code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import os
lowerCAmelCase : List[str] = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 1_00, 'D': 5_00, 'M': 10_00}
def A_( A : str):
UpperCamelCase = 0
UpperCamelCase = 0
while index < len(A) - 1:
UpperCamelCase = SYMBOLS[numerals[index]]
UpperCamelCase = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def A_( A : int):
UpperCamelCase = ''
UpperCamelCase = num // 1000
numerals += m_count * "M"
num %= 1000
UpperCamelCase = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
UpperCamelCase = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def A_( A : str = "/p089_roman.txt"):
UpperCamelCase = 0
with open(os.path.dirname(A) + roman_numerals_filename) as filea:
UpperCamelCase = filea.readlines()
for line in lines:
UpperCamelCase = line.strip()
UpperCamelCase = parse_roman_numerals(A)
UpperCamelCase = generate_roman_numerals(A)
savings += len(A) - len(A)
return savings
if __name__ == "__main__":
print(f"""{solution() = }""")
| 3 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileNetVaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation, MobileNetVaModel
from transformers.models.mobilenet_va.modeling_mobilenet_va import MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import MobileNetVaImageProcessor
class lowercase_ ( lowerCAmelCase_ ):
def _lowerCAmelCase ( self : Any ):
snake_case__ : Union[str, Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(__lowerCamelCase , 'tf_padding' ) )
self.parent.assertTrue(hasattr(__lowerCamelCase , 'depth_multiplier' ) )
class lowercase_ :
def __init__( self : Optional[int] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict=13 , __lowerCamelCase : Optional[Any]=3 , __lowerCamelCase : str=32 , __lowerCamelCase : str=0.2_5 , __lowerCamelCase : List[Any]=8 , __lowerCamelCase : List[Any]=8 , __lowerCamelCase : Union[str, Any]=6 , __lowerCamelCase : Optional[Any]=32 , __lowerCamelCase : Dict=True , __lowerCamelCase : str=True , __lowerCamelCase : int=True , __lowerCamelCase : Tuple="relu6" , __lowerCamelCase : List[str]=1280 , __lowerCamelCase : Optional[Any]=0.1 , __lowerCamelCase : Tuple=0.0_2 , __lowerCamelCase : str=True , __lowerCamelCase : int=True , __lowerCamelCase : int=10 , __lowerCamelCase : int=None , ):
snake_case__ : Dict = parent
snake_case__ : Union[str, Any] = batch_size
snake_case__ : List[Any] = num_channels
snake_case__ : List[str] = image_size
snake_case__ : Optional[int] = depth_multiplier
snake_case__ : int = depth_divisible_by
snake_case__ : List[Any] = min_depth
snake_case__ : Dict = expand_ratio
snake_case__ : Optional[Any] = tf_padding
snake_case__ : List[Any] = output_stride
snake_case__ : List[str] = first_layer_is_expansion
snake_case__ : Optional[Any] = finegrained_output
snake_case__ : int = hidden_act
snake_case__ : List[Any] = last_hidden_size if finegrained_output else int(last_hidden_size * depth_multiplier )
snake_case__ : List[str] = classifier_dropout_prob
snake_case__ : List[Any] = use_labels
snake_case__ : Tuple = is_training
snake_case__ : Dict = num_labels
snake_case__ : Any = initializer_range
snake_case__ : int = scope
def _lowerCAmelCase ( self : int ):
snake_case__ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case__ : Optional[int] = None
snake_case__ : Optional[int] = None
if self.use_labels:
snake_case__ : Dict = ids_tensor([self.batch_size] , self.num_labels )
snake_case__ : List[str] = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
snake_case__ : List[Any] = self.get_config()
return config, pixel_values, labels, pixel_labels
def _lowerCAmelCase ( self : Tuple ):
return MobileNetVaConfig(
num_channels=self.num_channels , image_size=self.image_size , depth_multiplier=self.depth_multiplier , depth_divisible_by=self.depth_divisible_by , min_depth=self.min_depth , expand_ratio=self.expand_ratio , output_stride=self.output_stride , first_layer_is_expansion=self.first_layer_is_expansion , finegrained_output=self.finegrained_output , hidden_act=self.hidden_act , tf_padding=self.tf_padding , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , )
def _lowerCAmelCase ( self : Dict , __lowerCamelCase : str , __lowerCamelCase : int , __lowerCamelCase : Dict , __lowerCamelCase : Union[str, Any] ):
snake_case__ : Optional[Any] = MobileNetVaModel(config=__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
snake_case__ : List[Any] = model(__lowerCamelCase )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
self.parent.assertEqual(
result.pooler_output.shape , (self.batch_size, self.last_hidden_size) , )
def _lowerCAmelCase ( self : str , __lowerCamelCase : str , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[int] , __lowerCamelCase : Dict ):
snake_case__ : Optional[int] = self.num_labels
snake_case__ : int = MobileNetVaForImageClassification(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
snake_case__ : int = model(__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _lowerCAmelCase ( self : Dict , __lowerCamelCase : int , __lowerCamelCase : Dict , __lowerCamelCase : Optional[int] , __lowerCamelCase : Union[str, Any] ):
snake_case__ : Tuple = self.num_labels
snake_case__ : Optional[Any] = MobileNetVaForSemanticSegmentation(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
snake_case__ : Optional[Any] = model(__lowerCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
snake_case__ : Dict = model(__lowerCamelCase , labels=__lowerCamelCase )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def _lowerCAmelCase ( self : Any ):
snake_case__ : List[Any] = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ , snake_case__ : str = config_and_inputs
snake_case__ : Union[str, Any] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class lowercase_ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
A_ = (
(MobileNetVaModel, MobileNetVaForImageClassification, MobileNetVaForSemanticSegmentation)
if is_torch_available()
else ()
)
A_ = (
{
"feature-extraction": MobileNetVaModel,
"image-classification": MobileNetVaForImageClassification,
"image-segmentation": MobileNetVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
A_ = False
A_ = False
A_ = False
A_ = False
def _lowerCAmelCase ( self : Dict ):
snake_case__ : List[Any] = MobileNetVaModelTester(self )
snake_case__ : str = MobileNetVaConfigTester(self , config_class=__lowerCamelCase , has_text_modality=__lowerCamelCase )
def _lowerCAmelCase ( self : Any ):
self.config_tester.run_common_tests()
@unittest.skip(reason='MobileNetV2 does not use inputs_embeds' )
def _lowerCAmelCase ( self : Union[str, Any] ):
pass
@unittest.skip(reason='MobileNetV2 does not support input and output embeddings' )
def _lowerCAmelCase ( self : int ):
pass
@unittest.skip(reason='MobileNetV2 does not output attentions' )
def _lowerCAmelCase ( self : Tuple ):
pass
def _lowerCAmelCase ( self : Any ):
snake_case__ , snake_case__ : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : str = model_class(__lowerCamelCase )
snake_case__ : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ : Union[str, Any] = [*signature.parameters.keys()]
snake_case__ : int = ['pixel_values']
self.assertListEqual(arg_names[:1] , __lowerCamelCase )
def _lowerCAmelCase ( self : List[str] ):
snake_case__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCamelCase )
def _lowerCAmelCase ( self : Tuple ):
def check_hidden_states_output(__lowerCamelCase : str , __lowerCamelCase : List[str] , __lowerCamelCase : List[str] ):
snake_case__ : Tuple = model_class(__lowerCamelCase )
model.to(__lowerCamelCase )
model.eval()
with torch.no_grad():
snake_case__ : List[str] = model(**self._prepare_for_class(__lowerCamelCase , __lowerCamelCase ) )
snake_case__ : List[str] = outputs.hidden_states
snake_case__ : List[Any] = 16
self.assertEqual(len(__lowerCamelCase ) , __lowerCamelCase )
snake_case__ , snake_case__ : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ : List[Any] = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case__ : List[Any] = True
check_hidden_states_output(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
def _lowerCAmelCase ( self : List[str] ):
snake_case__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase )
def _lowerCAmelCase ( self : str ):
snake_case__ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*__lowerCamelCase )
@slow
def _lowerCAmelCase ( self : Dict ):
for model_name in MOBILENET_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case__ : Any = MobileNetVaModel.from_pretrained(__lowerCamelCase )
self.assertIsNotNone(__lowerCamelCase )
def UpperCamelCase__ ( ) -> Dict:
snake_case__ : Tuple = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class lowercase_ ( unittest.TestCase ):
@cached_property
def _lowerCAmelCase ( self : Tuple ):
return (
MobileNetVaImageProcessor.from_pretrained('google/mobilenet_v2_1.0_224' ) if is_vision_available() else None
)
@slow
def _lowerCAmelCase ( self : int ):
snake_case__ : Dict = MobileNetVaForImageClassification.from_pretrained('google/mobilenet_v2_1.0_224' ).to(__lowerCamelCase )
snake_case__ : int = self.default_image_processor
snake_case__ : Optional[Any] = prepare_img()
snake_case__ : str = image_processor(images=__lowerCamelCase , return_tensors='pt' ).to(__lowerCamelCase )
# forward pass
with torch.no_grad():
snake_case__ : Any = model(**__lowerCamelCase )
# verify the logits
snake_case__ : Tuple = torch.Size((1, 1001) )
self.assertEqual(outputs.logits.shape , __lowerCamelCase )
snake_case__ : int = torch.tensor([0.2_4_4_5, -1.1_9_9_3, 0.1_9_0_5] ).to(__lowerCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCamelCase , atol=1E-4 ) )
@slow
def _lowerCAmelCase ( self : int ):
snake_case__ : str = MobileNetVaForSemanticSegmentation.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513' )
snake_case__ : int = model.to(__lowerCamelCase )
snake_case__ : Any = MobileNetVaImageProcessor.from_pretrained('google/deeplabv3_mobilenet_v2_1.0_513' )
snake_case__ : Any = prepare_img()
snake_case__ : Dict = image_processor(images=__lowerCamelCase , return_tensors='pt' ).to(__lowerCamelCase )
# forward pass
with torch.no_grad():
snake_case__ : Any = model(**__lowerCamelCase )
snake_case__ : Optional[int] = outputs.logits
# verify the logits
snake_case__ : str = torch.Size((1, 21, 65, 65) )
self.assertEqual(logits.shape , __lowerCamelCase )
snake_case__ : Optional[Any] = torch.tensor(
[
[[1_7.5_7_9_0, 1_7.7_5_8_1, 1_8.3_3_5_5], [1_8.3_2_5_7, 1_8.4_2_3_0, 1_8.8_9_7_3], [1_8.6_1_6_9, 1_8.8_6_5_0, 1_9.2_1_8_7]],
[[-2.1_5_9_5, -2.0_9_7_7, -2.3_7_4_1], [-2.4_2_2_6, -2.3_0_2_8, -2.6_8_3_5], [-2.7_8_1_9, -2.5_9_9_1, -2.7_7_0_6]],
[[4.2_0_5_8, 4.8_3_1_7, 4.7_6_3_8], [4.4_1_3_6, 5.0_3_6_1, 4.9_3_8_3], [4.5_0_2_8, 4.9_6_4_4, 4.8_7_3_4]],
] , device=__lowerCamelCase , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , __lowerCamelCase , atol=1E-4 ) )
| 270 | 0 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ) -> str:
if not (isinstance(__UpperCAmelCase , __UpperCAmelCase ) and isinstance(__UpperCAmelCase , __UpperCAmelCase )):
raise ValueError("longest_common_substring() takes two strings for inputs" )
SCREAMING_SNAKE_CASE__ = len(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = len(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = [[0] * (texta_length + 1) for _ in range(texta_length + 1 )]
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = 0
for i in range(1 , texta_length + 1 ):
for j in range(1 , texta_length + 1 ):
if texta[i - 1] == texta[j - 1]:
SCREAMING_SNAKE_CASE__ = 1 + dp[i - 1][j - 1]
if dp[i][j] > ans_length:
SCREAMING_SNAKE_CASE__ = i
SCREAMING_SNAKE_CASE__ = dp[i][j]
return texta[ans_index - ans_length : ans_index]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 715 | """simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_A = HUGGINGFACE_HUB_CACHE
_A = 'config.json'
_A = 'diffusion_pytorch_model.bin'
_A = 'diffusion_flax_model.msgpack'
_A = 'model.onnx'
_A = 'diffusion_pytorch_model.safetensors'
_A = 'weights.pb'
_A = 'https://huggingface.co'
_A = default_cache_path
_A = 'diffusers_modules'
_A = os.getenv('HF_MODULES_CACHE', os.path.join(hf_cache_home, 'modules'))
_A = ['fp16', 'non-ema']
_A = '.self_attn'
| 538 | 0 |
"""simple docstring"""
__magic_name__ = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def _A ( ):
"""simple docstring"""
lowerCamelCase__ = input("""Enter message: """ )
lowerCamelCase__ = input("""Enter key [alphanumeric]: """ )
lowerCamelCase__ = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
lowerCamelCase__ = """encrypt"""
lowerCamelCase__ = encrypt_message(__lowercase , __lowercase )
elif mode.lower().startswith("""d""" ):
lowerCamelCase__ = """decrypt"""
lowerCamelCase__ = decrypt_message(__lowercase , __lowercase )
print(f"""\n{mode.title()}ed message:""" )
print(__lowercase )
def _A ( __lowercase , __lowercase ):
"""simple docstring"""
return translate_message(__lowercase , __lowercase , """encrypt""" )
def _A ( __lowercase , __lowercase ):
"""simple docstring"""
return translate_message(__lowercase , __lowercase , """decrypt""" )
def _A ( __lowercase , __lowercase , __lowercase ):
"""simple docstring"""
lowerCamelCase__ = []
lowerCamelCase__ = 0
lowerCamelCase__ = key.upper()
for symbol in message:
lowerCamelCase__ = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(__lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(__lowercase ):
lowerCamelCase__ = 0
else:
translated.append(__lowercase )
return "".join(__lowercase )
if __name__ == "__main__":
main()
| 129 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE__ ( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
snake_case = CTRLTokenizer
snake_case = False
snake_case = False
def __UpperCAmelCase ( self : List[str] ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCamelCase__ = ["""adapt""", """re@@""", """a@@""", """apt""", """c@@""", """t""", """<unk>"""]
lowerCamelCase__ = dict(zip(SCREAMING_SNAKE_CASE_ , range(len(SCREAMING_SNAKE_CASE_ ) ) ) )
lowerCamelCase__ = ["""#version: 0.2""", """a p""", """ap t</w>""", """r e""", """a d""", """ad apt</w>""", """"""]
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 __UpperCAmelCase ( self : str , **SCREAMING_SNAKE_CASE_ : Optional[int] ):
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE_ )
def __UpperCAmelCase ( self : Dict , SCREAMING_SNAKE_CASE_ : List[Any] ):
lowerCamelCase__ = """adapt react readapt apt"""
lowerCamelCase__ = """adapt react readapt apt"""
return input_text, output_text
def __UpperCAmelCase ( self : List[Any] ):
lowerCamelCase__ = CTRLTokenizer(self.vocab_file , self.merges_file , **self.special_tokens_map )
lowerCamelCase__ = """adapt react readapt apt"""
lowerCamelCase__ = """adapt re@@ a@@ c@@ t re@@ adapt apt""".split()
lowerCamelCase__ = tokenizer.tokenize(SCREAMING_SNAKE_CASE_ )
self.assertListEqual(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCamelCase__ = tokens + [tokenizer.unk_token]
lowerCamelCase__ = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )
| 129 | 1 |
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self , a , a , a = True , a = False ) -> List[Any]:
snake_case_ = scheduler
snake_case_ = optimizers if isinstance(_UpperCAmelCase , (list, tuple) ) else [optimizers]
snake_case_ = split_batches
snake_case_ = step_with_optimizer
snake_case_ = GradientState()
def _UpperCamelCase ( self , *a , **a ) -> Optional[int]:
if not self.step_with_optimizer:
# No link between scheduler and optimizer -> just step
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
return
# Otherwise, first make sure the optimizer was stepped.
if not self.gradient_state.sync_gradients:
if self.gradient_state.adjust_scheduler:
self.scheduler._step_count += 1
return
for opt in self.optimizers:
if opt.step_was_skipped:
return
if self.split_batches:
# Split batches -> the training dataloader batch size is not changed so one step per training step
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
else:
# Otherwise the training dataloader batch size was multiplied by `num_processes`, so we need to do
# num_processes steps per training step
snake_case_ = AcceleratorState().num_processes
for _ in range(_UpperCAmelCase ):
# Special case when using OneCycle and `drop_last` was not used
if hasattr(self.scheduler , 'total_steps' ):
if self.scheduler._step_count <= self.scheduler.total_steps:
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
else:
self.scheduler.step(*_UpperCAmelCase , **_UpperCAmelCase )
def _UpperCamelCase ( self ) -> Any:
return self.scheduler.get_last_lr()
def _UpperCamelCase ( self ) -> Any:
return self.scheduler.state_dict()
def _UpperCamelCase ( self , a ) -> Optional[Any]:
self.scheduler.load_state_dict(_UpperCAmelCase )
def _UpperCamelCase ( self ) -> Optional[int]:
return self.scheduler.get_lr()
def _UpperCamelCase ( self , *a , **a ) -> Optional[int]:
return self.scheduler.print_lr(*_UpperCAmelCase , **_UpperCAmelCase )
| 714 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import re
from ..utils import cached_file
# docstyle-ignore
lowercase = "\nHuman: <<task>>\n\nAssistant: "
lowercase = "huggingface-tools/default-prompts"
lowercase = {"chat": "chat_prompt_template.txt", "run": "run_prompt_template.txt"}
def __UpperCAmelCase ( a_ , a_ , a_="run"):
if prompt_or_repo_id is None:
snake_case_ = DEFAULT_PROMPTS_REPO
# prompt is considered a repo ID when it does not contain any kind of space
if re.search('\\s' , a_) is not None:
return prompt_or_repo_id
snake_case_ = cached_file(
a_ , PROMPT_FILES[mode] , repo_type='dataset' , user_agent={'agent': agent_name})
with open(a_ , 'r' , encoding='utf-8') as f:
return f.read()
| 607 | 0 |
"""simple docstring"""
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
__A : Optional[Any] = {
'return_dict': False,
'output_hidden_states': True,
'output_attentions': True,
'torchscript': True,
'torch_dtype': 'float16',
'use_bfloat16': True,
'tf_legacy_loss': True,
'pruned_heads': {'a': 1},
'tie_word_embeddings': False,
'is_decoder': True,
'cross_attention_hidden_size': 1_2_8,
'add_cross_attention': True,
'tie_encoder_decoder': True,
'max_length': 5_0,
'min_length': 3,
'do_sample': True,
'early_stopping': True,
'num_beams': 3,
'num_beam_groups': 3,
'diversity_penalty': 0.5,
'temperature': 2.0,
'top_k': 1_0,
'top_p': 0.7,
'typical_p': 0.2,
'repetition_penalty': 0.8,
'length_penalty': 0.8,
'no_repeat_ngram_size': 5,
'encoder_no_repeat_ngram_size': 5,
'bad_words_ids': [1, 2, 3],
'num_return_sequences': 3,
'chunk_size_feed_forward': 5,
'output_scores': True,
'return_dict_in_generate': True,
'forced_bos_token_id': 2,
'forced_eos_token_id': 3,
'remove_invalid_values': True,
'architectures': ['BertModel'],
'finetuning_task': 'translation',
'id2label': {0: 'label'},
'label2id': {'label': '0'},
'tokenizer_class': 'BertTokenizerFast',
'prefix': 'prefix',
'bos_token_id': 6,
'pad_token_id': 7,
'eos_token_id': 8,
'sep_token_id': 9,
'decoder_start_token_id': 1_0,
'exponential_decay_length_penalty': (5, 1.01),
'suppress_tokens': [0, 1],
'begin_suppress_tokens': 2,
'task_specific_params': {'translation': 'some_params'},
'problem_type': 'regression',
}
@is_staging_test
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
@classmethod
def snake_case ( cls : str ):
__lowercase : List[str] = TOKEN
HfFolder.save_token(lowercase__ )
@classmethod
def snake_case ( cls : Optional[Any] ):
try:
delete_repo(token=cls._token , repo_id="test-config" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-config-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-config" )
except HTTPError:
pass
def snake_case ( self : Any ):
__lowercase : Optional[Any] = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub("test-config" , use_auth_token=self._token )
__lowercase : List[str] = BertConfig.from_pretrained(f'{USER}/test-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase__ , getattr(lowercase__ , lowercase__ ) )
# Reset repo
delete_repo(token=self._token , repo_id="test-config" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowercase__ , repo_id="test-config" , push_to_hub=lowercase__ , use_auth_token=self._token )
__lowercase : Optional[int] = BertConfig.from_pretrained(f'{USER}/test-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase__ , getattr(lowercase__ , lowercase__ ) )
def snake_case ( self : str ):
__lowercase : str = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub("valid_org/test-config-org" , use_auth_token=self._token )
__lowercase : Optional[int] = BertConfig.from_pretrained("valid_org/test-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase__ , getattr(lowercase__ , lowercase__ ) )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-config-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowercase__ , repo_id="valid_org/test-config-org" , push_to_hub=lowercase__ , use_auth_token=self._token )
__lowercase : List[Any] = BertConfig.from_pretrained("valid_org/test-config-org" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowercase__ , getattr(lowercase__ , lowercase__ ) )
def snake_case ( self : List[str] ):
CustomConfig.register_for_auto_class()
__lowercase : Any = CustomConfig(attribute=4_2 )
config.push_to_hub("test-dynamic-config" , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {"AutoConfig": "custom_configuration.CustomConfig"} )
__lowercase : List[str] = AutoConfig.from_pretrained(f'{USER}/test-dynamic-config' , trust_remote_code=lowercase__ )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , "CustomConfig" )
self.assertEqual(new_config.attribute , 4_2 )
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self : Union[str, Any] ):
__lowercase : Dict = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
__lowercase : Optional[int] = c.n_embd + 1 # int
__lowercase : int = c.resid_pdrop + 1.0 # float
__lowercase : int = not c.scale_attn_weights # bool
__lowercase : Union[str, Any] = c.summary_type + "foo" # str
c.update_from_string(
f'n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}' )
self.assertEqual(lowercase__ , c.n_embd , "mismatch for key: n_embd" )
self.assertEqual(lowercase__ , c.resid_pdrop , "mismatch for key: resid_pdrop" )
self.assertEqual(lowercase__ , c.scale_attn_weights , "mismatch for key: scale_attn_weights" )
self.assertEqual(lowercase__ , c.summary_type , "mismatch for key: summary_type" )
def snake_case ( self : str ):
__lowercase : Optional[Any] = PretrainedConfig()
__lowercase : List[Any] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
lowercase__ , ["is_encoder_decoder", "_name_or_path", "_commit_hash", "transformers_version"] )
__lowercase : Any = [key for key, value in config_common_kwargs.items() if value == getattr(lowercase__ , lowercase__ )]
if len(lowercase__ ) > 0:
raise ValueError(
"The following keys are set with the default values in"
" `test_configuration_common.config_common_kwargs` pick another value for them:"
f' {", ".join(lowercase__ )}.' )
def snake_case ( self : List[str] ):
with self.assertRaises(lowercase__ ):
# config is in subfolder, the following should not work without specifying the subfolder
__lowercase : str = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder" )
__lowercase : Optional[int] = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert-subfolder" , subfolder="bert" )
self.assertIsNotNone(lowercase__ )
def snake_case ( self : str ):
