code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
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"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
_UpperCamelCase: Any = logging.get_logger(__name__)
class a__ ( SCREAMING_SNAKE_CASE__ ):
_lowerCamelCase = ['input_values', 'padding_mask']
def __init__( self : Optional[int], lowerCAmelCase : int = 1, lowerCAmelCase : int = 24000, lowerCAmelCase : float = 0.0, lowerCAmelCase : float = None, lowerCAmelCase : float = None, **lowerCAmelCase : Union[str, Any], ) -> Dict:
super().__init__(feature_size=lowerCAmelCase, sampling_rate=lowerCAmelCase, padding_value=lowerCAmelCase, **lowerCAmelCase )
lowercase : Optional[Any] = chunk_length_s
lowercase : Union[str, Any] = overlap
@property
def lowercase ( self : List[str] ) -> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def lowercase ( self : List[Any] ) -> Optional[int]:
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1, int((1.0 - self.overlap) * self.chunk_length ) )
def __call__( self : Optional[int], lowerCAmelCase : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]], lowerCAmelCase : Optional[Union[bool, str, PaddingStrategy]] = None, lowerCAmelCase : Optional[bool] = False, lowerCAmelCase : Optional[int] = None, lowerCAmelCase : Optional[Union[str, TensorType]] = None, lowerCAmelCase : Optional[int] = None, ) -> BatchFeature:
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
f''' {self.sampling_rate} and not {sampling_rate}.''' )
else:
logger.warning(
'It is strongly recommended to pass the `sampling_rate` argument to this function. '
'Failing to do so can result in silent errors that might be hard to debug.' )
if padding and truncation:
raise ValueError('Both padding and truncation were set. Make sure you only set one.' )
elif padding is None:
# by default let's pad the inputs
lowercase : List[str] = True
lowercase : Any = bool(
isinstance(lowerCAmelCase, (list, tuple) ) and (isinstance(raw_audio[0], (np.ndarray, tuple, list) )) )
if is_batched:
lowercase : Optional[int] = [np.asarray(lowerCAmelCase, dtype=np.floataa ).T for audio in raw_audio]
elif not is_batched and not isinstance(lowerCAmelCase, np.ndarray ):
lowercase : List[str] = np.asarray(lowerCAmelCase, dtype=np.floataa )
elif isinstance(lowerCAmelCase, np.ndarray ) and raw_audio.dtype is np.dtype(np.floataa ):
lowercase : List[Any] = raw_audio.astype(np.floataa )
# always return batch
if not is_batched:
lowercase : Optional[int] = [np.asarray(lowerCAmelCase ).T]
# verify inputs are valid
for idx, example in enumerate(lowerCAmelCase ):
if example.ndim > 2:
raise ValueError(f'''Expected input shape (channels, length) but got shape {example.shape}''' )
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(f'''Expected mono audio but example has {example.shape[-1]} channels''' )
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(f'''Expected stereo audio but example has {example.shape[-1]} channels''' )
lowercase : Tuple = None
lowercase : Dict = BatchFeature({'input_values': raw_audio} )
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
lowercase : Optional[int] = min(array.shape[0] for array in raw_audio )
lowercase : Optional[Any] = int(np.floor(max_length / self.chunk_stride ) )
lowercase : str = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
lowercase : Optional[int] = max(array.shape[0] for array in raw_audio )
lowercase : Dict = int(np.ceil(max_length / self.chunk_stride ) )
lowercase : List[str] = (nb_step - 1) * self.chunk_stride + self.chunk_length
lowercase : Tuple = 'max_length'
else:
lowercase : Dict = input_values
# normal padding on batch
if padded_inputs is None:
lowercase : Tuple = self.pad(
lowerCAmelCase, max_length=lowerCAmelCase, truncation=lowerCAmelCase, padding=lowerCAmelCase, return_attention_mask=lowerCAmelCase, )
if padding:
lowercase : List[str] = padded_inputs.pop('attention_mask' )
lowercase : Union[str, Any] = []
for example in padded_inputs.pop('input_values' ):
if self.feature_size == 1:
lowercase : Any = example[..., None]
input_values.append(example.T )
lowercase : Dict = input_values
if return_tensors is not None:
lowercase : Union[str, Any] = padded_inputs.convert_to_tensors(lowerCAmelCase )
return padded_inputs
| 255 |
"""simple docstring"""
import math
def lowercase__ ( _UpperCAmelCase = 1_00 ) -> int:
'''simple docstring'''
lowercase : List[str] = sum(i * i for i in range(1 , n + 1 ) )
lowercase : Dict = int(math.pow(sum(range(1 , n + 1 ) ) , 2 ) )
return square_of_sum - sum_of_squares
if __name__ == "__main__":
print(f'''{solution() = }''')
| 255 | 1 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def UpperCamelCase ( _a , _a , _a , _a ) -> int:
'''simple docstring'''
lowercase_ :int = s.rsplit(_UpperCAmelCase , _UpperCAmelCase )
return new.join(_UpperCAmelCase )
def UpperCamelCase ( _a ) -> Optional[Any]:
'''simple docstring'''
return sum(param.float().sum() if '''encoder.embeddings''' not in key else 0 for key, param in state_dict.items() )
def UpperCamelCase ( _a ) -> int:
'''simple docstring'''
lowercase_ :Union[str, Any] = {}
lowercase_ :str = ['group_1', 'group_2', 'group_3', 'group_4']
for key, value in state_dict.items():
for group_key in group_keys:
if group_key in key:
lowercase_ :Tuple = key.replace(f"{group_key}." , f"{group_key}.group." )
if "res_path" in key:
lowercase_ :Optional[int] = key.replace('''res_path.''' , '''res_path.path.''' )
if key.endswith('''.w''' ):
lowercase_ :Dict = rreplace(_UpperCAmelCase , '''.w''' , '''.weight''' , 1 )
if key.endswith('''.b''' ):
lowercase_ :List[Any] = rreplace(_UpperCAmelCase , '''.b''' , '''.bias''' , 1 )
lowercase_ :List[str] = value.float()
return upgrade
@torch.no_grad()
def UpperCamelCase ( _a , _a , _a=None , _a=True ) -> List[Any]:
'''simple docstring'''
from dall_e import Encoder
lowercase_ :List[str] = Encoder()
if os.path.exists(_UpperCAmelCase ):
lowercase_ :str = torch.load(_UpperCAmelCase )
else:
lowercase_ :Any = torch.hub.load_state_dict_from_url(_UpperCAmelCase )
if isinstance(_UpperCAmelCase , _UpperCAmelCase ):
lowercase_ :Union[str, Any] = ckpt.state_dict()
encoder.load_state_dict(_UpperCAmelCase )
if config_path is not None:
lowercase_ :Union[str, Any] = FlavaImageCodebookConfig.from_pretrained(_UpperCAmelCase )
else:
lowercase_ :Tuple = FlavaImageCodebookConfig()
lowercase_ :str = FlavaImageCodebook(_UpperCAmelCase ).eval()
lowercase_ :int = encoder.state_dict()
lowercase_ :Dict = upgrade_state_dict(_UpperCAmelCase )
hf_model.load_state_dict(_UpperCAmelCase )
lowercase_ :Optional[Any] = hf_model.state_dict()
lowercase_ :Union[str, Any] = count_parameters(_UpperCAmelCase )
lowercase_ :List[str] = count_parameters(_UpperCAmelCase )
assert torch.allclose(_UpperCAmelCase , _UpperCAmelCase , atol=1E-3 )
if save_checkpoint:
hf_model.save_pretrained(_UpperCAmelCase )
else:
return hf_state_dict
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to flava checkpoint")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 350 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class UpperCamelCase :
'''simple docstring'''
lowercase : Any =PegasusConfig
lowercase : Any ={}
lowercase : int ="""gelu"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_=13 , UpperCamelCase_=7 , UpperCamelCase_=True , UpperCamelCase_=False , UpperCamelCase_=99 , UpperCamelCase_=32 , UpperCamelCase_=2 , UpperCamelCase_=4 , UpperCamelCase_=37 , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=40 , UpperCamelCase_=2 , UpperCamelCase_=1 , UpperCamelCase_=0 , ):
lowercase_ :Union[str, Any] = parent
lowercase_ :Tuple = batch_size
lowercase_ :Optional[Any] = seq_length
lowercase_ :Any = is_training
lowercase_ :Optional[int] = use_labels
lowercase_ :Optional[int] = vocab_size
lowercase_ :Optional[Any] = hidden_size
lowercase_ :List[Any] = num_hidden_layers
lowercase_ :Tuple = num_attention_heads
lowercase_ :Optional[Any] = intermediate_size
lowercase_ :List[Any] = hidden_dropout_prob
lowercase_ :Optional[Any] = attention_probs_dropout_prob
lowercase_ :Any = max_position_embeddings
lowercase_ :Any = eos_token_id
lowercase_ :List[str] = pad_token_id
lowercase_ :Optional[Any] = bos_token_id
def UpperCamelCase ( self ):
lowercase_ :Any = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
lowercase_ :Tuple = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
lowercase_ :int = tf.concat([input_ids, eos_tensor] , axis=1 )
lowercase_ :List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase_ :List[Any] = 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_ :Optional[int] = prepare_pegasus_inputs_dict(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
return config, inputs_dict
def UpperCamelCase ( self , UpperCamelCase_ , UpperCamelCase_ ):
lowercase_ :Optional[Any] = TFPegasusModel(config=UpperCamelCase_ ).get_decoder()
lowercase_ :Tuple = inputs_dict['''input_ids''']
lowercase_ :Optional[int] = input_ids[:1, :]
lowercase_ :Tuple = inputs_dict['''attention_mask'''][:1, :]
lowercase_ :List[str] = inputs_dict['''head_mask''']
lowercase_ :int = 1
# first forward pass
lowercase_ :Tuple = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , head_mask=UpperCamelCase_ , use_cache=UpperCamelCase_ )
lowercase_ , lowercase_ :Dict = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowercase_ :List[str] = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase_ :Optional[Any] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
lowercase_ :Optional[int] = tf.concat([input_ids, next_tokens] , axis=-1 )
lowercase_ :Optional[int] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
lowercase_ :Optional[Any] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ )[0]
lowercase_ :str = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , past_key_values=UpperCamelCase_ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
lowercase_ :Optional[Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
lowercase_ :Optional[int] = output_from_no_past[:, -3:, random_slice_idx]
lowercase_ :Any = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(UpperCamelCase_ , UpperCamelCase_ , rtol=1E-3 )
def UpperCamelCase ( _a , _a , _a , _a=None , _a=None , _a=None , _a=None , _a=None , ) -> Optional[int]:
'''simple docstring'''
if attention_mask is None:
lowercase_ :Dict = tf.cast(tf.math.not_equal(_a , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowercase_ :Dict = 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_ :List[str] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowercase_ :Optional[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowercase_ :Any = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase ( lowercase__ , lowercase__ , unittest.TestCase ):
'''simple docstring'''
lowercase : Tuple =(TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
lowercase : List[str] =(TFPegasusForConditionalGeneration,) if is_tf_available() else ()
lowercase : str =(
{
"""conversational""": TFPegasusForConditionalGeneration,
"""feature-extraction""": TFPegasusModel,
"""summarization""": TFPegasusForConditionalGeneration,
"""text2text-generation""": TFPegasusForConditionalGeneration,
"""translation""": TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
lowercase : Optional[int] =True
lowercase : List[str] =False
lowercase : Union[str, Any] =False
def UpperCamelCase ( self ):
lowercase_ :Dict = TFPegasusModelTester(self )
lowercase_ :str = ConfigTester(self , config_class=UpperCamelCase_ )
def UpperCamelCase ( self ):
self.config_tester.run_common_tests()
def UpperCamelCase ( self ):
lowercase_ :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase_ )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
lowercase : Tuple =[
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
lowercase : Optional[int] =[
"""California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
""" reduce the risk of wildfires.""",
"""N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
] # differs slightly from pytorch, likely due to numerical differences in linear layers
lowercase : Optional[Any] ="""google/pegasus-xsum"""
@cached_property
def UpperCamelCase ( self ):
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def UpperCamelCase ( self ):
lowercase_ :Union[str, Any] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def UpperCamelCase ( self , **UpperCamelCase_ ):
lowercase_ :Any = self.translate_src_text(**UpperCamelCase_ )
assert self.expected_text == generated_words
def UpperCamelCase ( self , **UpperCamelCase_ ):
lowercase_ :Dict = self.tokenizer(self.src_text , **UpperCamelCase_ , padding=UpperCamelCase_ , return_tensors='''tf''' )
lowercase_ :int = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=UpperCamelCase_ , )
lowercase_ :int = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=UpperCamelCase_ )
return generated_words
@slow
def UpperCamelCase ( self ):
self._assert_generated_batch_equal_expected()
| 252 | 0 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib import Path
from urllib.parse import urlparse
from zipfile import ZipFile, is_zipfile
import cva
import numpy as np
import requests
import wget
from filelock import FileLock
from PIL import Image
from tqdm.auto import tqdm
from yaml import Loader, dump, load
try:
import torch
lowercase : Optional[int] = True
except ImportError:
lowercase : int = False
try:
from torch.hub import _get_torch_home
lowercase : List[Any] = _get_torch_home()
except ImportError:
lowercase : Optional[int] = os.path.expanduser(
os.getenv("""TORCH_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """torch"""))
)
lowercase : List[str] = os.path.join(torch_cache_home, """transformers""")
lowercase : List[str] = """https://cdn.huggingface.co"""
lowercase : int = """https://s3.amazonaws.com/models.huggingface.co/bert"""
lowercase : int = """/""".join(str(Path(__file__).resolve()).split("""/""")[:-1])
lowercase : List[Any] = os.path.join(PATH, """config.yaml""")
lowercase : List[str] = os.path.join(PATH, """attributes.txt""")
lowercase : Optional[Any] = os.path.join(PATH, """objects.txt""")
lowercase : Tuple = os.getenv("""PYTORCH_PRETRAINED_BERT_CACHE""", default_cache_path)
lowercase : int = os.getenv("""PYTORCH_TRANSFORMERS_CACHE""", PYTORCH_PRETRAINED_BERT_CACHE)
lowercase : Optional[Any] = os.getenv("""TRANSFORMERS_CACHE""", PYTORCH_TRANSFORMERS_CACHE)
lowercase : Tuple = """pytorch_model.bin"""
lowercase : int = """config.yaml"""
def A_ ( A__=OBJECTS , A__=ATTRIBUTES ) -> Union[str, Any]:
a__ : Dict = []
with open(A__ ) as f:
for object in f.readlines():
vg_classes.append(object.split(',' )[0].lower().strip() )
a__ : Optional[int] = []
with open(A__ ) as f:
for object in f.readlines():
vg_attrs.append(object.split(',' )[0].lower().strip() )
return vg_classes, vg_attrs
def A_ ( A__ ) -> Union[str, Any]:
a__ : Optional[Any] = OrderedDict()
with open(A__ , 'rb' ) as f:
a__ : List[str] = pkl.load(A__ )['model']
for k in copy.deepcopy(list(ckp.keys() ) ):
a__ : Tuple = ckp.pop(A__ )
if isinstance(A__ , np.ndarray ):
a__ : str = torch.tensor(A__ )
else:
assert isinstance(A__ , torch.tensor ), type(A__ )
a__ : Optional[int] = v
return r
class A__ :
"""simple docstring"""
__A : Optional[Any] = {}
def __init__( self , lowercase , lowercase = "root" , lowercase=0) -> Union[str, Any]:
'''simple docstring'''
a__ : List[str] = name
a__ : Optional[Any] = level
a__ : Tuple = {}
for k, v in dictionary.items():
if v is None:
raise ValueError()
a__ : Dict = copy.deepcopy(lowercase)
a__ : Any = copy.deepcopy(lowercase)
if isinstance(lowercase , lowercase):
a__ : Tuple = Config(lowercase , name=lowercase , level=level + 1)
a__ : Dict = v
setattr(self , lowercase , lowercase)
a__ : List[Any] = d
def __repr__( self) -> Optional[int]:
'''simple docstring'''
return str(list((self._pointer.keys())))
def __setattr__( self , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
a__ : str = val
a__ : Tuple = val
a__ : int = key.split('.')
a__ : Tuple = len(lowercase) - 1
a__ : int = self._pointer
if len(lowercase) > 1:
for i, l in enumerate(lowercase):
if hasattr(self , lowercase) and isinstance(getattr(self , lowercase) , lowercase):
setattr(getattr(self , lowercase) , '.'.join(levels[i:]) , lowercase)
if l == last_level:
a__ : List[str] = val
else:
a__ : Tuple = pointer[l]
def __lowercase ( self) -> Tuple:
'''simple docstring'''
return self._pointer
def __lowercase ( self , lowercase , lowercase) -> str:
'''simple docstring'''
with open(F'{file_name}' , 'w') as stream:
dump(lowercase , lowercase)
def __lowercase ( self , lowercase , lowercase) -> Optional[int]:
'''simple docstring'''
with open(F'{file_name}' , 'w') as stream:
json.dump(lowercase , lowercase)
@staticmethod
def __lowercase ( lowercase) -> Optional[int]:
'''simple docstring'''
with open(lowercase) as stream:
a__ : Tuple = load(lowercase , Loader=lowercase)
return data
def __str__( self) -> Tuple:
'''simple docstring'''
a__ : Optional[int] = ' '
if self._name != "root":
a__ : Any = F'{t * (self._level-1)}{self._name}:\n'
else:
a__ : Dict = ''
a__ : Tuple = self._level
for i, (k, v) in enumerate(self._pointer.items()):
if isinstance(lowercase , lowercase):
r += F'{t * (self._level)}{v}\n'
self._level += 1
else:
r += F'{t * (self._level)}{k}: {v} ({type(lowercase).__name__})\n'
a__ : Tuple = level
return r[:-1]
@classmethod
def __lowercase ( cls , lowercase , **lowercase) -> Tuple:
'''simple docstring'''
a__ , a__ : Optional[Any] = cls.get_config_dict(lowercase , **lowercase)
return cls(lowercase)
@classmethod
def __lowercase ( cls , lowercase , **lowercase) -> Union[str, Any]:
'''simple docstring'''
a__ : Union[str, Any] = kwargs.pop('cache_dir' , lowercase)
a__ : Tuple = kwargs.pop('force_download' , lowercase)
a__ : str = kwargs.pop('resume_download' , lowercase)
a__ : Tuple = kwargs.pop('proxies' , lowercase)
a__ : List[str] = kwargs.pop('local_files_only' , lowercase)
if os.path.isdir(lowercase):
a__ : int = os.path.join(lowercase , lowercase)
elif os.path.isfile(lowercase) or is_remote_url(lowercase):
a__ : Union[str, Any] = pretrained_model_name_or_path
else:
a__ : Tuple = hf_bucket_url(lowercase , filename=lowercase , use_cdn=lowercase)
try:
# Load from URL or cache if already cached
a__ : Optional[Any] = cached_path(
lowercase , cache_dir=lowercase , force_download=lowercase , proxies=lowercase , resume_download=lowercase , local_files_only=lowercase , )
# Load config dict
if resolved_config_file is None:
raise EnvironmentError
a__ : Optional[int] = Config.load_yaml(lowercase)
except EnvironmentError:
a__ : List[str] = 'Can\'t load config for'
raise EnvironmentError(lowercase)
if resolved_config_file == config_file:
print('loading configuration file from path')
else:
print('loading configuration file cache')
return Config.load_yaml(lowercase), kwargs
def A_ ( A__ ) -> Union[str, Any]:
a__ : str = torch.load('dump.pt' , map_location=in_tensor.device )
a__ : Optional[Any] = in_tensor.numpy()
a__ : List[Any] = out_tensor.numpy()[0]
print(na.shape , na[0, 0, :5] )
print(na.shape , na[0, 0, :5] )
assert np.allclose(A__ , A__ , rtol=0.01 , atol=0.1 ), (
F'{sum([1 for x in np.isclose(A__ , A__ , rtol=0.01 , atol=0.1 ).flatten() if x is False] )/len(na.flatten() )*100:.4f} %'
" element-wise mismatch"
)
raise Exception('tensors are all good' )
# Hugging face functions below
def A_ ( A__ ) -> str:
a__ : Dict = urlparse(A__ )
return parsed.scheme in ("http", "https")
def A_ ( A__ , A__ , A__=True ) -> str:
a__ : int = CLOUDFRONT_DISTRIB_PREFIX if use_cdn else S3_BUCKET_PREFIX
a__ : Any = '/' not in model_id
if legacy_format:
return F'{endpoint}/{model_id}-{filename}'
else:
return F'{endpoint}/{model_id}/{filename}'
def A_ ( A__ , A__ , A__=None , A__=0 , A__=None , ) -> Union[str, Any]:
a__ : int = 'python/{}'.format(sys.version.split()[0] )
if _torch_available:
ua += "; torch/{}".format(torch.__version__ )
if isinstance(A__ , A__ ):
ua += "; " + "; ".join('{}/{}'.format(A__ , A__ ) for k, v in user_agent.items() )
elif isinstance(A__ , A__ ):
ua += "; " + user_agent
a__ : Dict = {'user-agent': ua}
if resume_size > 0:
a__ : Optional[Any] = 'bytes=%d-' % (resume_size,)
a__ : Any = requests.get(A__ , stream=A__ , proxies=A__ , headers=A__ )
if response.status_code == 416: # Range not satisfiable
return
a__ : List[Any] = response.headers.get('Content-Length' )
a__ : Optional[Any] = resume_size + int(A__ ) if content_length is not None else None
a__ : Any = tqdm(
unit='B' , unit_scale=A__ , total=A__ , initial=A__ , desc='Downloading' , )
for chunk in response.iter_content(chunk_size=1024 ):
if chunk: # filter out keep-alive new chunks
progress.update(len(A__ ) )
temp_file.write(A__ )
progress.close()
def A_ ( A__ , A__=None , A__=False , A__=None , A__=10 , A__=False , A__=None , A__=False , ) -> List[str]:
if cache_dir is None:
a__ : str = TRANSFORMERS_CACHE
if isinstance(A__ , A__ ):
a__ : Dict = str(A__ )
os.makedirs(A__ , exist_ok=A__ )
a__ : Union[str, Any] = None
if not local_files_only:
try:
a__ : Union[str, Any] = requests.head(A__ , allow_redirects=A__ , proxies=A__ , timeout=A__ )
if response.status_code == 200:
a__ : Union[str, Any] = response.headers.get('ETag' )
except (EnvironmentError, requests.exceptions.Timeout):
# etag is already None
pass
a__ : Optional[Any] = url_to_filename(A__ , A__ )
# get cache path to put the file
a__ : str = os.path.join(A__ , A__ )
# etag is None = we don't have a connection, or url doesn't exist, or is otherwise inaccessible.
# try to get the last downloaded one
if etag is None:
if os.path.exists(A__ ):
return cache_path
else:
a__ : List[Any] = [
file
for file in fnmatch.filter(os.listdir(A__ ) , filename + '.*' )
if not file.endswith('.json' ) and not file.endswith('.lock' )
]
if len(A__ ) > 0:
return os.path.join(A__ , matching_files[-1] )
else:
# If files cannot be found and local_files_only=True,
# the models might've been found if local_files_only=False
# Notify the user about that
if local_files_only:
raise ValueError(
'Cannot find the requested files in the cached path and outgoing traffic has been'
' disabled. To enable model look-ups and downloads online, set \'local_files_only\''
' to False.' )
return None
# From now on, etag is not None.
if os.path.exists(A__ ) and not force_download:
return cache_path
# Prevent parallel downloads of the same file with a lock.
a__ : str = cache_path + '.lock'
with FileLock(A__ ):
# If the download just completed while the lock was activated.
if os.path.exists(A__ ) and not force_download:
# Even if returning early like here, the lock will be released.
return cache_path
if resume_download:
a__ : Any = cache_path + '.incomplete'
@contextmanager
def _resumable_file_manager():
with open(A__ , 'a+b' ) as f:
yield f
a__ : Optional[Any] = _resumable_file_manager
if os.path.exists(A__ ):
a__ : str = os.stat(A__ ).st_size
else:
a__ : str = 0
else:
a__ : List[Any] = partial(tempfile.NamedTemporaryFile , dir=A__ , delete=A__ )
a__ : Optional[Any] = 0
# Download to temporary file, then copy to cache dir once finished.
# Otherwise you get corrupt cache entries if the download gets interrupted.
with temp_file_manager() as temp_file:
print(
'%s not found in cache or force_download set to True, downloading to %s' , A__ , temp_file.name , )
http_get(
A__ , A__ , proxies=A__ , resume_size=A__ , user_agent=A__ , )
os.replace(temp_file.name , A__ )
a__ : Dict = {'url': url, 'etag': etag}
a__ : str = cache_path + '.json'
with open(A__ , 'w' ) as meta_file:
json.dump(A__ , A__ )
return cache_path
def A_ ( A__ , A__=None ) -> Union[str, Any]:
a__ : int = url.encode('utf-8' )
a__ : str = shaaaa(A__ )
a__ : Union[str, Any] = url_hash.hexdigest()
if etag:
a__ : Optional[Any] = etag.encode('utf-8' )
a__ : Tuple = shaaaa(A__ )
filename += "." + etag_hash.hexdigest()
if url.endswith('.h5' ):
filename += ".h5"
return filename
def A_ ( A__ , A__=None , A__=False , A__=None , A__=False , A__=None , A__=False , A__=False , A__=False , ) -> Any:
if cache_dir is None:
a__ : Tuple = TRANSFORMERS_CACHE
if isinstance(A__ , A__ ):
a__ : Optional[Any] = str(A__ )
if isinstance(A__ , A__ ):
a__ : Optional[int] = str(A__ )
if is_remote_url(A__ ):
# URL, so get it from the cache (downloading if necessary)
a__ : Any = get_from_cache(
A__ , cache_dir=A__ , force_download=A__ , proxies=A__ , resume_download=A__ , user_agent=A__ , local_files_only=A__ , )
elif os.path.exists(A__ ):
# File, and it exists.
a__ : Tuple = url_or_filename
elif urlparse(A__ ).scheme == "":
# File, but it doesn't exist.
raise EnvironmentError('file {} not found'.format(A__ ) )
else:
# Something unknown
raise ValueError('unable to parse {} as a URL or as a local path'.format(A__ ) )
if extract_compressed_file:
if not is_zipfile(A__ ) and not tarfile.is_tarfile(A__ ):
return output_path
# Path where we extract compressed archives
# We avoid '.' in dir name and add "-extracted" at the end: "./model.zip" => "./model-zip-extracted/"
a__ , a__ : List[Any] = os.path.split(A__ )
a__ : Dict = output_file.replace('.' , '-' ) + '-extracted'
a__ : Union[str, Any] = os.path.join(A__ , A__ )
if os.path.isdir(A__ ) and os.listdir(A__ ) and not force_extract:
return output_path_extracted
# Prevent parallel extractions
a__ : List[Any] = output_path + '.lock'
with FileLock(A__ ):
shutil.rmtree(A__ , ignore_errors=A__ )
os.makedirs(A__ )
if is_zipfile(A__ ):
with ZipFile(A__ , 'r' ) as zip_file:
zip_file.extractall(A__ )
zip_file.close()
elif tarfile.is_tarfile(A__ ):
a__ : List[Any] = tarfile.open(A__ )
tar_file.extractall(A__ )
tar_file.close()
else:
raise EnvironmentError('Archive format of {} could not be identified'.format(A__ ) )
return output_path_extracted
return output_path
def A_ ( A__ , A__="," ) -> Union[str, Any]:
assert isinstance(A__ , A__ )
if os.path.isfile(A__ ):
with open(A__ ) as f:
a__ : List[Any] = eval(f.read() )
else:
a__ : Optional[Any] = requests.get(A__ )
try:
a__ : str = requests.json()
except Exception:
a__ : str = req.content.decode()
assert data is not None, "could not connect"
try:
a__ : Tuple = eval(A__ )
except Exception:
a__ : List[Any] = data.split('\n' )
req.close()
return data
def A_ ( A__ ) -> List[str]:
a__ : str = requests.get(A__ )
a__ : str = np.array(Image.open(BytesIO(response.content ) ) )
return img
def A_ ( A__ ) -> Any:
a__ : List[str] = url.split('/' )[-1]
if fn not in os.listdir(os.getcwd() ):
wget.download(A__ )
with open(A__ , 'rb' ) as stream:
a__ : Tuple = pkl.load(A__ )
a__ : Optional[Any] = weights.pop('model' )
a__ : int = {}
for k, v in model.items():
a__ : List[str] = torch.from_numpy(A__ )
if "running_var" in k:
a__ : str = torch.tensor([0] )
a__ : List[Any] = k.replace('running_var' , 'num_batches_tracked' )
a__ : int = zero
return new
def A_ ( ) -> List[Any]:
print(F'{os.path.abspath(os.path.join(A__ , os.pardir ) )}/demo.ipynb' )
def A_ ( A__ , A__="RGB" ) -> List[Any]:
assert isinstance(A__ , A__ )
if os.path.isfile(A__ ):
a__ : Any = cva.imread(A__ )
else:
a__ : Dict = get_image_from_url(A__ )
assert img is not None, F'could not connect to: {im}'
a__ : Dict = cva.cvtColor(A__ , cva.COLOR_BGR2RGB )
if input_format == "RGB":
a__ : Optional[int] = img[:, :, ::-1]
return img
def A_ ( A__ , A__=1 ) -> Tuple:
return (images[i : i + batch] for i in range(0 , len(A__ ) , A__ ))
| 99 |
def __lowerCamelCase ( snake_case__ ) -> list:
"""simple docstring"""
def merge(snake_case__ ,snake_case__ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(snake_case__ ) <= 1:
return collection
_SCREAMING_SNAKE_CASE = len(snake_case__ ) // 2
return merge(merge_sort(collection[:mid] ) ,merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase = input('''Enter numbers separated by a comma:\n''').strip()
UpperCamelCase = [int(item) for item in user_input.split(''',''')]
print(*merge_sort(unsorted), sep=''',''')
| 306 | 0 |
import shutil
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import (
is_pt_tf_cross_test,
require_tf,
require_torch,
require_torchvision,
require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, SamImageProcessor, SamProcessor
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
@require_vision
@require_torchvision
class __magic_name__ ( unittest.TestCase):
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = tempfile.mkdtemp()
UpperCamelCase__ : Union[str, Any] = SamImageProcessor()
UpperCamelCase__ : List[str] = SamProcessor(lowerCamelCase__ )
processor.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self : Optional[Any] , **lowerCamelCase__ : str ) -> Tuple:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase__ ).image_processor
def UpperCAmelCase__ ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase__ ( self : int ) -> Any:
'''simple docstring'''
UpperCamelCase__ : Tuple = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCamelCase__ : Optional[Any] = [Image.fromarray(np.moveaxis(lowerCamelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCAmelCase__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
UpperCamelCase__ : int = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase__ : Dict = self.get_image_processor(do_normalize=lowerCamelCase__ , padding_value=1.0 )
UpperCamelCase__ : Tuple = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowerCamelCase__ , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCamelCase__ )
def UpperCAmelCase__ ( self : List[str] ) -> Any:
'''simple docstring'''
UpperCamelCase__ : str = self.get_image_processor()
UpperCamelCase__ : str = SamProcessor(image_processor=lowerCamelCase__ )
UpperCamelCase__ : List[Any] = self.prepare_image_inputs()
UpperCamelCase__ : Any = image_processor(lowerCamelCase__ , return_tensors='''np''' )
UpperCamelCase__ : Optional[Any] = processor(images=lowerCamelCase__ , return_tensors='''np''' )
input_feat_extract.pop('''original_sizes''' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('''reshaped_input_sizes''' ) # pop original_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
@require_torch
def UpperCAmelCase__ ( self : Any ) -> int:
'''simple docstring'''
UpperCamelCase__ : List[str] = self.get_image_processor()
UpperCamelCase__ : int = SamProcessor(image_processor=lowerCamelCase__ )
UpperCamelCase__ : Optional[Any] = [torch.ones((1, 3, 5, 5) )]
UpperCamelCase__ : Union[str, Any] = [[1764, 2646]]
UpperCamelCase__ : str = [[683, 1024]]
UpperCamelCase__ : List[Any] = processor.post_process_masks(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
UpperCamelCase__ : Tuple = processor.post_process_masks(
lowerCamelCase__ , torch.tensor(lowerCamelCase__ ) , torch.tensor(lowerCamelCase__ ) )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
# should also work with np
UpperCamelCase__ : str = [np.ones((1, 3, 5, 5) )]
UpperCamelCase__ : str = processor.post_process_masks(lowerCamelCase__ , np.array(lowerCamelCase__ ) , np.array(lowerCamelCase__ ) )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
UpperCamelCase__ : int = [[1, 0], [0, 1]]
with self.assertRaises(lowerCamelCase__ ):
UpperCamelCase__ : Optional[Any] = processor.post_process_masks(lowerCamelCase__ , np.array(lowerCamelCase__ ) , np.array(lowerCamelCase__ ) )
@require_vision
@require_tf
class __magic_name__ ( unittest.TestCase):
def UpperCAmelCase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : str = tempfile.mkdtemp()
UpperCamelCase__ : Any = SamImageProcessor()
UpperCamelCase__ : List[Any] = SamProcessor(lowerCamelCase__ )
processor.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self : List[Any] , **lowerCamelCase__ : str ) -> Dict:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase__ ).image_processor
def UpperCAmelCase__ ( self : Optional[int] ) -> str:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase__ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCamelCase__ : int = [Image.fromarray(np.moveaxis(lowerCamelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCAmelCase__ ( self : Tuple ) -> str:
'''simple docstring'''
UpperCamelCase__ : Optional[Any] = SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
UpperCamelCase__ : Optional[Any] = self.get_image_processor(do_normalize=lowerCamelCase__ , padding_value=1.0 )
UpperCamelCase__ : Tuple = SamProcessor.from_pretrained(self.tmpdirname , do_normalize=lowerCamelCase__ , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCamelCase__ )
def UpperCAmelCase__ ( self : str ) -> int:
'''simple docstring'''
UpperCamelCase__ : List[Any] = self.get_image_processor()
UpperCamelCase__ : int = SamProcessor(image_processor=lowerCamelCase__ )
UpperCamelCase__ : Any = self.prepare_image_inputs()
UpperCamelCase__ : Tuple = image_processor(lowerCamelCase__ , return_tensors='''np''' )
UpperCamelCase__ : Optional[Any] = processor(images=lowerCamelCase__ , return_tensors='''np''' )
input_feat_extract.pop('''original_sizes''' ) # pop original_sizes as it is popped in the processor
input_feat_extract.pop('''reshaped_input_sizes''' ) # pop reshaped_input_sizes as it is popped in the processor
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
@require_tf
def UpperCAmelCase__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = self.get_image_processor()
UpperCamelCase__ : Any = SamProcessor(image_processor=lowerCamelCase__ )
UpperCamelCase__ : Any = [tf.ones((1, 3, 5, 5) )]
UpperCamelCase__ : Optional[Any] = [[1764, 2646]]
UpperCamelCase__ : Optional[int] = [[683, 1024]]
UpperCamelCase__ : Tuple = processor.post_process_masks(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , return_tensors='''tf''' )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
UpperCamelCase__ : Union[str, Any] = processor.post_process_masks(
lowerCamelCase__ , tf.convert_to_tensor(lowerCamelCase__ ) , tf.convert_to_tensor(lowerCamelCase__ ) , return_tensors='''tf''' , )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
# should also work with np
UpperCamelCase__ : Optional[Any] = [np.ones((1, 3, 5, 5) )]
UpperCamelCase__ : Optional[int] = processor.post_process_masks(
lowerCamelCase__ , np.array(lowerCamelCase__ ) , np.array(lowerCamelCase__ ) , return_tensors='''tf''' )
self.assertEqual(masks[0].shape , (1, 3, 1764, 2646) )
UpperCamelCase__ : Tuple = [[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError ):
UpperCamelCase__ : Any = processor.post_process_masks(
lowerCamelCase__ , np.array(lowerCamelCase__ ) , np.array(lowerCamelCase__ ) , return_tensors='''tf''' )
@require_vision
@require_torchvision
class __magic_name__ ( unittest.TestCase):
def UpperCAmelCase__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ : List[Any] = tempfile.mkdtemp()
UpperCamelCase__ : Optional[int] = SamImageProcessor()
UpperCamelCase__ : Optional[int] = SamProcessor(lowerCamelCase__ )
processor.save_pretrained(self.tmpdirname )
def UpperCAmelCase__ ( self : List[Any] , **lowerCamelCase__ : List[Any] ) -> Optional[Any]:
'''simple docstring'''
return AutoProcessor.from_pretrained(self.tmpdirname , **lowerCamelCase__ ).image_processor
def UpperCAmelCase__ ( self : Dict ) -> str:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
UpperCamelCase__ : Union[str, Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
UpperCamelCase__ : Union[str, Any] = [Image.fromarray(np.moveaxis(lowerCamelCase__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def UpperCAmelCase__ ( self : List[Any] ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ : Optional[int] = self.get_image_processor()
UpperCamelCase__ : int = SamProcessor(image_processor=lowerCamelCase__ )
UpperCamelCase__ : Tuple = np.random.randint(0 , 2 , size=(1, 3, 5, 5) ).astype(np.floataa )
UpperCamelCase__ : Union[str, Any] = [tf.convert_to_tensor(lowerCamelCase__ )]
UpperCamelCase__ : List[Any] = [torch.tensor(lowerCamelCase__ )]
UpperCamelCase__ : Optional[Any] = [[1764, 2646]]
UpperCamelCase__ : Any = [[683, 1024]]
UpperCamelCase__ : int = processor.post_process_masks(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , return_tensors='''tf''' )
UpperCamelCase__ : Optional[int] = processor.post_process_masks(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , return_tensors='''pt''' )
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy() ) )
@is_pt_tf_cross_test
def UpperCAmelCase__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ : str = self.get_image_processor()
UpperCamelCase__ : Tuple = SamProcessor(image_processor=lowerCamelCase__ )
UpperCamelCase__ : Optional[int] = self.prepare_image_inputs()
UpperCamelCase__ : List[str] = image_processor(lowerCamelCase__ , return_tensors='''pt''' )['''pixel_values'''].numpy()
UpperCamelCase__ : Dict = processor(images=lowerCamelCase__ , return_tensors='''pt''' )['''pixel_values'''].numpy()
UpperCamelCase__ : List[str] = image_processor(lowerCamelCase__ , return_tensors='''tf''' )['''pixel_values'''].numpy()
UpperCamelCase__ : List[str] = processor(images=lowerCamelCase__ , return_tensors='''tf''' )['''pixel_values'''].numpy()
self.assertTrue(np.allclose(lowerCamelCase__ , lowerCamelCase__ ) )
self.assertTrue(np.allclose(lowerCamelCase__ , lowerCamelCase__ ) )
self.assertTrue(np.allclose(lowerCamelCase__ , lowerCamelCase__ ) )
| 51 |
def _a ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
UpperCamelCase__ : List[str] = generate_pascal_triangle(SCREAMING_SNAKE_CASE )
for row_idx in range(SCREAMING_SNAKE_CASE ):
# Print left spaces
for _ in range(num_rows - row_idx - 1 ):
print(end=''' ''' )
# Print row values
for col_idx in range(row_idx + 1 ):
if col_idx != row_idx:
print(triangle[row_idx][col_idx] , end=''' ''' )
else:
print(triangle[row_idx][col_idx] , end='''''' )
print()
def _a ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError('''The input value of \'num_rows\' should be \'int\'''' )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
'''The input value of \'num_rows\' should be greater than or equal to 0''' )
UpperCamelCase__ : list[list[int]] = []
for current_row_idx in range(SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : str = populate_current_row(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
triangle.append(SCREAMING_SNAKE_CASE )
return triangle
def _a ( SCREAMING_SNAKE_CASE : list[list[int]] , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
UpperCamelCase__ : List[Any] = [-1] * (current_row_idx + 1)
# first and last elements of current row are equal to 1
UpperCamelCase__ , UpperCamelCase__ : Optional[int] = 1, 1
for current_col_idx in range(1 , SCREAMING_SNAKE_CASE ):
calculate_current_element(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return current_row
def _a ( SCREAMING_SNAKE_CASE : list[list[int]] , SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , ):
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = triangle[current_row_idx - 1][current_col_idx - 1]
UpperCamelCase__ : List[Any] = triangle[current_row_idx - 1][current_col_idx]
UpperCamelCase__ : Tuple = above_to_left_elt + above_to_right_elt
def _a ( SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
raise TypeError('''The input value of \'num_rows\' should be \'int\'''' )
if num_rows == 0:
return []
elif num_rows < 0:
raise ValueError(
'''The input value of \'num_rows\' should be greater than or equal to 0''' )
UpperCamelCase__ : list[list[int]] = [[1]]
for row_index in range(1 , SCREAMING_SNAKE_CASE ):
UpperCamelCase__ : Tuple = [0] + result[-1] + [0]
UpperCamelCase__ : Any = row_index + 1
# Calculate the number of distinct elements in a row
UpperCamelCase__ : str = sum(divmod(SCREAMING_SNAKE_CASE , 2 ) )
UpperCamelCase__ : Optional[int] = [
temp_row[i - 1] + temp_row[i] for i in range(1 , distinct_elements + 1 )
]
UpperCamelCase__ : int = row_first_half[: (row_index + 1) // 2]
row_second_half.reverse()
UpperCamelCase__ : List[Any] = row_first_half + row_second_half
result.append(SCREAMING_SNAKE_CASE )
return result
def _a ( ):
"""simple docstring"""
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(SCREAMING_SNAKE_CASE : Callable , SCREAMING_SNAKE_CASE : int ) -> None:
UpperCamelCase__ : Tuple = F"{func.__name__}({value})"
UpperCamelCase__ : Dict = timeit(F"__main__.{call}" , setup='''import __main__''' )
# print(f"{call:38} = {func(value)} -- {timing:.4f} seconds")
print(F"{call:38} -- {timing:.4f} seconds" )
for value in range(15 ): # (1, 7, 14):
for func in (generate_pascal_triangle, generate_pascal_triangle_optimized):
benchmark_a_function(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 51 | 1 |
'''simple docstring'''
import requests
def __lowerCamelCase ( lowerCAmelCase_ , lowerCAmelCase_ ) -> None:
_a : Tuple = {'Content-Type': 'application/json'}
_a : Tuple = requests.post(lowerCAmelCase_ , json={'text': message_body} , headers=lowerCAmelCase_ )
if response.status_code != 200:
_a : Optional[int] = (
'Request to slack returned an error '
f"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(lowerCAmelCase_ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message('''<YOUR MESSAGE BODY>''', '''<SLACK CHANNEL URL>''')
| 89 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : Dict = logging.get_logger(__name__)
__A : List[Any] = {'''ctrl''': '''https://huggingface.co/ctrl/resolve/main/config.json'''}
class __A ( lowerCAmelCase ):
lowerCAmelCase_ : str = "ctrl"
lowerCAmelCase_ : Optional[Any] = ["past_key_values"]
lowerCAmelCase_ : Dict = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Any , UpperCAmelCase_ : int=246534 , UpperCAmelCase_ : Optional[Any]=256 , UpperCAmelCase_ : Any=1280 , UpperCAmelCase_ : int=8192 , UpperCAmelCase_ : int=48 , UpperCAmelCase_ : Optional[Any]=16 , UpperCAmelCase_ : Dict=0.1 , UpperCAmelCase_ : Any=0.1 , UpperCAmelCase_ : List[str]=1E-6 , UpperCAmelCase_ : str=0.02 , UpperCAmelCase_ : Optional[Any]=True , **UpperCAmelCase_ : int , ):
lowerCAmelCase : int = vocab_size
lowerCAmelCase : int = n_positions
lowerCAmelCase : Optional[Any] = n_embd
lowerCAmelCase : Optional[Any] = n_layer
lowerCAmelCase : List[str] = n_head
lowerCAmelCase : Union[str, Any] = dff
lowerCAmelCase : Dict = resid_pdrop
lowerCAmelCase : List[Any] = embd_pdrop
lowerCAmelCase : List[Any] = layer_norm_epsilon
lowerCAmelCase : Dict = initializer_range
lowerCAmelCase : Union[str, Any] = use_cache
super().__init__(**UpperCAmelCase_ )
| 138 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import BeitConfig, BeitForImageClassification, BeitForMaskedImageModeling, BeitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase_ = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( __A : Optional[int] , __A : Tuple=False , __A : int=False ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = "backbone." if is_semantic else ""
_SCREAMING_SNAKE_CASE = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((f"""{prefix}blocks.{i}.norm1.weight""", f"""beit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((f"""{prefix}blocks.{i}.norm1.bias""", f"""beit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(f"""{prefix}blocks.{i}.attn.proj.weight""", f"""beit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append(
(f"""{prefix}blocks.{i}.attn.proj.bias""", f"""beit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((f"""{prefix}blocks.{i}.norm2.weight""", f"""beit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((f"""{prefix}blocks.{i}.norm2.bias""", f"""beit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.weight""", f"""beit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc1.bias""", f"""beit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.weight""", f"""beit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((f"""{prefix}blocks.{i}.mlp.fc2.bias""", f"""beit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
(f"""{prefix}cls_token""", "beit.embeddings.cls_token"),
(f"""{prefix}patch_embed.proj.weight""", "beit.embeddings.patch_embeddings.projection.weight"),
(f"""{prefix}patch_embed.proj.bias""", "beit.embeddings.patch_embeddings.projection.bias"),
(f"""{prefix}pos_embed""", "beit.embeddings.position_embeddings"),
] )
if has_lm_head:
# mask token + layernorm
rename_keys.extend(
[
("mask_token", "beit.embeddings.mask_token"),
("norm.weight", "layernorm.weight"),
("norm.bias", "layernorm.bias"),
] )
else:
# layernorm + classification head
rename_keys.extend(
[
("fc_norm.weight", "beit.pooler.layernorm.weight"),
("fc_norm.bias", "beit.pooler.layernorm.bias"),
("head.weight", "classifier.weight"),
("head.bias", "classifier.bias"),
] )
return rename_keys
def SCREAMING_SNAKE_CASE_ ( __A : Any , __A : Dict , __A : str=False , __A : List[str]=False ) -> Optional[int]:
for i in range(config.num_hidden_layers ):
_SCREAMING_SNAKE_CASE = "backbone." if is_semantic else ""
# queries, keys and values
_SCREAMING_SNAKE_CASE = state_dict.pop(f"""{prefix}blocks.{i}.attn.qkv.weight""" )
_SCREAMING_SNAKE_CASE = state_dict.pop(f"""{prefix}blocks.{i}.attn.q_bias""" )
_SCREAMING_SNAKE_CASE = state_dict.pop(f"""{prefix}blocks.{i}.attn.v_bias""" )
_SCREAMING_SNAKE_CASE = in_proj_weight[
: config.hidden_size, :
]
_SCREAMING_SNAKE_CASE = q_bias
_SCREAMING_SNAKE_CASE = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_SCREAMING_SNAKE_CASE = in_proj_weight[
-config.hidden_size :, :
]
_SCREAMING_SNAKE_CASE = v_bias
# gamma_1 and gamma_2
# we call them lambda because otherwise they are renamed when using .from_pretrained
_SCREAMING_SNAKE_CASE = state_dict.pop(f"""{prefix}blocks.{i}.gamma_1""" )
_SCREAMING_SNAKE_CASE = state_dict.pop(f"""{prefix}blocks.{i}.gamma_2""" )
_SCREAMING_SNAKE_CASE = gamma_a
_SCREAMING_SNAKE_CASE = gamma_a
def SCREAMING_SNAKE_CASE_ ( __A : Union[str, Any] , __A : List[str] , __A : Tuple ) -> Dict:
_SCREAMING_SNAKE_CASE = dct.pop(__A )
_SCREAMING_SNAKE_CASE = val
def SCREAMING_SNAKE_CASE_ ( ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = "http://images.cocodataset.org/val2017/000000039769.jpg"
_SCREAMING_SNAKE_CASE = Image.open(requests.get(__A , stream=__A ).raw )
return im
@torch.no_grad()
def SCREAMING_SNAKE_CASE_ ( __A : Optional[Any] , __A : Dict , __A : List[Any]=False ) -> Optional[int]:
_SCREAMING_SNAKE_CASE = False if "rvlcdip" in checkpoint_url else True
_SCREAMING_SNAKE_CASE = BeitConfig(use_absolute_position_embeddings=__A , use_mask_token=__A )
# size of the architecture
if "large" in checkpoint_url or "dit-l" in checkpoint_url:
_SCREAMING_SNAKE_CASE = 10_24
_SCREAMING_SNAKE_CASE = 40_96
_SCREAMING_SNAKE_CASE = 24
_SCREAMING_SNAKE_CASE = 16
# labels
if "rvlcdip" in checkpoint_url:
_SCREAMING_SNAKE_CASE = 16
_SCREAMING_SNAKE_CASE = "huggingface/label-files"
_SCREAMING_SNAKE_CASE = "rvlcdip-id2label.json"
_SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(__A , __A , repo_type="dataset" ) , "r" ) )
_SCREAMING_SNAKE_CASE = {int(__A ): v for k, v in idalabel.items()}
_SCREAMING_SNAKE_CASE = idalabel
_SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
# load state_dict of original model, remove and rename some keys
_SCREAMING_SNAKE_CASE = torch.hub.load_state_dict_from_url(__A , map_location="cpu" )["model"]
_SCREAMING_SNAKE_CASE = create_rename_keys(__A , has_lm_head=__A )
for src, dest in rename_keys:
rename_key(__A , __A , __A )
read_in_q_k_v(__A , __A , has_lm_head=__A )
# load HuggingFace model
_SCREAMING_SNAKE_CASE = BeitForMaskedImageModeling(__A ) if has_lm_head else BeitForImageClassification(__A )
model.eval()
model.load_state_dict(__A )
# Check outputs on an image
_SCREAMING_SNAKE_CASE = BeitImageProcessor(
size=config.image_size , resample=PILImageResampling.BILINEAR , do_center_crop=__A )
_SCREAMING_SNAKE_CASE = prepare_img()
_SCREAMING_SNAKE_CASE = image_processor(images=__A , return_tensors="pt" )
_SCREAMING_SNAKE_CASE = encoding["pixel_values"]
_SCREAMING_SNAKE_CASE = model(__A )
_SCREAMING_SNAKE_CASE = outputs.logits
# verify logits
_SCREAMING_SNAKE_CASE = [1, 16] if "rvlcdip" in checkpoint_url else [1, 1_96, 81_92]
assert logits.shape == torch.Size(__A ), "Shape of logits not as expected"
Path(__A ).mkdir(exist_ok=__A )
print(f"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(__A )
print(f"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(__A )
if push_to_hub:
if has_lm_head:
_SCREAMING_SNAKE_CASE = "dit-base" if "base" in checkpoint_url else "dit-large"
else:
_SCREAMING_SNAKE_CASE = "dit-base-finetuned-rvlcdip" if "dit-b" in checkpoint_url else "dit-large-finetuned-rvlcdip"
image_processor.push_to_hub(
repo_path_or_name=Path(__A , __A ) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=__A , )
model.push_to_hub(
repo_path_or_name=Path(__A , __A ) , organization="nielsr" , commit_message="Add model" , use_temp_dir=__A , )
if __name__ == "__main__":
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_url',
default='https://layoutlm.blob.core.windows.net/dit/dit-pts/dit-base-224-p16-500k-62d53a.pth',
type=str,
help='URL to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub',
action='store_true',
)
lowerCamelCase_ = parser.parse_args()
convert_dit_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub)
| 111 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'google/efficientnet-b7': 'https://huggingface.co/google/efficientnet-b7/resolve/main/config.json',
}
class lowercase_ ( A ):
"""simple docstring"""
lowerCamelCase_ = '''efficientnet'''
def __init__( self : Optional[Any] , __lowerCamelCase : int = 3 , __lowerCamelCase : int = 6_0_0 , __lowerCamelCase : float = 2.0 , __lowerCamelCase : float = 3.1 , __lowerCamelCase : int = 8 , __lowerCamelCase : List[int] = [3, 3, 5, 3, 5, 5, 3] , __lowerCamelCase : List[int] = [3_2, 1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2] , __lowerCamelCase : List[int] = [1_6, 2_4, 4_0, 8_0, 1_1_2, 1_9_2, 3_2_0] , __lowerCamelCase : List[int] = [] , __lowerCamelCase : List[int] = [1, 2, 2, 2, 1, 2, 1] , __lowerCamelCase : List[int] = [1, 2, 2, 3, 3, 4, 1] , __lowerCamelCase : List[int] = [1, 6, 6, 6, 6, 6, 6] , __lowerCamelCase : float = 0.2_5 , __lowerCamelCase : str = "swish" , __lowerCamelCase : int = 2_5_6_0 , __lowerCamelCase : str = "mean" , __lowerCamelCase : float = 0.0_2 , __lowerCamelCase : float = 0.0_0_1 , __lowerCamelCase : float = 0.9_9 , __lowerCamelCase : float = 0.5 , __lowerCamelCase : float = 0.2 , **__lowerCamelCase : Tuple , ):
"""simple docstring"""
super().__init__(**__lowerCamelCase )
_SCREAMING_SNAKE_CASE = num_channels
_SCREAMING_SNAKE_CASE = image_size
_SCREAMING_SNAKE_CASE = width_coefficient
_SCREAMING_SNAKE_CASE = depth_coefficient
_SCREAMING_SNAKE_CASE = depth_divisor
_SCREAMING_SNAKE_CASE = kernel_sizes
_SCREAMING_SNAKE_CASE = in_channels
_SCREAMING_SNAKE_CASE = out_channels
_SCREAMING_SNAKE_CASE = depthwise_padding
_SCREAMING_SNAKE_CASE = strides
_SCREAMING_SNAKE_CASE = num_block_repeats
_SCREAMING_SNAKE_CASE = expand_ratios
_SCREAMING_SNAKE_CASE = squeeze_expansion_ratio
_SCREAMING_SNAKE_CASE = hidden_act
_SCREAMING_SNAKE_CASE = hidden_dim
_SCREAMING_SNAKE_CASE = pooling_type
_SCREAMING_SNAKE_CASE = initializer_range
_SCREAMING_SNAKE_CASE = batch_norm_eps
_SCREAMING_SNAKE_CASE = batch_norm_momentum
_SCREAMING_SNAKE_CASE = dropout_rate
_SCREAMING_SNAKE_CASE = drop_connect_rate
_SCREAMING_SNAKE_CASE = sum(__lowerCamelCase ) * 4
class lowercase_ ( A ):
"""simple docstring"""
lowerCamelCase_ = version.parse('''1.11''' )
@property
def lowerCAmelCase_ ( self : Optional[Any] ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def lowerCAmelCase_ ( self : List[Any] ):
"""simple docstring"""
return 1e-5
| 111 | 1 |
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__SCREAMING_SNAKE_CASE : Dict = get_logger(__name__)
class lowerCamelCase_ :
'''simple docstring'''
def __init__( self : Dict , A : Dict , A : Union[str, Any]=None ):
_UpperCAmelCase : str = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith("__" ):
setattr(self , A , getattr(A , A ) )
_UpperCAmelCase : str = module._original_module if isinstance(A , _PatchedModuleObj ) else module
class lowerCamelCase_ :
'''simple docstring'''
__UpperCamelCase: Tuple = []
def __init__( self : int , A : int , A : str , A : Union[str, Any] , A : Dict=None ):
_UpperCAmelCase : Tuple = obj
_UpperCAmelCase : List[Any] = target
_UpperCAmelCase : Any = new
_UpperCAmelCase : str = target.split("." )[0]
_UpperCAmelCase : Optional[int] = {}
_UpperCAmelCase : str = attrs or []
def __enter__( self : List[str] ):
*_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = self.target.split("." )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(A ) ):
try:
_UpperCAmelCase : str = import_module(".".join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
_UpperCAmelCase : Optional[Any] = getattr(self.obj , A )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(A , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
_UpperCAmelCase : Optional[Any] = obj_attr
# patch at top level
setattr(self.obj , A , _PatchedModuleObj(A , attrs=self.attrs ) )
_UpperCAmelCase : int = getattr(self.obj , A )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(A , A , _PatchedModuleObj(getattr(A , A , A ) , attrs=self.attrs ) )
_UpperCAmelCase : str = getattr(A , A )
# finally set the target attribute
setattr(A , A , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
_UpperCAmelCase : int = getattr(import_module(".".join(A ) ) , A )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , A ) is attr_value:
_UpperCAmelCase : List[Any] = getattr(self.obj , A )
setattr(self.obj , A , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
_UpperCAmelCase : Union[str, Any] = globals()["__builtins__"][target_attr]
setattr(self.obj , A , self.new )
else:
raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""" )
def __exit__( self : Union[str, Any] , *A : str ):
for attr in list(self.original ):
setattr(self.obj , A , self.original.pop(A ) )
def _A ( self : Dict ):
self.__enter__()
self._active_patches.append(self )
def _A ( self : List[str] ):
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 31 |
'''simple docstring'''
import numpy
class __lowercase :
def __init__(self , A , A ):
lowerCamelCase_ : Optional[int] = input_array
# Random initial weights are assigned where first argument is the
# number of nodes in previous layer and second argument is the
# number of nodes in the next layer.
# Random initial weights are assigned.
# self.input_array.shape[1] is used to represent number of nodes in input layer.
# First hidden layer consists of 4 nodes.
lowerCamelCase_ : Optional[Any] = numpy.random.rand(
self.input_array.shape[1] , 4 )
# Random initial values for the first hidden layer.
# First hidden layer has 4 nodes.
# Second hidden layer has 3 nodes.
lowerCamelCase_ : Optional[Any] = numpy.random.rand(
4 , 3 )
# Random initial values for the second hidden layer.
# Second hidden layer has 3 nodes.
# Output layer has 1 node.
lowerCamelCase_ : Tuple = numpy.random.rand(3 , 1 )
# Real output values provided.
lowerCamelCase_ : Dict = output_array
# Predicted output values by the neural network.
# Predicted_output array initially consists of zeroes.
lowerCamelCase_ : Optional[int] = numpy.zeros(output_array.shape )
def UpperCAmelCase__ (self ):
lowerCamelCase_ : Dict = sigmoid(
numpy.dot(self.input_array , self.input_layer_and_first_hidden_layer_weights ) )
# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
# connecting the first hidden set of nodes with the second hidden set of nodes.
lowerCamelCase_ : Union[str, Any] = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
# layer_between_second_hidden_layer_and_output is the layer connecting
# second hidden layer with the output node.
lowerCamelCase_ : Optional[Any] = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return self.layer_between_second_hidden_layer_and_output
def UpperCAmelCase__ (self ):
lowerCamelCase_ : List[Any] = numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer.T , 2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , )
lowerCamelCase_ : List[Any] = numpy.dot(
self.layer_between_input_and_first_hidden_layer.T , numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , )
lowerCamelCase_ : Optional[int] = numpy.dot(
self.input_array.T , numpy.dot(
numpy.dot(
2
* (self.output_array - self.predicted_output)
* sigmoid_derivative(self.predicted_output ) , self.second_hidden_layer_and_output_layer_weights.T , )
* sigmoid_derivative(
self.layer_between_first_hidden_layer_and_second_hidden_layer ) , self.first_hidden_layer_and_second_hidden_layer_weights.T , )
* sigmoid_derivative(self.layer_between_input_and_first_hidden_layer ) , )
self.input_layer_and_first_hidden_layer_weights += (
updated_input_layer_and_first_hidden_layer_weights
)
self.first_hidden_layer_and_second_hidden_layer_weights += (
updated_first_hidden_layer_and_second_hidden_layer_weights
)
self.second_hidden_layer_and_output_layer_weights += (
updated_second_hidden_layer_and_output_layer_weights
)
def UpperCAmelCase__ (self , A , A , A ):
for iteration in range(1 , iterations + 1 ):
lowerCamelCase_ : Any = self.feedforward()
self.back_propagation()
if give_loss:
lowerCamelCase_ : List[str] = numpy.mean(numpy.square(output - self.feedforward() ) )
print(F"""Iteration {iteration} Loss: {loss}""" )
def UpperCAmelCase__ (self , A ):
lowerCamelCase_ : Optional[int] = input_arr
lowerCamelCase_ : List[Any] = sigmoid(
numpy.dot(self.array , self.input_layer_and_first_hidden_layer_weights ) )
lowerCamelCase_ : Optional[int] = sigmoid(
numpy.dot(
self.layer_between_input_and_first_hidden_layer , self.first_hidden_layer_and_second_hidden_layer_weights , ) )
lowerCamelCase_ : Union[str, Any] = sigmoid(
numpy.dot(
self.layer_between_first_hidden_layer_and_second_hidden_layer , self.second_hidden_layer_and_output_layer_weights , ) )
return int(self.layer_between_second_hidden_layer_and_output > 0.6 )
def lowercase_ ( _lowercase ) -> numpy.ndarray:
'''simple docstring'''
return 1 / (1 + numpy.exp(-value ))
def lowercase_ ( _lowercase ) -> numpy.ndarray:
'''simple docstring'''
return (value) * (1 - (value))
def lowercase_ ( ) -> int:
'''simple docstring'''
lowerCamelCase_ : int = numpy.array(
(
[0, 0, 0],
[0, 0, 1],
[0, 1, 0],
[0, 1, 1],
[1, 0, 0],
[1, 0, 1],
[1, 1, 0],
[1, 1, 1],
) , dtype=numpy.floataa , )
# True output values for the given input values.
lowerCamelCase_ : Union[str, Any] = numpy.array(([0], [1], [1], [0], [1], [0], [0], [1]) , dtype=numpy.floataa )
# Calling neural network class.
lowerCamelCase_ : Dict = TwoHiddenLayerNeuralNetwork(
input_array=_lowercase , output_array=_lowercase )
# Calling training function.
# Set give_loss to True if you want to see loss in every iteration.
neural_network.train(output=_lowercase , iterations=10 , give_loss=_lowercase )
return neural_network.predict(numpy.array(([1, 1, 1]) , dtype=numpy.floataa ) )
if __name__ == "__main__":
example()
| 318 | 0 |
'''simple docstring'''
# Copyright (c) 2021-, NVIDIA CORPORATION. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
####################################################################################################
#
# Note: If when running this conversion script you're getting an exception:
# ModuleNotFoundError: No module named 'megatron.model.enums'
# you need to tell python where to find the clone of Megatron-LM, e.g.:
#
# cd /tmp
# git clone https://github.com/NVIDIA/Megatron-LM
# PYTHONPATH=/tmp/Megatron-LM python src/transformers/models/megatron_gpt2/convert_megatron_gpt2_checkpoint.py ...
#
# if you already have it cloned elsewhere, simply adjust the path to the existing path
#
# If the training was done using a Megatron-LM fork, e.g.,
# https://github.com/microsoft/Megatron-DeepSpeed/ then chances are that you need to have that one
# in your path, i.e., /path/to/Megatron-DeepSpeed/
#
import argparse
import os
import re
import zipfile
import torch
from transformers import AutoTokenizer, GPTaConfig
def lowerCAmelCase__ ( lowerCamelCase : Any ,lowerCamelCase : Tuple ,lowerCamelCase : Union[str, Any]=0 ):
# Format the message.
if name is None:
_A : Union[str, Any] = None
else:
_A : Tuple = '.' * max(0 ,spaces - 2 ) + '# {:' + str(50 - spaces ) + 's}'
_A : Dict = fmt.format(lowerCamelCase )
# Print and recurse (if needed).
if isinstance(lowerCamelCase ,lowerCamelCase ):
if msg is not None:
print(lowerCamelCase )
for k in val.keys():
recursive_print(lowerCamelCase ,val[k] ,spaces + 2 )
elif isinstance(lowerCamelCase ,torch.Tensor ):
print(lowerCamelCase ,':' ,val.size() )
else:
print(lowerCamelCase ,':' ,lowerCamelCase )
def lowerCAmelCase__ ( lowerCamelCase : Tuple ,lowerCamelCase : Any ,lowerCamelCase : List[Any] ,lowerCamelCase : Optional[Any] ,lowerCamelCase : str ):
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
_A : int = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
_A : List[str] = (num_heads, hidden_size, num_splits) + input_shape[1:]
_A : Optional[Any] = param.view(*lowerCamelCase )
_A : Optional[int] = param.transpose(0 ,2 )
_A : Union[str, Any] = param.transpose(1 ,2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
_A : int = (num_heads, num_splits, hidden_size) + input_shape[1:]
_A : Optional[int] = param.view(*lowerCamelCase )
_A : Dict = param.transpose(0 ,1 ).contiguous()
_A : Tuple = param.view(*lowerCamelCase )
return param
def lowerCAmelCase__ ( lowerCamelCase : List[str] ,lowerCamelCase : List[Any] ,lowerCamelCase : Optional[Any] ):
# The converted output model.
_A : str = {}
# old versions did not store training args
_A : Optional[int] = input_state_dict.get('args' ,lowerCamelCase )
if ds_args is not None:
# do not make the user write a config file when the exact dimensions/sizes are already in the checkpoint
# from pprint import pprint
# pprint(vars(ds_args))
_A : Tuple = ds_args.padded_vocab_size
_A : Union[str, Any] = ds_args.max_position_embeddings
_A : str = ds_args.hidden_size
_A : str = ds_args.num_layers
_A : int = ds_args.num_attention_heads
_A : str = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
_A : List[Any] = config.n_head
# The hidden_size per head.
_A : Any = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
_A : List[Any] = input_state_dict['checkpoint_version']
else:
_A : Optional[Any] = 0.0
# The model.
_A : List[str] = input_state_dict['model']
# The language model.
_A : Optional[Any] = model['language_model']
# The embeddings.
_A : Any = lm['embedding']
# The word embeddings.
_A : str = embeddings['word_embeddings']['weight']
# Truncate the embedding table to vocab_size rows.
_A : str = word_embeddings[: config.vocab_size, :]
_A : Any = word_embeddings
# The position embeddings.
_A : List[Any] = embeddings['position_embeddings']['weight']
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
_A : Union[str, Any] = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
F'pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don\'t match' )
# Store the position embeddings.
_A : int = pos_embeddings
# The transformer.
_A : Any = lm['transformer'] if 'transformer' in lm.keys() else lm['encoder']
# The regex to extract layer names.
_A : List[str] = re.compile(R'layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)' )
# The simple map of names for "automated" rules.
_A : int = {
'attention.dense': '.attn.c_proj.',
'self_attention.dense': '.attn.c_proj.',
'mlp.dense_h_to_4h': '.mlp.c_fc.',
'mlp.dense_4h_to_h': '.mlp.c_proj.',
}
# Extract the layers.
for key, val in transformer.items():
# Match the name.
_A : Union[str, Any] = layer_re.match(lowerCamelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
_A : Tuple = int(m.group(1 ) )
# The name of the operation.
_A : List[Any] = m.group(2 )
# Is it a weight or a bias?
_A : List[Any] = m.group(3 )
# The name of the layer.
_A : int = F'transformer.h.{layer_idx}'
# For layernorm(s), simply store the layer norm.
if op_name.endswith('layernorm' ):
_A : Dict = 'ln_1' if op_name.startswith('input' ) else 'ln_2'
_A : List[str] = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
_A : str = torch.tril(torch.ones((n_positions, n_positions) ,dtype=torch.floataa ) ).view(
1 ,1 ,lowerCamelCase ,lowerCamelCase )
_A : Tuple = causal_mask
# Insert a "dummy" tensor for masked_bias.
_A : int = torch.tensor(-1E4 ,dtype=torch.floataa )
_A : Union[str, Any] = masked_bias
_A : int = fix_query_key_value_ordering(lowerCamelCase ,lowerCamelCase ,3 ,lowerCamelCase ,lowerCamelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
_A : Optional[Any] = out_val.transpose(0 ,1 ).contiguous()
# Store.
_A : int = out_val
# Transpose the bias.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "bias":
_A : List[str] = fix_query_key_value_ordering(lowerCamelCase ,lowerCamelCase ,3 ,lowerCamelCase ,lowerCamelCase )
# Store. No change of shape.
_A : str = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
_A : Union[str, Any] = megatron_to_transformers[op_name]
_A : List[Any] = val.transpose(0 ,1 )
# Copy the bias.
elif weight_or_bias == "bias":
_A : Dict = megatron_to_transformers[op_name]
_A : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
_A : str = transformer['final_layernorm.weight']
_A : Dict = transformer['final_layernorm.bias']
# For LM head, transformers' wants the matrix to weight embeddings.
_A : List[str] = word_embeddings
# It should be done!
return output_state_dict
def lowerCAmelCase__ ( ):
# Create the argument parser.
_A : Optional[Any] = argparse.ArgumentParser()
parser.add_argument('--print-checkpoint-structure' ,action='store_true' )
parser.add_argument(
'path_to_checkpoint' ,type=lowerCamelCase ,help='Path to the checkpoint file (.zip archive or direct .pt file)' ,)
parser.add_argument(
'--config_file' ,default='' ,type=lowerCamelCase ,help='An optional config json file describing the pre-trained model.' ,)
_A : str = parser.parse_args()
# Extract the basename.
_A : str = os.path.dirname(args.path_to_checkpoint )
# Load the model.
# the .zip is very optional, let's keep it for backward compatibility
print(F'Extracting PyTorch state dictionary from {args.path_to_checkpoint}' )
if args.path_to_checkpoint.endswith('.zip' ):
with zipfile.ZipFile(args.path_to_checkpoint ,'r' ) as checkpoint:
with checkpoint.open('release/mp_rank_00/model_optim_rng.pt' ) as pytorch_dict:
_A : List[str] = torch.load(lowerCamelCase ,map_location='cpu' )
else:
_A : Tuple = torch.load(args.path_to_checkpoint ,map_location='cpu' )
_A : Optional[int] = input_state_dict.get('args' ,lowerCamelCase )
# Read the config, or default to the model released by NVIDIA.
if args.config_file == "":
if ds_args is not None:
if ds_args.bias_gelu_fusion:
_A : Optional[int] = 'gelu_fast'
elif ds_args.openai_gelu:
_A : Optional[int] = 'gelu_new'
else:
_A : str = 'gelu'
else:
# in the very early days this used to be "gelu_new"
_A : Union[str, Any] = 'gelu_new'
# Spell out all parameters in case the defaults change.
_A : Dict = GPTaConfig(
vocab_size=50257 ,n_positions=1024 ,n_embd=1024 ,n_layer=24 ,n_head=16 ,n_inner=4096 ,activation_function=lowerCamelCase ,resid_pdrop=0.1 ,embd_pdrop=0.1 ,attn_pdrop=0.1 ,layer_norm_epsilon=1E-5 ,initializer_range=0.02 ,summary_type='cls_index' ,summary_use_proj=lowerCamelCase ,summary_activation=lowerCamelCase ,summary_proj_to_labels=lowerCamelCase ,summary_first_dropout=0.1 ,scale_attn_weights=lowerCamelCase ,use_cache=lowerCamelCase ,bos_token_id=50256 ,eos_token_id=50256 ,)
else:
_A : Optional[int] = GPTaConfig.from_json_file(args.config_file )
_A : str = ['GPT2LMHeadModel']
# Convert.
print('Converting' )
_A : Optional[Any] = convert_megatron_checkpoint(lowerCamelCase ,lowerCamelCase ,lowerCamelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(lowerCamelCase ,lowerCamelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
_A : str = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
_A : Any = 'gpt2'
elif tokenizer_type == "PretrainedFromHF":
_A : Union[str, Any] = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'Unrecognized tokenizer_type {tokenizer_type}' )
else:
_A : Tuple = 'gpt2'
_A : Union[str, Any] = AutoTokenizer.from_pretrained(lowerCamelCase )
_A : int = type(lowerCamelCase ).__name__
_A : int = tokenizer_class
# Store the config to file.
print('Saving config' )
config.save_pretrained(lowerCamelCase )
# Save tokenizer based on args
print(F'Adding {tokenizer_class} tokenizer files' )
tokenizer.save_pretrained(lowerCamelCase )
# Store the state_dict to file.
_A : Optional[Any] = os.path.join(lowerCamelCase ,'pytorch_model.bin' )
print(F'Saving checkpoint to "{output_checkpoint_file}"' )
torch.save(lowerCamelCase ,lowerCamelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 227 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __lowerCamelCase ( a_ , unittest.TestCase ):
"""simple docstring"""
a = KandinskyVaaPriorPipeline
a = ["prompt"]
a = ["prompt", "negative_prompt"]
a = [
"num_images_per_prompt",
"generator",
"num_inference_steps",
"latents",
"negative_prompt",
"guidance_scale",
"output_type",
"return_dict",
]
a = False
@property
def A ( self : List[str]):
return 32
@property
def A ( self : List[Any]):
return 32
@property
def A ( self : Dict):
return self.time_input_dim
@property
def A ( self : Tuple):
return self.time_input_dim * 4
@property
def A ( self : Optional[int]):
return 100
@property
def A ( self : Dict):
_A : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip')
return tokenizer
@property
def A ( self : Optional[Any]):
torch.manual_seed(0)
_A : Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(SCREAMING_SNAKE_CASE)
@property
def A ( self : List[Any]):
torch.manual_seed(0)
_A : Optional[Any] = {
'num_attention_heads': 2,
'attention_head_dim': 12,
'embedding_dim': self.text_embedder_hidden_size,
'num_layers': 1,
}
_A : Any = PriorTransformer(**SCREAMING_SNAKE_CASE)
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
_A : str = nn.Parameter(torch.ones(model.clip_std.shape))
return model
@property
def A ( self : List[str]):
torch.manual_seed(0)
_A : List[Any] = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=224 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=14 , )
_A : Union[str, Any] = CLIPVisionModelWithProjection(SCREAMING_SNAKE_CASE)
return model
@property
def A ( self : int):
_A : Optional[Any] = CLIPImageProcessor(
crop_size=224 , do_center_crop=SCREAMING_SNAKE_CASE , do_normalize=SCREAMING_SNAKE_CASE , do_resize=SCREAMING_SNAKE_CASE , image_mean=[0.4814_5466, 0.457_8275, 0.4082_1073] , image_std=[0.2686_2954, 0.2613_0258, 0.2757_7711] , resample=3 , size=224 , )
return image_processor
def A ( self : Optional[Any]):
_A : Optional[int] = self.dummy_prior
_A : Dict = self.dummy_image_encoder
_A : Dict = self.dummy_text_encoder
_A : str = self.dummy_tokenizer
_A : Optional[Any] = self.dummy_image_processor
_A : Optional[Any] = UnCLIPScheduler(
variance_type='fixed_small_log' , prediction_type='sample' , num_train_timesteps=1000 , clip_sample=SCREAMING_SNAKE_CASE , clip_sample_range=10.0 , )
_A : Dict = {
'prior': prior,
'image_encoder': image_encoder,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'scheduler': scheduler,
'image_processor': image_processor,
}
return components
def A ( self : Tuple , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[str]=0):
if str(SCREAMING_SNAKE_CASE).startswith('mps'):
_A : Dict = torch.manual_seed(SCREAMING_SNAKE_CASE)
else:
_A : int = torch.Generator(device=SCREAMING_SNAKE_CASE).manual_seed(SCREAMING_SNAKE_CASE)
_A : List[Any] = {
'prompt': 'horse',
'generator': generator,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def A ( self : List[Any]):
_A : str = 'cpu'
_A : Tuple = self.get_dummy_components()
_A : List[Any] = self.pipeline_class(**SCREAMING_SNAKE_CASE)
_A : Any = pipe.to(SCREAMING_SNAKE_CASE)
pipe.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE)
_A : Dict = pipe(**self.get_dummy_inputs(SCREAMING_SNAKE_CASE))
_A : str = output.image_embeds
_A : Optional[int] = pipe(
**self.get_dummy_inputs(SCREAMING_SNAKE_CASE) , return_dict=SCREAMING_SNAKE_CASE , )[0]
_A : Optional[int] = image[0, -10:]
_A : int = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
_A : Dict = np.array(
[-0.0532, 1.7120, 0.3656, -1.0852, -0.8946, -1.1756, 0.4348, 0.2482, 0.5146, -0.1156])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice).max() < 1e-2
@skip_mps
def A ( self : Any):
_A : Tuple = torch_device == 'cpu'
_A : Optional[int] = True
_A : Tuple = False
self._test_inference_batch_single_identical(
test_max_difference=SCREAMING_SNAKE_CASE , relax_max_difference=SCREAMING_SNAKE_CASE , test_mean_pixel_difference=SCREAMING_SNAKE_CASE , )
@skip_mps
def A ( self : int):
_A : Tuple = torch_device == 'cpu'
_A : Optional[Any] = False
self._test_attention_slicing_forward_pass(
test_max_difference=SCREAMING_SNAKE_CASE , test_mean_pixel_difference=SCREAMING_SNAKE_CASE , )
| 227 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _lowercase :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : Union[str, Any]=13 , __lowerCamelCase : str=32 , __lowerCamelCase : Optional[Any]=2 , __lowerCamelCase : List[Any]=3 , __lowerCamelCase : str=16 , __lowerCamelCase : Tuple=[1, 2, 1] , __lowerCamelCase : Tuple=[2, 2, 4] , __lowerCamelCase : str=2 , __lowerCamelCase : int=2.0 , __lowerCamelCase : Union[str, Any]=True , __lowerCamelCase : Tuple=0.0 , __lowerCamelCase : List[Any]=0.0 , __lowerCamelCase : int=0.1 , __lowerCamelCase : str="gelu" , __lowerCamelCase : int=False , __lowerCamelCase : List[Any]=True , __lowerCamelCase : Union[str, Any]=0.0_2 , __lowerCamelCase : Optional[Any]=1E-5 , __lowerCamelCase : Optional[int]=True , __lowerCamelCase : Any=None , __lowerCamelCase : List[str]=True , __lowerCamelCase : List[str]=10 , __lowerCamelCase : Optional[int]=8 , ):
'''simple docstring'''
lowerCamelCase__ : str = parent
lowerCamelCase__ : int = batch_size
lowerCamelCase__ : Union[str, Any] = image_size
lowerCamelCase__ : str = patch_size
lowerCamelCase__ : List[str] = num_channels
lowerCamelCase__ : Union[str, Any] = embed_dim
lowerCamelCase__ : Any = depths
lowerCamelCase__ : int = num_heads
lowerCamelCase__ : Tuple = window_size
lowerCamelCase__ : List[Any] = mlp_ratio
lowerCamelCase__ : Optional[int] = qkv_bias
lowerCamelCase__ : Dict = hidden_dropout_prob
lowerCamelCase__ : Dict = attention_probs_dropout_prob
lowerCamelCase__ : Any = drop_path_rate
lowerCamelCase__ : Dict = hidden_act
lowerCamelCase__ : Optional[int] = use_absolute_embeddings
lowerCamelCase__ : Union[str, Any] = patch_norm
lowerCamelCase__ : List[str] = layer_norm_eps
lowerCamelCase__ : Union[str, Any] = initializer_range
lowerCamelCase__ : Optional[int] = is_training
lowerCamelCase__ : List[Any] = scope
lowerCamelCase__ : Union[str, Any] = use_labels
lowerCamelCase__ : Dict = type_sequence_label_size
lowerCamelCase__ : Tuple = encoder_stride
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
lowerCamelCase__ : Dict = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase__ : Optional[Any] = None
if self.use_labels:
lowerCamelCase__ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase__ : Any = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase ( self : Any ):
'''simple docstring'''
return SwinvaConfig(
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 , )
def lowerCAmelCase ( self : str , __lowerCamelCase : List[str] , __lowerCamelCase : List[Any] , __lowerCamelCase : Tuple ):
'''simple docstring'''
lowerCamelCase__ : Tuple = SwinvaModel(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
lowerCamelCase__ : int = model(__SCREAMING_SNAKE_CASE )
lowerCamelCase__ : List[str] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowerCamelCase__ : List[str] = 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 lowerCAmelCase ( self : str , __lowerCamelCase : Dict , __lowerCamelCase : List[str] , __lowerCamelCase : Any ):
'''simple docstring'''
lowerCamelCase__ : List[str] = SwinvaForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
lowerCamelCase__ : Optional[int] = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCamelCase__ : str = 1
lowerCamelCase__ : Tuple = SwinvaForMaskedImageModeling(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
lowerCamelCase__ : Optional[int] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase__ : Tuple = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def lowerCAmelCase ( self : Union[str, Any] , __lowerCamelCase : Dict , __lowerCamelCase : List[str] , __lowerCamelCase : Any ):
'''simple docstring'''
lowerCamelCase__ : Tuple = self.type_sequence_label_size
lowerCamelCase__ : str = SwinvaForImageClassification(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
lowerCamelCase__ : List[Any] = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
lowerCamelCase__ : Any = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Tuple = config_and_inputs
lowerCamelCase__ : str = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _lowercase ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase):
"""simple docstring"""
A__ = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
A__ = (
{'''feature-extraction''': SwinvaModel, '''image-classification''': SwinvaForImageClassification}
if is_torch_available()
else {}
)
A__ = False
A__ = False
A__ = False
A__ = False
def lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = SwinvaModelTester(self )
lowerCamelCase__ : List[Any] = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , embed_dim=37 )
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCamelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
@unittest.skip(reason="Got `CUDA error: misaligned address` with PyTorch 2.0.0." )
def lowerCAmelCase ( self : str ):
'''simple docstring'''
pass
@unittest.skip(reason="Swinv2 does not use inputs_embeds" )
def lowerCAmelCase ( self : int ):
'''simple docstring'''
pass
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ : List[str] = model_class(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCamelCase__ : Any = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE , nn.Linear ) )
def lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase__ : Optional[int] = model_class(__SCREAMING_SNAKE_CASE )
lowerCamelCase__ : List[Any] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase__ : Optional[int] = [*signature.parameters.keys()]
lowerCamelCase__ : Optional[int] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : Any = True
for model_class in self.all_model_classes:
lowerCamelCase__ : int = True
lowerCamelCase__ : Tuple = False
lowerCamelCase__ : Optional[Any] = True
lowerCamelCase__ : Any = model_class(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
lowerCamelCase__ : Tuple = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
lowerCamelCase__ : Dict = outputs.attentions
lowerCamelCase__ : List[str] = len(self.model_tester.depths )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCamelCase__ : List[str] = True
lowerCamelCase__ : Optional[int] = config.window_size**2
lowerCamelCase__ : Any = model_class(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
lowerCamelCase__ : List[Any] = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
lowerCamelCase__ : Dict = outputs.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
lowerCamelCase__ : Union[str, Any] = len(__SCREAMING_SNAKE_CASE )
# Check attention is always last and order is fine
lowerCamelCase__ : Dict = True
lowerCamelCase__ : List[str] = True
lowerCamelCase__ : Dict = model_class(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
lowerCamelCase__ : Optional[Any] = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
if hasattr(self.model_tester , "num_hidden_states_types" ):
lowerCamelCase__ : Union[str, Any] = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
lowerCamelCase__ : List[Any] = 2
self.assertEqual(out_len + added_hidden_states , len(__SCREAMING_SNAKE_CASE ) )
lowerCamelCase__ : int = outputs.attentions
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_heads[0], window_size_squared, window_size_squared] , )
def lowerCAmelCase ( self : List[Any] , __lowerCamelCase : List[Any] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Any , __lowerCamelCase : Tuple ):
'''simple docstring'''
lowerCamelCase__ : int = model_class(__SCREAMING_SNAKE_CASE )
model.to(__SCREAMING_SNAKE_CASE )
model.eval()
with torch.no_grad():
lowerCamelCase__ : Union[str, Any] = model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
lowerCamelCase__ : List[str] = outputs.hidden_states
lowerCamelCase__ : str = getattr(
self.model_tester , "expected_num_hidden_layers" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
# Swinv2 has a different seq_length
lowerCamelCase__ : Union[str, Any] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowerCamelCase__ : Optional[int] = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
lowerCamelCase__ : Optional[int] = outputs.reshaped_hidden_states
self.assertEqual(len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[Any] = reshaped_hidden_states[0].shape
lowerCamelCase__ : int = (
reshaped_hidden_states[0].view(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , height * width ).permute(0 , 2 , 1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : List[str] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
lowerCamelCase__ : int = True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase__ : Tuple = True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self : Any ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : List[Any] = 3
lowerCamelCase__ : Dict = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
lowerCamelCase__ : List[Any] = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowerCamelCase__ : str = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowerCamelCase__ : List[Any] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowerCamelCase__ : int = True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCamelCase__ : Union[str, Any] = True
self.check_hidden_states_output(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , (padded_height, padded_width) )
def lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self : Any ):
'''simple docstring'''
lowerCamelCase__ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )
@slow
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase__ : str = SwinvaModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
lowerCamelCase__ , lowerCamelCase__ : Tuple = self.model_tester.prepare_config_and_inputs_for_common()
lowerCamelCase__ : Union[str, Any] = _config_zero_init(__SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
lowerCamelCase__ : Tuple = model_class(config=__SCREAMING_SNAKE_CASE )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() , [0.0, 1.0] , msg=f"Parameter {name} of model {model_class} seems not properly initialized" , )
@require_vision
@require_torch
class _lowercase ( unittest.TestCase):
"""simple docstring"""
@cached_property
def lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256" )
if is_vision_available()
else None
)
@slow
def lowerCAmelCase ( self : str ):
'''simple docstring'''
lowerCamelCase__ : List[Any] = SwinvaForImageClassification.from_pretrained("microsoft/swinv2-tiny-patch4-window8-256" ).to(
__SCREAMING_SNAKE_CASE )
lowerCamelCase__ : Optional[int] = self.default_image_processor
lowerCamelCase__ : Dict = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
lowerCamelCase__ : Any = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors="pt" ).to(__SCREAMING_SNAKE_CASE )
# forward pass
with torch.no_grad():
lowerCamelCase__ : List[Any] = model(**__SCREAMING_SNAKE_CASE )
# verify the logits
lowerCamelCase__ : Dict = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
lowerCamelCase__ : Optional[Any] = torch.tensor([-0.3_9_4_7, -0.4_3_0_6, 0.0_0_2_6] ).to(__SCREAMING_SNAKE_CASE )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1E-4 ) )
| 184 |
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class _A ( unittest.TestCase ):
def _lowerCamelCase ( self : List[Any]):
'''simple docstring'''
__a = [
'''safety_checker/pytorch_model.bin''',
'''safety_checker/model.safetensors''',
'''vae/diffusion_pytorch_model.bin''',
'''vae/diffusion_pytorch_model.safetensors''',
'''text_encoder/pytorch_model.bin''',
'''text_encoder/model.safetensors''',
'''unet/diffusion_pytorch_model.bin''',
'''unet/diffusion_pytorch_model.safetensors''',
]
self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__a = [
'''unet/diffusion_pytorch_model.bin''',
'''unet/diffusion_pytorch_model.safetensors''',
]
self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__a = [
'''safety_checker/pytorch_model.bin''',
'''safety_checker/model.safetensors''',
'''vae/diffusion_pytorch_model.bin''',
'''vae/diffusion_pytorch_model.safetensors''',
'''text_encoder/pytorch_model.bin''',
'''text_encoder/model.safetensors''',
'''unet/diffusion_pytorch_model.bin''',
# Removed: 'unet/diffusion_pytorch_model.safetensors',
]
self.assertFalse(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))
def _lowerCamelCase ( self : Dict):
'''simple docstring'''
__a = [
'''text_encoder/pytorch_model.bin''',
'''text_encoder/model.safetensors''',
]
self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__a = [
'''safety_checker/pytorch_model.bin''',
'''safety_checker/model.safetensors''',
'''vae/diffusion_pytorch_model.bin''',
'''vae/diffusion_pytorch_model.safetensors''',
'''text_encoder/pytorch_model.bin''',
# Removed: 'text_encoder/model.safetensors',
'''unet/diffusion_pytorch_model.bin''',
'''unet/diffusion_pytorch_model.safetensors''',
]
self.assertFalse(is_safetensors_compatible(__SCREAMING_SNAKE_CASE))
def _lowerCamelCase ( self : Tuple):
'''simple docstring'''
__a = [
'''safety_checker/pytorch_model.fp16.bin''',
'''safety_checker/model.fp16.safetensors''',
'''vae/diffusion_pytorch_model.fp16.bin''',
'''vae/diffusion_pytorch_model.fp16.safetensors''',
'''text_encoder/pytorch_model.fp16.bin''',
'''text_encoder/model.fp16.safetensors''',
'''unet/diffusion_pytorch_model.fp16.bin''',
'''unet/diffusion_pytorch_model.fp16.safetensors''',
]
__a = '''fp16'''
self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE , variant=__SCREAMING_SNAKE_CASE))
def _lowerCamelCase ( self : Dict):
'''simple docstring'''
__a = [
'''unet/diffusion_pytorch_model.fp16.bin''',
'''unet/diffusion_pytorch_model.fp16.safetensors''',
]
__a = '''fp16'''
self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE , variant=__SCREAMING_SNAKE_CASE))
def _lowerCamelCase ( self : Optional[Any]):
'''simple docstring'''
__a = [
'''unet/diffusion_pytorch_model.bin''',
'''unet/diffusion_pytorch_model.safetensors''',
]
__a = '''fp16'''
self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE , variant=__SCREAMING_SNAKE_CASE))
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
__a = [
'''safety_checker/pytorch_model.fp16.bin''',
'''safety_checker/model.fp16.safetensors''',
'''vae/diffusion_pytorch_model.fp16.bin''',
'''vae/diffusion_pytorch_model.fp16.safetensors''',
'''text_encoder/pytorch_model.fp16.bin''',
'''text_encoder/model.fp16.safetensors''',
'''unet/diffusion_pytorch_model.fp16.bin''',
# Removed: 'unet/diffusion_pytorch_model.fp16.safetensors',
]
__a = '''fp16'''
self.assertFalse(is_safetensors_compatible(__SCREAMING_SNAKE_CASE , variant=__SCREAMING_SNAKE_CASE))
def _lowerCamelCase ( self : Dict):
'''simple docstring'''
__a = [
'''text_encoder/pytorch_model.fp16.bin''',
'''text_encoder/model.fp16.safetensors''',
]
__a = '''fp16'''
self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE , variant=__SCREAMING_SNAKE_CASE))
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
__a = [
'''text_encoder/pytorch_model.bin''',
'''text_encoder/model.safetensors''',
]
__a = '''fp16'''
self.assertTrue(is_safetensors_compatible(__SCREAMING_SNAKE_CASE , variant=__SCREAMING_SNAKE_CASE))
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
__a = [
'''safety_checker/pytorch_model.fp16.bin''',
'''safety_checker/model.fp16.safetensors''',
'''vae/diffusion_pytorch_model.fp16.bin''',
'''vae/diffusion_pytorch_model.fp16.safetensors''',
'''text_encoder/pytorch_model.fp16.bin''',
# 'text_encoder/model.fp16.safetensors',
'''unet/diffusion_pytorch_model.fp16.bin''',
'''unet/diffusion_pytorch_model.fp16.safetensors''',
]
__a = '''fp16'''
self.assertFalse(is_safetensors_compatible(__SCREAMING_SNAKE_CASE , variant=__SCREAMING_SNAKE_CASE))
| 49 | 0 |
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase ) -> bool:
"""simple docstring"""
snake_case_ : Tuple = len(_UpperCamelCase )
snake_case_ : Optional[Any] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each arr value, a sum of zero(0) can be formed by not taking any element
# hence True/1
for i in range(arr_len + 1 ):
snake_case_ : Optional[Any] = True
# sum is not zero and set is empty then false
for i in range(1 , required_sum + 1 ):
snake_case_ : Dict = False
for i in range(1 , arr_len + 1 ):
for j in range(1 , required_sum + 1 ):
if arr[i - 1] > j:
snake_case_ : List[Any] = subset[i - 1][j]
if arr[i - 1] <= j:
snake_case_ : Optional[Any] = subset[i - 1][j] or subset[i - 1][j - arr[i - 1]]
return subset[arr_len][required_sum]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 368 |
import unittest
import numpy as np
import torch
from diffusers import PNDMPipeline, PNDMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class __lowerCAmelCase ( unittest.TestCase ):
@property
def lowerCamelCase (self ) -> Union[str, Any]:
'''simple docstring'''
torch.manual_seed(0 )
snake_case_ : int = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=('''DownBlock2D''', '''AttnDownBlock2D''') , up_block_types=('''AttnUpBlock2D''', '''UpBlock2D''') , )
return model
def lowerCamelCase (self ) -> Dict:
'''simple docstring'''
snake_case_ : Any = self.dummy_uncond_unet
snake_case_ : Optional[Any] = PNDMScheduler()
snake_case_ : Optional[Any] = PNDMPipeline(unet=__magic_name__ , scheduler=__magic_name__ )
pndm.to(__magic_name__ )
pndm.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : str = torch.manual_seed(0 )
snake_case_ : Dict = pndm(generator=__magic_name__ , num_inference_steps=20 , output_type='''numpy''' ).images
snake_case_ : str = torch.manual_seed(0 )
snake_case_ : str = pndm(generator=__magic_name__ , num_inference_steps=20 , output_type='''numpy''' , return_dict=__magic_name__ )[0]
snake_case_ : Any = image[0, -3:, -3:, -1]
snake_case_ : str = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ : str = np.array([1.0, 1.0, 0.0, 1.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase (self ) -> Any:
'''simple docstring'''
snake_case_ : Tuple = '''google/ddpm-cifar10-32'''
snake_case_ : Tuple = UNetaDModel.from_pretrained(__magic_name__ )
snake_case_ : Optional[Any] = PNDMScheduler()
snake_case_ : Any = PNDMPipeline(unet=__magic_name__ , scheduler=__magic_name__ )
pndm.to(__magic_name__ )
pndm.set_progress_bar_config(disable=__magic_name__ )
snake_case_ : int = torch.manual_seed(0 )
snake_case_ : Tuple = pndm(generator=__magic_name__ , output_type='''numpy''' ).images
snake_case_ : List[str] = image[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case_ : str = np.array([0.1_564, 0.14_645, 0.1_406, 0.14_715, 0.12_425, 0.14_045, 0.13_115, 0.12_175, 0.125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 279 | 0 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 346 |
'''simple docstring'''
from timeit import timeit
UpperCAmelCase_ = {
'MALAYALAM': True,
'String': False,
'rotor': True,
'level': True,
'A': True,
'BB': True,
'ABC': False,
'amanaplanacanalpanama': True, # "a man a plan a canal panama"
}
# Ensure our test data is valid
assert all((key == key[::-1]) is value for key, value in test_data.items())
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
UpperCAmelCase__ = 0
UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) - 1
while start_i < end_i:
if s[start_i] == s[end_i]:
start_i += 1
end_i -= 1
else:
return False
return True
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ ) // 2
UpperCAmelCase__ = len(SCREAMING_SNAKE_CASE__ )
