code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
import os
# Precomputes a list of the 100 first triangular numbers
__UpperCamelCase : Dict = [int(0.5 * n * (n + 1)) for n in range(1, 101)]
def snake_case ( ):
'''simple docstring'''
__lowercase = os.path.dirname(os.path.realpath(lowerCamelCase ) )
__lowercase = os.path.join(lowerCamelCase , """words.txt""" )
__lowercase = """"""
with open(lowerCamelCase ) as f:
__lowercase = f.readline()
__lowercase = [word.strip("""\"""" ) for word in words.strip("""\r\n""" ).split(""",""" )]
__lowercase = [
word
for word in [sum(ord(lowerCamelCase ) - 64 for x in word ) for word in words]
if word in TRIANGULAR_NUMBERS
]
return len(lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 80 |
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase : Union[str, Any] = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""
__UpperCamelCase : List[str] = """
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results['matthews_correlation'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results['matthews_correlation'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results['matthews_correlation'], 2))
-0.25
"""
__UpperCamelCase : Tuple = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def _a ( self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any]=None ) -> Optional[Any]:
"""simple docstring"""
return {
"matthews_correlation": float(matthews_corrcoef(_lowerCAmelCase , _lowerCAmelCase , sample_weight=_lowerCAmelCase ) ),
}
| 80 | 1 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MgpstrProcessor, ViTImageProcessor
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
__snake_case :str = ViTImageProcessor if is_vision_available() else None
@property
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
__lowercase = (3, 32, 128)
__lowercase = tempfile.mkdtemp()
# fmt: off
__lowercase = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
__lowercase = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
__lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(_lowerCAmelCase ) + """\n""" )
__lowercase = {
"""do_normalize""": False,
"""do_resize""": True,
"""image_processor_type""": """ViTImageProcessor""",
"""resample""": 3,
"""size""": {"""height""": 32, """width""": 128},
}
__lowercase = 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 : List[Any] , **_lowerCAmelCase : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def _a ( self : Union[str, Any] , **_lowerCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
return ViTImageProcessor.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )
__lowercase = Image.fromarray(np.moveaxis(_lowerCAmelCase , 0 , -1 ) )
return image_input
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = self.get_tokenizer()
__lowercase = self.get_image_processor()
__lowercase = MgpstrProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
__lowercase = MgpstrProcessor.from_pretrained(self.tmpdirname , use_fast=_lowerCAmelCase )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCAmelCase )
def _a ( self : Optional[int] ) -> Any:
"""simple docstring"""
__lowercase = self.get_tokenizer()
__lowercase = self.get_image_processor()
__lowercase = MgpstrProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
processor.save_pretrained(self.tmpdirname )
__lowercase = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
__lowercase = self.get_image_processor(do_normalize=_lowerCAmelCase , padding_value=1.0 )
__lowercase = MgpstrProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=_lowerCAmelCase , padding_value=1.0 )
self.assertEqual(processor.char_tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.char_tokenizer , _lowerCAmelCase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _lowerCAmelCase )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = self.get_tokenizer()
__lowercase = MgpstrProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
__lowercase = self.prepare_image_inputs()
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""np""" )
__lowercase = processor(images=_lowerCAmelCase , return_tensors="""np""" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() , input_processor[key].sum() , delta=1e-2 )
def _a ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = self.get_tokenizer()
__lowercase = MgpstrProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
__lowercase = """test"""
__lowercase = processor(text=_lowerCAmelCase )
__lowercase = tokenizer(_lowerCAmelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = self.get_tokenizer()
__lowercase = MgpstrProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
__lowercase = """test"""
__lowercase = self.prepare_image_inputs()
__lowercase = processor(text=_lowerCAmelCase , images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """labels"""] )
# test if it raises when no input is passed
with pytest.raises(_lowerCAmelCase ):
processor()
def _a ( self : int ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = self.get_tokenizer()
__lowercase = MgpstrProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
__lowercase = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9], [3, 4, 3, 1, 1, 8, 9]]
__lowercase = processor.char_decode(_lowerCAmelCase )
__lowercase = tokenizer.batch_decode(_lowerCAmelCase )
__lowercase = [seq.replace(""" """ , """""" ) for seq in decoded_tok]
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[Any] ) -> str:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = self.get_tokenizer()
__lowercase = MgpstrProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
__lowercase = None
__lowercase = self.prepare_image_inputs()
__lowercase = processor(text=_lowerCAmelCase , images=_lowerCAmelCase )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names )
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
__lowercase = self.get_image_processor()
__lowercase = self.get_tokenizer()
__lowercase = MgpstrProcessor(tokenizer=_lowerCAmelCase , image_processor=_lowerCAmelCase )
__lowercase = torch.randn(1 , 27 , 38 )
__lowercase = torch.randn(1 , 27 , 5_0257 )
__lowercase = torch.randn(1 , 27 , 3_0522 )
__lowercase = processor.batch_decode([char_input, bpe_input, wp_input] )
self.assertListEqual(list(results.keys() ) , ["""generated_text""", """scores""", """char_preds""", """bpe_preds""", """wp_preds"""] )
| 80 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
__UpperCamelCase : Optional[int] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
__UpperCamelCase : Dict = {"""facebook/blenderbot_small-90M""": 512}
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
__lowercase = set(lowerCamelCase )
return pairs
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = VOCAB_FILES_NAMES
__snake_case :Tuple = PRETRAINED_VOCAB_FILES_MAP
__snake_case :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case :str = ['input_ids', 'attention_mask']
def __init__( self : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str="__start__" , _lowerCAmelCase : int="__end__" , _lowerCAmelCase : Any="__unk__" , _lowerCAmelCase : List[Any]="__null__" , **_lowerCAmelCase : Tuple , ) -> str:
"""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:
__lowercase = json.load(_lowerCAmelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
with open(_lowerCAmelCase , encoding="""utf-8""" ) as merges_handle:
__lowercase = merges_handle.read().split("""\n""" )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in merges]
__lowercase = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
__lowercase = {}
@property
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return len(self.encoder )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _a ( self : str , _lowerCAmelCase : str ) -> str:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
__lowercase = re.sub("""([.,!?()])""" , r""" \1""" , _lowerCAmelCase )
__lowercase = re.sub("""(')""" , r""" \1 """ , _lowerCAmelCase )
__lowercase = re.sub(r"""\s{2,}""" , """ """ , _lowerCAmelCase )
if "\n" in token:
__lowercase = token.replace("""\n""" , """ __newln__""" )
__lowercase = token.split(""" """ )
__lowercase = []
for token in tokens:
if not len(_lowerCAmelCase ):
continue
__lowercase = token.lower()
__lowercase = tuple(_lowerCAmelCase )
__lowercase = tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
__lowercase = get_pairs(_lowerCAmelCase )
if not pairs:
words.append(_lowerCAmelCase )
continue
while True:
__lowercase = min(_lowerCAmelCase , key=lambda _lowerCAmelCase : self.bpe_ranks.get(_lowerCAmelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(_lowerCAmelCase ):
try:
__lowercase = word.index(_lowerCAmelCase , _lowerCAmelCase )
new_word.extend(word[i:j] )
__lowercase = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(_lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(_lowerCAmelCase )
__lowercase = new_word
if len(_lowerCAmelCase ) == 1:
break
else:
__lowercase = get_pairs(_lowerCAmelCase )
__lowercase = """@@ """.join(_lowerCAmelCase )
__lowercase = word[:-4]
__lowercase = word
words.append(_lowerCAmelCase )
return " ".join(_lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
__lowercase = []
__lowercase = re.findall(r"""\S+\n?""" , _lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(_lowerCAmelCase ).split(""" """ ) ) )
return split_tokens
def _a ( self : Tuple , _lowerCAmelCase : str ) -> int:
"""simple docstring"""
__lowercase = token.lower()
return self.encoder.get(_lowerCAmelCase , self.encoder.get(self.unk_token ) )
def _a ( self : Tuple , _lowerCAmelCase : int ) -> str:
"""simple docstring"""
return self.decoder.get(_lowerCAmelCase , self.unk_token )
def _a ( self : Dict , _lowerCAmelCase : List[str] ) -> str:
"""simple docstring"""
__lowercase = """ """.join(_lowerCAmelCase ).replace("""@@ """ , """""" ).strip()
return out_string
def _a ( self : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + """\n""" )
__lowercase = 0
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
""" Please check that the tokenizer is not corrupted!""" )
__lowercase = token_index
writer.write(""" """.join(_lowerCAmelCase ) + """\n""" )
index += 1
return vocab_file, merge_file
| 80 | 1 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
__UpperCamelCase : int = logging.get_logger(__name__) # pylint: disable=invalid-name
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Union[str, Any] , _lowerCAmelCase : CLIPSegForImageSegmentation , _lowerCAmelCase : CLIPSegProcessor , _lowerCAmelCase : AutoencoderKL , _lowerCAmelCase : CLIPTextModel , _lowerCAmelCase : CLIPTokenizer , _lowerCAmelCase : UNetaDConditionModel , _lowerCAmelCase : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , _lowerCAmelCase : StableDiffusionSafetyChecker , _lowerCAmelCase : CLIPImageProcessor , ) -> Any:
"""simple docstring"""
super().__init__()
if hasattr(scheduler.config , """steps_offset""" ) and scheduler.config.steps_offset != 1:
__lowercase = (
F'The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`'
F' should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure '
"""to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
""" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
""" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
""" file"""
)
deprecate("""steps_offset!=1""" , """1.0.0""" , _lowerCAmelCase , standard_warn=_lowerCAmelCase )
__lowercase = dict(scheduler.config )
__lowercase = 1
__lowercase = FrozenDict(_lowerCAmelCase )
if hasattr(scheduler.config , """skip_prk_steps""" ) and scheduler.config.skip_prk_steps is False:
__lowercase = (
F'The configuration file of this scheduler: {scheduler} has not set the configuration'
""" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
""" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
""" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
""" Hub, it would be very nice if you could open a Pull request for the"""
""" `scheduler/scheduler_config.json` file"""
)
deprecate("""skip_prk_steps not set""" , """1.0.0""" , _lowerCAmelCase , standard_warn=_lowerCAmelCase )
__lowercase = dict(scheduler.config )
__lowercase = True
__lowercase = FrozenDict(_lowerCAmelCase )
if safety_checker is None:
logger.warning(
F'You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure'
""" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
""" results in services or applications open to the public. Both the diffusers team and Hugging Face"""
""" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
""" it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
""" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .""" )
self.register_modules(
segmentation_model=_lowerCAmelCase , segmentation_processor=_lowerCAmelCase , vae=_lowerCAmelCase , text_encoder=_lowerCAmelCase , tokenizer=_lowerCAmelCase , unet=_lowerCAmelCase , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , )
def _a ( self : Tuple , _lowerCAmelCase : Optional[Union[str, int]] = "auto" ) -> Any:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
__lowercase = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
self.enable_attention_slicing(_lowerCAmelCase )
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
__lowercase = torch.device("""cuda""" )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(_lowerCAmelCase , _lowerCAmelCase )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
if self.device != torch.device("""meta""" ) or 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()
def __call__( self : Union[str, Any] , _lowerCAmelCase : Union[str, List[str]] , _lowerCAmelCase : Union[torch.FloatTensor, PIL.Image.Image] , _lowerCAmelCase : str , _lowerCAmelCase : int = 512 , _lowerCAmelCase : int = 512 , _lowerCAmelCase : int = 50 , _lowerCAmelCase : float = 7.5 , _lowerCAmelCase : Optional[Union[str, List[str]]] = None , _lowerCAmelCase : Optional[int] = 1 , _lowerCAmelCase : float = 0.0 , _lowerCAmelCase : Optional[torch.Generator] = None , _lowerCAmelCase : Optional[torch.FloatTensor] = None , _lowerCAmelCase : Optional[str] = "pil" , _lowerCAmelCase : bool = True , _lowerCAmelCase : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , _lowerCAmelCase : int = 1 , **_lowerCAmelCase : Optional[int] , ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.segmentation_processor(
text=[text] , images=[image] , padding="""max_length""" , return_tensors="""pt""" ).to(self.device )
__lowercase = self.segmentation_model(**_lowerCAmelCase )
__lowercase = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
__lowercase = self.numpy_to_pil(_lowerCAmelCase )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
__lowercase = StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , mask_image=_lowerCAmelCase , height=_lowerCAmelCase , width=_lowerCAmelCase , num_inference_steps=_lowerCAmelCase , guidance_scale=_lowerCAmelCase , negative_prompt=_lowerCAmelCase , num_images_per_prompt=_lowerCAmelCase , eta=_lowerCAmelCase , generator=_lowerCAmelCase , latents=_lowerCAmelCase , output_type=_lowerCAmelCase , return_dict=_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=_lowerCAmelCase , )
| 80 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Union[str, Any] = 'lxmert'
__snake_case :Union[str, Any] = {}
def __init__( self : List[str] , _lowerCAmelCase : Dict=3_0522 , _lowerCAmelCase : List[str]=768 , _lowerCAmelCase : Union[str, Any]=12 , _lowerCAmelCase : Union[str, Any]=9500 , _lowerCAmelCase : Union[str, Any]=1600 , _lowerCAmelCase : Optional[Any]=400 , _lowerCAmelCase : Tuple=3072 , _lowerCAmelCase : List[Any]="gelu" , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Tuple=512 , _lowerCAmelCase : Tuple=2 , _lowerCAmelCase : Optional[Any]=0.02 , _lowerCAmelCase : List[str]=1e-12 , _lowerCAmelCase : Any=9 , _lowerCAmelCase : Optional[Any]=5 , _lowerCAmelCase : Any=5 , _lowerCAmelCase : Dict=2048 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[Any]=6.67 , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , _lowerCAmelCase : str=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , **_lowerCAmelCase : Tuple , ) -> Dict:
"""simple docstring"""
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = num_qa_labels
__lowercase = num_object_labels
__lowercase = num_attr_labels
__lowercase = l_layers
__lowercase = x_layers
__lowercase = r_layers
__lowercase = visual_feat_dim
__lowercase = visual_pos_dim
__lowercase = visual_loss_normalizer
__lowercase = task_matched
__lowercase = task_mask_lm
__lowercase = task_obj_predict
__lowercase = task_qa
__lowercase = visual_obj_loss
__lowercase = visual_attr_loss
__lowercase = visual_feat_loss
__lowercase = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**_lowerCAmelCase )
| 80 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from ...utils.dataclasses import (
ComputeEnvironment,
DistributedType,
DynamoBackend,
PrecisionType,
SageMakerDistributedType,
)
from ..menu import BulletMenu
__UpperCamelCase : Dict = [
"""EAGER""",
"""AOT_EAGER""",
"""INDUCTOR""",
"""NVFUSER""",
"""AOT_NVFUSER""",
"""AOT_CUDAGRAPHS""",
"""OFI""",
"""FX2TRT""",
"""ONNXRT""",
"""IPEX""",
]
def snake_case ( lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None ):
'''simple docstring'''
__lowercase = True
while ask_again:
__lowercase = input(lowerCamelCase )
try:
if default is not None and len(lowerCamelCase ) == 0:
return default
return convert_value(lowerCamelCase ) if convert_value is not None else result
except Exception:
if error_message is not None:
print(lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase=[] , lowerCamelCase=None , lowerCamelCase=0 ):
'''simple docstring'''
__lowercase = BulletMenu(lowerCamelCase , lowerCamelCase )
__lowercase = menu.run(default_choice=lowerCamelCase )
return convert_value(lowerCamelCase ) if convert_value is not None else result
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = int(lowerCamelCase )
return ComputeEnvironment(["""LOCAL_MACHINE""", """AMAZON_SAGEMAKER"""][value] )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = int(lowerCamelCase )
return DistributedType(["""NO""", """MULTI_CPU""", """MULTI_XPU""", """MULTI_GPU""", """MULTI_NPU""", """TPU"""][value] )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = int(lowerCamelCase )
return DynamoBackend(DYNAMO_BACKENDS[value] ).value
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = int(lowerCamelCase )
return PrecisionType(["""no""", """fp16""", """bf16""", """fp8"""][value] )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = int(lowerCamelCase )
return SageMakerDistributedType(["""NO""", """DATA_PARALLEL""", """MODEL_PARALLEL"""][value] )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return {"yes": True, "no": False}[value.lower()]
class __UpperCamelCase ( argparse.RawDescriptionHelpFormatter ):
def _a ( self : int , _lowerCAmelCase : str , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
__lowercase = super()._format_usage(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = usage.replace("""<command> [<args>] """ , """""" )
return usage
| 80 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : int , _lowerCAmelCase : str , _lowerCAmelCase : List[str]=13 , _lowerCAmelCase : List[str]=7 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : Union[str, Any]=False , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=99 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : Optional[int]=5 , _lowerCAmelCase : Any=4 , _lowerCAmelCase : Tuple=37 , _lowerCAmelCase : str="gelu" , _lowerCAmelCase : List[str]=0.1 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : Union[str, Any]=512 , _lowerCAmelCase : Dict=16 , _lowerCAmelCase : List[Any]=2 , _lowerCAmelCase : Optional[int]=0.02 , _lowerCAmelCase : int=3 , _lowerCAmelCase : Dict=4 , _lowerCAmelCase : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_input_mask
__lowercase = use_token_type_ids
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = num_labels
__lowercase = num_choices
__lowercase = scope
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = None
if self.use_input_mask:
__lowercase = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase = None
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowercase = ids_tensor([self.batch_size] , self.num_choices )
__lowercase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _a ( self : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = DistilBertModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> List[str]:
"""simple docstring"""
__lowercase = DistilBertForMaskedLM(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertForQuestionAnswering(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForSequenceClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForTokenClassification(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ) -> str:
"""simple docstring"""
__lowercase = self.num_choices
__lowercase = DistilBertForMultipleChoice(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = model(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
((__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase)) = config_and_inputs
__lowercase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[Any] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
__snake_case :Dict = (
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__snake_case :Tuple = True
__snake_case :Tuple = True
__snake_case :List[str] = True
__snake_case :Optional[int] = True
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = DistilBertModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , dim=37 )
def _a ( self : Dict ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*_lowerCAmelCase )
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*_lowerCAmelCase )
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*_lowerCAmelCase )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*_lowerCAmelCase )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*_lowerCAmelCase )
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*_lowerCAmelCase )
@slow
def _a ( self : int ) -> Optional[Any]:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = DistilBertModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@slow
@require_torch_gpu
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
__lowercase = True
__lowercase = model_class(config=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = torch.jit.trace(
_lowerCAmelCase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , """traced_model.pt""" ) )
__lowercase = torch.jit.load(os.path.join(_lowerCAmelCase , """traced_model.pt""" ) , map_location=_lowerCAmelCase )
loaded(inputs_dict["""input_ids"""].to(_lowerCAmelCase ) , inputs_dict["""attention_mask"""].to(_lowerCAmelCase ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertModel.from_pretrained("""distilbert-base-uncased""" )
__lowercase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__lowercase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )[0]
__lowercase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _lowerCAmelCase )
__lowercase = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowerCAmelCase , atol=1e-4 ) )
| 80 | 1 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __UpperCamelCase :
__snake_case :float
__snake_case :TreeNode | None = None
__snake_case :TreeNode | None = None
def snake_case ( lowerCamelCase ):
'''simple docstring'''
def is_valid_tree(lowerCamelCase ) -> bool:
if node is None:
return True
if not isinstance(lowerCamelCase , lowerCamelCase ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(lowerCamelCase ):
raise ValueError(
"""Each node should be type of TreeNode and data should be float.""" )
def is_binary_search_tree_recursive_check(
lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , lowerCamelCase , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , lowerCamelCase )
)
return is_binary_search_tree_recursive_check(lowerCamelCase , -float("""inf""" ) , float("""inf""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCamelCase ( _lowerCAmelCase ):
# to overwrite at feature extractactor specific tests
__snake_case :Optional[int] = None
__snake_case :Dict = None
@property
def _a ( self : str ) -> List[str]:
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_lowerCAmelCase , """feature_size""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """sampling_rate""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """padding_value""" ) )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_lowerCAmelCase ) == len(_lowerCAmelCase ) for x, y in zip(_lowerCAmelCase , processed_features[input_name] ) ) )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _a ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _a ( self : str , _lowerCAmelCase : List[Any]=False ) -> int:
"""simple docstring"""
def _inputs_have_equal_length(_lowerCAmelCase : int ):
__lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCAmelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCAmelCase : Dict , _lowerCAmelCase : Tuple ):
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCAmelCase , _lowerCAmelCase ):
if not np.allclose(np.asarray(_lowerCAmelCase ) , np.asarray(_lowerCAmelCase ) , atol=1e-3 ):
return False
return True
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = self.feat_extract_tester.seq_length_diff
__lowercase = self.feat_extract_tester.max_seq_length + pad_diff
__lowercase = self.feat_extract_tester.min_seq_length
__lowercase = self.feat_extract_tester.batch_size
__lowercase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
__lowercase = feat_extract.pad(_lowerCAmelCase , padding=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )
__lowercase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""max_length""" )[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=_lowerCAmelCase , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
__lowercase = feat_extract.pad(_lowerCAmelCase , pad_to_multiple_of=10 )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , pad_to_multiple_of=10 )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = input_a[input_name]
self.assertTrue(all(len(_lowerCAmelCase ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_lowerCAmelCase ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
__lowercase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _a ( self : Tuple , _lowerCAmelCase : str=False ) -> Union[str, Any]:
"""simple docstring"""
def _inputs_have_equal_length(_lowerCAmelCase : Tuple ):
__lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCAmelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCAmelCase : Any , _lowerCAmelCase : str ):
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCAmelCase , _lowerCAmelCase ):
if not np.allclose(np.asarray(_lowerCAmelCase ) , np.asarray(_lowerCAmelCase ) , atol=1e-3 ):
return False
return True
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
__lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
# truncate to smallest with np
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=_lowerCAmelCase , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
# truncate to middle
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , truncation=_lowerCAmelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""longest""" , truncation=_lowerCAmelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""longest""" , truncation=_lowerCAmelCase )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , truncation=_lowerCAmelCase )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
__lowercase = 12
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCAmelCase , truncation=_lowerCAmelCase , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCAmelCase , )
__lowercase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
__lowercase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
__lowercase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self._check_padding(numpify=_lowerCAmelCase )
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
self._check_padding(numpify=_lowerCAmelCase )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
self._check_truncation(numpify=_lowerCAmelCase )
def _a ( self : str ) -> str:
"""simple docstring"""
self._check_truncation(numpify=_lowerCAmelCase )
@require_torch
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _a ( self : Any ) -> Any:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**_lowerCAmelCase )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = [len(_lowerCAmelCase ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _lowerCAmelCase )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _lowerCAmelCase )
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**_lowerCAmelCase )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = [len(_lowerCAmelCase ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = min(_lowerCAmelCase )
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=_lowerCAmelCase , truncation=_lowerCAmelCase , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _lowerCAmelCase )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 80 | 1 |
import unittest
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
if is_torch_available():
import torch
from transformers import AutoModelForImageClassification
if is_vision_available():
from transformers import AutoImageProcessor
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : Any ) -> Tuple:
"""simple docstring"""
__lowercase = AutoImageProcessor.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""" )
__lowercase = AutoModelForImageClassification.from_pretrained("""microsoft/dit-base-finetuned-rvlcdip""" )
model.to(_lowerCAmelCase )
from datasets import load_dataset
__lowercase = load_dataset("""nielsr/rvlcdip-demo""" )
__lowercase = dataset["""train"""][0]["""image"""].convert("""RGB""" )
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
__lowercase = outputs.logits
__lowercase = torch.Size((1, 16) )
self.assertEqual(logits.shape , _lowerCAmelCase )
__lowercase = torch.tensor(
[-0.4_158, -0.4_092, -0.4_347] , device=_lowerCAmelCase , dtype=torch.float , )
self.assertTrue(torch.allclose(logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
| 80 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lowerCamelCase ) <= key:
return input_string
for position, character in enumerate(lowerCamelCase ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(lowerCamelCase )
__lowercase = ["""""".join(lowerCamelCase ) for row in temp_grid]
__lowercase = """""".join(lowerCamelCase )
return output_string
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = []
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1:
return input_string
__lowercase = [[] for _ in range(lowerCamelCase )] # generates template
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("""*""" )
__lowercase = 0
for row in temp_grid: # fills in the characters
__lowercase = input_string[counter : counter + len(lowerCamelCase )]
grid.append(list(lowerCamelCase ) )
counter += len(lowerCamelCase )
__lowercase = """""" # reads as zigzag
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = {}
for key_guess in range(1 , len(lowerCamelCase ) ): # tries every key
__lowercase = decrypt(lowerCamelCase , lowerCamelCase )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = DPTConfig()
if "large" in checkpoint_url:
__lowercase = 1_024
__lowercase = 4_096
__lowercase = 24
__lowercase = 16
__lowercase = [5, 11, 17, 23]
__lowercase = [256, 512, 1_024, 1_024]
__lowercase = (1, 384, 384)
if "ade" in checkpoint_url:
__lowercase = True
__lowercase = 150
__lowercase = """huggingface/label-files"""
__lowercase = """ade20k-id2label.json"""
__lowercase = json.load(open(cached_download(hf_hub_url(lowerCamelCase , lowerCamelCase , repo_type="""dataset""" ) ) , """r""" ) )
__lowercase = {int(lowerCamelCase ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
__lowercase = [1, 150, 480, 480]
return config, expected_shape
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(lowerCamelCase , lowerCamelCase )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
__lowercase = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
__lowercase = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
__lowercase = name.replace("""patch_embed""" , """patch_embeddings""" )
if "pos_embed" in name:
__lowercase = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
__lowercase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
__lowercase = name.replace("""proj""" , """projection""" )
if "blocks" in name:
__lowercase = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
__lowercase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__lowercase = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name:
__lowercase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__lowercase = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
__lowercase = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
__lowercase = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
__lowercase = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
__lowercase = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
__lowercase = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
__lowercase = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
__lowercase = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
__lowercase = name.replace(F'refinenet{layer_idx}' , F'fusion_stage.layers.{abs(layer_idx-4 )}' )
if "out_conv" in name:
__lowercase = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
__lowercase = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
__lowercase = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
__lowercase = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
__lowercase = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
__lowercase = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
__lowercase = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
__lowercase = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
__lowercase = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
__lowercase = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
__lowercase = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
__lowercase = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
__lowercase = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
__lowercase = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
__lowercase = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
__lowercase = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
__lowercase = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
__lowercase = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
__lowercase = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
__lowercase = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
__lowercase = name.replace("""auxlayer""" , """auxiliary_head.head""" )
return name
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__lowercase = state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.weight' )
__lowercase = state_dict.pop(F'dpt.encoder.layer.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
__lowercase = in_proj_weight[: config.hidden_size, :]
__lowercase = in_proj_bias[: config.hidden_size]
__lowercase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__lowercase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__lowercase = in_proj_weight[
-config.hidden_size :, :
]
__lowercase = in_proj_bias[-config.hidden_size :]
def snake_case ( ):
'''simple docstring'''
__lowercase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowercase = Image.open(requests.get(lowerCamelCase , stream=lowerCamelCase ).raw )
return im
@torch.no_grad()
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase , __lowercase = get_dpt_config(lowerCamelCase )
# load original state_dict from URL
__lowercase = torch.hub.load_state_dict_from_url(lowerCamelCase , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(lowerCamelCase )
# rename keys
for key in state_dict.copy().keys():
__lowercase = state_dict.pop(lowerCamelCase )
__lowercase = val
# read in qkv matrices
read_in_q_k_v(lowerCamelCase , lowerCamelCase )
# load HuggingFace model
__lowercase = DPTForSemanticSegmentation(lowerCamelCase ) if """ade""" in checkpoint_url else DPTForDepthEstimation(lowerCamelCase )
model.load_state_dict(lowerCamelCase )
model.eval()
# Check outputs on an image
__lowercase = 480 if """ade""" in checkpoint_url else 384
__lowercase = DPTImageProcessor(size=lowerCamelCase )
__lowercase = prepare_img()
__lowercase = image_processor(lowerCamelCase , return_tensors="""pt""" )
# forward pass
__lowercase = model(**lowerCamelCase ).logits if """ade""" in checkpoint_url else model(**lowerCamelCase ).predicted_depth
# Assert logits
__lowercase = torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
if "ade" in checkpoint_url:
__lowercase = torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
assert outputs.shape == torch.Size(lowerCamelCase )
assert (
torch.allclose(outputs[0, 0, :3, :3] , lowerCamelCase , atol=1e-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , lowerCamelCase )
)
Path(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
print(F'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(lowerCamelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowerCamelCase )
if push_to_hub:
print("""Pushing model to hub...""" )
model.push_to_hub(
repo_path_or_name=Path(lowerCamelCase , lowerCamelCase ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=lowerCamelCase , )
image_processor.push_to_hub(
repo_path_or_name=Path(lowerCamelCase , lowerCamelCase ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=lowerCamelCase , )
if __name__ == "__main__":
__UpperCamelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt""",
type=str,
help="""URL of the original DPT checkpoint you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=str,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
)
parser.add_argument(
"""--model_name""",
default="""dpt-large""",
type=str,
help="""Name of the model, in case you're pushing to the hub.""",
)
__UpperCamelCase : Tuple = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 80 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , lowerCamelCase ):
__lowercase = 1
__lowercase = 0
for i in range(lowerCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Dict = logging.get_logger(__name__)
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_case :Optional[Any] = 'maskformer-swin'
__snake_case :Dict = {
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Optional[Any] , _lowerCAmelCase : Optional[Any]=224 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Any=3 , _lowerCAmelCase : List[Any]=96 , _lowerCAmelCase : List[str]=[2, 2, 6, 2] , _lowerCAmelCase : List[str]=[3, 6, 12, 24] , _lowerCAmelCase : Any=7 , _lowerCAmelCase : Optional[int]=4.0 , _lowerCAmelCase : str=True , _lowerCAmelCase : Optional[Any]=0.0 , _lowerCAmelCase : List[str]=0.0 , _lowerCAmelCase : Optional[int]=0.1 , _lowerCAmelCase : Optional[Any]="gelu" , _lowerCAmelCase : str=False , _lowerCAmelCase : Union[str, Any]=0.02 , _lowerCAmelCase : str=1e-5 , _lowerCAmelCase : Union[str, Any]=None , _lowerCAmelCase : Optional[int]=None , **_lowerCAmelCase : List[str] , ) -> Tuple:
"""simple docstring"""
super().__init__(**_lowerCAmelCase )
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = len(_lowerCAmelCase )
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = layer_norm_eps
__lowercase = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__lowercase = int(embed_dim * 2 ** (len(_lowerCAmelCase ) - 1) )
__lowercase = ["""stem"""] + [F'stage{idx}' for idx in range(1 , len(_lowerCAmelCase ) + 1 )]
__lowercase , __lowercase = get_aligned_output_features_output_indices(
out_features=_lowerCAmelCase , out_indices=_lowerCAmelCase , stage_names=self.stage_names )
| 80 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class __UpperCamelCase :
def __init__( self : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int]=13 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : Optional[Any]=2 , _lowerCAmelCase : Union[str, Any]=3 , _lowerCAmelCase : List[str]=16 , _lowerCAmelCase : List[str]=[1, 2, 1] , _lowerCAmelCase : Dict=[2, 2, 4] , _lowerCAmelCase : Optional[int]=2 , _lowerCAmelCase : Optional[Any]=2.0 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Optional[int]=0.0 , _lowerCAmelCase : Any=0.0 , _lowerCAmelCase : Optional[int]=0.1 , _lowerCAmelCase : Tuple="gelu" , _lowerCAmelCase : int=False , _lowerCAmelCase : Dict=True , _lowerCAmelCase : List[str]=0.02 , _lowerCAmelCase : Union[str, Any]=1e-5 , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : List[Any]=10 , _lowerCAmelCase : Tuple=8 , _lowerCAmelCase : List[Any]=["stage1", "stage2", "stage3"] , _lowerCAmelCase : Union[str, Any]=[1, 2, 3] , ) -> int:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = patch_norm
__lowercase = layer_norm_eps
__lowercase = initializer_range
__lowercase = is_training
__lowercase = scope
__lowercase = use_labels
__lowercase = type_sequence_label_size
__lowercase = encoder_stride
__lowercase = out_features
__lowercase = out_indices
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def _a ( self : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : str , _lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
__lowercase = MaskFormerSwinModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
__lowercase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__lowercase = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def _a ( self : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = MaskFormerSwinBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_lowerCAmelCase ):
__lowercase = ["""stem"""]
__lowercase = MaskFormerSwinBackbone(config=_lowerCAmelCase )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
__snake_case :Optional[int] = {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
__snake_case :Optional[int] = False
__snake_case :Any = False
__snake_case :List[str] = False
__snake_case :Tuple = False
__snake_case :Optional[int] = False
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__lowercase = MaskFormerSwinModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"""`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
""" `nn.DataParallel`"""
) )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
pass
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
return
def _a ( self : Any ) -> Tuple:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCAmelCase )
@unittest.skip("""Swin does not use inputs_embeds""" )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip("""Swin does not support feedforward chunking""" )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
pass
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions""" )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone""" )
def _a ( self : Any ) -> Any:
"""simple docstring"""
pass
def _a ( self : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.hidden_states
__lowercase = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
# Swin has a different seq_length
__lowercase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = 3
__lowercase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__lowercase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__lowercase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , (padded_height, padded_width) )
@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints""" )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a ( self : Any ) -> str:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
pass
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(_lowerCAmelCase : Optional[int] ):
__lowercase = 0
return t
def check_equivalence(_lowerCAmelCase : int , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int]={} ):
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase , return_dict=_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = model(**_lowerCAmelCase , return_dict=_lowerCAmelCase , **_lowerCAmelCase ).to_tuple()
def recursive_check(_lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ):
if isinstance(_lowerCAmelCase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_lowerCAmelCase , _lowerCAmelCase ):
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_lowerCAmelCase ) , set_nan_tensor_to_zero(_lowerCAmelCase ) , atol=1e-5 ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
F' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'
F' {torch.isnan(_lowerCAmelCase ).any()} and `inf`: {torch.isinf(_lowerCAmelCase )}. Dict has'
F' `nan`: {torch.isnan(_lowerCAmelCase ).any()} and `inf`: {torch.isinf(_lowerCAmelCase )}.'
) , )
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {"""output_hidden_states""": True} )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {"""output_hidden_states""": True} )
@require_torch
class __UpperCamelCase ( unittest.TestCase , _lowerCAmelCase ):
__snake_case :Optional[Any] = (MaskFormerSwinBackbone,) if is_torch_available() else ()
__snake_case :Dict = MaskFormerSwinConfig
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase = MaskFormerSwinModelTester(self )
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = inputs_dict["""pixel_values"""].shape[0]
for backbone_class in self.all_model_classes:
__lowercase = backbone_class(_lowerCAmelCase )
backbone.to(_lowerCAmelCase )
backbone.eval()
__lowercase = backbone(**_lowerCAmelCase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _lowerCAmelCase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
__lowercase = backbone(**_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
__lowercase , __lowercase , __lowercase = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
__lowercase = backbone(**_lowerCAmelCase , output_attentions=_lowerCAmelCase )
self.assertIsNotNone(outputs.attentions )
| 80 | 1 |
import unittest
import numpy as np
from transformers import MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING, TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
from transformers.pipelines import AudioClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_torchaudio,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class __UpperCamelCase ( unittest.TestCase ):
__snake_case :Any = MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
__snake_case :Tuple = TF_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING
def _a ( self : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Tuple , _lowerCAmelCase : int ) -> Any:
"""simple docstring"""
__lowercase = AudioClassificationPipeline(model=_lowerCAmelCase , feature_extractor=_lowerCAmelCase )
# test with a raw waveform
__lowercase = np.zeros((3_4000,) )
__lowercase = np.zeros((1_4000,) )
return audio_classifier, [audioa, audio]
def _a ( self : str , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = examples
__lowercase = audio_classifier(_lowerCAmelCase )
# by default a model is initialized with num_labels=2
self.assertEqual(
_lowerCAmelCase , [
{"""score""": ANY(_lowerCAmelCase ), """label""": ANY(_lowerCAmelCase )},
{"""score""": ANY(_lowerCAmelCase ), """label""": ANY(_lowerCAmelCase )},
] , )
__lowercase = audio_classifier(_lowerCAmelCase , top_k=1 )
self.assertEqual(
_lowerCAmelCase , [
{"""score""": ANY(_lowerCAmelCase ), """label""": ANY(_lowerCAmelCase )},
] , )
self.run_torchaudio(_lowerCAmelCase )
@require_torchaudio
def _a ( self : Union[str, Any] , _lowerCAmelCase : List[str] ) -> Optional[Any]:
"""simple docstring"""
import datasets
# test with a local file
__lowercase = datasets.load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
__lowercase = dataset[0]["""audio"""]["""array"""]
__lowercase = audio_classifier(_lowerCAmelCase )
self.assertEqual(
_lowerCAmelCase , [
{"""score""": ANY(_lowerCAmelCase ), """label""": ANY(_lowerCAmelCase )},
{"""score""": ANY(_lowerCAmelCase ), """label""": ANY(_lowerCAmelCase )},
] , )
@require_torch
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = """anton-l/wav2vec2-random-tiny-classifier"""
__lowercase = pipeline("""audio-classification""" , model=_lowerCAmelCase )
__lowercase = np.ones((8000,) )
__lowercase = audio_classifier(_lowerCAmelCase , top_k=4 )
__lowercase = [
{"""score""": 0.0_842, """label""": """no"""},
{"""score""": 0.0_838, """label""": """up"""},
{"""score""": 0.0_837, """label""": """go"""},
{"""score""": 0.0_834, """label""": """right"""},
]
__lowercase = [
{"""score""": 0.0_845, """label""": """stop"""},
{"""score""": 0.0_844, """label""": """on"""},
{"""score""": 0.0_841, """label""": """right"""},
{"""score""": 0.0_834, """label""": """left"""},
]
self.assertIn(nested_simplify(_lowerCAmelCase , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
__lowercase = {"""array""": np.ones((8000,) ), """sampling_rate""": audio_classifier.feature_extractor.sampling_rate}
__lowercase = audio_classifier(_lowerCAmelCase , top_k=4 )
self.assertIn(nested_simplify(_lowerCAmelCase , decimals=4 ) , [EXPECTED_OUTPUT, EXPECTED_OUTPUT_PT_2] )
@require_torch
@slow
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
import datasets
__lowercase = """superb/wav2vec2-base-superb-ks"""
__lowercase = pipeline("""audio-classification""" , model=_lowerCAmelCase )
__lowercase = datasets.load_dataset("""anton-l/superb_dummy""" , """ks""" , split="""test""" )
__lowercase = np.array(dataset[3]["""speech"""] , dtype=np.floataa )
__lowercase = audio_classifier(_lowerCAmelCase , top_k=4 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=3 ) , [
{"""score""": 0.981, """label""": """go"""},
{"""score""": 0.007, """label""": """up"""},
{"""score""": 0.006, """label""": """_unknown_"""},
{"""score""": 0.001, """label""": """down"""},
] , )
@require_tf
@unittest.skip("""Audio classification is not implemented for TF""" )
def _a ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
pass
| 80 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = torch.nn.Linear(10 , 10 )
__lowercase = torch.optim.SGD(model.parameters() , 0.1 )
__lowercase = Accelerator()
__lowercase = accelerator.prepare(_lowerCAmelCase )
try:
pickle.loads(pickle.dumps(_lowerCAmelCase ) )
except Exception as e:
self.fail(F'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 80 | 1 |
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = 0 , lowerCamelCase = 0 ):
'''simple docstring'''
__lowercase = right or len(lowerCamelCase ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(lowerCamelCase , lowerCamelCase , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
__UpperCamelCase : Dict = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
__UpperCamelCase : int = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
__UpperCamelCase : List[str] = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
__UpperCamelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 | 1 |
def snake_case ( lowerCamelCase = 10 , lowerCamelCase = 1_000 , lowerCamelCase = True ):
'''simple docstring'''
assert (
isinstance(lowerCamelCase , lowerCamelCase )
and isinstance(lowerCamelCase , lowerCamelCase )
and isinstance(lowerCamelCase , lowerCamelCase )
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError("""Invalid value for min_val or max_val (min_value < max_value)""" )
return min_val if option else max_val
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return int((number_a + number_a) / 2 )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
assert (
isinstance(lowerCamelCase , lowerCamelCase ) and isinstance(lowerCamelCase , lowerCamelCase ) and isinstance(lowerCamelCase , lowerCamelCase )
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError("""argument value for lower and higher must be(lower > higher)""" )
if not lower < to_guess < higher:
raise ValueError(
"""guess value must be within the range of lower and higher value""" )
def answer(lowerCamelCase ) -> str:
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print("""started...""" )
__lowercase = lower
__lowercase = higher
__lowercase = []
while True:
__lowercase = get_avg(lowerCamelCase , lowerCamelCase )
last_numbers.append(lowerCamelCase )
if answer(lowerCamelCase ) == "low":
__lowercase = number
elif answer(lowerCamelCase ) == "high":
__lowercase = number
else:
break
print(F'guess the number : {last_numbers[-1]}' )
print(F'details : {last_numbers!s}' )
def snake_case ( ):
'''simple docstring'''
__lowercase = int(input("""Enter lower value : """ ).strip() )
__lowercase = int(input("""Enter high value : """ ).strip() )
__lowercase = int(input("""Enter value to guess : """ ).strip() )
guess_the_number(lowerCamelCase , lowerCamelCase , lowerCamelCase )
if __name__ == "__main__":
main()
| 80 |
import os
from collections.abc import Iterator
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCamelCase ):
__lowercase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(lowerCamelCase )[1] in (".py", ".ipynb"):
yield os.path.join(lowerCamelCase , lowerCamelCase ).lstrip("""./""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return F'{i * " "}*' if i else "\n##"
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(lowerCamelCase ) or old_parts[i] != new_part) and new_part:
print(F'{md_prefix(lowerCamelCase )} {new_part.replace("_" , " " ).title()}' )
return new_path
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
__lowercase = """"""
for filepath in sorted(good_file_paths(lowerCamelCase ) ):
__lowercase , __lowercase = os.path.split(lowerCamelCase )
if filepath != old_path:
__lowercase = print_path(lowerCamelCase , lowerCamelCase )
__lowercase = (filepath.count(os.sep ) + 1) if filepath else 0
__lowercase = F'{filepath}/{filename}'.replace(""" """ , """%20""" )
__lowercase = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'{md_prefix(lowerCamelCase )} [{filename}]({url})' )
if __name__ == "__main__":
print_directory_md(""".""")
| 80 | 1 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
__UpperCamelCase : List[str] = 0B10_11_00_11_11_10_11_00_10_01_00_00_01_11_10_11_10_11_00_01_10_01_11_10
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
__UpperCamelCase : List[Any] = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class __UpperCamelCase :
def __init__( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = WATERMARK_BITS
__lowercase = WatermarkEncoder()
self.encoder.set_watermark("""bits""" , self.watermark )
def _a ( self : List[Any] , _lowerCAmelCase : torch.FloatTensor ) -> Union[str, Any]:
"""simple docstring"""
if images.shape[-1] < 256:
return images
__lowercase = (255 * (images / 2 + 0.5)).cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__lowercase = [self.encoder.encode(_lowerCAmelCase , """dwtDct""" ) for image in images]
__lowercase = torch.from_numpy(np.array(_lowerCAmelCase ) ).permute(0 , 3 , 1 , 2 )
__lowercase = torch.clamp(2 * (images / 255 - 0.5) , min=-1.0 , max=1.0 )
return images
| 80 |
from math import factorial
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(lowerCamelCase ) // (factorial(lowerCamelCase ) * factorial(n - k ))
if __name__ == "__main__":
print(
"""The number of five-card hands possible from a standard""",
F'''fifty-two card deck is: {combinations(52, 5)}\n''',
)
print(
"""If a class of 40 students must be arranged into groups of""",
F'''4 for group projects, there are {combinations(40, 4)} ways''',
"""to arrange them.\n""",
)
print(
"""If 10 teams are competing in a Formula One race, there""",
F'''are {combinations(10, 3)} ways that first, second and''',
"""third place can be awarded.""",
)
| 80 | 1 |
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
__UpperCamelCase : Dict = [
"""cross_validation.py""",
"""gradient_accumulation.py""",
"""local_sgd.py""",
"""multi_process_metrics.py""",
"""memory.py""",
"""automatic_gradient_accumulation.py""",
"""fsdp_with_peak_mem_tracking.py""",
"""deepspeed_with_config_support.py""",
"""megatron_lm_gpt_pretraining.py""",
]
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Dict , _lowerCAmelCase : str , _lowerCAmelCase : bool , _lowerCAmelCase : str = None , _lowerCAmelCase : list = None ) -> int:
"""simple docstring"""
__lowercase = None
__lowercase = os.path.abspath(os.path.join("""examples""" , """by_feature""" ) )
__lowercase = os.path.abspath("""examples""" )
for item in os.listdir(_lowerCAmelCase ):
if item not in EXCLUDE_EXAMPLES:
__lowercase = os.path.join(_lowerCAmelCase , _lowerCAmelCase )
if os.path.isfile(_lowerCAmelCase ) and ".py" in item_path:
with self.subTest(
tested_script=_lowerCAmelCase , feature_script=_lowerCAmelCase , tested_section="""main()""" if parser_only else """training_function()""" , ):
__lowercase = compare_against_test(
os.path.join(_lowerCAmelCase , _lowerCAmelCase ) , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = """\n""".join(_lowerCAmelCase )
if special_strings is not None:
for string in special_strings:
__lowercase = diff.replace(_lowerCAmelCase , """""" )
self.assertEqual(_lowerCAmelCase , """""" )
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
self.one_complete_example("""complete_nlp_example.py""" , _lowerCAmelCase )
self.one_complete_example("""complete_nlp_example.py""" , _lowerCAmelCase )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase = os.path.abspath(os.path.join("""examples""" , """cv_example.py""" ) )
__lowercase = [
""" """ * 16 + """{\n\n""",
""" """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
""" """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
""" """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
""" """ * 20 + """\"epoch\": epoch,\n\n""",
""" """ * 16 + """},\n\n""",
""" """ * 16 + """step=epoch,\n""",
""" """ * 12,
""" """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
]
self.one_complete_example("""complete_cv_example.py""" , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
self.one_complete_example("""complete_cv_example.py""" , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
@mock.patch.dict(os.environ , {'TESTING_MOCKED_DATALOADERS': '1'} )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = False
@classmethod
def _a ( cls : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
super().setUpClass()
__lowercase = tempfile.mkdtemp()
__lowercase = os.path.join(cls._tmpdir , """default_config.yml""" )
write_basic_config(save_location=cls.configPath )
__lowercase = ["""accelerate""", """launch""", """--config_file""", cls.configPath]
@classmethod
def _a ( cls : Dict ) -> Tuple:
"""simple docstring"""
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = F'\n examples/by_feature/checkpointing.py\n --checkpointing_steps epoch\n --output_dir {self.tmpdir}\n '.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , """epoch_0""" ) ) )
def _a ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = F'\n examples/by_feature/checkpointing.py\n --checkpointing_steps 1\n --output_dir {self.tmpdir}\n '.split()
__lowercase = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , """step_2""" ) ) )
def _a ( self : int ) -> Dict:
"""simple docstring"""
__lowercase = F'\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , "epoch_0" )}\n '.split()
__lowercase = run_command(self._launch_args + testargs , return_stdout=_lowerCAmelCase )
self.assertNotIn("""epoch 0:""" , _lowerCAmelCase )
self.assertIn("""epoch 1:""" , _lowerCAmelCase )
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = F'\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , "step_2" )}\n '.split()
__lowercase = run_command(self._launch_args + testargs , return_stdout=_lowerCAmelCase )
if torch.cuda.is_available():
__lowercase = torch.cuda.device_count()
else:
__lowercase = 1
if num_processes > 1:
self.assertNotIn("""epoch 0:""" , _lowerCAmelCase )
self.assertIn("""epoch 1:""" , _lowerCAmelCase )
else:
self.assertIn("""epoch 0:""" , _lowerCAmelCase )
self.assertIn("""epoch 1:""" , _lowerCAmelCase )
@slow
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
__lowercase = """
examples/by_feature/cross_validation.py
--num_folds 2
""".split()
with mock.patch.dict(os.environ , {"""TESTING_MOCKED_DATALOADERS""": """0"""} ):
__lowercase = run_command(self._launch_args + testargs , return_stdout=_lowerCAmelCase )
__lowercase = re.findall("""({.+})""" , _lowerCAmelCase )
__lowercase = [r for r in results if """accuracy""" in r][-1]
__lowercase = ast.literal_eval(_lowerCAmelCase )
self.assertGreaterEqual(results["""accuracy"""] , 0.75 )
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
__lowercase = ["""examples/by_feature/multi_process_metrics.py"""]
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {"""WANDB_MODE""": """offline"""} )
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmpdir:
__lowercase = F'\n examples/by_feature/tracking.py\n --with_tracking\n --project_dir {tmpdir}\n '.split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_lowerCAmelCase , """tracking""" ) ) )
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = ["""examples/by_feature/gradient_accumulation.py"""]
run_command(self._launch_args + testargs )
def _a ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = ["""examples/by_feature/local_sgd.py"""]
run_command(self._launch_args + testargs )
| 80 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case ( ):
'''simple docstring'''
__lowercase = [randint(-1_000 , 1_000 ) for i in range(10 )]
__lowercase = randint(-5_000 , 5_000 )
return (arr, r)
__UpperCamelCase : Any = make_dataset()
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
for triplet in permutations(lowerCamelCase , 3 ):
if sum(lowerCamelCase ) == target:
return tuple(sorted(lowerCamelCase ) )
return (0, 0, 0)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
arr.sort()
__lowercase = len(lowerCamelCase )
for i in range(n - 1 ):
__lowercase , __lowercase = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def snake_case ( ):
'''simple docstring'''
__lowercase = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
__lowercase = """
triplet_sum1(*dataset)
"""
__lowercase = """
triplet_sum2(*dataset)
"""
__lowercase = repeat(setup=lowerCamelCase , stmt=lowerCamelCase , repeat=5 , number=10_000 )
__lowercase = repeat(setup=lowerCamelCase , stmt=lowerCamelCase , repeat=5 , number=10_000 )
return (min(lowerCamelCase ), min(lowerCamelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
__UpperCamelCase : Tuple = solution_times()
print(F'''The time for naive implementation is {times[0]}.''')