# A mock response for an HTTP head request to emulate server down
__lowercase : Tuple = mock.Mock()
__lowercase : Optional[Any] = 5_0_0
__lowercase : Tuple = {}
__lowercase : List[Any] = HTTPError
__lowercase : Optional[Any] = {}
# Download this model to make sure it's in the cache.
__lowercase : str = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert" )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch("requests.Session.request" , return_value=lowercase__ ) as mock_head:
__lowercase : List[Any] = BertConfig.from_pretrained("hf-internal-testing/tiny-random-bert" )
# This check we did call the fake head request
mock_head.assert_called()
def snake_case ( self : Any ):
# This test is for deprecated behavior and can be removed in v5
__lowercase : Any = BertConfig.from_pretrained(
"https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json" )
def snake_case ( self : List[str] ):
__lowercase : Dict = AutoConfig.from_pretrained("bert-base-cased" )
__lowercase : Tuple = ["config.4.0.0.json"]
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(lowercase__ )
__lowercase : Tuple = 2
json.dump(configuration.to_dict() , open(os.path.join(lowercase__ , "config.4.0.0.json" ) , "w" ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
__lowercase : str = AutoConfig.from_pretrained(lowercase__ )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
__lowercase : int = ["config.42.0.0.json"]
__lowercase : Dict = 7_6_8
configuration.save_pretrained(lowercase__ )
shutil.move(os.path.join(lowercase__ , "config.4.0.0.json" ) , os.path.join(lowercase__ , "config.42.0.0.json" ) )
__lowercase : Optional[Any] = AutoConfig.from_pretrained(lowercase__ )
self.assertEqual(new_configuration.hidden_size , 7_6_8 )
def snake_case ( self : List[Any] ):
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
__lowercase : Any = "hf-internal-testing/test-two-configs"
import transformers as new_transformers
__lowercase : Any = "v4.0.0"
__lowercase ,__lowercase : str = new_transformers.models.auto.AutoConfig.from_pretrained(
lowercase__ , return_unused_kwargs=lowercase__ )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(lowercase__ , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
__lowercase : List[str] = "v3.0.0"
__lowercase : Dict = old_transformers.models.auto.AutoConfig.from_pretrained(lowercase__ )
self.assertEqual(old_configuration.hidden_size , 7_6_8 )
| 575 |
"""simple docstring"""
from math import factorial, pi
def snake_case__ ( _lowerCamelCase, _lowerCamelCase = 30 ) ->float:
"""simple docstring"""
if not isinstance(_lowerCamelCase, (int, float) ):
raise ValueError("maclaurin_sin() requires either an int or float for theta" )
if not isinstance(_lowerCamelCase, _lowerCamelCase ) or accuracy <= 0:
raise ValueError("maclaurin_sin() requires a positive int for accuracy" )
__lowercase : List[str] = float(_lowerCamelCase )
__lowercase : Any = theta // (2 * pi)
theta -= 2 * div * pi
return sum(
(-1) ** r * theta ** (2 * r + 1) / factorial(2 * r + 1 ) for r in range(_lowerCamelCase ) )
def snake_case__ ( _lowerCamelCase, _lowerCamelCase = 30 ) ->float:
"""simple docstring"""
if not isinstance(_lowerCamelCase, (int, float) ):
raise ValueError("maclaurin_cos() requires either an int or float for theta" )
if not isinstance(_lowerCamelCase, _lowerCamelCase ) or accuracy <= 0:
raise ValueError("maclaurin_cos() requires a positive int for accuracy" )
__lowercase : List[str] = float(_lowerCamelCase )
__lowercase : str = theta // (2 * pi)
theta -= 2 * div * pi
return sum((-1) ** r * theta ** (2 * r) / factorial(2 * r ) for r in range(_lowerCamelCase ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(maclaurin_sin(1_0))
print(maclaurin_sin(-1_0))
print(maclaurin_sin(1_0, 1_5))
print(maclaurin_sin(-1_0, 1_5))
print(maclaurin_cos(5))
print(maclaurin_cos(-5))
print(maclaurin_cos(1_0, 1_5))
print(maclaurin_cos(-1_0, 1_5))
| 575 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'abeja/gpt-neox-japanese-2.7b': 'https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/config.json',
}
class __a ( _snake_case ):
__SCREAMING_SNAKE_CASE : Tuple = 'gpt_neox_japanese'
def __init__( self : Any , lowercase__ : Dict=3_20_00 , lowercase__ : Dict=25_60 , lowercase__ : str=32 , lowercase__ : Optional[int]=32 , lowercase__ : int=4 , lowercase__ : Union[str, Any]="gelu" , lowercase__ : Dict=1.00 , lowercase__ : Optional[int]=1_00_00 , lowercase__ : Optional[int]=20_48 , lowercase__ : Any=0.02 , lowercase__ : List[Any]=1e-5 , lowercase__ : List[str]=True , lowercase__ : Optional[Any]=3_19_96 , lowercase__ : List[Any]=3_19_99 , lowercase__ : Any=0.1 , lowercase__ : Union[str, Any]=0.0 , **lowercase__ : Union[str, Any] , ) ->Any:
"""simple docstring"""
super().__init__(bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__)
_lowercase = vocab_size
_lowercase = max_position_embeddings
_lowercase = hidden_size
_lowercase = num_hidden_layers
_lowercase = num_attention_heads
_lowercase = intermediate_multiple_size
_lowercase = hidden_act
_lowercase = rotary_pct
_lowercase = rotary_emb_base
_lowercase = initializer_range
_lowercase = layer_norm_eps
_lowercase = use_cache
_lowercase = attention_dropout
_lowercase = hidden_dropout
| 721 |
'''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
_lowerCamelCase = logging.get_logger(__name__)
_lowerCamelCase = {
'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 __a ( _snake_case ):
__SCREAMING_SNAKE_CASE : Optional[Any] = 'owlvit_text_model'
def __init__( self : Union[str, Any] , lowercase__ : Union[str, Any]=4_94_08 , lowercase__ : List[str]=5_12 , lowercase__ : Optional[Any]=20_48 , lowercase__ : List[str]=12 , lowercase__ : List[Any]=8 , lowercase__ : List[Any]=16 , lowercase__ : List[str]="quick_gelu" , lowercase__ : Tuple=1e-5 , lowercase__ : int=0.0 , lowercase__ : str=0.02 , lowercase__ : List[Any]=1.0 , lowercase__ : int=0 , lowercase__ : int=4_94_06 , lowercase__ : int=4_94_07 , **lowercase__ : Any , ) ->Tuple:
"""simple docstring"""
super().__init__(pad_token_id=lowercase__ , bos_token_id=lowercase__ , eos_token_id=lowercase__ , **lowercase__)
_lowercase = vocab_size
_lowercase = hidden_size
_lowercase = intermediate_size
_lowercase = num_hidden_layers
_lowercase = num_attention_heads
_lowercase = max_position_embeddings
_lowercase = hidden_act
_lowercase = layer_norm_eps
_lowercase = attention_dropout
_lowercase = initializer_range
_lowercase = initializer_factor
@classmethod
def _UpperCAmelCase ( cls : List[Any] , lowercase__ : Union[str, os.PathLike] , **lowercase__ : Tuple) ->"PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(lowercase__)
_lowercase , _lowercase = cls.get_config_dict(lowercase__ , **lowercase__)
# get the text config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""") == "owlvit":
_lowercase = 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(lowercase__ , **lowercase__)
class __a ( _snake_case ):
__SCREAMING_SNAKE_CASE : str = 'owlvit_vision_model'
def __init__( self : Optional[int] , lowercase__ : Dict=7_68 , lowercase__ : Tuple=30_72 , lowercase__ : List[str]=12 , lowercase__ : str=12 , lowercase__ : Any=3 , lowercase__ : Union[str, Any]=7_68 , lowercase__ : Union[str, Any]=32 , lowercase__ : Dict="quick_gelu" , lowercase__ : Tuple=1e-5 , lowercase__ : List[Any]=0.0 , lowercase__ : List[str]=0.02 , lowercase__ : List[Any]=1.0 , **lowercase__ : List[Any] , ) ->int:
"""simple docstring"""
super().__init__(**lowercase__)
_lowercase = hidden_size
_lowercase = intermediate_size
_lowercase = num_hidden_layers
_lowercase = num_attention_heads
_lowercase = num_channels
_lowercase = image_size
_lowercase = patch_size
_lowercase = hidden_act
_lowercase = layer_norm_eps
_lowercase = attention_dropout
_lowercase = initializer_range
_lowercase = initializer_factor
@classmethod
def _UpperCAmelCase ( cls : Optional[int] , lowercase__ : Union[str, os.PathLike] , **lowercase__ : Optional[int]) ->"PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(lowercase__)
_lowercase , _lowercase = cls.get_config_dict(lowercase__ , **lowercase__)
# get the vision config dict if we are loading from OwlViTConfig
if config_dict.get("""model_type""") == "owlvit":
_lowercase = 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(lowercase__ , **lowercase__)
class __a ( _snake_case ):
__SCREAMING_SNAKE_CASE : Optional[Any] = 'owlvit'
__SCREAMING_SNAKE_CASE : Tuple = True
def __init__( self : str , lowercase__ : List[str]=None , lowercase__ : int=None , lowercase__ : str=5_12 , lowercase__ : Any=2.6592 , lowercase__ : List[str]=True , **lowercase__ : str , ) ->Tuple:
"""simple docstring"""
super().__init__(**lowercase__)
if text_config is None:
_lowercase = {}
logger.info("""text_config is None. Initializing the OwlViTTextConfig with default values.""")
if vision_config is None:
_lowercase = {}
logger.info("""vision_config is None. initializing the OwlViTVisionConfig with default values.""")
_lowercase = OwlViTTextConfig(**lowercase__)
_lowercase = OwlViTVisionConfig(**lowercase__)
_lowercase = projection_dim
_lowercase = logit_scale_init_value
_lowercase = return_dict
_lowercase = 1.0
@classmethod
def _UpperCAmelCase ( cls : int , lowercase__ : Union[str, os.PathLike] , **lowercase__ : List[str]) ->"PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(lowercase__)
_lowercase , _lowercase = cls.get_config_dict(lowercase__ , **lowercase__)
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(lowercase__ , **lowercase__)
@classmethod
def _UpperCAmelCase ( cls : Optional[int] , lowercase__ : Dict , lowercase__ : Dict , **lowercase__ : str) ->Union[str, Any]:
"""simple docstring"""
_lowercase = {}
_lowercase = text_config
_lowercase = vision_config
return cls.from_dict(lowercase__ , **lowercase__)
def _UpperCAmelCase ( self : Tuple) ->Tuple:
"""simple docstring"""
_lowercase = copy.deepcopy(self.__dict__)
_lowercase = self.text_config.to_dict()
_lowercase = self.vision_config.to_dict()
_lowercase = self.__class__.model_type
return output
class __a ( _snake_case ):
@property
def _UpperCAmelCase ( self : List[str]) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
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 _UpperCAmelCase ( self : Union[str, Any]) ->Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
("""logits_per_image""", {0: """batch"""}),
("""logits_per_text""", {0: """batch"""}),
("""text_embeds""", {0: """batch"""}),
("""image_embeds""", {0: """batch"""}),
])
@property
def _UpperCAmelCase ( self : str) ->float:
"""simple docstring"""
return 1e-4
def _UpperCAmelCase ( self : str , lowercase__ : "ProcessorMixin" , lowercase__ : int = -1 , lowercase__ : int = -1 , lowercase__ : Optional["TensorType"] = None , ) ->Mapping[str, Any]:
"""simple docstring"""
_lowercase = super().generate_dummy_inputs(
processor.tokenizer , batch_size=lowercase__ , seq_length=lowercase__ , framework=lowercase__)
_lowercase = super().generate_dummy_inputs(
processor.image_processor , batch_size=lowercase__ , framework=lowercase__)
return {**text_input_dict, **image_input_dict}
@property
def _UpperCAmelCase ( self : Any) ->int:
"""simple docstring"""
return 14
| 572 | 0 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def a (lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__=1E-1_2 ):
__a = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(__UpperCamelCase , axis=1 ) , a_min=__UpperCamelCase ) ).T
__a = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(__UpperCamelCase , axis=1 ) , a_min=__UpperCamelCase ) ).T
return jnp.matmul(__UpperCamelCase , norm_emb_a.T )
class __UpperCAmelCase ( nn.Module ):
"""simple docstring"""
_lowerCamelCase = 42
_lowerCamelCase = jnp.floataa
def snake_case_ ( self ):
__a = FlaxCLIPVisionModule(self.config.vision_config )
__a = nn.Dense(self.config.projection_dim , use_bias=A_ , dtype=self.dtype )
__a = self.param("""concept_embeds""" , jax.nn.initializers.ones , (17, self.config.projection_dim) )
__a = self.param(
"""special_care_embeds""" , jax.nn.initializers.ones , (3, self.config.projection_dim) )
__a = self.param("""concept_embeds_weights""" , jax.nn.initializers.ones , (17,) )
__a = self.param("""special_care_embeds_weights""" , jax.nn.initializers.ones , (3,) )
def __call__( self , __A ):
__a = self.vision_model(A_ )[1]
__a = self.visual_projection(A_ )
__a = jax_cosine_distance(A_ , self.special_care_embeds )
__a = jax_cosine_distance(A_ , self.concept_embeds )
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
__a = 0.0
__a = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
__a = jnp.round(A_ , 3 )
__a = jnp.any(special_scores > 0 , axis=1 , keepdims=A_ )
# Use a lower threshold if an image has any special care concept
__a = is_special_care * 0.01
__a = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
__a = jnp.round(A_ , 3 )
__a = jnp.any(concept_scores > 0 , axis=1 )
return has_nsfw_concepts
class __UpperCAmelCase ( UpperCamelCase_ ):
"""simple docstring"""
_lowerCamelCase = CLIPConfig
_lowerCamelCase = """clip_input"""
_lowerCamelCase = FlaxStableDiffusionSafetyCheckerModule
def __init__( self , __A , __A = None , __A = 0 , __A = jnp.floataa , __A = True , **__A , ):
if input_shape is None:
__a = (1, 224, 224, 3)
__a = self.module_class(config=A_ , dtype=A_ , **A_ )
super().__init__(A_ , A_ , input_shape=A_ , seed=A_ , dtype=A_ , _do_init=_do_init )
def snake_case_ ( self , __A , __A , __A = None ):
# init input tensor
__a = jax.random.normal(A_ , A_ )
__a = jax.random.split(A_ )
__a = {'''params''': params_rng, '''dropout''': dropout_rng}
__a = self.module.init(A_ , A_ )['''params''']
return random_params
def __call__( self , __A , __A = None , ):
__a = jnp.transpose(A_ , (0, 2, 3, 1) )
return self.module.apply(
{"""params""": params or self.params} , jnp.array(A_ , dtype=jnp.floataa ) , rngs={} , )
| 99 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def UpperCamelCase( __UpperCamelCase : int ,__UpperCamelCase : Dict ,__UpperCamelCase : Dict=1e-1_2 ):
lowerCAmelCase_ : Optional[Any] = jnp.divide(emb_a.T ,jnp.clip(jnp.linalg.norm(__UpperCamelCase ,axis=1 ) ,a_min=__UpperCamelCase ) ).T
lowerCAmelCase_ : int = jnp.divide(emb_a.T ,jnp.clip(jnp.linalg.norm(__UpperCamelCase ,axis=1 ) ,a_min=__UpperCamelCase ) ).T
return jnp.matmul(__UpperCamelCase ,norm_emb_a.T )
class __snake_case ( nn.Module ):
_a = 42
_a = jnp.floataa
def UpperCAmelCase__ ( self : str):
lowerCAmelCase_ : List[str] = FlaxCLIPVisionModule(self.config.vision_config)
lowerCAmelCase_ : Union[str, Any] = nn.Dense(self.config.projection_dim , use_bias=A_ , dtype=self.dtype)
lowerCAmelCase_ : List[str] = self.param('''concept_embeds''' , jax.nn.initializers.ones , (1_7, self.config.projection_dim))
lowerCAmelCase_ : Optional[int] = self.param(
'''special_care_embeds''' , jax.nn.initializers.ones , (3, self.config.projection_dim))
lowerCAmelCase_ : int = self.param('''concept_embeds_weights''' , jax.nn.initializers.ones , (1_7,))
lowerCAmelCase_ : str = self.param('''special_care_embeds_weights''' , jax.nn.initializers.ones , (3,))
def __call__( self : Optional[Any] , A_ : Tuple):
lowerCAmelCase_ : Tuple = self.vision_model(A_)[1]
lowerCAmelCase_ : Any = self.visual_projection(A_)
lowerCAmelCase_ : List[Any] = jax_cosine_distance(A_ , self.special_care_embeds)
lowerCAmelCase_ : Any = jax_cosine_distance(A_ , self.concept_embeds)
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
lowerCAmelCase_ : int = 0.0
lowerCAmelCase_ : List[Any] = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
lowerCAmelCase_ : str = jnp.round(A_ , 3)
lowerCAmelCase_ : int = jnp.any(special_scores > 0 , axis=1 , keepdims=A_)
# Use a lower threshold if an image has any special care concept
lowerCAmelCase_ : Any = is_special_care * 0.01
lowerCAmelCase_ : Dict = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
lowerCAmelCase_ : str = jnp.round(A_ , 3)
lowerCAmelCase_ : List[str] = jnp.any(concept_scores > 0 , axis=1)
return has_nsfw_concepts
class __snake_case ( UpperCamelCase_ ):
_a = CLIPConfig
_a = '''clip_input'''
_a = FlaxStableDiffusionSafetyCheckerModule
def __init__( self : Optional[Any] , A_ : CLIPConfig , A_ : Optional[Tuple] = None , A_ : int = 0 , A_ : jnp.dtype = jnp.floataa , A_ : bool = True , **A_ : Union[str, Any] , ):
if input_shape is None:
lowerCAmelCase_ : List[Any] = (1, 2_2_4, 2_2_4, 3)
lowerCAmelCase_ : List[Any] = self.module_class(config=A_ , dtype=A_ , **A_)
super().__init__(A_ , A_ , input_shape=A_ , seed=A_ , dtype=A_ , _do_init=_do_init)
def UpperCAmelCase__ ( self : List[str] , A_ : jax.random.KeyArray , A_ : Tuple , A_ : FrozenDict = None):
# init input tensor
lowerCAmelCase_ : Optional[Any] = jax.random.normal(A_ , A_)
lowerCAmelCase_ , lowerCAmelCase_ : Any = jax.random.split(A_)
lowerCAmelCase_ : Union[str, Any] = {'''params''': params_rng, '''dropout''': dropout_rng}
lowerCAmelCase_ : str = self.module.init(A_ , A_)['''params''']
return random_params
def __call__( self : Any , A_ : str , A_ : dict = None , ):
lowerCAmelCase_ : Dict = jnp.transpose(A_ , (0, 2, 3, 1))
return self.module.apply(
{'''params''': params or self.params} , jnp.array(A_ , dtype=jnp.floataa) , rngs={} , )
| 171 | 0 |
"""simple docstring"""
import argparse
from transformers import BigBirdConfig, BigBirdForPreTraining, BigBirdForQuestionAnswering, load_tf_weights_in_big_bird
from transformers.utils import logging
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE ( snake_case, snake_case, snake_case, snake_case):
# Initialise PyTorch model
__snake_case = BigBirdConfig.from_json_file(snake_case)
print(f"Building PyTorch model from configuration: {config}")
if is_trivia_qa:
__snake_case = BigBirdForQuestionAnswering(snake_case)
else:
__snake_case = BigBirdForPreTraining(snake_case)
# Load weights from tf checkpoint
load_tf_weights_in_big_bird(snake_case, snake_case, is_trivia_qa=snake_case)
# Save pytorch-model
print(f"Save PyTorch model to {pytorch_dump_path}")
model.save_pretrained(snake_case)
if __name__ == "__main__":
__lowercase : Union[str, 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(
"--big_bird_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--is_trivia_qa", action="store_true", help="Whether to convert a model with a trivia_qa head."
