerCAmelCase = config.max_speech_positions
_lowerCAmelCase = SpeechTaForTextToSpeech(snake_case_ )
elif task == "s2s":
_lowerCAmelCase = 1876
_lowerCAmelCase = config.max_speech_positions
_lowerCAmelCase = SpeechTaForSpeechToSpeech(snake_case_ )
else:
raise ValueError(F"""Unknown task name: {task}""" )
if vocab_path:
_lowerCAmelCase = SpeechTaTokenizer(snake_case_ , model_max_length=config.max_text_positions )
# Mask token behaves like a normal word, i.e. include the space before it
_lowerCAmelCase = AddedToken("""<mask>""" , lstrip=snake_case_ , rstrip=snake_case_ )
_lowerCAmelCase = mask_token
tokenizer.add_special_tokens({"""mask_token""": mask_token} )
tokenizer.add_tokens(["""<ctc_blank>"""] )
_lowerCAmelCase = SpeechTaFeatureExtractor()
_lowerCAmelCase = SpeechTaProcessor(tokenizer=snake_case_ , feature_extractor=snake_case_ )
processor.save_pretrained(snake_case_ )
_lowerCAmelCase = torch.load(snake_case_ )
recursively_load_weights(fairseq_checkpoint["""model"""] , snake_case_ , snake_case_ )
model.save_pretrained(snake_case_ )
if repo_id:
print("""Pushing to the hub...""" )
processor.push_to_hub(snake_case_ )
model.push_to_hub(snake_case_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Dict = argparse.ArgumentParser()
parser.add_argument(
'''--task''',
default='''s2t''',
type=str,
help='''Type of the SpeechT5 model you\'d like to convert. Should be one of \'s2t\', \'t2s\', \'s2s\'.''',
)
parser.add_argument('''--checkpoint_path''', required=True, default=None, type=str, help='''Path to fairseq checkpoint''')
parser.add_argument('''--vocab_path''', default=None, type=str, help='''Path to SentencePiece model''')
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to hf config.json of model to convert''')
parser.add_argument(
'''--pytorch_dump_folder_path''', required=True, default=None, type=str, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--push_to_hub''', default=None, type=str, help='''Where to upload the converted model on the 🤗 hub.'''
)
SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args()
convert_speechta_checkpoint(
args.task,
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.vocab_path,
args.push_to_hub,
)
| 317
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
SCREAMING_SNAKE_CASE : Optional[Any] = logging.getLogger(__name__)
@dataclass
class __lowerCamelCase :
__UpperCamelCase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Whether tp freeze the encoder.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class __lowerCamelCase :
__UpperCamelCase = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__UpperCamelCase = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
__UpperCamelCase = field(
default=1_024 , 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=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCamelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
__UpperCamelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCamelCase = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
__UpperCamelCase = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
__UpperCamelCase = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Source language id for translation.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Target language id for translation.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': '# num_beams to use for evaluation.'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def __UpperCAmelCase ( snake_case_ : Optional[int] , snake_case_ : Any , snake_case_ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(snake_case_ , os.path.join(snake_case_ , F"""{split}_results.json""" ) )
def __UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_args_into_dataclasses()
check_output_dir(snake_case_ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("""Training/evaluation parameters %s""" , snake_case_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_lowerCAmelCase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(snake_case_ , snake_case_ , snake_case_ ):
assert hasattr(snake_case_ , snake_case_ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(snake_case_ , snake_case_ , getattr(snake_case_ , snake_case_ ) )
_lowerCAmelCase = 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 = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=snake_case_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(snake_case_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCAmelCase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(snake_case_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(snake_case_ , snake_case_ ):
_lowerCAmelCase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCAmelCase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(snake_case_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCAmelCase = SeqaSeqDataset
# Get datasets
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCAmelCase = (
build_compute_metrics_fn(data_args.task , snake_case_ ) if training_args.predict_with_generate else None
)
_lowerCAmelCase = SeqaSeqTrainer(
model=snake_case_ , args=snake_case_ , data_args=snake_case_ , train_dataset=snake_case_ , eval_dataset=snake_case_ , data_collator=SeqaSeqDataCollator(
snake_case_ , snake_case_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=snake_case_ , tokenizer=snake_case_ , )
_lowerCAmelCase = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
_lowerCAmelCase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCAmelCase = train_result.metrics
_lowerCAmelCase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_lowerCAmelCase = trainer.evaluate(metric_key_prefix="""val""" )
_lowerCAmelCase = data_args.n_val
_lowerCAmelCase = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
_lowerCAmelCase = trainer.predict(test_dataset=snake_case_ , metric_key_prefix="""test""" )
_lowerCAmelCase = test_output.metrics
_lowerCAmelCase = data_args.n_test
if trainer.is_world_process_zero():
_lowerCAmelCase = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
if training_args.predict_with_generate:
_lowerCAmelCase = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=snake_case_ , clean_up_tokenization_spaces=snake_case_ )
_lowerCAmelCase = lmap(str.strip , snake_case_ )
write_txt_file(snake_case_ , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(snake_case_ , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def __UpperCAmelCase ( snake_case_ : Any ) -> Dict:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 317
| 1
|
"""simple docstring"""
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import logging
SCREAMING_SNAKE_CASE : Optional[int] = ['''bart.large''', '''bart.large.mnli''', '''bart.large.cnn''', '''bart_xsum/model.pt''']
SCREAMING_SNAKE_CASE : int = {'''bart.large''': BartModel, '''bart.large.mnli''': BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
raise Exception('''requires fairseq >= 0.9.0''')
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = ''' Hello world! cécé herlolip'''
SCREAMING_SNAKE_CASE : Union[str, Any] = [
('''model.classification_heads.mnli.dense.weight''', '''classification_head.dense.weight'''),
('''model.classification_heads.mnli.dense.bias''', '''classification_head.dense.bias'''),
('''model.classification_heads.mnli.out_proj.weight''', '''classification_head.out_proj.weight'''),
('''model.classification_heads.mnli.out_proj.bias''', '''classification_head.out_proj.bias'''),
]
def __UpperCAmelCase ( snake_case_ : Any ) -> int:
"""simple docstring"""
_lowerCAmelCase = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""_float_tensor""",
]
for k in ignore_keys:
state_dict.pop(snake_case_ , snake_case_ )
def __UpperCAmelCase ( snake_case_ : Any , snake_case_ : List[Any] , snake_case_ : str ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = dct.pop(snake_case_ )
_lowerCAmelCase = val
def __UpperCAmelCase ( snake_case_ : List[Any] ) -> Dict:
"""simple docstring"""
_lowerCAmelCase = torch.load(snake_case_ , map_location="""cpu""" )
_lowerCAmelCase = torch.hub.load("""pytorch/fairseq""" , """bart.large.cnn""" ).eval()
hub_interface.model.load_state_dict(sd["""model"""] )
return hub_interface
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = emb.weight.shape
_lowerCAmelCase = nn.Linear(snake_case_ , snake_case_ , bias=snake_case_ )
_lowerCAmelCase = emb.weight.data
return lin_layer
@torch.no_grad()
def __UpperCAmelCase ( snake_case_ : Any , snake_case_ : Any , snake_case_ : int=None ) -> str:
"""simple docstring"""
if not os.path.exists(snake_case_ ):
_lowerCAmelCase = torch.hub.load("""pytorch/fairseq""" , snake_case_ ).eval()
else:
_lowerCAmelCase = load_xsum_checkpoint(snake_case_ )
bart.model.upgrade_state_dict(bart.model.state_dict() )
if hf_checkpoint_name is None:
_lowerCAmelCase = checkpoint_path.replace(""".""" , """-""" )
_lowerCAmelCase = BartConfig.from_pretrained(snake_case_ )
_lowerCAmelCase = bart.encode(snake_case_ ).unsqueeze(0 )
_lowerCAmelCase = BartTokenizer.from_pretrained(snake_case_ ).encode(snake_case_ , return_tensors="""pt""" ).unsqueeze(0 )
if not torch.eq(snake_case_ , snake_case_ ).all():
raise ValueError(
F"""converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}""" )
if checkpoint_path == "bart.large.mnli":
_lowerCAmelCase = bart.state_dict()
remove_ignore_keys_(snake_case_ )
_lowerCAmelCase = state_dict["""model.decoder.embed_tokens.weight"""]
for src, dest in mnli_rename_keys:
rename_key(snake_case_ , snake_case_ , snake_case_ )
_lowerCAmelCase = BartForSequenceClassification(snake_case_ ).eval()
model.load_state_dict(snake_case_ )
_lowerCAmelCase = bart.predict("""mnli""" , snake_case_ , return_logits=snake_case_ )
_lowerCAmelCase = model(snake_case_ )[0] # logits
else: # no classification heads to worry about
_lowerCAmelCase = bart.model.state_dict()
remove_ignore_keys_(snake_case_ )
_lowerCAmelCase = state_dict["""decoder.embed_tokens.weight"""]
_lowerCAmelCase = bart.extract_features(snake_case_ )
if hf_checkpoint_name == "facebook/bart-large":
_lowerCAmelCase = BartModel(snake_case_ ).eval()
model.load_state_dict(snake_case_ )
_lowerCAmelCase = model(snake_case_ ).model[0]
else:
_lowerCAmelCase = BartForConditionalGeneration(snake_case_ ).eval() # an existing summarization ckpt
model.model.load_state_dict(snake_case_ )
if hasattr(snake_case_ , """lm_head""" ):
_lowerCAmelCase = make_linear_from_emb(model.model.shared )
_lowerCAmelCase = model.model(snake_case_ )[0]
# Check results
if fairseq_output.shape != new_model_outputs.shape:
raise ValueError(
F"""`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}""" )
if (fairseq_output != new_model_outputs).any().item():
raise ValueError("""Some values in `fairseq_output` are different from `new_model_outputs`""" )
Path(snake_case_ ).mkdir(exist_ok=snake_case_ )
model.save_pretrained(snake_case_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''fairseq_path''', type=str, help='''bart.large, bart.large.cnn or a path to a model.pt on local filesystem.'''
)
parser.add_argument('''pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
parser.add_argument(
'''--hf_config''', default=None, type=str, help='''Which huggingface architecture to use: bart-large-xsum'''
)
SCREAMING_SNAKE_CASE : Optional[Any] = parser.parse_args()
convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)
| 317
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : List[Any] = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = [
'''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FocalNetForImageClassification''',
'''FocalNetForMaskedImageModeling''',
'''FocalNetBackbone''',
'''FocalNetModel''',
'''FocalNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 317
| 1
|
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[Any] = {
'''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''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
def __UpperCAmelCase ( snake_case_ : Union[str, Any] , snake_case_ : Any , snake_case_ : Any , snake_case_ : List[str] , snake_case_ : int ) -> Tuple:
"""simple docstring"""
for attribute in key.split(""".""" ):
_lowerCAmelCase = getattr(snake_case_ , snake_case_ )
if weight_type is not None:
_lowerCAmelCase = getattr(snake_case_ , snake_case_ ).shape
else:
_lowerCAmelCase = hf_pointer.shape
assert hf_shape == value.shape, (
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":
_lowerCAmelCase = value
elif weight_type == "weight_g":
_lowerCAmelCase = value
elif weight_type == "weight_v":
_lowerCAmelCase = value
elif weight_type == "bias":
_lowerCAmelCase = value
else:
_lowerCAmelCase = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""" )
def __UpperCAmelCase ( snake_case_ : Union[str, Any] , snake_case_ : Optional[Any] , snake_case_ : str ) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = fairseq_model.state_dict()
_lowerCAmelCase = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
_lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , hf_model.config.feat_extract_norm == """group""" , )
_lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
_lowerCAmelCase = """hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or (key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0] and not is_finetuned):
_lowerCAmelCase = True
if "*" in mapped_key:
_lowerCAmelCase = name.split(snake_case_ )[0].split(""".""" )[-2]
_lowerCAmelCase = mapped_key.replace("""*""" , snake_case_ )
if "weight_g" in name:
_lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase = """weight_v"""
elif "weight" in name:
_lowerCAmelCase = """weight"""
elif "bias" in name:
_lowerCAmelCase = """bias"""
else:
_lowerCAmelCase = None
set_recursively(snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ )
continue
if not is_used:
unused_weights.append(snake_case_ )
logger.warning(F"""Unused weights: {unused_weights}""" )
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : Tuple , snake_case_ : Union[str, Any] , snake_case_ : Optional[int] , snake_case_ : List[str] ) -> str:
"""simple docstring"""
_lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
_lowerCAmelCase = name.split(""".""" )
_lowerCAmelCase = int(items[0] )
_lowerCAmelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
)
_lowerCAmelCase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
)
_lowerCAmelCase = 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:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
" found."
)
_lowerCAmelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
)
_lowerCAmelCase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(snake_case_ )
@torch.no_grad()
def __UpperCAmelCase ( snake_case_ : List[str] , snake_case_ : Optional[Any] , snake_case_ : Optional[int]=None , snake_case_ : Tuple=None , snake_case_ : Any=True ) -> Any:
"""simple docstring"""
if config_path is not None:
_lowerCAmelCase = HubertConfig.from_pretrained(snake_case_ )
else:
_lowerCAmelCase = HubertConfig()
if is_finetuned:
if dict_path:
_lowerCAmelCase = Dictionary.load(snake_case_ )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_lowerCAmelCase = target_dict.pad_index
_lowerCAmelCase = target_dict.bos_index
_lowerCAmelCase = target_dict.eos_index
_lowerCAmelCase = len(target_dict.symbols )
_lowerCAmelCase = os.path.join(snake_case_ , """vocab.json""" )
if not os.path.isdir(snake_case_ ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(snake_case_ ) )
return
os.makedirs(snake_case_ , exist_ok=snake_case_ )
with open(snake_case_ , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , snake_case_ )
_lowerCAmelCase = WavaVecaCTCTokenizer(
snake_case_ , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=snake_case_ , )
_lowerCAmelCase = True if config.feat_extract_norm == """layer""" else False
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16000 , padding_value=0 , do_normalize=snake_case_ , return_attention_mask=snake_case_ , )
_lowerCAmelCase = WavaVecaProcessor(feature_extractor=snake_case_ , tokenizer=snake_case_ )
processor.save_pretrained(snake_case_ )
_lowerCAmelCase = HubertForCTC(snake_case_ )
else:
_lowerCAmelCase = HubertModel(snake_case_ )
if is_finetuned:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
_lowerCAmelCase = model[0].eval()
recursively_load_weights(snake_case_ , snake_case_ , snake_case_ )
hf_wavavec.save_pretrained(snake_case_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Any = 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'''
)
SCREAMING_SNAKE_CASE : List[str] = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 317
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __lowerCamelCase ( unittest.TestCase ):
def __init__(self , lowerCamelCase , lowerCamelCase=7 , lowerCamelCase=3 , lowerCamelCase=18 , lowerCamelCase=30 , lowerCamelCase=400 , lowerCamelCase=True , lowerCamelCase=None , lowerCamelCase=True , lowerCamelCase=None , ):
'''simple docstring'''
_lowerCAmelCase = size if size is not None else {"""shortest_edge""": 20}
_lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = min_resolution
_lowerCAmelCase = max_resolution
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
def A__ (self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __lowerCamelCase ( __lowercase , unittest.TestCase ):
__UpperCamelCase = MobileNetVaImageProcessor if is_vision_available() else None
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = MobileNetVaImageProcessingTester(self )
@property
def A__ (self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(lowerCamelCase , """size""" ) )
self.assertTrue(hasattr(lowerCamelCase , """do_center_crop""" ) )
self.assertTrue(hasattr(lowerCamelCase , """crop_size""" ) )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 20} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} )
def A__ (self ):
'''simple docstring'''
pass
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , Image.Image )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , numpify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , np.ndarray )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , torchify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , torch.Tensor )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 317
| 1
|
"""simple docstring"""
import random
from typing import Any
def __UpperCAmelCase ( snake_case_ : list ) -> list[Any]:
"""simple docstring"""
for _ in range(len(snake_case_ ) ):
_lowerCAmelCase = random.randint(0 , len(snake_case_ ) - 1 )
_lowerCAmelCase = random.randint(0 , len(snake_case_ ) - 1 )
_lowerCAmelCase , _lowerCAmelCase = data[b], data[a]
return data
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : List[str] = [0, 1, 2, 3, 4, 5, 6, 7]
SCREAMING_SNAKE_CASE : List[Any] = ['''python''', '''says''', '''hello''', '''!''']