# We need to traverse till half of the length of string
# as we can get access of the i'th last element from
# i'th index.
# eg: [0,1,2,3,4,5] => 4th index can be accessed
# with the help of 1st index (i==n-i-1)
# where n is length of string
return all(s[i] == s[n - i - 1] for i in range(SCREAMING_SNAKE_CASE__ ) )
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
if len(SCREAMING_SNAKE_CASE__ ) <= 2:
return True
if s[0] == s[len(SCREAMING_SNAKE_CASE__ ) - 1]:
return is_palindrome_recursive(s[1:-1] )
else:
return False
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
return s == s[::-1]
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : str ):
'''simple docstring'''
UpperCAmelCase__ = F'''all({name}(key) is value for key, value in test_data.items())'''
UpperCAmelCase__ = F'''from __main__ import test_data, {name}'''
UpperCAmelCase__ = 500000
UpperCAmelCase__ = timeit(stmt=SCREAMING_SNAKE_CASE__ , setup=SCREAMING_SNAKE_CASE__ , number=SCREAMING_SNAKE_CASE__ )
print(F'''{name:<35} finished {number:,} runs in {result:.5f} seconds''' )
if __name__ == "__main__":
for key, value in test_data.items():
assert is_palindrome(key) is is_palindrome_recursive(key)
assert is_palindrome(key) is is_palindrome_slice(key)
print(f"{key:21} {value}")
print('a man a plan a canal panama')
# finished 500,000 runs in 0.46793 seconds
benchmark_function('is_palindrome_slice')
# finished 500,000 runs in 0.85234 seconds
benchmark_function('is_palindrome')
# finished 500,000 runs in 1.32028 seconds
benchmark_function('is_palindrome_recursive')
# finished 500,000 runs in 2.08679 seconds
benchmark_function('is_palindrome_traversal')
| 346 | 1 |
"""simple docstring"""
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 : List[Any] = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, 'r', encoding='utf-8') as f:
_UpperCamelCase : str = json.load(f)
@require_torch
class snake_case ( unittest.TestCase ):
def lowerCamelCase__ ( self : Optional[int] , A : Tuple ):
'''simple docstring'''
return FSMTTokenizer.from_pretrained(_a )
def lowerCamelCase__ ( self : Optional[Any] , A : List[Any] ):
'''simple docstring'''
a : Optional[Any] = FSMTForConditionalGeneration.from_pretrained(_a ).to(_a )
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 lowerCamelCase__ ( self : Dict , A : Dict , A : Optional[Any] ):
'''simple docstring'''
a : Union[str, Any] = F'''facebook/wmt19-{pair}'''
a : Dict = self.get_tokenizer(_a )
a : Any = self.get_model(_a )
a : Tuple = bleu_data[pair]["""src"""]
a : Any = bleu_data[pair]["""tgt"""]
a : Any = tokenizer(_a , return_tensors='pt' , truncation=_a , padding='longest' ).to(_a )
a : Union[str, Any] = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
a : List[Any] = tokenizer.batch_decode(
_a , skip_special_tokens=_a , clean_up_tokenization_spaces=_a )
a : Dict = calculate_bleu(_a , _a )
print(_a )
self.assertGreaterEqual(scores['bleu'] , _a )
| 362 |
"""simple docstring"""
_UpperCamelCase : List[Any] = 8.31_44_62 # Unit - J mol-1 K-1
def snake_case (A_ :float , A_ :float , A_ :float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('Invalid inputs. Enter positive value.' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def snake_case (A_ :float , A_ :float , A_ :float ):
'''simple docstring'''
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError('Invalid inputs. Enter positive value.' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 186 | 0 |
"""simple docstring"""
from typing import Any
class _SCREAMING_SNAKE_CASE :
def __init__( self , __A ) -> int:
lowerCAmelCase_ :List[str] = data
lowerCAmelCase_ :Union[str, Any] = None
class _SCREAMING_SNAKE_CASE :
def __init__( self ) -> int:
lowerCAmelCase_ :Tuple = None
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Tuple = self.head
while temp is not None:
print(temp.data , end=""" """ )
lowerCAmelCase_ :str = temp.next
print()
def __lowerCAmelCase ( self , __A ) -> Optional[Any]:
lowerCAmelCase_ :str = Node(__A )
lowerCAmelCase_ :int = self.head
lowerCAmelCase_ :Optional[int] = new_node
def __lowerCAmelCase ( self , __A , __A ) -> List[Any]:
if node_data_a == node_data_a:
return
else:
lowerCAmelCase_ :Optional[int] = self.head
while node_a is not None and node_a.data != node_data_a:
lowerCAmelCase_ :Optional[int] = node_a.next
lowerCAmelCase_ :Any = self.head
while node_a is not None and node_a.data != node_data_a:
lowerCAmelCase_ :List[str] = node_a.next
if node_a is None or node_a is None:
return
lowerCAmelCase_ , lowerCAmelCase_ :Optional[Any] = node_a.data, node_a.data
if __name__ == "__main__":
__UpperCAmelCase = LinkedList()
for i in range(5, 0, -1):
ll.push(i)
ll.print_list()
ll.swap_nodes(1, 4)
print('After swapping')
ll.print_list()
| 84 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoCBertForCausalLM',
'RoCBertForMaskedLM',
'RoCBertForMultipleChoice',
'RoCBertForPreTraining',
'RoCBertForQuestionAnswering',
'RoCBertForSequenceClassification',
'RoCBertForTokenClassification',
'RoCBertLayer',
'RoCBertModel',
'RoCBertPreTrainedModel',
'load_tf_weights_in_roc_bert',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 84 | 1 |
"""simple docstring"""
import importlib
import torch
import yaml
from omegaconf import OmegaConf
from taming.models.vqgan import VQModel
def lowercase ( A_ , A_=False )-> int:
'''simple docstring'''
a : Optional[int] = OmegaConf.load(A_ )
if display:
print(yaml.dump(OmegaConf.to_container(A_ ) ) )
return config
def lowercase ( A_ , A_=None , A_=None )-> List[Any]:
'''simple docstring'''
if conf_path is None:
a : str = "./model_checkpoints/vqgan_only.yaml"
a : Union[str, Any] = load_config(A_ , display=A_ )
a : Tuple = VQModel(**config.model.params )
if ckpt_path is None:
a : List[str] = "./model_checkpoints/vqgan_only.pt"
a : Tuple = torch.load(A_ , map_location=A_ )
if ".ckpt" in ckpt_path:
a : Optional[int] = sd["state_dict"]
model.load_state_dict(A_ , strict=A_ )
model.to(A_ )
del sd
return model
def lowercase ( A_ , A_ )-> List[Any]:
'''simple docstring'''
a , a , a : Optional[int] = model.encode(A_ )
print(F'''VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}''' )
a : Union[str, Any] = model.decode(A_ )
return xrec
def lowercase ( A_ , A_=False )-> Tuple:
'''simple docstring'''
a , a : Optional[Any] = string.rsplit("." , 1 )
if reload:
a : str = importlib.import_module(A_ )
importlib.reload(A_ )
return getattr(importlib.import_module(A_ , package=A_ ) , cls )
def lowercase ( A_ )-> Union[str, Any]:
'''simple docstring'''
if "target" not in config:
raise KeyError("Expected key `target` to instantiate." )
return get_obj_from_str(config["target"] )(**config.get("params" , {} ) )
def lowercase ( A_ , A_ , A_=True , A_=True )-> Any:
'''simple docstring'''
a : Optional[int] = instantiate_from_config(A_ )
if sd is not None:
model.load_state_dict(A_ )
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def lowercase ( A_ , A_ , A_ , A_ )-> Optional[Any]:
'''simple docstring'''
if ckpt:
a : int = torch.load(A_ , map_location="cpu" )
a : Any = pl_sd["global_step"]
print(F'''loaded model from global step {global_step}.''' )
else:
a : Tuple = {"state_dict": None}
a : int = None
a : List[str] = load_model_from_config(config.model , pl_sd["state_dict"] , gpu=A_ , eval_mode=A_ )["model"]
return model, global_step
| 226 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def lowercase ( A_ )-> Optional[Any]:
'''simple docstring'''
a : Optional[Any] = 384
if "tiny" in model_name:
a : List[str] = [3, 3, 9, 3]
a : Optional[Any] = [96, 192, 384, 768]
if "small" in model_name:
a : Tuple = [3, 3, 27, 3]
a : str = [96, 192, 384, 768]
if "base" in model_name:
a : Union[str, Any] = [3, 3, 27, 3]
a : Dict = [128, 256, 512, 1_024]
a : Any = 512
if "large" in model_name:
a : Optional[Any] = [3, 3, 27, 3]
a : str = [192, 384, 768, 1_536]
a : Dict = 768
if "xlarge" in model_name:
a : str = [3, 3, 27, 3]
a : List[Any] = [256, 512, 1_024, 2_048]
a : List[Any] = 1_024
# set label information
a : int = 150
a : str = "huggingface/label-files"
a : Tuple = "ade20k-id2label.json"
a : Dict = json.load(open(hf_hub_download(A_ , A_ , repo_type="dataset" ) , "r" ) )
a : int = {int(A_ ): v for k, v in idalabel.items()}
a : List[Any] = {v: k for k, v in idalabel.items()}
a : Optional[int] = ConvNextConfig(
depths=A_ , hidden_sizes=A_ , out_features=["stage1", "stage2", "stage3", "stage4"] )
a : Tuple = UperNetConfig(
backbone_config=A_ , auxiliary_in_channels=A_ , num_labels=A_ , idalabel=A_ , labelaid=A_ , )
return config
def lowercase ( A_ )-> Tuple:
'''simple docstring'''
a : int = []
# fmt: off
# stem
rename_keys.append(("backbone.downsample_layers.0.0.weight", "backbone.embeddings.patch_embeddings.weight") )
rename_keys.append(("backbone.downsample_layers.0.0.bias", "backbone.embeddings.patch_embeddings.bias") )
rename_keys.append(("backbone.downsample_layers.0.1.weight", "backbone.embeddings.layernorm.weight") )
rename_keys.append(("backbone.downsample_layers.0.1.bias", "backbone.embeddings.layernorm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F'''backbone.stages.{i}.{j}.gamma''', F'''backbone.encoder.stages.{i}.layers.{j}.layer_scale_parameter''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.weight''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.depthwise_conv.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.dwconv.bias''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.norm.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.weight''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.norm.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.layernorm.bias''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.weight''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv1.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv1.bias''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.weight''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.weight''') )
rename_keys.append((F'''backbone.stages.{i}.{j}.pointwise_conv2.bias''', F'''backbone.encoder.stages.{i}.layers.{j}.pwconv2.bias''') )
if i > 0:
rename_keys.append((F'''backbone.downsample_layers.{i}.0.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.weight''') )
rename_keys.append((F'''backbone.downsample_layers.{i}.0.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.0.bias''') )
rename_keys.append((F'''backbone.downsample_layers.{i}.1.weight''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.weight''') )
rename_keys.append((F'''backbone.downsample_layers.{i}.1.bias''', F'''backbone.encoder.stages.{i}.downsampling_layer.1.bias''') )
rename_keys.append((F'''backbone.norm{i}.weight''', F'''backbone.hidden_states_norms.stage{i+1}.weight''') )
rename_keys.append((F'''backbone.norm{i}.bias''', F'''backbone.hidden_states_norms.stage{i+1}.bias''') )
# decode head
rename_keys.extend(
[
("decode_head.conv_seg.weight", "decode_head.classifier.weight"),
("decode_head.conv_seg.bias", "decode_head.classifier.bias"),
("auxiliary_head.conv_seg.weight", "auxiliary_head.classifier.weight"),
("auxiliary_head.conv_seg.bias", "auxiliary_head.classifier.bias"),
] )
# fmt: on
return rename_keys
def lowercase ( A_ , A_ , A_ )-> str:
'''simple docstring'''
a : str = dct.pop(A_ )
a : str = val
def lowercase ( A_ , A_ , A_ )-> int:
'''simple docstring'''
a : Any = {
"upernet-convnext-tiny": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_tiny_fp16_512x512_160k_ade20k/upernet_convnext_tiny_fp16_512x512_160k_ade20k_20220227_124553-cad485de.pth",
"upernet-convnext-small": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_small_fp16_512x512_160k_ade20k/upernet_convnext_small_fp16_512x512_160k_ade20k_20220227_131208-1b1e394f.pth",
"upernet-convnext-base": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_base_fp16_512x512_160k_ade20k/upernet_convnext_base_fp16_512x512_160k_ade20k_20220227_181227-02a24fc6.pth",
"upernet-convnext-large": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_large_fp16_640x640_160k_ade20k/upernet_convnext_large_fp16_640x640_160k_ade20k_20220226_040532-e57aa54d.pth",
"upernet-convnext-xlarge": "https://download.openmmlab.com/mmsegmentation/v0.5/convnext/upernet_convnext_xlarge_fp16_640x640_160k_ade20k/upernet_convnext_xlarge_fp16_640x640_160k_ade20k_20220226_080344-95fc38c2.pth",
}
a : Tuple = model_name_to_url[model_name]
a : int = torch.hub.load_state_dict_from_url(A_ , map_location="cpu" )["state_dict"]
a : Optional[Any] = get_upernet_config(A_ )
a : int = UperNetForSemanticSegmentation(A_ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
a : Tuple = state_dict.pop(A_ )
if "bn" in key:
a : Optional[int] = key.replace("bn" , "batch_norm" )
a : Any = val
# rename keys
a : Any = create_rename_keys(A_ )
for src, dest in rename_keys:
rename_key(A_ , A_ , A_ )
model.load_state_dict(A_ )
# verify on image
a : Dict = "https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg"
a : Any = Image.open(requests.get(A_ , stream=A_ ).raw ).convert("RGB" )
a : Union[str, Any] = SegformerImageProcessor()
a : Dict = processor(A_ , return_tensors="pt" ).pixel_values
with torch.no_grad():
a : Any = model(A_ )
if model_name == "upernet-convnext-tiny":
a : List[str] = torch.tensor(
[[-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.8_1_1_0, -8.8_1_1_0, -8.6_5_2_1], [-8.7_7_4_6, -8.7_7_4_6, -8.6_1_3_0]] )
elif model_name == "upernet-convnext-small":
a : int = torch.tensor(
[[-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.8_2_3_6, -8.8_2_3_6, -8.6_7_7_1], [-8.7_6_3_8, -8.7_6_3_8, -8.6_2_4_0]] )
elif model_name == "upernet-convnext-base":
a : Union[str, Any] = torch.tensor(
[[-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.8_5_5_8, -8.8_5_5_8, -8.6_9_0_5], [-8.7_6_6_9, -8.7_6_6_9, -8.6_0_2_1]] )
elif model_name == "upernet-convnext-large":
a : Union[str, Any] = torch.tensor(
[[-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_6_6_0, -8.6_6_6_0, -8.6_2_1_0], [-8.6_3_1_0, -8.6_3_1_0, -8.5_9_6_4]] )
elif model_name == "upernet-convnext-xlarge":
a : str = torch.tensor(
[[-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_9_8_0, -8.4_9_8_0, -8.3_9_7_7], [-8.4_3_7_9, -8.4_3_7_9, -8.3_4_1_2]] )
print("Logits:" , outputs.logits[0, 0, :3, :3] )
assert torch.allclose(outputs.logits[0, 0, :3, :3] , A_ , atol=1e-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(A_ )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(A_ )
if push_to_hub:
print(F'''Pushing model and processor for {model_name} to hub''' )
model.push_to_hub(F'''openmmlab/{model_name}''' )
processor.push_to_hub(F'''openmmlab/{model_name}''' )
if __name__ == "__main__":
__lowercase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""upernet-convnext-tiny""",
type=str,
choices=[f'''upernet-convnext-{size}''' for size in ["""tiny""", """small""", """base""", """large""", """xlarge"""]],
help="""Name of the ConvNext UperNet model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
__lowercase = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 226 | 1 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
snake_case__ : int = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class A_ ( _a , unittest.TestCase ):
lowerCAmelCase__ = XLMRobertaTokenizer
lowerCAmelCase__ = XLMRobertaTokenizerFast
lowerCAmelCase__ = True
lowerCAmelCase__ = True
def _lowerCAmelCase (self :Any )-> List[Any]:
super().setUp()
# We have a SentencePiece fixture for testing
__A = XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowerCAmelCase (self :Dict )-> List[Any]:
__A = "<pad>"
__A = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def _lowerCAmelCase (self :Optional[Any] )-> Union[str, Any]:
__A = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '''<s>''' )
self.assertEqual(vocab_keys[1] , '''<pad>''' )
self.assertEqual(vocab_keys[-1] , '''<mask>''' )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , 1002 )
def _lowerCAmelCase (self :str )-> Union[str, Any]:
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def _lowerCAmelCase (self :int )-> Optional[Any]:
__A = XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
__A = tokenizer.tokenize('''This is a test''' )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , ['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
__A = tokenizer.tokenize('''I was born in 92000, and this is falsé.''' )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''9''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''é''',
'''.''',
] , )
__A = tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
__A = tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
SPIECE_UNDERLINE + '''I''',
SPIECE_UNDERLINE + '''was''',
SPIECE_UNDERLINE + '''b''',
'''or''',
'''n''',
SPIECE_UNDERLINE + '''in''',
SPIECE_UNDERLINE + '''''',
'''<unk>''',
'''2''',
'''0''',
'''0''',
'''0''',
''',''',
SPIECE_UNDERLINE + '''and''',
SPIECE_UNDERLINE + '''this''',
SPIECE_UNDERLINE + '''is''',
SPIECE_UNDERLINE + '''f''',
'''al''',
'''s''',
'''<unk>''',
'''.''',
] , )
def _lowerCAmelCase (self :Tuple )-> Union[str, Any]:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
__A = (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
__A = self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
__A = self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
__A = tempfile.mkdtemp()
__A = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__ )
__A = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
__A = tuple(f for f in tokenizer_r_files if '''tokenizer.json''' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Checks everything loads correctly in the same way
__A = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__ )
__A = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
# Save tokenizer rust, legacy_format=True
__A = tempfile.mkdtemp()
__A = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__ , legacy_format=SCREAMING_SNAKE_CASE__ )
__A = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Checks everything loads correctly in the same way
__A = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__ )
__A = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
# Save tokenizer rust, legacy_format=False
__A = tempfile.mkdtemp()
__A = tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__ , legacy_format=SCREAMING_SNAKE_CASE__ )
__A = tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('''tokenizer.json''' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
__A = tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__ )
__A = tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
@cached_property
def _lowerCAmelCase (self :List[str] )-> Optional[Any]:
return XLMRobertaTokenizer.from_pretrained('''xlm-roberta-base''' )
def _lowerCAmelCase (self :Union[str, Any] )-> str:
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(SCREAMING_SNAKE_CASE__ , f.name )
__A = XLMRobertaTokenizer(f.name , keep_accents=SCREAMING_SNAKE_CASE__ )
__A = pickle.dumps(SCREAMING_SNAKE_CASE__ )
pickle.loads(SCREAMING_SNAKE_CASE__ )
def _lowerCAmelCase (self :int )-> int:
if not self.test_rust_tokenizer:
return
__A = self.get_tokenizer()
__A = self.get_rust_tokenizer()
__A = "I was born in 92000, and this is falsé."
__A = tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
__A = rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__A = tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
__A = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__A = self.get_rust_tokenizer()
__A = tokenizer.encode(SCREAMING_SNAKE_CASE__ )
__A = rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@slow
def _lowerCAmelCase (self :Any )-> Union[str, Any]:
__A = "Hello World!"
__A = [0, 3_5378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(SCREAMING_SNAKE_CASE__ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__ ) )
@slow
def _lowerCAmelCase (self :Union[str, Any] )-> List[str]:
__A = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
__A = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
17_9459,
12_4850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
1_0114,
711,
152,
20,
6,
5,
2_2376,
642,
1221,
1_5190,
3_4153,
450,
5608,
959,
1119,
5_7702,
136,
186,
47,
1098,
2_9367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
5_0901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(SCREAMING_SNAKE_CASE__ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__ ) )
@slow
def _lowerCAmelCase (self :Tuple )-> str:
# fmt: off
__A = {"input_ids": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE__ , model_name='''xlm-roberta-base''' , revision='''d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3''' , )
| 117 |
'''simple docstring'''
import gc
import unittest
import torch
from parameterized import parameterized
from diffusers import AutoencoderKL
from diffusers.utils import floats_tensor, load_hf_numpy, require_torch_gpu, slow, torch_all_close, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class _snake_case ( _a , _a , unittest.TestCase ):
_A : Tuple = AutoencoderKL
_A : Union[str, Any] = '''sample'''
_A : int = 1E-2
@property
def __UpperCamelCase ( self : Union[str, Any] ):
SCREAMING_SNAKE_CASE:Tuple = 4
SCREAMING_SNAKE_CASE:Dict = 3
SCREAMING_SNAKE_CASE:str = (32, 32)
SCREAMING_SNAKE_CASE:List[str] = floats_tensor((batch_size, num_channels) + sizes ).to(SCREAMING_SNAKE_CASE__ )
return {"sample": image}
@property
def __UpperCamelCase ( self : Any ):
return (3, 32, 32)
@property
def __UpperCamelCase ( self : int ):
return (3, 32, 32)
def __UpperCamelCase ( self : Dict ):
SCREAMING_SNAKE_CASE:Optional[Any] = {
"block_out_channels": [32, 64],
"in_channels": 3,
"out_channels": 3,
"down_block_types": ["DownEncoderBlock2D", "DownEncoderBlock2D"],
"up_block_types": ["UpDecoderBlock2D", "UpDecoderBlock2D"],
"latent_channels": 4,
}
SCREAMING_SNAKE_CASE:List[Any] = self.dummy_input
return init_dict, inputs_dict
def __UpperCamelCase ( self : int ):
pass
def __UpperCamelCase ( self : Tuple ):
pass
@unittest.skipIf(torch_device == "mps" ,"Gradient checkpointing skipped on MPS" )
def __UpperCamelCase ( self : str ):
# enable deterministic behavior for gradient checkpointing
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE:Optional[Any] = self.prepare_init_args_and_inputs_for_common()
SCREAMING_SNAKE_CASE:Optional[Any] = self.model_class(**SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
assert not model.is_gradient_checkpointing and model.training
SCREAMING_SNAKE_CASE:str = model(**SCREAMING_SNAKE_CASE__ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model.zero_grad()
SCREAMING_SNAKE_CASE:str = torch.randn_like(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:List[str] = (out - labels).mean()
loss.backward()
# re-instantiate the model now enabling gradient checkpointing
SCREAMING_SNAKE_CASE:List[Any] = self.model_class(**SCREAMING_SNAKE_CASE__ )
# clone model
model_a.load_state_dict(model.state_dict() )
model_a.to(SCREAMING_SNAKE_CASE__ )
model_a.enable_gradient_checkpointing()
assert model_a.is_gradient_checkpointing and model_a.training
SCREAMING_SNAKE_CASE:Optional[int] = model_a(**SCREAMING_SNAKE_CASE__ ).sample
# run the backwards pass on the model. For backwards pass, for simplicity purpose,
# we won't calculate the loss and rather backprop on out.sum()
model_a.zero_grad()
SCREAMING_SNAKE_CASE:List[Any] = (out_a - labels).mean()
loss_a.backward()
# compare the output and parameters gradients
self.assertTrue((loss - loss_a).abs() < 1e-5 )
SCREAMING_SNAKE_CASE:Dict = dict(model.named_parameters() )
SCREAMING_SNAKE_CASE:Tuple = dict(model_a.named_parameters() )
for name, param in named_params.items():
self.assertTrue(torch_all_close(param.grad.data ,named_params_a[name].grad.data ,atol=5e-5 ) )
def __UpperCamelCase ( self : int ):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE:int = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" ,output_loading_info=SCREAMING_SNAKE_CASE__ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE__ )
self.assertEqual(len(loading_info["missing_keys"] ) ,0 )
model.to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Any = model(**self.dummy_input )
assert image is not None, "Make sure output is not None"
def __UpperCamelCase ( self : Optional[Any] ):
SCREAMING_SNAKE_CASE:Union[str, Any] = AutoencoderKL.from_pretrained("fusing/autoencoder-kl-dummy" )
SCREAMING_SNAKE_CASE:int = model.to(SCREAMING_SNAKE_CASE__ )
model.eval()
if torch_device == "mps":
SCREAMING_SNAKE_CASE:str = torch.manual_seed(0 )
else:
SCREAMING_SNAKE_CASE:Tuple = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(0 )
SCREAMING_SNAKE_CASE:Any = torch.randn(
1 ,model.config.in_channels ,model.config.sample_size ,model.config.sample_size ,generator=torch.manual_seed(0 ) ,)
SCREAMING_SNAKE_CASE:Optional[int] = image.to(SCREAMING_SNAKE_CASE__ )
with torch.no_grad():
SCREAMING_SNAKE_CASE:Union[str, Any] = model(SCREAMING_SNAKE_CASE__ ,sample_posterior=SCREAMING_SNAKE_CASE__ ,generator=SCREAMING_SNAKE_CASE__ ).sample
SCREAMING_SNAKE_CASE:str = output[0, -1, -3:, -3:].flatten().cpu()
# Since the VAE Gaussian prior's generator is seeded on the appropriate device,
# the expected output slices are not the same for CPU and GPU.
if torch_device == "mps":
SCREAMING_SNAKE_CASE:List[Any] = torch.tensor(
[
-4.0_078e-01,
-3.8_323e-04,
-1.2_681e-01,
-1.1_462e-01,
2.0_095e-01,
1.0_893e-01,
-8.8_247e-02,
-3.0_361e-01,
-9.8_644e-03,
] )
elif torch_device == "cpu":
SCREAMING_SNAKE_CASE:Optional[int] = torch.tensor(
[-0.1_352, 0.0_878, 0.0_419, -0.0_818, -0.1_069, 0.0_688, -0.1_458, -0.4_446, -0.0_026] )
else:
SCREAMING_SNAKE_CASE:Dict = torch.tensor(
[-0.2_421, 0.4_642, 0.2_507, -0.0_438, 0.0_682, 0.3_160, -0.2_018, -0.0_727, 0.2_485] )
self.assertTrue(torch_all_close(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,rtol=1e-2 ) )
@slow
class _snake_case ( unittest.TestCase ):
def __UpperCamelCase ( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : int ):
return F'''gaussian_noise_s={seed}_shape={"_".join([str(SCREAMING_SNAKE_CASE__ ) for s in shape] )}.npy'''
def __UpperCamelCase ( self : Tuple ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : Dict ,SCREAMING_SNAKE_CASE__ : Dict=0 ,SCREAMING_SNAKE_CASE__ : Any=(4, 3, 512, 512) ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=False ):
SCREAMING_SNAKE_CASE:str = torch.floataa if fpaa else torch.floataa
SCREAMING_SNAKE_CASE:List[Any] = torch.from_numpy(load_hf_numpy(self.get_file_format(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) ) ).to(SCREAMING_SNAKE_CASE__ ).to(SCREAMING_SNAKE_CASE__ )
return image
def __UpperCamelCase ( self : int ,SCREAMING_SNAKE_CASE__ : Dict="CompVis/stable-diffusion-v1-4" ,SCREAMING_SNAKE_CASE__ : int=False ):
SCREAMING_SNAKE_CASE:Union[str, Any] = "fp16" if fpaa else None
SCREAMING_SNAKE_CASE:Optional[Any] = torch.floataa if fpaa else torch.floataa
SCREAMING_SNAKE_CASE:Union[str, Any] = AutoencoderKL.from_pretrained(
SCREAMING_SNAKE_CASE__ ,subfolder="vae" ,torch_dtype=SCREAMING_SNAKE_CASE__ ,revision=SCREAMING_SNAKE_CASE__ ,)
model.to(SCREAMING_SNAKE_CASE__ ).eval()
return model
def __UpperCamelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : List[str]=0 ):
if torch_device == "mps":
return torch.manual_seed(SCREAMING_SNAKE_CASE__ )
return torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
@parameterized.expand(
[
# fmt: off
[33, [-0.1_603, 0.9_878, -0.0_495, -0.0_790, -0.2_709, 0.8_375, -0.2_060, -0.0_824], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
[47, [-0.2_376, 0.1_168, 0.1_332, -0.4_840, -0.2_508, -0.0_791, -0.0_493, -0.4_089], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
# fmt: on
] )
def __UpperCamelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : List[str] ,SCREAMING_SNAKE_CASE__ : Optional[Any] ,SCREAMING_SNAKE_CASE__ : List[Any] ):
SCREAMING_SNAKE_CASE:Any = self.get_sd_vae_model()
SCREAMING_SNAKE_CASE:Tuple = self.get_sd_image(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Dict = self.get_generator(SCREAMING_SNAKE_CASE__ )
with torch.no_grad():
SCREAMING_SNAKE_CASE:List[str] = model(SCREAMING_SNAKE_CASE__ ,generator=SCREAMING_SNAKE_CASE__ ,sample_posterior=SCREAMING_SNAKE_CASE__ ).sample
assert sample.shape == image.shape
SCREAMING_SNAKE_CASE:List[Any] = sample[-1, -2:, -2:, :2].flatten().float().cpu()
SCREAMING_SNAKE_CASE:Union[str, Any] = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[33, [-0.0_513, 0.0_289, 1.3_799, 0.2_166, -0.2_573, -0.0_871, 0.5_103, -0.0_999]],
[47, [-0.4_128, -0.1_320, -0.3_704, 0.1_965, -0.4_116, -0.2_332, -0.3_340, 0.2_247]],
# fmt: on
] )
@require_torch_gpu
def __UpperCamelCase ( self : str ,SCREAMING_SNAKE_CASE__ : List[str] ,SCREAMING_SNAKE_CASE__ : Tuple ):
SCREAMING_SNAKE_CASE:Tuple = self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:str = self.get_sd_image(SCREAMING_SNAKE_CASE__ ,fpaa=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Tuple = self.get_generator(SCREAMING_SNAKE_CASE__ )
with torch.no_grad():
SCREAMING_SNAKE_CASE:Optional[int] = model(SCREAMING_SNAKE_CASE__ ,generator=SCREAMING_SNAKE_CASE__ ,sample_posterior=SCREAMING_SNAKE_CASE__ ).sample
assert sample.shape == image.shape
SCREAMING_SNAKE_CASE:int = sample[-1, -2:, :2, -2:].flatten().float().cpu()
SCREAMING_SNAKE_CASE:Optional[int] = torch.tensor(SCREAMING_SNAKE_CASE__ )
assert torch_all_close(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.1_609, 0.9_866, -0.0_487, -0.0_777, -0.2_716, 0.8_368, -0.2_055, -0.0_814], [-0.2_395, 0.0_098, 0.0_102, -0.0_709, -0.2_840, -0.0_274, -0.0_718, -0.1_824]],
[47, [-0.2_377, 0.1_147, 0.1_333, -0.4_841, -0.2_506, -0.0_805, -0.0_491, -0.4_085], [0.0_350, 0.0_847, 0.0_467, 0.0_344, -0.0_842, -0.0_547, -0.0_633, -0.1_131]],
# fmt: on
] )
def __UpperCamelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : List[str] ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : Optional[Any] ):
SCREAMING_SNAKE_CASE:List[str] = self.get_sd_vae_model()
SCREAMING_SNAKE_CASE:Union[str, Any] = self.get_sd_image(SCREAMING_SNAKE_CASE__ )
with torch.no_grad():
SCREAMING_SNAKE_CASE:Union[str, Any] = model(SCREAMING_SNAKE_CASE__ ).sample
assert sample.shape == image.shape
SCREAMING_SNAKE_CASE:int = sample[-1, -2:, -2:, :2].flatten().float().cpu()
SCREAMING_SNAKE_CASE:str = torch.tensor(expected_slice_mps if torch_device == "mps" else expected_slice )
assert torch_all_close(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=3e-3 )
@parameterized.expand(
[
# fmt: off
[13, [-0.2_051, -0.1_803, -0.2_311, -0.2_114, -0.3_292, -0.3_574, -0.2_953, -0.3_323]],
[37, [-0.2_632, -0.2_625, -0.2_199, -0.2_741, -0.4_539, -0.4_990, -0.3_720, -0.4_925]],
# fmt: on
] )
@require_torch_gpu
def __UpperCamelCase ( self : int ,SCREAMING_SNAKE_CASE__ : Dict ,SCREAMING_SNAKE_CASE__ : Optional[int] ):
SCREAMING_SNAKE_CASE:Any = self.get_sd_vae_model()
SCREAMING_SNAKE_CASE:List[str] = self.get_sd_image(SCREAMING_SNAKE_CASE__ ,shape=(3, 4, 64, 64) )
with torch.no_grad():
SCREAMING_SNAKE_CASE:Optional[Any] = model.decode(SCREAMING_SNAKE_CASE__ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
SCREAMING_SNAKE_CASE:List[str] = sample[-1, -2:, :2, -2:].flatten().cpu()
SCREAMING_SNAKE_CASE:Any = torch.tensor(SCREAMING_SNAKE_CASE__ )
assert torch_all_close(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=1e-3 )
@parameterized.expand(
[
# fmt: off
[27, [-0.0_369, 0.0_207, -0.0_776, -0.0_682, -0.1_747, -0.1_930, -0.1_465, -0.2_039]],
[16, [-0.1_628, -0.2_134, -0.2_747, -0.2_642, -0.3_774, -0.4_404, -0.3_687, -0.4_277]],
# fmt: on
] )
@require_torch_gpu
def __UpperCamelCase ( self : Any ,SCREAMING_SNAKE_CASE__ : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : List[str] ):
SCREAMING_SNAKE_CASE:int = self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Dict = self.get_sd_image(SCREAMING_SNAKE_CASE__ ,shape=(3, 4, 64, 64) ,fpaa=SCREAMING_SNAKE_CASE__ )
with torch.no_grad():
SCREAMING_SNAKE_CASE:Optional[Any] = model.decode(SCREAMING_SNAKE_CASE__ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
SCREAMING_SNAKE_CASE:Tuple = sample[-1, -2:, :2, -2:].flatten().float().cpu()
SCREAMING_SNAKE_CASE:Any = torch.tensor(SCREAMING_SNAKE_CASE__ )
assert torch_all_close(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=5e-3 )
@parameterized.expand([(13,), (16,), (27,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() ,reason="xformers is not required when using PyTorch 2.0." )
def __UpperCamelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : List[str] ):
SCREAMING_SNAKE_CASE:str = self.get_sd_vae_model(fpaa=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Dict = self.get_sd_image(SCREAMING_SNAKE_CASE__ ,shape=(3, 4, 64, 64) ,fpaa=SCREAMING_SNAKE_CASE__ )
with torch.no_grad():
SCREAMING_SNAKE_CASE:List[str] = model.decode(SCREAMING_SNAKE_CASE__ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
SCREAMING_SNAKE_CASE:Optional[Any] = model.decode(SCREAMING_SNAKE_CASE__ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=1e-1 )
@parameterized.expand([(13,), (16,), (37,)] )
@require_torch_gpu
@unittest.skipIf(not is_xformers_available() ,reason="xformers is not required when using PyTorch 2.0." )
def __UpperCamelCase ( self : Tuple ,SCREAMING_SNAKE_CASE__ : Optional[int] ):
SCREAMING_SNAKE_CASE:List[Any] = self.get_sd_vae_model()
SCREAMING_SNAKE_CASE:List[Any] = self.get_sd_image(SCREAMING_SNAKE_CASE__ ,shape=(3, 4, 64, 64) )
with torch.no_grad():
SCREAMING_SNAKE_CASE:List[Any] = model.decode(SCREAMING_SNAKE_CASE__ ).sample
model.enable_xformers_memory_efficient_attention()
with torch.no_grad():
SCREAMING_SNAKE_CASE:int = model.decode(SCREAMING_SNAKE_CASE__ ).sample
assert list(sample.shape ) == [3, 3, 512, 512]
assert torch_all_close(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=1e-2 )
@parameterized.expand(
[
# fmt: off
[33, [-0.3_001, 0.0_918, -2.6_984, -3.9_720, -3.2_099, -5.0_353, 1.7_338, -0.2_065, 3.4_267]],
[47, [-1.5_030, -4.3_871, -6.0_355, -9.1_157, -1.6_661, -2.7_853, 2.1_607, -5.0_823, 2.5_633]],
# fmt: on
] )
def __UpperCamelCase ( self : List[str] ,SCREAMING_SNAKE_CASE__ : Optional[int] ,SCREAMING_SNAKE_CASE__ : Optional[int] ):
SCREAMING_SNAKE_CASE:int = self.get_sd_vae_model()
SCREAMING_SNAKE_CASE:List[Any] = self.get_sd_image(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Optional[Any] = self.get_generator(SCREAMING_SNAKE_CASE__ )
with torch.no_grad():
SCREAMING_SNAKE_CASE:List[Any] = model.encode(SCREAMING_SNAKE_CASE__ ).latent_dist
SCREAMING_SNAKE_CASE:int = dist.sample(generator=SCREAMING_SNAKE_CASE__ )
assert list(sample.shape ) == [image.shape[0], 4] + [i // 8 for i in image.shape[2:]]
SCREAMING_SNAKE_CASE:List[Any] = sample[0, -1, -3:, -3:].flatten().cpu()
SCREAMING_SNAKE_CASE:int = torch.tensor(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Any = 3e-3 if torch_device != "mps" else 1e-2
assert torch_all_close(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=SCREAMING_SNAKE_CASE__ )
| 139 | 0 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def _snake_case( SCREAMING_SNAKE_CASE__ = 3 ) -> qiskit.result.counts.Counts:
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
raise TypeError("""number of qubits must be a integer.""" )
if number_of_qubits <= 0:
raise ValueError("""number of qubits must be > 0.""" )
if math.floor(SCREAMING_SNAKE_CASE__ ) != number_of_qubits:
raise ValueError("""number of qubits must be exact integer.""" )
if number_of_qubits > 10:
raise ValueError("""number of qubits too large to simulate(>10).""" )
lowercase : str = QuantumRegister(SCREAMING_SNAKE_CASE__ , """qr""" )
lowercase : Optional[Any] = ClassicalRegister(SCREAMING_SNAKE_CASE__ , """cr""" )
lowercase : str = QuantumCircuit(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
lowercase : List[str] = number_of_qubits
for i in range(SCREAMING_SNAKE_CASE__ ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(SCREAMING_SNAKE_CASE__ ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(SCREAMING_SNAKE_CASE__ , number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# simulate with 10000 shots
lowercase : List[str] = Aer.get_backend("""qasm_simulator""" )
lowercase : Optional[int] = execute(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , shots=10_000 )
return job.result().get_counts(SCREAMING_SNAKE_CASE__ )
if __name__ == "__main__":
print(
F'''Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}'''
)
| 285 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : List[Any] = logging.get_logger(__name__)
lowercase : List[Any] = {
"""google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
"""google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class __snake_case ( lowerCAmelCase ):
_a : Dict= "mobilenet_v1"
def __init__( self ,snake_case=3 ,snake_case=224 ,snake_case=1.0 ,snake_case=8 ,snake_case="relu6" ,snake_case=True ,snake_case=0.999 ,snake_case=0.02 ,snake_case=0.001 ,**snake_case ,):
'''simple docstring'''
super().__init__(**snake_case )
if depth_multiplier <= 0:
raise ValueError("""depth_multiplier must be greater than zero.""" )
lowercase : int = num_channels
lowercase : Union[str, Any] = image_size
lowercase : int = depth_multiplier
lowercase : Tuple = min_depth
lowercase : Dict = hidden_act
lowercase : Dict = tf_padding
lowercase : Dict = classifier_dropout_prob
lowercase : int = initializer_range
lowercase : List[str] = layer_norm_eps
class __snake_case ( lowerCAmelCase ):
_a : int= version.parse("1.11" )
@property
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
return OrderedDict([("""pixel_values""", {0: """batch"""})] )
@property
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([("""logits""", {0: """batch"""})] )
else:
return OrderedDict([("""last_hidden_state""", {0: """batch"""}), ("""pooler_output""", {0: """batch"""})] )
@property
def _SCREAMING_SNAKE_CASE ( self ):
'''simple docstring'''
return 1e-4
| 285 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional
from packaging import version
if TYPE_CHECKING:
from ... import PreTrainedTokenizer, TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import is_torch_available, logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""bigscience/bloom""": """https://huggingface.co/bigscience/bloom/resolve/main/config.json""",
"""bigscience/bloom-560m""": """https://huggingface.co/bigscience/bloom-560m/blob/main/config.json""",
"""bigscience/bloom-1b1""": """https://huggingface.co/bigscience/bloom-1b1/blob/main/config.json""",
"""bigscience/bloom-1b7""": """https://huggingface.co/bigscience/bloom-1b7/blob/main/config.json""",
"""bigscience/bloom-3b""": """https://huggingface.co/bigscience/bloom-3b/blob/main/config.json""",
"""bigscience/bloom-7b1""": """https://huggingface.co/bigscience/bloom-7b1/blob/main/config.json""",
}
class A__ ( _lowerCamelCase):
A_ : int = 'bloom'
A_ : Tuple = ['past_key_values']
A_ : Union[str, Any] = {
'num_hidden_layers': 'n_layer',
'num_attention_heads': 'n_head',
}
def __init__( self , _SCREAMING_SNAKE_CASE=25_08_80 , _SCREAMING_SNAKE_CASE=64 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=8 , _SCREAMING_SNAKE_CASE=1E-5 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=0.0 , _SCREAMING_SNAKE_CASE=1 , _SCREAMING_SNAKE_CASE=False , **_SCREAMING_SNAKE_CASE , ):
__lowerCAmelCase : Any = vocab_size
# Backward compatibility with n_embed kwarg
__lowerCAmelCase : List[str] = kwargs.pop('n_embed' , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = hidden_size if n_embed is None else n_embed
__lowerCAmelCase : Optional[int] = n_layer
__lowerCAmelCase : List[Any] = n_head
__lowerCAmelCase : Dict = layer_norm_epsilon
__lowerCAmelCase : Optional[Any] = initializer_range
__lowerCAmelCase : Optional[Any] = use_cache
__lowerCAmelCase : Any = pretraining_tp
__lowerCAmelCase : Any = apply_residual_connection_post_layernorm
__lowerCAmelCase : int = hidden_dropout
__lowerCAmelCase : Any = attention_dropout
__lowerCAmelCase : List[Any] = bos_token_id
__lowerCAmelCase : Dict = eos_token_id
__lowerCAmelCase : str = slow_but_exact
super().__init__(bos_token_id=_SCREAMING_SNAKE_CASE , eos_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
class A__ ( _lowerCamelCase):
A_ : Union[str, Any] = version.parse('1.12')
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = "default" , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = False , ):
super().__init__(_SCREAMING_SNAKE_CASE , task=_SCREAMING_SNAKE_CASE , patching_specs=_SCREAMING_SNAKE_CASE , use_past=_SCREAMING_SNAKE_CASE )
if not getattr(self._config , 'pad_token_id' , _SCREAMING_SNAKE_CASE ):
# TODO: how to do that better?
__lowerCAmelCase : Optional[Any] = 0
@property
def __lowerCamelCase ( self ):
__lowerCAmelCase : int = OrderedDict({'input_ids': {0: 'batch', 1: 'sequence'}} )
if self.use_past:
# BLOOM stores values on dynamic axis 2. For more details see: https://github.com/huggingface/transformers/pull/18344
self.fill_with_past_key_values_(_SCREAMING_SNAKE_CASE , direction='inputs' , inverted_values_shape=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = {0: 'batch', 1: 'past_sequence + sequence'}
else:
__lowerCAmelCase : int = {0: 'batch', 1: 'sequence'}
return common_inputs
@property
def __lowerCamelCase ( self ):
return self._config.n_layer
@property
def __lowerCamelCase ( self ):
return self._config.n_head
@property
def __lowerCamelCase ( self ):
return 1E-3
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = -1 , _SCREAMING_SNAKE_CASE = -1 , _SCREAMING_SNAKE_CASE = False , _SCREAMING_SNAKE_CASE = None , ):
__lowerCAmelCase : Tuple = super(_SCREAMING_SNAKE_CASE , self ).generate_dummy_inputs(
_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE , seq_length=_SCREAMING_SNAKE_CASE , is_pair=_SCREAMING_SNAKE_CASE , framework=_SCREAMING_SNAKE_CASE )
# We need to order the input in the way they appears in the forward()
__lowerCAmelCase : List[str] = OrderedDict({'input_ids': common_inputs['input_ids']} )
# Need to add the past_keys
if self.use_past:
if not is_torch_available():
raise ValueError('Cannot generate dummy past_keys inputs without PyTorch installed.' )
else:
import torch
__lowerCAmelCase , __lowerCAmelCase : str = common_inputs['input_ids'].shape
# Not using the same length for past_key_values
__lowerCAmelCase : Tuple = seqlen + 2
__lowerCAmelCase : int = self._config.hidden_size // self.num_attention_heads
__lowerCAmelCase : Optional[Any] = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
__lowerCAmelCase : Union[str, Any] = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
__lowerCAmelCase : Optional[int] = [
(torch.zeros(_SCREAMING_SNAKE_CASE ), torch.zeros(_SCREAMING_SNAKE_CASE )) for _ in range(self.num_layers )
]
__lowerCAmelCase : List[str] = common_inputs['attention_mask']
if self.use_past:
__lowerCAmelCase : Optional[int] = ordered_inputs['attention_mask'].dtype
__lowerCAmelCase : List[Any] = torch.cat(
[ordered_inputs['attention_mask'], torch.ones(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , dtype=_SCREAMING_SNAKE_CASE )] , dim=1 )
return ordered_inputs
@property
def __lowerCamelCase ( self ):
return 13 | 86 |
"""simple docstring"""
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class A__ :
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=13 , _SCREAMING_SNAKE_CASE=7 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=99 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=37 , _SCREAMING_SNAKE_CASE="gelu" , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=0.1 , _SCREAMING_SNAKE_CASE=5_12 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=0.02 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=4 , _SCREAMING_SNAKE_CASE=None , ):
__lowerCAmelCase : Tuple = parent
__lowerCAmelCase : Optional[int] = 13
__lowerCAmelCase : List[Any] = 7
__lowerCAmelCase : int = True
__lowerCAmelCase : Optional[int] = True
__lowerCAmelCase : List[Any] = True
__lowerCAmelCase : Optional[int] = True
__lowerCAmelCase : Optional[Any] = 99
__lowerCAmelCase : int = 3_84
__lowerCAmelCase : Union[str, Any] = 2
__lowerCAmelCase : Tuple = 4
__lowerCAmelCase : str = 37
__lowerCAmelCase : Any = 'gelu'
__lowerCAmelCase : List[str] = 0.1
__lowerCAmelCase : Any = 0.1
__lowerCAmelCase : Union[str, Any] = 5_12
__lowerCAmelCase : int = 16
__lowerCAmelCase : Union[str, Any] = 2
__lowerCAmelCase : int = 0.02
__lowerCAmelCase : Dict = 3
__lowerCAmelCase : Tuple = 4
__lowerCAmelCase : Tuple = 1_28
__lowerCAmelCase : Optional[int] = 2
__lowerCAmelCase : List[str] = 9
__lowerCAmelCase : int = 1
__lowerCAmelCase : int = None
def __lowerCamelCase ( self ):
__lowerCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase : Optional[int] = None
if self.use_input_mask:
__lowerCAmelCase : str = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase : Tuple = None
if self.use_token_type_ids:
__lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowerCAmelCase : Optional[Any] = None
__lowerCAmelCase : Dict = None
__lowerCAmelCase : Union[str, Any] = None
if self.use_labels:
__lowerCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase : List[Any] = ids_tensor([self.batch_size] , self.num_choices )
__lowerCAmelCase : Union[str, Any] = ConvBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=_SCREAMING_SNAKE_CASE , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Optional[int] = TFConvBertModel(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = {'input_ids': input_ids, 'attention_mask': input_mask, 'token_type_ids': token_type_ids}
__lowerCAmelCase : Tuple = [input_ids, input_mask]
__lowerCAmelCase : Any = model(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Any = TFConvBertForMaskedLM(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
__lowerCAmelCase : Optional[Any] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Tuple = self.num_labels
__lowerCAmelCase : Optional[Any] = TFConvBertForSequenceClassification(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
__lowerCAmelCase : Union[str, Any] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : int = self.num_choices
__lowerCAmelCase : List[str] = TFConvBertForMultipleChoice(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
__lowerCAmelCase : Dict = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
__lowerCAmelCase : Union[str, Any] = tf.tile(tf.expand_dims(_SCREAMING_SNAKE_CASE , 1 ) , (1, self.num_choices, 1) )
__lowerCAmelCase : Tuple = {
'input_ids': multiple_choice_inputs_ids,
'attention_mask': multiple_choice_input_mask,
'token_type_ids': multiple_choice_token_type_ids,
}
__lowerCAmelCase : str = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : str = self.num_labels
__lowerCAmelCase : Any = TFConvBertForTokenClassification(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
__lowerCAmelCase : Union[str, Any] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : str = TFConvBertForQuestionAnswering(config=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = {
'input_ids': input_ids,
'attention_mask': input_mask,
'token_type_ids': token_type_ids,
}
__lowerCAmelCase : Optional[int] = model(_SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Union[str, Any] = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) : List[str] = config_and_inputs
__lowerCAmelCase : Any = {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_tf
class A__ ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase):
A_ : List[str] = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
A_ : str = (
{
'feature-extraction': TFConvBertModel,
'fill-mask': TFConvBertForMaskedLM,
'question-answering': TFConvBertForQuestionAnswering,
'text-classification': TFConvBertForSequenceClassification,
'token-classification': TFConvBertForTokenClassification,
'zero-shot': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
A_ : List[Any] = False
A_ : str = False
A_ : List[Any] = False
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = TFConvBertModelTester(self )
__lowerCAmelCase : Any = ConfigTester(self , config_class=_SCREAMING_SNAKE_CASE , hidden_size=37 )
def __lowerCamelCase ( self ):
self.config_tester.run_common_tests()
def __lowerCamelCase ( self ):
__lowerCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_SCREAMING_SNAKE_CASE )
@slow
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Any = True
__lowerCAmelCase : Dict = True
if hasattr(_SCREAMING_SNAKE_CASE , 'use_cache' ):
__lowerCAmelCase : int = True
__lowerCAmelCase : List[str] = getattr(self.model_tester , 'encoder_seq_length' , self.model_tester.seq_length )
__lowerCAmelCase : str = getattr(self.model_tester , 'key_length' , _SCREAMING_SNAKE_CASE )
for model_class in self.all_model_classes:
__lowerCAmelCase : str = self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : str = model_class(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = len(model(_SCREAMING_SNAKE_CASE ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_SCREAMING_SNAKE_CASE , saved_model=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = os.path.join(_SCREAMING_SNAKE_CASE , 'saved_model' , '1' )
__lowerCAmelCase : int = tf.keras.models.load_model(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = model(_SCREAMING_SNAKE_CASE )
if self.is_encoder_decoder:
__lowerCAmelCase : List[str] = outputs['encoder_hidden_states']
__lowerCAmelCase : Tuple = outputs['encoder_attentions']
else:
__lowerCAmelCase : Optional[int] = outputs['hidden_states']
__lowerCAmelCase : Tuple = outputs['attentions']
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = getattr(
self.model_tester , 'expected_num_hidden_layers' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def __lowerCamelCase ( self ):
__lowerCAmelCase : Tuple = TFConvBertModel.from_pretrained('YituTech/conv-bert-base' )
self.assertIsNotNone(_SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( self ):
__lowerCAmelCase , __lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase : Optional[Any] = True
__lowerCAmelCase : List[Any] = getattr(self.model_tester , 'decoder_seq_length' , self.model_tester.seq_length )
__lowerCAmelCase : str = getattr(self.model_tester , 'encoder_seq_length' , self.model_tester.seq_length )
__lowerCAmelCase : Tuple = getattr(self.model_tester , 'key_length' , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = getattr(self.model_tester , 'key_length' , _SCREAMING_SNAKE_CASE )
def check_decoder_attentions_output(_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Union[str, Any] = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(out_len % 2 , 0 )
__lowerCAmelCase : Optional[Any] = outputs.decoder_attentions
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(_SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : str = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
__lowerCAmelCase : List[str] = True
__lowerCAmelCase : Optional[int] = False
__lowerCAmelCase : List[Any] = model_class(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase : Tuple = len(_SCREAMING_SNAKE_CASE )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
if self.is_encoder_decoder:
__lowerCAmelCase : Any = model_class(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_decoder_attentions_output(_SCREAMING_SNAKE_CASE )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__lowerCAmelCase : Optional[Any] = True
__lowerCAmelCase : str = model_class(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
# Check attention is always last and order is fine
__lowerCAmelCase : Dict = True
__lowerCAmelCase : Optional[Any] = True
__lowerCAmelCase : List[Any] = model_class(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = model(self._prepare_for_class(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_SCREAMING_SNAKE_CASE ) )
self.assertEqual(model.config.output_hidden_states , _SCREAMING_SNAKE_CASE )
check_encoder_attentions_output(_SCREAMING_SNAKE_CASE )
@require_tf
class A__ ( unittest.TestCase):
@slow
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = TFConvBertModel.from_pretrained('YituTech/conv-bert-base' )
__lowerCAmelCase : int = tf.constant([[0, 1, 2, 3, 4, 5]] )
__lowerCAmelCase : Tuple = model(_SCREAMING_SNAKE_CASE )[0]
__lowerCAmelCase : Tuple = [1, 6, 7_68]
self.assertEqual(output.shape , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = tf.constant(
[
[
[-0.0347_5493, -0.468_6034, -0.3063_8832],
[0.2263_7248, -0.2698_8646, -0.742_3424],
[0.1032_4868, -0.4501_3508, -0.5828_0784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 ) | 86 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
def UpperCamelCase ( __lowercase : Dict ):
'''simple docstring'''
if "resnet-50" in model_name:
A_ : Any = ResNetConfig.from_pretrained('microsoft/resnet-50' )
elif "resnet-101" in model_name:
A_ : str = ResNetConfig.from_pretrained('microsoft/resnet-101' )
else:
raise ValueError('Model name should include either resnet50 or resnet101' )
A_ : Union[str, Any] = DetrConfig(use_timm_backbone=__lowercase ,backbone_config=__lowercase )
# set label attributes
A_ : Tuple = """panoptic""" in model_name
if is_panoptic:
A_ : List[Any] = 2_50
else:
A_ : Tuple = 91
A_ : str = """huggingface/label-files"""
A_ : Dict = """coco-detection-id2label.json"""
A_ : str = json.load(open(hf_hub_download(__lowercase ,__lowercase ,repo_type='dataset' ) ,'r' ) )
A_ : Optional[Any] = {int(__lowercase ): v for k, v in idalabel.items()}
A_ : Optional[Any] = idalabel
A_ : Dict = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def UpperCamelCase ( __lowercase : Union[str, Any] ):
'''simple docstring'''
A_ : Dict = []
# stem
# fmt: off
rename_keys.append(('backbone.0.body.conv1.weight', 'backbone.conv_encoder.model.embedder.embedder.convolution.weight') )
rename_keys.append(('backbone.0.body.bn1.weight', 'backbone.conv_encoder.model.embedder.embedder.normalization.weight') )
rename_keys.append(('backbone.0.body.bn1.bias', 'backbone.conv_encoder.model.embedder.embedder.normalization.bias') )
rename_keys.append(('backbone.0.body.bn1.running_mean', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_mean') )
rename_keys.append(('backbone.0.body.bn1.running_var', 'backbone.conv_encoder.model.embedder.embedder.normalization.running_var') )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var''',
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean''',
) )
rename_keys.append(
(
f'''backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var''',
f'''backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var''',
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''',
f'''encoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias''') )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''',
f'''decoder.layers.{i}.self_attn.out_proj.weight''',
) )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''') )
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.weight''',
f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
) )
rename_keys.append(
(
f'''transformer.decoder.layers.{i}.multihead_attn.out_proj.bias''',
f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
) )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias''') )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''') )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias''') )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''') )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''') )
rename_keys.append(
(f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight''') )
rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias''') )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
('input_proj.weight', 'input_projection.weight'),
('input_proj.bias', 'input_projection.bias'),
('query_embed.weight', 'query_position_embeddings.weight'),
('transformer.decoder.norm.weight', 'decoder.layernorm.weight'),
('transformer.decoder.norm.bias', 'decoder.layernorm.bias'),
('class_embed.weight', 'class_labels_classifier.weight'),
('class_embed.bias', 'class_labels_classifier.bias'),
('bbox_embed.layers.0.weight', 'bbox_predictor.layers.0.weight'),
('bbox_embed.layers.0.bias', 'bbox_predictor.layers.0.bias'),
('bbox_embed.layers.1.weight', 'bbox_predictor.layers.1.weight'),
('bbox_embed.layers.1.bias', 'bbox_predictor.layers.1.bias'),
('bbox_embed.layers.2.weight', 'bbox_predictor.layers.2.weight'),
('bbox_embed.layers.2.bias', 'bbox_predictor.layers.2.bias'),
] )
return rename_keys
def UpperCamelCase ( __lowercase : Union[str, Any] ,__lowercase : Any ,__lowercase : List[str] ):
'''simple docstring'''
A_ : Union[str, Any] = state_dict.pop(__lowercase )
A_ : Tuple = val
def UpperCamelCase ( __lowercase : str ,__lowercase : Optional[int]=False ):
'''simple docstring'''
A_ : Optional[int] = """"""
if is_panoptic:
A_ : Tuple = """detr."""
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
A_ : List[str] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
A_ : Tuple = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
A_ : int = in_proj_weight[:2_56, :]
A_ : Optional[int] = in_proj_bias[:2_56]
A_ : Optional[Any] = in_proj_weight[2_56:5_12, :]
A_ : Any = in_proj_bias[2_56:5_12]
A_ : List[Any] = in_proj_weight[-2_56:, :]
A_ : Optional[Any] = in_proj_bias[-2_56:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
A_ : Union[str, Any] = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight''' )
A_ : List[str] = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
A_ : str = in_proj_weight[:2_56, :]
A_ : Optional[int] = in_proj_bias[:2_56]
A_ : List[Any] = in_proj_weight[2_56:5_12, :]
A_ : List[Any] = in_proj_bias[2_56:5_12]
A_ : str = in_proj_weight[-2_56:, :]
A_ : List[Any] = in_proj_bias[-2_56:]
# read in weights + bias of input projection layer of cross-attention
A_ : List[str] = state_dict.pop(
f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight''' )
A_ : str = state_dict.pop(f'''{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) of cross-attention to the state dict
A_ : Optional[int] = in_proj_weight_cross_attn[:2_56, :]
A_ : Dict = in_proj_bias_cross_attn[:2_56]
A_ : int = in_proj_weight_cross_attn[2_56:5_12, :]
A_ : Tuple = in_proj_bias_cross_attn[2_56:5_12]
A_ : List[Any] = in_proj_weight_cross_attn[-2_56:, :]
A_ : Tuple = in_proj_bias_cross_attn[-2_56:]
def UpperCamelCase ( ):
'''simple docstring'''
A_ : Optional[int] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
A_ : Dict = Image.open(requests.get(__lowercase ,stream=__lowercase ).raw )
return im
@torch.no_grad()
def UpperCamelCase ( __lowercase : Optional[int] ,__lowercase : str=None ,__lowercase : int=False ):
'''simple docstring'''
A_ : Any = get_detr_config(__lowercase )
# load original model from torch hub
A_ : Dict = {
"""detr-resnet-50""": """detr_resnet50""",
"""detr-resnet-101""": """detr_resnet101""",
}
logger.info(f'''Converting model {model_name}...''' )
A_ : int = torch.hub.load('facebookresearch/detr' ,model_name_to_original_name[model_name] ,pretrained=__lowercase ).eval()
A_ : Union[str, Any] = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(__lowercase ):
if is_panoptic:
A_ : List[str] = """detr.""" + src
rename_key(__lowercase ,__lowercase ,__lowercase )
# query, key and value matrices need special treatment
read_in_q_k_v(__lowercase ,is_panoptic=__lowercase )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
A_ : Dict = """detr.model.""" if is_panoptic else """model."""
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('detr' )
and not key.startswith('class_labels_classifier' )
and not key.startswith('bbox_predictor' )
):
A_ : Optional[Any] = state_dict.pop(__lowercase )
A_ : Optional[Any] = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
A_ : Optional[int] = state_dict.pop(__lowercase )
A_ : Optional[int] = val
elif key.startswith('bbox_attention' ) or key.startswith('mask_head' ):
continue
else:
A_ : int = state_dict.pop(__lowercase )
A_ : List[Any] = val
else:
if not key.startswith('class_labels_classifier' ) and not key.startswith('bbox_predictor' ):
A_ : int = state_dict.pop(__lowercase )
A_ : int = val
# finally, create HuggingFace model and load state dict
A_ : Dict = DetrForSegmentation(__lowercase ) if is_panoptic else DetrForObjectDetection(__lowercase )
model.load_state_dict(__lowercase )
model.eval()
# verify our conversion on an image
A_ : Union[str, Any] = """coco_panoptic""" if is_panoptic else """coco_detection"""
A_ : Any = DetrImageProcessor(format=__lowercase )
A_ : Any = processor(images=prepare_img() ,return_tensors='pt' )
A_ : Union[str, Any] = encoding["""pixel_values"""]
A_ : Optional[int] = detr(__lowercase )
A_ : Any = model(__lowercase )
assert torch.allclose(outputs.logits ,original_outputs['pred_logits'] ,atol=1e-3 )
assert torch.allclose(outputs.pred_boxes ,original_outputs['pred_boxes'] ,atol=1e-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks ,original_outputs['pred_masks'] ,atol=1e-4 )
print('Looks ok!' )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(__lowercase ).mkdir(exist_ok=__lowercase )
model.save_pretrained(__lowercase )
processor.save_pretrained(__lowercase )
if push_to_hub:
# Upload model and image processor to the hub
logger.info('Uploading PyTorch model and image processor to the hub...' )
model.push_to_hub(f'''nielsr/{model_name}''' )
processor.push_to_hub(f'''nielsr/{model_name}''' )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
"""--model_name""",
default="""detr-resnet-50""",
type=str,
choices=["""detr-resnet-50""", """detr-resnet-101"""],
help="""Name of the DETR model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
)
parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Whether to push the model to the hub or not.""")
_UpperCAmelCase = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 360 | from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""AutoformerConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase = [
"""AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""AutoformerForPrediction""",
"""AutoformerModel""",
"""AutoformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_autoformer import (
AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
AutoformerConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_autoformer import (
AUTOFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
AutoformerForPrediction,
AutoformerModel,
AutoformerPreTrainedModel,
)
else:
import sys
_UpperCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 192 | 0 |
import unittest
from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
SCREAMING_SNAKE_CASE__ = get_tests_dir("""fixtures/test_sentencepiece_bpe.model""")
SCREAMING_SNAKE_CASE__ = """pt""" if is_torch_available() else """tf"""
@require_sentencepiece
@require_tokenizers
class A__ ( UpperCAmelCase__ , unittest.TestCase ):
lowerCAmelCase__ : int = CamembertTokenizer
lowerCAmelCase__ : Tuple = CamembertTokenizerFast
lowerCAmelCase__ : Optional[Any] = True
lowerCAmelCase__ : Optional[Any] = True
def a__ ( self : Optional[Any] ) -> List[Any]:
"""simple docstring"""
super().setUp()
# We have a SentencePiece fixture for testing
__lowercase = CamembertTokenizer(lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
def a__ ( self : List[str] ) -> int:
"""simple docstring"""
__lowercase = '''<pad>'''
__lowercase = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowercase_ ) , lowercase_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowercase_ ) , lowercase_ )
def a__ ( self : Dict ) -> Any:
"""simple docstring"""
__lowercase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>NOTUSED' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(lowercase_ ) , 10_04 )
def a__ ( self : Optional[Any] ) -> int:
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_05 )
def a__ ( self : Dict ) -> List[Any]:
"""simple docstring"""
__lowercase = CamembertTokenizer(lowercase_ )
tokenizer.save_pretrained(self.tmpdirname )
__lowercase = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
__lowercase = '''I was born in 92000, and this is falsé.'''
__lowercase = tokenizer.encode(lowercase_ )
__lowercase = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
__lowercase = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
__lowercase = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
# <unk> tokens are not the same for `rust` than for `slow`.
# Because spm gives back raw token instead of `unk` in EncodeAsPieces
# tokens = tokenizer.tokenize(sequence)
__lowercase = tokenizer.convert_ids_to_tokens(lowercase_ )
__lowercase = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
def a__ ( self : int ) -> int:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
__lowercase = self.get_tokenizer()
__lowercase = self.get_rust_tokenizer()
__lowercase = '''I was born in 92000, and this is falsé.'''
__lowercase = tokenizer.tokenize(lowercase_ )
__lowercase = rust_tokenizer.tokenize(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
__lowercase = tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
__lowercase = rust_tokenizer.encode(lowercase_ , add_special_tokens=lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
__lowercase = self.get_rust_tokenizer()
__lowercase = tokenizer.encode(lowercase_ )
__lowercase = rust_tokenizer.encode(lowercase_ )
self.assertListEqual(lowercase_ , lowercase_ )
@slow
def a__ ( self : Any ) -> Optional[Any]:
"""simple docstring"""
__lowercase = {'''input_ids''': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# camembert is a french model. So we also use french texts.
__lowercase = [
'''Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '''
'''utilisé principalement dans le domaine du traitement automatique des langues (TAL).''',
'''À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '''
'''pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '''
'''telles que la traduction et la synthèse de texte.''',
]
self.tokenizer_integration_test_util(
expected_encoding=lowercase_ , model_name='camembert-base' , revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf' , sequences=lowercase_ , )
| 325 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : Tuple = """▁"""
UpperCAmelCase_ : Optional[Any] = {"""vocab_file""": """sentencepiece.bpe.model"""}
UpperCAmelCase_ : str = {
"""vocab_file""": {
"""facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model""",
}
}
UpperCAmelCase_ : str = {
"""facebook/xglm-564M""": 2048,
}
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = VOCAB_FILES_NAMES
__UpperCamelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCamelCase = ["input_ids", "attention_mask"]
def __init__( self : List[Any] , lowercase_ : str , lowercase_ : Tuple="<s>" , lowercase_ : Any="</s>" , lowercase_ : Optional[int]="</s>" , lowercase_ : List[Any]="<s>" , lowercase_ : Union[str, Any]="<unk>" , lowercase_ : Union[str, Any]="<pad>" , lowercase_ : Optional[Dict[str, Any]] = None , **lowercase_ : Tuple , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
SCREAMING_SNAKE_CASE_ : List[str] = 7
SCREAMING_SNAKE_CASE_ : Tuple = [F'<madeupword{i}>' for i in range(self.num_madeup_words)]
SCREAMING_SNAKE_CASE_ : List[Any] = kwargs.get('''additional_special_tokens''' , [])
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=lowercase_ , eos_token=lowercase_ , unk_token=lowercase_ , sep_token=lowercase_ , cls_token=lowercase_ , pad_token=lowercase_ , sp_model_kwargs=self.sp_model_kwargs , **lowercase_ , )
SCREAMING_SNAKE_CASE_ : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(lowercase_))
SCREAMING_SNAKE_CASE_ : Union[str, Any] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 1
# Mimic fairseq token-to-id alignment for the first 4 token
SCREAMING_SNAKE_CASE_ : Optional[Any] = {'''<s>''': 0, '''<pad>''': 1, '''</s>''': 2, '''<unk>''': 3}
SCREAMING_SNAKE_CASE_ : List[Any] = len(self.sp_model)
SCREAMING_SNAKE_CASE_ : Optional[Any] = {F'<madeupword{i}>': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words)}
self.fairseq_tokens_to_ids.update(lowercase_)
SCREAMING_SNAKE_CASE_ : List[Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Dict):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = self.__dict__.copy()
SCREAMING_SNAKE_CASE_ : str = None
SCREAMING_SNAKE_CASE_ : Optional[int] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Tuple , lowercase_ : str):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs'''):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {}
SCREAMING_SNAKE_CASE_ : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None):
'''simple docstring'''
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
SCREAMING_SNAKE_CASE_ : Dict = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None , lowercase_ : bool = False):
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase_ , token_ids_a=lowercase_ , already_has_special_tokens=lowercase_)
if token_ids_a is None:
return [1] + ([0] * len(lowercase_))
return [1] + ([0] * len(lowercase_)) + [1, 1] + ([0] * len(lowercase_))
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowercase_ : List[int] , lowercase_ : Optional[List[int]] = None):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a) * [0]
@property
def _SCREAMING_SNAKE_CASE ( self : Optional[Any]):
'''simple docstring'''
return len(self.sp_model) + self.fairseq_offset + self.num_madeup_words
def _SCREAMING_SNAKE_CASE ( self : Any):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[str] = {self.convert_ids_to_tokens(lowercase_): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def _SCREAMING_SNAKE_CASE ( self : Dict , lowercase_ : str):
'''simple docstring'''
return self.sp_model.encode(lowercase_ , out_type=lowercase_)
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowercase_ : Union[str, Any]):
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.sp_model.PieceToId(lowercase_)
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _SCREAMING_SNAKE_CASE ( self : int , lowercase_ : Optional[Any]):
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset)
def _SCREAMING_SNAKE_CASE ( self : int , lowercase_ : Tuple):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[int] = ''''''.join(lowercase_).replace(lowercase_ , ''' ''').strip()
return out_string
def _SCREAMING_SNAKE_CASE ( self : str , lowercase_ : str , lowercase_ : Optional[str] = None):
'''simple docstring'''
if not os.path.isdir(lowercase_):
logger.error(F'Vocabulary path ({save_directory}) should be a directory')
return
SCREAMING_SNAKE_CASE_ : List[Any] = os.path.join(
lowercase_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''])
if os.path.abspath(self.vocab_file) != os.path.abspath(lowercase_) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , lowercase_)
elif not os.path.isfile(self.vocab_file):
with open(lowercase_ , '''wb''') as fi:
SCREAMING_SNAKE_CASE_ : int = self.sp_model.serialized_model_proto()
fi.write(lowercase_)
return (out_vocab_file,)
| 91 | 0 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
A = logging.getLogger(__name__)
class __lowercase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = '''token-classification'''
def __init__( self , _UpperCAmelCase ):
if type(_UpperCAmelCase ) == dict:
__a : Any = Namespace(**_UpperCAmelCase )
__a : Optional[int] = import_module('''tasks''' )
try:
__a : int = getattr(_UpperCAmelCase , hparams.task_type )
__a : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
__a : Dict = self.token_classification_task.get_labels(hparams.labels )
__a : Union[str, Any] = CrossEntropyLoss().ignore_index
super().__init__(_UpperCAmelCase , len(self.labels ) , self.mode )
def _lowerCamelCase ( self , **_UpperCAmelCase ):
return self.model(**_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase ):
__a : Any = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type != "distilbert":
__a : str = (
batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
) # XLM and RoBERTa don"t use token_type_ids
__a : str = self(**_UpperCAmelCase )
__a : Union[str, Any] = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def _lowerCamelCase ( self ):
__a : Dict = self.hparams
for mode in ["train", "dev", "test"]:
__a : Tuple = self._feature_file(_UpperCAmelCase )
if os.path.exists(_UpperCAmelCase ) and not args.overwrite_cache:
logger.info('''Loading features from cached file %s''' , _UpperCAmelCase )
__a : str = torch.load(_UpperCAmelCase )
else:
logger.info('''Creating features from dataset file at %s''' , args.data_dir )
__a : str = self.token_classification_task.read_examples_from_file(args.data_dir , _UpperCAmelCase )
__a : Optional[Any] = self.token_classification_task.convert_examples_to_features(
_UpperCAmelCase , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ['''xlnet'''] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ['''xlnet'''] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=_UpperCAmelCase , pad_on_left=bool(self.config.model_type in ['''xlnet'''] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info('''Saving features into cached file %s''' , _UpperCAmelCase )
torch.save(_UpperCAmelCase , _UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase = False ):
__a : Dict = self._feature_file(_UpperCAmelCase )
logger.info('''Loading features from cached file %s''' , _UpperCAmelCase )
__a : List[str] = torch.load(_UpperCAmelCase )
__a : Any = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
__a : int = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
__a : Optional[int] = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
__a : int = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
__a : Tuple = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) , batch_size=_UpperCAmelCase )
def _lowerCamelCase ( self , _UpperCAmelCase , _UpperCAmelCase ):
"""Compute validation""" ""
__a : List[Any] = {'''input_ids''': batch[0], '''attention_mask''': batch[1], '''labels''': batch[3]}
if self.config.model_type != "distilbert":
__a : Tuple = (
batch[2] if self.config.model_type in ['''bert''', '''xlnet'''] else None
) # XLM and RoBERTa don"t use token_type_ids
__a : Any = self(**_UpperCAmelCase )
__a , __a : Optional[Any] = outputs[:2]
__a : Union[str, Any] = logits.detach().cpu().numpy()
__a : Any = inputs['''labels'''].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _lowerCamelCase ( self , _UpperCAmelCase ):
__a : Optional[int] = torch.stack([x['''val_loss'''] for x in outputs] ).mean()
__a : List[str] = np.concatenate([x['''pred'''] for x in outputs] , axis=0 )
__a : str = np.argmax(_UpperCAmelCase , axis=2 )
__a : List[str] = np.concatenate([x['''target'''] for x in outputs] , axis=0 )
__a : int = dict(enumerate(self.labels ) )
__a : int = [[] for _ in range(out_label_ids.shape[0] )]
__a : Optional[Any] = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
__a : Optional[Any] = {
'''val_loss''': val_loss_mean,
'''accuracy_score''': accuracy_score(_UpperCAmelCase , _UpperCAmelCase ),
'''precision''': precision_score(_UpperCAmelCase , _UpperCAmelCase ),
'''recall''': recall_score(_UpperCAmelCase , _UpperCAmelCase ),
'''f1''': fa_score(_UpperCAmelCase , _UpperCAmelCase ),
}
__a : str = dict(results.items() )
__a : Tuple = results
return ret, preds_list, out_label_list
def _lowerCamelCase ( self , _UpperCAmelCase ):
# when stable
__a , __a , __a : Any = self._eval_end(_UpperCAmelCase )
__a : Union[str, Any] = ret['''log''']
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _lowerCamelCase ( self , _UpperCAmelCase ):
# updating to test_epoch_end instead of deprecated test_end
__a , __a , __a : int = self._eval_end(_UpperCAmelCase )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
__a : Dict = ret['''log''']
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _lowerCamelCase ( _UpperCAmelCase , _UpperCAmelCase ):
# Add NER specific options
BaseTransformer.add_model_specific_args(_UpperCAmelCase , _UpperCAmelCase )
parser.add_argument(
'''--task_type''' , default='''NER''' , type=_UpperCAmelCase , help='''Task type to fine tune in training (e.g. NER, POS, etc)''' )
parser.add_argument(
'''--max_seq_length''' , default=128 , type=_UpperCAmelCase , help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) , )
parser.add_argument(
'''--labels''' , default='''''' , type=_UpperCAmelCase , help='''Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.''' , )
parser.add_argument(
'''--gpus''' , default=0 , type=_UpperCAmelCase , help='''The number of GPUs allocated for this, it is by default 0 meaning none''' , )
parser.add_argument(
'''--overwrite_cache''' , action='''store_true''' , help='''Overwrite the cached training and evaluation sets''' )
return parser
if __name__ == "__main__":
A = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
A = NERTransformer.add_model_specific_args(parser, os.getcwd())
A = parser.parse_args()
A = NERTransformer(args)
A = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
A = sorted(glob.glob(os.path.join(args.output_dir, '''checkpoint-epoch=*.ckpt'''), recursive=True))
A = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model) | 188 |
"""simple docstring"""
from pathlib import Path
import numpy as np
from PIL import Image
def __A ( a_ :np.ndarray) -> np.ndarray:
__a , __a , __a : Union[str, Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2_9_8_9 * r + 0.5_8_7_0 * g + 0.1_1_4_0 * b
def __A ( a_ :np.ndarray) -> np.ndarray:
return (gray > 1_27) & (gray <= 2_55)
def __A ( a_ :np.ndarray , a_ :np.ndarray) -> np.ndarray:
__a : Optional[int] = np.zeros_like(a_)
__a : Dict = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1))
# Copy image to padded image
__a : int = image
# Iterate over image & apply kernel
for x in range(image.shape[1]):
for y in range(image.shape[0]):
__a : Optional[Any] = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
__a : Any = int(summation > 0)
return output
if __name__ == "__main__":
# read original image
A = Path(__file__).resolve().parent / '''image_data''' / '''lena.jpg'''
A = np.array(Image.open(lena_path))
# kernel to be applied
A = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
A = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
A = Image.fromarray(output).convert('''RGB''')
pil_img.save('''result_dilation.png''') | 188 | 1 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
lowerCAmelCase : List[Any] = None
lowerCAmelCase : str = logging.get_logger(__name__)
lowerCAmelCase : Union[str, Any] = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
lowerCAmelCase : Optional[Any] = {
"""vocab_file""": {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/spiece.model""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/spiece.model""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/spiece.model""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/spiece.model""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""t5-small""": """https://huggingface.co/t5-small/resolve/main/tokenizer.json""",
"""t5-base""": """https://huggingface.co/t5-base/resolve/main/tokenizer.json""",
"""t5-large""": """https://huggingface.co/t5-large/resolve/main/tokenizer.json""",
"""t5-3b""": """https://huggingface.co/t5-3b/resolve/main/tokenizer.json""",
"""t5-11b""": """https://huggingface.co/t5-11b/resolve/main/tokenizer.json""",
},
}
# TODO(PVP) - this should be removed in Transformers v5
lowerCAmelCase : str = {
"""t5-small""": 512,
"""t5-base""": 512,
"""t5-large""": 512,
"""t5-3b""": 512,
"""t5-11b""": 512,
}
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
_UpperCAmelCase : Any = VOCAB_FILES_NAMES
_UpperCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
_UpperCAmelCase : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCAmelCase : str = ['''input_ids''', '''attention_mask''']
_UpperCAmelCase : Optional[int] = TaTokenizer
_UpperCAmelCase : List[int] = []
def __init__( self : Any , lowerCAmelCase__ : Optional[int]=None , lowerCAmelCase__ : Dict=None , lowerCAmelCase__ : int="</s>" , lowerCAmelCase__ : List[Any]="<unk>" , lowerCAmelCase__ : str="<pad>" , lowerCAmelCase__ : List[Any]=100 , lowerCAmelCase__ : List[Any]=None , **lowerCAmelCase__ : Tuple , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
SCREAMING_SNAKE_CASE_: Optional[int] = [F"<extra_id_{i}>" for i in range(lowerCAmelCase__)]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
SCREAMING_SNAKE_CASE_: List[str] = len(set(filter(lambda lowerCAmelCase__: bool("extra_id_" in str(lowerCAmelCase__)) , lowerCAmelCase__)))
if extra_tokens != extra_ids:
raise ValueError(
F"Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are"
" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
" tokens")
super().__init__(
lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , extra_ids=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , **lowerCAmelCase__ , )
SCREAMING_SNAKE_CASE_: Dict = vocab_file
SCREAMING_SNAKE_CASE_: Dict = False if not self.vocab_file else True
SCREAMING_SNAKE_CASE_: List[str] = extra_ids
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Any):
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
SCREAMING_SNAKE_CASE_: List[str] = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"This tokenizer was incorrectly instantiated with a model max length of"
F" {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this"
" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
F" {pretrained_model_name_or_path} automatically truncating your input to"
F" {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences"
F" longer than {deprecated_max_model_length} you can either instantiate this tokenizer with"
" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
" instantiate this tokenizer with `model_max_length` set to your preferred value." , lowerCAmelCase__ , )
return max_model_length
def _SCREAMING_SNAKE_CASE ( self : Any , lowerCAmelCase__ : str , lowerCAmelCase__ : Optional[str] = None):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer.")
if not os.path.isdir(lowerCAmelCase__):
logger.error(F"Vocabulary path ({save_directory}) should be a directory")
return
SCREAMING_SNAKE_CASE_: Tuple = os.path.join(
lowerCAmelCase__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"])
if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase__):
copyfile(self.vocab_file , lowerCAmelCase__)
logger.info(F"Copy vocab file to {out_vocab_file}")
return (out_vocab_file,)
def _SCREAMING_SNAKE_CASE ( self : str , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None):
SCREAMING_SNAKE_CASE_: Optional[int] = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
SCREAMING_SNAKE_CASE_: Optional[int] = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : Optional[List[int]] = None):
SCREAMING_SNAKE_CASE_: List[Any] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos) * [0]
return len(token_ids_a + eos + token_ids_a + eos) * [0]
def _SCREAMING_SNAKE_CASE ( self : List[str]):
return list(
set(filter(lambda lowerCAmelCase__: bool(re.search(R"<extra_id_\d+>" , lowerCAmelCase__)) is not None , self.additional_special_tokens)))
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
return [self.convert_tokens_to_ids(lowerCAmelCase__) for token in self.get_sentinel_tokens()]
| 13 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tokenization_ta import TaTokenizer
else:
from ...utils.dummy_sentencepiece_objects import TaTokenizer
lowerCAmelCase : Any = TaTokenizer
if is_tokenizers_available():
from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
from ...utils.dummy_tokenizers_objects import TaTokenizerFast
lowerCAmelCase : Dict = TaTokenizerFast
lowerCAmelCase : Optional[int] = {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : int = [
"""MT5EncoderModel""",
"""MT5ForConditionalGeneration""",
"""MT5ForQuestionAnswering""",
"""MT5Model""",
"""MT5PreTrainedModel""",
"""MT5Stack""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : Tuple = ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase : List[str] = ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]
if TYPE_CHECKING:
from .configuration_mta import MTaConfig, MTaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mta import (
MTaEncoderModel,
MTaForConditionalGeneration,
MTaForQuestionAnswering,
MTaModel,
MTaPreTrainedModel,
MTaStack,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel
else:
import sys
lowerCAmelCase : Optional[Any] = _LazyModule(
__name__,
globals()["""__file__"""],
_import_structure,
extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
module_spec=__spec__,
)
| 13 | 1 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
lowerCamelCase__ = {
'''vocab_file''': {
'''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json''',
},
'''merges_file''': {
'''Salesforce/codegen-350M-mono''': '''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''Salesforce/codegen-350M-mono''': (
'''https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json'''
),
},
}
lowerCamelCase__ = {
'''Salesforce/codegen-350M-mono''': 2048,
}
class _UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
__A = VOCAB_FILES_NAMES
__A = PRETRAINED_VOCAB_FILES_MAP
__A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__A = ['''input_ids''', '''attention_mask''']
__A = CodeGenTokenizer
def __init__( self : str , lowercase_ : List[str]=None , lowercase_ : Any=None , lowercase_ : Optional[int]=None , lowercase_ : Optional[Any]="<|endoftext|>" , lowercase_ : str="<|endoftext|>" , lowercase_ : str="<|endoftext|>" , lowercase_ : List[Any]=False , **lowercase_ : List[Any] , ) -> List[str]:
"""simple docstring"""
super().__init__(
lowercase_ , lowercase_ , tokenizer_file=lowercase_ , unk_token=lowercase_ , bos_token=lowercase_ , eos_token=lowercase_ , add_prefix_space=lowercase_ , **lowercase_ , )
if kwargs.pop("add_bos_token" , lowercase_):
_UpperCamelCase = kwargs.pop("name_or_path" , "")
raise ValueError(
"Currenty GPT2's fast tokenizer does NOT support adding a BOS token."
"Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"
f'`CodeGenTokenizer.from_pretrained(\'{model_id}\')`\nor\n'
f'`AutoTokenizer.from_pretrained(\'{model_id}\', use_fast=False)`\n'
"This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."