print(F'''The time for optimized implementation is {times[1]}.''')
| 80 | 1 |
def snake_case ( lowerCamelCase = 1_000 ):
'''simple docstring'''
__lowercase = 2**power
__lowercase = str(lowerCamelCase )
__lowercase = list(lowerCamelCase )
__lowercase = 0
for i in list_num:
sum_of_num += int(lowerCamelCase )
return sum_of_num
if __name__ == "__main__":
__UpperCamelCase : int = int(input("""Enter the power of 2: """).strip())
print("""2 ^ """, power, """ = """, 2**power)
__UpperCamelCase : int = solution(power)
print("""Sum of the digits is: """, result)
| 80 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Any , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str=None ) -> int:
"""simple docstring"""
super().__init__(
_lowerCAmelCase , question_encoder_tokenizer=_lowerCAmelCase , generator_tokenizer=_lowerCAmelCase , index=_lowerCAmelCase , init_retrieval=_lowerCAmelCase , )
__lowercase = None
def _a ( self : int , _lowerCAmelCase : int ) -> Any:
"""simple docstring"""
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
__lowercase = self._infer_socket_ifname()
# avoid clash with the NCCL port
__lowercase = str(distributed_port + 1 )
__lowercase = dist.new_group(ranks=_lowerCAmelCase , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _a ( self : Tuple ) -> List[str]:
"""simple docstring"""
return dist.get_rank(group=self.process_group ) == 0
def _a ( self : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any]=torch.floataa ) -> Tuple:
"""simple docstring"""
__lowercase = torch.empty(_lowerCAmelCase , dtype=_lowerCAmelCase )
dist.scatter(_lowerCAmelCase , src=0 , scatter_list=_lowerCAmelCase , group=self.process_group )
return target_tensor
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
__lowercase = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
__lowercase = next((addr for addr in addrs if addr.startswith("""e""" )) , _lowerCAmelCase )
return ifname
def _a ( self : str , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : int ) -> Tuple[np.ndarray, List[dict]]:
"""simple docstring"""
if not dist.is_initialized():
__lowercase , __lowercase = self._main_retrieve(_lowerCAmelCase , _lowerCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCAmelCase )
# distributed training
__lowercase = dist.get_world_size(group=self.process_group )
# gather logic
__lowercase = None
if self._is_main():
__lowercase = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(_lowerCAmelCase )]
dist.gather(torch.tensor(_lowerCAmelCase ) , dst=0 , gather_list=_lowerCAmelCase , group=self.process_group )
# scatter logic
__lowercase = question_hidden_states.shape[0]
__lowercase = []
__lowercase = []
if self._is_main():
assert len(_lowerCAmelCase ) == world_size
__lowercase , __lowercase = self._main_retrieve(torch.cat(_lowerCAmelCase ).numpy() , _lowerCAmelCase )
__lowercase , __lowercase = torch.tensor(_lowerCAmelCase ), torch.tensor(_lowerCAmelCase )
__lowercase = self._chunk_tensor(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._chunk_tensor(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._scattered(_lowerCAmelCase , [n_queries, n_docs] , target_type=torch.intaa )
__lowercase = self._scattered(_lowerCAmelCase , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(_lowerCAmelCase )
| 80 | 1 |
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return len(set(lowerCamelCase ) ) == len(lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_case :List[Any] = 1
@register_to_config
def __init__( self : str , _lowerCAmelCase : int = 1000 , _lowerCAmelCase : Optional[Union[np.ndarray, List[float]]] = None ) -> Optional[int]:
"""simple docstring"""
self.set_timesteps(_lowerCAmelCase )
# standard deviation of the initial noise distribution
__lowercase = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
__lowercase = 4
# running values
__lowercase = []
def _a ( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Union[str, torch.device] = None ) -> int:
"""simple docstring"""
__lowercase = num_inference_steps
__lowercase = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
__lowercase = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
__lowercase = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
__lowercase = torch.sin(steps * math.pi / 2 ) ** 2
__lowercase = (1.0 - self.betas**2) ** 0.5
__lowercase = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
__lowercase = timesteps.to(_lowerCAmelCase )
__lowercase = []
def _a ( self : List[str] , _lowerCAmelCase : torch.FloatTensor , _lowerCAmelCase : int , _lowerCAmelCase : torch.FloatTensor , _lowerCAmelCase : bool = True , ) -> Union[SchedulerOutput, Tuple]:
"""simple docstring"""
if self.num_inference_steps is None:
raise ValueError(
"""Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )
__lowercase = (self.timesteps == timestep).nonzero().item()
__lowercase = timestep_index + 1
__lowercase = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(_lowerCAmelCase )
if len(self.ets ) == 1:
__lowercase = self.ets[-1]
elif len(self.ets ) == 2:
__lowercase = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
__lowercase = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
__lowercase = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
__lowercase = self._get_prev_sample(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_lowerCAmelCase )
def _a ( self : Union[str, Any] , _lowerCAmelCase : torch.FloatTensor , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : str ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def _a ( self : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = self.alphas[timestep_index]
__lowercase = self.betas[timestep_index]
__lowercase = self.alphas[prev_timestep_index]
__lowercase = self.betas[prev_timestep_index]
__lowercase = (sample - sigma * ets) / max(_lowerCAmelCase , 1e-8 )
__lowercase = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : Optional[Any] ) -> Dict:
"""simple docstring"""
return self.config.num_train_timesteps
| 80 | 1 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __UpperCamelCase ( _lowerCAmelCase ):
@slow
@require_torch
def _a ( self : str ) -> Any:
"""simple docstring"""
__lowercase = EncoderDecoderModel.from_encoder_decoder_pretrained("""prajjwal1/bert-tiny""" , """prajjwal1/bert-tiny""" )
__lowercase = BertTokenizer.from_pretrained("""bert-base-uncased""" )
__lowercase = bertabert.config.encoder.vocab_size
__lowercase = tokenizer.sep_token_id
__lowercase = tokenizer.cls_token_id
__lowercase = 128
__lowercase = datasets.load_dataset("""cnn_dailymail""" , """3.0.0""" , split="""train[:1%]""" )
__lowercase = datasets.load_dataset("""cnn_dailymail""" , """3.0.0""" , split="""validation[:1%]""" )
__lowercase = train_dataset.select(range(32 ) )
__lowercase = val_dataset.select(range(16 ) )
__lowercase = 4
def _map_to_encoder_decoder_inputs(_lowerCAmelCase : int ):
# Tokenizer will automatically set [BOS] <text> [EOS]
__lowercase = tokenizer(batch["""article"""] , padding="""max_length""" , truncation=_lowerCAmelCase , max_length=512 )
__lowercase = tokenizer(batch["""highlights"""] , padding="""max_length""" , truncation=_lowerCAmelCase , max_length=128 )
__lowercase = inputs.input_ids
__lowercase = inputs.attention_mask
__lowercase = outputs.input_ids
__lowercase = outputs.input_ids.copy()
__lowercase = [
[-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch["""labels"""]
]
__lowercase = outputs.attention_mask
assert all(len(_lowerCAmelCase ) == 512 for x in inputs.input_ids )
assert all(len(_lowerCAmelCase ) == 128 for x in outputs.input_ids )
return batch
def _compute_metrics(_lowerCAmelCase : Optional[int] ):
__lowercase = pred.label_ids
__lowercase = pred.predictions
# all unnecessary tokens are removed
__lowercase = tokenizer.batch_decode(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase )
__lowercase = tokenizer.batch_decode(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase )
__lowercase = sum([int(pred_str[i] == label_str[i] ) for i in range(len(_lowerCAmelCase ) )] ) / len(_lowerCAmelCase )
return {"accuracy": accuracy}
# map train dataset
__lowercase = train_dataset.map(
_map_to_encoder_decoder_inputs , batched=_lowerCAmelCase , batch_size=_lowerCAmelCase , remove_columns=["""article""", """highlights"""] , )
train_dataset.set_format(
type="""torch""" , columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] , )
# same for validation dataset
__lowercase = val_dataset.map(
_map_to_encoder_decoder_inputs , batched=_lowerCAmelCase , batch_size=_lowerCAmelCase , remove_columns=["""article""", """highlights"""] , )
val_dataset.set_format(
type="""torch""" , columns=["""input_ids""", """attention_mask""", """decoder_input_ids""", """decoder_attention_mask""", """labels"""] , )
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = SeqaSeqTrainingArguments(
output_dir=_lowerCAmelCase , per_device_train_batch_size=_lowerCAmelCase , per_device_eval_batch_size=_lowerCAmelCase , predict_with_generate=_lowerCAmelCase , evaluation_strategy="""steps""" , do_train=_lowerCAmelCase , do_eval=_lowerCAmelCase , warmup_steps=0 , eval_steps=2 , logging_steps=2 , )
# instantiate trainer
__lowercase = SeqaSeqTrainer(
model=_lowerCAmelCase , args=_lowerCAmelCase , compute_metrics=_compute_metrics , train_dataset=_lowerCAmelCase , eval_dataset=_lowerCAmelCase , tokenizer=_lowerCAmelCase , )
# start training
trainer.train()
| 80 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar("""T""")
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] , _lowerCAmelCase : T ) -> List[str]:
"""simple docstring"""
__lowercase = data
__lowercase = None
def __str__( self : List[str] ) -> str:
"""simple docstring"""
return F'{self.data}'
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] ) -> None:
"""simple docstring"""
__lowercase = None
def __iter__( self : int ) -> Iterator[T]:
"""simple docstring"""
__lowercase = self.top
while node:
yield node.data
__lowercase = node.next
def __str__( self : List[str] ) -> str:
"""simple docstring"""
return "->".join([str(_lowerCAmelCase ) for item in self] )
def __len__( self : Any ) -> int:
"""simple docstring"""
return len(tuple(iter(self ) ) )
def _a ( self : str ) -> bool:
"""simple docstring"""
return self.top is None
def _a ( self : List[str] , _lowerCAmelCase : T ) -> None:
"""simple docstring"""
__lowercase = Node(_lowerCAmelCase )
if not self.is_empty():
__lowercase = self.top
__lowercase = node
def _a ( self : Union[str, Any] ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError("""pop from empty stack""" )
assert isinstance(self.top , _lowerCAmelCase )
__lowercase = self.top
__lowercase = self.top.next
return pop_node.data
def _a ( self : int ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError("""peek from empty stack""" )
assert self.top is not None
return self.top.data
def _a ( self : int ) -> None:
"""simple docstring"""
__lowercase = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 80 | 1 |
import torch
def snake_case ( ):
'''simple docstring'''
if torch.cuda.is_available():
__lowercase = torch.cuda.device_count()
else:
__lowercase = 0
print(F'Successfully ran on {num_gpus} GPUs' )
if __name__ == "__main__":
main()
| 80 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
class __UpperCamelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt="""first prompt""" , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="""numpy""" , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCAmelCase )
__lowercase = VersatileDiffusionPipeline.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = generator.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt="""first prompt""" , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="""numpy""" , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def _a ( self : Any ) -> Dict:
"""simple docstring"""
__lowercase = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """cyberpunk 2077"""
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__lowercase = """A painting of a squirrel eating a burger """
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.text_to_image(
prompt=_lowerCAmelCase , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__lowercase = pipe.image_variation(_lowerCAmelCase , generator=_lowerCAmelCase , output_type="""numpy""" ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 80 | 1 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""asapp/sew-tiny-100k""": """https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json""",
# See all SEW models at https://huggingface.co/models?filter=sew
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Dict = 'sew'
def __init__( self : Optional[Any] , _lowerCAmelCase : Tuple=32 , _lowerCAmelCase : Tuple=768 , _lowerCAmelCase : List[Any]=12 , _lowerCAmelCase : Dict=12 , _lowerCAmelCase : int=3072 , _lowerCAmelCase : Any=2 , _lowerCAmelCase : List[Any]="gelu" , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : int=0.0 , _lowerCAmelCase : Optional[int]=0.1 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Optional[Any]=0.02 , _lowerCAmelCase : Optional[int]=1e-5 , _lowerCAmelCase : Dict="group" , _lowerCAmelCase : Dict="gelu" , _lowerCAmelCase : Union[str, Any]=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , _lowerCAmelCase : Union[str, Any]=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , _lowerCAmelCase : Union[str, Any]=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , _lowerCAmelCase : Any=False , _lowerCAmelCase : Optional[Any]=128 , _lowerCAmelCase : Tuple=16 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=0.05 , _lowerCAmelCase : List[Any]=10 , _lowerCAmelCase : str=2 , _lowerCAmelCase : List[str]=0.0 , _lowerCAmelCase : str=10 , _lowerCAmelCase : Tuple=0 , _lowerCAmelCase : str="mean" , _lowerCAmelCase : int=False , _lowerCAmelCase : str=False , _lowerCAmelCase : Union[str, Any]=256 , _lowerCAmelCase : str=0 , _lowerCAmelCase : Any=1 , _lowerCAmelCase : str=2 , **_lowerCAmelCase : Dict , ) -> int:
"""simple docstring"""
super().__init__(**_lowerCAmelCase , pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase )
__lowercase = hidden_size
__lowercase = feat_extract_norm
__lowercase = feat_extract_activation
__lowercase = list(_lowerCAmelCase )
__lowercase = list(_lowerCAmelCase )
__lowercase = list(_lowerCAmelCase )
__lowercase = conv_bias
__lowercase = num_conv_pos_embeddings
__lowercase = num_conv_pos_embedding_groups
__lowercase = len(self.conv_dim )
__lowercase = num_hidden_layers
__lowercase = intermediate_size
__lowercase = squeeze_factor
__lowercase = hidden_act
__lowercase = num_attention_heads
__lowercase = hidden_dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = feat_proj_dropout
__lowercase = final_dropout
__lowercase = layerdrop
__lowercase = layer_norm_eps
__lowercase = initializer_range
__lowercase = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect."""
"""It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"""
F'but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'
F'= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__lowercase = apply_spec_augment
__lowercase = mask_time_prob
__lowercase = mask_time_length
__lowercase = mask_time_min_masks
__lowercase = mask_feature_prob
__lowercase = mask_feature_length
__lowercase = mask_feature_min_masks
# ctc loss
__lowercase = ctc_loss_reduction
__lowercase = ctc_zero_infinity
# sequence classification
__lowercase = use_weighted_layer_sum
__lowercase = classifier_proj_size
@property
def _a ( self : List[str] ) -> int:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 80 |
from __future__ import annotations
from collections.abc import MutableSequence
class __UpperCamelCase :
def __init__( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : MutableSequence[float] ) -> None:
"""simple docstring"""
if len(_lowerCAmelCase ) != degree + 1:
raise ValueError(
"""The number of coefficients should be equal to the degree + 1.""" )
__lowercase = list(_lowerCAmelCase )
__lowercase = degree
def __add__( self : Optional[int] , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
if self.degree > polynomial_a.degree:
__lowercase = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , _lowerCAmelCase )
else:
__lowercase = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , _lowerCAmelCase )
def __sub__( self : int , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : Union[str, Any] ) -> Polynomial:
"""simple docstring"""
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Optional[int] , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , _lowerCAmelCase )
def _a ( self : Optional[int] , _lowerCAmelCase : int | float ) -> int | float:
"""simple docstring"""
__lowercase = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : Dict ) -> str:
"""simple docstring"""
__lowercase = """"""
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(_lowerCAmelCase )
return polynomial
def __repr__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return self.__str__()
def _a ( self : List[str] ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * self.degree
for i in range(self.degree ):
__lowercase = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , _lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : int | float = 0 ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * (self.degree + 2)
__lowercase = constant
for i in range(self.degree + 1 ):
__lowercase = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , _lowerCAmelCase )
def __eq__( self : List[str] , _lowerCAmelCase : object ) -> bool:
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : Dict , _lowerCAmelCase : object ) -> bool:
"""simple docstring"""
return not self.__eq__(_lowerCAmelCase )
| 80 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, PNDMScheduler, StableDiffusionInpaintPipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Union[str, Any] = StableDiffusionInpaintPipeline
__snake_case :Optional[int] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
__snake_case :Union[str, Any] = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__snake_case :Any = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
__snake_case :Dict = frozenset([] )
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=9 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=_lowerCAmelCase , )
__lowercase = PNDMScheduler(skip_prk_steps=_lowerCAmelCase )
torch.manual_seed(0 )
__lowercase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
__lowercase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act="""gelu""" , projection_dim=512 , )
__lowercase = CLIPTextModel(_lowerCAmelCase )
__lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowercase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _a ( self : Tuple , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int]=0 ) -> Any:
"""simple docstring"""
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
__lowercase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowercase = Image.fromarray(np.uinta(_lowerCAmelCase ) ).convert("""RGB""" ).resize((64, 64) )
__lowercase = Image.fromarray(np.uinta(image + 4 ) ).convert("""RGB""" ).resize((64, 64) )
if str(_lowerCAmelCase ).startswith("""mps""" ):
__lowercase = torch.manual_seed(_lowerCAmelCase )
else:
__lowercase = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": init_image,
"""mask_image""": mask_image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = StableDiffusionInpaintPipeline(**_lowerCAmelCase )
__lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs(_lowerCAmelCase )
__lowercase = sd_pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowercase = np.array([0.4_727, 0.5_735, 0.3_941, 0.5_446, 0.5_926, 0.4_394, 0.5_062, 0.4_654, 0.4_476] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Any ) -> int:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Any ) -> Any:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : List[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
__lowercase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
"""/yellow_cat_sitting_on_a_park_bench.npy""" )
__lowercase = """stabilityai/stable-diffusion-2-inpainting"""
__lowercase = StableDiffusionInpaintPipeline.from_pretrained(_lowerCAmelCase , safety_checker=_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
__lowercase = """Face of a yellow cat, high resolution, sitting on a park bench"""
__lowercase = torch.manual_seed(0 )
__lowercase = pipe(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , mask_image=_lowerCAmelCase , generator=_lowerCAmelCase , output_type="""np""" , )
__lowercase = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 9e-3
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
__lowercase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint"""
"""/yellow_cat_sitting_on_a_park_bench_fp16.npy""" )
__lowercase = """stabilityai/stable-diffusion-2-inpainting"""
__lowercase = StableDiffusionInpaintPipeline.from_pretrained(
_lowerCAmelCase , torch_dtype=torch.floataa , safety_checker=_lowerCAmelCase , )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
__lowercase = """Face of a yellow cat, high resolution, sitting on a park bench"""
__lowercase = torch.manual_seed(0 )
__lowercase = pipe(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , mask_image=_lowerCAmelCase , generator=_lowerCAmelCase , output_type="""np""" , )
__lowercase = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 5e-1
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/sd2-inpaint/init_image.png""" )
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-inpaint/mask.png""" )
__lowercase = """stabilityai/stable-diffusion-2-inpainting"""
__lowercase = PNDMScheduler.from_pretrained(_lowerCAmelCase , subfolder="""scheduler""" )
__lowercase = StableDiffusionInpaintPipeline.from_pretrained(
_lowerCAmelCase , safety_checker=_lowerCAmelCase , scheduler=_lowerCAmelCase , torch_dtype=torch.floataa , )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__lowercase = """Face of a yellow cat, high resolution, sitting on a park bench"""
__lowercase = torch.manual_seed(0 )
__lowercase = pipe(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , mask_image=_lowerCAmelCase , generator=_lowerCAmelCase , num_inference_steps=2 , output_type="""np""" , )
__lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 2.65 GB is allocated
assert mem_bytes < 2.65 * 10**9
| 80 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__lowercase = len(lowerCamelCase )
__lowercase = max(lowerCamelCase )
__lowercase = min(lowerCamelCase )
# create the counting array
__lowercase = coll_max + 1 - coll_min
__lowercase = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , lowerCamelCase ):
__lowercase = counting_arr[i] + counting_arr[i - 1]
# create the output collection
__lowercase = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , lowerCamelCase ) ):
__lowercase = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return "".join([chr(lowerCamelCase ) for i in counting_sort([ord(lowerCamelCase ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string("""thisisthestring""") == "eghhiiinrsssttt"
__UpperCamelCase : str = input("""Enter numbers separated by a comma:\n""").strip()
__UpperCamelCase : Union[str, Any] = [int(item) for item in user_input.split(""",""")]
print(counting_sort(unsorted))
| 80 | 1 |
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__UpperCamelCase : Any = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__UpperCamelCase : Tuple = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F'''{len(upper_files)} files contain uppercase characters:''')
print("""\n""".join(upper_files) + """\n""")
__UpperCamelCase : Dict = [file for file in filepaths if """ """ in file]
if space_files:
print(F'''{len(space_files)} files contain space characters:''')
print("""\n""".join(space_files) + """\n""")
__UpperCamelCase : Optional[Any] = [file for file in filepaths if """-""" in file]
if hyphen_files:
print(F'''{len(hyphen_files)} files contain hyphen characters:''')
print("""\n""".join(hyphen_files) + """\n""")
__UpperCamelCase : List[str] = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F'''{len(nodir_files)} files are not in a directory:''')
print("""\n""".join(nodir_files) + """\n""")
__UpperCamelCase : List[str] = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 80 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __UpperCamelCase :
def __init__( self : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : int=13 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : str=3 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[int]=[10, 20, 30, 40] , _lowerCAmelCase : Optional[Any]=[2, 2, 3, 2] , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : List[str]=37 , _lowerCAmelCase : List[str]="gelu" , _lowerCAmelCase : List[Any]=10 , _lowerCAmelCase : int=0.02 , _lowerCAmelCase : str=["stage2", "stage3", "stage4"] , _lowerCAmelCase : Dict=[2, 3, 4] , _lowerCAmelCase : Tuple=None , ) -> Any:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = num_channels
__lowercase = num_stages
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = is_training
__lowercase = use_labels
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = num_labels
__lowercase = initializer_range
__lowercase = out_features
__lowercase = out_indices
__lowercase = scope
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.num_labels )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def _a ( self : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self : Optional[int] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__lowercase = None
__lowercase = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[Any] = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
__snake_case :List[str] = (
{'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
if is_torch_available()
else {}
)
__snake_case :str = True
__snake_case :Any = False
__snake_case :Any = False
__snake_case :Any = False
__snake_case :int = False
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason="""ConvNext does not use inputs_embeds""" )
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not support input and output embeddings""" )
def _a ( self : Dict ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not use feedforward chunking""" )
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
pass
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int] ):
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = self.model_tester.num_stages
self.assertEqual(len(_lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = ConvNextModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224""" ) if is_vision_available() else None
@slow
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""" ).to(_lowerCAmelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# verify the logits
__lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = torch.tensor([-0.0_260, -0.4_739, 0.1_911] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase , _lowerCAmelCase ):
__snake_case :Union[str, Any] = (ConvNextBackbone,) if is_torch_available() else ()
__snake_case :str = ConvNextConfig
__snake_case :Optional[Any] = False
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = ConvNextModelTester(self )
| 80 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : str = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = 'vit_msn'
def __init__( self : Optional[Any] , _lowerCAmelCase : List[Any]=768 , _lowerCAmelCase : Union[str, Any]=12 , _lowerCAmelCase : Optional[int]=12 , _lowerCAmelCase : Dict=3072 , _lowerCAmelCase : Any="gelu" , _lowerCAmelCase : str=0.0 , _lowerCAmelCase : int=0.0 , _lowerCAmelCase : Optional[Any]=0.02 , _lowerCAmelCase : str=1e-06 , _lowerCAmelCase : Optional[int]=224 , _lowerCAmelCase : Any=16 , _lowerCAmelCase : Union[str, Any]=3 , _lowerCAmelCase : Dict=True , **_lowerCAmelCase : Optional[int] , ) -> Tuple:
"""simple docstring"""
super().__init__(**_lowerCAmelCase )
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = qkv_bias
| 80 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : List[str] = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
__UpperCamelCase : Tuple = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
__UpperCamelCase : Optional[Any] = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
__UpperCamelCase : Optional[Any] = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
__UpperCamelCase : int = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
__UpperCamelCase : List[Any] = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
__UpperCamelCase : List[Any] = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
__UpperCamelCase : List[str] = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
__UpperCamelCase : List[str] = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
__UpperCamelCase : int = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
__UpperCamelCase : Dict = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
__UpperCamelCase : str = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
__UpperCamelCase : Optional[int] = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
__UpperCamelCase : Dict = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
__UpperCamelCase : List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__UpperCamelCase : str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__UpperCamelCase : Optional[int] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__UpperCamelCase : Dict = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__UpperCamelCase : Dict = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : int = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__UpperCamelCase : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__UpperCamelCase : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__UpperCamelCase : Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__UpperCamelCase : str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__UpperCamelCase : List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__UpperCamelCase : Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Tuple = FLAX_MODEL_MAPPING
__UpperCamelCase : Tuple = auto_class_update(FlaxAutoModel)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Union[str, Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
__UpperCamelCase : List[Any] = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
__UpperCamelCase : Union[str, Any] = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :List[Any] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
__UpperCamelCase : Dict = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__UpperCamelCase : Optional[Any] = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[int] = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
__UpperCamelCase : Union[str, Any] = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :List[Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[int] = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[int] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
__UpperCamelCase : int = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :str = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
__UpperCamelCase : int = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[Any] = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
__UpperCamelCase : Tuple = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
__UpperCamelCase : str = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 80 | 1 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return 10 - x * x
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if equation(lowerCamelCase ) * equation(lowerCamelCase ) >= 0:
raise ValueError("""Wrong space!""" )
__lowercase = a
while (b - a) >= 0.01:
# Find middle point
__lowercase = (a + b) / 2
# Check if middle point is root
if equation(lowerCamelCase ) == 0.0:
break
# Decide the side to repeat the steps
if equation(lowerCamelCase ) * equation(lowerCamelCase ) < 0:
__lowercase = c
else:
__lowercase = c
return c
if __name__ == "__main__":
import doctest
doctest.testmod()
print(bisection(-2, 5))
print(bisection(0, 6))
| 80 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCamelCase : int = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
__UpperCamelCase : List[Any] = {
"""configuration_perceiver""": ["""PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PerceiverConfig""", """PerceiverOnnxConfig"""],
"""tokenization_perceiver""": ["""PerceiverTokenizer"""],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Union[str, Any] = ["""PerceiverFeatureExtractor"""]
__UpperCamelCase : str = ["""PerceiverImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[Any] = [
"""PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""PerceiverForImageClassificationConvProcessing""",
"""PerceiverForImageClassificationFourier""",
"""PerceiverForImageClassificationLearned""",
"""PerceiverForMaskedLM""",
"""PerceiverForMultimodalAutoencoding""",
"""PerceiverForOpticalFlow""",
"""PerceiverForSequenceClassification""",
"""PerceiverLayer""",
"""PerceiverModel""",
"""PerceiverPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
__UpperCamelCase : List[str] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 |
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase : Union[str, Any] = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""
__UpperCamelCase : List[str] = """
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results['matthews_correlation'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results['matthews_correlation'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results['matthews_correlation'], 2))
-0.25
"""
__UpperCamelCase : Tuple = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def _a ( self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any]=None ) -> Optional[Any]:
"""simple docstring"""
return {
"matthews_correlation": float(matthews_corrcoef(_lowerCAmelCase , _lowerCAmelCase , sample_weight=_lowerCAmelCase ) ),
}
| 80 | 1 |
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
__UpperCamelCase : List[Any] = 0
__UpperCamelCase : Optional[int] = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
__UpperCamelCase : Tuple = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
__UpperCamelCase : Dict = tuple[int, int]
class __UpperCamelCase :
def __init__( self : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : Node | None , ) -> None:
"""simple docstring"""
__lowercase = pos_x
__lowercase = pos_y
__lowercase = (pos_y, pos_x)
__lowercase = goal_x
__lowercase = goal_y
__lowercase = g_cost
__lowercase = parent
__lowercase = self.calculate_heuristic()
__lowercase = self.g_cost + self.h_cost
def _a ( self : Optional[Any] ) -> float:
"""simple docstring"""
__lowercase = self.pos_x - self.goal_x
__lowercase = self.pos_y - self.goal_y
if HEURISTIC == 1:
return abs(_lowerCAmelCase ) + abs(_lowerCAmelCase )
else:
return sqrt(dy**2 + dx**2 )
def __lt__( self : int , _lowerCAmelCase : Node ) -> bool:
"""simple docstring"""
return self.f_cost < other.f_cost
class __UpperCamelCase :
def __init__( self : Optional[Any] , _lowerCAmelCase : TPosition , _lowerCAmelCase : TPosition ) -> List[Any]:
"""simple docstring"""
__lowercase = Node(start[1] , start[0] , goal[1] , goal[0] , 0 , _lowerCAmelCase )
__lowercase = Node(goal[1] , goal[0] , goal[1] , goal[0] , 9_9999 , _lowerCAmelCase )
__lowercase = [self.start]
__lowercase = []
__lowercase = False
def _a ( self : Optional[int] ) -> list[TPosition]:
"""simple docstring"""
while self.open_nodes:
# Open Nodes are sorted using __lt__
self.open_nodes.sort()
__lowercase = self.open_nodes.pop(0 )
if current_node.pos == self.target.pos:
return self.retrace_path(_lowerCAmelCase )
self.closed_nodes.append(_lowerCAmelCase )
__lowercase = self.get_successors(_lowerCAmelCase )
for child_node in successors:
if child_node in self.closed_nodes:
continue
if child_node not in self.open_nodes:
self.open_nodes.append(_lowerCAmelCase )
else:
# retrieve the best current path
__lowercase = self.open_nodes.pop(self.open_nodes.index(_lowerCAmelCase ) )
if child_node.g_cost < better_node.g_cost:
self.open_nodes.append(_lowerCAmelCase )
else:
self.open_nodes.append(_lowerCAmelCase )
return [self.start.pos]
def _a ( self : Dict , _lowerCAmelCase : Node ) -> list[Node]:
"""simple docstring"""
__lowercase = []
for action in delta:
__lowercase = parent.pos_x + action[1]
__lowercase = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_lowerCAmelCase ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(
_lowerCAmelCase , _lowerCAmelCase , self.target.pos_y , self.target.pos_x , parent.g_cost + 1 , _lowerCAmelCase , ) )
return successors
def _a ( self : List[Any] , _lowerCAmelCase : Node | None ) -> list[TPosition]:
"""simple docstring"""
__lowercase = node
__lowercase = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
__lowercase = current_node.parent
path.reverse()
return path
class __UpperCamelCase :
def __init__( self : List[Any] , _lowerCAmelCase : TPosition , _lowerCAmelCase : TPosition ) -> None:
"""simple docstring"""
__lowercase = AStar(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = AStar(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = False
def _a ( self : Any ) -> list[TPosition]:
"""simple docstring"""
while self.fwd_astar.open_nodes or self.bwd_astar.open_nodes:
self.fwd_astar.open_nodes.sort()
self.bwd_astar.open_nodes.sort()
__lowercase = self.fwd_astar.open_nodes.pop(0 )
__lowercase = self.bwd_astar.open_nodes.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
return self.retrace_bidirectional_path(
_lowerCAmelCase , _lowerCAmelCase )
self.fwd_astar.closed_nodes.append(_lowerCAmelCase )
self.bwd_astar.closed_nodes.append(_lowerCAmelCase )
__lowercase = current_bwd_node
__lowercase = current_fwd_node
__lowercase = {
self.fwd_astar: self.fwd_astar.get_successors(_lowerCAmelCase ),
self.bwd_astar: self.bwd_astar.get_successors(_lowerCAmelCase ),
}
for astar in [self.fwd_astar, self.bwd_astar]:
for child_node in successors[astar]:
if child_node in astar.closed_nodes:
continue
if child_node not in astar.open_nodes:
astar.open_nodes.append(_lowerCAmelCase )
else:
# retrieve the best current path
__lowercase = astar.open_nodes.pop(
astar.open_nodes.index(_lowerCAmelCase ) )
if child_node.g_cost < better_node.g_cost:
astar.open_nodes.append(_lowerCAmelCase )
else:
astar.open_nodes.append(_lowerCAmelCase )
return [self.fwd_astar.start.pos]
def _a ( self : int , _lowerCAmelCase : Node , _lowerCAmelCase : Node ) -> list[TPosition]:
"""simple docstring"""
__lowercase = self.fwd_astar.retrace_path(_lowerCAmelCase )
__lowercase = self.bwd_astar.retrace_path(_lowerCAmelCase )
bwd_path.pop()
bwd_path.reverse()
__lowercase = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
__UpperCamelCase : Union[str, Any] = (0, 0)
__UpperCamelCase : Optional[int] = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
__UpperCamelCase : Dict = time.time()
__UpperCamelCase : Any = AStar(init, goal)
__UpperCamelCase : Any = a_star.search()
__UpperCamelCase : str = time.time() - start_time
print(F'''AStar execution time = {end_time:f} seconds''')
__UpperCamelCase : Union[str, Any] = time.time()
__UpperCamelCase : Dict = BidirectionalAStar(init, goal)
__UpperCamelCase : int = time.time() - bd_start_time
print(F'''BidirectionalAStar execution time = {bd_end_time:f} seconds''')
| 80 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
__UpperCamelCase : Optional[int] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
__UpperCamelCase : Dict = {"""facebook/blenderbot_small-90M""": 512}
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
__lowercase = set(lowerCamelCase )
return pairs
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = VOCAB_FILES_NAMES
__snake_case :Tuple = PRETRAINED_VOCAB_FILES_MAP
__snake_case :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case :str = ['input_ids', 'attention_mask']
def __init__( self : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str="__start__" , _lowerCAmelCase : int="__end__" , _lowerCAmelCase : Any="__unk__" , _lowerCAmelCase : List[Any]="__null__" , **_lowerCAmelCase : Tuple , ) -> str:
"""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:
__lowercase = json.load(_lowerCAmelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
with open(_lowerCAmelCase , encoding="""utf-8""" ) as merges_handle:
__lowercase = merges_handle.read().split("""\n""" )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in merges]
__lowercase = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
__lowercase = {}
@property
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return len(self.encoder )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _a ( self : str , _lowerCAmelCase : str ) -> str:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
__lowercase = re.sub("""([.,!?()])""" , r""" \1""" , _lowerCAmelCase )
__lowercase = re.sub("""(')""" , r""" \1 """ , _lowerCAmelCase )
__lowercase = re.sub(r"""\s{2,}""" , """ """ , _lowerCAmelCase )
if "\n" in token:
__lowercase = token.replace("""\n""" , """ __newln__""" )
__lowercase = token.split(""" """ )
__lowercase = []
for token in tokens:
if not len(_lowerCAmelCase ):
continue
__lowercase = token.lower()
__lowercase = tuple(_lowerCAmelCase )
__lowercase = tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
__lowercase = get_pairs(_lowerCAmelCase )
if not pairs:
words.append(_lowerCAmelCase )
continue
while True:
__lowercase = min(_lowerCAmelCase , key=lambda _lowerCAmelCase : self.bpe_ranks.get(_lowerCAmelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(_lowerCAmelCase ):
try:
__lowercase = word.index(_lowerCAmelCase , _lowerCAmelCase )
new_word.extend(word[i:j] )
__lowercase = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(_lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(_lowerCAmelCase )
__lowercase = new_word
if len(_lowerCAmelCase ) == 1:
break
else:
__lowercase = get_pairs(_lowerCAmelCase )
__lowercase = """@@ """.join(_lowerCAmelCase )
__lowercase = word[:-4]
__lowercase = word
words.append(_lowerCAmelCase )
return " ".join(_lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
__lowercase = []
__lowercase = re.findall(r"""\S+\n?""" , _lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(_lowerCAmelCase ).split(""" """ ) ) )
return split_tokens
def _a ( self : Tuple , _lowerCAmelCase : str ) -> int:
"""simple docstring"""
__lowercase = token.lower()
return self.encoder.get(_lowerCAmelCase , self.encoder.get(self.unk_token ) )
def _a ( self : Tuple , _lowerCAmelCase : int ) -> str:
"""simple docstring"""
return self.decoder.get(_lowerCAmelCase , self.unk_token )
def _a ( self : Dict , _lowerCAmelCase : List[str] ) -> str:
"""simple docstring"""
__lowercase = """ """.join(_lowerCAmelCase ).replace("""@@ """ , """""" ).strip()
return out_string
def _a ( self : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + """\n""" )
__lowercase = 0
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
""" Please check that the tokenizer is not corrupted!""" )
__lowercase = token_index
writer.write(""" """.join(_lowerCAmelCase ) + """\n""" )
index += 1
return vocab_file, merge_file
| 80 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[str] = ["""NllbTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = ["""NllbTokenizerFast"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Union[str, Any] = 'lxmert'
__snake_case :Union[str, Any] = {}
def __init__( self : List[str] , _lowerCAmelCase : Dict=3_0522 , _lowerCAmelCase : List[str]=768 , _lowerCAmelCase : Union[str, Any]=12 , _lowerCAmelCase : Union[str, Any]=9500 , _lowerCAmelCase : Union[str, Any]=1600 , _lowerCAmelCase : Optional[Any]=400 , _lowerCAmelCase : Tuple=3072 , _lowerCAmelCase : List[Any]="gelu" , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Tuple=512 , _lowerCAmelCase : Tuple=2 , _lowerCAmelCase : Optional[Any]=0.02 , _lowerCAmelCase : List[str]=1e-12 , _lowerCAmelCase : Any=9 , _lowerCAmelCase : Optional[Any]=5 , _lowerCAmelCase : Any=5 , _lowerCAmelCase : Dict=2048 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[Any]=6.67 , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , _lowerCAmelCase : str=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , **_lowerCAmelCase : Tuple , ) -> Dict:
"""simple docstring"""
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = num_qa_labels
__lowercase = num_object_labels
__lowercase = num_attr_labels
__lowercase = l_layers
__lowercase = x_layers
__lowercase = r_layers
__lowercase = visual_feat_dim
__lowercase = visual_pos_dim
__lowercase = visual_loss_normalizer
__lowercase = task_matched
__lowercase = task_mask_lm
__lowercase = task_obj_predict
__lowercase = task_qa
__lowercase = visual_obj_loss
__lowercase = visual_attr_loss
__lowercase = visual_feat_loss
__lowercase = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**_lowerCAmelCase )
| 80 | 1 |
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
# TODO: is there an appropriate internal test set?
__snake_case :Union[str, Any] = 'ssube/stable-diffusion-x4-upscaler-onnx'
def _a ( self : Optional[int] , _lowerCAmelCase : Dict=0 ) -> Optional[int]:
"""simple docstring"""
__lowercase = floats_tensor((1, 3, 128, 128) , rng=random.Random(_lowerCAmelCase ) )
__lowercase = torch.manual_seed(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : int ) -> Dict:
"""simple docstring"""
__lowercase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array(
[0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223] )
assert np.abs(image_slice - expected_slice ).max() < 1e-1
def _a ( self : Tuple ) -> Dict:
"""simple docstring"""
__lowercase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array(
[0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _a ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array(
[0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _a ( self : str ) -> Tuple:
"""simple docstring"""
__lowercase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array(
[0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
def _a ( self : str ) -> List[Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array(
[0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
@nightly
@require_onnxruntime
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
@property
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ort.SessionOptions()
__lowercase = False
return options
def _a ( self : List[str] ) -> Tuple:
"""simple docstring"""
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
__lowercase = init_image.resize((128, 128) )
# using the PNDM scheduler by default
__lowercase = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """A fantasy landscape, trending on artstation"""
__lowercase = torch.manual_seed(0 )
__lowercase = pipe(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCAmelCase , output_type="""np""" , )
__lowercase = output.images
__lowercase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
__lowercase = np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
__lowercase = init_image.resize((128, 128) )
__lowercase = LMSDiscreteScheduler.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , subfolder="""scheduler""" )
__lowercase = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , scheduler=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """A fantasy landscape, trending on artstation"""
__lowercase = torch.manual_seed(0 )
__lowercase = pipe(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=20 , generator=_lowerCAmelCase , output_type="""np""" , )
__lowercase = output.images
__lowercase = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
__lowercase = np.array(
[0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
| 80 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : int , _lowerCAmelCase : str , _lowerCAmelCase : List[str]=13 , _lowerCAmelCase : List[str]=7 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : Union[str, Any]=False , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=99 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : Optional[int]=5 , _lowerCAmelCase : Any=4 , _lowerCAmelCase : Tuple=37 , _lowerCAmelCase : str="gelu" , _lowerCAmelCase : List[str]=0.1 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : Union[str, Any]=512 , _lowerCAmelCase : Dict=16 , _lowerCAmelCase : List[Any]=2 , _lowerCAmelCase : Optional[int]=0.02 , _lowerCAmelCase : int=3 , _lowerCAmelCase : Dict=4 , _lowerCAmelCase : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_input_mask
__lowercase = use_token_type_ids
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = num_labels
__lowercase = num_choices
__lowercase = scope
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = None
if self.use_input_mask:
__lowercase = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase = None
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowercase = ids_tensor([self.batch_size] , self.num_choices )
__lowercase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _a ( self : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = DistilBertModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> List[str]:
"""simple docstring"""
__lowercase = DistilBertForMaskedLM(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertForQuestionAnswering(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForSequenceClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForTokenClassification(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ) -> str:
"""simple docstring"""
__lowercase = self.num_choices
__lowercase = DistilBertForMultipleChoice(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = model(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
((__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase)) = config_and_inputs
__lowercase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[Any] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
__snake_case :Dict = (
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__snake_case :Tuple = True
__snake_case :Tuple = True
__snake_case :List[str] = True
__snake_case :Optional[int] = True
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = DistilBertModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , dim=37 )
def _a ( self : Dict ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*_lowerCAmelCase )
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*_lowerCAmelCase )
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*_lowerCAmelCase )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*_lowerCAmelCase )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*_lowerCAmelCase )
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*_lowerCAmelCase )
@slow
def _a ( self : int ) -> Optional[Any]:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = DistilBertModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@slow
@require_torch_gpu
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
__lowercase = True
__lowercase = model_class(config=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = torch.jit.trace(
_lowerCAmelCase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , """traced_model.pt""" ) )
__lowercase = torch.jit.load(os.path.join(_lowerCAmelCase , """traced_model.pt""" ) , map_location=_lowerCAmelCase )
loaded(inputs_dict["""input_ids"""].to(_lowerCAmelCase ) , inputs_dict["""attention_mask"""].to(_lowerCAmelCase ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertModel.from_pretrained("""distilbert-base-uncased""" )
__lowercase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__lowercase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )[0]
__lowercase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _lowerCAmelCase )
__lowercase = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowerCAmelCase , atol=1e-4 ) )
| 80 | 1 |
from __future__ import annotations
import numpy as np
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return np.maximum(0 , lowerCamelCase )
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 80 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCamelCase ( _lowerCAmelCase ):
# to overwrite at feature extractactor specific tests
__snake_case :Optional[int] = None
__snake_case :Dict = None
@property
def _a ( self : str ) -> List[str]:
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_lowerCAmelCase , """feature_size""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """sampling_rate""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """padding_value""" ) )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_lowerCAmelCase ) == len(_lowerCAmelCase ) for x, y in zip(_lowerCAmelCase , processed_features[input_name] ) ) )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _a ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _a ( self : str , _lowerCAmelCase : List[Any]=False ) -> int:
"""simple docstring"""
def _inputs_have_equal_length(_lowerCAmelCase : int ):
__lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCAmelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCAmelCase : Dict , _lowerCAmelCase : Tuple ):
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCAmelCase , _lowerCAmelCase ):
if not np.allclose(np.asarray(_lowerCAmelCase ) , np.asarray(_lowerCAmelCase ) , atol=1e-3 ):
return False
return True
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = self.feat_extract_tester.seq_length_diff
__lowercase = self.feat_extract_tester.max_seq_length + pad_diff
__lowercase = self.feat_extract_tester.min_seq_length
__lowercase = self.feat_extract_tester.batch_size
__lowercase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
__lowercase = feat_extract.pad(_lowerCAmelCase , padding=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )
__lowercase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""max_length""" )[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=_lowerCAmelCase , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
__lowercase = feat_extract.pad(_lowerCAmelCase , pad_to_multiple_of=10 )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , pad_to_multiple_of=10 )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = input_a[input_name]
self.assertTrue(all(len(_lowerCAmelCase ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_lowerCAmelCase ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
__lowercase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _a ( self : Tuple , _lowerCAmelCase : str=False ) -> Union[str, Any]:
"""simple docstring"""
def _inputs_have_equal_length(_lowerCAmelCase : Tuple ):
__lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCAmelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCAmelCase : Any , _lowerCAmelCase : str ):
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCAmelCase , _lowerCAmelCase ):
if not np.allclose(np.asarray(_lowerCAmelCase ) , np.asarray(_lowerCAmelCase ) , atol=1e-3 ):
return False
return True
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
__lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
# truncate to smallest with np
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=_lowerCAmelCase , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
# truncate to middle
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , truncation=_lowerCAmelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""longest""" , truncation=_lowerCAmelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""longest""" , truncation=_lowerCAmelCase )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , truncation=_lowerCAmelCase )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
__lowercase = 12
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCAmelCase , truncation=_lowerCAmelCase , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCAmelCase , )
__lowercase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
__lowercase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
__lowercase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self._check_padding(numpify=_lowerCAmelCase )
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
self._check_padding(numpify=_lowerCAmelCase )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
self._check_truncation(numpify=_lowerCAmelCase )
def _a ( self : str ) -> str:
"""simple docstring"""
self._check_truncation(numpify=_lowerCAmelCase )
@require_torch
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _a ( self : Any ) -> Any:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**_lowerCAmelCase )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = [len(_lowerCAmelCase ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _lowerCAmelCase )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _lowerCAmelCase )
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**_lowerCAmelCase )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = [len(_lowerCAmelCase ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = min(_lowerCAmelCase )
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=_lowerCAmelCase , truncation=_lowerCAmelCase , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _lowerCAmelCase )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 80 | 1 |
from math import ceil, sqrt
def snake_case ( lowerCamelCase = 1_000_000 ):
'''simple docstring'''
__lowercase = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
__lowercase = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
__lowercase = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lowerCamelCase ) <= key:
return input_string
for position, character in enumerate(lowerCamelCase ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(lowerCamelCase )
__lowercase = ["""""".join(lowerCamelCase ) for row in temp_grid]
__lowercase = """""".join(lowerCamelCase )
return output_string
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = []
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1:
return input_string
__lowercase = [[] for _ in range(lowerCamelCase )] # generates template
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("""*""" )
__lowercase = 0
for row in temp_grid: # fills in the characters
__lowercase = input_string[counter : counter + len(lowerCamelCase )]
grid.append(list(lowerCamelCase ) )
counter += len(lowerCamelCase )
__lowercase = """""" # reads as zigzag
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = {}
for key_guess in range(1 , len(lowerCamelCase ) ): # tries every key
__lowercase = decrypt(lowerCamelCase , lowerCamelCase )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 | 1 |
from collections import deque
class __UpperCamelCase :
def __init__( self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> None:
"""simple docstring"""
__lowercase = process_name # process name
__lowercase = arrival_time # arrival time of the process
# completion time of finished process or last interrupted time
__lowercase = arrival_time
__lowercase = burst_time # remaining burst time
__lowercase = 0 # total time of the process wait in ready queue
__lowercase = 0 # time from arrival time to completion time
class __UpperCamelCase :
def __init__( self : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : list[int] , _lowerCAmelCase : deque[Process] , _lowerCAmelCase : int , ) -> None:
"""simple docstring"""
__lowercase = number_of_queues
# time slice of queues that round robin algorithm applied
__lowercase = time_slices
# unfinished process is in this ready_queue
__lowercase = queue
# current time
__lowercase = current_time
# finished process is in this sequence queue
__lowercase = deque()
def _a ( self : List[Any] ) -> list[str]:
"""simple docstring"""
__lowercase = []
for i in range(len(self.finish_queue ) ):
sequence.append(self.finish_queue[i].process_name )
return sequence
def _a ( self : List[str] , _lowerCAmelCase : list[Process] ) -> list[int]:
"""simple docstring"""
__lowercase = []
for i in range(len(_lowerCAmelCase ) ):
waiting_times.append(queue[i].waiting_time )
return waiting_times
def _a ( self : List[str] , _lowerCAmelCase : list[Process] ) -> list[int]:
"""simple docstring"""
__lowercase = []
for i in range(len(_lowerCAmelCase ) ):
turnaround_times.append(queue[i].turnaround_time )
return turnaround_times
def _a ( self : Any , _lowerCAmelCase : list[Process] ) -> list[int]:
"""simple docstring"""
__lowercase = []
for i in range(len(_lowerCAmelCase ) ):
completion_times.append(queue[i].stop_time )
return completion_times
def _a ( self : Optional[int] , _lowerCAmelCase : deque[Process] ) -> list[int]:
"""simple docstring"""
return [q.burst_time for q in queue]
def _a ( self : Dict , _lowerCAmelCase : Process ) -> int:
"""simple docstring"""
process.waiting_time += self.current_time - process.stop_time
return process.waiting_time
def _a ( self : Tuple , _lowerCAmelCase : deque[Process] ) -> deque[Process]:
"""simple docstring"""
__lowercase = deque() # sequence deque of finished process
while len(_lowerCAmelCase ) != 0:
__lowercase = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of current process
self.update_waiting_time(_lowerCAmelCase )
# update current time
self.current_time += cp.burst_time
# finish the process and set the process's burst-time 0
__lowercase = 0
# set the process's turnaround time because it is finished
__lowercase = self.current_time - cp.arrival_time
# set the completion time
__lowercase = self.current_time
# add the process to queue that has finished queue
finished.append(_lowerCAmelCase )
self.finish_queue.extend(_lowerCAmelCase ) # add finished process to finish queue
# FCFS will finish all remaining processes
return finished
def _a ( self : int , _lowerCAmelCase : deque[Process] , _lowerCAmelCase : int ) -> tuple[deque[Process], deque[Process]]:
"""simple docstring"""
__lowercase = deque() # sequence deque of terminated process
# just for 1 cycle and unfinished processes will go back to queue
for _ in range(len(_lowerCAmelCase ) ):
__lowercase = ready_queue.popleft() # current process
# if process's arrival time is later than current time, update current time
if self.current_time < cp.arrival_time:
self.current_time += cp.arrival_time
# update waiting time of unfinished processes
self.update_waiting_time(_lowerCAmelCase )
# if the burst time of process is bigger than time-slice
if cp.burst_time > time_slice:
# use CPU for only time-slice
self.current_time += time_slice
# update remaining burst time
cp.burst_time -= time_slice
# update end point time
__lowercase = self.current_time
# locate the process behind the queue because it is not finished
ready_queue.append(_lowerCAmelCase )
else:
# use CPU for remaining burst time
self.current_time += cp.burst_time
# set burst time 0 because the process is finished
__lowercase = 0
# set the finish time
__lowercase = self.current_time
# update the process' turnaround time because it is finished
__lowercase = self.current_time - cp.arrival_time
# add the process to queue that has finished queue
finished.append(_lowerCAmelCase )
self.finish_queue.extend(_lowerCAmelCase ) # add finished process to finish queue
# return finished processes queue and remaining processes queue
return finished, ready_queue
def _a ( self : Any ) -> deque[Process]:
"""simple docstring"""
for i in range(self.number_of_queues - 1 ):
__lowercase , __lowercase = self.round_robin(
self.ready_queue , self.time_slices[i] )
# the last queue has first_come_first_served algorithm
self.first_come_first_served(self.ready_queue )
return self.finish_queue
if __name__ == "__main__":
import doctest
__UpperCamelCase : Optional[int] = Process("""P1""", 0, 53)
__UpperCamelCase : List[Any] = Process("""P2""", 0, 17)
__UpperCamelCase : Tuple = Process("""P3""", 0, 68)
__UpperCamelCase : Union[str, Any] = Process("""P4""", 0, 24)
__UpperCamelCase : Union[str, Any] = 3
__UpperCamelCase : Union[str, Any] = [17, 25]
__UpperCamelCase : Dict = deque([Pa, Pa, Pa, Pa])
if len(time_slices) != number_of_queues - 1:
raise SystemExit(0)
doctest.testmod(extraglobs={"""queue""": deque([Pa, Pa, Pa, Pa])})
__UpperCamelCase : int = Process("""P1""", 0, 53)
__UpperCamelCase : Union[str, Any] = Process("""P2""", 0, 17)
__UpperCamelCase : Any = Process("""P3""", 0, 68)
__UpperCamelCase : Dict = Process("""P4""", 0, 24)
__UpperCamelCase : int = 3
__UpperCamelCase : Optional[int] = [17, 25]
__UpperCamelCase : Optional[Any] = deque([Pa, Pa, Pa, Pa])
__UpperCamelCase : Dict = MLFQ(number_of_queues, time_slices, queue, 0)
__UpperCamelCase : List[Any] = mlfq.multi_level_feedback_queue()
# print total waiting times of processes(P1, P2, P3, P4)
print(
F'''waiting time:\
\t\t\t{MLFQ.calculate_waiting_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print completion times of processes(P1, P2, P3, P4)
print(
F'''completion time:\
\t\t{MLFQ.calculate_completion_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print total turnaround times of processes(P1, P2, P3, P4)
print(
F'''turnaround time:\
\t\t{MLFQ.calculate_turnaround_time(mlfq, [Pa, Pa, Pa, Pa])}'''
)
# print sequence of finished processes
print(
F'''sequence of finished processes:\
{mlfq.calculate_sequence_of_finish_queue()}'''
)
| 80 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , lowerCamelCase ):
__lowercase = 1
__lowercase = 0
for i in range(lowerCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Union[str, Any] = 'lxmert'
__snake_case :Union[str, Any] = {}
def __init__( self : List[str] , _lowerCAmelCase : Dict=3_0522 , _lowerCAmelCase : List[str]=768 , _lowerCAmelCase : Union[str, Any]=12 , _lowerCAmelCase : Union[str, Any]=9500 , _lowerCAmelCase : Union[str, Any]=1600 , _lowerCAmelCase : Optional[Any]=400 , _lowerCAmelCase : Tuple=3072 , _lowerCAmelCase : List[Any]="gelu" , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Tuple=512 , _lowerCAmelCase : Tuple=2 , _lowerCAmelCase : Optional[Any]=0.02 , _lowerCAmelCase : List[str]=1e-12 , _lowerCAmelCase : Any=9 , _lowerCAmelCase : Optional[Any]=5 , _lowerCAmelCase : Any=5 , _lowerCAmelCase : Dict=2048 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[Any]=6.67 , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , _lowerCAmelCase : str=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , **_lowerCAmelCase : Tuple , ) -> Dict:
"""simple docstring"""
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = num_qa_labels
__lowercase = num_object_labels
__lowercase = num_attr_labels
__lowercase = l_layers
__lowercase = x_layers
__lowercase = r_layers
__lowercase = visual_feat_dim
__lowercase = visual_pos_dim
__lowercase = visual_loss_normalizer
__lowercase = task_matched
__lowercase = task_mask_lm
__lowercase = task_obj_predict
__lowercase = task_qa
__lowercase = visual_obj_loss
__lowercase = visual_attr_loss
__lowercase = visual_feat_loss
__lowercase = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**_lowerCAmelCase )
| 80 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class __UpperCamelCase :
def __init__( self : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int]=13 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : Optional[Any]=2 , _lowerCAmelCase : Union[str, Any]=3 , _lowerCAmelCase : List[str]=16 , _lowerCAmelCase : List[str]=[1, 2, 1] , _lowerCAmelCase : Dict=[2, 2, 4] , _lowerCAmelCase : Optional[int]=2 , _lowerCAmelCase : Optional[Any]=2.0 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Optional[int]=0.0 , _lowerCAmelCase : Any=0.0 , _lowerCAmelCase : Optional[int]=0.1 , _lowerCAmelCase : Tuple="gelu" , _lowerCAmelCase : int=False , _lowerCAmelCase : Dict=True , _lowerCAmelCase : List[str]=0.02 , _lowerCAmelCase : Union[str, Any]=1e-5 , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : List[Any]=10 , _lowerCAmelCase : Tuple=8 , _lowerCAmelCase : List[Any]=["stage1", "stage2", "stage3"] , _lowerCAmelCase : Union[str, Any]=[1, 2, 3] , ) -> int:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = patch_norm
__lowercase = layer_norm_eps
__lowercase = initializer_range
__lowercase = is_training
__lowercase = scope
__lowercase = use_labels
__lowercase = type_sequence_label_size
__lowercase = encoder_stride
__lowercase = out_features
__lowercase = out_indices
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def _a ( self : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : str , _lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
__lowercase = MaskFormerSwinModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
__lowercase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__lowercase = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def _a ( self : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = MaskFormerSwinBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_lowerCAmelCase ):
__lowercase = ["""stem"""]
__lowercase = MaskFormerSwinBackbone(config=_lowerCAmelCase )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
__snake_case :Optional[int] = {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
__snake_case :Optional[int] = False
__snake_case :Any = False
__snake_case :List[str] = False
__snake_case :Tuple = False
__snake_case :Optional[int] = False
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__lowercase = MaskFormerSwinModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"""`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
""" `nn.DataParallel`"""
) )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
pass
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
return
def _a ( self : Any ) -> Tuple:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCAmelCase )
@unittest.skip("""Swin does not use inputs_embeds""" )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip("""Swin does not support feedforward chunking""" )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
pass
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions""" )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone""" )
def _a ( self : Any ) -> Any:
"""simple docstring"""
pass
def _a ( self : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.hidden_states
__lowercase = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
# Swin has a different seq_length
__lowercase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = 3
__lowercase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__lowercase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__lowercase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , (padded_height, padded_width) )
@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints""" )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a ( self : Any ) -> str:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
pass
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(_lowerCAmelCase : Optional[int] ):
__lowercase = 0
return t
def check_equivalence(_lowerCAmelCase : int , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int]={} ):
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase , return_dict=_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = model(**_lowerCAmelCase , return_dict=_lowerCAmelCase , **_lowerCAmelCase ).to_tuple()
def recursive_check(_lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ):
if isinstance(_lowerCAmelCase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_lowerCAmelCase , _lowerCAmelCase ):
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_lowerCAmelCase ) , set_nan_tensor_to_zero(_lowerCAmelCase ) , atol=1e-5 ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
F' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'
F' {torch.isnan(_lowerCAmelCase ).any()} and `inf`: {torch.isinf(_lowerCAmelCase )}. Dict has'
F' `nan`: {torch.isnan(_lowerCAmelCase ).any()} and `inf`: {torch.isinf(_lowerCAmelCase )}.'