print('''Fisher-Yates Shuffle:''')
print('''List''', integers, strings)
print('''FY Shuffle''', fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 317
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : list ) -> list:
"""simple docstring"""
for i in range(len(snake_case_ ) - 1 , 0 , -1 ):
_lowerCAmelCase = False
for j in range(snake_case_ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
_lowerCAmelCase , _lowerCAmelCase = unsorted[j - 1], unsorted[j]
_lowerCAmelCase = True
for j in range(snake_case_ ):
if unsorted[j] > unsorted[j + 1]:
_lowerCAmelCase , _lowerCAmelCase = unsorted[j + 1], unsorted[j]
_lowerCAmelCase = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : List[Any] = input('''Enter numbers separated by a comma:\n''').strip()
SCREAMING_SNAKE_CASE : List[str] = [int(item) for item in user_input.split(''',''')]
print(F'{cocktail_shaker_sort(unsorted) = }')
| 317
| 1
|
"""simple docstring"""
import numpy as np
import qiskit
def __UpperCAmelCase ( snake_case_ : int = 8 , snake_case_ : int | None = None ) -> str:
"""simple docstring"""
_lowerCAmelCase = np.random.default_rng(seed=snake_case_ )
# Roughly 25% of the qubits will contribute to the key.
# So we take more than we need.
_lowerCAmelCase = 6 * key_len
# Measurement basis for Alice's qubits.
_lowerCAmelCase = rng.integers(2 , size=snake_case_ )
# The set of states Alice will prepare.
_lowerCAmelCase = rng.integers(2 , size=snake_case_ )
# Measurement basis for Bob's qubits.
_lowerCAmelCase = rng.integers(2 , size=snake_case_ )
# Quantum Circuit to simulate BB84
_lowerCAmelCase = qiskit.QuantumCircuit(snake_case_ , name="""BB84""" )
# Alice prepares her qubits according to rules above.
for index, _ in enumerate(snake_case_ ):
if alice_state[index] == 1:
bbaa_circ.x(snake_case_ )
if alice_basis[index] == 1:
bbaa_circ.h(snake_case_ )
bbaa_circ.barrier()
# Bob measures the received qubits according to rules above.
for index, _ in enumerate(snake_case_ ):
if bob_basis[index] == 1:
bbaa_circ.h(snake_case_ )
bbaa_circ.barrier()
bbaa_circ.measure_all()
# Simulate the quantum circuit.
_lowerCAmelCase = qiskit.Aer.get_backend("""aer_simulator""" )
# We only need to run one shot because the key is unique.
# Multiple shots will produce the same key.
_lowerCAmelCase = qiskit.execute(snake_case_ , snake_case_ , shots=1 , seed_simulator=snake_case_ )
# Returns the result of measurement.
_lowerCAmelCase = job.result().get_counts(snake_case_ ).most_frequent()
# Extracting the generated key from the simulation results.
# Only keep measurement results where Alice and Bob chose the same basis.
_lowerCAmelCase = """""".join(
[
result_bit
for alice_basis_bit, bob_basis_bit, result_bit in zip(
snake_case_ , snake_case_ , snake_case_ )
if alice_basis_bit == bob_basis_bit
] )
# Get final key. Pad with 0 if too short, otherwise truncate.
_lowerCAmelCase = gen_key[:key_len] if len(snake_case_ ) >= key_len else gen_key.ljust(snake_case_ , """0""" )
return key
if __name__ == "__main__":
print(F'The generated key is : {bbaa(8, seed=0)}')
from doctest import testmod
testmod()
| 317
|
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, logging
from .benchmark_utils import (
Benchmark,
Memory,
MemorySummary,
measure_peak_memory_cpu,
start_memory_tracing,
stop_memory_tracing,
)
if is_tf_available():
import tensorflow as tf
from tensorflow.python.framework.errors_impl import ResourceExhaustedError
from .benchmark_args_tf import TensorFlowBenchmarkArguments
if is_pyanvml_available():
import pyanvml.pyanvml as nvml
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
def __UpperCAmelCase ( snake_case_ : bool , snake_case_ : bool ) -> Tuple:
"""simple docstring"""
def run_func(snake_case_ : Union[str, Any] ):
@wraps(snake_case_ )
def run_in_eager_mode(*snake_case_ : Optional[int] , **snake_case_ : Union[str, Any] ):
return func(*snake_case_ , **snake_case_ )
@wraps(snake_case_ )
@tf.function(experimental_compile=snake_case_ )
def run_in_graph_mode(*snake_case_ : Dict , **snake_case_ : Union[str, Any] ):
return func(*snake_case_ , **snake_case_ )
if do_eager_mode is True:
if use_xla is not False:
raise ValueError(
"""Cannot run model in XLA, if `args.eager_mode` is set to `True`. Please set `args.eager_mode=False`.""" )
return run_in_eager_mode
else:
return run_in_graph_mode
return run_func
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : int , snake_case_ : int ) -> ["tf.Tensor"]:
"""simple docstring"""
_lowerCAmelCase = random.Random()
_lowerCAmelCase = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(snake_case_ , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 42
__UpperCamelCase = 42
__UpperCamelCase = "TensorFlow"
@property
def A__ (self ):
'''simple docstring'''
return tf.__version__
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_inference_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_speed(_inference )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_train_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_speed(_train )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase )
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_inference_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_memory(_inference )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , lowerCamelCase )
_lowerCAmelCase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
_lowerCAmelCase = self._prepare_train_func(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return self._measure_memory(_train )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
_lowerCAmelCase = (
hasattr(lowerCamelCase , """architectures""" )
and isinstance(config.architectures , lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model_cls(lowerCamelCase )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
_lowerCAmelCase = TF_MODEL_MAPPING[config.__class__](lowerCamelCase )
# encoder-decoder has vocab size saved differently
_lowerCAmelCase = config.vocab_size if hasattr(lowerCamelCase , """vocab_size""" ) else config.encoder.vocab_size
_lowerCAmelCase = random_input_ids(lowerCamelCase , lowerCamelCase , lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_forward():
return model(lowerCamelCase , decoder_input_ids=lowerCamelCase , training=lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_forward():
return model(lowerCamelCase , training=lowerCamelCase )
_lowerCAmelCase = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.config_dict[model_name]
if self.args.eager_mode is not False:
raise ValueError("""Training cannot be done in eager mode. Please make sure that `args.eager_mode = False`.""" )
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
_lowerCAmelCase = (
hasattr(lowerCamelCase , """architectures""" )
and isinstance(config.architectures , lowerCamelCase )
and len(config.architectures ) > 0
)
if not self.args.only_pretrain_model and has_model_class_in_config:
try:
_lowerCAmelCase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
_lowerCAmelCase = __import__("""transformers""" , fromlist=[model_class] )
_lowerCAmelCase = getattr(lowerCamelCase , lowerCamelCase )
_lowerCAmelCase = model_cls(lowerCamelCase )
except ImportError:
raise ImportError(
f"""{model_class} does not exist. If you just want to test the pretrained model, you might want to"""
""" set `--only_pretrain_model` or `args.only_pretrain_model=True`.""" )
else:
_lowerCAmelCase = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCamelCase )
# encoder-decoder has vocab size saved differently
_lowerCAmelCase = config.vocab_size if hasattr(lowerCamelCase , """vocab_size""" ) else config.encoder.vocab_size
_lowerCAmelCase = random_input_ids(lowerCamelCase , lowerCamelCase , lowerCamelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
_lowerCAmelCase = model(lowerCamelCase , decoder_input_ids=lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )[0]
_lowerCAmelCase = tf.gradients(lowerCamelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
_lowerCAmelCase = model(lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )[0]
_lowerCAmelCase = tf.gradients(lowerCamelCase , model.trainable_variables )
return gradients
_lowerCAmelCase = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def A__ (self , lowerCamelCase ):
'''simple docstring'''
with self.args.strategy.scope():
try:
if self.args.is_tpu or self.args.use_xla:
# run additional 10 times to stabilize compilation for tpu
logger.info("""Do inference on TPU. Running model 5 times to stabilize compilation""" )
timeit.repeat(lowerCamelCase , repeat=1 , number=5 )
# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
_lowerCAmelCase = timeit.repeat(
lowerCamelCase , repeat=self.args.repeat , number=10 , )
return min(lowerCamelCase ) / 10.0
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
logger.info(
"""Note that TensorFlow allocates more memory than """
"""it might need to speed up computation. """
"""The memory reported here corresponds to the memory """
"""reported by `nvidia-smi`, which can vary depending """
"""on total available memory on the GPU that is used.""" )
with self.args.strategy.scope():
try:
if self.args.trace_memory_line_by_line:
if not self.args.eager_mode:
raise ValueError(
"""`args.eager_mode` is set to `False`. Make sure to run model in eager mode to measure memory"""
""" consumption line by line.""" )
_lowerCAmelCase = start_memory_tracing("""transformers""" )
if self.args.is_tpu:
# tpu
raise NotImplementedError(
"""Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking"""
""" with `args.memory=False`""" )
elif self.args.is_gpu:
# gpu
if not is_pyanvml_available():
logger.warning(
"""py3nvml not installed, we won't log GPU memory usage. """
"""Install py3nvml (pip install py3nvml) to log information about GPU.""" )
_lowerCAmelCase = """N/A"""
else:
logger.info(
"""Measuring total GPU usage on GPU device. Make sure to not have additional processes"""
""" running on the same GPU.""" )
# init nvml
nvml.nvmlInit()
func()
_lowerCAmelCase = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
_lowerCAmelCase = nvml.nvmlDeviceGetMemoryInfo(lowerCamelCase )
_lowerCAmelCase = meminfo.used
_lowerCAmelCase = Memory(lowerCamelCase )
# shutdown nvml
nvml.nvmlShutdown()
else:
# cpu
if self.args.trace_memory_line_by_line:
logger.info(
"""When enabling line by line tracing, the max peak memory for CPU is inaccurate in"""
""" TensorFlow.""" )
_lowerCAmelCase = None
else:
_lowerCAmelCase = measure_peak_memory_cpu(lowerCamelCase )
_lowerCAmelCase = Memory(lowerCamelCase ) if isinstance(lowerCamelCase , lowerCamelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
_lowerCAmelCase = stop_memory_tracing(lowerCamelCase )
if memory is None:
_lowerCAmelCase = summary.total
else:
_lowerCAmelCase = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(f"""Doesn't fit on GPU. {e}""" )
return "N/A", None
| 317
| 1
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 10**9 ) -> int:
"""simple docstring"""
_lowerCAmelCase = 1
_lowerCAmelCase = 2
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
_lowerCAmelCase = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(F'{solution() = }')
| 317
|
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Any = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'transfo-xl'
__UpperCamelCase = ['mems']
__UpperCamelCase = {
'n_token': 'vocab_size',
'hidden_size': 'd_model',
'num_attention_heads': 'n_head',
'num_hidden_layers': 'n_layer',
}
def __init__(self , lowerCamelCase=267_735 , lowerCamelCase=[20_000, 40_000, 200_000] , lowerCamelCase=1_024 , lowerCamelCase=1_024 , lowerCamelCase=16 , lowerCamelCase=64 , lowerCamelCase=4_096 , lowerCamelCase=4 , lowerCamelCase=False , lowerCamelCase=18 , lowerCamelCase=1_600 , lowerCamelCase=1_000 , lowerCamelCase=True , lowerCamelCase=True , lowerCamelCase=0 , lowerCamelCase=-1 , lowerCamelCase=True , lowerCamelCase=0.1 , lowerCamelCase=0.0 , lowerCamelCase=True , lowerCamelCase="normal" , lowerCamelCase=0.01 , lowerCamelCase=0.01 , lowerCamelCase=0.02 , lowerCamelCase=1e-5 , lowerCamelCase=0 , **lowerCamelCase , ):
'''simple docstring'''
_lowerCAmelCase = vocab_size
_lowerCAmelCase = []
self.cutoffs.extend(lowerCamelCase )
if proj_share_all_but_first:
_lowerCAmelCase = [False] + [True] * len(self.cutoffs )
else:
_lowerCAmelCase = [False] + [False] * len(self.cutoffs )
_lowerCAmelCase = d_model
_lowerCAmelCase = d_embed
_lowerCAmelCase = d_head
_lowerCAmelCase = d_inner
_lowerCAmelCase = div_val
_lowerCAmelCase = pre_lnorm
_lowerCAmelCase = n_layer
_lowerCAmelCase = n_head
_lowerCAmelCase = mem_len
_lowerCAmelCase = same_length
_lowerCAmelCase = attn_type
_lowerCAmelCase = clamp_len
_lowerCAmelCase = sample_softmax
_lowerCAmelCase = adaptive
_lowerCAmelCase = dropout
_lowerCAmelCase = dropatt
_lowerCAmelCase = untie_r
_lowerCAmelCase = init
_lowerCAmelCase = init_range
_lowerCAmelCase = proj_init_std
_lowerCAmelCase = init_std
_lowerCAmelCase = layer_norm_epsilon
super().__init__(eos_token_id=lowerCamelCase , **lowerCamelCase )
@property
def A__ (self ):
'''simple docstring'''
logger.info(f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def A__ (self , lowerCamelCase ):
'''simple docstring'''
raise NotImplementedError(
f"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 317
| 1
|
"""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
SCREAMING_SNAKE_CASE : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : Optional[int] = {
'''google/efficientnet-b7''': '''https://huggingface.co/google/efficientnet-b7/resolve/main/config.json''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'efficientnet'
def __init__(self , lowerCamelCase = 3 , lowerCamelCase = 600 , lowerCamelCase = 2.0 , lowerCamelCase = 3.1 , lowerCamelCase = 8 , lowerCamelCase = [3, 3, 5, 3, 5, 5, 3] , lowerCamelCase = [32, 16, 24, 40, 80, 112, 192] , lowerCamelCase = [16, 24, 40, 80, 112, 192, 320] , lowerCamelCase = [] , lowerCamelCase = [1, 2, 2, 2, 1, 2, 1] , lowerCamelCase = [1, 2, 2, 3, 3, 4, 1] , lowerCamelCase = [1, 6, 6, 6, 6, 6, 6] , lowerCamelCase = 0.25 , lowerCamelCase = "swish" , lowerCamelCase = 2_560 , lowerCamelCase = "mean" , lowerCamelCase = 0.02 , lowerCamelCase = 0.001 , lowerCamelCase = 0.99 , lowerCamelCase = 0.5 , lowerCamelCase = 0.2 , **lowerCamelCase , ):
'''simple docstring'''
super().__init__(**lowerCamelCase )
_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(lowerCamelCase ) * 4
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = version.parse('1.11' )
@property
def A__ (self ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def A__ (self ):
'''simple docstring'''
return 1e-5
| 317
|
"""simple docstring"""
import math
def __UpperCAmelCase ( snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = 2
_lowerCAmelCase = int(math.sqrt(snake_case_ ) ) # Size of every segment
_lowerCAmelCase = [True] * (end + 1)
_lowerCAmelCase = []
while start <= end:
if temp[start] is True:
in_prime.append(snake_case_ )
for i in range(start * start , end + 1 , snake_case_ ):
_lowerCAmelCase = False
start += 1
prime += in_prime
_lowerCAmelCase = end + 1
_lowerCAmelCase = min(2 * end , snake_case_ )
while low <= n:
_lowerCAmelCase = [True] * (high - low + 1)
for each in in_prime:
_lowerCAmelCase = math.floor(low / each ) * each
if t < low:
t += each
for j in range(snake_case_ , high + 1 , snake_case_ ):
_lowerCAmelCase = False
for j in range(len(snake_case_ ) ):
if temp[j] is True:
prime.append(j + low )
_lowerCAmelCase = high + 1
_lowerCAmelCase = min(high + end , snake_case_ )
return prime
print(sieve(1_0**6))
| 317
| 1
|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = {'''vocab_file''': '''vocab.txt'''}
SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''vocab_file''': {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt''',
}
}
SCREAMING_SNAKE_CASE : int = {
'''YituTech/conv-bert-base''': 5_1_2,
'''YituTech/conv-bert-medium-small''': 5_1_2,
'''YituTech/conv-bert-small''': 5_1_2,
}
SCREAMING_SNAKE_CASE : Dict = {
'''YituTech/conv-bert-base''': {'''do_lower_case''': True},
'''YituTech/conv-bert-medium-small''': {'''do_lower_case''': True},
'''YituTech/conv-bert-small''': {'''do_lower_case''': True},
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_INIT_CONFIGURATION
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ConvBertTokenizer
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__(
lowerCamelCase , tokenizer_file=lowerCamelCase , do_lower_case=lowerCamelCase , unk_token=lowerCamelCase , sep_token=lowerCamelCase , pad_token=lowerCamelCase , cls_token=lowerCamelCase , mask_token=lowerCamelCase , tokenize_chinese_chars=lowerCamelCase , strip_accents=lowerCamelCase , **lowerCamelCase , )
_lowerCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , lowerCamelCase ) != do_lower_case
or normalizer_state.get("""strip_accents""" , lowerCamelCase ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , lowerCamelCase ) != tokenize_chinese_chars
):
_lowerCAmelCase = getattr(lowerCamelCase , normalizer_state.pop("""type""" ) )
_lowerCAmelCase = do_lower_case
_lowerCAmelCase = strip_accents
_lowerCAmelCase = tokenize_chinese_chars
_lowerCAmelCase = normalizer_class(**lowerCamelCase )
_lowerCAmelCase = do_lower_case
def A__ (self , lowerCamelCase , lowerCamelCase=None ):
'''simple docstring'''
_lowerCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def A__ (self , lowerCamelCase , lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def A__ (self , lowerCamelCase , lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase = self._tokenizer.model.save(lowerCamelCase , name=lowerCamelCase )
return tuple(lowerCamelCase )
| 317
|
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
SCREAMING_SNAKE_CASE : Any = (7_2_0, 1_2_8_0) # Height, Width
SCREAMING_SNAKE_CASE : List[str] = (0.4, 0.6) # if height or width lower than this scale, drop it.