" so that the fast tokenizer works correctly.")
_UpperCamelCase = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
if pre_tok_state.get("add_prefix_space" , lowercase_) != add_prefix_space:
_UpperCamelCase = getattr(lowercase_ , pre_tok_state.pop("type"))
_UpperCamelCase = add_prefix_space
_UpperCamelCase = pre_tok_class(**lowercase_)
_UpperCamelCase = add_prefix_space
def __UpperCAmelCase ( self : Dict , *lowercase_ : Union[str, Any] , **lowercase_ : int) -> BatchEncoding:
"""simple docstring"""
_UpperCamelCase = kwargs.get("is_split_into_words" , lowercase_)
assert self.add_prefix_space or not is_split_into_words, (
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*lowercase_ , **lowercase_)
def __UpperCAmelCase ( self : str , *lowercase_ : Tuple , **lowercase_ : Dict) -> BatchEncoding:
"""simple docstring"""
_UpperCamelCase = kwargs.get("is_split_into_words" , lowercase_)
assert self.add_prefix_space or not is_split_into_words, (
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._encode_plus(*lowercase_ , **lowercase_)
def __UpperCAmelCase ( self : List[Any] , lowercase_ : str , lowercase_ : Optional[str] = None) -> Tuple[str]:
"""simple docstring"""
_UpperCamelCase = self._tokenizer.model.save(lowercase_ , name=lowercase_)
return tuple(lowercase_)
def __UpperCAmelCase ( self : List[str] , lowercase_ : Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"] , lowercase_ : bool = False , lowercase_ : bool = None , lowercase_ : Optional[List[str]] = None , **lowercase_ : int , ) -> str:
"""simple docstring"""
_UpperCamelCase = super().decode(
token_ids=lowercase_ , skip_special_tokens=lowercase_ , clean_up_tokenization_spaces=lowercase_ , **lowercase_ , )
if truncate_before_pattern is not None and len(lowercase_) > 0:
_UpperCamelCase = self.truncate(lowercase_ , lowercase_)
return decoded_text
def __UpperCAmelCase ( self : Union[str, Any] , lowercase_ : Union[str, Any] , lowercase_ : Any) -> Any:
"""simple docstring"""
def find_re(lowercase_ : Dict , lowercase_ : List[Any] , lowercase_ : int):
_UpperCamelCase = pattern.search(lowercase_ , lowercase_)
return m.start() if m else -1
_UpperCamelCase = [re.compile(lowercase_ , re.MULTILINE) for pattern in truncate_before_pattern]
_UpperCamelCase = list(re.finditer("^print" , lowercase_ , re.MULTILINE))
if len(lowercase_) > 1:
_UpperCamelCase = completion[: prints[1].start()]
_UpperCamelCase = list(re.finditer("^def" , lowercase_ , re.MULTILINE))
if len(lowercase_) > 1:
_UpperCamelCase = completion[: defs[1].start()]
_UpperCamelCase = 0
_UpperCamelCase = [
pos for pos in [find_re(lowercase_ , lowercase_ , lowercase_) for terminal in terminals] if pos != -1
]
if len(lowercase_) > 0:
return completion[: min(lowercase_)]
else:
return completion
| 63 | import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : str) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
_UpperCamelCase = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowercase_)
_UpperCamelCase = -1
_UpperCamelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size).to(lowercase_)
_UpperCamelCase = model.generate(lowercase_ , max_new_tokens=10 , do_sample=lowercase_)
_UpperCamelCase = tokenizer.decode(greedy_ids[0])
with CaptureStdout() as cs:
_UpperCamelCase = TextStreamer(lowercase_)
model.generate(lowercase_ , max_new_tokens=10 , do_sample=lowercase_ , streamer=lowercase_)
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_UpperCamelCase = cs.out[:-1]
self.assertEqual(lowercase_ , lowercase_)
def __UpperCAmelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
_UpperCamelCase = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowercase_)
_UpperCamelCase = -1
_UpperCamelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size).to(lowercase_)
_UpperCamelCase = model.generate(lowercase_ , max_new_tokens=10 , do_sample=lowercase_)
_UpperCamelCase = tokenizer.decode(greedy_ids[0])
_UpperCamelCase = TextIteratorStreamer(lowercase_)
_UpperCamelCase = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
_UpperCamelCase = Thread(target=model.generate , kwargs=lowercase_)
thread.start()
_UpperCamelCase = ""
for new_text in streamer:
streamer_text += new_text
self.assertEqual(lowercase_ , lowercase_)
def __UpperCAmelCase ( self : List[str]) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
_UpperCamelCase = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowercase_)
_UpperCamelCase = -1
_UpperCamelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size).to(lowercase_)
_UpperCamelCase = model.generate(lowercase_ , max_new_tokens=10 , do_sample=lowercase_)
_UpperCamelCase = greedy_ids[:, input_ids.shape[1] :]
_UpperCamelCase = tokenizer.decode(new_greedy_ids[0])
with CaptureStdout() as cs:
_UpperCamelCase = TextStreamer(lowercase_ , skip_prompt=lowercase_)
model.generate(lowercase_ , max_new_tokens=10 , do_sample=lowercase_ , streamer=lowercase_)
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_UpperCamelCase = cs.out[:-1]
self.assertEqual(lowercase_ , lowercase_)
def __UpperCAmelCase ( self : Optional[int]) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = AutoTokenizer.from_pretrained("distilgpt2")
_UpperCamelCase = AutoModelForCausalLM.from_pretrained("distilgpt2").to(lowercase_)
_UpperCamelCase = -1
_UpperCamelCase = torch.ones((1, 5) , device=lowercase_).long() * model.config.bos_token_id
with CaptureStdout() as cs:
_UpperCamelCase = TextStreamer(lowercase_ , skip_special_tokens=lowercase_)
model.generate(lowercase_ , max_new_tokens=1 , do_sample=lowercase_ , streamer=lowercase_)
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
_UpperCamelCase = cs.out[:-1] # Remove the final "\n"
_UpperCamelCase = tokenizer(lowercase_ , return_tensors="pt")
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1))
def __UpperCAmelCase ( self : List[str]) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2")
_UpperCamelCase = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2").to(lowercase_)
_UpperCamelCase = -1
_UpperCamelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size).to(lowercase_)
_UpperCamelCase = TextIteratorStreamer(lowercase_ , timeout=0.0_01)
_UpperCamelCase = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
_UpperCamelCase = Thread(target=model.generate , kwargs=lowercase_)
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(lowercase_):
_UpperCamelCase = ""
for new_text in streamer:
streamer_text += new_text
| 63 | 1 |
def lowerCAmelCase_ (lowerCAmelCase__: int , lowerCAmelCase__: int ):
"""simple docstring"""
return numa ^ numa < 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 147 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
a : List[str] = logging.get_logger(__name__)
a : List[Any] = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
a : List[str] = {
'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
'tokenizer_config_file': {
'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
},
}
a : int = {'facebook/blenderbot-3B': 128}
class _a ( _lowerCAmelCase ):
A = VOCAB_FILES_NAMES
A = PRETRAINED_VOCAB_FILES_MAP
A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A = ['''input_ids''', '''attention_mask''']
A = BlenderbotTokenizer
def __init__(self, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_="replace", SCREAMING_SNAKE_CASE_="<s>", SCREAMING_SNAKE_CASE_="</s>", SCREAMING_SNAKE_CASE_="</s>", SCREAMING_SNAKE_CASE_="<s>", SCREAMING_SNAKE_CASE_="<unk>", SCREAMING_SNAKE_CASE_="<pad>", SCREAMING_SNAKE_CASE_="<mask>", SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=True, **SCREAMING_SNAKE_CASE_, ) -> Dict:
super().__init__(
SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, tokenizer_file=SCREAMING_SNAKE_CASE_, errors=SCREAMING_SNAKE_CASE_, bos_token=SCREAMING_SNAKE_CASE_, eos_token=SCREAMING_SNAKE_CASE_, sep_token=SCREAMING_SNAKE_CASE_, cls_token=SCREAMING_SNAKE_CASE_, unk_token=SCREAMING_SNAKE_CASE_, pad_token=SCREAMING_SNAKE_CASE_, mask_token=SCREAMING_SNAKE_CASE_, add_prefix_space=SCREAMING_SNAKE_CASE_, trim_offsets=SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_, )
UpperCAmelCase_: Any = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""", SCREAMING_SNAKE_CASE_ ) != add_prefix_space:
UpperCAmelCase_: int = getattr(SCREAMING_SNAKE_CASE_, pre_tok_state.pop("""type""" ) )
UpperCAmelCase_: Union[str, Any] = add_prefix_space
UpperCAmelCase_: List[Any] = pre_tok_class(**SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Tuple = add_prefix_space
UpperCAmelCase_: Optional[Any] = """post_processor"""
UpperCAmelCase_: List[str] = getattr(self.backend_tokenizer, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
if tokenizer_component_instance:
UpperCAmelCase_: Optional[Any] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
UpperCAmelCase_: Optional[Any] = tuple(state["""sep"""] )
if "cls" in state:
UpperCAmelCase_: int = tuple(state["""cls"""] )
UpperCAmelCase_: int = False
if state.get("""add_prefix_space""", SCREAMING_SNAKE_CASE_ ) != add_prefix_space:
UpperCAmelCase_: Any = add_prefix_space
UpperCAmelCase_: str = True
if state.get("""trim_offsets""", SCREAMING_SNAKE_CASE_ ) != trim_offsets:
UpperCAmelCase_: Tuple = trim_offsets
UpperCAmelCase_: Tuple = True
if changes_to_apply:
UpperCAmelCase_: str = getattr(SCREAMING_SNAKE_CASE_, state.pop("""type""" ) )
UpperCAmelCase_: str = component_class(**SCREAMING_SNAKE_CASE_ )
setattr(self.backend_tokenizer, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
@property
# Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
def __snake_case (self ) -> str:
if self._mask_token is None:
if self.verbose:
logger.error("""Using mask_token, but it is not set yet.""" )
return None
return str(self._mask_token )
@mask_token.setter
def __snake_case (self, SCREAMING_SNAKE_CASE_ ) -> int:
UpperCAmelCase_: Dict = AddedToken(SCREAMING_SNAKE_CASE_, lstrip=SCREAMING_SNAKE_CASE_, rstrip=SCREAMING_SNAKE_CASE_ ) if isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) else value
UpperCAmelCase_: Optional[int] = value
def __snake_case (self, *SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> BatchEncoding:
UpperCAmelCase_: List[str] = kwargs.get("""is_split_into_words""", SCREAMING_SNAKE_CASE_ )
assert self.add_prefix_space or not is_split_into_words, (
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
def __snake_case (self, *SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> BatchEncoding:
UpperCAmelCase_: List[Any] = kwargs.get("""is_split_into_words""", SCREAMING_SNAKE_CASE_ )
assert self.add_prefix_space or not is_split_into_words, (
f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
"to use it with pretokenized inputs."
)
return super()._encode_plus(*SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
UpperCAmelCase_: str = self._tokenizer.model.save(SCREAMING_SNAKE_CASE_, name=SCREAMING_SNAKE_CASE_ )
return tuple(SCREAMING_SNAKE_CASE_ )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None ) -> List[int]:
UpperCAmelCase_: List[Any] = [self.sep_token_id]
UpperCAmelCase_: int = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None ) -> Union[str, Any]:
return token_ids_a + [self.eos_token_id]
def __snake_case (self, SCREAMING_SNAKE_CASE_ ) -> List[int]:
UpperCAmelCase_: Optional[Any] = []
for is_user, text in conversation.iter_texts():
if is_user:
# We need to space prefix as it's being done within blenderbot
inputs.append(""" """ + text )
else:
# Generated responses should contain them already.
inputs.append(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: int = """ """.join(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Tuple = self.encode(SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) > self.model_max_length:
UpperCAmelCase_: int = input_ids[-self.model_max_length :]
logger.warning(f'Trimmed input from conversation as it was longer than {self.model_max_length} tokens.' )
return input_ids
| 147 | 1 |
"""simple docstring"""
import os
from math import logaa
def lowercase__(A = "base_exp.txt" ) ->int:
"""simple docstring"""
lowercase__ : float= 0
lowercase__ : Optional[Any]= 0
for i, line in enumerate(open(os.path.join(os.path.dirname(A ) , A ) ) ):
lowercase__ : Tuple= list(map(A , line.split("," ) ) )
if x * logaa(A ) > largest:
lowercase__ : Tuple= x * logaa(A )
lowercase__ : Optional[Any]= i + 1
return result
if __name__ == "__main__":
print(solution())
| 362 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ = None , ):
'''simple docstring'''
super().__init__()
self.register_modules(transformer=snake_case__ , vae=snake_case__ , scheduler=snake_case__ )
# create a imagenet -> id dictionary for easier use
lowercase__ : int= {}
if idalabel is not None:
for key, value in idalabel.items():
for label in value.split("," ):
lowercase__ : Tuple= int(snake_case__ )
lowercase__ : Union[str, Any]= dict(sorted(self.labels.items() ) )
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
if not isinstance(snake_case__ , snake_case__ ):
lowercase__ : List[Any]= list(snake_case__ )
for l in label:
if l not in self.labels:
raise ValueError(
F'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.''' )
return [self.labels[l] for l in label]
@torch.no_grad()
def __call__( self , snake_case__ , snake_case__ = 4.0 , snake_case__ = None , snake_case__ = 50 , snake_case__ = "pil" , snake_case__ = True , ):
'''simple docstring'''
lowercase__ : List[Any]= len(snake_case__ )
lowercase__ : Optional[int]= self.transformer.config.sample_size
lowercase__ : List[str]= self.transformer.config.in_channels
lowercase__ : Any= randn_tensor(
shape=(batch_size, latent_channels, latent_size, latent_size) , generator=snake_case__ , device=self.device , dtype=self.transformer.dtype , )
lowercase__ : Any= torch.cat([latents] * 2 ) if guidance_scale > 1 else latents
lowercase__ : Tuple= torch.tensor(snake_case__ , device=self.device ).reshape(-1 )
lowercase__ : Any= torch.tensor([1000] * batch_size , device=self.device )
lowercase__ : Tuple= torch.cat([class_labels, class_null] , 0 ) if guidance_scale > 1 else class_labels
# set step values
self.scheduler.set_timesteps(snake_case__ )
for t in self.progress_bar(self.scheduler.timesteps ):
if guidance_scale > 1:
lowercase__ : List[str]= latent_model_input[: len(snake_case__ ) // 2]
lowercase__ : int= torch.cat([half, half] , dim=0 )
lowercase__ : Union[str, Any]= self.scheduler.scale_model_input(snake_case__ , snake_case__ )
lowercase__ : Optional[int]= t
if not torch.is_tensor(snake_case__ ):
# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
# This would be a good case for the `match` statement (Python 3.10+)
lowercase__ : List[str]= latent_model_input.device.type == "mps"
if isinstance(snake_case__ , snake_case__ ):
lowercase__ : int= torch.floataa if is_mps else torch.floataa
else:
lowercase__ : Dict= torch.intaa if is_mps else torch.intaa
lowercase__ : Tuple= torch.tensor([timesteps] , dtype=snake_case__ , device=latent_model_input.device )
elif len(timesteps.shape ) == 0:
lowercase__ : Dict= timesteps[None].to(latent_model_input.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
lowercase__ : int= timesteps.expand(latent_model_input.shape[0] )
# predict noise model_output
lowercase__ : Union[str, Any]= self.transformer(
snake_case__ , timestep=snake_case__ , class_labels=snake_case__ ).sample
# perform guidance
if guidance_scale > 1:
lowercase__, lowercase__ : Tuple= noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
lowercase__, lowercase__ : Union[str, Any]= torch.split(snake_case__ , len(snake_case__ ) // 2 , dim=0 )
lowercase__ : str= uncond_eps + guidance_scale * (cond_eps - uncond_eps)
lowercase__ : Dict= torch.cat([half_eps, half_eps] , dim=0 )
lowercase__ : Optional[int]= torch.cat([eps, rest] , dim=1 )
# learned sigma
if self.transformer.config.out_channels // 2 == latent_channels:
lowercase__, lowercase__ : Union[str, Any]= torch.split(snake_case__ , snake_case__ , dim=1 )
else:
lowercase__ : int= noise_pred
# compute previous image: x_t -> x_t-1
lowercase__ : List[Any]= self.scheduler.step(snake_case__ , snake_case__ , snake_case__ ).prev_sample
if guidance_scale > 1:
lowercase__, lowercase__ : Any= latent_model_input.chunk(2 , dim=0 )
else:
lowercase__ : str= latent_model_input
lowercase__ : Dict= 1 / self.vae.config.scaling_factor * latents
lowercase__ : Any= self.vae.decode(snake_case__ ).sample
lowercase__ : Tuple= (samples / 2 + 0.5).clamp(0 , 1 )
# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
lowercase__ : List[Any]= samples.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowercase__ : Optional[Any]= self.numpy_to_pil(snake_case__ )
if not return_dict:
return (samples,)
return ImagePipelineOutput(images=snake_case__ )
| 150 | 0 |
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
lowerCAmelCase_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class _A ( _lowerCamelCase ):
def __init__( self : Dict , _A : CLIPSegForImageSegmentation , _A : CLIPSegProcessor , _A : AutoencoderKL , _A : CLIPTextModel , _A : CLIPTokenizer , _A : UNetaDConditionModel , _A : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , _A : StableDiffusionSafetyChecker , _A : CLIPImageProcessor , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__()
if hasattr(scheduler.config , '''steps_offset''' ) and scheduler.config.steps_offset != 1:
lowercase : int = (
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''' , _A , standard_warn=_A )
lowercase : str = dict(scheduler.config )
lowercase : int = 1
lowercase : Dict = FrozenDict(_A )
if hasattr(scheduler.config , '''skip_prk_steps''' ) and scheduler.config.skip_prk_steps is False:
lowercase : Tuple = (
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''' , _A , standard_warn=_A )
lowercase : Optional[Any] = dict(scheduler.config )
lowercase : List[Any] = True
lowercase : str = FrozenDict(_A )
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=_A , segmentation_processor=_A , vae=_A , text_encoder=_A , tokenizer=_A , unet=_A , scheduler=_A , safety_checker=_A , feature_extractor=_A , )
def __a ( self : Optional[Any] , _A : Optional[Union[str, int]] = "auto" ) -> List[str]:
"""simple docstring"""
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
lowercase : List[Any] = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(_A )
def __a ( self : Dict ) -> List[Any]:
"""simple docstring"""
self.enable_attention_slicing(_A )
def __a ( self : Dict ) -> Dict:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('''Please install accelerate via `pip install accelerate`''' )
lowercase : Any = 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(_A , _A )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __a ( self : List[str] ) -> Dict:
"""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(_A , '''_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] , _A : Union[str, List[str]] , _A : Union[torch.FloatTensor, PIL.Image.Image] , _A : str , _A : int = 512 , _A : int = 512 , _A : int = 50 , _A : float = 7.5 , _A : Optional[Union[str, List[str]]] = None , _A : Optional[int] = 1 , _A : float = 0.0 , _A : Optional[torch.Generator] = None , _A : Optional[torch.FloatTensor] = None , _A : Optional[str] = "pil" , _A : bool = True , _A : Optional[Callable[[int, int, torch.FloatTensor], None]] = None , _A : int = 1 , **_A : List[Any] , ) -> List[Any]:
"""simple docstring"""
lowercase : Tuple = self.segmentation_processor(
text=[text] , images=[image] , padding='''max_length''' , return_tensors='''pt''' ).to(self.device )
lowercase : str = self.segmentation_model(**_A )
lowercase : int = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
lowercase : List[Any] = self.numpy_to_pil(_A )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
lowercase : Union[str, Any] = 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=_A , image=_A , mask_image=_A , height=_A , width=_A , num_inference_steps=_A , guidance_scale=_A , negative_prompt=_A , num_images_per_prompt=_A , eta=_A , generator=_A , latents=_A , output_type=_A , return_dict=_A , callback=_A , callback_steps=_A , ) | 308 |
# Lint as: python3
import os
import re
import urllib.parse
from pathlib import Path
from typing import Callable, List, Optional, Union
from zipfile import ZipFile
from ..utils.file_utils import cached_path, hf_github_url
from ..utils.logging import get_logger
from ..utils.version import Version
lowerCAmelCase_ = get_logger(__name__)
class _A :
_UpperCamelCase : int = '''dummy_data'''
_UpperCamelCase : Tuple = '''datasets'''
_UpperCamelCase : Optional[int] = False
def __init__( self : Any , _A : str , _A : str , _A : Union[Version, str] , _A : Optional[str] = None , _A : bool = False , _A : bool = True , _A : Optional[List[Callable]] = None , ) -> Dict:
"""simple docstring"""
lowercase : Tuple = 0
lowercase : List[Any] = dataset_name
lowercase : int = cache_dir
lowercase : str = use_local_dummy_data
lowercase : Union[str, Any] = config
# download_callbacks take a single url as input
lowercase : List[Callable] = download_callbacks or []
# if False, it doesn't load existing files and it returns the paths of the dummy files relative
# to the dummy_data zip file root
lowercase : Any = load_existing_dummy_data
# TODO(PVP, QL) might need to make this more general
lowercase : Union[str, Any] = str(_A )
# to be downloaded
lowercase : Tuple = None
lowercase : Optional[int] = None
@property
def __a ( self : str ) -> Dict:
"""simple docstring"""
if self._dummy_file is None:
lowercase : Optional[Any] = self.download_dummy_data()
return self._dummy_file
@property
def __a ( self : int ) -> Optional[Any]:
"""simple docstring"""
if self.config is not None:
# structure is dummy / config_name / version_name
return os.path.join('''dummy''' , self.config.name , self.version_name )
# structure is dummy / version_name
return os.path.join('''dummy''' , self.version_name )
@property
def __a ( self : List[Any] ) -> int:
"""simple docstring"""
return os.path.join(self.dummy_data_folder , '''dummy_data.zip''' )
def __a ( self : str ) -> int:
"""simple docstring"""
lowercase : str = (
self.local_path_to_dummy_data if self.use_local_dummy_data is True else self.github_path_to_dummy_data
)
lowercase : List[str] = cached_path(
_A , cache_dir=self.cache_dir , extract_compressed_file=_A , force_extract=_A )
return os.path.join(_A , self.dummy_file_name )
@property
def __a ( self : str ) -> Tuple:
"""simple docstring"""
return os.path.join(self.datasets_scripts_dir , self.dataset_name , self.dummy_zip_file )
@property
def __a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
if self._bucket_url is None:
lowercase : Optional[Any] = hf_github_url(self.dataset_name , self.dummy_zip_file.replace(os.sep , '''/''' ) )
return self._bucket_url
@property
def __a ( self : Tuple ) -> List[str]:
"""simple docstring"""
if os.path.isdir(self.dummy_file ):
return self.dummy_file
# else cut off path to file -> example `xsum`.
return "/".join(self.dummy_file.replace(os.sep , '''/''' ).split('''/''' )[:-1] )
def __a ( self : Union[str, Any] , _A : Dict , *_A : Union[str, Any] ) -> Optional[Any]:
"""simple docstring"""
if self.load_existing_dummy_data:
# dummy data is downloaded and tested
lowercase : Union[str, Any] = self.dummy_file
else:
# dummy data cannot be downloaded and only the path to dummy file is returned
lowercase : Optional[Any] = self.dummy_file_name
# special case when data_url is a dict
if isinstance(_A , _A ):
return self.create_dummy_data_dict(_A , _A )
elif isinstance(_A , (list, tuple) ):
return self.create_dummy_data_list(_A , _A )
else:
return self.create_dummy_data_single(_A , _A )
def __a ( self : str , _A : Union[str, Any] , *_A : Dict ) -> Dict:
"""simple docstring"""
return self.download_and_extract(_A )
def __a ( self : str , _A : List[str] , _A : Any ) -> Union[str, Any]:
"""simple docstring"""
return self.download_and_extract(_A )
def __a ( self : Optional[int] , _A : Tuple , *_A : str , **_A : Any ) -> Optional[Any]:
"""simple docstring"""
return path
def __a ( self : List[str] ) -> str:
"""simple docstring"""
return {}
def __a ( self : List[str] , _A : Union[str, Any] , _A : List[Any] ) -> Optional[Any]:
"""simple docstring"""
lowercase : Any = {}
for key, single_urls in data_url.items():
for download_callback in self.download_callbacks:
if isinstance(_A , _A ):
for single_url in single_urls:
download_callback(_A )
else:
lowercase : List[str] = single_urls
download_callback(_A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
if isinstance(_A , _A ):
lowercase : int = [os.path.join(_A , urllib.parse.quote_plus(Path(_A ).name ) ) for x in single_urls]
else:
lowercase : int = single_urls
lowercase : Any = os.path.join(_A , urllib.parse.quote_plus(Path(_A ).name ) )
lowercase : str = value
# make sure that values are unique
if all(isinstance(_A , _A ) for i in dummy_data_dict.values() ) and len(set(dummy_data_dict.values() ) ) < len(
dummy_data_dict.values() ):
# append key to value to make its name unique
lowercase : str = {key: value + key for key, value in dummy_data_dict.items()}
return dummy_data_dict
def __a ( self : Optional[int] , _A : List[Any] , _A : Tuple ) -> Tuple:
"""simple docstring"""
lowercase : Optional[Any] = []
# trick: if there are many shards named like `data.txt-000001-of-00300`, only use the first one
lowercase : Union[str, Any] = all(bool(re.findall('''[0-9]{3,}-of-[0-9]{3,}''' , _A ) ) for url in data_url )
lowercase : str = all(
url.startswith('''https://ftp.ncbi.nlm.nih.gov/pubmed/baseline/pubmed''' ) for url in data_url )
if data_url and (is_tf_records or is_pubmed_records):
lowercase : List[str] = [data_url[0]] * len(_A )
for single_url in data_url:
for download_callback in self.download_callbacks:
download_callback(_A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase : Optional[int] = os.path.join(_A , urllib.parse.quote_plus(single_url.split('''/''' )[-1] ) )
dummy_data_list.append(_A )
return dummy_data_list
def __a ( self : Optional[Any] , _A : List[str] , _A : Union[str, Any] ) -> List[str]:
"""simple docstring"""
for download_callback in self.download_callbacks:
download_callback(_A )
# we force the name of each key to be the last file / folder name of the url path
# if the url has arguments, we need to encode them with urllib.parse.quote_plus
lowercase : Dict = os.path.join(_A , urllib.parse.quote_plus(data_url.split('''/''' )[-1] ) )
if os.path.exists(_A ) or not self.load_existing_dummy_data:
return value
else:
# Backward compatibility, maybe deprecate at one point.
# For many datasets with single url calls to dl_manager.download_and_extract,
# the dummy_data.zip file is actually the zipped downloaded file
# while now we expected the dummy_data.zip file to be a directory containing
# the downloaded file.
return path_to_dummy_data
def __a ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
pass
def __a ( self : Any ) -> Dict:
"""simple docstring"""
pass
def __a ( self : int , _A : Optional[Any] ) -> Dict:
"""simple docstring"""
def _iter_archive_members(_A : Optional[int] ):
# this preserves the order of the members inside the ZIP archive
lowercase : int = Path(self.dummy_file ).parent
lowercase : List[str] = path.relative_to(_A )
with ZipFile(self.local_path_to_dummy_data ) as zip_file:
lowercase : Optional[int] = zip_file.namelist()
for member in members:
if member.startswith(relative_path.as_posix() ):
yield dummy_parent_path.joinpath(_A )
lowercase : Tuple = Path(_A )
lowercase : List[Any] = _iter_archive_members(_A ) if self.use_local_dummy_data else path.rglob('''*''' )
for file_path in file_paths:
if file_path.is_file() and not file_path.name.startswith(('''.''', '''__''') ):
yield file_path.relative_to(_A ).as_posix(), file_path.open('''rb''' )
def __a ( self : Optional[Any] , _A : Dict ) -> Union[str, Any]:
"""simple docstring"""
if not isinstance(_A , _A ):
lowercase : Dict = [paths]
for path in paths:
if os.path.isfile(_A ):
if os.path.basename(_A ).startswith(('''.''', '''__''') ):
return
yield path
else:
for dirpath, dirnames, filenames in os.walk(_A ):
if os.path.basename(_A ).startswith(('''.''', '''__''') ):
continue
dirnames.sort()
for filename in sorted(_A ):
if filename.startswith(('''.''', '''__''') ):
continue
yield os.path.join(_A , _A ) | 308 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCAmelCase__ ( __magic_name__ , unittest.TestCase ):
SCREAMING_SNAKE_CASE_ =BioGptTokenizer
SCREAMING_SNAKE_CASE_ =False
def __a ( self : Dict ):
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCAmelCase__ : Union[str, Any] = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
UpperCAmelCase__ : Union[str, Any] = dict(zip(snake_case__ , range(len(snake_case__ ) ) ) )
UpperCAmelCase__ : Optional[int] = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
UpperCAmelCase__ : Optional[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
UpperCAmelCase__ : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" ) as fp:
fp.write(json.dumps(snake_case__ ) )
with open(self.merges_file , "w" ) as fp:
fp.write("\n".join(snake_case__ ) )
def __a ( self : Dict , snake_case__ : Any ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = "lower newer"
UpperCAmelCase__ : List[Any] = "lower newer"
return input_text, output_text
def __a ( self : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = BioGptTokenizer(self.vocab_file , self.merges_file )
UpperCAmelCase__ : str = "lower"
UpperCAmelCase__ : Tuple = ["low", "er</w>"]
UpperCAmelCase__ : Any = tokenizer.tokenize(snake_case__ )
self.assertListEqual(snake_case__ , snake_case__ )
UpperCAmelCase__ : str = tokens + ["<unk>"]
UpperCAmelCase__ : Tuple = [1_4, 1_5, 2_0]
self.assertListEqual(tokenizer.convert_tokens_to_ids(snake_case__ ) , snake_case__ )
@slow
def __a ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
UpperCAmelCase__ : Dict = tokenizer.encode("sequence builders" , add_special_tokens=snake_case__ )
UpperCAmelCase__ : int = tokenizer.encode("multi-sequence build" , add_special_tokens=snake_case__ )
UpperCAmelCase__ : str = tokenizer.build_inputs_with_special_tokens(snake_case__ )
UpperCAmelCase__ : List[str] = tokenizer.build_inputs_with_special_tokens(snake_case__ , snake_case__ )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 360 |
"""simple docstring"""
import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida
from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
EntryNotFoundError,
RepositoryNotFoundError,
RevisionNotFoundError,
is_jinja_available,
)
from packaging import version
from requests import HTTPError
from .. import __version__
from .constants import (
DEPRECATED_REVISION_ARGS,
DIFFUSERS_CACHE,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
SAFETENSORS_WEIGHTS_NAME,
WEIGHTS_NAME,
)
from .import_utils import (
ENV_VARS_TRUE_VALUES,
_flax_version,
_jax_version,
_onnxruntime_version,
_torch_version,
is_flax_available,
is_onnx_available,
is_torch_available,
)
from .logging import get_logger
_lowerCAmelCase : Optional[int] = get_logger(__name__)
_lowerCAmelCase : Any = Path(__file__).parent / """model_card_template.md"""
_lowerCAmelCase : Dict = uuida().hex
_lowerCAmelCase : Optional[int] = os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
_lowerCAmelCase : Optional[int] = os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
_lowerCAmelCase : int = HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""
def SCREAMING_SNAKE_CASE__ ( snake_case : Union[Dict, str, None] = None )-> str:
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = f'diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}'
if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
return ua + "; telemetry/off"
if is_torch_available():
ua += f'; torch/{_torch_version}'
if is_flax_available():
ua += f'; jax/{_jax_version}'
ua += f'; flax/{_flax_version}'
if is_onnx_available():
ua += f'; onnxruntime/{_onnxruntime_version}'
# CI will set this value to True
if os.environ.get("DIFFUSERS_IS_CI" , "" ).upper() in ENV_VARS_TRUE_VALUES:
ua += "; is_ci/true"
if isinstance(snake_case , snake_case ):
ua += "; " + "; ".join(f'{k}/{v}' for k, v in user_agent.items() )
elif isinstance(snake_case , snake_case ):
ua += "; " + user_agent
return ua
def SCREAMING_SNAKE_CASE__ ( snake_case : str , snake_case : Optional[str] = None , snake_case : Optional[str] = None )-> List[str]:
'''simple docstring'''
if token is None:
UpperCAmelCase__ : Optional[Any] = HfFolder.get_token()
if organization is None:
UpperCAmelCase__ : Tuple = whoami(snake_case )["name"]
return f'{username}/{model_id}'
else:
return f'{organization}/{model_id}'
def SCREAMING_SNAKE_CASE__ ( snake_case : int , snake_case : List[Any] )-> List[Any]:
'''simple docstring'''
if not is_jinja_available():
raise ValueError(
"Modelcard rendering is based on Jinja templates."
" Please make sure to have `jinja` installed before using `create_model_card`."
" To install it, please run `pip install Jinja2`." )
if hasattr(snake_case , "local_rank" ) and args.local_rank not in [-1, 0]:
return
UpperCAmelCase__ : int = args.hub_token if hasattr(snake_case , "hub_token" ) else None
UpperCAmelCase__ : Optional[Any] = get_full_repo_name(snake_case , token=snake_case )
UpperCAmelCase__ : Tuple = ModelCard.from_template(
card_data=ModelCardData( # Card metadata object that will be converted to YAML block
language="en" , license="apache-2.0" , library_name="diffusers" , tags=[] , datasets=args.dataset_name , metrics=[] , ) , template_path=snake_case , model_name=snake_case , repo_name=snake_case , dataset_name=args.dataset_name if hasattr(snake_case , "dataset_name" ) else None , learning_rate=args.learning_rate , train_batch_size=args.train_batch_size , eval_batch_size=args.eval_batch_size , gradient_accumulation_steps=(
args.gradient_accumulation_steps if hasattr(snake_case , "gradient_accumulation_steps" ) else None
) , adam_betaa=args.adam_betaa if hasattr(snake_case , "adam_beta1" ) else None , adam_betaa=args.adam_betaa if hasattr(snake_case , "adam_beta2" ) else None , adam_weight_decay=args.adam_weight_decay if hasattr(snake_case , "adam_weight_decay" ) else None , adam_epsilon=args.adam_epsilon if hasattr(snake_case , "adam_epsilon" ) else None , lr_scheduler=args.lr_scheduler if hasattr(snake_case , "lr_scheduler" ) else None , lr_warmup_steps=args.lr_warmup_steps if hasattr(snake_case , "lr_warmup_steps" ) else None , ema_inv_gamma=args.ema_inv_gamma if hasattr(snake_case , "ema_inv_gamma" ) else None , ema_power=args.ema_power if hasattr(snake_case , "ema_power" ) else None , ema_max_decay=args.ema_max_decay if hasattr(snake_case , "ema_max_decay" ) else None , mixed_precision=args.mixed_precision , )
UpperCAmelCase__ : List[str] = os.path.join(args.output_dir , "README.md" )
model_card.save(snake_case )
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[str] , snake_case : Optional[str] = None )-> Tuple:
'''simple docstring'''
if resolved_file is None or commit_hash is not None:
return commit_hash
UpperCAmelCase__ : Dict = str(Path(snake_case ).as_posix() )
UpperCAmelCase__ : Optional[int] = re.search(r"snapshots/([^/]+)/" , snake_case )
if search is None:
return None
UpperCAmelCase__ : Dict = search.groups()[0]
return commit_hash if REGEX_COMMIT_HASH.match(snake_case ) else None
# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
_lowerCAmelCase : Dict = os.path.expanduser(
os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
_lowerCAmelCase : List[Any] = os.path.join(hf_cache_home, """diffusers""")
def SCREAMING_SNAKE_CASE__ ( snake_case : Optional[str] = None , snake_case : Optional[str] = None )-> None:
'''simple docstring'''
if new_cache_dir is None:
UpperCAmelCase__ : Union[str, Any] = DIFFUSERS_CACHE
if old_cache_dir is None:
UpperCAmelCase__ : str = old_diffusers_cache
UpperCAmelCase__ : List[str] = Path(snake_case ).expanduser()
UpperCAmelCase__ : Any = Path(snake_case ).expanduser()
for old_blob_path in old_cache_dir.glob("**/blobs/*" ):
if old_blob_path.is_file() and not old_blob_path.is_symlink():
UpperCAmelCase__ : Dict = new_cache_dir / old_blob_path.relative_to(snake_case )
new_blob_path.parent.mkdir(parents=snake_case , exist_ok=snake_case )
os.replace(snake_case , snake_case )
try:
os.symlink(snake_case , snake_case )
except OSError:
logger.warning(
"Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded." )
# At this point, old_cache_dir contains symlinks to the new cache (it can still be used).
_lowerCAmelCase : Tuple = os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
_lowerCAmelCase : Any = 0
else:
with open(cache_version_file) as f:
try:
_lowerCAmelCase : List[str] = int(f.read())
except ValueError:
_lowerCAmelCase : Optional[int] = 0
if cache_version < 1:
_lowerCAmelCase : List[str] = os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
if old_cache_is_not_empty:
logger.warning(
"""The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
"""existing cached models. This is a one-time operation, you can interrupt it or run it """
"""later by calling `diffusers.utils.hub_utils.move_cache()`."""
)
try:
move_cache()
except Exception as e:
_lowerCAmelCase : Dict = """\n""".join(traceback.format_tb(e.__traceback__))
logger.error(
F"""There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease """
"""file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
"""message and we will do our best to help."""
)
if cache_version < 1:
try:
os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
with open(cache_version_file, """w""") as f:
f.write("""1""")
except Exception:
logger.warning(
F"""There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure """
"""the directory exists and can be written to."""
)
def SCREAMING_SNAKE_CASE__ ( snake_case : str , snake_case : Optional[str] = None )-> str:
'''simple docstring'''
if variant is not None:
UpperCAmelCase__ : int = weights_name.split("." )
UpperCAmelCase__ : Optional[Any] = splits[:-1] + [variant] + splits[-1:]
UpperCAmelCase__ : Optional[int] = ".".join(snake_case )
return weights_name
def SCREAMING_SNAKE_CASE__ ( snake_case : Tuple , *,
snake_case : Union[str, Any] , snake_case : Optional[Any] , snake_case : str , snake_case : List[str] , snake_case : Dict , snake_case : Any , snake_case : Any , snake_case : Tuple , snake_case : List[str] , snake_case : Any , snake_case : Optional[int]=None , )-> Tuple:
'''simple docstring'''
UpperCAmelCase__ : List[str] = str(snake_case )
if os.path.isfile(snake_case ):
return pretrained_model_name_or_path
elif os.path.isdir(snake_case ):
if os.path.isfile(os.path.join(snake_case , snake_case ) ):
# Load from a PyTorch checkpoint
UpperCAmelCase__ : Any = os.path.join(snake_case , snake_case )
return model_file
elif subfolder is not None and os.path.isfile(
os.path.join(snake_case , snake_case , snake_case ) ):
UpperCAmelCase__ : str = os.path.join(snake_case , snake_case , snake_case )
return model_file
else:
raise EnvironmentError(
f'Error no file named {weights_name} found in directory {pretrained_model_name_or_path}.' )
else:
# 1. First check if deprecated way of loading from branches is used
if (
revision in DEPRECATED_REVISION_ARGS
and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
and version.parse(version.parse(snake_case ).base_version ) >= version.parse("0.20.0" )
):
try:
UpperCAmelCase__ : List[Any] = hf_hub_download(
snake_case , filename=_add_variant(snake_case , snake_case ) , cache_dir=snake_case , force_download=snake_case , proxies=snake_case , resume_download=snake_case , local_files_only=snake_case , use_auth_token=snake_case , user_agent=snake_case , subfolder=snake_case , revision=revision or commit_hash , )
warnings.warn(
f'Loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'` is deprecated. Loading instead from `revision=\'main\'` with `variant={revision}`. Loading model variants via `revision=\'{revision}\'` will be removed in diffusers v1. Please use `variant=\'{revision}\'` instead.' , snake_case , )
return model_file
except: # noqa: E722
warnings.warn(
f'You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision=\'{revision}\'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant=\'{revision}\'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(snake_case , snake_case )} file in the \'main\' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title \'{pretrained_model_name_or_path} is missing {_add_variant(snake_case , snake_case )}\' so that the correct variant file can be added.' , snake_case , )
try:
# 2. Load model file as usual
UpperCAmelCase__ : Dict = hf_hub_download(
snake_case , filename=snake_case , cache_dir=snake_case , force_download=snake_case , proxies=snake_case , resume_download=snake_case , local_files_only=snake_case , use_auth_token=snake_case , user_agent=snake_case , subfolder=snake_case , revision=revision or commit_hash , )
return model_file
except RepositoryNotFoundError:
raise EnvironmentError(
f'{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier '
"listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a "
"token having permission to this repo with `use_auth_token` or log in with `huggingface-cli "
"login`." )
except RevisionNotFoundError:
raise EnvironmentError(
f'{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for '
"this model name. Check the model page at "
f'\'https://huggingface.co/{pretrained_model_name_or_path}\' for available revisions.' )
except EntryNotFoundError:
raise EnvironmentError(
f'{pretrained_model_name_or_path} does not appear to have a file named {weights_name}.' )
except HTTPError as err:
raise EnvironmentError(
f'There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}' )
except ValueError:
raise EnvironmentError(
f'We couldn\'t connect to \'{HUGGINGFACE_CO_RESOLVE_ENDPOINT}\' to load this model, couldn\'t find it'
f' in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a'
f' directory containing a file named {weights_name} or'
" \nCheckout your internet connection or see how to run the library in"
" offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'." )
except EnvironmentError:
raise EnvironmentError(
f'Can\'t load the model for \'{pretrained_model_name_or_path}\'. If you were trying to load it from '
"'https://huggingface.co/models', make sure you don't have a local directory with the same name. "
f'Otherwise, make sure \'{pretrained_model_name_or_path}\' is the correct path to a directory '
f'containing a file named {weights_name}' )
| 298 | 0 |
'''simple docstring'''
import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
__UpperCAmelCase = logging.getLogger(__name__)
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : str , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : int=None , lowerCamelCase_ : int=None ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.layer[current_layer](lowerCamelCase_ , lowerCamelCase_ , head_mask[current_layer] )
SCREAMING_SNAKE_CASE : Any = layer_outputs[0]
return hidden_states
@add_start_docstrings(
'''The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top.''' , lowercase_ , )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Tuple , lowerCamelCase_ : Union[str, Any] ):
'''simple docstring'''
super().__init__(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = BertEncoderWithPabee(lowerCamelCase_ )
self.init_weights()
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : Optional[int] = 0
SCREAMING_SNAKE_CASE : Any = 0
SCREAMING_SNAKE_CASE : Dict = 0
def lowerCamelCase_ ( self : Dict , lowerCamelCase_ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = threshold
def lowerCamelCase_ ( self : List[str] , lowerCamelCase_ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = patience
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = 0
SCREAMING_SNAKE_CASE : List[str] = 0
def lowerCamelCase_ ( self : Optional[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.inference_layers_num / self.inference_instances_num
SCREAMING_SNAKE_CASE : Dict = (
f'''*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ='''
f''' {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***'''
)
print(lowerCamelCase_ )
@add_start_docstrings_to_model_forward(lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : List[str]=None , lowerCamelCase_ : Any=None , lowerCamelCase_ : List[str]=None , lowerCamelCase_ : Dict=None , lowerCamelCase_ : Optional[Any]=None , lowerCamelCase_ : Union[str, Any]=None , lowerCamelCase_ : str=None , lowerCamelCase_ : Optional[int]=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : int=None , lowerCamelCase_ : Any=False , ):
'''simple docstring'''
if input_ids is not None and inputs_embeds is not None:
raise ValueError("""You cannot specify both input_ids and inputs_embeds at the same time""" )
elif input_ids is not None:
SCREAMING_SNAKE_CASE : Any = input_ids.size()
elif inputs_embeds is not None:
SCREAMING_SNAKE_CASE : Optional[int] = inputs_embeds.size()[:-1]
else:
raise ValueError("""You have to specify either input_ids or inputs_embeds""" )
SCREAMING_SNAKE_CASE : Any = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
SCREAMING_SNAKE_CASE : List[Any] = torch.ones(lowerCamelCase_ , device=lowerCamelCase_ )
if token_type_ids is None:
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.zeros(lowerCamelCase_ , dtype=torch.long , device=lowerCamelCase_ )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
SCREAMING_SNAKE_CASE : torch.Tensor = self.get_extended_attention_mask(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE : str = encoder_hidden_states.size()
SCREAMING_SNAKE_CASE : Union[str, Any] = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
SCREAMING_SNAKE_CASE : int = torch.ones(lowerCamelCase_ , device=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = self.invert_attention_mask(lowerCamelCase_ )
else:
SCREAMING_SNAKE_CASE : Any = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
SCREAMING_SNAKE_CASE : List[str] = self.get_head_mask(lowerCamelCase_ , self.config.num_hidden_layers )
SCREAMING_SNAKE_CASE : int = self.embeddings(
input_ids=lowerCamelCase_ , position_ids=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , inputs_embeds=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = embedding_output
if self.training:
SCREAMING_SNAKE_CASE : Any = []
for i in range(self.config.num_hidden_layers ):
SCREAMING_SNAKE_CASE : Union[str, Any] = self.encoder.adaptive_forward(
lowerCamelCase_ , current_layer=lowerCamelCase_ , attention_mask=lowerCamelCase_ , head_mask=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : str = self.pooler(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = output_layers[i](output_dropout(lowerCamelCase_ ) )
res.append(lowerCamelCase_ )
elif self.patience == 0: # Use all layers for inference
SCREAMING_SNAKE_CASE : Tuple = self.encoder(
lowerCamelCase_ , attention_mask=lowerCamelCase_ , head_mask=lowerCamelCase_ , encoder_hidden_states=lowerCamelCase_ , encoder_attention_mask=lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : Dict = self.pooler(encoder_outputs[0] )
SCREAMING_SNAKE_CASE : List[str] = [output_layers[self.config.num_hidden_layers - 1](lowerCamelCase_ )]
else:
SCREAMING_SNAKE_CASE : int = 0
SCREAMING_SNAKE_CASE : List[Any] = None
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
SCREAMING_SNAKE_CASE : Dict = self.encoder.adaptive_forward(
lowerCamelCase_ , current_layer=lowerCamelCase_ , attention_mask=lowerCamelCase_ , head_mask=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = self.pooler(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = output_layers[i](lowerCamelCase_ )
if regression:
SCREAMING_SNAKE_CASE : List[Any] = logits.detach()
if patient_result is not None:
SCREAMING_SNAKE_CASE : Optional[int] = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
SCREAMING_SNAKE_CASE : Any = 0
else:
SCREAMING_SNAKE_CASE : Any = logits.detach().argmax(dim=1 )
if patient_result is not None:
SCREAMING_SNAKE_CASE : int = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(lowerCamelCase_ ) ):
patient_counter += 1
else:
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : str = logits
if patient_counter == self.patience:
break
SCREAMING_SNAKE_CASE : Any = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
'''Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of
the pooled output) e.g. for GLUE tasks. ''' , lowercase_ , )
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
def __init__( self : Union[str, Any] , lowerCamelCase_ : Tuple ):
'''simple docstring'''
super().__init__(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = config.num_labels
SCREAMING_SNAKE_CASE : str = BertModelWithPabee(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = nn.Dropout(config.hidden_dropout_prob )
SCREAMING_SNAKE_CASE : Tuple = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(lowerCamelCase_ )
def lowerCamelCase_ ( self : Tuple , lowerCamelCase_ : int=None , lowerCamelCase_ : List[str]=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : Union[str, Any]=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : List[Any]=None , lowerCamelCase_ : Any=None , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.bert(
input_ids=lowerCamelCase_ , attention_mask=lowerCamelCase_ , token_type_ids=lowerCamelCase_ , position_ids=lowerCamelCase_ , head_mask=lowerCamelCase_ , inputs_embeds=lowerCamelCase_ , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
SCREAMING_SNAKE_CASE : List[str] = (logits[-1],)
if labels is not None:
SCREAMING_SNAKE_CASE : Union[str, Any] = None
SCREAMING_SNAKE_CASE : Optional[int] = 0
for ix, logits_item in enumerate(lowerCamelCase_ ):
if self.num_labels == 1:
# We are doing regression
SCREAMING_SNAKE_CASE : Union[str, Any] = MSELoss()
SCREAMING_SNAKE_CASE : List[str] = loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
SCREAMING_SNAKE_CASE : Tuple = CrossEntropyLoss()
SCREAMING_SNAKE_CASE : int = loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
SCREAMING_SNAKE_CASE : Dict = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
SCREAMING_SNAKE_CASE : Optional[Any] = (total_loss / total_weights,) + outputs
return outputs
| 323 |
'''simple docstring'''
import torch
from diffusers import CMStochasticIterativeScheduler
from .test_schedulers import SchedulerCommonTest
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE__ = (CMStochasticIterativeScheduler,)
SCREAMING_SNAKE_CASE__ = 10
def lowerCamelCase_ ( self : List[str] , **lowerCamelCase_ : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = {
"""num_train_timesteps""": 2_01,
"""sigma_min""": 0.002,
"""sigma_max""": 80.0,
}
config.update(**lowerCamelCase_ )
return config
def lowerCamelCase_ ( self : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Dict = 10
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : int = self.scheduler_classes[0](**lowerCamelCase_ )
scheduler.set_timesteps(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = scheduler.timesteps[0]
SCREAMING_SNAKE_CASE : Dict = scheduler.timesteps[1]
SCREAMING_SNAKE_CASE : Optional[Any] = self.dummy_sample
SCREAMING_SNAKE_CASE : List[str] = 0.1 * sample
SCREAMING_SNAKE_CASE : Union[str, Any] = scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ).prev_sample
SCREAMING_SNAKE_CASE : Optional[Any] = scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ).prev_sample
self.assertEqual(output_a.shape , sample.shape )
self.assertEqual(output_a.shape , output_a.shape )
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
for timesteps in [10, 50, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=lowerCamelCase_ )
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
for clip_denoised in [True, False]:
self.check_over_configs(clip_denoised=lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : List[str] = scheduler_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Dict = 1
scheduler.set_timesteps(lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = scheduler.timesteps
SCREAMING_SNAKE_CASE : str = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = self.dummy_model()
SCREAMING_SNAKE_CASE : Optional[Any] = self.dummy_sample_deter * scheduler.init_noise_sigma
for i, t in enumerate(lowerCamelCase_ ):
# 1. scale model input
SCREAMING_SNAKE_CASE : Optional[int] = scheduler.scale_model_input(lowerCamelCase_ , lowerCamelCase_ )
# 2. predict noise residual
SCREAMING_SNAKE_CASE : Optional[int] = model(lowerCamelCase_ , lowerCamelCase_ )
# 3. predict previous sample x_t-1
SCREAMING_SNAKE_CASE : List[str] = scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , generator=lowerCamelCase_ ).prev_sample
SCREAMING_SNAKE_CASE : Union[str, Any] = pred_prev_sample
SCREAMING_SNAKE_CASE : Any = torch.sum(torch.abs(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Optional[int] = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_sum.item() - 192.7_614 ) < 1e-2
assert abs(result_mean.item() - 0.2_510 ) < 1e-3
def lowerCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : Tuple = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : int = scheduler_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Optional[int] = [1_06, 0]
scheduler.set_timesteps(timesteps=lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Tuple = scheduler.timesteps
SCREAMING_SNAKE_CASE : Tuple = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Any = self.dummy_model()
SCREAMING_SNAKE_CASE : List[str] = self.dummy_sample_deter * scheduler.init_noise_sigma
for t in timesteps:
# 1. scale model input
SCREAMING_SNAKE_CASE : Optional[Any] = scheduler.scale_model_input(lowerCamelCase_ , lowerCamelCase_ )
# 2. predict noise residual
SCREAMING_SNAKE_CASE : Any = model(lowerCamelCase_ , lowerCamelCase_ )
# 3. predict previous sample x_t-1
SCREAMING_SNAKE_CASE : str = scheduler.step(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , generator=lowerCamelCase_ ).prev_sample
SCREAMING_SNAKE_CASE : Dict = pred_prev_sample
SCREAMING_SNAKE_CASE : Any = torch.sum(torch.abs(lowerCamelCase_ ) )
SCREAMING_SNAKE_CASE : Tuple = torch.mean(torch.abs(lowerCamelCase_ ) )
assert abs(result_sum.item() - 347.6_357 ) < 1e-2
assert abs(result_mean.item() - 0.4_527 ) < 1e-3
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : Optional[int] = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : Any = scheduler_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : Any = [39, 30, 12, 15, 0]
with self.assertRaises(lowerCamelCase_ , msg="""`timesteps` must be in descending order.""" ):
scheduler.set_timesteps(timesteps=lowerCamelCase_ )
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : Dict = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : Optional[int] = scheduler_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : int = [39, 30, 12, 1, 0]
SCREAMING_SNAKE_CASE : Optional[Any] = len(lowerCamelCase_ )
with self.assertRaises(lowerCamelCase_ , msg="""Can only pass one of `num_inference_steps` or `timesteps`.""" ):
scheduler.set_timesteps(num_inference_steps=lowerCamelCase_ , timesteps=lowerCamelCase_ )
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[str] = self.scheduler_classes[0]
SCREAMING_SNAKE_CASE : Any = self.get_scheduler_config()
SCREAMING_SNAKE_CASE : int = scheduler_class(**lowerCamelCase_ )
SCREAMING_SNAKE_CASE : List[str] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
lowerCamelCase_ , msg="""`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}""" , ):
scheduler.set_timesteps(timesteps=lowerCamelCase_ )
| 323 | 1 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : int ):
UpperCAmelCase : Tuple = int(UpperCamelCase )
if n_element < 1:
UpperCAmelCase : Tuple = ValueError("""a should be a positive number""" )
raise my_error
UpperCAmelCase : Tuple = [1]
UpperCAmelCase : Union[str, Any] = (0, 0, 0)
UpperCAmelCase : Any = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
A: int = input("Enter the last number (nth term) of the Hamming Number Series: ")
print("Formula of Hamming Number Series => 2^i * 3^j * 5^k")
A: List[str] = hamming(int(n))
print("-----------------------------------------------------")
print(f"""The list with nth numbers is: {hamming_numbers}""")
print("-----------------------------------------------------") | 359 |
"""simple docstring"""
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the same n-dimensional space""" )
return float(sum(abs(a - b ) for a, b in zip(UpperCamelCase , UpperCamelCase ) ) )
def _snake_case ( UpperCamelCase : list[float] ):
if point:
if isinstance(UpperCamelCase , UpperCamelCase ):
for item in point:
if not isinstance(UpperCamelCase , (int, float) ):
UpperCAmelCase : Any = (
"""Expected a list of numbers as input, found """
F"{type(UpperCamelCase ).__name__}"
)
raise TypeError(UpperCamelCase )
else:
UpperCAmelCase : int = F"Expected a list of numbers as input, found {type(UpperCamelCase ).__name__}"
raise TypeError(UpperCamelCase )
else:
raise ValueError("""Missing an input""" )
def _snake_case ( UpperCamelCase : list , UpperCamelCase : list ):
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("""Both points must be in the same n-dimensional space""" )
return float(sum(abs(x - y ) for x, y in zip(UpperCamelCase , UpperCamelCase ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 76 | 0 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : List[Any] , a : Optional[Any] , a : List[Any]=99 , a : Union[str, Any]=13 , a : List[str]=16 , a : Tuple=7 , a : Dict=True , a : List[str]=True , a : Union[str, Any]=True , a : Optional[int]=False , a : Union[str, Any]=True , a : List[str]=2 , a : Dict=32 , a : List[Any]=4 , a : Optional[Any]=4 , a : List[str]=30 , a : str=0 , a : str=1 , a : Tuple=2 , a : Optional[Any]=None , ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : Tuple = batch_size
SCREAMING_SNAKE_CASE : Dict = decoder_seq_length
# For common tests
SCREAMING_SNAKE_CASE : Dict = self.decoder_seq_length
SCREAMING_SNAKE_CASE : Tuple = is_training
SCREAMING_SNAKE_CASE : int = use_attention_mask
SCREAMING_SNAKE_CASE : str = use_labels
SCREAMING_SNAKE_CASE : List[str] = vocab_size
SCREAMING_SNAKE_CASE : Union[str, Any] = d_model
SCREAMING_SNAKE_CASE : List[Any] = d_model
SCREAMING_SNAKE_CASE : Tuple = decoder_layers
SCREAMING_SNAKE_CASE : Any = decoder_layers
SCREAMING_SNAKE_CASE : Tuple = decoder_ffn_dim
SCREAMING_SNAKE_CASE : int = decoder_attention_heads
SCREAMING_SNAKE_CASE : int = decoder_attention_heads
SCREAMING_SNAKE_CASE : Any = eos_token_id
SCREAMING_SNAKE_CASE : int = bos_token_id
SCREAMING_SNAKE_CASE : Optional[int] = pad_token_id
SCREAMING_SNAKE_CASE : str = decoder_start_token_id
SCREAMING_SNAKE_CASE : Optional[Any] = use_cache
SCREAMING_SNAKE_CASE : List[Any] = max_position_embeddings
SCREAMING_SNAKE_CASE : int = None
SCREAMING_SNAKE_CASE : Optional[int] = decoder_seq_length
SCREAMING_SNAKE_CASE : Union[str, Any] = 2
SCREAMING_SNAKE_CASE : Tuple = 1
def __UpperCamelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : List[Any] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
SCREAMING_SNAKE_CASE : Optional[Any] = None
if self.use_labels:
SCREAMING_SNAKE_CASE : List[Any] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Optional[int] = TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def __UpperCamelCase ( self : Tuple , a : Optional[Any] , a : int , a : Tuple , a : List[Any] , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : Optional[int] = TrOCRDecoder(config=a ).to(a ).eval()
SCREAMING_SNAKE_CASE : Any = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
SCREAMING_SNAKE_CASE : Union[str, Any] = model(a , use_cache=a )
SCREAMING_SNAKE_CASE : Tuple = model(a )
SCREAMING_SNAKE_CASE : Tuple = model(a , use_cache=a )
self.parent.assertTrue(len(a ) == len(a ) )
self.parent.assertTrue(len(a ) == len(a ) + 1 )
SCREAMING_SNAKE_CASE : Any = outputs["past_key_values"]
# create hypothetical next token and extent to next_input_ids
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.cat([input_ids, next_tokens] , dim=-1 )
SCREAMING_SNAKE_CASE : Any = model(a )["last_hidden_state"]
SCREAMING_SNAKE_CASE : List[str] = model(a , past_key_values=a )["last_hidden_state"]
# select random slice
SCREAMING_SNAKE_CASE : Tuple = ids_tensor((1,) , output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE : Optional[int] = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
SCREAMING_SNAKE_CASE : Union[str, Any] = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(a , a , atol=1e-3 )
def __UpperCamelCase ( self : Dict ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Optional[Any] = config_and_inputs
SCREAMING_SNAKE_CASE : Tuple = {"input_ids": input_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __A , __A , __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =(TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
lowerCamelCase__ =(TrOCRForCausalLM,) if is_torch_available() else ()
lowerCamelCase__ ={'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
lowerCamelCase__ =True
lowerCamelCase__ =False
def __UpperCamelCase ( self : int ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = TrOCRStandaloneDecoderModelTester(self , is_training=a )
SCREAMING_SNAKE_CASE : str = ConfigTester(self , config_class=a )
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
pass
def __UpperCamelCase ( self : Dict ) -> Any:
"""simple docstring"""
pass
def __UpperCamelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
pass
def __UpperCamelCase ( self : Tuple ) -> List[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*a )
def __UpperCamelCase ( self : List[Any] ) -> Any:
"""simple docstring"""
return
@unittest.skip("The model doesn't support left padding" ) # and it's not used enough to be worth fixing :)
def __UpperCamelCase ( self : List[str] ) -> int:
"""simple docstring"""
pass | 76 |
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
a_ = logging.get_logger(__name__)
def lowerCamelCase__ ( _a):
if isinstance(_a , (list, tuple)) and isinstance(videos[0] , (list, tuple)) and is_valid_image(videos[0][0]):
return videos
elif isinstance(_a , (list, tuple)) and is_valid_image(videos[0]):
return [videos]
elif is_valid_image(_a):
return [[videos]]
raise ValueError(f"Could not make batched video from {videos}")
class _UpperCamelCase ( __A ):
'''simple docstring'''
lowerCamelCase__ =['pixel_values']
def __init__( self : Optional[Any] , a : bool = True , a : Dict[str, int] = None , a : PILImageResampling = PILImageResampling.BILINEAR , a : bool = True , a : Dict[str, int] = None , a : bool = True , a : Union[int, float] = 1 / 255 , a : bool = True , a : bool = True , a : Optional[Union[float, List[float]]] = None , a : Optional[Union[float, List[float]]] = None , **a : Tuple , ) -> None:
"""simple docstring"""
super().__init__(**a )
SCREAMING_SNAKE_CASE : Tuple = size if size is not None else {"shortest_edge": 256}
SCREAMING_SNAKE_CASE : Tuple = get_size_dict(a , default_to_square=a )
SCREAMING_SNAKE_CASE : List[str] = crop_size if crop_size is not None else {"height": 224, "width": 224}
SCREAMING_SNAKE_CASE : str = get_size_dict(a , param_name="crop_size" )
SCREAMING_SNAKE_CASE : Dict = do_resize
SCREAMING_SNAKE_CASE : List[Any] = size
SCREAMING_SNAKE_CASE : Optional[int] = do_center_crop
SCREAMING_SNAKE_CASE : int = crop_size
SCREAMING_SNAKE_CASE : int = resample
SCREAMING_SNAKE_CASE : Any = do_rescale
SCREAMING_SNAKE_CASE : int = rescale_factor
SCREAMING_SNAKE_CASE : Tuple = offset
SCREAMING_SNAKE_CASE : str = do_normalize
SCREAMING_SNAKE_CASE : Optional[int] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
SCREAMING_SNAKE_CASE : Dict = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __UpperCamelCase ( self : Optional[Any] , a : np.ndarray , a : Dict[str, int] , a : PILImageResampling = PILImageResampling.BILINEAR , a : Optional[Union[str, ChannelDimension]] = None , **a : Union[str, Any] , ) -> np.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = get_size_dict(a , default_to_square=a )
if "shortest_edge" in size:
SCREAMING_SNAKE_CASE : str = get_resize_output_image_size(a , size["shortest_edge"] , default_to_square=a )
elif "height" in size and "width" in size:
SCREAMING_SNAKE_CASE : Dict = (size["height"], size["width"])
else:
raise ValueError(F"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}" )
return resize(a , size=a , resample=a , data_format=a , **a )
def __UpperCamelCase ( self : List[str] , a : np.ndarray , a : Dict[str, int] , a : Optional[Union[str, ChannelDimension]] = None , **a : str , ) -> np.ndarray:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = get_size_dict(a )
if "height" not in size or "width" not in size:
raise ValueError(F"Size must have 'height' and 'width' as keys. Got {size.keys()}" )
return center_crop(a , size=(size["height"], size["width"]) , data_format=a , **a )
def __UpperCamelCase ( self : List[Any] , a : np.ndarray , a : Union[int, float] , a : bool = True , a : Optional[Union[str, ChannelDimension]] = None , **a : Tuple , ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = image.astype(np.floataa )
if offset:
SCREAMING_SNAKE_CASE : Union[str, Any] = image - (scale / 2)
return rescale(a , scale=a , data_format=a , **a )
def __UpperCamelCase ( self : int , a : np.ndarray , a : Union[float, List[float]] , a : Union[float, List[float]] , a : Optional[Union[str, ChannelDimension]] = None , **a : List[str] , ) -> np.ndarray:
"""simple docstring"""
return normalize(a , mean=a , std=a , data_format=a , **a )
def __UpperCamelCase ( self : Tuple , a : ImageInput , a : bool = None , a : Dict[str, int] = None , a : PILImageResampling = None , a : bool = None , a : Dict[str, int] = None , a : bool = None , a : float = None , a : bool = None , a : bool = None , a : Optional[Union[float, List[float]]] = None , a : Optional[Union[float, List[float]]] = None , a : Optional[ChannelDimension] = ChannelDimension.FIRST , ) -> np.ndarray:
"""simple docstring"""
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
if offset and not do_rescale:
raise ValueError("For offset, do_rescale must also be set to True." )
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE : List[str] = to_numpy_array(a )
if do_resize:
SCREAMING_SNAKE_CASE : Optional[Any] = self.resize(image=a , size=a , resample=a )
if do_center_crop:
SCREAMING_SNAKE_CASE : Union[str, Any] = self.center_crop(a , size=a )
if do_rescale:
SCREAMING_SNAKE_CASE : Any = self.rescale(image=a , scale=a , offset=a )
if do_normalize:
SCREAMING_SNAKE_CASE : Tuple = self.normalize(image=a , mean=a , std=a )
SCREAMING_SNAKE_CASE : Optional[int] = to_channel_dimension_format(a , a )
return image
def __UpperCamelCase ( self : Dict , a : ImageInput , a : bool = None , a : Dict[str, int] = None , a : PILImageResampling = None , a : bool = None , a : Dict[str, int] = None , a : bool = None , a : float = None , a : bool = None , a : bool = None , a : Optional[Union[float, List[float]]] = None , a : Optional[Union[float, List[float]]] = None , a : Optional[Union[str, TensorType]] = None , a : ChannelDimension = ChannelDimension.FIRST , **a : Tuple , ) -> PIL.Image.Image:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE : Union[str, Any] = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE : int = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE : str = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE : List[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE : Optional[Any] = offset if offset is not None else self.offset
SCREAMING_SNAKE_CASE : str = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE : Optional[int] = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE : Optional[Any] = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE : int = size if size is not None else self.size
SCREAMING_SNAKE_CASE : List[Any] = get_size_dict(a , default_to_square=a )
SCREAMING_SNAKE_CASE : Tuple = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE : Union[str, Any] = get_size_dict(a , param_name="crop_size" )
if not valid_images(a ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
SCREAMING_SNAKE_CASE : Optional[int] = make_batched(a )
SCREAMING_SNAKE_CASE : List[Any] = [
[
self._preprocess_image(
image=a , do_resize=a , size=a , resample=a , do_center_crop=a , crop_size=a , do_rescale=a , rescale_factor=a , offset=a , do_normalize=a , image_mean=a , image_std=a , data_format=a , )
for img in video
]
for video in videos
]
SCREAMING_SNAKE_CASE : Optional[int] = {"pixel_values": videos}
return BatchFeature(data=a , tensor_type=a ) | 76 | 1 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
class __a ( unittest.TestCase ):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=7 , lowerCAmelCase__=3 , lowerCAmelCase__=30 , lowerCAmelCase__=400 , lowerCAmelCase__=True , lowerCAmelCase__=None , lowerCAmelCase__=True , lowerCAmelCase__=1 / 255 , lowerCAmelCase__=True , lowerCAmelCase__=[0.5, 0.5, 0.5] , lowerCAmelCase__=[0.5, 0.5, 0.5] , lowerCAmelCase__=True , ) -> List[str]:
'''simple docstring'''
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
lowercase__: Dict = size if size is not None else {'shortest_edge': 18, 'longest_edge': 1_333}
lowercase__: Tuple = parent
lowercase__: Optional[Any] = batch_size
lowercase__: Any = num_channels
lowercase__: str = min_resolution
lowercase__: Dict = max_resolution
lowercase__: Any = do_resize
lowercase__: str = size
lowercase__: Any = do_rescale
lowercase__: Union[str, Any] = rescale_factor
lowercase__: Optional[int] = do_normalize
lowercase__: Union[str, Any] = image_mean
lowercase__: List[str] = image_std
lowercase__: Optional[Any] = do_pad
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def SCREAMING_SNAKE_CASE__ ( self , lowerCAmelCase__ , lowerCAmelCase__=False ) -> int:
'''simple docstring'''
if not batched:
lowercase__: List[Any] = image_inputs[0]
if isinstance(lowerCAmelCase__ , Image.Image ):
lowercase__ , lowercase__: List[str] = image.size
else:
lowercase__ , lowercase__: str = image.shape[1], image.shape[2]
if w < h:
lowercase__: Optional[int] = int(self.size['shortest_edge'] * h / w )
lowercase__: int = self.size['shortest_edge']
elif w > h:
lowercase__: Tuple = self.size['shortest_edge']
lowercase__: int = int(self.size['shortest_edge'] * w / h )
else:
lowercase__: Tuple = self.size['shortest_edge']
lowercase__: Optional[Any] = self.size['shortest_edge']
else:
lowercase__: str = []
for image in image_inputs:
lowercase__ , lowercase__: Any = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
lowercase__: Union[str, Any] = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[0] )[0]
lowercase__: Any = max(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __a ( __UpperCamelCase , unittest.TestCase ):
__lowercase : Tuple = DetrImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
lowercase__: Optional[int] = DetrImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
'''simple docstring'''
lowercase__: Dict = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCAmelCase__ , 'image_mean' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'image_std' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'do_normalize' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'do_rescale' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'rescale_factor' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'do_resize' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'size' ) )
self.assertTrue(hasattr(lowerCAmelCase__ , 'do_pad' ) )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
'''simple docstring'''
lowercase__: int = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {'shortest_edge': 18, 'longest_edge': 1_333} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
lowercase__: str = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=lowerCAmelCase__ )
self.assertEqual(image_processor.size , {'shortest_edge': 42, 'longest_edge': 84} )
self.assertEqual(image_processor.do_pad , lowerCAmelCase__ )
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
'''simple docstring'''
# Initialize image_processing
lowercase__: Dict = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
lowercase__: str = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , Image.Image )
# Test not batched input
lowercase__: Optional[Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
lowercase__ , lowercase__: Tuple = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__ , lowercase__: Union[str, Any] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
lowercase__: Dict = image_processing(lowerCAmelCase__ , return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
'''simple docstring'''
# Initialize image_processing
lowercase__: Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
lowercase__: Optional[int] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , numpify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , np.ndarray )
# Test not batched input
lowercase__: Union[str, Any] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
lowercase__ , lowercase__: Optional[int] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__: Union[str, Any] = image_processing(lowerCAmelCase__ , return_tensors='pt' ).pixel_values
lowercase__ , lowercase__: Dict = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[Any]:
'''simple docstring'''
# Initialize image_processing
lowercase__: Tuple = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
lowercase__: Union[str, Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowerCAmelCase__ , torchify=lowerCAmelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCAmelCase__ , torch.Tensor )
# Test not batched input
lowercase__: Optional[int] = image_processing(image_inputs[0] , return_tensors='pt' ).pixel_values
lowercase__ , lowercase__: Dict = self.image_processor_tester.get_expected_values(lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
lowercase__: Optional[Any] = image_processing(lowerCAmelCase__ , return_tensors='pt' ).pixel_values
lowercase__ , lowercase__: Optional[Any] = self.image_processor_tester.get_expected_values(lowerCAmelCase__ , batched=lowerCAmelCase__ )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
# prepare image and target
lowercase__: Any = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt' , 'r' ) as f:
lowercase__: Optional[int] = json.loads(f.read() )
lowercase__: Optional[Any] = {'image_id': 39_769, 'annotations': target}
# encode them
lowercase__: Optional[Any] = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50' )
lowercase__: List[Any] = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , return_tensors='pt' )
# verify pixel values
lowercase__: Optional[int] = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['pixel_values'].shape , lowerCAmelCase__ )
lowercase__: Optional[Any] = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
lowercase__: List[Any] = torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , lowerCAmelCase__ ) )
# verify boxes
lowercase__: Union[str, Any] = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , lowerCAmelCase__ )
lowercase__: int = torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
lowercase__: List[Any] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , lowerCAmelCase__ ) )
# verify is_crowd
lowercase__: Union[str, Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , lowerCAmelCase__ ) )
# verify class_labels
lowercase__: List[Any] = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , lowerCAmelCase__ ) )
# verify orig_size
lowercase__: Tuple = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , lowerCAmelCase__ ) )
# verify size
lowercase__: Tuple = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , lowerCAmelCase__ ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
'''simple docstring'''
# prepare image, target and masks_path
lowercase__: List[str] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt' , 'r' ) as f:
lowercase__: Tuple = json.loads(f.read() )
lowercase__: Tuple = {'file_name': '000000039769.png', 'image_id': 39_769, 'segments_info': target}
lowercase__: List[str] = pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic' )
# encode them
lowercase__: Dict = DetrImageProcessor.from_pretrained('facebook/detr-resnet-50-panoptic' )
lowercase__: Dict = image_processing(images=lowerCAmelCase__ , annotations=lowerCAmelCase__ , masks_path=lowerCAmelCase__ , return_tensors='pt' )
# verify pixel values
lowercase__: Any = torch.Size([1, 3, 800, 1_066] )
self.assertEqual(encoding['pixel_values'].shape , lowerCAmelCase__ )
lowercase__: Any = torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1] )
self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3] , lowerCAmelCase__ , atol=1E-4 ) )
# verify area
lowercase__: str = torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7] )
self.assertTrue(torch.allclose(encoding['labels'][0]['area'] , lowerCAmelCase__ ) )
# verify boxes
lowercase__: Dict = torch.Size([6, 4] )
self.assertEqual(encoding['labels'][0]['boxes'].shape , lowerCAmelCase__ )
lowercase__: str = torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5] )
self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0] , lowerCAmelCase__ , atol=1E-3 ) )
# verify image_id
lowercase__: Optional[int] = torch.tensor([39_769] )
self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'] , lowerCAmelCase__ ) )
# verify is_crowd
lowercase__: List[Any] = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'] , lowerCAmelCase__ ) )
# verify class_labels
lowercase__: Optional[int] = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'] , lowerCAmelCase__ ) )
# verify masks
lowercase__: str = 822_873
self.assertEqual(encoding['labels'][0]['masks'].sum().item() , lowerCAmelCase__ )
# verify orig_size
lowercase__: Optional[int] = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'] , lowerCAmelCase__ ) )
# verify size
lowercase__: Optional[int] = torch.tensor([800, 1_066] )
self.assertTrue(torch.allclose(encoding['labels'][0]['size'] , lowerCAmelCase__ ) )
| 288 |
from __future__ import annotations
def snake_case_ ( snake_case , snake_case ) -> list[str]:
if nth_term == "":
return [""]
lowercase__: Tuple = int(snake_case )
lowercase__: int = int(snake_case )
lowercase__: list[str] = []
for temp in range(int(snake_case ) ):
series.append(f'1 / {pow(temp + 1 , int(snake_case ) )}' if series else '1' )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowerCAmelCase = int(input('''Enter the last number (nth term) of the P-Series'''))
__lowerCAmelCase = int(input('''Enter the power for P-Series'''))
print('''Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p''')
print(p_series(nth_term, power))
| 288 | 1 |
'''simple docstring'''
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ : Dict =logging.get_logger(__name__)
A__ : List[str] ={
'''google/owlvit-base-patch32''': '''https://huggingface.co/google/owlvit-base-patch32/resolve/main/config.json''',
'''google/owlvit-base-patch16''': '''https://huggingface.co/google/owlvit-base-patch16/resolve/main/config.json''',
'''google/owlvit-large-patch14''': '''https://huggingface.co/google/owlvit-large-patch14/resolve/main/config.json''',
}
class UpperCAmelCase ( snake_case_ ):
_lowercase: List[str] = '''owlvit_text_model'''
def __init__( self : List[Any] , __snake_case : int=4_94_08 , __snake_case : str=5_12 , __snake_case : List[Any]=20_48 , __snake_case : str=12 , __snake_case : Optional[int]=8 , __snake_case : List[str]=16 , __snake_case : Union[str, Any]="quick_gelu" , __snake_case : Tuple=1E-5 , __snake_case : str=0.0 , __snake_case : int=0.02 , __snake_case : Union[str, Any]=1.0 , __snake_case : Optional[int]=0 , __snake_case : Optional[int]=4_94_06 , __snake_case : List[str]=4_94_07 , **__snake_case : Optional[Any] , ) -> int:
super().__init__(pad_token_id=__snake_case , bos_token_id=__snake_case , eos_token_id=__snake_case , **__snake_case )
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = hidden_act
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = initializer_range
_lowerCAmelCase = initializer_factor
@classmethod
def lowercase__ ( cls : Optional[Any] , __snake_case : Union[str, os.PathLike] , **__snake_case : Dict ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__snake_case )
_lowerCAmelCase , _lowerCAmelCase = cls.get_config_dict(__snake_case , **__snake_case )
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
_lowerCAmelCase = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(__snake_case , **__snake_case )
class UpperCAmelCase ( snake_case_ ):
_lowercase: List[str] = '''owlvit_vision_model'''
def __init__( self : str , __snake_case : Union[str, Any]=7_68 , __snake_case : Optional[Any]=30_72 , __snake_case : List[Any]=12 , __snake_case : Dict=12 , __snake_case : Tuple=3 , __snake_case : Dict=7_68 , __snake_case : Optional[int]=32 , __snake_case : Tuple="quick_gelu" , __snake_case : str=1E-5 , __snake_case : List[str]=0.0 , __snake_case : int=0.02 , __snake_case : Any=1.0 , **__snake_case : int , ) -> str:
super().__init__(**__snake_case )
_lowerCAmelCase = hidden_size
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = num_channels
_lowerCAmelCase = image_size
_lowerCAmelCase = patch_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = attention_dropout
_lowerCAmelCase = initializer_range
_lowerCAmelCase = initializer_factor
@classmethod
def lowercase__ ( cls : int , __snake_case : Union[str, os.PathLike] , **__snake_case : Tuple ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__snake_case )
_lowerCAmelCase , _lowerCAmelCase = cls.get_config_dict(__snake_case , **__snake_case )
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""" ) == "owlvit":
_lowerCAmelCase = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(__snake_case , **__snake_case )
class UpperCAmelCase ( snake_case_ ):
_lowercase: Tuple = '''owlvit'''
_lowercase: Optional[Any] = True
def __init__( self : Optional[int] , __snake_case : Optional[Any]=None , __snake_case : int=None , __snake_case : Dict=5_12 , __snake_case : List[Any]=2.65_92 , __snake_case : Union[str, Any]=True , **__snake_case : str , ) -> Dict:
super().__init__(**__snake_case )
if text_config is None:
_lowerCAmelCase = {}
logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""" )
if vision_config is None:
_lowerCAmelCase = {}
logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""" )
_lowerCAmelCase = OwlViTTextConfig(**__snake_case )
_lowerCAmelCase = OwlViTVisionConfig(**__snake_case )
_lowerCAmelCase = projection_dim
_lowerCAmelCase = logit_scale_init_value
_lowerCAmelCase = return_dict
_lowerCAmelCase = 1.0
@classmethod
def lowercase__ ( cls : str , __snake_case : Union[str, os.PathLike] , **__snake_case : Optional[Any] ) -> "PretrainedConfig":
cls._set_token_in_kwargs(__snake_case )
_lowerCAmelCase , _lowerCAmelCase = cls.get_config_dict(__snake_case , **__snake_case )
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
f"{cls.model_type}. This is not supported for all configurations of models and can yield errors." )
return cls.from_dict(__snake_case , **__snake_case )
@classmethod
def lowercase__ ( cls : List[Any] , __snake_case : Dict , __snake_case : Dict , **__snake_case : Optional[int] ) -> Union[str, Any]:
_lowerCAmelCase = {}
_lowerCAmelCase = text_config
_lowerCAmelCase = vision_config
return cls.from_dict(__snake_case , **__snake_case )
def lowercase__ ( self : Union[str, Any] ) -> List[Any]:
_lowerCAmelCase = copy.deepcopy(self.__dict__ )
_lowerCAmelCase = self.text_config.to_dict()
_lowerCAmelCase = self.vision_config.to_dict()
_lowerCAmelCase = self.__class__.model_type
return output
class UpperCAmelCase ( snake_case_ ):
@property
def lowercase__ ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """sequence"""}),
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""attention_mask""", {0: """batch""", 1: """sequence"""}),
] )
@property
def lowercase__ ( self : str ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""logits_per_image""", {0: """batch"""}),
("""logits_per_text""", {0: """batch"""}),
("""text_embeds""", {0: """batch"""}),
("""image_embeds""", {0: """batch"""}),
] )
@property
def lowercase__ ( self : List[str] ) -> float:
return 1E-4
def lowercase__ ( self : Any , __snake_case : "ProcessorMixin" , __snake_case : int = -1 , __snake_case : int = -1 , __snake_case : Optional["TensorType"] = None , ) -> Mapping[str, Any]:
_lowerCAmelCase = super().generate_dummy_inputs(
processor.tokenizer , batch_size=__snake_case , seq_length=__snake_case , framework=__snake_case )
_lowerCAmelCase = super().generate_dummy_inputs(
processor.image_processor , batch_size=__snake_case , framework=__snake_case )
return {**text_input_dict, **image_input_dict}
@property
def lowercase__ ( self : Union[str, Any] ) -> int:
return 14
| 70 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class UpperCAmelCase ( snake_case_ , snake_case_ , snake_case_ , unittest.TestCase ):
_lowercase: Optional[int] = StableDiffusionControlNetImgaImgPipeline
_lowercase: Tuple = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
_lowercase: str = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_lowercase: Tuple = IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'''control_image'''} )
_lowercase: Union[str, Any] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def lowercase__ ( self : List[str] ) -> List[str]:
torch.manual_seed(0 )
_lowerCAmelCase = 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 , )
torch.manual_seed(0 )
_lowerCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
_lowerCAmelCase = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="""scaled_linear""" , clip_sample=__snake_case , set_alpha_to_one=__snake_case , )
torch.manual_seed(0 )
_lowerCAmelCase = 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 )
_lowerCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
_lowerCAmelCase = CLIPTextModel(__snake_case )
_lowerCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_lowerCAmelCase = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowercase__ ( self : Any , __snake_case : str , __snake_case : Any=0 ) -> str:
if str(__snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(__snake_case )
else:
_lowerCAmelCase = torch.Generator(device=__snake_case ).manual_seed(__snake_case )
_lowerCAmelCase = 2
_lowerCAmelCase = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__snake_case , device=torch.device(__snake_case ) , )
_lowerCAmelCase = floats_tensor(control_image.shape , rng=random.Random(__snake_case ) ).to(__snake_case )
_lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCAmelCase = Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((64, 64) )
_lowerCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def lowercase__ ( self : Optional[int] ) -> List[Any]:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def lowercase__ ( self : Tuple ) -> Optional[int]:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def lowercase__ ( self : Tuple ) -> Optional[int]:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class UpperCAmelCase ( snake_case_ , snake_case_ , unittest.TestCase ):
_lowercase: Any = StableDiffusionControlNetImgaImgPipeline
_lowercase: Dict = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width'''}
_lowercase: List[Any] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
_lowercase: Any = frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def lowercase__ ( self : Optional[Any] ) -> Union[str, Any]:
torch.manual_seed(0 )
_lowerCAmelCase = 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 , )
torch.manual_seed(0 )
def init_weights(__snake_case : Optional[Any] ):
if isinstance(__snake_case , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
_lowerCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(__snake_case )
torch.manual_seed(0 )
_lowerCAmelCase = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(__snake_case )
torch.manual_seed(0 )
_lowerCAmelCase = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="""scaled_linear""" , clip_sample=__snake_case , set_alpha_to_one=__snake_case , )
torch.manual_seed(0 )
_lowerCAmelCase = 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 )
_lowerCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
_lowerCAmelCase = CLIPTextModel(__snake_case )
_lowerCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
_lowerCAmelCase = MultiControlNetModel([controlneta, controlneta] )
_lowerCAmelCase = {
"""unet""": unet,
"""controlnet""": controlnet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""safety_checker""": None,
"""feature_extractor""": None,
}
return components
def lowercase__ ( self : Tuple , __snake_case : int , __snake_case : List[str]=0 ) -> Union[str, Any]:
if str(__snake_case ).startswith("""mps""" ):
_lowerCAmelCase = torch.manual_seed(__snake_case )
else:
_lowerCAmelCase = torch.Generator(device=__snake_case ).manual_seed(__snake_case )
_lowerCAmelCase = 2
_lowerCAmelCase = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__snake_case , device=torch.device(__snake_case ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=__snake_case , device=torch.device(__snake_case ) , ),
]
_lowerCAmelCase = floats_tensor(control_image[0].shape , rng=random.Random(__snake_case ) ).to(__snake_case )
_lowerCAmelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCAmelCase = Image.fromarray(np.uinta(__snake_case ) ).convert("""RGB""" ).resize((64, 64) )
_lowerCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """numpy""",
"""image""": image,
"""control_image""": control_image,
}
return inputs
def lowercase__ ( self : List[str] ) -> Dict:
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**__snake_case )
pipe.to(__snake_case )
_lowerCAmelCase = 10.0
_lowerCAmelCase = 4
_lowerCAmelCase = self.get_dummy_inputs(__snake_case )
_lowerCAmelCase = steps
_lowerCAmelCase = scale
_lowerCAmelCase = pipe(**__snake_case )[0]
_lowerCAmelCase = self.get_dummy_inputs(__snake_case )
_lowerCAmelCase = steps
_lowerCAmelCase = scale
_lowerCAmelCase = pipe(**__snake_case , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
_lowerCAmelCase = self.get_dummy_inputs(__snake_case )
_lowerCAmelCase = steps
_lowerCAmelCase = scale
_lowerCAmelCase = pipe(**__snake_case , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
_lowerCAmelCase = self.get_dummy_inputs(__snake_case )
_lowerCAmelCase = steps
_lowerCAmelCase = scale
_lowerCAmelCase = pipe(**__snake_case , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def lowercase__ ( self : int ) -> str:
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def lowercase__ ( self : Optional[Any] ) -> Dict:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def lowercase__ ( self : int ) -> str:
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def lowercase__ ( self : Union[str, Any] ) -> Optional[Any]:
_lowerCAmelCase = self.get_dummy_components()
_lowerCAmelCase = self.pipeline_class(**__snake_case )
pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(__snake_case )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
def lowercase__ ( self : Union[str, Any] ) -> int:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase__ ( self : List[str] ) -> Any:
_lowerCAmelCase = ControlNetModel.from_pretrained("""lllyasviel/sd-controlnet-canny""" )
_lowerCAmelCase = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , safety_checker=__snake_case , controlnet=__snake_case )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__snake_case )
_lowerCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
_lowerCAmelCase = """evil space-punk bird"""
_lowerCAmelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png""" ).resize((5_12, 5_12) )
_lowerCAmelCase = load_image(
"""https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png""" ).resize((5_12, 5_12) )
_lowerCAmelCase = pipe(
__snake_case , __snake_case , control_image=__snake_case , generator=__snake_case , output_type="""np""" , num_inference_steps=50 , strength=0.6 , )
_lowerCAmelCase = output.images[0]
assert image.shape == (5_12, 5_12, 3)
_lowerCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy""" )
assert np.abs(expected_image - image ).max() < 9E-2
| 70 | 1 |
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Optional[int]:
# Initialise PyTorch model
__snake_case: int = MobileBertConfig.from_json_file(SCREAMING_SNAKE_CASE__)
print(F'''Building PyTorch model from configuration: {config}''')
__snake_case: Optional[Any] = MobileBertForPreTraining(SCREAMING_SNAKE_CASE__)
# Load weights from tf checkpoint
__snake_case: Optional[Any] = load_tf_weights_in_mobilebert(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
# Save pytorch-model
print(F'''Save PyTorch model to {pytorch_dump_path}''')
torch.save(model.state_dict() , SCREAMING_SNAKE_CASE__)
if __name__ == "__main__":
__UpperCAmelCase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--mobilebert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained MobileBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__UpperCAmelCase : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 293 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : int = {
"RWKV/rwkv-4-169m-pile": "https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json",
"RWKV/rwkv-4-430m-pile": "https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json",
"RWKV/rwkv-4-1b5-pile": "https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json",
"RWKV/rwkv-4-3b-pile": "https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json",
"RWKV/rwkv-4-7b-pile": "https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json",
"RWKV/rwkv-4-14b-pile": "https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json",
"RWKV/rwkv-raven-1b5": "https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json",
"RWKV/rwkv-raven-3b": "https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json",
"RWKV/rwkv-raven-7b": "https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json",
"RWKV/rwkv-raven-14b": "https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json",
}
class __snake_case ( __lowerCamelCase ):
'''simple docstring'''
lowerCAmelCase__ = """rwkv"""
lowerCAmelCase__ = {"""max_position_embeddings""": """context_length"""}
def __init__( self : Dict , A : List[Any]=50_277 , A : List[Any]=1_024 , A : Union[str, Any]=4_096 , A : Tuple=32 , A : List[Any]=None , A : Tuple=None , A : Tuple=1E-5 , A : int=0 , A : Optional[int]=0 , A : Dict=6 , A : Dict=False , A : int=True , **A : List[Any] , ):
__snake_case: Tuple = vocab_size
__snake_case: Any = context_length
__snake_case: Dict = hidden_size
__snake_case: Dict = num_hidden_layers
__snake_case: Union[str, Any] = attention_hidden_size if attention_hidden_size is not None else hidden_size
__snake_case: str = intermediate_size if intermediate_size is not None else 4 * hidden_size
__snake_case: Any = layer_norm_epsilon
__snake_case: int = rescale_every
__snake_case: str = use_cache
__snake_case: Dict = bos_token_id
__snake_case: Union[str, Any] = eos_token_id
super().__init__(
tie_word_embeddings=A , bos_token_id=A , eos_token_id=A , **A )
| 293 | 1 |
'''simple docstring'''
import warnings
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ : Optional[int] =logging.get_logger(__name__)
A__ : Optional[Any] ={
'''nvidia/segformer-b0-finetuned-ade-512-512''': (
'''https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512/resolve/main/config.json'''
),
# See all SegFormer models at https://huggingface.co/models?filter=segformer
}
class UpperCAmelCase ( snake_case_ ):
_lowercase: List[Any] = '''segformer'''
def __init__( self : Any , __snake_case : Any=3 , __snake_case : List[str]=4 , __snake_case : Union[str, Any]=[2, 2, 2, 2] , __snake_case : Dict=[8, 4, 2, 1] , __snake_case : List[str]=[32, 64, 1_60, 2_56] , __snake_case : Optional[int]=[7, 3, 3, 3] , __snake_case : Optional[Any]=[4, 2, 2, 2] , __snake_case : Any=[1, 2, 5, 8] , __snake_case : List[Any]=[4, 4, 4, 4] , __snake_case : List[str]="gelu" , __snake_case : List[str]=0.0 , __snake_case : Optional[int]=0.0 , __snake_case : Optional[int]=0.1 , __snake_case : Optional[int]=0.02 , __snake_case : Tuple=0.1 , __snake_case : Any=1E-6 , __snake_case : Any=2_56 , __snake_case : Optional[int]=2_55 , **__snake_case : str , ) -> List[str]:
super().__init__(**__snake_case )
if "reshape_last_stage" in kwargs and kwargs["reshape_last_stage"] is False:
warnings.warn(
"""Reshape_last_stage is set to False in this config. This argument is deprecated and will soon be"""
""" removed, as the behaviour will default to that of reshape_last_stage = True.""" , __snake_case , )
_lowerCAmelCase = num_channels
_lowerCAmelCase = num_encoder_blocks
_lowerCAmelCase = depths
_lowerCAmelCase = sr_ratios
_lowerCAmelCase = hidden_sizes
_lowerCAmelCase = patch_sizes
_lowerCAmelCase = strides
_lowerCAmelCase = mlp_ratios
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = classifier_dropout_prob
_lowerCAmelCase = initializer_range
_lowerCAmelCase = drop_path_rate
_lowerCAmelCase = layer_norm_eps
_lowerCAmelCase = decoder_hidden_size
_lowerCAmelCase = kwargs.get("""reshape_last_stage""" , __snake_case )
_lowerCAmelCase = semantic_loss_ignore_index
class UpperCAmelCase ( snake_case_ ):
_lowercase: Optional[Any] = version.parse('''1.11''' )
@property
def lowercase__ ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowercase__ ( self : Any ) -> float:
return 1E-4
@property
def lowercase__ ( self : List[Any] ) -> int:
return 12
| 70 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase_ = {"""configuration_mbart""": ["""MBART_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MBartConfig""", """MBartOnnxConfig"""]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["""MBartTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["""MBartTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"""MBART_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MBartForCausalLM""",
"""MBartForConditionalGeneration""",
"""MBartForQuestionAnswering""",
"""MBartForSequenceClassification""",
"""MBartModel""",
"""MBartPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"""TFMBartForConditionalGeneration""",
"""TFMBartModel""",
"""TFMBartPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
"""FlaxMBartForConditionalGeneration""",
"""FlaxMBartForQuestionAnswering""",
"""FlaxMBartForSequenceClassification""",
"""FlaxMBartModel""",
"""FlaxMBartPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mbart import MBART_PRETRAINED_CONFIG_ARCHIVE_MAP, MBartConfig, MBartOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart import MBartTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mbart_fast import MBartTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mbart import (
MBART_PRETRAINED_MODEL_ARCHIVE_LIST,
MBartForCausalLM,
MBartForConditionalGeneration,
MBartForQuestionAnswering,
MBartForSequenceClassification,
MBartModel,
MBartPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mbart import TFMBartForConditionalGeneration, TFMBartModel, TFMBartPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_mbart import (
FlaxMBartForConditionalGeneration,
FlaxMBartForQuestionAnswering,
FlaxMBartForSequenceClassification,
FlaxMBartModel,
FlaxMBartPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 58 | 0 |
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING
lowerCAmelCase__ : List[str] =logging.get_logger(__name__)
@add_end_docstrings(UpperCamelCase_ )
class UpperCAmelCase_ ( UpperCamelCase_ ):
'''simple docstring'''
def __init__( self , *_A , **_A ):
'''simple docstring'''
super().__init__(*_A , **_A )
requires_backends(self , 'vision' )
self.check_model_type(
TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == 'tf' else MODEL_FOR_VISION_2_SEQ_MAPPING )
def _A ( self , _A=None , _A=None , _A=None ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = {}
if prompt is not None:
__SCREAMING_SNAKE_CASE = prompt
if generate_kwargs is not None:
__SCREAMING_SNAKE_CASE = generate_kwargs
if max_new_tokens is not None:
if "generate_kwargs" not in forward_kwargs:
__SCREAMING_SNAKE_CASE = {}
if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
raise ValueError(
'\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'
' please use only one' )
__SCREAMING_SNAKE_CASE = max_new_tokens
return preprocess_params, forward_kwargs, {}
def __call__( self , _A , **_A ):
'''simple docstring'''
return super().__call__(_A , **_A )
def _A ( self , _A , _A=None ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = load_image(_A )
if prompt is not None:
if not isinstance(_A , _A ):
raise ValueError(
f"""Received an invalid text input, got - {type(_A )} - but expected a single string. """
'Note also that one single text can be provided for conditional image to text generation.' )
__SCREAMING_SNAKE_CASE = self.model.config.model_type
if model_type == "git":
__SCREAMING_SNAKE_CASE = self.image_processor(images=_A , return_tensors=self.framework )
__SCREAMING_SNAKE_CASE = self.tokenizer(text=_A , add_special_tokens=_A ).input_ids
__SCREAMING_SNAKE_CASE = [self.tokenizer.cls_token_id] + input_ids
__SCREAMING_SNAKE_CASE = torch.tensor(_A ).unsqueeze(0 )
model_inputs.update({'input_ids': input_ids} )
elif model_type == "pix2struct":
__SCREAMING_SNAKE_CASE = self.image_processor(images=_A , header_text=_A , return_tensors=self.framework )
elif model_type != "vision-encoder-decoder":
# vision-encoder-decoder does not support conditional generation
__SCREAMING_SNAKE_CASE = self.image_processor(images=_A , return_tensors=self.framework )
__SCREAMING_SNAKE_CASE = self.tokenizer(_A , return_tensors=self.framework )
model_inputs.update(_A )
else:
raise ValueError(f"""Model type {model_type} does not support conditional text generation""" )
else:
__SCREAMING_SNAKE_CASE = self.image_processor(images=_A , return_tensors=self.framework )
if self.model.config.model_type == "git" and prompt is None:
__SCREAMING_SNAKE_CASE = None
return model_inputs
def _A ( self , _A , _A=None ):
'''simple docstring'''
if (
"input_ids" in model_inputs
and isinstance(model_inputs['input_ids'] , _A )
and all(x is None for x in model_inputs['input_ids'] )
):
__SCREAMING_SNAKE_CASE = None
if generate_kwargs is None:
__SCREAMING_SNAKE_CASE = {}
# FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
# parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
# the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
# in the `_prepare_model_inputs` method.
__SCREAMING_SNAKE_CASE = model_inputs.pop(self.model.main_input_name )
__SCREAMING_SNAKE_CASE = self.model.generate(_A , **_A , **_A )
return model_outputs
def _A ( self , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = []
for output_ids in model_outputs:
__SCREAMING_SNAKE_CASE = {
'generated_text': self.tokenizer.decode(
_A , skip_special_tokens=_A , )
}
records.append(_A )
return records
| 118 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : Optional[int] ={'''configuration_fnet''': ['''FNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FNetConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : Optional[int] =['''FNetTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : Optional[Any] =['''FNetTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : Tuple =[
'''FNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''FNetForMaskedLM''',
'''FNetForMultipleChoice''',
'''FNetForNextSentencePrediction''',
'''FNetForPreTraining''',
'''FNetForQuestionAnswering''',
'''FNetForSequenceClassification''',
'''FNetForTokenClassification''',
'''FNetLayer''',
'''FNetModel''',
'''FNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_fnet import FNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet import FNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_fnet_fast import FNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_fnet import (
FNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FNetForMaskedLM,
FNetForMultipleChoice,
FNetForNextSentencePrediction,
FNetForPreTraining,
FNetForQuestionAnswering,
FNetForSequenceClassification,
FNetForTokenClassification,
FNetLayer,
FNetModel,
FNetPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ : Any =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 118 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
_A : Union[str, Any] = logging.get_logger(__name__)
if is_vision_available():
import PIL
class a__ ( a_ ):
__lowerCAmelCase = ["""pixel_values"""]
def __init__( self , _a = True , _a = None , _a = PILImageResampling.BICUBIC , _a = True , _a = None , _a = True , _a = 1 / 255 , _a = True , _a = None , _a = None , _a = True , **_a , ):
super().__init__(**_a )
lowercase : int = size if size is not None else {"shortest_edge": 224}
lowercase : Optional[Any] = get_size_dict(_a , default_to_square=_a )
lowercase : Optional[int] = crop_size if crop_size is not None else {"height": 224, "width": 224}
lowercase : int = get_size_dict(_a , default_to_square=_a , param_name="crop_size" )
lowercase : Optional[int] = do_resize
lowercase : str = size
lowercase : Any = resample
lowercase : Tuple = do_center_crop
lowercase : Dict = crop_size
lowercase : Dict = do_rescale
lowercase : List[Any] = rescale_factor
lowercase : str = do_normalize
lowercase : Dict = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
lowercase : Optional[Any] = image_std if image_std is not None else OPENAI_CLIP_STD
lowercase : List[Any] = do_convert_rgb
def __magic_name__ ( self , _a , _a , _a = PILImageResampling.BICUBIC , _a = None , **_a , ):
lowercase : Dict = get_size_dict(_a , default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(f"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
lowercase : Union[str, Any] = get_resize_output_image_size(_a , size=size["shortest_edge"] , default_to_square=_a )
return resize(_a , size=_a , resample=_a , data_format=_a , **_a )
def __magic_name__ ( self , _a , _a , _a = None , **_a , ):
lowercase : Optional[Any] = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(_a , size=(size["height"], size["width"]) , data_format=_a , **_a )
def __magic_name__ ( self , _a , _a , _a = None , **_a , ):
return rescale(_a , scale=_a , data_format=_a , **_a )
def __magic_name__ ( self , _a , _a , _a , _a = None , **_a , ):
return normalize(_a , mean=_a , std=_a , data_format=_a , **_a )
def __magic_name__ ( self , _a , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = ChannelDimension.FIRST , **_a , ):
lowercase : Optional[int] = do_resize if do_resize is not None else self.do_resize
lowercase : Optional[int] = size if size is not None else self.size
lowercase : int = get_size_dict(_a , param_name="size" , default_to_square=_a )
lowercase : str = resample if resample is not None else self.resample
lowercase : str = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase : List[Any] = crop_size if crop_size is not None else self.crop_size
lowercase : Optional[int] = get_size_dict(_a , param_name="crop_size" , default_to_square=_a )
lowercase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
lowercase : Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase : Any = do_normalize if do_normalize is not None else self.do_normalize
lowercase : str = image_mean if image_mean is not None else self.image_mean
lowercase : Optional[Any] = image_std if image_std is not None else self.image_std
lowercase : Tuple = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowercase : Optional[Any] = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None:
raise ValueError("Size must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowercase : Any = [convert_to_rgb(_a ) for image in images]
# All transformations expect numpy arrays.
lowercase : str = [to_numpy_array(_a ) for image in images]
if do_resize:
lowercase : Union[str, Any] = [self.resize(image=_a , size=_a , resample=_a ) for image in images]
if do_center_crop:
lowercase : List[Any] = [self.center_crop(image=_a , size=_a ) for image in images]
if do_rescale:
lowercase : Dict = [self.rescale(image=_a , scale=_a ) for image in images]
if do_normalize:
lowercase : int = [self.normalize(image=_a , mean=_a , std=_a ) for image in images]
lowercase : str = [to_channel_dimension_format(_a , _a ) for image in images]
lowercase : List[Any] = {"pixel_values": images}
return BatchFeature(data=_a , tensor_type=_a )
| 202 |
"""simple docstring"""
import argparse
import OmegaConf
import torch
from diffusers import DDIMScheduler, LDMPipeline, UNetLDMModel, VQModel
def __magic_name__ ( __snake_case : List[str] , __snake_case : Tuple , __snake_case : Tuple ) -> int:
lowercase : Union[str, Any] = OmegaConf.load(__snake_case )
lowercase : int = torch.load(__snake_case , map_location="cpu" )["model"]
lowercase : Optional[Any] = list(state_dict.keys() )
# extract state_dict for VQVAE
lowercase : Optional[int] = {}
lowercase : Union[str, Any] = "first_stage_model."