)
__lowercase : Tuple = parser.parse_args()
convert_tf_checkpoint_to_pytorch(
args.tf_checkpoint_path, args.big_bird_config_file, args.pytorch_dump_path, args.is_trivia_qa
)
| 705 | """simple docstring"""
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Tuple ) -> str:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase ( self : int ) -> Tuple:
__snake_case = StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
__snake_case = sd_pipe.to(A_ )
sd_pipe.set_progress_bar_config(disable=A_ )
sd_pipe.set_scheduler('''sample_euler''' )
__snake_case = '''A painting of a squirrel eating a burger'''
__snake_case = torch.manual_seed(0 )
__snake_case = sd_pipe([prompt] , generator=A_ , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
__snake_case = output.images
__snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__snake_case = np.array([0.04_47, 0.04_92, 0.04_68, 0.04_08, 0.03_83, 0.04_08, 0.03_54, 0.03_80, 0.03_39] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowercase ( self : Optional[Any] ) -> Tuple:
__snake_case = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
__snake_case = sd_pipe.to(A_ )
sd_pipe.set_progress_bar_config(disable=A_ )
sd_pipe.set_scheduler('''sample_euler''' )
__snake_case = '''A painting of a squirrel eating a burger'''
__snake_case = torch.manual_seed(0 )
__snake_case = sd_pipe([prompt] , generator=A_ , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
__snake_case = output.images
__snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__snake_case = np.array([0.12_37, 0.13_20, 0.14_38, 0.13_59, 0.13_90, 0.11_32, 0.12_77, 0.11_75, 0.11_12] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1
def lowercase ( self : List[str] ) -> Optional[Any]:
__snake_case = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
__snake_case = sd_pipe.to(A_ )
sd_pipe.set_progress_bar_config(disable=A_ )
sd_pipe.set_scheduler('''sample_dpmpp_2m''' )
__snake_case = '''A painting of a squirrel eating a burger'''
__snake_case = torch.manual_seed(0 )
__snake_case = sd_pipe(
[prompt] , generator=A_ , guidance_scale=7.5 , num_inference_steps=15 , output_type='''np''' , use_karras_sigmas=A_ , )
__snake_case = output.images
__snake_case = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__snake_case = np.array(
[0.11_38_16_89, 0.12_11_29_21, 0.1_38_94_57, 0.12_54_96_06, 0.1_24_49_64, 0.10_83_15_17, 0.11_56_28_66, 0.10_86_78_16, 0.10_49_90_48] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2 | 93 | 0 |
'''simple docstring'''
from collections import namedtuple
lowercase =namedtuple('from_to', 'from_ to')
lowercase ={
'cubicmeter': from_to(1, 1),
'litre': from_to(0.001, 1000),
'kilolitre': from_to(1, 1),
'gallon': from_to(0.0_0454, 264.172),
'cubicyard': from_to(0.7_6455, 1.3_0795),
'cubicfoot': from_to(0.028, 35.3147),
'cup': from_to(0.0_0023_6588, 4226.75),
}
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : List[Any] , __lowerCamelCase : int ):
'''simple docstring'''
if from_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid \'from_type\' value: {from_type!r} Supported values are:\n"
+ ', '.join(__lowerCamelCase ) )
if to_type not in METRIC_CONVERSION:
raise ValueError(
f"Invalid \'to_type\' value: {to_type!r}. Supported values are:\n"
+ ', '.join(__lowerCamelCase ) )
return value * METRIC_CONVERSION[from_type].from_ * METRIC_CONVERSION[to_type].to
if __name__ == "__main__":
import doctest
doctest.testmod()
| 446 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class A_ :
'''simple docstring'''
def __init__(self , lowercase__ , lowercase__=2 , lowercase__=True , lowercase__=False , lowercase__=10 , lowercase__=3 , lowercase__=32 * 8 , lowercase__=32 * 8 , lowercase__=4 , lowercase__=64 , ) -> Dict:
__UpperCAmelCase = parent
__UpperCAmelCase = batch_size
__UpperCAmelCase = is_training
__UpperCAmelCase = use_auxiliary_loss
__UpperCAmelCase = num_queries
__UpperCAmelCase = num_channels
__UpperCAmelCase = min_size
__UpperCAmelCase = max_size
__UpperCAmelCase = num_labels
__UpperCAmelCase = hidden_dim
__UpperCAmelCase = hidden_dim
def lowerCAmelCase_ (self ) -> Dict:
__UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
lowercase__ )
__UpperCAmelCase = torch.ones([self.batch_size, self.min_size, self.max_size] , device=lowercase__ )
__UpperCAmelCase = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] , device=lowercase__ ) > 0.5
).float()
__UpperCAmelCase = (torch.rand((self.batch_size, self.num_labels) , device=lowercase__ ) > 0.5).long()
__UpperCAmelCase = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def lowerCAmelCase_ (self ) -> Dict:
__UpperCAmelCase = MaskaFormerConfig(
hidden_size=self.hidden_dim , )
__UpperCAmelCase = self.num_queries
__UpperCAmelCase = self.num_labels
__UpperCAmelCase = [1, 1, 1, 1]
__UpperCAmelCase = self.num_channels
__UpperCAmelCase = 64
__UpperCAmelCase = 128
__UpperCAmelCase = self.hidden_dim
__UpperCAmelCase = self.hidden_dim
__UpperCAmelCase = self.hidden_dim
return config
def lowerCAmelCase_ (self ) -> Optional[int]:
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = self.prepare_config_and_inputs()
__UpperCAmelCase = {'''pixel_values''': pixel_values, '''pixel_mask''': pixel_mask}
return config, inputs_dict
def lowerCAmelCase_ (self , lowercase__ , lowercase__ ) -> Optional[int]:
__UpperCAmelCase = output.encoder_hidden_states
__UpperCAmelCase = output.pixel_decoder_hidden_states
__UpperCAmelCase = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(lowercase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowercase__ ) , len(config.backbone_config.depths ) )
self.parent.assertTrue(len(lowercase__ ) , config.decoder_layers )
def lowerCAmelCase_ (self , lowercase__ , lowercase__ , lowercase__ , lowercase__=False ) -> Union[str, Any]:
with torch.no_grad():
__UpperCAmelCase = MaskaFormerModel(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__UpperCAmelCase = model(pixel_values=lowercase__ , pixel_mask=lowercase__ )
__UpperCAmelCase = model(lowercase__ , output_hidden_states=lowercase__ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape , (self.batch_size, self.num_queries, self.hidden_dim) , )
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(lowercase__ , lowercase__ )
def lowerCAmelCase_ (self , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ) -> List[str]:
__UpperCAmelCase = MaskaFormerForUniversalSegmentation(config=lowercase__ )
model.to(lowercase__ )
model.eval()
def comm_check_on_output(lowercase__ ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape , (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) , )
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape , (self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
__UpperCAmelCase = model(pixel_values=lowercase__ , pixel_mask=lowercase__ )
__UpperCAmelCase = model(lowercase__ )
comm_check_on_output(lowercase__ )
__UpperCAmelCase = model(
pixel_values=lowercase__ , pixel_mask=lowercase__ , mask_labels=lowercase__ , class_labels=lowercase__ )
comm_check_on_output(lowercase__ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape , torch.Size([1] ) )
@require_torch
class A_ ( _a , _a , unittest.TestCase ):
'''simple docstring'''
a__ = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
a__ = {"feature-extraction": MaskaFormerModel} if is_torch_available() else {}
a__ = False
a__ = False
a__ = False
a__ = False
def lowerCAmelCase_ (self ) -> Optional[Any]:
__UpperCAmelCase = MaskaFormerModelTester(self )
__UpperCAmelCase = ConfigTester(self , config_class=lowercase__ , has_text_modality=lowercase__ )
def lowerCAmelCase_ (self ) -> Any:
self.config_tester.run_common_tests()
def lowerCAmelCase_ (self ) -> Union[str, Any]:
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowercase__ , **lowercase__ , output_hidden_states=lowercase__ )
def lowerCAmelCase_ (self ) -> Any:
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*lowercase__ )
@unittest.skip(reason='''Mask2Former does not use inputs_embeds''' )
def lowerCAmelCase_ (self ) -> Tuple:
pass
@unittest.skip(reason='''Mask2Former does not have a get_input_embeddings method''' )
def lowerCAmelCase_ (self ) -> str:
pass
@unittest.skip(reason='''Mask2Former is not a generative model''' )
def lowerCAmelCase_ (self ) -> Optional[Any]:
pass
@unittest.skip(reason='''Mask2Former does not use token embeddings''' )
def lowerCAmelCase_ (self ) -> List[Any]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='''Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''' )
def lowerCAmelCase_ (self ) -> Optional[int]:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowerCAmelCase_ (self ) -> Optional[Any]:
pass
def lowerCAmelCase_ (self ) -> Dict:
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase = model_class(lowercase__ )
__UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase = [*signature.parameters.keys()]
__UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase__ )
@slow
def lowerCAmelCase_ (self ) -> int:
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
__UpperCAmelCase = MaskaFormerModel.from_pretrained(lowercase__ )
self.assertIsNotNone(lowercase__ )
def lowerCAmelCase_ (self ) -> List[str]:
__UpperCAmelCase = (self.model_tester.min_size,) * 2
__UpperCAmelCase = {
'''pixel_values''': torch.randn((2, 3, *size) , device=lowercase__ ),
'''mask_labels''': torch.randn((2, 10, *size) , device=lowercase__ ),
'''class_labels''': torch.zeros(2 , 10 , device=lowercase__ ).long(),
}
__UpperCAmelCase = self.model_tester.get_config()
__UpperCAmelCase = MaskaFormerForUniversalSegmentation(lowercase__ ).to(lowercase__ )
__UpperCAmelCase = model(**lowercase__ )
self.assertTrue(outputs.loss is not None )
def lowerCAmelCase_ (self ) -> Any:
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(lowercase__ , **lowercase__ , output_hidden_states=lowercase__ )
def lowerCAmelCase_ (self ) -> Dict:
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase = model_class(lowercase__ ).to(lowercase__ )
__UpperCAmelCase = model(**lowercase__ , output_attentions=lowercase__ )
self.assertTrue(outputs.attentions is not None )
def lowerCAmelCase_ (self ) -> str:
if not self.model_tester.is_training:
return
__UpperCAmelCase = self.all_model_classes[1]
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
__UpperCAmelCase = model_class(lowercase__ )
model.to(lowercase__ )
model.train()
__UpperCAmelCase = model(lowercase__ , mask_labels=lowercase__ , class_labels=lowercase__ ).loss
loss.backward()
def lowerCAmelCase_ (self ) -> List[Any]:
__UpperCAmelCase = self.all_model_classes[1]
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
__UpperCAmelCase = True
__UpperCAmelCase = True
__UpperCAmelCase = model_class(lowercase__ ).to(lowercase__ )
model.train()
__UpperCAmelCase = model(lowercase__ , mask_labels=lowercase__ , class_labels=lowercase__ )
__UpperCAmelCase = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
__UpperCAmelCase = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
__UpperCAmelCase = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
__UpperCAmelCase = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=lowercase__ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
A_ : List[Any] = 1e-4
def __a ( ) -> str:
'''simple docstring'''
__UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_vision
@slow
class A_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ (self ) -> Union[str, Any]:
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def lowerCAmelCase_ (self ) -> List[str]:
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def lowerCAmelCase_ (self ) -> Optional[Any]:
__UpperCAmelCase = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(lowercase__ )
__UpperCAmelCase = self.default_image_processor
__UpperCAmelCase = prepare_img()
__UpperCAmelCase = image_processor(lowercase__ , return_tensors='''pt''' ).to(lowercase__ )
__UpperCAmelCase = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowercase__ , (1, 3, 384, 384) )
with torch.no_grad():
__UpperCAmelCase = model(**lowercase__ )
__UpperCAmelCase = torch.tensor(
[[-0.2790, -1.0717, -1.1668], [-0.5128, -0.3128, -0.4987], [-0.5832, 0.1971, -0.0197]] ).to(lowercase__ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] , lowercase__ , atol=lowercase__ ) )
__UpperCAmelCase = torch.tensor(
[[0.8973, 1.1847, 1.1776], [1.1934, 1.5040, 1.5128], [1.1153, 1.4486, 1.4951]] ).to(lowercase__ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] , lowercase__ , atol=lowercase__ ) )
__UpperCAmelCase = torch.tensor(
[[2.1152, 1.7000, -0.8603], [1.5808, 1.8004, -0.9353], [1.6043, 1.7495, -0.5999]] ).to(lowercase__ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] , lowercase__ , atol=lowercase__ ) )
def lowerCAmelCase_ (self ) -> List[str]:
__UpperCAmelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowercase__ ).eval()
__UpperCAmelCase = self.default_image_processor
__UpperCAmelCase = prepare_img()
__UpperCAmelCase = image_processor(lowercase__ , return_tensors='''pt''' ).to(lowercase__ )
__UpperCAmelCase = inputs['''pixel_values'''].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(lowercase__ , (1, 3, 384, 384) )
with torch.no_grad():
__UpperCAmelCase = model(**lowercase__ )
# masks_queries_logits
__UpperCAmelCase = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape , (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
__UpperCAmelCase = [
[-8.7839, -9.0056, -8.8121],
[-7.4104, -7.0313, -6.5401],
[-6.6105, -6.3427, -6.4675],
]
__UpperCAmelCase = torch.tensor(lowercase__ ).to(lowercase__ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] , lowercase__ , atol=lowercase__ ) )
# class_queries_logits
__UpperCAmelCase = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape , (1, model.config.num_queries, model.config.num_labels + 1) )
__UpperCAmelCase = torch.tensor(
[
[1.8324, -8.0835, -4.1922],
[0.8450, -9.0050, -3.6053],
[0.3045, -7.7293, -3.0275],
] ).to(lowercase__ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] , lowercase__ , atol=lowercase__ ) )
def lowerCAmelCase_ (self ) -> Optional[int]:
__UpperCAmelCase = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(lowercase__ ).eval()
__UpperCAmelCase = self.default_image_processor
__UpperCAmelCase = image_processor(
[np.zeros((3, 800, 1_333) ), np.zeros((3, 800, 1_333) )] , segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] , return_tensors='''pt''' , )
__UpperCAmelCase = inputs['''pixel_values'''].to(lowercase__ )
__UpperCAmelCase = [el.to(lowercase__ ) for el in inputs['''mask_labels''']]
__UpperCAmelCase = [el.to(lowercase__ ) for el in inputs['''class_labels''']]
with torch.no_grad():
__UpperCAmelCase = model(**lowercase__ )
self.assertTrue(outputs.loss is not None )
| 303 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
snake_case__ : Optional[Any] = 1.054571817e-34 # unit of ℏ : J * s
snake_case__ : List[Any] = 3e8 # unit of c : m * s^-1
def _snake_case ( _snake_case : float , _snake_case : float , _snake_case : float ):
if (force, area, distance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if force < 0:
raise ValueError('''Magnitude of force can not be negative''' )
if distance < 0:
raise ValueError('''Distance can not be negative''' )
if area < 0:
raise ValueError('''Area can not be negative''' )
if force == 0:
lowerCAmelCase : Union[str, Any] = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
240 * (distance) ** 4
)
return {"force": force}
elif area == 0:
lowerCAmelCase : List[str] = (240 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
lowerCAmelCase : Optional[Any] = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError('''One and only one argument must be 0''' )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 637 |
"""simple docstring"""
def _snake_case ( _snake_case : float , _snake_case : list[float] ):
if discount_rate < 0:
raise ValueError('''Discount rate cannot be negative''' )
if not cash_flows:
raise ValueError('''Cash flows list cannot be empty''' )
lowerCAmelCase : List[str] = sum(
cash_flow / ((1 + discount_rate) ** i) for i, cash_flow in enumerate(_snake_case ) )
return round(_snake_case , ndigits=2 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 637 | 1 |
"""simple docstring"""
from math import factorial
def _snake_case ( _snake_case : Tuple , _snake_case : List[Any] ) -> int:
'''simple docstring'''
if n < k or k < 0:
raise ValueError('Please enter positive integers for n and k where n >= k' )
return factorial(snake_case__ ) // (factorial(snake_case__ ) * factorial(n - k ))
if __name__ == "__main__":
print(
'''The number of five-card hands possible from a standard''',
F'''fifty-two card deck is: {combinations(52, 5)}\n''',
)
print(
'''If a class of 40 students must be arranged into groups of''',
F'''4 for group projects, there are {combinations(40, 4)} ways''',
'''to arrange them.\n''',
)
print(
'''If 10 teams are competing in a Formula One race, there''',
F'''are {combinations(10, 3)} ways that first, second and''',
'''third place can be awarded.''',
)
| 7 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
UpperCamelCase__ :List[Any] = logging.get_logger(__name__)
UpperCamelCase__ :Tuple = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
# See all MVP models at https://huggingface.co/models?filter=mvp
UpperCamelCase__ :List[str] = {
"""vocab_file""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
},
"""added_tokens.json""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
},
"""merges_file""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
},
}
UpperCamelCase__ :List[Any] = {
"""RUCAIBox/mvp""": 1_024,
}
class A( lowerCamelCase__ ):
"""simple docstring"""
A = VOCAB_FILES_NAMES
A = PRETRAINED_VOCAB_FILES_MAP
A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
A = ["input_ids", "attention_mask"]
A = MvpTokenizer
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__ , ) -> List[str]:
"""simple docstring"""
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 :int = 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 :Optional[int] = add_prefix_space
_UpperCamelCase :List[Any] = pre_tok_class(**SCREAMING_SNAKE_CASE__ )
_UpperCamelCase :Optional[int] = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
_UpperCamelCase :Any = '''post_processor'''
_UpperCamelCase :Optional[Any] = 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 :Optional[Any] = tuple(state['''cls'''] )
_UpperCamelCase :str = False
if state.get('''add_prefix_space''' , SCREAMING_SNAKE_CASE__ ) != add_prefix_space:
_UpperCamelCase :Optional[int] = add_prefix_space
_UpperCamelCase :Union[str, Any] = True
if state.get('''trim_offsets''' , SCREAMING_SNAKE_CASE__ ) != trim_offsets:
_UpperCamelCase :Union[str, Any] = trim_offsets
_UpperCamelCase :Optional[Any] = True
if changes_to_apply:
_UpperCamelCase :Union[str, Any] = getattr(SCREAMING_SNAKE_CASE__ , state.pop('''type''' ) )
_UpperCamelCase :Optional[Any] = component_class(**SCREAMING_SNAKE_CASE__ )
setattr(self.backend_tokenizer , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@property
def _UpperCamelCase( self ) -> str:
"""simple docstring"""
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ ) -> int:
"""simple docstring"""
_UpperCamelCase :List[Any] = AddedToken(SCREAMING_SNAKE_CASE__ , lstrip=SCREAMING_SNAKE_CASE__ , rstrip=SCREAMING_SNAKE_CASE__ ) if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else value
_UpperCamelCase :int = value
def _UpperCamelCase( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> BatchEncoding:
"""simple docstring"""
_UpperCamelCase :int = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase( self , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> BatchEncoding:
"""simple docstring"""
_UpperCamelCase :Dict = kwargs.get('''is_split_into_words''' , SCREAMING_SNAKE_CASE__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ) -> Tuple[str]:
"""simple docstring"""
_UpperCamelCase :Optional[Any] = self._tokenizer.model.save(SCREAMING_SNAKE_CASE__ , name=SCREAMING_SNAKE_CASE__ )
return tuple(SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=None ) -> str:
"""simple docstring"""
_UpperCamelCase :Optional[int] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ) -> List[int]:
"""simple docstring"""
_UpperCamelCase :Any = [self.sep_token_id]
_UpperCamelCase :List[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 + sep + token_ids_a + sep ) * [0]
| 355 | 0 |
import argparse
import os
import torch
from transformers import FlavaImageCodebook, FlavaImageCodebookConfig
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> Tuple:
UpperCamelCase_ = s.rsplit(UpperCamelCase_ , UpperCamelCase_ )
return new.join(UpperCamelCase_ )
def lowerCAmelCase_ ( UpperCamelCase_ ) -> Union[str, Any]:
# encoder.embeddings are double copied in original FLAVA
return sum(param.float().sum() if "encoder.embeddings" not in key else 0 for key, param in state_dict.items() )
def lowerCAmelCase_ ( UpperCamelCase_ ) -> str:
UpperCamelCase_ = {}
UpperCamelCase_ = ["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:
UpperCamelCase_ = key.replace(F'''{group_key}.''' , F'''{group_key}.group.''' )
if "res_path" in key:
UpperCamelCase_ = key.replace("res_path." , "res_path.path." )
if key.endswith(".w" ):
UpperCamelCase_ = rreplace(UpperCamelCase_ , ".w" , ".weight" , 1 )
if key.endswith(".b" ):
UpperCamelCase_ = rreplace(UpperCamelCase_ , ".b" , ".bias" , 1 )
UpperCamelCase_ = value.float()
return upgrade
@torch.no_grad()
def lowerCAmelCase_ ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=None , UpperCamelCase_=True ) -> str:
from dall_e import Encoder
UpperCamelCase_ = Encoder()
if os.path.exists(UpperCamelCase_ ):
UpperCamelCase_ = torch.load(UpperCamelCase_ )
else:
UpperCamelCase_ = torch.hub.load_state_dict_from_url(UpperCamelCase_ )
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCase_ = ckpt.state_dict()
encoder.load_state_dict(UpperCamelCase_ )
if config_path is not None:
UpperCamelCase_ = FlavaImageCodebookConfig.from_pretrained(UpperCamelCase_ )
else:
UpperCamelCase_ = FlavaImageCodebookConfig()
UpperCamelCase_ = FlavaImageCodebook(UpperCamelCase_ ).eval()
UpperCamelCase_ = encoder.state_dict()
UpperCamelCase_ = upgrade_state_dict(UpperCamelCase_ )
hf_model.load_state_dict(UpperCamelCase_ )
UpperCamelCase_ = hf_model.state_dict()
UpperCamelCase_ = count_parameters(UpperCamelCase_ )
UpperCamelCase_ = 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__":
_UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to flava checkpoint')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
_UpperCAmelCase = parser.parse_args()
convert_dalle_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)
| 721 |
from typing import List, Optional
from tokenizers import ByteLevelBPETokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'vocab_file': 'vocab.json',
'merges_file': 'merges.txt',
'tokenizer_config_file': 'tokenizer_config.json',
}
_UpperCAmelCase = {
'vocab_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'
},
'merges_file': {