SCREAMING_SNAKE_CASE : List[Any] = 1 / 1_0_0
SCREAMING_SNAKE_CASE : Optional[Any] = ''''''
SCREAMING_SNAKE_CASE : Dict = ''''''
SCREAMING_SNAKE_CASE : List[Any] = ''''''
SCREAMING_SNAKE_CASE : Dict = 2_5_0
def __UpperCAmelCase ( ) -> None:
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = get_dataset(snake_case_ , snake_case_ )
for index in range(snake_case_ ):
_lowerCAmelCase = random.sample(range(len(snake_case_ ) ) , 4 )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = update_image_and_anno(
snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , filter_scale=snake_case_ , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_lowerCAmelCase = random_chars(32 )
_lowerCAmelCase = path.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
_lowerCAmelCase = F"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
cva.imwrite(F"""{file_root}.jpg""" , snake_case_ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
_lowerCAmelCase = []
for anno in new_annos:
_lowerCAmelCase = anno[3] - anno[1]
_lowerCAmelCase = anno[4] - anno[2]
_lowerCAmelCase = anno[1] + width / 2
_lowerCAmelCase = anno[2] + height / 2
_lowerCAmelCase = F"""{anno[0]} {x_center} {y_center} {width} {height}"""
annos_list.append(snake_case_ )
with open(F"""{file_root}.txt""" , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : str ) -> tuple[list, list]:
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = []
for label_file in glob.glob(os.path.join(snake_case_ , """*.txt""" ) ):
_lowerCAmelCase = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(snake_case_ ) as in_file:
_lowerCAmelCase = in_file.readlines()
_lowerCAmelCase = os.path.join(snake_case_ , F"""{label_name}.jpg""" )
_lowerCAmelCase = []
for obj_list in obj_lists:
_lowerCAmelCase = obj_list.rstrip("""\n""" ).split(""" """ )
_lowerCAmelCase = float(obj[1] ) - float(obj[3] ) / 2
_lowerCAmelCase = float(obj[2] ) - float(obj[4] ) / 2
_lowerCAmelCase = float(obj[1] ) + float(obj[3] ) / 2
_lowerCAmelCase = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(snake_case_ )
labels.append(snake_case_ )
return img_paths, labels
def __UpperCAmelCase ( snake_case_ : list , snake_case_ : list , snake_case_ : list[int] , snake_case_ : tuple[int, int] , snake_case_ : tuple[float, float] , snake_case_ : float = 0.0 , ) -> tuple[list, list, str]:
"""simple docstring"""
_lowerCAmelCase = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
_lowerCAmelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_lowerCAmelCase = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_lowerCAmelCase = int(scale_x * output_size[1] )
_lowerCAmelCase = int(scale_y * output_size[0] )
_lowerCAmelCase = []
_lowerCAmelCase = []
for i, index in enumerate(snake_case_ ):
_lowerCAmelCase = all_img_list[index]
path_list.append(snake_case_ )
_lowerCAmelCase = all_annos[index]
_lowerCAmelCase = cva.imread(snake_case_ )
if i == 0: # top-left
_lowerCAmelCase = cva.resize(snake_case_ , (divid_point_x, divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = bbox[1] * scale_x
_lowerCAmelCase = bbox[2] * scale_y
_lowerCAmelCase = bbox[3] * scale_x
_lowerCAmelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
_lowerCAmelCase = cva.resize(snake_case_ , (output_size[1] - divid_point_x, divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = scale_x + bbox[1] * (1 - scale_x)
_lowerCAmelCase = bbox[2] * scale_y
_lowerCAmelCase = scale_x + bbox[3] * (1 - scale_x)
_lowerCAmelCase = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
_lowerCAmelCase = cva.resize(snake_case_ , (divid_point_x, output_size[0] - divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = bbox[1] * scale_x
_lowerCAmelCase = scale_y + bbox[2] * (1 - scale_y)
_lowerCAmelCase = bbox[3] * scale_x
_lowerCAmelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
_lowerCAmelCase = cva.resize(
snake_case_ , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
_lowerCAmelCase = img
for bbox in img_annos:
_lowerCAmelCase = scale_x + bbox[1] * (1 - scale_x)
_lowerCAmelCase = scale_y + bbox[2] * (1 - scale_y)
_lowerCAmelCase = scale_x + bbox[3] * (1 - scale_x)
_lowerCAmelCase = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
_lowerCAmelCase = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def __UpperCAmelCase ( snake_case_ : int ) -> str:
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
_lowerCAmelCase = ascii_lowercase + digits
return "".join(random.choice(snake_case_ ) for _ in range(snake_case_ ) )
if __name__ == "__main__":
main()
print('''DONE ✅''')
| 317
| 1
|
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__) # pylint: disable=invalid-name
SCREAMING_SNAKE_CASE : Any = '''
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)["depth"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline("depth-estimation")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to("cuda")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> img = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/cat.png"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
>>> prompt = "A robot, 4k photo"
>>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
>>> generator = torch.Generator(device="cuda").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save("robot_cat.png")
```
'''
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : List[Any] , snake_case_ : Any=8 ) -> str:
"""simple docstring"""
_lowerCAmelCase = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
_lowerCAmelCase = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class __lowerCamelCase ( __lowercase ):
def __init__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase , ):
'''simple docstring'''
super().__init__()
self.register_modules(
unet=lowerCamelCase , scheduler=lowerCamelCase , movq=lowerCamelCase , )
_lowerCAmelCase = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if latents is None:
_lowerCAmelCase = randn_tensor(lowerCamelCase , generator=lowerCamelCase , device=lowerCamelCase , dtype=lowerCamelCase )
else:
if latents.shape != shape:
raise ValueError(f"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
_lowerCAmelCase = latents.to(lowerCamelCase )
_lowerCAmelCase = latents * scheduler.init_noise_sigma
return latents
def A__ (self , lowerCamelCase=0 ):
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
_lowerCAmelCase = torch.device(f"""cuda:{gpu_id}""" )
_lowerCAmelCase = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(lowerCamelCase , lowerCamelCase )
def A__ (self , lowerCamelCase=0 ):
'''simple docstring'''
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
_lowerCAmelCase = torch.device(f"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=lowerCamelCase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
_lowerCAmelCase = None
for cpu_offloaded_model in [self.unet, self.movq]:
_lowerCAmelCase , _lowerCAmelCase = cpu_offload_with_hook(lowerCamelCase , lowerCamelCase , prev_module_hook=lowerCamelCase )
# We'll offload the last model manually.
_lowerCAmelCase = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def A__ (self ):
'''simple docstring'''
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCamelCase , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(lowerCamelCase )
def __call__(self , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 512 , lowerCamelCase = 512 , lowerCamelCase = 100 , lowerCamelCase = 4.0 , lowerCamelCase = 1 , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = "pil" , lowerCamelCase = True , ):
'''simple docstring'''
_lowerCAmelCase = self._execution_device
_lowerCAmelCase = guidance_scale > 1.0
if isinstance(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = torch.cat(lowerCamelCase , dim=0 )
if isinstance(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = torch.cat(lowerCamelCase , dim=0 )
if isinstance(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = torch.cat(lowerCamelCase , dim=0 )
_lowerCAmelCase = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
_lowerCAmelCase = image_embeds.repeat_interleave(lowerCamelCase , dim=0 )
_lowerCAmelCase = negative_image_embeds.repeat_interleave(lowerCamelCase , dim=0 )
_lowerCAmelCase = hint.repeat_interleave(lowerCamelCase , dim=0 )
_lowerCAmelCase = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase )
_lowerCAmelCase = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=lowerCamelCase )
self.scheduler.set_timesteps(lowerCamelCase , device=lowerCamelCase )
_lowerCAmelCase = self.scheduler.timesteps
_lowerCAmelCase = self.movq.config.latent_channels
_lowerCAmelCase , _lowerCAmelCase = downscale_height_and_width(lowerCamelCase , lowerCamelCase , self.movq_scale_factor )
# create initial latent
_lowerCAmelCase = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , lowerCamelCase , lowerCamelCase , lowerCamelCase , self.scheduler , )
for i, t in enumerate(self.progress_bar(lowerCamelCase ) ):
# expand the latents if we are doing classifier free guidance
_lowerCAmelCase = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_lowerCAmelCase = {"""image_embeds""": image_embeds, """hint""": hint}
_lowerCAmelCase = self.unet(
sample=lowerCamelCase , timestep=lowerCamelCase , encoder_hidden_states=lowerCamelCase , added_cond_kwargs=lowerCamelCase , return_dict=lowerCamelCase , )[0]
if do_classifier_free_guidance:
_lowerCAmelCase , _lowerCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
_lowerCAmelCase , _lowerCAmelCase = noise_pred.chunk(2 )
_lowerCAmelCase , _lowerCAmelCase = variance_pred.chunk(2 )
_lowerCAmelCase = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
_lowerCAmelCase = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
_lowerCAmelCase , _lowerCAmelCase = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
_lowerCAmelCase = self.scheduler.step(
lowerCamelCase , lowerCamelCase , lowerCamelCase , generator=lowerCamelCase , )[0]
# post-processing
_lowerCAmelCase = self.movq.decode(lowerCamelCase , force_not_quantize=lowerCamelCase )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" )
if output_type in ["np", "pil"]:
_lowerCAmelCase = image * 0.5 + 0.5
_lowerCAmelCase = image.clamp(0 , 1 )
_lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
_lowerCAmelCase = self.numpy_to_pil(lowerCamelCase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=lowerCamelCase )
| 317
|
"""simple docstring"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkouts and running tests.