for key in keys:
if key.startswith(__snake_case ):
lowercase : List[Any] = state_dict[key]
# extract state_dict for UNetLDM
lowercase : Optional[int] = {}
lowercase : List[Any] = "model.diffusion_model."
for key in keys:
if key.startswith(__snake_case ):
lowercase : Optional[Any] = state_dict[key]
lowercase : Dict = config.model.params.first_stage_config.params
lowercase : List[str] = config.model.params.unet_config.params
lowercase : Union[str, Any] = VQModel(**__snake_case ).eval()
vqvae.load_state_dict(__snake_case )
lowercase : List[Any] = UNetLDMModel(**__snake_case ).eval()
unet.load_state_dict(__snake_case )
lowercase : Dict = DDIMScheduler(
timesteps=config.model.params.timesteps , beta_schedule="scaled_linear" , beta_start=config.model.params.linear_start , beta_end=config.model.params.linear_end , clip_sample=__snake_case , )
lowercase : Optional[Any] = LDMPipeline(__snake_case , __snake_case , __snake_case )
pipeline.save_pretrained(__snake_case )
if __name__ == "__main__":
_A : Optional[Any] = argparse.ArgumentParser()
parser.add_argument("""--checkpoint_path""", type=str, required=True)
parser.add_argument("""--config_path""", type=str, required=True)
parser.add_argument("""--output_path""", type=str, required=True)
_A : Dict = parser.parse_args()
convert_ldm_original(args.checkpoint_path, args.config_path, args.output_path)
| 202 | 1 |
from __future__ import annotations
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : int | None = None , UpperCamelCase__ : int | None = None ) -> None:
"""simple docstring"""
if start is None:
__lowerCamelCase = 0
if end is None:
__lowerCamelCase = len(UpperCamelCase__ ) - 1
if start >= end:
return
__lowerCamelCase = (start + end) // 2
slowsort(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
slowsort(UpperCamelCase__ , mid + 1 , UpperCamelCase__ )
if sequence[end] < sequence[mid]:
__lowerCamelCase , __lowerCamelCase = sequence[mid], sequence[end]
slowsort(UpperCamelCase__ , UpperCamelCase__ , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 348 |
# 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
class __lowerCAmelCase ( __magic_name__ ):
"""simple docstring"""
snake_case_ = '''philschmid/bart-large-cnn-samsum'''
snake_case_ = (
'''This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, '''
'''and returns a summary of the text.'''
)
snake_case_ = '''summarizer'''
snake_case_ = AutoTokenizer
snake_case_ = AutoModelForSeqaSeqLM
snake_case_ = ['''text''']
snake_case_ = ['''text''']
def lowercase_ ( self , lowerCamelCase__ ) -> Optional[int]:
'''simple docstring'''
return self.pre_processor(lowerCamelCase__ , return_tensors='pt' , truncation=lowerCamelCase__ )
def lowercase_ ( self , lowerCamelCase__ ) -> Dict:
'''simple docstring'''
return self.model.generate(**lowerCamelCase__ )[0]
def lowercase_ ( self , lowerCamelCase__ ) -> Any:
'''simple docstring'''
return self.pre_processor.decode(lowerCamelCase__ , skip_special_tokens=lowerCamelCase__ , clean_up_tokenization_spaces=lowerCamelCase__ )
| 348 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
lowercase_ = {
'''configuration_clip''': [
'''CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPConfig''',
'''CLIPOnnxConfig''',
'''CLIPTextConfig''',
'''CLIPVisionConfig''',
],
'''processing_clip''': ['''CLIPProcessor'''],
'''tokenization_clip''': ['''CLIPTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['''CLIPTokenizerFast''']
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ['''CLIPFeatureExtractor''']
lowercase_ = ['''CLIPImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CLIPModel''',
'''CLIPPreTrainedModel''',
'''CLIPTextModel''',
'''CLIPTextModelWithProjection''',
'''CLIPVisionModel''',
'''CLIPVisionModelWithProjection''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFCLIPModel''',
'''TFCLIPPreTrainedModel''',
'''TFCLIPTextModel''',
'''TFCLIPVisionModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = [
'''FlaxCLIPModel''',
'''FlaxCLIPPreTrainedModel''',
'''FlaxCLIPTextModel''',
'''FlaxCLIPTextPreTrainedModel''',
'''FlaxCLIPVisionModel''',
'''FlaxCLIPVisionPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_clip import (
CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
CLIPConfig,
CLIPOnnxConfig,
CLIPTextConfig,
CLIPVisionConfig,
)
from .processing_clip import CLIPProcessor
from .tokenization_clip import CLIPTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_clip_fast import CLIPTokenizerFast
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clip import CLIPFeatureExtractor
from .image_processing_clip import CLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_clip import (
CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
CLIPModel,
CLIPPreTrainedModel,
CLIPTextModel,
CLIPTextModelWithProjection,
CLIPVisionModel,
CLIPVisionModelWithProjection,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_clip import (
TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
TFCLIPModel,
TFCLIPPreTrainedModel,
TFCLIPTextModel,
TFCLIPVisionModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_clip import (
FlaxCLIPModel,
FlaxCLIPPreTrainedModel,
FlaxCLIPTextModel,
FlaxCLIPTextPreTrainedModel,
FlaxCLIPVisionModel,
FlaxCLIPVisionPreTrainedModel,
)
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 303 | # 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.
from argparse import ArgumentParser
from accelerate.commands.config import get_config_parser
from accelerate.commands.env import env_command_parser
from accelerate.commands.launch import launch_command_parser
from accelerate.commands.test import test_command_parser
from accelerate.commands.tpu import tpu_command_parser
def _snake_case ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = ArgumentParser("Accelerate CLI tool" , usage="accelerate <command> [<args>]" , allow_abbrev=lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[int] = parser.add_subparsers(help="accelerate command helpers" )
# Register commands
get_config_parser(subparsers=lowerCAmelCase )
env_command_parser(subparsers=lowerCAmelCase )
launch_command_parser(subparsers=lowerCAmelCase )
tpu_command_parser(subparsers=lowerCAmelCase )
test_command_parser(subparsers=lowerCAmelCase )
# Let's go
SCREAMING_SNAKE_CASE_ : Dict = parser.parse_args()
if not hasattr(lowerCAmelCase , "func" ):
parser.print_help()
exit(1 )
# Run
args.func(lowerCAmelCase )
if __name__ == "__main__":
main()
| 18 | 0 |
from __future__ import annotations
import copy
import inspect
import unittest
import numpy as np
from transformers import is_tf_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_MODEL_FOR_MULTIPLE_CHOICE_MAPPING,
TF_MODEL_FOR_QUESTION_ANSWERING_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING,
LayoutLMvaConfig,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
TFLayoutLMvaModel,
)
if is_vision_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class snake_case__:
"""simple docstring"""
def __init__( self : str , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : int=2 , SCREAMING_SNAKE_CASE : List[str]=3 , SCREAMING_SNAKE_CASE : Union[str, Any]=4 , SCREAMING_SNAKE_CASE : str=2 , SCREAMING_SNAKE_CASE : Union[str, Any]=7 , SCREAMING_SNAKE_CASE : int=True , SCREAMING_SNAKE_CASE : str=True , SCREAMING_SNAKE_CASE : List[str]=True , SCREAMING_SNAKE_CASE : Dict=True , SCREAMING_SNAKE_CASE : Union[str, Any]=99 , SCREAMING_SNAKE_CASE : int=36 , SCREAMING_SNAKE_CASE : Tuple=2 , SCREAMING_SNAKE_CASE : Union[str, Any]=4 , SCREAMING_SNAKE_CASE : int=37 , SCREAMING_SNAKE_CASE : Dict="gelu" , SCREAMING_SNAKE_CASE : Any=0.1 , SCREAMING_SNAKE_CASE : str=0.1 , SCREAMING_SNAKE_CASE : Dict=512 , SCREAMING_SNAKE_CASE : int=16 , SCREAMING_SNAKE_CASE : Any=2 , SCREAMING_SNAKE_CASE : Optional[int]=0.02 , SCREAMING_SNAKE_CASE : Dict=6 , SCREAMING_SNAKE_CASE : Optional[Any]=6 , SCREAMING_SNAKE_CASE : Any=3 , SCREAMING_SNAKE_CASE : List[str]=4 , SCREAMING_SNAKE_CASE : Optional[int]=None , SCREAMING_SNAKE_CASE : Optional[Any]=1_000 , ):
lowercase__ : str = parent
lowercase__ : int = batch_size
lowercase__ : int = num_channels
lowercase__ : Dict = image_size
lowercase__ : List[str] = patch_size
lowercase__ : List[str] = is_training
lowercase__ : Dict = use_input_mask
lowercase__ : Any = use_token_type_ids
lowercase__ : Any = use_labels
lowercase__ : List[Any] = vocab_size
lowercase__ : Union[str, Any] = hidden_size
lowercase__ : Optional[int] = num_hidden_layers
lowercase__ : Any = num_attention_heads
lowercase__ : Tuple = intermediate_size
lowercase__ : Any = hidden_act
lowercase__ : Tuple = hidden_dropout_prob
lowercase__ : Optional[int] = attention_probs_dropout_prob
lowercase__ : int = max_position_embeddings
lowercase__ : Optional[Any] = type_vocab_size
lowercase__ : List[str] = type_sequence_label_size
lowercase__ : Dict = initializer_range
lowercase__ : Optional[Any] = coordinate_size
lowercase__ : Optional[Any] = shape_size
lowercase__ : Union[str, Any] = num_labels
lowercase__ : Union[str, Any] = num_choices
lowercase__ : List[Any] = scope
lowercase__ : str = range_bbox
# LayoutLMv3's sequence length equals the number of text tokens + number of patches + 1 (we add 1 for the CLS token)
lowercase__ : List[Any] = text_seq_length
lowercase__ : Any = (image_size // patch_size) ** 2 + 1
lowercase__ : Any = self.text_seq_length + self.image_seq_length
def snake_case ( self : Optional[Any] ):
lowercase__ : Dict = ids_tensor([self.batch_size, self.text_seq_length] , self.vocab_size )
lowercase__ : str = ids_tensor([self.batch_size, self.text_seq_length, 4] , self.range_bbox )
lowercase__ : List[str] = bbox.numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
lowercase__ : Union[str, Any] = bbox[i, j, 3]
lowercase__ : List[Any] = bbox[i, j, 1]
lowercase__ : Optional[Any] = tmp_coordinate
if bbox[i, j, 2] < bbox[i, j, 0]:
lowercase__ : Dict = bbox[i, j, 2]
lowercase__ : str = bbox[i, j, 0]
lowercase__ : List[str] = tmp_coordinate
lowercase__ : Optional[Any] = tf.constant(SCREAMING_SNAKE_CASE )
lowercase__ : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : Tuple = None
if self.use_input_mask:
lowercase__ : Dict = random_attention_mask([self.batch_size, self.text_seq_length] )
lowercase__ : List[Any] = None
if self.use_token_type_ids:
lowercase__ : Tuple = ids_tensor([self.batch_size, self.text_seq_length] , self.type_vocab_size )
lowercase__ : List[Any] = None
lowercase__ : Tuple = None
if self.use_labels:
lowercase__ : List[Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : List[str] = ids_tensor([self.batch_size, self.text_seq_length] , self.num_labels )
lowercase__ : str = LayoutLMvaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , coordinate_size=self.coordinate_size , shape_size=self.shape_size , input_size=self.image_size , patch_size=self.patch_size , )
return config, input_ids, bbox, pixel_values, token_type_ids, input_mask, sequence_labels, token_labels
def snake_case ( self : int , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Tuple ):
lowercase__ : Union[str, Any] = TFLayoutLMvaModel(config=SCREAMING_SNAKE_CASE )
# text + image
lowercase__ : Dict = model(SCREAMING_SNAKE_CASE , pixel_values=SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = model(
SCREAMING_SNAKE_CASE , bbox=SCREAMING_SNAKE_CASE , pixel_values=SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE , )
lowercase__ : Tuple = model(SCREAMING_SNAKE_CASE , bbox=SCREAMING_SNAKE_CASE , pixel_values=SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
# text only
lowercase__ : str = model(SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.text_seq_length, self.hidden_size) )
# image only
lowercase__ : Any = model({"pixel_values": pixel_values} , training=SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.image_seq_length, self.hidden_size) )
def snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : List[str] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : List[str] ):
lowercase__ : List[Any] = self.num_labels
lowercase__ : Union[str, Any] = TFLayoutLMvaForSequenceClassification(config=SCREAMING_SNAKE_CASE )
lowercase__ : List[str] = model(
SCREAMING_SNAKE_CASE , bbox=SCREAMING_SNAKE_CASE , pixel_values=SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Dict ):
lowercase__ : Tuple = self.num_labels
lowercase__ : Optional[Any] = TFLayoutLMvaForTokenClassification(config=SCREAMING_SNAKE_CASE )
lowercase__ : int = model(
SCREAMING_SNAKE_CASE , bbox=SCREAMING_SNAKE_CASE , pixel_values=SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , labels=SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.text_seq_length, self.num_labels) )
def snake_case ( self : Union[str, Any] , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Tuple , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Optional[Any] , SCREAMING_SNAKE_CASE : List[str] ):
lowercase__ : str = 2
lowercase__ : Dict = TFLayoutLMvaForQuestionAnswering(config=SCREAMING_SNAKE_CASE )
lowercase__ : Optional[int] = model(
SCREAMING_SNAKE_CASE , bbox=SCREAMING_SNAKE_CASE , pixel_values=SCREAMING_SNAKE_CASE , attention_mask=SCREAMING_SNAKE_CASE , token_type_ids=SCREAMING_SNAKE_CASE , start_positions=SCREAMING_SNAKE_CASE , end_positions=SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case ( self : List[str] ):
lowercase__ : List[str] = self.prepare_config_and_inputs()
((lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__) , (lowercase__)) : Optional[int] = config_and_inputs
lowercase__ : Dict = {
"input_ids": input_ids,
"bbox": bbox,
"pixel_values": pixel_values,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_tf
class snake_case__(_UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
lowercase_ = (
(
TFLayoutLMvaModel,
TFLayoutLMvaForQuestionAnswering,
TFLayoutLMvaForSequenceClassification,
TFLayoutLMvaForTokenClassification,
)
if is_tf_available()
else ()
)
lowercase_ = (
{"""document-question-answering""": TFLayoutLMvaForQuestionAnswering, """feature-extraction""": TFLayoutLMvaModel}
if is_tf_available()
else {}
)
lowercase_ = False
lowercase_ = False
lowercase_ = False
def snake_case ( self : Dict , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : Any , SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Dict , SCREAMING_SNAKE_CASE : Dict ):
return True
def snake_case ( self : Optional[Any] , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : str=False ):
lowercase__ : Any = copy.deepcopy(SCREAMING_SNAKE_CASE )
if model_class in get_values(SCREAMING_SNAKE_CASE ):
lowercase__ : List[str] = {
k: tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE , 1 ) , (1, self.model_tester.num_choices) + (1,) * (v.ndim - 1) )
if isinstance(SCREAMING_SNAKE_CASE , tf.Tensor ) and v.ndim > 0
else v
for k, v in inputs_dict.items()
}
if return_labels:
if model_class in get_values(SCREAMING_SNAKE_CASE ):
lowercase__ : Optional[Any] = tf.ones(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE ):
lowercase__ : List[str] = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
lowercase__ : Tuple = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE ):
lowercase__ : Dict = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
elif model_class in get_values(SCREAMING_SNAKE_CASE ):
lowercase__ : str = tf.zeros(
(self.model_tester.batch_size, self.model_tester.text_seq_length) , dtype=tf.intaa )
return inputs_dict
def snake_case ( self : Optional[int] ):
lowercase__ : Optional[Any] = TFLayoutLMvaModelTester(self )
lowercase__ : Dict = ConfigTester(self , config_class=SCREAMING_SNAKE_CASE , hidden_size=37 )
def snake_case ( self : Tuple ):
self.config_tester.run_common_tests()
def snake_case ( self : str ):
lowercase__ , lowercase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : List[Any] = model_class(SCREAMING_SNAKE_CASE )
if getattr(SCREAMING_SNAKE_CASE , "hf_compute_loss" , SCREAMING_SNAKE_CASE ):
# The number of elements in the loss should be the same as the number of elements in the label
lowercase__ : List[str] = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE , return_labels=SCREAMING_SNAKE_CASE )
lowercase__ : Optional[Any] = prepared_for_class[
sorted(prepared_for_class.keys() - inputs_dict.keys() , reverse=SCREAMING_SNAKE_CASE )[0]
]
lowercase__ : List[str] = added_label.shape.as_list()[:1]
# Test that model correctly compute the loss with kwargs
lowercase__ : str = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE , return_labels=SCREAMING_SNAKE_CASE )
lowercase__ : List[str] = prepared_for_class.pop("input_ids" )
lowercase__ : Optional[int] = model(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss when we mask some positions
lowercase__ : List[str] = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE , return_labels=SCREAMING_SNAKE_CASE )
lowercase__ : str = prepared_for_class.pop("input_ids" )
if "labels" in prepared_for_class:
lowercase__ : Union[str, Any] = prepared_for_class["labels"].numpy()
if len(labels.shape ) > 1 and labels.shape[1] != 1:
lowercase__ : List[Any] = -100
lowercase__ : List[Any] = tf.convert_to_tensor(SCREAMING_SNAKE_CASE )
lowercase__ : List[Any] = model(SCREAMING_SNAKE_CASE , **SCREAMING_SNAKE_CASE )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
self.assertTrue(not np.any(np.isnan(loss.numpy() ) ) )
# Test that model correctly compute the loss with a dict
lowercase__ : Tuple = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE , return_labels=SCREAMING_SNAKE_CASE )
lowercase__ : Any = model(SCREAMING_SNAKE_CASE )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
# Test that model correctly compute the loss with a tuple
lowercase__ : List[str] = self._prepare_for_class(inputs_dict.copy() , SCREAMING_SNAKE_CASE , return_labels=SCREAMING_SNAKE_CASE )
# Get keys that were added with the _prepare_for_class function
lowercase__ : Tuple = prepared_for_class.keys() - inputs_dict.keys()
lowercase__ : List[Any] = inspect.signature(model.call ).parameters
lowercase__ : int = list(signature.keys() )
# Create a dictionary holding the location of the tensors in the tuple
lowercase__ : List[str] = {0: "input_ids"}
for label_key in label_keys:
lowercase__ : str = signature_names.index(SCREAMING_SNAKE_CASE )
lowercase__ : List[str] = label_key
lowercase__ : int = sorted(tuple_index_mapping.items() )
# Initialize a list with their default values, update the values and convert to a tuple
lowercase__ : List[Any] = []
for name in signature_names:
if name != "kwargs":
list_input.append(signature[name].default )
for index, value in sorted_tuple_index_mapping:
lowercase__ : List[Any] = prepared_for_class[value]
lowercase__ : List[Any] = tuple(SCREAMING_SNAKE_CASE )
# Send to model
lowercase__ : Dict = model(tuple_input[:-1] )[0]
self.assertTrue(loss.shape.as_list() == expected_loss_size or loss.shape.as_list() == [1] )
def snake_case ( self : Optional[int] ):
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def snake_case ( self : Tuple ):
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : Tuple = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
lowercase__ : List[Any] = type
self.model_tester.create_and_check_model(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def snake_case ( self : str ):
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def snake_case ( self : Optional[int] ):
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def snake_case ( self : List[str] ):
(
(
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) , (
lowercase__
) ,
) : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@slow
def snake_case ( self : List[str] ):
for model_name in TF_LAYOUTLMV3_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : str = TFLayoutLMvaModel.from_pretrained(SCREAMING_SNAKE_CASE )
self.assertIsNotNone(SCREAMING_SNAKE_CASE )
def __lowerCamelCase ( ):
"""simple docstring"""
lowercase__ : Any = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_tf
class snake_case__(unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case ( self : Union[str, Any] ):
return LayoutLMvaImageProcessor(apply_ocr=SCREAMING_SNAKE_CASE ) if is_vision_available() else None
@slow
def snake_case ( self : Optional[int] ):
lowercase__ : Dict = TFLayoutLMvaModel.from_pretrained("microsoft/layoutlmv3-base" )
lowercase__ : Any = self.default_image_processor
lowercase__ : Optional[Any] = prepare_img()
lowercase__ : str = image_processor(images=SCREAMING_SNAKE_CASE , return_tensors="tf" ).pixel_values
lowercase__ : Any = tf.constant([[1, 2]] )
lowercase__ : str = tf.expand_dims(tf.constant([[1, 2, 3, 4], [5, 6, 7, 8]] ) , axis=0 )
# forward pass
lowercase__ : Union[str, Any] = model(input_ids=SCREAMING_SNAKE_CASE , bbox=SCREAMING_SNAKE_CASE , pixel_values=SCREAMING_SNAKE_CASE , training=SCREAMING_SNAKE_CASE )
# verify the logits
lowercase__ : Any = (1, 199, 768)
self.assertEqual(outputs.last_hidden_state.shape , SCREAMING_SNAKE_CASE )
lowercase__ : int = tf.constant(
[[-0.0_529, 0.3_618, 0.1_632], [-0.1_587, -0.1_667, -0.0_400], [-0.1_557, -0.1_671, -0.0_505]] )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , SCREAMING_SNAKE_CASE , atol=1E-4 ) )
| 121 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( lowerCamelCase__ ):
"""simple docstring"""
lowercase__ : Optional[Any] = {}
lowercase__ : Tuple = tokenizer(example["content"] , truncation=lowerCamelCase__ )["input_ids"]
lowercase__ : Optional[int] = len(example["content"] ) / len(output["input_ids"] )
return output
lowerCAmelCase__ = HfArgumentParser(PretokenizationArguments)
lowerCAmelCase__ = parser.parse_args()
if args.num_workers is None:
lowerCAmelCase__ = multiprocessing.cpu_count()
lowerCAmelCase__ = AutoTokenizer.from_pretrained(args.tokenizer_dir)
lowerCAmelCase__ = time.time()
lowerCAmelCase__ = load_dataset(args.dataset_name, split='''train''')
print(f'''Dataset loaded in {time.time()-t_start:.2f}s''')
lowerCAmelCase__ = time.time()
lowerCAmelCase__ = ds.map(
tokenize,
num_proc=args.num_workers,
remove_columns=[
'''repo_name''',
'''path''',
'''copies''',
'''size''',
'''content''',
'''license''',
'''hash''',
'''line_mean''',
'''line_max''',
'''alpha_frac''',
'''autogenerated''',
],
)
print(f'''Dataset tokenized in {time.time()-t_start:.2f}s''')
lowerCAmelCase__ = time.time()
ds.push_to_hub(args.tokenized_data_repo)
print(f'''Data pushed to the hub in {time.time()-t_start:.2f}s''')
| 121 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class _SCREAMING_SNAKE_CASE ( snake_case_ , unittest.TestCase ):
lowerCAmelCase__ = DanceDiffusionPipeline
lowerCAmelCase__ = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
lowerCAmelCase__ = PipelineTesterMixin.required_optional_params - {
'callback',
'latents',
'callback_steps',
'output_type',
'num_images_per_prompt',
}
lowerCAmelCase__ = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=512 , sample_rate=16000 , in_channels=2 , out_channels=2 , flip_sin_to_cos=lowercase , use_timestep_embedding=lowercase , time_embedding_type="fourier" , mid_block_type="UNetMidBlock1D" , down_block_types=("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") , up_block_types=("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") , )
lowerCamelCase_ = IPNDMScheduler()
lowerCamelCase_ = {
"unet": unet,
"scheduler": scheduler,
}
return components
def SCREAMING_SNAKE_CASE_( self , lowercase , lowercase=0 ) -> Tuple:
if str(lowercase ).startswith("mps" ):
lowerCamelCase_ = torch.manual_seed(lowercase )
else:
lowerCamelCase_ = torch.Generator(device=lowercase ).manual_seed(lowercase )
lowerCamelCase_ = {
"batch_size": 1,
"generator": generator,
"num_inference_steps": 4,
}
return inputs
def SCREAMING_SNAKE_CASE_( self ) -> str:
lowerCamelCase_ = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ = self.get_dummy_components()
lowerCamelCase_ = DanceDiffusionPipeline(**lowercase )
lowerCamelCase_ = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
lowerCamelCase_ = self.get_dummy_inputs(lowercase )
lowerCamelCase_ = pipe(**lowercase )
lowerCamelCase_ = output.audios
lowerCamelCase_ = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
lowerCamelCase_ = np.array([-0.7_2_6_5, 1.0_0_0_0, -0.8_3_8_8, 0.1_1_7_5, 0.9_4_9_8, -1.0_0_0_0] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def SCREAMING_SNAKE_CASE_( self ) -> Dict:
return super().test_save_load_local()
@skip_mps
def SCREAMING_SNAKE_CASE_( self ) -> int:
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
@skip_mps
def SCREAMING_SNAKE_CASE_( self ) -> Union[str, Any]:
return super().test_save_load_optional_components()
@skip_mps
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
return super().test_attention_slicing_forward_pass()
def SCREAMING_SNAKE_CASE_( self ) -> Optional[Any]:
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
@slow
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE_( self ) -> List[str]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE_( self ) -> Any:
lowerCamelCase_ = torch_device
lowerCamelCase_ = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k" )
lowerCamelCase_ = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
lowerCamelCase_ = torch.manual_seed(0 )
lowerCamelCase_ = pipe(generator=lowercase , num_inference_steps=100 , audio_length_in_s=4.0_9_6 )
lowerCamelCase_ = output.audios
lowerCamelCase_ = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCamelCase_ = np.array([-0.0_1_9_2, -0.0_2_3_1, -0.0_3_1_8, -0.0_0_5_9, 0.0_0_0_2, -0.0_0_2_0] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE_( self ) -> List[Any]:
lowerCamelCase_ = torch_device
lowerCamelCase_ = DanceDiffusionPipeline.from_pretrained("harmonai/maestro-150k" , torch_dtype=torch.floataa )
lowerCamelCase_ = pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
lowerCamelCase_ = torch.manual_seed(0 )
lowerCamelCase_ = pipe(generator=lowercase , num_inference_steps=100 , audio_length_in_s=4.0_9_6 )
lowerCamelCase_ = output.audios
lowerCamelCase_ = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
lowerCamelCase_ = np.array([-0.0_3_6_7, -0.0_4_8_8, -0.0_7_7_1, -0.0_5_2_5, -0.0_4_4_4, -0.0_3_4_1] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1e-2
| 19 |
"""simple docstring"""
def _A ( ):
"""simple docstring"""
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def _A ( lowercase ):
"""simple docstring"""
a =1
a =2
while i * i <= n:
a =0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def _A ( ):
"""simple docstring"""
return next(i for i in triangle_number_generator() if count_divisors(lowercase ) > 5_00 )
if __name__ == "__main__":
print(solution()) | 81 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_pytesseract, require_torch
from transformers.utils import is_pytesseract_available, is_torch_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_pytesseract_available():
from PIL import Image
from transformers import LayoutLMvaImageProcessor
class UpperCAmelCase__ ( unittest.TestCase):
def __init__( self , lowercase , lowercase=7 , lowercase=3 , lowercase=1_8 , lowercase=3_0 , lowercase=4_0_0 , lowercase=True , lowercase=None , lowercase=True , ) -> str:
__UpperCamelCase = size if size is not None else {"""height""": 1_8, """width""": 1_8}
__UpperCamelCase = parent
__UpperCamelCase = batch_size
__UpperCamelCase = num_channels
__UpperCamelCase = image_size
__UpperCamelCase = min_resolution
__UpperCamelCase = max_resolution
__UpperCamelCase = do_resize
__UpperCamelCase = size
__UpperCamelCase = apply_ocr
def __lowerCamelCase ( self ) -> Tuple:
return {"do_resize": self.do_resize, "size": self.size, "apply_ocr": self.apply_ocr}
@require_torch
@require_pytesseract
class UpperCAmelCase__ ( UpperCAmelCase_ , unittest.TestCase):
__SCREAMING_SNAKE_CASE = LayoutLMvaImageProcessor if is_pytesseract_available() else None
def __lowerCamelCase ( self ) -> Tuple:
__UpperCamelCase = LayoutLMvaImageProcessingTester(self )
@property
def __lowerCamelCase ( self ) -> str:
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCamelCase ( self ) -> str:
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase , """do_resize""" ) )
self.assertTrue(hasattr(lowercase , """size""" ) )
self.assertTrue(hasattr(lowercase , """apply_ocr""" ) )
def __lowerCamelCase ( self ) -> Optional[int]:
__UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""height""": 1_8, """width""": 1_8} )
__UpperCamelCase = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 )
self.assertEqual(image_processor.size , {"""height""": 4_2, """width""": 4_2} )
def __lowerCamelCase ( self ) -> List[Any]:
pass
def __lowerCamelCase ( self ) -> Optional[int]:
# Initialize image_processing
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
__UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , Image.Image )
# Test not batched input
__UpperCamelCase = image_processing(image_inputs[0] , return_tensors="""pt""" )
self.assertEqual(
encoding.pixel_values.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
self.assertIsInstance(encoding.words , lowercase )
self.assertIsInstance(encoding.boxes , lowercase )
# Test batched
__UpperCamelCase = image_processing(lowercase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def __lowerCamelCase ( self ) -> List[str]:
# Initialize image_processing
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
__UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase , numpify=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , np.ndarray )
# Test not batched input
__UpperCamelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
__UpperCamelCase = image_processing(lowercase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def __lowerCamelCase ( self ) -> List[str]:
# Initialize image_processing
__UpperCamelCase = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
__UpperCamelCase = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase , torchify=lowercase )
for image in image_inputs:
self.assertIsInstance(lowercase , torch.Tensor )
# Test not batched input
__UpperCamelCase = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
# Test batched
__UpperCamelCase = image_processing(lowercase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.size["""height"""],
self.image_processor_tester.size["""width"""],
) , )
def __lowerCamelCase ( self ) -> Tuple:
# with apply_OCR = True
__UpperCamelCase = LayoutLMvaImageProcessor()
from datasets import load_dataset
__UpperCamelCase = load_dataset("""hf-internal-testing/fixtures_docvqa""" , split="""test""" )
__UpperCamelCase = Image.open(ds[0]["""file"""] ).convert("""RGB""" )
__UpperCamelCase = image_processing(lowercase , return_tensors="""pt""" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) )
self.assertEqual(len(encoding.words ) , len(encoding.boxes ) )
# fmt: off
# the words and boxes were obtained with Tesseract 4.1.1
__UpperCamelCase = [["""11:14""", """to""", """11:39""", """a.m""", """11:39""", """to""", """11:44""", """a.m.""", """11:44""", """a.m.""", """to""", """12:25""", """p.m.""", """12:25""", """to""", """12:58""", """p.m.""", """12:58""", """to""", """4:00""", """p.m.""", """2:00""", """to""", """5:00""", """p.m.""", """Coffee""", """Break""", """Coffee""", """will""", """be""", """served""", """for""", """men""", """and""", """women""", """in""", """the""", """lobby""", """adjacent""", """to""", """exhibit""", """area.""", """Please""", """move""", """into""", """exhibit""", """area.""", """(Exhibits""", """Open)""", """TRRF""", """GENERAL""", """SESSION""", """(PART""", """|)""", """Presiding:""", """Lee""", """A.""", """Waller""", """TRRF""", """Vice""", """President""", """“Introductory""", """Remarks”""", """Lee""", """A.""", """Waller,""", """TRRF""", """Vice""", """Presi-""", """dent""", """Individual""", """Interviews""", """with""", """TRRF""", """Public""", """Board""", """Members""", """and""", """Sci-""", """entific""", """Advisory""", """Council""", """Mem-""", """bers""", """Conducted""", """by""", """TRRF""", """Treasurer""", """Philip""", """G.""", """Kuehn""", """to""", """get""", """answers""", """which""", """the""", """public""", """refrigerated""", """warehousing""", """industry""", """is""", """looking""", """for.""", """Plus""", """questions""", """from""", """the""", """floor.""", """Dr.""", """Emil""", """M.""", """Mrak,""", """University""", """of""", """Cal-""", """ifornia,""", """Chairman,""", """TRRF""", """Board;""", """Sam""", """R.""", """Cecil,""", """University""", """of""", """Georgia""", """College""", """of""", """Agriculture;""", """Dr.""", """Stanley""", """Charm,""", """Tufts""", """University""", """School""", """of""", """Medicine;""", """Dr.""", """Robert""", """H.""", """Cotton,""", """ITT""", """Continental""", """Baking""", """Company;""", """Dr.""", """Owen""", """Fennema,""", """University""", """of""", """Wis-""", """consin;""", """Dr.""", """Robert""", """E.""", """Hardenburg,""", """USDA.""", """Questions""", """and""", """Answers""", """Exhibits""", """Open""", """Capt.""", """Jack""", """Stoney""", """Room""", """TRRF""", """Scientific""", """Advisory""", """Council""", """Meeting""", """Ballroom""", """Foyer"""]] # noqa: E231
__UpperCamelCase = [[[1_4_1, 5_7, 2_1_4, 6_9], [2_2_8, 5_8, 2_5_2, 6_9], [1_4_1, 7_5, 2_1_6, 8_8], [2_3_0, 7_9, 2_8_0, 8_8], [1_4_2, 2_6_0, 2_1_8, 2_7_3], [2_3_0, 2_6_1, 2_5_5, 2_7_3], [1_4_3, 2_7_9, 2_1_8, 2_9_0], [2_3_1, 2_8_2, 2_9_0, 2_9_1], [1_4_3, 3_4_2, 2_1_8, 3_5_4], [2_3_1, 3_4_5, 2_8_9, 3_5_5], [2_0_2, 3_6_2, 2_2_7, 3_7_3], [1_4_3, 3_7_9, 2_2_0, 3_9_2], [2_3_1, 3_8_2, 2_9_1, 3_9_4], [1_4_4, 7_1_4, 2_2_0, 7_2_6], [2_3_1, 7_1_5, 2_5_6, 7_2_6], [1_4_4, 7_3_2, 2_2_0, 7_4_5], [2_3_2, 7_3_6, 2_9_1, 7_4_7], [1_4_4, 7_6_9, 2_1_8, 7_8_2], [2_3_1, 7_7_0, 2_5_6, 7_8_2], [1_4_1, 7_8_8, 2_0_2, 8_0_1], [2_1_5, 7_9_1, 2_7_4, 8_0_4], [1_4_3, 8_2_6, 2_0_4, 8_3_8], [2_1_5, 8_2_6, 2_4_0, 8_3_8], [1_4_2, 8_4_4, 2_0_2, 8_5_7], [2_1_5, 8_4_7, 2_7_4, 8_5_9], [3_3_4, 5_7, 4_2_7, 6_9], [4_4_0, 5_7, 5_2_2, 6_9], [3_6_9, 7_5, 4_6_1, 8_8], [4_6_9, 7_5, 5_1_6, 8_8], [5_2_8, 7_6, 5_6_2, 8_8], [5_7_0, 7_6, 6_6_7, 8_8], [6_7_5, 7_5, 7_1_1, 8_7], [7_2_1, 7_9, 7_7_8, 8_8], [7_8_9, 7_5, 8_4_0, 8_8], [3_6_9, 9_7, 4_7_0, 1_0_7], [4_8_4, 9_4, 5_0_7, 1_0_6], [5_1_8, 9_4, 5_6_2, 1_0_7], [5_7_6, 9_4, 6_5_5, 1_1_0], [6_6_8, 9_4, 7_9_2, 1_0_9], [8_0_4, 9_5, 8_2_9, 1_0_7], [3_6_9, 1_1_3, 4_6_5, 1_2_5], [4_7_7, 1_1_6, 5_4_7, 1_2_5], [5_6_2, 1_1_3, 6_5_8, 1_2_5], [6_7_1, 1_1_6, 7_4_8, 1_2_5], [7_6_1, 1_1_3, 8_1_1, 1_2_5], [3_6_9, 1_3_1, 4_6_5, 1_4_3], [4_7_7, 1_3_3, 5_4_8, 1_4_3], [5_6_3, 1_3_0, 6_9_8, 1_4_5], [7_1_0, 1_3_0, 8_0_2, 1_4_6], [3_3_6, 1_7_1, 4_1_2, 1_8_3], [4_2_3, 1_7_1, 5_7_2, 1_8_3], [5_8_2, 1_7_0, 7_1_6, 1_8_4], [7_2_8, 1_7_1, 8_1_7, 1_8_7], [8_2_9, 1_7_1, 8_4_4, 1_8_6], [3_3_8, 1_9_7, 4_8_2, 2_1_2], [5_0_7, 1_9_6, 5_5_7, 2_0_9], [5_6_9, 1_9_6, 5_9_5, 2_0_8], [6_1_0, 1_9_6, 7_0_2, 2_0_9], [5_0_5, 2_1_4, 5_8_3, 2_2_6], [5_9_5, 2_1_4, 6_5_6, 2_2_7], [6_7_0, 2_1_5, 8_0_7, 2_2_7], [3_3_5, 2_5_9, 5_4_3, 2_7_4], [5_5_6, 2_5_9, 7_0_8, 2_7_2], [3_7_2, 2_7_9, 4_2_2, 2_9_1], [4_3_5, 2_7_9, 4_6_0, 2_9_1], [4_7_4, 2_7_9, 5_7_4, 2_9_2], [5_8_7, 2_7_8, 6_6_4, 2_9_1], [6_7_6, 2_7_8, 7_3_8, 2_9_1], [7_5_1, 2_7_9, 8_3_4, 2_9_1], [3_7_2, 2_9_8, 4_3_4, 3_1_0], [3_3_5, 3_4_1, 4_8_3, 3_5_4], [4_9_7, 3_4_1, 6_5_5, 3_5_4], [6_6_7, 3_4_1, 7_2_8, 3_5_4], [7_4_0, 3_4_1, 8_2_5, 3_5_4], [3_3_5, 3_6_0, 4_3_0, 3_7_2], [4_4_2, 3_6_0, 5_3_4, 3_7_2], [5_4_5, 3_5_9, 6_8_7, 3_7_2], [6_9_7, 3_6_0, 7_5_4, 3_7_2], [7_6_5, 3_6_0, 8_2_3, 3_7_3], [3_3_4, 3_7_8, 4_2_8, 3_9_1], [4_4_0, 3_7_8, 5_7_7, 3_9_4], [5_9_0, 3_7_8, 7_0_5, 3_9_1], [7_2_0, 3_7_8, 8_0_1, 3_9_1], [3_3_4, 3_9_7, 4_0_0, 4_0_9], [3_7_0, 4_1_6, 5_2_9, 4_2_9], [5_4_4, 4_1_6, 5_7_6, 4_3_2], [5_8_7, 4_1_6, 6_6_5, 4_2_8], [6_7_7, 4_1_6, 8_1_4, 4_2_9], [3_7_2, 4_3_5, 4_5_2, 4_5_0], [4_6_5, 4_3_4, 4_9_5, 4_4_7], [5_1_1, 4_3_4, 6_0_0, 4_4_7], [6_1_1, 4_3_6, 6_3_7, 4_4_7], [6_4_9, 4_3_6, 6_9_4, 4_5_1], [7_0_5, 4_3_8, 8_2_4, 4_4_7], [3_6_9, 4_5_3, 4_5_2, 4_6_6], [4_6_4, 4_5_4, 5_0_9, 4_6_6], [5_2_2, 4_5_3, 6_1_1, 4_6_9], [6_2_5, 4_5_3, 7_9_2, 4_6_9], [3_7_0, 4_7_2, 5_5_6, 4_8_8], [5_7_0, 4_7_2, 6_8_4, 4_8_7], [6_9_7, 4_7_2, 7_1_8, 4_8_5], [7_3_2, 4_7_2, 8_3_5, 4_8_8], [3_6_9, 4_9_0, 4_1_1, 5_0_3], [4_2_5, 4_9_0, 4_8_4, 5_0_3], [4_9_6, 4_9_0, 6_3_5, 5_0_6], [6_4_5, 4_9_0, 7_0_7, 5_0_3], [7_1_8, 4_9_1, 7_6_1, 5_0_3], [7_7_1, 4_9_0, 8_4_0, 5_0_3], [3_3_6, 5_1_0, 3_7_4, 5_2_1], [3_8_8, 5_1_0, 4_4_7, 5_2_2], [4_6_0, 5_1_0, 4_8_9, 5_2_1], [5_0_3, 5_1_0, 5_8_0, 5_2_2], [5_9_2, 5_0_9, 7_3_6, 5_2_5], [7_4_5, 5_0_9, 7_7_0, 5_2_2], [7_8_1, 5_0_9, 8_4_0, 5_2_2], [3_3_8, 5_2_8, 4_3_4, 5_4_1], [4_4_8, 5_2_8, 5_9_6, 5_4_1], [6_0_9, 5_2_7, 6_8_7, 5_4_0], [7_0_0, 5_2_8, 7_9_2, 5_4_1], [3_3_6, 5_4_6, 3_9_7, 5_5_9], [4_0_7, 5_4_6, 4_3_1, 5_5_9], [4_4_3, 5_4_6, 5_2_5, 5_6_0], [5_3_7, 5_4_6, 6_8_0, 5_6_2], [6_8_8, 5_4_6, 7_1_4, 5_5_9], [7_2_2, 5_4_6, 8_3_7, 5_6_2], [3_3_6, 5_6_5, 4_4_9, 5_8_1], [4_6_1, 5_6_5, 4_8_5, 5_7_7], [4_9_7, 5_6_5, 6_6_5, 5_8_1], [6_8_1, 5_6_5, 7_1_8, 5_7_7], [7_3_2, 5_6_5, 8_3_7, 5_8_0], [3_3_7, 5_8_4, 4_3_8, 5_9_7], [4_5_2, 5_8_3, 5_2_1, 5_9_6], [5_3_5, 5_8_4, 6_7_7, 5_9_9], [6_9_0, 5_8_3, 7_8_7, 5_9_6], [8_0_1, 5_8_3, 8_2_5, 5_9_6], [3_3_8, 6_0_2, 4_7_8, 6_1_5], [4_9_2, 6_0_2, 5_3_0, 6_1_4], [5_4_3, 6_0_2, 6_3_8, 6_1_5], [6_5_0, 6_0_2, 6_7_6, 6_1_4], [6_8_8, 6_0_2, 7_8_8, 6_1_5], [8_0_2, 6_0_2, 8_4_3, 6_1_4], [3_3_7, 6_2_1, 5_0_2, 6_3_3], [5_1_6, 6_2_1, 6_1_5, 6_3_7], [6_2_9, 6_2_1, 7_7_4, 6_3_6], [7_8_9, 6_2_1, 8_2_7, 6_3_3], [3_3_7, 6_3_9, 4_1_8, 6_5_2], [4_3_2, 6_4_0, 5_7_1, 6_5_3], [5_8_7, 6_3_9, 7_3_1, 6_5_5], [7_4_3, 6_3_9, 7_6_9, 6_5_2], [7_8_0, 6_3_9, 8_4_1, 6_5_2], [3_3_8, 6_5_8, 4_4_0, 6_7_3], [4_5_5, 6_5_8, 4_9_1, 6_7_0], [5_0_8, 6_5_8, 6_0_2, 6_7_1], [6_1_6, 6_5_8, 6_3_8, 6_7_0], [6_5_4, 6_5_8, 8_3_5, 6_7_4], [3_3_7, 6_7_7, 4_2_9, 6_8_9], [3_3_7, 7_1_4, 4_8_2, 7_2_6], [4_9_5, 7_1_4, 5_4_8, 7_2_6], [5_6_1, 7_1_4, 6_8_3, 7_2_6], [3_3_8, 7_7_0, 4_6_1, 7_8_2], [4_7_4, 7_6_9, 5_5_4, 7_8_5], [4_8_9, 7_8_8, 5_6_2, 8_0_3], [5_7_6, 7_8_8, 6_4_3, 8_0_1], [6_5_6, 7_8_7, 7_5_1, 8_0_4], [7_6_4, 7_8_8, 8_4_4, 8_0_1], [3_3_4, 8_2_5, 4_2_1, 8_3_8], [4_3_0, 8_2_4, 5_7_4, 8_3_8], [5_8_4, 8_2_4, 7_2_3, 8_4_1], [3_3_5, 8_4_4, 4_5_0, 8_5_7], [4_6_4, 8_4_3, 5_8_3, 8_6_0], [6_2_8, 8_6_2, 7_5_5, 8_7_5], [7_6_9, 8_6_1, 8_4_8, 8_7_8]]] # noqa: E231
# fmt: on
self.assertListEqual(encoding.words , lowercase )
self.assertListEqual(encoding.boxes , lowercase )
# with apply_OCR = False
__UpperCamelCase = LayoutLMvaImageProcessor(apply_ocr=lowercase )
__UpperCamelCase = image_processing(lowercase , return_tensors="""pt""" )
self.assertEqual(encoding.pixel_values.shape , (1, 3, 2_2_4, 2_2_4) )
| 369 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = '''Salesforce/blip-image-captioning-base'''
__SCREAMING_SNAKE_CASE = (
'''This is a tool that generates a description of an image. It takes an input named `image` which should be the '''
'''image to caption, and returns a text that contains the description in English.'''
)
__SCREAMING_SNAKE_CASE = '''image_captioner'''
__SCREAMING_SNAKE_CASE = AutoModelForVisionaSeq
__SCREAMING_SNAKE_CASE = ['''image''']
__SCREAMING_SNAKE_CASE = ['''text''']
def __init__( self , *lowercase , **lowercase ) -> Optional[int]:
requires_backends(self , ["""vision"""] )
super().__init__(*lowercase , **lowercase )
def __lowerCamelCase ( self , lowercase ) -> Any:
return self.pre_processor(images=lowercase , return_tensors="""pt""" )
def __lowerCamelCase ( self , lowercase ) -> Optional[int]:
return self.model.generate(**lowercase )
def __lowerCamelCase ( self , lowercase ) -> List[str]:
return self.pre_processor.batch_decode(lowercase , skip_special_tokens=lowercase )[0].strip()
| 243 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ : int = logging.get_logger(__name__)
lowerCamelCase_ : int = {
"""uw-madison/mra-base-512-4""": """https://huggingface.co/uw-madison/mra-base-512-4/resolve/main/config.json""",
}
class __A ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__lowerCAmelCase = "mra"
def __init__( self , __A=5_0265 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=1 , __A=0.02 , __A=1E-5 , __A="absolute" , __A=4 , __A="full" , __A=0 , __A=0 , __A=1 , __A=0 , __A=2 , **__A , ) -> List[str]:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
a =vocab_size
a =max_position_embeddings
a =hidden_size
a =num_hidden_layers
a =num_attention_heads
a =intermediate_size
a =hidden_act
a =hidden_dropout_prob
a =attention_probs_dropout_prob
a =initializer_range
a =type_vocab_size
a =layer_norm_eps
a =position_embedding_type
a =block_per_row
a =approx_mode
a =initial_prior_first_n_blocks
a =initial_prior_diagonal_n_blocks | 81 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase_ : Union[str, Any] = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
"""TableTransformerOnnxConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : List[Any] = [
"""TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TableTransformerForObjectDetection""",
"""TableTransformerModel""",
"""TableTransformerPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_table_transformer import (
TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
TableTransformerConfig,
TableTransformerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_table_transformer import (
TABLE_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TableTransformerForObjectDetection,
TableTransformerModel,
TableTransformerPreTrainedModel,
)
else:
import sys
lowerCamelCase_ : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 81 | 1 |
from .imports import is_rich_available
if is_rich_available():
from rich.traceback import install
install(show_locals=False)
else:
raise ModuleNotFoundError('To use the rich extension, install rich with `pip install rich`')
| 177 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCamelCase = {
'configuration_conditional_detr': [
'CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP',
'ConditionalDetrConfig',
'ConditionalDetrOnnxConfig',
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = ['ConditionalDetrFeatureExtractor']
_lowerCamelCase = ['ConditionalDetrImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCamelCase = [
'CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConditionalDetrForObjectDetection',
'ConditionalDetrForSegmentation',
'ConditionalDetrModel',
'ConditionalDetrPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
_lowerCamelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 177 | 1 |
import csv
import tweepy
# Twitter API credentials
__a = ''
__a = ''
__a = ''
__a = ''
def a ( snake_case__: str ):
'''simple docstring'''
# authorize twitter, initialize tweepy
lowercase_ = tweepy.OAuthHandler(snake_case__ , snake_case__ )
auth.set_access_token(snake_case__ , snake_case__ )
lowercase_ = tweepy.API(snake_case__ )
# initialize a list to hold all the tweepy Tweets
lowercase_ = []
# make initial request for most recent tweets (200 is the maximum allowed count)
lowercase_ = api.user_timeline(screen_name=snake_case__ , count=200 )
# save most recent tweets
alltweets.extend(snake_case__ )
# save the id of the oldest tweet less one
lowercase_ = alltweets[-1].id - 1
# keep grabbing tweets until there are no tweets left to grab
while len(snake_case__ ) > 0:
print(F'''getting tweets before {oldest}''' )
# all subsequent requests use the max_id param to prevent duplicates
lowercase_ = api.user_timeline(
screen_name=snake_case__ , count=200 , max_id=snake_case__ )
# save most recent tweets
alltweets.extend(snake_case__ )
# update the id of the oldest tweet less one
lowercase_ = alltweets[-1].id - 1
print(F'''...{len(snake_case__ )} tweets downloaded so far''' )
# transform the tweepy tweets into a 2D array that will populate the csv
lowercase_ = [[tweet.id_str, tweet.created_at, tweet.text] for tweet in alltweets]
# write the csv
with open(F'''new_{screen_name}_tweets.csv''' , '''w''' ) as f:
lowercase_ = csv.writer(snake_case__ )
writer.writerow(['''id''', '''created_at''', '''text'''] )
writer.writerows(snake_case__ )
if __name__ == "__main__":
# pass in the username of the account you want to download
get_all_tweets('FirePing32')
| 30 |
def a ( snake_case__: list ):
'''simple docstring'''
if len(snake_case__ ) <= 1:
return [tuple(snake_case__ )]
lowercase_ = []
def generate(snake_case__: int , snake_case__: list ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , snake_case__ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
lowercase_ , lowercase_ = arr[k - 1], arr[i]
else: # k is odd
lowercase_ , lowercase_ = arr[k - 1], arr[0]
generate(k - 1 , snake_case__ )
generate(len(snake_case__ ) , snake_case__ )
return res
if __name__ == "__main__":
__a = input('Enter numbers separated by a comma:\n').strip()
__a = [int(item) for item in user_input.split(',')]
print(heaps(arr))
| 30 | 1 |
"""simple docstring"""
from collections import namedtuple
import requests
from lxml import html # type: ignore
a_ = namedtuple("covid_data", "cases deaths recovered")
def a__ ( __lowercase = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
_A = "//div[@class = \"maincounter-number\"]/span/text()"
return covid_data(*html.fromstring(requests.get(__lowercase ).content ).xpath(__lowercase ) )
a_ = "Total COVID-19 cases in the world: {}\nTotal deaths due to COVID-19 in the world: {}\nTotal COVID-19 patients recovered in the world: {}"
print(fmt.format(*covid_stats())) | 369 |
"""simple docstring"""
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def a__ ( __lowercase , __lowercase ) -> Dict:
_A = old_name
if "patch_embed" in old_name:
_A , _A , _A = old_name.split("." )
if layer == "0":
_A = old_name.replace("0" , "convolution1" )
elif layer == "1":
_A = old_name.replace("1" , "batchnorm_before" )
elif layer == "3":
_A = old_name.replace("3" , "convolution2" )
else:
_A = old_name.replace("4" , "batchnorm_after" )
if "network" in old_name and re.search(R"\d\.\d" , __lowercase ):
_A = R"\b\d{2}\b"
if bool(re.search(__lowercase , __lowercase ) ):
_A = re.search(R"\d\.\d\d." , __lowercase ).group()
else:
_A = re.search(R"\d\.\d." , __lowercase ).group()
if int(match[0] ) < 6:
_A = old_name.replace(__lowercase , "" )
_A = trimmed_name.replace("network" , match[0] + ".meta4D_layers.blocks." + match[2:-1] )
_A = "intermediate_stages." + trimmed_name
else:
_A = old_name.replace(__lowercase , "" )
if int(match[2] ) < num_meta4D_last_stage:
_A = trimmed_name.replace("network" , "meta4D_layers.blocks." + match[2] )
else:
_A = str(int(match[2] ) - num_meta4D_last_stage )
_A = trimmed_name.replace("network" , "meta3D_layers.blocks." + layer_index )
if "norm1" in old_name:
_A = trimmed_name.replace("norm1" , "layernorm1" )
elif "norm2" in old_name:
_A = trimmed_name.replace("norm2" , "layernorm2" )
elif "fc1" in old_name:
_A = trimmed_name.replace("fc1" , "linear_in" )
elif "fc2" in old_name:
_A = trimmed_name.replace("fc2" , "linear_out" )
_A = "last_stage." + trimmed_name
elif "network" in old_name and re.search(R".\d." , __lowercase ):
_A = old_name.replace("network" , "intermediate_stages" )
if "fc" in new_name:
_A = new_name.replace("fc" , "convolution" )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
_A = new_name.replace("norm1" , "batchnorm_before" )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
_A = new_name.replace("norm2" , "batchnorm_after" )
if "proj" in new_name:
_A = new_name.replace("proj" , "projection" )
if "dist_head" in new_name:
_A = new_name.replace("dist_head" , "distillation_classifier" )
elif "head" in new_name:
_A = new_name.replace("head" , "classifier" )
elif "patch_embed" in new_name:
_A = "efficientformer." + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
_A = new_name.replace("norm" , "layernorm" )
_A = "efficientformer." + new_name
else:
_A = "efficientformer.encoder." + new_name
return new_name
def a__ ( __lowercase , __lowercase ) -> List[str]:
for key in checkpoint.copy().keys():
_A = checkpoint.pop(__lowercase )
_A = val
return checkpoint
def a__ ( ) -> Dict:
_A = "http://images.cocodataset.org/val2017/000000039769.jpg"
_A = Image.open(requests.get(__lowercase , stream=__lowercase ).raw )
return image
def a__ ( __lowercase , __lowercase , __lowercase , __lowercase ) -> str:
_A = torch.load(__lowercase , map_location="cpu" )["model"]
_A = EfficientFormerConfig.from_json_file(__lowercase )
_A = EfficientFormerForImageClassificationWithTeacher(__lowercase )
_A = "_".join(checkpoint_path.split("/" )[-1].split("." )[0].split("_" )[:-1] )
_A = config.depths[-1] - config.num_metaad_blocks + 1
_A = convert_torch_checkpoint(__lowercase , __lowercase )
model.load_state_dict(__lowercase )
model.eval()
_A = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
# prepare image
_A = prepare_img()
_A = 256
_A = 224
_A = EfficientFormerImageProcessor(
size={"shortest_edge": image_size} , crop_size={"height": crop_size, "width": crop_size} , resample=pillow_resamplings["bicubic"] , )
_A = processor(images=__lowercase , return_tensors="pt" ).pixel_values
# original processing pipeline
_A = Compose(
[
Resize(__lowercase , interpolation=pillow_resamplings["bicubic"] ),
CenterCrop(__lowercase ),
ToTensor(),
Normalize(__lowercase , __lowercase ),
] )
_A = image_transforms(__lowercase ).unsqueeze(0 )
assert torch.allclose(__lowercase , __lowercase )
_A = model(__lowercase )
_A = outputs.logits
_A = (1, 1000)
if "l1" in model_name:
_A = torch.Tensor(
[-0.1_312, 0.4_353, -1.0_499, -0.5_124, 0.4_183, -0.6_793, -1.3_777, -0.0_893, -0.7_358, -2.4_328] )
assert torch.allclose(logits[0, :10] , __lowercase , atol=1E-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
_A = torch.Tensor(
[-1.3_150, -1.5_456, -1.2_556, -0.8_496, -0.7_127, -0.7_897, -0.9_728, -0.3_052, 0.3_751, -0.3_127] )
assert torch.allclose(logits[0, :10] , __lowercase , atol=1E-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
_A = torch.Tensor(
[-1.0_283, -1.4_131, -0.5_644, -1.3_115, -0.5_785, -1.2_049, -0.7_528, 0.1_992, -0.3_822, -0.0_878] )
assert logits.shape == expected_shape
else:
raise ValueError(
f"""Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7""" )
# Save Checkpoints
Path(__lowercase ).mkdir(exist_ok=__lowercase )
model.save_pretrained(__lowercase )
print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
processor.save_pretrained(__lowercase )
print(f"""Processor successfuly saved at {pytorch_dump_path}""" )
if push_to_hub:
print("Pushing model to the hub..." )
model.push_to_hub(
repo_id=f"""Bearnardd/{pytorch_dump_path}""" , commit_message="Add model" , use_temp_dir=__lowercase , )
processor.push_to_hub(
repo_id=f"""Bearnardd/{pytorch_dump_path}""" , commit_message="Add image processor" , use_temp_dir=__lowercase , )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_model_path",
default=None,
type=str,
required=True,
help="Path to EfficientFormer pytorch checkpoint.",
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The json file for EfficientFormer model config.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
parser.add_argument(
"--no-push_to_hub",
dest="push_to_hub",
action="store_false",
help="Do not push model and image processor to the hub",
)
parser.set_defaults(push_to_hub=True)
a_ = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
) | 163 | 0 |
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = '▁'
__UpperCAmelCase = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
'tokenizer_config_file': 'tokenizer_config.json',
}
__UpperCAmelCase = {
'vocab_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json',
},
'spm_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model',
},
'tokenizer_config_file': {
'facebook/m2m100_418M': 'https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json',
'facebook/m2m100_1.2B': 'https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json',
},
}
__UpperCAmelCase = {
'facebook/m2m100_418M': 10_24,
}
# fmt: off
__UpperCAmelCase = {
'm2m100': ['af', 'am', 'ar', 'ast', 'az', 'ba', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'ceb', 'cs', 'cy', 'da', 'de', 'el', 'en', 'es', 'et', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gu', 'ha', 'he', 'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'ilo', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko', 'lb', 'lg', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my', 'ne', 'nl', 'no', 'ns', 'oc', 'or', 'pa', 'pl', 'ps', 'pt', 'ro', 'ru', 'sd', 'si', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'th', 'tl', 'tn', 'tr', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zh', 'zu'],
'wmt21': ['en', 'ha', 'is', 'ja', 'cs', 'ru', 'zh', 'de']
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Dict = VOCAB_FILES_NAMES
UpperCAmelCase_ :List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase_ :Optional[int] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase_ :Tuple = ["input_ids", "attention_mask"]
UpperCAmelCase_ :List[int] = []
UpperCAmelCase_ :List[int] = []
def __init__( self , __A , __A , __A=None , __A=None , __A="<s>" , __A="</s>" , __A="</s>" , __A="<pad>" , __A="<unk>" , __A="m2m100" , __A = None , __A=8 , **__A , ) -> None:
lowerCAmelCase_ :Optional[int] = {} if sp_model_kwargs is None else sp_model_kwargs
lowerCAmelCase_ :List[str] = language_codes
lowerCAmelCase_ :Dict = FAIRSEQ_LANGUAGE_CODES[language_codes]
lowerCAmelCase_ :List[str] = {lang_code: f"""__{lang_code}__""" for lang_code in fairseq_language_code}
lowerCAmelCase_ :List[Any] = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(__A )
for lang_code in fairseq_language_code
if self.get_lang_token(__A ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=__A , tgt_lang=__A , bos_token=__A , eos_token=__A , sep_token=__A , unk_token=__A , pad_token=__A , language_codes=__A , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=__A , **__A , )
lowerCAmelCase_ :int = vocab_file
lowerCAmelCase_ :Tuple = load_json(__A )
lowerCAmelCase_ :Tuple = {v: k for k, v in self.encoder.items()}
lowerCAmelCase_ :List[str] = spm_file
lowerCAmelCase_ :Optional[Any] = load_spm(__A , self.sp_model_kwargs )
lowerCAmelCase_ :str = len(self.encoder )
lowerCAmelCase_ :Union[str, Any] = {
self.get_lang_token(__A ): self.encoder_size + i for i, lang_code in enumerate(__A )
}
lowerCAmelCase_ :List[str] = {lang_code: self.encoder_size + i for i, lang_code in enumerate(__A )}
lowerCAmelCase_ :Dict = {v: k for k, v in self.lang_token_to_id.items()}
lowerCAmelCase_ :List[str] = src_lang if src_lang is not None else """en"""
lowerCAmelCase_ :int = tgt_lang
lowerCAmelCase_ :Optional[Any] = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
lowerCAmelCase_ :Optional[int] = num_madeup_words
@property
def __lowerCAmelCase ( self ) -> int:
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def __lowerCAmelCase ( self ) -> str:
return self._src_lang
@src_lang.setter
def __lowerCAmelCase ( self , __A ) -> None:
lowerCAmelCase_ :Dict = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def __lowerCAmelCase ( self , __A ) -> List[str]:
return self.sp_model.encode(__A , out_type=__A )
def __lowerCAmelCase ( self , __A ) -> Any:
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(__A , self.encoder[self.unk_token] )
def __lowerCAmelCase ( self , __A ) -> str:
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(__A , self.unk_token )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
lowerCAmelCase_ :str = []
lowerCAmelCase_ :int = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(__A ) + token
lowerCAmelCase_ :Dict = []
else:
current_sub_tokens.append(__A )
out_string += self.sp_model.decode(__A )
return out_string.strip()
def __lowerCAmelCase ( self , __A , __A = None , __A = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__A , token_ids_a=__A , already_has_special_tokens=__A )
lowerCAmelCase_ :Any = [1] * len(self.prefix_tokens )
lowerCAmelCase_ :Union[str, Any] = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(__A )) + suffix_ones
return prefix_ones + ([0] * len(__A )) + ([0] * len(__A )) + suffix_ones
def __lowerCAmelCase ( self , __A , __A = None ) -> List[int]:
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :int = {self.convert_ids_to_tokens(__A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Dict:
lowerCAmelCase_ :List[Any] = self.__dict__.copy()
lowerCAmelCase_ :List[str] = None
return state
def __setstate__( self , __A ) -> None:
lowerCAmelCase_ :List[Any] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
lowerCAmelCase_ :List[Any] = {}
lowerCAmelCase_ :str = load_spm(self.spm_file , self.sp_model_kwargs )
def __lowerCAmelCase ( self , __A , __A = None ) -> Tuple[str]:
lowerCAmelCase_ :Optional[Any] = Path(__A )
if not save_dir.is_dir():
raise OSError(f"""{save_directory} should be a directory""" )
lowerCAmelCase_ :Any = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
lowerCAmelCase_ :Tuple = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , __A )
if os.path.abspath(self.spm_file ) != os.path.abspath(__A ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , __A )
elif not os.path.isfile(self.spm_file ):
with open(__A , """wb""" ) as fi:
lowerCAmelCase_ :Union[str, Any] = self.sp_model.serialized_model_proto()
fi.write(__A )
return (str(__A ), str(__A ))
def __lowerCAmelCase ( self , __A , __A = "en" , __A = None , __A = "ro" , **__A , ) -> BatchEncoding:
lowerCAmelCase_ :Union[str, Any] = src_lang
lowerCAmelCase_ :List[Any] = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(__A , __A , **__A )
def __lowerCAmelCase ( self , __A , __A , __A , **__A ) -> Tuple:
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
lowerCAmelCase_ :Dict = src_lang
lowerCAmelCase_ :Optional[int] = self(__A , add_special_tokens=__A , **__A )
lowerCAmelCase_ :Union[str, Any] = self.get_lang_id(__A )
lowerCAmelCase_ :Union[str, Any] = tgt_lang_id
return inputs
def __lowerCAmelCase ( self ) -> Any:
self.set_src_lang_special_tokens(self.src_lang )
def __lowerCAmelCase ( self ) -> Dict:
self.set_tgt_lang_special_tokens(self.tgt_lang )
def __lowerCAmelCase ( self , __A ) -> None:
lowerCAmelCase_ :str = self.get_lang_token(__A )
lowerCAmelCase_ :Any = self.lang_token_to_id[lang_token]
lowerCAmelCase_ :Optional[Any] = [self.cur_lang_id]
lowerCAmelCase_ :List[str] = [self.eos_token_id]
def __lowerCAmelCase ( self , __A ) -> None:
lowerCAmelCase_ :str = self.get_lang_token(__A )
lowerCAmelCase_ :int = self.lang_token_to_id[lang_token]
lowerCAmelCase_ :str = [self.cur_lang_id]
lowerCAmelCase_ :Union[str, Any] = [self.eos_token_id]
def __lowerCAmelCase ( self , __A ) -> str:
return self.lang_code_to_token[lang]
def __lowerCAmelCase ( self , __A ) -> int:
lowerCAmelCase_ :Optional[Any] = self.get_lang_token(__A )
return self.lang_token_to_id[lang_token]
def _snake_case ( lowercase__ : str , lowercase__ : Dict[str, Any] ) -> sentencepiece.SentencePieceProcessor:
'''simple docstring'''
lowerCAmelCase_ :Any = sentencepiece.SentencePieceProcessor(**lowercase__ )
spm.Load(str(lowercase__ ) )
return spm
def _snake_case ( lowercase__ : str ) -> Union[Dict, List]:
'''simple docstring'''
with open(lowercase__ , """r""" ) as f:
return json.load(lowercase__ )
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : str ) -> None:
'''simple docstring'''
with open(lowercase__ , """w""" ) as f:
json.dump(lowercase__ , lowercase__ , indent=2 )
| 84 |
"""simple docstring"""
from PIL import Image
def _snake_case ( lowercase__ : Image , lowercase__ : float ) -> Image:
'''simple docstring'''
def brightness(lowercase__ : int ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(lowercase__ )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
__UpperCAmelCase = change_brightness(img, 1_00)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 84 | 1 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )-> np.ndarray:
"""simple docstring"""
if (ksize % 2) == 0:
UpperCamelCase_ = ksize + 1
UpperCamelCase_ = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(SCREAMING_SNAKE_CASE_ ):
for x in range(SCREAMING_SNAKE_CASE_ ):
# distance from center
UpperCamelCase_ = x - ksize // 2
UpperCamelCase_ = y - ksize // 2
# degree to radiant
UpperCamelCase_ = theta / 1_8_0 * np.pi
UpperCamelCase_ = np.cos(_theta )
UpperCamelCase_ = np.sin(_theta )
# get kernel x
UpperCamelCase_ = cos_theta * px + sin_theta * py
# get kernel y
UpperCamelCase_ = -sin_theta * px + cos_theta * py
# fill kernel
UpperCamelCase_ = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
SCREAMING_SNAKE_CASE :Dict = imread("""../image_data/lena.jpg""")
# turn image in gray scale value
SCREAMING_SNAKE_CASE :Tuple = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
SCREAMING_SNAKE_CASE :Dict = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
SCREAMING_SNAKE_CASE :str = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
SCREAMING_SNAKE_CASE :List[Any] = out / out.max() * 255
SCREAMING_SNAKE_CASE :Any = out.astype(np.uinta)
imshow("""Original""", gray)
imshow("""Gabor filter with 20x20 mask and 6 directions""", out)
waitKey(0)
| 60 |
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class __magic_name__ ( unittest.TestCase ):
def __init__( self , _lowercase , _lowercase=13 , _lowercase=7 , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=True , _lowercase=99 , _lowercase=32 , _lowercase=5 , _lowercase=4 , _lowercase=37 , _lowercase="gelu" , _lowercase=0.1 , _lowercase=0.1 , _lowercase=512 , _lowercase=16 , _lowercase=2 , _lowercase=0.02 , _lowercase=4 , )-> Union[str, Any]:
UpperCamelCase_ = parent
UpperCamelCase_ = batch_size
UpperCamelCase_ = seq_length
UpperCamelCase_ = is_training
UpperCamelCase_ = use_attention_mask
UpperCamelCase_ = use_token_type_ids
UpperCamelCase_ = use_labels
UpperCamelCase_ = vocab_size
UpperCamelCase_ = hidden_size
UpperCamelCase_ = num_hidden_layers
UpperCamelCase_ = num_attention_heads
UpperCamelCase_ = intermediate_size
UpperCamelCase_ = hidden_act
UpperCamelCase_ = hidden_dropout_prob
UpperCamelCase_ = attention_probs_dropout_prob
UpperCamelCase_ = max_position_embeddings
UpperCamelCase_ = type_vocab_size
UpperCamelCase_ = type_sequence_label_size
UpperCamelCase_ = initializer_range
UpperCamelCase_ = num_choices
def UpperCAmelCase_ ( self )-> Optional[Any]:
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase_ = None
if self.use_attention_mask:
UpperCamelCase_ = random_attention_mask([self.batch_size, self.seq_length] )
UpperCamelCase_ = None
if self.use_token_type_ids:
UpperCamelCase_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
UpperCamelCase_ = AlbertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=_lowercase , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase_ ( self )-> str:
UpperCamelCase_ = self.prepare_config_and_inputs()
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ = config_and_inputs
UpperCamelCase_ = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class __magic_name__ ( snake_case , unittest.TestCase ):
UpperCamelCase_ :Optional[Any] = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase_ ( self )-> Optional[int]:
UpperCamelCase_ = FlaxAlbertModelTester(self )
@slow
def UpperCAmelCase_ ( self )-> str:
for model_class_name in self.all_model_classes:
UpperCamelCase_ = model_class_name.from_pretrained("albert-base-v2" )
UpperCamelCase_ = model(np.ones((1, 1) ) )
self.assertIsNotNone(_lowercase )
@require_flax
class __magic_name__ ( unittest.TestCase ):
@slow
def UpperCAmelCase_ ( self )-> List[str]:
UpperCamelCase_ = FlaxAlbertModel.from_pretrained("albert-base-v2" )
UpperCamelCase_ = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
UpperCamelCase_ = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
UpperCamelCase_ = model(_lowercase , attention_mask=_lowercase )[0]
UpperCamelCase_ = (1, 11, 768)
self.assertEqual(output.shape , _lowercase )
UpperCamelCase_ = np.array(
[[[-0.6_513, 1.5_035, -0.2_766], [-0.6_515, 1.5_046, -0.2_780], [-0.6_512, 1.5_049, -0.2_784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , _lowercase , atol=1e-4 ) )
| 60 | 1 |
_a = 8.31_44_62 # Unit - J mol-1 K-1
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError('Invalid inputs. Enter positive value.' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def __A ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )-> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError('Invalid inputs. Enter positive value.' )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 39 |
'''simple docstring'''
import os
import numpy
import onnx
def a__ ( lowercase : List[str], lowercase : str ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = a.name
_UpperCamelCase = b.name
_UpperCamelCase = ''''''
_UpperCamelCase = ''''''
_UpperCamelCase = a == b
_UpperCamelCase = name_a
_UpperCamelCase = name_b
return res
def a__ ( lowercase : List[str], lowercase : List[Any], lowercase : Tuple ) -> int:
"""simple docstring"""
for i, input_name in enumerate(node_proto.input ):
if input_name == name:
node_proto.input.insert(lowercase, lowercase )
node_proto.input.pop(i + 1 )
if node_proto.op_type == "If":
_graph_replace_input_with(node_proto.attribute[0].g, lowercase, lowercase )
_graph_replace_input_with(node_proto.attribute[1].g, lowercase, lowercase )
if node_proto.op_type == "Loop":
_graph_replace_input_with(node_proto.attribute[0].g, lowercase, lowercase )
def a__ ( lowercase : Any, lowercase : Union[str, Any], lowercase : Dict ) -> Tuple:
"""simple docstring"""
for n in graph_proto.node:
_node_replace_input_with(lowercase, lowercase, lowercase )
def a__ ( lowercase : Optional[int], lowercase : Union[str, Any], lowercase : Optional[int] ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = list(model.graph.initializer )
_UpperCamelCase = list(model_without_ext.graph.initializer )
for i, ref_i in ind_to_replace:
assert inits_with_data[i].name == inits[i].name
assert inits_with_data[ref_i].name == inits[ref_i].name
assert i > ref_i
_UpperCamelCase = inits[i].name
_UpperCamelCase = inits[ref_i].name
model_without_ext.graph.initializer.remove(inits[i] )
# for n in model.graph.node:
_graph_replace_input_with(model_without_ext.graph, lowercase, lowercase )
def a__ ( lowercase : Dict ) -> Dict:
"""simple docstring"""
_UpperCamelCase = os.path.dirname(lowercase )
_UpperCamelCase = os.path.basename(lowercase )
_UpperCamelCase = onnx.load(os.path.join(lowercase, lowercase ) )
_UpperCamelCase = list(model.graph.initializer )
_UpperCamelCase = set()
_UpperCamelCase = {}
_UpperCamelCase = []
_UpperCamelCase = 0
for i in range(len(lowercase ) ):
if i in dup_set:
continue
for j in range(i + 1, len(lowercase ) ):
if j in dup_set:
continue
if _is_equal_tensor_proto(inits[i], inits[j] ):
dup_set.add(lowercase )
dup_set.add(lowercase )
_UpperCamelCase = inits[j].data_type
_UpperCamelCase = numpy.prod(inits[j].dims )
if dtype == 1:
mem_size *= 4
elif dtype == 6:
mem_size *= 4
elif dtype == 7 or dtype == 11:
mem_size *= 8
else:
print('''unexpected data type: ''', lowercase )
total_reduced_size += mem_size
_UpperCamelCase = inits[i].name
_UpperCamelCase = inits[j].name
if name_i in dup_map:
dup_map[name_i].append(lowercase )
else:
_UpperCamelCase = [name_j]
ind_to_replace.append((j, i) )
print('''total reduced size: ''', total_reduced_size / 1024 / 1024 / 1024, '''GB''' )
_UpperCamelCase = sorted(lowercase )
_remove_dup_initializers_from_model(lowercase, lowercase, lowercase )
_UpperCamelCase = '''optimized_''' + model_file_name
_UpperCamelCase = os.path.join(lowercase, lowercase )
onnx.save(lowercase, lowercase )
return new_model
| 324 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
UpperCamelCase__ = {
'''configuration_roc_bert''': ['''ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''RoCBertConfig'''],
'''tokenization_roc_bert''': ['''RoCBertTokenizer'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'''ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''RoCBertForCausalLM''',
'''RoCBertForMaskedLM''',
'''RoCBertForMultipleChoice''',
'''RoCBertForPreTraining''',
'''RoCBertForQuestionAnswering''',
'''RoCBertForSequenceClassification''',
'''RoCBertForTokenClassification''',
'''RoCBertLayer''',
'''RoCBertModel''',
'''RoCBertPreTrainedModel''',
'''load_tf_weights_in_roc_bert''',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 370 |
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def a__ ( lowerCAmelCase__ ) -> None:
UpperCAmelCase__ , UpperCAmelCase__ : Optional[Any] = analyze_text(lowerCAmelCase__ )
UpperCAmelCase__ : List[Any] = list(''' ''' + ascii_lowercase )
# what is our total sum of probabilities.
UpperCAmelCase__ : str = sum(single_char_strings.values() )
# one length string
UpperCAmelCase__ : int = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
UpperCAmelCase__ : Optional[int] = single_char_strings[ch]
UpperCAmelCase__ : int = my_str / all_sum
my_fir_sum += prob * math.loga(lowerCAmelCase__ ) # entropy formula.