) , )
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {"""output_hidden_states""": True} )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {"""output_hidden_states""": True} )
@require_torch
class __UpperCamelCase ( unittest.TestCase , _lowerCAmelCase ):
__snake_case :Optional[Any] = (MaskFormerSwinBackbone,) if is_torch_available() else ()
__snake_case :Dict = MaskFormerSwinConfig
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase = MaskFormerSwinModelTester(self )
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = inputs_dict["""pixel_values"""].shape[0]
for backbone_class in self.all_model_classes:
__lowercase = backbone_class(_lowerCAmelCase )
backbone.to(_lowerCAmelCase )
backbone.eval()
__lowercase = backbone(**_lowerCAmelCase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _lowerCAmelCase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
__lowercase = backbone(**_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
__lowercase , __lowercase , __lowercase = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
__lowercase = backbone(**_lowerCAmelCase , output_attentions=_lowerCAmelCase )
self.assertIsNotNone(outputs.attentions )
| 80 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
__UpperCamelCase : str = get_tests_dir() + """/test_data/fsmt/fsmt_val_data.json"""
with io.open(filename, """r""", encoding="""utf-8""") as f:
__UpperCamelCase : List[Any] = json.load(f)
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Optional[int] , _lowerCAmelCase : Union[str, Any] ) -> Any:
"""simple docstring"""
return FSMTTokenizer.from_pretrained(_lowerCAmelCase )
def _a ( self : List[Any] , _lowerCAmelCase : Dict ) -> int:
"""simple docstring"""
__lowercase = FSMTForConditionalGeneration.from_pretrained(_lowerCAmelCase ).to(_lowerCAmelCase )
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
["""en-ru""", 26.0],
["""ru-en""", 22.0],
["""en-de""", 22.0],
["""de-en""", 29.0],
] )
@slow
def _a ( self : Optional[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any] ) -> int:
"""simple docstring"""
__lowercase = F'facebook/wmt19-{pair}'
__lowercase = self.get_tokenizer(_lowerCAmelCase )
__lowercase = self.get_model(_lowerCAmelCase )
__lowercase = bleu_data[pair]["""src"""]
__lowercase = bleu_data[pair]["""tgt"""]
__lowercase = tokenizer(_lowerCAmelCase , return_tensors="""pt""" , truncation=_lowerCAmelCase , padding="""longest""" ).to(_lowerCAmelCase )
__lowercase = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
__lowercase = tokenizer.batch_decode(
_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase , clean_up_tokenization_spaces=_lowerCAmelCase )
__lowercase = calculate_bleu(_lowerCAmelCase , _lowerCAmelCase )
print(_lowerCAmelCase )
self.assertGreaterEqual(scores["""bleu"""] , _lowerCAmelCase )
| 80 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = torch.nn.Linear(10 , 10 )
__lowercase = torch.optim.SGD(model.parameters() , 0.1 )
__lowercase = Accelerator()
__lowercase = accelerator.prepare(_lowerCAmelCase )
try:
pickle.loads(pickle.dumps(_lowerCAmelCase ) )
except Exception as e:
self.fail(F'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 80 | 1 |
import math
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = []
__lowercase = 2
__lowercase = int(math.sqrt(lowerCamelCase ) ) # Size of every segment
__lowercase = [True] * (end + 1)
__lowercase = []
while start <= end:
if temp[start] is True:
in_prime.append(lowerCamelCase )
for i in range(start * start , end + 1 , lowerCamelCase ):
__lowercase = False
start += 1
prime += in_prime
__lowercase = end + 1
__lowercase = min(2 * end , lowerCamelCase )
while low <= n:
__lowercase = [True] * (high - low + 1)
for each in in_prime:
__lowercase = math.floor(low / each ) * each
if t < low:
t += each
for j in range(lowerCamelCase , high + 1 , lowerCamelCase ):
__lowercase = False
for j in range(len(lowerCamelCase ) ):
if temp[j] is True:
prime.append(j + low )
__lowercase = high + 1
__lowercase = min(high + end , lowerCamelCase )
return prime
print(sieve(10**6))
| 80 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
__UpperCamelCase : Dict = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
__UpperCamelCase : int = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
__UpperCamelCase : List[str] = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
__UpperCamelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 | 1 |
from math import asin, atan, cos, radians, sin, sqrt, tan
__UpperCamelCase : Optional[Any] = 6_3_7_8_1_3_7.0
__UpperCamelCase : Tuple = 6_3_5_6_7_5_2.3_1_4_2_4_5
__UpperCamelCase : Any = 6378137
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = (AXIS_A - AXIS_B) / AXIS_A
__lowercase = atan((1 - flattening) * tan(radians(lowerCamelCase ) ) )
__lowercase = atan((1 - flattening) * tan(radians(lowerCamelCase ) ) )
__lowercase = radians(lowerCamelCase )
__lowercase = radians(lowerCamelCase )
# Equation
__lowercase = sin((phi_a - phi_a) / 2 )
__lowercase = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
__lowercase = sqrt(sin_sq_phi + (cos(lowerCamelCase ) * cos(lowerCamelCase ) * sin_sq_lambda) )
return 2 * RADIUS * asin(lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 |
import os
from collections.abc import Iterator
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCamelCase ):
__lowercase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(lowerCamelCase )[1] in (".py", ".ipynb"):
yield os.path.join(lowerCamelCase , lowerCamelCase ).lstrip("""./""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return F'{i * " "}*' if i else "\n##"
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(lowerCamelCase ) or old_parts[i] != new_part) and new_part:
print(F'{md_prefix(lowerCamelCase )} {new_part.replace("_" , " " ).title()}' )
return new_path
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
__lowercase = """"""
for filepath in sorted(good_file_paths(lowerCamelCase ) ):
__lowercase , __lowercase = os.path.split(lowerCamelCase )
if filepath != old_path:
__lowercase = print_path(lowerCamelCase , lowerCamelCase )
__lowercase = (filepath.count(os.sep ) + 1) if filepath else 0
__lowercase = F'{filepath}/{filename}'.replace(""" """ , """%20""" )
__lowercase = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'{md_prefix(lowerCamelCase )} [{filename}]({url})' )
if __name__ == "__main__":
print_directory_md(""".""")
| 80 | 1 |
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
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
def run_func(lowerCamelCase ):
@wraps(lowerCamelCase )
def run_in_eager_mode(*lowerCamelCase , **lowerCamelCase ):
return func(*lowerCamelCase , **lowerCamelCase )
@wraps(lowerCamelCase )
@tf.function(experimental_compile=lowerCamelCase )
def run_in_graph_mode(*lowerCamelCase , **lowerCamelCase ):
return func(*lowerCamelCase , **lowerCamelCase )
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 snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = random.Random()
__lowercase = [rng.randint(0 , vocab_size - 1 ) for i in range(batch_size * sequence_length )]
return tf.constant(lowerCamelCase , shape=(batch_size, sequence_length) , dtype=tf.intaa )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :TensorFlowBenchmarkArguments
__snake_case :PretrainedConfig
__snake_case :str = "TensorFlow"
@property
def _a ( self : str ) -> Any:
"""simple docstring"""
return tf.__version__
def _a ( self : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> float:
"""simple docstring"""
__lowercase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
__lowercase = self._prepare_inference_func(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return self._measure_speed(_inference )
def _a ( self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> float:
"""simple docstring"""
__lowercase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
__lowercase = self._prepare_train_func(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return self._measure_speed(_train )
def _a ( self : Dict , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> [Memory, Optional[MemorySummary]]:
"""simple docstring"""
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _lowerCAmelCase )
__lowercase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
__lowercase = self._prepare_inference_func(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return self._measure_memory(_inference )
def _a ( self : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> [Memory, Optional[MemorySummary]]:
"""simple docstring"""
if self.args.is_gpu:
tf.config.experimental.set_memory_growth(self.args.gpu_list[self.args.device_idx] , _lowerCAmelCase )
__lowercase = self.args.strategy
if strategy is None:
raise ValueError("""A device strategy has to be initialized before using TensorFlow.""" )
__lowercase = self._prepare_train_func(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return self._measure_memory(_train )
def _a ( self : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> Callable[[], None]:
"""simple docstring"""
__lowercase = self.config_dict[model_name]
if self.args.fpaa:
raise NotImplementedError("""Mixed precision is currently not supported.""" )
__lowercase = (
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:
__lowercase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
__lowercase = __import__("""transformers""" , fromlist=[model_class] )
__lowercase = getattr(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = 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:
__lowercase = TF_MODEL_MAPPING[config.__class__](_lowerCAmelCase )
# encoder-decoder has vocab size saved differently
__lowercase = config.vocab_size if hasattr(_lowerCAmelCase , """vocab_size""" ) else config.encoder.vocab_size
__lowercase = 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 )
__lowercase = encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
return _inference
def _a ( self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> Callable[[], None]:
"""simple docstring"""
__lowercase = 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.""" )
__lowercase = (
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:
__lowercase = """TF""" + config.architectures[0] # prepend 'TF' for tensorflow model
__lowercase = __import__("""transformers""" , fromlist=[model_class] )
__lowercase = getattr(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = 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:
__lowercase = TF_MODEL_WITH_LM_HEAD_MAPPING[config.__class__](_lowerCAmelCase )
# encoder-decoder has vocab size saved differently
__lowercase = config.vocab_size if hasattr(_lowerCAmelCase , """vocab_size""" ) else config.encoder.vocab_size
__lowercase = random_input_ids(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_decoder_train():
__lowercase = model(_lowerCAmelCase , decoder_input_ids=_lowerCAmelCase , labels=_lowerCAmelCase , training=_lowerCAmelCase )[0]
__lowercase = tf.gradients(_lowerCAmelCase , model.trainable_variables )
return gradients
@run_with_tf_optimizations(self.args.eager_mode , self.args.use_xla )
def encoder_train():
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase , training=_lowerCAmelCase )[0]
__lowercase = tf.gradients(_lowerCAmelCase , model.trainable_variables )
return gradients
__lowercase = encoder_decoder_train if config.is_encoder_decoder else encoder_train
return _train
def _a ( self : Dict , _lowerCAmelCase : Any ) -> float:
"""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
__lowercase = 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 : str , _lowerCAmelCase : Callable[[], None] ) -> [Memory, MemorySummary]:
"""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.""" )
__lowercase = 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.""" )
__lowercase = """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()
__lowercase = nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx )
__lowercase = nvml.nvmlDeviceGetMemoryInfo(_lowerCAmelCase )
__lowercase = meminfo.used
__lowercase = 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.""" )
__lowercase = None
else:
__lowercase = measure_peak_memory_cpu(_lowerCAmelCase )
__lowercase = Memory(_lowerCAmelCase ) if isinstance(_lowerCAmelCase , _lowerCAmelCase ) else memory_bytes
if self.args.trace_memory_line_by_line:
__lowercase = stop_memory_tracing(_lowerCAmelCase )
if memory is None:
__lowercase = summary.total
else:
__lowercase = None
return memory, summary
except ResourceExhaustedError as e:
self.print_fn(F'Doesn\'t fit on GPU. {e}' )
return "N/A", None
| 80 |
from math import factorial
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(lowerCamelCase ) // (factorial(lowerCamelCase ) * factorial(n - k ))
if __name__ == "__main__":
print(
"""The number of five-card hands possible from a standard""",
F'''fifty-two card deck is: {combinations(52, 5)}\n''',
)
print(
"""If a class of 40 students must be arranged into groups of""",
F'''4 for group projects, there are {combinations(40, 4)} ways''',
"""to arrange them.\n""",
)
print(
"""If 10 teams are competing in a Formula One race, there""",
F'''are {combinations(10, 3)} ways that first, second and''',
"""third place can be awarded.""",
)
| 80 | 1 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
__UpperCamelCase : List[str] = logging.getLogger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return (preds == labels).mean()
@dataclass
class __UpperCamelCase :
__snake_case :str = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
__snake_case :Optional[str] = field(
default=_lowerCAmelCase , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
@dataclass
class __UpperCamelCase :
__snake_case :str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys() )} )
__snake_case :str = field(metadata={'help': 'Should contain the data files for the task.'} )
__snake_case :int = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def snake_case ( ):
'''simple docstring'''
__lowercase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
__lowercase , __lowercase , __lowercase = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
""" --overwrite_output_dir to overcome.""" )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
"""Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s""" , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info("""Training/evaluation parameters %s""" , lowerCamelCase )
# Set seed
set_seed(training_args.seed )
try:
__lowercase = processors[data_args.task_name]()
__lowercase = processor.get_labels()
__lowercase = len(lowerCamelCase )
except KeyError:
raise ValueError("""Task not found: %s""" % (data_args.task_name) )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
__lowercase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowerCamelCase , finetuning_task=data_args.task_name , cache_dir=model_args.cache_dir , )
__lowercase = 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 , )
__lowercase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=lowerCamelCase , cache_dir=model_args.cache_dir , )
# Get datasets
__lowercase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=lowerCamelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.train , )
if training_args.do_train
else None
)
__lowercase = (
MultipleChoiceDataset(
data_dir=data_args.data_dir , tokenizer=lowerCamelCase , task=data_args.task_name , max_seq_length=data_args.max_seq_length , overwrite_cache=data_args.overwrite_cache , mode=Split.dev , )
if training_args.do_eval
else None
)
def compute_metrics(lowerCamelCase ) -> Dict:
__lowercase = np.argmax(p.predictions , axis=1 )
return {"acc": simple_accuracy(lowerCamelCase , p.label_ids )}
# Data collator
__lowercase = DataCollatorWithPadding(lowerCamelCase , pad_to_multiple_of=8 ) if training_args.fpaa else None
# Initialize our Trainer
__lowercase = Trainer(
model=lowerCamelCase , args=lowerCamelCase , train_dataset=lowerCamelCase , eval_dataset=lowerCamelCase , compute_metrics=lowerCamelCase , data_collator=lowerCamelCase , )
# Training
if training_args.do_train:
trainer.train(
model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path ) else None )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
__lowercase = {}
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
__lowercase = trainer.evaluate()
__lowercase = os.path.join(training_args.output_dir , """eval_results.txt""" )
if trainer.is_world_master():
with open(lowerCamelCase , """w""" ) as writer:
logger.info("""***** Eval results *****""" )
for key, value in result.items():
logger.info(""" %s = %s""" , lowerCamelCase , lowerCamelCase )
writer.write("""%s = %s\n""" % (key, value) )
results.update(lowerCamelCase )
return results
def snake_case ( lowerCamelCase ):
'''simple docstring'''
main()
if __name__ == "__main__":
main()
| 80 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case ( ):
'''simple docstring'''
__lowercase = [randint(-1_000 , 1_000 ) for i in range(10 )]
__lowercase = randint(-5_000 , 5_000 )
return (arr, r)
__UpperCamelCase : Any = make_dataset()
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
for triplet in permutations(lowerCamelCase , 3 ):
if sum(lowerCamelCase ) == target:
return tuple(sorted(lowerCamelCase ) )
return (0, 0, 0)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
arr.sort()
__lowercase = len(lowerCamelCase )
for i in range(n - 1 ):
__lowercase , __lowercase = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def snake_case ( ):
'''simple docstring'''
__lowercase = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
__lowercase = """
triplet_sum1(*dataset)
"""
__lowercase = """
triplet_sum2(*dataset)
"""
__lowercase = repeat(setup=lowerCamelCase , stmt=lowerCamelCase , repeat=5 , number=10_000 )
__lowercase = repeat(setup=lowerCamelCase , stmt=lowerCamelCase , repeat=5 , number=10_000 )
return (min(lowerCamelCase ), min(lowerCamelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
__UpperCamelCase : Tuple = solution_times()
print(F'''The time for naive implementation is {times[0]}.''')
print(F'''The time for optimized implementation is {times[1]}.''')
| 80 | 1 |
def snake_case ( lowerCamelCase = 100 ):
'''simple docstring'''
__lowercase = n * (n + 1) * (2 * n + 1) / 6
__lowercase = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Any , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str=None ) -> int:
"""simple docstring"""
super().__init__(
_lowerCAmelCase , question_encoder_tokenizer=_lowerCAmelCase , generator_tokenizer=_lowerCAmelCase , index=_lowerCAmelCase , init_retrieval=_lowerCAmelCase , )
__lowercase = None
def _a ( self : int , _lowerCAmelCase : int ) -> Any:
"""simple docstring"""
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
__lowercase = self._infer_socket_ifname()
# avoid clash with the NCCL port
__lowercase = str(distributed_port + 1 )
__lowercase = dist.new_group(ranks=_lowerCAmelCase , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _a ( self : Tuple ) -> List[str]:
"""simple docstring"""
return dist.get_rank(group=self.process_group ) == 0
def _a ( self : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any]=torch.floataa ) -> Tuple:
"""simple docstring"""
__lowercase = torch.empty(_lowerCAmelCase , dtype=_lowerCAmelCase )
dist.scatter(_lowerCAmelCase , src=0 , scatter_list=_lowerCAmelCase , group=self.process_group )
return target_tensor
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
__lowercase = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
__lowercase = next((addr for addr in addrs if addr.startswith("""e""" )) , _lowerCAmelCase )
return ifname
def _a ( self : str , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : int ) -> Tuple[np.ndarray, List[dict]]:
"""simple docstring"""
if not dist.is_initialized():
__lowercase , __lowercase = self._main_retrieve(_lowerCAmelCase , _lowerCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCAmelCase )
# distributed training
__lowercase = dist.get_world_size(group=self.process_group )
# gather logic
__lowercase = None
if self._is_main():
__lowercase = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(_lowerCAmelCase )]
dist.gather(torch.tensor(_lowerCAmelCase ) , dst=0 , gather_list=_lowerCAmelCase , group=self.process_group )
# scatter logic
__lowercase = question_hidden_states.shape[0]
__lowercase = []
__lowercase = []
if self._is_main():
assert len(_lowerCAmelCase ) == world_size
__lowercase , __lowercase = self._main_retrieve(torch.cat(_lowerCAmelCase ).numpy() , _lowerCAmelCase )
__lowercase , __lowercase = torch.tensor(_lowerCAmelCase ), torch.tensor(_lowerCAmelCase )
__lowercase = self._chunk_tensor(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._chunk_tensor(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._scattered(_lowerCAmelCase , [n_queries, n_docs] , target_type=torch.intaa )
__lowercase = self._scattered(_lowerCAmelCase , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(_lowerCAmelCase )
| 80 | 1 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskFormerForInstanceSegmentation, MaskFormerModel
if is_vision_available():
from transformers import MaskFormerImageProcessor
if is_vision_available():
from PIL import Image
class __UpperCamelCase :
def __init__( self : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any]=2 , _lowerCAmelCase : Union[str, Any]=True , _lowerCAmelCase : Union[str, Any]=False , _lowerCAmelCase : Optional[Any]=10 , _lowerCAmelCase : Any=3 , _lowerCAmelCase : str=32 * 4 , _lowerCAmelCase : List[str]=32 * 6 , _lowerCAmelCase : int=4 , _lowerCAmelCase : int=32 , ) -> Dict:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = is_training
__lowercase = use_auxiliary_loss
__lowercase = num_queries
__lowercase = num_channels
__lowercase = min_size
__lowercase = max_size
__lowercase = num_labels
__lowercase = mask_feature_size
def _a ( self : str ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
_lowerCAmelCase )
__lowercase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=_lowerCAmelCase )
__lowercase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=_lowerCAmelCase ) > 0.5
).float()
__lowercase = (torch.rand((self.batch_size, self.num_labels) , device=_lowerCAmelCase ) > 0.5).long()
__lowercase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return MaskFormerConfig.from_backbone_and_decoder_configs(
backbone_config=SwinConfig(
depths=[1, 1, 1, 1] , ) , decoder_config=DetrConfig(
decoder_ffn_dim=128 , num_queries=self.num_queries , decoder_attention_heads=2 , d_model=self.mask_feature_size , ) , mask_feature_size=self.mask_feature_size , fpn_feature_size=self.mask_feature_size , num_channels=self.num_channels , num_labels=self.num_labels , )
def _a ( self : int ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase = self.prepare_config_and_inputs()
__lowercase = {"""pixel_values""": pixel_values, """pixel_mask""": pixel_mask}
return config, inputs_dict
def _a ( self : List[str] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = output.encoder_hidden_states
__lowercase = output.pixel_decoder_hidden_states
__lowercase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(_lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCAmelCase ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(_lowerCAmelCase ) , config.decoder_config.decoder_layers )
def _a ( self : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int]=False ) -> int:
"""simple docstring"""
with torch.no_grad():
__lowercase = MaskFormerModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(pixel_values=_lowerCAmelCase , pixel_mask=_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
# the correct shape of output.transformer_decoder_hidden_states ensure the correcteness of the
# encoder and pixel decoder
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.mask_feature_size) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Dict , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = MaskFormerForInstanceSegmentation(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
def comm_check_on_output(_lowerCAmelCase : List[str] ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__lowercase = model(pixel_values=_lowerCAmelCase , pixel_mask=_lowerCAmelCase )
__lowercase = model(_lowerCAmelCase )
comm_check_on_output(_lowerCAmelCase )
__lowercase = model(
pixel_values=_lowerCAmelCase , pixel_mask=_lowerCAmelCase , mask_labels=_lowerCAmelCase , class_labels=_lowerCAmelCase )
comm_check_on_output(_lowerCAmelCase )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :str = (MaskFormerModel, MaskFormerForInstanceSegmentation) if is_torch_available() else ()
__snake_case :Optional[int] = (
{'feature-extraction': MaskFormerModel, 'image-segmentation': MaskFormerForInstanceSegmentation}
if is_torch_available()
else {}
)
__snake_case :Any = False
__snake_case :str = False
__snake_case :Any = False
__snake_case :Optional[int] = False
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase = MaskFormerModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCAmelCase , **_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
def _a ( self : List[str] ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskformer_instance_segmentation_head_model(*_lowerCAmelCase )
@unittest.skip(reason="""MaskFormer does not use inputs_embeds""" )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormer does not have a get_input_embeddings method""" )
def _a ( self : List[str] ) -> str:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormer is not a generative model""" )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormer does not use token embeddings""" )
def _a ( self : List[str] ) -> Optional[Any]:
"""simple docstring"""
pass
@require_torch_multi_gpu
@unittest.skip(
reason="""MaskFormer has some layers using `add_module` which doesn't work well with `nn.DataParallel`""" )
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
pass
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
@slow
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
for model_name in ["facebook/maskformer-swin-small-coco"]:
__lowercase = MaskFormerModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = (self.model_tester.min_size,) * 2
__lowercase = {
"""pixel_values""": torch.randn((2, 3, *size) , device=_lowerCAmelCase ),
"""mask_labels""": torch.randn((2, 10, *size) , device=_lowerCAmelCase ),
"""class_labels""": torch.zeros(2 , 10 , device=_lowerCAmelCase ).long(),
}
__lowercase = MaskFormerForInstanceSegmentation(MaskFormerConfig() ).to(_lowerCAmelCase )
__lowercase = model(**_lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskformer_model(_lowerCAmelCase , **_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase ).to(_lowerCAmelCase )
__lowercase = model(**_lowerCAmelCase , output_attentions=_lowerCAmelCase )
self.assertTrue(outputs.attentions is not None )
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
if not self.model_tester.is_training:
return
# only MaskFormerForInstanceSegmentation has the loss
__lowercase = self.all_model_classes[1]
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase = self.model_tester.prepare_config_and_inputs()
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.train()
__lowercase = model(_lowerCAmelCase , mask_labels=_lowerCAmelCase , class_labels=_lowerCAmelCase ).loss
loss.backward()
def _a ( self : List[Any] ) -> List[str]:
"""simple docstring"""
__lowercase = self.all_model_classes[1]
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase = self.model_tester.prepare_config_and_inputs()
__lowercase = True
__lowercase = True
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.train()
__lowercase = model(_lowerCAmelCase , mask_labels=_lowerCAmelCase , class_labels=_lowerCAmelCase )
__lowercase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__lowercase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
# we requires_grad=True in inputs_embeds (line 2152), the original implementation don't
__lowercase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__lowercase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=_lowerCAmelCase )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
__UpperCamelCase : Dict = 1e-4
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_vision
@slow
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
return (
MaskFormerImageProcessor.from_pretrained("""facebook/maskformer-swin-small-coco""" )
if is_vision_available()
else None
)
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = MaskFormerModel.from_pretrained("""facebook/maskformer-swin-small-coco""" ).to(_lowerCAmelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
__lowercase = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
__lowercase = torch.tensor(
[[-0.0_482, 0.9_228, 0.4_951], [-0.2_547, 0.8_017, 0.8_527], [-0.0_069, 0.3_385, -0.0_089]] ).to(_lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
__lowercase = torch.tensor(
[[-0.8_422, -0.8_434, -0.9_718], [-1.0_144, -0.5_565, -0.4_195], [-1.0_038, -0.4_484, -0.1_961]] ).to(_lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
__lowercase = torch.tensor(
[[0.2_852, -0.0_159, 0.9_735], [0.6_254, 0.1_858, 0.8_529], [-0.0_680, -0.4_116, 1.8_413]] ).to(_lowerCAmelCase )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(_lowerCAmelCase )
.eval()
)
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
__lowercase = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# masks_queries_logits
__lowercase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__lowercase = [
[-1.3_737_124, -1.7_724_937, -1.9_364_233],
[-1.5_977_281, -1.9_867_939, -2.1_523_695],
[-1.5_795_398, -1.9_269_832, -2.093_942],
]
__lowercase = torch.tensor(_lowerCAmelCase ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
# class_queries_logits
__lowercase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__lowercase = torch.tensor(
[
[1.6512e00, -5.2572e00, -3.3519e00],
[3.6169e-02, -5.9025e00, -2.9313e00],
[1.0766e-04, -7.7630e00, -5.1263e00],
] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
def _a ( self : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-resnet101-coco-stuff""" )
.to(_lowerCAmelCase )
.eval()
)
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
__lowercase = inputs["""pixel_values"""].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(_lowerCAmelCase , (1, 3, 800, 1088) )
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# masks_queries_logits
__lowercase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.decoder_config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) , )
__lowercase = [[-0.9_046, -2.6_366, -4.6_062], [-3.4_179, -5.7_890, -8.8_057], [-4.9_179, -7.6_560, -10.7_711]]
__lowercase = torch.tensor(_lowerCAmelCase ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
# class_queries_logits
__lowercase = outputs.class_queries_logits
self.assertEqual(
class_queries_logits.shape , (1, model.config.decoder_config.num_queries, model.config.num_labels + 1) )
__lowercase = torch.tensor(
[[4.7_188, -3.2_585, -2.8_857], [6.6_871, -2.9_181, -1.2_487], [7.2_449, -2.2_764, -2.1_874]] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , _lowerCAmelCase , atol=_lowerCAmelCase ) )
def _a ( self : str ) -> str:
"""simple docstring"""
__lowercase = (
MaskFormerForInstanceSegmentation.from_pretrained("""facebook/maskformer-swin-small-coco""" )
.to(_lowerCAmelCase )
.eval()
)
__lowercase = self.default_image_processor
__lowercase = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors="""pt""" , )
__lowercase = inputs["""pixel_values"""].to(_lowerCAmelCase )
__lowercase = [el.to(_lowerCAmelCase ) for el in inputs["""mask_labels"""]]
__lowercase = [el.to(_lowerCAmelCase ) for el in inputs["""class_labels"""]]
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
self.assertTrue(outputs.loss is not None )
| 80 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_case :List[Any] = 1
@register_to_config
def __init__( self : str , _lowerCAmelCase : int = 1000 , _lowerCAmelCase : Optional[Union[np.ndarray, List[float]]] = None ) -> Optional[int]:
"""simple docstring"""
self.set_timesteps(_lowerCAmelCase )
# standard deviation of the initial noise distribution
__lowercase = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
__lowercase = 4
# running values
__lowercase = []
def _a ( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Union[str, torch.device] = None ) -> int:
"""simple docstring"""
__lowercase = num_inference_steps
__lowercase = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
__lowercase = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
__lowercase = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
__lowercase = torch.sin(steps * math.pi / 2 ) ** 2
__lowercase = (1.0 - self.betas**2) ** 0.5
__lowercase = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
__lowercase = timesteps.to(_lowerCAmelCase )
__lowercase = []
def _a ( self : List[str] , _lowerCAmelCase : torch.FloatTensor , _lowerCAmelCase : int , _lowerCAmelCase : torch.FloatTensor , _lowerCAmelCase : bool = True , ) -> Union[SchedulerOutput, Tuple]:
"""simple docstring"""
if self.num_inference_steps is None:
raise ValueError(
"""Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )
__lowercase = (self.timesteps == timestep).nonzero().item()
__lowercase = timestep_index + 1
__lowercase = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(_lowerCAmelCase )
if len(self.ets ) == 1:
__lowercase = self.ets[-1]
elif len(self.ets ) == 2:
__lowercase = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
__lowercase = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
__lowercase = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
__lowercase = self._get_prev_sample(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_lowerCAmelCase )
def _a ( self : Union[str, Any] , _lowerCAmelCase : torch.FloatTensor , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : str ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def _a ( self : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = self.alphas[timestep_index]
__lowercase = self.betas[timestep_index]
__lowercase = self.alphas[prev_timestep_index]
__lowercase = self.betas[prev_timestep_index]
__lowercase = (sample - sigma * ets) / max(_lowerCAmelCase , 1e-8 )
__lowercase = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : Optional[Any] ) -> Dict:
"""simple docstring"""
return self.config.num_train_timesteps
| 80 | 1 |
from maths.prime_check import is_prime
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
__lowercase = F'Input value of [number={number}] must be an integer'
raise TypeError(lowerCamelCase )
if is_prime(lowerCamelCase ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar("""T""")
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] , _lowerCAmelCase : T ) -> List[str]:
"""simple docstring"""
__lowercase = data
__lowercase = None
def __str__( self : List[str] ) -> str:
"""simple docstring"""
return F'{self.data}'
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] ) -> None:
"""simple docstring"""
__lowercase = None
def __iter__( self : int ) -> Iterator[T]:
"""simple docstring"""
__lowercase = self.top
while node:
yield node.data
__lowercase = node.next
def __str__( self : List[str] ) -> str:
"""simple docstring"""
return "->".join([str(_lowerCAmelCase ) for item in self] )
def __len__( self : Any ) -> int:
"""simple docstring"""
return len(tuple(iter(self ) ) )
def _a ( self : str ) -> bool:
"""simple docstring"""
return self.top is None
def _a ( self : List[str] , _lowerCAmelCase : T ) -> None:
"""simple docstring"""
__lowercase = Node(_lowerCAmelCase )
if not self.is_empty():
__lowercase = self.top
__lowercase = node
def _a ( self : Union[str, Any] ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError("""pop from empty stack""" )
assert isinstance(self.top , _lowerCAmelCase )
__lowercase = self.top
__lowercase = self.top.next
return pop_node.data
def _a ( self : int ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError("""peek from empty stack""" )
assert self.top is not None
return self.top.data
def _a ( self : int ) -> None:
"""simple docstring"""
__lowercase = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 80 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
__UpperCamelCase : int = {"""configuration_speech_encoder_decoder""": ["""SpeechEncoderDecoderConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Union[str, Any] = ["""SpeechEncoderDecoderModel"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = ["""FlaxSpeechEncoderDecoderModel"""]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
__UpperCamelCase : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
class __UpperCamelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt="""first prompt""" , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="""numpy""" , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCAmelCase )
__lowercase = VersatileDiffusionPipeline.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = generator.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt="""first prompt""" , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="""numpy""" , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def _a ( self : Any ) -> Dict:
"""simple docstring"""
__lowercase = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """cyberpunk 2077"""
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__lowercase = """A painting of a squirrel eating a burger """
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.text_to_image(
prompt=_lowerCAmelCase , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__lowercase = pipe.image_variation(_lowerCAmelCase , generator=_lowerCAmelCase , output_type="""numpy""" ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 80 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
enable_full_determinism()
class __UpperCamelCase ( unittest.TestCase ):
__snake_case :Union[str, Any] = StableDiffusionLDMaDPipeline
__snake_case :List[str] = TEXT_TO_IMAGE_PARAMS
__snake_case :List[str] = TEXT_TO_IMAGE_BATCH_PARAMS
__snake_case :str = TEXT_TO_IMAGE_IMAGE_PARAMS
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
__lowercase = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" , clip_sample=_lowerCAmelCase , set_alpha_to_one=_lowerCAmelCase , )
torch.manual_seed(0 )
__lowercase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=6 , out_channels=6 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
__lowercase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__lowercase = CLIPTextModel(_lowerCAmelCase )
__lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowercase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _a ( self : str , _lowerCAmelCase : Any , _lowerCAmelCase : Dict=0 ) -> List[Any]:
"""simple docstring"""
if str(_lowerCAmelCase ).startswith("""mps""" ):
__lowercase = torch.manual_seed(_lowerCAmelCase )
else:
__lowercase = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = StableDiffusionLDMaDPipeline(**_lowerCAmelCase )
__lowercase = ldmad_pipe.to(_lowerCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs(_lowerCAmelCase )
__lowercase = ldmad_pipe(**_lowerCAmelCase )
__lowercase , __lowercase = output.rgb, output.depth
__lowercase = rgb[0, -3:, -3:, -1]
__lowercase = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
__lowercase = np.array(
[0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262] )
__lowercase = np.array([103.46_727, 85.812_004, 87.849_236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.get_dummy_components()
__lowercase = StableDiffusionLDMaDPipeline(**_lowerCAmelCase )
__lowercase = ldmad_pipe.to(_lowerCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs(_lowerCAmelCase )
__lowercase = 3 * [inputs["""prompt"""]]
# forward
__lowercase = ldmad_pipe(**_lowerCAmelCase )
__lowercase , __lowercase = output.rgb, output.depth
__lowercase = rgb_slice_a[0, -3:, -3:, -1]
__lowercase = depth_slice_a[0, -3:, -1]
__lowercase = self.get_dummy_inputs(_lowerCAmelCase )
__lowercase = 3 * [inputs.pop("""prompt""" )]
__lowercase = ldmad_pipe.tokenizer(
_lowerCAmelCase , padding="""max_length""" , max_length=ldmad_pipe.tokenizer.model_max_length , truncation=_lowerCAmelCase , return_tensors="""pt""" , )
__lowercase = text_inputs["""input_ids"""].to(_lowerCAmelCase )
__lowercase = ldmad_pipe.text_encoder(_lowerCAmelCase )[0]
__lowercase = prompt_embeds
# forward
__lowercase = ldmad_pipe(**_lowerCAmelCase )
__lowercase , __lowercase = output.rgb, output.depth
__lowercase = rgb_slice_a[0, -3:, -3:, -1]
__lowercase = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def _a ( self : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = PNDMScheduler(skip_prk_steps=_lowerCAmelCase )
__lowercase = StableDiffusionLDMaDPipeline(**_lowerCAmelCase )
__lowercase = ldmad_pipe.to(_lowerCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs(_lowerCAmelCase )
__lowercase = """french fries"""
__lowercase = ldmad_pipe(**_lowerCAmelCase , negative_prompt=_lowerCAmelCase )
__lowercase , __lowercase = output.rgb, output.depth
__lowercase = rgb[0, -3:, -3:, -1]
__lowercase = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
__lowercase = np.array(
[0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217] )
__lowercase = np.array([107.84_738, 84.62_802, 89.962_135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : int ) -> Union[str, Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : List[Any]="cpu" , _lowerCAmelCase : int=torch.floataa , _lowerCAmelCase : int=0 ) -> Optional[int]:
"""simple docstring"""
__lowercase = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
__lowercase = np.random.RandomState(_lowerCAmelCase ).standard_normal((1, 4, 64, 64) )
__lowercase = torch.from_numpy(_lowerCAmelCase ).to(device=_lowerCAmelCase , dtype=_lowerCAmelCase )
__lowercase = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
__lowercase = StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d""" )
__lowercase = ldmad_pipe.to(_lowerCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_inputs(_lowerCAmelCase )
__lowercase = ldmad_pipe(**_lowerCAmelCase )
__lowercase , __lowercase = output.rgb, output.depth
__lowercase = rgb[0, -3:, -3:, -1].flatten()
__lowercase = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512)
__lowercase = np.array(
[0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706] )
__lowercase = np.array(
[0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : int ) -> Tuple:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any]="cpu" , _lowerCAmelCase : Dict=torch.floataa , _lowerCAmelCase : Optional[int]=0 ) -> Tuple:
"""simple docstring"""
__lowercase = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
__lowercase = np.random.RandomState(_lowerCAmelCase ).standard_normal((1, 4, 64, 64) )
__lowercase = torch.from_numpy(_lowerCAmelCase ).to(device=_lowerCAmelCase , dtype=_lowerCAmelCase )
__lowercase = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 50,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
__lowercase = StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d""" ).to(_lowerCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_inputs(_lowerCAmelCase )
__lowercase = ldmad_pipe(**_lowerCAmelCase )
__lowercase , __lowercase = output.rgb, output.depth
__lowercase = 0.495_586
__lowercase = 0.33_795_515
__lowercase = 112.48_518
__lowercase = 98.489_746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def _a ( self : Tuple ) -> Dict:
"""simple docstring"""
__lowercase = StableDiffusionLDMaDPipeline.from_pretrained("""Intel/ldm3d-4c""" ).to(_lowerCAmelCase )
ldmad_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_inputs(_lowerCAmelCase )
__lowercase = ldmad_pipe(**_lowerCAmelCase )
__lowercase , __lowercase = output.rgb, output.depth
__lowercase = 0.4_194_127
__lowercase = 0.35_375_586
__lowercase = 0.5_638_502
__lowercase = 0.34_686_103
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
| 80 |
from __future__ import annotations
from collections.abc import MutableSequence
class __UpperCamelCase :
def __init__( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : MutableSequence[float] ) -> None:
"""simple docstring"""
if len(_lowerCAmelCase ) != degree + 1:
raise ValueError(
"""The number of coefficients should be equal to the degree + 1.""" )
__lowercase = list(_lowerCAmelCase )
__lowercase = degree
def __add__( self : Optional[int] , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
if self.degree > polynomial_a.degree:
__lowercase = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , _lowerCAmelCase )
else:
__lowercase = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , _lowerCAmelCase )
def __sub__( self : int , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : Union[str, Any] ) -> Polynomial:
"""simple docstring"""
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Optional[int] , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , _lowerCAmelCase )
def _a ( self : Optional[int] , _lowerCAmelCase : int | float ) -> int | float:
"""simple docstring"""
__lowercase = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : Dict ) -> str:
"""simple docstring"""
__lowercase = """"""
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(_lowerCAmelCase )
return polynomial
def __repr__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return self.__str__()
def _a ( self : List[str] ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * self.degree
for i in range(self.degree ):
__lowercase = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , _lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : int | float = 0 ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * (self.degree + 2)
__lowercase = constant
for i in range(self.degree + 1 ):
__lowercase = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , _lowerCAmelCase )
def __eq__( self : List[str] , _lowerCAmelCase : object ) -> bool:
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : Dict , _lowerCAmelCase : object ) -> bool:
"""simple docstring"""
return not self.__eq__(_lowerCAmelCase )
| 80 | 1 |
from pathlib import Path
import fire
from tqdm import tqdm
def snake_case ( lowerCamelCase="ro" , lowerCamelCase="en" , lowerCamelCase="wmt16" , lowerCamelCase=None ):
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError("""run pip install datasets""" )
__lowercase = F'{src_lang}-{tgt_lang}'
print(F'Converting {dataset}-{pair}' )
__lowercase = datasets.load_dataset(lowerCamelCase , lowerCamelCase )
if save_dir is None:
__lowercase = F'{dataset}-{pair}'
__lowercase = Path(lowerCamelCase )
save_dir.mkdir(exist_ok=lowerCamelCase )
for split in ds.keys():
print(F'Splitting {split} with {ds[split].num_rows} records' )
# to save to val.source, val.target like summary datasets
__lowercase = """val""" if split == """validation""" else split
__lowercase = save_dir.joinpath(F'{fn}.source' )
__lowercase = save_dir.joinpath(F'{fn}.target' )
__lowercase = src_path.open("""w+""" )
__lowercase = tgt_path.open("""w+""" )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
__lowercase = x["""translation"""]
src_fp.write(ex[src_lang] + """\n""" )
tgt_fp.write(ex[tgt_lang] + """\n""" )
print(F'Saved {dataset} dataset to {save_dir}' )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset)
| 80 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__lowercase = len(lowerCamelCase )
__lowercase = max(lowerCamelCase )
__lowercase = min(lowerCamelCase )
# create the counting array
__lowercase = coll_max + 1 - coll_min
__lowercase = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , lowerCamelCase ):
__lowercase = counting_arr[i] + counting_arr[i - 1]
# create the output collection
__lowercase = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , lowerCamelCase ) ):
__lowercase = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return "".join([chr(lowerCamelCase ) for i in counting_sort([ord(lowerCamelCase ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string("""thisisthestring""") == "eghhiiinrsssttt"
__UpperCamelCase : str = input("""Enter numbers separated by a comma:\n""").strip()
__UpperCamelCase : Union[str, Any] = [int(item) for item in user_input.split(""",""")]
print(counting_sort(unsorted))
| 80 | 1 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__UpperCamelCase : int = 16
__UpperCamelCase : str = 32
def snake_case ( lowerCamelCase , lowerCamelCase = 16 , lowerCamelCase = "bert-base-cased" ):
'''simple docstring'''
__lowercase = AutoTokenizer.from_pretrained(lowerCamelCase )
__lowercase = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(lowerCamelCase ):
# max_length=None => use the model max length (it's actually the default)
__lowercase = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=lowerCamelCase , max_length=lowerCamelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
__lowercase = datasets.map(
lowerCamelCase , batched=lowerCamelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , load_from_cache_file=lowerCamelCase )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowercase = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(lowerCamelCase ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase , padding="""max_length""" , max_length=128 , return_tensors="""pt""" )
return tokenizer.pad(lowerCamelCase , padding="""longest""" , return_tensors="""pt""" )