SCREAMING_SNAKE_CASE : Dict = abspath(join(dirname(dirname(__file__)), '''src'''))
sys.path.insert(1, git_repo_path)
# silence FutureWarning warnings in tests since often we can't act on them until
# they become normal warnings - i.e. the tests still need to test the current functionality
warnings.simplefilter(action='''ignore''', category=FutureWarning)
def __UpperCAmelCase ( snake_case_ : Optional[int] ) -> List[str]:
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_addoption_shared
pytest_addoption_shared(snake_case_ )
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> int:
"""simple docstring"""
from diffusers.utils.testing_utils import pytest_terminal_summary_main
_lowerCAmelCase = terminalreporter.config.getoption("""--make-reports""" )
if make_reports:
pytest_terminal_summary_main(snake_case_ , id=snake_case_ )
| 317
| 1
|
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('TEST_SAGEMAKER' , 'False' ) ) is not True , reason='Skipping test because should only be run when releasing minor transformers version' , )
@pytest.mark.usefixtures('sm_env' )
@parameterized_class(
[
{
'framework': 'pytorch',
'script': 'run_glue.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 650, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'pytorch',
'script': 'run_ddp.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 600, 'eval_accuracy': 0.7, 'eval_loss': 0.6},
},
{
'framework': 'tensorflow',
'script': 'run_tf_dist.py',
'model_name_or_path': 'distilbert-base-cased',
'instance_type': 'ml.p3.16xlarge',
'results': {'train_runtime': 600, 'eval_accuracy': 0.6, 'eval_loss': 0.7},
},
] )
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=lowerCamelCase , )
assert hasattr(self , """env""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = f"""{self.env.base_job_name}-{instance_count}-{"ddp" if "ddp" in self.script else "smd"}"""
# distributed data settings
_lowerCAmelCase = {"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=lowerCamelCase , instance_count=lowerCamelCase , instance_type=self.instance_type , debugger_hook_config=lowerCamelCase , hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=lowerCamelCase , py_version="""py36""" , )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
TrainingJobAnalytics(lowerCamelCase ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(2,)] )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.create_estimator(lowerCamelCase )
# run training
estimator.fit()
# result dataframe
_lowerCAmelCase = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
_lowerCAmelCase = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
_lowerCAmelCase = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
_lowerCAmelCase = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 999_999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , lowerCamelCase )
| 317
|
"""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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
SCREAMING_SNAKE_CASE : Optional[Any] = {
'''Acehnese Arabic''': '''ace_Arab''',
'''Acehnese Latin''': '''ace_Latn''',
'''Mesopotamian Arabic''': '''acm_Arab''',
'''Ta\'izzi-Adeni Arabic''': '''acq_Arab''',
'''Tunisian Arabic''': '''aeb_Arab''',
'''Afrikaans''': '''afr_Latn''',
'''South Levantine Arabic''': '''ajp_Arab''',
'''Akan''': '''aka_Latn''',
'''Amharic''': '''amh_Ethi''',
'''North Levantine Arabic''': '''apc_Arab''',
'''Modern Standard Arabic''': '''arb_Arab''',
'''Modern Standard Arabic Romanized''': '''arb_Latn''',
'''Najdi Arabic''': '''ars_Arab''',
'''Moroccan Arabic''': '''ary_Arab''',
'''Egyptian Arabic''': '''arz_Arab''',
'''Assamese''': '''asm_Beng''',
'''Asturian''': '''ast_Latn''',
'''Awadhi''': '''awa_Deva''',
'''Central Aymara''': '''ayr_Latn''',
'''South Azerbaijani''': '''azb_Arab''',
'''North Azerbaijani''': '''azj_Latn''',
'''Bashkir''': '''bak_Cyrl''',
'''Bambara''': '''bam_Latn''',
'''Balinese''': '''ban_Latn''',
'''Belarusian''': '''bel_Cyrl''',
'''Bemba''': '''bem_Latn''',
'''Bengali''': '''ben_Beng''',
'''Bhojpuri''': '''bho_Deva''',
'''Banjar Arabic''': '''bjn_Arab''',
'''Banjar Latin''': '''bjn_Latn''',
'''Standard Tibetan''': '''bod_Tibt''',
'''Bosnian''': '''bos_Latn''',
'''Buginese''': '''bug_Latn''',
'''Bulgarian''': '''bul_Cyrl''',
'''Catalan''': '''cat_Latn''',
'''Cebuano''': '''ceb_Latn''',
'''Czech''': '''ces_Latn''',
'''Chokwe''': '''cjk_Latn''',
'''Central Kurdish''': '''ckb_Arab''',
'''Crimean Tatar''': '''crh_Latn''',
'''Welsh''': '''cym_Latn''',
'''Danish''': '''dan_Latn''',
'''German''': '''deu_Latn''',
'''Southwestern Dinka''': '''dik_Latn''',
'''Dyula''': '''dyu_Latn''',
'''Dzongkha''': '''dzo_Tibt''',
'''Greek''': '''ell_Grek''',
'''English''': '''eng_Latn''',
'''Esperanto''': '''epo_Latn''',
'''Estonian''': '''est_Latn''',
'''Basque''': '''eus_Latn''',
'''Ewe''': '''ewe_Latn''',
'''Faroese''': '''fao_Latn''',
'''Fijian''': '''fij_Latn''',
'''Finnish''': '''fin_Latn''',
'''Fon''': '''fon_Latn''',
'''French''': '''fra_Latn''',
'''Friulian''': '''fur_Latn''',
'''Nigerian Fulfulde''': '''fuv_Latn''',
'''Scottish Gaelic''': '''gla_Latn''',
'''Irish''': '''gle_Latn''',
'''Galician''': '''glg_Latn''',
'''Guarani''': '''grn_Latn''',
'''Gujarati''': '''guj_Gujr''',
'''Haitian Creole''': '''hat_Latn''',
'''Hausa''': '''hau_Latn''',
'''Hebrew''': '''heb_Hebr''',
'''Hindi''': '''hin_Deva''',
'''Chhattisgarhi''': '''hne_Deva''',
'''Croatian''': '''hrv_Latn''',
'''Hungarian''': '''hun_Latn''',
'''Armenian''': '''hye_Armn''',
'''Igbo''': '''ibo_Latn''',
'''Ilocano''': '''ilo_Latn''',
'''Indonesian''': '''ind_Latn''',
'''Icelandic''': '''isl_Latn''',
'''Italian''': '''ita_Latn''',
'''Javanese''': '''jav_Latn''',
'''Japanese''': '''jpn_Jpan''',
'''Kabyle''': '''kab_Latn''',
'''Jingpho''': '''kac_Latn''',
'''Kamba''': '''kam_Latn''',
'''Kannada''': '''kan_Knda''',
'''Kashmiri Arabic''': '''kas_Arab''',
'''Kashmiri Devanagari''': '''kas_Deva''',
'''Georgian''': '''kat_Geor''',
'''Central Kanuri Arabic''': '''knc_Arab''',
'''Central Kanuri Latin''': '''knc_Latn''',
'''Kazakh''': '''kaz_Cyrl''',
'''Kabiyè''': '''kbp_Latn''',
'''Kabuverdianu''': '''kea_Latn''',
'''Khmer''': '''khm_Khmr''',
'''Kikuyu''': '''kik_Latn''',
'''Kinyarwanda''': '''kin_Latn''',
'''Kyrgyz''': '''kir_Cyrl''',
'''Kimbundu''': '''kmb_Latn''',
'''Northern Kurdish''': '''kmr_Latn''',
'''Kikongo''': '''kon_Latn''',
'''Korean''': '''kor_Hang''',
'''Lao''': '''lao_Laoo''',
'''Ligurian''': '''lij_Latn''',
'''Limburgish''': '''lim_Latn''',
'''Lingala''': '''lin_Latn''',
'''Lithuanian''': '''lit_Latn''',
'''Lombard''': '''lmo_Latn''',
'''Latgalian''': '''ltg_Latn''',
'''Luxembourgish''': '''ltz_Latn''',
'''Luba-Kasai''': '''lua_Latn''',
'''Ganda''': '''lug_Latn''',
'''Luo''': '''luo_Latn''',
'''Mizo''': '''lus_Latn''',
'''Standard Latvian''': '''lvs_Latn''',
'''Magahi''': '''mag_Deva''',
'''Maithili''': '''mai_Deva''',
'''Malayalam''': '''mal_Mlym''',
'''Marathi''': '''mar_Deva''',
'''Minangkabau Arabic ''': '''min_Arab''',
'''Minangkabau Latin''': '''min_Latn''',
'''Macedonian''': '''mkd_Cyrl''',
'''Plateau Malagasy''': '''plt_Latn''',
'''Maltese''': '''mlt_Latn''',
'''Meitei Bengali''': '''mni_Beng''',
'''Halh Mongolian''': '''khk_Cyrl''',
'''Mossi''': '''mos_Latn''',
'''Maori''': '''mri_Latn''',
'''Burmese''': '''mya_Mymr''',
'''Dutch''': '''nld_Latn''',
'''Norwegian Nynorsk''': '''nno_Latn''',
'''Norwegian Bokmål''': '''nob_Latn''',
'''Nepali''': '''npi_Deva''',
'''Northern Sotho''': '''nso_Latn''',
'''Nuer''': '''nus_Latn''',
'''Nyanja''': '''nya_Latn''',
'''Occitan''': '''oci_Latn''',
'''West Central Oromo''': '''gaz_Latn''',
'''Odia''': '''ory_Orya''',
'''Pangasinan''': '''pag_Latn''',
'''Eastern Panjabi''': '''pan_Guru''',
'''Papiamento''': '''pap_Latn''',
'''Western Persian''': '''pes_Arab''',
'''Polish''': '''pol_Latn''',
'''Portuguese''': '''por_Latn''',
'''Dari''': '''prs_Arab''',
'''Southern Pashto''': '''pbt_Arab''',
'''Ayacucho Quechua''': '''quy_Latn''',
'''Romanian''': '''ron_Latn''',
'''Rundi''': '''run_Latn''',
'''Russian''': '''rus_Cyrl''',
'''Sango''': '''sag_Latn''',
'''Sanskrit''': '''san_Deva''',
'''Santali''': '''sat_Olck''',
'''Sicilian''': '''scn_Latn''',
'''Shan''': '''shn_Mymr''',
'''Sinhala''': '''sin_Sinh''',
'''Slovak''': '''slk_Latn''',
'''Slovenian''': '''slv_Latn''',
'''Samoan''': '''smo_Latn''',
'''Shona''': '''sna_Latn''',
'''Sindhi''': '''snd_Arab''',
'''Somali''': '''som_Latn''',
'''Southern Sotho''': '''sot_Latn''',
'''Spanish''': '''spa_Latn''',
'''Tosk Albanian''': '''als_Latn''',
'''Sardinian''': '''srd_Latn''',
'''Serbian''': '''srp_Cyrl''',
'''Swati''': '''ssw_Latn''',
'''Sundanese''': '''sun_Latn''',
'''Swedish''': '''swe_Latn''',
'''Swahili''': '''swh_Latn''',
'''Silesian''': '''szl_Latn''',
'''Tamil''': '''tam_Taml''',
'''Tatar''': '''tat_Cyrl''',
'''Telugu''': '''tel_Telu''',
'''Tajik''': '''tgk_Cyrl''',
'''Tagalog''': '''tgl_Latn''',
'''Thai''': '''tha_Thai''',
'''Tigrinya''': '''tir_Ethi''',
'''Tamasheq Latin''': '''taq_Latn''',
'''Tamasheq Tifinagh''': '''taq_Tfng''',
'''Tok Pisin''': '''tpi_Latn''',
'''Tswana''': '''tsn_Latn''',
'''Tsonga''': '''tso_Latn''',
'''Turkmen''': '''tuk_Latn''',
'''Tumbuka''': '''tum_Latn''',
'''Turkish''': '''tur_Latn''',
'''Twi''': '''twi_Latn''',
'''Central Atlas Tamazight''': '''tzm_Tfng''',
'''Uyghur''': '''uig_Arab''',
'''Ukrainian''': '''ukr_Cyrl''',
'''Umbundu''': '''umb_Latn''',
'''Urdu''': '''urd_Arab''',
'''Northern Uzbek''': '''uzn_Latn''',
'''Venetian''': '''vec_Latn''',
'''Vietnamese''': '''vie_Latn''',
'''Waray''': '''war_Latn''',
'''Wolof''': '''wol_Latn''',
'''Xhosa''': '''xho_Latn''',
'''Eastern Yiddish''': '''ydd_Hebr''',
'''Yoruba''': '''yor_Latn''',
'''Yue Chinese''': '''yue_Hant''',
'''Chinese Simplified''': '''zho_Hans''',
'''Chinese Traditional''': '''zho_Hant''',
'''Standard Malay''': '''zsm_Latn''',
'''Zulu''': '''zul_Latn''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'facebook/nllb-200-distilled-600M'
__UpperCamelCase = (
'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
)
__UpperCamelCase = 'translator'
__UpperCamelCase = AutoTokenizer
__UpperCamelCase = AutoModelForSeqaSeqLM
__UpperCamelCase = LANGUAGE_CODES
__UpperCamelCase = ['text', 'text', 'text']
__UpperCamelCase = ['text']
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if src_lang not in self.lang_to_code:
raise ValueError(f"""{src_lang} is not a supported language.""" )
if tgt_lang not in self.lang_to_code:
raise ValueError(f"""{tgt_lang} is not a supported language.""" )
_lowerCAmelCase = self.lang_to_code[src_lang]
_lowerCAmelCase = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
lowerCamelCase , return_tensors="""pt""" , src_lang=lowerCamelCase , tgt_lang=lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.model.generate(**lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=lowerCamelCase )
| 317
| 1
|
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
class __lowerCamelCase ( __lowercase ):
def __init__(self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
warnings.warn(
"""The class BeitFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use BeitImageProcessor instead.""" , lowerCamelCase , )
super().__init__(*lowerCamelCase , **lowerCamelCase )
| 317
|
"""simple docstring"""
from math import isqrt
def __UpperCAmelCase ( snake_case_ : int ) -> list[int]:
"""simple docstring"""
_lowerCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , snake_case_ , snake_case_ ):
_lowerCAmelCase = False
return [i for i in range(2 , snake_case_ ) if is_prime[i]]
def __UpperCAmelCase ( snake_case_ : int = 10**8 ) -> int:
"""simple docstring"""
_lowerCAmelCase = calculate_prime_numbers(max_number // 2 )
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = len(snake_case_ ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 1
|
"""simple docstring"""
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerModel,
)
from transformers.models.efficientformer.modeling_tf_efficientformer import (
TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_vision_available():
from PIL import Image
from transformers import EfficientFormerImageProcessor
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase = 13 , lowerCamelCase = 64 , lowerCamelCase = 2 , lowerCamelCase = 3 , lowerCamelCase = 3 , lowerCamelCase = True , lowerCamelCase = True , lowerCamelCase = 128 , lowerCamelCase=[16, 32, 64, 128] , lowerCamelCase = 7 , lowerCamelCase = 4 , lowerCamelCase = 37 , lowerCamelCase = "gelu" , lowerCamelCase = 0.1 , lowerCamelCase = 0.1 , lowerCamelCase = 10 , lowerCamelCase = 0.02 , lowerCamelCase = 2 , lowerCamelCase = 1 , lowerCamelCase = 128 , lowerCamelCase = [2, 2, 2, 2] , lowerCamelCase = 2 , lowerCamelCase = 2 , ):
'''simple docstring'''
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = image_size
_lowerCAmelCase = patch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = is_training
_lowerCAmelCase = use_labels
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = encoder_stride
_lowerCAmelCase = num_attention_outputs
_lowerCAmelCase = embed_dim
_lowerCAmelCase = embed_dim + 1
_lowerCAmelCase = resolution
_lowerCAmelCase = depths
_lowerCAmelCase = hidden_sizes
_lowerCAmelCase = dim
_lowerCAmelCase = mlp_expansion_ratio
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def A__ (self ):
'''simple docstring'''
return EfficientFormerConfig(
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=lowerCamelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , resolution=self.resolution , depths=self.depths , hidden_sizes=self.hidden_sizes , dim=self.dim , mlp_expansion_ratio=self.mlp_expansion_ratio , )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = TFEfficientFormerModel(config=lowerCamelCase )
_lowerCAmelCase = model(lowerCamelCase , training=lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.type_sequence_label_size
_lowerCAmelCase = TFEfficientFormerForImageClassification(lowerCamelCase )
_lowerCAmelCase = model(lowerCamelCase , labels=lowerCamelCase , training=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_lowerCAmelCase = 1
_lowerCAmelCase = TFEfficientFormerForImageClassification(lowerCamelCase )
_lowerCAmelCase = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_lowerCAmelCase = model(lowerCamelCase , labels=lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.prepare_config_and_inputs()
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = config_and_inputs
_lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class __lowerCamelCase ( __lowercase , __lowercase , unittest.TestCase ):
__UpperCamelCase = (
(
TFEfficientFormerModel,
TFEfficientFormerForImageClassificationWithTeacher,
TFEfficientFormerForImageClassification,
)
if is_tf_available()
else ()
)
__UpperCamelCase = (
{
'feature-extraction': TFEfficientFormerModel,
'image-classification': (
TFEfficientFormerForImageClassification,
TFEfficientFormerForImageClassificationWithTeacher,
),
}
if is_tf_available()
else {}
)
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
__UpperCamelCase = False
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = TFEfficientFormerModelTester(self )
_lowerCAmelCase = ConfigTester(
self , config_class=lowerCamelCase , has_text_modality=lowerCamelCase , hidden_size=37 )
def A__ (self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""EfficientFormer does not use inputs_embeds""" )
def A__ (self ):
'''simple docstring'''
pass
@unittest.skip(reason="""EfficientFormer does not support input and output embeddings""" )