# print entropy
print(F"""{round(-1 * my_fir_sum ):.1f}""" )
# two len string
UpperCAmelCase__ : str = sum(two_char_strings.values() )
UpperCAmelCase__ : Optional[Any] = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
UpperCAmelCase__ : Optional[int] = cha + cha
if sequence in two_char_strings:
UpperCAmelCase__ : Dict = two_char_strings[sequence]
UpperCAmelCase__ : Optional[int] = int(lowerCAmelCase__ ) / all_sum
my_sec_sum += prob * math.loga(lowerCAmelCase__ )
# print second entropy
print(F"""{round(-1 * my_sec_sum ):.1f}""" )
# print the difference between them
print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )
def a__ ( lowerCAmelCase__ ) -> tuple[dict, dict]:
UpperCAmelCase__ : Union[str, Any] = Counter() # type: ignore
UpperCAmelCase__ : Tuple = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 , len(lowerCAmelCase__ ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def a__ ( ) -> Tuple:
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 299 | 0 |
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
class UpperCAmelCase ( a_ ):
'''simple docstring'''
def __init__( self : int ,*A : str ,**A : Dict ):
warnings.warn(
"The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use CLIPImageProcessor instead." ,snake_case__ ,)
super().__init__(*snake_case__ ,**snake_case__ )
| 15 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
UpperCAmelCase : Dict =TypeVar("""T""")
class _lowercase (Generic[T] ):
'''simple docstring'''
def __init__( self , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = data
UpperCamelCase_ = None
def __str__( self ):
'''simple docstring'''
return F"""{self.data}"""
class _lowercase (Generic[T] ):
'''simple docstring'''
def __init__( self ):
'''simple docstring'''
UpperCamelCase_ = None
def __iter__( self ):
'''simple docstring'''
UpperCamelCase_ = self.top
while node:
yield node.data
UpperCamelCase_ = node.next
def __str__( self ):
'''simple docstring'''
return "->".join([str(snake_case__ ) for item in self] )
def __len__( self ):
'''simple docstring'''
return len(tuple(iter(self ) ) )
def _lowerCamelCase ( self ):
'''simple docstring'''
return self.top is None
def _lowerCamelCase ( self , snake_case__ ):
'''simple docstring'''
UpperCamelCase_ = Node(snake_case__ )
if not self.is_empty():
UpperCamelCase_ = self.top
UpperCamelCase_ = node
def _lowerCamelCase ( self ):
'''simple docstring'''
if self.is_empty():
raise IndexError("pop from empty stack" )
assert isinstance(self.top , snake_case__ )
UpperCamelCase_ = self.top
UpperCamelCase_ = self.top.next
return pop_node.data
def _lowerCamelCase ( self ):
'''simple docstring'''
if self.is_empty():
raise IndexError("peek from empty stack" )
assert self.top is not None
return self.top.data
def _lowerCamelCase ( self ):
'''simple docstring'''
UpperCamelCase_ = None
if __name__ == "__main__":
from doctest import testmod
testmod()
| 128 | 0 |
'''simple docstring'''
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__snake_case : Optional[int] = HfArgumentParser(InitializationArguments)
__snake_case : int = parser.parse_args()
# Load codeparrot tokenizer trained for Python code tokenization
__snake_case : List[str] = AutoTokenizer.from_pretrained(args.tokenizer_name)
# Config: "scale_attn_by_layer_idx" and "reorder_and_upcast_attn" are Mistral stability tweaks
__snake_case : Optional[Any] = {
'vocab_size': len(tokenizer),
'scale_attn_by_inverse_layer_idx': True,
'reorder_and_upcast_attn': True,
}
# Load model config (GPT-2 large in this case)
__snake_case : Optional[Any] = AutoConfig.from_pretrained(args.config_name, **config_kwargs)
# Initialize new model with config
__snake_case : Union[str, Any] = AutoModelForCausalLM.from_config(config)
# Save model to the hub
model.save_pretrained(args.model_name, push_to_hub=args.push_to_hub)
| 363 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPSegProcessor, ViTImageProcessor
@require_vision
class lowerCamelCase ( unittest.TestCase ):
'''simple docstring'''
def lowercase__ ( self : Optional[int] ) -> int:
'''simple docstring'''
A__ : int =tempfile.mkdtemp()
# fmt: off
A__ : Optional[int] =["""l""", """o""", """w""", """e""", """r""", """s""", """t""", """i""", """d""", """n""", """lo""", """l</w>""", """w</w>""", """r</w>""", """t</w>""", """low</w>""", """er</w>""", """lowest</w>""", """newer</w>""", """wider""", """<unk>""", """<|startoftext|>""", """<|endoftext|>"""]
# fmt: on
A__ : List[Any] =dict(zip(lowerCAmelCase_ , range(len(lowerCAmelCase_ ) ) ) )
A__ : Tuple =["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
A__ : int ={"""unk_token""": """<unk>"""}
A__ : Optional[Any] =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
A__ : int =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCAmelCase_ ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCAmelCase_ ) )
A__ : Dict ={
"""do_resize""": True,
"""size""": 20,
"""do_center_crop""": True,
"""crop_size""": 18,
"""do_normalize""": True,
"""image_mean""": [0.48145466, 0.4578275, 0.40821073],
"""image_std""": [0.26862954, 0.26130258, 0.27577711],
}
A__ : Dict =os.path.join(self.tmpdirname , lowerCAmelCase_ )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(lowerCAmelCase_ , lowerCAmelCase_ )
def lowercase__ ( self : Optional[Any] , **lowerCAmelCase_ : Union[str, Any] ) -> int:
'''simple docstring'''
return CLIPTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase_ )
def lowercase__ ( self : Optional[Any] , **lowerCAmelCase_ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **lowerCAmelCase_ )
def lowercase__ ( self : Union[str, Any] , **lowerCAmelCase_ : str ) -> Union[str, Any]:
'''simple docstring'''
return ViTImageProcessor.from_pretrained(self.tmpdirname , **lowerCAmelCase_ )
def lowercase__ ( self : Dict ) -> str:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def lowercase__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
A__ : Dict =[np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
A__ : int =[Image.fromarray(np.moveaxis(lowerCAmelCase_ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def lowercase__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
A__ : int =self.get_tokenizer()
A__ : Optional[int] =self.get_rust_tokenizer()
A__ : Any =self.get_image_processor()
A__ : int =CLIPSegProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
processor_slow.save_pretrained(self.tmpdirname )
A__ : Dict =CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=lowerCAmelCase_ )
A__ : int =CLIPSegProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
processor_fast.save_pretrained(self.tmpdirname )
A__ : Optional[int] =CLIPSegProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() , tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() , tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() , tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer , lowerCAmelCase_ )
self.assertIsInstance(processor_fast.tokenizer , lowerCAmelCase_ )
self.assertEqual(processor_slow.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() , image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor , lowerCAmelCase_ )
self.assertIsInstance(processor_fast.image_processor , lowerCAmelCase_ )
def lowercase__ ( self : str ) -> List[str]:
'''simple docstring'''
A__ : List[str] =CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A__ : List[Any] =self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
A__ : Union[str, Any] =self.get_image_processor(do_normalize=lowerCAmelCase_ , padding_value=1.0 )
A__ : Optional[int] =CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowerCAmelCase_ , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowerCAmelCase_ )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowerCAmelCase_ )
def lowercase__ ( self : str ) -> str:
'''simple docstring'''
A__ : Optional[Any] =self.get_image_processor()
A__ : int =self.get_tokenizer()
A__ : Optional[int] =CLIPSegProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
A__ : Dict =self.prepare_image_inputs()
A__ : List[Any] =image_processor(lowerCAmelCase_ , return_tensors="""np""" )
A__ : List[Any] =processor(images=lowerCAmelCase_ , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def lowercase__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
A__ : Any =self.get_image_processor()
A__ : Optional[Any] =self.get_tokenizer()
A__ : int =CLIPSegProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
A__ : Any ="""lower newer"""
A__ : Optional[int] =processor(text=lowerCAmelCase_ )
A__ : Optional[int] =tokenizer(lowerCAmelCase_ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def lowercase__ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
A__ : Any =self.get_image_processor()
A__ : Optional[Any] =self.get_tokenizer()
A__ : Union[str, Any] =CLIPSegProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
A__ : Optional[Any] ="""lower newer"""
A__ : List[str] =self.prepare_image_inputs()
A__ : Optional[Any] =processor(text=lowerCAmelCase_ , images=lowerCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase_ ):
processor()
def lowercase__ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
A__ : Optional[Any] =self.get_image_processor()
A__ : List[str] =self.get_tokenizer()
A__ : Optional[int] =CLIPSegProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
A__ : Tuple =self.prepare_image_inputs()
A__ : str =self.prepare_image_inputs()
A__ : int =processor(images=lowerCAmelCase_ , visual_prompt=lowerCAmelCase_ )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """conditional_pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(lowerCAmelCase_ ):
processor()
def lowercase__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
A__ : List[str] =self.get_image_processor()
A__ : Optional[int] =self.get_tokenizer()
A__ : Any =CLIPSegProcessor(tokenizer=lowerCAmelCase_ , image_processor=lowerCAmelCase_ )
A__ : List[Any] =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A__ : Tuple =processor.batch_decode(lowerCAmelCase_ )
A__ : List[Any] =tokenizer.batch_decode(lowerCAmelCase_ )
self.assertListEqual(lowerCAmelCase_ , lowerCAmelCase_ )
| 136 | 0 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Dict:
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
UpperCamelCase__ : Dict = ksize + 1
UpperCamelCase__ : Any = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(__lowerCamelCase ):
for x in range(__lowerCamelCase ):
# distance from center
UpperCamelCase__ : Any = x - ksize // 2
UpperCamelCase__ : Tuple = y - ksize // 2
# degree to radiant
UpperCamelCase__ : int = theta / 180 * np.pi
UpperCamelCase__ : int = np.cos(_theta )
UpperCamelCase__ : Tuple = np.sin(_theta )
# get kernel x
UpperCamelCase__ : List[Any] = cos_theta * px + sin_theta * py
# get kernel y
UpperCamelCase__ : Any = -sin_theta * px + cos_theta * py
# fill kernel
UpperCamelCase__ : List[Any] = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
lowerCamelCase : int =imread('''../image_data/lena.jpg''')
# turn image in gray scale value
lowerCamelCase : Dict =cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
lowerCamelCase : Dict =np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
lowerCamelCase : Dict =gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
lowerCamelCase : List[Any] =out / out.max() * 255
lowerCamelCase : str =out.astype(np.uinta)
imshow('''Original''', gray)
imshow('''Gabor filter with 20x20 mask and 6 directions''', out)
waitKey(0) | 189 |
"""simple docstring"""
import argparse
import os
import re
_lowercase : str = "src/diffusers"
# Pattern that looks at the indentation in a line.
_lowercase : List[Any] = re.compile(r"^(\s*)\S")
# Pattern that matches `"key":" and puts `key` in group 0.
_lowercase : int = re.compile(r"^\s*\"([^\"]+)\":")
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_lowercase : Optional[int] = re.compile(r"^\s*_import_structure\[\"([^\"]+)\"\]")
# Pattern that matches `"key",` and puts `key` in group 0.
_lowercase : List[Any] = re.compile(r"^\s*\"([^\"]+)\",\s*$")
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_lowercase : str = re.compile(r"\[([^\]]+)\]")
def snake_case__ ( __lowerCamelCase : Optional[int] ):
"""simple docstring"""
lowerCamelCase__ : List[str] =_re_indent.search(__lowerCamelCase )
return "" if search is None else search.groups()[0]
def snake_case__ ( __lowerCamelCase : Any , __lowerCamelCase : List[Any]="" , __lowerCamelCase : int=None , __lowerCamelCase : Optional[int]=None ):
"""simple docstring"""
lowerCamelCase__ : Optional[int] =0
lowerCamelCase__ : Any =code.split('''\n''' )
if start_prompt is not None:
while not lines[index].startswith(__lowerCamelCase ):
index += 1
lowerCamelCase__ : Dict =['''\n'''.join(lines[:index] )]
else:
lowerCamelCase__ : Tuple =[]
# We split into blocks until we get to the `end_prompt` (or the end of the block).
lowerCamelCase__ : int =[lines[index]]
index += 1
while index < len(__lowerCamelCase ) and (end_prompt is None or not lines[index].startswith(__lowerCamelCase )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(__lowerCamelCase ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ''' ''' ):
current_block.append(lines[index] )
blocks.append('''\n'''.join(__lowerCamelCase ) )
if index < len(__lowerCamelCase ) - 1:
lowerCamelCase__ : str =[lines[index + 1]]
index += 1
else:
lowerCamelCase__ : str =[]
else:
blocks.append('''\n'''.join(__lowerCamelCase ) )
lowerCamelCase__ : str =[lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(__lowerCamelCase ) > 0:
blocks.append('''\n'''.join(__lowerCamelCase ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(__lowerCamelCase ):
blocks.append('''\n'''.join(lines[index:] ) )
return blocks
def snake_case__ ( __lowerCamelCase : Any ):
"""simple docstring"""
def _inner(__lowerCamelCase : Any ):
return key(__lowerCamelCase ).lower().replace('''_''' , '''''' )
return _inner
def snake_case__ ( __lowerCamelCase : int , __lowerCamelCase : Any=None ):
"""simple docstring"""
# If no key is provided, we use a noop.
def noop(__lowerCamelCase : List[str] ):
return x
if key is None:
lowerCamelCase__ : Tuple =noop
# Constants are all uppercase, they go first.
lowerCamelCase__ : Union[str, Any] =[obj for obj in objects if key(__lowerCamelCase ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
lowerCamelCase__ : Optional[int] =[obj for obj in objects if key(__lowerCamelCase )[0].isupper() and not key(__lowerCamelCase ).isupper()]
# Functions begin with a lowercase, they go last.
lowerCamelCase__ : Optional[Any] =[obj for obj in objects if not key(__lowerCamelCase )[0].isupper()]
lowerCamelCase__ : int =ignore_underscore(__lowerCamelCase )
return sorted(__lowerCamelCase , key=__lowerCamelCase ) + sorted(__lowerCamelCase , key=__lowerCamelCase ) + sorted(__lowerCamelCase , key=__lowerCamelCase )
def snake_case__ ( __lowerCamelCase : Optional[Any] ):
"""simple docstring"""
# This inner function sort imports between [ ].
def _replace(__lowerCamelCase : Optional[Any] ):
lowerCamelCase__ : Dict =match.groups()[0]
if "," not in imports:
return f'''[{imports}]'''
lowerCamelCase__ : List[Any] =[part.strip().replace('''"''' , '''''' ) for part in imports.split(''',''' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowerCamelCase__ : Optional[int] =keys[:-1]
return "[" + ", ".join([f'''"{k}"''' for k in sort_objects(__lowerCamelCase )] ) + "]"
lowerCamelCase__ : List[Any] =import_statement.split('''\n''' )
if len(__lowerCamelCase ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
lowerCamelCase__ : Tuple =2 if lines[1].strip() == '''[''' else 1
lowerCamelCase__ : Any =[(i, _re_strip_line.search(__lowerCamelCase ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
lowerCamelCase__ : List[Any] =sort_objects(__lowerCamelCase , key=lambda __lowerCamelCase : x[1] )
lowerCamelCase__ : Union[str, Any] =[lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(__lowerCamelCase ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
lowerCamelCase__ : List[str] =_re_bracket_content.sub(_replace , lines[1] )
else:
lowerCamelCase__ : str =[part.strip().replace('''"''' , '''''' ) for part in lines[1].split(''',''' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowerCamelCase__ : Any =keys[:-1]
lowerCamelCase__ : List[Any] =get_indent(lines[1] ) + ''', '''.join([f'''"{k}"''' for k in sort_objects(__lowerCamelCase )] )
return "\n".join(__lowerCamelCase )
else:
# Finally we have to deal with imports fitting on one line
lowerCamelCase__ : Union[str, Any] =_re_bracket_content.sub(_replace , __lowerCamelCase )
return import_statement
def snake_case__ ( __lowerCamelCase : Optional[int] , __lowerCamelCase : List[Any]=True ):
"""simple docstring"""
with open(__lowerCamelCase , '''r''' ) as f:
lowerCamelCase__ : Optional[int] =f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
lowerCamelCase__ : int =split_code_in_indented_blocks(
__lowerCamelCase , start_prompt='''_import_structure = {''' , end_prompt='''if TYPE_CHECKING:''' )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(__lowerCamelCase ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
lowerCamelCase__ : Optional[Any] =main_blocks[block_idx]
lowerCamelCase__ : List[str] =block.split('''\n''' )
# Get to the start of the imports.
lowerCamelCase__ : Any =0
while line_idx < len(__lowerCamelCase ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
lowerCamelCase__ : Tuple =len(__lowerCamelCase )
else:
line_idx += 1
if line_idx >= len(__lowerCamelCase ):
continue
# Ignore beginning and last line: they don't contain anything.
lowerCamelCase__ : Any ='''\n'''.join(block_lines[line_idx:-1] )
lowerCamelCase__ : Dict =get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
lowerCamelCase__ : List[Any] =split_code_in_indented_blocks(__lowerCamelCase , indent_level=__lowerCamelCase )
# We have two categories of import key: list or _import_structure[key].append/extend
lowerCamelCase__ : List[Any] =_re_direct_key if '''_import_structure''' in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
lowerCamelCase__ : Union[str, Any] =[(pattern.search(__lowerCamelCase ).groups()[0] if pattern.search(__lowerCamelCase ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
lowerCamelCase__ : Optional[Any] =[(i, key) for i, key in enumerate(__lowerCamelCase ) if key is not None]
lowerCamelCase__ : List[Any] =[x[0] for x in sorted(__lowerCamelCase , key=lambda __lowerCamelCase : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
lowerCamelCase__ : Optional[Any] =0
lowerCamelCase__ : Tuple =[]
for i in range(len(__lowerCamelCase ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
lowerCamelCase__ : List[Any] =sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(__lowerCamelCase )
count += 1
# And we put our main block back together with its first and last line.
lowerCamelCase__ : str ='''\n'''.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(__lowerCamelCase ):
if check_only:
return True
else:
print(f'''Overwriting {file}.''' )
with open(__lowerCamelCase , '''w''' ) as f:
f.write('''\n'''.join(__lowerCamelCase ) )
def snake_case__ ( __lowerCamelCase : Optional[Any]=True ):
"""simple docstring"""
lowerCamelCase__ : Any =[]
for root, _, files in os.walk(__lowerCamelCase ):
if "__init__.py" in files:
lowerCamelCase__ : Tuple =sort_imports(os.path.join(__lowerCamelCase , '''__init__.py''' ) , check_only=__lowerCamelCase )
if result:
lowerCamelCase__ : List[str] =[os.path.join(__lowerCamelCase , '''__init__.py''' )]
if len(__lowerCamelCase ) > 0:
raise ValueError(f'''Would overwrite {len(__lowerCamelCase )} files, run `make style`.''' )
if __name__ == "__main__":
_lowercase : Any = argparse.ArgumentParser()
parser.add_argument("--check_only", action="store_true", help="Whether to only check or fix style.")
_lowercase : List[Any] = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 238 | 0 |
'''simple docstring'''
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
def _lowerCAmelCase ( lowercase ) -> List[str]:
return 1.0 / (1.0 + np.exp(-_outputs ))
def _lowerCAmelCase ( lowercase ) -> Optional[int]:
__lowerCAmelCase = np.max(_outputs , axis=-1 , keepdims=lowercase )
__lowerCAmelCase = np.exp(_outputs - maxes )
return shifted_exp / shifted_exp.sum(axis=-1 , keepdims=lowercase )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Dict ="""sigmoid"""
a : Optional[int] ="""softmax"""
a : List[str] ="""none"""
@add_end_docstrings(
lowerCAmelCase_ , R"""
return_all_scores (`bool`, *optional*, defaults to `False`):
Whether to return all prediction scores or just the one of the predicted class.
function_to_apply (`str`, *optional*, defaults to `\"default\"`):
The function to apply to the model outputs in order to retrieve the scores. Accepts four different values:
- `\"default\"`: if the model has a single label, will apply the sigmoid function on the output. If the model
has several labels, will apply the softmax function on the output.
- `\"sigmoid\"`: Applies the sigmoid function on the output.
- `\"softmax\"`: Applies the softmax function on the output.
- `\"none\"`: Does not apply any function on the output.
""" , )
class _UpperCAmelCase ( lowerCAmelCase_ ):
a : Optional[Any] =False
a : Optional[int] =ClassificationFunction.NONE
def __init__( self,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
super().__init__(**__SCREAMING_SNAKE_CASE )
self.check_model_type(
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE="",**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = tokenizer_kwargs
__lowerCAmelCase = {}
if hasattr(self.model.config,"""return_all_scores""" ) and return_all_scores is None:
__lowerCAmelCase = self.model.config.return_all_scores
if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) or top_k is None:
__lowerCAmelCase = top_k
__lowerCAmelCase = False
elif return_all_scores is not None:
warnings.warn(
"""`return_all_scores` is now deprecated, if want a similar functionality use `top_k=None` instead of"""
""" `return_all_scores=True` or `top_k=1` instead of `return_all_scores=False`.""",__SCREAMING_SNAKE_CASE,)
if return_all_scores:
__lowerCAmelCase = None
else:
__lowerCAmelCase = 1
if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
__lowerCAmelCase = ClassificationFunction[function_to_apply.upper()]
if function_to_apply is not None:
__lowerCAmelCase = function_to_apply
return preprocess_params, {}, postprocess_params
def __call__( self,*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = super().__call__(*__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
# TODO try and retrieve it in a nicer way from _sanitize_parameters.
__lowerCAmelCase = """top_k""" not in kwargs
if isinstance(args[0],__SCREAMING_SNAKE_CASE ) and _legacy:
# This pipeline is odd, and return a list when single item is run
return [result]
else:
return result
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__lowerCAmelCase = self.framework
if isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
return self.tokenizer(**__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
elif isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ) and len(__SCREAMING_SNAKE_CASE ) == 1 and isinstance(inputs[0],__SCREAMING_SNAKE_CASE ) and len(inputs[0] ) == 2:
# It used to be valid to use a list of list of list for text pairs, keeping this path for BC
return self.tokenizer(
text=inputs[0][0],text_pair=inputs[0][1],return_tensors=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
elif isinstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE ):
# This is likely an invalid usage of the pipeline attempting to pass text pairs.
raise ValueError(
"""The pipeline received invalid inputs, if you are trying to send text pairs, you can try to send a"""
""" dictionary `{\"text\": \"My text\", \"text_pair\": \"My pair\"}` in order to send a text pair.""" )
return self.tokenizer(__SCREAMING_SNAKE_CASE,return_tensors=__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return self.model(**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE=None,__SCREAMING_SNAKE_CASE=1,__SCREAMING_SNAKE_CASE=True ):
'''simple docstring'''
if function_to_apply is None:
if self.model.config.problem_type == "multi_label_classification" or self.model.config.num_labels == 1:
__lowerCAmelCase = ClassificationFunction.SIGMOID
elif self.model.config.problem_type == "single_label_classification" or self.model.config.num_labels > 1:
__lowerCAmelCase = ClassificationFunction.SOFTMAX
elif hasattr(self.model.config,"""function_to_apply""" ) and function_to_apply is None:
__lowerCAmelCase = self.model.config.function_to_apply
else:
__lowerCAmelCase = ClassificationFunction.NONE
__lowerCAmelCase = model_outputs["""logits"""][0]
__lowerCAmelCase = outputs.numpy()
if function_to_apply == ClassificationFunction.SIGMOID:
__lowerCAmelCase = sigmoid(__SCREAMING_SNAKE_CASE )
elif function_to_apply == ClassificationFunction.SOFTMAX:
__lowerCAmelCase = softmax(__SCREAMING_SNAKE_CASE )
elif function_to_apply == ClassificationFunction.NONE:
__lowerCAmelCase = outputs
else:
raise ValueError(f'Unrecognized `function_to_apply` argument: {function_to_apply}' )
if top_k == 1 and _legacy:
return {"label": self.model.config.idalabel[scores.argmax().item()], "score": scores.max().item()}
__lowerCAmelCase = [
{"""label""": self.model.config.idalabel[i], """score""": score.item()} for i, score in enumerate(__SCREAMING_SNAKE_CASE )
]
if not _legacy:
dict_scores.sort(key=lambda __SCREAMING_SNAKE_CASE : x["score"],reverse=__SCREAMING_SNAKE_CASE )
if top_k is not None:
__lowerCAmelCase = dict_scores[:top_k]
return dict_scores
| 356 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors
@require_tokenizers
class _UpperCAmelCase ( lowerCAmelCase_ , unittest.TestCase ):
a : Dict =MvpTokenizer
a : int =MvpTokenizerFast
a : Any =True
a : int =filter_roberta_detectors
def lowerCamelCase__ ( self ):
'''simple docstring'''
super().setUp()
__lowerCAmelCase = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
__lowerCAmelCase = dict(zip(__SCREAMING_SNAKE_CASE,range(len(__SCREAMING_SNAKE_CASE ) ) ) )
__lowerCAmelCase = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
__lowerCAmelCase = {"""unk_token""": """<unk>"""}
__lowerCAmelCase = os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["""vocab_file"""] )
__lowerCAmelCase = os.path.join(self.tmpdirname,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file,"""w""",encoding="""utf-8""" ) as fp:
fp.write(json.dumps(__SCREAMING_SNAKE_CASE ) + """\n""" )
with open(self.merges_file,"""w""",encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(__SCREAMING_SNAKE_CASE ) )
def lowerCamelCase__ ( self,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,**__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname,**__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( self,__SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return "lower newer", "lower newer"
@cached_property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return MvpTokenizer.from_pretrained("""RUCAIBox/mvp""" )
@cached_property
def lowerCamelCase__ ( self ):
'''simple docstring'''
return MvpTokenizerFast.from_pretrained("""RUCAIBox/mvp""" )
@require_torch
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
__lowerCAmelCase = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__lowerCAmelCase = tokenizer(__SCREAMING_SNAKE_CASE,max_length=len(__SCREAMING_SNAKE_CASE ),padding=__SCREAMING_SNAKE_CASE,return_tensors="""pt""" )
self.assertIsInstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
self.assertEqual((2, 9),batch.input_ids.shape )
self.assertEqual((2, 9),batch.attention_mask.shape )
__lowerCAmelCase = batch.input_ids.tolist()[0]
self.assertListEqual(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
# Test that special tokens are reset
@require_torch
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = ["""A long paragraph for summarization.""", """Another paragraph for summarization."""]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__lowerCAmelCase = tokenizer(__SCREAMING_SNAKE_CASE,padding=__SCREAMING_SNAKE_CASE,return_tensors="""pt""" )
# check if input_ids are returned and no labels
self.assertIn("""input_ids""",__SCREAMING_SNAKE_CASE )
self.assertIn("""attention_mask""",__SCREAMING_SNAKE_CASE )
self.assertNotIn("""labels""",__SCREAMING_SNAKE_CASE )
self.assertNotIn("""decoder_attention_mask""",__SCREAMING_SNAKE_CASE )
@require_torch
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = [
"""Summary of the text.""",
"""Another summary.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__lowerCAmelCase = tokenizer(text_target=__SCREAMING_SNAKE_CASE,max_length=32,padding="""max_length""",return_tensors="""pt""" )
self.assertEqual(32,targets["""input_ids"""].shape[1] )
@require_torch
def lowerCamelCase__ ( self ):
'''simple docstring'''
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__lowerCAmelCase = tokenizer(
["""I am a small frog""" * 10_24, """I am a small frog"""],padding=__SCREAMING_SNAKE_CASE,truncation=__SCREAMING_SNAKE_CASE,return_tensors="""pt""" )
self.assertIsInstance(__SCREAMING_SNAKE_CASE,__SCREAMING_SNAKE_CASE )
self.assertEqual(batch.input_ids.shape,(2, 10_24) )
@require_torch
def lowerCamelCase__ ( self ):
'''simple docstring'''
__lowerCAmelCase = ["""A long paragraph for summarization."""]
__lowerCAmelCase = [
"""Summary of the text.""",
]
for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
__lowerCAmelCase = tokenizer(__SCREAMING_SNAKE_CASE,text_target=__SCREAMING_SNAKE_CASE,return_tensors="""pt""" )
__lowerCAmelCase = inputs["""input_ids"""]
__lowerCAmelCase = inputs["""labels"""]
self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )
def lowerCamelCase__ ( self ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self ):
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
__lowerCAmelCase = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE,**__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = """A, <mask> AllenNLP sentence."""
__lowerCAmelCase = tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE,add_special_tokens=__SCREAMING_SNAKE_CASE,return_token_type_ids=__SCREAMING_SNAKE_CASE )
__lowerCAmelCase = tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE,add_special_tokens=__SCREAMING_SNAKE_CASE,return_token_type_ids=__SCREAMING_SNAKE_CASE )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r["""token_type_ids"""] ),sum(tokens_p["""token_type_ids"""] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r["""attention_mask"""] ) / len(tokens_r["""attention_mask"""] ),sum(tokens_p["""attention_mask"""] ) / len(tokens_p["""attention_mask"""] ),)
__lowerCAmelCase = tokenizer_r.convert_ids_to_tokens(tokens_r["""input_ids"""] )
__lowerCAmelCase = tokenizer_p.convert_ids_to_tokens(tokens_p["""input_ids"""] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p["""input_ids"""],[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r["""input_ids"""],[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
__SCREAMING_SNAKE_CASE,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
self.assertSequenceEqual(
__SCREAMING_SNAKE_CASE,["""<s>""", """A""", """,""", """<mask>""", """ĠAllen""", """N""", """LP""", """Ġsentence""", """.""", """</s>"""] )
| 46 | 0 |
'''simple docstring'''
def _a( UpperCamelCase__ : dict ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : set[int] =set()
# To detect a back edge, keep track of vertices currently in the recursion stack
SCREAMING_SNAKE_CASE__ : set[int] =set()
return any(
node not in visited and depth_first_search(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ )
for node in graph )
def _a( UpperCamelCase__ : dict, UpperCamelCase__ : int, UpperCamelCase__ : set, UpperCamelCase__ : set ):
'''simple docstring'''
visited.add(UpperCamelCase__ )
rec_stk.add(UpperCamelCase__ )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(UpperCamelCase__, UpperCamelCase__, UpperCamelCase__, UpperCamelCase__ ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(UpperCamelCase__ )
return False
if __name__ == "__main__":
from doctest import testmod
testmod() | 152 |
'''simple docstring'''
import re
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''', str_ )]
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =split_input(str_ )
return "".join(
[''''''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def _a( UpperCamelCase__ : str, UpperCamelCase__ : bool, UpperCamelCase__ : str ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Any =split_input(UpperCamelCase__ )
if upper:
SCREAMING_SNAKE_CASE__ : int =''''''.join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
SCREAMING_SNAKE_CASE__ : Any =''''''.join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
return to_simple_case(UpperCamelCase__ )
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : List[str] =to_simple_case(UpperCamelCase__ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def _a( UpperCamelCase__ : str, UpperCamelCase__ : bool ):
'''simple docstring'''
return to_complex_case(UpperCamelCase__, UpperCamelCase__, '''_''' )
def _a( UpperCamelCase__ : str, UpperCamelCase__ : bool ):
'''simple docstring'''
return to_complex_case(UpperCamelCase__, UpperCamelCase__, '''-''' )
if __name__ == "__main__":
__import__('doctest').testmod() | 152 | 1 |
'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = None
def snake_case__ ( lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Tuple=0.999 , lowerCamelCase__ : List[Any]="cosine" , ) -> Optional[int]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(lowerCamelCase__ : List[Any] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(lowerCamelCase__ : Optional[int] ):
return math.exp(t * -12.0 )
else:
raise ValueError(f'Unsupported alpha_tranform_type: {alpha_transform_type}' )
A_ : List[Any] = []
for i in range(lowerCamelCase__ ):
A_ : Tuple = i / num_diffusion_timesteps
A_ : Union[str, Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(lowerCamelCase__ ) / alpha_bar_fn(lowerCamelCase__ ) , lowerCamelCase__ ) )
return torch.tensor(lowerCamelCase__ , dtype=torch.floataa )
class UpperCamelCase_ (a__, a__ ):
"""simple docstring"""
_lowerCAmelCase = 1
@register_to_config
def __init__( self : Any , _lowerCamelCase : int = 1000 , _lowerCamelCase : float = 0.00_01 , _lowerCamelCase : float = 0.02 , _lowerCamelCase : str = "linear" , _lowerCamelCase : Optional[Union[np.ndarray, List[float]]] = None , _lowerCamelCase : bool = True , _lowerCamelCase : bool = True , _lowerCamelCase : int = 0 , _lowerCamelCase : str = "epsilon" , _lowerCamelCase : float = 1.0 , **_lowerCamelCase : Tuple , ):
"""simple docstring"""
if kwargs.get('''set_alpha_to_one''' , _lowerCamelCase ) is not None:
A_ : Optional[Any] = (
'''The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead.'''
)
deprecate('''set_alpha_to_one''' , '''1.0.0''' , _lowerCamelCase , standard_warn=_lowerCamelCase )
A_ : Union[str, Any] = kwargs['''set_alpha_to_one''']
if trained_betas is not None:
A_ : List[Any] = torch.tensor(_lowerCamelCase , dtype=torch.floataa )
elif beta_schedule == "linear":
A_ : List[Any] = torch.linspace(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
A_ : int = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , _lowerCamelCase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
A_ : List[str] = betas_for_alpha_bar(_lowerCamelCase )
else:
raise NotImplementedError(f'{beta_schedule} does is not implemented for {self.__class__}' )
A_ : Tuple = 1.0 - self.betas
A_ : Optional[int] = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
A_ : List[str] = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
A_ : Union[str, Any] = 1.0
# setable values
A_ : Optional[int] = None
A_ : List[Any] = torch.from_numpy(np.arange(0 , _lowerCamelCase ).copy().astype(np.intaa ) )
def _a ( self : Any , _lowerCamelCase : torch.FloatTensor , _lowerCamelCase : Optional[int] = None ):
"""simple docstring"""
return sample
def _a ( self : List[str] , _lowerCamelCase : int , _lowerCamelCase : Union[str, torch.device] = None ):
"""simple docstring"""
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f'`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'
f' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'
f' maximal {self.config.num_train_timesteps} timesteps.' )
A_ : List[str] = num_inference_steps
A_ : Any = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
A_ : Union[str, Any] = (np.arange(0 , _lowerCamelCase ) * step_ratio).round().copy().astype(np.intaa )
A_ : List[Any] = torch.from_numpy(_lowerCamelCase ).to(_lowerCamelCase )
self.timesteps += self.config.steps_offset
def _a ( self : Tuple , _lowerCamelCase : torch.FloatTensor , _lowerCamelCase : int , _lowerCamelCase : torch.FloatTensor , _lowerCamelCase : float = 0.0 , _lowerCamelCase : bool = False , _lowerCamelCase : Optional[torch.FloatTensor] = None , _lowerCamelCase : bool = True , ):
"""simple docstring"""
A_ : Union[str, Any] = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
A_ : int = self.alphas_cumprod[timestep]
A_ : Optional[Any] = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
A_ : Tuple = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
A_ : Dict = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
A_ : Dict = model_output
elif self.config.prediction_type == "sample":
A_ : Tuple = model_output
A_ : List[str] = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
A_ : Dict = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
A_ : Union[str, Any] = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'
''' `v_prediction`''' )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
A_ : List[Any] = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
A_ : List[str] = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
A_ : Optional[int] = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=_lowerCamelCase , pred_original_sample=_lowerCamelCase )
def __len__( self : Any ):
"""simple docstring"""
return self.config.num_train_timesteps
| 4 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgumentParser, TrainingArguments
from transformers.hf_argparser import make_choice_type_function, string_to_bool
# Since Python 3.10, we can use the builtin `|` operator for Union types
# See PEP 604: https://peps.python.org/pep-0604
snake_case__ = sys.version_info >= (3, 10)
def snake_case__ ( lowerCamelCase__ : Union[str, Any]=None , lowerCamelCase__ : str=None ) -> List[Any]:
return field(default_factory=lambda: default , metadata=lowerCamelCase__ )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
_lowerCAmelCase = 42
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 4_2
_lowerCAmelCase = field(default='toto', metadata={'help': 'help message'} )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = True
_lowerCAmelCase = None
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'titi'
_lowerCAmelCase = 'toto'
class UpperCamelCase_ (a__ ):
"""simple docstring"""
_lowerCAmelCase = 'titi'
_lowerCAmelCase = 'toto'
_lowerCAmelCase = 4_2
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Optional[int] = BasicEnum(self.foo )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Optional[Any] = MixedTypeEnum(self.foo )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = field(default=a__, metadata={'help': 'help message'} )
_lowerCAmelCase = None
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[] )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[1, 2, 3] )
_lowerCAmelCase = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
_lowerCAmelCase = list_field(default=[0.1, 0.2, 0.3] )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = field()
_lowerCAmelCase = field()
_lowerCAmelCase = field()
def _a ( self : Tuple ):
"""simple docstring"""
A_ : Tuple = BasicEnum(self.required_enum )
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = 42
_lowerCAmelCase = field()
_lowerCAmelCase = None
_lowerCAmelCase = field(default='toto', metadata={'help': 'help message'} )
_lowerCAmelCase = list_field(default=['Hallo', 'Bonjour', 'Hello'] )
if is_python_no_less_than_3_10:
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = False
_lowerCAmelCase = True
_lowerCAmelCase = None
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = None
_lowerCAmelCase = field(default=a__, metadata={'help': 'help message'} )
_lowerCAmelCase = None
_lowerCAmelCase = list_field(default=[] )
_lowerCAmelCase = list_field(default=[] )
class UpperCamelCase_ (unittest.TestCase ):
"""simple docstring"""
def _a ( self : List[str] , _lowerCamelCase : argparse.ArgumentParser , _lowerCamelCase : argparse.ArgumentParser ):
"""simple docstring"""
self.assertEqual(len(a._actions ) , len(b._actions ) )
for x, y in zip(a._actions , b._actions ):
A_ : Union[str, Any] = {k: v for k, v in vars(_lowerCamelCase ).items() if k != '''container'''}
A_ : Optional[Any] = {k: v for k, v in vars(_lowerCamelCase ).items() if k != '''container'''}
# Choices with mixed type have custom function as "type"
# So we need to compare results directly for equality
if xx.get('''choices''' , _lowerCamelCase ) and yy.get('''choices''' , _lowerCamelCase ):
for expected_choice in yy["choices"] + xx["choices"]:
self.assertEqual(xx['''type'''](_lowerCamelCase ) , yy['''type'''](_lowerCamelCase ) )
del xx["type"], yy["type"]
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Optional[int] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--bar''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--baz''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--flag''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Union[str, Any] = ['''--foo''', '''1''', '''--baz''', '''quux''', '''--bar''', '''0.5''']
((A_) ,) : List[str] = parser.parse_args_into_dataclasses(_lowerCamelCase , look_for_args_file=_lowerCamelCase )
self.assertFalse(example.flag )
def _a ( self : Dict ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : int = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=42 , type=_lowerCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=_lowerCamelCase , help='''help message''' )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Any = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
expected.add_argument('''--baz''' , type=_lowerCamelCase , default=_lowerCamelCase , const=_lowerCamelCase , nargs='''?''' )
# A boolean no_* argument always has to come after its "default: True" regular counter-part
# and its default must be set to False
expected.add_argument('''--no_baz''' , action='''store_false''' , default=_lowerCamelCase , dest='''baz''' )
expected.add_argument('''--opt''' , type=_lowerCamelCase , default=_lowerCamelCase )
A_ : Dict = [WithDefaultBoolExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCamelCase )
for dataclass_type in dataclass_types:
A_ : Any = HfArgumentParser(_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = parser.parse_args([] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : Optional[int] = parser.parse_args(['''--foo''', '''--no_baz'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : Union[str, Any] = parser.parse_args(['''--foo''', '''--baz'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : List[str] = parser.parse_args(['''--foo''', '''True''', '''--baz''', '''True''', '''--opt''', '''True'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
A_ : List[Any] = parser.parse_args(['''--foo''', '''False''', '''--baz''', '''False''', '''--opt''', '''False'''] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , baz=_lowerCamelCase , opt=_lowerCamelCase ) )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : str = HfArgumentParser(_lowerCamelCase )
A_ : Optional[int] = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=['''titi''', '''toto''', 42] , type=make_choice_type_function(['''titi''', '''toto''', 42] ) , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : str = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
A_ : List[Any] = parser.parse_args_into_dataclasses([] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.toto )
A_ : int = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
A_ : Dict = parser.parse_args_into_dataclasses(['''--foo''', '''titi'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.titi )
A_ : Tuple = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 42 )
A_ : List[str] = parser.parse_args_into_dataclasses(['''--foo''', '''42'''] )[0]
self.assertEqual(enum_ex.foo , MixedTypeEnum.fourtytwo )
def _a ( self : Optional[int] ):
"""simple docstring"""
@dataclass
class UpperCamelCase_ :
"""simple docstring"""
_lowerCAmelCase = "toto"
A_ : List[str] = HfArgumentParser(_lowerCamelCase )
A_ : Tuple = argparse.ArgumentParser()
expected.add_argument(
'''--foo''' , default='''toto''' , choices=('''titi''', '''toto''', 42) , type=make_choice_type_function(['''titi''', '''toto''', 42] ) , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Tuple = parser.parse_args([] )
self.assertEqual(args.foo , '''toto''' )
A_ : List[str] = parser.parse_args(['''--foo''', '''titi'''] )
self.assertEqual(args.foo , '''titi''' )
A_ : int = parser.parse_args(['''--foo''', '''42'''] )
self.assertEqual(args.foo , 42 )
def _a ( self : Dict ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : List[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo_int''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
expected.add_argument('''--bar_int''' , nargs='''+''' , default=[1, 2, 3] , type=_lowerCamelCase )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=_lowerCamelCase )
expected.add_argument('''--foo_float''' , nargs='''+''' , default=[0.1, 0.2, 0.3] , type=_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[int] = parser.parse_args([] )
self.assertEqual(
_lowerCamelCase , Namespace(foo_int=[] , bar_int=[1, 2, 3] , foo_str=['''Hallo''', '''Bonjour''', '''Hello'''] , foo_float=[0.1, 0.2, 0.3] ) , )
A_ : str = parser.parse_args('''--foo_int 1 --bar_int 2 3 --foo_str a b c --foo_float 0.1 0.7'''.split() )
self.assertEqual(_lowerCamelCase , Namespace(foo_int=[1] , bar_int=[2, 3] , foo_str=['''a''', '''b''', '''c'''] , foo_float=[0.1, 0.7] ) )
def _a ( self : Dict ):
"""simple docstring"""
A_ : Optional[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , default=_lowerCamelCase , type=_lowerCamelCase )
expected.add_argument('''--bar''' , default=_lowerCamelCase , type=_lowerCamelCase , help='''help message''' )
expected.add_argument('''--baz''' , default=_lowerCamelCase , type=_lowerCamelCase )
expected.add_argument('''--ces''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
expected.add_argument('''--des''' , nargs='''+''' , default=[] , type=_lowerCamelCase )
A_ : Tuple = [OptionalExample]
if is_python_no_less_than_3_10:
dataclass_types.append(_lowerCamelCase )
for dataclass_type in dataclass_types:
A_ : int = HfArgumentParser(_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
A_ : List[Any] = parser.parse_args([] )
self.assertEqual(_lowerCamelCase , Namespace(foo=_lowerCamelCase , bar=_lowerCamelCase , baz=_lowerCamelCase , ces=[] , des=[] ) )
A_ : Optional[Any] = parser.parse_args('''--foo 12 --bar 3.14 --baz 42 --ces a b c --des 1 2 3'''.split() )
self.assertEqual(_lowerCamelCase , Namespace(foo=12 , bar=3.14 , baz='''42''' , ces=['''a''', '''b''', '''c'''] , des=[1, 2, 3] ) )
def _a ( self : List[Any] ):
"""simple docstring"""
A_ : List[Any] = HfArgumentParser(_lowerCamelCase )
A_ : Dict = argparse.ArgumentParser()
expected.add_argument('''--required_list''' , nargs='''+''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument('''--required_str''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=_lowerCamelCase , )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : List[Any] = argparse.ArgumentParser()
expected.add_argument('''--foo''' , type=_lowerCamelCase , required=_lowerCamelCase )
expected.add_argument(
'''--required_enum''' , type=make_choice_type_function(['''titi''', '''toto'''] ) , choices=['''titi''', '''toto'''] , required=_lowerCamelCase , )
expected.add_argument('''--opt''' , type=_lowerCamelCase , default=_lowerCamelCase )
expected.add_argument('''--baz''' , default='''toto''' , type=_lowerCamelCase , help='''help message''' )
expected.add_argument('''--foo_str''' , nargs='''+''' , default=['''Hallo''', '''Bonjour''', '''Hello'''] , type=_lowerCamelCase )
self.argparsersEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Tuple ):
"""simple docstring"""
A_ : List[Any] = HfArgumentParser(_lowerCamelCase )
A_ : Union[str, Any] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
A_ : Optional[int] = parser.parse_dict(_lowerCamelCase )[0]
A_ : str = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : List[str] ):
"""simple docstring"""
A_ : Any = HfArgumentParser(_lowerCamelCase )
A_ : List[str] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
'''extra''': 42,
}
self.assertRaises(_lowerCamelCase , parser.parse_dict , _lowerCamelCase , allow_extra_keys=_lowerCamelCase )
def _a ( self : Optional[Any] ):
"""simple docstring"""
A_ : Union[str, Any] = HfArgumentParser(_lowerCamelCase )
A_ : List[str] = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : Tuple = os.path.join(_lowerCamelCase , '''temp_json''' )
os.mkdir(_lowerCamelCase )
with open(temp_local_path + '''.json''' , '''w+''' ) as f:
json.dump(_lowerCamelCase , _lowerCamelCase )
A_ : List[str] = parser.parse_yaml_file(Path(temp_local_path + '''.json''' ) )[0]
A_ : Optional[Any] = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : int ):
"""simple docstring"""
A_ : int = HfArgumentParser(_lowerCamelCase )
A_ : Tuple = {
'''foo''': 12,
'''bar''': 3.14,
'''baz''': '''42''',
'''flag''': True,
}
with tempfile.TemporaryDirectory() as tmp_dir:
A_ : int = os.path.join(_lowerCamelCase , '''temp_yaml''' )
os.mkdir(_lowerCamelCase )
with open(temp_local_path + '''.yaml''' , '''w+''' ) as f:
yaml.dump(_lowerCamelCase , _lowerCamelCase )
A_ : Optional[Any] = parser.parse_yaml_file(Path(temp_local_path + '''.yaml''' ) )[0]
A_ : int = BasicExample(**_lowerCamelCase )
self.assertEqual(_lowerCamelCase , _lowerCamelCase )
def _a ( self : Union[str, Any] ):
"""simple docstring"""
A_ : Dict = HfArgumentParser(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
| 4 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging
__SCREAMING_SNAKE_CASE : int = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Tuple = {
'Helsinki-NLP/opus-mt-en-de': 'https://huggingface.co/Helsinki-NLP/opus-mt-en-de/resolve/main/config.json',
# See all Marian models at https://huggingface.co/models?filter=marian
}
class __A (snake_case__):
'''simple docstring'''
__lowercase: Tuple = """marian"""
__lowercase: Dict = ["""past_key_values"""]
__lowercase: Optional[Any] = {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}
def __init__( self : Optional[Any] , UpperCAmelCase_ : Dict=58_101 , UpperCAmelCase_ : Union[str, Any]=None , UpperCAmelCase_ : Tuple=1_024 , UpperCAmelCase_ : Any=12 , UpperCAmelCase_ : List[Any]=4_096 , UpperCAmelCase_ : int=16 , UpperCAmelCase_ : Tuple=12 , UpperCAmelCase_ : Optional[int]=4_096 , UpperCAmelCase_ : Any=16 , UpperCAmelCase_ : Tuple=0.0 , UpperCAmelCase_ : Union[str, Any]=0.0 , UpperCAmelCase_ : str=True , UpperCAmelCase_ : int=True , UpperCAmelCase_ : int="gelu" , UpperCAmelCase_ : Any=1_024 , UpperCAmelCase_ : Union[str, Any]=0.1 , UpperCAmelCase_ : Union[str, Any]=0.0 , UpperCAmelCase_ : str=0.0 , UpperCAmelCase_ : Any=0.02 , UpperCAmelCase_ : Union[str, Any]=58_100 , UpperCAmelCase_ : str=False , UpperCAmelCase_ : Tuple=58_100 , UpperCAmelCase_ : int=0 , UpperCAmelCase_ : Dict=0 , UpperCAmelCase_ : List[str]=True , **UpperCAmelCase_ : Optional[Any] , ) ->str:
"""simple docstring"""
snake_case_ = vocab_size
snake_case_ = decoder_vocab_size or vocab_size
snake_case_ = max_position_embeddings
snake_case_ = d_model
snake_case_ = encoder_ffn_dim
snake_case_ = encoder_layers
snake_case_ = encoder_attention_heads
snake_case_ = decoder_ffn_dim
snake_case_ = decoder_layers
snake_case_ = decoder_attention_heads
snake_case_ = dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = activation_function
snake_case_ = init_std
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = use_cache
snake_case_ = encoder_layers
snake_case_ = scale_embedding # scale factor will be sqrt(d_model) if True
snake_case_ = share_encoder_decoder_embeddings
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 , )
class __A (snake_case__):
'''simple docstring'''
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.inputs
def lowerCAmelCase ( self : Union[str, Any] ) ->List[str]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
snake_case_ = {0: """batch"""}
snake_case_ = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
snake_case_ = {0: """batch""", 1: """decoder_sequence"""}
snake_case_ = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(_lowerCAmelCase , direction="""inputs""" )
elif self.task == "causal-lm":
# TODO: figure this case out.
snake_case_ = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
] )
if self.use_past:
snake_case_ , snake_case_ = self.num_layers
for i in range(_lowerCAmelCase ):
snake_case_ = {0: """batch""", 2: """past_sequence + sequence"""}
snake_case_ = {0: """batch""", 2: """past_sequence + sequence"""}
else:
snake_case_ = OrderedDict(
[
("""input_ids""", {0: """batch""", 1: """encoder_sequence"""}),
("""attention_mask""", {0: """batch""", 1: """encoder_sequence"""}),
("""decoder_input_ids""", {0: """batch""", 1: """decoder_sequence"""}),
("""decoder_attention_mask""", {0: """batch""", 1: """decoder_sequence"""}),
] )
return common_inputs
@property
# Copied from transformers.models.bart.configuration_bart.BartOnnxConfig.outputs
def lowerCAmelCase ( self : int ) ->List[Any]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super().outputs
else:
snake_case_ = super(_lowerCAmelCase , self ).outputs
if self.use_past:
snake_case_ , snake_case_ = self.num_layers
for i in range(_lowerCAmelCase ):
snake_case_ = {0: """batch""", 2: """past_sequence + sequence"""}
snake_case_ = {0: """batch""", 2: """past_sequence + sequence"""}
return common_outputs
def lowerCAmelCase ( self : int , UpperCAmelCase_ : PreTrainedTokenizer , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[TensorType] = None , ) ->int:
"""simple docstring"""
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# Generate decoder inputs
snake_case_ = seq_length if not self.use_past else 1
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
snake_case_ = {F"""decoder_{name}""": tensor for name, tensor in decoder_inputs.items()}
snake_case_ = dict(**_lowerCAmelCase , **_lowerCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
snake_case_ , snake_case_ = common_inputs["""input_ids"""].shape
snake_case_ = common_inputs["""decoder_input_ids"""].shape[1]
snake_case_ , snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = decoder_seq_length + 3
snake_case_ = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
snake_case_ = torch.cat(
[common_inputs["""decoder_attention_mask"""], torch.ones(_lowerCAmelCase , _lowerCAmelCase )] , dim=1 )
snake_case_ = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
snake_case_ , snake_case_ = self.num_layers
snake_case_ = min(_lowerCAmelCase , _lowerCAmelCase )
snake_case_ = max(_lowerCAmelCase , _lowerCAmelCase ) - min_num_layers
snake_case_ = """encoder""" if num_encoder_layers > num_decoder_layers else """decoder"""
for _ in range(_lowerCAmelCase ):
common_inputs["past_key_values"].append(
(
torch.zeros(_lowerCAmelCase ),
torch.zeros(_lowerCAmelCase ),
torch.zeros(_lowerCAmelCase ),
torch.zeros(_lowerCAmelCase ),
) )
# TODO: test this.
snake_case_ = encoder_shape if remaining_side_name == """encoder""" else decoder_shape
for _ in range(_lowerCAmelCase , _lowerCAmelCase ):
common_inputs["past_key_values"].append((torch.zeros(_lowerCAmelCase ), torch.zeros(_lowerCAmelCase )) )
return common_inputs
def lowerCAmelCase ( self : int , UpperCAmelCase_ : PreTrainedTokenizer , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[TensorType] = None , ) ->Dict:
"""simple docstring"""
snake_case_ = self._generate_dummy_inputs_for_encoder_and_decoder(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
if self.use_past:
if not is_torch_available():
raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed.""" )
else:
import torch
snake_case_ , snake_case_ = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
snake_case_ = seqlen + 2
snake_case_ , snake_case_ = self.num_layers
snake_case_ , snake_case_ = self.num_attention_heads
snake_case_ = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
snake_case_ = common_inputs["""attention_mask"""].dtype
snake_case_ = torch.cat(
[common_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase , _lowerCAmelCase , dtype=_lowerCAmelCase )] , dim=1 )
snake_case_ = [
(torch.zeros(_lowerCAmelCase ), torch.zeros(_lowerCAmelCase )) for _ in range(_lowerCAmelCase )
]
return common_inputs
def lowerCAmelCase ( self : Dict , UpperCAmelCase_ : PreTrainedTokenizer , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[TensorType] = None , ) ->Tuple:
"""simple docstring"""
snake_case_ = compute_effective_axis_dimension(
_lowerCAmelCase , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
snake_case_ = tokenizer.num_special_tokens_to_add(_lowerCAmelCase )
snake_case_ = compute_effective_axis_dimension(
_lowerCAmelCase , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=_lowerCAmelCase )
# Generate dummy inputs according to compute batch and sequence
snake_case_ = [""" """.join([tokenizer.unk_token] ) * seq_length] * batch_size
snake_case_ = dict(tokenizer(_lowerCAmelCase , return_tensors=_lowerCAmelCase ) )
return common_inputs
def lowerCAmelCase ( self : Any , UpperCAmelCase_ : PreTrainedTokenizer , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional[TensorType] = None , ) ->Optional[Any]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
_lowerCAmelCase , batch_size=_lowerCAmelCase , seq_length=_lowerCAmelCase , is_pair=_lowerCAmelCase , framework=_lowerCAmelCase )
else:
snake_case_ = self._generate_dummy_inputs_for_causal_lm(
_lowerCAmelCase , batch_size=_lowerCAmelCase , seq_length=_lowerCAmelCase , is_pair=_lowerCAmelCase , framework=_lowerCAmelCase )
return common_inputs
def lowerCAmelCase ( self : Tuple , UpperCAmelCase_ : Any , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : str ) ->Optional[int]:
"""simple docstring"""
if self.task in ["default", "seq2seq-lm"]:
snake_case_ = super()._flatten_past_key_values_(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
else:
snake_case_ = super(_lowerCAmelCase , self )._flatten_past_key_values_(
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
@property
def lowerCAmelCase ( self : Dict ) ->List[str]:
"""simple docstring"""
return 1E-4
| 347 |
'''simple docstring'''
from typing import List
import numpy as np
def _A ( _lowerCAmelCase ):
"""simple docstring"""
__lowercase ={key: len(_lowerCAmelCase ) for key, value in gen_kwargs.items() if isinstance(_lowerCAmelCase , _lowerCAmelCase )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
'Sharding is ambiguous for this dataset: '
+ 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
+ '\n'.join(f"""\t- key {key} has length {length}""" for key, length in lists_lengths.items() )
+ '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
+ 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
) )
__lowercase =max(lists_lengths.values() , default=0 )
return max(1 , _lowerCAmelCase )
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =[]
for group_idx in range(_lowerCAmelCase ):
__lowercase =num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
__lowercase =shards_indices_per_group[-1].stop if shards_indices_per_group else 0
__lowercase =range(_lowerCAmelCase , start + num_shards_to_add )
shards_indices_per_group.append(_lowerCAmelCase )
return shards_indices_per_group
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase =_number_of_shards_in_gen_kwargs(_lowerCAmelCase )
if num_shards == 1:
return [dict(_lowerCAmelCase )]
else:
__lowercase =_distribute_shards(num_shards=_lowerCAmelCase , max_num_jobs=_lowerCAmelCase )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(_lowerCAmelCase , _lowerCAmelCase )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(_lowerCAmelCase ) )
]
def _A ( _lowerCAmelCase ):
"""simple docstring"""
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , _lowerCAmelCase )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def _A ( _lowerCAmelCase , _lowerCAmelCase ):
"""simple docstring"""
__lowercase ={len(_lowerCAmelCase ) for value in gen_kwargs.values() if isinstance(_lowerCAmelCase , _lowerCAmelCase )}
__lowercase ={}
for size in list_sizes:
__lowercase =list(range(_lowerCAmelCase ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
__lowercase =dict(_lowerCAmelCase )
for key, value in shuffled_kwargs.items():
if isinstance(_lowerCAmelCase , _lowerCAmelCase ):
__lowercase =[value[i] for i in indices_per_size[len(_lowerCAmelCase )]]
return shuffled_kwargs
| 166 | 0 |
"""simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :str ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(lowerCAmelCase__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 32 | """simple docstring"""
def UpperCAmelCase__ ( lowerCAmelCase__ :list[list] ) -> list[list]:
'''simple docstring'''
lowercase = current_set.copy()
for row_index, row in enumerate(lowerCAmelCase__ ):
lowercase = row[0]
for column_index, column in enumerate(lowerCAmelCase__ ):
if magnitude == 0:
lowercase = column
continue
lowercase = column / magnitude
# Subtract to cancel term
lowercase = current_set[0]
lowercase = [first_row]
lowercase = current_set[1::]
for row in current_set:
lowercase = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(lowerCAmelCase__ )
continue
for column_index in range(len(lowerCAmelCase__ ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(lowerCAmelCase__ )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
lowercase = final_set[0]
lowercase = []
lowercase = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
lowercase = simplify(lowerCAmelCase__ )
for i in range(len(lowerCAmelCase__ ) ):
resultant[i].insert(0 , current_first_column[i] )
resultant.insert(0 , lowerCAmelCase__ )
lowercase = resultant
return final_set
def UpperCAmelCase__ ( lowerCAmelCase__ :list[list] ) -> list:
'''simple docstring'''
if len(lowerCAmelCase__ ) == 0:
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
lowercase = len(lowerCAmelCase__ ) + 1
if any(len(lowerCAmelCase__ ) != _length for item in equations ):
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
for row in equations:
if any(not isinstance(lowerCAmelCase__ , (int, float) ) for column in row ):
raise ValueError("""solve_simultaneous() requires lists of integers""" )
if len(lowerCAmelCase__ ) == 1:
return [equations[0][-1] / equations[0][0]]
lowercase = equations.copy()
if any(0 in row for row in data_set ):
lowercase = data_set.copy()
lowercase = []
for row_index, row in enumerate(lowerCAmelCase__ ):
if 0 not in row:
lowercase = data_set.pop(lowerCAmelCase__ )
break
if not full_row:
raise ValueError("""solve_simultaneous() requires at least 1 full equation""" )
data_set.insert(0 , lowerCAmelCase__ )
lowercase = data_set.copy()
lowercase = simplify(lowerCAmelCase__ )
lowercase = simplified[::-1]
lowercase = []
for row in simplified:
lowercase = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
lowercase = row.copy()[: len(lowerCAmelCase__ ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(lowerCAmelCase__ ) == 0:
solutions.append(0 )
continue
lowercase = temp_row[1::]
lowercase = temp_row[::-1]
for column_index, column in enumerate(lowerCAmelCase__ ):
current_solution -= column * solutions[column_index]
solutions.append(lowerCAmelCase__ )
lowercase = []
for item in solutions:
final.append(float(round(lowerCAmelCase__ , 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
__lowerCAmelCase : List[str] =[
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 32 | 1 |
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def _a ( SCREAMING_SNAKE_CASE : Union[str, Any] , SCREAMING_SNAKE_CASE : Union[str, Any] = True , SCREAMING_SNAKE_CASE : Any = math.inf , SCREAMING_SNAKE_CASE : str = -math.inf , SCREAMING_SNAKE_CASE : str = math.inf , SCREAMING_SNAKE_CASE : Any = -math.inf , SCREAMING_SNAKE_CASE : str = False , SCREAMING_SNAKE_CASE : Optional[int] = 100 , SCREAMING_SNAKE_CASE : List[str] = 0.01 , SCREAMING_SNAKE_CASE : Union[str, Any] = 1 , ):
"""simple docstring"""
UpperCamelCase__ : Optional[Any] = False
UpperCamelCase__ : str = search_prob
UpperCamelCase__ : Any = start_temperate
UpperCamelCase__ : Dict = []
UpperCamelCase__ : str = 0
UpperCamelCase__ : Optional[Any] = None
while not search_end:
UpperCamelCase__ : Dict = current_state.score()
if best_state is None or current_score > best_state.score():
UpperCamelCase__ : Dict = current_state
scores.append(SCREAMING_SNAKE_CASE )
iterations += 1
UpperCamelCase__ : List[str] = None
UpperCamelCase__ : int = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
UpperCamelCase__ : Any = random.randint(0 , len(SCREAMING_SNAKE_CASE ) - 1 ) # picking a random neighbor
UpperCamelCase__ : Optional[int] = neighbors.pop(SCREAMING_SNAKE_CASE )
UpperCamelCase__ : Dict = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
UpperCamelCase__ : int = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
UpperCamelCase__ : Tuple = picked_neighbor
else:
UpperCamelCase__ : Any = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
UpperCamelCase__ : str = picked_neighbor
UpperCamelCase__ : Optional[int] = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
UpperCamelCase__ : List[str] = True
else:
UpperCamelCase__ : int = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(SCREAMING_SNAKE_CASE ) , SCREAMING_SNAKE_CASE )
plt.xlabel('''Iterations''' )
plt.ylabel('''Function values''' )
plt.show()
return best_state
if __name__ == "__main__":
def _a ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
__UpperCamelCase : str = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
__UpperCamelCase : int = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
__UpperCamelCase : Optional[int] = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
__UpperCamelCase : int = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
"The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 "
f"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def _a ( SCREAMING_SNAKE_CASE : Optional[int] , SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
return (3 * x**2) - (6 * y)
__UpperCamelCase : Dict = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
__UpperCamelCase : Optional[Any] = simulated_annealing(prob, find_max=False, visualization=True)
print(
"The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
__UpperCamelCase : Optional[Any] = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
__UpperCamelCase : Union[str, Any] = simulated_annealing(prob, find_max=True, visualization=True)
print(
"The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: "
f"{local_min.score()}"
)
| 146 | '''simple docstring'''
def __UpperCamelCase ( UpperCAmelCase = 1 , UpperCAmelCase = 1000 ):
lowercase__ : Dict = 1
lowercase__ : Dict = 0
for divide_by_number in range(UpperCAmelCase , digit + 1 ):
lowercase__ : list[int] = []
lowercase__ : Union[str, Any] = numerator
for _ in range(1 , digit + 1 ):
if now_divide in has_been_divided:
if longest_list_length < len(UpperCAmelCase ):
lowercase__ : Dict = len(UpperCAmelCase )
lowercase__ : Optional[Any] = divide_by_number
else:
has_been_divided.append(UpperCAmelCase )
lowercase__ : int = now_divide * 10 % divide_by_number
return the_digit
# Tests
if __name__ == "__main__":
import doctest
doctest.testmod()
| 198 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
UpperCAmelCase_ = abs(__UpperCamelCase )
UpperCAmelCase_ = 0
while n > 0:
res += n % 10
n //= 10
return res
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
UpperCAmelCase_ = abs(__UpperCamelCase )
return n if n < 10 else n % 10 + sum_of_digits(n // 10 )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : int ) -> int:
return sum(int(__UpperCamelCase ) for c in str(abs(__UpperCamelCase ) ) )
def SCREAMING_SNAKE_CASE ( ) -> None:
from collections.abc import Callable
from timeit import timeit
def benchmark_a_function(__UpperCamelCase : Callable , __UpperCamelCase : int ) -> None:
UpperCAmelCase_ = f'{func.__name__}({value})'
UpperCAmelCase_ = timeit(f'__main__.{call}' , setup='''import __main__''' )
print(f'{call:56} = {func(__UpperCamelCase )} -- {timing:.4f} seconds' )
for value in (26_2144, 1125_8999_0684_2624, 126_7650_6002_2822_9401_4967_0320_5376):
for func in (sum_of_digits, sum_of_digits_recursion, sum_of_digits_compact):
benchmark_a_function(__UpperCamelCase , __UpperCamelCase )
print()
if __name__ == "__main__":
import doctest
doctest.testmod()
benchmark()
| 365 |
import baseaa
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : str ) -> bytes:
return baseaa.baaencode(string.encode('''utf-8''' ) )
def SCREAMING_SNAKE_CASE ( __UpperCamelCase : bytes ) -> str:
return baseaa.baadecode(__UpperCamelCase ).decode('''utf-8''' )
if __name__ == "__main__":
_lowerCamelCase = 'Hello World!'
_lowerCamelCase = baseaa_encode(test)
print(encoded)
_lowerCamelCase = baseaa_decode(encoded)
print(decoded)
| 177 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
A__ : Optional[int] = logging.get_logger(__name__)
A__ : Tuple = {
'openai/imagegpt-small': '',
'openai/imagegpt-medium': '',
'openai/imagegpt-large': '',
}
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :List[str] = "imagegpt"
_UpperCAmelCase :Any = ["past_key_values"]
_UpperCAmelCase :Dict = {
"hidden_size": "n_embd",
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : int , snake_case__ : Any=512 + 1 , snake_case__ : Optional[Any]=32 * 32 , snake_case__ : Union[str, Any]=512 , snake_case__ : Optional[int]=24 , snake_case__ : Optional[int]=8 , snake_case__ : Tuple=None , snake_case__ : str="quick_gelu" , snake_case__ : List[str]=0.1 , snake_case__ : int=0.1 , snake_case__ : str=0.1 , snake_case__ : Any=1E-5 , snake_case__ : Optional[Any]=0.02 , snake_case__ : List[str]=True , snake_case__ : List[str]=True , snake_case__ : List[Any]=False , snake_case__ : List[str]=False , snake_case__ : Optional[int]=False , **snake_case__ : List[str] , ):
lowerCamelCase_ : List[Any] =vocab_size
lowerCamelCase_ : Optional[Any] =n_positions
lowerCamelCase_ : Dict =n_embd
lowerCamelCase_ : Optional[int] =n_layer
lowerCamelCase_ : Optional[Any] =n_head
lowerCamelCase_ : List[Any] =n_inner
lowerCamelCase_ : Optional[Any] =activation_function
lowerCamelCase_ : Optional[int] =resid_pdrop
lowerCamelCase_ : List[Any] =embd_pdrop
lowerCamelCase_ : Optional[Any] =attn_pdrop
lowerCamelCase_ : Optional[int] =layer_norm_epsilon
lowerCamelCase_ : Optional[Any] =initializer_range
lowerCamelCase_ : List[str] =scale_attn_weights
lowerCamelCase_ : str =use_cache
lowerCamelCase_ : List[Any] =scale_attn_by_inverse_layer_idx
lowerCamelCase_ : List[Any] =reorder_and_upcast_attn
lowerCamelCase_ : Optional[Any] =tie_word_embeddings
super().__init__(tie_word_embeddings=snake_case__ , **snake_case__ )
class lowercase__ ( snake_case__ ):
@property
def UpperCAmelCase__ ( self : Optional[Any] ):
return OrderedDict(
[
("input_ids", {0: "batch", 1: "sequence"}),
] )
def UpperCAmelCase__ ( self : Optional[Any] , snake_case__ : "FeatureExtractionMixin" , snake_case__ : int = 1 , snake_case__ : int = -1 , snake_case__ : bool = False , snake_case__ : Optional["TensorType"] = None , snake_case__ : int = 3 , snake_case__ : int = 32 , snake_case__ : int = 32 , ):
lowerCamelCase_ : Any =self._generate_dummy_images(snake_case__ , snake_case__ , snake_case__ , snake_case__ )
lowerCamelCase_ : List[str] =dict(preprocessor(images=snake_case__ , return_tensors=snake_case__ ) )
return inputs
| 144 |
"""simple docstring"""
import os
def _snake_case ( ) -> Dict:
with open(os.path.dirname(lowerCamelCase__ ) + "/p022_names.txt" ) as file:
lowerCamelCase_ : str =str(file.readlines()[0] )
lowerCamelCase_ : Union[str, Any] =names.replace("\"" , "" ).split("," )
names.sort()
lowerCamelCase_ : str =0
lowerCamelCase_ : Optional[int] =0
for i, name in enumerate(lowerCamelCase__ ):
for letter in name:
name_score += ord(lowerCamelCase__ ) - 64
total_score += (i + 1) * name_score
lowerCamelCase_ : List[Any] =0
return total_score
if __name__ == "__main__":
print(solution())
| 144 | 1 |
'''simple docstring'''
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class UpperCamelCase__( unittest.TestCase ):
@parameterized.expand([(None,), ('''foo.json''',)] )
def a__( self : Union[str, Any] , lowerCAmelCase : Tuple )-> List[str]:
"""simple docstring"""
UpperCAmelCase = GenerationConfig(
do_sample=lowerCAmelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCAmelCase , config_name=lowerCAmelCase )
UpperCAmelCase = GenerationConfig.from_pretrained(lowerCAmelCase , config_name=lowerCAmelCase )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , lowerCAmelCase )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , lowerCAmelCase )
def a__( self : List[str] )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase = AutoConfig.from_pretrained('''gpt2''' )
UpperCAmelCase = GenerationConfig.from_model_config(lowerCAmelCase )
UpperCAmelCase = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowerCAmelCase , lowerCAmelCase )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def a__( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase = GenerationConfig()
UpperCAmelCase = {
'''max_new_tokens''': 1024,
'''foo''': '''bar''',
}
UpperCAmelCase = copy.deepcopy(lowerCAmelCase )
UpperCAmelCase = generation_config.update(**lowerCAmelCase )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowerCAmelCase , lowerCAmelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowerCAmelCase , {'''foo''': '''bar'''} )
def a__( self : int )-> Any:
"""simple docstring"""
UpperCAmelCase = GenerationConfig()
UpperCAmelCase = '''bar'''
with tempfile.TemporaryDirectory('''test-generation-config''' ) as tmp_dir:
generation_config.save_pretrained(lowerCAmelCase )
UpperCAmelCase = GenerationConfig.from_pretrained(lowerCAmelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , '''bar''' )
UpperCAmelCase = GenerationConfig.from_model_config(lowerCAmelCase )
assert not hasattr(lowerCAmelCase , '''foo''' ) # no new kwargs should be initialized if from config
def a__( self : Union[str, Any] )-> List[Any]:
"""simple docstring"""
UpperCAmelCase = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , lowerCAmelCase )
self.assertEqual(default_config.num_beams , 1 )
UpperCAmelCase = GenerationConfig(
do_sample=lowerCAmelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , lowerCAmelCase )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCAmelCase )
UpperCAmelCase = GenerationConfig.from_pretrained(lowerCAmelCase , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , lowerCAmelCase )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class UpperCamelCase__( unittest.TestCase ):
@classmethod
def a__( cls : List[str] )-> List[Any]:
"""simple docstring"""
UpperCAmelCase = TOKEN
HfFolder.save_token(lowerCAmelCase )
@classmethod
def a__( cls : int )-> int:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id='''test-generation-config''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-generation-config-org''' )
except HTTPError:
pass
def a__( self : Tuple )-> int:
"""simple docstring"""
UpperCAmelCase = GenerationConfig(
do_sample=lowerCAmelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''test-generation-config''' , use_auth_token=self._token )
UpperCAmelCase = GenerationConfig.from_pretrained(F"""{USER}/test-generation-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCAmelCase , getattr(lowerCAmelCase , lowerCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-generation-config''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCAmelCase , repo_id='''test-generation-config''' , push_to_hub=lowerCAmelCase , use_auth_token=self._token )
UpperCAmelCase = GenerationConfig.from_pretrained(F"""{USER}/test-generation-config""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCAmelCase , getattr(lowerCAmelCase , lowerCAmelCase ) )
def a__( self : Tuple )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = GenerationConfig(
do_sample=lowerCAmelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''valid_org/test-generation-config-org''' , use_auth_token=self._token )
UpperCAmelCase = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCAmelCase , getattr(lowerCAmelCase , lowerCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-generation-config-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCAmelCase , repo_id='''valid_org/test-generation-config-org''' , push_to_hub=lowerCAmelCase , use_auth_token=self._token )
UpperCAmelCase = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCAmelCase , getattr(lowerCAmelCase , lowerCAmelCase ) )
| 91 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class UpperCamelCase__( nn.Module ):
__magic_name__ : int
__magic_name__ : int
__magic_name__ : float = 0.0
__magic_name__ : int = 1
__magic_name__ : int = 1
__magic_name__ : bool = True
__magic_name__ : bool = False
__magic_name__ : bool = False
__magic_name__ : bool = False
__magic_name__ : jnp.dtype = jnp.floataa
def a__( self : str )-> Dict:
"""simple docstring"""
UpperCAmelCase = []
UpperCAmelCase = []
for i in range(self.num_layers ):
UpperCAmelCase = self.in_channels if i == 0 else self.out_channels
UpperCAmelCase = FlaxResnetBlockaD(
in_channels=lowerCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowerCAmelCase )
UpperCAmelCase = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(lowerCAmelCase )
UpperCAmelCase = resnets
UpperCAmelCase = attentions
if self.add_downsample:
UpperCAmelCase = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : List[Any] , lowerCAmelCase : Any , lowerCAmelCase : int , lowerCAmelCase : Dict , lowerCAmelCase : List[str]=True )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase = ()
for resnet, attn in zip(self.resnets , self.attentions ):
UpperCAmelCase = resnet(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
UpperCAmelCase = attn(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
UpperCAmelCase = self.downsamplers_a(lowerCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class UpperCamelCase__( nn.Module ):
__magic_name__ : int
__magic_name__ : int
__magic_name__ : float = 0.0
__magic_name__ : int = 1
__magic_name__ : bool = True
__magic_name__ : jnp.dtype = jnp.floataa
def a__( self : List[str] )-> Any:
"""simple docstring"""
UpperCAmelCase = []
for i in range(self.num_layers ):
UpperCAmelCase = self.in_channels if i == 0 else self.out_channels
UpperCAmelCase = FlaxResnetBlockaD(
in_channels=lowerCAmelCase , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowerCAmelCase )
UpperCAmelCase = resnets
if self.add_downsample:
UpperCAmelCase = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : List[str] , lowerCAmelCase : Tuple , lowerCAmelCase : Dict , lowerCAmelCase : List[str]=True )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase = ()
for resnet in self.resnets:
UpperCAmelCase = resnet(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
output_states += (hidden_states,)
if self.add_downsample:
UpperCAmelCase = self.downsamplers_a(lowerCAmelCase )
output_states += (hidden_states,)
return hidden_states, output_states
class UpperCamelCase__( nn.Module ):
__magic_name__ : int
__magic_name__ : int
__magic_name__ : int
__magic_name__ : float = 0.0
__magic_name__ : int = 1
__magic_name__ : int = 1
__magic_name__ : bool = True
__magic_name__ : bool = False
__magic_name__ : bool = False
__magic_name__ : bool = False
__magic_name__ : jnp.dtype = jnp.floataa
def a__( self : List[str] )-> Tuple:
"""simple docstring"""
UpperCAmelCase = []
UpperCAmelCase = []
for i in range(self.num_layers ):
UpperCAmelCase = self.in_channels if (i == self.num_layers - 1) else self.out_channels
UpperCAmelCase = self.prev_output_channel if i == 0 else self.out_channels
UpperCAmelCase = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowerCAmelCase )
UpperCAmelCase = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(lowerCAmelCase )
UpperCAmelCase = resnets
UpperCAmelCase = attentions
if self.add_upsample:
UpperCAmelCase = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Union[str, Any] , lowerCAmelCase : List[str] , lowerCAmelCase : Optional[int] , lowerCAmelCase : List[str] , lowerCAmelCase : Any , lowerCAmelCase : Union[str, Any]=True )-> Optional[int]:
"""simple docstring"""
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
UpperCAmelCase = res_hidden_states_tuple[-1]
UpperCAmelCase = res_hidden_states_tuple[:-1]
UpperCAmelCase = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
UpperCAmelCase = resnet(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
UpperCAmelCase = attn(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
if self.add_upsample:
UpperCAmelCase = self.upsamplers_a(lowerCAmelCase )
return hidden_states
class UpperCamelCase__( nn.Module ):
__magic_name__ : int
__magic_name__ : int
__magic_name__ : int
__magic_name__ : float = 0.0
__magic_name__ : int = 1
__magic_name__ : bool = True
__magic_name__ : jnp.dtype = jnp.floataa
def a__( self : Optional[int] )-> str:
"""simple docstring"""
UpperCAmelCase = []
for i in range(self.num_layers ):
UpperCAmelCase = self.in_channels if (i == self.num_layers - 1) else self.out_channels
UpperCAmelCase = self.prev_output_channel if i == 0 else self.out_channels
UpperCAmelCase = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowerCAmelCase )
UpperCAmelCase = resnets
if self.add_upsample:
UpperCAmelCase = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Union[str, Any] , lowerCAmelCase : Dict , lowerCAmelCase : Union[str, Any] , lowerCAmelCase : Tuple , lowerCAmelCase : Dict=True )-> Tuple:
"""simple docstring"""
for resnet in self.resnets:
# pop res hidden states
UpperCAmelCase = res_hidden_states_tuple[-1]
UpperCAmelCase = res_hidden_states_tuple[:-1]
UpperCAmelCase = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
UpperCAmelCase = resnet(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
if self.add_upsample:
UpperCAmelCase = self.upsamplers_a(lowerCAmelCase )
return hidden_states
class UpperCamelCase__( nn.Module ):
__magic_name__ : int
__magic_name__ : float = 0.0
__magic_name__ : int = 1
__magic_name__ : int = 1
__magic_name__ : bool = False
__magic_name__ : bool = False
__magic_name__ : jnp.dtype = jnp.floataa
def a__( self : int )-> Optional[int]:
"""simple docstring"""
UpperCAmelCase = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
UpperCAmelCase = []
for _ in range(self.num_layers ):
UpperCAmelCase = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(lowerCAmelCase )
UpperCAmelCase = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(lowerCAmelCase )
UpperCAmelCase = resnets
UpperCAmelCase = attentions
def __call__( self : Dict , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Tuple , lowerCAmelCase : Dict , lowerCAmelCase : Any=True )-> Union[str, Any]:
"""simple docstring"""
UpperCAmelCase = self.resnets[0](lowerCAmelCase , lowerCAmelCase )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
UpperCAmelCase = attn(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
UpperCAmelCase = resnet(lowerCAmelCase , lowerCAmelCase , deterministic=lowerCAmelCase )
return hidden_states
| 91 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_lxmert import LxmertTokenizer
a_ : Optional[Any] = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
a_ : Optional[int] = {
"""vocab_file""": {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""unc-nlp/lxmert-base-uncased""": (
"""https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/tokenizer.json"""
),
},
}
a_ : Dict = {
"""unc-nlp/lxmert-base-uncased""": 512,
}
a_ : Optional[int] = {
"""unc-nlp/lxmert-base-uncased""": {"""do_lower_case""": True},
}
class snake_case ( lowercase ):
"""simple docstring"""
_lowerCamelCase = VOCAB_FILES_NAMES
_lowerCamelCase = PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase = PRETRAINED_INIT_CONFIGURATION
_lowerCamelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCamelCase = LxmertTokenizer
def __init__( self , UpperCamelCase=None , UpperCamelCase=None , UpperCamelCase=True , UpperCamelCase="[UNK]" , UpperCamelCase="[SEP]" , UpperCamelCase="[PAD]" , UpperCamelCase="[CLS]" , UpperCamelCase="[MASK]" , UpperCamelCase=True , UpperCamelCase=None , **UpperCamelCase , ):
"""simple docstring"""
super().__init__(
UpperCamelCase , tokenizer_file=UpperCamelCase , do_lower_case=UpperCamelCase , unk_token=UpperCamelCase , sep_token=UpperCamelCase , pad_token=UpperCamelCase , cls_token=UpperCamelCase , mask_token=UpperCamelCase , tokenize_chinese_chars=UpperCamelCase , strip_accents=UpperCamelCase , **UpperCamelCase , )
lowerCamelCase_ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , UpperCamelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , UpperCamelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , UpperCamelCase ) != tokenize_chinese_chars
):
lowerCamelCase_ = getattr(UpperCamelCase , normalizer_state.pop("type" ) )
lowerCamelCase_ = do_lower_case
lowerCamelCase_ = strip_accents
lowerCamelCase_ = tokenize_chinese_chars
lowerCamelCase_ = normalizer_class(**UpperCamelCase )
lowerCamelCase_ = do_lower_case
def snake_case ( self , UpperCamelCase , UpperCamelCase=None ):
"""simple docstring"""
lowerCamelCase_ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def snake_case ( self , UpperCamelCase , UpperCamelCase = None ):
"""simple docstring"""
lowerCamelCase_ = [self.sep_token_id]
lowerCamelCase_ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def snake_case ( self , UpperCamelCase , UpperCamelCase = None ):
"""simple docstring"""
lowerCamelCase_ = self._tokenizer.model.save(UpperCamelCase , name=UpperCamelCase )
return tuple(UpperCamelCase )
| 55 | """simple docstring"""
import inspect
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_config_docstrings.py
__UpperCamelCase = '''src/transformers'''
# This is to make sure the transformers module imported is the one in the repo.
__UpperCamelCase = direct_transformers_import(PATH_TO_TRANSFORMERS)
__UpperCamelCase = transformers.models.auto.configuration_auto.CONFIG_MAPPING
# Regex pattern used to find the checkpoint mentioned in the docstring of `config_class`.
# For example, `[bert-base-uncased](https://huggingface.co/bert-base-uncased)`
__UpperCamelCase = re.compile(r'''\[(.+?)\]\((https://huggingface\.co/.+?)\)''')
__UpperCamelCase = {
'''DecisionTransformerConfig''',
'''EncoderDecoderConfig''',
'''MusicgenConfig''',
'''RagConfig''',
'''SpeechEncoderDecoderConfig''',
'''TimmBackboneConfig''',
'''VisionEncoderDecoderConfig''',
'''VisionTextDualEncoderConfig''',
'''LlamaConfig''',
}
def UpperCAmelCase ( UpperCAmelCase ) -> List[Any]:
snake_case_ = None
# source code of `config_class`
snake_case_ = inspect.getsource(UpperCAmelCase )
snake_case_ = _re_checkpoint.findall(UpperCAmelCase )
# Each `checkpoint` is a tuple of a checkpoint name and a checkpoint link.
# For example, `('bert-base-uncased', 'https://huggingface.co/bert-base-uncased')`
for ckpt_name, ckpt_link in checkpoints:
# allow the link to end with `/`
if ckpt_link.endswith('/' ):
snake_case_ = ckpt_link[:-1]
# verify the checkpoint name corresponds to the checkpoint link
snake_case_ = f'https://huggingface.co/{ckpt_name}'
if ckpt_link == ckpt_link_from_name:
snake_case_ = ckpt_name
break
return checkpoint
def UpperCAmelCase ( ) -> Union[str, Any]:
snake_case_ = []
for config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in config_class.__module__:
continue
snake_case_ = get_checkpoint_from_config_class(UpperCAmelCase )
snake_case_ = config_class.__name__
if checkpoint is None and name not in CONFIG_CLASSES_TO_IGNORE_FOR_DOCSTRING_CHECKPOINT_CHECK:
configs_without_checkpoint.append(UpperCAmelCase )
if len(UpperCAmelCase ) > 0:
snake_case_ = '\n'.join(sorted(UpperCAmelCase ) )
raise ValueError(f'The following configurations don\'t contain any valid checkpoint:\n{message}' )
if __name__ == "__main__":
check_config_docstrings_have_checkpoints()
| 69 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __A :
@staticmethod
def __A ( *a__ , **a__ ):
pass
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : Optional[Any] ) -> List[str]:
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
_a : Dict = (
'https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'
)
@is_pipeline_test
@require_torch
@require_vision
class __A ( unittest.TestCase ):
_UpperCamelCase : str = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def __A ( self , a__ , a__ , a__ ):
_lowerCAmelCase : Optional[int] = pipeline(
"""document-question-answering""" , model=a__ , tokenizer=a__ , image_processor=a__ )
_lowerCAmelCase : Tuple = INVOICE_URL
_lowerCAmelCase : List[str] = list(zip(*apply_tesseract(load_image(a__ ) , a__ , """""" ) ) )
_lowerCAmelCase : Union[str, Any] = """What is the placebo?"""
_lowerCAmelCase : str = [
{
"""image""": load_image(a__ ),
"""question""": question,
},
{
"""image""": image,
"""question""": question,
},
{
"""image""": image,
"""question""": question,
"""word_boxes""": word_boxes,
},
]
return dqa_pipeline, examples
def __A ( self , a__ , a__ ):
_lowerCAmelCase : List[str] = dqa_pipeline(a__ , top_k=2 )
self.assertEqual(
a__ , [
[
{"""score""": ANY(a__ ), """answer""": ANY(a__ ), """start""": ANY(a__ ), """end""": ANY(a__ )},
{"""score""": ANY(a__ ), """answer""": ANY(a__ ), """start""": ANY(a__ ), """end""": ANY(a__ )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def __A ( self ):
_lowerCAmelCase : Tuple = pipeline("""document-question-answering""" , model="""hf-internal-testing/tiny-random-layoutlmv2""" )
_lowerCAmelCase : List[Any] = INVOICE_URL
_lowerCAmelCase : Optional[Any] = """How many cats are there?"""
_lowerCAmelCase : str = [
{"""score""": 0.0_0_0_1, """answer""": """oy 2312/2019""", """start""": 38, """end""": 39},
{"""score""": 0.0_0_0_1, """answer""": """oy 2312/2019 DUE""", """start""": 38, """end""": 40},
]
_lowerCAmelCase : Dict = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(nested_simplify(a__ , decimals=4 ) , a__ )
_lowerCAmelCase : List[str] = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(nested_simplify(a__ , decimals=4 ) , a__ )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
_lowerCAmelCase : Any = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
_lowerCAmelCase : int = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(a__ , [] )
# We can optionnally pass directly the words and bounding boxes
_lowerCAmelCase : Dict = """./tests/fixtures/tests_samples/COCO/000000039769.png"""
_lowerCAmelCase : Optional[int] = []
_lowerCAmelCase : List[str] = []
_lowerCAmelCase : List[Any] = dqa_pipeline(image=a__ , question=a__ , words=a__ , boxes=a__ , top_k=2 )
self.assertEqual(a__ , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __A ( self ):
_lowerCAmelCase : List[str] = pipeline(
"""document-question-answering""" , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa""" , revision="""9977165""" , )
_lowerCAmelCase : int = INVOICE_URL
_lowerCAmelCase : Optional[Any] = """What is the invoice number?"""
_lowerCAmelCase : str = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"""score""": 0.9_9_4_4, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_0_0_9, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
_lowerCAmelCase : Optional[int] = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"""score""": 0.9_9_4_4, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_0_0_9, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
_lowerCAmelCase : Tuple = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{"""score""": 0.9_9_4_4, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_0_0_9, """answer""": """us-001""", """start""": 16, """end""": 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def __A ( self ):
_lowerCAmelCase : Optional[Any] = pipeline(
"""document-question-answering""" , model="""tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa""" , revision="""9977165""" , max_seq_len=50 , )
_lowerCAmelCase : Dict = INVOICE_URL
_lowerCAmelCase : Union[str, Any] = """What is the invoice number?"""
_lowerCAmelCase : int = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"""score""": 0.9_9_7_4, """answer""": """1110212019""", """start""": 23, """end""": 23},
{"""score""": 0.9_9_4_8, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
_lowerCAmelCase : List[str] = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"""score""": 0.9_9_7_4, """answer""": """1110212019""", """start""": 23, """end""": 23},
{"""score""": 0.9_9_4_8, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
_lowerCAmelCase : Dict = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{"""score""": 0.9_9_7_4, """answer""": """1110212019""", """start""": 23, """end""": 23},
{"""score""": 0.9_9_4_8, """answer""": """us-001""", """start""": 16, """end""": 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def __A ( self ):
_lowerCAmelCase : str = AutoTokenizer.from_pretrained(
"""impira/layoutlm-document-qa""" , revision="""3dc6de3""" , add_prefix_space=a__ )
_lowerCAmelCase : Tuple = pipeline(
"""document-question-answering""" , model="""impira/layoutlm-document-qa""" , tokenizer=a__ , revision="""3dc6de3""" , )
_lowerCAmelCase : Optional[int] = INVOICE_URL
_lowerCAmelCase : List[Any] = """What is the invoice number?"""
_lowerCAmelCase : int = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"""score""": 0.4_2_5_1, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_8_1_9, """answer""": """1110212019""", """start""": 23, """end""": 23},
] , )
_lowerCAmelCase : int = dqa_pipeline({"""image""": image, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"""score""": 0.4_2_5_1, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_8_1_9, """answer""": """1110212019""", """start""": 23, """end""": 23},
] , )
_lowerCAmelCase : Optional[int] = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{"""score""": 0.4_2_5_1, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_8_1_9, """answer""": """1110212019""", """start""": 23, """end""": 23},
]
]
* 2 , )
_lowerCAmelCase : str = list(zip(*apply_tesseract(load_image(a__ ) , a__ , """""" ) ) )
# This model should also work if `image` is set to None
_lowerCAmelCase : Optional[Any] = dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"""score""": 0.4_2_5_1, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.0_8_1_9, """answer""": """1110212019""", """start""": 23, """end""": 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def __A ( self ):
_lowerCAmelCase : Optional[int] = AutoTokenizer.from_pretrained(
"""impira/layoutlm-document-qa""" , revision="""3dc6de3""" , add_prefix_space=a__ )
_lowerCAmelCase : List[Any] = pipeline(
"""document-question-answering""" , model="""impira/layoutlm-document-qa""" , tokenizer=a__ , revision="""3dc6de3""" , max_seq_len=50 , )
_lowerCAmelCase : Optional[Any] = INVOICE_URL
_lowerCAmelCase : Union[str, Any] = """What is the invoice number?"""