'facebook/blenderbot_small-90M': 'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'
},
'tokenizer_config_file': {
'facebook/blenderbot_small-90M': (
'https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'
)
},
}
_UpperCAmelCase = {
'facebook/blenderbot_small-90M': 5_1_2,
}
class _UpperCamelCase ( lowerCAmelCase_ ):
_UpperCamelCase : Optional[Any] = VOCAB_FILES_NAMES
_UpperCamelCase : str = PRETRAINED_VOCAB_FILES_MAP
_UpperCamelCase : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_UpperCamelCase : str = BlenderbotSmallTokenizer
def __init__( self: Dict , _SCREAMING_SNAKE_CASE: Optional[int]=None , _SCREAMING_SNAKE_CASE: List[str]=None , _SCREAMING_SNAKE_CASE: Optional[int]="<|endoftext|>" , _SCREAMING_SNAKE_CASE: List[Any]="<|endoftext|>" , _SCREAMING_SNAKE_CASE: List[str]="<|endoftext|>" , _SCREAMING_SNAKE_CASE: Optional[Any]=False , _SCREAMING_SNAKE_CASE: int=True , **_SCREAMING_SNAKE_CASE: Optional[int] , ) -> int:
"""simple docstring"""
super().__init__(
ByteLevelBPETokenizer(
vocab=_SCREAMING_SNAKE_CASE , merges=_SCREAMING_SNAKE_CASE , add_prefix_space=_SCREAMING_SNAKE_CASE , trim_offsets=_SCREAMING_SNAKE_CASE , ) , bos_token=_SCREAMING_SNAKE_CASE , eos_token=_SCREAMING_SNAKE_CASE , unk_token=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
UpperCamelCase_ = add_prefix_space
def lowercase ( self: Any , _SCREAMING_SNAKE_CASE: Any , _SCREAMING_SNAKE_CASE: Any=None ) -> List[str]:
"""simple docstring"""
UpperCamelCase_ = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowercase ( self: int , _SCREAMING_SNAKE_CASE: List[int] , _SCREAMING_SNAKE_CASE: Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
UpperCamelCase_ = [self.sep_token_id]
UpperCamelCase_ = [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]
| 371 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def UpperCamelCase__ ( _A: Optional[Any] , _A: Dict=10 ):
'''simple docstring'''
__lowerCamelCase = []
for _ in range(_A ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def UpperCamelCase__ ( _A: Tuple , _A: Optional[Any]=10 ):
'''simple docstring'''
__lowerCamelCase = []
for step in range(_A ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
__lowerCamelCase = os.path.join(_A , """schedule.bin""" )
torch.save(scheduler.state_dict() , _A )
__lowerCamelCase = torch.load(_A )
scheduler.load_state_dict(_A )
return lrs
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ):
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) )
for a, b in zip(UpperCAmelCase , UpperCAmelCase ):
self.assertAlmostEqual(UpperCAmelCase , UpperCAmelCase , delta=UpperCAmelCase )
def lowerCamelCase_ ( self ):
__lowerCamelCase = torch.tensor([0.1, -0.2, -0.1] , requires_grad=UpperCAmelCase )
__lowerCamelCase = torch.tensor([0.4, 0.2, -0.5] )
__lowerCamelCase = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
__lowerCamelCase = AdamW(params=[w] , lr=2E-1 , weight_decay=0.0 )
for _ in range(1_0_0 ):
__lowerCamelCase = criterion(UpperCAmelCase , UpperCAmelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
def lowerCamelCase_ ( self ):
__lowerCamelCase = torch.tensor([0.1, -0.2, -0.1] , requires_grad=UpperCAmelCase )
__lowerCamelCase = torch.tensor([0.4, 0.2, -0.5] )
__lowerCamelCase = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
__lowerCamelCase = Adafactor(
params=[w] , lr=1E-2 , eps=(1E-30, 1E-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=UpperCAmelCase , weight_decay=0.0 , relative_step=UpperCAmelCase , scale_parameter=UpperCAmelCase , warmup_init=UpperCAmelCase , )
for _ in range(1_0_0_0 ):
__lowerCamelCase = criterion(UpperCAmelCase , UpperCAmelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
@require_torch
class UpperCamelCase_ ( unittest.TestCase ):
"""simple docstring"""
A = nn.Linear(50 ,50 ) if is_torch_available() else None
A = AdamW(m.parameters() ,lr=10.0 ) if is_torch_available() else None
A = 10
def lowerCamelCase_ ( self , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase=None ):
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) )
for a, b in zip(UpperCAmelCase , UpperCAmelCase ):
self.assertAlmostEqual(UpperCAmelCase , UpperCAmelCase , delta=UpperCAmelCase , msg=UpperCAmelCase )
def lowerCamelCase_ ( self ):
__lowerCamelCase = {"""num_warmup_steps""": 2, """num_training_steps""": 1_0}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
__lowerCamelCase = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{"""num_warmup_steps""": 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, """num_cycles""": 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, """power""": 2.0, """lr_end""": 1E-7},
[0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
),
get_inverse_sqrt_schedule: (
{"""num_warmup_steps""": 2},
[0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
),
}
for scheduler_func, data in scheds.items():
__lowerCamelCase , __lowerCamelCase = data
__lowerCamelCase = scheduler_func(self.optimizer , **UpperCAmelCase )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
__lowerCamelCase = unwrap_schedule(UpperCAmelCase , self.num_steps )
self.assertListAlmostEqual(
UpperCAmelCase , UpperCAmelCase , tol=1E-2 , msg=f'''failed for {scheduler_func} in normal scheduler''' , )
__lowerCamelCase = scheduler_func(self.optimizer , **UpperCAmelCase )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(UpperCAmelCase ) # wrap to test picklability of the schedule
__lowerCamelCase = unwrap_and_save_reload_schedule(UpperCAmelCase , self.num_steps )
self.assertListEqual(UpperCAmelCase , UpperCAmelCase , msg=f'''failed for {scheduler_func} in save and reload''' )
class UpperCamelCase_ :
"""simple docstring"""
def __init__( self , UpperCAmelCase ):
__lowerCamelCase = fn
def __call__( self , *UpperCAmelCase , **UpperCAmelCase ):
return self.fn(*UpperCAmelCase , **UpperCAmelCase )
@classmethod
def lowerCamelCase_ ( self , UpperCAmelCase ):
__lowerCamelCase = list(map(self , scheduler.lr_lambdas ) )
| 479 |
from __future__ import annotations
def UpperCamelCase__ ( _A: float , _A: float , _A: float ):
'''simple docstring'''
if days_between_payments <= 0:
raise ValueError("""days_between_payments must be > 0""" )
if daily_interest_rate < 0:
raise ValueError("""daily_interest_rate must be >= 0""" )
if principal <= 0:
raise ValueError("""principal must be > 0""" )
return principal * daily_interest_rate * days_between_payments
def UpperCamelCase__ ( _A: float , _A: float , _A: float , ):
'''simple docstring'''
if number_of_compounding_periods <= 0:
raise ValueError("""number_of_compounding_periods must be > 0""" )
if nominal_annual_interest_rate_percentage < 0:
raise ValueError("""nominal_annual_interest_rate_percentage must be >= 0""" )
if principal <= 0:
raise ValueError("""principal must be > 0""" )
return principal * (
(1 + nominal_annual_interest_rate_percentage) ** number_of_compounding_periods
- 1
)
def UpperCamelCase__ ( _A: float , _A: float , _A: float , ):
'''simple docstring'''
if number_of_years <= 0:
raise ValueError("""number_of_years must be > 0""" )
if nominal_annual_percentage_rate < 0:
raise ValueError("""nominal_annual_percentage_rate must be >= 0""" )
if principal <= 0:
raise ValueError("""principal must be > 0""" )
return compound_interest(
_A , nominal_annual_percentage_rate / 365 , number_of_years * 365 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 479 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
_snake_case = {
"""configuration_encodec""": [
"""ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""EncodecConfig""",
],
"""feature_extraction_encodec""": ["""EncodecFeatureExtractor"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_snake_case = [
"""ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""EncodecModel""",
"""EncodecPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
_snake_case = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 611 |
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import MaskFormerSwinBackbone
from transformers.models.maskformer import MaskFormerSwinModel
class lowerCAmelCase :
def __init__( self :Union[str, Any] , _lowercase :Dict , _lowercase :int=13 , _lowercase :Dict=32 , _lowercase :List[Any]=2 , _lowercase :Any=3 , _lowercase :Optional[Any]=16 , _lowercase :str=[1, 2, 1] , _lowercase :Tuple=[2, 2, 4] , _lowercase :int=2 , _lowercase :Optional[Any]=2.0 , _lowercase :List[Any]=True , _lowercase :Tuple=0.0 , _lowercase :List[str]=0.0 , _lowercase :List[str]=0.1 , _lowercase :Optional[int]="gelu" , _lowercase :Dict=False , _lowercase :Union[str, Any]=True , _lowercase :str=0.02 , _lowercase :str=1e-5 , _lowercase :Optional[Any]=True , _lowercase :Any=None , _lowercase :int=True , _lowercase :Any=10 , _lowercase :Optional[int]=8 , _lowercase :List[str]=["stage1", "stage2", "stage3"] , _lowercase :int=[1, 2, 3] , ):
'''simple docstring'''
lowercase__ = parent
lowercase__ = batch_size
lowercase__ = image_size
lowercase__ = patch_size
lowercase__ = num_channels
lowercase__ = embed_dim
lowercase__ = depths
lowercase__ = num_heads
lowercase__ = window_size
lowercase__ = mlp_ratio
lowercase__ = qkv_bias
lowercase__ = hidden_dropout_prob
lowercase__ = attention_probs_dropout_prob
lowercase__ = drop_path_rate
lowercase__ = hidden_act
lowercase__ = use_absolute_embeddings
lowercase__ = patch_norm
lowercase__ = layer_norm_eps
lowercase__ = initializer_range
lowercase__ = is_training
lowercase__ = scope
lowercase__ = use_labels
lowercase__ = type_sequence_label_size
lowercase__ = encoder_stride
lowercase__ = out_features
lowercase__ = out_indices
def UpperCAmelCase ( self :int ):
'''simple docstring'''
lowercase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ = None
if self.use_labels:
lowercase__ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ = self.get_config()
return config, pixel_values, labels
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
return MaskFormerSwinConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , embed_dim=self.embed_dim , depths=self.depths , num_heads=self.num_heads , window_size=self.window_size , mlp_ratio=self.mlp_ratio , qkv_bias=self.qkv_bias , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , drop_path_rate=self.drop_path_rate , hidden_act=self.hidden_act , use_absolute_embeddings=self.use_absolute_embeddings , path_norm=self.patch_norm , layer_norm_eps=self.layer_norm_eps , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , out_features=self.out_features , out_indices=self.out_indices , )
def UpperCAmelCase ( self :Any , _lowercase :List[Any] , _lowercase :List[str] , _lowercase :Optional[Any] ):
'''simple docstring'''
lowercase__ = MaskFormerSwinModel(config=_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase )
lowercase__ = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
lowercase__ = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, expected_seq_len, expected_dim) )
def UpperCAmelCase ( self :Tuple , _lowercase :int , _lowercase :Union[str, Any] , _lowercase :Any ):
'''simple docstring'''
lowercase__ = MaskFormerSwinBackbone(config=_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = model(_lowercase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [13, 16, 16, 16] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , [16, 32, 64] )
# verify ValueError
with self.parent.assertRaises(_lowercase ):
lowercase__ = ["stem"]
lowercase__ = MaskFormerSwinBackbone(config=_lowercase )
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
lowercase__ = self.prepare_config_and_inputs()
lowercase__ , lowercase__ , lowercase__ = config_and_inputs
lowercase__ = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class lowerCAmelCase ( lowercase_ , lowercase_ , unittest.TestCase ):
__lowerCamelCase = (
(
MaskFormerSwinModel,
MaskFormerSwinBackbone,
)
if is_torch_available()
else ()
)
__lowerCamelCase = {'feature-extraction': MaskFormerSwinModel} if is_torch_available() else {}
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = MaskFormerSwinModelTester(self )
lowercase__ = ConfigTester(self , config_class=_lowercase , embed_dim=37 )
@require_torch_multi_gpu
@unittest.skip(
reason=(
"`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn't work well with"
" `nn.DataParallel`"
) )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
pass
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def UpperCAmelCase ( self :str ):
'''simple docstring'''
return
def UpperCAmelCase ( self :Any ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowercase )
def UpperCAmelCase ( self :Tuple ):
'''simple docstring'''
lowercase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowercase )
@unittest.skip("Swin does not use inputs_embeds" )
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
pass
@unittest.skip("Swin does not support feedforward chunking" )
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
pass
def UpperCAmelCase ( self :List[str] ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(_lowercase , nn.Linear ) )
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ = model_class(_lowercase )
lowercase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ = [*signature.parameters.keys()]
lowercase__ = ["pixel_values"]
self.assertListEqual(arg_names[:1] , _lowercase )
@unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn't support output_attentions" )
def UpperCAmelCase ( self :Union[str, Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="MaskFormerSwin is only used as an internal backbone" )
def UpperCAmelCase ( self :List[Any] ):
'''simple docstring'''
pass
def UpperCAmelCase ( self :int , _lowercase :List[Any] , _lowercase :Dict , _lowercase :str , _lowercase :str ):
'''simple docstring'''
lowercase__ = model_class(_lowercase )
model.to(_lowercase )
model.eval()
with torch.no_grad():
lowercase__ = model(**self._prepare_for_class(_lowercase , _lowercase ) )
lowercase__ = outputs.hidden_states
lowercase__ = getattr(
self.model_tester , "expected_num_hidden_layers" , len(self.model_tester.depths ) + 1 )
self.assertEqual(len(_lowercase ) , _lowercase )
# Swin has a different seq_length
lowercase__ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [num_patches, self.model_tester.embed_dim] , )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
lowercase__ = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , _lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , _lowercase )
def UpperCAmelCase ( self :Optional[int] ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = 3
lowercase__ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size , collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
lowercase__ = (
config.patch_size
if isinstance(config.patch_size , collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
lowercase__ = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
lowercase__ = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
lowercase__ = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , (padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowercase__ = True
self.check_hidden_states_output(_lowercase , _lowercase , _lowercase , (padded_height, padded_width) )
@unittest.skip(reason="MaskFormerSwin doesn't have pretrained checkpoints" )
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
pass
@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
pass
@unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin" )
def UpperCAmelCase ( self :Optional[Any] ):
'''simple docstring'''
pass
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
def set_nan_tensor_to_zero(_lowercase :Optional[Any] ):
lowercase__ = 0
return t
def check_equivalence(_lowercase :Optional[int] , _lowercase :List[str] , _lowercase :Optional[Any] , _lowercase :str={} ):
with torch.no_grad():
lowercase__ = model(**_lowercase , return_dict=_lowercase , **_lowercase )
lowercase__ = model(**_lowercase , return_dict=_lowercase , **_lowercase ).to_tuple()
def recursive_check(_lowercase :int , _lowercase :Dict ):
if isinstance(_lowercase , (List, Tuple) ):
for tuple_iterable_value, dict_iterable_value in zip(_lowercase , _lowercase ):
recursive_check(_lowercase , _lowercase )
elif isinstance(_lowercase , _lowercase ):
for tuple_iterable_value, dict_iterable_value in zip(
tuple_object.values() , dict_object.values() ):
recursive_check(_lowercase , _lowercase )
elif tuple_object is None:
return
else:
self.assertTrue(
torch.allclose(
set_nan_tensor_to_zero(_lowercase ) , set_nan_tensor_to_zero(_lowercase ) , atol=1e-5 ) , msg=(
"Tuple and dict output are not equal. Difference:"
f''' {torch.max(torch.abs(tuple_object - dict_object ) )}. Tuple has `nan`:'''
f''' {torch.isnan(_lowercase ).any()} and `inf`: {torch.isinf(_lowercase )}. Dict has'''
f''' `nan`: {torch.isnan(_lowercase ).any()} and `inf`: {torch.isinf(_lowercase )}.'''
) , )
recursive_check(_lowercase , _lowercase )
for model_class in self.all_model_classes:
lowercase__ = model_class(_lowercase )
model.to(_lowercase )
model.eval()
lowercase__ = self._prepare_for_class(_lowercase , _lowercase )
lowercase__ = self._prepare_for_class(_lowercase , _lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase )
lowercase__ = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
lowercase__ = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase )
lowercase__ = self._prepare_for_class(_lowercase , _lowercase )
lowercase__ = self._prepare_for_class(_lowercase , _lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase , {"output_hidden_states": True} )
lowercase__ = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
lowercase__ = self._prepare_for_class(_lowercase , _lowercase , return_labels=_lowercase )
check_equivalence(_lowercase , _lowercase , _lowercase , {"output_hidden_states": True} )
@require_torch
class lowerCAmelCase ( unittest.TestCase , lowercase_ ):
__lowerCamelCase = (MaskFormerSwinBackbone,) if is_torch_available() else ()
__lowerCamelCase = MaskFormerSwinConfig
def UpperCAmelCase ( self :Dict ):
'''simple docstring'''
lowercase__ = MaskFormerSwinModelTester(self )
def UpperCAmelCase ( self :str ):
'''simple docstring'''
lowercase__ , lowercase__ = self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ = inputs_dict["pixel_values"].shape[0]
for backbone_class in self.all_model_classes:
lowercase__ = backbone_class(_lowercase )
backbone.to(_lowercase )
backbone.eval()
lowercase__ = backbone(**_lowercase )
# Test default outputs and verify feature maps
self.assertIsInstance(outputs.feature_maps , _lowercase )
self.assertTrue(len(outputs.feature_maps ) == len(backbone.channels ) )
for feature_map, n_channels in zip(outputs.feature_maps , backbone.channels ):
self.assertTrue(feature_map.shape[:2] , (batch_size, n_channels) )
self.assertIsNone(outputs.hidden_states )
self.assertIsNone(outputs.attentions )
# Test output_hidden_states=True
lowercase__ = backbone(**_lowercase , output_hidden_states=_lowercase )
self.assertIsNotNone(outputs.hidden_states )
self.assertTrue(len(outputs.hidden_states ) , len(backbone.stage_names ) )
# We skip the stem layer
for hidden_states, n_channels in zip(outputs.hidden_states[1:] , backbone.channels ):
for hidden_state in hidden_states:
# Hidden states are in the format (batch_size, (height * width), n_channels)
lowercase__ , lowercase__ , lowercase__ = hidden_state.shape
self.assertTrue((h_batch_size, h_n_channels) , (batch_size, n_channels) )
# Test output_attentions=True
if self.has_attentions:
lowercase__ = backbone(**_lowercase , output_attentions=_lowercase )
self.assertIsNotNone(outputs.attentions )
| 611 | 1 |
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class _A ( UpperCAmelCase_ ):
def __init__( self : Any , lowerCamelCase__ : pyspark.sql.DataFrame , lowerCamelCase__ : Optional[NamedSplit] = None , lowerCamelCase__ : Optional[Features] = None , lowerCamelCase__ : bool = True , lowerCamelCase__ : str = None , lowerCamelCase__ : bool = False , lowerCamelCase__ : str = None , lowerCamelCase__ : bool = True , lowerCamelCase__ : str = "arrow" , **lowerCamelCase__ : Tuple , ):
"""simple docstring"""
super().__init__(
split=lowerCamelCase__ , features=lowerCamelCase__ , cache_dir=lowerCamelCase__ , keep_in_memory=lowerCamelCase__ , streaming=lowerCamelCase__ , **lowerCamelCase__ , )
__UpperCamelCase : Union[str, Any] = load_from_cache_file
__UpperCamelCase : Dict = file_format
__UpperCamelCase : str = Spark(
df=lowerCamelCase__ , features=lowerCamelCase__ , cache_dir=lowerCamelCase__ , working_dir=lowerCamelCase__ , **lowerCamelCase__ , )
def a ( self : List[str] ):
"""simple docstring"""
if self.streaming:
return self.builder.as_streaming_dataset(split=self.split )
__UpperCamelCase : Optional[int] = None if self._load_from_cache_file else DownloadMode.FORCE_REDOWNLOAD
self.builder.download_and_prepare(
download_mode=lowerCamelCase__ , file_format=self._file_format , )
return self.builder.as_dataset(split=self.split )
| 269 |
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, 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 ( UpperCAmelCase_ , unittest.TestCase ):
lowercase_ : int = KandinskyVaaPipeline
lowercase_ : Union[str, Any] = [
'''image_embeds''',
'''negative_image_embeds''',
]
lowercase_ : int = ['''image_embeds''', '''negative_image_embeds''']
lowercase_ : str = [
'''generator''',
'''height''',
'''width''',
'''latents''',
'''guidance_scale''',
'''num_inference_steps''',
'''return_dict''',
'''guidance_scale''',
'''num_images_per_prompt''',
'''output_type''',
'''return_dict''',
]
lowercase_ : str = False
@property
def a ( self : int ):
"""simple docstring"""
return 32
@property
def a ( self : str ):
"""simple docstring"""
return 32
@property
def a ( self : Optional[int] ):
"""simple docstring"""
return self.time_input_dim
@property
def a ( self : Optional[int] ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def a ( self : Optional[Any] ):
"""simple docstring"""
return 1_00
@property
def a ( self : str ):
"""simple docstring"""
torch.manual_seed(0 )
__UpperCamelCase : int = {
"""in_channels""": 4,
# 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,
}
__UpperCamelCase : Any = UNetaDConditionModel(**lowerCamelCase__ )
return model
@property
def a ( self : Union[str, Any] ):
"""simple docstring"""
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 a ( self : int ):
"""simple docstring"""
torch.manual_seed(0 )
__UpperCamelCase : Tuple = VQModel(**self.dummy_movq_kwargs )
return model
def a ( self : Tuple ):
"""simple docstring"""
__UpperCamelCase : str = self.dummy_unet
__UpperCamelCase : Optional[int] = self.dummy_movq
__UpperCamelCase : List[str] = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule="""linear""" , beta_start=0.0_0085 , beta_end=0.012 , clip_sample=lowerCamelCase__ , set_alpha_to_one=lowerCamelCase__ , steps_offset=1 , prediction_type="""epsilon""" , thresholding=lowerCamelCase__ , )
__UpperCamelCase : str = {
"""unet""": unet,
"""scheduler""": scheduler,
"""movq""": movq,
}
return components