def A__ (self ):
'''simple docstring'''
pass
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = model_class(lowerCamelCase )
_lowerCAmelCase = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowerCAmelCase = [*signature.parameters.keys()]
_lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowerCamelCase )
def A__ (self ):
'''simple docstring'''
def check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = model_class(lowerCamelCase )
_lowerCAmelCase = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) , training=lowerCamelCase )
_lowerCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_lowerCAmelCase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(lowerCamelCase ) , lowerCamelCase )
if hasattr(self.model_tester , """encoder_seq_length""" ):
_lowerCAmelCase = self.model_tester.encoder_seq_length
if hasattr(self.model_tester , """chunk_length""" ) and self.model_tester.chunk_length > 1:
_lowerCAmelCase = seq_length * self.model_tester.chunk_length
else:
_lowerCAmelCase = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
if config.is_encoder_decoder:
_lowerCAmelCase = outputs.decoder_hidden_states
self.asseretIsInstance(lowerCamelCase , (list, tuple) )
self.assertEqual(len(lowerCamelCase ) , lowerCamelCase )
_lowerCAmelCase = getattr(self.model_tester , """seq_length""" , lowerCamelCase )
_lowerCAmelCase = getattr(self.model_tester , """decoder_seq_length""" , lowerCamelCase )
self.assertListEqual(
list(hidden_states[-1].shape[-2:] ) , [decoder_seq_length, self.model_tester.hidden_size] , )
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase = True
check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_lowerCAmelCase = True
check_hidden_states_output(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase=False ):
'''simple docstring'''
_lowerCAmelCase = super()._prepare_for_class(lowerCamelCase , lowerCamelCase , return_labels=lowerCamelCase )
if return_labels:
if model_class.__name__ == "TFEfficientFormerForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase )
@unittest.skip(reason="""EfficientFormer does not implement masked image modeling yet""" )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase )
@slow
def A__ (self ):
'''simple docstring'''
for model_name in TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = TFEfficientFormerModel.from_pretrained(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
_lowerCAmelCase = True
_lowerCAmelCase = getattr(self.model_tester , """seq_length""" , lowerCamelCase )
_lowerCAmelCase = getattr(self.model_tester , """encoder_seq_length""" , lowerCamelCase )
_lowerCAmelCase = getattr(self.model_tester , """key_length""" , lowerCamelCase )
_lowerCAmelCase = getattr(self.model_tester , """chunk_length""" , lowerCamelCase )
if chunk_length is not None and hasattr(self.model_tester , """num_hashes""" ):
_lowerCAmelCase = encoder_seq_length * self.model_tester.num_hashes
for model_class in self.all_model_classes:
_lowerCAmelCase = True
_lowerCAmelCase = False
_lowerCAmelCase = True
_lowerCAmelCase = model_class(lowerCamelCase )
_lowerCAmelCase = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) , training=lowerCamelCase )
_lowerCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCamelCase ) , self.model_tester.num_attention_outputs )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
_lowerCAmelCase = True
_lowerCAmelCase = model_class(lowerCamelCase )
_lowerCAmelCase = model(**self._prepare_for_class(lowerCamelCase , lowerCamelCase ) , training=lowerCamelCase )
_lowerCAmelCase = outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions
self.assertEqual(len(lowerCamelCase ) , self.model_tester.num_attention_outputs )
if chunk_length is not None:
self.assertListEqual(
list(attentions[0].shape[-4:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, chunk_length, encoder_key_length] , )
else:
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, encoder_seq_length, encoder_key_length] , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# Prepare our model
_lowerCAmelCase = model_class(lowerCamelCase )
# These are maximally general inputs for the model, with multiple None dimensions
# Hopefully this will catch any conditionals that fail for flexible shapes
_lowerCAmelCase = {
key: tf.keras.Input(shape=val.shape[1:] , dtype=val.dtype , name=lowerCamelCase )
for key, val in model.input_signature.items()
if key in model.dummy_inputs
}
_lowerCAmelCase = model(lowerCamelCase )
self.assertTrue(outputs_dict is not None )
def __UpperCAmelCase ( ) -> Dict:
"""simple docstring"""
_lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class __lowerCamelCase ( unittest.TestCase ):
@cached_property
def A__ (self ):
'''simple docstring'''
return (
EfficientFormerImageProcessor.from_pretrained("""snap-research/efficientformer-l1-300""" )
if is_vision_available()
else None
)
@slow
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = TFEfficientFormerForImageClassification.from_pretrained("""snap-research/efficientformer-l1-300""" )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=lowerCamelCase , return_tensors="""tf""" )
# forward pass
_lowerCAmelCase = model(**lowerCamelCase , training=lowerCamelCase )
# verify the logits
_lowerCAmelCase = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowerCamelCase )
_lowerCAmelCase = tf.constant([-0.0555, 0.4825, -0.0852] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , lowerCamelCase , atol=1e-4 ) )
@slow
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = TFEfficientFormerForImageClassificationWithTeacher.from_pretrained(
"""snap-research/efficientformer-l1-300""" )
_lowerCAmelCase = self.default_image_processor
_lowerCAmelCase = prepare_img()
_lowerCAmelCase = image_processor(images=lowerCamelCase , return_tensors="""tf""" )
# forward pass
_lowerCAmelCase = model(**lowerCamelCase , training=lowerCamelCase )
# verify the logits
_lowerCAmelCase = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowerCamelCase )
_lowerCAmelCase = tf.constant([-0.1312, 0.4353, -1.0499] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , lowerCamelCase , atol=1e-4 ) )
| 317
|
"""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 numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = (
'This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'
'It takes two arguments named `image` which should be the original image, and `label` which should be a text '
'describing the elements what should be identified in the segmentation mask. The tool returns the mask.'
)
__UpperCamelCase = 'CIDAS/clipseg-rd64-refined'
__UpperCamelCase = 'image_segmenter'
__UpperCamelCase = CLIPSegForImageSegmentation
__UpperCamelCase = ['image', 'text']
__UpperCamelCase = ['image']
def __init__(self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
requires_backends(self , ["""vision"""] )
super().__init__(*lowerCamelCase , **lowerCamelCase )
def A__ (self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return self.pre_processor(text=[label] , images=[image] , padding=lowerCamelCase , return_tensors="""pt""" )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
with torch.no_grad():
_lowerCAmelCase = self.model(**lowerCamelCase ).logits
return logits
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = outputs.cpu().detach().numpy()
_lowerCAmelCase = 0
_lowerCAmelCase = 1
return Image.fromarray((array * 255).astype(np.uinta ) )
| 317
| 1
|
"""simple docstring"""
import string
import numpy
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : int ) -> int:
"""simple docstring"""
return b if a == 0 else greatest_common_divisor(b % a , snake_case_ )
class __lowerCamelCase :
__UpperCamelCase = string.ascii_uppercase + string.digits
# This cipher takes alphanumerics into account
# i.e. a total of 36 characters
# take x and return x % len(key_string)
__UpperCamelCase = numpy.vectorize(lambda __lowercase : x % 36 )
__UpperCamelCase = numpy.vectorize(__lowercase )
def __init__(self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.modulus(lowerCamelCase ) # mod36 calc's on the encrypt key
self.check_determinant() # validate the determinant of the encryption key
_lowerCAmelCase = encrypt_key.shape[0]
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.key_string.index(lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.key_string[round(lowerCamelCase )]
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
_lowerCAmelCase = det % len(self.key_string )
_lowerCAmelCase = len(self.key_string )
if greatest_common_divisor(lowerCamelCase , len(self.key_string ) ) != 1:
_lowerCAmelCase = (
f"""determinant modular {req_l} of encryption key({det}) """
f"""is not co prime w.r.t {req_l}.\nTry another key."""
)
raise ValueError(lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = [char for char in text.upper() if char in self.key_string]
_lowerCAmelCase = chars[-1]
while len(lowerCamelCase ) % self.break_key != 0:
chars.append(lowerCamelCase )
return "".join(lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.process_text(text.upper() )
_lowerCAmelCase = """"""
for i in range(0 , len(lowerCamelCase ) - self.break_key + 1 , self.break_key ):
_lowerCAmelCase = text[i : i + self.break_key]
_lowerCAmelCase = [self.replace_letters(lowerCamelCase ) for char in batch]
_lowerCAmelCase = numpy.array([vec] ).T
_lowerCAmelCase = self.modulus(self.encrypt_key.dot(lowerCamelCase ) ).T.tolist()[
0
]
_lowerCAmelCase = """""".join(
self.replace_digits(lowerCamelCase ) for num in batch_encrypted )
encrypted += encrypted_batch
return encrypted
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = round(numpy.linalg.det(self.encrypt_key ) )
if det < 0:
_lowerCAmelCase = det % len(self.key_string )
_lowerCAmelCase = None
for i in range(len(self.key_string ) ):
if (det * i) % len(self.key_string ) == 1:
_lowerCAmelCase = i
break
_lowerCAmelCase = (
det_inv
* numpy.linalg.det(self.encrypt_key )
* numpy.linalg.inv(self.encrypt_key )
)
return self.to_int(self.modulus(lowerCamelCase ) )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = self.make_decrypt_key()
_lowerCAmelCase = self.process_text(text.upper() )
_lowerCAmelCase = """"""
for i in range(0 , len(lowerCamelCase ) - self.break_key + 1 , self.break_key ):
_lowerCAmelCase = text[i : i + self.break_key]
_lowerCAmelCase = [self.replace_letters(lowerCamelCase ) for char in batch]
_lowerCAmelCase = numpy.array([vec] ).T
_lowerCAmelCase = self.modulus(decrypt_key.dot(lowerCamelCase ) ).T.tolist()[0]
_lowerCAmelCase = """""".join(
self.replace_digits(lowerCamelCase ) for num in batch_decrypted )
decrypted += decrypted_batch
return decrypted
def __UpperCAmelCase ( ) -> None:
"""simple docstring"""
_lowerCAmelCase = int(input("""Enter the order of the encryption key: """ ) )
_lowerCAmelCase = []
print("""Enter each row of the encryption key with space separated integers""" )
for _ in range(snake_case_ ):
_lowerCAmelCase = [int(snake_case_ ) for x in input().split()]
hill_matrix.append(snake_case_ )
_lowerCAmelCase = HillCipher(numpy.array(snake_case_ ) )
print("""Would you like to encrypt or decrypt some text? (1 or 2)""" )
_lowerCAmelCase = input("""\n1. Encrypt\n2. Decrypt\n""" )
if option == "1":
_lowerCAmelCase = input("""What text would you like to encrypt?: """ )
print("""Your encrypted text is:""" )
print(hc.encrypt(snake_case_ ) )
elif option == "2":
_lowerCAmelCase = input("""What text would you like to decrypt?: """ )
print("""Your decrypted text is:""" )
print(hc.decrypt(snake_case_ ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 317
|
"""simple docstring"""
from __future__ import annotations
import queue
class __lowerCamelCase :
def __init__(self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = data
_lowerCAmelCase = None
_lowerCAmelCase = None
def __UpperCAmelCase ( ) -> TreeNode:
"""simple docstring"""
print("""\n********Press N to stop entering at any point of time********\n""" )
_lowerCAmelCase = input("""Enter the value of the root node: """ ).strip().lower()
_lowerCAmelCase = queue.Queue()
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = q.get()
_lowerCAmelCase = F"""Enter the left node of {node_found.data}: """
_lowerCAmelCase = input(snake_case_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
_lowerCAmelCase = left_node
q.put(snake_case_ )
_lowerCAmelCase = F"""Enter the right node of {node_found.data}: """
_lowerCAmelCase = input(snake_case_ ).strip().lower() or """n"""
if check == "n":
return tree_node
_lowerCAmelCase = TreeNode(int(snake_case_ ) )
_lowerCAmelCase = right_node
q.put(snake_case_ )
raise
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
print(node.data , end=""",""" )
pre_order(node.left )
pre_order(node.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
in_order(node.left )
print(node.data , end=""",""" )
in_order(node.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
post_order(node.left )
post_order(node.right )
print(node.data , end=""",""" )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = queue.Queue()
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
q.put(node_dequeued.left )
if node_dequeued.right:
q.put(node_dequeued.right )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = queue.Queue()
q.put(snake_case_ )
while not q.empty():
_lowerCAmelCase = []
while not q.empty():
_lowerCAmelCase = q.get()
print(node_dequeued.data , end=""",""" )
if node_dequeued.left:
list_.append(node_dequeued.left )
if node_dequeued.right:
list_.append(node_dequeued.right )
print()
for node in list_:
q.put(snake_case_ )
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = []
_lowerCAmelCase = node
while n or stack:
while n: # start from root node, find its left child
print(n.data , end=""",""" )
stack.append(snake_case_ )
_lowerCAmelCase = n.left
# end of while means current node doesn't have left child
_lowerCAmelCase = stack.pop()
# start to traverse its right child
_lowerCAmelCase = n.right
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase = []
_lowerCAmelCase = node
while n or stack:
while n:
stack.append(snake_case_ )
_lowerCAmelCase = n.left
_lowerCAmelCase = stack.pop()
print(n.data , end=""",""" )
_lowerCAmelCase = n.right
def __UpperCAmelCase ( snake_case_ : TreeNode ) -> None:
"""simple docstring"""
if not isinstance(snake_case_ , snake_case_ ) or not node:
return
_lowerCAmelCase , _lowerCAmelCase = [], []
_lowerCAmelCase = node
stacka.append(snake_case_ )
while stacka: # to find the reversed order of post order, store it in stack2
_lowerCAmelCase = stacka.pop()
if n.left:
stacka.append(n.left )
if n.right:
stacka.append(n.right )
stacka.append(snake_case_ )
while stacka: # pop up from stack2 will be the post order
print(stacka.pop().data , end=""",""" )
def __UpperCAmelCase ( snake_case_ : str = "" , snake_case_ : int=50 , snake_case_ : Dict="*" ) -> str:
"""simple docstring"""
if not s:
return "\n" + width * char
_lowerCAmelCase , _lowerCAmelCase = divmod(width - len(snake_case_ ) - 2 , 2 )
return F"""{left * char} {s} {(left + extra) * char}"""
if __name__ == "__main__":
import doctest
doctest.testmod()
print(prompt('''Binary Tree Traversals'''))
SCREAMING_SNAKE_CASE : TreeNode = build_tree()
print(prompt('''Pre Order Traversal'''))
pre_order(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal'''))
in_order(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal'''))
post_order(node)
print(prompt() + '''\n''')
print(prompt('''Level Order Traversal'''))
level_order(node)