_lowerCAmelCase : List[str] = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"""score""": 0.9_9_9_9, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.9_9_9_8, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
_lowerCAmelCase : Union[str, Any] = dqa_pipeline(
[{"""image""": image, """question""": question}, {"""image""": image, """question""": question}] , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
[
{"""score""": 0.9_9_9_9, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.9_9_9_8, """answer""": """us-001""", """start""": 16, """end""": 16},
]
]
* 2 , )
_lowerCAmelCase : Union[str, Any] = list(zip(*apply_tesseract(load_image(a__ ) , a__ , """""" ) ) )
# This model should also work if `image` is set to None
_lowerCAmelCase : int = dqa_pipeline({"""image""": None, """word_boxes""": word_boxes, """question""": question} , top_k=2 )
self.assertEqual(
nested_simplify(a__ , decimals=4 ) , [
{"""score""": 0.9_9_9_9, """answer""": """us-001""", """start""": 16, """end""": 16},
{"""score""": 0.9_9_9_8, """answer""": """us-001""", """start""": 16, """end""": 16},
] , )
@slow
@require_torch
def __A ( self ):
_lowerCAmelCase : str = pipeline(
"""document-question-answering""" , model="""naver-clova-ix/donut-base-finetuned-docvqa""" , tokenizer=AutoTokenizer.from_pretrained("""naver-clova-ix/donut-base-finetuned-docvqa""" ) , feature_extractor="""naver-clova-ix/donut-base-finetuned-docvqa""" , )
_lowerCAmelCase : List[str] = INVOICE_URL
_lowerCAmelCase : int = """What is the invoice number?"""
_lowerCAmelCase : List[str] = dqa_pipeline(image=a__ , question=a__ , top_k=2 )
self.assertEqual(nested_simplify(a__ , decimals=4 ) , [{"""answer""": """us-001"""}] )
@require_tf
@unittest.skip("""Document question answering not implemented in TF""" )
def __A ( self ):
pass
| 350 | """simple docstring"""
from manim import *
class __A ( SCREAMING_SNAKE_CASE_ ):
def __A ( self ):
_lowerCAmelCase : Any = Rectangle(height=0.5 , width=0.5 )
_lowerCAmelCase : List[Any] = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
_lowerCAmelCase : List[str] = [mem.copy() for i in range(6 )]
_lowerCAmelCase : Any = [mem.copy() for i in range(6 )]
_lowerCAmelCase : Optional[int] = VGroup(*a__ ).arrange(a__ , buff=0 )
_lowerCAmelCase : Tuple = VGroup(*a__ ).arrange(a__ , buff=0 )
_lowerCAmelCase : Optional[Any] = VGroup(a__ , a__ ).arrange(a__ , buff=0 )
_lowerCAmelCase : Dict = Text("""CPU""" , font_size=24 )
_lowerCAmelCase : str = Group(a__ , a__ ).arrange(a__ , buff=0.5 , aligned_edge=a__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(a__ )
_lowerCAmelCase : Dict = [mem.copy() for i in range(4 )]
_lowerCAmelCase : Any = VGroup(*a__ ).arrange(a__ , buff=0 )
_lowerCAmelCase : Tuple = Text("""GPU""" , font_size=24 )
_lowerCAmelCase : Optional[int] = Group(a__ , a__ ).arrange(a__ , buff=0.5 , aligned_edge=a__ )
gpu.move_to([-1, -1, 0] )
self.add(a__ )
_lowerCAmelCase : Optional[int] = [mem.copy() for i in range(6 )]
_lowerCAmelCase : Any = VGroup(*a__ ).arrange(a__ , buff=0 )
_lowerCAmelCase : List[str] = Text("""Model""" , font_size=24 )
_lowerCAmelCase : Any = Group(a__ , a__ ).arrange(a__ , buff=0.5 , aligned_edge=a__ )
model.move_to([3, -1.0, 0] )
self.add(a__ )
_lowerCAmelCase : Tuple = []
for i, rect in enumerate(a__ ):
rect.set_stroke(a__ )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
_lowerCAmelCase : List[str] = Rectangle(height=0.4_6 / 4 , width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(a__ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.0_2 , direction=a__ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=a__ , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=a__ , buff=0.0 )
self.add(a__ )
cpu_targs.append(a__ )
_lowerCAmelCase : Any = [mem.copy() for i in range(6 )]
_lowerCAmelCase : List[str] = VGroup(*a__ ).arrange(a__ , buff=0 )
_lowerCAmelCase : int = Text("""Loaded Checkpoint""" , font_size=24 )
_lowerCAmelCase : Optional[int] = Group(a__ , a__ ).arrange(a__ , aligned_edge=a__ , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
_lowerCAmelCase : List[Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_lowerCAmelCase : List[str] = MarkupText(
F"<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model" , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(a__ , a__ )
_lowerCAmelCase : int = MarkupText(
F"<span fgcolor='{BLUE}'>●</span> Checkpoint" , font_size=18 , )
blue_text.next_to(a__ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
_lowerCAmelCase : List[Any] = MarkupText(
F"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(a__ ) , Write(a__ ) )
self.play(Write(a__ , run_time=1 ) , Create(a__ , run_time=1 ) )
_lowerCAmelCase : int = []
_lowerCAmelCase : List[Any] = []
for i, rect in enumerate(a__ ):
_lowerCAmelCase : Tuple = fill.copy().set_fill(a__ , opacity=0.7 )
target.move_to(a__ )
first_animations.append(GrowFromCenter(a__ , run_time=1 ) )
_lowerCAmelCase : Optional[Any] = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(a__ , run_time=1.5 ) )
self.play(*a__ )
self.play(*a__ )
self.wait()
| 126 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetaImageProcessor
class __lowerCAmelCase ( unittest.TestCase ):
def __init__( self :str , __magic_name__ :Any , __magic_name__ :str=7 , __magic_name__ :List[Any]=3 , __magic_name__ :str=30 , __magic_name__ :Optional[int]=400 , __magic_name__ :Union[str, Any]=True , __magic_name__ :str=None , __magic_name__ :List[str]=True , __magic_name__ :int=[0.5, 0.5, 0.5] , __magic_name__ :List[Any]=[0.5, 0.5, 0.5] , __magic_name__ :Tuple=True , __magic_name__ :Optional[int]=1 / 255 , __magic_name__ :str=True , ):
'''simple docstring'''
a = size if size is not None else {"""shortest_edge""": 18, """longest_edge""": 1333}
a = parent
a = batch_size
a = num_channels
a = min_resolution
a = max_resolution
a = do_resize
a = size
a = do_normalize
a = image_mean
a = image_std
a = do_rescale
a = rescale_factor
a = do_pad
def lowerCamelCase__ ( self :Optional[int] ):
'''simple docstring'''
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_pad": self.do_pad,
}
def lowerCamelCase__ ( self :Any , __magic_name__ :Dict , __magic_name__ :Optional[Any]=False ):
'''simple docstring'''
if not batched:
a = image_inputs[0]
if isinstance(_lowerCamelCase , Image.Image ):
a = image.size
else:
a = image.shape[1], image.shape[2]
if w < h:
a = int(self.size["""shortest_edge"""] * h / w )
a = self.size["""shortest_edge"""]
elif w > h:
a = self.size["""shortest_edge"""]
a = int(self.size["""shortest_edge"""] * w / h )
else:
a = self.size["""shortest_edge"""]
a = self.size["""shortest_edge"""]
else:
a = []
for image in image_inputs:
a = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
a = max(_lowerCamelCase , key=lambda __magic_name__ : item[0] )[0]
a = max(_lowerCamelCase , key=lambda __magic_name__ : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __lowerCAmelCase ( __A , unittest.TestCase ):
UpperCamelCase__ = DetaImageProcessor if is_vision_available() else None
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = DetaImageProcessingTester(self )
@property
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def lowerCamelCase__ ( self :Any ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(_lowerCamelCase , """image_mean""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """image_std""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """do_normalize""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """do_resize""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """do_rescale""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """do_pad""" ) )
self.assertTrue(hasattr(_lowerCamelCase , """size""" ) )
def lowerCamelCase__ ( self :Dict ):
'''simple docstring'''
a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"""shortest_edge""": 18, """longest_edge""": 1333} )
self.assertEqual(image_processor.do_pad , _lowerCamelCase )
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
pass
def lowerCamelCase__ ( self :int ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , Image.Image )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
a = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
a = image_processing(_lowerCamelCase , return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase__ ( self :Union[str, Any] ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , numpify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , np.ndarray )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
a = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a = image_processing(_lowerCamelCase , return_tensors="""pt""" ).pixel_values
a = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
a = prepare_image_inputs(self.image_processor_tester , equal_resolution=_lowerCamelCase , torchify=_lowerCamelCase )
for image in image_inputs:
self.assertIsInstance(_lowerCamelCase , torch.Tensor )
# Test not batched input
a = image_processing(image_inputs[0] , return_tensors="""pt""" ).pixel_values
a = self.image_processor_tester.get_expected_values(_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
a = image_processing(_lowerCamelCase , return_tensors="""pt""" ).pixel_values
a = self.image_processor_tester.get_expected_values(_lowerCamelCase , batched=_lowerCamelCase )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def lowerCamelCase__ ( self :Optional[Any] ):
'''simple docstring'''
a = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_annotations.txt""" , """r""" ) as f:
a = json.loads(f.read() )
a = {"""image_id""": 3_9769, """annotations""": target}
# encode them
a = DetaImageProcessor()
a = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , return_tensors="""pt""" )
# verify pixel values
a = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , _lowerCamelCase )
a = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , _lowerCamelCase , atol=1E-4 ) )
# verify area
a = torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , _lowerCamelCase ) )
# verify boxes
a = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , _lowerCamelCase )
a = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , _lowerCamelCase , atol=1E-3 ) )
# verify image_id
a = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , _lowerCamelCase ) )
# verify is_crowd
a = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , _lowerCamelCase ) )
# verify class_labels
a = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , _lowerCamelCase ) )
# verify orig_size
a = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , _lowerCamelCase ) )
# verify size
a = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , _lowerCamelCase ) )
@slow
def lowerCamelCase__ ( self :Tuple ):
'''simple docstring'''
a = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
with open("""./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt""" , """r""" ) as f:
a = json.loads(f.read() )
a = {"""file_name""": """000000039769.png""", """image_id""": 3_9769, """segments_info""": target}
a = pathlib.Path("""./tests/fixtures/tests_samples/COCO/coco_panoptic""" )
# encode them
a = DetaImageProcessor(format="""coco_panoptic""" )
a = image_processing(images=_lowerCamelCase , annotations=_lowerCamelCase , masks_path=_lowerCamelCase , return_tensors="""pt""" )
# verify pixel values
a = torch.Size([1, 3, 800, 1066] )
self.assertEqual(encoding["""pixel_values"""].shape , _lowerCamelCase )
a = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["""pixel_values"""][0, 0, 0, :3] , _lowerCamelCase , atol=1E-4 ) )
# verify area
a = torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""area"""] , _lowerCamelCase ) )
# verify boxes
a = torch.Size([6, 4] )
self.assertEqual(encoding["""labels"""][0]["""boxes"""].shape , _lowerCamelCase )
a = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""boxes"""][0] , _lowerCamelCase , atol=1E-3 ) )
# verify image_id
a = torch.tensor([3_9769] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""image_id"""] , _lowerCamelCase ) )
# verify is_crowd
a = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""iscrowd"""] , _lowerCamelCase ) )
# verify class_labels
a = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""class_labels"""] , _lowerCamelCase ) )
# verify masks
a = 82_2873
self.assertEqual(encoding["""labels"""][0]["""masks"""].sum().item() , _lowerCamelCase )
# verify orig_size
a = torch.tensor([480, 640] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""orig_size"""] , _lowerCamelCase ) )
# verify size
a = torch.tensor([800, 1066] )
self.assertTrue(torch.allclose(encoding["""labels"""][0]["""size"""] , _lowerCamelCase ) )
| 228 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class _lowerCAmelCase :
"""simple docstring"""
def __init__( self , _lowerCamelCase , _lowerCamelCase=13 , _lowerCamelCase=30 , _lowerCamelCase=2 , _lowerCamelCase=3 , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=32 , _lowerCamelCase=5 , _lowerCamelCase=4 , _lowerCamelCase=37 , _lowerCamelCase="gelu" , _lowerCamelCase=0.1 , _lowerCamelCase=0.1 , _lowerCamelCase=10 , _lowerCamelCase=0.02 , _lowerCamelCase=3 , _lowerCamelCase=None , _lowerCamelCase=2 , ) -> str:
A_ : Optional[int] = parent
A_ : Dict = batch_size
A_ : List[Any] = image_size
A_ : Optional[int] = patch_size
A_ : List[str] = num_channels
A_ : List[Any] = is_training
A_ : Union[str, Any] = use_labels
A_ : Union[str, Any] = hidden_size
A_ : str = num_hidden_layers
A_ : List[str] = num_attention_heads
A_ : Union[str, Any] = intermediate_size
A_ : Any = hidden_act
A_ : Optional[Any] = hidden_dropout_prob
A_ : List[Any] = attention_probs_dropout_prob
A_ : Dict = type_sequence_label_size
A_ : Optional[int] = initializer_range
A_ : str = scope
A_ : Optional[Any] = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
A_ : Tuple = (image_size // patch_size) ** 2
A_ : Union[str, Any] = num_patches + 2
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
A_ : Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A_ : Dict = None
if self.use_labels:
A_ : Any = ids_tensor([self.batch_size] , self.type_sequence_label_size )
A_ : Optional[Any] = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase_ ( self ) -> int:
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=_lowerCamelCase , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
A_ : List[str] = DeiTModel(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Dict = model(_lowerCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> int:
A_ : int = DeiTForMaskedImageModeling(config=_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A_ : Dict = 1
A_ : Optional[int] = DeiTForMaskedImageModeling(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : Optional[int] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : int = model(_lowerCamelCase )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) -> Union[str, Any]:
A_ : Tuple = self.type_sequence_label_size
A_ : Tuple = DeiTForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : int = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
A_ : Dict = 1
A_ : Any = DeiTForImageClassification(_lowerCamelCase )
model.to(_lowerCamelCase )
model.eval()
A_ : str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A_ : List[str] = model(_lowerCamelCase , labels=_lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase_ ( self ) -> List[str]:
A_ : List[Any] = self.prepare_config_and_inputs()
(
(
A_
) , (
A_
) , (
A_
) ,
) : Union[str, Any] = config_and_inputs
A_ : Tuple = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _lowerCAmelCase ( __A, __A, unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
lowerCamelCase = (
{
'''feature-extraction''': DeiTModel,
'''image-classification''': (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
A_ : int = DeiTModelTester(self )
A_ : str = ConfigTester(self , config_class=_lowerCamelCase , has_text_modality=_lowerCamelCase , hidden_size=37 )
def UpperCAmelCase_ ( self ) -> List[str]:
self.config_tester.run_common_tests()
@unittest.skip(reason="""DeiT does not use inputs_embeds""" )
def UpperCAmelCase_ ( self ) -> Optional[int]:
pass
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
A_ , A_ : List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : List[Any] = model_class(_lowerCamelCase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A_ : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowerCamelCase , nn.Linear ) )
def UpperCAmelCase_ ( self ) -> Optional[Any]:
A_ , A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A_ : List[str] = model_class(_lowerCamelCase )
A_ : str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A_ : Union[str, Any] = [*signature.parameters.keys()]
A_ : List[str] = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCamelCase )
def UpperCAmelCase_ ( self ) -> List[str]:
A_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCamelCase )
def UpperCAmelCase_ ( self ) -> Union[str, Any]:
A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*_lowerCamelCase )
def UpperCAmelCase_ ( self ) -> Optional[Any]:
A_ : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCamelCase )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=False ) -> Union[str, Any]:
A_ : int = super()._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def UpperCAmelCase_ ( self ) -> Optional[Any]:
if not self.model_tester.is_training:
return
A_ , A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Optional[Any] = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(_lowerCamelCase )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
A_ : List[str] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
A_ : List[str] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
A_ : List[str] = model(**_lowerCamelCase ).loss
loss.backward()
def UpperCAmelCase_ ( self ) -> int:
A_ , A_ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
A_ : Any = False
A_ : Union[str, Any] = True
for model_class in self.all_model_classes:
if model_class in get_values(_lowerCamelCase ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
A_ : List[Any] = model_class(_lowerCamelCase )
model.gradient_checkpointing_enable()
model.to(_lowerCamelCase )
model.train()
A_ : str = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
A_ : Union[str, Any] = model(**_lowerCamelCase ).loss
loss.backward()
def UpperCAmelCase_ ( self ) -> Tuple:
A_ , A_ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
A_ : Optional[Any] = [
{"""title""": """multi_label_classification""", """num_labels""": 2, """dtype""": torch.float},
{"""title""": """single_label_classification""", """num_labels""": 1, """dtype""": torch.long},
{"""title""": """regression""", """num_labels""": 1, """dtype""": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(_lowerCamelCase ),
*get_values(_lowerCamelCase ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F"Testing {model_class} with {problem_type['title']}" ):
A_ : Dict = problem_type["""title"""]
A_ : List[Any] = problem_type["""num_labels"""]
A_ : List[str] = model_class(_lowerCamelCase )
model.to(_lowerCamelCase )
model.train()
A_ : List[Any] = self._prepare_for_class(_lowerCamelCase , _lowerCamelCase , return_labels=_lowerCamelCase )
if problem_type["num_labels"] > 1:
A_ : Tuple = inputs["""labels"""].unsqueeze(1 ).repeat(1 , problem_type["""num_labels"""] )
A_ : Union[str, Any] = inputs["""labels"""].to(problem_type["""dtype"""] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=_lowerCamelCase ) as warning_list:
A_ : List[str] = model(**_lowerCamelCase ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F"Something is going wrong in the regression problem: intercepted {w.message}" )
loss.backward()
@slow
def UpperCAmelCase_ ( self ) -> Tuple:
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A_ : int = DeiTModel.from_pretrained(_lowerCamelCase )
self.assertIsNotNone(_lowerCamelCase )
def UpperCAmelCase ( ) -> Tuple:
"""simple docstring"""
A_ : Optional[Any] = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase_ ( self ) -> Optional[Any]:
return (
DeiTImageProcessor.from_pretrained("""facebook/deit-base-distilled-patch16-224""" )
if is_vision_available()
else None
)
@slow
def UpperCAmelCase_ ( self ) -> Tuple:
A_ : Any = DeiTForImageClassificationWithTeacher.from_pretrained("""facebook/deit-base-distilled-patch16-224""" ).to(
_lowerCamelCase )
A_ : Optional[int] = self.default_image_processor
A_ : str = prepare_img()
A_ : Any = image_processor(images=_lowerCamelCase , return_tensors="""pt""" ).to(_lowerCamelCase )
# forward pass
with torch.no_grad():
A_ : Any = model(**_lowerCamelCase )
# verify the logits
A_ : Tuple = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCamelCase )
A_ : List[Any] = torch.tensor([-1.0266, 0.1912, -1.2861] ).to(_lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCamelCase , atol=1e-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def UpperCAmelCase_ ( self ) -> Tuple:
A_ : Optional[Any] = DeiTModel.from_pretrained(
"""facebook/deit-base-distilled-patch16-224""" , torch_dtype=torch.floataa , device_map="""auto""" )
A_ : int = self.default_image_processor
A_ : List[str] = prepare_img()
A_ : List[Any] = image_processor(images=_lowerCamelCase , return_tensors="""pt""" )
A_ : Union[str, Any] = inputs.pixel_values.to(_lowerCamelCase )
# forward pass to make sure inference works in fp16
with torch.no_grad():
A_ : List[Any] = model(_lowerCamelCase )
| 344 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import MutableSequence
class __snake_case:
'''simple docstring'''
def __init__( self , A_ , A_ ) -> None:
if len(A_ ) != degree + 1:
raise ValueError(
"""The number of coefficients should be equal to the degree + 1.""" )
lowerCAmelCase = list(A_ )
lowerCAmelCase = degree
def __add__( self , A_ ) -> Polynomial:
if self.degree > polynomial_a.degree:
lowerCAmelCase = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , A_ )
else:
lowerCAmelCase = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , A_ )
def __sub__( self , A_ ) -> Polynomial:
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self ) -> Polynomial:
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self , A_ ) -> Polynomial:
lowerCAmelCase = [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 , A_ )
def __snake_case ( self , A_ ) -> int | float:
lowerCAmelCase = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self ) -> str:
lowerCAmelCase = """"""
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(A_ )
return polynomial
def __repr__( self ) -> str:
return self.__str__()
def __snake_case ( self ) -> Polynomial:
lowerCAmelCase = [0] * self.degree
for i in range(self.degree ):
lowerCAmelCase = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , A_ )
def __snake_case ( self , A_ = 0 ) -> Polynomial:
lowerCAmelCase = [0] * (self.degree + 2)
lowerCAmelCase = constant
for i in range(self.degree + 1 ):
lowerCAmelCase = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , A_ )
def __eq__( self , A_ ) -> bool:
if not isinstance(A_ , A_ ):
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 , A_ ) -> bool:
return not self.__eq__(A_ ) | 187 |
'''simple docstring'''
import cmath
import math
def _snake_case ( _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float , _SCREAMING_SNAKE_CASE : float ) -> complex:
"""simple docstring"""
lowerCAmelCase = math.radians(_SCREAMING_SNAKE_CASE )
lowerCAmelCase = math.radians(_SCREAMING_SNAKE_CASE )
# Convert voltage and current to rectangular form
lowerCAmelCase = cmath.rect(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCAmelCase = cmath.rect(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod() | 187 | 1 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class A__ ( nn.Module ):
A__ = 42
A__ = 42
A__ = 0.0
A__ = 1
A__ = 1
A__ = True
A__ = False
A__ = False
A__ = False
A__ = jnp.floataa
def A ( self : str ) -> List[str]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[]
_SCREAMING_SNAKE_CASE =[]
for i in range(self.num_layers ):
_SCREAMING_SNAKE_CASE =self.in_channels if i == 0 else self.out_channels
_SCREAMING_SNAKE_CASE =FlaxResnetBlockaD(
in_channels=_a , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_a )
_SCREAMING_SNAKE_CASE =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_a )
_SCREAMING_SNAKE_CASE =resnets
_SCREAMING_SNAKE_CASE =attentions
if self.add_downsample:
_SCREAMING_SNAKE_CASE =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , _a : Union[str, Any] , _a : Union[str, Any] , _a : str , _a : Optional[Any]=True ) -> List[Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =()
for resnet, attn in zip(self.resnets , self.attentions ):
_SCREAMING_SNAKE_CASE =resnet(_a , _a , deterministic=_a )
_SCREAMING_SNAKE_CASE =attn(_a , _a , deterministic=_a )
output_states += (hidden_states,)
if self.add_downsample:
_SCREAMING_SNAKE_CASE =self.downsamplers_a(_a )
output_states += (hidden_states,)
return hidden_states, output_states
class A__ ( nn.Module ):
A__ = 42
A__ = 42
A__ = 0.0
A__ = 1
A__ = True
A__ = jnp.floataa
def A ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[]
for i in range(self.num_layers ):
_SCREAMING_SNAKE_CASE =self.in_channels if i == 0 else self.out_channels
_SCREAMING_SNAKE_CASE =FlaxResnetBlockaD(
in_channels=_a , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_a )
_SCREAMING_SNAKE_CASE =resnets
if self.add_downsample:
_SCREAMING_SNAKE_CASE =FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Dict , _a : int , _a : Tuple , _a : Union[str, Any]=True ) -> int:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =()
for resnet in self.resnets:
_SCREAMING_SNAKE_CASE =resnet(_a , _a , deterministic=_a )
output_states += (hidden_states,)
if self.add_downsample:
_SCREAMING_SNAKE_CASE =self.downsamplers_a(_a )
output_states += (hidden_states,)
return hidden_states, output_states
class A__ ( nn.Module ):
A__ = 42
A__ = 42
A__ = 42
A__ = 0.0
A__ = 1
A__ = 1
A__ = True
A__ = False
A__ = False
A__ = False
A__ = jnp.floataa
def A ( self : int ) -> Dict:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[]
_SCREAMING_SNAKE_CASE =[]
for i in range(self.num_layers ):
_SCREAMING_SNAKE_CASE =self.in_channels if (i == self.num_layers - 1) else self.out_channels
_SCREAMING_SNAKE_CASE =self.prev_output_channel if i == 0 else self.out_channels
_SCREAMING_SNAKE_CASE =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_a )
_SCREAMING_SNAKE_CASE =FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_a )
_SCREAMING_SNAKE_CASE =resnets
_SCREAMING_SNAKE_CASE =attentions
if self.add_upsample:
_SCREAMING_SNAKE_CASE =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Optional[Any] , _a : Optional[Any] , _a : Dict , _a : Union[str, Any] , _a : str , _a : List[str]=True ) -> int:
'''simple docstring'''
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
_SCREAMING_SNAKE_CASE =res_hidden_states_tuple[-1]
_SCREAMING_SNAKE_CASE =res_hidden_states_tuple[:-1]
_SCREAMING_SNAKE_CASE =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_SCREAMING_SNAKE_CASE =resnet(_a , _a , deterministic=_a )
_SCREAMING_SNAKE_CASE =attn(_a , _a , deterministic=_a )
if self.add_upsample:
_SCREAMING_SNAKE_CASE =self.upsamplers_a(_a )
return hidden_states
class A__ ( nn.Module ):
A__ = 42
A__ = 42
A__ = 42
A__ = 0.0
A__ = 1
A__ = True
A__ = jnp.floataa
def A ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[]
for i in range(self.num_layers ):
_SCREAMING_SNAKE_CASE =self.in_channels if (i == self.num_layers - 1) else self.out_channels
_SCREAMING_SNAKE_CASE =self.prev_output_channel if i == 0 else self.out_channels
_SCREAMING_SNAKE_CASE =FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_a )
_SCREAMING_SNAKE_CASE =resnets
if self.add_upsample:
_SCREAMING_SNAKE_CASE =FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : str , _a : Dict , _a : Dict , _a : Optional[Any] , _a : str=True ) -> Optional[int]:
'''simple docstring'''
for resnet in self.resnets:
# pop res hidden states
_SCREAMING_SNAKE_CASE =res_hidden_states_tuple[-1]
_SCREAMING_SNAKE_CASE =res_hidden_states_tuple[:-1]
_SCREAMING_SNAKE_CASE =jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
_SCREAMING_SNAKE_CASE =resnet(_a , _a , deterministic=_a )
if self.add_upsample:
_SCREAMING_SNAKE_CASE =self.upsamplers_a(_a )
return hidden_states
class A__ ( nn.Module ):
A__ = 42
A__ = 0.0
A__ = 1
A__ = 1
A__ = False
A__ = False
A__ = jnp.floataa
def A ( self : List[str] ) -> Any:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =[
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
_SCREAMING_SNAKE_CASE =[]
for _ in range(self.num_layers ):
_SCREAMING_SNAKE_CASE =FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(_a )
_SCREAMING_SNAKE_CASE =FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(_a )
_SCREAMING_SNAKE_CASE =resnets
_SCREAMING_SNAKE_CASE =attentions
def __call__( self : Union[str, Any] , _a : List[Any] , _a : Tuple , _a : Optional[Any] , _a : str=True ) -> Union[str, Any]:
'''simple docstring'''
_SCREAMING_SNAKE_CASE =self.resnets[0](_a , _a )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
_SCREAMING_SNAKE_CASE =attn(_a , _a , deterministic=_a )
_SCREAMING_SNAKE_CASE =resnet(_a , _a , deterministic=_a )
return hidden_states
| 47 |
'''simple docstring'''
from math import ceil
def UpperCamelCase_ ( A__ : int = 10_01 ):
'''simple docstring'''
lowerCAmelCase_ : List[Any] = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
lowerCAmelCase_ : int = 2 * i + 1
lowerCAmelCase_ : Tuple = 2 * i
lowerCAmelCase_ : Tuple = total + 4 * odd**2 - 6 * even
return total
if __name__ == "__main__":
import sys
if len(sys.argv) == 1:
print(solution())
else:
try:
__A : str = int(sys.argv[1])
print(solution(n))
except ValueError:
print("Invalid entry - please enter a number")
| 120 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : Any = {"configuration_plbart": ["PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP", "PLBartConfig"]}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[Any] = ["PLBartTokenizer"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
"PLBART_PRETRAINED_MODEL_ARCHIVE_LIST",
"PLBartForCausalLM",
"PLBartForConditionalGeneration",
"PLBartForSequenceClassification",
"PLBartModel",
"PLBartPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_plbart import PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP, PLBartConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_plbart import PLBartTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_plbart import (
PLBART_PRETRAINED_MODEL_ARCHIVE_LIST,
PLBartForCausalLM,
PLBartForConditionalGeneration,
PLBartForSequenceClassification,
PLBartModel,
PLBartPreTrainedModel,
)
else:
import sys
__A : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure)
| 89 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def UpperCamelCase_ ( A__ : bytes , A__ : int ):
'''simple docstring'''
lowerCAmelCase_ : int = f'{sampling_rate}'
lowerCAmelCase_ : str = """1"""
lowerCAmelCase_ : Optional[int] = """f32le"""
lowerCAmelCase_ : Any = [
"""ffmpeg""",
"""-i""",
"""pipe:0""",
"""-ac""",
ac,
"""-ar""",
ar,
"""-f""",
format_for_conversion,
"""-hide_banner""",
"""-loglevel""",
"""quiet""",
"""pipe:1""",
]
try:
with subprocess.Popen(A__ , stdin=subprocess.PIPE , stdout=subprocess.PIPE ) as ffmpeg_process:
lowerCAmelCase_ : Optional[int] = ffmpeg_process.communicate(A__ )
except FileNotFoundError as error:
raise ValueError("""ffmpeg was not found but is required to load audio files from filename""" ) from error
lowerCAmelCase_ : Optional[Any] = output_stream[0]
lowerCAmelCase_ : Optional[int] = np.frombuffer(A__ , np.floataa )
if audio.shape[0] == 0:
raise ValueError("""Malformed soundfile""" )
return audio
def UpperCamelCase_ ( A__ : int , A__ : float , A__ : str = "f32le" , ):
'''simple docstring'''
lowerCAmelCase_ : int = f'{sampling_rate}'
lowerCAmelCase_ : Any = """1"""
if format_for_conversion == "s16le":
lowerCAmelCase_ : Optional[Any] = 2
elif format_for_conversion == "f32le":
lowerCAmelCase_ : Union[str, Any] = 4
else:
raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )
lowerCAmelCase_ : int = platform.system()
if system == "Linux":
lowerCAmelCase_ : int = """alsa"""
lowerCAmelCase_ : int = """default"""
elif system == "Darwin":
lowerCAmelCase_ : List[str] = """avfoundation"""
lowerCAmelCase_ : Union[str, Any] = """:0"""
elif system == "Windows":
lowerCAmelCase_ : List[Any] = """dshow"""
lowerCAmelCase_ : Union[str, Any] = """default"""
lowerCAmelCase_ : Tuple = [
"""ffmpeg""",
"""-f""",
format_,
"""-i""",
input_,
"""-ac""",
ac,
"""-ar""",
ar,
"""-f""",
format_for_conversion,
"""-fflags""",
"""nobuffer""",
"""-hide_banner""",
"""-loglevel""",
"""quiet""",
"""pipe:1""",
]
lowerCAmelCase_ : Tuple = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
lowerCAmelCase_ : List[str] = _ffmpeg_stream(A__ , A__ )
for item in iterator:
yield item
def UpperCamelCase_ ( A__ : int , A__ : float , A__ : Optional[int] = None , A__ : Optional[Union[Tuple[float, float], float]] = None , A__ : str = "f32le" , ):
'''simple docstring'''
if stream_chunk_s is not None:
lowerCAmelCase_ : Union[str, Any] = stream_chunk_s
else:
lowerCAmelCase_ : Tuple = chunk_length_s
lowerCAmelCase_ : List[Any] = ffmpeg_microphone(A__ , A__ , format_for_conversion=A__ )
if format_for_conversion == "s16le":
lowerCAmelCase_ : Tuple = np.intaa
lowerCAmelCase_ : List[Any] = 2
elif format_for_conversion == "f32le":
lowerCAmelCase_ : Dict = np.floataa
lowerCAmelCase_ : int = 4
else:
raise ValueError(f'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )
if stride_length_s is None:
lowerCAmelCase_ : Optional[Any] = chunk_length_s / 6
lowerCAmelCase_ : List[str] = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(A__ , (int, float) ):
lowerCAmelCase_ : int = [stride_length_s, stride_length_s]
lowerCAmelCase_ : str = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
lowerCAmelCase_ : List[str] = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
lowerCAmelCase_ : Dict = datetime.datetime.now()
lowerCAmelCase_ : Any = datetime.timedelta(seconds=A__ )
for item in chunk_bytes_iter(A__ , A__ , stride=(stride_left, stride_right) , stream=A__ ):
# Put everything back in numpy scale
lowerCAmelCase_ : Optional[int] = np.frombuffer(item["""raw"""] , dtype=A__ )
lowerCAmelCase_ : Dict = (
item["""stride"""][0] // size_of_sample,
item["""stride"""][1] // size_of_sample,
)
lowerCAmelCase_ : Dict = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def UpperCamelCase_ ( A__ : Any , A__ : int , A__ : Tuple[int, int] , A__ : bool = False ):
'''simple docstring'''
lowerCAmelCase_ : List[str] = B""""""
lowerCAmelCase_, lowerCAmelCase_ : Any = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
f'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}' )
lowerCAmelCase_ : Union[str, Any] = 0
for raw in iterator:
acc += raw
if stream and len(A__ ) < chunk_len:
lowerCAmelCase_ : Dict = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(A__ ) >= chunk_len:
# We are flushing the accumulator
lowerCAmelCase_ : Optional[Any] = (_stride_left, stride_right)
lowerCAmelCase_ : List[str] = {"""raw""": acc[:chunk_len], """stride""": stride}
if stream:
lowerCAmelCase_ : List[Any] = False
yield item
lowerCAmelCase_ : str = stride_left
lowerCAmelCase_ : str = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(A__ ) > stride_left:
lowerCAmelCase_ : Tuple = {"""raw""": acc, """stride""": (_stride_left, 0)}
if stream:
lowerCAmelCase_ : Optional[Any] = False
yield item
def UpperCamelCase_ ( A__ : List[str] , A__ : int ):
'''simple docstring'''
lowerCAmelCase_ : Dict = 2**24 # 16Mo
try:
with subprocess.Popen(A__ , stdout=subprocess.PIPE , bufsize=A__ ) as ffmpeg_process:
while True:
lowerCAmelCase_ : Union[str, Any] = ffmpeg_process.stdout.read(A__ )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError("""ffmpeg was not found but is required to stream audio files from filename""" ) from error
| 89 | 1 |
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
__A : Tuple = {
'''User-Agent''': '''Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'''
''' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582'''
}
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = "dhaka", _UpperCAmelCase = 5 ) -> int:
'''simple docstring'''
lowerCAmelCase : List[Any] = min(_UpperCAmelCase, 50 ) # Prevent abuse!
lowerCAmelCase : str = {
'q': query,
'tbm': 'isch',
'hl': 'en',
'ijn': '0',
}
lowerCAmelCase : Optional[Any] = requests.get('https://www.google.com/search', params=_UpperCAmelCase, headers=_UpperCAmelCase )
lowerCAmelCase : int = BeautifulSoup(html.text, 'html.parser' )
lowerCAmelCase : List[Any] = ''.join(
re.findall(r'AF_initDataCallback\(([^<]+)\);', str(soup.select('script' ) ) ) )
lowerCAmelCase : Optional[int] = json.dumps(_UpperCAmelCase )
lowerCAmelCase : str = json.loads(_UpperCAmelCase )
lowerCAmelCase : str = re.findall(
r'\[\"GRID_STATE0\",null,\[\[1,\[0,\".*?\",(.*),\"All\",', _UpperCAmelCase, )
if not matched_google_image_data:
return 0
lowerCAmelCase : Tuple = re.sub(
r'\[\"(https\:\/\/encrypted-tbn0\.gstatic\.com\/images\?.*?)\",\d+,\d+\]', '', str(_UpperCAmelCase ), )
lowerCAmelCase : Dict = re.findall(
r'(?:\'|,),\[\"(https:|http.*?)\",\d+,\d+\]', _UpperCAmelCase, )
for index, fixed_full_res_image in enumerate(_UpperCAmelCase ):
if index >= max_images:
return index
lowerCAmelCase : Any = bytes(_UpperCAmelCase, 'ascii' ).decode(
'unicode-escape' )
lowerCAmelCase : Tuple = bytes(_UpperCAmelCase, 'ascii' ).decode(
'unicode-escape' )
lowerCAmelCase : Optional[Any] = urllib.request.build_opener()
lowerCAmelCase : Any = [
(
'User-Agent',
'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.19582',
)
]
urllib.request.install_opener(_UpperCAmelCase )
lowerCAmelCase : List[str] = f"query_{query.replace(' ', '_' )}"
if not os.path.exists(_UpperCAmelCase ):
os.makedirs(_UpperCAmelCase )
urllib.request.urlretrieve( # noqa: S310
_UpperCAmelCase, f"{path_name}/original_size_img_{index}.jpg" )
return index
if __name__ == "__main__":
try:
__A : Tuple = download_images_from_google_query(sys.argv[1])
print(F'{image_count} images were downloaded to disk.')
except IndexError:
print('''Please provide a search term.''')
raise
| 138 |
__A : dict[tuple[int, int, int], int] = {}
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> int:
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:
return 1
# No easy solution, so now we need to do the recursive calculation
# First, check if the combination is already in the cache, and
# if yes, return the stored value from there since we already
# know the number of possible prize strings from this point on
lowerCAmelCase : Dict = (days, absent, late)
if key in cache:
return cache[key]
# now we calculate the three possible ways that can unfold from
# this point on, depending on our attendance today
# 1) if we are late (but not absent), the "absent" counter stays as
# it is, but the "late" counter increases by one
lowerCAmelCase : int = _calculate(days - 1, _UpperCAmelCase, late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
lowerCAmelCase : List[Any] = _calculate(days - 1, absent + 1, 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
lowerCAmelCase : Optional[Any] = _calculate(days - 1, _UpperCAmelCase, 0 )
lowerCAmelCase : int = state_late + state_absent + state_ontime
lowerCAmelCase : Any = prizestrings
return prizestrings
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase = 30 ) -> int:
'''simple docstring'''
return _calculate(_UpperCAmelCase, absent=0, late=0 )
if __name__ == "__main__":
print(solution())
| 138 | 1 |
from functools import lru_cache
def a( A : int ) -> set:
"""simple docstring"""
a = 2
a = set()
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.add(_snake_case )
if n > 1:
factors.add(_snake_case )
return factors
@lru_cache
def a( A : int ) -> int:
"""simple docstring"""
return len(unique_prime_factors(_snake_case ) )
def a( A : list ) -> bool:
"""simple docstring"""
return len(set(_snake_case ) ) in (0, 1)
def a( A : int ) -> list:
"""simple docstring"""
a = 2
while True:
# Increment each value of a generated range
a = [base + i for i in range(_snake_case )]
# Run elements through out unique_prime_factors function
# Append our target number to the end.
a = [upf_len(_snake_case ) for x in group]
checker.append(_snake_case )
# If all numbers in the list are equal, return the group variable.
if equality(_snake_case ):
return group
# Increment our base variable by 1
base += 1
def a( A : int = 4 ) -> int:
"""simple docstring"""
a = run(_snake_case )
return results[0] if len(_snake_case ) else None
if __name__ == "__main__":
print(solution())
| 369 |
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Audio, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowerCAmelCase )
class _lowercase ( lowerCAmelCase ):
"""simple docstring"""
__A = field(default="automatic-speech-recognition", metadata={"include_in_asdict_even_if_is_default": True} )
__A = Features({"audio": Audio()} )
__A = Features({"transcription": Value("string" )} )
__A = "audio"
__A = "transcription"
def UpperCamelCase_ (self , lowerCamelCase_ ):
"""simple docstring"""
if self.audio_column not in features:
raise ValueError(F'''Column {self.audio_column} is not present in features.''' )
if not isinstance(features[self.audio_column] , lowerCamelCase_ ):
raise ValueError(F'''Column {self.audio_column} is not an Audio type.''' )
a = copy.deepcopy(self )
a = self.input_schema.copy()
a = features[self.audio_column]
a = input_schema
return task_template
@property
def UpperCamelCase_ (self ):
"""simple docstring"""
return {self.audio_column: "audio", self.transcription_column: "transcription"}
| 71 | 0 |
from torch import nn
class a ( nn.Module ):
def __init__( self :Tuple ,__lowercase :Optional[int] ,__lowercase :int ):
super().__init__()
snake_case__ : Optional[Any] = class_size
snake_case__ : Dict = embed_size
# self.mlp1 = nn.Linear(embed_size, embed_size)
# self.mlp2 = (nn.Linear(embed_size, class_size))
snake_case__ : Dict = nn.Linear(__lowercase ,__lowercase )
def __lowerCamelCase ( self :str ,__lowercase :int ):
# hidden_state = nn.functional.relu(self.mlp1(hidden_state))
# hidden_state = self.mlp2(hidden_state)
snake_case__ : Optional[Any] = self.mlp(__lowercase )
return logits
| 230 |
from math import ceil, sqrt
def _lowerCAmelCase ( __lowerCAmelCase = 1000000 ) -> int:
"""simple docstring"""
snake_case__ : Dict = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
snake_case__ : Any = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
snake_case__ : int = 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() = }""")
| 230 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowercase : Union[str, Any] = logging.get_logger(__name__)
def lowerCAmelCase__ ( _a : List[Any] ):
snake_case_ : Dict = "huggingface/label-files"
snake_case_ : Optional[Any] = "imagenet-1k-id2label.json"
snake_case_ : str = json.load(open(hf_hub_download(_a , _a , repo_type="dataset" ) , "r" ) )
snake_case_ : Any = {int(_a ): v for k, v in idalabel.items()}
snake_case_ : Optional[int] = {v: k for k, v in idalabel.items()}
snake_case_ : int = "std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
snake_case_ : Union[str, Any] = BitConfig(
conv_layer=_a , num_labels=10_00 , idalabel=_a , labelaid=_a , )
return config
def lowerCAmelCase__ ( _a : str ):
if "stem.conv" in name:
snake_case_ : Tuple = name.replace("stem.conv" , "bit.embedder.convolution" )
if "blocks" in name:
snake_case_ : Dict = name.replace("blocks" , "layers" )
if "head.fc" in name:
snake_case_ : Optional[int] = name.replace("head.fc" , "classifier.1" )
if name.startswith("norm" ):
snake_case_ : int = "bit." + name
if "bit" not in name and "classifier" not in name:
snake_case_ : Tuple = "bit.encoder." + name
return name
def lowerCAmelCase__ ( ):
snake_case_ : Dict = "http://images.cocodataset.org/val2017/000000039769.jpg"
snake_case_ : Union[str, Any] = Image.open(requests.get(_a , stream=_a ).raw )
return im
@torch.no_grad()
def lowerCAmelCase__ ( _a : Dict , _a : Tuple , _a : Dict=False ):
snake_case_ : int = get_config(_a )
# load original model from timm
snake_case_ : str = create_model(_a , pretrained=_a )
timm_model.eval()
# load state_dict of original model
snake_case_ : Tuple = timm_model.state_dict()
for key in state_dict.copy().keys():
snake_case_ : str = state_dict.pop(_a )
snake_case_ : Union[str, Any] = val.squeeze() if "head" in key else val
# load HuggingFace model
snake_case_ : int = BitForImageClassification(_a )
model.eval()
model.load_state_dict(_a )
# create image processor
snake_case_ : Optional[Any] = create_transform(**resolve_data_config({} , model=_a ) )
snake_case_ : Tuple = transform.transforms
snake_case_ : Union[str, Any] = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
snake_case_ : Union[str, Any] = BitImageProcessor(
do_resize=_a , size={"shortest_edge": timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=_a , crop_size={"height": timm_transforms[1].size[0], "width": timm_transforms[1].size[1]} , do_normalize=_a , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
snake_case_ : str = prepare_img()
snake_case_ : Dict = transform(_a ).unsqueeze(0 )
snake_case_ : Optional[Any] = processor(_a , return_tensors="pt" ).pixel_values
# verify pixel values
assert torch.allclose(_a , _a )
# verify logits
with torch.no_grad():
snake_case_ : Any = model(_a )
snake_case_ : Optional[Any] = outputs.logits
print("Logits:" , logits[0, :3] )
print("Predicted class:" , model.config.idalabel[logits.argmax(-1 ).item()] )
snake_case_ : List[Any] = timm_model(_a )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(_a , outputs.logits , atol=1E-3 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(_a ).mkdir(exist_ok=_a )
print(F'''Saving model {model_name} and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(_a )
processor.save_pretrained(_a )
if push_to_hub:
print(F'''Pushing model {model_name} and processor to the hub''' )
model.push_to_hub(F'''ybelkada/{model_name}''' )
processor.push_to_hub(F'''ybelkada/{model_name}''' )
if __name__ == "__main__":
lowercase : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--model_name''',
default='''resnetv2_50x1_bitm''',
type=str,
help='''Name of the BiT timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
parser.add_argument(
'''--push_to_hub''',
action='''store_true''',
help='''Whether to push the model to the hub.''',
)
lowercase : List[Any] = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 351 |
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class UpperCAmelCase_ ( SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
A : Tuple = ['image_processor', 'tokenizer']
A : Tuple = 'AutoImageProcessor'
A : Dict = 'AutoTokenizer'
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[Any]:
super().__init__(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
snake_case_ : Union[str, Any] = self.image_processor
def __call__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , **_SCREAMING_SNAKE_CASE ) -> List[str]:
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none." )
if text is not None:
snake_case_ : Tuple = self.tokenizer(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if images is not None:
snake_case_ : Tuple = self.image_processor(_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if text is not None and images is not None:
snake_case_ : List[Any] = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**_SCREAMING_SNAKE_CASE ) , tensor_type=_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Any:
return self.tokenizer.batch_decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def _lowerCAmelCase ( self , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> List[Any]:
return self.tokenizer.decode(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@property
def _lowerCAmelCase ( self ) -> Dict:
return ["input_ids", "attention_mask", "pixel_values"]
| 36 | 0 |
from __future__ import annotations
lowerCamelCase__ = """Muhammad Umer Farooq"""
lowerCamelCase__ = """MIT"""
lowerCamelCase__ = """1.0.0"""
lowerCamelCase__ = """Muhammad Umer Farooq"""
lowerCamelCase__ = """contact@muhammadumerfarooq.me"""
lowerCamelCase__ = """Alpha"""
import re
from html.parser import HTMLParser
from urllib import parse
import requests
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
def __init__( self : str , __lowercase : str ):
'''simple docstring'''
super().__init__()
__a = []
__a = domain
def UpperCamelCase_ ( self : Optional[int] , __lowercase : str , __lowercase : list[tuple[str, str | None]] ):
'''simple docstring'''
# Only parse the 'anchor' tag.
if tag == "a":
# Check the list of defined attributes.
for name, value in attrs:
# If href is defined, and not empty nor # print it.
if name == "href" and value != "#" and value != "":
# If not already in urls.
if value not in self.urls:
__a = parse.urljoin(self.domain , __lowercase )
self.urls.append(__lowercase )
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
return ".".join(get_sub_domain_name(_SCREAMING_SNAKE_CASE ).split(""".""" )[-2:] )
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
return parse.urlparse(_SCREAMING_SNAKE_CASE ).netloc
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : str = "https://github.com" ):
"""simple docstring"""
__a = get_domain_name(_SCREAMING_SNAKE_CASE )
# Initialize the parser
__a = Parser(_SCREAMING_SNAKE_CASE )
try:
# Open URL
__a = requests.get(_SCREAMING_SNAKE_CASE )
# pass the raw HTML to the parser to get links
parser.feed(r.text )
# Get links and loop through
__a = set()
for link in parser.urls:
# open URL.
# read = requests.get(link)
try:
__a = requests.get(_SCREAMING_SNAKE_CASE )
# Get the valid email.
__a = re.findall("""[a-zA-Z0-9]+@""" + domain , read.text )
# If not in list then append it.
for email in emails:
valid_emails.add(_SCREAMING_SNAKE_CASE )
except ValueError:
pass
except ValueError:
raise SystemExit(1 )
# Finally return a sorted list of email addresses with no duplicates.
return sorted(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowerCamelCase__ = emails_from_url("""https://github.com""")
print(F"""{len(emails)} emails found:""")
print("""\n""".join(sorted(emails)))
| 302 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import AlbertTokenizer
else:
lowerCamelCase__ = None
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
lowerCamelCase__ = {
"""vocab_file""": {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
},
}
lowerCamelCase__ = {
"""albert-base-v1""": 512,
"""albert-large-v1""": 512,
"""albert-xlarge-v1""": 512,
"""albert-xxlarge-v1""": 512,
"""albert-base-v2""": 512,
"""albert-large-v2""": 512,
"""albert-xlarge-v2""": 512,
"""albert-xxlarge-v2""": 512,
}
lowerCamelCase__ = """▁"""
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
__lowerCamelCase : List[Any] =VOCAB_FILES_NAMES
__lowerCamelCase : Union[str, Any] =PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : List[str] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Any =AlbertTokenizer
def __init__( self : Tuple , __lowercase : Union[str, Any]=None , __lowercase : Optional[int]=None , __lowercase : int=True , __lowercase : Dict=True , __lowercase : str=False , __lowercase : str="[CLS]" , __lowercase : List[Any]="[SEP]" , __lowercase : Any="<unk>" , __lowercase : List[Any]="[SEP]" , __lowercase : List[Any]="<pad>" , __lowercase : Optional[Any]="[CLS]" , __lowercase : List[str]="[MASK]" , **__lowercase : str , ):
'''simple docstring'''
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
__a = (
AddedToken(__lowercase , lstrip=__lowercase , rstrip=__lowercase , normalized=__lowercase )
if isinstance(__lowercase , __lowercase )
else mask_token
)
super().__init__(
__lowercase , tokenizer_file=__lowercase , do_lower_case=__lowercase , remove_space=__lowercase , keep_accents=__lowercase , bos_token=__lowercase , eos_token=__lowercase , unk_token=__lowercase , sep_token=__lowercase , pad_token=__lowercase , cls_token=__lowercase , mask_token=__lowercase , **__lowercase , )
__a = do_lower_case
__a = remove_space
__a = keep_accents
__a = vocab_file
__a = False if not self.vocab_file else True
def UpperCamelCase_ ( self : Dict , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ):
'''simple docstring'''
__a = [self.sep_token_id]
__a = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def UpperCamelCase_ ( self : str , __lowercase : List[int] , __lowercase : Optional[List[int]] = None ):
'''simple docstring'''
__a = [self.sep_token_id]
__a = [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 UpperCamelCase_ ( self : Tuple , __lowercase : str , __lowercase : Optional[str] = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(__lowercase ):
logger.error(F"Vocabulary path ({save_directory}) should be a directory" )
return
__a = os.path.join(
__lowercase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowercase ):
copyfile(self.vocab_file , __lowercase )
return (out_vocab_file,)
| 302 | 1 |
"""simple docstring"""
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> str:
'''simple docstring'''
lowerCAmelCase : Tuple = 1.5
lowerCAmelCase : Union[str, Any] = int(factor * num_class_images )
lowerCAmelCase : Any = ClipClient(
url='https://knn.laion.ai/knn-service', indice_name='laion_400m', num_images=SCREAMING_SNAKE_CASE_, aesthetic_weight=0.1 )
os.makedirs(f"{class_data_dir}/images", exist_ok=SCREAMING_SNAKE_CASE_ )
if len(list(Path(f"{class_data_dir}/images" ).iterdir() ) ) >= num_class_images:
return
while True:
lowerCAmelCase : str = client.query(text=SCREAMING_SNAKE_CASE_ )
if len(SCREAMING_SNAKE_CASE_ ) >= factor * num_class_images or num_images > 1e4:
break
else:
lowerCAmelCase : str = int(factor * num_images )
lowerCAmelCase : str = ClipClient(
url='https://knn.laion.ai/knn-service', indice_name='laion_400m', num_images=SCREAMING_SNAKE_CASE_, aesthetic_weight=0.1, )
lowerCAmelCase : Tuple = 0
lowerCAmelCase : str = 0
lowerCAmelCase : Optional[int] = tqdm(desc='downloading real regularization images', total=SCREAMING_SNAKE_CASE_ )
with open(f"{class_data_dir}/caption.txt", 'w' ) as fa, open(f"{class_data_dir}/urls.txt", 'w' ) as fa, open(
f"{class_data_dir}/images.txt", 'w' ) as fa:
while total < num_class_images:
lowerCAmelCase : Dict = class_images[count]
count += 1
try:
lowerCAmelCase : List[str] = requests.get(images['url'] )
if img.status_code == 200:
lowerCAmelCase : int = Image.open(BytesIO(img.content ) )
with open(f"{class_data_dir}/images/{total}.jpg", 'wb' ) as f:
f.write(img.content )
fa.write(images['caption'] + '\n' )
fa.write(images['url'] + '\n' )
fa.write(f"{class_data_dir}/images/{total}.jpg" + '\n' )
total += 1
pbar.update(1 )
else:
continue
except Exception:
continue
return
def SCREAMING_SNAKE_CASE__ ( ) -> List[str]:
'''simple docstring'''
lowerCAmelCase : int = argparse.ArgumentParser('', add_help=SCREAMING_SNAKE_CASE_ )
parser.add_argument('--class_prompt', help='text prompt to retrieve images', required=SCREAMING_SNAKE_CASE_, type=SCREAMING_SNAKE_CASE_ )
parser.add_argument('--class_data_dir', help='path to save images', required=SCREAMING_SNAKE_CASE_, type=SCREAMING_SNAKE_CASE_ )
parser.add_argument('--num_class_images', help='number of images to download', default=200, type=SCREAMING_SNAKE_CASE_ )
return parser.parse_args()
if __name__ == "__main__":
__A : Optional[Any] = parse_args()
retrieve(args.class_prompt, args.class_data_dir, args.num_class_images)
| 366 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType
__A : List[Any] = logging.get_logger(__name__)
__A : List[Any] = {
'''microsoft/deberta-v2-xlarge''': '''https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json''',
'''microsoft/deberta-v2-xxlarge''': '''https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json''',
'''microsoft/deberta-v2-xlarge-mnli''': (
'''https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json'''
),
'''microsoft/deberta-v2-xxlarge-mnli''': (
'''https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json'''
),
}
class __A ( lowerCAmelCase ):
lowerCAmelCase_ : Union[str, Any] = "deberta-v2"
def __init__( self : int , UpperCAmelCase_ : Dict=128100 , UpperCAmelCase_ : Optional[int]=1536 , UpperCAmelCase_ : Tuple=24 , UpperCAmelCase_ : Any=24 , UpperCAmelCase_ : Any=6144 , UpperCAmelCase_ : Tuple="gelu" , UpperCAmelCase_ : List[str]=0.1 , UpperCAmelCase_ : List[str]=0.1 , UpperCAmelCase_ : List[Any]=512 , UpperCAmelCase_ : List[str]=0 , UpperCAmelCase_ : List[Any]=0.02 , UpperCAmelCase_ : Optional[int]=1E-7 , UpperCAmelCase_ : List[str]=False , UpperCAmelCase_ : Dict=-1 , UpperCAmelCase_ : Tuple=0 , UpperCAmelCase_ : int=True , UpperCAmelCase_ : int=None , UpperCAmelCase_ : List[Any]=0 , UpperCAmelCase_ : int="gelu" , **UpperCAmelCase_ : int , ):
super().__init__(**UpperCAmelCase_ )
lowerCAmelCase : Dict = hidden_size
lowerCAmelCase : List[Any] = num_hidden_layers
lowerCAmelCase : str = num_attention_heads
lowerCAmelCase : List[str] = intermediate_size
lowerCAmelCase : List[Any] = hidden_act
lowerCAmelCase : int = hidden_dropout_prob
lowerCAmelCase : int = attention_probs_dropout_prob
lowerCAmelCase : Any = max_position_embeddings
lowerCAmelCase : str = type_vocab_size
lowerCAmelCase : Union[str, Any] = initializer_range
lowerCAmelCase : Union[str, Any] = relative_attention
lowerCAmelCase : List[Any] = max_relative_positions
lowerCAmelCase : List[Any] = pad_token_id
lowerCAmelCase : Optional[Any] = position_biased_input
# Backwards compatibility
if type(UpperCAmelCase_ ) == str:
lowerCAmelCase : Tuple = [x.strip() for x in pos_att_type.lower().split('|' )]
lowerCAmelCase : str = pos_att_type
lowerCAmelCase : Dict = vocab_size
lowerCAmelCase : Optional[Any] = layer_norm_eps
lowerCAmelCase : str = kwargs.get('pooler_hidden_size' , UpperCAmelCase_ )
lowerCAmelCase : Tuple = pooler_dropout
lowerCAmelCase : Union[str, Any] = pooler_hidden_act
class __A ( lowerCAmelCase ):
@property
def lowercase__ ( self : Optional[Any] ):
if self.task == "multiple-choice":
lowerCAmelCase : Tuple = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
lowerCAmelCase : Union[str, Any] = {0: 'batch', 1: 'sequence'}
if self._config.type_vocab_size > 0:
return OrderedDict(
[('input_ids', dynamic_axis), ('attention_mask', dynamic_axis), ('token_type_ids', dynamic_axis)] )
else:
return OrderedDict([('input_ids', dynamic_axis), ('attention_mask', dynamic_axis)] )
@property
def lowercase__ ( self : int ):
return 12
def lowercase__ ( self : Union[str, Any] , UpperCAmelCase_ : Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"] , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : int = -1 , UpperCAmelCase_ : bool = False , UpperCAmelCase_ : Optional["TensorType"] = None , UpperCAmelCase_ : int = 3 , UpperCAmelCase_ : int = 40 , UpperCAmelCase_ : int = 40 , UpperCAmelCase_ : "PreTrainedTokenizerBase" = None , ):
lowerCAmelCase : List[str] = super().generate_dummy_inputs(preprocessor=UpperCAmelCase_ , framework=UpperCAmelCase_ )
if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
del dummy_inputs["token_type_ids"]
return dummy_inputs
| 323 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path
import timm
import torch
from huggingface_hub import hf_hub_download
from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger()
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = True ):
'''simple docstring'''
print(F"""Converting {name}...""" )
with torch.no_grad():
if hidden_sizes == 1_2_8:
if name[-1] == "S":
_a : Optional[Any] = timm.create_model("""levit_128s""" , pretrained=UpperCamelCase__ )
else:
_a : Any = timm.create_model("""levit_128""" , pretrained=UpperCamelCase__ )
if hidden_sizes == 1_9_2:
_a : int = timm.create_model("""levit_192""" , pretrained=UpperCamelCase__ )
if hidden_sizes == 2_5_6:
_a : str = timm.create_model("""levit_256""" , pretrained=UpperCamelCase__ )
if hidden_sizes == 3_8_4:
_a : List[Any] = timm.create_model("""levit_384""" , pretrained=UpperCamelCase__ )
from_model.eval()
_a : Dict = LevitForImageClassificationWithTeacher(UpperCamelCase__ ).eval()
_a : Union[str, Any] = OrderedDict()
_a : Tuple = from_model.state_dict()
_a : Any = list(from_model.state_dict().keys() )
_a : Dict = list(our_model.state_dict().keys() )
print(len(UpperCamelCase__ ) , len(UpperCamelCase__ ) )
for i in range(len(UpperCamelCase__ ) ):
_a : Union[str, Any] = weights[og_keys[i]]
our_model.load_state_dict(UpperCamelCase__ )
_a : int = torch.randn((2, 3, 2_2_4, 2_2_4) )
_a : Dict = from_model(UpperCamelCase__ )
_a : List[Any] = our_model(UpperCamelCase__ ).logits
assert torch.allclose(UpperCamelCase__ , UpperCamelCase__ ), "The model logits don't match the original one."
_a : Dict = name
print(UpperCamelCase__ )
if push_to_hub:
our_model.save_pretrained(save_directory / checkpoint_name )
_a : List[Any] = LevitImageProcessor()
image_processor.save_pretrained(save_directory / checkpoint_name )
print(F"""Pushed {checkpoint_name}""" )
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ = None , UpperCamelCase__ = True ):
'''simple docstring'''
_a : Dict = """imagenet-1k-id2label.json"""
_a : int = 1_0_0_0
_a : Any = (1, num_labels)
_a : List[Any] = """huggingface/label-files"""
_a : Optional[int] = num_labels
_a : str = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="""dataset""" ) , """r""" ) )
_a : Union[str, Any] = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_a : Tuple = idalabel
_a : Dict = {v: k for k, v in idalabel.items()}
_a : int = partial(UpperCamelCase__ , num_labels=UpperCamelCase__ , idalabel=UpperCamelCase__ , labelaid=UpperCamelCase__ )
_a : Any = {
"""levit-128S""": 1_2_8,
"""levit-128""": 1_2_8,
"""levit-192""": 1_9_2,
"""levit-256""": 2_5_6,
"""levit-384""": 3_8_4,
}
_a : Tuple = {
"""levit-128S""": ImageNetPreTrainedConfig(
hidden_sizes=[1_2_8, 2_5_6, 3_8_4] , num_attention_heads=[4, 6, 8] , depths=[2, 3, 4] , key_dim=[1_6, 1_6, 1_6] , drop_path_rate=0 , ),
"""levit-128""": ImageNetPreTrainedConfig(
hidden_sizes=[1_2_8, 2_5_6, 3_8_4] , num_attention_heads=[4, 8, 1_2] , depths=[4, 4, 4] , key_dim=[1_6, 1_6, 1_6] , drop_path_rate=0 , ),
"""levit-192""": ImageNetPreTrainedConfig(
hidden_sizes=[1_9_2, 2_8_8, 3_8_4] , num_attention_heads=[3, 5, 6] , depths=[4, 4, 4] , key_dim=[3_2, 3_2, 3_2] , drop_path_rate=0 , ),
"""levit-256""": ImageNetPreTrainedConfig(
hidden_sizes=[2_5_6, 3_8_4, 5_1_2] , num_attention_heads=[4, 6, 8] , depths=[4, 4, 4] , key_dim=[3_2, 3_2, 3_2] , drop_path_rate=0 , ),
"""levit-384""": ImageNetPreTrainedConfig(
hidden_sizes=[3_8_4, 5_1_2, 7_6_8] , num_attention_heads=[6, 9, 1_2] , depths=[4, 4, 4] , key_dim=[3_2, 3_2, 3_2] , drop_path_rate=0.1 , ),
}
if model_name:
convert_weight_and_push(
names_to_hidden_sizes[model_name] , UpperCamelCase__ , names_to_config[model_name] , UpperCamelCase__ , UpperCamelCase__ )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(names_to_hidden_sizes[model_name] , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
return config, expected_shape
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default=None,
type=str,
help='The name of the model you wish to convert, it must be one of the supported Levit* architecture,',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='levit-dump-folder/',
type=Path,
required=False,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
parser.add_argument(
'--no-push_to_hub',
dest='push_to_hub',
action='store_false',
help='Do not push model and image processor to the hub',
)
_snake_case = parser.parse_args()
_snake_case = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 294 |
'''simple docstring'''
from ....utils import logging
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
def __init__( self : Tuple , __a : int , __a : Any=None , __a : Optional[int]=20_48 ):
_a = config.__dict__
_a = modal_hidden_size
if num_labels:
_a = num_labels
| 63 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
_snake_case = logging.get_logger(__name__)
_snake_case = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_snake_case = {
'''vocab_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'''
),
'''distilbert-base-german-cased''': '''https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt''',
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''distilbert-base-uncased''': '''https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json''',
'''distilbert-base-uncased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-cased''': '''https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json''',
'''distilbert-base-cased-distilled-squad''': (
'''https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'''
),
'''distilbert-base-german-cased''': (
'''https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'''
),
'''distilbert-base-multilingual-cased''': (
'''https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'''
),
},
}
_snake_case = {
'''distilbert-base-uncased''': 512,
'''distilbert-base-uncased-distilled-squad''': 512,
'''distilbert-base-cased''': 512,
'''distilbert-base-cased-distilled-squad''': 512,
'''distilbert-base-german-cased''': 512,
'''distilbert-base-multilingual-cased''': 512,
}
_snake_case = {
'''distilbert-base-uncased''': {'''do_lower_case''': True},
'''distilbert-base-uncased-distilled-squad''': {'''do_lower_case''': True},
'''distilbert-base-cased''': {'''do_lower_case''': False},
'''distilbert-base-cased-distilled-squad''': {'''do_lower_case''': False},
'''distilbert-base-german-cased''': {'''do_lower_case''': False},
'''distilbert-base-multilingual-cased''': {'''do_lower_case''': False},
}
class _snake_case ( _lowercase ):
lowerCamelCase__: Any = VOCAB_FILES_NAMES
lowerCamelCase__: Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase__: Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase__: List[str] = PRETRAINED_INIT_CONFIGURATION
lowerCamelCase__: Optional[int] = ["input_ids", "attention_mask"]
lowerCamelCase__: Dict = DistilBertTokenizer
def __init__( self: Dict , __lowerCamelCase: List[Any]=None , __lowerCamelCase: str=None , __lowerCamelCase: Any=True , __lowerCamelCase: List[Any]="[UNK]" , __lowerCamelCase: Any="[SEP]" , __lowerCamelCase: List[str]="[PAD]" , __lowerCamelCase: Union[str, Any]="[CLS]" , __lowerCamelCase: Optional[Any]="[MASK]" , __lowerCamelCase: Optional[Any]=True , __lowerCamelCase: str=None , **__lowerCamelCase: Tuple , ) -> List[Any]:
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 , )
__UpperCAmelCase : str = 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
):
__UpperCAmelCase : List[Any] = getattr(__lowerCamelCase , normalizer_state.pop("type" ) )
__UpperCAmelCase : List[str] = do_lower_case
__UpperCAmelCase : Any = strip_accents
__UpperCAmelCase : List[str] = tokenize_chinese_chars
__UpperCAmelCase : List[str] = normalizer_class(**__lowerCamelCase )
__UpperCAmelCase : List[str] = do_lower_case
def _lowerCamelCase ( self: List[str] , __lowerCamelCase: List[str] , __lowerCamelCase: Dict=None ) -> str:
__UpperCAmelCase : Union[str, Any] = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _lowerCamelCase ( self: int , __lowerCamelCase: List[int] , __lowerCamelCase: Optional[List[int]] = None ) -> List[int]:
__UpperCAmelCase : Optional[int] = [self.sep_token_id]
__UpperCAmelCase : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _lowerCamelCase ( self: Optional[Any] , __lowerCamelCase: str , __lowerCamelCase: Optional[str] = None ) -> Tuple[str]:
__UpperCAmelCase : Optional[Any] = self._tokenizer.model.save(__lowerCamelCase , name=__lowerCamelCase )
return tuple(__lowerCamelCase )
| 342 | import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
_snake_case = pytest.mark.integration
@require_faiss
class _snake_case ( _lowercase ):
def _lowerCamelCase ( self: Union[str, Any] ) -> str:
__UpperCAmelCase : Optional[int] = Dataset.from_dict({"filename": ["my_name-train" + "_" + str(__lowerCamelCase ) for x in np.arange(30 ).tolist()]} )
return dset
def _lowerCamelCase ( self: Optional[Any] ) -> Tuple:
import faiss
__UpperCAmelCase : Dataset = self._create_dummy_dataset()
__UpperCAmelCase : int = dset.map(
lambda __lowerCamelCase , __lowerCamelCase : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=__lowerCamelCase , keep_in_memory=__lowerCamelCase )
__UpperCAmelCase : Tuple = dset.add_faiss_index("vecs" , batch_size=1_00 , metric_type=faiss.METRIC_INNER_PRODUCT )
__UpperCAmelCase , __UpperCAmelCase : Dict = dset.get_nearest_examples("vecs" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
dset.drop_index("vecs" )
def _lowerCamelCase ( self: List[str] ) -> int:
import faiss
__UpperCAmelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" , batch_size=1_00 , metric_type=faiss.METRIC_INNER_PRODUCT , )
__UpperCAmelCase , __UpperCAmelCase : Tuple = dset.get_nearest_examples("vecs" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
def _lowerCamelCase ( self: Optional[int] ) -> Dict:
import faiss
__UpperCAmelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__lowerCamelCase ) as tmp_file:
dset.save_faiss_index("vecs" , tmp_file.name )
dset.load_faiss_index("vecs2" , tmp_file.name )
os.unlink(tmp_file.name )
__UpperCAmelCase , __UpperCAmelCase : List[Any] = dset.get_nearest_examples("vecs2" , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
def _lowerCamelCase ( self: List[Any] ) -> List[Any]:
__UpperCAmelCase : Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name="vecs" )
dset.drop_index("vecs" )
self.assertRaises(__lowerCamelCase , partial(dset.get_nearest_examples , "vecs2" , np.ones(5 , dtype=np.floataa ) ) )
def _lowerCamelCase ( self: List[str] ) -> Dict:
from elasticsearch import Elasticsearch
__UpperCAmelCase : Dataset = self._create_dummy_dataset()
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
__UpperCAmelCase : int = {"acknowledged": True}
mocked_bulk.return_value([(True, None)] * 30 )
__UpperCAmelCase : Dict = {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
__UpperCAmelCase : Any = Elasticsearch()
dset.add_elasticsearch_index("filename" , es_client=__lowerCamelCase )
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = dset.get_nearest_examples("filename" , "my_name-train_29" )
self.assertEqual(examples["filename"][0] , "my_name-train_29" )
@require_faiss
class _snake_case ( _lowercase ):
def _lowerCamelCase ( self: List[str] ) -> Optional[int]:
import faiss
__UpperCAmelCase : int = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
__UpperCAmelCase : Dict = np.zeros(5 , dtype=np.floataa )
__UpperCAmelCase : List[str] = 1
__UpperCAmelCase , __UpperCAmelCase : List[str] = index.search(__lowerCamelCase )
self.assertRaises(__lowerCamelCase , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
__UpperCAmelCase : List[str] = np.eye(5 , dtype=np.floataa )[::-1]
__UpperCAmelCase , __UpperCAmelCase : Any = index.search_batch(__lowerCamelCase )
self.assertRaises(__lowerCamelCase , index.search_batch , queries[0] )
__UpperCAmelCase : Dict = [scores[0] for scores in total_scores]
__UpperCAmelCase : int = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__lowerCamelCase ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , __lowerCamelCase )
def _lowerCamelCase ( self: Any ) -> List[str]:
import faiss
__UpperCAmelCase : Dict = FaissIndex(string_factory="Flat" )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
__UpperCAmelCase : Optional[Any] = FaissIndex(string_factory="LSH" )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(__lowerCamelCase ):
__UpperCAmelCase : Any = FaissIndex(string_factory="Flat" , custom_index=faiss.IndexFlat(5 ) )
def _lowerCamelCase ( self: List[str] ) -> Dict:
import faiss
__UpperCAmelCase : str = faiss.IndexFlat(5 )
__UpperCAmelCase : int = FaissIndex(custom_index=__lowerCamelCase )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def _lowerCamelCase ( self: Union[str, Any] ) -> int:
import faiss
__UpperCAmelCase : Any = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=__lowerCamelCase ) as tmp_file:
index.save(tmp_file.name )
__UpperCAmelCase : List[str] = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
__UpperCAmelCase : Tuple = np.zeros(5 , dtype=np.floataa )
__UpperCAmelCase : Tuple = 1
__UpperCAmelCase , __UpperCAmelCase : List[Any] = index.search(__lowerCamelCase )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def _UpperCamelCase ( snake_case__ ) -> Optional[Any]:
import faiss
__UpperCAmelCase : Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5, dtype=np.floataa ) )
__UpperCAmelCase : Optional[Any] = "index.faiss"
__UpperCAmelCase : Optional[int] = f'''mock://{index_name}'''
index.save(snake_case__, storage_options=mockfs.storage_options )
__UpperCAmelCase : Dict = FaissIndex.load(snake_case__, storage_options=mockfs.storage_options )
__UpperCAmelCase : str = np.zeros(5, dtype=np.floataa )
__UpperCAmelCase : Any = 1
__UpperCAmelCase , __UpperCAmelCase : List[str] = index.search(snake_case__ )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class _snake_case ( _lowercase ):
def _lowerCamelCase ( self: str ) -> Union[str, Any]:
from elasticsearch import Elasticsearch
with patch("elasticsearch.Elasticsearch.search" ) as mocked_search, patch(
"elasticsearch.client.IndicesClient.create" ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk" ) as mocked_bulk:
__UpperCAmelCase : Optional[Any] = Elasticsearch()
__UpperCAmelCase : Dict = {"acknowledged": True}
__UpperCAmelCase : Any = ElasticSearchIndex(es_client=__lowerCamelCase )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(["foo", "bar", "foobar"] )
# single query
__UpperCAmelCase : Dict = "foo"
__UpperCAmelCase : Optional[Any] = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
__UpperCAmelCase , __UpperCAmelCase : Optional[int] = index.search(__lowerCamelCase )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
__UpperCAmelCase : int = "foo"
__UpperCAmelCase : Optional[Any] = {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = index.search(__lowerCamelCase , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
__UpperCAmelCase : int = ["foo", "bar", "foobar"]
__UpperCAmelCase : Union[str, Any] = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
__UpperCAmelCase , __UpperCAmelCase : List[Any] = index.search_batch(__lowerCamelCase )
__UpperCAmelCase : Tuple = [scores[0] for scores in total_scores]
__UpperCAmelCase : Optional[int] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__lowerCamelCase ) , 0 )
self.assertListEqual([1, 1, 1] , __lowerCamelCase )
# batched queries with timeout
__UpperCAmelCase : str = ["foo", "bar", "foobar"]
__UpperCAmelCase : Tuple = {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = index.search_batch(__lowerCamelCase , request_timeout=30 )
__UpperCAmelCase : Union[str, Any] = [scores[0] for scores in total_scores]
__UpperCAmelCase : List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(__lowerCamelCase ) , 0 )
self.assertListEqual([1, 1, 1] , __lowerCamelCase )
| 342 | 1 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from transformers import (
SPIECE_UNDERLINE,
AddedToken,
BatchEncoding,
NllbTokenizer,
NllbTokenizerFast,
is_torch_available,
)
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
)
from ...test_tokenization_common import TokenizerTesterMixin
__lowerCAmelCase = get_tests_dir('''fixtures/test_sentencepiece.model''')
if is_torch_available():
from transformers.models.mam_aaa.modeling_mam_aaa import shift_tokens_right
__lowerCAmelCase = 256_047
__lowerCAmelCase = 256_145
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( _UpperCamelCase , unittest.TestCase ):
lowerCAmelCase : Tuple = NllbTokenizer
lowerCAmelCase : Tuple = NllbTokenizerFast
lowerCAmelCase : List[str] = True
lowerCAmelCase : Union[str, Any] = True
lowerCAmelCase : List[str] = {}
def __lowercase ( self : List[str] ):
super().setUp()
# We have a SentencePiece fixture for testing
_a : int = NllbTokenizer(_UpperCAmelCase ,keep_accents=_UpperCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def __lowercase ( self : Any ):
_a : Optional[Any] = NllbTokenizer(_UpperCAmelCase ,keep_accents=_UpperCAmelCase )
_a : List[str] = tokenizer.tokenize('This is a test' )
self.assertListEqual(_UpperCAmelCase ,['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) ,[value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] ,)
_a : Optional[int] = tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
_UpperCAmelCase ,[
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] ,)
_a : Tuple = tokenizer.convert_tokens_to_ids(_UpperCAmelCase )
self.assertListEqual(
_UpperCAmelCase ,[
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] ,)
_a : Dict = tokenizer.convert_ids_to_tokens(_UpperCAmelCase )
self.assertListEqual(
_UpperCAmelCase ,[
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] ,)
def __lowercase ( self : Optional[Any] ):
_a : Tuple = (self.rust_tokenizer_class, 'hf-internal-testing/tiny-random-nllb', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_a : List[str] = self.rust_tokenizer_class.from_pretrained(_UpperCAmelCase ,**_UpperCAmelCase )
_a : Tuple = self.tokenizer_class.from_pretrained(_UpperCAmelCase ,**_UpperCAmelCase )
_a : Tuple = tempfile.mkdtemp()
_a : str = tokenizer_r.save_pretrained(_UpperCAmelCase )
_a : str = tokenizer_p.save_pretrained(_UpperCAmelCase )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
_a : int = tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(_UpperCAmelCase ,_UpperCAmelCase )
# Checks everything loads correctly in the same way
_a : Union[str, Any] = tokenizer_r.from_pretrained(_UpperCAmelCase )
_a : Optional[Any] = tokenizer_p.from_pretrained(_UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_UpperCAmelCase ,_UpperCAmelCase ) )
shutil.rmtree(_UpperCAmelCase )
# Save tokenizer rust, legacy_format=True
_a : Any = tempfile.mkdtemp()
_a : Tuple = tokenizer_r.save_pretrained(_UpperCAmelCase ,legacy_format=_UpperCAmelCase )
_a : Optional[int] = tokenizer_p.save_pretrained(_UpperCAmelCase )
# Checks it save with the same files
self.assertSequenceEqual(_UpperCAmelCase ,_UpperCAmelCase )
# Checks everything loads correctly in the same way
_a : Optional[Any] = tokenizer_r.from_pretrained(_UpperCAmelCase )
_a : Optional[Any] = tokenizer_p.from_pretrained(_UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_UpperCAmelCase ,_UpperCAmelCase ) )
shutil.rmtree(_UpperCAmelCase )
# Save tokenizer rust, legacy_format=False
_a : Dict = tempfile.mkdtemp()
_a : Any = tokenizer_r.save_pretrained(_UpperCAmelCase ,legacy_format=_UpperCAmelCase )
_a : Union[str, Any] = tokenizer_p.save_pretrained(_UpperCAmelCase )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
_a : int = tokenizer_r.from_pretrained(_UpperCAmelCase )
_a : List[str] = tokenizer_p.from_pretrained(_UpperCAmelCase )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(_UpperCAmelCase ,_UpperCAmelCase ) )
shutil.rmtree(_UpperCAmelCase )
@require_torch
def __lowercase ( self : int ):
if not self.test_seqaseq:
return
_a : List[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# Longer text that will definitely require truncation.