# Instantiate dataloaders.
__lowercase = DataLoader(
tokenized_datasets["""train"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase )
__lowercase = DataLoader(
tokenized_datasets["""validation"""] , shuffle=lowerCamelCase , collate_fn=lowerCamelCase , batch_size=lowerCamelCase )
return train_dataloader, eval_dataloader
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
model.eval()
__lowercase = 0
for step, batch in enumerate(lowerCamelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowercase = model(**lowerCamelCase )
__lowercase = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
__lowercase , __lowercase = accelerator.gather(
(predictions, batch["""labels"""]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowerCamelCase ) - 1:
__lowercase = predictions[: len(eval_dataloader.dataset ) - samples_seen]
__lowercase = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowerCamelCase , references=lowerCamelCase , )
__lowercase = metric.compute()
return eval_metric["accuracy"]
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowercase = config["""lr"""]
__lowercase = int(config["""num_epochs"""] )
__lowercase = int(config["""seed"""] )
__lowercase = int(config["""batch_size"""] )
__lowercase = args.model_name_or_path
set_seed(lowerCamelCase )
__lowercase , __lowercase = get_dataloaders(lowerCamelCase , lowerCamelCase , lowerCamelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowercase = AutoModelForSequenceClassification.from_pretrained(lowerCamelCase , return_dict=lowerCamelCase )
# Instantiate optimizer
__lowercase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or """optimizer""" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
__lowercase = optimizer_cls(params=model.parameters() , lr=lowerCamelCase )
if accelerator.state.deepspeed_plugin is not None:
__lowercase = accelerator.state.deepspeed_plugin.deepspeed_config[
"""gradient_accumulation_steps"""
]
else:
__lowercase = 1
__lowercase = (len(lowerCamelCase ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
__lowercase = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase , num_warmup_steps=0 , num_training_steps=lowerCamelCase , )
else:
__lowercase = DummyScheduler(lowerCamelCase , total_num_steps=lowerCamelCase , warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase = accelerator.prepare(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
# We need to keep track of how many total steps we have iterated over
__lowercase = 0
# We also need to keep track of the stating epoch so files are named properly
__lowercase = 0
__lowercase = evaluate.load("""glue""" , """mrpc""" )
__lowercase = num_epochs
if args.partial_train_epoch is not None:
__lowercase = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
__lowercase = args.resume_from_checkpoint.split("""epoch_""" )[1]
__lowercase = """"""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
__lowercase = int(lowerCamelCase ) + 1
__lowercase = evaluation_loop(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
accelerator.print("""resumed checkpoint performance:""" , lowerCamelCase )
accelerator.print("""resumed checkpoint's scheduler's lr:""" , lr_scheduler.get_lr()[0] )
accelerator.print("""resumed optimizers's lr:""" , optimizer.param_groups[0]["""lr"""] )
with open(os.path.join(args.output_dir , F'state_{starting_epoch-1}.json' ) , """r""" ) as f:
__lowercase = json.load(lowerCamelCase )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
__lowercase = {}
for epoch in range(lowerCamelCase , lowerCamelCase ):
model.train()
for step, batch in enumerate(lowerCamelCase ):
__lowercase = model(**lowerCamelCase )
__lowercase = outputs.loss
__lowercase = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
__lowercase = F'epoch_{epoch}'
__lowercase = os.path.join(args.output_dir , lowerCamelCase )
accelerator.save_state(lowerCamelCase )
__lowercase = evaluation_loop(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = accuracy
__lowercase = lr_scheduler.get_lr()[0]
__lowercase = optimizer.param_groups[0]["""lr"""]
__lowercase = epoch
__lowercase = overall_step
accelerator.print(F'epoch {epoch}:' , lowerCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir , F'state_{epoch}.json' ) , """w""" ) as f:
json.dump(lowerCamelCase , lowerCamelCase )
def snake_case ( ):
'''simple docstring'''
__lowercase = argparse.ArgumentParser(description="""Simple example of training script tracking peak GPU memory usage.""" )
parser.add_argument(
"""--model_name_or_path""" , type=lowerCamelCase , default="""bert-base-cased""" , help="""Path to pretrained model or model identifier from huggingface.co/models.""" , required=lowerCamelCase , )
parser.add_argument(
"""--output_dir""" , type=lowerCamelCase , default=""".""" , help="""Optional save directory where all checkpoint folders will be stored. Default is the current working directory.""" , )
parser.add_argument(
"""--resume_from_checkpoint""" , type=lowerCamelCase , default=lowerCamelCase , help="""If the training should continue from a checkpoint folder.""" , )
parser.add_argument(
"""--partial_train_epoch""" , type=lowerCamelCase , default=lowerCamelCase , help="""If passed, the training will stop after this number of epochs.""" , )
parser.add_argument(
"""--num_epochs""" , type=lowerCamelCase , default=2 , help="""Number of train epochs.""" , )
__lowercase = parser.parse_args()
__lowercase = {"""lr""": 2e-5, """num_epochs""": args.num_epochs, """seed""": 42, """batch_size""": 16}
training_function(lowerCamelCase , lowerCamelCase )
if __name__ == "__main__":
main()
| 80 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __UpperCamelCase :
def __init__( self : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : int=13 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : str=3 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[int]=[10, 20, 30, 40] , _lowerCAmelCase : Optional[Any]=[2, 2, 3, 2] , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : List[str]=37 , _lowerCAmelCase : List[str]="gelu" , _lowerCAmelCase : List[Any]=10 , _lowerCAmelCase : int=0.02 , _lowerCAmelCase : str=["stage2", "stage3", "stage4"] , _lowerCAmelCase : Dict=[2, 3, 4] , _lowerCAmelCase : Tuple=None , ) -> Any:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = num_channels
__lowercase = num_stages
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = is_training
__lowercase = use_labels
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = num_labels
__lowercase = initializer_range
__lowercase = out_features
__lowercase = out_indices
__lowercase = scope
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.num_labels )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def _a ( self : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self : Optional[int] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__lowercase = None
__lowercase = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[Any] = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
__snake_case :List[str] = (
{'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
if is_torch_available()
else {}
)
__snake_case :str = True
__snake_case :Any = False
__snake_case :Any = False
__snake_case :Any = False
__snake_case :int = False
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason="""ConvNext does not use inputs_embeds""" )
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not support input and output embeddings""" )
def _a ( self : Dict ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not use feedforward chunking""" )
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
pass
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int] ):
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = self.model_tester.num_stages
self.assertEqual(len(_lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = ConvNextModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224""" ) if is_vision_available() else None
@slow
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""" ).to(_lowerCAmelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# verify the logits
__lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = torch.tensor([-0.0_260, -0.4_739, 0.1_911] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase , _lowerCAmelCase ):
__snake_case :Union[str, Any] = (ConvNextBackbone,) if is_torch_available() else ()
__snake_case :str = ConvNextConfig
__snake_case :Optional[Any] = False
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = ConvNextModelTester(self )
| 80 | 1 |
from collections.abc import Sequence
from queue import Queue
class __UpperCamelCase :
def __init__( self : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any=None , _lowerCAmelCase : Dict=None ) -> List[Any]:
"""simple docstring"""
__lowercase = start
__lowercase = end
__lowercase = val
__lowercase = (start + end) // 2
__lowercase = left
__lowercase = right
def __repr__( self : List[Any] ) -> List[Any]:
"""simple docstring"""
return F'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'
class __UpperCamelCase :
def __init__( self : Dict , _lowerCAmelCase : Sequence , _lowerCAmelCase : str ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = collection
__lowercase = function
if self.collection:
__lowercase = self._build_tree(0 , len(_lowerCAmelCase ) - 1 )
def _a ( self : int , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
self._update_tree(self.root , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple ) -> List[Any]:
"""simple docstring"""
return self._query_range(self.root , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Any , _lowerCAmelCase : str , _lowerCAmelCase : str ) -> str:
"""simple docstring"""
if start == end:
return SegmentTreeNode(_lowerCAmelCase , _lowerCAmelCase , self.collection[start] )
__lowercase = (start + end) // 2
__lowercase = self._build_tree(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._build_tree(mid + 1 , _lowerCAmelCase )
return SegmentTreeNode(_lowerCAmelCase , _lowerCAmelCase , self.fn(left.val , right.val ) , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : List[str] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any] ) -> Tuple:
"""simple docstring"""
if node.start == i and node.end == i:
__lowercase = val
return
if i <= node.mid:
self._update_tree(node.left , _lowerCAmelCase , _lowerCAmelCase )
else:
self._update_tree(node.right , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self.fn(node.left.val , node.right.val )
def _a ( self : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left , _lowerCAmelCase , _lowerCAmelCase )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left , _lowerCAmelCase , node.mid ) , self._query_range(node.right , node.mid + 1 , _lowerCAmelCase ) , )
else:
# range in right child tree
return self._query_range(node.right , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : List[Any] ) -> Tuple:
"""simple docstring"""
if self.root is not None:
__lowercase = Queue()
queue.put(self.root )
while not queue.empty():
__lowercase = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print("""*""" * 50)
__UpperCamelCase : Union[str, Any] = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 80 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : List[str] = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
__UpperCamelCase : Tuple = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
__UpperCamelCase : Optional[Any] = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
__UpperCamelCase : Optional[Any] = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
__UpperCamelCase : int = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
__UpperCamelCase : List[Any] = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
__UpperCamelCase : List[Any] = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
__UpperCamelCase : List[str] = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
__UpperCamelCase : List[str] = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
__UpperCamelCase : int = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
__UpperCamelCase : Dict = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
__UpperCamelCase : str = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
__UpperCamelCase : Optional[int] = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
__UpperCamelCase : Dict = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
__UpperCamelCase : List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__UpperCamelCase : str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__UpperCamelCase : Optional[int] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__UpperCamelCase : Dict = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__UpperCamelCase : Dict = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : int = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__UpperCamelCase : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__UpperCamelCase : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__UpperCamelCase : Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__UpperCamelCase : str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__UpperCamelCase : List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__UpperCamelCase : Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Tuple = FLAX_MODEL_MAPPING
__UpperCamelCase : Tuple = auto_class_update(FlaxAutoModel)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Union[str, Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
__UpperCamelCase : List[Any] = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
__UpperCamelCase : Union[str, Any] = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :List[Any] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
__UpperCamelCase : Dict = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__UpperCamelCase : Optional[Any] = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[int] = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
__UpperCamelCase : Union[str, Any] = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :List[Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[int] = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[int] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
__UpperCamelCase : int = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :str = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
__UpperCamelCase : int = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[Any] = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
__UpperCamelCase : Tuple = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
__UpperCamelCase : str = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 80 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Any = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerConfig""",
"""SwiftFormerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[int] = [
"""SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""SwiftFormerForImageClassification""",
"""SwiftFormerModel""",
"""SwiftFormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
__UpperCamelCase : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCamelCase : int = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import logging
from ..processors.glue import glue_convert_examples_to_features, glue_output_modes, glue_processors
from ..processors.utils import InputFeatures
__UpperCamelCase : List[str] = logging.get_logger(__name__)
@dataclass
class __UpperCamelCase :
__snake_case :str = field(metadata={'help': 'The name of the task to train on: ' + ', '.join(glue_processors.keys() )} )
__snake_case :str = field(
metadata={'help': 'The input data dir. Should contain the .tsv files (or other data files) for the task.'} )
__snake_case :int = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
__snake_case :bool = field(
default=_lowerCAmelCase , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.task_name.lower()
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Optional[Any] = 'train'
__snake_case :int = 'dev'
__snake_case :Optional[int] = 'test'
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :GlueDataTrainingArguments
__snake_case :str
__snake_case :List[InputFeatures]
def __init__( self : int , _lowerCAmelCase : GlueDataTrainingArguments , _lowerCAmelCase : PreTrainedTokenizerBase , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : Union[str, Split] = Split.train , _lowerCAmelCase : Optional[str] = None , ) -> str:
"""simple docstring"""
warnings.warn(
"""This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets """
"""library. You can have a look at this example script for pointers: """
"""https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py""" , _lowerCAmelCase , )
__lowercase = args
__lowercase = glue_processors[args.task_name]()
__lowercase = glue_output_modes[args.task_name]
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
try:
__lowercase = Split[mode]
except KeyError:
raise KeyError("""mode is not a valid split name""" )
# Load data features from cache or dataset file
__lowercase = os.path.join(
cache_dir if cache_dir is not None else args.data_dir , F'cached_{mode.value}_{tokenizer.__class__.__name__}_{args.max_seq_length}_{args.task_name}' , )
__lowercase = self.processor.get_labels()
if args.task_name in ["mnli", "mnli-mm"] and tokenizer.__class__.__name__ in (
"RobertaTokenizer",
"RobertaTokenizerFast",
"XLMRobertaTokenizer",
"BartTokenizer",
"BartTokenizerFast",
):
# HACK(label indices are swapped in RoBERTa pretrained model)
__lowercase , __lowercase = label_list[2], label_list[1]
__lowercase = label_list
# Make sure only the first process in distributed training processes the dataset,
# and the others will use the cache.
__lowercase = cached_features_file + """.lock"""
with FileLock(_lowerCAmelCase ):
if os.path.exists(_lowerCAmelCase ) and not args.overwrite_cache:
__lowercase = time.time()
__lowercase = torch.load(_lowerCAmelCase )
logger.info(
F'Loading features from cached file {cached_features_file} [took %.3f s]' , time.time() - start )
else:
logger.info(F'Creating features from dataset file at {args.data_dir}' )
if mode == Split.dev:
__lowercase = self.processor.get_dev_examples(args.data_dir )
elif mode == Split.test:
__lowercase = self.processor.get_test_examples(args.data_dir )
else:
__lowercase = self.processor.get_train_examples(args.data_dir )
if limit_length is not None:
__lowercase = examples[:limit_length]
__lowercase = glue_convert_examples_to_features(
_lowerCAmelCase , _lowerCAmelCase , max_length=args.max_seq_length , label_list=_lowerCAmelCase , output_mode=self.output_mode , )
__lowercase = time.time()
torch.save(self.features , _lowerCAmelCase )
# ^ This seems to take a lot of time so I want to investigate why and how we can improve.
logger.info(
F'Saving features into cached file {cached_features_file} [took {time.time() - start:.3f} s]' )
def __len__( self : Any ) -> Optional[Any]:
"""simple docstring"""
return len(self.features )
def __getitem__( self : Any , _lowerCAmelCase : Optional[Any] ) -> InputFeatures:
"""simple docstring"""
return self.features[i]
def _a ( self : int ) -> Optional[Any]:
"""simple docstring"""
return self.label_list
| 80 |
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase : Union[str, Any] = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""
__UpperCamelCase : List[str] = """
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results['matthews_correlation'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results['matthews_correlation'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results['matthews_correlation'], 2))
-0.25
"""
__UpperCamelCase : Tuple = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def _a ( self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any]=None ) -> Optional[Any]:
"""simple docstring"""
return {
"matthews_correlation": float(matthews_corrcoef(_lowerCAmelCase , _lowerCAmelCase , sample_weight=_lowerCAmelCase ) ),
}
| 80 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
__UpperCamelCase : Dict = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
__UpperCamelCase : int = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
__UpperCamelCase : List[str] = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
__UpperCamelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
__UpperCamelCase : Optional[int] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
__UpperCamelCase : Dict = {"""facebook/blenderbot_small-90M""": 512}
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
__lowercase = set(lowerCamelCase )
return pairs
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = VOCAB_FILES_NAMES
__snake_case :Tuple = PRETRAINED_VOCAB_FILES_MAP
__snake_case :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case :str = ['input_ids', 'attention_mask']
def __init__( self : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str="__start__" , _lowerCAmelCase : int="__end__" , _lowerCAmelCase : Any="__unk__" , _lowerCAmelCase : List[Any]="__null__" , **_lowerCAmelCase : Tuple , ) -> str:
"""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:
__lowercase = json.load(_lowerCAmelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
with open(_lowerCAmelCase , encoding="""utf-8""" ) as merges_handle:
__lowercase = merges_handle.read().split("""\n""" )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in merges]
__lowercase = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
__lowercase = {}
@property
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return len(self.encoder )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _a ( self : str , _lowerCAmelCase : str ) -> str:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
__lowercase = re.sub("""([.,!?()])""" , r""" \1""" , _lowerCAmelCase )
__lowercase = re.sub("""(')""" , r""" \1 """ , _lowerCAmelCase )
__lowercase = re.sub(r"""\s{2,}""" , """ """ , _lowerCAmelCase )
if "\n" in token:
__lowercase = token.replace("""\n""" , """ __newln__""" )
__lowercase = token.split(""" """ )
__lowercase = []
for token in tokens:
if not len(_lowerCAmelCase ):
continue
__lowercase = token.lower()
__lowercase = tuple(_lowerCAmelCase )
__lowercase = tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
__lowercase = get_pairs(_lowerCAmelCase )
if not pairs:
words.append(_lowerCAmelCase )
continue
while True:
__lowercase = min(_lowerCAmelCase , key=lambda _lowerCAmelCase : self.bpe_ranks.get(_lowerCAmelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(_lowerCAmelCase ):
try:
__lowercase = word.index(_lowerCAmelCase , _lowerCAmelCase )
new_word.extend(word[i:j] )
__lowercase = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(_lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(_lowerCAmelCase )
__lowercase = new_word
if len(_lowerCAmelCase ) == 1:
break
else:
__lowercase = get_pairs(_lowerCAmelCase )
__lowercase = """@@ """.join(_lowerCAmelCase )
__lowercase = word[:-4]
__lowercase = word
words.append(_lowerCAmelCase )
return " ".join(_lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
__lowercase = []
__lowercase = re.findall(r"""\S+\n?""" , _lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(_lowerCAmelCase ).split(""" """ ) ) )
return split_tokens
def _a ( self : Tuple , _lowerCAmelCase : str ) -> int:
"""simple docstring"""
__lowercase = token.lower()
return self.encoder.get(_lowerCAmelCase , self.encoder.get(self.unk_token ) )
def _a ( self : Tuple , _lowerCAmelCase : int ) -> str:
"""simple docstring"""
return self.decoder.get(_lowerCAmelCase , self.unk_token )
def _a ( self : Dict , _lowerCAmelCase : List[str] ) -> str:
"""simple docstring"""
__lowercase = """ """.join(_lowerCAmelCase ).replace("""@@ """ , """""" ).strip()
return out_string
def _a ( self : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + """\n""" )
__lowercase = 0
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
""" Please check that the tokenizer is not corrupted!""" )
__lowercase = token_index
writer.write(""" """.join(_lowerCAmelCase ) + """\n""" )
index += 1
return vocab_file, merge_file
| 80 | 1 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : int ) -> int:
"""simple docstring"""
__lowercase = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
__lowercase = get_activation("""gelu""" )
self.assertTrue(torch.allclose(gelu_python(_lowerCAmelCase ) , torch_builtin(_lowerCAmelCase ) ) )
self.assertFalse(torch.allclose(gelu_python(_lowerCAmelCase ) , gelu_new(_lowerCAmelCase ) ) )
def _a ( self : List[Any] ) -> str:
"""simple docstring"""
__lowercase = torch.tensor([-100, -1, -0.1, 0, 0.1, 1.0, 100] )
__lowercase = get_activation("""gelu""" )
__lowercase = get_activation("""gelu_10""" )
__lowercase = torch_builtin(_lowerCAmelCase )
__lowercase = geluaa(_lowerCAmelCase )
__lowercase = torch.where(y_gelu_aa < 10.0 , 1 , 0 )
self.assertTrue(torch.max(_lowerCAmelCase ).item() == 10.0 )
self.assertTrue(torch.allclose(y_gelu * clipped_mask , y_gelu_aa * clipped_mask ) )
def _a ( self : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
get_activation("""gelu""" )
get_activation("""gelu_10""" )
get_activation("""gelu_fast""" )
get_activation("""gelu_new""" )
get_activation("""gelu_python""" )
get_activation("""gelu_pytorch_tanh""" )
get_activation("""linear""" )
get_activation("""mish""" )
get_activation("""quick_gelu""" )
get_activation("""relu""" )
get_activation("""sigmoid""" )
get_activation("""silu""" )
get_activation("""swish""" )
get_activation("""tanh""" )
with self.assertRaises(_lowerCAmelCase ):
get_activation("""bogus""" )
with self.assertRaises(_lowerCAmelCase ):
get_activation(_lowerCAmelCase )
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
__lowercase = get_activation("""gelu""" )
__lowercase = 1
__lowercase = get_activation("""gelu""" )
self.assertEqual(acta.a , 1 )
with self.assertRaises(_lowerCAmelCase ):
__lowercase = acta.a
| 80 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Union[str, Any] = 'lxmert'
__snake_case :Union[str, Any] = {}
def __init__( self : List[str] , _lowerCAmelCase : Dict=3_0522 , _lowerCAmelCase : List[str]=768 , _lowerCAmelCase : Union[str, Any]=12 , _lowerCAmelCase : Union[str, Any]=9500 , _lowerCAmelCase : Union[str, Any]=1600 , _lowerCAmelCase : Optional[Any]=400 , _lowerCAmelCase : Tuple=3072 , _lowerCAmelCase : List[Any]="gelu" , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Tuple=512 , _lowerCAmelCase : Tuple=2 , _lowerCAmelCase : Optional[Any]=0.02 , _lowerCAmelCase : List[str]=1e-12 , _lowerCAmelCase : Any=9 , _lowerCAmelCase : Optional[Any]=5 , _lowerCAmelCase : Any=5 , _lowerCAmelCase : Dict=2048 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[Any]=6.67 , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , _lowerCAmelCase : str=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , **_lowerCAmelCase : Tuple , ) -> Dict:
"""simple docstring"""
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = num_qa_labels
__lowercase = num_object_labels
__lowercase = num_attr_labels
__lowercase = l_layers
__lowercase = x_layers
__lowercase = r_layers
__lowercase = visual_feat_dim
__lowercase = visual_pos_dim
__lowercase = visual_loss_normalizer
__lowercase = task_matched
__lowercase = task_mask_lm
__lowercase = task_obj_predict
__lowercase = task_qa
__lowercase = visual_obj_loss
__lowercase = visual_attr_loss
__lowercase = visual_feat_loss
__lowercase = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**_lowerCAmelCase )
| 80 | 1 |
def snake_case ( lowerCamelCase = 1_000 ):
'''simple docstring'''
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 80 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : int , _lowerCAmelCase : str , _lowerCAmelCase : List[str]=13 , _lowerCAmelCase : List[str]=7 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : Union[str, Any]=False , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=99 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : Optional[int]=5 , _lowerCAmelCase : Any=4 , _lowerCAmelCase : Tuple=37 , _lowerCAmelCase : str="gelu" , _lowerCAmelCase : List[str]=0.1 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : Union[str, Any]=512 , _lowerCAmelCase : Dict=16 , _lowerCAmelCase : List[Any]=2 , _lowerCAmelCase : Optional[int]=0.02 , _lowerCAmelCase : int=3 , _lowerCAmelCase : Dict=4 , _lowerCAmelCase : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_input_mask
__lowercase = use_token_type_ids
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = num_labels
__lowercase = num_choices
__lowercase = scope
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = None
if self.use_input_mask:
__lowercase = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase = None
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowercase = ids_tensor([self.batch_size] , self.num_choices )
__lowercase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _a ( self : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = DistilBertModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> List[str]:
"""simple docstring"""
__lowercase = DistilBertForMaskedLM(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertForQuestionAnswering(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForSequenceClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForTokenClassification(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ) -> str:
"""simple docstring"""
__lowercase = self.num_choices
__lowercase = DistilBertForMultipleChoice(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = model(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
((__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase)) = config_and_inputs
__lowercase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[Any] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
__snake_case :Dict = (
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__snake_case :Tuple = True
__snake_case :Tuple = True
__snake_case :List[str] = True
__snake_case :Optional[int] = True
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = DistilBertModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , dim=37 )
def _a ( self : Dict ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*_lowerCAmelCase )
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*_lowerCAmelCase )
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*_lowerCAmelCase )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*_lowerCAmelCase )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*_lowerCAmelCase )
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*_lowerCAmelCase )
@slow
def _a ( self : int ) -> Optional[Any]:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = DistilBertModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@slow
@require_torch_gpu
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
__lowercase = True
__lowercase = model_class(config=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = torch.jit.trace(
_lowerCAmelCase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , """traced_model.pt""" ) )
__lowercase = torch.jit.load(os.path.join(_lowerCAmelCase , """traced_model.pt""" ) , map_location=_lowerCAmelCase )
loaded(inputs_dict["""input_ids"""].to(_lowerCAmelCase ) , inputs_dict["""attention_mask"""].to(_lowerCAmelCase ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertModel.from_pretrained("""distilbert-base-uncased""" )
__lowercase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__lowercase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )[0]
__lowercase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _lowerCAmelCase )
__lowercase = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowerCAmelCase , atol=1e-4 ) )
| 80 | 1 |
from ... import PretrainedConfig
__UpperCamelCase : int = {
"""sijunhe/nezha-cn-base""": """https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json""",
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Any = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
__snake_case :Dict = 'nezha'
def __init__( self : int , _lowerCAmelCase : List[Any]=2_1128 , _lowerCAmelCase : Tuple=768 , _lowerCAmelCase : Dict=12 , _lowerCAmelCase : Optional[Any]=12 , _lowerCAmelCase : Optional[Any]=3072 , _lowerCAmelCase : Union[str, Any]="gelu" , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Optional[int]=0.1 , _lowerCAmelCase : List[Any]=512 , _lowerCAmelCase : List[Any]=64 , _lowerCAmelCase : Dict=2 , _lowerCAmelCase : int=0.02 , _lowerCAmelCase : Any=1e-12 , _lowerCAmelCase : Union[str, Any]=0.1 , _lowerCAmelCase : int=0 , _lowerCAmelCase : Any=2 , _lowerCAmelCase : Optional[int]=3 , _lowerCAmelCase : int=True , **_lowerCAmelCase : Optional[int] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = max_relative_position
__lowercase = type_vocab_size
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = classifier_dropout
__lowercase = use_cache
| 80 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCamelCase ( _lowerCAmelCase ):
# to overwrite at feature extractactor specific tests
__snake_case :Optional[int] = None
__snake_case :Dict = None
@property
def _a ( self : str ) -> List[str]:
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_lowerCAmelCase , """feature_size""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """sampling_rate""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """padding_value""" ) )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_lowerCAmelCase ) == len(_lowerCAmelCase ) for x, y in zip(_lowerCAmelCase , processed_features[input_name] ) ) )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _a ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _a ( self : str , _lowerCAmelCase : List[Any]=False ) -> int:
"""simple docstring"""
def _inputs_have_equal_length(_lowerCAmelCase : int ):
__lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCAmelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCAmelCase : Dict , _lowerCAmelCase : Tuple ):
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCAmelCase , _lowerCAmelCase ):
if not np.allclose(np.asarray(_lowerCAmelCase ) , np.asarray(_lowerCAmelCase ) , atol=1e-3 ):
return False
return True
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = self.feat_extract_tester.seq_length_diff
__lowercase = self.feat_extract_tester.max_seq_length + pad_diff
__lowercase = self.feat_extract_tester.min_seq_length
__lowercase = self.feat_extract_tester.batch_size
__lowercase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
__lowercase = feat_extract.pad(_lowerCAmelCase , padding=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )
__lowercase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""max_length""" )[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=_lowerCAmelCase , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
__lowercase = feat_extract.pad(_lowerCAmelCase , pad_to_multiple_of=10 )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , pad_to_multiple_of=10 )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = input_a[input_name]
self.assertTrue(all(len(_lowerCAmelCase ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_lowerCAmelCase ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
__lowercase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _a ( self : Tuple , _lowerCAmelCase : str=False ) -> Union[str, Any]:
"""simple docstring"""
def _inputs_have_equal_length(_lowerCAmelCase : Tuple ):
__lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCAmelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCAmelCase : Any , _lowerCAmelCase : str ):
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCAmelCase , _lowerCAmelCase ):
if not np.allclose(np.asarray(_lowerCAmelCase ) , np.asarray(_lowerCAmelCase ) , atol=1e-3 ):
return False
return True
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
__lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
# truncate to smallest with np
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=_lowerCAmelCase , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
# truncate to middle
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , truncation=_lowerCAmelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""longest""" , truncation=_lowerCAmelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""longest""" , truncation=_lowerCAmelCase )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , truncation=_lowerCAmelCase )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
__lowercase = 12
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCAmelCase , truncation=_lowerCAmelCase , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCAmelCase , )
__lowercase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
__lowercase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
__lowercase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self._check_padding(numpify=_lowerCAmelCase )
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
self._check_padding(numpify=_lowerCAmelCase )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
self._check_truncation(numpify=_lowerCAmelCase )
def _a ( self : str ) -> str:
"""simple docstring"""
self._check_truncation(numpify=_lowerCAmelCase )
@require_torch
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _a ( self : Any ) -> Any:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**_lowerCAmelCase )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = [len(_lowerCAmelCase ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _lowerCAmelCase )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _lowerCAmelCase )
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**_lowerCAmelCase )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = [len(_lowerCAmelCase ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = min(_lowerCAmelCase )
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=_lowerCAmelCase , truncation=_lowerCAmelCase , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _lowerCAmelCase )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 80 | 1 |
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,
)
| 80 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lowerCamelCase ) <= key:
return input_string
for position, character in enumerate(lowerCamelCase ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(lowerCamelCase )
__lowercase = ["""""".join(lowerCamelCase ) for row in temp_grid]
__lowercase = """""".join(lowerCamelCase )
return output_string
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = []
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1:
return input_string
__lowercase = [[] for _ in range(lowerCamelCase )] # generates template
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("""*""" )
__lowercase = 0
for row in temp_grid: # fills in the characters
__lowercase = input_string[counter : counter + len(lowerCamelCase )]
grid.append(list(lowerCamelCase ) )
counter += len(lowerCamelCase )
__lowercase = """""" # reads as zigzag
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = {}
for key_guess in range(1 , len(lowerCamelCase ) ): # tries every key
__lowercase = decrypt(lowerCamelCase , lowerCamelCase )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 | 1 |
import os
from collections.abc import Iterator
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCamelCase ):
__lowercase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(lowerCamelCase )[1] in (".py", ".ipynb"):
yield os.path.join(lowerCamelCase , lowerCamelCase ).lstrip("""./""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return F'{i * " "}*' if i else "\n##"
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(lowerCamelCase ) or old_parts[i] != new_part) and new_part:
print(F'{md_prefix(lowerCamelCase )} {new_part.replace("_" , " " ).title()}' )
return new_path
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
__lowercase = """"""
for filepath in sorted(good_file_paths(lowerCamelCase ) ):
__lowercase , __lowercase = os.path.split(lowerCamelCase )
if filepath != old_path:
__lowercase = print_path(lowerCamelCase , lowerCamelCase )
__lowercase = (filepath.count(os.sep ) + 1) if filepath else 0
__lowercase = F'{filepath}/{filename}'.replace(""" """ , """%20""" )
__lowercase = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'{md_prefix(lowerCamelCase )} [{filename}]({url})' )
if __name__ == "__main__":
print_directory_md(""".""")
| 80 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , lowerCamelCase ):
__lowercase = 1
__lowercase = 0
for i in range(lowerCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 | 1 |
import os
import unittest
from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer
from transformers.testing_utils import get_tests_dir
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCamelCase : int = get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :List[Any] = BartphoTokenizer
__snake_case :int = False
__snake_case :Union[str, Any] = True
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
super().setUp()
__lowercase = ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""]
__lowercase = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
__lowercase = {"""unk_token""": """<unk>"""}
__lowercase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""monolingual_vocab_file"""] )
with open(self.monolingual_vocab_file , """w""" , encoding="""utf-8""" ) as fp:
for token in vocab_tokens:
fp.write(F'{token} {vocab_tokens[token]}\n' )
__lowercase = BartphoTokenizer(_lowerCAmelCase , self.monolingual_vocab_file , **self.special_tokens_map )
tokenizer.save_pretrained(self.tmpdirname )
def _a ( self : List[str] , **_lowerCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
kwargs.update(self.special_tokens_map )
return BartphoTokenizer.from_pretrained(self.tmpdirname , **_lowerCAmelCase )
def _a ( self : str , _lowerCAmelCase : List[Any] ) -> Any:
"""simple docstring"""
__lowercase = """This is a là test"""
__lowercase = """This is a<unk><unk> test"""
return input_text, output_text
def _a ( self : int ) -> str:
"""simple docstring"""
__lowercase = BartphoTokenizer(_lowerCAmelCase , self.monolingual_vocab_file , **self.special_tokens_map )
__lowercase = """This is a là test"""
__lowercase = """▁This ▁is ▁a ▁l à ▁t est""".split()
__lowercase = tokenizer.tokenize(_lowerCAmelCase )
self.assertListEqual(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = tokens + [tokenizer.unk_token]
__lowercase = [4, 5, 6, 3, 3, 7, 8, 3]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCAmelCase ) , _lowerCAmelCase )
| 80 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class __UpperCamelCase :
def __init__( self : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int]=13 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : Optional[Any]=2 , _lowerCAmelCase : Union[str, Any]=3 , _lowerCAmelCase : List[str]=16 , _lowerCAmelCase : List[str]=[1, 2, 1] , _lowerCAmelCase : Dict=[2, 2, 4] , _lowerCAmelCase : Optional[int]=2 , _lowerCAmelCase : Optional[Any]=2.0 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Optional[int]=0.0 , _lowerCAmelCase : Any=0.0 , _lowerCAmelCase : Optional[int]=0.1 , _lowerCAmelCase : Tuple="gelu" , _lowerCAmelCase : int=False , _lowerCAmelCase : Dict=True , _lowerCAmelCase : List[str]=0.02 , _lowerCAmelCase : Union[str, Any]=1e-5 , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : List[Any]=10 , _lowerCAmelCase : Tuple=8 , _lowerCAmelCase : List[Any]=["stage1", "stage2", "stage3"] , _lowerCAmelCase : Union[str, Any]=[1, 2, 3] , ) -> int:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = patch_norm
__lowercase = layer_norm_eps
__lowercase = initializer_range
__lowercase = is_training
__lowercase = scope
__lowercase = use_labels
__lowercase = type_sequence_label_size
__lowercase = encoder_stride
__lowercase = out_features
__lowercase = out_indices
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def _a ( self : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : str , _lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
__lowercase = MaskFormerSwinModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
__lowercase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__lowercase = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def _a ( self : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = MaskFormerSwinBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_lowerCAmelCase ):
__lowercase = ["""stem"""]
__lowercase = MaskFormerSwinBackbone(config=_lowerCAmelCase )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
__snake_case :Optional[int] = {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
__snake_case :Optional[int] = False
__snake_case :Any = False
__snake_case :List[str] = False
__snake_case :Tuple = False
__snake_case :Optional[int] = False
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__lowercase = MaskFormerSwinModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"""`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
""" `nn.DataParallel`"""
) )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
pass
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
return
def _a ( self : Any ) -> Tuple:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCAmelCase )
@unittest.skip("""Swin does not use inputs_embeds""" )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip("""Swin does not support feedforward chunking""" )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
pass
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions""" )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone""" )
def _a ( self : Any ) -> Any:
"""simple docstring"""
pass
def _a ( self : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.hidden_states
__lowercase = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
# Swin has a different seq_length
__lowercase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = 3
__lowercase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__lowercase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__lowercase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , (padded_height, padded_width) )
@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints""" )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a ( self : Any ) -> str:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
pass
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(_lowerCAmelCase : Optional[int] ):
__lowercase = 0
return t
def check_equivalence(_lowerCAmelCase : int , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int]={} ):
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase , return_dict=_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = model(**_lowerCAmelCase , return_dict=_lowerCAmelCase , **_lowerCAmelCase ).to_tuple()
def recursive_check(_lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ):
if isinstance(_lowerCAmelCase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_lowerCAmelCase , _lowerCAmelCase ):
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_lowerCAmelCase ) , set_nan_tensor_to_zero(_lowerCAmelCase ) , atol=1e-5 ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
F' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'
F' {torch.isnan(_lowerCAmelCase ).any()} and `inf`: {torch.isinf(_lowerCAmelCase )}. Dict has'
F' `nan`: {torch.isnan(_lowerCAmelCase ).any()} and `inf`: {torch.isinf(_lowerCAmelCase )}.'
) , )
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {"""output_hidden_states""": True} )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {"""output_hidden_states""": True} )
@require_torch
class __UpperCamelCase ( unittest.TestCase , _lowerCAmelCase ):
__snake_case :Optional[Any] = (MaskFormerSwinBackbone,) if is_torch_available() else ()
__snake_case :Dict = MaskFormerSwinConfig
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase = MaskFormerSwinModelTester(self )
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = inputs_dict["""pixel_values"""].shape[0]
for backbone_class in self.all_model_classes:
__lowercase = backbone_class(_lowerCAmelCase )
backbone.to(_lowerCAmelCase )
backbone.eval()
__lowercase = backbone(**_lowerCAmelCase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _lowerCAmelCase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
__lowercase = backbone(**_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
__lowercase , __lowercase , __lowercase = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
__lowercase = backbone(**_lowerCAmelCase , output_attentions=_lowerCAmelCase )
self.assertIsNotNone(outputs.attentions )
| 80 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCamelCase : Union[str, Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : int = ["""BartphoTokenizer"""]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
__UpperCamelCase : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = torch.nn.Linear(10 , 10 )
__lowercase = torch.optim.SGD(model.parameters() , 0.1 )
__lowercase = Accelerator()
__lowercase = accelerator.prepare(_lowerCAmelCase )
try:
pickle.loads(pickle.dumps(_lowerCAmelCase ) )
except Exception as e:
self.fail(F'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 80 | 1 |
import unittest
from diffusers import FlaxAutoencoderKL
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax
from .test_modeling_common_flax import FlaxModelTesterMixin
if is_flax_available():
import jax
@require_flax
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = FlaxAutoencoderKL
@property
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__lowercase = 4
__lowercase = 3
__lowercase = (32, 32)
__lowercase = jax.random.PRNGKey(0 )
__lowercase = jax.random.uniform(_lowerCAmelCase , ((batch_size, num_channels) + sizes) )
return {"sample": image, "prng_key": prng_key}
def _a ( self : str ) -> Optional[int]:
"""simple docstring"""
__lowercase = {
"""block_out_channels""": [32, 64],
"""in_channels""": 3,
"""out_channels""": 3,
"""down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
"""up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
"""latent_channels""": 4,
}
__lowercase = self.dummy_input
return init_dict, inputs_dict
| 80 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
__UpperCamelCase : Dict = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
__UpperCamelCase : int = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
__UpperCamelCase : List[str] = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
__UpperCamelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 | 1 |
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
__UpperCamelCase : Optional[List[str]] = None
__UpperCamelCase : Union[str, Any] = """<""" 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
__UpperCamelCase : int = [
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 __UpperCamelCase :
__snake_case :bool = True
__snake_case :Optional[str] = None
# Automatically constructed
__snake_case :ClassVar[str] = "PIL.Image.Image"
__snake_case :ClassVar[Any] = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
__snake_case :str = field(default='Image' , init=_lowerCAmelCase , repr=_lowerCAmelCase )
def __call__( self : Dict ) -> Optional[int]:
"""simple docstring"""
return self.pa_type
def _a ( self : int , _lowerCAmelCase : Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"] ) -> dict:
"""simple docstring"""
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
__lowercase = 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 : Union[str, Any] , _lowerCAmelCase : dict , _lowerCAmelCase : Union[str, Any]=None ) -> "PIL.Image.Image":
"""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:
__lowercase = {}
__lowercase , __lowercase = 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 ):
__lowercase = PIL.Image.open(_lowerCAmelCase )
else:
__lowercase = path.split("""::""" )[-1]
try:
__lowercase = string_to_dict(_lowerCAmelCase , config.HUB_DATASETS_URL )["""repo_id"""]
__lowercase = token_per_repo_id.get(_lowerCAmelCase )
except ValueError:
__lowercase = None
with xopen(_lowerCAmelCase , """rb""" , use_auth_token=_lowerCAmelCase ) as f:
__lowercase = BytesIO(f.read() )
__lowercase = PIL.Image.open(bytes_ )
else:
__lowercase = PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def _a ( self : Any ) -> Union["FeatureType", Dict[str, "FeatureType"]]:
"""simple docstring"""
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("""binary""" ),
"path": Value("""string""" ),
}
)
def _a ( self : int , _lowerCAmelCase : Union[pa.StringArray, pa.StructArray, pa.ListArray] ) -> pa.StructArray:
"""simple docstring"""
if pa.types.is_string(storage.type ):
__lowercase = pa.array([None] * len(_lowerCAmelCase ) , type=pa.binary() )
__lowercase = pa.StructArray.from_arrays([bytes_array, storage] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
__lowercase = pa.array([None] * len(_lowerCAmelCase ) , type=pa.string() )
__lowercase = 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:
__lowercase = storage.field("""bytes""" )
else:
__lowercase = pa.array([None] * len(_lowerCAmelCase ) , type=pa.binary() )
if storage.type.get_field_index("""path""" ) >= 0:
__lowercase = storage.field("""path""" )
else:
__lowercase = pa.array([None] * len(_lowerCAmelCase ) , type=pa.string() )
__lowercase = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
__lowercase = 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() , )
__lowercase = pa.array([None] * len(_lowerCAmelCase ) , type=pa.string() )
__lowercase = 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 : Tuple , _lowerCAmelCase : pa.StructArray ) -> pa.StructArray:
"""simple docstring"""
@no_op_if_value_is_null
def path_to_bytes(_lowerCAmelCase : Dict ):
with xopen(_lowerCAmelCase , """rb""" ) as f:
__lowercase = f.read()
return bytes_
__lowercase = 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() , )
__lowercase = pa.array(
[os.path.basename(_lowerCAmelCase ) if path is not None else None for path in storage.field("""path""" ).to_pylist()] , type=pa.string() , )
__lowercase = pa.StructArray.from_arrays([bytes_array, path_array] , ["""bytes""", """path"""] , mask=bytes_array.is_null() )
return array_cast(_lowerCAmelCase , self.pa_type )
def snake_case ( ):
'''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()
__lowercase = list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = BytesIO()
if image.format in list_image_compression_formats():
__lowercase = image.format
else:
__lowercase = """PNG""" if image.mode in ["""1""", """L""", """LA""", """RGB""", """RGBA"""] else """TIFF"""
image.save(lowerCamelCase , format=lowerCamelCase )
return buffer.getvalue()
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if hasattr(lowerCamelCase , """filename""" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(lowerCamelCase )}
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
__lowercase = array.dtype
__lowercase = dtype.byteorder if dtype.byteorder != """=""" else _NATIVE_BYTEORDER
__lowercase = dtype.kind
__lowercase = dtype.itemsize
__lowercase = None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
__lowercase = 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:
__lowercase = 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:
__lowercase = dtype_byteorder + dtype_kind + str(lowerCamelCase )
__lowercase = np.dtype(lowerCamelCase )
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}' )
__lowercase = PIL.Image.fromarray(array.astype(lowerCamelCase ) )
return {"path": None, "bytes": image_to_bytes(lowerCamelCase )}
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("""To support encoding images, please install 'Pillow'.""" )
if objs:
__lowercase , __lowercase = first_non_null_value(lowerCamelCase )
if isinstance(lowerCamelCase , lowerCamelCase ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(lowerCamelCase , np.ndarray ):
__lowercase = no_op_if_value_is_null(lowerCamelCase )
return [obj_to_image_dict_func(lowerCamelCase ) for obj in objs]
elif isinstance(lowerCamelCase , PIL.Image.Image ):
__lowercase = no_op_if_value_is_null(lowerCamelCase )
return [obj_to_image_dict_func(lowerCamelCase ) for obj in objs]
else:
return objs
else:
return objs
| 80 |
import os
from collections.abc import Iterator
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCamelCase ):
__lowercase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(lowerCamelCase )[1] in (".py", ".ipynb"):
yield os.path.join(lowerCamelCase , lowerCamelCase ).lstrip("""./""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return F'{i * " "}*' if i else "\n##"
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(lowerCamelCase ) or old_parts[i] != new_part) and new_part:
print(F'{md_prefix(lowerCamelCase )} {new_part.replace("_" , " " ).title()}' )
return new_path
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
__lowercase = """"""
for filepath in sorted(good_file_paths(lowerCamelCase ) ):
__lowercase , __lowercase = os.path.split(lowerCamelCase )
if filepath != old_path:
__lowercase = print_path(lowerCamelCase , lowerCamelCase )
__lowercase = (filepath.count(os.sep ) + 1) if filepath else 0
__lowercase = F'{filepath}/{filename}'.replace(""" """ , """%20""" )
__lowercase = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'{md_prefix(lowerCamelCase )} [{filename}]({url})' )
if __name__ == "__main__":
print_directory_md(""".""")
| 80 | 1 |
from __future__ import annotations
__UpperCamelCase : Optional[Any] = list[list[int]]
# assigning initial values to the grid
__UpperCamelCase : Matrix = [
[3, 0, 6, 5, 0, 8, 4, 0, 0],
[5, 2, 0, 0, 0, 0, 0, 0, 0],
[0, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
# a grid with no solution
__UpperCamelCase : Matrix = [
[5, 0, 6, 5, 0, 8, 4, 0, 3],
[5, 2, 0, 0, 0, 0, 0, 0, 2],
[1, 8, 7, 0, 0, 0, 0, 3, 1],
[0, 0, 3, 0, 1, 0, 0, 8, 0],
[9, 0, 0, 8, 6, 3, 0, 0, 5],
[0, 5, 0, 0, 9, 0, 6, 0, 0],
[1, 3, 0, 0, 0, 0, 2, 5, 0],
[0, 0, 0, 0, 0, 0, 0, 7, 4],
[0, 0, 5, 2, 0, 6, 3, 0, 0],
]
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
for i in range(9 ):
if grid[row][i] == n or grid[i][column] == n:
return False
for i in range(3 ):
for j in range(3 ):
if grid[(row - row % 3) + i][(column - column % 3) + j] == n:
return False
return True
def snake_case ( lowerCamelCase ):
'''simple docstring'''
for i in range(9 ):
for j in range(9 ):
if grid[i][j] == 0:
return i, j
return None
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if location := find_empty_location(lowerCamelCase ):
__lowercase , __lowercase = location
else:
# If the location is ``None``, then the grid is solved.
return grid
for digit in range(1 , 10 ):
if is_safe(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__lowercase = digit
if sudoku(lowerCamelCase ) is not None:
return grid
__lowercase = 0
return None
def snake_case ( lowerCamelCase ):
'''simple docstring'''
for row in grid:
for cell in row:
print(lowerCamelCase , end=""" """ )
print()
if __name__ == "__main__":
# make a copy of grid so that you can compare with the unmodified grid
for example_grid in (initial_grid, no_solution):
print("""\nExample grid:\n""" + """=""" * 20)
print_solution(example_grid)
print("""\nExample grid solution:""")
__UpperCamelCase : Optional[Any] = sudoku(example_grid)
if solution is not None:
print_solution(solution)
else:
print("""Cannot find a solution.""")
| 80 |
from math import factorial
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(lowerCamelCase ) // (factorial(lowerCamelCase ) * factorial(n - k ))
if __name__ == "__main__":
print(
"""The number of five-card hands possible from a standard""",
F'''fifty-two card deck is: {combinations(52, 5)}\n''',
)
print(
"""If a class of 40 students must be arranged into groups of""",
F'''4 for group projects, there are {combinations(40, 4)} ways''',
"""to arrange them.\n""",
)
print(
"""If 10 teams are competing in a Formula One race, there""",
F'''are {combinations(10, 3)} ways that first, second and''',
"""third place can be awarded.""",
)
| 80 | 1 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
__lowercase = set()
return any(
node not in visited and depth_first_search(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
for node in graph )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
visited.add(lowerCamelCase )
rec_stk.add(lowerCamelCase )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(lowerCamelCase )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 80 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case ( ):
'''simple docstring'''
__lowercase = [randint(-1_000 , 1_000 ) for i in range(10 )]
__lowercase = randint(-5_000 , 5_000 )
return (arr, r)
__UpperCamelCase : Any = make_dataset()
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
for triplet in permutations(lowerCamelCase , 3 ):
if sum(lowerCamelCase ) == target:
return tuple(sorted(lowerCamelCase ) )
return (0, 0, 0)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
arr.sort()
__lowercase = len(lowerCamelCase )
for i in range(n - 1 ):
__lowercase , __lowercase = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def snake_case ( ):
'''simple docstring'''
__lowercase = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
__lowercase = """
triplet_sum1(*dataset)
"""
__lowercase = """
triplet_sum2(*dataset)
"""
__lowercase = repeat(setup=lowerCamelCase , stmt=lowerCamelCase , repeat=5 , number=10_000 )
__lowercase = repeat(setup=lowerCamelCase , stmt=lowerCamelCase , repeat=5 , number=10_000 )
return (min(lowerCamelCase ), min(lowerCamelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
__UpperCamelCase : Tuple = solution_times()
print(F'''The time for naive implementation is {times[0]}.''')
print(F'''The time for optimized implementation is {times[1]}.''')