print(prompt() + '''\n''')
print(prompt('''Actual Level Order Traversal'''))
level_order_actual(node)
print('''*''' * 5_0 + '''\n''')
print(prompt('''Pre Order Traversal - Iteration Version'''))
pre_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''In Order Traversal - Iteration Version'''))
in_order_iter(node)
print(prompt() + '''\n''')
print(prompt('''Post Order Traversal - Iteration Version'''))
post_order_iter(node)
print(prompt())
| 317
| 1
|
"""simple docstring"""
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
SCREAMING_SNAKE_CASE : Optional[Any] = {
'''Acehnese Arabic''': '''ace_Arab''',
'''Acehnese Latin''': '''ace_Latn''',
'''Mesopotamian Arabic''': '''acm_Arab''',
'''Ta\'izzi-Adeni Arabic''': '''acq_Arab''',
'''Tunisian Arabic''': '''aeb_Arab''',
'''Afrikaans''': '''afr_Latn''',
'''South Levantine Arabic''': '''ajp_Arab''',
'''Akan''': '''aka_Latn''',
'''Amharic''': '''amh_Ethi''',
'''North Levantine Arabic''': '''apc_Arab''',
'''Modern Standard Arabic''': '''arb_Arab''',
'''Modern Standard Arabic Romanized''': '''arb_Latn''',
'''Najdi Arabic''': '''ars_Arab''',
'''Moroccan Arabic''': '''ary_Arab''',
'''Egyptian Arabic''': '''arz_Arab''',
'''Assamese''': '''asm_Beng''',
'''Asturian''': '''ast_Latn''',
'''Awadhi''': '''awa_Deva''',
'''Central Aymara''': '''ayr_Latn''',
'''South Azerbaijani''': '''azb_Arab''',
'''North Azerbaijani''': '''azj_Latn''',
'''Bashkir''': '''bak_Cyrl''',
'''Bambara''': '''bam_Latn''',
'''Balinese''': '''ban_Latn''',
'''Belarusian''': '''bel_Cyrl''',
'''Bemba''': '''bem_Latn''',
'''Bengali''': '''ben_Beng''',
'''Bhojpuri''': '''bho_Deva''',
'''Banjar Arabic''': '''bjn_Arab''',
'''Banjar Latin''': '''bjn_Latn''',
'''Standard Tibetan''': '''bod_Tibt''',
'''Bosnian''': '''bos_Latn''',
'''Buginese''': '''bug_Latn''',
'''Bulgarian''': '''bul_Cyrl''',
'''Catalan''': '''cat_Latn''',
'''Cebuano''': '''ceb_Latn''',
'''Czech''': '''ces_Latn''',
'''Chokwe''': '''cjk_Latn''',
'''Central Kurdish''': '''ckb_Arab''',
'''Crimean Tatar''': '''crh_Latn''',
'''Welsh''': '''cym_Latn''',
'''Danish''': '''dan_Latn''',
'''German''': '''deu_Latn''',
'''Southwestern Dinka''': '''dik_Latn''',
'''Dyula''': '''dyu_Latn''',
'''Dzongkha''': '''dzo_Tibt''',
'''Greek''': '''ell_Grek''',
'''English''': '''eng_Latn''',
'''Esperanto''': '''epo_Latn''',
'''Estonian''': '''est_Latn''',
'''Basque''': '''eus_Latn''',
'''Ewe''': '''ewe_Latn''',
'''Faroese''': '''fao_Latn''',
'''Fijian''': '''fij_Latn''',
'''Finnish''': '''fin_Latn''',
'''Fon''': '''fon_Latn''',
'''French''': '''fra_Latn''',
'''Friulian''': '''fur_Latn''',
'''Nigerian Fulfulde''': '''fuv_Latn''',
'''Scottish Gaelic''': '''gla_Latn''',
'''Irish''': '''gle_Latn''',
'''Galician''': '''glg_Latn''',
'''Guarani''': '''grn_Latn''',
'''Gujarati''': '''guj_Gujr''',
'''Haitian Creole''': '''hat_Latn''',
'''Hausa''': '''hau_Latn''',
'''Hebrew''': '''heb_Hebr''',
'''Hindi''': '''hin_Deva''',
'''Chhattisgarhi''': '''hne_Deva''',
'''Croatian''': '''hrv_Latn''',
'''Hungarian''': '''hun_Latn''',
'''Armenian''': '''hye_Armn''',
'''Igbo''': '''ibo_Latn''',
'''Ilocano''': '''ilo_Latn''',
'''Indonesian''': '''ind_Latn''',
'''Icelandic''': '''isl_Latn''',
'''Italian''': '''ita_Latn''',
'''Javanese''': '''jav_Latn''',
'''Japanese''': '''jpn_Jpan''',
'''Kabyle''': '''kab_Latn''',
'''Jingpho''': '''kac_Latn''',
'''Kamba''': '''kam_Latn''',
'''Kannada''': '''kan_Knda''',
'''Kashmiri Arabic''': '''kas_Arab''',
'''Kashmiri Devanagari''': '''kas_Deva''',
'''Georgian''': '''kat_Geor''',
'''Central Kanuri Arabic''': '''knc_Arab''',
'''Central Kanuri Latin''': '''knc_Latn''',
'''Kazakh''': '''kaz_Cyrl''',
'''Kabiyè''': '''kbp_Latn''',
'''Kabuverdianu''': '''kea_Latn''',
'''Khmer''': '''khm_Khmr''',
'''Kikuyu''': '''kik_Latn''',
'''Kinyarwanda''': '''kin_Latn''',
'''Kyrgyz''': '''kir_Cyrl''',
'''Kimbundu''': '''kmb_Latn''',
'''Northern Kurdish''': '''kmr_Latn''',
'''Kikongo''': '''kon_Latn''',
'''Korean''': '''kor_Hang''',
'''Lao''': '''lao_Laoo''',
'''Ligurian''': '''lij_Latn''',
'''Limburgish''': '''lim_Latn''',
'''Lingala''': '''lin_Latn''',
'''Lithuanian''': '''lit_Latn''',
'''Lombard''': '''lmo_Latn''',
'''Latgalian''': '''ltg_Latn''',
'''Luxembourgish''': '''ltz_Latn''',
'''Luba-Kasai''': '''lua_Latn''',
'''Ganda''': '''lug_Latn''',
'''Luo''': '''luo_Latn''',
'''Mizo''': '''lus_Latn''',
'''Standard Latvian''': '''lvs_Latn''',
'''Magahi''': '''mag_Deva''',
'''Maithili''': '''mai_Deva''',
'''Malayalam''': '''mal_Mlym''',
'''Marathi''': '''mar_Deva''',
'''Minangkabau Arabic ''': '''min_Arab''',
'''Minangkabau Latin''': '''min_Latn''',
'''Macedonian''': '''mkd_Cyrl''',
'''Plateau Malagasy''': '''plt_Latn''',
'''Maltese''': '''mlt_Latn''',
'''Meitei Bengali''': '''mni_Beng''',
'''Halh Mongolian''': '''khk_Cyrl''',
'''Mossi''': '''mos_Latn''',
'''Maori''': '''mri_Latn''',
'''Burmese''': '''mya_Mymr''',
'''Dutch''': '''nld_Latn''',
'''Norwegian Nynorsk''': '''nno_Latn''',
'''Norwegian Bokmål''': '''nob_Latn''',
'''Nepali''': '''npi_Deva''',
'''Northern Sotho''': '''nso_Latn''',
'''Nuer''': '''nus_Latn''',
'''Nyanja''': '''nya_Latn''',
'''Occitan''': '''oci_Latn''',
'''West Central Oromo''': '''gaz_Latn''',
'''Odia''': '''ory_Orya''',
'''Pangasinan''': '''pag_Latn''',
'''Eastern Panjabi''': '''pan_Guru''',
'''Papiamento''': '''pap_Latn''',
'''Western Persian''': '''pes_Arab''',
'''Polish''': '''pol_Latn''',
'''Portuguese''': '''por_Latn''',
'''Dari''': '''prs_Arab''',
'''Southern Pashto''': '''pbt_Arab''',
'''Ayacucho Quechua''': '''quy_Latn''',
'''Romanian''': '''ron_Latn''',
'''Rundi''': '''run_Latn''',
'''Russian''': '''rus_Cyrl''',
'''Sango''': '''sag_Latn''',
'''Sanskrit''': '''san_Deva''',
'''Santali''': '''sat_Olck''',
'''Sicilian''': '''scn_Latn''',
'''Shan''': '''shn_Mymr''',
'''Sinhala''': '''sin_Sinh''',
'''Slovak''': '''slk_Latn''',
'''Slovenian''': '''slv_Latn''',
'''Samoan''': '''smo_Latn''',
'''Shona''': '''sna_Latn''',
'''Sindhi''': '''snd_Arab''',
'''Somali''': '''som_Latn''',
'''Southern Sotho''': '''sot_Latn''',
'''Spanish''': '''spa_Latn''',
'''Tosk Albanian''': '''als_Latn''',
'''Sardinian''': '''srd_Latn''',
'''Serbian''': '''srp_Cyrl''',
'''Swati''': '''ssw_Latn''',
'''Sundanese''': '''sun_Latn''',
'''Swedish''': '''swe_Latn''',
'''Swahili''': '''swh_Latn''',
'''Silesian''': '''szl_Latn''',
'''Tamil''': '''tam_Taml''',
'''Tatar''': '''tat_Cyrl''',
'''Telugu''': '''tel_Telu''',
'''Tajik''': '''tgk_Cyrl''',
'''Tagalog''': '''tgl_Latn''',
'''Thai''': '''tha_Thai''',
'''Tigrinya''': '''tir_Ethi''',
'''Tamasheq Latin''': '''taq_Latn''',
'''Tamasheq Tifinagh''': '''taq_Tfng''',
'''Tok Pisin''': '''tpi_Latn''',
'''Tswana''': '''tsn_Latn''',
'''Tsonga''': '''tso_Latn''',
'''Turkmen''': '''tuk_Latn''',
'''Tumbuka''': '''tum_Latn''',
'''Turkish''': '''tur_Latn''',
'''Twi''': '''twi_Latn''',
'''Central Atlas Tamazight''': '''tzm_Tfng''',
'''Uyghur''': '''uig_Arab''',
'''Ukrainian''': '''ukr_Cyrl''',
'''Umbundu''': '''umb_Latn''',
'''Urdu''': '''urd_Arab''',
'''Northern Uzbek''': '''uzn_Latn''',
'''Venetian''': '''vec_Latn''',
'''Vietnamese''': '''vie_Latn''',
'''Waray''': '''war_Latn''',
'''Wolof''': '''wol_Latn''',
'''Xhosa''': '''xho_Latn''',
'''Eastern Yiddish''': '''ydd_Hebr''',
'''Yoruba''': '''yor_Latn''',
'''Yue Chinese''': '''yue_Hant''',
'''Chinese Simplified''': '''zho_Hans''',
'''Chinese Traditional''': '''zho_Hant''',
'''Standard Malay''': '''zsm_Latn''',
'''Zulu''': '''zul_Latn''',
}
class __lowerCamelCase ( __lowercase ):
__UpperCamelCase = 'facebook/nllb-200-distilled-600M'
__UpperCamelCase = (
'This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '
'be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '
'which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '
'plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'
)
__UpperCamelCase = 'translator'
__UpperCamelCase = AutoTokenizer
__UpperCamelCase = AutoModelForSeqaSeqLM
__UpperCamelCase = LANGUAGE_CODES
__UpperCamelCase = ['text', 'text', 'text']
__UpperCamelCase = ['text']
def A__ (self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if src_lang not in self.lang_to_code:
raise ValueError(f"""{src_lang} is not a supported language.""" )
if tgt_lang not in self.lang_to_code:
raise ValueError(f"""{tgt_lang} is not a supported language.""" )
_lowerCAmelCase = self.lang_to_code[src_lang]
_lowerCAmelCase = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
lowerCamelCase , return_tensors="""pt""" , src_lang=lowerCamelCase , tgt_lang=lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.model.generate(**lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=lowerCamelCase )
| 317
|
"""simple docstring"""
from __future__ import annotations
class __lowerCamelCase :
def __init__(self , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = text, pattern
_lowerCAmelCase , _lowerCAmelCase = len(lowerCamelCase ), len(lowerCamelCase )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def A__ (self , lowerCamelCase ):
'''simple docstring'''
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = []
for i in range(self.textLen - self.patLen + 1 ):
_lowerCAmelCase = self.mismatch_in_text(lowerCamelCase )
if mismatch_index == -1:
positions.append(lowerCamelCase )
else:
_lowerCAmelCase = self.match_in_pattern(self.text[mismatch_index] )
_lowerCAmelCase = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
SCREAMING_SNAKE_CASE : Any = '''ABAABA'''
SCREAMING_SNAKE_CASE : Optional[int] = '''AB'''
SCREAMING_SNAKE_CASE : str = BoyerMooreSearch(text, pattern)
SCREAMING_SNAKE_CASE : Tuple = bms.bad_character_heuristic()
if len(positions) == 0:
print('''No match found''')
else:
print('''Pattern found in following positions: ''')
print(positions)
| 317
| 1
|
"""simple docstring"""
from bisect import bisect
from itertools import accumulate
def __UpperCAmelCase ( snake_case_ : List[str] , snake_case_ : str , snake_case_ : Dict , snake_case_ : Tuple ) -> Dict:
"""simple docstring"""
_lowerCAmelCase = sorted(zip(snake_case_ , snake_case_ ) , key=lambda snake_case_ : x[0] / x[1] , reverse=snake_case_ )
_lowerCAmelCase , _lowerCAmelCase = [i[0] for i in r], [i[1] for i in r]
_lowerCAmelCase = list(accumulate(snake_case_ ) )
_lowerCAmelCase = bisect(snake_case_ , snake_case_ )
return (
0
if k == 0
else sum(vl[:k] ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
if k != n
else sum(vl[:k] )
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 317
|
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
SCREAMING_SNAKE_CASE : List[str] = False
class __lowerCamelCase ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(
image=lowerCamelCase , generator=lowerCamelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
_lowerCAmelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
_lowerCAmelCase = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 317
| 1
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : Any ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = [], []
while len(snake_case_ ) > 1:
_lowerCAmelCase , _lowerCAmelCase = min(snake_case_ ), max(snake_case_ )
start.append(snake_case_ )
end.append(snake_case_ )
collection.remove(snake_case_ )
collection.remove(snake_case_ )
end.reverse()
return start + collection + end
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Tuple = input('''Enter numbers separated by a comma:\n''').strip()
SCREAMING_SNAKE_CASE : Any = [int(item) for item in user_input.split(''',''')]
print(*merge_sort(unsorted), sep=''',''')
| 317
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCamelCase ( __lowercase , unittest.TestCase ):
__UpperCamelCase = DiTPipeline
__UpperCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
__UpperCamelCase = PipelineTesterMixin.required_optional_params - {
'latents',
'num_images_per_prompt',
'callback',
'callback_steps',
}
__UpperCamelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
__UpperCamelCase = False
def A__ (self ):
'''simple docstring'''
torch.manual_seed(0 )
_lowerCAmelCase = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=lowerCamelCase , activation_fn="""gelu-approximate""" , num_embeds_ada_norm=1_000 , norm_type="""ada_norm_zero""" , norm_elementwise_affine=lowerCamelCase , )
_lowerCAmelCase = AutoencoderKL()
_lowerCAmelCase = DDIMScheduler()
_lowerCAmelCase = {"""transformer""": transformer.eval(), """vae""": vae.eval(), """scheduler""": scheduler}
return components
def A__ (self , lowerCamelCase , lowerCamelCase=0 ):
'''simple docstring'''
if str(lowerCamelCase ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(lowerCamelCase )
else:
_lowerCAmelCase = torch.Generator(device=lowerCamelCase ).manual_seed(lowerCamelCase )
_lowerCAmelCase = {
"""class_labels""": [1],
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = """cpu"""
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**lowerCamelCase )
pipe.to(lowerCamelCase )
pipe.set_progress_bar_config(disable=lowerCamelCase )
_lowerCAmelCase = self.get_dummy_inputs(lowerCamelCase )
_lowerCAmelCase = pipe(**lowerCamelCase ).images
_lowerCAmelCase = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_lowerCAmelCase = np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457] )
_lowerCAmelCase = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowerCamelCase , 1e-3 )
def A__ (self ):
'''simple docstring'''
self._test_inference_batch_single_identical(relax_max_difference=lowerCamelCase , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def A__ (self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class __lowerCamelCase ( unittest.TestCase ):
def A__ (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-256""" )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella""", """white shark""", """white wolf"""]
_lowerCAmelCase = pipe.get_label_ids(lowerCamelCase )
_lowerCAmelCase = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=40 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = load_numpy(
f"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-2
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = DiTPipeline.from_pretrained("""facebook/DiT-XL-2-512""" )
_lowerCAmelCase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("""cuda""" )
_lowerCAmelCase = ["""vase""", """umbrella"""]
_lowerCAmelCase = pipe.get_label_ids(lowerCamelCase )
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(lowerCamelCase , generator=lowerCamelCase , num_inference_steps=25 , output_type="""np""" ).images
for word, image in zip(lowerCamelCase , lowerCamelCase ):
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
f"""/dit/{word}_512.npy""" )
assert np.abs((expected_image - image).max() ) < 1e-1
| 317
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
SCREAMING_SNAKE_CASE : List[str] = {'''tokenization_wav2vec2_phoneme''': ['''Wav2Vec2PhonemeCTCTokenizer''']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
SCREAMING_SNAKE_CASE : Tuple = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 317
|
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def __UpperCAmelCase ( snake_case_ : Union[str, Any] ) -> Dict:
"""simple docstring"""
return getitem, k
def __UpperCAmelCase ( snake_case_ : Dict , snake_case_ : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return setitem, k, v
def __UpperCAmelCase ( snake_case_ : str ) -> Optional[int]:
"""simple docstring"""
return delitem, k
def __UpperCAmelCase ( snake_case_ : Optional[Any] , snake_case_ : Tuple , *snake_case_ : Tuple ) -> str:
"""simple docstring"""
try:
return fun(snake_case_ , *snake_case_ ), None