_a : Any = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for'
' Syria is that \'there is no military solution\' to the nearly five-year conflict and more weapons'
' will only worsen the violence and misery for millions of people.',
]
_a : Optional[Any] = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al'
' Rusiei pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi'
' că noi arme nu vor face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
try:
_a : Optional[int] = tokenizer.prepare_seqaseq_batch(
src_texts=_UpperCAmelCase ,tgt_texts=_UpperCAmelCase ,max_length=3 ,max_target_length=10 ,return_tensors='pt' ,src_lang='eng_Latn' ,tgt_lang='ron_Latn' ,)
except NotImplementedError:
return
self.assertEqual(batch.input_ids.shape[1] ,3 )
self.assertEqual(batch.labels.shape[1] ,10 )
# max_target_length will default to max_length if not specified
_a : List[str] = tokenizer.prepare_seqaseq_batch(
_UpperCAmelCase ,tgt_texts=_UpperCAmelCase ,max_length=3 ,return_tensors='pt' )
self.assertEqual(batch.input_ids.shape[1] ,3 )
self.assertEqual(batch.labels.shape[1] ,3 )
_a : Dict = tokenizer.prepare_seqaseq_batch(
src_texts=_UpperCAmelCase ,max_length=3 ,max_target_length=10 ,return_tensors='pt' )
self.assertEqual(batch_encoder_only.input_ids.shape[1] ,3 )
self.assertEqual(batch_encoder_only.attention_mask.shape[1] ,3 )
self.assertNotIn('decoder_input_ids' ,_UpperCAmelCase )
@unittest.skip('Unfortunately way too slow to build a BPE with SentencePiece.' )
def __lowercase ( self : List[str] ):
pass
def __lowercase ( self : Dict ):
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
_a : Dict = [AddedToken('<special>' ,lstrip=_UpperCAmelCase )]
_a : Dict = self.rust_tokenizer_class.from_pretrained(
_UpperCAmelCase ,additional_special_tokens=_UpperCAmelCase ,**_UpperCAmelCase )
_a : Tuple = tokenizer_r.encode('Hey this is a <special> token' )
_a : Dict = tokenizer_r.encode('<special>' ,add_special_tokens=_UpperCAmelCase )[0]
self.assertTrue(special_token_id in r_output )
if self.test_slow_tokenizer:
_a : Tuple = self.rust_tokenizer_class.from_pretrained(
_UpperCAmelCase ,additional_special_tokens=_UpperCAmelCase ,**_UpperCAmelCase ,)
_a : Any = self.tokenizer_class.from_pretrained(
_UpperCAmelCase ,additional_special_tokens=_UpperCAmelCase ,**_UpperCAmelCase )
_a : Tuple = tokenizer_p.encode('Hey this is a <special> token' )
_a : Optional[Any] = tokenizer_cr.encode('Hey this is a <special> token' )
self.assertEqual(_UpperCAmelCase ,_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase ,_UpperCAmelCase )
self.assertTrue(special_token_id in p_output )
self.assertTrue(special_token_id in cr_output )
@require_torch
@require_sentencepiece
@require_tokenizers
class __magic_name__ ( unittest.TestCase ):
lowerCAmelCase : Optional[int] = 'facebook/nllb-200-distilled-600M'
lowerCAmelCase : int = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
lowerCAmelCase : Optional[int] = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
lowerCAmelCase : int = [
2_5_6_0_4_7,
1_6_2_9_7,
1_3_4_4_0_8,
8_1_6_5,
2_4_8_0_6_6,
1_4_7_3_4,
9_5_0,
1_1_3_5,
1_0_5_7_2_1,
3_5_7_3,
8_3,
2_7_3_5_2,
1_0_8,
4_9_4_8_6,
2,
]
@classmethod
def __lowercase ( cls : Union[str, Any] ):
_a : NllbTokenizer = NllbTokenizer.from_pretrained(
cls.checkpoint_name ,src_lang='eng_Latn' ,tgt_lang='ron_Latn' )
_a : Optional[Any] = 1
return cls
def __lowercase ( self : Any ):
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Arab'] ,256001 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['ace_Latn'] ,256002 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids['fra_Latn'] ,256057 )
def __lowercase ( self : List[Any] ):
_a : int = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens ,_UpperCAmelCase )
def __lowercase ( self : Any ):
self.assertIn(_UpperCAmelCase ,self.tokenizer.all_special_ids )
# fmt: off
_a : Dict = [RO_CODE, 4254, 98068, 112923, 39072, 3909, 713, 102767, 26, 17314, 35642, 14683, 33118, 2022, 66987, 2, 256047]
# fmt: on
_a : Tuple = self.tokenizer.decode(_UpperCAmelCase ,skip_special_tokens=_UpperCAmelCase )
_a : List[str] = self.tokenizer.decode(generated_ids[1:] ,skip_special_tokens=_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase ,_UpperCAmelCase )
self.assertNotIn(self.tokenizer.eos_token ,_UpperCAmelCase )
def __lowercase ( self : Optional[int] ):
_a : Optional[int] = ['this is gunna be a long sentence ' * 20]
assert isinstance(src_text[0] ,_UpperCAmelCase )
_a : List[str] = 10
_a : List[Any] = self.tokenizer(_UpperCAmelCase ,max_length=_UpperCAmelCase ,truncation=_UpperCAmelCase ).input_ids[0]
self.assertEqual(ids[-1] ,2 )
self.assertEqual(ids[0] ,_UpperCAmelCase )
self.assertEqual(len(_UpperCAmelCase ) ,_UpperCAmelCase )
def __lowercase ( self : Dict ):
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(['<mask>', 'ar_AR'] ) ,[256203, 3] )
def __lowercase ( self : List[Any] ):
_a : Union[str, Any] = tempfile.mkdtemp()
_a : Tuple = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(_UpperCAmelCase )
_a : Tuple = NllbTokenizer.from_pretrained(_UpperCAmelCase )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids ,_UpperCAmelCase )
@require_torch
def __lowercase ( self : Optional[int] ):
_a : Optional[Any] = self.tokenizer(
self.src_text ,text_target=self.tgt_text ,padding=_UpperCAmelCase ,truncation=_UpperCAmelCase ,max_length=len(self.expected_src_tokens ) ,return_tensors='pt' ,)
_a : Tuple = shift_tokens_right(
batch['labels'] ,self.tokenizer.pad_token_id ,self.tokenizer.lang_code_to_id['ron_Latn'] )
self.assertIsInstance(_UpperCAmelCase ,_UpperCAmelCase )
self.assertEqual((2, 15) ,batch.input_ids.shape )
self.assertEqual((2, 15) ,batch.attention_mask.shape )
_a : Optional[int] = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens ,_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase ,batch.decoder_input_ids[0, 0] ) # EOS
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens ,[EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens ,[self.tokenizer.eos_token_id] )
def __lowercase ( self : List[str] ):
_a : int = self.tokenizer(self.src_text ,padding=_UpperCAmelCase ,truncation=_UpperCAmelCase ,max_length=3 ,return_tensors='pt' )
_a : str = self.tokenizer(
text_target=self.tgt_text ,padding=_UpperCAmelCase ,truncation=_UpperCAmelCase ,max_length=10 ,return_tensors='pt' )
_a : Union[str, Any] = targets['input_ids']
_a : Dict = shift_tokens_right(
_UpperCAmelCase ,self.tokenizer.pad_token_id ,decoder_start_token_id=self.tokenizer.lang_code_to_id[self.tokenizer.tgt_lang] ,)
self.assertEqual(batch.input_ids.shape[1] ,3 )
self.assertEqual(batch.decoder_input_ids.shape[1] ,10 )
@require_torch
def __lowercase ( self : List[Any] ):
_a : int = self.tokenizer._build_translation_inputs(
'A test' ,return_tensors='pt' ,src_lang='eng_Latn' ,tgt_lang='fra_Latn' )
self.assertEqual(
nested_simplify(_UpperCAmelCase ) ,{
# A, test, EOS, en_XX
'input_ids': [[256047, 70, 7356, 2]],
'attention_mask': [[1, 1, 1, 1]],
# ar_AR
'forced_bos_token_id': 256057,
} ,)
@require_torch
def __lowercase ( self : Union[str, Any] ):
_a : List[str] = True
_a : str = self.tokenizer(
'UN Chief says there is no military solution in Syria' ,src_lang='eng_Latn' ,tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids ,[16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2, 256047] )
_a : Tuple = False
_a : str = self.tokenizer(
'UN Chief says there is no military solution in Syria' ,src_lang='eng_Latn' ,tgt_lang='fra_Latn' )
self.assertEqual(
inputs.input_ids ,[256047, 16297, 134408, 25653, 6370, 248, 254, 103929, 94995, 108, 49486, 2] )
| 89 |
'''simple docstring'''
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __magic_name__ ( _UpperCamelCase , unittest.TestCase ):
lowerCAmelCase : str = LayoutLMTokenizer
lowerCAmelCase : Tuple = LayoutLMTokenizerFast
lowerCAmelCase : List[Any] = True
lowerCAmelCase : int = True
def __lowercase ( self : Dict ):
super().setUp()
_a : int = [
'[UNK]',
'[CLS]',
'[SEP]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
_a : List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file ,'w' ,encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def __lowercase ( self : Dict ,**_UpperCAmelCase : List[str] ):
return LayoutLMTokenizer.from_pretrained(self.tmpdirname ,**_UpperCAmelCase )
def __lowercase ( self : Optional[Any] ,_UpperCAmelCase : Tuple ):
_a : Optional[int] = 'UNwant\u00E9d,running'
_a : List[Any] = 'unwanted, running'
return input_text, output_text
def __lowercase ( self : Optional[int] ):
_a : Optional[Any] = self.tokenizer_class(self.vocab_file )
_a : Optional[Any] = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(_UpperCAmelCase ,['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_UpperCAmelCase ) ,[7, 4, 5, 10, 8, 9] )
def __lowercase ( self : Optional[int] ):
pass
| 89 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : List[Any] = logging.get_logger(__name__)
a : List[Any] = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class a ( _lowerCamelCase ):
snake_case_ = "time_series_transformer"
snake_case_ = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
"num_hidden_layers": "encoder_layers",
}
def __init__( self : int , lowercase_ : Optional[int] = None , lowercase_ : Optional[int] = None , lowercase_ : str = "student_t" , lowercase_ : str = "nll" , lowercase_ : int = 1 , lowercase_ : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowercase_ : Optional[Union[str, bool]] = "mean" , lowercase_ : int = 0 , lowercase_ : int = 0 , lowercase_ : int = 0 , lowercase_ : int = 0 , lowercase_ : Optional[List[int]] = None , lowercase_ : Optional[List[int]] = None , lowercase_ : int = 32 , lowercase_ : int = 32 , lowercase_ : int = 2 , lowercase_ : int = 2 , lowercase_ : int = 2 , lowercase_ : int = 2 , lowercase_ : bool = True , lowercase_ : str = "gelu" , lowercase_ : int = 64 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : float = 0.1 , lowercase_ : int = 100 , lowercase_ : float = 0.02 , lowercase_ : Union[str, Any]=True , **lowercase_ : Dict , ):
# time series specific configuration
snake_case_ = prediction_length
snake_case_ = context_length or prediction_length
snake_case_ = distribution_output
snake_case_ = loss
snake_case_ = input_size
snake_case_ = num_time_features
snake_case_ = lags_sequence
snake_case_ = scaling
snake_case_ = num_dynamic_real_features
snake_case_ = num_static_real_features
snake_case_ = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(__A ) != num_static_categorical_features:
raise ValueError(
'''The cardinality should be a list of the same length as `num_static_categorical_features`''' )
snake_case_ = cardinality
else:
snake_case_ = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(__A ) != num_static_categorical_features:
raise ValueError(
'''The embedding dimension should be a list of the same length as `num_static_categorical_features`''' )
snake_case_ = embedding_dimension
else:
snake_case_ = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
snake_case_ = num_parallel_samples
# Transformer architecture configuration
snake_case_ = input_size * len(__A ) + self._number_of_features
snake_case_ = d_model
snake_case_ = encoder_attention_heads
snake_case_ = decoder_attention_heads
snake_case_ = encoder_ffn_dim
snake_case_ = decoder_ffn_dim
snake_case_ = encoder_layers
snake_case_ = decoder_layers
snake_case_ = dropout
snake_case_ = attention_dropout
snake_case_ = activation_dropout
snake_case_ = encoder_layerdrop
snake_case_ = decoder_layerdrop
snake_case_ = activation_function
snake_case_ = init_std
snake_case_ = use_cache
super().__init__(is_encoder_decoder=__A , **__A )
@property
def A_ ( self : List[str] ):
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 358 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a : Optional[int] = logging.get_logger(__name__)
a : Optional[Any] = {
'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class a ( _lowerCamelCase ):
snake_case_ = "xlm-roberta-xl"
def __init__( self : Optional[Any] , lowercase_ : Optional[Any]=25_0880 , lowercase_ : Tuple=2560 , lowercase_ : str=36 , lowercase_ : List[str]=32 , lowercase_ : Optional[Any]=1_0240 , lowercase_ : List[str]="gelu" , lowercase_ : Union[str, Any]=0.1 , lowercase_ : Union[str, Any]=0.1 , lowercase_ : str=514 , lowercase_ : Any=1 , lowercase_ : Optional[Any]=0.02 , lowercase_ : Dict=1e-05 , lowercase_ : List[Any]=1 , lowercase_ : str=0 , lowercase_ : Dict=2 , lowercase_ : Optional[Any]="absolute" , lowercase_ : str=True , lowercase_ : str=None , **lowercase_ : Tuple , ):
super().__init__(pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_ )
snake_case_ = vocab_size
snake_case_ = hidden_size
snake_case_ = num_hidden_layers
snake_case_ = num_attention_heads
snake_case_ = hidden_act
snake_case_ = intermediate_size
snake_case_ = hidden_dropout_prob
snake_case_ = attention_probs_dropout_prob
snake_case_ = max_position_embeddings
snake_case_ = type_vocab_size
snake_case_ = initializer_range
snake_case_ = layer_norm_eps
snake_case_ = position_embedding_type
snake_case_ = use_cache
snake_case_ = classifier_dropout
class a ( _lowerCamelCase ):
@property
def A_ ( self : Optional[Any] ):
if self.task == "multiple-choice":
snake_case_ = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
snake_case_ = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
] )
| 72 | 0 |
'''simple docstring'''
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import PTtoTFCommand
from .run import RunCommand
from .serving import ServeCommand
from .user import UserCommands
def lowercase__ ( ) -> Optional[int]:
"""simple docstring"""
__UpperCamelCase = ArgumentParser('Transformers CLI tool' , usage='transformers-cli <command> [<args>]' )
__UpperCamelCase = parser.add_subparsers(help='transformers-cli command helpers' )
# Register commands
ConvertCommand.register_subcommand(__SCREAMING_SNAKE_CASE )
DownloadCommand.register_subcommand(__SCREAMING_SNAKE_CASE )
EnvironmentCommand.register_subcommand(__SCREAMING_SNAKE_CASE )
RunCommand.register_subcommand(__SCREAMING_SNAKE_CASE )
ServeCommand.register_subcommand(__SCREAMING_SNAKE_CASE )
UserCommands.register_subcommand(__SCREAMING_SNAKE_CASE )
AddNewModelCommand.register_subcommand(__SCREAMING_SNAKE_CASE )
AddNewModelLikeCommand.register_subcommand(__SCREAMING_SNAKE_CASE )
LfsCommands.register_subcommand(__SCREAMING_SNAKE_CASE )
PTtoTFCommand.register_subcommand(__SCREAMING_SNAKE_CASE )
# Let's go
__UpperCamelCase = parser.parse_args()
if not hasattr(__SCREAMING_SNAKE_CASE , 'func' ):
parser.print_help()
exit(1 )
# Run
__UpperCamelCase = args.func(__SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 53 |
'''simple docstring'''
from math import isqrt, loga
def snake_case_ ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
lowercase_ : Any = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase_ : Optional[Any] = False
return [i for i in range(2 , __SCREAMING_SNAKE_CASE ) if is_prime[i]]
def snake_case_ ( __SCREAMING_SNAKE_CASE : int = 800800 , __SCREAMING_SNAKE_CASE : int = 800800 ):
"""simple docstring"""
lowercase_ : Union[str, Any] = degree * loga(__SCREAMING_SNAKE_CASE )
lowercase_ : Any = int(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[Any] = calculate_prime_numbers(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[Any] = 0
lowercase_ : List[Any] = 0
lowercase_ : Union[str, Any] = len(__SCREAMING_SNAKE_CASE ) - 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() = }""")
| 93 | 0 |
'''simple docstring'''
import re
def __lowerCamelCase ( __lowerCAmelCase : str ) -> bool:
snake_case = re.compile(
r"""^(?:0|94|\+94|0{2}94)""" r"""7(0|1|2|4|5|6|7|8)""" r"""(-| |)""" r"""\d{7}$""" )
return bool(re.search(lowerCAmelCase__ , lowerCAmelCase__ ) )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = "0094702343221"
print(is_sri_lankan_phone_number(phone))
| 365 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
# Register SEW's fairseq modules
from sew_asapp import tasks # noqa: F401
from transformers import (
SEWConfig,
SEWForCTC,
SEWModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"post_extract_proj": "feature_projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.upsample.0": "encoder.upsample.projection",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "layer_norm",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
def __lowerCamelCase ( __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : Any , __lowerCAmelCase : str ) -> Union[str, Any]:
for attribute in key.split(""".""" ):
snake_case = getattr(__lowerCAmelCase , __lowerCAmelCase )
if weight_type is not None:
snake_case = getattr(__lowerCAmelCase , __lowerCAmelCase ).shape
else:
snake_case = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
snake_case = value
elif weight_type == "weight_g":
snake_case = value
elif weight_type == "weight_v":
snake_case = value
elif weight_type == "bias":
snake_case = value
else:
snake_case = value
logger.info(F'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def __lowerCamelCase ( __lowerCAmelCase : str , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any] ) -> int:
snake_case = []
snake_case = fairseq_model.state_dict()
snake_case = hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
snake_case = False
if "conv_layers" in name:
load_conv_layer(
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , hf_model.config.feat_extract_norm == """group""" , )
snake_case = True
else:
for key, mapped_key in MAPPING.items():
snake_case = """sew.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
snake_case = True
if "*" in mapped_key:
snake_case = name.split(__lowerCAmelCase )[0].split(""".""" )[-2]
snake_case = mapped_key.replace("""*""" , __lowerCAmelCase )
if "weight_g" in name:
snake_case = """weight_g"""
elif "weight_v" in name:
snake_case = """weight_v"""
elif "weight" in name:
snake_case = """weight"""
elif "bias" in name:
snake_case = """bias"""
else:
snake_case = None
set_recursively(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
continue
if not is_used:
unused_weights.append(__lowerCAmelCase )
logger.warning(F'''Unused weights: {unused_weights}''' )
def __lowerCamelCase ( __lowerCAmelCase : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any] , __lowerCAmelCase : Tuple ) -> List[str]:
snake_case = full_name.split("""conv_layers.""" )[-1]
snake_case = name.split(""".""" )
snake_case = int(items[0] )
snake_case = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
snake_case = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
snake_case = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
snake_case = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
snake_case = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__lowerCAmelCase )
def __lowerCamelCase ( __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Any ) -> List[str]:
snake_case = SEWConfig()
if is_finetuned:
snake_case = model.wav_encoder.wav_model.cfg
else:
snake_case = model.cfg
snake_case = fs_config.conv_bias
snake_case = eval(fs_config.conv_feature_layers )
snake_case = [x[0] for x in conv_layers]
snake_case = [x[1] for x in conv_layers]
snake_case = [x[2] for x in conv_layers]
snake_case = """gelu"""
snake_case = """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
snake_case = 0.0
snake_case = fs_config.activation_fn.name
snake_case = fs_config.encoder_embed_dim
snake_case = 0.02
snake_case = fs_config.encoder_ffn_embed_dim
snake_case = 1e-5
snake_case = fs_config.encoder_layerdrop
snake_case = fs_config.encoder_attention_heads
snake_case = fs_config.conv_pos_groups
snake_case = fs_config.conv_pos
snake_case = len(__lowerCAmelCase )
snake_case = fs_config.encoder_layers
snake_case = fs_config.squeeze_factor
# take care of any params that are overridden by the Wav2VecCtc model
if is_finetuned:
snake_case = model.cfg
snake_case = fs_config.final_dropout
snake_case = fs_config.layerdrop
snake_case = fs_config.activation_dropout
snake_case = fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
snake_case = fs_config.attention_dropout
snake_case = fs_config.dropout_input
snake_case = fs_config.dropout
snake_case = fs_config.mask_channel_length
snake_case = fs_config.mask_channel_prob
snake_case = fs_config.mask_length
snake_case = fs_config.mask_prob
snake_case = """Wav2Vec2FeatureExtractor"""
snake_case = """Wav2Vec2CTCTokenizer"""
return config
@torch.no_grad()
def __lowerCamelCase ( __lowerCAmelCase : List[str] , __lowerCAmelCase : Tuple , __lowerCAmelCase : List[Any]=None , __lowerCAmelCase : int=None , __lowerCAmelCase : str=True ) -> Any:
if is_finetuned:
snake_case , snake_case , snake_case = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
snake_case , snake_case , snake_case = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
if config_path is not None:
snake_case = SEWConfig.from_pretrained(__lowerCAmelCase )
else:
snake_case = convert_config(model[0] , __lowerCAmelCase )
snake_case = model[0].eval()
snake_case = True if config.feat_extract_norm == """layer""" else False
snake_case = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__lowerCAmelCase , return_attention_mask=__lowerCAmelCase , )
if is_finetuned:
if dict_path:
snake_case = Dictionary.load(__lowerCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
snake_case = target_dict.pad_index
snake_case = target_dict.bos_index
snake_case = target_dict.pad_index
snake_case = target_dict.bos_index
snake_case = target_dict.eos_index
snake_case = len(target_dict.symbols )
snake_case = os.path.join(__lowerCAmelCase , """vocab.json""" )
if not os.path.isdir(__lowerCAmelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowerCAmelCase ) )
return
os.makedirs(__lowerCAmelCase , exist_ok=__lowerCAmelCase )
with open(__lowerCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , __lowerCAmelCase )
snake_case = WavaVecaCTCTokenizer(
__lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=__lowerCAmelCase , )
snake_case = WavaVecaProcessor(feature_extractor=__lowerCAmelCase , tokenizer=__lowerCAmelCase )
processor.save_pretrained(__lowerCAmelCase )
snake_case = SEWForCTC(__lowerCAmelCase )
else:
snake_case = SEWModel(__lowerCAmelCase )
feature_extractor.save_pretrained(__lowerCAmelCase )
recursively_load_weights(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
hf_model.save_pretrained(__lowerCAmelCase )
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
_SCREAMING_SNAKE_CASE = parser.parse_args()
convert_sew_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
)
| 3 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanticSegmentation,
SegformerImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
a_ = logging.get_logger(__name__)
def _a( UpperCamelCase__ : int, UpperCamelCase__ : Optional[int]=False ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict =OrderedDict()
for key, value in state_dict.items():
if encoder_only and not key.startswith('''head''' ):
SCREAMING_SNAKE_CASE__ : List[str] ='''segformer.encoder.''' + key
if key.startswith('''backbone''' ):
SCREAMING_SNAKE_CASE__ : Union[str, Any] =key.replace('''backbone''', '''segformer.encoder''' )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
SCREAMING_SNAKE_CASE__ : str =key[key.find('''patch_embed''' ) + len('''patch_embed''' )]
SCREAMING_SNAKE_CASE__ : Optional[Any] =key.replace(f"patch_embed{idx}", f"patch_embeddings.{int(UpperCamelCase__ )-1}" )
if "norm" in key:
SCREAMING_SNAKE_CASE__ : List[Any] =key.replace('''norm''', '''layer_norm''' )
if "segformer.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
SCREAMING_SNAKE_CASE__ : List[str] =key[key.find('''segformer.encoder.layer_norm''' ) + len('''segformer.encoder.layer_norm''' )]
SCREAMING_SNAKE_CASE__ : List[Any] =key.replace(f"layer_norm{idx}", f"layer_norm.{int(UpperCamelCase__ )-1}" )
if "layer_norm1" in key:
SCREAMING_SNAKE_CASE__ : Tuple =key.replace('''layer_norm1''', '''layer_norm_1''' )
if "layer_norm2" in key:
SCREAMING_SNAKE_CASE__ : List[Any] =key.replace('''layer_norm2''', '''layer_norm_2''' )
if "block" in key:
# replace for example block1 by block.0
SCREAMING_SNAKE_CASE__ : List[Any] =key[key.find('''block''' ) + len('''block''' )]
SCREAMING_SNAKE_CASE__ : str =key.replace(f"block{idx}", f"block.{int(UpperCamelCase__ )-1}" )
if "attn.q" in key:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =key.replace('''attn.q''', '''attention.self.query''' )
if "attn.proj" in key:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =key.replace('''attn.proj''', '''attention.output.dense''' )
if "attn" in key:
SCREAMING_SNAKE_CASE__ : Any =key.replace('''attn''', '''attention.self''' )
if "fc1" in key:
SCREAMING_SNAKE_CASE__ : Tuple =key.replace('''fc1''', '''dense1''' )
if "fc2" in key:
SCREAMING_SNAKE_CASE__ : Tuple =key.replace('''fc2''', '''dense2''' )
if "linear_pred" in key:
SCREAMING_SNAKE_CASE__ : List[str] =key.replace('''linear_pred''', '''classifier''' )
if "linear_fuse" in key:
SCREAMING_SNAKE_CASE__ : List[Any] =key.replace('''linear_fuse.conv''', '''linear_fuse''' )
SCREAMING_SNAKE_CASE__ : List[Any] =key.replace('''linear_fuse.bn''', '''batch_norm''' )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
SCREAMING_SNAKE_CASE__ : int =key[key.find('''linear_c''' ) + len('''linear_c''' )]
SCREAMING_SNAKE_CASE__ : List[Any] =key.replace(f"linear_c{idx}", f"linear_c.{int(UpperCamelCase__ )-1}" )
if key.startswith('''head''' ):
SCREAMING_SNAKE_CASE__ : Tuple =key.replace('''head''', '''classifier''' )
SCREAMING_SNAKE_CASE__ : Tuple =value
return new_state_dict
def _a( UpperCamelCase__ : List[Any], UpperCamelCase__ : Dict ):
'''simple docstring'''
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
SCREAMING_SNAKE_CASE__ : Optional[Any] =state_dict.pop(f"segformer.encoder.block.{i}.{j}.attention.self.kv.weight" )
SCREAMING_SNAKE_CASE__ : str =state_dict.pop(f"segformer.encoder.block.{i}.{j}.attention.self.kv.bias" )
# next, add keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE__ : Optional[Any] =kv_weight[
: config.hidden_sizes[i], :
]
SCREAMING_SNAKE_CASE__ : int =kv_bias[: config.hidden_sizes[i]]
SCREAMING_SNAKE_CASE__ : str =kv_weight[
config.hidden_sizes[i] :, :
]
SCREAMING_SNAKE_CASE__ : Union[str, Any] =kv_bias[
config.hidden_sizes[i] :
]
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
SCREAMING_SNAKE_CASE__ : List[Any] =Image.open(requests.get(UpperCamelCase__, stream=UpperCamelCase__ ).raw )
return image
@torch.no_grad()
def _a( UpperCamelCase__ : int, UpperCamelCase__ : str, UpperCamelCase__ : List[str] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict =SegformerConfig()
SCREAMING_SNAKE_CASE__ : str =False
# set attributes based on model_name
SCREAMING_SNAKE_CASE__ : Dict ='''huggingface/label-files'''
if "segformer" in model_name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =model_name[len('''segformer.''' ) : len('''segformer.''' ) + 2]
if "ade" in model_name:
SCREAMING_SNAKE_CASE__ : Tuple =1_5_0
SCREAMING_SNAKE_CASE__ : Any ='''ade20k-id2label.json'''
SCREAMING_SNAKE_CASE__ : List[str] =(1, 1_5_0, 1_2_8, 1_2_8)
elif "city" in model_name:
SCREAMING_SNAKE_CASE__ : Any =1_9
SCREAMING_SNAKE_CASE__ : int ='''cityscapes-id2label.json'''
SCREAMING_SNAKE_CASE__ : str =(1, 1_9, 1_2_8, 1_2_8)
else:
raise ValueError(f"Model {model_name} not supported" )
elif "mit" in model_name:
SCREAMING_SNAKE_CASE__ : str =True
SCREAMING_SNAKE_CASE__ : Optional[Any] =model_name[4:6]
SCREAMING_SNAKE_CASE__ : str =1_0_0_0
SCREAMING_SNAKE_CASE__ : List[Any] ='''imagenet-1k-id2label.json'''
SCREAMING_SNAKE_CASE__ : Dict =(1, 1_0_0_0)
else:
raise ValueError(f"Model {model_name} not supported" )
# set config attributes
SCREAMING_SNAKE_CASE__ : List[Any] =json.load(open(hf_hub_download(UpperCamelCase__, UpperCamelCase__, repo_type='''dataset''' ), '''r''' ) )
SCREAMING_SNAKE_CASE__ : Union[str, Any] ={int(UpperCamelCase__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE__ : Any =idalabel
SCREAMING_SNAKE_CASE__ : List[Any] ={v: k for k, v in idalabel.items()}
if size == "b0":
pass
elif size == "b1":
SCREAMING_SNAKE_CASE__ : List[str] =[6_4, 1_2_8, 3_2_0, 5_1_2]
SCREAMING_SNAKE_CASE__ : List[str] =2_5_6
elif size == "b2":
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[6_4, 1_2_8, 3_2_0, 5_1_2]
SCREAMING_SNAKE_CASE__ : Optional[Any] =7_6_8
SCREAMING_SNAKE_CASE__ : Any =[3, 4, 6, 3]
elif size == "b3":
SCREAMING_SNAKE_CASE__ : Optional[Any] =[6_4, 1_2_8, 3_2_0, 5_1_2]
SCREAMING_SNAKE_CASE__ : Any =7_6_8
SCREAMING_SNAKE_CASE__ : Tuple =[3, 4, 1_8, 3]
elif size == "b4":
SCREAMING_SNAKE_CASE__ : List[Any] =[6_4, 1_2_8, 3_2_0, 5_1_2]
SCREAMING_SNAKE_CASE__ : int =7_6_8
SCREAMING_SNAKE_CASE__ : str =[3, 8, 2_7, 3]
elif size == "b5":
SCREAMING_SNAKE_CASE__ : Optional[int] =[6_4, 1_2_8, 3_2_0, 5_1_2]
SCREAMING_SNAKE_CASE__ : Dict =7_6_8
SCREAMING_SNAKE_CASE__ : Union[str, Any] =[3, 6, 4_0, 3]
else:
raise ValueError(f"Size {size} not supported" )
# load image processor (only resize + normalize)
SCREAMING_SNAKE_CASE__ : Any =SegformerImageProcessor(
image_scale=(5_1_2, 5_1_2), keep_ratio=UpperCamelCase__, align=UpperCamelCase__, do_random_crop=UpperCamelCase__ )
# prepare image
SCREAMING_SNAKE_CASE__ : Dict =prepare_img()
SCREAMING_SNAKE_CASE__ : Optional[Any] =image_processor(images=UpperCamelCase__, return_tensors='''pt''' ).pixel_values
logger.info(f"Converting model {model_name}..." )
# load original state dict
if encoder_only:
SCREAMING_SNAKE_CASE__ : Dict =torch.load(UpperCamelCase__, map_location=torch.device('''cpu''' ) )
else:
SCREAMING_SNAKE_CASE__ : Any =torch.load(UpperCamelCase__, map_location=torch.device('''cpu''' ) )['''state_dict''']
# rename keys
SCREAMING_SNAKE_CASE__ : Tuple =rename_keys(UpperCamelCase__, encoder_only=UpperCamelCase__ )
if not encoder_only:
del state_dict["decode_head.conv_seg.weight"]
del state_dict["decode_head.conv_seg.bias"]
# key and value matrices need special treatment
read_in_k_v(UpperCamelCase__, UpperCamelCase__ )
# create HuggingFace model and load state dict
if encoder_only:
SCREAMING_SNAKE_CASE__ : List[str] =False
SCREAMING_SNAKE_CASE__ : Tuple =SegformerForImageClassification(UpperCamelCase__ )
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =SegformerForSemanticSegmentation(UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
model.eval()
# forward pass
SCREAMING_SNAKE_CASE__ : Optional[Any] =model(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Optional[int] =outputs.logits
# set expected_slice based on model name
# ADE20k checkpoints
if model_name == "segformer.b0.512x512.ade.160k":
SCREAMING_SNAKE_CASE__ : Dict =torch.tensor(
[
[[-4.6_3_1_0, -5.5_2_3_2, -6.2_3_5_6], [-5.1_9_2_1, -6.1_4_4_4, -6.5_9_9_6], [-5.4_4_2_4, -6.2_7_9_0, -6.7_5_7_4]],
[[-1_2.1_3_9_1, -1_3.3_1_2_2, -1_3.9_5_5_4], [-1_2.8_7_3_2, -1_3.9_3_5_2, -1_4.3_5_6_3], [-1_2.9_4_3_8, -1_3.8_2_2_6, -1_4.2_5_1_3]],
[[-1_2.5_1_3_4, -1_3.4_6_8_6, -1_4.4_9_1_5], [-1_2.8_6_6_9, -1_4.4_3_4_3, -1_4.7_7_5_8], [-1_3.2_5_2_3, -1_4.5_8_1_9, -1_5.0_6_9_4]],
] )
elif model_name == "segformer.b1.512x512.ade.160k":
SCREAMING_SNAKE_CASE__ : str =torch.tensor(
[
[[-7.5_8_2_0, -8.7_2_3_1, -8.3_2_1_5], [-8.0_6_0_0, -1_0.3_5_2_9, -1_0.0_3_0_4], [-7.5_2_0_8, -9.4_1_0_3, -9.6_2_3_9]],
[[-1_2.6_9_1_8, -1_3.8_9_9_4, -1_3.7_1_3_7], [-1_3.3_1_9_6, -1_5.7_5_2_3, -1_5.4_7_8_9], [-1_2.9_3_4_3, -1_4.8_7_5_7, -1_4.9_6_8_9]],
[[-1_1.1_9_1_1, -1_1.9_4_2_1, -1_1.3_2_4_3], [-1_1.3_3_4_2, -1_3.6_8_3_9, -1_3.3_5_8_1], [-1_0.3_9_0_9, -1_2.1_8_3_2, -1_2.4_8_5_8]],
] )
elif model_name == "segformer.b2.512x512.ade.160k":
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.tensor(
[
[[-1_1.8_1_7_3, -1_4.3_8_5_0, -1_6.3_1_2_8], [-1_4.5_6_4_8, -1_6.5_8_0_4, -1_8.6_5_6_8], [-1_4.7_2_2_3, -1_5.7_3_8_7, -1_8.4_2_1_8]],
[[-1_5.7_2_9_0, -1_7.9_1_7_1, -1_9.4_4_2_3], [-1_8.3_1_0_5, -1_9.9_4_4_8, -2_1.4_6_6_1], [-1_7.9_2_9_6, -1_8.6_4_9_7, -2_0.7_9_1_0]],
[[-1_5.0_7_8_3, -1_7.0_3_3_6, -1_8.2_7_8_9], [-1_6.8_7_7_1, -1_8.6_8_7_0, -2_0.1_6_1_2], [-1_6.2_4_5_4, -1_7.1_4_2_6, -1_9.5_0_5_5]],
] )
elif model_name == "segformer.b3.512x512.ade.160k":
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.tensor(
[
[[-9.0_8_7_8, -1_0.2_0_8_1, -1_0.1_8_9_1], [-9.3_1_4_4, -1_0.7_9_4_1, -1_0.9_8_4_3], [-9.2_2_9_4, -1_0.3_8_5_5, -1_0.5_7_0_4]],
[[-1_2.2_3_1_6, -1_3.9_0_6_8, -1_3.6_1_0_2], [-1_2.9_1_6_1, -1_4.3_7_0_2, -1_4.3_2_3_5], [-1_2.5_2_3_3, -1_3.7_1_7_4, -1_3.7_9_3_2]],
[[-1_4.6_2_7_5, -1_5.2_4_9_0, -1_4.9_7_2_7], [-1_4.3_4_0_0, -1_5.9_6_8_7, -1_6.2_8_2_7], [-1_4.1_4_8_4, -1_5.4_0_3_3, -1_5.8_9_3_7]],
] )
elif model_name == "segformer.b4.512x512.ade.160k":
SCREAMING_SNAKE_CASE__ : List[str] =torch.tensor(
[
[[-1_2.3_1_4_4, -1_3.2_4_4_7, -1_4.0_8_0_2], [-1_3.3_6_1_4, -1_4.5_8_1_6, -1_5.6_1_1_7], [-1_3.3_3_4_0, -1_4.4_4_3_3, -1_6.2_2_1_9]],
[[-1_9.2_7_8_1, -2_0.4_1_2_8, -2_0.7_5_0_6], [-2_0.6_1_5_3, -2_1.6_5_6_6, -2_2.0_9_9_8], [-1_9.9_8_0_0, -2_1.0_4_3_0, -2_2.1_4_9_4]],
[[-1_8.8_7_3_9, -1_9.7_8_0_4, -2_1.1_8_3_4], [-2_0.1_2_3_3, -2_1.6_7_6_5, -2_3.2_9_4_4], [-2_0.0_3_1_5, -2_1.2_6_4_1, -2_3.6_9_4_4]],
] )
elif model_name == "segformer.b5.640x640.ade.160k":
SCREAMING_SNAKE_CASE__ : str =torch.tensor(
[
[[-9.5_5_2_4, -1_2.0_8_3_5, -1_1.7_3_4_8], [-1_0.5_2_2_9, -1_3.6_4_4_6, -1_4.5_6_6_2], [-9.5_8_4_2, -1_2.8_8_5_1, -1_3.9_4_1_4]],
[[-1_5.3_4_3_2, -1_7.5_3_2_3, -1_7.0_8_1_8], [-1_6.3_3_3_0, -1_8.9_2_5_5, -1_9.2_1_0_1], [-1_5.1_3_4_0, -1_7.7_8_4_8, -1_8.3_9_7_1]],
[[-1_2.6_0_7_2, -1_4.9_4_8_6, -1_4.6_6_3_1], [-1_3.7_6_2_9, -1_7.0_9_0_7, -1_7.7_7_4_5], [-1_2.7_8_9_9, -1_6.1_6_9_5, -1_7.1_6_7_1]],
] )
# Cityscapes checkpoints
elif model_name == "segformer.b0.1024x1024.city.160k":
SCREAMING_SNAKE_CASE__ : Any =torch.tensor(
[
[[-1_1.9_2_9_5, -1_3.4_0_5_7, -1_4.8_1_0_6], [-1_3.3_4_3_1, -1_4.8_1_7_9, -1_5.3_7_8_1], [-1_4.2_8_3_6, -1_5.5_9_4_2, -1_6.1_5_8_8]],
[[-1_1.4_9_0_6, -1_2.8_0_6_7, -1_3.6_5_6_4], [-1_3.1_1_8_9, -1_4.0_5_0_0, -1_4.1_5_4_3], [-1_3.8_7_4_8, -1_4.5_1_3_6, -1_4.8_7_8_9]],
[[0.5_3_7_4, 0.1_0_6_7, -0.4_7_4_2], [0.1_1_4_1, -0.2_2_5_5, -0.7_0_9_9], [-0.3_0_0_0, -0.5_9_2_4, -1.3_1_0_5]],
] )
elif model_name == "segformer.b0.512x1024.city.160k":
SCREAMING_SNAKE_CASE__ : List[Any] =torch.tensor(
[
[[-7.8_2_1_7, -9.8_7_6_7, -1_0.1_7_1_7], [-9.4_4_3_8, -1_0.9_0_5_8, -1_1.4_0_4_7], [-9.7_9_3_9, -1_2.3_4_9_5, -1_2.1_0_7_9]],
[[-7.1_5_1_4, -9.5_3_3_6, -1_0.0_8_6_0], [-9.7_7_7_6, -1_1.6_8_2_2, -1_1.8_4_3_9], [-1_0.1_4_1_1, -1_2.7_6_5_5, -1_2.8_9_7_2]],
[[0.3_0_2_1, 0.0_8_0_5, -0.2_3_1_0], [-0.0_3_2_8, -0.1_6_0_5, -0.2_7_1_4], [-0.1_4_0_8, -0.5_4_7_7, -0.6_9_7_6]],
] )
elif model_name == "segformer.b0.640x1280.city.160k":
SCREAMING_SNAKE_CASE__ : int =torch.tensor(
[
[
[-1.1372e01, -1.2787e01, -1.3477e01],
[-1.2536e01, -1.4194e01, -1.4409e01],
[-1.3217e01, -1.4888e01, -1.5327e01],
],
[
[-1.4791e01, -1.7122e01, -1.8277e01],
[-1.7163e01, -1.9192e01, -1.9533e01],
[-1.7897e01, -1.9991e01, -2.0315e01],
],
[
[7.6723e-01, 4.1921e-01, -7.7878e-02],
[4.7772e-01, 9.5557e-03, -2.8082e-01],
[3.6032e-01, -2.4826e-01, -5.1168e-01],
],
] )
elif model_name == "segformer.b0.768x768.city.160k":
SCREAMING_SNAKE_CASE__ : Any =torch.tensor(
[
[[-9.4_9_5_9, -1_1.3_0_8_7, -1_1.7_4_7_9], [-1_1.0_0_2_5, -1_2.6_5_4_0, -1_2.3_3_1_9], [-1_1.4_0_6_4, -1_3.0_4_8_7, -1_2.9_9_0_5]],
[[-9.8_9_0_5, -1_1.3_0_8_4, -1_2.0_8_5_4], [-1_1.1_7_2_6, -1_2.7_6_9_8, -1_2.9_5_8_3], [-1_1.5_9_8_5, -1_3.3_2_7_8, -1_4.1_7_7_4]],
[[0.2_2_1_3, 0.0_1_9_2, -0.2_4_6_6], [-0.1_7_3_1, -0.4_2_1_3, -0.4_8_7_4], [-0.3_1_2_6, -0.6_5_4_1, -1.1_3_8_9]],
] )
elif model_name == "segformer.b1.1024x1024.city.160k":
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.tensor(
[
[[-1_3.5_7_4_8, -1_3.9_1_1_1, -1_2.6_5_0_0], [-1_4.3_5_0_0, -1_5.3_6_8_3, -1_4.2_3_2_8], [-1_4.7_5_3_2, -1_6.0_4_2_4, -1_5.6_0_8_7]],
[[-1_7.1_6_5_1, -1_5.8_7_2_5, -1_2.9_6_5_3], [-1_7.2_5_8_0, -1_7.3_7_1_8, -1_4.8_2_2_3], [-1_6.6_0_5_8, -1_6.8_7_8_3, -1_6.7_4_5_2]],
[[-3.6_4_5_6, -3.0_2_0_9, -1.4_2_0_3], [-3.0_7_9_7, -3.1_9_5_9, -2.0_0_0_0], [-1.8_7_5_7, -1.9_2_1_7, -1.6_9_9_7]],
] )
elif model_name == "segformer.b2.1024x1024.city.160k":
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.tensor(
[
[[-1_6.0_9_7_6, -1_6.4_8_5_6, -1_7.3_9_6_2], [-1_6.6_2_3_4, -1_9.0_3_4_2, -1_9.7_6_8_5], [-1_6.0_9_0_0, -1_8.0_6_6_1, -1_9.1_1_8_0]],
[[-1_8.4_7_5_0, -1_8.8_4_8_8, -1_9.5_0_7_4], [-1_9.4_0_3_0, -2_2.1_5_7_0, -2_2.5_9_7_7], [-1_9.1_1_9_1, -2_0.8_4_8_6, -2_2.3_7_8_3]],
[[-4.5_1_7_8, -5.5_0_3_7, -6.5_1_0_9], [-5.0_8_8_4, -7.2_1_7_4, -8.0_3_3_4], [-4.4_1_5_6, -5.8_1_1_7, -7.2_9_7_0]],
] )
elif model_name == "segformer.b3.1024x1024.city.160k":
SCREAMING_SNAKE_CASE__ : List[str] =torch.tensor(
[
[[-1_4.2_0_8_1, -1_4.4_7_3_2, -1_4.1_9_7_7], [-1_4.5_8_6_7, -1_6.4_4_2_3, -1_6.6_3_5_6], [-1_3.4_4_4_1, -1_4.9_6_8_5, -1_6.8_6_9_6]],
[[-1_4.4_5_7_6, -1_4.7_0_7_3, -1_5.0_4_5_1], [-1_5.0_8_1_6, -1_7.6_2_3_7, -1_7.9_8_7_3], [-1_4.4_2_1_3, -1_6.0_1_9_9, -1_8.5_9_9_2]],
[[-4.7_3_4_9, -4.9_5_8_8, -5.0_9_6_6], [-4.3_2_1_0, -6.9_3_2_5, -7.2_5_9_1], [-3.4_3_1_2, -4.7_4_8_4, -7.1_9_1_7]],
] )
elif model_name == "segformer.b4.1024x1024.city.160k":
SCREAMING_SNAKE_CASE__ : Tuple =torch.tensor(
[
[[-1_1.7_7_3_7, -1_1.9_5_2_6, -1_1.3_2_7_3], [-1_3.6_6_9_2, -1_4.4_5_7_4, -1_3.8_8_7_8], [-1_3.8_9_3_7, -1_4.6_9_2_4, -1_5.9_3_4_5]],
[[-1_4.6_7_0_6, -1_4.5_3_3_0, -1_4.1_3_0_6], [-1_6.1_5_0_2, -1_6.8_1_8_0, -1_6.4_2_6_9], [-1_6.8_3_3_8, -1_7.8_9_3_9, -2_0.1_7_4_6]],
[[1.0_4_9_1, 0.8_2_8_9, 1.0_3_1_0], [1.1_0_4_4, 0.5_2_1_9, 0.8_0_5_5], [1.0_8_9_9, 0.6_9_2_6, 0.5_5_9_0]],
] )
elif model_name == "segformer.b5.1024x1024.city.160k":
SCREAMING_SNAKE_CASE__ : Dict =torch.tensor(
[
[[-1_2.5_6_4_1, -1_3.4_7_7_7, -1_3.0_6_8_4], [-1_3.9_5_8_7, -1_5.8_9_8_3, -1_6.6_5_5_7], [-1_3.3_1_0_9, -1_5.7_3_5_0, -1_6.3_1_4_1]],
[[-1_4.7_0_7_4, -1_5.4_3_5_2, -1_4.5_9_4_4], [-1_6.6_3_5_3, -1_8.1_6_6_3, -1_8.6_1_2_0], [-1_5.1_7_0_2, -1_8.0_3_2_9, -1_8.1_5_4_7]],
[[-1.7_9_9_0, -2.0_9_5_1, -1.7_7_8_4], [-2.6_3_9_7, -3.8_2_4_5, -3.9_6_8_6], [-1.5_2_6_4, -2.8_1_2_6, -2.9_3_1_6]],
] )
else:
SCREAMING_SNAKE_CASE__ : int =logits.argmax(-1 ).item()
print('''Predicted class:''', model.config.idalabel[predicted_class_idx] )
# verify logits
if not encoder_only:
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3, :3, :3], UpperCamelCase__, atol=1e-2 )
# finally, save model and image processor
logger.info(f"Saving PyTorch model and image processor to {pytorch_dump_folder_path}..." )
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
image_processor.save_pretrained(UpperCamelCase__ )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
parser.add_argument(
'--model_name',
default='segformer.b0.512x512.ade.160k',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--checkpoint_path', default=None, type=str, help='Path to the original PyTorch checkpoint (.pth file).'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
a_ = parser.parse_args()
convert_segformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path) | 152 |
'''simple docstring'''
import argparse
import re
from pathlib import Path
import requests
import torch
from PIL import Image
from torchvision.transforms import CenterCrop, Compose, Normalize, Resize, ToTensor
from transformers import (
EfficientFormerConfig,
EfficientFormerForImageClassificationWithTeacher,
EfficientFormerImageProcessor,
)
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, PILImageResampling
def _a( UpperCamelCase__ : int, UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[Any] =old_name
if "patch_embed" in old_name:
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int =old_name.split('''.''' )
if layer == "0":
SCREAMING_SNAKE_CASE__ : int =old_name.replace('''0''', '''convolution1''' )
elif layer == "1":
SCREAMING_SNAKE_CASE__ : Tuple =old_name.replace('''1''', '''batchnorm_before''' )
elif layer == "3":
SCREAMING_SNAKE_CASE__ : List[Any] =old_name.replace('''3''', '''convolution2''' )
else:
SCREAMING_SNAKE_CASE__ : Dict =old_name.replace('''4''', '''batchnorm_after''' )
if "network" in old_name and re.search(R'''\d\.\d''', UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : Tuple =R'''\b\d{2}\b'''
if bool(re.search(UpperCamelCase__, UpperCamelCase__ ) ):
SCREAMING_SNAKE_CASE__ : int =re.search(R'''\d\.\d\d.''', UpperCamelCase__ ).group()
else:
SCREAMING_SNAKE_CASE__ : Tuple =re.search(R'''\d\.\d.''', UpperCamelCase__ ).group()
if int(match[0] ) < 6:
SCREAMING_SNAKE_CASE__ : List[str] =old_name.replace(UpperCamelCase__, '''''' )
SCREAMING_SNAKE_CASE__ : Any =trimmed_name.replace('''network''', match[0] + '''.meta4D_layers.blocks.''' + match[2:-1] )
SCREAMING_SNAKE_CASE__ : Any ='''intermediate_stages.''' + trimmed_name
else:
SCREAMING_SNAKE_CASE__ : Optional[Any] =old_name.replace(UpperCamelCase__, '''''' )
if int(match[2] ) < num_meta4D_last_stage:
SCREAMING_SNAKE_CASE__ : str =trimmed_name.replace('''network''', '''meta4D_layers.blocks.''' + match[2] )
else:
SCREAMING_SNAKE_CASE__ : int =str(int(match[2] ) - num_meta4D_last_stage )
SCREAMING_SNAKE_CASE__ : Any =trimmed_name.replace('''network''', '''meta3D_layers.blocks.''' + layer_index )
if "norm1" in old_name:
SCREAMING_SNAKE_CASE__ : Optional[int] =trimmed_name.replace('''norm1''', '''layernorm1''' )
elif "norm2" in old_name:
SCREAMING_SNAKE_CASE__ : List[Any] =trimmed_name.replace('''norm2''', '''layernorm2''' )
elif "fc1" in old_name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =trimmed_name.replace('''fc1''', '''linear_in''' )
elif "fc2" in old_name:
SCREAMING_SNAKE_CASE__ : str =trimmed_name.replace('''fc2''', '''linear_out''' )
SCREAMING_SNAKE_CASE__ : Any ='''last_stage.''' + trimmed_name
elif "network" in old_name and re.search(R'''.\d.''', UpperCamelCase__ ):
SCREAMING_SNAKE_CASE__ : int =old_name.replace('''network''', '''intermediate_stages''' )
if "fc" in new_name:
SCREAMING_SNAKE_CASE__ : str =new_name.replace('''fc''', '''convolution''' )
elif ("norm1" in new_name) and ("layernorm1" not in new_name):
SCREAMING_SNAKE_CASE__ : Tuple =new_name.replace('''norm1''', '''batchnorm_before''' )
elif ("norm2" in new_name) and ("layernorm2" not in new_name):
SCREAMING_SNAKE_CASE__ : List[str] =new_name.replace('''norm2''', '''batchnorm_after''' )
if "proj" in new_name:
SCREAMING_SNAKE_CASE__ : Optional[int] =new_name.replace('''proj''', '''projection''' )
if "dist_head" in new_name:
SCREAMING_SNAKE_CASE__ : Optional[Any] =new_name.replace('''dist_head''', '''distillation_classifier''' )
elif "head" in new_name:
SCREAMING_SNAKE_CASE__ : Tuple =new_name.replace('''head''', '''classifier''' )
elif "patch_embed" in new_name:
SCREAMING_SNAKE_CASE__ : Optional[int] ='''efficientformer.''' + new_name
elif new_name == "norm.weight" or new_name == "norm.bias":
SCREAMING_SNAKE_CASE__ : Any =new_name.replace('''norm''', '''layernorm''' )
SCREAMING_SNAKE_CASE__ : Optional[Any] ='''efficientformer.''' + new_name
else:
SCREAMING_SNAKE_CASE__ : str ='''efficientformer.encoder.''' + new_name
return new_name
def _a( UpperCamelCase__ : int, UpperCamelCase__ : Union[str, Any] ):
'''simple docstring'''
for key in checkpoint.copy().keys():
SCREAMING_SNAKE_CASE__ : List[str] =checkpoint.pop(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str =val
return checkpoint
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict ='''http://images.cocodataset.org/val2017/000000039769.jpg'''
SCREAMING_SNAKE_CASE__ : List[str] =Image.open(requests.get(UpperCamelCase__, stream=UpperCamelCase__ ).raw )
return image
def _a( UpperCamelCase__ : Path, UpperCamelCase__ : Path, UpperCamelCase__ : Path, UpperCamelCase__ : bool ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict =torch.load(UpperCamelCase__, map_location='''cpu''' )['''model''']
SCREAMING_SNAKE_CASE__ : Optional[int] =EfficientFormerConfig.from_json_file(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : List[Any] =EfficientFormerForImageClassificationWithTeacher(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : str ='''_'''.join(checkpoint_path.split('''/''' )[-1].split('''.''' )[0].split('''_''' )[:-1] )
SCREAMING_SNAKE_CASE__ : Tuple =config.depths[-1] - config.num_metaad_blocks + 1
SCREAMING_SNAKE_CASE__ : Tuple =convert_torch_checkpoint(UpperCamelCase__, UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE__ : Any ={
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
# prepare image
SCREAMING_SNAKE_CASE__ : Any =prepare_img()
SCREAMING_SNAKE_CASE__ : List[str] =2_5_6
SCREAMING_SNAKE_CASE__ : Optional[int] =2_2_4
SCREAMING_SNAKE_CASE__ : List[Any] =EfficientFormerImageProcessor(
size={'''shortest_edge''': image_size}, crop_size={'''height''': crop_size, '''width''': crop_size}, resample=pillow_resamplings['''bicubic'''], )
SCREAMING_SNAKE_CASE__ : str =processor(images=UpperCamelCase__, return_tensors='''pt''' ).pixel_values
# original processing pipeline
SCREAMING_SNAKE_CASE__ : List[Any] =Compose(
[
Resize(UpperCamelCase__, interpolation=pillow_resamplings['''bicubic'''] ),
CenterCrop(UpperCamelCase__ ),
ToTensor(),
Normalize(UpperCamelCase__, UpperCamelCase__ ),
] )
SCREAMING_SNAKE_CASE__ : List[str] =image_transforms(UpperCamelCase__ ).unsqueeze(0 )
assert torch.allclose(UpperCamelCase__, UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : int =model(UpperCamelCase__ )
SCREAMING_SNAKE_CASE__ : Tuple =outputs.logits
SCREAMING_SNAKE_CASE__ : Dict =(1, 1_0_0_0)
if "l1" in model_name:
SCREAMING_SNAKE_CASE__ : Union[str, Any] =torch.Tensor(
[-0.1_3_1_2, 0.4_3_5_3, -1.0_4_9_9, -0.5_1_2_4, 0.4_1_8_3, -0.6_7_9_3, -1.3_7_7_7, -0.0_8_9_3, -0.7_3_5_8, -2.4_3_2_8] )
assert torch.allclose(logits[0, :1_0], UpperCamelCase__, atol=1e-3 )
assert logits.shape == expected_shape
elif "l3" in model_name:
SCREAMING_SNAKE_CASE__ : Optional[int] =torch.Tensor(
[-1.3_1_5_0, -1.5_4_5_6, -1.2_5_5_6, -0.8_4_9_6, -0.7_1_2_7, -0.7_8_9_7, -0.9_7_2_8, -0.3_0_5_2, 0.3_7_5_1, -0.3_1_2_7] )
assert torch.allclose(logits[0, :1_0], UpperCamelCase__, atol=1e-3 )
assert logits.shape == expected_shape
elif "l7" in model_name:
SCREAMING_SNAKE_CASE__ : Optional[Any] =torch.Tensor(
[-1.0_2_8_3, -1.4_1_3_1, -0.5_6_4_4, -1.3_1_1_5, -0.5_7_8_5, -1.2_0_4_9, -0.7_5_2_8, 0.1_9_9_2, -0.3_8_2_2, -0.0_8_7_8] )
assert logits.shape == expected_shape
else:
raise ValueError(
f"Unknown model checkpoint: {checkpoint_path}. Supported version of efficientformer are l1, l3 and l7" )
# Save Checkpoints
Path(UpperCamelCase__ ).mkdir(exist_ok=UpperCamelCase__ )
model.save_pretrained(UpperCamelCase__ )
print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}" )
processor.save_pretrained(UpperCamelCase__ )
print(f"Processor successfuly saved at {pytorch_dump_path}" )
if push_to_hub:
print('''Pushing model to the hub...''' )
model.push_to_hub(
repo_id=f"Bearnardd/{pytorch_dump_path}", commit_message='''Add model''', use_temp_dir=UpperCamelCase__, )
processor.push_to_hub(
repo_id=f"Bearnardd/{pytorch_dump_path}", commit_message='''Add image processor''', use_temp_dir=UpperCamelCase__, )
if __name__ == "__main__":
a_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--pytorch_model_path',
default=None,
type=str,
required=True,
help='Path to EfficientFormer pytorch checkpoint.',
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The json file for EfficientFormer model config.',
)
parser.add_argument(
'--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
parser.add_argument(
'--no-push_to_hub',
dest='push_to_hub',
action='store_false',
help='Do not push model and image processor to the hub',
)
parser.set_defaults(push_to_hub=True)
a_ = parser.parse_args()
convert_efficientformer_checkpoint(
checkpoint_path=args.pytorch_model_path,
efficientformer_config_file=args.config_file,
pytorch_dump_path=args.pytorch_dump_path,
push_to_hub=args.push_to_hub,
) | 152 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a =logging.get_logger(__name__)
a ={
"""google/pix2struct-textcaps-base""": (
"""https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"""
),
}
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : Dict = '''pix2struct_text_model'''
_UpperCAmelCase : Union[str, Any] = ['''past_key_values''']
_UpperCAmelCase : str = {
'''hidden_size''': '''hidden_size''',
'''num_attention_heads''': '''num_heads''',
'''num_hidden_layers''': '''num_layers''',
}
def __init__( self : Tuple ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=5_0_2_4_4 ,SCREAMING_SNAKE_CASE__ : Optional[int]=7_6_8 ,SCREAMING_SNAKE_CASE__ : str=6_4 ,SCREAMING_SNAKE_CASE__ : Dict=2_0_4_8 ,SCREAMING_SNAKE_CASE__ : List[str]=1_2 ,SCREAMING_SNAKE_CASE__ : int=1_2 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=3_2 ,SCREAMING_SNAKE_CASE__ : List[str]=1_2_8 ,SCREAMING_SNAKE_CASE__ : List[Any]=0.1 ,SCREAMING_SNAKE_CASE__ : Any=1E-6 ,SCREAMING_SNAKE_CASE__ : str=1.0 ,SCREAMING_SNAKE_CASE__ : Optional[Any]="gelu_new" ,SCREAMING_SNAKE_CASE__ : Tuple=0 ,SCREAMING_SNAKE_CASE__ : List[str]=False ,SCREAMING_SNAKE_CASE__ : str=0 ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=1 ,SCREAMING_SNAKE_CASE__ : int=False ,SCREAMING_SNAKE_CASE__ : Dict=True ,**SCREAMING_SNAKE_CASE__ : Tuple ,):
__lowerCamelCase : int = vocab_size
__lowerCamelCase : List[str] = hidden_size
__lowerCamelCase : int = d_kv
__lowerCamelCase : str = d_ff
__lowerCamelCase : Optional[int] = num_layers
__lowerCamelCase : Optional[int] = num_heads
__lowerCamelCase : Optional[Any] = relative_attention_num_buckets
__lowerCamelCase : Any = relative_attention_max_distance
__lowerCamelCase : int = dropout_rate
__lowerCamelCase : Union[str, Any] = layer_norm_epsilon
__lowerCamelCase : Optional[Any] = initializer_factor
__lowerCamelCase : Optional[Any] = use_cache
__lowerCamelCase : str = eos_token_id
__lowerCamelCase : List[str] = decoder_start_token_id
# for backwards compatibility
__lowerCamelCase : int = dense_act_fn
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE__ ,eos_token_id=SCREAMING_SNAKE_CASE__ ,decoder_start_token_id=SCREAMING_SNAKE_CASE__ ,tie_word_embeddings=SCREAMING_SNAKE_CASE__ ,is_decoder=SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ ,)
@classmethod
def lowerCAmelCase ( cls : str ,SCREAMING_SNAKE_CASE__ : Union[str, os.PathLike] ,**SCREAMING_SNAKE_CASE__ : Dict):
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__)
__lowerCamelCase , __lowerCamelCase : Any = cls.get_config_dict(SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__)
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type') == "pix2struct":
__lowerCamelCase : Dict = config_dict['text_config']
if "model_type" in config_dict and hasattr(cls ,'model_type') and config_dict["model_type"] != cls.model_type:
logger.warning(
F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
F"{cls.model_type}. This is not supported for all configurations of models and can yield errors.")
return cls.from_dict(SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__)
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : Optional[Any] = '''pix2struct_vision_model'''
def __init__( self : Any ,SCREAMING_SNAKE_CASE__ : Dict=7_6_8 ,SCREAMING_SNAKE_CASE__ : List[Any]=7_6_8 ,SCREAMING_SNAKE_CASE__ : List[Any]=2_0_4_8 ,SCREAMING_SNAKE_CASE__ : Dict=6_4 ,SCREAMING_SNAKE_CASE__ : Any=1_2 ,SCREAMING_SNAKE_CASE__ : str=1_2 ,SCREAMING_SNAKE_CASE__ : Union[str, Any]="gelu_new" ,SCREAMING_SNAKE_CASE__ : Optional[int]=1E-6 ,SCREAMING_SNAKE_CASE__ : str=0.0 ,SCREAMING_SNAKE_CASE__ : str=0.0 ,SCREAMING_SNAKE_CASE__ : int=1E-10 ,SCREAMING_SNAKE_CASE__ : Optional[Any]=1.0 ,SCREAMING_SNAKE_CASE__ : str=4_0_9_6 ,SCREAMING_SNAKE_CASE__ : List[Any]=3_2 ,SCREAMING_SNAKE_CASE__ : int=1_2_8 ,**SCREAMING_SNAKE_CASE__ : List[str] ,):
super().__init__(**SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Any = hidden_size
__lowerCamelCase : Any = patch_embed_hidden_size
__lowerCamelCase : Any = d_ff
__lowerCamelCase : Optional[Any] = dropout_rate
__lowerCamelCase : List[str] = num_hidden_layers
__lowerCamelCase : Optional[int] = num_attention_heads
__lowerCamelCase : Dict = initializer_range
__lowerCamelCase : List[str] = initializer_factor
__lowerCamelCase : Union[str, Any] = attention_dropout
__lowerCamelCase : Any = layer_norm_eps
__lowerCamelCase : Dict = dense_act_fn
__lowerCamelCase : Tuple = seq_len
__lowerCamelCase : Union[str, Any] = relative_attention_num_buckets
__lowerCamelCase : Optional[Any] = relative_attention_max_distance
__lowerCamelCase : Tuple = d_kv
@classmethod
def lowerCAmelCase ( cls : Optional[int] ,SCREAMING_SNAKE_CASE__ : Union[str, os.PathLike] ,**SCREAMING_SNAKE_CASE__ : Optional[int]):
cls._set_token_in_kwargs(SCREAMING_SNAKE_CASE__)
__lowerCamelCase , __lowerCamelCase : List[Any] = cls.get_config_dict(SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__)
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get('model_type') == "pix2struct":
__lowerCamelCase : Tuple = config_dict['vision_config']
if "model_type" in config_dict and hasattr(cls ,'model_type') and config_dict["model_type"] != cls.model_type:
logger.warning(
F"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
F"{cls.model_type}. This is not supported for all configurations of models and can yield errors.")
return cls.from_dict(SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__)
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : Optional[int] = '''pix2struct'''
_UpperCAmelCase : List[str] = True
def __init__( self : List[Any] ,SCREAMING_SNAKE_CASE__ : List[str]=None ,SCREAMING_SNAKE_CASE__ : Optional[int]=None ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=1.0 ,SCREAMING_SNAKE_CASE__ : str=0.02 ,SCREAMING_SNAKE_CASE__ : Tuple=False ,SCREAMING_SNAKE_CASE__ : Optional[Any]=False ,SCREAMING_SNAKE_CASE__ : Union[str, Any]=True ,**SCREAMING_SNAKE_CASE__ : List[Any] ,):
super().__init__(tie_word_embeddings=SCREAMING_SNAKE_CASE__ ,is_encoder_decoder=SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__)
if text_config is None:
__lowerCamelCase : Any = {}
logger.info('text_config is None. Initializing the Pix2StructTextConfig with default values.')
if vision_config is None:
__lowerCamelCase : Union[str, Any] = {}
logger.info('vision_config is None. Initializing the Pix2StructVisionConfig with default values.')
__lowerCamelCase : Dict = PixaStructTextConfig(**SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Any = PixaStructVisionConfig(**SCREAMING_SNAKE_CASE__)
__lowerCamelCase : int = self.text_config.decoder_start_token_id
__lowerCamelCase : Optional[int] = self.text_config.pad_token_id
__lowerCamelCase : Any = self.text_config.eos_token_id
__lowerCamelCase : Optional[Any] = initializer_factor
__lowerCamelCase : Optional[Any] = initializer_range
__lowerCamelCase : Optional[Any] = self.initializer_range
__lowerCamelCase : Tuple = self.initializer_range
__lowerCamelCase : Any = is_vqa
@classmethod
def lowerCAmelCase ( cls : Optional[int] ,SCREAMING_SNAKE_CASE__ : PixaStructTextConfig ,SCREAMING_SNAKE_CASE__ : PixaStructVisionConfig ,**SCREAMING_SNAKE_CASE__ : Dict):
return cls(text_config=text_config.to_dict() ,vision_config=vision_config.to_dict() ,**SCREAMING_SNAKE_CASE__)
def lowerCAmelCase ( self : Union[str, Any]):
__lowerCamelCase : List[Any] = copy.deepcopy(self.__dict__)
__lowerCamelCase : Union[str, Any] = self.text_config.to_dict()
__lowerCamelCase : Dict = self.vision_config.to_dict()
__lowerCamelCase : Dict = self.__class__.model_type
return output
| 113 |
import os
import random
import sys
from . import cryptomath_module as cryptomath
from . import rabin_miller
a =3
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> int:
print('Generating primitive root of p' )
while True:
__lowerCamelCase : Tuple = random.randrange(3 , lowerCamelCase__ )
if pow(lowerCamelCase__ , 2 , lowerCamelCase__ ) == 1:
continue
if pow(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) == 1:
continue
return g
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> tuple[tuple[int, int, int, int], tuple[int, int]]:
print('Generating prime p...' )
__lowerCamelCase : List[str] = rabin_miller.generate_large_prime(lowerCamelCase__ ) # select large prime number.
__lowerCamelCase : Dict = primitive_root(lowerCamelCase__ ) # one primitive root on modulo p.
__lowerCamelCase : Optional[int] = random.randrange(3 , lowerCamelCase__ ) # private_key -> have to be greater than 2 for safety.
__lowerCamelCase : List[Any] = cryptomath.find_mod_inverse(pow(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) , lowerCamelCase__ )
__lowerCamelCase : int = (key_size, e_a, e_a, p)
__lowerCamelCase : str = (key_size, d)
return public_key, private_key
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> None:
if os.path.exists(F"{name}_pubkey.txt" ) or os.path.exists(F"{name}_privkey.txt" ):
print('\nWARNING:' )
print(
F"\"{name}_pubkey.txt\" or \"{name}_privkey.txt\" already exists. \n"
'Use a different name or delete these files and re-run this program.' )
sys.exit()
__lowerCamelCase , __lowerCamelCase : List[Any] = generate_key(lowerCamelCase__ )
print(F"\nWriting public key to file {name}_pubkey.txt..." )
with open(F"{name}_pubkey.txt" , 'w' ) as fo:
fo.write(F"{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}" )
print(F"Writing private key to file {name}_privkey.txt..." )
with open(F"{name}_privkey.txt" , 'w' ) as fo:
fo.write(F"{private_key[0]},{private_key[1]}" )
def SCREAMING_SNAKE_CASE__ ( ) -> None:
print('Making key files...' )
make_key_files('elgamal' , 2_0_4_8 )
print('Key files generation successful' )
if __name__ == "__main__":
main()
| 113 | 1 |
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
_lowercase: Union[str, Any] = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n"
_lowercase: Optional[Any] = "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"
_lowercase: Dict = "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for 'record': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'prediction_text': the predicted answer text\n - for 'multirc': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question-answer pair as specified by the dataset\n - 'prediction': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for 'record': list of question-answers dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'answers': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for 'record':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1': F1 score\n - for 'multirc':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1_m': Per-question macro-F1 score\n - 'f1_a': Average F1 score over all answers\n - for 'axb':\n 'matthews_correlation': Matthew Correlation\n - for 'cb':\n - 'accuracy': Accuracy\n - 'f1': F1 score\n - for all others:\n - 'accuracy': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n"
def a( A : Tuple , A : Any ) -> Optional[int]:
"""simple docstring"""
return float((preds == labels).mean() )
def a( A : Tuple , A : Optional[int] , A : str="binary" ) -> int:
"""simple docstring"""
a = simple_accuracy(A , A )
a = float(fa_score(y_true=A , y_pred=A , average=A ) )
return {
"accuracy": acc,
"f1": fa,
}
def a( A : int , A : List[str] ) -> Any:
"""simple docstring"""
a = {}
for id_pred, label in zip(A , A ):
a = f'''{id_pred['idx']['paragraph']}-{id_pred['idx']['question']}'''
a = id_pred["prediction"]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
a = [(pred, label)]
a , a = [], []
for question, preds_labels in question_map.items():
a , a = zip(*A )
a = fa_score(y_true=A , y_pred=A , average="macro" )
fas.append(A )
a = int(sum(pred == label for pred, label in preds_labels ) == len(A ) )
ems.append(A )
a = float(sum(A ) / len(A ) )
a = sum(A ) / len(A )
a = float(fa_score(y_true=A , y_pred=[id_pred["prediction"] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class _lowercase ( datasets.Metric ):
"""simple docstring"""
def UpperCamelCase_ (self ):
"""simple docstring"""
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None , )
def UpperCamelCase_ (self ):
"""simple docstring"""
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"prediction_text": datasets.Value("string" ),
},
"references": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"answers": datasets.Sequence(datasets.Value("string" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("int64" ),
"paragraph": datasets.Value("int64" ),
"question": datasets.Value("int64" ),
},
"prediction": datasets.Value("int64" ),
},
"references": datasets.Value("int64" ),
}
else:
return {
"predictions": datasets.Value("int64" ),
"references": datasets.Value("int64" ),
}
def UpperCamelCase_ (self , lowerCamelCase_ , lowerCamelCase_ ):
"""simple docstring"""
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(lowerCamelCase_ , lowerCamelCase_ )}
elif self.config_name == "cb":
return acc_and_fa(lowerCamelCase_ , lowerCamelCase_ , fa_avg="macro" )
elif self.config_name == "record":
a = [
{
"qas": [
{"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
for ref in references
]
}
]
a = {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
return evaluate_record(lowerCamelCase_ , lowerCamelCase_ )[0]
elif self.config_name == "multirc":
return evaluate_multirc(lowerCamelCase_ , lowerCamelCase_ )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(lowerCamelCase_ , lowerCamelCase_ )}
else:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
| 227 |
import random
def a( A : Optional[Any] , A : Optional[Any] , A : str ) -> List[Any]:
"""simple docstring"""
a = a[left_index]
a = left_index + 1
for j in range(left_index + 1 , A ):
if a[j] < pivot:
a , a = a[i], a[j]
i += 1
a , a = a[i - 1], a[left_index]
return i - 1
def a( A : List[Any] , A : List[Any] , A : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
if left < right:
a = random.randint(A , right - 1 )
a , a = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
a = partition(A , A , A )
quick_sort_random(
A , A , A ) # recursive quicksort to the left of the pivot point
quick_sort_random(
A , pivot_index + 1 , A ) # recursive quicksort to the right of the pivot point
def a( ) -> Any:
"""simple docstring"""
a = input("Enter numbers separated by a comma:\n" ).strip()
a = [int(A ) for item in user_input.split("," )]
quick_sort_random(A , 0 , len(A ) )
print(A )
if __name__ == "__main__":
main()
| 227 | 1 |
'''simple docstring'''
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class UpperCamelCase__( unittest.TestCase ):
def a__( self : List[str] )-> Union[str, Any]:
"""simple docstring"""
debug_launcher(test_script.main )
def a__( self : List[Any] )-> Optional[int]:
"""simple docstring"""
debug_launcher(test_ops.main )
| 91 |
'''simple docstring'''
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def lowerCamelCase__ ( A : int , A : int , A : int , A : int , A : int , A : int ):
'''simple docstring'''
if (ksize % 2) == 0:
UpperCAmelCase = ksize + 1
UpperCAmelCase = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(A ):
for x in range(A ):
# distance from center
UpperCAmelCase = x - ksize // 2
UpperCAmelCase = y - ksize // 2
# degree to radiant
UpperCAmelCase = theta / 1_80 * np.pi
UpperCAmelCase = np.cos(_theta )
UpperCAmelCase = np.sin(_theta )
# get kernel x
UpperCAmelCase = cos_theta * px + sin_theta * py
# get kernel y
UpperCAmelCase = -sin_theta * px + cos_theta * py
# fill kernel
UpperCAmelCase = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
_lowercase : Tuple = imread("""../image_data/lena.jpg""")
# turn image in gray scale value
_lowercase : int = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
_lowercase : List[str] = np.zeros(gray.shape[:2])
for theta in [0, 30, 60, 90, 120, 150]:
_lowercase : List[Any] = gabor_filter_kernel(10, 8, theta, 10, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
_lowercase : Optional[int] = out / out.max() * 255
_lowercase : Optional[int] = out.astype(np.uinta)
imshow("""Original""", gray)
imshow("""Gabor filter with 20x20 mask and 6 directions""", out)
waitKey(0)
| 91 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
DiffusionPipeline,
UnCLIPImageVariationPipeline,
UnCLIPScheduler,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.pipelines.unclip.text_proj import UnCLIPTextProjModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, load_image, require_torch_gpu, skip_mps
from ..pipeline_params import IMAGE_VARIATION_BATCH_PARAMS, IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class _UpperCamelCase ( __A , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =UnCLIPImageVariationPipeline
lowerCamelCase__ =IMAGE_VARIATION_PARAMS - {'height', 'width', 'guidance_scale'}
lowerCamelCase__ =IMAGE_VARIATION_BATCH_PARAMS
lowerCamelCase__ =[
'generator',
'return_dict',
'decoder_num_inference_steps',
'super_res_num_inference_steps',
]
lowerCamelCase__ =False
@property
def __UpperCamelCase ( self : int ) -> List[Any]:
"""simple docstring"""
return 32
@property
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
return 32
@property
def __UpperCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
return self.time_input_dim
@property
def __UpperCamelCase ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
return self.time_input_dim * 4
@property
def __UpperCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
return 100
@property
def __UpperCamelCase ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
return tokenizer
@property
def __UpperCamelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
return CLIPTextModelWithProjection(a )
@property
def __UpperCamelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , projection_dim=self.text_embedder_hidden_size , num_hidden_layers=5 , num_attention_heads=4 , image_size=32 , intermediate_size=37 , patch_size=1 , )
return CLIPVisionModelWithProjection(a )
@property
def __UpperCamelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : List[str] = {
"clip_embeddings_dim": self.text_embedder_hidden_size,
"time_embed_dim": self.time_embed_dim,
"cross_attention_dim": self.cross_attention_dim,
}
SCREAMING_SNAKE_CASE : Dict = UnCLIPTextProjModel(**a )
return model
@property
def __UpperCamelCase ( self : int ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : str = {
"sample_size": 32,
# RGB in channels
"in_channels": 3,
# Out channels is double in channels because predicts mean and variance
"out_channels": 6,
"down_block_types": ("ResnetDownsampleBlock2D", "SimpleCrossAttnDownBlock2D"),
"up_block_types": ("SimpleCrossAttnUpBlock2D", "ResnetUpsampleBlock2D"),
"mid_block_type": "UNetMidBlock2DSimpleCrossAttn",
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"layers_per_block": 1,
"cross_attention_dim": self.cross_attention_dim,
"attention_head_dim": 4,
"resnet_time_scale_shift": "scale_shift",
"class_embed_type": "identity",
}
SCREAMING_SNAKE_CASE : int = UNetaDConditionModel(**a )
return model
@property
def __UpperCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
return {
"sample_size": 64,
"layers_per_block": 1,
"down_block_types": ("ResnetDownsampleBlock2D", "ResnetDownsampleBlock2D"),
"up_block_types": ("ResnetUpsampleBlock2D", "ResnetUpsampleBlock2D"),
"block_out_channels": (self.block_out_channels_a, self.block_out_channels_a * 2),
"in_channels": 6,
"out_channels": 3,
}
@property
def __UpperCamelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE : Optional[Any] = UNetaDModel(**self.dummy_super_res_kwargs )
return model
@property
def __UpperCamelCase ( self : Any ) -> Optional[Any]:
"""simple docstring"""
torch.manual_seed(1 )
SCREAMING_SNAKE_CASE : Tuple = UNetaDModel(**self.dummy_super_res_kwargs )
return model
def __UpperCamelCase ( self : int ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = self.dummy_decoder
SCREAMING_SNAKE_CASE : List[str] = self.dummy_text_proj
SCREAMING_SNAKE_CASE : int = self.dummy_text_encoder
SCREAMING_SNAKE_CASE : int = self.dummy_tokenizer
SCREAMING_SNAKE_CASE : str = self.dummy_super_res_first
SCREAMING_SNAKE_CASE : List[Any] = self.dummy_super_res_last
SCREAMING_SNAKE_CASE : str = UnCLIPScheduler(
variance_type="learned_range" , prediction_type="epsilon" , num_train_timesteps=1000 , )
SCREAMING_SNAKE_CASE : str = UnCLIPScheduler(
variance_type="fixed_small_log" , prediction_type="epsilon" , num_train_timesteps=1000 , )
SCREAMING_SNAKE_CASE : List[str] = CLIPImageProcessor(crop_size=32 , size=32 )
SCREAMING_SNAKE_CASE : Dict = self.dummy_image_encoder
return {
"decoder": decoder,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"text_proj": text_proj,
"feature_extractor": feature_extractor,
"image_encoder": image_encoder,
"super_res_first": super_res_first,
"super_res_last": super_res_last,
"decoder_scheduler": decoder_scheduler,
"super_res_scheduler": super_res_scheduler,
}
def __UpperCamelCase ( self : Any , a : str , a : Union[str, Any]=0 , a : Tuple=True ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = floats_tensor((1, 3, 32, 32) , rng=random.Random(a ) ).to(a )
if str(a ).startswith("mps" ):
SCREAMING_SNAKE_CASE : Tuple = torch.manual_seed(a )
else:
SCREAMING_SNAKE_CASE : int = torch.Generator(device=a ).manual_seed(a )
if pil_image:
SCREAMING_SNAKE_CASE : Dict = input_image * 0.5 + 0.5
SCREAMING_SNAKE_CASE : List[Any] = input_image.clamp(0 , 1 )
SCREAMING_SNAKE_CASE : Union[str, Any] = input_image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
SCREAMING_SNAKE_CASE : Tuple = DiffusionPipeline.numpy_to_pil(a )[0]
return {
"image": input_image,
"generator": generator,
"decoder_num_inference_steps": 2,
"super_res_num_inference_steps": 2,
"output_type": "np",
}
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = "cpu"
SCREAMING_SNAKE_CASE : Optional[Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE : List[str] = self.pipeline_class(**a )
SCREAMING_SNAKE_CASE : int = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : List[str] = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : Dict = pipe(**a )
SCREAMING_SNAKE_CASE : Any = output.images
SCREAMING_SNAKE_CASE : int = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : int = pipe(
**a , return_dict=a , )[0]
SCREAMING_SNAKE_CASE : List[Any] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : List[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE : Union[str, Any] = np.array(
[
0.9997,
0.0002,
0.9997,
0.9997,
0.9969,
0.0023,
0.9997,
0.9969,
0.9970,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : int ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = "cpu"
SCREAMING_SNAKE_CASE : Optional[int] = self.get_dummy_components()
SCREAMING_SNAKE_CASE : List[str] = self.pipeline_class(**a )
SCREAMING_SNAKE_CASE : str = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Any = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : Optional[int] = pipe(**a )
SCREAMING_SNAKE_CASE : Optional[Any] = output.images
SCREAMING_SNAKE_CASE : List[str] = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : Dict = pipe(
**a , return_dict=a , )[0]
SCREAMING_SNAKE_CASE : List[str] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
SCREAMING_SNAKE_CASE : int = np.array([0.9997, 0.0003, 0.9997, 0.9997, 0.9970, 0.0024, 0.9997, 0.9971, 0.9971] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[str] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = "cpu"
SCREAMING_SNAKE_CASE : List[Any] = self.get_dummy_components()
SCREAMING_SNAKE_CASE : List[str] = self.pipeline_class(**a )
SCREAMING_SNAKE_CASE : Union[str, Any] = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : str = [
pipeline_inputs["image"],
pipeline_inputs["image"],
]
SCREAMING_SNAKE_CASE : Dict = pipe(**a )
SCREAMING_SNAKE_CASE : Optional[int] = output.images
SCREAMING_SNAKE_CASE : int = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : str = [
tuple_pipeline_inputs["image"],
tuple_pipeline_inputs["image"],
]
SCREAMING_SNAKE_CASE : List[str] = pipe(
**a , return_dict=a , )[0]
SCREAMING_SNAKE_CASE : Optional[int] = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (2, 64, 64, 3)
SCREAMING_SNAKE_CASE : List[Any] = np.array(
[
0.9997,
0.9989,
0.0008,
0.0021,
0.9960,
0.0018,
0.0014,
0.0002,
0.9933,
] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = torch.device("cpu" )
class _UpperCamelCase :
'''simple docstring'''
lowerCamelCase__ =1
SCREAMING_SNAKE_CASE : str = self.get_dummy_components()
SCREAMING_SNAKE_CASE : Optional[int] = self.pipeline_class(**a )
SCREAMING_SNAKE_CASE : str = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : str = torch.Generator(device=a ).manual_seed(0 )
SCREAMING_SNAKE_CASE : Dict = pipe.decoder.dtype
SCREAMING_SNAKE_CASE : List[str] = 1
SCREAMING_SNAKE_CASE : List[str] = (
batch_size,
pipe.decoder.config.in_channels,
pipe.decoder.config.sample_size,
pipe.decoder.config.sample_size,
)
SCREAMING_SNAKE_CASE : List[Any] = pipe.prepare_latents(
a , dtype=a , device=a , generator=a , latents=a , scheduler=DummyScheduler() )
SCREAMING_SNAKE_CASE : List[str] = (
batch_size,
pipe.super_res_first.config.in_channels // 2,
pipe.super_res_first.config.sample_size,
pipe.super_res_first.config.sample_size,
)
SCREAMING_SNAKE_CASE : int = pipe.prepare_latents(
a , dtype=a , device=a , generator=a , latents=a , scheduler=DummyScheduler() )
SCREAMING_SNAKE_CASE : str = self.get_dummy_inputs(a , pil_image=a )
SCREAMING_SNAKE_CASE : Union[str, Any] = pipe(
**a , decoder_latents=a , super_res_latents=a ).images
SCREAMING_SNAKE_CASE : str = self.get_dummy_inputs(a , pil_image=a )
# Don't pass image, instead pass embedding
SCREAMING_SNAKE_CASE : List[str] = pipeline_inputs.pop("image" )
SCREAMING_SNAKE_CASE : str = pipe.image_encoder(a ).image_embeds
SCREAMING_SNAKE_CASE : Optional[Any] = pipe(
**a , decoder_latents=a , super_res_latents=a , image_embeddings=a , ).images
# make sure passing text embeddings manually is identical
assert np.abs(img_out_a - img_out_a ).max() < 1e-4
@skip_mps
def __UpperCamelCase ( self : Optional[Any] ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = torch_device == "cpu"
# Check is relaxed because there is not a torch 2.0 sliced attention added kv processor
SCREAMING_SNAKE_CASE : List[Any] = 1e-2
self._test_attention_slicing_forward_pass(
test_max_difference=a , expected_max_diff=a )
@skip_mps
def __UpperCamelCase ( self : Optional[int] ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = torch_device == "cpu"
SCREAMING_SNAKE_CASE : Optional[int] = True
SCREAMING_SNAKE_CASE : List[Any] = [
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
self._test_inference_batch_single_identical(
test_max_difference=a , relax_max_difference=a , additional_params_copy_to_batched_inputs=a , )
def __UpperCamelCase ( self : Union[str, Any] ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = [
"decoder_num_inference_steps",
"super_res_num_inference_steps",
]
if torch_device == "mps":
# TODO: MPS errors with larger batch sizes
SCREAMING_SNAKE_CASE : List[str] = [2, 3]
self._test_inference_batch_consistent(
batch_sizes=a , additional_params_copy_to_batched_inputs=a , )
else:
self._test_inference_batch_consistent(
additional_params_copy_to_batched_inputs=a )
@skip_mps
def __UpperCamelCase ( self : Tuple ) -> Any:
"""simple docstring"""
return super().test_dict_tuple_outputs_equivalent()
@skip_mps
def __UpperCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
return super().test_save_load_local()
@skip_mps
def __UpperCamelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
return super().test_save_load_optional_components()
@slow
@require_torch_gpu
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : List[Any] ) -> int:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __UpperCamelCase ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/unclip/cat.png" )
SCREAMING_SNAKE_CASE : Optional[Any] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/unclip/karlo_v1_alpha_cat_variation_fp16.npy" )
SCREAMING_SNAKE_CASE : str = UnCLIPImageVariationPipeline.from_pretrained(
"kakaobrain/karlo-v1-alpha-image-variations" , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE : Tuple = pipeline.to(a )
pipeline.set_progress_bar_config(disable=a )
SCREAMING_SNAKE_CASE : Optional[int] = torch.Generator(device="cpu" ).manual_seed(0 )
SCREAMING_SNAKE_CASE : str = pipeline(
a , generator=a , output_type="np" , )
SCREAMING_SNAKE_CASE : Union[str, Any] = output.images[0]
assert image.shape == (256, 256, 3)
assert_mean_pixel_difference(a , a , 15 ) | 76 |
'''simple docstring'''
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
_lowerCAmelCase = '''src/transformers'''
_lowerCAmelCase = '''docs/source/en/tasks'''
def __lowerCAmelCase ( snake_case__ , snake_case__ , snake_case__ ):
with open(snake_case__ , "r" , encoding="utf-8" , newline="\n" ) as f:
__UpperCamelCase : str = f.readlines()
# Find the start prompt.
__UpperCamelCase : Dict = 0
while not lines[start_index].startswith(snake_case__ ):
start_index += 1
start_index += 1
__UpperCamelCase : Dict = start_index
while not lines[end_index].startswith(snake_case__ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
_lowerCAmelCase = direct_transformers_import(TRANSFORMERS_PATH)
_lowerCAmelCase = {
'''asr.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
'''audio_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
'''language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
'''image_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
'''masked_language_modeling.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
'''multiple_choice.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
'''object_detection.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
'''question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
'''semantic_segmentation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
'''sequence_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
'''summarization.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'''token_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
'''translation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'''video_classification.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
'''document_question_answering.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
'''monocular_depth_estimation.md''': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
_lowerCAmelCase = {
'''summarization.md''': ('''nllb''',),
'''translation.md''': ('''nllb''',),
}
def __lowerCAmelCase ( snake_case__ ):
__UpperCamelCase : Optional[Any] = TASK_GUIDE_TO_MODELS[task_guide]
__UpperCamelCase : str = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(snake_case__ , set() )
__UpperCamelCase : Union[str, Any] = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([F"[{name}](../model_doc/{code})" for code, name in model_names.items()] ) + "\n"
def __lowerCAmelCase ( snake_case__ , snake_case__=False ):
__UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase : Union[str, Any] = _find_text_in_file(
filename=os.path.join(snake_case__ , snake_case__ ) , start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->" , end_prompt="<!--End of the generated tip-->" , )
__UpperCamelCase : List[str] = get_model_list_for_task(snake_case__ )
if current_list != new_list:
if overwrite:
with open(os.path.join(snake_case__ , snake_case__ ) , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
F"The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"
" to fix this." )
if __name__ == "__main__":
_lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
_lowerCAmelCase = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 298 | 0 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase , __UpperCAmelCase ) -> tuple[float, float]:
# Check if the input is valid
if not len(__UpperCAmelCase ) == len(__UpperCAmelCase ) == 3:
raise ValueError("""Please enter a valid equation.""" )
if equationa[0] == equationa[1] == equationa[0] == equationa[1] == 0:
raise ValueError("""Both a & b of two equations can't be zero.""" )
# Extract the coefficients
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Tuple = equationa
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = equationa
# Calculate the determinants of the matrices
lowerCAmelCase__ : int = aa * ba - aa * ba
lowerCAmelCase__ : str = ca * ba - ca * ba
lowerCAmelCase__ : List[str] = aa * ca - aa * ca
# Check if the system of linear equations has a solution (using Cramer's rule)
if determinant == 0:
if determinant_x == determinant_y == 0:
raise ValueError("""Infinite solutions. (Consistent system)""" )
else:
raise ValueError("""No solution. (Inconsistent system)""" )
else:
if determinant_x == determinant_y == 0:
# Trivial solution (Inconsistent system)
return (0.0, 0.0)
else:
lowerCAmelCase__ : str = determinant_x / determinant
lowerCAmelCase__ : List[Any] = determinant_y / determinant
# Non-Trivial Solution (Consistent system)
return (x, y)
| 212 |
"""simple docstring"""
import argparse
import glob
import logging
import os
from argparse import Namespace
from importlib import import_module
import numpy as np
import torch
from lightning_base import BaseTransformer, add_generic_args, generic_train
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch.nn import CrossEntropyLoss
from torch.utils.data import DataLoader, TensorDataset
from utils_ner import TokenClassificationTask
_A = logging.getLogger(__name__)
class _lowerCamelCase ( a_ ):
_lowerCamelCase :Union[str, Any] = "token-classification"
def __init__( self : Dict , UpperCamelCase : Any ) -> Optional[int]:
"""simple docstring"""
if type(UpperCamelCase ) == dict:
lowerCAmelCase__ : Optional[int] = Namespace(**UpperCamelCase )
lowerCAmelCase__ : Tuple = import_module("""tasks""" )
try:
lowerCAmelCase__ : Union[str, Any] = getattr(UpperCamelCase , hparams.task_type )
lowerCAmelCase__ : TokenClassificationTask = token_classification_task_clazz()
except AttributeError:
raise ValueError(
f"""Task {hparams.task_type} needs to be defined as a TokenClassificationTask subclass in {module}. """
f"""Available tasks classes are: {TokenClassificationTask.__subclasses__()}""" )
lowerCAmelCase__ : Optional[Any] = self.token_classification_task.get_labels(hparams.labels )
lowerCAmelCase__ : Dict = CrossEntropyLoss().ignore_index
super().__init__(UpperCamelCase , len(self.labels ) , self.mode )
def _lowerCAmelCase ( self : int , **UpperCamelCase : List[Any] ) -> str:
"""simple docstring"""
return self.model(**UpperCamelCase )
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : Union[str, Any] , UpperCamelCase : Any ) -> Dict:
"""simple docstring"""
lowerCAmelCase__ : Tuple = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
lowerCAmelCase__ : List[str] = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
lowerCAmelCase__ : Tuple = self(**UpperCamelCase )
lowerCAmelCase__ : List[Any] = outputs[0]
# tensorboard_logs = {"loss": loss, "rate": self.lr_scheduler.get_last_lr()[-1]}
return {"loss": loss}
def _lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
lowerCAmelCase__ : Optional[int] = self.hparams
for mode in ["train", "dev", "test"]:
lowerCAmelCase__ : Union[str, Any] = self._feature_file(UpperCamelCase )
if os.path.exists(UpperCamelCase ) and not args.overwrite_cache:
logger.info("""Loading features from cached file %s""" , UpperCamelCase )
lowerCAmelCase__ : Tuple = torch.load(UpperCamelCase )
else:
logger.info("""Creating features from dataset file at %s""" , args.data_dir )
lowerCAmelCase__ : Union[str, Any] = self.token_classification_task.read_examples_from_file(args.data_dir , UpperCamelCase )
lowerCAmelCase__ : Tuple = self.token_classification_task.convert_examples_to_features(
UpperCamelCase , self.labels , args.max_seq_length , self.tokenizer , cls_token_at_end=bool(self.config.model_type in ["""xlnet"""] ) , cls_token=self.tokenizer.cls_token , cls_token_segment_id=2 if self.config.model_type in ["""xlnet"""] else 0 , sep_token=self.tokenizer.sep_token , sep_token_extra=UpperCamelCase , pad_on_left=bool(self.config.model_type in ["""xlnet"""] ) , pad_token=self.tokenizer.pad_token_id , pad_token_segment_id=self.tokenizer.pad_token_type_id , pad_token_label_id=self.pad_token_label_id , )
logger.info("""Saving features into cached file %s""" , UpperCamelCase )
torch.save(UpperCamelCase , UpperCamelCase )
def _lowerCAmelCase ( self : Union[str, Any] , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : bool = False ) -> DataLoader:
"""simple docstring"""
lowerCAmelCase__ : int = self._feature_file(UpperCamelCase )
logger.info("""Loading features from cached file %s""" , UpperCamelCase )
lowerCAmelCase__ : int = torch.load(UpperCamelCase )
lowerCAmelCase__ : str = torch.tensor([f.input_ids for f in features] , dtype=torch.long )
lowerCAmelCase__ : Any = torch.tensor([f.attention_mask for f in features] , dtype=torch.long )
if features[0].token_type_ids is not None:
lowerCAmelCase__ : Optional[int] = torch.tensor([f.token_type_ids for f in features] , dtype=torch.long )
else:
lowerCAmelCase__ : Union[str, Any] = torch.tensor([0 for f in features] , dtype=torch.long )
# HACK(we will not use this anymore soon)
lowerCAmelCase__ : Union[str, Any] = torch.tensor([f.label_ids for f in features] , dtype=torch.long )
return DataLoader(
TensorDataset(UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase ) , batch_size=UpperCamelCase )
def _lowerCAmelCase ( self : Optional[int] , UpperCamelCase : Optional[Any] , UpperCamelCase : List[str] ) -> List[str]:
"""simple docstring"""
"""Compute validation""" ""
lowerCAmelCase__ : str = {"""input_ids""": batch[0], """attention_mask""": batch[1], """labels""": batch[3]}
if self.config.model_type != "distilbert":
lowerCAmelCase__ : List[Any] = (
batch[2] if self.config.model_type in ["""bert""", """xlnet"""] else None
) # XLM and RoBERTa don"t use token_type_ids
lowerCAmelCase__ : Union[str, Any] = self(**UpperCamelCase )
lowerCAmelCase__ , lowerCAmelCase__ : List[str] = outputs[:2]
lowerCAmelCase__ : Optional[Any] = logits.detach().cpu().numpy()
lowerCAmelCase__ : Optional[Any] = inputs["""labels"""].detach().cpu().numpy()
return {"val_loss": tmp_eval_loss.detach().cpu(), "pred": preds, "target": out_label_ids}
def _lowerCAmelCase ( self : Tuple , UpperCamelCase : Optional[int] ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : str = torch.stack([x["""val_loss"""] for x in outputs] ).mean()
lowerCAmelCase__ : Any = np.concatenate([x["""pred"""] for x in outputs] , axis=0 )
lowerCAmelCase__ : List[str] = np.argmax(UpperCamelCase , axis=2 )
lowerCAmelCase__ : str = np.concatenate([x["""target"""] for x in outputs] , axis=0 )
lowerCAmelCase__ : Any = dict(enumerate(self.labels ) )
lowerCAmelCase__ : str = [[] for _ in range(out_label_ids.shape[0] )]
lowerCAmelCase__ : Optional[Any] = [[] for _ in range(out_label_ids.shape[0] )]
for i in range(out_label_ids.shape[0] ):
for j in range(out_label_ids.shape[1] ):
if out_label_ids[i, j] != self.pad_token_label_id:
out_label_list[i].append(label_map[out_label_ids[i][j]] )
preds_list[i].append(label_map[preds[i][j]] )
lowerCAmelCase__ : Optional[int] = {
"""val_loss""": val_loss_mean,
"""accuracy_score""": accuracy_score(UpperCamelCase , UpperCamelCase ),
"""precision""": precision_score(UpperCamelCase , UpperCamelCase ),
"""recall""": recall_score(UpperCamelCase , UpperCamelCase ),
"""f1""": fa_score(UpperCamelCase , UpperCamelCase ),
}
lowerCAmelCase__ : Dict = dict(results.items() )
lowerCAmelCase__ : List[Any] = results
return ret, preds_list, out_label_list
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : List[Any] ) -> Any:
"""simple docstring"""
# when stable
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Any = self._eval_end(UpperCamelCase )
lowerCAmelCase__ : Optional[int] = ret["""log"""]
return {"val_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
def _lowerCAmelCase ( self : Dict , UpperCamelCase : int ) -> Optional[Any]:
"""simple docstring"""
# updating to test_epoch_end instead of deprecated test_end
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ : Optional[int] = self._eval_end(UpperCamelCase )
# Converting to the dict required by pl
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master/\
# pytorch_lightning/trainer/logging.py#L139
lowerCAmelCase__ : int = ret["""log"""]
# `val_loss` is the key returned by `self._eval_end()` but actually refers to `test_loss`
return {"avg_test_loss": logs["val_loss"], "log": logs, "progress_bar": logs}
@staticmethod
def _lowerCAmelCase ( UpperCamelCase : List[str] , UpperCamelCase : Union[str, Any] ) -> List[str]:
"""simple docstring"""
# Add NER specific options
BaseTransformer.add_model_specific_args(UpperCamelCase , UpperCamelCase )
parser.add_argument(
"""--task_type""" , default="""NER""" , type=UpperCamelCase , help="""Task type to fine tune in training (e.g. NER, POS, etc)""" )
parser.add_argument(
"""--max_seq_length""" , default=1_28 , type=UpperCamelCase , help=(
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
) , )
parser.add_argument(
"""--labels""" , default="""""" , type=UpperCamelCase , help="""Path to a file containing all labels. If not specified, CoNLL-2003 labels are used.""" , )
parser.add_argument(
"""--gpus""" , default=0 , type=UpperCamelCase , help="""The number of GPUs allocated for this, it is by default 0 meaning none""" , )
parser.add_argument(
"""--overwrite_cache""" , action="""store_true""" , help="""Overwrite the cached training and evaluation sets""" )
return parser
if __name__ == "__main__":
_A = argparse.ArgumentParser()
add_generic_args(parser, os.getcwd())
_A = NERTransformer.add_model_specific_args(parser, os.getcwd())
_A = parser.parse_args()
_A = NERTransformer(args)
_A = generic_train(model, args)
if args.do_predict:
# See https://github.com/huggingface/transformers/issues/3159
# pl use this default format to create a checkpoint:
# https://github.com/PyTorchLightning/pytorch-lightning/blob/master\
# /pytorch_lightning/callbacks/model_checkpoint.py#L322
_A = sorted(glob.glob(os.path.join(args.output_dir, """checkpoint-epoch=*.ckpt"""), recursive=True))
_A = model.load_from_checkpoint(checkpoints[-1])
trainer.test(model)
| 212 | 1 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A_ : Any = logging.get_logger(__name__)
A_ : Tuple = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
A_ : Union[str, Any] = {
'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'
)
},
}
A_ : int = {'facebook/blenderbot_small-90M': 512}
def __a ( SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
__UpperCAmelCase = set()
__UpperCAmelCase = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
__UpperCAmelCase = char
__UpperCAmelCase = set(SCREAMING_SNAKE_CASE )
return pairs
class A_ ( _a ):
'''simple docstring'''
a__ = VOCAB_FILES_NAMES
a__ = PRETRAINED_VOCAB_FILES_MAP
a__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ = ["input_ids", "attention_mask"]
def __init__(self , lowercase__ , lowercase__ , lowercase__="__start__" , lowercase__="__end__" , lowercase__="__unk__" , lowercase__="__null__" , **lowercase__ , ) -> int:
super().__init__(unk_token=lowercase__ , bos_token=lowercase__ , eos_token=lowercase__ , pad_token=lowercase__ , **lowercase__ )
with open(lowercase__ , encoding='''utf-8''' ) as vocab_handle:
__UpperCAmelCase = json.load(lowercase__ )
__UpperCAmelCase = {v: k for k, v in self.encoder.items()}
with open(lowercase__ , encoding='''utf-8''' ) as merges_handle:
__UpperCAmelCase = merges_handle.read().split('''\n''' )[1:-1]
__UpperCAmelCase = [tuple(merge.split() ) for merge in merges]
__UpperCAmelCase = dict(zip(lowercase__ , range(len(lowercase__ ) ) ) )
__UpperCAmelCase = {}
@property
def lowerCAmelCase_ (self ) -> int:
return len(self.encoder )
def lowerCAmelCase_ (self ) -> Dict:
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase_ (self , lowercase__ ) -> str:
if token in self.cache:
return self.cache[token]
__UpperCAmelCase = re.sub('''([.,!?()])''' , R''' \1''' , lowercase__ )
__UpperCAmelCase = re.sub('''(\')''' , R''' \1 ''' , lowercase__ )
__UpperCAmelCase = re.sub(R'''\s{2,}''' , ''' ''' , lowercase__ )
if "\n" in token:
__UpperCAmelCase = token.replace('''\n''' , ''' __newln__''' )
__UpperCAmelCase = token.split(''' ''' )
__UpperCAmelCase = []
for token in tokens:
if not len(lowercase__ ):
continue
__UpperCAmelCase = token.lower()
__UpperCAmelCase = tuple(lowercase__ )
__UpperCAmelCase = tuple(list(word[:-1] ) + [word[-1] + '''</w>'''] )
__UpperCAmelCase = get_pairs(lowercase__ )
if not pairs:
words.append(lowercase__ )
continue
while True:
__UpperCAmelCase = min(lowercase__ , key=lambda lowercase__ : self.bpe_ranks.get(lowercase__ , float('''inf''' ) ) )
if bigram not in self.bpe_ranks:
break
__UpperCAmelCase , __UpperCAmelCase = bigram
__UpperCAmelCase = []
__UpperCAmelCase = 0
while i < len(lowercase__ ):
try:
__UpperCAmelCase = word.index(lowercase__ , lowercase__ )
new_word.extend(word[i:j] )
__UpperCAmelCase = j
except ValueError:
new_word.extend(word[i:] )
break
if word[i] == first and i < len(lowercase__ ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
__UpperCAmelCase = tuple(lowercase__ )
__UpperCAmelCase = new_word
if len(lowercase__ ) == 1:
break
else:
__UpperCAmelCase = get_pairs(lowercase__ )
__UpperCAmelCase = '''@@ '''.join(lowercase__ )
__UpperCAmelCase = word[:-4]
__UpperCAmelCase = word
words.append(lowercase__ )
return " ".join(lowercase__ )
def lowerCAmelCase_ (self , lowercase__ ) -> List[str]:
__UpperCAmelCase = []
__UpperCAmelCase = re.findall(R'''\S+\n?''' , lowercase__ )
for token in words:
split_tokens.extend(list(self.bpe(lowercase__ ).split(''' ''' ) ) )
return split_tokens
def lowerCAmelCase_ (self , lowercase__ ) -> int:
__UpperCAmelCase = token.lower()
return self.encoder.get(lowercase__ , self.encoder.get(self.unk_token ) )
def lowerCAmelCase_ (self , lowercase__ ) -> str:
return self.decoder.get(lowercase__ , self.unk_token )
def lowerCAmelCase_ (self , lowercase__ ) -> str:
__UpperCAmelCase = ''' '''.join(lowercase__ ).replace('''@@ ''' , '''''' ).strip()
return out_string
def lowerCAmelCase_ (self , lowercase__ , lowercase__ = None ) -> Tuple[str]:
if not os.path.isdir(lowercase__ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
__UpperCAmelCase = os.path.join(
lowercase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
__UpperCAmelCase = os.path.join(
lowercase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file'''] )
with open(lowercase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.encoder , indent=2 , sort_keys=lowercase__ , ensure_ascii=lowercase__ ) + '''\n''' )
__UpperCAmelCase = 0
with open(lowercase__ , '''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 lowercase__ : 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!''' )
__UpperCAmelCase = token_index
writer.write(''' '''.join(lowercase__ ) + '''\n''' )
index += 1
return vocab_file, merge_file
| 333 |
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
A_ : Tuple = logging.get_logger(__name__)
class A_ ( _a ):
'''simple docstring'''
a__ = "linear"
a__ = "cosine"
a__ = "cosine_with_restarts"
a__ = "polynomial"
a__ = "constant"
a__ = "constant_with_warmup"
a__ = "piecewise_constant"
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = -1 ) -> Tuple:
'''simple docstring'''
return LambdaLR(SCREAMING_SNAKE_CASE , lambda SCREAMING_SNAKE_CASE : 1 , last_epoch=SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = -1 ) -> Union[str, Any]:
'''simple docstring'''
def lr_lambda(SCREAMING_SNAKE_CASE ):
if current_step < num_warmup_steps:
return float(SCREAMING_SNAKE_CASE ) / float(max(1.0 , SCREAMING_SNAKE_CASE ) )
return 1.0
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = -1 ) -> List[Any]:
'''simple docstring'''
__UpperCAmelCase = {}
__UpperCAmelCase = step_rules.split(''',''' )
for rule_str in rule_list[:-1]:
__UpperCAmelCase , __UpperCAmelCase = rule_str.split(''':''' )
__UpperCAmelCase = int(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = float(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = value
__UpperCAmelCase = float(rule_list[-1] )
def create_rules_function(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
def rule_func(SCREAMING_SNAKE_CASE ) -> float:
__UpperCAmelCase = sorted(rules_dict.keys() )
for i, sorted_step in enumerate(SCREAMING_SNAKE_CASE ):
if steps < sorted_step:
return rules_dict[sorted_steps[i]]
return last_lr_multiple
return rule_func
__UpperCAmelCase = create_rules_function(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=-1 ) -> Optional[Any]:
'''simple docstring'''
def lr_lambda(SCREAMING_SNAKE_CASE ):
if current_step < num_warmup_steps:
return float(SCREAMING_SNAKE_CASE ) / float(max(1 , SCREAMING_SNAKE_CASE ) )
return max(
0.0 , float(num_training_steps - current_step ) / float(max(1 , num_training_steps - num_warmup_steps ) ) )
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 0.5 , SCREAMING_SNAKE_CASE = -1 ) -> int:
'''simple docstring'''
def lr_lambda(SCREAMING_SNAKE_CASE ):
if current_step < num_warmup_steps:
return float(SCREAMING_SNAKE_CASE ) / float(max(1 , SCREAMING_SNAKE_CASE ) )
__UpperCAmelCase = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * float(SCREAMING_SNAKE_CASE ) * 2.0 * progress )) )
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = -1 ) -> Dict:
'''simple docstring'''
def lr_lambda(SCREAMING_SNAKE_CASE ):
if current_step < num_warmup_steps:
return float(SCREAMING_SNAKE_CASE ) / float(max(1 , SCREAMING_SNAKE_CASE ) )
__UpperCAmelCase = float(current_step - num_warmup_steps ) / float(max(1 , num_training_steps - num_warmup_steps ) )
if progress >= 1.0:
return 0.0
return max(0.0 , 0.5 * (1.0 + math.cos(math.pi * ((float(SCREAMING_SNAKE_CASE ) * progress) % 1.0) )) )
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=1e-7 , SCREAMING_SNAKE_CASE=1.0 , SCREAMING_SNAKE_CASE=-1 ) -> List[str]:
'''simple docstring'''
__UpperCAmelCase = optimizer.defaults['''lr''']
if not (lr_init > lr_end):
raise ValueError(f'''lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})''' )
def lr_lambda(SCREAMING_SNAKE_CASE ):
if current_step < num_warmup_steps:
return float(SCREAMING_SNAKE_CASE ) / float(max(1 , SCREAMING_SNAKE_CASE ) )
elif current_step > num_training_steps:
return lr_end / lr_init # as LambdaLR multiplies by lr_init
else:
__UpperCAmelCase = lr_init - lr_end
__UpperCAmelCase = num_training_steps - num_warmup_steps
__UpperCAmelCase = 1 - (current_step - num_warmup_steps) / decay_steps
__UpperCAmelCase = lr_range * pct_remaining**power + lr_end
return decay / lr_init # as LambdaLR multiplies by lr_init
return LambdaLR(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
A_ : Optional[Any] = {
SchedulerType.LINEAR: get_linear_schedule_with_warmup,
SchedulerType.COSINE: get_cosine_schedule_with_warmup,
SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
SchedulerType.CONSTANT: get_constant_schedule,
SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}
def __a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = None , SCREAMING_SNAKE_CASE = 1 , SCREAMING_SNAKE_CASE = 1.0 , SCREAMING_SNAKE_CASE = -1 , ) -> Union[str, Any]:
'''simple docstring'''
__UpperCAmelCase = SchedulerType(SCREAMING_SNAKE_CASE )
__UpperCAmelCase = TYPE_TO_SCHEDULER_FUNCTION[name]
if name == SchedulerType.CONSTANT:
return schedule_func(SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
if name == SchedulerType.PIECEWISE_CONSTANT:
return schedule_func(SCREAMING_SNAKE_CASE , step_rules=SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
# All other schedulers require `num_warmup_steps`
if num_warmup_steps is None:
raise ValueError(f'''{name} requires `num_warmup_steps`, please provide that argument.''' )
if name == SchedulerType.CONSTANT_WITH_WARMUP:
return schedule_func(SCREAMING_SNAKE_CASE , num_warmup_steps=SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
# All other schedulers require `num_training_steps`
if num_training_steps is None:
raise ValueError(f'''{name} requires `num_training_steps`, please provide that argument.''' )
if name == SchedulerType.COSINE_WITH_RESTARTS:
return schedule_func(
SCREAMING_SNAKE_CASE , num_warmup_steps=SCREAMING_SNAKE_CASE , num_training_steps=SCREAMING_SNAKE_CASE , num_cycles=SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE , )
if name == SchedulerType.POLYNOMIAL:
return schedule_func(
SCREAMING_SNAKE_CASE , num_warmup_steps=SCREAMING_SNAKE_CASE , num_training_steps=SCREAMING_SNAKE_CASE , power=SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE , )
return schedule_func(
SCREAMING_SNAKE_CASE , num_warmup_steps=SCREAMING_SNAKE_CASE , num_training_steps=SCREAMING_SNAKE_CASE , last_epoch=SCREAMING_SNAKE_CASE )
| 333 | 1 |
'''simple docstring'''
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class A__ ( _snake_case , _snake_case , unittest.TestCase ):
lowercase = IFPipeline
lowercase = TEXT_TO_IMAGE_PARAMS - {"width", "height", "latents"}
lowercase = TEXT_TO_IMAGE_BATCH_PARAMS
lowercase = PipelineTesterMixin.required_optional_params - {"latents"}
def snake_case_ ( self ) -> Any:
'''simple docstring'''
return self._get_dummy_components()
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__=0 ) -> List[str]:
'''simple docstring'''
if str(UpperCamelCase__ ).startswith("""mps""" ):
A_ = torch.manual_seed(UpperCamelCase__ )
else:
A_ = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
A_ = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def snake_case_ ( self ) -> Union[str, Any]:
'''simple docstring'''
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def snake_case_ ( self ) -> Tuple:
'''simple docstring'''
# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
super().test_save_load_floataa(expected_max_diff=1e-1 )
def snake_case_ ( self ) -> int:
'''simple docstring'''
self._test_attention_slicing_forward_pass(expected_max_diff=1e-2 )
def snake_case_ ( self ) -> int:
'''simple docstring'''
self._test_save_load_local()
def snake_case_ ( self ) -> Any:
'''simple docstring'''
self._test_inference_batch_single_identical(
expected_max_diff=1e-2 , )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def snake_case_ ( self ) -> List[Any]:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@slow
@require_torch_gpu
class A__ ( unittest.TestCase ):
def snake_case_ ( self ) -> List[str]:
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self ) -> Optional[Any]:
'''simple docstring'''
# if
A_ = IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa )
A_ = IFSuperResolutionPipeline.from_pretrained(
"""DeepFloyd/IF-II-L-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa , text_encoder=UpperCamelCase__ , tokenizer=UpperCamelCase__ )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to("""cuda""" )
A_ , A_ = pipe_a.encode_prompt("""anime turtle""" , device="""cuda""" )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
A_ = None
A_ = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
A_ = IFImgaImgPipeline(**pipe_a.components )
A_ = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
A_ = IFInpaintingPipeline(**pipe_a.components )
A_ = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Tuple:
'''simple docstring'''
# pipeline 1
_start_torch_memory_measurement()
A_ = torch.Generator(device="""cpu""" ).manual_seed(0 )
A_ = pipe_a(
prompt_embeds=UpperCamelCase__ , negative_prompt_embeds=UpperCamelCase__ , num_inference_steps=2 , generator=UpperCamelCase__ , output_type="""np""" , )
A_ = output.images[0]
assert image.shape == (64, 64, 3)
A_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
A_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""" )
assert_mean_pixel_difference(UpperCamelCase__ , UpperCamelCase__ )
# pipeline 2
_start_torch_memory_measurement()
A_ = torch.Generator(device="""cpu""" ).manual_seed(0 )
A_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(UpperCamelCase__ )
A_ = pipe_a(
prompt_embeds=UpperCamelCase__ , negative_prompt_embeds=UpperCamelCase__ , image=UpperCamelCase__ , generator=UpperCamelCase__ , num_inference_steps=2 , output_type="""np""" , )
A_ = output.images[0]
assert image.shape == (256, 256, 3)
A_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
A_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(UpperCamelCase__ , UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
# pipeline 1
_start_torch_memory_measurement()
A_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(UpperCamelCase__ )
A_ = torch.Generator(device="""cpu""" ).manual_seed(0 )
A_ = pipe_a(
prompt_embeds=UpperCamelCase__ , negative_prompt_embeds=UpperCamelCase__ , image=UpperCamelCase__ , num_inference_steps=2 , generator=UpperCamelCase__ , output_type="""np""" , )
A_ = output.images[0]
assert image.shape == (64, 64, 3)
A_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
A_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""" )
assert_mean_pixel_difference(UpperCamelCase__ , UpperCamelCase__ )
# pipeline 2
_start_torch_memory_measurement()
A_ = torch.Generator(device="""cpu""" ).manual_seed(0 )
A_ = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(UpperCamelCase__ )
A_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(UpperCamelCase__ )
A_ = pipe_a(
prompt_embeds=UpperCamelCase__ , negative_prompt_embeds=UpperCamelCase__ , image=UpperCamelCase__ , original_image=UpperCamelCase__ , generator=UpperCamelCase__ , num_inference_steps=2 , output_type="""np""" , )
A_ = output.images[0]
assert image.shape == (256, 256, 3)
A_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
A_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(UpperCamelCase__ , UpperCamelCase__ )
def snake_case_ ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
'''simple docstring'''
# pipeline 1
_start_torch_memory_measurement()
A_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(UpperCamelCase__ )
A_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(UpperCamelCase__ )
A_ = torch.Generator(device="""cpu""" ).manual_seed(0 )
A_ = pipe_a(
prompt_embeds=UpperCamelCase__ , negative_prompt_embeds=UpperCamelCase__ , image=UpperCamelCase__ , mask_image=UpperCamelCase__ , num_inference_steps=2 , generator=UpperCamelCase__ , output_type="""np""" , )
A_ = output.images[0]
assert image.shape == (64, 64, 3)
A_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
A_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""" )
assert_mean_pixel_difference(UpperCamelCase__ , UpperCamelCase__ )
# pipeline 2
_start_torch_memory_measurement()
A_ = torch.Generator(device="""cpu""" ).manual_seed(0 )
A_ = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(UpperCamelCase__ )
A_ = floats_tensor((1, 3, 256, 256) , rng=random.Random(0 ) ).to(UpperCamelCase__ )
A_ = floats_tensor((1, 3, 256, 256) , rng=random.Random(1 ) ).to(UpperCamelCase__ )
A_ = pipe_a(
prompt_embeds=UpperCamelCase__ , negative_prompt_embeds=UpperCamelCase__ , image=UpperCamelCase__ , mask_image=UpperCamelCase__ , original_image=UpperCamelCase__ , generator=UpperCamelCase__ , num_inference_steps=2 , output_type="""np""" , )
A_ = output.images[0]
assert image.shape == (256, 256, 3)
A_ = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
A_ = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(UpperCamelCase__ , UpperCamelCase__ )
def UpperCAmelCase__ ( ) -> Dict:
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 101 |
'''simple docstring'''
from functools import lru_cache
@lru_cache
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> int:
if num < 0:
raise ValueError("""Number should not be negative.""" )
return 1 if num in (0, 1) else num * factorial(num - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 101 | 1 |
from random import randint
from tempfile import TemporaryFile
import numpy as np
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> Tuple:
'''simple docstring'''
lowerCAmelCase : Tuple = 0
if start < end:
lowerCAmelCase : Tuple = randint(__lowerCAmelCase, __lowerCAmelCase )
lowerCAmelCase : str = a[end]
lowerCAmelCase : str = a[pivot]
lowerCAmelCase : Tuple = temp
lowerCAmelCase : Optional[Any] = _in_place_partition(__lowerCAmelCase, __lowerCAmelCase, __lowerCAmelCase )
count += _in_place_quick_sort(__lowerCAmelCase, __lowerCAmelCase, p - 1 )
count += _in_place_quick_sort(__lowerCAmelCase, p + 1, __lowerCAmelCase )
return count
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase : int = 0
lowerCAmelCase : int = randint(__lowerCAmelCase, __lowerCAmelCase )
lowerCAmelCase : Tuple = a[end]
lowerCAmelCase : Optional[int] = a[pivot]
lowerCAmelCase : int = temp
lowerCAmelCase : Any = start - 1
for index in range(__lowerCAmelCase, __lowerCAmelCase ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
lowerCAmelCase : str = new_pivot_index + 1
lowerCAmelCase : List[str] = a[new_pivot_index]
lowerCAmelCase : List[Any] = a[index]
lowerCAmelCase : Any = temp
lowerCAmelCase : List[Any] = a[new_pivot_index + 1]
lowerCAmelCase : Union[str, Any] = a[end]
lowerCAmelCase : List[str] = temp
return new_pivot_index + 1, count
__A : List[str] = TemporaryFile()
__A : Optional[Any] = 100 # 1000 elements are to be sorted
__A : Optional[int] = 0, 1 # mean and standard deviation
__A : Dict = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print('''The array is''')
print(X)
outfile.seek(0) # using the same array
__A : str = np.load(outfile)
__A : List[Any] = len(M) - 1
__A : Dict = _in_place_quick_sort(M, 0, r)
print(
'''No of Comparisons for 100 elements selected from a standard normal distribution'''
'''is :'''
)
print(z)
| 138 |
"""simple docstring"""
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
a :Union[str, Any] = 500_000
a ,a :Union[str, Any] = os.path.split(__file__)
a :Union[str, Any] = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_duration
def _lowercase ( __lowerCAmelCase , **__lowerCAmelCase ) -> Optional[Any]:
SCREAMING_SNAKE_CASE__ : str = dataset.map(**__lowerCAmelCase )
@get_duration
def _lowercase ( __lowerCAmelCase , **__lowerCAmelCase ) -> int:
SCREAMING_SNAKE_CASE__ : List[str] = dataset.filter(**__lowerCAmelCase )
def _lowercase ( ) -> str:
SCREAMING_SNAKE_CASE__ : str = {"""num examples""": SPEED_TEST_N_EXAMPLES}
with tempfile.TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE__ : Tuple = datasets.Features({"""text""": datasets.Value("""string""" ), """numbers""": datasets.Value("""float32""" )} )
SCREAMING_SNAKE_CASE__ : Any = generate_example_dataset(
os.path.join(__lowerCAmelCase , """dataset.arrow""" ) , __lowerCAmelCase , num_examples=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Any = transformers.AutoTokenizer.from_pretrained("""bert-base-cased""" , use_fast=__lowerCAmelCase )
def tokenize(__lowerCAmelCase ):
return tokenizer(examples["""text"""] )
SCREAMING_SNAKE_CASE__ : List[str] = map(__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Dict = map(__lowerCAmelCase , batched=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : Optional[Any] = map(__lowerCAmelCase , function=lambda __lowerCAmelCase : None , batched=__lowerCAmelCase )
with dataset.formatted_as(type="""numpy""" ):
SCREAMING_SNAKE_CASE__ : Any = map(__lowerCAmelCase , function=lambda __lowerCAmelCase : None , batched=__lowerCAmelCase )
with dataset.formatted_as(type="""pandas""" ):
SCREAMING_SNAKE_CASE__ : Optional[int] = map(__lowerCAmelCase , function=lambda __lowerCAmelCase : None , batched=__lowerCAmelCase )
with dataset.formatted_as(type="""torch""" , columns="""numbers""" ):
SCREAMING_SNAKE_CASE__ : Any = map(__lowerCAmelCase , function=lambda __lowerCAmelCase : None , batched=__lowerCAmelCase )
with dataset.formatted_as(type="""tensorflow""" , columns="""numbers""" ):
SCREAMING_SNAKE_CASE__ : int = map(__lowerCAmelCase , function=lambda __lowerCAmelCase : None , batched=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : List[Any] = map(__lowerCAmelCase , function=__lowerCAmelCase , batched=__lowerCAmelCase )
SCREAMING_SNAKE_CASE__ : int = filter(__lowerCAmelCase )
# Activate later when tokenizer support batched inputs
# with dataset.formatted_as(type='numpy'):
# times[func.__name__ + " fast-tokenizer batched numpy"] = func(dataset, function=tokenize, batched=True)
with open(__lowerCAmelCase , """wb""" ) as f:
f.write(json.dumps(__lowerCAmelCase ).encode("""utf-8""" ) )
if __name__ == "__main__": # useful to run the profiler
benchmark_map_filter()
| 132 | 0 |
from __future__ import annotations
import unittest
from transformers import is_tf_available, is_torch_available
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, SMALL_MODEL_IDENTIFIER, is_pt_tf_cross_test, slow
if is_tf_available():
from transformers import (
AutoConfig,
BertConfig,
GPTaConfig,
TaConfig,
TFAutoModel,
TFAutoModelForCausalLM,
TFAutoModelForMaskedLM,
TFAutoModelForPreTraining,
TFAutoModelForQuestionAnswering,
TFAutoModelForSeqaSeqLM,
TFAutoModelForSequenceClassification,
TFAutoModelWithLMHead,
TFBertForMaskedLM,
TFBertForPreTraining,
TFBertForQuestionAnswering,
TFBertForSequenceClassification,
TFBertModel,
TFGPTaLMHeadModel,
TFRobertaForMaskedLM,
TFTaForConditionalGeneration,
)
from transformers.models.bert.modeling_tf_bert import TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.gpta.modeling_tf_gpta import TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST
from transformers.models.ta.modeling_tf_ta import TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST
if is_torch_available():
from transformers import (
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
AutoModelForSeqaSeqLM,
AutoModelForSequenceClassification,
AutoModelWithLMHead,
BertForMaskedLM,
BertForPreTraining,
BertForQuestionAnswering,
BertForSequenceClassification,
BertModel,
GPTaLMHeadModel,
RobertaForMaskedLM,
TaForConditionalGeneration,
)
@is_pt_tf_cross_test
class a ( unittest.TestCase ):
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
_UpperCAmelCase : Optional[Any] = AutoConfig.from_pretrained(A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : Any = TFAutoModel.from_pretrained(A_ , from_pt=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : List[Any] = AutoModel.from_pretrained(A_ , from_tf=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
_UpperCAmelCase : List[str] = AutoConfig.from_pretrained(A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : List[Any] = TFAutoModelForPreTraining.from_pretrained(A_ , from_pt=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : List[str] = AutoModelForPreTraining.from_pretrained(A_ , from_tf=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in TF_GPT2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : List[Any] = AutoConfig.from_pretrained(A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : Optional[Any] = TFAutoModelForCausalLM.from_pretrained(A_ , from_pt=A_ )
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = TFAutoModelForCausalLM.from_pretrained(
A_ , output_loading_info=A_ , from_pt=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : Optional[int] = AutoModelForCausalLM.from_pretrained(A_ , from_tf=A_ )
_UpperCAmelCase , _UpperCAmelCase : str = AutoModelForCausalLM.from_pretrained(
A_ , output_loading_info=A_ , from_tf=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : List[Any] = AutoConfig.from_pretrained(A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : Optional[int] = TFAutoModelWithLMHead.from_pretrained(A_ , from_pt=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : str = AutoModelWithLMHead.from_pretrained(A_ , from_tf=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in TF_BERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : List[Any] = AutoConfig.from_pretrained(A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : int = TFAutoModelForMaskedLM.from_pretrained(A_ , from_pt=A_ )
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = TFAutoModelForMaskedLM.from_pretrained(
A_ , output_loading_info=A_ , from_pt=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : List[str] = AutoModelForMaskedLM.from_pretrained(A_ , from_tf=A_ )
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = AutoModelForMaskedLM.from_pretrained(
A_ , output_loading_info=A_ , from_tf=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_UpperCAmelCase : Tuple = AutoConfig.from_pretrained(A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : str = TFAutoModelForSeqaSeqLM.from_pretrained(A_ , from_pt=A_ )
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = TFAutoModelForSeqaSeqLM.from_pretrained(
A_ , output_loading_info=A_ , from_pt=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : str = AutoModelForSeqaSeqLM.from_pretrained(A_ , from_tf=A_ )
_UpperCAmelCase , _UpperCAmelCase : Any = AutoModelForSeqaSeqLM.from_pretrained(
A_ , output_loading_info=A_ , from_tf=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
_UpperCAmelCase : Optional[int] = AutoConfig.from_pretrained(A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : List[Any] = TFAutoModelForSequenceClassification.from_pretrained(A_ , from_pt=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : Tuple = AutoModelForSequenceClassification.from_pretrained(A_ , from_tf=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
@slow
def _UpperCAmelCase ( self ):
'''simple docstring'''
for model_name in ["bert-base-uncased"]:
_UpperCAmelCase : int = AutoConfig.from_pretrained(A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : Any = TFAutoModelForQuestionAnswering.from_pretrained(A_ , from_pt=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
_UpperCAmelCase : Dict = AutoModelForQuestionAnswering.from_pretrained(A_ , from_tf=A_ )
self.assertIsNotNone(A_ )
self.assertIsInstance(A_ , A_ )
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Any = TFAutoModelWithLMHead.from_pretrained(A_ , from_pt=A_ )
self.assertIsInstance(A_ , A_ )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=A_ ) , 14410 )
_UpperCAmelCase : Optional[Any] = AutoModelWithLMHead.from_pretrained(A_ , from_tf=A_ )
self.assertIsInstance(A_ , A_ )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=A_ ) , 14410 )
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = TFAutoModelWithLMHead.from_pretrained(A_ , from_pt=A_ )
self.assertIsInstance(A_ , A_ )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=A_ ) , 14410 )
_UpperCAmelCase : List[str] = AutoModelWithLMHead.from_pretrained(A_ , from_tf=A_ )
self.assertIsInstance(A_ , A_ )
self.assertEqual(model.num_parameters() , 14410 )
self.assertEqual(model.num_parameters(only_trainable=A_ ) , 14410 )
| 189 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class a ( UpperCAmelCase ):
_lowercase = ["image_processor", "tokenizer"]
_lowercase = "OwlViTImageProcessor"
_lowercase = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__( self , A_=None , A_=None , **A_ ):
'''simple docstring'''
_UpperCAmelCase : List[str] = None
if "feature_extractor" in kwargs:
warnings.warn(
"The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
" instead." , A_ , )
_UpperCAmelCase : Union[str, Any] = kwargs.pop("feature_extractor" )
_UpperCAmelCase : Any = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(A_ , A_ )
def __call__( self , A_=None , A_=None , A_=None , A_="max_length" , A_="np" , **A_ ):
'''simple docstring'''
if text is None and query_images is None and images is None:
raise ValueError(
"You have to specify at least one text or query image or image. All three cannot be none." )
if text is not None:
if isinstance(A_ , A_ ) or (isinstance(A_ , A_ ) and not isinstance(text[0] , A_ )):
_UpperCAmelCase : Optional[int] = [self.tokenizer(A_ , padding=A_ , return_tensors=A_ , **A_ )]
elif isinstance(A_ , A_ ) and isinstance(text[0] , A_ ):
_UpperCAmelCase : Optional[int] = []
# Maximum number of queries across batch
_UpperCAmelCase : Optional[Any] = max([len(A_ ) for t in text] )
# Pad all batch samples to max number of text queries
for t in text:
if len(A_ ) != max_num_queries:
_UpperCAmelCase : Optional[int] = t + [" "] * (max_num_queries - len(A_ ))
_UpperCAmelCase : str = self.tokenizer(A_ , padding=A_ , return_tensors=A_ , **A_ )
encodings.append(A_ )
else:
raise TypeError("Input text should be a string, a list of strings or a nested list of strings" )
if return_tensors == "np":
_UpperCAmelCase : List[str] = np.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 )
_UpperCAmelCase : Tuple = np.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 )
elif return_tensors == "jax" and is_flax_available():
import jax.numpy as jnp
_UpperCAmelCase : Optional[Any] = jnp.concatenate([encoding["input_ids"] for encoding in encodings] , axis=0 )
_UpperCAmelCase : str = jnp.concatenate([encoding["attention_mask"] for encoding in encodings] , axis=0 )
elif return_tensors == "pt" and is_torch_available():
import torch
_UpperCAmelCase : str = torch.cat([encoding["input_ids"] for encoding in encodings] , dim=0 )
_UpperCAmelCase : Dict = torch.cat([encoding["attention_mask"] for encoding in encodings] , dim=0 )
elif return_tensors == "tf" and is_tf_available():
import tensorflow as tf
_UpperCAmelCase : Union[str, Any] = tf.stack([encoding["input_ids"] for encoding in encodings] , axis=0 )
_UpperCAmelCase : Optional[int] = tf.stack([encoding["attention_mask"] for encoding in encodings] , axis=0 )
else:
raise ValueError("Target return tensor type could not be returned" )
_UpperCAmelCase : Optional[int] = BatchEncoding()
_UpperCAmelCase : str = input_ids
_UpperCAmelCase : Optional[Any] = attention_mask
if query_images is not None:
_UpperCAmelCase : int = BatchEncoding()
_UpperCAmelCase : str = self.image_processor(
A_ , return_tensors=A_ , **A_ ).pixel_values
_UpperCAmelCase : Optional[Any] = query_pixel_values
if images is not None:
_UpperCAmelCase : int = self.image_processor(A_ , return_tensors=A_ , **A_ )
if text is not None and images is not None:
_UpperCAmelCase : Optional[int] = image_features.pixel_values
return encoding
elif query_images is not None and images is not None:
_UpperCAmelCase : Any = image_features.pixel_values
return encoding
elif text is not None or query_images is not None:
return encoding
else:
return BatchEncoding(data=dict(**A_ ) , tensor_type=A_ )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
return self.image_processor.post_process(*A_ , **A_ )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
return self.image_processor.post_process_object_detection(*A_ , **A_ )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
return self.image_processor.post_process_image_guided_detection(*A_ , **A_ )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
return self.tokenizer.batch_decode(*A_ , **A_ )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
return self.tokenizer.decode(*A_ , **A_ )
@property
def _UpperCAmelCase ( self ):
'''simple docstring'''
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , A_ , )
return self.image_processor_class
@property
def _UpperCAmelCase ( self ):
'''simple docstring'''
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , A_ , )
return self.image_processor
| 189 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE :Any = logging.get_logger(__name__)
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
__A = SwinConfig.from_pretrained(
"microsoft/swin-tiny-patch4-window7-224" , out_features=["stage1", "stage2", "stage3", "stage4"] )
__A = MaskFormerConfig(backbone_config=a_ )
__A = "huggingface/label-files"
if "ade20k-full" in model_name:
# this should be ok
__A = 8_4_7
__A = "maskformer-ade20k-full-id2label.json"
elif "ade" in model_name:
# this should be ok
__A = 1_5_0
__A = "ade20k-id2label.json"
elif "coco-stuff" in model_name:
# this should be ok
__A = 1_7_1
__A = "maskformer-coco-stuff-id2label.json"
elif "coco" in model_name:
# TODO
__A = 1_3_3
__A = "coco-panoptic-id2label.json"
elif "cityscapes" in model_name:
# this should be ok
__A = 1_9
__A = "cityscapes-id2label.json"
elif "vistas" in model_name:
# this should be ok
__A = 6_5
__A = "mapillary-vistas-id2label.json"
__A = json.load(open(hf_hub_download(a_ , a_ , repo_type="dataset" ) , "r" ) )
__A = {int(a_ ): v for k, v in idalabel.items()}
return config
def UpperCAmelCase ( a_ ) -> Any:
"""simple docstring"""
__A = []
# stem
# fmt: off
rename_keys.append(("backbone.patch_embed.proj.weight", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.weight") )
rename_keys.append(("backbone.patch_embed.proj.bias", "model.pixel_level_module.encoder.model.embeddings.patch_embeddings.projection.bias") )
rename_keys.append(("backbone.patch_embed.norm.weight", "model.pixel_level_module.encoder.model.embeddings.norm.weight") )
rename_keys.append(("backbone.patch_embed.norm.bias", "model.pixel_level_module.encoder.model.embeddings.norm.bias") )
# stages
for i in range(len(config.backbone_config.depths ) ):
for j in range(config.backbone_config.depths[i] ):
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.weight''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_before.bias''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_bias_table''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_bias_table''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.relative_position_index''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.self.relative_position_index''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.weight''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.attn.proj.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.attention.output.dense.bias''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.weight''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.norm2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.layernorm_after.bias''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.weight''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc1.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.intermediate.dense.bias''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.weight''') )
rename_keys.append((F'''backbone.layers.{i}.blocks.{j}.mlp.fc2.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.blocks.{j}.output.dense.bias''') )
if i < 3:
rename_keys.append((F'''backbone.layers.{i}.downsample.reduction.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.reduction.weight''') )
rename_keys.append((F'''backbone.layers.{i}.downsample.norm.weight''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.weight''') )
rename_keys.append((F'''backbone.layers.{i}.downsample.norm.bias''', F'''model.pixel_level_module.encoder.model.encoder.layers.{i}.downsample.norm.bias''') )
rename_keys.append((F'''backbone.norm{i}.weight''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.weight''') )
rename_keys.append((F'''backbone.norm{i}.bias''', F'''model.pixel_level_module.encoder.hidden_states_norms.{i}.bias''') )
# FPN
rename_keys.append(("sem_seg_head.layer_4.weight", "model.pixel_level_module.decoder.fpn.stem.0.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.weight", "model.pixel_level_module.decoder.fpn.stem.1.weight") )
rename_keys.append(("sem_seg_head.layer_4.norm.bias", "model.pixel_level_module.decoder.fpn.stem.1.bias") )
for source_index, target_index in zip(range(3 , 0 , -1 ) , range(0 , 3 ) ):
rename_keys.append((F'''sem_seg_head.adapter_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.0.weight''') )
rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.weight''') )
rename_keys.append((F'''sem_seg_head.adapter_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.proj.1.bias''') )
rename_keys.append((F'''sem_seg_head.layer_{source_index}.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.0.weight''') )
rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.weight''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.weight''') )
rename_keys.append((F'''sem_seg_head.layer_{source_index}.norm.bias''', F'''model.pixel_level_module.decoder.fpn.layers.{target_index}.block.1.bias''') )
rename_keys.append(("sem_seg_head.mask_features.weight", "model.pixel_level_module.decoder.mask_projection.weight") )
rename_keys.append(("sem_seg_head.mask_features.bias", "model.pixel_level_module.decoder.mask_projection.bias") )
# Transformer decoder
for idx in range(config.decoder_config.decoder_layers ):
# self-attention out projection
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn.out_proj.bias''') )
# cross-attention out projection
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.out_proj.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn.out_proj.bias''') )
# MLP 1
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc1.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear1.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc1.bias''') )
# MLP 2
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.weight''', F'''model.transformer_module.decoder.layers.{idx}.fc2.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.linear2.bias''', F'''model.transformer_module.decoder.layers.{idx}.fc2.bias''') )
# layernorm 1 (self-attention layernorm)
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.weight''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm1.bias''', F'''model.transformer_module.decoder.layers.{idx}.self_attn_layer_norm.bias''') )
# layernorm 2 (cross-attention layernorm)
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.weight''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm2.bias''', F'''model.transformer_module.decoder.layers.{idx}.encoder_attn_layer_norm.bias''') )
# layernorm 3 (final layernorm)
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.weight''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.norm3.bias''', F'''model.transformer_module.decoder.layers.{idx}.final_layer_norm.bias''') )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.weight", "model.transformer_module.decoder.layernorm.weight") )
rename_keys.append(("sem_seg_head.predictor.transformer.decoder.norm.bias", "model.transformer_module.decoder.layernorm.bias") )
# heads on top
rename_keys.append(("sem_seg_head.predictor.query_embed.weight", "model.transformer_module.queries_embedder.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.weight", "model.transformer_module.input_projection.weight") )
rename_keys.append(("sem_seg_head.predictor.input_proj.bias", "model.transformer_module.input_projection.bias") )
rename_keys.append(("sem_seg_head.predictor.class_embed.weight", "class_predictor.weight") )
rename_keys.append(("sem_seg_head.predictor.class_embed.bias", "class_predictor.bias") )
for i in range(3 ):
rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.weight''', F'''mask_embedder.{i}.0.weight''') )
rename_keys.append((F'''sem_seg_head.predictor.mask_embed.layers.{i}.bias''', F'''mask_embedder.{i}.0.bias''') )
# fmt: on
return rename_keys
def UpperCAmelCase ( a_ , a_ , a_ ) -> List[str]:
"""simple docstring"""
__A = dct.pop(a_ )
__A = val
def UpperCAmelCase ( a_ , a_ ) -> Dict:
"""simple docstring"""
__A = [int(backbone_config.embed_dim * 2**i ) for i in range(len(backbone_config.depths ) )]
for i in range(len(backbone_config.depths ) ):
__A = num_features[i]
for j in range(backbone_config.depths[i] ):
# fmt: off
# read in weights + bias of input projection layer (in original implementation, this is a single matrix + bias)
__A = state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.weight''' )
__A = state_dict.pop(F'''backbone.layers.{i}.blocks.{j}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
__A = in_proj_weight[:dim, :]
__A = in_proj_bias[: dim]
__A = in_proj_weight[
dim : dim * 2, :
]
__A = in_proj_bias[
dim : dim * 2
]
__A = in_proj_weight[
-dim :, :
]
__A = in_proj_bias[-dim :]
# fmt: on
def UpperCAmelCase ( a_ , a_ ) -> str:
"""simple docstring"""
__A = config.decoder_config.hidden_size
for idx in range(config.decoder_config.decoder_layers ):
# read in weights + bias of self-attention input projection layer (in the original implementation, this is a single matrix + bias)
__A = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_weight''' )
__A = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
__A = in_proj_weight[: hidden_size, :]
__A = in_proj_bias[:config.hidden_size]
__A = in_proj_weight[hidden_size : hidden_size * 2, :]
__A = in_proj_bias[hidden_size : hidden_size * 2]
__A = in_proj_weight[-hidden_size :, :]
__A = in_proj_bias[-hidden_size :]
# read in weights + bias of cross-attention input projection layer (in the original implementation, this is a single matrix + bias)
__A = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_weight''' )
__A = state_dict.pop(F'''sem_seg_head.predictor.transformer.decoder.layers.{idx}.multihead_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
__A = in_proj_weight[: hidden_size, :]
__A = in_proj_bias[:config.hidden_size]
__A = in_proj_weight[hidden_size : hidden_size * 2, :]
__A = in_proj_bias[hidden_size : hidden_size * 2]
__A = in_proj_weight[-hidden_size :, :]
__A = in_proj_bias[-hidden_size :]
# fmt: on
def UpperCAmelCase ( ) -> torch.Tensor:
"""simple docstring"""
__A = "http://images.cocodataset.org/val2017/000000039769.jpg"
__A = Image.open(requests.get(a_ , stream=a_ ).raw )
return im
@torch.no_grad()
def UpperCAmelCase ( a_ , a_ , a_ , a_ = False ) -> Union[str, Any]:
"""simple docstring"""
__A = get_maskformer_config(a_ )
# load original state_dict
with open(a_ , "rb" ) as f:
__A = pickle.load(a_ )
__A = data["model"]
# for name, param in state_dict.items():
# print(name, param.shape)
# rename keys
__A = create_rename_keys(a_ )
for src, dest in rename_keys:
rename_key(a_ , a_ , a_ )
read_in_swin_q_k_v(a_ , config.backbone_config )
read_in_decoder_q_k_v(a_ , a_ )
# update to torch tensors
for key, value in state_dict.items():
__A = torch.from_numpy(a_ )
# load 🤗 model
__A = MaskFormerForInstanceSegmentation(a_ )
model.eval()
for name, param in model.named_parameters():
print(a_ , param.shape )
__A , __A = model.load_state_dict(a_ , strict=a_ )
assert missing_keys == [
"model.pixel_level_module.encoder.model.layernorm.weight",
"model.pixel_level_module.encoder.model.layernorm.bias",
]
assert len(a_ ) == 0, F'''Unexpected keys: {unexpected_keys}'''
# verify results
__A = prepare_img()
if "vistas" in model_name:
__A = 6_5
elif "cityscapes" in model_name:
__A = 6_5_5_3_5
else:
__A = 2_5_5
__A = True if "ade" in model_name else False
__A = MaskFormerImageProcessor(ignore_index=a_ , reduce_labels=a_ )
__A = image_processor(a_ , return_tensors="pt" )
__A = model(**a_ )
print("Logits:" , outputs.class_queries_logits[0, :3, :3] )
if model_name == "maskformer-swin-tiny-ade":
__A = torch.tensor(
[[3.6_353, -4.4_770, -2.6_065], [0.5_081, -4.2_394, -3.5_343], [2.1_909, -5.0_353, -1.9_323]] )
assert torch.allclose(outputs.class_queries_logits[0, :3, :3] , a_ , atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
print(F'''Saving model and image processor to {pytorch_dump_folder_path}''' )
Path(a_ ).mkdir(exist_ok=a_ )
model.save_pretrained(a_ )
image_processor.save_pretrained(a_ )
if push_to_hub:
print("Pushing model and image processor to the hub..." )
model.push_to_hub(F'''nielsr/{model_name}''' )
image_processor.push_to_hub(F'''nielsr/{model_name}''' )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE :int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='maskformer-swin-tiny-ade',
type=str,
help=('Name of the MaskFormer model you\'d like to convert',),
)
parser.add_argument(
'--checkpoint_path',
default='/Users/nielsrogge/Documents/MaskFormer_checkpoints/MaskFormer-Swin-tiny-ADE20k/model.pkl',
type=str,
help='Path to the original state dict (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
)
SCREAMING_SNAKE_CASE :Optional[Any] = parser.parse_args()
convert_maskformer_checkpoint(
args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 15 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse('9.1.0'):
SCREAMING_SNAKE_CASE :Any = {
'linear': PIL.Image.Resampling.BILINEAR,
'bilinear': PIL.Image.Resampling.BILINEAR,
'bicubic': PIL.Image.Resampling.BICUBIC,
'lanczos': PIL.Image.Resampling.LANCZOS,
'nearest': PIL.Image.Resampling.NEAREST,
}
else:
SCREAMING_SNAKE_CASE :int = {
'linear': PIL.Image.LINEAR,
'bilinear': PIL.Image.BILINEAR,
'bicubic': PIL.Image.BICUBIC,
'lanczos': PIL.Image.LANCZOS,
'nearest': PIL.Image.NEAREST,
}
def UpperCAmelCase ( a_ ) -> Optional[Any]:
"""simple docstring"""
__A = (images / 2 + 0.5).clamp(0 , 1 )
__A = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
__A = numpy_to_pil(a_ )
return images
def UpperCAmelCase ( a_ ) -> int:
"""simple docstring"""
if images.ndim == 3:
__A = images[None, ...]
__A = (images * 2_5_5).round().astype("uint8" )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
__A = [Image.fromarray(image.squeeze() , mode="L" ) for image in images]
else:
__A = [Image.fromarray(a_ ) for image in images]
return pil_images
| 15 | 1 |
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def lowerCAmelCase_ ( __UpperCAmelCase: int ) -> bool:
UpperCamelCase__ : int = int(number**0.5 )
return number == sq * sq
def lowerCAmelCase_ ( __UpperCAmelCase: int , __UpperCAmelCase: int , __UpperCAmelCase: int , __UpperCAmelCase: int , __UpperCAmelCase: int , __UpperCAmelCase: int ) -> tuple[int, int]:
UpperCamelCase__ : int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
UpperCamelCase__ : int = x_den * y_den * z_den
UpperCamelCase__ : int = gcd(__UpperCAmelCase , __UpperCAmelCase )
top //= hcf
bottom //= hcf
return top, bottom
def lowerCAmelCase_ ( __UpperCAmelCase: int = 35 ) -> int:
UpperCamelCase__ : set = set()
UpperCamelCase__ : int
UpperCamelCase__ : Fraction = Fraction(0 )
UpperCamelCase__ : tuple[int, int]
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
UpperCamelCase__ : Dict = x_num * y_den + x_den * y_num
UpperCamelCase__ : str = x_den * y_den
UpperCamelCase__ : List[str] = gcd(__UpperCAmelCase , __UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ : Union[str, Any] = add_three(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
unique_s.add(__UpperCAmelCase )
# n=2
UpperCamelCase__ : Tuple = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
UpperCamelCase__ : Tuple = x_den * x_den * y_den * y_den
if is_sq(__UpperCAmelCase ) and is_sq(__UpperCAmelCase ):
UpperCamelCase__ : List[Any] = int(sqrt(__UpperCAmelCase ) )
UpperCamelCase__ : int = int(sqrt(__UpperCAmelCase ) )
UpperCamelCase__ : Union[str, Any] = gcd(__UpperCAmelCase , __UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ : int = add_three(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
unique_s.add(__UpperCAmelCase )
# n=-1
UpperCamelCase__ : Optional[Any] = x_num * y_num
UpperCamelCase__ : Optional[int] = x_den * y_num + x_num * y_den
UpperCamelCase__ : Optional[Any] = gcd(__UpperCAmelCase , __UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ : Any = add_three(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
unique_s.add(__UpperCAmelCase )
# n=2
UpperCamelCase__ : Optional[int] = x_num * x_num * y_num * y_num
UpperCamelCase__ : Any = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(__UpperCAmelCase ) and is_sq(__UpperCAmelCase ):
UpperCamelCase__ : Optional[int] = int(sqrt(__UpperCAmelCase ) )
UpperCamelCase__ : Union[str, Any] = int(sqrt(__UpperCAmelCase ) )
UpperCamelCase__ : Optional[Any] = gcd(__UpperCAmelCase , __UpperCAmelCase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
UpperCamelCase__ : List[str] = add_three(
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
unique_s.add(__UpperCAmelCase )
for num, den in unique_s:
total += Fraction(__UpperCAmelCase , __UpperCAmelCase )
return total.denominator + total.numerator
if __name__ == "__main__":
print(F'''{solution() = }''')
| 247 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCAmelCase_ ( ) -> List[str]:
UpperCamelCase__ : List[str] = {
'''repo_name''': ['''test_repo1''', '''test_repo2''', '''test_repo3'''],
'''path''': ['''test_1.py''', '''test_2.py''', '''unit_test.py'''],
'''content''': ['''a ''' * 20, '''a ''' * 30, '''b ''' * 7],
}
UpperCamelCase__ : Dict = Dataset.from_dict(__UpperCAmelCase )
return dataset
class lowercase__ ( __lowerCamelCase ):
'''simple docstring'''
def UpperCamelCase__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase__ : List[Any] = get_dataset()
UpperCamelCase__ : List[str] = make_duplicate_clusters(__magic_name__, 0.85 )
self.assertEqual(len(duplicate_clusters[0] ), 2 )
def UpperCamelCase__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase__ : List[Any] = get_dataset()
UpperCamelCase__ ,UpperCamelCase__ : Dict = deduplicate_dataset(__magic_name__ )
self.assertEqual(len(__magic_name__ ), 2 )
print(__magic_name__ )
self.assertEqual(duplicate_clusters[0][0]['''copies'''], 2 )
self.assertEqual(duplicate_clusters[0][0]['''is_extreme'''], __magic_name__ )
| 247 | 1 |
"""simple docstring"""
def lowerCamelCase__ ( _lowerCamelCase : str , _lowerCamelCase : int ) -> list:
lowerCamelCase_ = word.split()
def justify(_lowerCamelCase : list , _lowerCamelCase : int , _lowerCamelCase : int ) -> str:
lowerCamelCase_ = max_width - width
lowerCamelCase_ = len(_lowerCamelCase )
if len(_lowerCamelCase ) == 1:
# if there is only word in line
# just insert overall_spaces_count for the remainder of line
return line[0] + " " * overall_spaces_count
else:
lowerCamelCase_ = words_count - 1
# num_spaces_between_words_list[i] : tells you to insert
# num_spaces_between_words_list[i] spaces
# after word on line[i]
lowerCamelCase_ = spaces_to_insert_between_words * [
overall_spaces_count // spaces_to_insert_between_words
]
lowerCamelCase_ = (
overall_spaces_count % spaces_to_insert_between_words
)
# distribute spaces via round robin to the left words
for i in range(_lowerCamelCase ):
num_spaces_between_words_list[i] += 1
lowerCamelCase_ = []
for i in range(_lowerCamelCase ):
# add the word
aligned_words_list.append(line[i] )
# add the spaces to insert
aligned_words_list.append(num_spaces_between_words_list[i] * ' ' )
# just add the last word to the sentence
aligned_words_list.append(line[-1] )
# join the aligned words list to form a justified line
return "".join(_lowerCamelCase )
lowerCamelCase_ = []
lowerCamelCase_ = []
lowerCamelCase_ = 0
for word in words:
if width + len(_lowerCamelCase ) + len(_lowerCamelCase ) <= max_width:
# keep adding words until we can fill out max_width
# width = sum of length of all words (without overall_spaces_count)
# len(word) = length of current word
# len(line) = number of overall_spaces_count to insert between words
line.append(_lowerCamelCase )
width += len(_lowerCamelCase )
else:
# justify the line and add it to result
answer.append(justify(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase ) )
# reset new line and new width
lowerCamelCase_ , lowerCamelCase_ = [word], len(_lowerCamelCase )
lowerCamelCase_ = max_width - width - len(_lowerCamelCase )
answer.append(' '.join(_lowerCamelCase ) + (remaining_spaces + 1) * ' ' )
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 183 |
"""simple docstring"""
from math import pow, sqrt
def lowerCamelCase__ ( *_lowerCamelCase : float ) -> bool:
lowerCamelCase_ = len(_lowerCamelCase ) > 0 and all(value > 0.0 for value in values )
return result
def lowerCamelCase__ ( _lowerCamelCase : float , _lowerCamelCase : float ) -> float | ValueError:
return (
round(sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_lowerCamelCase , _lowerCamelCase )
else ValueError('Input Error: Molar mass values must greater than 0.' )
)
def lowerCamelCase__ ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> float | ValueError:
return (
round(effusion_rate * sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def lowerCamelCase__ ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> float | ValueError:
return (
round(effusion_rate / sqrt(molar_mass_a / molar_mass_a ) , 6 )
if validate(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def lowerCamelCase__ ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> float | ValueError:
return (
round(molar_mass / pow(effusion_rate_a / effusion_rate_a , 2 ) , 6 )
if validate(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
def lowerCamelCase__ ( _lowerCamelCase : float , _lowerCamelCase : float , _lowerCamelCase : float ) -> float | ValueError:
return (
round(pow(effusion_rate_a / effusion_rate_a , 2 ) / molar_mass , 6 )
if validate(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
else ValueError(
'Input Error: Molar mass and effusion rate values must greater than 0.' )
)
| 183 | 1 |
def A ( _lowercase ):
SCREAMING_SNAKE_CASE : Optional[Any] = len(_lowercase )
for i in range(1 , _lowercase ):
SCREAMING_SNAKE_CASE : Any = collection[i]
SCREAMING_SNAKE_CASE : Any = 0
SCREAMING_SNAKE_CASE : str = i - 1
while low <= high:
SCREAMING_SNAKE_CASE : Optional[int] = (low + high) // 2
if val < collection[mid]:
SCREAMING_SNAKE_CASE : Any = mid - 1
else:
SCREAMING_SNAKE_CASE : Union[str, Any] = mid + 1
for j in range(_lowercase , _lowercase , -1 ):
SCREAMING_SNAKE_CASE : str = collection[j - 1]
SCREAMING_SNAKE_CASE : Union[str, Any] = val
return collection
if __name__ == "__main__":
__UpperCamelCase : str = input('Enter numbers separated by a comma:\n').strip()
__UpperCamelCase : Union[str, Any] = [int(item) for item in user_input.split(',')]
print(binary_insertion_sort(unsorted))
| 258 | from __future__ import annotations
from math import pi
def A ( _lowercase , _lowercase , _lowercase ):
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
raise ValueError('''Inductance cannot be negative''' )
if frequency < 0:
raise ValueError('''Frequency cannot be negative''' )
if reactance < 0:
raise ValueError('''Inductive reactance cannot be negative''' )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 258 | 1 |
"""simple docstring"""
from __future__ import annotations
# This is the precision for this function which can be altered.
# It is recommended for users to keep this number greater than or equal to 10.
a__ : Optional[Any] = 1_0
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
for i in range(lowerCAmelCase_ , lowerCAmelCase_ ):
if array[i] == target:
return i
return -1
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = len(lowerCAmelCase_ )
while left <= right:
if right - left < precision:
return lin_search(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = (left + right) // 3 + 1
__SCREAMING_SNAKE_CASE = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
__SCREAMING_SNAKE_CASE = one_third - 1
elif array[two_third] < target:
__SCREAMING_SNAKE_CASE = two_third + 1
else:
__SCREAMING_SNAKE_CASE = one_third + 1
__SCREAMING_SNAKE_CASE = two_third - 1
else:
return -1
def UpperCAmelCase__ (lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
if left < right:
if right - left < precision:
return lin_search(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
__SCREAMING_SNAKE_CASE = (left + right) // 3 + 1
__SCREAMING_SNAKE_CASE = 2 * (left + right) // 3 + 1
if array[one_third] == target:
return one_third
elif array[two_third] == target:
return two_third
elif target < array[one_third]:
return rec_ternary_search(lowerCAmelCase_ , one_third - 1 , lowerCAmelCase_ , lowerCAmelCase_ )
elif array[two_third] < target:
return rec_ternary_search(two_third + 1 , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
else:
return rec_ternary_search(one_third + 1 , two_third - 1 , lowerCAmelCase_ , lowerCAmelCase_ )
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
a__ : List[Any] = input('''Enter numbers separated by comma:\n''').strip()
a__ : Any = [int(item.strip()) for item in user_input.split(''',''')]
assert collection == sorted(collection), F"List must be ordered.\n{collection}."
a__ : List[str] = int(input('''Enter the number to be found in the list:\n''').strip())
a__ : Optional[Any] = ite_ternary_search(collection, target)
a__ : Optional[int] = rec_ternary_search(0, len(collection) - 1, collection, target)
if resulta != -1:
print(F"Iterative search: {target} found at positions: {resulta}")
print(F"Recursive search: {target} found at positions: {resulta}")
else:
print('''Not found''')
| 54 |
"""simple docstring"""
import unittest
from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
return x + 2
class UpperCamelCase_ ( unittest.TestCase):
"""simple docstring"""
def UpperCAmelCase_ ( self : Any ) -> Any:
__SCREAMING_SNAKE_CASE = "x = 3"
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
assert result == 3
self.assertDictEqual(UpperCAmelCase__ , {"x": 3} )
__SCREAMING_SNAKE_CASE = "x = y"
__SCREAMING_SNAKE_CASE = {"y": 5}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 5, "y": 5} )
def UpperCAmelCase_ ( self : Dict ) -> List[str]:
__SCREAMING_SNAKE_CASE = "y = add_two(x)"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"add_two": add_two} , state=UpperCAmelCase__ )
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "y": 5} )
# Won't work without the tool
with CaptureStdout() as out:
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
assert result is None
assert "tried to execute add_two" in out.out
def UpperCAmelCase_ ( self : List[Any] ) -> List[Any]:
__SCREAMING_SNAKE_CASE = "x = 3"
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
assert result == 3
self.assertDictEqual(UpperCAmelCase__ , {"x": 3} )
def UpperCAmelCase_ ( self : str ) -> Any:
__SCREAMING_SNAKE_CASE = "test_dict = {'x': x, 'y': add_two(x)}"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"add_two": add_two} , state=UpperCAmelCase__ )
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "y": 5} )
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def UpperCAmelCase_ ( self : int ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = "x = 3\ny = 5"
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "y": 5} )
def UpperCAmelCase_ ( self : Any ) -> Any:
__SCREAMING_SNAKE_CASE = "text = f'This is x: {x}.'"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
# evaluate returns the value of the last assignment.
assert result == "This is x: 3."
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "text": "This is x: 3."} )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = "if x <= 3:\n y = 2\nelse:\n y = 5"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
# evaluate returns the value of the last assignment.
assert result == 2
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "y": 2} )
__SCREAMING_SNAKE_CASE = {"x": 8}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
# evaluate returns the value of the last assignment.
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 8, "y": 5} )
def UpperCAmelCase_ ( self : Tuple ) -> str:
__SCREAMING_SNAKE_CASE = "test_list = [x, add_two(x)]"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"add_two": add_two} , state=UpperCAmelCase__ )
self.assertListEqual(UpperCAmelCase__ , [3, 5] )
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "test_list": [3, 5]} )
def UpperCAmelCase_ ( self : Any ) -> int:
__SCREAMING_SNAKE_CASE = "y = x"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {} , state=UpperCAmelCase__ )
assert result == 3
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "y": 3} )
def UpperCAmelCase_ ( self : Tuple ) -> int:
__SCREAMING_SNAKE_CASE = "test_list = [x, add_two(x)]\ntest_list[1]"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"add_two": add_two} , state=UpperCAmelCase__ )
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "test_list": [3, 5]} )
__SCREAMING_SNAKE_CASE = "test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
__SCREAMING_SNAKE_CASE = {"x": 3}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"add_two": add_two} , state=UpperCAmelCase__ )
assert result == 5
self.assertDictEqual(UpperCAmelCase__ , {"x": 3, "test_dict": {"x": 3, "y": 5}} )
def UpperCAmelCase_ ( self : List[str] ) -> List[str]:
__SCREAMING_SNAKE_CASE = "x = 0\nfor i in range(3):\n x = i"
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = evaluate(UpperCAmelCase__ , {"range": range} , state=UpperCAmelCase__ )
assert result == 2
self.assertDictEqual(UpperCAmelCase__ , {"x": 2, "i": 2} )
| 54 | 1 |
'''simple docstring'''
import numpy as np
def _lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def _lowerCAmelCase ( _UpperCamelCase : np.ndarray ) -> np.ndarray:
"""simple docstring"""
return vector * sigmoid(_UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 114 |
'''simple docstring'''
import warnings
warnings.warn(
"memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: "
"`from accelerate import find_executable_batch_size` to avoid this warning.",
FutureWarning,
)
| 114 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__lowerCamelCase = {
'''configuration_bloom''': ['''BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BloomConfig''', '''BloomOnnxConfig'''],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = ['''BloomTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
'''BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BloomForCausalLM''',
'''BloomModel''',
'''BloomPreTrainedModel''',
'''BloomForSequenceClassification''',
'''BloomForTokenClassification''',
'''BloomForQuestionAnswering''',
]
if TYPE_CHECKING:
from .configuration_bloom import BLOOM_PRETRAINED_CONFIG_ARCHIVE_MAP, BloomConfig, BloomOnnxConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bloom_fast import BloomTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bloom import (
BLOOM_PRETRAINED_MODEL_ARCHIVE_LIST,
BloomForCausalLM,
BloomForQuestionAnswering,
BloomForSequenceClassification,
BloomForTokenClassification,
BloomModel,
BloomPreTrainedModel,
)
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 162 |
'''simple docstring'''
from __future__ import annotations
class A__ :
def __init__( self , UpperCamelCase__=None ) -> Any:
'''simple docstring'''
A_ = data
A_ = None
def __repr__( self ) -> List[str]:
'''simple docstring'''
A_ = []
A_ = self
while temp:
string_rep.append(f'''{temp.data}''' )
A_ = temp.next
return "->".join(UpperCamelCase__ )
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> Tuple:
if not elements_list:
raise Exception("""The Elements List is empty""" )
A_ = A_ = Node(elements_list[0] )
for i in range(1, len(UpperCAmelCase__ ) ):
A_ = Node(elements_list[i] )
A_ = current.next
return head
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> None:
if head_node is not None and isinstance(UpperCAmelCase__, UpperCAmelCase__ ):
print_reverse(head_node.next )
print(head_node.data )
def UpperCAmelCase__ ( ) -> Optional[Any]:
from doctest import testmod
testmod()
A_ = make_linked_list([14, 52, 14, 12, 43] )
print("""Linked List:""" )
print(UpperCAmelCase__ )
print("""Elements in Reverse:""" )
print_reverse(UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 162 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCamelCase__ = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
"""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
lowerCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 182 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class A__ ( _lowerCamelCase , unittest.TestCase):
A_ : List[Any] = KandinskyVaaInpaintPipeline
A_ : Optional[int] = ['image_embeds', 'negative_image_embeds', 'image', 'mask_image']
A_ : Any = [
'image_embeds',
'negative_image_embeds',
'image',
'mask_image',
]
A_ : Any = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
A_ : Any = False
@property
def __lowerCamelCase ( self ):
return 32
@property
def __lowerCamelCase ( self ):
return 32
@property
def __lowerCamelCase ( self ):
return self.time_input_dim
@property
def __lowerCamelCase ( self ):
return self.time_input_dim * 4
@property
def __lowerCamelCase ( self ):
return 1_00
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
__lowerCAmelCase : Optional[int] = {
'in_channels': 9,
# Out channels is double in channels because predicts mean and variance
'out_channels': 8,
'addition_embed_type': 'image',
'down_block_types': ('ResnetDownsampleBlock2D', 'SimpleCrossAttnDownBlock2D'),
'up_block_types': ('SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'),
'mid_block_type': 'UNetMidBlock2DSimpleCrossAttn',
'block_out_channels': (self.block_out_channels_a, self.block_out_channels_a * 2),
'layers_per_block': 1,
'encoder_hid_dim': self.text_embedder_hidden_size,
'encoder_hid_dim_type': 'image_proj',
'cross_attention_dim': self.cross_attention_dim,
'attention_head_dim': 4,
'resnet_time_scale_shift': 'scale_shift',
'class_embed_type': None,
}
__lowerCAmelCase : Any = UNetaDConditionModel(**_SCREAMING_SNAKE_CASE )
return model
@property
def __lowerCamelCase ( self ):
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def __lowerCamelCase ( self ):
torch.manual_seed(0 )
__lowerCAmelCase : Optional[Any] = VQModel(**self.dummy_movq_kwargs )
return model
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = self.dummy_unet
__lowerCAmelCase : Optional[Any] = self.dummy_movq
__lowerCAmelCase : Tuple = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='linear' , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=_SCREAMING_SNAKE_CASE , set_alpha_to_one=_SCREAMING_SNAKE_CASE , steps_offset=1 , prediction_type='epsilon' , thresholding=_SCREAMING_SNAKE_CASE , )
__lowerCAmelCase : str = {
'unet': unet,
'scheduler': scheduler,
'movq': movq,
}
return components
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=0 ):
__lowerCAmelCase : List[Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
_SCREAMING_SNAKE_CASE )
# create init_image
__lowerCAmelCase : int = floats_tensor((1, 3, 64, 64) , rng=random.Random(_SCREAMING_SNAKE_CASE ) ).to(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__lowerCAmelCase : str = Image.fromarray(np.uinta(_SCREAMING_SNAKE_CASE ) ).convert('RGB' ).resize((2_56, 2_56) )
# create mask
__lowerCAmelCase : Dict = np.ones((64, 64) , dtype=np.floataa )
__lowerCAmelCase : List[str] = 0
if str(_SCREAMING_SNAKE_CASE ).startswith('mps' ):
__lowerCAmelCase : Optional[int] = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
__lowerCAmelCase : List[Any] = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = {
'image': init_image,
'mask_image': mask,
'image_embeds': image_embeds,
'negative_image_embeds': negative_image_embeds,
'generator': generator,
'height': 64,
'width': 64,
'num_inference_steps': 2,
'guidance_scale': 4.0,
'output_type': 'np',
}
return inputs
def __lowerCamelCase ( self ):
__lowerCAmelCase : List[Any] = 'cpu'
__lowerCAmelCase : Dict = self.get_dummy_components()
__lowerCAmelCase : Union[str, Any] = self.pipeline_class(**_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : int = pipe.to(_SCREAMING_SNAKE_CASE )
pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[Any] = pipe(**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase : Optional[Any] = output.images
__lowerCAmelCase : Any = pipe(
**self.get_dummy_inputs(_SCREAMING_SNAKE_CASE ) , return_dict=_SCREAMING_SNAKE_CASE , )[0]
__lowerCAmelCase : Union[str, Any] = image[0, -3:, -3:, -1]
__lowerCAmelCase : Any = image_from_tuple[0, -3:, -3:, -1]
print(f"image.shape {image.shape}" )
assert image.shape == (1, 64, 64, 3)
__lowerCAmelCase : str = np.array(
[0.5077_5903, 0.4952_7195, 0.4882_4543, 0.5019_2237, 0.4864_4906, 0.4937_3814, 0.478_0598, 0.4723_4827, 0.4832_7848] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
), f" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
def __lowerCamelCase ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class A__ ( unittest.TestCase):
def __lowerCamelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCamelCase ( self ):
__lowerCAmelCase : Dict = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/kandinskyv22/kandinskyv22_inpaint_cat_with_hat_fp16.npy' )
__lowerCAmelCase : Union[str, Any] = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main' '/kandinsky/cat.png' )
__lowerCAmelCase : Any = np.ones((7_68, 7_68) , dtype=np.floataa )
__lowerCAmelCase : int = 0
__lowerCAmelCase : str = 'a hat'
__lowerCAmelCase : str = KandinskyVaaPriorPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-prior' , torch_dtype=torch.floataa )
pipe_prior.to(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[int] = KandinskyVaaInpaintPipeline.from_pretrained(
'kandinsky-community/kandinsky-2-2-decoder-inpaint' , torch_dtype=torch.floataa )
__lowerCAmelCase : Tuple = pipeline.to(_SCREAMING_SNAKE_CASE )
pipeline.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = torch.Generator(device='cpu' ).manual_seed(0 )
__lowerCAmelCase , __lowerCAmelCase : Any = pipe_prior(
_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=5 , negative_prompt='' , ).to_tuple()
__lowerCAmelCase : Tuple = pipeline(
image=_SCREAMING_SNAKE_CASE , mask_image=_SCREAMING_SNAKE_CASE , image_embeds=_SCREAMING_SNAKE_CASE , negative_image_embeds=_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=1_00 , height=7_68 , width=7_68 , output_type='np' , )
__lowerCAmelCase : Optional[Any] = output.images[0]
assert image.shape == (7_68, 7_68, 3)
assert_mean_pixel_difference(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) | 182 | 1 |
import argparse
import re
from typing import Dict
import torch
from datasets import Audio, Dataset, load_dataset, load_metric
from transformers import AutoFeatureExtractor, pipeline
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
lowercase = args.log_outputs
lowercase = '_'.join(args.dataset.split('/' ) + [args.config, args.split] )
# load metric
lowercase = load_metric('wer' )
lowercase = load_metric('cer' )
# compute metrics
lowercase = wer.compute(references=result['target'] , predictions=result['prediction'] )
lowercase = cer.compute(references=result['target'] , predictions=result['prediction'] )
# print & log results
lowercase = F'''WER: {wer_result}\nCER: {cer_result}'''
print(__SCREAMING_SNAKE_CASE )
with open(F'''{dataset_id}_eval_results.txt''' , 'w' ) as f:
f.write(__SCREAMING_SNAKE_CASE )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
lowercase = F'''log_{dataset_id}_predictions.txt'''
lowercase = F'''log_{dataset_id}_targets.txt'''
with open(__SCREAMING_SNAKE_CASE , 'w' ) as p, open(__SCREAMING_SNAKE_CASE , 'w' ) as t:
# mapping function to write output
def write_to_file(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
p.write(F'''{i}''' + '\n' )
p.write(batch['prediction'] + '\n' )
t.write(F'''{i}''' + '\n' )
t.write(batch['target'] + '\n' )
result.map(__SCREAMING_SNAKE_CASE , with_indices=__SCREAMING_SNAKE_CASE )
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = '[,?.!\-\;\:"“%‘”�—’…–]' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
lowercase = re.sub(__SCREAMING_SNAKE_CASE , '' , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
lowercase = ['\n\n', '\n', ' ', ' ']
for t in token_sequences_to_ignore:
lowercase = ' '.join(text.split(__SCREAMING_SNAKE_CASE ) )
return text
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
# load dataset
lowercase = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=__SCREAMING_SNAKE_CASE )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
lowercase = AutoFeatureExtractor.from_pretrained(args.model_id )
lowercase = feature_extractor.sampling_rate
# resample audio
lowercase = dataset.cast_column('audio' , Audio(sampling_rate=__SCREAMING_SNAKE_CASE ) )
# load eval pipeline
if args.device is None:
lowercase = 0 if torch.cuda.is_available() else -1
lowercase = pipeline('automatic-speech-recognition' , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(__SCREAMING_SNAKE_CASE ):
lowercase = asr(
batch['audio']['array'] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
lowercase = prediction['text']
lowercase = normalize_text(batch['sentence'] )
return batch
# run inference on all examples
lowercase = dataset.map(__SCREAMING_SNAKE_CASE , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'''--model_id''', type=str, required=True, help='''Model identifier. Should be loadable with 🤗 Transformers'''
)
parser.add_argument(
'''--dataset''',
type=str,
required=True,
help='''Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets''',
)
parser.add_argument(
'''--config''', type=str, required=True, help='''Config of the dataset. *E.g.* `\'en\'` for Common Voice'''
)
parser.add_argument('''--split''', type=str, required=True, help='''Split of the dataset. *E.g.* `\'test\'`''')
parser.add_argument(
'''--chunk_length_s''', type=float, default=None, help='''Chunk length in seconds. Defaults to 5 seconds.'''
)
parser.add_argument(
'''--stride_length_s''', type=float, default=None, help='''Stride of the audio chunks. Defaults to 1 second.'''
)
parser.add_argument(
'''--log_outputs''', action='''store_true''', help='''If defined, write outputs to log file for analysis.'''
)
parser.add_argument(
'''--device''',
type=int,
default=None,
help='''The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.''',
)
UpperCAmelCase = parser.parse_args()
main(args)
| 195 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
UpperCAmelCase = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = test_results.split(' ' )
lowercase = 0
lowercase = 0
# When the output is short enough, the output is surrounded by = signs: "== OUTPUT =="
# When it is too long, those signs are not present.
lowercase = expressions[-2] if '=' in expressions[-1] else expressions[-1]
for i, expression in enumerate(__SCREAMING_SNAKE_CASE ):
if "failed" in expression:
failed += int(expressions[i - 1] )
if "passed" in expression:
success += int(expressions[i - 1] )
return failed, success, time_spent
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = {}
lowercase = None
lowercase = False
for line in failures_short_lines.split('\n' ):
if re.search(r'_ \[doctest\]' , __SCREAMING_SNAKE_CASE ):
lowercase = True
lowercase = line.split(' ' )[2]
elif in_error and not line.split(' ' )[0].isdigit():
lowercase = line
lowercase = False
return failures
class A_ :
'''simple docstring'''
def __init__( self , snake_case , snake_case ):
lowercase = title
lowercase = doc_test_results['time_spent'].split(',' )[0]
lowercase = doc_test_results['success']
lowercase = doc_test_results['failures']
lowercase = self.n_success + self.n_failures
# Failures and success of the modeling tests
lowercase = doc_test_results
@property
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [self._time_spent]
lowercase = 0
for time in time_spent:
lowercase = time.split(':' )
# Time can be formatted as xx:xx:xx, as .xx, or as x.xx if the time spent was less than a minute.
if len(snake_case ) == 1:
lowercase = [0, 0, time_parts[0]]
lowercase , lowercase , lowercase = int(time_parts[0] ), int(time_parts[1] ), float(time_parts[2] )
total_secs += hours * 3600 + minutes * 60 + seconds
lowercase , lowercase , lowercase = total_secs // 3600, (total_secs % 3600) // 60, total_secs % 60
return F'''{int(snake_case )}h{int(snake_case )}m{int(snake_case )}s'''
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return {"type": "header", "text": {"type": "plain_text", "text": self.title}}
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": F'''🌞 There were no failures: all {self.n_tests} tests passed. The suite ran in {self.time}.''',
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return {
"type": "section",
"text": {
"type": "plain_text",
"text": (
F'''There were {self.n_failures} failures, out of {self.n_tests} tests.\nThe suite ran in'''
F''' {self.time}.'''
),
"emoji": True,
},
"accessory": {
"type": "button",
"text": {"type": "plain_text", "text": "Check Action results", "emoji": True},
"url": F'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
@property
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = 40
lowercase = {k: v['failed'] for k, v in doc_test_results.items() if isinstance(snake_case , snake_case )}
lowercase = ''
for category, failures in category_failures.items():
if len(snake_case ) == 0:
continue
if report != "":
report += "\n\n"
report += F'''*{category} failures*:'''.ljust(line_length // 2 ).rjust(line_length // 2 ) + "\n"
report += "`"
report += "`\n`".join(snake_case )
report += "`"
return {
"type": "section",
"text": {
"type": "mrkdwn",
"text": F'''The following examples had failures:\n\n\n{report}\n''',
},
}
@property
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = [self.header]
if self.n_failures > 0:
blocks.append(self.failures )
if self.n_failures > 0:
blocks.extend([self.category_failures] )
if self.n_failures == 0:
blocks.append(self.no_failures )
return json.dumps(snake_case )
@staticmethod
def SCREAMING_SNAKE_CASE__ ( ):
lowercase = [
{
'type': 'section',
'text': {
'type': 'plain_text',
'text': 'There was an issue running the tests.',
},
'accessory': {
'type': 'button',
'text': {'type': 'plain_text', 'text': 'Check Action results', 'emoji': True},
'url': F'''https://github.com/huggingface/transformers/actions/runs/{os.environ['GITHUB_RUN_ID']}''',
},
}
]
print('Sending the following payload' )
print(json.dumps({'blocks': json.loads(snake_case )} ) )
client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , text='There was an issue running the tests.' , blocks=snake_case , )
def SCREAMING_SNAKE_CASE__ ( self ):
print('Sending the following payload' )
print(json.dumps({'blocks': json.loads(self.payload )} ) )
lowercase = F'''{self.n_failures} failures out of {self.n_tests} tests,''' if self.n_failures else 'All tests passed.'
lowercase = client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , blocks=self.payload , text=snake_case , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case , snake_case , snake_case , snake_case ):
lowercase = ''
for key, value in failures.items():
lowercase = value[:200] + ' [Truncated]' if len(snake_case ) > 250 else value
failures_text += F'''*{key}*\n_{value}_\n\n'''
lowercase = job_name
lowercase = {'type': 'section', 'text': {'type': 'mrkdwn', 'text': text}}
if job_link is not None:
lowercase = {
'type': 'button',
'text': {'type': 'plain_text', 'text': 'GitHub Action job', 'emoji': True},
'url': job_link,
}
return [
{"type": "header", "text": {"type": "plain_text", "text": title.upper(), "emoji": True}},
content,
{"type": "section", "text": {"type": "mrkdwn", "text": failures_text}},
]
def SCREAMING_SNAKE_CASE__ ( self ):
if self.thread_ts is None:
raise ValueError('Can only post reply if a post has been made.' )
lowercase = self.doc_test_results.pop('job_link' )
self.doc_test_results.pop('failures' )
self.doc_test_results.pop('success' )
self.doc_test_results.pop('time_spent' )
lowercase = sorted(self.doc_test_results.items() , key=lambda snake_case : t[0] )
for job, job_result in sorted_dict:
if len(job_result['failures'] ):
lowercase = F'''*Num failures* :{len(job_result['failed'] )} \n'''
lowercase = job_result['failures']
lowercase = self.get_reply_blocks(snake_case , snake_case , snake_case , text=snake_case )
print('Sending the following reply' )
print(json.dumps({'blocks': blocks} ) )
client.chat_postMessage(
channel=os.environ['CI_SLACK_CHANNEL_ID_DAILY'] , text=F'''Results for {job}''' , blocks=snake_case , thread_ts=self.thread_ts['ts'] , )
time.sleep(1 )
def UpperCAmelCase_ ( ):
lowercase = os.environ['GITHUB_RUN_ID']
lowercase = F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{run_id}/jobs?per_page=100'''
lowercase = requests.get(__SCREAMING_SNAKE_CASE ).json()
lowercase = {}
try:
jobs.update({job['name']: job['html_url'] for job in result['jobs']} )
lowercase = math.ceil((result['total_count'] - 100) / 100 )
for i in range(__SCREAMING_SNAKE_CASE ):
lowercase = requests.get(url + F'''&page={i + 2}''' ).json()
jobs.update({job['name']: job['html_url'] for job in result['jobs']} )
return jobs
except Exception as e:
print('Unknown error, could not fetch links.' , __SCREAMING_SNAKE_CASE )
return {}
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = {}
if os.path.exists(__SCREAMING_SNAKE_CASE ):
lowercase = os.listdir(__SCREAMING_SNAKE_CASE )
for file in files:
try:
with open(os.path.join(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) , encoding='utf-8' ) as f:
lowercase = f.read()
except UnicodeDecodeError as e:
raise ValueError(F'''Could not open {os.path.join(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )}.''' ) from e
return _artifact
def UpperCAmelCase_ ( ):
class A_ :
'''simple docstring'''
def __init__( self , snake_case ):
lowercase = name
lowercase = []
def __str__( self ):
return self.name
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
self.paths.append({'name': self.name, 'path': path} )
lowercase = {}
lowercase = filter(os.path.isdir , os.listdir() )
for directory in directories:
lowercase = directory
if artifact_name not in _available_artifacts:
lowercase = Artifact(__SCREAMING_SNAKE_CASE )
_available_artifacts[artifact_name].add_path(__SCREAMING_SNAKE_CASE )
return _available_artifacts
if __name__ == "__main__":
UpperCAmelCase = get_job_links()
UpperCAmelCase = retrieve_available_artifacts()
UpperCAmelCase = collections.OrderedDict(
[
('''*.py''', '''API Examples'''),
('''*.md''', '''MD Examples'''),
]
)
# This dict will contain all the information relative to each doc test category:
# - failed: list of failed tests
# - failures: dict in the format 'test': 'error_message'
UpperCAmelCase = {
v: {
'''failed''': [],
'''failures''': {},
}
for v in docs.values()
}
# Link to the GitHub Action job
UpperCAmelCase = github_actions_job_links.get('''run_doctests''')
UpperCAmelCase = available_artifacts['''doc_tests_gpu_test_reports'''].paths[0]
UpperCAmelCase = retrieve_artifact(artifact_path['''name'''])
if "stats" in artifact:
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = handle_test_results(artifact['''stats'''])
UpperCAmelCase = failed
UpperCAmelCase = success
UpperCAmelCase = time_spent[1:-1] + ''', '''
UpperCAmelCase = extract_first_line_failure(artifact['''failures_short'''])
for line in artifact["summary_short"].split('''\n'''):
if re.search('''FAILED''', line):
UpperCAmelCase = line.replace('''FAILED ''', '''''')
UpperCAmelCase = line.split()[0].replace('''\n''', '''''')
if "::" in line:
UpperCAmelCase , UpperCAmelCase = line.split('''::''')
else:
UpperCAmelCase , UpperCAmelCase = line, line
for file_regex in docs.keys():
if fnmatch(file_path, file_regex):
UpperCAmelCase = docs[file_regex]
doc_test_results[category]["failed"].append(test)
UpperCAmelCase = all_failures[test] if test in all_failures else '''N/A'''
UpperCAmelCase = failure
break
UpperCAmelCase = Message('''🤗 Results of the doc tests.''', doc_test_results)
message.post()
message.post_reply()
| 195 | 1 |
'''simple docstring'''
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class __magic_name__ ( _UpperCamelCase ):
lowerCAmelCase : int = CustomTokenizer
pass
| 107 |
'''simple docstring'''
from __future__ import annotations
import collections
import pprint
from pathlib import Path
def __lowerCamelCase ( lowerCAmelCase_ ) -> str:
return "".join(sorted(lowerCAmelCase_ ) )
def __lowerCamelCase ( lowerCAmelCase_ ) -> list[str]:
return word_by_signature[signature(lowerCAmelCase_ )]
__lowerCAmelCase = Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
__lowerCAmelCase = sorted({word.strip().lower() for word in data.splitlines()})
__lowerCAmelCase = collections.defaultdict(list)
for word in word_list:
word_by_signature[signature(word)].append(word)
if __name__ == "__main__":
__lowerCAmelCase = {word: anagram(word) for word in word_list if len(anagram(word)) > 1}
with open('''anagrams.txt''', '''w''') as file:
file.write('''all_anagrams = \n ''')
file.write(pprint.pformat(all_anagrams))
| 107 | 1 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATURE_EXTRACTOR_NAME,
FLAX_WEIGHTS_NAME,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
MODEL_CARD_NAME,
MULTIPLE_CHOICE_DUMMY_INPUTS,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
SENTENCEPIECE_UNDERLINE,
SPIECE_UNDERLINE,
TF2_WEIGHTS_NAME,
TF_WEIGHTS_NAME,
TORCH_FX_REQUIRED_VERSION,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
USE_JAX,
USE_TF,
USE_TORCH,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
ContextManagers,
DummyObject,
EntryNotFoundError,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
TensorType,
_LazyModule,
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
cached_property,
copy_func,
default_cache_path,
define_sagemaker_information,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
get_torch_version,
has_file,
http_user_agent,
is_apex_available,
is_bsa_available,
is_coloredlogs_available,
is_datasets_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_librosa_available,
is_offline_mode,
is_onnx_available,
is_pandas_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_tensor,
is_tensorflow_probability_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_training_run_on_sagemaker,
is_vision_available,
replace_return_docstrings,
requires_backends,
to_numpy,
to_py_obj,
torch_only_method,
)
| 107 |
from jiwer import compute_measures
import datasets
lowerCAmelCase : Tuple = '\\n@inproceedings{inproceedings,\n author = {Morris, Andrew and Maier, Viktoria and Green, Phil},\n year = {2004},\n month = {01},\n pages = {},\n title = {From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition.}\n}\n'
lowerCAmelCase : List[Any] = '\\nWord error rate (WER) is a common metric of the performance of an automatic speech recognition system.\n\nThe general difficulty of measuring performance lies in the fact that the recognized word sequence can have a different length from the reference word sequence (supposedly the correct one). The WER is derived from the Levenshtein distance, working at the word level instead of the phoneme level. The WER is a valuable tool for comparing different systems as well as for evaluating improvements within one system. This kind of measurement, however, provides no details on the nature of translation errors and further work is therefore required to identify the main source(s) of error and to focus any research effort.\n\nThis problem is solved by first aligning the recognized word sequence with the reference (spoken) word sequence using dynamic string alignment. Examination of this issue is seen through a theory called the power law that states the correlation between perplexity and word error rate.\n\nWord error rate can then be computed as:\n\nWER = (S + D + I) / N = (S + D + I) / (S + D + C)\n\nwhere\n\nS is the number of substitutions,\nD is the number of deletions,\nI is the number of insertions,\nC is the number of correct words,\nN is the number of words in the reference (N=S+D+C).\n\nThis value indicates the average number of errors per reference word. The lower the value, the better the\nperformance of the ASR system with a WER of 0 being a perfect score.\n'
lowerCAmelCase : Dict = '\nCompute WER score of transcribed segments against references.\n\nArgs:\n references: List of references for each speech input.\n predictions: List of transcriptions to score.\n concatenate_texts (bool, default=False): Whether to concatenate all input texts or compute WER iteratively.\n\nReturns:\n (float): the word error rate\n\nExamples:\n\n >>> predictions = ["this is the prediction", "there is an other sample"]\n >>> references = ["this is the reference", "there is another one"]\n >>> wer = datasets.load_metric("wer")\n >>> wer_score = wer.compute(predictions=predictions, references=references)\n >>> print(wer_score)\n 0.5\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class _A ( datasets.Metric):
def UpperCAmelCase ( self ):
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/jitsi/jiwer/'] , reference_urls=[
'https://en.wikipedia.org/wiki/Word_error_rate',
] , )
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=False ):
"""simple docstring"""
if concatenate_texts:
return compute_measures(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )["wer"]
else:
SCREAMING_SNAKE_CASE_ : str = 0
SCREAMING_SNAKE_CASE_ : Union[str, Any] = 0
for prediction, reference in zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE_ : str = compute_measures(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 253 | 0 |
import doctest
from collections import deque
import numpy as np
class SCREAMING_SNAKE_CASE__ :
'''simple docstring'''
def __init__( self ):
A : str = [2, 1, 2, -1]
A : int = [1, 2, 3, 4]
def _lowerCAmelCase ( self ):
A : Any = len(self.first_signal )
A : int = len(self.second_signal )
A : str = max(lowerCamelCase__, lowerCamelCase__ )
# create a zero matrix of max_length x max_length
A : int = [[0] * max_length for i in range(lowerCamelCase__ )]
# fills the smaller signal with zeros to make both signals of same length
if length_first_signal < length_second_signal:
self.first_signal += [0] * (max_length - length_first_signal)
elif length_first_signal > length_second_signal:
self.second_signal += [0] * (max_length - length_second_signal)
for i in range(lowerCamelCase__ ):
A : Tuple = deque(self.second_signal )
rotated_signal.rotate(lowerCamelCase__ )
for j, item in enumerate(lowerCamelCase__ ):
matrix[i][j] += item
# multiply the matrix with the first signal
A : int = np.matmul(np.transpose(lowerCamelCase__ ), np.transpose(self.first_signal ) )
# rounding-off to two decimal places
return [round(lowerCamelCase__, 2 ) for i in final_signal]
if __name__ == "__main__":
doctest.testmod()
| 115 |
import os
from pathlib import Path
def __UpperCamelCase ( ) -> Any:
"""simple docstring"""
from torch.utils.cpp_extension import load
A : Any = Path(_lowerCAmelCase ).resolve().parent.parent.parent / """kernels""" / """deformable_detr"""
A : int = [
root / filename
for filename in [
"""vision.cpp""",
os.path.join("""cpu""" , """ms_deform_attn_cpu.cpp""" ),
os.path.join("""cuda""" , """ms_deform_attn_cuda.cu""" ),
]
]
load(
"""MultiScaleDeformableAttention""" , _lowerCAmelCase , with_cuda=_lowerCAmelCase , extra_include_paths=[str(_lowerCAmelCase )] , extra_cflags=["""-DWITH_CUDA=1"""] , extra_cuda_cflags=[
"""-DCUDA_HAS_FP16=1""",
"""-D__CUDA_NO_HALF_OPERATORS__""",
"""-D__CUDA_NO_HALF_CONVERSIONS__""",
"""-D__CUDA_NO_HALF2_OPERATORS__""",
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 115 | 1 |
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