def a ( self : Optional[int] , lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : Any=0 ):
"""simple docstring"""
__UpperCamelCase : Tuple = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(lowerCamelCase__ ) ).to(lowerCamelCase__ )
__UpperCamelCase : Optional[int] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
lowerCamelCase__ )
if str(lowerCamelCase__ ).startswith("""mps""" ):
__UpperCamelCase : Optional[int] = torch.manual_seed(lowerCamelCase__ )
else:
__UpperCamelCase : int = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
__UpperCamelCase : Dict = {
"""image_embeds""": image_embeds,
"""negative_image_embeds""": negative_image_embeds,
"""generator""": generator,
"""height""": 64,
"""width""": 64,
"""guidance_scale""": 4.0,
"""num_inference_steps""": 2,
"""output_type""": """np""",
}
return inputs
def a ( self : Dict ):
"""simple docstring"""
__UpperCamelCase : Dict = """cpu"""
__UpperCamelCase : Dict = self.get_dummy_components()
__UpperCamelCase : Union[str, Any] = self.pipeline_class(**lowerCamelCase__ )
__UpperCamelCase : int = pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : List[Any] = pipe(**self.get_dummy_inputs(lowerCamelCase__ ) )
__UpperCamelCase : List[Any] = output.images
__UpperCamelCase : int = pipe(
**self.get_dummy_inputs(lowerCamelCase__ ) , return_dict=lowerCamelCase__ , )[0]
__UpperCamelCase : List[str] = image[0, -3:, -3:, -1]
__UpperCamelCase : Tuple = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__UpperCamelCase : int = np.array(
[0.623_7976, 1.0, 0.3644_1332, 1.0, 0.7063_9634, 0.2987_7186, 0.8565_2125, 0.521_6843, 0.5445_4046] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f' expected_slice {expected_slice}, but got {image_slice.flatten()}'
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f' expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}'
@slow
@require_torch_gpu
class _A ( unittest.TestCase ):
def a ( self : Optional[int] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a ( self : List[Any] ):
"""simple docstring"""
__UpperCamelCase : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy""" )
__UpperCamelCase : List[str] = KandinskyVaaPriorPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-prior""" , torch_dtype=torch.floataa )
pipe_prior.to(lowerCamelCase__ )
__UpperCamelCase : int = KandinskyVaaPipeline.from_pretrained(
"""kandinsky-community/kandinsky-2-2-decoder""" , torch_dtype=torch.floataa )
__UpperCamelCase : Optional[int] = pipeline.to(lowerCamelCase__ )
pipeline.set_progress_bar_config(disable=lowerCamelCase__ )
__UpperCamelCase : Optional[Any] = """red cat, 4k photo"""
__UpperCamelCase : Tuple = torch.Generator(device="""cuda""" ).manual_seed(0 )
__UpperCamelCase , __UpperCamelCase : Union[str, Any] = pipe_prior(
lowerCamelCase__ , generator=lowerCamelCase__ , num_inference_steps=5 , negative_prompt="""""" , ).to_tuple()
__UpperCamelCase : Any = torch.Generator(device="""cuda""" ).manual_seed(0 )
__UpperCamelCase : Union[str, Any] = pipeline(
image_embeds=lowerCamelCase__ , negative_image_embeds=lowerCamelCase__ , generator=lowerCamelCase__ , num_inference_steps=1_00 , output_type="""np""" , )
__UpperCamelCase : Any = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(lowerCamelCase__ , lowerCamelCase__ )
| 269 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_realm import RealmTokenizer
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {"""vocab_file""": """vocab.txt""", """tokenizer_file""": """tokenizer.json"""}
__UpperCAmelCase = {
"""vocab_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/vocab.txt"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/vocab.txt"""
),
"""google/realm-orqa-nq-openqa""": """https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-nq-reader""": """https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-openqa""": """https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/vocab.txt""",
"""google/realm-orqa-wq-reader""": """https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/vocab.txt""",
},
"""tokenizer_file""": {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/tokenizer.jsont"""
),
"""google/realm-cc-news-pretrained-encoder""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-encoder/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-scorer""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-scorer/resolve/main/tokenizer.json"""
),
"""google/realm-cc-news-pretrained-openqa""": (
"""https://huggingface.co/google/realm-cc-news-pretrained-openqa/aresolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-openqa""": (
"""https://huggingface.co/google/realm-orqa-nq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-nq-reader""": (
"""https://huggingface.co/google/realm-orqa-nq-reader/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-openqa""": (
"""https://huggingface.co/google/realm-orqa-wq-openqa/resolve/main/tokenizer.json"""
),
"""google/realm-orqa-wq-reader""": (
"""https://huggingface.co/google/realm-orqa-wq-reader/resolve/main/tokenizer.json"""
),
},
}
__UpperCAmelCase = {
"""google/realm-cc-news-pretrained-embedder""": 512,
"""google/realm-cc-news-pretrained-encoder""": 512,
"""google/realm-cc-news-pretrained-scorer""": 512,
"""google/realm-cc-news-pretrained-openqa""": 512,
"""google/realm-orqa-nq-openqa""": 512,
"""google/realm-orqa-nq-reader""": 512,
"""google/realm-orqa-wq-openqa""": 512,
"""google/realm-orqa-wq-reader""": 512,
}
__UpperCAmelCase = {
"""google/realm-cc-news-pretrained-embedder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-encoder""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-scorer""": {"""do_lower_case""": True},
"""google/realm-cc-news-pretrained-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-nq-reader""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-openqa""": {"""do_lower_case""": True},
"""google/realm-orqa-wq-reader""": {"""do_lower_case""": True},
}
class SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowerCamelCase : Dict =VOCAB_FILES_NAMES
lowerCamelCase : Union[str, Any] =PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : List[Any] =PRETRAINED_INIT_CONFIGURATION
lowerCamelCase : List[Any] =PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : Optional[int] =RealmTokenizer
def __init__( self : Dict , lowerCAmelCase : Dict=None , lowerCAmelCase : str=None , lowerCAmelCase : Optional[Any]=True , lowerCAmelCase : Optional[Any]="[UNK]" , lowerCAmelCase : int="[SEP]" , lowerCAmelCase : List[str]="[PAD]" , lowerCAmelCase : List[str]="[CLS]" , lowerCAmelCase : str="[MASK]" , lowerCAmelCase : Optional[int]=True , lowerCAmelCase : List[str]=None , **lowerCAmelCase : Optional[int] , ) -> Any:
"""simple docstring"""
super().__init__(
lowerCAmelCase , tokenizer_file=lowerCAmelCase , do_lower_case=lowerCAmelCase , unk_token=lowerCAmelCase , sep_token=lowerCAmelCase , pad_token=lowerCAmelCase , cls_token=lowerCAmelCase , mask_token=lowerCAmelCase , tokenize_chinese_chars=lowerCAmelCase , strip_accents=lowerCAmelCase , **lowerCAmelCase , )
__lowerCAmelCase : Dict = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("""lowercase""" , lowerCAmelCase ) != do_lower_case
or normalizer_state.get("""strip_accents""" , lowerCAmelCase ) != strip_accents
or normalizer_state.get("""handle_chinese_chars""" , lowerCAmelCase ) != tokenize_chinese_chars
):
__lowerCAmelCase : Dict = getattr(lowerCAmelCase , normalizer_state.pop("""type""" ) )
__lowerCAmelCase : Any = do_lower_case
__lowerCAmelCase : Tuple = strip_accents
__lowerCAmelCase : Any = tokenize_chinese_chars
__lowerCAmelCase : Optional[Any] = normalizer_class(**lowerCAmelCase )
__lowerCAmelCase : List[Any] = do_lower_case
def SCREAMING_SNAKE_CASE ( self : Optional[Any] , lowerCAmelCase : Union[str, Any] , **lowerCAmelCase : List[Any] ) -> Tuple:
"""simple docstring"""
__lowerCAmelCase : List[str] = PaddingStrategy.MAX_LENGTH
__lowerCAmelCase : List[Any] = text
__lowerCAmelCase : Union[str, Any] = kwargs.pop("""text_pair""" , lowerCAmelCase )
__lowerCAmelCase : int = kwargs.pop("""return_tensors""" , lowerCAmelCase )
__lowerCAmelCase : int = {
"""input_ids""": [],
"""attention_mask""": [],
"""token_type_ids""": [],
}
for idx, candidate_text in enumerate(lowerCAmelCase ):
if batch_text_pair is not None:
__lowerCAmelCase : str = batch_text_pair[idx]
else:
__lowerCAmelCase : Optional[int] = None
__lowerCAmelCase : Any = super().__call__(lowerCAmelCase , lowerCAmelCase , return_tensors=lowerCAmelCase , **lowerCAmelCase )
__lowerCAmelCase : Dict = encoded_candidates.get("""input_ids""" )
__lowerCAmelCase : List[str] = encoded_candidates.get("""attention_mask""" )
__lowerCAmelCase : str = encoded_candidates.get("""token_type_ids""" )
if encoded_input_ids is not None:
output_data["input_ids"].append(lowerCAmelCase )
if encoded_attention_mask is not None:
output_data["attention_mask"].append(lowerCAmelCase )
if encoded_token_type_ids is not None:
output_data["token_type_ids"].append(lowerCAmelCase )
__lowerCAmelCase : Optional[int] = {key: item for key, item in output_data.items() if len(lowerCAmelCase ) != 0}
return BatchEncoding(lowerCAmelCase , tensor_type=lowerCAmelCase )
def SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase : Optional[int] , lowerCAmelCase : Any=None ) -> Any:
"""simple docstring"""
__lowerCAmelCase : 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 SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase : List[int] , lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
"""simple docstring"""
__lowerCAmelCase : Optional[int] = [self.sep_token_id]
__lowerCAmelCase : Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase : str , lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
"""simple docstring"""
__lowerCAmelCase : int = self._tokenizer.model.save(lowerCAmelCase , name=lowerCAmelCase )
return tuple(lowerCAmelCase )
| 218 |
from __future__ import annotations
def snake_case_ (__A : list[int] , __A : int ) -> list[int]:
__lowerCAmelCase : List[Any] = 0
__lowerCAmelCase : Optional[Any] = len(__A ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
elif nums[i] + nums[j] < target:
__lowerCAmelCase : int = i + 1
else:
__lowerCAmelCase : List[str] = j - 1
return []
if __name__ == "__main__":
import doctest
doctest.testmod()
print(F'{two_pointer([2, 7, 11, 15], 9) = }')
| 218 | 1 |
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffusers_config,
renew_vae_attention_paths,
renew_vae_resnet_paths,
)
def a__ ( lowercase__ , lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =checkpoint
UpperCAmelCase_ ={}
UpperCAmelCase_ =vae_state_dict["encoder.conv_in.weight"]
UpperCAmelCase_ =vae_state_dict["encoder.conv_in.bias"]
UpperCAmelCase_ =vae_state_dict["encoder.conv_out.weight"]
UpperCAmelCase_ =vae_state_dict["encoder.conv_out.bias"]
UpperCAmelCase_ =vae_state_dict["encoder.norm_out.weight"]
UpperCAmelCase_ =vae_state_dict["encoder.norm_out.bias"]
UpperCAmelCase_ =vae_state_dict["decoder.conv_in.weight"]
UpperCAmelCase_ =vae_state_dict["decoder.conv_in.bias"]
UpperCAmelCase_ =vae_state_dict["decoder.conv_out.weight"]
UpperCAmelCase_ =vae_state_dict["decoder.conv_out.bias"]
UpperCAmelCase_ =vae_state_dict["decoder.norm_out.weight"]
UpperCAmelCase_ =vae_state_dict["decoder.norm_out.bias"]
UpperCAmelCase_ =vae_state_dict["quant_conv.weight"]
UpperCAmelCase_ =vae_state_dict["quant_conv.bias"]
UpperCAmelCase_ =vae_state_dict["post_quant_conv.weight"]
UpperCAmelCase_ =vae_state_dict["post_quant_conv.bias"]
# Retrieves the keys for the encoder down blocks only
UpperCAmelCase_ =len({".".join(layer.split("." )[:3] ) for layer in vae_state_dict if "encoder.down" in layer} )
UpperCAmelCase_ ={
layer_id: [key for key in vae_state_dict if F'down.{layer_id}' in key] for layer_id in range(lowercase__ )
}
# Retrieves the keys for the decoder up blocks only
UpperCAmelCase_ =len({".".join(layer.split("." )[:3] ) for layer in vae_state_dict if "decoder.up" in layer} )
UpperCAmelCase_ ={
layer_id: [key for key in vae_state_dict if F'up.{layer_id}' in key] for layer_id in range(lowercase__ )
}
for i in range(lowercase__ ):
UpperCAmelCase_ =[key for key in down_blocks[i] if F'down.{i}' in key and F'down.{i}.downsample' not in key]
if F'encoder.down.{i}.downsample.conv.weight' in vae_state_dict:
UpperCAmelCase_ =vae_state_dict.pop(
F'encoder.down.{i}.downsample.conv.weight' )
UpperCAmelCase_ =vae_state_dict.pop(
F'encoder.down.{i}.downsample.conv.bias' )
UpperCAmelCase_ =renew_vae_resnet_paths(lowercase__ )
UpperCAmelCase_ ={"old": F'down.{i}.block', "new": F'down_blocks.{i}.resnets'}
assign_to_checkpoint(lowercase__ , lowercase__ , lowercase__ , additional_replacements=[meta_path] , config=lowercase__ )
UpperCAmelCase_ =[key for key in vae_state_dict if "encoder.mid.block" in key]
UpperCAmelCase_ =2
for i in range(1 , num_mid_res_blocks + 1 ):
UpperCAmelCase_ =[key for key in mid_resnets if F'encoder.mid.block_{i}' in key]
UpperCAmelCase_ =renew_vae_resnet_paths(lowercase__ )
UpperCAmelCase_ ={"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
assign_to_checkpoint(lowercase__ , lowercase__ , lowercase__ , additional_replacements=[meta_path] , config=lowercase__ )
UpperCAmelCase_ =[key for key in vae_state_dict if "encoder.mid.attn" in key]
UpperCAmelCase_ =renew_vae_attention_paths(lowercase__ )
UpperCAmelCase_ ={"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(lowercase__ , lowercase__ , lowercase__ , additional_replacements=[meta_path] , config=lowercase__ )
conv_attn_to_linear(lowercase__ )
for i in range(lowercase__ ):
UpperCAmelCase_ =num_up_blocks - 1 - i
UpperCAmelCase_ =[
key for key in up_blocks[block_id] if F'up.{block_id}' in key and F'up.{block_id}.upsample' not in key
]
if F'decoder.up.{block_id}.upsample.conv.weight' in vae_state_dict:
UpperCAmelCase_ =vae_state_dict[
F'decoder.up.{block_id}.upsample.conv.weight'
]
UpperCAmelCase_ =vae_state_dict[
F'decoder.up.{block_id}.upsample.conv.bias'
]
UpperCAmelCase_ =renew_vae_resnet_paths(lowercase__ )
UpperCAmelCase_ ={"old": F'up.{block_id}.block', "new": F'up_blocks.{i}.resnets'}
assign_to_checkpoint(lowercase__ , lowercase__ , lowercase__ , additional_replacements=[meta_path] , config=lowercase__ )
UpperCAmelCase_ =[key for key in vae_state_dict if "decoder.mid.block" in key]
UpperCAmelCase_ =2
for i in range(1 , num_mid_res_blocks + 1 ):
UpperCAmelCase_ =[key for key in mid_resnets if F'decoder.mid.block_{i}' in key]
UpperCAmelCase_ =renew_vae_resnet_paths(lowercase__ )
UpperCAmelCase_ ={"old": F'mid.block_{i}', "new": F'mid_block.resnets.{i - 1}'}
assign_to_checkpoint(lowercase__ , lowercase__ , lowercase__ , additional_replacements=[meta_path] , config=lowercase__ )
UpperCAmelCase_ =[key for key in vae_state_dict if "decoder.mid.attn" in key]
UpperCAmelCase_ =renew_vae_attention_paths(lowercase__ )
UpperCAmelCase_ ={"old": "mid.attn_1", "new": "mid_block.attentions.0"}
assign_to_checkpoint(lowercase__ , lowercase__ , lowercase__ , additional_replacements=[meta_path] , config=lowercase__ )
conv_attn_to_linear(lowercase__ )
return new_checkpoint
def a__ ( lowercase__ , lowercase__ , ):
'''simple docstring'''
UpperCAmelCase_ =requests.get(
" https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml" )
UpperCAmelCase_ =io.BytesIO(r.content )
UpperCAmelCase_ =OmegaConf.load(lowercase__ )
UpperCAmelCase_ =5_1_2
UpperCAmelCase_ ="cuda" if torch.cuda.is_available() else "cpu"
if checkpoint_path.endswith("safetensors" ):
from safetensors import safe_open
UpperCAmelCase_ ={}
with safe_open(lowercase__ , framework="pt" , device="cpu" ) as f:
for key in f.keys():
UpperCAmelCase_ =f.get_tensor(lowercase__ )
else:
UpperCAmelCase_ =torch.load(lowercase__ , map_location=lowercase__ )["state_dict"]
# Convert the VAE model.
UpperCAmelCase_ =create_vae_diffusers_config(lowercase__ , image_size=lowercase__ )
UpperCAmelCase_ =custom_convert_ldm_vae_checkpoint(lowercase__ , lowercase__ )
UpperCAmelCase_ =AutoencoderKL(**lowercase__ )
vae.load_state_dict(lowercase__ )
vae.save_pretrained(lowercase__ )
if __name__ == "__main__":
__lowercase : Tuple =argparse.ArgumentParser()
parser.add_argument("""--vae_pt_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the VAE.pt to convert.""")
__lowercase : Optional[int] =parser.parse_args()
vae_pt_to_vae_diffuser(args.vae_pt_path, args.dump_path)
| 54 |
import argparse
import logging
import os
import re
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
DataCollatorForLanguageModeling,
PushToHubCallback,
TFAutoModelForMaskedLM,
create_optimizer,
)
__lowercase : Tuple =logging.getLogger(__name__)
__lowercase : Optional[int] =tf.data.AUTOTUNE
def a__ ( ):
'''simple docstring'''
UpperCAmelCase_ =argparse.ArgumentParser(description="Train a masked language model on TPU." )
parser.add_argument(
"--pretrained_model_config" , type=lowercase__ , default="roberta-base" , help="The model config to use. Note that we don't copy the model's weights, only the config!" , )
parser.add_argument(
"--tokenizer" , type=lowercase__ , default="unigram-tokenizer-wikitext" , help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size." , )
parser.add_argument(
"--per_replica_batch_size" , type=lowercase__ , default=8 , help="Batch size per TPU core." , )
parser.add_argument(
"--no_tpu" , action="store_true" , help="If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances." , )
parser.add_argument(
"--tpu_name" , type=lowercase__ , help="Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs." , default="local" , )
parser.add_argument(
"--tpu_zone" , type=lowercase__ , help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes." , )
parser.add_argument(
"--gcp_project" , type=lowercase__ , help="Google cloud project name. Only used for non-Colab TPU nodes." )
parser.add_argument(
"--bfloat16" , action="store_true" , help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU." , )
parser.add_argument(
"--train_dataset" , type=lowercase__ , help="Path to training dataset to load. If the path begins with `gs://`"
" then the dataset will be loaded from a Google Cloud Storage bucket." , )
parser.add_argument(
"--shuffle_buffer_size" , type=lowercase__ , default=2**1_8 , help="Size of the shuffle buffer (in samples)" , )
parser.add_argument(
"--eval_dataset" , type=lowercase__ , help="Path to evaluation dataset to load. If the path begins with `gs://`"
" then the dataset will be loaded from a Google Cloud Storage bucket." , )
parser.add_argument(
"--num_epochs" , type=lowercase__ , default=1 , help="Number of epochs to train for." , )
parser.add_argument(
"--learning_rate" , type=lowercase__ , default=1E-4 , help="Learning rate to use for training." , )
parser.add_argument(
"--weight_decay_rate" , type=lowercase__ , default=1E-3 , help="Weight decay rate to use for training." , )
parser.add_argument(
"--max_length" , type=lowercase__ , default=5_1_2 , help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py" , )
parser.add_argument(
"--mlm_probability" , type=lowercase__ , default=0.15 , help="Fraction of tokens to mask during training." , )
parser.add_argument("--output_dir" , type=lowercase__ , required=lowercase__ , help="Path to save model checkpoints to." )
parser.add_argument("--hub_model_id" , type=lowercase__ , help="Model ID to upload to on the Hugging Face Hub." )
UpperCAmelCase_ =parser.parse_args()
return args
def a__ ( lowercase__ ):
'''simple docstring'''
try:
if args.tpu_name:
UpperCAmelCase_ =tf.distribute.cluster_resolver.TPUClusterResolver(
args.tpu_name , zone=args.tpu_zone , project=args.gcp_project )
else:
UpperCAmelCase_ =tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
raise RuntimeError(
"Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or "
"--gcp_project. When running on a TPU VM, use --tpu_name local." )
tf.config.experimental_connect_to_cluster(lowercase__ )
tf.tpu.experimental.initialize_tpu_system(lowercase__ )
return tpu
def a__ ( lowercase__ ):
'''simple docstring'''
UpperCAmelCase_ =0
for file in file_list:
UpperCAmelCase_ =file.split("/" )[-1]
UpperCAmelCase_ =re.search(R"-\d+-(\d+)\.tfrecord" , lowercase__ ).group(1 )
UpperCAmelCase_ =int(lowercase__ )
num_samples += sample_count
return num_samples
def a__ ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__=None ):
'''simple docstring'''
UpperCAmelCase_ =count_samples(lowercase__ )
UpperCAmelCase_ =tf.data.Dataset.from_tensor_slices(lowercase__ )
if shuffle:
UpperCAmelCase_ =dataset.shuffle(len(lowercase__ ) )
UpperCAmelCase_ =tf.data.TFRecordDataset(lowercase__ , num_parallel_reads=lowercase__ )
# TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
UpperCAmelCase_ =dataset.apply(tf.data.experimental.assert_cardinality(lowercase__ ) )
UpperCAmelCase_ =dataset.map(lowercase__ , num_parallel_calls=lowercase__ )
if shuffle:
assert shuffle_buffer_size is not None
UpperCAmelCase_ =dataset.shuffle(args.shuffle_buffer_size )
UpperCAmelCase_ =dataset.batch(lowercase__ , drop_remainder=lowercase__ )
UpperCAmelCase_ =dataset.map(lowercase__ , num_parallel_calls=lowercase__ )
UpperCAmelCase_ =dataset.prefetch(lowercase__ )
return dataset
def a__ ( lowercase__ ):
'''simple docstring'''
if not args.no_tpu:
UpperCAmelCase_ =initialize_tpu(lowercase__ )
UpperCAmelCase_ =tf.distribute.TPUStrategy(lowercase__ )
else:
UpperCAmelCase_ =tf.distribute.OneDeviceStrategy(device="/gpu:0" )
if args.bfloataa:
tf.keras.mixed_precision.set_global_policy("mixed_bfloat16" )
UpperCAmelCase_ =AutoTokenizer.from_pretrained(args.tokenizer )
UpperCAmelCase_ =AutoConfig.from_pretrained(args.pretrained_model_config )
UpperCAmelCase_ =tokenizer.vocab_size
UpperCAmelCase_ =tf.io.gfile.glob(os.path.join(args.train_dataset , "*.tfrecord" ) )
if not training_records:
raise ValueError(F'No .tfrecord files found in {args.train_dataset}.' )
UpperCAmelCase_ =tf.io.gfile.glob(os.path.join(args.eval_dataset , "*.tfrecord" ) )
if not eval_records:
raise ValueError(F'No .tfrecord files found in {args.eval_dataset}.' )
UpperCAmelCase_ =count_samples(lowercase__ )
UpperCAmelCase_ =num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
UpperCAmelCase_ =steps_per_epoch * args.num_epochs
with strategy.scope():
UpperCAmelCase_ =TFAutoModelForMaskedLM.from_config(lowercase__ )
model(model.dummy_inputs ) # Pass some dummy inputs through the model to ensure all the weights are built
UpperCAmelCase_ , UpperCAmelCase_ =create_optimizer(
num_train_steps=lowercase__ , num_warmup_steps=total_train_steps // 2_0 , init_lr=args.learning_rate , weight_decay_rate=args.weight_decay_rate , )
# Transformers models compute the right loss for their task by default when labels are passed, and will
# use this for training unless you specify your own loss function in compile().
model.compile(optimizer=lowercase__ , metrics=["accuracy"] )
def decode_fn(lowercase__ ):
UpperCAmelCase_ ={
"input_ids": tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
"attention_mask": tf.io.FixedLenFeature(dtype=tf.intaa , shape=(args.max_length,) ),
}
return tf.io.parse_single_example(lowercase__ , lowercase__ )
# Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
# use their methods in our data pipeline.
UpperCAmelCase_ =DataCollatorForLanguageModeling(
tokenizer=lowercase__ , mlm_probability=args.mlm_probability , mlm=lowercase__ , return_tensors="tf" )
def mask_with_collator(lowercase__ ):
# TF really needs an isin() function
UpperCAmelCase_ =(
~tf.cast(batch["attention_mask"] , tf.bool )
| (batch["input_ids"] == tokenizer.cls_token_id)
| (batch["input_ids"] == tokenizer.sep_token_id)
)
UpperCAmelCase_ , UpperCAmelCase_ =data_collator.tf_mask_tokens(
batch["input_ids"] , vocab_size=len(lowercase__ ) , mask_token_id=tokenizer.mask_token_id , special_tokens_mask=lowercase__ , )
return batch
UpperCAmelCase_ =args.per_replica_batch_size * strategy.num_replicas_in_sync
UpperCAmelCase_ =prepare_dataset(
lowercase__ , decode_fn=lowercase__ , mask_fn=lowercase__ , batch_size=lowercase__ , shuffle=lowercase__ , shuffle_buffer_size=args.shuffle_buffer_size , )
UpperCAmelCase_ =prepare_dataset(
lowercase__ , decode_fn=lowercase__ , mask_fn=lowercase__ , batch_size=lowercase__ , shuffle=lowercase__ , )
UpperCAmelCase_ =[]
if args.hub_model_id:
callbacks.append(
PushToHubCallback(output_dir=args.output_dir , hub_model_id=args.hub_model_id , tokenizer=lowercase__ ) )
model.fit(
lowercase__ , validation_data=lowercase__ , epochs=args.num_epochs , callbacks=lowercase__ , )
model.save_pretrained(args.output_dir )
if __name__ == "__main__":
__lowercase : Union[str, Any] =parse_args()
main(args)
| 54 | 1 |
'''simple docstring'''
class lowercase :
'''simple docstring'''
def __init__( self : Union[str, Any] ) -> None:
'''simple docstring'''
lowerCamelCase__ = {} # Mapping from char to TrieNode
lowerCamelCase__ = False
def a__ ( self : List[Any] , __lowerCamelCase : list[str] ) -> None:
'''simple docstring'''
for word in words:
self.insert(__lowerCamelCase )
def a__ ( self : int , __lowerCamelCase : str ) -> None:
'''simple docstring'''
lowerCamelCase__ = self
for char in word:
if char not in curr.nodes:
lowerCamelCase__ = TrieNode()
lowerCamelCase__ = curr.nodes[char]
lowerCamelCase__ = True
def a__ ( self : Optional[Any] , __lowerCamelCase : str ) -> bool:
'''simple docstring'''
lowerCamelCase__ = self
for char in word:
if char not in curr.nodes:
return False
lowerCamelCase__ = curr.nodes[char]
return curr.is_leaf
def a__ ( self : Optional[Any] , __lowerCamelCase : str ) -> None:
'''simple docstring'''
def _delete(__lowerCamelCase : TrieNode , __lowerCamelCase : str , __lowerCamelCase : int ) -> bool:
if index == len(__lowerCamelCase ):
# If word does not exist
if not curr.is_leaf:
return False
lowerCamelCase__ = False
return len(curr.nodes ) == 0
lowerCamelCase__ = word[index]
lowerCamelCase__ = curr.nodes.get(__lowerCamelCase )
# If char not in current trie node
if not char_node:
return False
# Flag to check if node can be deleted
lowerCamelCase__ = _delete(__lowerCamelCase , __lowerCamelCase , index + 1 )
if delete_curr:
del curr.nodes[char]
return len(curr.nodes ) == 0
return delete_curr
_delete(self , __lowerCamelCase , 0 )
def lowerCamelCase_ ( lowercase__ , lowercase__) -> Union[str, Any]:
if node.is_leaf:
print(lowercase__ , end=" ")
for key, value in node.nodes.items():
print_words(lowercase__ , word + key)
def lowerCamelCase_ ( ) -> Optional[Any]:
lowerCamelCase__ = "banana bananas bandana band apple all beast".split()
lowerCamelCase__ = TrieNode()
root.insert_many(lowercase__)
# print_words(root, "")
assert all(root.find(lowercase__) for word in words)
assert root.find("banana")
assert not root.find("bandanas")
assert not root.find("apps")
assert root.find("apple")
assert root.find("all")
root.delete("all")
assert not root.find("all")
root.delete("banana")
assert not root.find("banana")
assert root.find("bananas")
return True
def lowerCamelCase_ ( lowercase__ , lowercase__) -> Tuple:
print(str(lowercase__) , "works!" if passes else "doesn't work :(")
def lowerCamelCase_ ( ) -> List[Any]:
assert test_trie()
def lowerCamelCase_ ( ) -> List[str]:
print_results("Testing trie functionality" , test_trie())
if __name__ == "__main__":
main()
| 712 |
'''simple docstring'''
from pathlib import Path
import cva
import numpy as np
from matplotlib import pyplot as plt
def lowerCamelCase_ ( lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__):
lowerCamelCase__ = cva.getAffineTransform(lowercase__ , lowercase__)
return cva.warpAffine(lowercase__ , lowercase__ , (rows, cols))
if __name__ == "__main__":
# read original image
__A : Any = cva.imread(
str(Path(__file__).resolve().parent.parent / """image_data""" / """lena.jpg""")
)
# turn image in gray scale value
__A : int = cva.cvtColor(image, cva.COLOR_BGR2GRAY)
# get image shape
__A , __A : Dict = gray_img.shape
# set different points to rotate image
__A : int = np.array([[50, 50], [2_00, 50], [50, 2_00]], np.floataa)
__A : Any = np.array([[10, 1_00], [2_00, 50], [1_00, 2_50]], np.floataa)
__A : str = np.array([[50, 50], [1_50, 50], [1_20, 2_00]], np.floataa)
__A : Optional[int] = np.array([[10, 1_00], [80, 50], [1_80, 2_50]], np.floataa)
# add all rotated images in a list
__A : List[Any] = [
gray_img,
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
]
# plot different image rotations
__A : int = plt.figure(1)
__A : int = ["""Original""", """Rotation 1""", """Rotation 2""", """Rotation 3"""]
for i, image in enumerate(images):
plt.subplot(2, 2, i + 1), plt.imshow(image, """gray""")
plt.title(titles[i])
plt.axis("""off""")
plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
plt.show()
| 187 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import ConvNextBackbone, ConvNextForImageClassification, ConvNextModel
from transformers.models.convnext.modeling_convnext import CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __UpperCamelCase :
def __init__( self : Optional[int] , _lowerCAmelCase : List[str] , _lowerCAmelCase : int=13 , _lowerCAmelCase : Any=32 , _lowerCAmelCase : str=3 , _lowerCAmelCase : int=4 , _lowerCAmelCase : Optional[int]=[10, 20, 30, 40] , _lowerCAmelCase : Optional[Any]=[2, 2, 3, 2] , _lowerCAmelCase : List[str]=True , _lowerCAmelCase : int=True , _lowerCAmelCase : List[str]=37 , _lowerCAmelCase : List[str]="gelu" , _lowerCAmelCase : List[Any]=10 , _lowerCAmelCase : int=0.02 , _lowerCAmelCase : str=["stage2", "stage3", "stage4"] , _lowerCAmelCase : Dict=[2, 3, 4] , _lowerCAmelCase : Tuple=None , ) -> Any:
"""simple docstring"""
__lowercase = parent
__lowercase = batch_size
__lowercase = image_size
__lowercase = num_channels
__lowercase = num_stages
__lowercase = hidden_sizes
__lowercase = depths
__lowercase = is_training
__lowercase = use_labels
__lowercase = intermediate_size
__lowercase = hidden_act
__lowercase = num_labels
__lowercase = initializer_range
__lowercase = out_features
__lowercase = out_indices
__lowercase = scope
def _a ( self : Dict ) -> Optional[int]:
"""simple docstring"""
__lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowercase = None
if self.use_labels:
__lowercase = ids_tensor([self.batch_size] , self.num_labels )
__lowercase = self.get_config()
return config, pixel_values, labels
def _a ( self : List[str] ) -> Any:
"""simple docstring"""
return ConvNextConfig(
num_channels=self.num_channels , hidden_sizes=self.hidden_sizes , depths=self.depths , num_stages=self.num_stages , hidden_act=self.hidden_act , is_decoder=_lowerCAmelCase , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def _a ( self : Optional[Any] , _lowerCAmelCase : List[str] , _lowerCAmelCase : Union[str, Any] , _lowerCAmelCase : Tuple ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextModel(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def _a ( self : Optional[int] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : Optional[Any] ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextForImageClassification(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase , labels=_lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _a ( self : List[Any] , _lowerCAmelCase : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify hidden states
self.parent.assertEqual(len(result.feature_maps ) , len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[1], 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) , len(config.out_features ) )
self.parent.assertListEqual(model.channels , config.hidden_sizes[1:] )
# verify backbone works with out_features=None
__lowercase = None
__lowercase = ConvNextBackbone(config=_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
__lowercase = model(_lowerCAmelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) , 1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) , [self.batch_size, self.hidden_sizes[-1], 1, 1] )
# verify channels
self.parent.assertEqual(len(model.channels ) , 1 )
self.parent.assertListEqual(model.channels , [config.hidden_sizes[-1]] )
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = self.prepare_config_and_inputs()
__lowercase , __lowercase , __lowercase = config_and_inputs
__lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCamelCase ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
__snake_case :Optional[Any] = (
(
ConvNextModel,
ConvNextForImageClassification,
ConvNextBackbone,
)
if is_torch_available()
else ()
)
__snake_case :List[str] = (
{'feature-extraction': ConvNextModel, 'image-classification': ConvNextForImageClassification}
if is_torch_available()
else {}
)
__snake_case :str = True
__snake_case :Any = False
__snake_case :Any = False
__snake_case :Any = False
__snake_case :int = False
def _a ( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = ConvNextModelTester(self )
__lowercase = ConfigTester(self , config_class=_lowerCAmelCase , has_text_modality=_lowerCAmelCase , hidden_size=37 )
def _a ( self : Optional[Any] ) -> int:
"""simple docstring"""
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self : Any ) -> Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason="""ConvNext does not use inputs_embeds""" )
def _a ( self : List[Any] ) -> Any:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not support input and output embeddings""" )
def _a ( self : Dict ) -> int:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNext does not use feedforward chunking""" )
def _a ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
pass
def _a ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = model_class(_lowerCAmelCase )
__lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowercase = [*signature.parameters.keys()]
__lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , _lowerCAmelCase )
def _a ( self : Any ) -> List[str]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_lowerCAmelCase )
def _a ( self : Any ) -> Optional[int]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*_lowerCAmelCase )
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
def check_hidden_states_output(_lowerCAmelCase : Tuple , _lowerCAmelCase : int , _lowerCAmelCase : Optional[int] ):
__lowercase = model_class(_lowerCAmelCase )
model.to(_lowerCAmelCase )
model.eval()
with torch.no_grad():
__lowercase = model(**self._prepare_for_class(_lowerCAmelCase , _lowerCAmelCase ) )
__lowercase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowercase = self.model_tester.num_stages
self.assertEqual(len(_lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__lowercase , __lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowercase = True
check_hidden_states_output(_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase )
def _a ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
__lowercase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_lowerCAmelCase )
@slow
def _a ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
for model_name in CONVNEXT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowercase = ConvNextModel.from_pretrained(_lowerCAmelCase )
self.assertIsNotNone(_lowerCAmelCase )
def snake_case ( ):
'''simple docstring'''
__lowercase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __UpperCamelCase ( unittest.TestCase ):
@cached_property
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/convnext-tiny-224""" ) if is_vision_available() else None
@slow
def _a ( self : str ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ConvNextForImageClassification.from_pretrained("""facebook/convnext-tiny-224""" ).to(_lowerCAmelCase )
__lowercase = self.default_image_processor
__lowercase = prepare_img()
__lowercase = image_processor(images=_lowerCAmelCase , return_tensors="""pt""" ).to(_lowerCAmelCase )
# forward pass
with torch.no_grad():
__lowercase = model(**_lowerCAmelCase )
# verify the logits
__lowercase = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , _lowerCAmelCase )
__lowercase = torch.tensor([-0.0_260, -0.4_739, 0.1_911] ).to(_lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , _lowerCAmelCase , atol=1e-4 ) )
@require_torch
class __UpperCamelCase ( unittest.TestCase , _lowerCAmelCase ):
__snake_case :Union[str, Any] = (ConvNextBackbone,) if is_torch_available() else ()
__snake_case :str = ConvNextConfig
__snake_case :Optional[Any] = False
def _a ( self : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase = ConvNextModelTester(self )
| 80 |
import itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_torch_available():
import torch
a : List[Any] = random.Random()
def lowercase_ ( _UpperCamelCase , _UpperCamelCase=1.0 , _UpperCamelCase=None , _UpperCamelCase=None ):
'''simple docstring'''
if rng is None:
__lowercase = global_rng
__lowercase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , snake_case_ , snake_case_=7 , snake_case_=4_0_0 , snake_case_=2_0_0_0 , snake_case_=1 , snake_case_=0.0 , snake_case_=1_6_0_0_0 , snake_case_=True , snake_case_=8_0 , snake_case_=1_6 , snake_case_=6_4 , snake_case_="hann_window" , snake_case_=8_0 , snake_case_=7_6_0_0 , snake_case_=1e-1_0 , snake_case_=True , ) -> Tuple:
'''simple docstring'''
__lowercase = parent
__lowercase = batch_size
__lowercase = min_seq_length
__lowercase = max_seq_length
__lowercase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
__lowercase = feature_size
__lowercase = padding_value
__lowercase = sampling_rate
__lowercase = do_normalize
__lowercase = num_mel_bins
__lowercase = hop_length
__lowercase = win_length
__lowercase = win_function
__lowercase = fmin
__lowercase = fmax
__lowercase = mel_floor
__lowercase = return_attention_mask
def A ( self ) -> str:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"do_normalize": self.do_normalize,
"num_mel_bins": self.num_mel_bins,
"hop_length": self.hop_length,
"win_length": self.win_length,
"win_function": self.win_function,
"fmin": self.fmin,
"fmax": self.fmax,
"mel_floor": self.mel_floor,
"return_attention_mask": self.return_attention_mask,
}
def A ( self , snake_case_=False , snake_case_=False ) -> Tuple:
'''simple docstring'''
def _flatten(snake_case_ ):
return list(itertools.chain(*snake_case_ ) )
if equal_length:
__lowercase = floats_list((self.batch_size, self.max_seq_length) )
else:
# make sure that inputs increase in size
__lowercase = [
_flatten(floats_list((x, self.feature_size) ) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__lowercase = [np.asarray(snake_case_ ) for x in speech_inputs]
return speech_inputs
def A ( self , snake_case_=False , snake_case_=False ) -> Any:
'''simple docstring'''
if equal_length:
__lowercase = [floats_list((self.max_seq_length, self.num_mel_bins) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
__lowercase = [
floats_list((x, self.num_mel_bins) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
__lowercase = [np.asarray(snake_case_ ) for x in speech_inputs]
return speech_inputs
@require_torch
class lowerCamelCase_ ( lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase = SpeechTaFeatureExtractor
def A ( self ) -> Dict:
'''simple docstring'''
__lowercase = SpeechTaFeatureExtractionTester(self )
def A ( self , snake_case_ ) -> str:
'''simple docstring'''
self.assertTrue(np.all(np.mean(snake_case_ , axis=0 ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(snake_case_ , axis=0 ) - 1 ) < 1e-3 ) )
def A ( self ) -> int:
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__lowercase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
__lowercase = [np.asarray(snake_case_ ) for speech_input in speech_inputs]
# Test not batched input
__lowercase = feat_extract(speech_inputs[0] , return_tensors='''np''' ).input_values
__lowercase = feat_extract(np_speech_inputs[0] , return_tensors='''np''' ).input_values
self.assertTrue(np.allclose(snake_case_ , snake_case_ , atol=1e-3 ) )
# Test batched
__lowercase = feat_extract(snake_case_ , return_tensors='''np''' ).input_values
__lowercase = feat_extract(snake_case_ , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(snake_case_ , snake_case_ ):
self.assertTrue(np.allclose(snake_case_ , snake_case_ , atol=1e-3 ) )
def A ( self ) -> Any:
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__lowercase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
__lowercase = ['''longest''', '''max_length''', '''do_not_pad''']
__lowercase = [None, 1_6_0_0, None]
for max_length, padding in zip(snake_case_ , snake_case_ ):
__lowercase = feat_extract(snake_case_ , padding=snake_case_ , max_length=snake_case_ , return_tensors='''np''' )
__lowercase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self.assertTrue(input_values[0][8_0_0:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self.assertTrue(input_values[0][1_0_0_0:].sum() < 1e-6 )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def A ( self ) -> List[str]:
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__lowercase = range(8_0_0 , 1_4_0_0 , 2_0_0 )
__lowercase = [floats_list((1, x) )[0] for x in lengths]
__lowercase = ['''longest''', '''max_length''', '''do_not_pad''']
__lowercase = [None, 1_6_0_0, None]
for max_length, padding in zip(snake_case_ , snake_case_ ):
__lowercase = feat_extract(snake_case_ , max_length=snake_case_ , padding=snake_case_ )
__lowercase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0][:8_0_0] )
self._check_zero_mean_unit_variance(input_values[1][:1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2][:1_2_0_0] )
def A ( self ) -> List[Any]:
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__lowercase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
__lowercase = feat_extract(
snake_case_ , truncation=snake_case_ , max_length=1_0_0_0 , padding='''max_length''' , return_tensors='''np''' )
__lowercase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1] )
self._check_zero_mean_unit_variance(input_values[2] )
def A ( self ) -> List[str]:
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__lowercase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
__lowercase = feat_extract(
snake_case_ , truncation=snake_case_ , max_length=1_0_0_0 , padding='''longest''' , return_tensors='''np''' )
__lowercase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length < longest -> then pad to max_length
self.assertTrue(input_values.shape == (3, 1_0_0_0) )
__lowercase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
__lowercase = feat_extract(
snake_case_ , truncation=snake_case_ , max_length=2_0_0_0 , padding='''longest''' , return_tensors='''np''' )
__lowercase = processed.input_values
self._check_zero_mean_unit_variance(input_values[0, :8_0_0] )
self._check_zero_mean_unit_variance(input_values[1, :1_0_0_0] )
self._check_zero_mean_unit_variance(input_values[2] )
# make sure that if max_length > longest -> then pad to longest
self.assertTrue(input_values.shape == (3, 1_2_0_0) )
def A ( self ) -> str:
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
__lowercase = np.random.rand(1_0_0 ).astype(np.floataa )
__lowercase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
__lowercase = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_values.dtype == np.floataa )
__lowercase = feature_extractor.pad([{'''input_values''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_values.dtype == torch.floataa )
def A ( self ) -> List[str]:
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
__lowercase = [floats_list((1, x) )[0] for x in range(8_0_0 , 1_4_0_0 , 2_0_0 )]
__lowercase = [np.asarray(snake_case_ ) for speech_input in speech_inputs]
# Test feature size
__lowercase = feature_extractor(audio_target=snake_case_ , padding=snake_case_ , return_tensors='''np''' ).input_values
self.assertTrue(input_values.ndim == 3 )
self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins )
# Test not batched input
__lowercase = feature_extractor(speech_inputs[0] , return_tensors='''np''' ).input_values
__lowercase = feature_extractor(np_speech_inputs[0] , return_tensors='''np''' ).input_values
self.assertTrue(np.allclose(snake_case_ , snake_case_ , atol=1e-3 ) )
# Test batched
__lowercase = feature_extractor(snake_case_ , return_tensors='''np''' ).input_values
__lowercase = feature_extractor(snake_case_ , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(snake_case_ , snake_case_ ):
self.assertTrue(np.allclose(snake_case_ , snake_case_ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
__lowercase = [floats_list((1, x) )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
__lowercase = np.asarray(snake_case_ )
__lowercase = feature_extractor(snake_case_ , return_tensors='''np''' ).input_values
__lowercase = feature_extractor(snake_case_ , return_tensors='''np''' ).input_values
for enc_seq_a, enc_seq_a in zip(snake_case_ , snake_case_ ):
self.assertTrue(np.allclose(snake_case_ , snake_case_ , atol=1e-3 ) )
def A ( self ) -> Tuple:
'''simple docstring'''
__lowercase = self.feat_extract_tester.prepare_inputs_for_target()
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
self.assertTrue(all(len(snake_case_ ) == len(snake_case_ ) for x, y in zip(snake_case_ , processed_features[input_name] ) ) )
__lowercase = self.feat_extract_tester.prepare_inputs_for_target(equal_length=snake_case_ )
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type='''np''' )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def A ( self ) -> List[str]:
'''simple docstring'''
__lowercase = self.feat_extract_tester.prepare_inputs_for_target(equal_length=snake_case_ )
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} , tensor_type='''pt''' )
__lowercase = processed_features[input_name]
if len(batch_features_input.shape ) < 3:
__lowercase = batch_features_input[:, :, None]
self.assertTrue(
batch_features_input.shape
== (self.feat_extract_tester.batch_size, len(speech_inputs[0] ), self.feat_extract_tester.num_mel_bins) )
@require_torch
def A ( self ) -> Tuple:
'''simple docstring'''
__lowercase = self.feature_extraction_class(**self.feat_extract_dict )
__lowercase = self.feat_extract_tester.prepare_inputs_for_target()
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.num_mel_bins # hack!