except Exception as e:
return None, e
SCREAMING_SNAKE_CASE : int = (
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
)
SCREAMING_SNAKE_CASE : List[Any] = [
_set('''key_a''', '''val_a'''),
_set('''key_a''', '''val_b'''),
]
SCREAMING_SNAKE_CASE : Any = [
_set('''key_a''', '''val_a'''),
_set('''key_b''', '''val_b'''),
_del('''key_a'''),
_del('''key_b'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
]
SCREAMING_SNAKE_CASE : Union[str, Any] = [
_get('''key_a'''),
_del('''key_a'''),
_set('''key_a''', '''val_a'''),
_del('''key_a'''),
_del('''key_a'''),
_get('''key_a'''),
]
SCREAMING_SNAKE_CASE : Optional[Any] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
]
SCREAMING_SNAKE_CASE : Optional[int] = [
*[_set(x, x) for x in range(5)], # guaranteed upsize
*[_del(x) for x in range(5)],
_set('''key_a''', '''val_b'''),
]
@pytest.mark.parametrize(
"""operations""" , (
pytest.param(_add_items , id="""add items""" ),
pytest.param(_overwrite_items , id="""overwrite items""" ),
pytest.param(_delete_items , id="""delete items""" ),
pytest.param(_access_absent_items , id="""access absent items""" ),
pytest.param(_add_with_resize_up , id="""add with resize up""" ),
pytest.param(_add_with_resize_down , id="""add with resize down""" ),
) , )
def __UpperCAmelCase ( snake_case_ : List[Any] ) -> Tuple:
"""simple docstring"""
_lowerCAmelCase = HashMap(initial_block_size=4 )
_lowerCAmelCase = {}
for _, (fun, *args) in enumerate(snake_case_ ):
_lowerCAmelCase , _lowerCAmelCase = _run_operation(snake_case_ , snake_case_ , *snake_case_ )
_lowerCAmelCase , _lowerCAmelCase = _run_operation(snake_case_ , snake_case_ , *snake_case_ )
assert my_res == py_res
assert str(snake_case_ ) == str(snake_case_ )
assert set(snake_case_ ) == set(snake_case_ )
assert len(snake_case_ ) == len(snake_case_ )
assert set(my.items() ) == set(py.items() )
def __UpperCAmelCase ( ) -> Tuple:
"""simple docstring"""
def is_public(snake_case_ : str ) -> bool:
return not name.startswith("""_""" )
_lowerCAmelCase = {name for name in dir({} ) if is_public(snake_case_ )}
_lowerCAmelCase = {name for name in dir(HashMap() ) if is_public(snake_case_ )}
assert dict_public_names > hash_public_names
| 317
| 1
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 50 ) -> int:
"""simple docstring"""
_lowerCAmelCase = [[0] * 3 for _ in range(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 ):
different_colour_ways_number[row_length][tile_length - 2] += (
different_colour_ways_number[row_length - tile_start - tile_length][
tile_length - 2
]
+ 1
)
return sum(different_colour_ways_number[length] )
if __name__ == "__main__":
print(F'{solution() = }')
| 350
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations(snake_case_ : int ) -> int:
if target < 0:
return 0
if target == 0:
return 1
return sum(count_of_possible_combinations(target - item ) for item in array )
return count_of_possible_combinations(snake_case_ )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
def count_of_possible_combinations_with_dp_array(
snake_case_ : int , snake_case_ : list[int] ) -> int:
if target < 0:
return 0
if target == 0:
return 1
if dp_array[target] != -1:
return dp_array[target]
_lowerCAmelCase = sum(
count_of_possible_combinations_with_dp_array(target - item , snake_case_ )
for item in array )
_lowerCAmelCase = answer
return answer
_lowerCAmelCase = [-1] * (target + 1)
return count_of_possible_combinations_with_dp_array(snake_case_ , snake_case_ )
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : list[int] , snake_case_ : int ) -> int:
"""simple docstring"""
_lowerCAmelCase = [0] * (target + 1)
_lowerCAmelCase = 1
for i in range(1 , target + 1 ):
for j in range(snake_case_ ):
if i - array[j] >= 0:
dp_array[i] += dp_array[i - array[j]]
return dp_array[target]
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : Tuple = 3
SCREAMING_SNAKE_CASE : Any = 5
SCREAMING_SNAKE_CASE : Optional[int] = [1, 2, 5]
print(combination_sum_iv(n, array, target))
| 317
| 0
|
"""simple docstring"""
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[Any] = {'''tokenizer_file''': '''tokenizer.json'''}
SCREAMING_SNAKE_CASE : Tuple = {
'''tokenizer_file''': {
'''bigscience/tokenizer''': '''https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json''',
'''bigscience/bloom-560m''': '''https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json''',
'''bigscience/bloom-1b1''': '''https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json''',
'''bigscience/bloom-1b7''': '''https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json''',
'''bigscience/bloom-3b''': '''https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json''',
'''bigscience/bloom-7b1''': '''https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json''',
'''bigscience/bloom''': '''https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json''',
},
}
class __lowerCamelCase ( A__ ):
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = ["input_ids", "attention_mask"]
__UpperCamelCase = None
def __init__(self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase="<unk>" , lowerCamelCase="<s>" , lowerCamelCase="</s>" , lowerCamelCase="<pad>" , lowerCamelCase=False , lowerCamelCase=False , **lowerCamelCase , ):
'''simple docstring'''
super().__init__(
__A , __A , tokenizer_file=__A , unk_token=__A , bos_token=__A , eos_token=__A , pad_token=__A , add_prefix_space=__A , clean_up_tokenization_spaces=__A , **__A , )
_lowerCAmelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" , __A ) != add_prefix_space:
_lowerCAmelCase = getattr(__A , pre_tok_state.pop("""type""" ) )
_lowerCAmelCase = add_prefix_space
_lowerCAmelCase = pre_tok_class(**__A )
_lowerCAmelCase = add_prefix_space
def A__ (self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = kwargs.get("""is_split_into_words""" , __A )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
""" pretokenized inputs.""" )
return super()._batch_encode_plus(*__A , **__A )
def A__ (self , *lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = kwargs.get("""is_split_into_words""" , __A )
if not (self.add_prefix_space or not is_split_into_words):
raise Exception(
f"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
""" pretokenized inputs.""" )
return super()._encode_plus(*__A , **__A )
def A__ (self , lowerCamelCase , lowerCamelCase = None ):
'''simple docstring'''
_lowerCAmelCase = self._tokenizer.model.save(__A , name=__A )
return tuple(__A )
def A__ (self , lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase = []
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:
_lowerCAmelCase = input_ids[-self.model_max_length :]
return input_ids
| 351
|
"""simple docstring"""
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
SCREAMING_SNAKE_CASE : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
SCREAMING_SNAKE_CASE : list[int] = [ord(letter) for letter in string.ascii_lowercase]
SCREAMING_SNAKE_CASE : set[int] = {ord(char) for char in VALID_CHARS}
SCREAMING_SNAKE_CASE : list[str] = ["the", "be", "to", "of", "and", "in", "that", "have"]
def __UpperCAmelCase ( snake_case_ : list[int] , snake_case_ : tuple[int, ...] ) -> str | None:
"""simple docstring"""
_lowerCAmelCase = ""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
for keychar, cipherchar in zip(cycle(snake_case_ ) , snake_case_ ):
_lowerCAmelCase = cipherchar ^ keychar
if decodedchar not in VALID_INTS:
return None
decoded += chr(snake_case_ )
return decoded
def __UpperCAmelCase ( snake_case_ : list[int] ) -> list[str]:
"""simple docstring"""
_lowerCAmelCase = []
for key in product(snake_case_ , repeat=3 ):
_lowerCAmelCase = try_key(snake_case_ , snake_case_ )
if encoded is not None:
possibles.append(snake_case_ )
return possibles
def __UpperCAmelCase ( snake_case_ : list[str] , snake_case_ : str ) -> list[str]:
"""simple docstring"""
return [possible for possible in possibles if common_word in possible.lower()]
def __UpperCAmelCase ( snake_case_ : str = "p059_cipher.txt" ) -> int:
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = Path(snake_case_ ).parent.joinpath(snake_case_ ).read_text(encoding="""utf-8""" )
_lowerCAmelCase = [int(snake_case_ ) for number in data.strip().split(""",""" )]
_lowerCAmelCase = filter_valid_chars(snake_case_ )
for common_word in COMMON_WORDS:
_lowerCAmelCase = filter_common_word(snake_case_ , snake_case_ )
if len(snake_case_ ) == 1:
break
_lowerCAmelCase = possibles[0]
return sum(ord(snake_case_ ) for char in decoded_text )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : Any = {
'''configuration_mgp_str''': ['''MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MgpstrConfig'''],
'''processing_mgp_str''': ['''MgpstrProcessor'''],
'''tokenization_mgp_str''': ['''MgpstrTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Optional[int] = [
'''MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MgpstrModel''',
'''MgpstrPreTrainedModel''',
'''MgpstrForSceneTextRecognition''',
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : str = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 352
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 1000000 ) -> int:
"""simple docstring"""
_lowerCAmelCase = limit + 1
_lowerCAmelCase = [0] * limit
for first_term in range(1 , snake_case_ ):
for n in range(snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
_lowerCAmelCase = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 0
|
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
from .timesteps import (
fastaa_timesteps,
smartaa_timesteps,
smartaa_timesteps,
smartaaa_timesteps,
smartaaa_timesteps,
superaa_timesteps,
superaa_timesteps,
superaaa_timesteps,
)
@dataclass
class __lowerCamelCase ( A__ ):
__UpperCamelCase = 42
__UpperCamelCase = 42
__UpperCamelCase = 42
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_if import IFPipeline
from .pipeline_if_imgaimg import IFImgaImgPipeline
from .pipeline_if_imgaimg_superresolution import IFImgaImgSuperResolutionPipeline
from .pipeline_if_inpainting import IFInpaintingPipeline
from .pipeline_if_inpainting_superresolution import IFInpaintingSuperResolutionPipeline
from .pipeline_if_superresolution import IFSuperResolutionPipeline
from .safety_checker import IFSafetyChecker
from .watermark import IFWatermarker
| 353
|
"""simple docstring"""
from functools import reduce
SCREAMING_SNAKE_CASE : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''12540698747158523863050715693290963295227443043557'''
'''66896648950445244523161731856403098711121722383113'''
'''62229893423380308135336276614282806444486645238749'''
'''30358907296290491560440772390713810515859307960866'''
'''70172427121883998797908792274921901699720888093776'''
'''65727333001053367881220235421809751254540594752243'''
'''52584907711670556013604839586446706324415722155397'''
'''53697817977846174064955149290862569321978468622482'''
'''83972241375657056057490261407972968652414535100474'''
'''82166370484403199890008895243450658541227588666881'''
'''16427171479924442928230863465674813919123162824586'''
'''17866458359124566529476545682848912883142607690042'''
'''24219022671055626321111109370544217506941658960408'''
'''07198403850962455444362981230987879927244284909188'''
'''84580156166097919133875499200524063689912560717606'''
'''05886116467109405077541002256983155200055935729725'''
'''71636269561882670428252483600823257530420752963450'''
)
def __UpperCAmelCase ( snake_case_ : str = N ) -> int:
"""simple docstring"""
return max(
# mypy cannot properly interpret reduce
int(reduce(lambda snake_case_ , snake_case_ : str(int(snake_case_ ) * int(snake_case_ ) ) , n[i : i + 13] ) )
for i in range(len(snake_case_ ) - 12 ) )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 0
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : list ) -> list:
"""simple docstring"""
if len(lowerCamelCase__ ) <= 1:
return [tuple(lowerCamelCase__ )]
_lowerCAmelCase = []
def generate(snake_case_ : int , snake_case_ : list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , lowerCamelCase__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
_lowerCAmelCase = arr[k - 1], arr[i]
else: # k is odd
_lowerCAmelCase = arr[k - 1], arr[0]
generate(k - 1 , lowerCamelCase__ )
generate(len(lowerCamelCase__ ) , lowerCamelCase__ )
return res
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : str = input('''Enter numbers separated by a comma:\n''').strip()
__SCREAMING_SNAKE_CASE : str = [int(item) for item in user_input.split(''',''')]
print(heaps(arr))
| 354
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : int = 600851475143 ) -> int:
"""simple docstring"""
try:
_lowerCAmelCase = int(snake_case_ )
except (TypeError, ValueError):
raise TypeError("""Parameter n must be int or castable to int.""" )
if n <= 0:
raise ValueError("""Parameter n must be greater than or equal to one.""" )
_lowerCAmelCase = 1
_lowerCAmelCase = 2
while i * i <= n:
while n % i == 0:
_lowerCAmelCase = i
n //= i
i += 1
if n > 1:
_lowerCAmelCase = n
return int(snake_case_ )
if __name__ == "__main__":
print(F'{solution() = }')
| 317
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : List[Any] = {
'''configuration_efficientnet''': [
'''EFFICIENTNET_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''EfficientNetConfig''',
'''EfficientNetOnnxConfig''',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Dict = ['''EfficientNetImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Optional[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
SCREAMING_SNAKE_CASE : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 355
|
"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import SeqaSeqTrainer
from seqaseq_training_args import SeqaSeqTrainingArguments
import transformers
from transformers import (
AutoConfig,
AutoModelForSeqaSeqLM,
AutoTokenizer,
HfArgumentParser,
MBartTokenizer,
MBartTokenizerFast,
set_seed,
)
from transformers.trainer_utils import EvaluationStrategy, is_main_process
from transformers.training_args import ParallelMode
from utils import (
SeqaSeqDataCollator,
SeqaSeqDataset,
assert_all_frozen,
build_compute_metrics_fn,
check_output_dir,
freeze_embeds,
freeze_params,
lmap,
save_json,
use_task_specific_params,
write_txt_file,
)
SCREAMING_SNAKE_CASE : Optional[Any] = logging.getLogger(__name__)
@dataclass
class __lowerCamelCase :
__UpperCamelCase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Whether tp freeze the encoder.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Whether to freeze the embeddings.'} )
@dataclass
class __lowerCamelCase :
__UpperCamelCase = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__UpperCamelCase = field(
default='summarization' , metadata={'help': 'Task name, summarization (or summarization_{dataset} for pegasus) or translation'} , )
__UpperCamelCase = field(
default=1_024 , 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=128 , metadata={
'help': (
'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCamelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. '
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
)
} , )
__UpperCamelCase = field(
default=142 , metadata={
'help': (
'The maximum total sequence length for test target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__UpperCamelCase = field(default=-1 , metadata={'help': '# training examples. -1 means use all.'} )