| 80 | 1 |
from math import sqrt
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(lowerCamelCase ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def snake_case ( lowerCamelCase = 10_001 ):
'''simple docstring'''
__lowercase = 0
__lowercase = 1
while count != nth and number < 3:
number += 1
if is_prime(lowerCamelCase ):
count += 1
while count != nth:
number += 2
if is_prime(lowerCamelCase ):
count += 1
return number
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Any , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str=None ) -> int:
"""simple docstring"""
super().__init__(
_lowerCAmelCase , question_encoder_tokenizer=_lowerCAmelCase , generator_tokenizer=_lowerCAmelCase , index=_lowerCAmelCase , init_retrieval=_lowerCAmelCase , )
__lowercase = None
def _a ( self : int , _lowerCAmelCase : int ) -> Any:
"""simple docstring"""
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
__lowercase = self._infer_socket_ifname()
# avoid clash with the NCCL port
__lowercase = str(distributed_port + 1 )
__lowercase = dist.new_group(ranks=_lowerCAmelCase , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _a ( self : Tuple ) -> List[str]:
"""simple docstring"""
return dist.get_rank(group=self.process_group ) == 0
def _a ( self : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any]=torch.floataa ) -> Tuple:
"""simple docstring"""
__lowercase = torch.empty(_lowerCAmelCase , dtype=_lowerCAmelCase )
dist.scatter(_lowerCAmelCase , src=0 , scatter_list=_lowerCAmelCase , group=self.process_group )
return target_tensor
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
__lowercase = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
__lowercase = next((addr for addr in addrs if addr.startswith("""e""" )) , _lowerCAmelCase )
return ifname
def _a ( self : str , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : int ) -> Tuple[np.ndarray, List[dict]]:
"""simple docstring"""
if not dist.is_initialized():
__lowercase , __lowercase = self._main_retrieve(_lowerCAmelCase , _lowerCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCAmelCase )
# distributed training
__lowercase = dist.get_world_size(group=self.process_group )
# gather logic
__lowercase = None
if self._is_main():
__lowercase = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(_lowerCAmelCase )]
dist.gather(torch.tensor(_lowerCAmelCase ) , dst=0 , gather_list=_lowerCAmelCase , group=self.process_group )
# scatter logic
__lowercase = question_hidden_states.shape[0]
__lowercase = []
__lowercase = []
if self._is_main():
assert len(_lowerCAmelCase ) == world_size
__lowercase , __lowercase = self._main_retrieve(torch.cat(_lowerCAmelCase ).numpy() , _lowerCAmelCase )
__lowercase , __lowercase = torch.tensor(_lowerCAmelCase ), torch.tensor(_lowerCAmelCase )
__lowercase = self._chunk_tensor(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._chunk_tensor(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._scattered(_lowerCAmelCase , [n_queries, n_docs] , target_type=torch.intaa )
__lowercase = self._scattered(_lowerCAmelCase , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(_lowerCAmelCase )
| 80 | 1 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Dict , _lowerCAmelCase : Distribution , _lowerCAmelCase : List[str]=None , _lowerCAmelCase : Dict=None , _lowerCAmelCase : Any=0 ) -> str:
"""simple docstring"""
__lowercase = 1.0 if scale is None else scale
__lowercase = 0.0 if loc is None else loc
super().__init__(_lowerCAmelCase , [AffineTransform(loc=self.loc , scale=self.scale , event_dim=_lowerCAmelCase )] )
@property
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
return self.base_dist.mean * self.scale + self.loc
@property
def _a ( self : Any ) -> str:
"""simple docstring"""
return self.base_dist.variance * self.scale**2
@property
def _a ( self : int ) -> int:
"""simple docstring"""
return self.variance.sqrt()
class __UpperCamelCase ( nn.Module ):
def __init__( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Dict[str, int] , _lowerCAmelCase : Callable[..., Tuple[torch.Tensor]] , **_lowerCAmelCase : Optional[int] ) -> None:
"""simple docstring"""
super().__init__(**_lowerCAmelCase )
__lowercase = args_dim
__lowercase = nn.ModuleList([nn.Linear(_lowerCAmelCase , _lowerCAmelCase ) for dim in args_dim.values()] )
__lowercase = domain_map
def _a ( self : int , _lowerCAmelCase : torch.Tensor ) -> Tuple[torch.Tensor]:
"""simple docstring"""
__lowercase = [proj(_lowerCAmelCase ) for proj in self.proj]
return self.domain_map(*_lowerCAmelCase )
class __UpperCamelCase ( nn.Module ):
def __init__( self : str , _lowerCAmelCase : Tuple ) -> str:
"""simple docstring"""
super().__init__()
__lowercase = function
def _a ( self : str , _lowerCAmelCase : List[Any] , *_lowerCAmelCase : Union[str, Any] ) -> int:
"""simple docstring"""
return self.function(_lowerCAmelCase , *_lowerCAmelCase )
class __UpperCamelCase :
__snake_case :type
__snake_case :int
__snake_case :Dict[str, int]
def __init__( self : Dict , _lowerCAmelCase : int = 1 ) -> None:
"""simple docstring"""
__lowercase = dim
__lowercase = {k: dim * self.args_dim[k] for k in self.args_dim}
def _a ( self : List[str] , _lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
if self.dim == 1:
return self.distribution_class(*_lowerCAmelCase )
else:
return Independent(self.distribution_class(*_lowerCAmelCase ) , 1 )
def _a ( self : Optional[int] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[torch.Tensor] = None , _lowerCAmelCase : Optional[torch.Tensor] = None , ) -> Distribution:
"""simple docstring"""
__lowercase = self._base_distribution(_lowerCAmelCase )
if loc is None and scale is None:
return distr
else:
return AffineTransformed(_lowerCAmelCase , loc=_lowerCAmelCase , scale=_lowerCAmelCase , event_dim=self.event_dim )
@property
def _a ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
return () if self.dim == 1 else (self.dim,)
@property
def _a ( self : str ) -> int:
"""simple docstring"""
return len(self.event_shape )
@property
def _a ( self : Optional[Any] ) -> float:
"""simple docstring"""
return 0.0
def _a ( self : Union[str, Any] , _lowerCAmelCase : int ) -> nn.Module:
"""simple docstring"""
return ParameterProjection(
in_features=_lowerCAmelCase , args_dim=self.args_dim , domain_map=LambdaLayer(self.domain_map ) , )
def _a ( self : int , *_lowerCAmelCase : torch.Tensor ) -> Tuple:
"""simple docstring"""
raise NotImplementedError()
@staticmethod
def _a ( _lowerCAmelCase : torch.Tensor ) -> torch.Tensor:
"""simple docstring"""
return (x + torch.sqrt(torch.square(_lowerCAmelCase ) + 4.0 )) / 2.0
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Dict[str, int] = {"df": 1, "loc": 1, "scale": 1}
__snake_case :type = StudentT
@classmethod
def _a ( cls : str , _lowerCAmelCase : torch.Tensor , _lowerCAmelCase : torch.Tensor , _lowerCAmelCase : torch.Tensor ) -> Tuple:
"""simple docstring"""
__lowercase = cls.squareplus(_lowerCAmelCase ).clamp_min(torch.finfo(scale.dtype ).eps )
__lowercase = 2.0 + cls.squareplus(_lowerCAmelCase )
return df.squeeze(-1 ), loc.squeeze(-1 ), scale.squeeze(-1 )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Dict[str, int] = {"loc": 1, "scale": 1}
__snake_case :type = Normal
@classmethod
def _a ( cls : Union[str, Any] , _lowerCAmelCase : torch.Tensor , _lowerCAmelCase : torch.Tensor ) -> int:
"""simple docstring"""
__lowercase = cls.squareplus(_lowerCAmelCase ).clamp_min(torch.finfo(scale.dtype ).eps )
return loc.squeeze(-1 ), scale.squeeze(-1 )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Dict[str, int] = {"total_count": 1, "logits": 1}
__snake_case :type = NegativeBinomial
@classmethod
def _a ( cls : Optional[int] , _lowerCAmelCase : torch.Tensor , _lowerCAmelCase : torch.Tensor ) -> str:
"""simple docstring"""
__lowercase = cls.squareplus(_lowerCAmelCase )
return total_count.squeeze(-1 ), logits.squeeze(-1 )
def _a ( self : List[Any] , _lowerCAmelCase : List[str] ) -> Distribution:
"""simple docstring"""
__lowercase , __lowercase = distr_args
if self.dim == 1:
return self.distribution_class(total_count=_lowerCAmelCase , logits=_lowerCAmelCase )
else:
return Independent(self.distribution_class(total_count=_lowerCAmelCase , logits=_lowerCAmelCase ) , 1 )
def _a ( self : Optional[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[torch.Tensor] = None , _lowerCAmelCase : Optional[torch.Tensor] = None ) -> Distribution:
"""simple docstring"""
__lowercase , __lowercase = distr_args
if scale is not None:
# See scaling property of Gamma.
logits += scale.log()
return self._base_distribution((total_count, logits) )
| 80 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_case :List[Any] = 1
@register_to_config
def __init__( self : str , _lowerCAmelCase : int = 1000 , _lowerCAmelCase : Optional[Union[np.ndarray, List[float]]] = None ) -> Optional[int]:
"""simple docstring"""
self.set_timesteps(_lowerCAmelCase )
# standard deviation of the initial noise distribution
__lowercase = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
__lowercase = 4
# running values
__lowercase = []
def _a ( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Union[str, torch.device] = None ) -> int:
"""simple docstring"""
__lowercase = num_inference_steps
__lowercase = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
__lowercase = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
__lowercase = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
__lowercase = torch.sin(steps * math.pi / 2 ) ** 2
__lowercase = (1.0 - self.betas**2) ** 0.5
__lowercase = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
__lowercase = timesteps.to(_lowerCAmelCase )
__lowercase = []
def _a ( self : List[str] , _lowerCAmelCase : torch.FloatTensor , _lowerCAmelCase : int , _lowerCAmelCase : torch.FloatTensor , _lowerCAmelCase : bool = True , ) -> Union[SchedulerOutput, Tuple]:
"""simple docstring"""
if self.num_inference_steps is None:
raise ValueError(
"""Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )
__lowercase = (self.timesteps == timestep).nonzero().item()
__lowercase = timestep_index + 1
__lowercase = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(_lowerCAmelCase )
if len(self.ets ) == 1:
__lowercase = self.ets[-1]
elif len(self.ets ) == 2:
__lowercase = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
__lowercase = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
__lowercase = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
__lowercase = self._get_prev_sample(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_lowerCAmelCase )
def _a ( self : Union[str, Any] , _lowerCAmelCase : torch.FloatTensor , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : str ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def _a ( self : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = self.alphas[timestep_index]
__lowercase = self.betas[timestep_index]
__lowercase = self.alphas[prev_timestep_index]
__lowercase = self.betas[prev_timestep_index]
__lowercase = (sample - sigma * ets) / max(_lowerCAmelCase , 1e-8 )
__lowercase = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : Optional[Any] ) -> Dict:
"""simple docstring"""
return self.config.num_train_timesteps
| 80 | 1 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return "".join([hex(lowerCamelCase )[2:].zfill(2 ).upper() for byte in list(lowerCamelCase )] )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if (len(lowerCamelCase ) % 2) != 0:
raise ValueError(
"""Base16 encoded data is invalid:
Data does not have an even number of hex digits.""" )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(lowerCamelCase ) <= set("""0123456789ABCDEF""" ):
raise ValueError(
"""Base16 encoded data is invalid:
Data is not uppercase hex or it contains invalid characters.""" )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(lowerCamelCase ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar("""T""")
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] , _lowerCAmelCase : T ) -> List[str]:
"""simple docstring"""
__lowercase = data
__lowercase = None
def __str__( self : List[str] ) -> str:
"""simple docstring"""
return F'{self.data}'
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] ) -> None:
"""simple docstring"""
__lowercase = None
def __iter__( self : int ) -> Iterator[T]:
"""simple docstring"""
__lowercase = self.top
while node:
yield node.data
__lowercase = node.next
def __str__( self : List[str] ) -> str:
"""simple docstring"""
return "->".join([str(_lowerCAmelCase ) for item in self] )
def __len__( self : Any ) -> int:
"""simple docstring"""
return len(tuple(iter(self ) ) )
def _a ( self : str ) -> bool:
"""simple docstring"""
return self.top is None
def _a ( self : List[str] , _lowerCAmelCase : T ) -> None:
"""simple docstring"""
__lowercase = Node(_lowerCAmelCase )
if not self.is_empty():
__lowercase = self.top
__lowercase = node
def _a ( self : Union[str, Any] ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError("""pop from empty stack""" )
assert isinstance(self.top , _lowerCAmelCase )
__lowercase = self.top
__lowercase = self.top.next
return pop_node.data
def _a ( self : int ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError("""peek from empty stack""" )
assert self.top is not None
return self.top.data
def _a ( self : int ) -> None:
"""simple docstring"""
__lowercase = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 80 | 1 |
import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(""">=""", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default_planner import DefaultLoadPlanner, DefaultSavePlanner
from torch.distributed.checkpoint.optimizer import load_sharded_optimizer_state_dict
from torch.distributed.fsdp.fully_sharded_data_parallel import FullyShardedDataParallel as FSDP
from torch.distributed.fsdp.fully_sharded_data_parallel import StateDictType
__UpperCamelCase : Union[str, Any] = get_logger(__name__)
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=0 ):
'''simple docstring'''
os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase )
with FSDP.state_dict_type(
lowerCamelCase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowercase = model.state_dict()
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowercase = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
if accelerator.process_index == 0:
logger.info(F'Saving model to {output_model_file}' )
torch.save(lowerCamelCase , lowerCamelCase )
logger.info(F'Model saved to {output_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowercase = (
F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
if model_index == 0
else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
)
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
logger.info(F'Saving model to {output_model_file}' )
torch.save(lowerCamelCase , lowerCamelCase )
logger.info(F'Model saved to {output_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowercase = os.path.join(lowerCamelCase , F'{MODEL_NAME}_{model_index}' )
os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase )
logger.info(F'Saving model to {ckpt_dir}' )
__lowercase = {"""model""": state_dict}
dist_cp.save_state_dict(
state_dict=lowerCamelCase , storage_writer=dist_cp.FileSystemWriter(lowerCamelCase ) , planner=DefaultSavePlanner() , )
logger.info(F'Model saved to {ckpt_dir}' )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=0 ):
'''simple docstring'''
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
lowerCamelCase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if type(lowerCamelCase ) != FSDP and accelerator.process_index != 0:
if not fsdp_plugin.sync_module_states:
raise ValueError(
"""Set the `sync_module_states` flag to `True` so that model states are synced across processes when """
"""initializing FSDP object""" )
return
__lowercase = F'{MODEL_NAME}.bin' if model_index == 0 else F'{MODEL_NAME}_{model_index}.bin'
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
logger.info(F'Loading model from {input_model_file}' )
__lowercase = torch.load(lowerCamelCase )
logger.info(F'Model loaded from {input_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.LOCAL_STATE_DICT:
__lowercase = (
F'{MODEL_NAME}_rank{accelerator.process_index}.bin'
if model_index == 0
else F'{MODEL_NAME}_{model_index}_rank{accelerator.process_index}.bin'
)
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
logger.info(F'Loading model from {input_model_file}' )
__lowercase = torch.load(lowerCamelCase )
logger.info(F'Model loaded from {input_model_file}' )
elif fsdp_plugin.state_dict_type == StateDictType.SHARDED_STATE_DICT:
__lowercase = (
os.path.join(lowerCamelCase , F'{MODEL_NAME}_{model_index}' )
if F'{MODEL_NAME}' not in input_dir
else input_dir
)
logger.info(F'Loading model from {ckpt_dir}' )
__lowercase = {"""model""": model.state_dict()}
dist_cp.load_state_dict(
state_dict=lowerCamelCase , storage_reader=dist_cp.FileSystemReader(lowerCamelCase ) , planner=DefaultLoadPlanner() , )
__lowercase = state_dict["""model"""]
logger.info(F'Model loaded from {ckpt_dir}' )
model.load_state_dict(lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=0 ):
'''simple docstring'''
os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase )
with FSDP.state_dict_type(
lowerCamelCase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
__lowercase = FSDP.optim_state_dict(lowerCamelCase , lowerCamelCase )
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
if accelerator.process_index == 0:
__lowercase = (
F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
)
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
logger.info(F'Saving Optimizer state to {output_optimizer_file}' )
torch.save(lowerCamelCase , lowerCamelCase )
logger.info(F'Optimizer state saved in {output_optimizer_file}' )
else:
__lowercase = os.path.join(lowerCamelCase , F'{OPTIMIZER_NAME}_{optimizer_index}' )
os.makedirs(lowerCamelCase , exist_ok=lowerCamelCase )
logger.info(F'Saving Optimizer state to {ckpt_dir}' )
dist_cp.save_state_dict(
state_dict={"""optimizer""": optim_state} , storage_writer=dist_cp.FileSystemWriter(lowerCamelCase ) , planner=DefaultSavePlanner() , )
logger.info(F'Optimizer state saved in {ckpt_dir}' )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=0 ):
'''simple docstring'''
accelerator.wait_for_everyone()
with FSDP.state_dict_type(
lowerCamelCase , fsdp_plugin.state_dict_type , fsdp_plugin.state_dict_config , fsdp_plugin.optim_state_dict_config ):
if fsdp_plugin.state_dict_type == StateDictType.FULL_STATE_DICT:
__lowercase = None
# below check should work but currently it isn't working (mostly opytorch issue),
# in the meantime disabling it at the cost of excess memory usage
# if accelerator.process_index == 0 or not fsdp_plugin.optim_state_dict_config.rank0_only:
__lowercase = (
F'{OPTIMIZER_NAME}.bin' if optimizer_index == 0 else F'{OPTIMIZER_NAME}_{optimizer_index}.bin'
)
__lowercase = os.path.join(lowerCamelCase , lowerCamelCase )
logger.info(F'Loading Optimizer state from {input_optimizer_file}' )
__lowercase = torch.load(lowerCamelCase )
logger.info(F'Optimizer state loaded from {input_optimizer_file}' )
else:
__lowercase = (
os.path.join(lowerCamelCase , F'{OPTIMIZER_NAME}_{optimizer_index}' )
if F'{OPTIMIZER_NAME}' not in input_dir
else input_dir
)
logger.info(F'Loading Optimizer from {ckpt_dir}' )
__lowercase = load_sharded_optimizer_state_dict(
model_state_dict=model.state_dict() , optimizer_key="""optimizer""" , storage_reader=dist_cp.FileSystemReader(lowerCamelCase ) , )
__lowercase = optim_state["""optimizer"""]
logger.info(F'Optimizer loaded from {ckpt_dir}' )
__lowercase = FSDP.optim_state_dict_to_load(lowerCamelCase , lowerCamelCase , lowerCamelCase )
optimizer.load_state_dict(lowerCamelCase )
| 80 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
class __UpperCamelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt="""first prompt""" , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="""numpy""" , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCAmelCase )
__lowercase = VersatileDiffusionPipeline.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = generator.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt="""first prompt""" , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="""numpy""" , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def _a ( self : Any ) -> Dict:
"""simple docstring"""
__lowercase = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """cyberpunk 2077"""
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__lowercase = """A painting of a squirrel eating a burger """
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.text_to_image(
prompt=_lowerCAmelCase , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__lowercase = pipe.image_variation(_lowerCAmelCase , generator=_lowerCAmelCase , output_type="""numpy""" ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 80 | 1 |
__UpperCamelCase : Optional[int] = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
__UpperCamelCase : str = frozenset(["""prompt""", """negative_prompt"""])
__UpperCamelCase : Optional[int] = frozenset([])
__UpperCamelCase : Optional[Any] = frozenset(["""image"""])
__UpperCamelCase : List[str] = frozenset(
[
"""image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
__UpperCamelCase : Dict = frozenset(["""image"""])
__UpperCamelCase : Tuple = frozenset(
[
"""prompt""",
"""image""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
]
)
__UpperCamelCase : int = frozenset(["""prompt""", """image""", """negative_prompt"""])
__UpperCamelCase : int = frozenset(
[
# Text guided image variation with an image mask
"""prompt""",
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
]
)
__UpperCamelCase : Tuple = frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])
__UpperCamelCase : List[str] = frozenset(
[
# image variation with an image mask
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
__UpperCamelCase : int = frozenset(["""image""", """mask_image"""])
__UpperCamelCase : Tuple = frozenset(
[
"""example_image""",
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
__UpperCamelCase : Any = frozenset(["""example_image""", """image""", """mask_image"""])
__UpperCamelCase : Union[str, Any] = frozenset(["""class_labels"""])
__UpperCamelCase : Tuple = frozenset(["""class_labels"""])
__UpperCamelCase : Tuple = frozenset(["""batch_size"""])
__UpperCamelCase : List[Any] = frozenset([])
__UpperCamelCase : str = frozenset(["""batch_size"""])
__UpperCamelCase : List[str] = frozenset([])
__UpperCamelCase : Optional[Any] = frozenset(
[
"""prompt""",
"""audio_length_in_s""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
__UpperCamelCase : Tuple = frozenset(["""prompt""", """negative_prompt"""])
__UpperCamelCase : Optional[int] = frozenset(["""input_tokens"""])
__UpperCamelCase : Union[str, Any] = frozenset(["""input_tokens"""])
| 80 |
from __future__ import annotations
from collections.abc import MutableSequence
class __UpperCamelCase :
def __init__( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : MutableSequence[float] ) -> None:
"""simple docstring"""
if len(_lowerCAmelCase ) != degree + 1:
raise ValueError(
"""The number of coefficients should be equal to the degree + 1.""" )
__lowercase = list(_lowerCAmelCase )
__lowercase = degree
def __add__( self : Optional[int] , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
if self.degree > polynomial_a.degree:
__lowercase = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , _lowerCAmelCase )
else:
__lowercase = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , _lowerCAmelCase )
def __sub__( self : int , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : Union[str, Any] ) -> Polynomial:
"""simple docstring"""
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Optional[int] , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , _lowerCAmelCase )
def _a ( self : Optional[int] , _lowerCAmelCase : int | float ) -> int | float:
"""simple docstring"""
__lowercase = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : Dict ) -> str:
"""simple docstring"""
__lowercase = """"""
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(_lowerCAmelCase )
return polynomial
def __repr__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return self.__str__()
def _a ( self : List[str] ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * self.degree
for i in range(self.degree ):
__lowercase = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , _lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : int | float = 0 ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * (self.degree + 2)
__lowercase = constant
for i in range(self.degree + 1 ):
__lowercase = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , _lowerCAmelCase )
def __eq__( self : List[str] , _lowerCAmelCase : object ) -> bool:
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : Dict , _lowerCAmelCase : object ) -> bool:
"""simple docstring"""
return not self.__eq__(_lowerCAmelCase )
| 80 | 1 |
# 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
__UpperCamelCase : int = {
"""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 __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Union[str, Any] = 'facebook/nllb-200-distilled-600M'
__snake_case :Optional[Any] = (
'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`.'
)
__snake_case :str = 'translator'
__snake_case :Optional[Any] = AutoTokenizer
__snake_case :Optional[Any] = AutoModelForSeqaSeqLM
__snake_case :Dict = LANGUAGE_CODES
__snake_case :Any = ['text', 'text', 'text']
__snake_case :Dict = ['text']
def _a ( self : str , _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : Any ) -> str:
"""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.' )
__lowercase = self.lang_to_code[src_lang]
__lowercase = 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 : Tuple , _lowerCAmelCase : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return self.model.generate(**_lowerCAmelCase )
def _a ( self : List[Any] , _lowerCAmelCase : int ) -> Optional[Any]:
"""simple docstring"""
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=_lowerCAmelCase )
| 80 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__lowercase = len(lowerCamelCase )
__lowercase = max(lowerCamelCase )
__lowercase = min(lowerCamelCase )
# create the counting array
__lowercase = coll_max + 1 - coll_min
__lowercase = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , lowerCamelCase ):
__lowercase = counting_arr[i] + counting_arr[i - 1]
# create the output collection
__lowercase = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , lowerCamelCase ) ):
__lowercase = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return "".join([chr(lowerCamelCase ) for i in counting_sort([ord(lowerCamelCase ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string("""thisisthestring""") == "eghhiiinrsssttt"
__UpperCamelCase : str = input("""Enter numbers separated by a comma:\n""").strip()
__UpperCamelCase : Union[str, Any] = [int(item) for item in user_input.split(""",""")]
print(counting_sort(unsorted))
| 80 | 1 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : int , _lowerCAmelCase : Union[List[ControlNetModel], Tuple[ControlNetModel]] ) -> List[str]:
"""simple docstring"""
super().__init__()
__lowercase = nn.ModuleList(_lowerCAmelCase )
def _a ( self : Dict , _lowerCAmelCase : torch.FloatTensor , _lowerCAmelCase : Union[torch.Tensor, float, int] , _lowerCAmelCase : torch.Tensor , _lowerCAmelCase : List[torch.tensor] , _lowerCAmelCase : List[float] , _lowerCAmelCase : Optional[torch.Tensor] = None , _lowerCAmelCase : Optional[torch.Tensor] = None , _lowerCAmelCase : Optional[torch.Tensor] = None , _lowerCAmelCase : Optional[Dict[str, Any]] = None , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = True , ) -> Union[ControlNetOutput, Tuple]:
"""simple docstring"""
for i, (image, scale, controlnet) in enumerate(zip(_lowerCAmelCase , _lowerCAmelCase , self.nets ) ):
__lowercase , __lowercase = controlnet(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , )
# merge samples
if i == 0:
__lowercase , __lowercase = down_samples, mid_sample
else:
__lowercase = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(_lowerCAmelCase , _lowerCAmelCase )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def _a ( self : List[Any] , _lowerCAmelCase : Union[str, os.PathLike] , _lowerCAmelCase : bool = True , _lowerCAmelCase : Callable = None , _lowerCAmelCase : bool = False , _lowerCAmelCase : Optional[str] = None , ) -> str:
"""simple docstring"""
__lowercase = 0
__lowercase = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
_lowerCAmelCase , is_main_process=_lowerCAmelCase , save_function=_lowerCAmelCase , safe_serialization=_lowerCAmelCase , variant=_lowerCAmelCase , )
idx += 1
__lowercase = model_path_to_save + F'_{idx}'
@classmethod
def _a ( cls : List[Any] , _lowerCAmelCase : Optional[Union[str, os.PathLike]] , **_lowerCAmelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = 0
__lowercase = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
__lowercase = pretrained_model_path
while os.path.isdir(_lowerCAmelCase ):
__lowercase = ControlNetModel.from_pretrained(_lowerCAmelCase , **_lowerCAmelCase )
controlnets.append(_lowerCAmelCase )
idx += 1
__lowercase = pretrained_model_path + F'_{idx}'
logger.info(F'{len(_lowerCAmelCase )} controlnets loaded from {pretrained_model_path}.' )
if len(_lowerCAmelCase ) == 0:
raise ValueError(
F'No ControlNets found under {os.path.dirname(_lowerCAmelCase )}. Expected at least {pretrained_model_path + "_0"}.' )
return cls(_lowerCAmelCase )
| 80 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __UpperCamelCase :
def __init__( self : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : int=13 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : str=3 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[int]=[10, 20, 30, 40] , _lowerCAmelCase : Optional[Any]=[2, 2, 3, 2] , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : List[str]=37 , _lowerCAmelCase : List[str]="gelu" , _lowerCAmelCase : List[Any]=10 , _lowerCAmelCase : int=0.02 , _lowerCAmelCase : str=["stage2", "stage3", "stage4"] , _lowerCAmelCase : Dict=[2, 3, 4] , _lowerCAmelCase : Tuple=None , ) -> Any:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = num_channels
__lowercase = num_stages
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = is_training
__lowercase = use_labels
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = num_labels
__lowercase = initializer_range
__lowercase = out_features
__lowercase = out_indices
__lowercase = scope
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.num_labels )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def _a ( self : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self : Optional[int] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__lowercase = None
__lowercase = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[Any] = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
__snake_case :List[str] = (
{'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
if is_torch_available()
else {}
)
__snake_case :str = True
__snake_case :Any = False
__snake_case :Any = False
__snake_case :Any = False
__snake_case :int = False
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason="""ConvNext does not use inputs_embeds""" )
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not support input and output embeddings""" )
def _a ( self : Dict ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not use feedforward chunking""" )
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
pass
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int] ):
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = self.model_tester.num_stages
self.assertEqual(len(_lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = ConvNextModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224""" ) if is_vision_available() else None
@slow
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""" ).to(_lowerCAmelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# verify the logits
__lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = torch.tensor([-0.0_260, -0.4_739, 0.1_911] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase , _lowerCAmelCase ):
__snake_case :Union[str, Any] = (ConvNextBackbone,) if is_torch_available() else ()
__snake_case :str = ConvNextConfig
__snake_case :Optional[Any] = False
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = ConvNextModelTester(self )
| 80 | 1 |
from itertools import product
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = sides_number
__lowercase = max_face_number * dice_number
__lowercase = [0] * (max_total + 1)
__lowercase = 1
__lowercase = range(lowerCamelCase , max_face_number + 1 )
for dice_numbers in product(lowerCamelCase , repeat=lowerCamelCase ):
__lowercase = sum(lowerCamelCase )
totals_frequencies[total] += 1
return totals_frequencies
def snake_case ( ):
'''simple docstring'''
__lowercase = total_frequency_distribution(
sides_number=4 , dice_number=9 )
__lowercase = total_frequency_distribution(
sides_number=6 , dice_number=6 )
__lowercase = 0
__lowercase = 9
__lowercase = 4 * 9
__lowercase = 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] )
__lowercase = (4**9) * (6**6)
__lowercase = peter_wins_count / total_games_number
__lowercase = round(lowerCamelCase , ndigits=7 )
return rounded_peter_win_probability
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : List[str] = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
__UpperCamelCase : Tuple = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
__UpperCamelCase : Optional[Any] = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
__UpperCamelCase : Optional[Any] = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
__UpperCamelCase : int = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
__UpperCamelCase : List[Any] = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
__UpperCamelCase : List[Any] = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
__UpperCamelCase : List[str] = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
__UpperCamelCase : List[str] = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
__UpperCamelCase : int = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
__UpperCamelCase : Dict = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
__UpperCamelCase : str = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
__UpperCamelCase : Optional[int] = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
__UpperCamelCase : Dict = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
__UpperCamelCase : List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__UpperCamelCase : str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__UpperCamelCase : Optional[int] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__UpperCamelCase : Dict = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__UpperCamelCase : Dict = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : int = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__UpperCamelCase : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__UpperCamelCase : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__UpperCamelCase : Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__UpperCamelCase : str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__UpperCamelCase : List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__UpperCamelCase : Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Tuple = FLAX_MODEL_MAPPING
__UpperCamelCase : Tuple = auto_class_update(FlaxAutoModel)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Union[str, Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
__UpperCamelCase : List[Any] = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
__UpperCamelCase : Union[str, Any] = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :List[Any] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
__UpperCamelCase : Dict = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__UpperCamelCase : Optional[Any] = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[int] = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
__UpperCamelCase : Union[str, Any] = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :List[Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[int] = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[int] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
__UpperCamelCase : int = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :str = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
__UpperCamelCase : int = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[Any] = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
__UpperCamelCase : Tuple = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
__UpperCamelCase : str = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 80 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : List[Any] = {
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Dict = 'gpt_neox'
def __init__( self : Optional[int] , _lowerCAmelCase : str=5_0432 , _lowerCAmelCase : Optional[Any]=6144 , _lowerCAmelCase : List[Any]=44 , _lowerCAmelCase : Union[str, Any]=64 , _lowerCAmelCase : List[str]=2_4576 , _lowerCAmelCase : List[Any]="gelu" , _lowerCAmelCase : str=0.25 , _lowerCAmelCase : List[Any]=1_0000 , _lowerCAmelCase : str=0.0 , _lowerCAmelCase : Any=0.0 , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : List[str]=2048 , _lowerCAmelCase : Union[str, Any]=0.02 , _lowerCAmelCase : List[str]=1e-5 , _lowerCAmelCase : List[Any]=True , _lowerCAmelCase : List[str]=0 , _lowerCAmelCase : Dict=2 , _lowerCAmelCase : List[str]=False , _lowerCAmelCase : List[Any]=True , _lowerCAmelCase : Dict=None , **_lowerCAmelCase : str , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = vocab_size
__lowercase = max_position_embeddings
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = rotary_pct
__lowercase = rotary_emb_base
__lowercase = attention_dropout
__lowercase = hidden_dropout
__lowercase = classifier_dropout
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = use_cache
__lowercase = tie_word_embeddings
__lowercase = use_parallel_residual
__lowercase = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
"""The hidden size is not divisble by the number of attention heads! Make sure to update them!""" )
def _a ( self : Any ) -> int:
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , _lowerCAmelCase ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
F'got {self.rope_scaling}' )
__lowercase = self.rope_scaling.get("""type""" , _lowerCAmelCase )
__lowercase = self.rope_scaling.get("""factor""" , _lowerCAmelCase )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(_lowerCAmelCase , _lowerCAmelCase ) or rope_scaling_factor <= 1.0:
raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 80 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCamelCase : int = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 | 1 |
import sys
from collections import defaultdict
class __UpperCamelCase :
def __init__( self : Any ) -> Tuple:
"""simple docstring"""
__lowercase = []
def _a ( self : int , _lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
return self.node_position[vertex]
def _a ( self : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = pos
def _a ( self : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
if start > size // 2 - 1:
return
else:
if 2 * start + 2 >= size:
__lowercase = 2 * start + 1
else:
if heap[2 * start + 1] < heap[2 * start + 2]:
__lowercase = 2 * start + 1
else:
__lowercase = 2 * start + 2
if heap[smallest_child] < heap[start]:
__lowercase , __lowercase = heap[smallest_child], positions[smallest_child]
__lowercase , __lowercase = (
heap[start],
positions[start],
)
__lowercase , __lowercase = temp, tempa
__lowercase = self.get_position(positions[smallest_child] )
self.set_position(
positions[smallest_child] , self.get_position(positions[start] ) )
self.set_position(positions[start] , _lowerCAmelCase )
self.top_to_bottom(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = position[index]
while index != 0:
__lowercase = int((index - 2) / 2 ) if index % 2 == 0 else int((index - 1) / 2 )
if val < heap[parent]:
__lowercase = heap[parent]
__lowercase = position[parent]
self.set_position(position[parent] , _lowerCAmelCase )
else:
__lowercase = val
__lowercase = temp
self.set_position(_lowerCAmelCase , _lowerCAmelCase )
break
__lowercase = parent
else:
__lowercase = val
__lowercase = temp
self.set_position(_lowerCAmelCase , 0 )
def _a ( self : Optional[Any] , _lowerCAmelCase : Dict , _lowerCAmelCase : int ) -> int:
"""simple docstring"""
__lowercase = len(_lowerCAmelCase ) // 2 - 1
for i in range(_lowerCAmelCase , -1 , -1 ):
self.top_to_bottom(_lowerCAmelCase , _lowerCAmelCase , len(_lowerCAmelCase ) , _lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : int ) -> str:
"""simple docstring"""
__lowercase = positions[0]
__lowercase = sys.maxsize
self.top_to_bottom(_lowerCAmelCase , 0 , len(_lowerCAmelCase ) , _lowerCAmelCase )
return temp
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = Heap()
__lowercase = [0] * len(lowerCamelCase )
__lowercase = [-1] * len(lowerCamelCase ) # Neighboring Tree Vertex of selected vertex
# Minimum Distance of explored vertex with neighboring vertex of partial tree
# formed in graph
__lowercase = [] # Heap of Distance of vertices from their neighboring vertex
__lowercase = []
for vertex in range(len(lowerCamelCase ) ):
distance_tv.append(sys.maxsize )
positions.append(lowerCamelCase )
heap.node_position.append(lowerCamelCase )
__lowercase = []
__lowercase = 1
__lowercase = sys.maxsize
for neighbor, distance in adjacency_list[0]:
__lowercase = 0
__lowercase = distance
heap.heapify(lowerCamelCase , lowerCamelCase )
for _ in range(1 , len(lowerCamelCase ) ):
__lowercase = heap.delete_minimum(lowerCamelCase , lowerCamelCase )
if visited[vertex] == 0:
tree_edges.append((nbr_tv[vertex], vertex) )
__lowercase = 1
for neighbor, distance in adjacency_list[vertex]:
if (
visited[neighbor] == 0
and distance < distance_tv[heap.get_position(lowerCamelCase )]
):
__lowercase = distance
heap.bottom_to_top(
lowerCamelCase , heap.get_position(lowerCamelCase ) , lowerCamelCase , lowerCamelCase )
__lowercase = vertex
return tree_edges
if __name__ == "__main__": # pragma: no cover
# < --------- Prims Algorithm --------- >
__UpperCamelCase : int = int(input("""Enter number of edges: """).strip())
__UpperCamelCase : Optional[Any] = defaultdict(list)
for _ in range(edges_number):
__UpperCamelCase : List[Any] = [int(x) for x in input().strip().split()]
adjacency_list[edge[0]].append([edge[1], edge[2]])
adjacency_list[edge[1]].append([edge[0], edge[2]])
print(prisms_algorithm(adjacency_list))
| 80 |
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase : Union[str, Any] = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""
__UpperCamelCase : List[str] = """
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results['matthews_correlation'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results['matthews_correlation'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results['matthews_correlation'], 2))
-0.25
"""
__UpperCamelCase : Tuple = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def _a ( self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any]=None ) -> Optional[Any]:
"""simple docstring"""
return {
"matthews_correlation": float(matthews_corrcoef(_lowerCAmelCase , _lowerCAmelCase , sample_weight=_lowerCAmelCase ) ),
}
| 80 | 1 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return "".join(chr(ord(lowerCamelCase ) - 32 ) if """a""" <= char <= """z""" else char for char in word )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 80 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
__UpperCamelCase : Optional[int] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
__UpperCamelCase : Dict = {"""facebook/blenderbot_small-90M""": 512}
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
__lowercase = set(lowerCamelCase )
return pairs
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = VOCAB_FILES_NAMES
__snake_case :Tuple = PRETRAINED_VOCAB_FILES_MAP
__snake_case :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case :str = ['input_ids', 'attention_mask']
def __init__( self : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str="__start__" , _lowerCAmelCase : int="__end__" , _lowerCAmelCase : Any="__unk__" , _lowerCAmelCase : List[Any]="__null__" , **_lowerCAmelCase : Tuple , ) -> str:
"""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:
__lowercase = json.load(_lowerCAmelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
with open(_lowerCAmelCase , encoding="""utf-8""" ) as merges_handle:
__lowercase = merges_handle.read().split("""\n""" )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in merges]
__lowercase = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
__lowercase = {}
@property
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return len(self.encoder )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _a ( self : str , _lowerCAmelCase : str ) -> str:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
__lowercase = re.sub("""([.,!?()])""" , r""" \1""" , _lowerCAmelCase )
__lowercase = re.sub("""(')""" , r""" \1 """ , _lowerCAmelCase )
__lowercase = re.sub(r"""\s{2,}""" , """ """ , _lowerCAmelCase )
if "\n" in token:
__lowercase = token.replace("""\n""" , """ __newln__""" )
__lowercase = token.split(""" """ )
__lowercase = []
for token in tokens:
if not len(_lowerCAmelCase ):
continue
__lowercase = token.lower()
__lowercase = tuple(_lowerCAmelCase )
__lowercase = tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
__lowercase = get_pairs(_lowerCAmelCase )
if not pairs:
words.append(_lowerCAmelCase )
continue
while True:
__lowercase = min(_lowerCAmelCase , key=lambda _lowerCAmelCase : self.bpe_ranks.get(_lowerCAmelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(_lowerCAmelCase ):
try:
__lowercase = word.index(_lowerCAmelCase , _lowerCAmelCase )
new_word.extend(word[i:j] )
__lowercase = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(_lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(_lowerCAmelCase )
__lowercase = new_word
if len(_lowerCAmelCase ) == 1:
break
else:
__lowercase = get_pairs(_lowerCAmelCase )
__lowercase = """@@ """.join(_lowerCAmelCase )
__lowercase = word[:-4]
__lowercase = word
words.append(_lowerCAmelCase )
return " ".join(_lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
__lowercase = []
__lowercase = re.findall(r"""\S+\n?""" , _lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(_lowerCAmelCase ).split(""" """ ) ) )
return split_tokens
def _a ( self : Tuple , _lowerCAmelCase : str ) -> int:
"""simple docstring"""
__lowercase = token.lower()
return self.encoder.get(_lowerCAmelCase , self.encoder.get(self.unk_token ) )
def _a ( self : Tuple , _lowerCAmelCase : int ) -> str:
"""simple docstring"""
return self.decoder.get(_lowerCAmelCase , self.unk_token )
def _a ( self : Dict , _lowerCAmelCase : List[str] ) -> str:
"""simple docstring"""
__lowercase = """ """.join(_lowerCAmelCase ).replace("""@@ """ , """""" ).strip()
return out_string
def _a ( self : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + """\n""" )
__lowercase = 0
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
""" Please check that the tokenizer is not corrupted!""" )
__lowercase = token_index
writer.write(""" """.join(_lowerCAmelCase ) + """\n""" )
index += 1
return vocab_file, merge_file
| 80 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[int] = StableDiffusionInstructPixaPixPipeline
__snake_case :List[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
__snake_case :Any = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
__snake_case :int = IMAGE_TO_IMAGE_IMAGE_PARAMS
__snake_case :int = IMAGE_TO_IMAGE_IMAGE_PARAMS
def _a ( self : List[Any] ) -> Tuple:
"""simple docstring"""
torch.manual_seed(0 )
__lowercase = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=8 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , cross_attention_dim=32 , )
__lowercase = PNDMScheduler(skip_prk_steps=_lowerCAmelCase )
torch.manual_seed(0 )
__lowercase = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , )
torch.manual_seed(0 )
__lowercase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
__lowercase = CLIPTextModel(_lowerCAmelCase )
__lowercase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
__lowercase = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def _a ( self : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any]=0 ) -> Any:
"""simple docstring"""
__lowercase = floats_tensor((1, 3, 32, 32) , rng=random.Random(_lowerCAmelCase ) ).to(_lowerCAmelCase )
__lowercase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowercase = Image.fromarray(np.uinta(_lowerCAmelCase ) ).convert("""RGB""" )
if str(_lowerCAmelCase ).startswith("""mps""" ):
__lowercase = torch.manual_seed(_lowerCAmelCase )
else:
__lowercase = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""image_guidance_scale""": 1,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : str ) -> Tuple:
"""simple docstring"""
__lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase )
__lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs(_lowerCAmelCase )
__lowercase = sd_pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowercase = np.array([0.7_526, 0.3_750, 0.4_547, 0.6_117, 0.5_866, 0.5_016, 0.4_327, 0.5_642, 0.4_815] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Any ) -> Any:
"""simple docstring"""
__lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase )
__lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs(_lowerCAmelCase )
__lowercase = """french fries"""
__lowercase = sd_pipe(**_lowerCAmelCase , negative_prompt=_lowerCAmelCase )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
__lowercase = np.array([0.7_511, 0.3_642, 0.4_553, 0.6_236, 0.5_797, 0.5_013, 0.4_343, 0.5_611, 0.4_831] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase )
__lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs(_lowerCAmelCase )
__lowercase = [inputs["""prompt"""]] * 2
__lowercase = np.array(inputs["""image"""] ).astype(np.floataa ) / 255.0
__lowercase = torch.from_numpy(_lowerCAmelCase ).unsqueeze(0 ).to(_lowerCAmelCase )
__lowercase = image / 2 + 0.5
__lowercase = image.permute(0 , 3 , 1 , 2 )
__lowercase = image.repeat(2 , 1 , 1 , 1 )
__lowercase = sd_pipe(**_lowerCAmelCase ).images
__lowercase = image[-1, -3:, -3:, -1]
assert image.shape == (2, 32, 32, 3)
__lowercase = np.array([0.5_812, 0.5_748, 0.5_222, 0.5_908, 0.5_695, 0.7_174, 0.6_804, 0.5_523, 0.5_579] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : int ) -> List[str]:
"""simple docstring"""
__lowercase = """cpu""" # ensure determinism for the device-dependent torch.Generator
__lowercase = self.get_dummy_components()
__lowercase = EulerAncestralDiscreteScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="""scaled_linear""" )
__lowercase = StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase )
__lowercase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs(_lowerCAmelCase )
__lowercase = sd_pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
__lowercase = [round(_lowerCAmelCase , 4 ) for x in image_slice.flatten().tolist()]
print(""",""".join([str(_lowerCAmelCase ) for x in slice] ) )
assert image.shape == (1, 32, 32, 3)
__lowercase = np.array([0.7_417, 0.3_842, 0.4_732, 0.5_776, 0.5_891, 0.5_139, 0.4_052, 0.5_673, 0.4_986] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : List[str] ) -> Dict:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
__lowercase = self.get_dummy_components()
__lowercase = StableDiffusionInstructPixaPixPipeline(**_lowerCAmelCase )
__lowercase = VaeImageProcessor(do_resize=_lowerCAmelCase , do_normalize=_lowerCAmelCase )
__lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = pipe(**self.get_dummy_inputs_by_type(_lowerCAmelCase , input_image_type="""pt""" ) )[0]
__lowercase = components["""vae"""]
__lowercase = self.get_dummy_inputs_by_type(_lowerCAmelCase , input_image_type="""pt""" )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
__lowercase = vae.encode(inputs[image_param] ).latent_dist.mode()
__lowercase = pipe(**_lowerCAmelCase )[0]
__lowercase = np.abs(out - out_latents_inputs ).max()
self.assertLess(_lowerCAmelCase , 1e-4 , """passing latents as image input generate different result from passing image""" )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Dict , _lowerCAmelCase : str=0 ) -> Optional[int]:
"""simple docstring"""
__lowercase = torch.manual_seed(_lowerCAmelCase )
__lowercase = load_image(
"""https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg""" )
__lowercase = {
"""prompt""": """turn him into a cyborg""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""image_guidance_scale""": 1.0,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : Tuple ) -> Optional[Any]:
"""simple docstring"""
__lowercase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
__lowercase = self.get_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.5_902, 0.6_015, 0.6_027, 0.5_983, 0.6_092, 0.6_061, 0.5_765, 0.5_785, 0.5_555] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=_lowerCAmelCase )
__lowercase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
__lowercase = self.get_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.6_578, 0.6_817, 0.6_972, 0.6_761, 0.6_856, 0.6_916, 0.6_428, 0.6_516, 0.6_301] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=_lowerCAmelCase )
__lowercase = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
__lowercase = self.get_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.3_828, 0.3_834, 0.3_818, 0.3_792, 0.3_865, 0.3_752, 0.3_792, 0.3_847, 0.3_753] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
__lowercase = 0
def callback_fn(_lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : torch.FloatTensor ) -> None:
__lowercase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
__lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array([-0.2_463, -0.4_644, -0.9_756, 1.5_176, 1.4_414, 0.7_866, 0.9_897, 0.8_521, 0.7_983] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
__lowercase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array([-0.2_644, -0.4_626, -0.9_653, 1.5_176, 1.4_551, 0.7_686, 0.9_805, 0.8_452, 0.8_115] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
__lowercase = False
__lowercase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=_lowerCAmelCase , torch_dtype=torch.floataa )
__lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
__lowercase = self.get_inputs()
pipe(**_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
__lowercase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
"""timbrooks/instruct-pix2pix""" , safety_checker=_lowerCAmelCase , torch_dtype=torch.floataa )
__lowercase = pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
__lowercase = self.get_inputs()
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 10**9
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
__lowercase = inputs["""image"""].resize((504, 504) )
__lowercase = """timbrooks/instruct-pix2pix"""
__lowercase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
_lowerCAmelCase , safety_checker=_lowerCAmelCase , )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0]
__lowercase = image[255:258, 383:386, -1]
assert image.shape == (504, 504, 3)
__lowercase = np.array([0.2_726, 0.2_529, 0.2_664, 0.2_655, 0.2_641, 0.2_642, 0.2_591, 0.2_649, 0.2_590] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 80 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Union[str, Any] = 'lxmert'
__snake_case :Union[str, Any] = {}
def __init__( self : List[str] , _lowerCAmelCase : Dict=3_0522 , _lowerCAmelCase : List[str]=768 , _lowerCAmelCase : Union[str, Any]=12 , _lowerCAmelCase : Union[str, Any]=9500 , _lowerCAmelCase : Union[str, Any]=1600 , _lowerCAmelCase : Optional[Any]=400 , _lowerCAmelCase : Tuple=3072 , _lowerCAmelCase : List[Any]="gelu" , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Tuple=512 , _lowerCAmelCase : Tuple=2 , _lowerCAmelCase : Optional[Any]=0.02 , _lowerCAmelCase : List[str]=1e-12 , _lowerCAmelCase : Any=9 , _lowerCAmelCase : Optional[Any]=5 , _lowerCAmelCase : Any=5 , _lowerCAmelCase : Dict=2048 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[Any]=6.67 , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , _lowerCAmelCase : str=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , **_lowerCAmelCase : Tuple , ) -> Dict:
"""simple docstring"""
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = num_qa_labels
__lowercase = num_object_labels
__lowercase = num_attr_labels
__lowercase = l_layers
__lowercase = x_layers
__lowercase = r_layers
__lowercase = visual_feat_dim
__lowercase = visual_pos_dim
__lowercase = visual_loss_normalizer
__lowercase = task_matched
__lowercase = task_mask_lm
__lowercase = task_obj_predict
__lowercase = task_qa
__lowercase = visual_obj_loss
__lowercase = visual_attr_loss
__lowercase = visual_feat_loss
__lowercase = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**_lowerCAmelCase )
| 80 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""SCUT-DLVCLab/lilt-roberta-en-base""": (
"""https://huggingface.co/SCUT-DLVCLab/lilt-roberta-en-base/resolve/main/config.json"""
),
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Tuple = 'lilt'
def __init__( self : Any , _lowerCAmelCase : str=3_0522 , _lowerCAmelCase : Tuple=768 , _lowerCAmelCase : Any=12 , _lowerCAmelCase : Dict=12 , _lowerCAmelCase : Tuple=3072 , _lowerCAmelCase : Any="gelu" , _lowerCAmelCase : str=0.1 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : List[str]=512 , _lowerCAmelCase : List[Any]=2 , _lowerCAmelCase : Tuple=0.02 , _lowerCAmelCase : Any=1e-12 , _lowerCAmelCase : Tuple=0 , _lowerCAmelCase : Optional[Any]="absolute" , _lowerCAmelCase : str=None , _lowerCAmelCase : Union[str, Any]=4 , _lowerCAmelCase : Union[str, Any]=1024 , **_lowerCAmelCase : Dict , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(pad_token_id=_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = position_embedding_type
__lowercase = classifier_dropout
__lowercase = channel_shrink_ratio
__lowercase = max_ad_position_embeddings
| 80 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : int , _lowerCAmelCase : str , _lowerCAmelCase : List[str]=13 , _lowerCAmelCase : List[str]=7 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : Union[str, Any]=False , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=99 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : Optional[int]=5 , _lowerCAmelCase : Any=4 , _lowerCAmelCase : Tuple=37 , _lowerCAmelCase : str="gelu" , _lowerCAmelCase : List[str]=0.1 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : Union[str, Any]=512 , _lowerCAmelCase : Dict=16 , _lowerCAmelCase : List[Any]=2 , _lowerCAmelCase : Optional[int]=0.02 , _lowerCAmelCase : int=3 , _lowerCAmelCase : Dict=4 , _lowerCAmelCase : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_input_mask
__lowercase = use_token_type_ids
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = num_labels
__lowercase = num_choices
__lowercase = scope
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = None
if self.use_input_mask:
__lowercase = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase = None
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowercase = ids_tensor([self.batch_size] , self.num_choices )
__lowercase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _a ( self : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = DistilBertModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> List[str]:
"""simple docstring"""
__lowercase = DistilBertForMaskedLM(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertForQuestionAnswering(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForSequenceClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForTokenClassification(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ) -> str:
"""simple docstring"""
__lowercase = self.num_choices
__lowercase = DistilBertForMultipleChoice(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = model(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
((__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase)) = config_and_inputs
__lowercase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[Any] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
__snake_case :Dict = (
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__snake_case :Tuple = True
__snake_case :Tuple = True
__snake_case :List[str] = True
__snake_case :Optional[int] = True
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = DistilBertModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , dim=37 )
def _a ( self : Dict ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*_lowerCAmelCase )
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*_lowerCAmelCase )
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*_lowerCAmelCase )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*_lowerCAmelCase )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*_lowerCAmelCase )
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*_lowerCAmelCase )
@slow
def _a ( self : int ) -> Optional[Any]:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = DistilBertModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@slow
@require_torch_gpu
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
__lowercase = True
__lowercase = model_class(config=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = torch.jit.trace(
_lowerCAmelCase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , """traced_model.pt""" ) )
__lowercase = torch.jit.load(os.path.join(_lowerCAmelCase , """traced_model.pt""" ) , map_location=_lowerCAmelCase )
loaded(inputs_dict["""input_ids"""].to(_lowerCAmelCase ) , inputs_dict["""attention_mask"""].to(_lowerCAmelCase ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertModel.from_pretrained("""distilbert-base-uncased""" )
__lowercase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__lowercase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )[0]
__lowercase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _lowerCAmelCase )
__lowercase = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowerCAmelCase , atol=1e-4 ) )
| 80 | 1 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = [0] * len(lowerCamelCase )
__lowercase = []
__lowercase = []
__lowercase = 0
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(lowerCamelCase ) ):
if indegree[i] == 0:
queue.append(lowerCamelCase )
while queue:
__lowercase = queue.pop(0 )
cnt += 1
topo.append(lowerCamelCase )
for x in graph[vertex]:
indegree[x] -= 1
if indegree[x] == 0:
queue.append(lowerCamelCase )
if cnt != len(lowerCamelCase ):
print("""Cycle exists""" )
else:
print(lowerCamelCase )
# Adjacency List of Graph
__UpperCamelCase : Dict = {0: [1, 2], 1: [3], 2: [3], 3: [4, 5], 4: [], 5: []}
topological_sort(graph)
| 80 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCamelCase ( _lowerCAmelCase ):
# to overwrite at feature extractactor specific tests
__snake_case :Optional[int] = None
__snake_case :Dict = None
@property
def _a ( self : str ) -> List[str]:
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_lowerCAmelCase , """feature_size""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """sampling_rate""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """padding_value""" ) )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_lowerCAmelCase ) == len(_lowerCAmelCase ) for x, y in zip(_lowerCAmelCase , processed_features[input_name] ) ) )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _a ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _a ( self : str , _lowerCAmelCase : List[Any]=False ) -> int:
"""simple docstring"""
def _inputs_have_equal_length(_lowerCAmelCase : int ):
__lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCAmelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCAmelCase : Dict , _lowerCAmelCase : Tuple ):
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCAmelCase , _lowerCAmelCase ):
if not np.allclose(np.asarray(_lowerCAmelCase ) , np.asarray(_lowerCAmelCase ) , atol=1e-3 ):
return False
return True
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = self.feat_extract_tester.seq_length_diff
__lowercase = self.feat_extract_tester.max_seq_length + pad_diff
__lowercase = self.feat_extract_tester.min_seq_length
__lowercase = self.feat_extract_tester.batch_size
__lowercase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
__lowercase = feat_extract.pad(_lowerCAmelCase , padding=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )
__lowercase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""max_length""" )[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=_lowerCAmelCase , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
__lowercase = feat_extract.pad(_lowerCAmelCase , pad_to_multiple_of=10 )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , pad_to_multiple_of=10 )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = input_a[input_name]
self.assertTrue(all(len(_lowerCAmelCase ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_lowerCAmelCase ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
__lowercase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _a ( self : Tuple , _lowerCAmelCase : str=False ) -> Union[str, Any]:
"""simple docstring"""
def _inputs_have_equal_length(_lowerCAmelCase : Tuple ):
__lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCAmelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCAmelCase : Any , _lowerCAmelCase : str ):
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCAmelCase , _lowerCAmelCase ):
if not np.allclose(np.asarray(_lowerCAmelCase ) , np.asarray(_lowerCAmelCase ) , atol=1e-3 ):
return False
return True
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
__lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
# truncate to smallest with np
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=_lowerCAmelCase , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
# truncate to middle
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , truncation=_lowerCAmelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""longest""" , truncation=_lowerCAmelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""longest""" , truncation=_lowerCAmelCase )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , truncation=_lowerCAmelCase )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
__lowercase = 12
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCAmelCase , truncation=_lowerCAmelCase , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCAmelCase , )
__lowercase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
__lowercase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
__lowercase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self._check_padding(numpify=_lowerCAmelCase )
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
self._check_padding(numpify=_lowerCAmelCase )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
self._check_truncation(numpify=_lowerCAmelCase )
def _a ( self : str ) -> str:
"""simple docstring"""
self._check_truncation(numpify=_lowerCAmelCase )
@require_torch
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _a ( self : Any ) -> Any:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**_lowerCAmelCase )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = [len(_lowerCAmelCase ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _lowerCAmelCase )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _lowerCAmelCase )
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**_lowerCAmelCase )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = [len(_lowerCAmelCase ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = min(_lowerCAmelCase )
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=_lowerCAmelCase , truncation=_lowerCAmelCase , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _lowerCAmelCase )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 80 | 1 |
__UpperCamelCase : Tuple = {
"""a""": """AAAAA""",
"""b""": """AAAAB""",
"""c""": """AAABA""",
"""d""": """AAABB""",
"""e""": """AABAA""",
"""f""": """AABAB""",
"""g""": """AABBA""",
"""h""": """AABBB""",
"""i""": """ABAAA""",
"""j""": """BBBAA""",
"""k""": """ABAAB""",
"""l""": """ABABA""",
"""m""": """ABABB""",
"""n""": """ABBAA""",
"""o""": """ABBAB""",
"""p""": """ABBBA""",
"""q""": """ABBBB""",
"""r""": """BAAAA""",
"""s""": """BAAAB""",
"""t""": """BAABA""",
"""u""": """BAABB""",
"""v""": """BBBAB""",
"""w""": """BABAA""",
"""x""": """BABAB""",
"""y""": """BABBA""",
"""z""": """BABBB""",
""" """: """ """,
}
__UpperCamelCase : str = {value: key for key, value in encode_dict.items()}
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = """"""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("""encode() accepts only letters of the alphabet and spaces""" )
return encoded
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if set(lowerCamelCase ) - {"A", "B", " "} != set():
raise Exception("""decode() accepts only 'A', 'B' and spaces""" )
__lowercase = """"""
for word in coded.split():
while len(lowerCamelCase ) != 0:
decoded += decode_dict[word[:5]]
__lowercase = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 80 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lowerCamelCase ) <= key:
return input_string
for position, character in enumerate(lowerCamelCase ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(lowerCamelCase )
__lowercase = ["""""".join(lowerCamelCase ) for row in temp_grid]
__lowercase = """""".join(lowerCamelCase )
return output_string
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = []
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1:
return input_string
__lowercase = [[] for _ in range(lowerCamelCase )] # generates template
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("""*""" )
__lowercase = 0
for row in temp_grid: # fills in the characters
__lowercase = input_string[counter : counter + len(lowerCamelCase )]
grid.append(list(lowerCamelCase ) )
counter += len(lowerCamelCase )
__lowercase = """""" # reads as zigzag
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = {}
for key_guess in range(1 , len(lowerCamelCase ) ): # tries every key
__lowercase = decrypt(lowerCamelCase , lowerCamelCase )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 | 1 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case ( ):
'''simple docstring'''
__lowercase = [randint(-1_000 , 1_000 ) for i in range(10 )]
__lowercase = randint(-5_000 , 5_000 )
return (arr, r)
__UpperCamelCase : Any = make_dataset()
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
for triplet in permutations(lowerCamelCase , 3 ):
if sum(lowerCamelCase ) == target:
return tuple(sorted(lowerCamelCase ) )
return (0, 0, 0)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
arr.sort()
__lowercase = len(lowerCamelCase )
for i in range(n - 1 ):
__lowercase , __lowercase = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def snake_case ( ):
'''simple docstring'''
__lowercase = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
__lowercase = """
triplet_sum1(*dataset)
"""
__lowercase = """
triplet_sum2(*dataset)
"""
__lowercase = repeat(setup=lowerCamelCase , stmt=lowerCamelCase , repeat=5 , number=10_000 )
__lowercase = repeat(setup=lowerCamelCase , stmt=lowerCamelCase , repeat=5 , number=10_000 )
return (min(lowerCamelCase ), min(lowerCamelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
__UpperCamelCase : Tuple = solution_times()
print(F'''The time for naive implementation is {times[0]}.''')