__lowercase = feat_extract.pad(snake_case_ , padding='''longest''' , return_tensors='''np''' )[input_name]
__lowercase = feat_extract.pad(snake_case_ , padding='''longest''' , return_tensors='''pt''' )[input_name]
self.assertTrue(abs(input_np.astype(np.floataa ).sum() - input_pt.numpy().astype(np.floataa ).sum() ) < 1e-2 )
def A ( self ) -> Dict:
'''simple docstring'''
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**snake_case_ )
__lowercase = self.feat_extract_tester.prepare_inputs_for_target()
__lowercase = [len(snake_case_ ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = feat_extract.num_mel_bins # hack!
__lowercase = feat_extract.pad(snake_case_ , padding='''longest''' , return_tensors='''np''' )
self.assertIn('''attention_mask''' , snake_case_ )
self.assertListEqual(list(processed.attention_mask.shape ) , list(processed[input_name].shape[:2] ) )
self.assertListEqual(processed.attention_mask.sum(-1 ).tolist() , snake_case_ )
def A ( self ) -> Optional[Any]:
'''simple docstring'''
__lowercase = self.feat_extract_dict
__lowercase = True
__lowercase = self.feature_extraction_class(**snake_case_ )
__lowercase = self.feat_extract_tester.prepare_inputs_for_target()
__lowercase = [len(snake_case_ ) for x in speech_inputs]
__lowercase = feat_extract.model_input_names[0]
__lowercase = BatchFeature({input_name: speech_inputs} )
__lowercase = min(snake_case_ )
__lowercase = feat_extract.num_mel_bins # hack!
__lowercase = feat_extract.pad(
snake_case_ , padding='''max_length''' , max_length=snake_case_ , truncation=snake_case_ , return_tensors='''np''' )
self.assertIn('''attention_mask''' , snake_case_ )
self.assertListEqual(
list(processed_pad.attention_mask.shape ) , [processed_pad[input_name].shape[0], max_length] )
self.assertListEqual(
processed_pad.attention_mask[:, :max_length].sum(-1 ).tolist() , [max_length for x in speech_inputs] )
def A ( self , snake_case_ ) -> str:
'''simple docstring'''
from datasets import load_dataset
__lowercase = load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
__lowercase = ds.sort('''id''' ).select(range(snake_case_ ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def A ( self ) -> Optional[int]:
'''simple docstring'''
__lowercase = torch.tensor(
[2.3_8_0_4e-0_3, 2.0_7_5_2e-0_3, 1.9_8_3_6e-0_3, 2.1_0_5_7e-0_3, 1.6_1_7_4e-0_3,
3.0_5_1_8e-0_4, 9.1_5_5_3e-0_5, 3.3_5_6_9e-0_4, 9.7_6_5_6e-0_4, 1.8_3_1_1e-0_3,
2.0_1_4_2e-0_3, 2.1_0_5_7e-0_3, 1.7_3_9_5e-0_3, 4.5_7_7_6e-0_4, -3.9_6_7_3e-0_4,
4.5_7_7_6e-0_4, 1.0_0_7_1e-0_3, 9.1_5_5_3e-0_5, 4.8_8_2_8e-0_4, 1.1_5_9_7e-0_3,
7.3_2_4_2e-0_4, 9.4_6_0_4e-0_4, 1.8_0_0_5e-0_3, 1.8_3_1_1e-0_3, 8.8_5_0_1e-0_4,
4.2_7_2_5e-0_4, 4.8_8_2_8e-0_4, 7.3_2_4_2e-0_4, 1.0_9_8_6e-0_3, 2.1_0_5_7e-0_3] )
# fmt: on
__lowercase = self._load_datasamples(1 )
__lowercase = SpeechTaFeatureExtractor()
__lowercase = feature_extractor(snake_case_ , return_tensors='''pt''' ).input_values
self.assertEquals(input_values.shape , (1, 9_3_6_8_0) )
self.assertTrue(torch.allclose(input_values[0, :3_0] , snake_case_ , atol=1e-6 ) )
def A ( self ) -> str:
'''simple docstring'''
__lowercase = torch.tensor(
[-2.6_8_7_0, -3.0_1_0_4, -3.1_3_5_6, -3.5_3_5_2, -3.0_0_4_4, -3.0_3_5_3, -3.4_7_1_9, -3.6_7_7_7,
-3.1_5_2_0, -2.9_4_3_5, -2.6_5_5_3, -2.8_7_9_5, -2.9_9_4_4, -2.5_9_2_1, -3.0_2_7_9, -3.0_3_8_6,
-3.0_8_6_4, -3.1_2_9_1, -3.2_3_5_3, -2.7_4_4_4, -2.6_8_3_1, -2.7_2_8_7, -3.1_7_6_1, -3.1_5_7_1,
-3.2_7_2_6, -3.0_5_8_2, -3.1_0_0_7, -3.4_5_3_3, -3.4_6_9_5, -3.0_9_9_8] )
# fmt: on
__lowercase = self._load_datasamples(1 )
__lowercase = SpeechTaFeatureExtractor()
__lowercase = feature_extractor(audio_target=snake_case_ , return_tensors='''pt''' ).input_values
self.assertEquals(input_values.shape , (1, 3_6_6, 8_0) )
self.assertTrue(torch.allclose(input_values[0, 0, :3_0] , snake_case_ , atol=1e-4 ) )
| 639 | 0 |
'''simple docstring'''
import io
import json
import fsspec
import pytest
from datasets import Dataset, DatasetDict, Features, NamedSplit, Value
from datasets.io.json import JsonDatasetReader, JsonDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def _snake_case ( lowercase , lowercase ) -> Union[str, Any]:
assert isinstance(lowercase , lowercase )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def _snake_case ( lowercase , lowercase , lowercase ) -> Dict:
__a : Optional[int] = tmp_path / """cache"""
__a : Optional[int] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__a : List[str] = JsonDatasetReader(lowercase , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_json_dataset(lowercase , lowercase )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def _snake_case ( lowercase , lowercase , lowercase ) -> Any:
__a : int = tmp_path / """cache"""
__a : Dict = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
__a : Union[str, Any] = features.copy() if features else default_expected_features
__a : Optional[Any] = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
__a : List[Any] = JsonDatasetReader(lowercase , features=lowercase , cache_dir=lowercase ).read()
_check_json_dataset(lowercase , lowercase )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""},
] , )
def _snake_case ( lowercase , lowercase , lowercase ) -> Any:
__a : Tuple = tmp_path / """cache"""
__a : Union[str, Any] = {"""col_3""": """float64""", """col_1""": """string""", """col_2""": """int64"""}
__a : Tuple = features.copy() if features else default_expected_features
__a : List[Any] = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
__a : str = JsonDatasetReader(lowercase , features=lowercase , cache_dir=lowercase ).read()
assert isinstance(lowercase , lowercase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_3", "col_1", "col_2"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
def _snake_case ( lowercase , lowercase ) -> str:
# jsonl_312_path features are {"col_3": "float64", "col_1": "string", "col_2": "int64"}
__a : int = {"""col_2""": """int64""", """col_3""": """float64""", """col_1""": """string"""}
__a : List[str] = features.copy()
__a : str = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
__a : Tuple = tmp_path / """cache"""
__a : Any = JsonDatasetReader(lowercase , features=lowercase , cache_dir=lowercase ).read()
assert isinstance(lowercase , lowercase )
assert dataset.num_rows == 2
assert dataset.num_columns == 3
assert dataset.column_names == ["col_2", "col_3", "col_1"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def _snake_case ( lowercase , lowercase , lowercase ) -> str:
__a : List[Any] = tmp_path / """cache"""
__a : List[str] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
__a : List[str] = JsonDatasetReader(lowercase , cache_dir=lowercase , split=lowercase ).read()
_check_json_dataset(lowercase , lowercase )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def _snake_case ( lowercase , lowercase , lowercase ) -> List[Any]:
if issubclass(lowercase , lowercase ):
__a : str = jsonl_path
elif issubclass(lowercase , lowercase ):
__a : Tuple = [jsonl_path]
__a : Any = tmp_path / """cache"""
__a : Tuple = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
__a : Optional[int] = JsonDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_json_dataset(lowercase , lowercase )
def _snake_case ( lowercase , lowercase , lowercase=("train",) ) -> Optional[int]:
assert isinstance(lowercase , lowercase )
for split in splits:
__a : Optional[int] = dataset_dict[split]
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@pytest.mark.parametrize("""keep_in_memory""" , [False, True] )
def _snake_case ( lowercase , lowercase , lowercase ) -> List[Any]:
__a : Optional[int] = tmp_path / """cache"""
__a : List[str] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
__a : int = JsonDatasetReader({"""train""": jsonl_path} , cache_dir=lowercase , keep_in_memory=lowercase ).read()
_check_json_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize(
"""features""" , [
None,
{"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""},
{"""col_1""": """string""", """col_2""": """string""", """col_3""": """string"""},
{"""col_1""": """int32""", """col_2""": """int32""", """col_3""": """int32"""},
{"""col_1""": """float32""", """col_2""": """float32""", """col_3""": """float32"""},
] , )
def _snake_case ( lowercase , lowercase , lowercase ) -> Any:
__a : str = tmp_path / """cache"""
__a : int = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
__a : str = features.copy() if features else default_expected_features
__a : List[str] = (
Features({feature: Value(lowercase ) for feature, dtype in features.items()} ) if features is not None else None
)
__a : List[str] = JsonDatasetReader({"""train""": jsonl_path} , features=lowercase , cache_dir=lowercase ).read()
_check_json_datasetdict(lowercase , lowercase )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def _snake_case ( lowercase , lowercase , lowercase ) -> Dict:
if split:
__a : str = {split: jsonl_path}
else:
__a : Dict = """train"""
__a : Optional[int] = {"""train""": jsonl_path, """test""": jsonl_path}
__a : int = tmp_path / """cache"""
__a : Dict = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
__a : Optional[int] = JsonDatasetReader(lowercase , cache_dir=lowercase ).read()
_check_json_datasetdict(lowercase , lowercase , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def _snake_case ( lowercase ) -> Any:
return json.load(lowercase )
def _snake_case ( lowercase ) -> Union[str, Any]:
return [json.loads(lowercase ) for line in buffer]
class SCREAMING_SNAKE_CASE__ :
@pytest.mark.parametrize("""lines, load_json_function""" , [(True, load_json_lines), (False, load_json)] )
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(__UpperCamelCase , __UpperCamelCase , lines=__UpperCamelCase ).write()
buffer.seek(0 )
__a : List[str] = load_json_function(__UpperCamelCase )
assert isinstance(__UpperCamelCase , __UpperCamelCase )
assert isinstance(exported_content[0] , __UpperCamelCase )
assert len(__UpperCamelCase ) == 10
@pytest.mark.parametrize(
"""orient, container, keys, len_at""" , [
("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
("""split""", dict, {"""columns""", """data"""}, """data"""),
("""index""", dict, set("""0123456789""" ), None),
("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
("""values""", list, None, None),
("""table""", dict, {"""schema""", """data"""}, """data"""),
] , )
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(__UpperCamelCase , __UpperCamelCase , lines=__UpperCamelCase , orient=__UpperCamelCase ).write()
buffer.seek(0 )
__a : List[str] = load_json(__UpperCamelCase )
assert isinstance(__UpperCamelCase , __UpperCamelCase )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(__UpperCamelCase , """keys""" ) and not hasattr(exported_content[0] , """keys""" )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(__UpperCamelCase ) == 10
@pytest.mark.parametrize("""lines, load_json_function""" , [(True, load_json_lines), (False, load_json)] )
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(__UpperCamelCase , __UpperCamelCase , lines=__UpperCamelCase , num_proc=2 ).write()
buffer.seek(0 )
__a : List[str] = load_json_function(__UpperCamelCase )
assert isinstance(__UpperCamelCase , __UpperCamelCase )
assert isinstance(exported_content[0] , __UpperCamelCase )
assert len(__UpperCamelCase ) == 10
@pytest.mark.parametrize(
"""orient, container, keys, len_at""" , [
("""records""", list, {"""tokens""", """labels""", """answers""", """id"""}, None),
("""split""", dict, {"""columns""", """data"""}, """data"""),
("""index""", dict, set("""0123456789""" ), None),
("""columns""", dict, {"""tokens""", """labels""", """answers""", """id"""}, """tokens"""),
("""values""", list, None, None),
("""table""", dict, {"""schema""", """data"""}, """data"""),
] , )
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
with io.BytesIO() as buffer:
JsonDatasetWriter(__UpperCamelCase , __UpperCamelCase , lines=__UpperCamelCase , orient=__UpperCamelCase , num_proc=2 ).write()
buffer.seek(0 )
__a : Any = load_json(__UpperCamelCase )
assert isinstance(__UpperCamelCase , __UpperCamelCase )
if keys:
if container is dict:
assert exported_content.keys() == keys
else:
assert exported_content[0].keys() == keys
else:
assert not hasattr(__UpperCamelCase , """keys""" ) and not hasattr(exported_content[0] , """keys""" )
if len_at:
assert len(exported_content[len_at] ) == 10
else:
assert len(__UpperCamelCase ) == 10
def __lowerCamelCase ( self , __UpperCamelCase ):
'''simple docstring'''
with pytest.raises(__UpperCamelCase ):
with io.BytesIO() as buffer:
JsonDatasetWriter(__UpperCamelCase , __UpperCamelCase , num_proc=0 )
@pytest.mark.parametrize("""compression, extension""" , [("""gzip""", """gz"""), ("""bz2""", """bz2"""), ("""xz""", """xz""")] )
def __lowerCamelCase ( self , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
'''simple docstring'''
__a : Tuple = tmp_path_factory.mktemp("""data""" ) / f"""test.json.{extension}"""
__a : Optional[Any] = str(shared_datadir / f"""test_file.json.{extension}""" )
JsonDatasetWriter(__UpperCamelCase , __UpperCamelCase , compression=__UpperCamelCase ).write()
with fsspec.open(__UpperCamelCase , """rb""" , compression="""infer""" ) as f:
__a : Optional[int] = f.read()
with fsspec.open(__UpperCamelCase , """rb""" , compression="""infer""" ) as f:
__a : Dict = f.read()
assert exported_content == original_content | 697 |
'''simple docstring'''
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE : Optional[Any] = {'configuration_focalnet': ['FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FocalNetConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : List[Any] = [
'FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST',
'FocalNetForImageClassification',
'FocalNetForMaskedImageModeling',
'FocalNetBackbone',
'FocalNetModel',
'FocalNetPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_focalnet import FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP, FocalNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_focalnet import (
FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST,
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
FocalNetPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[int] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 697 | 1 |
from ...utils import is_torch_available, is_transformers_available
if is_transformers_available() and is_torch_available():
from .pipeline_vq_diffusion import LearnedClassifierFreeSamplingEmbeddings, VQDiffusionPipeline
| 47 |
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE__ = TypeVar('''T''')
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
return (position - 1) // 2
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
return (2 * position) + 1
def UpperCAmelCase__ ( lowerCamelCase_ : int ):
return (2 * position) + 2
class _UpperCamelCase( Generic[T] ):
def __init__( self : List[str] ):
'''simple docstring'''
__a : list[tuple[T, int]] = []
__a : dict[T, int] = {}
__a : int = 0
def __len__( self : Any ):
'''simple docstring'''
return self.elements
def __repr__( self : Any ):
'''simple docstring'''
return str(self.heap )
def __lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
return self.elements == 0
def __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
self.heap.append((elem, weight) )
__a : List[Any] = self.elements
self.elements += 1
self._bubble_up(SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
if self.elements > 1:
self._swap_nodes(0 , self.elements - 1 )
__a , __a : Union[str, Any] = self.heap.pop()
del self.position_map[elem]
self.elements -= 1
if self.elements > 0:
__a , __a : Dict = self.heap[0]
self._bubble_down(SCREAMING_SNAKE_CASE__ )
return elem
def __lowerCAmelCase ( self : List[str] , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
__a : List[Any] = self.position_map[elem]
__a : str = (elem, weight)
if position > 0:
__a : Tuple = get_parent_position(SCREAMING_SNAKE_CASE__ )
__a , __a : Dict = self.heap[parent_position]
if parent_weight > weight:
self._bubble_up(SCREAMING_SNAKE_CASE__ )
else:
self._bubble_down(SCREAMING_SNAKE_CASE__ )
else:
self._bubble_down(SCREAMING_SNAKE_CASE__ )
def __lowerCAmelCase ( self : Any , SCREAMING_SNAKE_CASE__ : T ):
'''simple docstring'''
__a : List[Any] = self.position_map[elem]
if curr_pos == 0:
return None
__a : List[str] = get_parent_position(SCREAMING_SNAKE_CASE__ )
__a , __a : str = self.heap[curr_pos]
__a , __a : Optional[int] = self.heap[parent_position]
if parent_weight > weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_up(SCREAMING_SNAKE_CASE__ )
return None
def __lowerCAmelCase ( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : T ):
'''simple docstring'''
__a : int = self.position_map[elem]
__a , __a : Optional[Any] = self.heap[curr_pos]
__a : Tuple = get_child_left_position(SCREAMING_SNAKE_CASE__ )
__a : Optional[Any] = get_child_right_position(SCREAMING_SNAKE_CASE__ )
if child_left_position < self.elements and child_right_position < self.elements:
__a , __a : str = self.heap[child_left_position]
__a , __a : List[str] = self.heap[child_right_position]
if child_right_weight < child_left_weight and child_right_weight < weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_down(SCREAMING_SNAKE_CASE__ )
if child_left_position < self.elements:
__a , __a : Any = self.heap[child_left_position]
if child_left_weight < weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_down(SCREAMING_SNAKE_CASE__ )
else:
return None
if child_right_position < self.elements:
__a , __a : Union[str, Any] = self.heap[child_right_position]
if child_right_weight < weight:
self._swap_nodes(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return self._bubble_down(SCREAMING_SNAKE_CASE__ )
return None
def __lowerCAmelCase ( self : List[Any] , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
__a : Optional[Any] = self.heap[nodea_pos][0]
__a : str = self.heap[nodea_pos][0]
__a , __a : int = (
self.heap[nodea_pos],
self.heap[nodea_pos],
)
__a : str = nodea_pos
__a : Optional[int] = nodea_pos
class _UpperCamelCase( Generic[T] ):
def __init__( self : List[Any] ):
'''simple docstring'''
__a : dict[T, dict[T, int]] = {}
__a : int = 0
def __repr__( self : Tuple ):
'''simple docstring'''
return str(self.connections )
def __len__( self : Dict ):
'''simple docstring'''
return self.nodes
def __lowerCAmelCase ( self : Optional[int] , SCREAMING_SNAKE_CASE__ : T ):
'''simple docstring'''
if node not in self.connections:
__a : Tuple = {}
self.nodes += 1
def __lowerCAmelCase ( self : Any , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : T , SCREAMING_SNAKE_CASE__ : int ):
'''simple docstring'''
self.add_node(SCREAMING_SNAKE_CASE__ )
self.add_node(SCREAMING_SNAKE_CASE__ )
__a : Optional[Any] = weight
__a : Any = weight
def UpperCAmelCase__ ( lowerCamelCase_ : GraphUndirectedWeighted[T] , ):
__a : dict[T, int] = {node: maxsize for node in graph.connections}
__a : dict[T, T | None] = {node: None for node in graph.connections}
__a : MinPriorityQueue[T] = MinPriorityQueue()
for node, weight in dist.items():
priority_queue.push(lowerCamelCase_ , lowerCamelCase_ )
if priority_queue.is_empty():
return dist, parent
# initialization
__a : Optional[int] = priority_queue.extract_min()
__a : int = 0
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
__a : str = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(lowerCamelCase_ , dist[neighbour] )
__a : Optional[int] = node
# running prim's algorithm
while not priority_queue.is_empty():
__a : Any = priority_queue.extract_min()
for neighbour in graph.connections[node]:
if dist[neighbour] > dist[node] + graph.connections[node][neighbour]:
__a : Tuple = dist[node] + graph.connections[node][neighbour]
priority_queue.update_key(lowerCamelCase_ , dist[neighbour] )
__a : Dict = node
return dist, parent
| 47 | 1 |
from jiwer import compute_measures
import datasets
__SCREAMING_SNAKE_CASE : Union[str, Any] = '\\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'
__SCREAMING_SNAKE_CASE : Optional[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'
__SCREAMING_SNAKE_CASE : Any = '\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 lowercase_ ( datasets.Metric ):
def UpperCamelCase ( self ):
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 , lowercase_=None , lowercase_=None , lowercase_=False ):
if concatenate_texts:
return compute_measures(lowercase_ , lowercase_ )["wer"]
else:
_snake_case : List[str] = 0
_snake_case : List[str] = 0
for prediction, reference in zip(lowercase_ , lowercase_ ):
_snake_case : Union[str, Any] = compute_measures(lowercase_ , lowercase_ )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total | 580 | import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class lowercase_ :
def __init__( self , lowercase_ , lowercase_=None , lowercase_=None , lowercase_=None , lowercase_="resnet50" , lowercase_=3 , lowercase_=32 , lowercase_=3 , lowercase_=True , lowercase_=True , ):
_snake_case : Any = parent
_snake_case : int = out_indices if out_indices is not None else [4]
_snake_case : Any = stage_names
_snake_case : Optional[Any] = out_features
_snake_case : Dict = backbone
_snake_case : List[str] = batch_size
_snake_case : Optional[int] = image_size