__UpperCamelCase = field(default=-1 , metadata={'help': '# validation examples. -1 means use all.'} )
__UpperCamelCase = field(default=-1 , metadata={'help': '# test examples. -1 means use all.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Source language id for translation.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': 'Target language id for translation.'} )
__UpperCamelCase = field(default=__lowercase , metadata={'help': '# num_beams to use for evaluation.'} )
__UpperCamelCase = field(
default=__lowercase , metadata={'help': 'If only pad tokens should be ignored. This assumes that `config.pad_token_id` is defined.'} , )
def __UpperCAmelCase ( snake_case_ : Optional[int] , snake_case_ : Any , snake_case_ : Union[str, Any] ) -> Tuple:
"""simple docstring"""
logger.info(F"""***** {split} metrics *****""" )
for key in sorted(metrics.keys() ):
logger.info(F""" {key} = {metrics[key]}""" )
save_json(snake_case_ , os.path.join(snake_case_ , F"""{split}_results.json""" ) )
def __UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, SeqaSeqTrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_args_into_dataclasses()
check_output_dir(snake_case_ )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.parallel_mode == ParallelMode.DISTRIBUTED ) , training_args.fpaa , )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
logger.info("""Training/evaluation parameters %s""" , snake_case_ )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
_lowerCAmelCase = ("""encoder_layerdrop""", """decoder_layerdrop""", """dropout""", """attention_dropout""")
for p in extra_model_params:
if getattr(snake_case_ , snake_case_ , snake_case_ ):
assert hasattr(snake_case_ , snake_case_ ), F"""({config.__class__.__name__}) doesn't have a `{p}` attribute"""
setattr(snake_case_ , snake_case_ , getattr(snake_case_ , snake_case_ ) )
_lowerCAmelCase = 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 = AutoModelForSeqaSeqLM.from_pretrained(
model_args.model_name_or_path , from_tf=""".ckpt""" in model_args.model_name_or_path , config=snake_case_ , cache_dir=model_args.cache_dir , )
# use task specific params
use_task_specific_params(snake_case_ , data_args.task )
# set num_beams for evaluation
if data_args.eval_beams is None:
_lowerCAmelCase = model.config.num_beams
# set decoder_start_token_id for MBart
if model.config.decoder_start_token_id is None and isinstance(snake_case_ , (MBartTokenizer, MBartTokenizerFast) ):
assert (
data_args.tgt_lang is not None and data_args.src_lang is not None
), "mBart requires --tgt_lang and --src_lang"
if isinstance(snake_case_ , snake_case_ ):
_lowerCAmelCase = tokenizer.lang_code_to_id[data_args.tgt_lang]
else:
_lowerCAmelCase = tokenizer.convert_tokens_to_ids(data_args.tgt_lang )
if model_args.freeze_embeds:
freeze_embeds(snake_case_ )
if model_args.freeze_encoder:
freeze_params(model.get_encoder() )
assert_all_frozen(model.get_encoder() )
_lowerCAmelCase = SeqaSeqDataset
# Get datasets
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""train""" , data_dir=data_args.data_dir , n_obs=data_args.n_train , max_target_length=data_args.max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_train
else None
)
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""val""" , data_dir=data_args.data_dir , n_obs=data_args.n_val , max_target_length=data_args.val_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_eval or training_args.evaluation_strategy != EvaluationStrategy.NO
else None
)
_lowerCAmelCase = (
dataset_class(
snake_case_ , type_path="""test""" , data_dir=data_args.data_dir , n_obs=data_args.n_test , max_target_length=data_args.test_max_target_length , max_source_length=data_args.max_source_length , prefix=model.config.prefix or """""" , )
if training_args.do_predict
else None
)
# Initialize our Trainer
_lowerCAmelCase = (
build_compute_metrics_fn(data_args.task , snake_case_ ) if training_args.predict_with_generate else None
)
_lowerCAmelCase = SeqaSeqTrainer(
model=snake_case_ , args=snake_case_ , data_args=snake_case_ , train_dataset=snake_case_ , eval_dataset=snake_case_ , data_collator=SeqaSeqDataCollator(
snake_case_ , snake_case_ , model.config.decoder_start_token_id , training_args.tpu_num_cores ) , compute_metrics=snake_case_ , tokenizer=snake_case_ , )
_lowerCAmelCase = {}
# Training
if training_args.do_train:
logger.info("""*** Train ***""" )
_lowerCAmelCase = trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
_lowerCAmelCase = train_result.metrics
_lowerCAmelCase = data_args.n_train
trainer.save_model() # this also saves the tokenizer
if trainer.is_world_process_zero():
handle_metrics("""train""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir , """trainer_state.json""" ) )
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_lowerCAmelCase = trainer.evaluate(metric_key_prefix="""val""" )
_lowerCAmelCase = data_args.n_val
_lowerCAmelCase = round(metrics["""val_loss"""] , 4 )
if trainer.is_world_process_zero():
handle_metrics("""val""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
if training_args.do_predict:
logger.info("""*** Predict ***""" )
_lowerCAmelCase = trainer.predict(test_dataset=snake_case_ , metric_key_prefix="""test""" )
_lowerCAmelCase = test_output.metrics
_lowerCAmelCase = data_args.n_test
if trainer.is_world_process_zero():
_lowerCAmelCase = round(metrics["""test_loss"""] , 4 )
handle_metrics("""test""" , snake_case_ , training_args.output_dir )
all_metrics.update(snake_case_ )
if training_args.predict_with_generate:
_lowerCAmelCase = tokenizer.batch_decode(
test_output.predictions , skip_special_tokens=snake_case_ , clean_up_tokenization_spaces=snake_case_ )
_lowerCAmelCase = lmap(str.strip , snake_case_ )
write_txt_file(snake_case_ , os.path.join(training_args.output_dir , """test_generations.txt""" ) )
if trainer.is_world_process_zero():
save_json(snake_case_ , os.path.join(training_args.output_dir , """all_results.json""" ) )
return all_metrics
def __UpperCAmelCase ( snake_case_ : Any ) -> Dict:
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 317
| 0
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : List[str] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : List[str] = [
"""PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""PegasusXForConditionalGeneration""",
"""PegasusXModel""",
"""PegasusXPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_pegasus_x import PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP, PegasusXConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pegasus_x import (
PEGASUS_X_PRETRAINED_MODEL_ARCHIVE_LIST,
PegasusXForConditionalGeneration,
PegasusXModel,
PegasusXPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 356
|
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE : List[Any] = {'''configuration_focalnet''': ['''FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FocalNetConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = [
'''FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FocalNetForImageClassification''',
'''FocalNetForMaskedImageModeling''',
'''FocalNetBackbone''',
'''FocalNetModel''',
'''FocalNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE : Optional[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 317
| 0
|
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('''1.0.0a'''):
raise Exception('''requires fairseq >= 1.0.0a''')
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[str] = "Hello world! cécé herlolip"
def __UpperCAmelCase ( snake_case_ : str , snake_case_ : str , snake_case_ : bool ) -> str:
"""simple docstring"""
_lowerCAmelCase = FairseqRobertaModel.from_pretrained(__A )
roberta.eval() # disable dropout
_lowerCAmelCase = roberta.model.encoder.sentence_encoder
_lowerCAmelCase = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=514 , type_vocab_size=1 , layer_norm_eps=1e-5 , )
if classification_head:
_lowerCAmelCase = roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our RoBERTa config:""" , __A )
_lowerCAmelCase = XLMRobertaXLForSequenceClassification(__A ) if classification_head else XLMRobertaXLForMaskedLM(__A )
model.eval()
# Now let's copy all the weights.
# Embeddings
_lowerCAmelCase = roberta_sent_encoder.embed_tokens.weight
_lowerCAmelCase = roberta_sent_encoder.embed_positions.weight
_lowerCAmelCase = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
_lowerCAmelCase = roberta_sent_encoder.layer_norm.weight
_lowerCAmelCase = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
_lowerCAmelCase = model.roberta.encoder.layer[i]
_lowerCAmelCase = roberta_sent_encoder.layers[i]
_lowerCAmelCase = layer.attention
_lowerCAmelCase = roberta_layer.self_attn_layer_norm.weight
_lowerCAmelCase = roberta_layer.self_attn_layer_norm.bias
# self attention
_lowerCAmelCase = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
_lowerCAmelCase = roberta_layer.self_attn.q_proj.weight
_lowerCAmelCase = roberta_layer.self_attn.q_proj.bias
_lowerCAmelCase = roberta_layer.self_attn.k_proj.weight
_lowerCAmelCase = roberta_layer.self_attn.k_proj.bias
_lowerCAmelCase = roberta_layer.self_attn.v_proj.weight
_lowerCAmelCase = roberta_layer.self_attn.v_proj.bias
# self-attention output
_lowerCAmelCase = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
_lowerCAmelCase = roberta_layer.self_attn.out_proj.weight
_lowerCAmelCase = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
_lowerCAmelCase = roberta_layer.final_layer_norm.weight
_lowerCAmelCase = roberta_layer.final_layer_norm.bias
# intermediate
_lowerCAmelCase = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
_lowerCAmelCase = roberta_layer.fca.weight
_lowerCAmelCase = roberta_layer.fca.bias
# output
_lowerCAmelCase = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
_lowerCAmelCase = roberta_layer.fca.weight
_lowerCAmelCase = roberta_layer.fca.bias
# end of layer
if classification_head:
_lowerCAmelCase = roberta.model.classification_heads["""mnli"""].dense.weight
_lowerCAmelCase = roberta.model.classification_heads["""mnli"""].dense.bias
_lowerCAmelCase = roberta.model.classification_heads["""mnli"""].out_proj.weight
_lowerCAmelCase = roberta.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
_lowerCAmelCase = roberta.model.encoder.lm_head.dense.weight
_lowerCAmelCase = roberta.model.encoder.lm_head.dense.bias
_lowerCAmelCase = roberta.model.encoder.lm_head.layer_norm.weight
_lowerCAmelCase = roberta.model.encoder.lm_head.layer_norm.bias
_lowerCAmelCase = roberta.model.encoder.lm_head.weight
_lowerCAmelCase = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
_lowerCAmelCase = roberta.encode(__A ).unsqueeze(0 ) # batch of size 1
_lowerCAmelCase = model(__A )[0]
if classification_head:
_lowerCAmelCase = roberta.model.classification_heads["""mnli"""](roberta.extract_features(__A ) )
else:
_lowerCAmelCase = roberta.model(__A )[0]
print(our_output.shape , their_output.shape )
_lowerCAmelCase = torch.max(torch.abs(our_output - their_output ) ).item()
print(F"""max_absolute_diff = {max_absolute_diff}""" ) # ~ 1e-7
_lowerCAmelCase = torch.allclose(__A , __A , atol=1e-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
pathlib.Path(__A ).mkdir(parents=__A , exist_ok=__A )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--roberta_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
)
SCREAMING_SNAKE_CASE : str = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
)
| 357
|
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class __lowerCamelCase ( unittest.TestCase ):
def __init__(self , lowerCamelCase , lowerCamelCase=7 , lowerCamelCase=3 , lowerCamelCase=18 , lowerCamelCase=30 , lowerCamelCase=400 , lowerCamelCase=True , lowerCamelCase=None , lowerCamelCase=True , lowerCamelCase=None , ):
'''simple docstring'''
_lowerCAmelCase = size if size is not None else {"""shortest_edge""": 20}
_lowerCAmelCase = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = min_resolution
_lowerCAmelCase = max_resolution
_lowerCAmelCase = do_resize
_lowerCAmelCase = size
_lowerCAmelCase = do_center_crop
_lowerCAmelCase = crop_size
def A__ (self ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class __lowerCamelCase ( __lowercase , unittest.TestCase ):
__UpperCamelCase = MobileNetVaImageProcessor if is_vision_available() else None
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = MobileNetVaImageProcessingTester(self )
@property
def A__ (self ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(lowerCamelCase , """size""" ) )
self.assertTrue(hasattr(lowerCamelCase , """do_center_crop""" ) )
self.assertTrue(hasattr(lowerCamelCase , """crop_size""" ) )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 20} )
self.assertEqual(image_processor.crop_size , {"""height""": 18, """width""": 18} )
_lowerCAmelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size , {"""height""": 84, """width""": 84} )
def A__ (self ):
'''simple docstring'''
pass
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , Image.Image )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , numpify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , np.ndarray )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
def A__ (self ):
'''simple docstring'''
_lowerCAmelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_lowerCAmelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCamelCase , torchify=lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase , torch.Tensor )
# Test not batched input
_lowerCAmelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
# Test batched
_lowerCAmelCase = image_processing(lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) , )
| 317
| 0
|
from itertools import product
def __UpperCAmelCase ( snake_case_ : int , snake_case_ : int ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = sides_number
_lowerCAmelCase = max_face_number * dice_number
_lowerCAmelCase = [0] * (max_total + 1)
_lowerCAmelCase = 1
_lowerCAmelCase = range(lowerCamelCase_ , max_face_number + 1 )
for dice_numbers in product(lowerCamelCase_ , repeat=lowerCamelCase_ ):
_lowerCAmelCase = sum(lowerCamelCase_ )
totals_frequencies[total] += 1
return totals_frequencies
def __UpperCAmelCase ( ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
_lowerCAmelCase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
_lowerCAmelCase = 0
_lowerCAmelCase = 9
_lowerCAmelCase = 4 * 9
_lowerCAmelCase = 6
for peter_total in range(lowerCamelCase_ , max_peter_total + 1 ):
peter_wins_count += peter_totals_frequencies[peter_total] * sum(
colin_totals_frequencies[min_colin_total:peter_total] )
_lowerCAmelCase = (4**9) * (6**6)
_lowerCAmelCase = peter_wins_count / total_games_number
_lowerCAmelCase = round(lowerCamelCase_ , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F'{solution() = }')
| 358
|
"""simple docstring"""
def __UpperCAmelCase ( snake_case_ : list ) -> list:
"""simple docstring"""
for i in range(len(snake_case_ ) - 1 , 0 , -1 ):
_lowerCAmelCase = False
for j in range(snake_case_ , 0 , -1 ):
if unsorted[j] < unsorted[j - 1]:
_lowerCAmelCase , _lowerCAmelCase = unsorted[j - 1], unsorted[j]
_lowerCAmelCase = True
for j in range(snake_case_ ):
if unsorted[j] > unsorted[j + 1]:
_lowerCAmelCase , _lowerCAmelCase = unsorted[j + 1], unsorted[j]
_lowerCAmelCase = True
if not swapped:
break
return unsorted
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE : List[Any] = input('''Enter numbers separated by a comma:\n''').strip()
SCREAMING_SNAKE_CASE : List[str] = [int(item) for item in user_input.split(''',''')]
print(F'{cocktail_shaker_sort(unsorted) = }')
| 317
| 0
|
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