print(F'''The time for optimized implementation is {times[1]}.''')
| 80 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , lowerCamelCase ):
__lowercase = 1
__lowercase = 0
for i in range(lowerCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 | 1 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , lowerCamelCase ):
__lowercase = 1
__lowercase = 0
for i in range(lowerCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class __UpperCamelCase :
def __init__( self : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int]=13 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : Optional[Any]=2 , _lowerCAmelCase : Union[str, Any]=3 , _lowerCAmelCase : List[str]=16 , _lowerCAmelCase : List[str]=[1, 2, 1] , _lowerCAmelCase : Dict=[2, 2, 4] , _lowerCAmelCase : Optional[int]=2 , _lowerCAmelCase : Optional[Any]=2.0 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Optional[int]=0.0 , _lowerCAmelCase : Any=0.0 , _lowerCAmelCase : Optional[int]=0.1 , _lowerCAmelCase : Tuple="gelu" , _lowerCAmelCase : int=False , _lowerCAmelCase : Dict=True , _lowerCAmelCase : List[str]=0.02 , _lowerCAmelCase : Union[str, Any]=1e-5 , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : List[Any]=10 , _lowerCAmelCase : Tuple=8 , _lowerCAmelCase : List[Any]=["stage1", "stage2", "stage3"] , _lowerCAmelCase : Union[str, Any]=[1, 2, 3] , ) -> int:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = patch_norm
__lowercase = layer_norm_eps
__lowercase = initializer_range
__lowercase = is_training
__lowercase = scope
__lowercase = use_labels
__lowercase = type_sequence_label_size
__lowercase = encoder_stride
__lowercase = out_features
__lowercase = out_indices
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def _a ( self : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : str , _lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
__lowercase = MaskFormerSwinModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
__lowercase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__lowercase = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def _a ( self : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = MaskFormerSwinBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_lowerCAmelCase ):
__lowercase = ["""stem"""]
__lowercase = MaskFormerSwinBackbone(config=_lowerCAmelCase )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
__snake_case :Optional[int] = {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
__snake_case :Optional[int] = False
__snake_case :Any = False
__snake_case :List[str] = False
__snake_case :Tuple = False
__snake_case :Optional[int] = False
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__lowercase = MaskFormerSwinModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"""`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
""" `nn.DataParallel`"""
) )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
pass
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
return
def _a ( self : Any ) -> Tuple:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCAmelCase )
@unittest.skip("""Swin does not use inputs_embeds""" )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip("""Swin does not support feedforward chunking""" )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
pass
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions""" )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone""" )
def _a ( self : Any ) -> Any:
"""simple docstring"""
pass
def _a ( self : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.hidden_states
__lowercase = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
# Swin has a different seq_length
__lowercase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = 3
__lowercase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__lowercase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__lowercase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , (padded_height, padded_width) )
@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints""" )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a ( self : Any ) -> str:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
pass
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(_lowerCAmelCase : Optional[int] ):
__lowercase = 0
return t
def check_equivalence(_lowerCAmelCase : int , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int]={} ):
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase , return_dict=_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = model(**_lowerCAmelCase , return_dict=_lowerCAmelCase , **_lowerCAmelCase ).to_tuple()
def recursive_check(_lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ):
if isinstance(_lowerCAmelCase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_lowerCAmelCase , _lowerCAmelCase ):
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_lowerCAmelCase ) , set_nan_tensor_to_zero(_lowerCAmelCase ) , atol=1e-5 ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
F' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'
F' {torch.isnan(_lowerCAmelCase ).any()} and `inf`: {torch.isinf(_lowerCAmelCase )}. Dict has'
F' `nan`: {torch.isnan(_lowerCAmelCase ).any()} and `inf`: {torch.isinf(_lowerCAmelCase )}.'
) , )
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {"""output_hidden_states""": True} )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {"""output_hidden_states""": True} )
@require_torch
class __UpperCamelCase ( unittest.TestCase , _lowerCAmelCase ):
__snake_case :Optional[Any] = (MaskFormerSwinBackbone,) if is_torch_available() else ()
__snake_case :Dict = MaskFormerSwinConfig
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase = MaskFormerSwinModelTester(self )
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = inputs_dict["""pixel_values"""].shape[0]
for backbone_class in self.all_model_classes:
__lowercase = backbone_class(_lowerCAmelCase )
backbone.to(_lowerCAmelCase )
backbone.eval()
__lowercase = backbone(**_lowerCAmelCase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _lowerCAmelCase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
__lowercase = backbone(**_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
__lowercase , __lowercase , __lowercase = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
__lowercase = backbone(**_lowerCAmelCase , output_attentions=_lowerCAmelCase )
self.assertIsNotNone(outputs.attentions )
| 80 | 1 |
import json
import os
from dataclasses import dataclass
from functools import partial
from typing import Callable
import flax.linen as nn
import jax
import jax.numpy as jnp
import joblib
import optax
import wandb
from flax import jax_utils, struct, traverse_util
from flax.serialization import from_bytes, to_bytes
from flax.training import train_state
from flax.training.common_utils import shard
from tqdm.auto import tqdm
from transformers import BigBirdConfig, FlaxBigBirdForQuestionAnswering
from transformers.models.big_bird.modeling_flax_big_bird import FlaxBigBirdForQuestionAnsweringModule
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :BigBirdConfig
__snake_case :jnp.dtype = jnp.floataa
__snake_case :bool = True
def _a ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
super().setup()
__lowercase = nn.Dense(5 , dtype=self.dtype )
def __call__( self : Optional[int] , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = super().__call__(*_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = self.cls(outputs[2] )
return outputs[:2] + (cls_out,)
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Tuple = FlaxBigBirdForNaturalQuestionsModule
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
def cross_entropy(lowerCamelCase , lowerCamelCase , lowerCamelCase=None ):
__lowercase = logits.shape[-1]
__lowercase = (labels[..., None] == jnp.arange(lowerCamelCase )[None]).astype("""f4""" )
__lowercase = jax.nn.log_softmax(lowerCamelCase , axis=-1 )
__lowercase = -jnp.sum(labels * logits , axis=-1 )
if reduction is not None:
__lowercase = reduction(lowerCamelCase )
return loss
__lowercase = partial(lowerCamelCase , reduction=jnp.mean )
__lowercase = cross_entropy(lowerCamelCase , lowerCamelCase )
__lowercase = cross_entropy(lowerCamelCase , lowerCamelCase )
__lowercase = cross_entropy(lowerCamelCase , lowerCamelCase )
return (start_loss + end_loss + pooled_loss) / 3
@dataclass
class __UpperCamelCase :
__snake_case :str = "google/bigbird-roberta-base"
__snake_case :int = 3_0_0_0
__snake_case :int = 1_0_5_0_0
__snake_case :int = 1_2_8
__snake_case :int = 3
__snake_case :int = 1
__snake_case :int = 5
# tx_args
__snake_case :float = 3e-5
__snake_case :float = 0.0
__snake_case :int = 2_0_0_0_0
__snake_case :float = 0.00_95
__snake_case :str = "bigbird-roberta-natural-questions"
__snake_case :str = "training-expt"
__snake_case :str = "data/nq-training.jsonl"
__snake_case :str = "data/nq-validation.jsonl"
def _a ( self : Dict ) -> List[str]:
"""simple docstring"""
os.makedirs(self.base_dir , exist_ok=_lowerCAmelCase )
__lowercase = os.path.join(self.base_dir , self.save_dir )
__lowercase = self.batch_size_per_device * jax.device_count()
@dataclass
class __UpperCamelCase :
__snake_case :int
__snake_case :int = 4_0_9_6 # no dynamic padding on TPUs
def __call__( self : Any , _lowerCAmelCase : str ) -> Tuple:
"""simple docstring"""
__lowercase = self.collate_fn(_lowerCAmelCase )
__lowercase = jax.tree_util.tree_map(_lowerCAmelCase , _lowerCAmelCase )
return batch
def _a ( self : Dict , _lowerCAmelCase : Tuple ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.fetch_inputs(features["""input_ids"""] )
__lowercase = {
"""input_ids""": jnp.array(_lowerCAmelCase , dtype=jnp.intaa ),
"""attention_mask""": jnp.array(_lowerCAmelCase , dtype=jnp.intaa ),
"""start_labels""": jnp.array(features["""start_token"""] , dtype=jnp.intaa ),
"""end_labels""": jnp.array(features["""end_token"""] , dtype=jnp.intaa ),
"""pooled_labels""": jnp.array(features["""category"""] , dtype=jnp.intaa ),
}
return batch
def _a ( self : Optional[Any] , _lowerCAmelCase : list ) -> Optional[Any]:
"""simple docstring"""
__lowercase = [self._fetch_inputs(_lowerCAmelCase ) for ids in input_ids]
return zip(*_lowerCAmelCase )
def _a ( self : int , _lowerCAmelCase : list ) -> Tuple:
"""simple docstring"""
__lowercase = [1 for _ in range(len(_lowerCAmelCase ) )]
while len(_lowerCAmelCase ) < self.max_length:
input_ids.append(self.pad_id )
attention_mask.append(0 )
return input_ids, attention_mask
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase=None ):
'''simple docstring'''
if seed is not None:
__lowercase = dataset.shuffle(seed=lowerCamelCase )
for i in range(len(lowerCamelCase ) // batch_size ):
__lowercase = dataset[i * batch_size : (i + 1) * batch_size]
yield dict(lowerCamelCase )
@partial(jax.pmap , axis_name="""batch""" )
def snake_case ( lowerCamelCase , lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
def loss_fn(lowerCamelCase ):
__lowercase = model_inputs.pop("""start_labels""" )
__lowercase = model_inputs.pop("""end_labels""" )
__lowercase = model_inputs.pop("""pooled_labels""" )
__lowercase = state.apply_fn(**lowerCamelCase , params=lowerCamelCase , dropout_rng=lowerCamelCase , train=lowerCamelCase )
__lowercase , __lowercase , __lowercase = outputs
return state.loss_fn(
lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , )
__lowercase , __lowercase = jax.random.split(lowerCamelCase )
__lowercase = jax.value_and_grad(lowerCamelCase )
__lowercase , __lowercase = grad_fn(state.params )
__lowercase = jax.lax.pmean({"""loss""": loss} , axis_name="""batch""" )
__lowercase = jax.lax.pmean(lowerCamelCase , """batch""" )
__lowercase = state.apply_gradients(grads=lowerCamelCase )
return state, metrics, new_drp_rng
@partial(jax.pmap , axis_name="""batch""" )
def snake_case ( lowerCamelCase , **lowerCamelCase ):
'''simple docstring'''
__lowercase = model_inputs.pop("""start_labels""" )
__lowercase = model_inputs.pop("""end_labels""" )
__lowercase = model_inputs.pop("""pooled_labels""" )
__lowercase = state.apply_fn(**lowerCamelCase , params=state.params , train=lowerCamelCase )
__lowercase , __lowercase , __lowercase = outputs
__lowercase = state.loss_fn(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = jax.lax.pmean({"""loss""": loss} , axis_name="""batch""" )
return metrics
class __UpperCamelCase ( train_state.TrainState ):
__snake_case :Callable = struct.field(pytree_node=_lowerCAmelCase )
@dataclass
class __UpperCamelCase :
__snake_case :Args
__snake_case :Callable
__snake_case :Callable
__snake_case :Callable
__snake_case :Callable
__snake_case :wandb
__snake_case :Callable = None
def _a ( self : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Tuple=None ) -> int:
"""simple docstring"""
__lowercase = model.params
__lowercase = TrainState.create(
apply_fn=model.__call__ , params=_lowerCAmelCase , tx=_lowerCAmelCase , loss_fn=_lowerCAmelCase , )
if ckpt_dir is not None:
__lowercase , __lowercase , __lowercase , __lowercase , __lowercase = restore_checkpoint(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = {
"""lr""": args.lr,
"""init_lr""": args.init_lr,
"""warmup_steps""": args.warmup_steps,
"""num_train_steps""": num_train_steps,
"""weight_decay""": args.weight_decay,
}
__lowercase , __lowercase = build_tx(**_lowerCAmelCase )
__lowercase = train_state.TrainState(
step=_lowerCAmelCase , apply_fn=model.__call__ , params=_lowerCAmelCase , tx=_lowerCAmelCase , opt_state=_lowerCAmelCase , )
__lowercase = args
__lowercase = data_collator
__lowercase = lr
__lowercase = params
__lowercase = jax_utils.replicate(_lowerCAmelCase )
return state
def _a ( self : str , _lowerCAmelCase : Any , _lowerCAmelCase : str , _lowerCAmelCase : List[str] ) -> Tuple:
"""simple docstring"""
__lowercase = self.args
__lowercase = len(_lowerCAmelCase ) // args.batch_size
__lowercase = jax.random.PRNGKey(0 )
__lowercase = jax.random.split(_lowerCAmelCase , jax.device_count() )
for epoch in range(args.max_epochs ):
__lowercase = jnp.array(0 , dtype=jnp.floataa )
__lowercase = get_batched_dataset(_lowerCAmelCase , args.batch_size , seed=_lowerCAmelCase )
__lowercase = 0
for batch in tqdm(_lowerCAmelCase , total=_lowerCAmelCase , desc=F'Running EPOCH-{epoch}' ):
__lowercase = self.data_collator(_lowerCAmelCase )
__lowercase , __lowercase , __lowercase = self.train_step_fn(_lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase )
running_loss += jax_utils.unreplicate(metrics["""loss"""] )
i += 1
if i % args.logging_steps == 0:
__lowercase = jax_utils.unreplicate(state.step )
__lowercase = running_loss.item() / i
__lowercase = self.scheduler_fn(state_step - 1 )
__lowercase = self.evaluate(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = {
"""step""": state_step.item(),
"""eval_loss""": eval_loss.item(),
"""tr_loss""": tr_loss,
"""lr""": lr.item(),
}
tqdm.write(str(_lowerCAmelCase ) )
self.logger.log(_lowerCAmelCase , commit=_lowerCAmelCase )
if i % args.save_steps == 0:
self.save_checkpoint(args.save_dir + F'-e{epoch}-s{i}' , state=_lowerCAmelCase )
def _a ( self : int , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = get_batched_dataset(_lowerCAmelCase , self.args.batch_size )
__lowercase = len(_lowerCAmelCase ) // self.args.batch_size
__lowercase = jnp.array(0 , dtype=jnp.floataa )
__lowercase = 0
for batch in tqdm(_lowerCAmelCase , total=_lowerCAmelCase , desc="""Evaluating ... """ ):
__lowercase = self.data_collator(_lowerCAmelCase )
__lowercase = self.val_step_fn(_lowerCAmelCase , **_lowerCAmelCase )
running_loss += jax_utils.unreplicate(metrics["""loss"""] )
i += 1
return running_loss / i
def _a ( self : Union[str, Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = jax_utils.unreplicate(_lowerCAmelCase )
print(F'SAVING CHECKPOINT IN {save_dir}' , end=""" ... """ )
self.model_save_fn(_lowerCAmelCase , params=state.params )
with open(os.path.join(_lowerCAmelCase , """opt_state.msgpack""" ) , """wb""" ) as f:
f.write(to_bytes(state.opt_state ) )
joblib.dump(self.args , os.path.join(_lowerCAmelCase , """args.joblib""" ) )
joblib.dump(self.data_collator , os.path.join(_lowerCAmelCase , """data_collator.joblib""" ) )
with open(os.path.join(_lowerCAmelCase , """training_state.json""" ) , """w""" ) as f:
json.dump({"""step""": state.step.item()} , _lowerCAmelCase )
print("""DONE""" )
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
print(F'RESTORING CHECKPOINT FROM {save_dir}' , end=""" ... """ )
with open(os.path.join(lowerCamelCase , """flax_model.msgpack""" ) , """rb""" ) as f:
__lowercase = from_bytes(state.params , f.read() )
with open(os.path.join(lowerCamelCase , """opt_state.msgpack""" ) , """rb""" ) as f:
__lowercase = from_bytes(state.opt_state , f.read() )
__lowercase = joblib.load(os.path.join(lowerCamelCase , """args.joblib""" ) )
__lowercase = joblib.load(os.path.join(lowerCamelCase , """data_collator.joblib""" ) )
with open(os.path.join(lowerCamelCase , """training_state.json""" ) , """r""" ) as f:
__lowercase = json.load(lowerCamelCase )
__lowercase = training_state["""step"""]
print("""DONE""" )
return params, opt_state, step, args, data_collator
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = num_train_steps - warmup_steps
__lowercase = optax.linear_schedule(init_value=lowerCamelCase , end_value=lowerCamelCase , transition_steps=lowerCamelCase )
__lowercase = optax.linear_schedule(init_value=lowerCamelCase , end_value=1e-7 , transition_steps=lowerCamelCase )
__lowercase = optax.join_schedules(schedules=[warmup_fn, decay_fn] , boundaries=[warmup_steps] )
return lr
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
def weight_decay_mask(lowerCamelCase ):
__lowercase = traverse_util.flatten_dict(lowerCamelCase )
__lowercase = {k: (v[-1] != """bias""" and v[-2:] != ("""LayerNorm""", """scale""")) for k, v in params.items()}
return traverse_util.unflatten_dict(lowerCamelCase )
__lowercase = scheduler_fn(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = optax.adamw(learning_rate=lowerCamelCase , weight_decay=lowerCamelCase , mask=lowerCamelCase )
return tx, lr
| 80 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = torch.nn.Linear(10 , 10 )
__lowercase = torch.optim.SGD(model.parameters() , 0.1 )
__lowercase = Accelerator()
__lowercase = accelerator.prepare(_lowerCAmelCase )
try:
pickle.loads(pickle.dumps(_lowerCAmelCase ) )
except Exception as e:
self.fail(F'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 80 | 1 |
import argparse
import json
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
VideoMAEConfig,
VideoMAEForPreTraining,
VideoMAEForVideoClassification,
VideoMAEImageProcessor,
)
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = VideoMAEConfig()
set_architecture_configs(lowerCamelCase , lowerCamelCase )
if "finetuned" not in model_name:
__lowercase = False
if "finetuned" in model_name:
__lowercase = """huggingface/label-files"""
if "kinetics" in model_name:
__lowercase = 400
__lowercase = """kinetics400-id2label.json"""
elif "ssv2" in model_name:
__lowercase = 174
__lowercase = """something-something-v2-id2label.json"""
else:
raise ValueError("""Model name should either contain 'kinetics' or 'ssv2' in case it's fine-tuned.""" )
__lowercase = json.load(open(hf_hub_download(lowerCamelCase , lowerCamelCase , repo_type="""dataset""" ) , """r""" ) )
__lowercase = {int(lowerCamelCase ): v for k, v in idalabel.items()}
__lowercase = idalabel
__lowercase = {v: k for k, v in idalabel.items()}
return config
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if "small" in model_name:
__lowercase = 384
__lowercase = 1_536
__lowercase = 12
__lowercase = 16
__lowercase = 12
__lowercase = 3
__lowercase = 192
__lowercase = 768
elif "large" in model_name:
__lowercase = 1_024
__lowercase = 4_096
__lowercase = 24
__lowercase = 16
__lowercase = 12
__lowercase = 8
__lowercase = 512
__lowercase = 2_048
elif "huge" in model_name:
__lowercase = 1_280
__lowercase = 5_120
__lowercase = 32
__lowercase = 16
__lowercase = 12
__lowercase = 8
__lowercase = 640
__lowercase = 2_560
elif "base" not in model_name:
raise ValueError("""Model name should include either \"small\", \"base\", \"large\", or \"huge\"""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if "encoder." in name:
__lowercase = name.replace("""encoder.""" , """""" )
if "cls_token" in name:
__lowercase = name.replace("""cls_token""" , """videomae.embeddings.cls_token""" )
if "decoder_pos_embed" in name:
__lowercase = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
__lowercase = name.replace("""pos_embed""" , """videomae.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
__lowercase = name.replace("""patch_embed.proj""" , """videomae.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
__lowercase = name.replace("""patch_embed.norm""" , """videomae.embeddings.norm""" )
if "decoder.blocks" in name:
__lowercase = name.replace("""decoder.blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
__lowercase = name.replace("""blocks""" , """videomae.encoder.layer""" )
if "attn.proj" in name:
__lowercase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name and "bias" not in name:
__lowercase = name.replace("""attn""" , """attention.self""" )
if "attn" in name:
__lowercase = name.replace("""attn""" , """attention.attention""" )
if "norm1" in name:
__lowercase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
__lowercase = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
__lowercase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
__lowercase = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
__lowercase = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
__lowercase = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
__lowercase = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name and "fc" not in name:
__lowercase = name.replace("""norm.weight""" , """videomae.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name and "fc" not in name:
__lowercase = name.replace("""norm.bias""" , """videomae.layernorm.bias""" )
if "head" in name and "decoder" not in name:
__lowercase = name.replace("""head""" , """classifier""" )
return name
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
__lowercase = orig_state_dict.pop(lowerCamelCase )
if key.startswith("""encoder.""" ):
__lowercase = key.replace("""encoder.""" , """""" )
if "qkv" in key:
__lowercase = key.split(""".""" )
if key.startswith("""decoder.blocks""" ):
__lowercase = config.decoder_hidden_size
__lowercase = int(key_split[2] )
__lowercase = """decoder.decoder_layers."""
if "weight" in key:
__lowercase = val[:dim, :]
__lowercase = val[dim : dim * 2, :]
__lowercase = val[-dim:, :]
else:
__lowercase = config.hidden_size
__lowercase = int(key_split[1] )
__lowercase = """videomae.encoder.layer."""
if "weight" in key:
__lowercase = val[:dim, :]
__lowercase = val[dim : dim * 2, :]
__lowercase = val[-dim:, :]
else:
__lowercase = val
return orig_state_dict
def snake_case ( ):
'''simple docstring'''
__lowercase = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
__lowercase = np.load(lowerCamelCase )
return list(lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = get_videomae_config(lowerCamelCase )
if "finetuned" in model_name:
__lowercase = VideoMAEForVideoClassification(lowerCamelCase )
else:
__lowercase = VideoMAEForPreTraining(lowerCamelCase )
# download original checkpoint, hosted on Google Drive
__lowercase = """pytorch_model.bin"""
gdown.cached_download(lowerCamelCase , lowerCamelCase , quiet=lowerCamelCase )
__lowercase = torch.load(lowerCamelCase , map_location="""cpu""" )
if "model" in files:
__lowercase = files["""model"""]
else:
__lowercase = files["""module"""]
__lowercase = convert_state_dict(lowerCamelCase , lowerCamelCase )
model.load_state_dict(lowerCamelCase )
model.eval()
# verify model on basic input
__lowercase = VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
__lowercase = prepare_video()
__lowercase = image_processor(lowerCamelCase , return_tensors="""pt""" )
if "finetuned" not in model_name:
__lowercase = hf_hub_download(repo_id="""hf-internal-testing/bool-masked-pos""" , filename="""bool_masked_pos.pt""" )
__lowercase = torch.load(lowerCamelCase )
__lowercase = model(**lowerCamelCase )
__lowercase = outputs.logits
__lowercase = [
"""videomae-small-finetuned-kinetics""",
"""videomae-small-finetuned-ssv2""",
# Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
"""videomae-base-short""",
"""videomae-base-short-finetuned-kinetics""",
"""videomae-base""",
"""videomae-base-finetuned-kinetics""",
"""videomae-large""",
"""videomae-large-finetuned-kinetics""",
"""videomae-huge-finetuned-kinetics""",
# Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
"""videomae-base-short-ssv2""",
"""videomae-base-short-finetuned-ssv2""",
"""videomae-base-ssv2""",
"""videomae-base-finetuned-ssv2""",
]
# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
if model_name == "videomae-small-finetuned-kinetics":
__lowercase = torch.Size([1, 400] )
__lowercase = torch.tensor([-0.9291, -0.4061, -0.9307] )
elif model_name == "videomae-small-finetuned-ssv2":
__lowercase = torch.Size([1, 174] )
__lowercase = torch.tensor([0.2671, -0.4689, -0.8235] )
elif model_name == "videomae-base":
__lowercase = torch.Size([1, 1_408, 1_536] )
__lowercase = torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]] )
elif model_name == "videomae-base-short":
__lowercase = torch.Size([1, 1_408, 1_536] )
__lowercase = torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]] )
# we verified the loss both for normalized and unnormalized targets for this one
__lowercase = torch.tensor([0.5142] ) if config.norm_pix_loss else torch.tensor([0.6469] )
elif model_name == "videomae-large":
__lowercase = torch.Size([1, 1_408, 1_536] )
__lowercase = torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]] )
elif model_name == "videomae-large-finetuned-kinetics":
__lowercase = torch.Size([1, 400] )
__lowercase = torch.tensor([0.0771, 0.0011, -0.3625] )
elif model_name == "videomae-huge-finetuned-kinetics":
__lowercase = torch.Size([1, 400] )
__lowercase = torch.tensor([0.2433, 0.1632, -0.4894] )
elif model_name == "videomae-base-short-finetuned-kinetics":
__lowercase = torch.Size([1, 400] )
__lowercase = torch.tensor([0.6588, 0.0990, -0.2493] )
elif model_name == "videomae-base-finetuned-kinetics":
__lowercase = torch.Size([1, 400] )
__lowercase = torch.tensor([0.3669, -0.0688, -0.2421] )
elif model_name == "videomae-base-short-ssv2":
__lowercase = torch.Size([1, 1_408, 1_536] )
__lowercase = torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]] )
elif model_name == "videomae-base-short-finetuned-ssv2":
__lowercase = torch.Size([1, 174] )
__lowercase = torch.tensor([-0.0537, -0.1539, -0.3266] )
elif model_name == "videomae-base-ssv2":
__lowercase = torch.Size([1, 1_408, 1_536] )
__lowercase = torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]] )
elif model_name == "videomae-base-finetuned-ssv2":
__lowercase = torch.Size([1, 174] )
__lowercase = torch.tensor([0.1961, -0.8337, -0.6389] )
else:
raise ValueError(F'Model name not supported. Should be one of {model_names}' )
# verify logits
assert logits.shape == expected_shape
if "finetuned" in model_name:
assert torch.allclose(logits[0, :3] , lowerCamelCase , atol=1e-4 )
else:
print("""Logits:""" , logits[0, :3, :3] )
assert torch.allclose(logits[0, :3, :3] , lowerCamelCase , atol=1e-4 )
print("""Logits ok!""" )
# verify loss, if applicable
if model_name == "videomae-base-short":
__lowercase = outputs.loss
assert torch.allclose(lowerCamelCase , lowerCamelCase , atol=1e-4 )
print("""Loss ok!""" )
if pytorch_dump_folder_path is not None:
print(F'Saving model and image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(lowerCamelCase )
model.save_pretrained(lowerCamelCase )
if push_to_hub:
print("""Pushing to the hub...""" )
model.push_to_hub(lowerCamelCase , organization="""nielsr""" )
if __name__ == "__main__":
__UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--checkpoint_url""",
default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&export=download&confirm=t&uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
type=str,
help=(
"""URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
""" download link."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
type=str,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__UpperCamelCase : Optional[int] = parser.parse_args()
convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 80 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
__UpperCamelCase : Dict = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
__UpperCamelCase : int = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
__UpperCamelCase : List[str] = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
__UpperCamelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Dict = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[int] = [
"""MEGA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MegaForCausalLM""",
"""MegaForMaskedLM""",
"""MegaForMultipleChoice""",
"""MegaForQuestionAnswering""",
"""MegaForSequenceClassification""",
"""MegaForTokenClassification""",
"""MegaModel""",
"""MegaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
__UpperCamelCase : Union[str, Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 |
import os
from collections.abc import Iterator
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCamelCase ):
__lowercase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(lowerCamelCase )[1] in (".py", ".ipynb"):
yield os.path.join(lowerCamelCase , lowerCamelCase ).lstrip("""./""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return F'{i * " "}*' if i else "\n##"
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(lowerCamelCase ) or old_parts[i] != new_part) and new_part:
print(F'{md_prefix(lowerCamelCase )} {new_part.replace("_" , " " ).title()}' )
return new_path
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
__lowercase = """"""
for filepath in sorted(good_file_paths(lowerCamelCase ) ):
__lowercase , __lowercase = os.path.split(lowerCamelCase )
if filepath != old_path:
__lowercase = print_path(lowerCamelCase , lowerCamelCase )
__lowercase = (filepath.count(os.sep ) + 1) if filepath else 0
__lowercase = F'{filepath}/{filename}'.replace(""" """ , """%20""" )
__lowercase = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'{md_prefix(lowerCamelCase )} [{filename}]({url})' )
if __name__ == "__main__":
print_directory_md(""".""")
| 80 | 1 |
from __future__ import annotations
import unittest
from transformers import BlenderbotConfig, BlenderbotTokenizer, is_tf_available
from transformers.testing_utils import require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFBlenderbotForConditionalGeneration, TFBlenderbotModel
@require_tf
class __UpperCamelCase :
__snake_case :List[str] = BlenderbotConfig
__snake_case :int = {}
__snake_case :Optional[Any] = 'gelu'
def __init__( self : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[Any]=13 , _lowerCAmelCase : Optional[int]=7 , _lowerCAmelCase : str=True , _lowerCAmelCase : Any=False , _lowerCAmelCase : List[str]=99 , _lowerCAmelCase : List[Any]=32 , _lowerCAmelCase : Any=2 , _lowerCAmelCase : str=4 , _lowerCAmelCase : Any=37 , _lowerCAmelCase : Any=0.1 , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : Dict=20 , _lowerCAmelCase : List[str]=2 , _lowerCAmelCase : int=1 , _lowerCAmelCase : Optional[int]=0 , ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = eos_token_id
__lowercase = pad_token_id
__lowercase = bos_token_id
def _a ( self : int ) -> Dict:
"""simple docstring"""
__lowercase = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
__lowercase = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
__lowercase = tf.concat([input_ids, eos_tensor] , axis=1 )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
__lowercase = prepare_blenderbot_inputs_dict(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return config, inputs_dict
def _a ( self : List[str] , _lowerCAmelCase : int , _lowerCAmelCase : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = TFBlenderbotModel(config=_lowerCAmelCase ).get_decoder()
__lowercase = inputs_dict["""input_ids"""]
__lowercase = input_ids[:1, :]
__lowercase = inputs_dict["""attention_mask"""][:1, :]
__lowercase = inputs_dict["""head_mask"""]
__lowercase = 1
# first forward pass
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , head_mask=_lowerCAmelCase , use_cache=_lowerCAmelCase )
__lowercase , __lowercase = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
__lowercase = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowercase = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
__lowercase = tf.concat([input_ids, next_tokens] , axis=-1 )
__lowercase = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )[0]
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , past_key_values=_lowerCAmelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
__lowercase = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
__lowercase = output_from_no_past[:, -3:, random_slice_idx]
__lowercase = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(_lowerCAmelCase , _lowerCAmelCase , rtol=1e-3 )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , ):
'''simple docstring'''
if attention_mask is None:
__lowercase = tf.cast(tf.math.not_equal(lowerCamelCase , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
__lowercase = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
__lowercase = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__lowercase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__lowercase = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Union[str, Any] = (TFBlenderbotForConditionalGeneration, TFBlenderbotModel) if is_tf_available() else ()
__snake_case :List[Any] = (TFBlenderbotForConditionalGeneration,) if is_tf_available() else ()
__snake_case :Union[str, Any] = (
{
'conversational': TFBlenderbotForConditionalGeneration,
'feature-extraction': TFBlenderbotModel,
'summarization': TFBlenderbotForConditionalGeneration,
'text2text-generation': TFBlenderbotForConditionalGeneration,
'translation': TFBlenderbotForConditionalGeneration,
}
if is_tf_available()
else {}
)
__snake_case :Any = True
__snake_case :Dict = False
__snake_case :Optional[int] = False
def _a ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = TFBlenderbotModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*_lowerCAmelCase )
@require_tokenizers
@require_tf
class __UpperCamelCase ( unittest.TestCase ):
__snake_case :str = ['My friends are cool but they eat too many carbs.']
__snake_case :List[str] = 'facebook/blenderbot-400M-distill'
@cached_property
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
return BlenderbotTokenizer.from_pretrained(self.model_name )
@cached_property
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
__lowercase = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
@slow
def _a ( self : str ) -> Dict:
"""simple docstring"""
__lowercase = self.tokenizer(self.src_text , return_tensors="""tf""" )
__lowercase = self.model.generate(
model_inputs.input_ids , )
__lowercase = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=_lowerCAmelCase )[0]
assert (
generated_words
== " That's unfortunate. Are they trying to lose weight or are they just trying to be healthier?"
)
| 80 |
from math import factorial
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(lowerCamelCase ) // (factorial(lowerCamelCase ) * factorial(n - k ))
if __name__ == "__main__":
print(
"""The number of five-card hands possible from a standard""",
F'''fifty-two card deck is: {combinations(52, 5)}\n''',
)
print(
"""If a class of 40 students must be arranged into groups of""",
F'''4 for group projects, there are {combinations(40, 4)} ways''',
"""to arrange them.\n""",
)
print(
"""If 10 teams are competing in a Formula One race, there""",
F'''are {combinations(10, 3)} ways that first, second and''',
"""third place can be awarded.""",
)
| 80 | 1 |
from math import isqrt, loga
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowerCamelCase , lowerCamelCase ):
__lowercase = False
return [i for i in range(2 , lowerCamelCase ) if is_prime[i]]
def snake_case ( lowerCamelCase = 800_800 , lowerCamelCase = 800_800 ):
'''simple docstring'''
__lowercase = degree * loga(lowerCamelCase )
__lowercase = int(lowerCamelCase )
__lowercase = calculate_prime_numbers(lowerCamelCase )
__lowercase = 0
__lowercase = 0
__lowercase = len(lowerCamelCase ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def snake_case ( ):
'''simple docstring'''
__lowercase = [randint(-1_000 , 1_000 ) for i in range(10 )]
__lowercase = randint(-5_000 , 5_000 )
return (arr, r)
__UpperCamelCase : Any = make_dataset()
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
for triplet in permutations(lowerCamelCase , 3 ):
if sum(lowerCamelCase ) == target:
return tuple(sorted(lowerCamelCase ) )
return (0, 0, 0)
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
arr.sort()
__lowercase = len(lowerCamelCase )
for i in range(n - 1 ):
__lowercase , __lowercase = i + 1, n - 1
while left < right:
if arr[i] + arr[left] + arr[right] == target:
return (arr[i], arr[left], arr[right])
elif arr[i] + arr[left] + arr[right] < target:
left += 1
elif arr[i] + arr[left] + arr[right] > target:
right -= 1
return (0, 0, 0)
def snake_case ( ):
'''simple docstring'''
__lowercase = """
from __main__ import dataset, triplet_sum1, triplet_sum2
"""
__lowercase = """
triplet_sum1(*dataset)
"""
__lowercase = """
triplet_sum2(*dataset)
"""
__lowercase = repeat(setup=lowerCamelCase , stmt=lowerCamelCase , repeat=5 , number=10_000 )
__lowercase = repeat(setup=lowerCamelCase , stmt=lowerCamelCase , repeat=5 , number=10_000 )
return (min(lowerCamelCase ), min(lowerCamelCase ))
if __name__ == "__main__":
from doctest import testmod
testmod()
__UpperCamelCase : Tuple = solution_times()
print(F'''The time for naive implementation is {times[0]}.''')
print(F'''The time for optimized implementation is {times[1]}.''')
| 80 | 1 |
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
__UpperCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"""--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
"""--original_config_file""",
default=None,
type=str,
help="""The YAML config file corresponding to the original architecture.""",
)
parser.add_argument(
"""--num_in_channels""",
default=None,
type=int,
help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
)
parser.add_argument(
"""--scheduler_type""",
default="""pndm""",
type=str,
help="""Type of scheduler to use. Should be one of ['pndm', 'lms', 'ddim', 'euler', 'euler-ancestral', 'dpm']""",
)
parser.add_argument(
"""--pipeline_type""",
default=None,
type=str,
help=(
"""The pipeline type. One of 'FrozenOpenCLIPEmbedder', 'FrozenCLIPEmbedder', 'PaintByExample'"""
""". If `None` pipeline will be automatically inferred."""
),
)
parser.add_argument(
"""--image_size""",
default=None,
type=int,
help=(
"""The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
""" Base. Use 768 for Stable Diffusion v2."""
),
)
parser.add_argument(
"""--prediction_type""",
default=None,
type=str,
help=(
"""The prediction type that the model was trained on. Use 'epsilon' for Stable Diffusion v1.X and Stable"""
""" Diffusion v2 Base. Use 'v_prediction' for Stable Diffusion v2."""
),
)
parser.add_argument(
"""--extract_ema""",
action="""store_true""",
help=(
"""Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
""" or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
""" higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
),
)
parser.add_argument(
"""--upcast_attention""",
action="""store_true""",
help=(
"""Whether the attention computation should always be upcasted. This is necessary when running stable"""
""" diffusion 2.1."""
),
)
parser.add_argument(
"""--from_safetensors""",
action="""store_true""",
help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
)
parser.add_argument(
"""--to_safetensors""",
action="""store_true""",
help="""Whether to store pipeline in safetensors format or not.""",
)
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")
parser.add_argument(
"""--stable_unclip""",
type=str,
default=None,
required=False,
help="""Set if this is a stable unCLIP model. One of 'txt2img' or 'img2img'.""",
)
parser.add_argument(
"""--stable_unclip_prior""",
type=str,
default=None,
required=False,
help="""Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.""",
)
parser.add_argument(
"""--clip_stats_path""",
type=str,
help="""Path to the clip stats file. Only required if the stable unclip model's config specifies `model.params.noise_aug_config.params.clip_stats_path`.""",
required=False,
)
parser.add_argument(
"""--controlnet""", action="""store_true""", default=None, help="""Set flag if this is a controlnet checkpoint."""
)
parser.add_argument("""--half""", action="""store_true""", help="""Save weights in half precision.""")
parser.add_argument(
"""--vae_path""",
type=str,
default=None,
required=False,
help="""Set to a path, hub id to an already converted vae to not convert it again.""",
)
__UpperCamelCase : str = parser.parse_args()
__UpperCamelCase : str = download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 80 |
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__UpperCamelCase : Union[str, Any] = logging.getLogger(__name__)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Any , _lowerCAmelCase : str , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : str=None ) -> int:
"""simple docstring"""
super().__init__(
_lowerCAmelCase , question_encoder_tokenizer=_lowerCAmelCase , generator_tokenizer=_lowerCAmelCase , index=_lowerCAmelCase , init_retrieval=_lowerCAmelCase , )
__lowercase = None
def _a ( self : int , _lowerCAmelCase : int ) -> Any:
"""simple docstring"""
logger.info("""initializing retrieval""" )
# initializing a separate process group for retrieval as the default
# nccl backend doesn't support gather/scatter operations while gloo
# is too slow to replace nccl for the core gpu communication
if dist.is_initialized():
logger.info("""dist initialized""" )
# needs to be set manually
__lowercase = self._infer_socket_ifname()
# avoid clash with the NCCL port
__lowercase = str(distributed_port + 1 )
__lowercase = dist.new_group(ranks=_lowerCAmelCase , backend="""gloo""" )
# initialize retriever only on the main worker
if not dist.is_initialized() or self._is_main():
logger.info("""dist not initialized / main""" )
self.index.init_index()
# all processes wait untill the retriever is initialized by the main process
if dist.is_initialized():
torch.distributed.barrier(group=self.process_group )
def _a ( self : Tuple ) -> List[str]:
"""simple docstring"""
return dist.get_rank(group=self.process_group ) == 0
def _a ( self : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any]=torch.floataa ) -> Tuple:
"""simple docstring"""
__lowercase = torch.empty(_lowerCAmelCase , dtype=_lowerCAmelCase )
dist.scatter(_lowerCAmelCase , src=0 , scatter_list=_lowerCAmelCase , group=self.process_group )
return target_tensor
def _a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
__lowercase = psutil.net_if_addrs()
# a hacky way to deal with varying network interface names
__lowercase = next((addr for addr in addrs if addr.startswith("""e""" )) , _lowerCAmelCase )
return ifname
def _a ( self : str , _lowerCAmelCase : np.ndarray , _lowerCAmelCase : int ) -> Tuple[np.ndarray, List[dict]]:
"""simple docstring"""
if not dist.is_initialized():
__lowercase , __lowercase = self._main_retrieve(_lowerCAmelCase , _lowerCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCAmelCase )
# distributed training
__lowercase = dist.get_world_size(group=self.process_group )
# gather logic
__lowercase = None
if self._is_main():
__lowercase = [torch.empty(question_hidden_states.shape , dtype=torch.floataa ) for _ in range(_lowerCAmelCase )]
dist.gather(torch.tensor(_lowerCAmelCase ) , dst=0 , gather_list=_lowerCAmelCase , group=self.process_group )
# scatter logic
__lowercase = question_hidden_states.shape[0]
__lowercase = []
__lowercase = []
if self._is_main():
assert len(_lowerCAmelCase ) == world_size
__lowercase , __lowercase = self._main_retrieve(torch.cat(_lowerCAmelCase ).numpy() , _lowerCAmelCase )
__lowercase , __lowercase = torch.tensor(_lowerCAmelCase ), torch.tensor(_lowerCAmelCase )
__lowercase = self._chunk_tensor(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._chunk_tensor(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._scattered(_lowerCAmelCase , [n_queries, n_docs] , target_type=torch.intaa )
__lowercase = self._scattered(_lowerCAmelCase , [n_queries, n_docs, question_hidden_states.shape[1]] )
return retrieved_doc_embeds.numpy(), doc_ids.numpy(), self.index.get_doc_dicts(_lowerCAmelCase )
| 80 | 1 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
__UpperCamelCase : str = logging.getLogger(__name__)
class __UpperCamelCase :
def __init__( self : Union[str, Any] ) -> int:
"""simple docstring"""
__lowercase = False
def _a ( self : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
if not self.initialized:
__lowercase = RagRetriever(
_lowerCAmelCase , question_encoder_tokenizer=_lowerCAmelCase , generator_tokenizer=_lowerCAmelCase , index=_lowerCAmelCase , init_retrieval=_lowerCAmelCase , )
__lowercase = True
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
self.retriever.index.init_index()
def _a ( self : Dict , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
__lowercase , __lowercase = self.retriever._main_retrieve(_lowerCAmelCase , _lowerCAmelCase )
return doc_ids, retrieved_doc_embeds
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Optional[int] , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : List[str]=None ) -> str:
"""simple docstring"""
if index is not None and index.is_initialized() and len(_lowerCAmelCase ) > 0:
raise ValueError(
"""When using Ray for distributed fine-tuning, """
"""you'll need to provide the paths instead, """
"""as the dataset and the index are loaded """
"""separately. More info in examples/rag/use_own_knowledge_dataset.py """ )
super().__init__(
_lowerCAmelCase , question_encoder_tokenizer=_lowerCAmelCase , generator_tokenizer=_lowerCAmelCase , index=_lowerCAmelCase , init_retrieval=_lowerCAmelCase , )
__lowercase = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
for worker in self.retrieval_workers
] )
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
logger.info("""initializing retrieval""" )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def _a ( self : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Dict ) -> Tuple:
"""simple docstring"""
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
__lowercase = self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
__lowercase , __lowercase = ray.get(random_worker.retrieve.remote(_lowerCAmelCase , _lowerCAmelCase ) )
else:
__lowercase , __lowercase = self._main_retrieve(_lowerCAmelCase , _lowerCAmelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(_lowerCAmelCase )
@classmethod
def _a ( cls : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str=None , **_lowerCAmelCase : str ) -> Optional[Any]:
"""simple docstring"""
return super(_lowerCAmelCase , cls ).get_tokenizers(_lowerCAmelCase , _lowerCAmelCase , **_lowerCAmelCase )
@classmethod
def _a ( cls : Optional[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str]=None , **_lowerCAmelCase : Optional[int] ) -> str:
"""simple docstring"""
__lowercase = kwargs.pop("""config""" , _lowerCAmelCase ) or RagConfig.from_pretrained(_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = RagTokenizer.from_pretrained(_lowerCAmelCase , config=_lowerCAmelCase )
__lowercase = rag_tokenizer.question_encoder
__lowercase = rag_tokenizer.generator
if indexed_dataset is not None:
__lowercase = """custom"""
__lowercase = CustomHFIndex(config.retrieval_vector_size , _lowerCAmelCase )
else:
__lowercase = cls._build_index(_lowerCAmelCase )
return cls(
_lowerCAmelCase , question_encoder_tokenizer=_lowerCAmelCase , generator_tokenizer=_lowerCAmelCase , retrieval_workers=_lowerCAmelCase , index=_lowerCAmelCase , )
| 80 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_case :List[Any] = 1
@register_to_config
def __init__( self : str , _lowerCAmelCase : int = 1000 , _lowerCAmelCase : Optional[Union[np.ndarray, List[float]]] = None ) -> Optional[int]:
"""simple docstring"""
self.set_timesteps(_lowerCAmelCase )
# standard deviation of the initial noise distribution
__lowercase = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
__lowercase = 4
# running values
__lowercase = []
def _a ( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Union[str, torch.device] = None ) -> int:
"""simple docstring"""
__lowercase = num_inference_steps
__lowercase = torch.linspace(1 , 0 , num_inference_steps + 1 )[:-1]
__lowercase = torch.cat([steps, torch.tensor([0.0] )] )
if self.config.trained_betas is not None:
__lowercase = torch.tensor(self.config.trained_betas , dtype=torch.floataa )
else:
__lowercase = torch.sin(steps * math.pi / 2 ) ** 2
__lowercase = (1.0 - self.betas**2) ** 0.5
__lowercase = (torch.atana(self.betas , self.alphas ) / math.pi * 2)[:-1]
__lowercase = timesteps.to(_lowerCAmelCase )
__lowercase = []
def _a ( self : List[str] , _lowerCAmelCase : torch.FloatTensor , _lowerCAmelCase : int , _lowerCAmelCase : torch.FloatTensor , _lowerCAmelCase : bool = True , ) -> Union[SchedulerOutput, Tuple]:
"""simple docstring"""
if self.num_inference_steps is None:
raise ValueError(
"""Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler""" )
__lowercase = (self.timesteps == timestep).nonzero().item()
__lowercase = timestep_index + 1
__lowercase = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(_lowerCAmelCase )
if len(self.ets ) == 1:
__lowercase = self.ets[-1]
elif len(self.ets ) == 2:
__lowercase = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets ) == 3:
__lowercase = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
__lowercase = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
__lowercase = self._get_prev_sample(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_lowerCAmelCase )
def _a ( self : Union[str, Any] , _lowerCAmelCase : torch.FloatTensor , *_lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : str ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def _a ( self : Optional[Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = self.alphas[timestep_index]
__lowercase = self.betas[timestep_index]
__lowercase = self.alphas[prev_timestep_index]
__lowercase = self.betas[prev_timestep_index]
__lowercase = (sample - sigma * ets) / max(_lowerCAmelCase , 1e-8 )
__lowercase = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : Optional[Any] ) -> Dict:
"""simple docstring"""
return self.config.num_train_timesteps
| 80 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : List[Any] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
# See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Tuple = 'pegasus'
__snake_case :Optional[int] = ['past_key_values']
__snake_case :Tuple = {'num_attention_heads': 'encoder_attention_heads', 'hidden_size': 'd_model'}
def __init__( self : Tuple , _lowerCAmelCase : str=5_0265 , _lowerCAmelCase : Union[str, Any]=1024 , _lowerCAmelCase : List[Any]=12 , _lowerCAmelCase : Union[str, Any]=4096 , _lowerCAmelCase : int=16 , _lowerCAmelCase : Dict=12 , _lowerCAmelCase : Optional[int]=4096 , _lowerCAmelCase : List[Any]=16 , _lowerCAmelCase : Optional[int]=0.0 , _lowerCAmelCase : Any=0.0 , _lowerCAmelCase : int=True , _lowerCAmelCase : Union[str, Any]=True , _lowerCAmelCase : Any="gelu" , _lowerCAmelCase : int=1024 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : int=0.0 , _lowerCAmelCase : Optional[int]=0.0 , _lowerCAmelCase : List[str]=0.02 , _lowerCAmelCase : Any=0 , _lowerCAmelCase : int=False , _lowerCAmelCase : Optional[int]=0 , _lowerCAmelCase : Union[str, Any]=1 , _lowerCAmelCase : Optional[Any]=1 , **_lowerCAmelCase : Optional[Any] , ) -> int:
"""simple docstring"""
__lowercase = vocab_size
__lowercase = max_position_embeddings
__lowercase = d_model
__lowercase = encoder_ffn_dim
__lowercase = encoder_layers
__lowercase = encoder_attention_heads
__lowercase = decoder_ffn_dim
__lowercase = decoder_layers
__lowercase = decoder_attention_heads
__lowercase = dropout
__lowercase = attention_dropout
__lowercase = activation_dropout
__lowercase = activation_function
__lowercase = init_std
__lowercase = encoder_layerdrop
__lowercase = decoder_layerdrop
__lowercase = use_cache
__lowercase = encoder_layers
__lowercase = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , is_encoder_decoder=_lowerCAmelCase , decoder_start_token_id=_lowerCAmelCase , forced_eos_token_id=_lowerCAmelCase , **_lowerCAmelCase , )
@property
def _a ( self : Tuple ) -> int:
"""simple docstring"""
return self.encoder_attention_heads
@property
def _a ( self : str ) -> int:
"""simple docstring"""
return self.d_model
| 80 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
__UpperCamelCase : Tuple = TypeVar("""T""")
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] , _lowerCAmelCase : T ) -> List[str]:
"""simple docstring"""
__lowercase = data
__lowercase = None
def __str__( self : List[str] ) -> str:
"""simple docstring"""
return F'{self.data}'
class __UpperCamelCase ( Generic[T] ):
def __init__( self : Optional[Any] ) -> None:
"""simple docstring"""
__lowercase = None
def __iter__( self : int ) -> Iterator[T]:
"""simple docstring"""
__lowercase = self.top
while node:
yield node.data
__lowercase = node.next
def __str__( self : List[str] ) -> str:
"""simple docstring"""
return "->".join([str(_lowerCAmelCase ) for item in self] )
def __len__( self : Any ) -> int:
"""simple docstring"""
return len(tuple(iter(self ) ) )
def _a ( self : str ) -> bool:
"""simple docstring"""
return self.top is None
def _a ( self : List[str] , _lowerCAmelCase : T ) -> None:
"""simple docstring"""
__lowercase = Node(_lowerCAmelCase )
if not self.is_empty():
__lowercase = self.top
__lowercase = node
def _a ( self : Union[str, Any] ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError("""pop from empty stack""" )
assert isinstance(self.top , _lowerCAmelCase )
__lowercase = self.top
__lowercase = self.top.next
return pop_node.data
def _a ( self : int ) -> T:
"""simple docstring"""
if self.is_empty():
raise IndexError("""peek from empty stack""" )
assert self.top is not None
return self.top.data
def _a ( self : int ) -> None:
"""simple docstring"""
__lowercase = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 80 | 1 |
from typing import Dict
from transformers import EvalPrediction, HfArgumentParser, TrainingArguments, is_torch_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
get_torch_dist_unique_port,
require_torch_multi_gpu,
require_torch_neuroncore,
)
from transformers.training_args import ParallelMode
from transformers.utils import logging
__UpperCamelCase : Dict = logging.get_logger(__name__)
if is_torch_available():
import torch
from torch import nn
from torch.utils.data import Dataset
from transformers import Trainer
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : Optional[Any] , _lowerCAmelCase : int = 101 ) -> Any:
"""simple docstring"""
__lowercase = length
def __len__( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
return self.length
def __getitem__( self : Tuple , _lowerCAmelCase : str ) -> int:
"""simple docstring"""
return i
class __UpperCamelCase :
def __call__( self : List[Any] , _lowerCAmelCase : Dict ) -> str:
"""simple docstring"""
return {"input_ids": torch.tensor(_lowerCAmelCase ), "labels": torch.tensor(_lowerCAmelCase )}
class __UpperCamelCase ( nn.Module ):
def __init__( self : List[str] ) -> Dict:
"""simple docstring"""
super().__init__()
# Add some (unused) params otherwise DDP will complain.
__lowercase = nn.Linear(120 , 80 )
def _a ( self : str , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int]=None ) -> Optional[int]:
"""simple docstring"""
if labels is not None:
return torch.tensor(0.0 , device=input_ids.device ), input_ids
else:
return input_ids
class __UpperCamelCase ( _lowerCAmelCase ):
@require_torch_neuroncore
def _a ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = F'--nproc_per_node=2\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n '.split()
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F'--output_dir {output_dir}'.split()
__lowercase = ["""torchrun"""] + distributed_args + args
execute_subprocess_async(_lowerCAmelCase , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
class __UpperCamelCase ( _lowerCAmelCase ):
@require_torch_multi_gpu
def _a ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = F'--nproc_per_node={torch.cuda.device_count()}\n --master_port={get_torch_dist_unique_port()}\n {self.test_file_dir}/test_trainer_distributed.py\n '.split()
__lowercase = self.get_auto_remove_tmp_dir()
__lowercase = F'--output_dir {output_dir}'.split()
__lowercase = ["""torchrun"""] + distributed_args + args
execute_subprocess_async(_lowerCAmelCase , env=self.get_env() )
# successful return here == success - any errors would have caused an error in the sub-call
if __name__ == "__main__":
# The script below is meant to be run under torch.distributed, on a machine with multiple GPUs:
#
# PYTHONPATH="src" python -m torch.distributed.run --nproc_per_node 2 --output_dir output_dir ./tests/test_trainer_distributed.py
__UpperCamelCase : Dict = HfArgumentParser((TrainingArguments,))
__UpperCamelCase : Dict = parser.parse_args_into_dataclasses()[0]
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, '''
F'''distributed training: {training_args.parallel_mode != ParallelMode.NOT_DISTRIBUTED}'''
)
# Essentially, what we want to verify in the distributed case is that we get all samples back,
# in the right order. (this is crucial for prediction for instance)
for dataset_length in [101, 40, 7]:
__UpperCamelCase : str = DummyDataset(dataset_length)
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = list(range(len(lowerCamelCase ) ) )
__lowercase = p.predictions.tolist() == sequential and p.label_ids.tolist() == sequential
if not success and training_args.local_rank == 0:
logger.warning(
"""Predictions and/or labels do not match expected results:\n - predictions: """
F'{p.predictions.tolist()}\n - labels: {p.label_ids.tolist()}\n - expected: {sequential}' )
return {"success": success}
__UpperCamelCase : int = Trainer(
model=DummyModel(),
args=training_args,
data_collator=DummyDataCollator(),
eval_dataset=dataset,
compute_metrics=compute_metrics,
)
__UpperCamelCase : Dict = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
__UpperCamelCase : Tuple = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
__UpperCamelCase : List[str] = 2
__UpperCamelCase : Optional[Any] = trainer.evaluate()
logger.info(metrics)
if metrics["eval_success"] is not True:
logger.error(metrics)
exit(1)
__UpperCamelCase : List[Any] = trainer.predict(dataset)
logger.info(p.metrics)
if p.metrics["test_success"] is not True:
logger.error(p.metrics)
exit(1)
__UpperCamelCase : Tuple = None
| 80 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
__UpperCamelCase : Union[str, Any] = False
class __UpperCamelCase ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _a ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
__lowercase = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt="""first prompt""" , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="""numpy""" , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCAmelCase )
__lowercase = VersatileDiffusionPipeline.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = generator.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt="""first prompt""" , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="""numpy""" , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def _a ( self : Any ) -> Dict:
"""simple docstring"""
__lowercase = VersatileDiffusionPipeline.from_pretrained("""shi-labs/versatile-diffusion""" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """cyberpunk 2077"""
__lowercase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.dual_guided(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.1_448, 0.1_619, 0.1_741, 0.1_086, 0.1_147, 0.1_128, 0.1_199, 0.1_165, 0.1_001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__lowercase = """A painting of a squirrel eating a burger """
__lowercase = torch.manual_seed(0 )
__lowercase = pipe.text_to_image(
prompt=_lowerCAmelCase , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.3_367, 0.3_169, 0.2_656, 0.3_870, 0.4_790, 0.3_796, 0.4_009, 0.4_878, 0.4_778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
__lowercase = pipe.image_variation(_lowerCAmelCase , generator=_lowerCAmelCase , output_type="""numpy""" ).images
__lowercase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.3_076, 0.3_123, 0.3_284, 0.3_782, 0.3_770, 0.3_894, 0.4_297, 0.4_331, 0.4_456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 80 | 1 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lowerCamelCase ) <= key:
return input_string
for position, character in enumerate(lowerCamelCase ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(lowerCamelCase )
__lowercase = ["""""".join(lowerCamelCase ) for row in temp_grid]
__lowercase = """""".join(lowerCamelCase )
return output_string
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = []
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1:
return input_string
__lowercase = [[] for _ in range(lowerCamelCase )] # generates template
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("""*""" )
__lowercase = 0
for row in temp_grid: # fills in the characters
__lowercase = input_string[counter : counter + len(lowerCamelCase )]
grid.append(list(lowerCamelCase ) )
counter += len(lowerCamelCase )
__lowercase = """""" # reads as zigzag
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = {}
for key_guess in range(1 , len(lowerCamelCase ) ): # tries every key
__lowercase = decrypt(lowerCamelCase , lowerCamelCase )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 |
from __future__ import annotations
from collections.abc import MutableSequence
class __UpperCamelCase :
def __init__( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : MutableSequence[float] ) -> None:
"""simple docstring"""
if len(_lowerCAmelCase ) != degree + 1:
raise ValueError(
"""The number of coefficients should be equal to the degree + 1.""" )
__lowercase = list(_lowerCAmelCase )
__lowercase = degree
def __add__( self : Optional[int] , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
if self.degree > polynomial_a.degree:
__lowercase = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , _lowerCAmelCase )
else:
__lowercase = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , _lowerCAmelCase )
def __sub__( self : int , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : Union[str, Any] ) -> Polynomial:
"""simple docstring"""
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Optional[int] , _lowerCAmelCase : Polynomial ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * (self.degree + polynomial_a.degree + 1)
for i in range(self.degree + 1 ):
for j in range(polynomial_a.degree + 1 ):
coefficients[i + j] += (
self.coefficients[i] * polynomial_a.coefficients[j]
)
return Polynomial(self.degree + polynomial_a.degree , _lowerCAmelCase )
def _a ( self : Optional[int] , _lowerCAmelCase : int | float ) -> int | float:
"""simple docstring"""
__lowercase = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : Dict ) -> str:
"""simple docstring"""
__lowercase = """"""
for i in range(self.degree , -1 , -1 ):
if self.coefficients[i] == 0:
continue
elif self.coefficients[i] > 0:
if polynomial:
polynomial += " + "
else:
polynomial += " - "
if i == 0:
polynomial += str(abs(self.coefficients[i] ) )
elif i == 1:
polynomial += str(abs(self.coefficients[i] ) ) + "x"
else:
polynomial += str(abs(self.coefficients[i] ) ) + "x^" + str(_lowerCAmelCase )
return polynomial
def __repr__( self : Union[str, Any] ) -> str:
"""simple docstring"""
return self.__str__()
def _a ( self : List[str] ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * self.degree
for i in range(self.degree ):
__lowercase = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , _lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : int | float = 0 ) -> Polynomial:
"""simple docstring"""
__lowercase = [0] * (self.degree + 2)
__lowercase = constant
for i in range(self.degree + 1 ):
__lowercase = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , _lowerCAmelCase )
def __eq__( self : List[str] , _lowerCAmelCase : object ) -> bool:
"""simple docstring"""
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
return False
if self.degree != polynomial_a.degree:
return False
for i in range(self.degree + 1 ):
if self.coefficients[i] != polynomial_a.coefficients[i]:
return False
return True
def __ne__( self : Dict , _lowerCAmelCase : object ) -> bool:
"""simple docstring"""
return not self.__eq__(_lowerCAmelCase )
| 80 | 1 |
import operator as op
__UpperCamelCase : int = """scaler.pt"""
__UpperCamelCase : int = """pytorch_model"""
__UpperCamelCase : Union[str, Any] = """random_states"""
__UpperCamelCase : Any = """optimizer"""
__UpperCamelCase : Tuple = """scheduler"""
__UpperCamelCase : List[Any] = """pytorch_model.bin"""
__UpperCamelCase : Union[str, Any] = """pytorch_model.bin.index.json"""
__UpperCamelCase : List[str] = """model.safetensors"""
__UpperCamelCase : str = """model.safetensors.index.json"""
__UpperCamelCase : Dict = """1.10.2"""
__UpperCamelCase : Any = """py38"""
__UpperCamelCase : Union[str, Any] = """4.17.0"""
__UpperCamelCase : Optional[Any] = ["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
__UpperCamelCase : Tuple = ["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
__UpperCamelCase : List[Any] = ["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
__UpperCamelCase : str = ["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
__UpperCamelCase : Optional[int] = ["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
__UpperCamelCase : int = """2.0.1"""
__UpperCamelCase : Optional[int] = ["""pdsh""", """standard""", """openmpi""", """mvapich"""]
__UpperCamelCase : Union[str, Any] = ["""default""", """reduce-overhead""", """max-autotune"""]
__UpperCamelCase : str = {""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
__UpperCamelCase : Dict = [
"""nnodes""",
"""nproc_per_node""",
"""rdzv_backend""",
"""rdzv_endpoint""",
"""rdzv_id""",
"""rdzv_conf""",
"""standalone""",
"""max_restarts""",
"""monitor_interval""",
"""start_method""",
"""role""",
"""module""",
"""m""",
"""no_python""",
"""run_path""",
"""log_dir""",
"""r""",
"""redirects""",
"""t""",
"""tee""",
"""node_rank""",
"""master_addr""",
"""master_port""",
]
__UpperCamelCase : List[str] = ["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
__UpperCamelCase : Tuple = ["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]
| 80 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if collection == []:
return []
# get some information about the collection
__lowercase = len(lowerCamelCase )
__lowercase = max(lowerCamelCase )
__lowercase = min(lowerCamelCase )
# create the counting array
__lowercase = coll_max + 1 - coll_min
__lowercase = [0] * counting_arr_length
# count how much a number appears in the collection
for number in collection:
counting_arr[number - coll_min] += 1
# sum each position with it's predecessors. now, counting_arr[i] tells
# us how many elements <= i has in the collection
for i in range(1 , lowerCamelCase ):
__lowercase = counting_arr[i] + counting_arr[i - 1]
# create the output collection
__lowercase = [0] * coll_len
# place the elements in the output, respecting the original order (stable
# sort) from end to begin, updating counting_arr
for i in reversed(range(0 , lowerCamelCase ) ):
__lowercase = collection[i]
counting_arr[collection[i] - coll_min] -= 1
return ordered
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return "".join([chr(lowerCamelCase ) for i in counting_sort([ord(lowerCamelCase ) for c in string] )] )
if __name__ == "__main__":
# Test string sort
assert counting_sort_string("""thisisthestring""") == "eghhiiinrsssttt"
__UpperCamelCase : str = input("""Enter numbers separated by a comma:\n""").strip()
__UpperCamelCase : Union[str, Any] = [int(item) for item in user_input.split(""",""")]
print(counting_sort(unsorted))
| 80 | 1 |
from __future__ import annotations
from fractions import Fraction
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = []
__lowercase = 11
__lowercase = int("""1""" + """0""" * digit_len )
for num in range(lowerCamelCase , lowerCamelCase ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(lowerCamelCase , lowerCamelCase ):
solutions.append(F'{num}/{den}' )
den += 1
num += 1
__lowercase = 10
return solutions
def snake_case ( lowerCamelCase = 2 ):
'''simple docstring'''
__lowercase = 1.0
for fraction in fraction_list(lowerCamelCase ):
__lowercase = Fraction(lowerCamelCase )
result *= frac.denominator / frac.numerator
return int(lowerCamelCase )
if __name__ == "__main__":
print(solution())
| 80 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __UpperCamelCase :
def __init__( self : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : int=13 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : str=3 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[int]=[10, 20, 30, 40] , _lowerCAmelCase : Optional[Any]=[2, 2, 3, 2] , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : List[str]=37 , _lowerCAmelCase : List[str]="gelu" , _lowerCAmelCase : List[Any]=10 , _lowerCAmelCase : int=0.02 , _lowerCAmelCase : str=["stage2", "stage3", "stage4"] , _lowerCAmelCase : Dict=[2, 3, 4] , _lowerCAmelCase : Tuple=None , ) -> Any:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = num_channels
__lowercase = num_stages
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = is_training
__lowercase = use_labels
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = num_labels
__lowercase = initializer_range
__lowercase = out_features
__lowercase = out_indices
__lowercase = scope
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.num_labels )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def _a ( self : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self : Optional[int] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__lowercase = None
__lowercase = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[Any] = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
__snake_case :List[str] = (
{'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
if is_torch_available()
else {}
)
__snake_case :str = True
__snake_case :Any = False
__snake_case :Any = False
__snake_case :Any = False
__snake_case :int = False
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason="""ConvNext does not use inputs_embeds""" )
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not support input and output embeddings""" )
def _a ( self : Dict ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not use feedforward chunking""" )
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
pass
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int] ):
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = self.model_tester.num_stages
self.assertEqual(len(_lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = ConvNextModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224""" ) if is_vision_available() else None
@slow
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""" ).to(_lowerCAmelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# verify the logits
__lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = torch.tensor([-0.0_260, -0.4_739, 0.1_911] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase , _lowerCAmelCase ):
__snake_case :Union[str, Any] = (ConvNextBackbone,) if is_torch_available() else ()
__snake_case :str = ConvNextConfig
__snake_case :Optional[Any] = False
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = ConvNextModelTester(self )
| 80 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = {
"""google/bigbird-roberta-base""": """https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json""",
"""google/bigbird-roberta-large""": """https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json""",
"""google/bigbird-base-trivia-itc""": """https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json""",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = 'big_bird'
def __init__( self : str , _lowerCAmelCase : Any=5_0358 , _lowerCAmelCase : Optional[int]=768 , _lowerCAmelCase : Union[str, Any]=12 , _lowerCAmelCase : Optional[int]=12 , _lowerCAmelCase : Optional[Any]=3072 , _lowerCAmelCase : List[str]="gelu_new" , _lowerCAmelCase : Optional[Any]=0.1 , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : Optional[int]=4096 , _lowerCAmelCase : Any=2 , _lowerCAmelCase : List[Any]=0.02 , _lowerCAmelCase : List[Any]=1e-12 , _lowerCAmelCase : List[Any]=True , _lowerCAmelCase : Union[str, Any]=0 , _lowerCAmelCase : Tuple=1 , _lowerCAmelCase : Dict=2 , _lowerCAmelCase : Optional[int]=66 , _lowerCAmelCase : Dict="block_sparse" , _lowerCAmelCase : Optional[int]=True , _lowerCAmelCase : str=False , _lowerCAmelCase : str=64 , _lowerCAmelCase : Union[str, Any]=3 , _lowerCAmelCase : Union[str, Any]=None , **_lowerCAmelCase : List[Any] , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(
pad_token_id=_lowerCAmelCase , bos_token_id=_lowerCAmelCase , eos_token_id=_lowerCAmelCase , sep_token_id=_lowerCAmelCase , **_lowerCAmelCase , )
__lowercase = vocab_size
__lowercase = max_position_embeddings
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = initializer_range
__lowercase = type_vocab_size
__lowercase = layer_norm_eps
__lowercase = use_cache
__lowercase = rescale_embeddings
__lowercase = attention_type
__lowercase = use_bias
__lowercase = block_size
__lowercase = num_random_blocks
__lowercase = classifier_dropout
class __UpperCamelCase ( _lowerCAmelCase ):
@property
def _a ( self : List[str] ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
__lowercase = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__lowercase = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 80 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : List[str] = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
__UpperCamelCase : Tuple = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
__UpperCamelCase : Optional[Any] = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
__UpperCamelCase : Optional[Any] = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
__UpperCamelCase : int = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
__UpperCamelCase : List[Any] = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
__UpperCamelCase : List[Any] = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
__UpperCamelCase : List[str] = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
__UpperCamelCase : List[str] = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
__UpperCamelCase : int = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
__UpperCamelCase : Dict = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
__UpperCamelCase : str = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
__UpperCamelCase : Optional[int] = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
__UpperCamelCase : Dict = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
__UpperCamelCase : List[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
__UpperCamelCase : str = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
__UpperCamelCase : Optional[int] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
__UpperCamelCase : Dict = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
__UpperCamelCase : Dict = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : int = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
__UpperCamelCase : Optional[Any] = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
__UpperCamelCase : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : Any = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
__UpperCamelCase : Union[str, Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
__UpperCamelCase : Tuple = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
__UpperCamelCase : str = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
__UpperCamelCase : List[str] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
__UpperCamelCase : Optional[Any] = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Tuple = FLAX_MODEL_MAPPING
__UpperCamelCase : Tuple = auto_class_update(FlaxAutoModel)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Union[str, Any] = FLAX_MODEL_FOR_PRETRAINING_MAPPING
__UpperCamelCase : List[Any] = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
__UpperCamelCase : Union[str, Any] = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :List[Any] = FLAX_MODEL_FOR_MASKED_LM_MAPPING
__UpperCamelCase : Dict = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__UpperCamelCase : Optional[Any] = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[int] = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
__UpperCamelCase : Union[str, Any] = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :List[Any] = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[int] = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[int] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
__UpperCamelCase : int = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :str = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
__UpperCamelCase : int = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
__UpperCamelCase : Optional[Any] = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Dict = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
__UpperCamelCase : Tuple = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class __UpperCamelCase ( _BaseAutoModelClass ):
__snake_case :Optional[Any] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
__UpperCamelCase : str = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 80 | 1 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return str(lowerCamelCase ) == str(lowerCamelCase )[::-1]
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return int(lowerCamelCase ) + int(str(lowerCamelCase )[::-1] )
def snake_case ( lowerCamelCase = 10_000 ):
'''simple docstring'''
__lowercase = []
for num in range(1 , lowerCamelCase ):
__lowercase = 0
__lowercase = num
while iterations < 50:
__lowercase = sum_reverse(lowerCamelCase )
iterations += 1
if is_palindrome(lowerCamelCase ):
break
else:
lychrel_nums.append(lowerCamelCase )
return len(lowerCamelCase )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__UpperCamelCase : int = {"""tokenization_wav2vec2_phoneme""": ["""Wav2Vec2PhonemeCTCTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
__UpperCamelCase : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : Dict = {
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mask2FormerConfig""",
],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : Optional[int] = ["""Mask2FormerImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[str] = [
"""MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Mask2FormerForUniversalSegmentation""",
"""Mask2FormerModel""",
"""Mask2FormerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_maskaformer import MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskaFormerConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_maskaformer import MaskaFormerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_maskaformer import (
MASK2FORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
MaskaFormerForUniversalSegmentation,
MaskaFormerModel,
MaskaFormerPreTrainedModel,
)
else:
import sys
__UpperCamelCase : Dict = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 80 |
from sklearn.metrics import matthews_corrcoef
import datasets
__UpperCamelCase : Union[str, Any] = """
Compute the Matthews correlation coefficient (MCC)
The Matthews correlation coefficient is used in machine learning as a
measure of the quality of binary and multiclass classifications. It takes
into account true and false positives and negatives and is generally
regarded as a balanced measure which can be used even if the classes are of
very different sizes. The MCC is in essence a correlation coefficient value
between -1 and +1. A coefficient of +1 represents a perfect prediction, 0
an average random prediction and -1 an inverse prediction. The statistic
is also known as the phi coefficient. [source: Wikipedia]
"""
__UpperCamelCase : List[str] = """
Args:
predictions (list of int): Predicted labels, as returned by a model.
references (list of int): Ground truth labels.
sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.
Returns:
matthews_correlation (dict containing float): Matthews correlation.
Examples:
Example 1, a basic example with only predictions and references as inputs:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3])
>>> print(round(results['matthews_correlation'], 2))
0.54
Example 2, the same example as above, but also including sample weights:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 3, 1, 1, 1, 2])
>>> print(round(results['matthews_correlation'], 2))
0.1
Example 3, the same example as above, but with sample weights that cause a negative correlation:
>>> matthews_metric = datasets.load_metric(\"matthews_correlation\")
>>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],
... predictions=[1, 2, 2, 0, 3, 3],
... sample_weight=[0.5, 1, 0, 0, 0, 1])
>>> print(round(results['matthews_correlation'], 2))
-0.25
"""
__UpperCamelCase : Tuple = """\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class __UpperCamelCase ( datasets.Metric ):
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""int32""" ),
"""references""": datasets.Value("""int32""" ),
} ) , reference_urls=[
"""https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html"""
] , )
def _a ( self : Union[str, Any] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[Any]=None ) -> Optional[Any]:
"""simple docstring"""
return {
"matthews_correlation": float(matthews_corrcoef(_lowerCAmelCase , _lowerCAmelCase , sample_weight=_lowerCAmelCase ) ),
}
| 80 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
__UpperCamelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCamelCase : int = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__UpperCamelCase : int = {
"""vocab_file""": {
"""squeezebert/squeezebert-uncased""": (
"""https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt"""
),
"""squeezebert/squeezebert-mnli""": """https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt""",
"""squeezebert/squeezebert-mnli-headless""": (
"""https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt"""
),
},
"""tokenizer_file""": {
"""squeezebert/squeezebert-uncased""": (
"""https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json"""
),
"""squeezebert/squeezebert-mnli""": (
"""https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json"""
),
"""squeezebert/squeezebert-mnli-headless""": (
"""https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json"""
),
},
}
__UpperCamelCase : Tuple = {
"""squeezebert/squeezebert-uncased""": 512,
"""squeezebert/squeezebert-mnli""": 512,
"""squeezebert/squeezebert-mnli-headless""": 512,
}
__UpperCamelCase : Optional[Any] = {
"""squeezebert/squeezebert-uncased""": {"""do_lower_case""": True},
"""squeezebert/squeezebert-mnli""": {"""do_lower_case""": True},
"""squeezebert/squeezebert-mnli-headless""": {"""do_lower_case""": True},
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Tuple = VOCAB_FILES_NAMES
__snake_case :Any = PRETRAINED_VOCAB_FILES_MAP
__snake_case :Optional[int] = PRETRAINED_INIT_CONFIGURATION
__snake_case :Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case :Union[str, Any] = SqueezeBertTokenizer
def __init__( self : Optional[Any] , _lowerCAmelCase : str=None , _lowerCAmelCase : List[Any]=None , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : Tuple="[UNK]" , _lowerCAmelCase : int="[SEP]" , _lowerCAmelCase : str="[PAD]" , _lowerCAmelCase : Dict="[CLS]" , _lowerCAmelCase : Optional[int]="[MASK]" , _lowerCAmelCase : Any=True , _lowerCAmelCase : str=None , **_lowerCAmelCase : Optional[Any] , ) -> str:
"""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 , )
__lowercase = 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
):
__lowercase = getattr(_lowerCAmelCase , normalizer_state.pop("""type""" ) )
__lowercase = do_lower_case
__lowercase = strip_accents
__lowercase = tokenize_chinese_chars
__lowercase = normalizer_class(**_lowerCAmelCase )
__lowercase = do_lower_case
def _a ( self : Tuple , _lowerCAmelCase : Tuple , _lowerCAmelCase : Optional[int]=None ) -> Tuple:
"""simple docstring"""
__lowercase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _a ( self : List[Any] , _lowerCAmelCase : List[int] , _lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__lowercase = [self.sep_token_id]
__lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _a ( self : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
__lowercase = self._tokenizer.model.save(_lowerCAmelCase , name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
| 80 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCamelCase : str = logging.get_logger(__name__)
__UpperCamelCase : Dict = {
"""vocab_file""": """vocab.json""",
"""merges_file""": """merges.txt""",
"""tokenizer_config_file""": """tokenizer_config.json""",
}
__UpperCamelCase : Optional[int] = {
"""vocab_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json"""
},
"""merges_file""": {
"""facebook/blenderbot_small-90M""": """https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt"""
},
"""tokenizer_config_file""": {
"""facebook/blenderbot_small-90M""": (
"""https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json"""
)
},
}
__UpperCamelCase : Dict = {"""facebook/blenderbot_small-90M""": 512}
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = set()
__lowercase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__lowercase = char
__lowercase = set(lowerCamelCase )
return pairs
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = VOCAB_FILES_NAMES
__snake_case :Tuple = PRETRAINED_VOCAB_FILES_MAP
__snake_case :Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case :str = ['input_ids', 'attention_mask']
def __init__( self : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : str="__start__" , _lowerCAmelCase : int="__end__" , _lowerCAmelCase : Any="__unk__" , _lowerCAmelCase : List[Any]="__null__" , **_lowerCAmelCase : Tuple , ) -> str:
"""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:
__lowercase = json.load(_lowerCAmelCase )
__lowercase = {v: k for k, v in self.encoder.items()}
with open(_lowerCAmelCase , encoding="""utf-8""" ) as merges_handle:
__lowercase = merges_handle.read().split("""\n""" )[1:-1]
__lowercase = [tuple(merge.split() ) for merge in merges]
__lowercase = dict(zip(_lowerCAmelCase , range(len(_lowerCAmelCase ) ) ) )
__lowercase = {}
@property
def _a ( self : Union[str, Any] ) -> int:
"""simple docstring"""
return len(self.encoder )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
return dict(self.encoder , **self.added_tokens_encoder )
def _a ( self : str , _lowerCAmelCase : str ) -> str:
"""simple docstring"""
if token in self.cache:
return self.cache[token]
__lowercase = re.sub("""([.,!?()])""" , r""" \1""" , _lowerCAmelCase )
__lowercase = re.sub("""(')""" , r""" \1 """ , _lowerCAmelCase )
__lowercase = re.sub(r"""\s{2,}""" , """ """ , _lowerCAmelCase )
if "\n" in token:
__lowercase = token.replace("""\n""" , """ __newln__""" )
__lowercase = token.split(""" """ )
__lowercase = []
for token in tokens:
if not len(_lowerCAmelCase ):
continue
__lowercase = token.lower()
__lowercase = tuple(_lowerCAmelCase )
__lowercase = tuple(list(word[:-1] ) + [word[-1] + """</w>"""] )
__lowercase = get_pairs(_lowerCAmelCase )
if not pairs:
words.append(_lowerCAmelCase )
continue
while True:
__lowercase = min(_lowerCAmelCase , key=lambda _lowerCAmelCase : self.bpe_ranks.get(_lowerCAmelCase , float("""inf""" ) ) )
if bigram not in self.bpe_ranks:
break
__lowercase , __lowercase = bigram
__lowercase = []
__lowercase = 0
while i < len(_lowerCAmelCase ):
try:
__lowercase = word.index(_lowerCAmelCase , _lowerCAmelCase )
new_word.extend(word[i:j] )
__lowercase = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(_lowerCAmelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__lowercase = tuple(_lowerCAmelCase )
__lowercase = new_word
if len(_lowerCAmelCase ) == 1:
break
else:
__lowercase = get_pairs(_lowerCAmelCase )
__lowercase = """@@ """.join(_lowerCAmelCase )
__lowercase = word[:-4]
__lowercase = word
words.append(_lowerCAmelCase )
return " ".join(_lowerCAmelCase )
def _a ( self : Optional[Any] , _lowerCAmelCase : str ) -> List[str]:
"""simple docstring"""
__lowercase = []
__lowercase = re.findall(r"""\S+\n?""" , _lowerCAmelCase )
for token in words:
split_tokens.extend(list(self.bpe(_lowerCAmelCase ).split(""" """ ) ) )
return split_tokens
def _a ( self : Tuple , _lowerCAmelCase : str ) -> int:
"""simple docstring"""
__lowercase = token.lower()
return self.encoder.get(_lowerCAmelCase , self.encoder.get(self.unk_token ) )
def _a ( self : Tuple , _lowerCAmelCase : int ) -> str:
"""simple docstring"""
return self.decoder.get(_lowerCAmelCase , self.unk_token )
def _a ( self : Dict , _lowerCAmelCase : List[str] ) -> str:
"""simple docstring"""
__lowercase = """ """.join(_lowerCAmelCase ).replace("""@@ """ , """""" ).strip()
return out_string
def _a ( self : List[str] , _lowerCAmelCase : str , _lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_lowerCAmelCase ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
__lowercase = os.path.join(
_lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""merges_file"""] )
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=_lowerCAmelCase , ensure_ascii=_lowerCAmelCase ) + """\n""" )
__lowercase = 0
with open(_lowerCAmelCase , """w""" , encoding="""utf-8""" ) as writer:
writer.write("""#version: 0.2\n""" )
for bpe_tokens, token_index in sorted(self.bpe_ranks.items() , key=lambda _lowerCAmelCase : kv[1] ):
if index != token_index:
logger.warning(
F'Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'
""" Please check that the tokenizer is not corrupted!""" )
__lowercase = token_index
writer.write(""" """.join(_lowerCAmelCase ) + """\n""" )
index += 1
return vocab_file, merge_file
| 80 | 1 |
from sympy import diff, lambdify, symbols
from sympy.functions import * # noqa: F403
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase = "x" , lowerCamelCase = 10**-10 , lowerCamelCase = 1 , ):
'''simple docstring'''
__lowercase = symbols(lowerCamelCase )
__lowercase = lambdify(lowerCamelCase , lowerCamelCase )
__lowercase = lambdify(lowerCamelCase , diff(lowerCamelCase , lowerCamelCase ) )
__lowercase = starting_point
while True:
if diff_function(lowerCamelCase ) != 0:
__lowercase = prev_guess - multiplicity * func(lowerCamelCase ) / diff_function(
lowerCamelCase )
else:
raise ZeroDivisionError("""Could not find root""" ) from None
# Precision is checked by comparing the difference of consecutive guesses
if abs(next_guess - prev_guess ) < precision:
return next_guess
__lowercase = next_guess
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(F'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
# Find root of polynomial
# Find fourth Root of 5
print(F'''The root of x**4 - 5 = 0 is {newton_raphson("x**4 -5", 0.4 +5j)}''')
# Find value of e
print(
"""The root of log(y) - 1 = 0 is """,
F'''{newton_raphson("log(y) - 1", 2, variable="y")}''',
)
# Exponential Roots
print(
"""The root of exp(x) - 1 = 0 is""",
F'''{newton_raphson("exp(x) - 1", 10, precision=0.0_0_5)}''',
)
# Find root of cos(x)
print(F'''The root of cos(x) = 0 is {newton_raphson("cos(x)", 0)}''')
| 80 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : Tuple = logging.get_logger(__name__)
__UpperCamelCase : int = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Union[str, Any] = 'lxmert'
__snake_case :Union[str, Any] = {}
def __init__( self : List[str] , _lowerCAmelCase : Dict=3_0522 , _lowerCAmelCase : List[str]=768 , _lowerCAmelCase : Union[str, Any]=12 , _lowerCAmelCase : Union[str, Any]=9500 , _lowerCAmelCase : Union[str, Any]=1600 , _lowerCAmelCase : Optional[Any]=400 , _lowerCAmelCase : Tuple=3072 , _lowerCAmelCase : List[Any]="gelu" , _lowerCAmelCase : int=0.1 , _lowerCAmelCase : Dict=0.1 , _lowerCAmelCase : Tuple=512 , _lowerCAmelCase : Tuple=2 , _lowerCAmelCase : Optional[Any]=0.02 , _lowerCAmelCase : List[str]=1e-12 , _lowerCAmelCase : Any=9 , _lowerCAmelCase : Optional[Any]=5 , _lowerCAmelCase : Any=5 , _lowerCAmelCase : Dict=2048 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[Any]=6.67 , _lowerCAmelCase : Optional[Any]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , _lowerCAmelCase : str=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : int=True , _lowerCAmelCase : int=True , **_lowerCAmelCase : Tuple , ) -> Dict:
"""simple docstring"""
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_attention_heads
__lowercase = hidden_act
__lowercase = intermediate_size
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = initializer_range
__lowercase = layer_norm_eps
__lowercase = num_qa_labels
__lowercase = num_object_labels
__lowercase = num_attr_labels
__lowercase = l_layers
__lowercase = x_layers
__lowercase = r_layers
__lowercase = visual_feat_dim
__lowercase = visual_pos_dim
__lowercase = visual_loss_normalizer
__lowercase = task_matched
__lowercase = task_mask_lm
__lowercase = task_obj_predict
__lowercase = task_qa
__lowercase = visual_obj_loss
__lowercase = visual_attr_loss
__lowercase = visual_feat_loss
__lowercase = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**_lowerCAmelCase )
| 80 | 1 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase = 10
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = [1, 2, 3, 4]
__lowercase = [1, 2, 3, 4, 0, 0, 0, 0, 0, 0]
self.assertEqual(truncate_or_pad(_lowerCAmelCase , self.block_size , 0 ) , _lowerCAmelCase )
def _a ( self : str ) -> Tuple:
"""simple docstring"""
__lowercase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
__lowercase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_lowerCAmelCase , self.block_size , 0 ) , _lowerCAmelCase )
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13]
__lowercase = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
self.assertEqual(truncate_or_pad(_lowerCAmelCase , self.block_size , 0 ) , _lowerCAmelCase )
def _a ( self : int ) -> str:
"""simple docstring"""
__lowercase = """It was the year of Our Lord one thousand seven hundred and
seventy-five.\n\nSpiritual revelations were conceded to England at that
favoured period, as at this."""
__lowercase , __lowercase = process_story(_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , [] )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = """"""
__lowercase , __lowercase = process_story(_lowerCAmelCase )
self.assertEqual(_lowerCAmelCase , [] )
self.assertEqual(_lowerCAmelCase , [] )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__lowercase = (
"""It was the year of Our Lord one thousand seven hundred and """
"""seventy-five\n\nSpiritual revelations were conceded to England """
"""at that favoured period, as at this.\n@highlight\n\nIt was the best of times"""
)
__lowercase , __lowercase = process_story(_lowerCAmelCase )
__lowercase = [
"""It was the year of Our Lord one thousand seven hundred and seventy-five.""",
"""Spiritual revelations were conceded to England at that favoured period, as at this.""",
]
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = ["""It was the best of times."""]
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Any ) -> Any:
"""simple docstring"""
__lowercase = torch.tensor([1, 2, 3, 4] )
__lowercase = torch.tensor([1, 1, 1, 1] )
np.testing.assert_array_equal(build_mask(_lowerCAmelCase , 0 ).numpy() , expected.numpy() )
def _a ( self : str ) -> Any:
"""simple docstring"""
__lowercase = torch.tensor([1, 2, 3, 4, 23, 23, 23] )
__lowercase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_lowerCAmelCase , 23 ).numpy() , expected.numpy() )
def _a ( self : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = torch.tensor([8, 2, 3, 4, 1, 1, 1] )
__lowercase = torch.tensor([1, 1, 1, 1, 0, 0, 0] )
np.testing.assert_array_equal(build_mask(_lowerCAmelCase , 1 ).numpy() , expected.numpy() )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = 101
__lowercase = torch.tensor([[1, 2, 3, 4, 5, 6], [1, 2, 3, 101, 5, 6], [1, 101, 3, 4, 101, 6]] )
__lowercase = torch.tensor([[1, 1, 1, 1, 1, 1], [1, 1, 1, 0, 0, 0], [1, 0, 0, 0, 1, 1]] )
__lowercase = compute_token_type_ids(_lowerCAmelCase , _lowerCAmelCase )
np.testing.assert_array_equal(_lowerCAmelCase , _lowerCAmelCase )
| 80 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class __UpperCamelCase ( _lowerCAmelCase ):
def __init__( self : int , _lowerCAmelCase : str , _lowerCAmelCase : List[str]=13 , _lowerCAmelCase : List[str]=7 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Dict=True , _lowerCAmelCase : Union[str, Any]=False , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=99 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : Optional[int]=5 , _lowerCAmelCase : Any=4 , _lowerCAmelCase : Tuple=37 , _lowerCAmelCase : str="gelu" , _lowerCAmelCase : List[str]=0.1 , _lowerCAmelCase : Tuple=0.1 , _lowerCAmelCase : Union[str, Any]=512 , _lowerCAmelCase : Dict=16 , _lowerCAmelCase : List[Any]=2 , _lowerCAmelCase : Optional[int]=0.02 , _lowerCAmelCase : int=3 , _lowerCAmelCase : Dict=4 , _lowerCAmelCase : List[Any]=None , ) -> List[Any]:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = seq_length
__lowercase = is_training
__lowercase = use_input_mask
__lowercase = use_token_type_ids
__lowercase = use_labels
__lowercase = vocab_size
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = num_attention_heads
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = max_position_embeddings
__lowercase = type_vocab_size
__lowercase = type_sequence_label_size
__lowercase = initializer_range
__lowercase = num_labels
__lowercase = num_choices
__lowercase = scope
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase = None
if self.use_input_mask:
__lowercase = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase = None
__lowercase = None
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowercase = ids_tensor([self.batch_size] , self.num_choices )
__lowercase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def _a ( self : List[Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = DistilBertModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = model(_lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def _a ( self : List[Any] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : str , _lowerCAmelCase : Any ) -> List[str]:
"""simple docstring"""
__lowercase = DistilBertForMaskedLM(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : str , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : Optional[int] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertForQuestionAnswering(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : List[Any] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForSequenceClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : Union[str, Any] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Any , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.num_labels
__lowercase = DistilBertForTokenClassification(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = 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 : Tuple , _lowerCAmelCase : str , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Any , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ) -> str:
"""simple docstring"""
__lowercase = self.num_choices
__lowercase = DistilBertForMultipleChoice(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__lowercase = model(
_lowerCAmelCase , attention_mask=_lowerCAmelCase , labels=_lowerCAmelCase , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
((__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase) , (__lowercase)) = config_and_inputs
__lowercase = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[Any] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
__snake_case :Dict = (
{
'feature-extraction': DistilBertModel,
'fill-mask': DistilBertForMaskedLM,
'question-answering': DistilBertForQuestionAnswering,
'text-classification': DistilBertForSequenceClassification,
'token-classification': DistilBertForTokenClassification,
'zero-shot': DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
__snake_case :Tuple = True
__snake_case :Tuple = True
__snake_case :List[str] = True
__snake_case :Optional[int] = True
def _a ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = DistilBertModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , dim=37 )
def _a ( self : Dict ) -> str:
"""simple docstring"""
self.config_tester.run_common_tests()
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*_lowerCAmelCase )
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*_lowerCAmelCase )
def _a ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*_lowerCAmelCase )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*_lowerCAmelCase )
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*_lowerCAmelCase )
def _a ( self : List[str] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*_lowerCAmelCase )
@slow
def _a ( self : int ) -> Optional[Any]:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = DistilBertModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
@slow
@require_torch_gpu
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
__lowercase = True
__lowercase = model_class(config=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = torch.jit.trace(
_lowerCAmelCase , (inputs_dict["""input_ids"""].to("""cpu""" ), inputs_dict["""attention_mask"""].to("""cpu""" )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(_lowerCAmelCase , os.path.join(_lowerCAmelCase , """traced_model.pt""" ) )
__lowercase = torch.jit.load(os.path.join(_lowerCAmelCase , """traced_model.pt""" ) , map_location=_lowerCAmelCase )
loaded(inputs_dict["""input_ids"""].to(_lowerCAmelCase ) , inputs_dict["""attention_mask"""].to(_lowerCAmelCase ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase ):
@slow
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = DistilBertModel.from_pretrained("""distilbert-base-uncased""" )
__lowercase = torch.tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
__lowercase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__lowercase = model(_lowerCAmelCase , attention_mask=_lowerCAmelCase )[0]
__lowercase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , _lowerCAmelCase )
__lowercase = torch.tensor(
[[[-0.1_639, 0.3_299, 0.1_648], [-0.1_746, 0.3_289, 0.1_710], [-0.1_884, 0.3_357, 0.1_810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , _lowerCAmelCase , atol=1e-4 ) )
| 80 | 1 |
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoImageProcessor, ViTImageProcessor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / """utils"""))
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
__UpperCamelCase : int = get_tests_dir("""fixtures""")
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = mock.Mock()
__lowercase = 500
__lowercase = {}
__lowercase = HTTPError
__lowercase = {}
# Download this model to make sure it's in the cache.
__lowercase = ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit""" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("""requests.Session.request""" , return_value=_lowerCAmelCase ) as mock_head:
__lowercase = ViTImageProcessor.from_pretrained("""hf-internal-testing/tiny-random-vit""" )
# This check we did call the fake head request
mock_head.assert_called()
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__lowercase = ViTImageProcessor.from_pretrained(
"""https://huggingface.co/hf-internal-testing/tiny-random-vit/resolve/main/preprocessor_config.json""" )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
with self.assertRaises(_lowerCAmelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
__lowercase = AutoImageProcessor.from_pretrained("""hf-internal-testing/stable-diffusion-all-variants""" )
__lowercase = AutoImageProcessor.from_pretrained(
"""hf-internal-testing/stable-diffusion-all-variants""" , subfolder="""feature_extractor""" )
self.assertIsNotNone(_lowerCAmelCase )
@is_staging_test
class __UpperCamelCase ( unittest.TestCase ):
@classmethod
def _a ( cls : Any ) -> Any:
"""simple docstring"""
__lowercase = TOKEN
HfFolder.save_token(_lowerCAmelCase )
@classmethod
def _a ( cls : Any ) -> List[str]:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="""test-image-processor""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""valid_org/test-image-processor-org""" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="""test-dynamic-image-processor""" )
except HTTPError:
pass
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
__lowercase = ViTImageProcessor.from_pretrained(_lowerCAmelCase )
image_processor.push_to_hub("""test-image-processor""" , use_auth_token=self._token )
__lowercase = ViTImageProcessor.from_pretrained(F'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="""test-image-processor""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCAmelCase , repo_id="""test-image-processor""" , push_to_hub=_lowerCAmelCase , use_auth_token=self._token )
__lowercase = ViTImageProcessor.from_pretrained(F'{USER}/test-image-processor' )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
def _a ( self : Any ) -> Tuple:
"""simple docstring"""
__lowercase = ViTImageProcessor.from_pretrained(_lowerCAmelCase )
image_processor.push_to_hub("""valid_org/test-image-processor""" , use_auth_token=self._token )
__lowercase = ViTImageProcessor.from_pretrained("""valid_org/test-image-processor""" )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id="""valid_org/test-image-processor""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(
_lowerCAmelCase , repo_id="""valid_org/test-image-processor-org""" , push_to_hub=_lowerCAmelCase , use_auth_token=self._token )
__lowercase = ViTImageProcessor.from_pretrained("""valid_org/test-image-processor-org""" )
for k, v in image_processor.__dict__.items():
self.assertEqual(_lowerCAmelCase , getattr(_lowerCAmelCase , _lowerCAmelCase ) )
def _a ( self : str ) -> str:
"""simple docstring"""
CustomImageProcessor.register_for_auto_class()
__lowercase = CustomImageProcessor.from_pretrained(_lowerCAmelCase )
image_processor.push_to_hub("""test-dynamic-image-processor""" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
image_processor.auto_map , {"""AutoImageProcessor""": """custom_image_processing.CustomImageProcessor"""} , )
__lowercase = AutoImageProcessor.from_pretrained(
F'{USER}/test-dynamic-image-processor' , trust_remote_code=_lowerCAmelCase )
# Can't make an isinstance check because the new_image_processor is from the CustomImageProcessor class of a dynamic module
self.assertEqual(new_image_processor.__class__.__name__ , """CustomImageProcessor""" )
| 80 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class __UpperCamelCase ( _lowerCAmelCase ):
# to overwrite at feature extractactor specific tests
__snake_case :Optional[int] = None
__snake_case :Dict = None
@property
def _a ( self : str ) -> List[str]:
"""simple docstring"""
return self.feat_extract_tester.prepare_feat_extract_dict()
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(_lowerCAmelCase , """feature_size""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """sampling_rate""" ) )
self.assertTrue(hasattr(_lowerCAmelCase , """padding_value""" ) )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(_lowerCAmelCase ) == len(_lowerCAmelCase ) for x, y in zip(_lowerCAmelCase , processed_features[input_name] ) ) )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""np""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_torch
def _a ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""pt""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
@require_tf
def _a ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(equal_length=_lowerCAmelCase )
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type="""tf""" )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.feature_size) )
def _a ( self : str , _lowerCAmelCase : List[Any]=False ) -> int:
"""simple docstring"""
def _inputs_have_equal_length(_lowerCAmelCase : int ):
__lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCAmelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCAmelCase : Dict , _lowerCAmelCase : Tuple ):
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCAmelCase , _lowerCAmelCase ):
if not np.allclose(np.asarray(_lowerCAmelCase ) , np.asarray(_lowerCAmelCase ) , atol=1e-3 ):
return False
return True
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = self.feat_extract_tester.seq_length_diff
__lowercase = self.feat_extract_tester.max_seq_length + pad_diff
__lowercase = self.feat_extract_tester.min_seq_length
__lowercase = self.feat_extract_tester.batch_size
__lowercase = self.feat_extract_tester.feature_size
# test padding for List[int] + numpy
__lowercase = feat_extract.pad(_lowerCAmelCase , padding=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[-1] ) )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )
__lowercase = input_a[input_name]
# max_length parameter has to be provided when setting `padding="max_length"`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""max_length""" )[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=_lowerCAmelCase , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
self.assertTrue(len(input_a[0] ) == pad_min_length )
self.assertTrue(len(input_a[1] ) == pad_min_length + pad_diff )
self.assertTrue(input_a.shape[:2] == (batch_size, len(input_a[0] )) )
self.assertTrue(input_a.shape[:2] == (batch_size, pad_max_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == input_a.shape[2] == feature_size )
# test padding for `pad_to_multiple_of` for List[int] + numpy
__lowercase = feat_extract.pad(_lowerCAmelCase , pad_to_multiple_of=10 )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , pad_to_multiple_of=10 )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , pad_to_multiple_of=10 , max_length=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = input_a[input_name]
self.assertTrue(all(len(_lowerCAmelCase ) % 10 == 0 for x in input_a ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = pad_max_length if pad_max_length % 10 == 0 else (pad_max_length // 10 + 1) * 10
self.assertTrue(all(len(_lowerCAmelCase ) == expected_mult_pad_length for x in input_a ) )
self.assertEqual(input_a.shape[:2] , (batch_size, expected_mult_pad_length) )
if feature_size > 1:
self.assertTrue(input_a.shape[2] == feature_size )
# Check padding value is correct
__lowercase = (np.ones(self.feat_extract_tester.feature_size ) * feat_extract.padding_value).sum()
self.assertTrue(
abs(np.asarray(input_a[0] )[pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[1] )[pad_min_length + pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - pad_diff) )
< 1e-3 )
self.assertTrue(
abs(
np.asarray(input_a[2] )[pad_min_length + 2 * pad_diff :].sum()
- padding_vector_sum * (pad_max_length - pad_min_length - 2 * pad_diff) )
< 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (pad_max_length - pad_min_length) ) < 1e-3 )
self.assertTrue(
abs(input_a[0, pad_min_length:].sum() - padding_vector_sum * (expected_mult_pad_length - pad_min_length) )
< 1e-3 )
def _a ( self : Tuple , _lowerCAmelCase : str=False ) -> Union[str, Any]:
"""simple docstring"""
def _inputs_have_equal_length(_lowerCAmelCase : Tuple ):
__lowercase = len(input[0] )
for input_slice in input[1:]:
if len(_lowerCAmelCase ) != length:
return False
return True
def _inputs_are_equal(_lowerCAmelCase : Any , _lowerCAmelCase : str ):
if len(_lowerCAmelCase ) != len(_lowerCAmelCase ):
return False
for input_slice_a, input_slice_a in zip(_lowerCAmelCase , _lowerCAmelCase ):
if not np.allclose(np.asarray(_lowerCAmelCase ) , np.asarray(_lowerCAmelCase ) , atol=1e-3 ):
return False
return True
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common(numpify=_lowerCAmelCase )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
# truncate to smallest
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , truncation=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) )
__lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
# truncate to smallest with np
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" , truncation=_lowerCAmelCase , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(input_a.shape[1] == len(speech_inputs[0] ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
# truncate to middle
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , truncation=_lowerCAmelCase )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[1] ) , return_tensors="""np""" )
__lowercase = input_a[input_name]
self.assertTrue(input_a.shape[1] == len(speech_inputs[1] ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(_inputs_are_equal(_lowerCAmelCase , _lowerCAmelCase ) )
# since truncation forces padding to be smaller than longest input
# function can't return `np.ndarray`, but has to return list
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertTrue(len(input_a[-1] ) == len(speech_inputs[-1] ) )
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , truncation=_lowerCAmelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""longest""" , truncation=_lowerCAmelCase )[input_name]
# padding has to be max_length when setting `truncation=True`
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""longest""" , truncation=_lowerCAmelCase )[input_name]
# max_length parameter has to be provided when setting `truncation=True` and padding="max_length"
with self.assertRaises(_lowerCAmelCase ):
feat_extract.pad(_lowerCAmelCase , padding="""max_length""" , truncation=_lowerCAmelCase )[input_name]
# test truncation for `pad_to_multiple_of` for List[int] + numpy
__lowercase = 12
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCAmelCase , truncation=_lowerCAmelCase , )
__lowercase = input_a[input_name]
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=len(speech_inputs[0] ) , pad_to_multiple_of=_lowerCAmelCase , )
__lowercase = input_a[input_name]
# retrieve expected_length as multiple of pad_to_multiple_of
__lowercase = len(speech_inputs[0] )
if expected_length % pad_to_multiple_of != 0:
__lowercase = ((len(speech_inputs[0] ) // pad_to_multiple_of) + 1) * pad_to_multiple_of
self.assertTrue(len(input_a[0] ) == expected_length )
self.assertTrue(_inputs_have_equal_length(_lowerCAmelCase ) )
self.assertFalse(_inputs_have_equal_length(_lowerCAmelCase ) )
def _a ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self._check_padding(numpify=_lowerCAmelCase )
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
self._check_padding(numpify=_lowerCAmelCase )
def _a ( self : int ) -> Tuple:
"""simple docstring"""
self._check_truncation(numpify=_lowerCAmelCase )
def _a ( self : str ) -> str:
"""simple docstring"""
self._check_truncation(numpify=_lowerCAmelCase )
@require_torch
def _a ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""pt""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
@require_tf
def _a ( self : Any ) -> Any:
"""simple docstring"""
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )[input_name]
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""tf""" )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_tf.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def _a ( self : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**_lowerCAmelCase )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = [len(_lowerCAmelCase ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.pad(_lowerCAmelCase , padding="""longest""" , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _lowerCAmelCase )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , _lowerCAmelCase )
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**_lowerCAmelCase )
__lowercase = self.feat_extract_tester.prepare_inputs_for_common()
__lowercase = [len(_lowerCAmelCase ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = min(_lowerCAmelCase )
__lowercase = feat_extract.pad(
_lowerCAmelCase , padding="""max_length""" , max_length=_lowerCAmelCase , truncation=_lowerCAmelCase , return_tensors="""np""" )
self.assertIn("""attention_mask""" , _lowerCAmelCase )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
| 80 | 1 |
import os
from typing import List, Optional, Union
from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[str] = ['image_processor', 'tokenizer']
__snake_case :Dict = 'BlipImageProcessor'
__snake_case :Any = 'AutoTokenizer'
def __init__( self : Optional[int] , _lowerCAmelCase : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] ) -> Tuple:
"""simple docstring"""
super().__init__(_lowerCAmelCase , _lowerCAmelCase )
# add QFormer tokenizer
__lowercase = qformer_tokenizer
def __call__( self : List[str] , _lowerCAmelCase : ImageInput = None , _lowerCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , _lowerCAmelCase : bool = True , _lowerCAmelCase : Union[bool, str, PaddingStrategy] = False , _lowerCAmelCase : Union[bool, str, TruncationStrategy] = None , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : int = 0 , _lowerCAmelCase : Optional[int] = None , _lowerCAmelCase : Optional[bool] = None , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = False , _lowerCAmelCase : bool = True , _lowerCAmelCase : Optional[Union[str, TensorType]] = None , **_lowerCAmelCase : List[Any] , ) -> BatchFeature:
"""simple docstring"""
if images is None and text is None:
raise ValueError("""You have to specify at least images or text.""" )
__lowercase = BatchFeature()
if text is not None:
__lowercase = 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 , )
encoding.update(_lowerCAmelCase )
__lowercase = self.qformer_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 , )
__lowercase = qformer_text_encoding.pop("""input_ids""" )
__lowercase = qformer_text_encoding.pop("""attention_mask""" )
if images is not None:
__lowercase = self.image_processor(_lowerCAmelCase , return_tensors=_lowerCAmelCase )
encoding.update(_lowerCAmelCase )
return encoding
def _a ( self : Optional[int] , *_lowerCAmelCase : Optional[int] , **_lowerCAmelCase : int ) -> Union[str, Any]:
"""simple docstring"""
return self.tokenizer.batch_decode(*_lowerCAmelCase , **_lowerCAmelCase )
def _a ( self : List[Any] , *_lowerCAmelCase : List[str] , **_lowerCAmelCase : Union[str, Any] ) -> Any:
"""simple docstring"""
return self.tokenizer.decode(*_lowerCAmelCase , **_lowerCAmelCase )
@property
# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = self.tokenizer.model_input_names
__lowercase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
def _a ( self : Any , _lowerCAmelCase : str , **_lowerCAmelCase : List[str] ) -> List[str]:
"""simple docstring"""
if os.path.isfile(_lowerCAmelCase ):
raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file' )
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
__lowercase = os.path.join(_lowerCAmelCase , """qformer_tokenizer""" )
self.qformer_tokenizer.save_pretrained(_lowerCAmelCase )
return super().save_pretrained(_lowerCAmelCase , **_lowerCAmelCase )
@classmethod
def _a ( cls : Any , _lowerCAmelCase : Optional[Any] , **_lowerCAmelCase : Tuple ) -> int:
"""simple docstring"""
__lowercase = AutoTokenizer.from_pretrained(_lowerCAmelCase , subfolder="""qformer_tokenizer""" )
__lowercase = cls._get_arguments_from_pretrained(_lowerCAmelCase , **_lowerCAmelCase )
args.append(_lowerCAmelCase )
return cls(*_lowerCAmelCase )
| 80 |
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = [[] for _ in range(lowerCamelCase )]
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1 or len(lowerCamelCase ) <= key:
return input_string
for position, character in enumerate(lowerCamelCase ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(lowerCamelCase )
__lowercase = ["""""".join(lowerCamelCase ) for row in temp_grid]
__lowercase = """""".join(lowerCamelCase )
return output_string
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = []
__lowercase = key - 1
if key <= 0:
raise ValueError("""Height of grid can't be 0 or negative""" )
if key == 1:
return input_string
__lowercase = [[] for _ in range(lowerCamelCase )] # generates template
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("""*""" )
__lowercase = 0
for row in temp_grid: # fills in the characters
__lowercase = input_string[counter : counter + len(lowerCamelCase )]
grid.append(list(lowerCamelCase ) )
counter += len(lowerCamelCase )
__lowercase = """""" # reads as zigzag
for position in range(len(lowerCamelCase ) ):
__lowercase = position % (lowest * 2) # puts it in bounds
__lowercase = min(lowerCamelCase , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = {}
for key_guess in range(1 , len(lowerCamelCase ) ): # tries every key
__lowercase = decrypt(lowerCamelCase , lowerCamelCase )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 | 1 |
from __future__ import annotations
import math
class __UpperCamelCase :
def __init__( self : List[str] , _lowerCAmelCase : int ) -> None:
"""simple docstring"""
__lowercase = size
# approximate the overall size of segment tree with given value
__lowercase = [0 for i in range(0 , 4 * size )]
# create array to store lazy update
__lowercase = [0 for i in range(0 , 4 * size )]
__lowercase = [0 for i in range(0 , 4 * size )] # flag for lazy update
def _a ( self : int , _lowerCAmelCase : int ) -> int:
"""simple docstring"""
return idx * 2
def _a ( self : Any , _lowerCAmelCase : int ) -> int:
"""simple docstring"""
return idx * 2 + 1
def _a ( self : Union[str, Any] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : list[int] ) -> None:
"""simple docstring"""
if left_element == right_element:
__lowercase = a[left_element - 1]
else:
__lowercase = (left_element + right_element) // 2
self.build(self.left(_lowerCAmelCase ) , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
self.build(self.right(_lowerCAmelCase ) , mid + 1 , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = max(
self.segment_tree[self.left(_lowerCAmelCase )] , self.segment_tree[self.right(_lowerCAmelCase )] )
def _a ( self : Optional[Any] , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> bool:
"""simple docstring"""
if self.flag[idx] is True:
__lowercase = self.lazy[idx]
__lowercase = False
if left_element != right_element:
__lowercase = self.lazy[idx]
__lowercase = self.lazy[idx]
__lowercase = True
__lowercase = True
if right_element < a or left_element > b:
return True
if left_element >= a and right_element <= b:
__lowercase = val
if left_element != right_element:
__lowercase = val
__lowercase = val
__lowercase = True
__lowercase = True
return True
__lowercase = (left_element + right_element) // 2
self.update(self.left(_lowerCAmelCase ) , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
self.update(self.right(_lowerCAmelCase ) , mid + 1 , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = max(
self.segment_tree[self.left(_lowerCAmelCase )] , self.segment_tree[self.right(_lowerCAmelCase )] )
return True
def _a ( self : str , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : int ) -> int | float:
"""simple docstring"""
if self.flag[idx] is True:
__lowercase = self.lazy[idx]
__lowercase = False
if left_element != right_element:
__lowercase = self.lazy[idx]
__lowercase = self.lazy[idx]
__lowercase = True
__lowercase = True
if right_element < a or left_element > b:
return -math.inf
if left_element >= a and right_element <= b:
return self.segment_tree[idx]
__lowercase = (left_element + right_element) // 2
__lowercase = self.query(self.left(_lowerCAmelCase ) , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self.query(self.right(_lowerCAmelCase ) , mid + 1 , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return max(_lowerCAmelCase , _lowerCAmelCase )
def __str__( self : str ) -> str:
"""simple docstring"""
return str([self.query(1 , 1 , self.size , _lowerCAmelCase , _lowerCAmelCase ) for i in range(1 , self.size + 1 )] )
if __name__ == "__main__":
__UpperCamelCase : int = [1, 2, -4, 7, 3, -5, 6, 11, -20, 9, 14, 15, 5, 2, -8]
__UpperCamelCase : Union[str, Any] = 15
__UpperCamelCase : int = SegmentTree(size)
segt.build(1, 1, size, A)
print(segt.query(1, 1, size, 4, 6))
print(segt.query(1, 1, size, 7, 11))
print(segt.query(1, 1, size, 7, 12))
segt.update(1, 1, size, 1, 3, 111)
print(segt.query(1, 1, size, 1, 15))
segt.update(1, 1, size, 7, 8, 235)
print(segt)
| 80 |
def snake_case ( lowerCamelCase = 2_000_000 ):
'''simple docstring'''
__lowercase = [0 for i in range(n + 1 )]
__lowercase = 1
__lowercase = 1
for i in range(2 , int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i , n + 1 , lowerCamelCase ):
__lowercase = 1
__lowercase = 0
for i in range(lowerCamelCase ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F'''{solution() = }''')
| 80 | 1 |
class __UpperCamelCase :
def __init__( self : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : List[Any] ) -> List[Any]:
"""simple docstring"""
__lowercase = name
__lowercase = val
def __str__( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
return F'{self.__class__.__name__}({self.name}, {self.val})'
def __lt__( self : Optional[int] , _lowerCAmelCase : int ) -> Any:
"""simple docstring"""
return self.val < other.val
class __UpperCamelCase :
def __init__( self : Union[str, Any] , _lowerCAmelCase : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = {}
__lowercase = {}
__lowercase = self.build_heap(_lowerCAmelCase )
def __getitem__( self : List[Any] , _lowerCAmelCase : int ) -> Union[str, Any]:
"""simple docstring"""
return self.get_value(_lowerCAmelCase )
def _a ( self : str , _lowerCAmelCase : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return (idx - 1) // 2
def _a ( self : Optional[Any] , _lowerCAmelCase : Optional[int] ) -> int:
"""simple docstring"""
return idx * 2 + 1
def _a ( self : List[Any] , _lowerCAmelCase : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
return idx * 2 + 2
def _a ( self : int , _lowerCAmelCase : Optional[Any] ) -> str:
"""simple docstring"""
return self.heap_dict[key]
def _a ( self : Optional[Any] , _lowerCAmelCase : Any ) -> Any:
"""simple docstring"""
__lowercase = len(_lowerCAmelCase ) - 1
__lowercase = self.get_parent_idx(_lowerCAmelCase )
for idx, i in enumerate(_lowerCAmelCase ):
__lowercase = idx
__lowercase = i.val
for i in range(_lowerCAmelCase , -1 , -1 ):
self.sift_down(_lowerCAmelCase , _lowerCAmelCase )
return array
def _a ( self : List[str] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Tuple ) -> List[str]:
"""simple docstring"""
while True:
__lowercase = self.get_left_child_idx(_lowerCAmelCase ) # noqa: E741
__lowercase = self.get_right_child_idx(_lowerCAmelCase )
__lowercase = idx
if l < len(_lowerCAmelCase ) and array[l] < array[idx]:
__lowercase = l
if r < len(_lowerCAmelCase ) and array[r] < array[smallest]:
__lowercase = r
if smallest != idx:
__lowercase , __lowercase = array[smallest], array[idx]
(
(
__lowercase
) , (
__lowercase
) ,
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
__lowercase = smallest
else:
break
def _a ( self : int , _lowerCAmelCase : Dict ) -> List[str]:
"""simple docstring"""
__lowercase = self.get_parent_idx(_lowerCAmelCase )
while p >= 0 and self.heap[p] > self.heap[idx]:
__lowercase , __lowercase = self.heap[idx], self.heap[p]
__lowercase , __lowercase = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
__lowercase = p
__lowercase = self.get_parent_idx(_lowerCAmelCase )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
return self.heap[0]
def _a ( self : str ) -> str:
"""simple docstring"""
__lowercase , __lowercase = self.heap[-1], self.heap[0]
__lowercase , __lowercase = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
__lowercase = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def _a ( self : Union[str, Any] , _lowerCAmelCase : List[str] ) -> Optional[int]:
"""simple docstring"""
self.heap.append(_lowerCAmelCase )
__lowercase = len(self.heap ) - 1
__lowercase = node.val
self.sift_up(len(self.heap ) - 1 )
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
return len(self.heap ) == 0
def _a ( self : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] ) -> int:
"""simple docstring"""
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
__lowercase = new_value
__lowercase = new_value
self.sift_up(self.idx_of_element[node] )
__UpperCamelCase : Tuple = Node("""R""", -1)
__UpperCamelCase : Union[str, Any] = Node("""B""", 6)
__UpperCamelCase : Optional[Any] = Node("""A""", 3)
__UpperCamelCase : Union[str, Any] = Node("""X""", 1)
__UpperCamelCase : Any = Node("""E""", 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
__UpperCamelCase : Tuple = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
print(i)
print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 80 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class __UpperCamelCase :
def __init__( self : Optional[int] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int]=13 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : Optional[Any]=2 , _lowerCAmelCase : Union[str, Any]=3 , _lowerCAmelCase : List[str]=16 , _lowerCAmelCase : List[str]=[1, 2, 1] , _lowerCAmelCase : Dict=[2, 2, 4] , _lowerCAmelCase : Optional[int]=2 , _lowerCAmelCase : Optional[Any]=2.0 , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : Optional[int]=0.0 , _lowerCAmelCase : Any=0.0 , _lowerCAmelCase : Optional[int]=0.1 , _lowerCAmelCase : Tuple="gelu" , _lowerCAmelCase : int=False , _lowerCAmelCase : Dict=True , _lowerCAmelCase : List[str]=0.02 , _lowerCAmelCase : Union[str, Any]=1e-5 , _lowerCAmelCase : Tuple=True , _lowerCAmelCase : Dict=None , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : List[Any]=10 , _lowerCAmelCase : Tuple=8 , _lowerCAmelCase : List[Any]=["stage1", "stage2", "stage3"] , _lowerCAmelCase : Union[str, Any]=[1, 2, 3] , ) -> int:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = patch_size
__lowercase = num_channels
__lowercase = embed_dim
__lowercase = depths
__lowercase = num_heads
__lowercase = window_size
__lowercase = mlp_ratio
__lowercase = qkv_bias
__lowercase = hidden_dropout_prob
__lowercase = attention_probs_dropout_prob
__lowercase = drop_path_rate
__lowercase = hidden_act
__lowercase = use_absolute_embeddings
__lowercase = patch_norm
__lowercase = layer_norm_eps
__lowercase = initializer_range
__lowercase = is_training
__lowercase = scope
__lowercase = use_labels
__lowercase = type_sequence_label_size
__lowercase = encoder_stride
__lowercase = out_features
__lowercase = out_indices
def _a ( self : List[Any] ) -> int:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def _a ( self : List[str] , _lowerCAmelCase : Dict , _lowerCAmelCase : str , _lowerCAmelCase : int ) -> Dict:
"""simple docstring"""
__lowercase = MaskFormerSwinModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
__lowercase = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
__lowercase = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def _a ( self : Optional[Any] , _lowerCAmelCase : Tuple , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = MaskFormerSwinBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_lowerCAmelCase ):
__lowercase = ["""stem"""]
__lowercase = MaskFormerSwinBackbone(config=_lowerCAmelCase )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Any = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
__snake_case :Optional[int] = {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
__snake_case :Optional[int] = False
__snake_case :Any = False
__snake_case :List[str] = False
__snake_case :Tuple = False
__snake_case :Optional[int] = False
def _a ( self : Tuple ) -> Optional[int]:
"""simple docstring"""
__lowercase = MaskFormerSwinModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"""`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"""
""" `nn.DataParallel`"""
) )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
pass
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
return
def _a ( self : Any ) -> Tuple:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : Optional[int] ) -> str:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCAmelCase )
@unittest.skip("""Swin does not use inputs_embeds""" )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip("""Swin does not support feedforward chunking""" )
def _a ( self : Tuple ) -> str:
"""simple docstring"""
pass
def _a ( self : List[Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__lowercase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCAmelCase , nn.Linear ) )
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
@unittest.skip(reason="""MaskFormerSwin is only used as backbone and doesn't support output_attentions""" )
def _a ( self : Optional[int] ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""MaskFormerSwin is only used as an internal backbone""" )
def _a ( self : Any ) -> Any:
"""simple docstring"""
pass
def _a ( self : Tuple , _lowerCAmelCase : Any , _lowerCAmelCase : Any , _lowerCAmelCase : int , _lowerCAmelCase : Any ) -> Dict:
"""simple docstring"""
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.hidden_states
__lowercase = getattr(
self.model_tester , """expected_num_hidden_layers""" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_lowerCAmelCase ) , _lowerCAmelCase )
# Swin has a different seq_length
__lowercase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Dict ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = 3
__lowercase = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
__lowercase = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
__lowercase = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
__lowercase = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
self.check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , (padded_height, padded_width) )
@unittest.skip(reason="""MaskFormerSwin doesn't have pretrained checkpoints""" )
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a ( self : Any ) -> str:
"""simple docstring"""
pass
@unittest.skip(reason="""This will be fixed once MaskFormerSwin is replaced by native Swin""" )
def _a ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
pass
def _a ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(_lowerCAmelCase : Optional[int] ):
__lowercase = 0
return t
def check_equivalence(_lowerCAmelCase : int , _lowerCAmelCase : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Optional[int]={} ):
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase , return_dict=_lowerCAmelCase , **_lowerCAmelCase )
__lowercase = model(**_lowerCAmelCase , return_dict=_lowerCAmelCase , **_lowerCAmelCase ).to_tuple()
def recursive_check(_lowerCAmelCase : Optional[Any] , _lowerCAmelCase : List[Any] ):
if isinstance(_lowerCAmelCase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_lowerCAmelCase , _lowerCAmelCase ):
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
elif isinstance(_lowerCAmelCase , _lowerCAmelCase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_lowerCAmelCase ) , set_nan_tensor_to_zero(_lowerCAmelCase ) , atol=1e-5 ) , msg=(
"""Tuple and dict output are not equal. Difference:"""
F' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'
F' {torch.isnan(_lowerCAmelCase ).any()} and `inf`: {torch.isinf(_lowerCAmelCase )}. Dict has'
F' `nan`: {torch.isnan(_lowerCAmelCase ).any()} and `inf`: {torch.isinf(_lowerCAmelCase )}.'
) , )
recursive_check(_lowerCAmelCase , _lowerCAmelCase )
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {"""output_hidden_states""": True} )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
__lowercase = self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase , return_labels=_lowerCAmelCase )
check_equivalence(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , {"""output_hidden_states""": True} )
@require_torch
class __UpperCamelCase ( unittest.TestCase , _lowerCAmelCase ):
__snake_case :Optional[Any] = (MaskFormerSwinBackbone,) if is_torch_available() else ()
__snake_case :Dict = MaskFormerSwinConfig
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase = MaskFormerSwinModelTester(self )
def _a ( self : List[Any] ) -> Dict:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase = inputs_dict["""pixel_values"""].shape[0]
for backbone_class in self.all_model_classes:
__lowercase = backbone_class(_lowerCAmelCase )
backbone.to(_lowerCAmelCase )
backbone.eval()
__lowercase = backbone(**_lowerCAmelCase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _lowerCAmelCase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
__lowercase = backbone(**_lowerCAmelCase , output_hidden_states=_lowerCAmelCase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
__lowercase , __lowercase , __lowercase = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
__lowercase = backbone(**_lowerCAmelCase , output_attentions=_lowerCAmelCase )
self.assertIsNotNone(outputs.attentions )
| 80 | 1 |
import os
import time
import numpy as np
import onnxruntime as ort
__UpperCamelCase : int = """1"""
__UpperCamelCase : Dict = """0"""
__UpperCamelCase : str = """1"""
__UpperCamelCase : int = ort.SessionOptions()
__UpperCamelCase : Dict = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print("""Create inference session...""")
__UpperCamelCase : Tuple = ["""TensorrtExecutionProvider""", """CUDAExecutionProvider"""]
__UpperCamelCase : int = ort.InferenceSession("""model.onnx""", sess_options=sess_opt, providers=execution_provider)
__UpperCamelCase : Tuple = ort.RunOptions()
__UpperCamelCase : int = 128
__UpperCamelCase : List[str] = 1
__UpperCamelCase : List[Any] = np.ones((batch, sequence), dtype=np.intaa)
__UpperCamelCase : str = np.ones((batch, sequence), dtype=np.intaa)
__UpperCamelCase : Optional[Any] = np.ones((batch, sequence), dtype=np.intaa)
print("""Warm up phase...""")
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Start inference...""")
__UpperCamelCase : int = time.time()
__UpperCamelCase : Dict = 2000
__UpperCamelCase : Optional[Any] = {}
for iter in range(max_iters):
__UpperCamelCase : Union[str, Any] = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print("""Average Inference Time = {:.3f} ms""".format((time.time() - start_time) * 1000 / max_iters))
| 80 |
import pickle
import unittest
import torch
from accelerate import Accelerator
from accelerate.state import AcceleratorState
from accelerate.test_utils import require_cpu
@require_cpu
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : List[str] ) -> str:
"""simple docstring"""
__lowercase = torch.nn.Linear(10 , 10 )
__lowercase = torch.optim.SGD(model.parameters() , 0.1 )
__lowercase = Accelerator()
__lowercase = accelerator.prepare(_lowerCAmelCase )
try:
pickle.loads(pickle.dumps(_lowerCAmelCase ) )
except Exception as e:
self.fail(F'Accelerated optimizer pickling failed with {e}' )
AcceleratorState._reset_state()
| 80 | 1 |
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
__UpperCamelCase : List[Any] = ["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
__UpperCamelCase : Optional[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()
__UpperCamelCase : List[str] = logging.get_logger(__name__)
__UpperCamelCase : Tuple = """ Hello world! cécé herlolip"""
__UpperCamelCase : List[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 snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""_float_tensor""",
]
for k in ignore_keys:
state_dict.pop(lowerCamelCase , lowerCamelCase )
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = dct.pop(lowerCamelCase )
__lowercase = val
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = torch.load(lowerCamelCase , map_location="""cpu""" )
__lowercase = torch.hub.load("""pytorch/fairseq""" , """bart.large.cnn""" ).eval()
hub_interface.model.load_state_dict(sd["""model"""] )
return hub_interface
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase , __lowercase = emb.weight.shape
__lowercase = nn.Linear(lowerCamelCase , lowerCamelCase , bias=lowerCamelCase )
__lowercase = emb.weight.data
return lin_layer
@torch.no_grad()
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase=None ):
'''simple docstring'''
if not os.path.exists(lowerCamelCase ):
__lowercase = torch.hub.load("""pytorch/fairseq""" , lowerCamelCase ).eval()
else:
__lowercase = load_xsum_checkpoint(lowerCamelCase )
bart.model.upgrade_state_dict(bart.model.state_dict() )
if hf_checkpoint_name is None:
__lowercase = checkpoint_path.replace(""".""" , """-""" )
__lowercase = BartConfig.from_pretrained(lowerCamelCase )
__lowercase = bart.encode(lowerCamelCase ).unsqueeze(0 )
__lowercase = BartTokenizer.from_pretrained(lowerCamelCase ).encode(lowerCamelCase , return_tensors="""pt""" ).unsqueeze(0 )
if not torch.eq(lowerCamelCase , lowerCamelCase ).all():
raise ValueError(
F'converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}' )
if checkpoint_path == "bart.large.mnli":
__lowercase = bart.state_dict()
remove_ignore_keys_(lowerCamelCase )
__lowercase = state_dict["""model.decoder.embed_tokens.weight"""]
for src, dest in mnli_rename_keys:
rename_key(lowerCamelCase , lowerCamelCase , lowerCamelCase )
__lowercase = BartForSequenceClassification(lowerCamelCase ).eval()
model.load_state_dict(lowerCamelCase )
__lowercase = bart.predict("""mnli""" , lowerCamelCase , return_logits=lowerCamelCase )
__lowercase = model(lowerCamelCase )[0] # logits
else: # no classification heads to worry about
__lowercase = bart.model.state_dict()
remove_ignore_keys_(lowerCamelCase )
__lowercase = state_dict["""decoder.embed_tokens.weight"""]
__lowercase = bart.extract_features(lowerCamelCase )
if hf_checkpoint_name == "facebook/bart-large":
__lowercase = BartModel(lowerCamelCase ).eval()
model.load_state_dict(lowerCamelCase )
__lowercase = model(lowerCamelCase ).model[0]
else:
__lowercase = BartForConditionalGeneration(lowerCamelCase ).eval() # an existing summarization ckpt
model.model.load_state_dict(lowerCamelCase )
if hasattr(lowerCamelCase , """lm_head""" ):
__lowercase = make_linear_from_emb(model.model.shared )
__lowercase = model.model(lowerCamelCase )[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(lowerCamelCase ).mkdir(exist_ok=lowerCamelCase )
model.save_pretrained(lowerCamelCase )
if __name__ == "__main__":
__UpperCamelCase : Tuple = 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"""
)
__UpperCamelCase : Any = parser.parse_args()
convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)
| 80 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCamelCase : List[Any] = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
__UpperCamelCase : Dict = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
__UpperCamelCase : int = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
__UpperCamelCase : List[str] = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
__UpperCamelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 80 | 1 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
__UpperCamelCase : Tuple = get_logger(__name__)
class __UpperCamelCase :
def __init__( self : List[Any] , _lowerCAmelCase : Optional[str] = None ) -> Optional[int]:
"""simple docstring"""
__lowercase = (
os.path.join(_lowerCAmelCase , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
__lowercase = Extractor
def _a ( self : List[Any] , _lowerCAmelCase : str ) -> str:
"""simple docstring"""
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
__lowercase = os.path.abspath(_lowerCAmelCase )
return os.path.join(self.extract_dir , hash_url_to_filename(_lowerCAmelCase ) )
def _a ( self : Any , _lowerCAmelCase : str , _lowerCAmelCase : bool ) -> bool:
"""simple docstring"""
return force_extract or (
not os.path.isfile(_lowerCAmelCase ) and not (os.path.isdir(_lowerCAmelCase ) and os.listdir(_lowerCAmelCase ))
)
def _a ( self : Optional[int] , _lowerCAmelCase : str , _lowerCAmelCase : bool = False ) -> str:
"""simple docstring"""
__lowercase = self.extractor.infer_extractor_format(_lowerCAmelCase )
if not extractor_format:
return input_path
__lowercase = self._get_output_path(_lowerCAmelCase )
if self._do_extract(_lowerCAmelCase , _lowerCAmelCase ):
self.extractor.extract(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
return output_path
class __UpperCamelCase ( _lowerCAmelCase ):
@classmethod
@abstractmethod
def _a ( cls : str , _lowerCAmelCase : Union[Path, str] , **_lowerCAmelCase : Union[str, Any] ) -> bool:
"""simple docstring"""
...
@staticmethod
@abstractmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Union[Path, str] ) -> None:
"""simple docstring"""
...
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase ):
__snake_case :List[bytes] = []
@staticmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : int ) -> Tuple:
"""simple docstring"""
with open(_lowerCAmelCase , """rb""" ) as f:
return f.read(_lowerCAmelCase )
@classmethod
def _a ( cls : Dict , _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : bytes = b"" ) -> bool:
"""simple docstring"""
if not magic_number:
__lowercase = max(len(_lowerCAmelCase ) for cls_magic_number in cls.magic_numbers )
try:
__lowercase = cls.read_magic_number(_lowerCAmelCase , _lowerCAmelCase )
except OSError:
return False
return any(magic_number.startswith(_lowerCAmelCase ) for cls_magic_number in cls.magic_numbers )
class __UpperCamelCase ( _lowerCAmelCase ):
@classmethod
def _a ( cls : Optional[int] , _lowerCAmelCase : Union[Path, str] , **_lowerCAmelCase : List[Any] ) -> bool:
"""simple docstring"""
return tarfile.is_tarfile(_lowerCAmelCase )
@staticmethod
def _a ( _lowerCAmelCase : Any , _lowerCAmelCase : str ) -> Any:
"""simple docstring"""
def resolved(_lowerCAmelCase : str ) -> str:
return os.path.realpath(os.path.abspath(_lowerCAmelCase ) )
def badpath(_lowerCAmelCase : str , _lowerCAmelCase : str ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(_lowerCAmelCase , _lowerCAmelCase ) ).startswith(_lowerCAmelCase )
def badlink(_lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : str ) -> bool:
# Links are interpreted relative to the directory containing the link
__lowercase = resolved(os.path.join(_lowerCAmelCase , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=_lowerCAmelCase )
__lowercase = resolved(_lowerCAmelCase )
for finfo in members:
if badpath(finfo.name , _lowerCAmelCase ):
logger.error(F'Extraction of {finfo.name} is blocked (illegal path)' )
elif finfo.issym() and badlink(_lowerCAmelCase , _lowerCAmelCase ):
logger.error(F'Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}' )
elif finfo.islnk() and badlink(_lowerCAmelCase , _lowerCAmelCase ):
logger.error(F'Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}' )
else:
yield finfo
@staticmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Union[Path, str] ) -> None:
"""simple docstring"""
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
__lowercase = tarfile.open(_lowerCAmelCase )
tar_file.extractall(_lowerCAmelCase , members=TarExtractor.safemembers(_lowerCAmelCase , _lowerCAmelCase ) )
tar_file.close()
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Dict = [B'\x1F\x8B']
@staticmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Union[Path, str] ) -> None:
"""simple docstring"""
with gzip.open(_lowerCAmelCase , """rb""" ) as gzip_file:
with open(_lowerCAmelCase , """wb""" ) as extracted_file:
shutil.copyfileobj(_lowerCAmelCase , _lowerCAmelCase )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[str] = [
B'PK\x03\x04',
B'PK\x05\x06', # empty archive
B'PK\x07\x08', # spanned archive
]
@classmethod
def _a ( cls : Tuple , _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : bytes = b"" ) -> bool:
"""simple docstring"""
if super().is_extractable(_lowerCAmelCase , magic_number=_lowerCAmelCase ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(_lowerCAmelCase , """rb""" ) as fp:
__lowercase = _EndRecData(_lowerCAmelCase )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
__lowercase = fp.read(_lowerCAmelCase ) # CD is where we expect it to be
if len(_lowerCAmelCase ) == sizeCentralDir:
__lowercase = struct.unpack(_lowerCAmelCase , _lowerCAmelCase ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Union[Path, str] ) -> None:
"""simple docstring"""
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
with zipfile.ZipFile(_lowerCAmelCase , """r""" ) as zip_file:
zip_file.extractall(_lowerCAmelCase )
zip_file.close()
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Tuple = [B'\xFD\x37\x7A\x58\x5A\x00']
@staticmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Union[Path, str] ) -> None:
"""simple docstring"""
with lzma.open(_lowerCAmelCase ) as compressed_file:
with open(_lowerCAmelCase , """wb""" ) as extracted_file:
shutil.copyfileobj(_lowerCAmelCase , _lowerCAmelCase )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Union[str, Any] = [B'Rar!\x1a\x07\x00', B'Rar!\x1a\x07\x01\x00'] # RAR_ID # RAR5_ID
@staticmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Union[Path, str] ) -> None:
"""simple docstring"""
if not config.RARFILE_AVAILABLE:
raise ImportError("""Please pip install rarfile""" )
import rarfile
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
__lowercase = rarfile.RarFile(_lowerCAmelCase )
rf.extractall(_lowerCAmelCase )
rf.close()
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Dict = [B'\x28\xb5\x2F\xFD']
@staticmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Union[Path, str] ) -> None:
"""simple docstring"""
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("""Please pip install zstandard""" )
import zstandard as zstd
__lowercase = zstd.ZstdDecompressor()
with open(_lowerCAmelCase , """rb""" ) as ifh, open(_lowerCAmelCase , """wb""" ) as ofh:
dctx.copy_stream(_lowerCAmelCase , _lowerCAmelCase )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = [B'\x42\x5A\x68']
@staticmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Union[Path, str] ) -> None:
"""simple docstring"""
with bza.open(_lowerCAmelCase , """rb""" ) as compressed_file:
with open(_lowerCAmelCase , """wb""" ) as extracted_file:
shutil.copyfileobj(_lowerCAmelCase , _lowerCAmelCase )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :List[Any] = [B'\x37\x7A\xBC\xAF\x27\x1C']
@staticmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Union[Path, str] ) -> None:
"""simple docstring"""
if not config.PY7ZR_AVAILABLE:
raise ImportError("""Please pip install py7zr""" )
import pyazr
os.makedirs(_lowerCAmelCase , exist_ok=_lowerCAmelCase )
with pyazr.SevenZipFile(_lowerCAmelCase , """r""" ) as archive:
archive.extractall(_lowerCAmelCase )
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :Any = [B'\x04\x22\x4D\x18']
@staticmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Union[Path, str] ) -> None:
"""simple docstring"""
if not config.LZ4_AVAILABLE:
raise ImportError("""Please pip install lz4""" )
import lza.frame
with lza.frame.open(_lowerCAmelCase , """rb""" ) as compressed_file:
with open(_lowerCAmelCase , """wb""" ) as extracted_file:
shutil.copyfileobj(_lowerCAmelCase , _lowerCAmelCase )
class __UpperCamelCase :
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
__snake_case :Dict[str, Type[BaseExtractor]] = {
"tar": TarExtractor,
"gzip": GzipExtractor,
"zip": ZipExtractor,
"xz": XzExtractor,
"rar": RarExtractor,
"zstd": ZstdExtractor,
"bz2": BzipaExtractor,
"7z": SevenZipExtractor, # <Added version="2.4.0"/>
"lz4": LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def _a ( cls : Tuple ) -> Any:
"""simple docstring"""
return max(
len(_lowerCAmelCase )
for extractor in cls.extractors.values()
if issubclass(_lowerCAmelCase , _lowerCAmelCase )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def _a ( _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : int ) -> Union[str, Any]:
"""simple docstring"""
try:
return MagicNumberBaseExtractor.read_magic_number(_lowerCAmelCase , magic_number_length=_lowerCAmelCase )
except OSError:
return b""
@classmethod
def _a ( cls : List[str] , _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : bool = False ) -> bool:
"""simple docstring"""
warnings.warn(
"""Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'infer_extractor_format' instead.""" , category=_lowerCAmelCase , )
__lowercase = cls.infer_extractor_format(_lowerCAmelCase )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def _a ( cls : int , _lowerCAmelCase : Union[Path, str] ) -> str: # <Added version="2.4.0"/>
"""simple docstring"""
__lowercase = cls._get_magic_number_max_length()
__lowercase = cls._read_magic_number(_lowerCAmelCase , _lowerCAmelCase )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(_lowerCAmelCase , magic_number=_lowerCAmelCase ):
return extractor_format
@classmethod
def _a ( cls : Tuple , _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Union[Path, str] , _lowerCAmelCase : Optional[str] = None , _lowerCAmelCase : Optional[BaseExtractor] = "deprecated" , ) -> None:
"""simple docstring"""
os.makedirs(os.path.dirname(_lowerCAmelCase ) , exist_ok=_lowerCAmelCase )
# Prevent parallel extractions
__lowercase = str(Path(_lowerCAmelCase ).with_suffix(""".lock""" ) )
with FileLock(_lowerCAmelCase ):
shutil.rmtree(_lowerCAmelCase , ignore_errors=_lowerCAmelCase )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(_lowerCAmelCase , _lowerCAmelCase ): # passed as positional arg
warnings.warn(
"""Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. """
"""Use 'extractor_format' instead.""" , category=_lowerCAmelCase , )
__lowercase = extractor if extractor != """deprecated""" else extractor_format
else:
__lowercase = cls.extractors[extractor_format]
return extractor.extract(_lowerCAmelCase , _lowerCAmelCase )
else:
warnings.warn(
"""Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an """
"""exception in 3.0.0.""" , category=_lowerCAmelCase , )
for extractor in cls.extractors.values():
if extractor.is_extractable(_lowerCAmelCase ):
return extractor.extract(_lowerCAmelCase , _lowerCAmelCase )
| 80 |
import os
from collections.abc import Iterator
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
for dir_path, dir_names, filenames in os.walk(lowerCamelCase ):
__lowercase = [d for d in dir_names if d != """scripts""" and d[0] not in """._"""]
for filename in filenames:
if filename == "__init__.py":
continue
if os.path.splitext(lowerCamelCase )[1] in (".py", ".ipynb"):
yield os.path.join(lowerCamelCase , lowerCamelCase ).lstrip("""./""" )
def snake_case ( lowerCamelCase ):
'''simple docstring'''
return F'{i * " "}*' if i else "\n##"
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
__lowercase = old_path.split(os.sep )
for i, new_part in enumerate(new_path.split(os.sep ) ):
if (i + 1 > len(lowerCamelCase ) or old_parts[i] != new_part) and new_part:
print(F'{md_prefix(lowerCamelCase )} {new_part.replace("_" , " " ).title()}' )
return new_path
def snake_case ( lowerCamelCase = "." ):
'''simple docstring'''
__lowercase = """"""
for filepath in sorted(good_file_paths(lowerCamelCase ) ):
__lowercase , __lowercase = os.path.split(lowerCamelCase )
if filepath != old_path:
__lowercase = print_path(lowerCamelCase , lowerCamelCase )
__lowercase = (filepath.count(os.sep ) + 1) if filepath else 0
__lowercase = F'{filepath}/{filename}'.replace(""" """ , """%20""" )
__lowercase = os.path.splitext(filename.replace("""_""" , """ """ ).title() )[0]
print(F'{md_prefix(lowerCamelCase )} [{filename}]({url})' )
if __name__ == "__main__":
print_directory_md(""".""")
| 80 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def snake_case ( lowerCamelCase=None ):
'''simple docstring'''
if subparsers is not None:
__lowercase = subparsers.add_parser("""test""" )
else:
__lowercase = argparse.ArgumentParser("""Accelerate test command""" )
parser.add_argument(
"""--config_file""" , default=lowerCamelCase , help=(
"""The path to use to store the config file. Will default to a file named default_config.yaml in the cache """
"""location, which is the content of the environment `HF_HOME` suffixed with 'accelerate', or if you don't have """
"""such an environment variable, your cache directory ('~/.cache' or the content of `XDG_CACHE_HOME`) suffixed """
"""with 'huggingface'."""
) , )
if subparsers is not None:
parser.set_defaults(func=lowerCamelCase )
return parser
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ["""test_utils""", """scripts""", """test_script.py"""] )
if args.config_file is None:
__lowercase = script_name
else:
__lowercase = F'--config_file={args.config_file} {script_name}'
__lowercase = ["""accelerate-launch"""] + test_args.split()
__lowercase = execute_subprocess_async(lowerCamelCase , env=os.environ.copy() )
if result.returncode == 0:
print("""Test is a success! You are ready for your distributed training!""" )
def snake_case ( ):
'''simple docstring'''
__lowercase = test_command_parser()
__lowercase = parser.parse_args()
test_command(lowerCamelCase )
if __name__ == "__main__":
main()
| 80 |
from math import factorial
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if n < k or k < 0:
raise ValueError("""Please enter positive integers for n and k where n >= k""" )
return factorial(lowerCamelCase ) // (factorial(lowerCamelCase ) * factorial(n - k ))
if __name__ == "__main__":
print(
"""The number of five-card hands possible from a standard""",
F'''fifty-two card deck is: {combinations(52, 5)}\n''',
)
print(
"""If a class of 40 students must be arranged into groups of""",
F'''4 for group projects, there are {combinations(40, 4)} ways''',
"""to arrange them.\n""",
)
print(
"""If 10 teams are competing in a Formula One race, there""",
F'''are {combinations(10, 3)} ways that first, second and''',
"""third place can be awarded.""",
)
| 80 | 1 |
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