_snake_case : str = num_channels
_snake_case : Optional[Any] = use_pretrained_backbone
_snake_case : str = is_training
def UpperCamelCase ( self ):
_snake_case : Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_snake_case : List[str] = self.get_config()
return config, pixel_values
def UpperCamelCase ( self ):
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def UpperCamelCase ( self , lowercase_ , lowercase_ ):
_snake_case : Dict = TimmBackbone(config=lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
_snake_case : List[Any] = model(lowercase_ )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 14, 14) , )
def UpperCamelCase ( self ):
_snake_case : Dict = self.prepare_config_and_inputs()
_snake_case ,_snake_case : List[Any] = config_and_inputs
_snake_case : int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class lowercase_ ( __snake_case , __snake_case , __snake_case , unittest.TestCase ):
_lowerCamelCase = (TimmBackbone,) if is_torch_available() else ()
_lowerCamelCase = {'feature-extraction': TimmBackbone} if is_torch_available() else {}
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
_lowerCamelCase = False
def UpperCamelCase ( self ):
_snake_case : Dict = TimmBackboneModelTester(self )
_snake_case : Optional[int] = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ )
def UpperCamelCase ( self ):
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 UpperCamelCase ( self ):
_snake_case : Dict = "resnet18"
_snake_case : Tuple = "microsoft/resnet-18"
_snake_case : Tuple = AutoBackbone.from_pretrained(lowercase_ , use_timm_backbone=lowercase_ )
_snake_case : List[str] = AutoBackbone.from_pretrained(lowercase_ )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
_snake_case : List[str] = AutoBackbone.from_pretrained(lowercase_ , use_timm_backbone=lowercase_ , out_indices=[1, 2, 3] )
_snake_case : Optional[int] = AutoBackbone.from_pretrained(lowercase_ , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip("TimmBackbone doesn't support feed forward chunking" )
def UpperCamelCase ( self ):
pass
@unittest.skip("TimmBackbone doesn't have num_hidden_layers attribute" )
def UpperCamelCase ( self ):
pass
@unittest.skip("TimmBackbone initialization is managed on the timm side" )
def UpperCamelCase ( self ):
pass
@unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
def UpperCamelCase ( self ):
pass
@unittest.skip("TimmBackbone models doesn't have inputs_embeds" )
def UpperCamelCase ( self ):
pass
@unittest.skip("TimmBackbone model cannot be created without specifying a backbone checkpoint" )
def UpperCamelCase ( self ):
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def UpperCamelCase ( self ):
pass
@unittest.skip("model weights aren't tied in TimmBackbone." )
def UpperCamelCase ( self ):
pass
@unittest.skip("model weights aren't tied in TimmBackbone." )
def UpperCamelCase ( self ):
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def UpperCamelCase ( self ):
pass
@unittest.skip("Only checkpoints on timm can be loaded into TimmBackbone" )
def UpperCamelCase ( self ):
pass
@unittest.skip("TimmBackbone doesn't have hidden size info in its configuration." )
def UpperCamelCase ( self ):
pass
@unittest.skip("TimmBackbone doesn't support output_attentions." )
def UpperCamelCase ( self ):
pass
@unittest.skip("Safetensors is not supported by timm." )
def UpperCamelCase ( self ):
pass
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def UpperCamelCase ( self ):
pass
def UpperCamelCase ( self ):
_snake_case ,_snake_case : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : Tuple = model_class(lowercase_ )
_snake_case : Optional[int] = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_snake_case : Union[str, Any] = [*signature.parameters.keys()]
_snake_case : Optional[int] = ["pixel_values"]
self.assertListEqual(arg_names[:1] , lowercase_ )
def UpperCamelCase ( self ):
_snake_case ,_snake_case : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
_snake_case : Tuple = True
_snake_case : Optional[Any] = self.has_attentions
# no need to test all models as different heads yield the same functionality
_snake_case : Dict = self.all_model_classes[0]
_snake_case : List[Any] = model_class(lowercase_ )
model.to(lowercase_ )
_snake_case : List[str] = self._prepare_for_class(lowercase_ , lowercase_ )
_snake_case : List[Any] = model(**lowercase_ )
_snake_case : Optional[int] = outputs[0][-1]
# Encoder-/Decoder-only models
_snake_case : List[Any] = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
_snake_case : Union[str, Any] = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=lowercase_ )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def UpperCamelCase ( self ):
_snake_case ,_snake_case : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_snake_case : int = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
_snake_case : Any = model(**lowercase_ )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
_snake_case : Union[str, Any] = copy.deepcopy(lowercase_ )
_snake_case : int = None
_snake_case : Optional[int] = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
_snake_case : int = model(**lowercase_ )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
_snake_case : Dict = copy.deepcopy(lowercase_ )
_snake_case : Dict = False
_snake_case : List[Any] = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
_snake_case : Any = model(**lowercase_ ) | 580 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ :List[str] = logging.get_logger(__name__)
UpperCamelCase__ :str = {
"""transfo-xl-wt103""": """https://huggingface.co/transfo-xl-wt103/resolve/main/config.json""",
}
class A( lowerCamelCase__ ):
"""simple docstring"""
A = "transfo-xl"
A = ["mems"]
A = {
"n_token": "vocab_size",
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , SCREAMING_SNAKE_CASE__=26_77_35 , SCREAMING_SNAKE_CASE__=[2_00_00, 4_00_00, 20_00_00] , SCREAMING_SNAKE_CASE__=10_24 , SCREAMING_SNAKE_CASE__=10_24 , SCREAMING_SNAKE_CASE__=16 , SCREAMING_SNAKE_CASE__=64 , SCREAMING_SNAKE_CASE__=40_96 , SCREAMING_SNAKE_CASE__=4 , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=18 , SCREAMING_SNAKE_CASE__=16_00 , SCREAMING_SNAKE_CASE__=10_00 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=0 , SCREAMING_SNAKE_CASE__=-1 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=0.1 , SCREAMING_SNAKE_CASE__=0.0 , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__="normal" , SCREAMING_SNAKE_CASE__=0.0_1 , SCREAMING_SNAKE_CASE__=0.0_1 , SCREAMING_SNAKE_CASE__=0.0_2 , SCREAMING_SNAKE_CASE__=1E-5 , SCREAMING_SNAKE_CASE__=0 , **SCREAMING_SNAKE_CASE__ , ) -> Union[str, Any]:
"""simple docstring"""
_UpperCamelCase :str = vocab_size
_UpperCamelCase :Optional[int] = []
self.cutoffs.extend(SCREAMING_SNAKE_CASE__ )
if proj_share_all_but_first:
_UpperCamelCase :Tuple = [False] + [True] * len(self.cutoffs )
else:
_UpperCamelCase :Optional[Any] = [False] + [False] * len(self.cutoffs )
_UpperCamelCase :Tuple = d_model
_UpperCamelCase :int = d_embed
_UpperCamelCase :List[str] = d_head
_UpperCamelCase :Dict = d_inner
_UpperCamelCase :List[str] = div_val
_UpperCamelCase :Dict = pre_lnorm
_UpperCamelCase :Tuple = n_layer
_UpperCamelCase :List[str] = n_head
_UpperCamelCase :Any = mem_len
_UpperCamelCase :Optional[int] = same_length
_UpperCamelCase :Optional[Any] = attn_type
_UpperCamelCase :List[Any] = clamp_len
_UpperCamelCase :Dict = sample_softmax
_UpperCamelCase :Any = adaptive
_UpperCamelCase :Tuple = dropout
_UpperCamelCase :Optional[int] = dropatt
_UpperCamelCase :Union[str, Any] = untie_r
_UpperCamelCase :int = init
_UpperCamelCase :Dict = init_range
_UpperCamelCase :Dict = proj_init_std
_UpperCamelCase :List[str] = init_std
_UpperCamelCase :Dict = layer_norm_epsilon
super().__init__(eos_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
@property
def _UpperCamelCase( self ) -> int:
"""simple docstring"""
logger.info(f"The model {self.model_type} is one of the few models that has no sequence length limit." )
return -1
@max_position_embeddings.setter
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ ) -> List[Any]:
"""simple docstring"""
raise NotImplementedError(
f"The model {self.model_type} is one of the few models that has no sequence length limit." )
| 355 |
"""simple docstring"""
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS
logging.basicConfig(level=logging.DEBUG)
UpperCamelCase__ :str = logging.getLogger()
def A_ ( snake_case__ , snake_case__ ) -> Optional[Any]:
_UpperCamelCase :Optional[int] = '''\n'''.join(snake_case__ )
Path(snake_case__ ).open('''w''' ).writelines(snake_case__ )
UpperCamelCase__ :Dict = """patrickvonplaten/t5-tiny-random"""
UpperCamelCase__ :List[Any] = """sshleifer/bart-tiny-random"""
UpperCamelCase__ :str = """sshleifer/tiny-mbart"""
UpperCamelCase__ :Any = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL) # remove noisy download output from tracebacks
class A( lowerCamelCase__ ):
"""simple docstring"""
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase :List[Any] = Path(self.get_auto_remove_tmp_dir() ) / '''utest_input.source'''
_UpperCamelCase :Union[str, Any] = input_file_name.parent / '''utest_output.txt'''
assert not output_file_name.exists()
_UpperCamelCase :int = [''' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.''']
_dump_articles(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
_UpperCamelCase :str = str(Path(self.get_auto_remove_tmp_dir() ) / '''scores.json''' )
_UpperCamelCase :Any = '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
_UpperCamelCase :Union[str, Any] = f"\n run_eval_search.py\n {model}\n {input_file_name}\n {output_file_name}\n --score_path {score_path}\n --task {task}\n --num_beams 2\n --length_penalty 2.0\n ".split()
with patch.object(SCREAMING_SNAKE_CASE__ , '''argv''' , SCREAMING_SNAKE_CASE__ ):
run_generate()
assert Path(SCREAMING_SNAKE_CASE__ ).exists()
# os.remove(Path(output_file_name))
def _UpperCamelCase( self ) -> Optional[int]:
"""simple docstring"""
self.run_eval_tester(SCREAMING_SNAKE_CASE__ )
@parameterized.expand([BART_TINY, MBART_TINY] )
@slow
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ ) -> List[str]:
"""simple docstring"""
self.run_eval_tester(SCREAMING_SNAKE_CASE__ )
@parameterized.expand([T5_TINY, MBART_TINY] )
@slow
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase :Dict = Path(self.get_auto_remove_tmp_dir() ) / '''utest_input.source'''
_UpperCamelCase :Any = input_file_name.parent / '''utest_output.txt'''
assert not output_file_name.exists()
_UpperCamelCase :Union[str, Any] = {
'''en''': ['''Machine learning is great, isn\'t it?''', '''I like to eat bananas''', '''Tomorrow is another great day!'''],
'''de''': [
'''Maschinelles Lernen ist großartig, oder?''',
'''Ich esse gerne Bananen''',
'''Morgen ist wieder ein toller Tag!''',
],
}
_UpperCamelCase :str = Path(self.get_auto_remove_tmp_dir() )
_UpperCamelCase :Optional[int] = str(tmp_dir / '''scores.json''' )
_UpperCamelCase :int = str(tmp_dir / '''val.target''' )
_dump_articles(SCREAMING_SNAKE_CASE__ , text['''en'''] )
_dump_articles(SCREAMING_SNAKE_CASE__ , text['''de'''] )
_UpperCamelCase :List[str] = '''translation_en_to_de''' if model == T5_TINY else '''summarization'''
_UpperCamelCase :str = f"\n run_eval_search.py\n {model}\n {str(SCREAMING_SNAKE_CASE__ )}\n {str(SCREAMING_SNAKE_CASE__ )}\n --score_path {score_path}\n --reference_path {reference_path}\n --task {task}\n ".split()
testargs.extend(['''--search''', '''num_beams=1:2 length_penalty=0.9:1.0'''] )
with patch.object(SCREAMING_SNAKE_CASE__ , '''argv''' , SCREAMING_SNAKE_CASE__ ):
with CaptureStdout() as cs:
run_search()
_UpperCamelCase :Union[str, Any] = [''' num_beams | length_penalty''', model, '''Best score args''']
_UpperCamelCase :List[Any] = ['''Info''']
if "translation" in task:
expected_strings.append('''bleu''' )
else:
expected_strings.extend(SCREAMING_SNAKE_CASE__ )
for w in expected_strings:
assert w in cs.out
for w in un_expected_strings:
assert w not in cs.out
assert Path(SCREAMING_SNAKE_CASE__ ).exists()
os.remove(Path(SCREAMING_SNAKE_CASE__ ) )
| 355 | 1 |
class snake_case ( UpperCamelCase_ ):
pass
class snake_case ( UpperCamelCase_ ):
pass
class snake_case :
def __init__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = [
[],
[],
[],
]
def __lowercase( self : int , a_ : int , a_ : int )-> None:
"""simple docstring"""
try:
if len(self.queues[priority] ) >= 100:
raise OverflowError('Maximum queue size is 100' )
self.queues[priority].append(a_ )
except IndexError:
raise ValueError('Valid priorities are 0, 1, and 2' )
def __lowercase( self : int )-> int:
"""simple docstring"""
for queue in self.queues:
if queue:
return queue.pop(0 )
raise UnderFlowError('All queues are empty' )
def __str__( self : Any )-> str:
"""simple docstring"""
return "\n".join(F'''Priority {i}: {q}''' for i, q in enumerate(self.queues ) )
class snake_case :
def __init__( self : Union[str, Any] )-> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = []
def __lowercase( self : List[str] , a_ : int )-> None:
"""simple docstring"""
if len(self.queue ) == 100:
raise OverFlowError('Maximum queue size is 100' )
self.queue.append(a_ )
def __lowercase( self : int )-> int:
"""simple docstring"""
if not self.queue:
raise UnderFlowError('The queue is empty' )
else:
SCREAMING_SNAKE_CASE__ : Optional[int] = min(self.queue )
self.queue.remove(a_ )
return data
def __str__( self : List[str] )-> str:
"""simple docstring"""
return str(self.queue )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int = FixedPriorityQueue()
fpq.enqueue(0 , 10 )
fpq.enqueue(1 , 70 )
fpq.enqueue(0 , 1_00 )
fpq.enqueue(2 , 1 )
fpq.enqueue(2 , 5 )
fpq.enqueue(1 , 7 )
fpq.enqueue(2 , 4 )
fpq.enqueue(1 , 64 )
fpq.enqueue(0 , 1_28 )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(lowercase__ )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
print(fpq.dequeue() )
def _a ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Dict = ElementPriorityQueue()
epq.enqueue(10 )
epq.enqueue(70 )
epq.enqueue(1_00 )
epq.enqueue(1 )
epq.enqueue(5 )
epq.enqueue(7 )
epq.enqueue(4 )
epq.enqueue(64 )
epq.enqueue(1_28 )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(lowercase__ )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
print(epq.dequeue() )
if __name__ == "__main__":
fixed_priority_queue()
element_priority_queue()
| 720 | import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
from .transformer_engine import convert_model
from .versions import is_torch_version
if is_deepspeed_available():
from deepspeed import DeepSpeedEngine
if is_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
def _a ( lowercase__ : int ):
'''simple docstring'''
if is_torch_version('<' , '2.0.0' ) or not hasattr(lowercase__ , '_dynamo' ):
return False
return isinstance(lowercase__ , torch._dynamo.eval_frame.OptimizedModule )
def _a ( lowercase__ : Optional[Any] , lowercase__ : bool = True ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = (torch.nn.parallel.DistributedDataParallel, torch.nn.DataParallel)
SCREAMING_SNAKE_CASE__ : Dict = is_compiled_module(lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : Tuple = model
SCREAMING_SNAKE_CASE__ : int = model._orig_mod
if is_deepspeed_available():
options += (DeepSpeedEngine,)
while isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : Any = model.module
if not keep_fpaa_wrapper:
SCREAMING_SNAKE_CASE__ : List[Any] = getattr(lowercase__ , 'forward' )
SCREAMING_SNAKE_CASE__ : str = model.__dict__.pop('_original_forward' , lowercase__ )
if original_forward is not None:
while hasattr(lowercase__ , '__wrapped__' ):
SCREAMING_SNAKE_CASE__ : Dict = forward.__wrapped__
if forward == original_forward:
break
SCREAMING_SNAKE_CASE__ : Dict = forward
if getattr(lowercase__ , '_converted_to_transformer_engine' , lowercase__ ):
convert_model(lowercase__ , to_transformer_engine=lowercase__ )
if is_compiled:
SCREAMING_SNAKE_CASE__ : List[Any] = model
SCREAMING_SNAKE_CASE__ : Optional[Any] = compiled_model
return model
def _a ( ):
'''simple docstring'''
PartialState().wait_for_everyone()
def _a ( lowercase__ : str , lowercase__ : Optional[Any] ):
'''simple docstring'''
if PartialState().distributed_type == DistributedType.TPU:
xm.save(lowercase__ , lowercase__ )
elif PartialState().local_process_index == 0:
torch.save(lowercase__ , lowercase__ )
@contextmanager
def _a ( **lowercase__ : str ):
'''simple docstring'''
for key, value in kwargs.items():
SCREAMING_SNAKE_CASE__ : int = str(lowercase__ )
yield
for key in kwargs:
if key.upper() in os.environ:
del os.environ[key.upper()]
def _a ( lowercase__ : Optional[Any] ):
'''simple docstring'''
if not hasattr(lowercase__ , '__qualname__' ) and not hasattr(lowercase__ , '__name__' ):
SCREAMING_SNAKE_CASE__ : Any = getattr(lowercase__ , '__class__' , lowercase__ )
if hasattr(lowercase__ , '__qualname__' ):
return obj.__qualname__
if hasattr(lowercase__ , '__name__' ):
return obj.__name__
return str(lowercase__ )
def _a ( lowercase__ : List[str] , lowercase__ : List[Any] ):
'''simple docstring'''
for key, value in source.items():
if isinstance(lowercase__ , lowercase__ ):
SCREAMING_SNAKE_CASE__ : List[str] = destination.setdefault(lowercase__ , {} )
merge_dicts(lowercase__ , lowercase__ )
else:
SCREAMING_SNAKE_CASE__ : List[Any] = value
return destination
def _a ( lowercase__ : int = None ):
'''simple docstring'''
if port is None:
SCREAMING_SNAKE_CASE__ : int = 2_95_00
with socket.socket(socket.AF_INET , socket.SOCK_STREAM ) as s:
return s.connect_ex(('localhost', port) ) == 0
| 636 | 0 |
'''simple docstring'''
import os
from argparse import ArgumentParser, Namespace
from ..data import SingleSentenceClassificationProcessor as Processor
from ..pipelines import TextClassificationPipeline
from ..utils import is_tf_available, is_torch_available, logging
from . import BaseTransformersCLICommand
if not is_tf_available() and not is_torch_available():
raise RuntimeError("At least one of PyTorch or TensorFlow 2.0+ should be installed to use CLI training")
# TF training parameters
_a : Optional[int] = False
_a : Dict = False
def _a (lowercase__ : Namespace ) -> int:
"""simple docstring"""
return TrainCommand(lowercase__ )
class _lowercase ( __lowercase ):
@staticmethod
def a ( SCREAMING_SNAKE_CASE_ : ArgumentParser ) -> Any:
__snake_case = parser.add_parser('train' , help='CLI tool to train a model on a task.' )
train_parser.add_argument(
'--train_data' , type=SCREAMING_SNAKE_CASE_ , required=SCREAMING_SNAKE_CASE_ , help='path to train (and optionally evaluation) dataset as a csv with tab separated labels and sentences.' , )
train_parser.add_argument(
'--column_label' , type=SCREAMING_SNAKE_CASE_ , default=0 , help='Column of the dataset csv file with example labels.' )
train_parser.add_argument(
'--column_text' , type=SCREAMING_SNAKE_CASE_ , default=1 , help='Column of the dataset csv file with example texts.' )
train_parser.add_argument(
'--column_id' , type=SCREAMING_SNAKE_CASE_ , default=2 , help='Column of the dataset csv file with example ids.' )
train_parser.add_argument(
'--skip_first_row' , action='store_true' , help='Skip the first row of the csv file (headers).' )
train_parser.add_argument('--validation_data' , type=SCREAMING_SNAKE_CASE_ , default='' , help='path to validation dataset.' )
train_parser.add_argument(
'--validation_split' , type=SCREAMING_SNAKE_CASE_ , default=0.1 , help='if validation dataset is not provided, fraction of train dataset to use as validation dataset.' , )
train_parser.add_argument('--output' , type=SCREAMING_SNAKE_CASE_ , default='./' , help='path to saved the trained model.' )
train_parser.add_argument(
'--task' , type=SCREAMING_SNAKE_CASE_ , default='text_classification' , help='Task to train the model on.' )
train_parser.add_argument(
'--model' , type=SCREAMING_SNAKE_CASE_ , default='bert-base-uncased' , help='Model\'s name or path to stored model.' )
train_parser.add_argument('--train_batch_size' , type=SCREAMING_SNAKE_CASE_ , default=32 , help='Batch size for training.' )
train_parser.add_argument('--valid_batch_size' , type=SCREAMING_SNAKE_CASE_ , default=64 , help='Batch size for validation.' )
train_parser.add_argument('--learning_rate' , type=SCREAMING_SNAKE_CASE_ , default=3e-5 , help='Learning rate.' )
train_parser.add_argument('--adam_epsilon' , type=SCREAMING_SNAKE_CASE_ , default=1e-08 , help='Epsilon for Adam optimizer.' )
train_parser.set_defaults(func=SCREAMING_SNAKE_CASE_ )
def __init__( self : str , SCREAMING_SNAKE_CASE_ : Namespace ) -> Optional[int]:
__snake_case = logging.get_logger('transformers-cli/training' )
__snake_case = 'tf' if is_tf_available() else 'torch'
os.makedirs(args.output , exist_ok=SCREAMING_SNAKE_CASE_ )
__snake_case = args.output
__snake_case = args.column_label
__snake_case = args.column_text
__snake_case = args.column_id
self.logger.info(f'Loading {args.task} pipeline for {args.model}' )
if args.task == "text_classification":
__snake_case = TextClassificationPipeline.from_pretrained(args.model )
elif args.task == "token_classification":
raise NotImplementedError
elif args.task == "question_answering":
raise NotImplementedError
self.logger.info(f'Loading dataset from {args.train_data}' )
__snake_case = Processor.create_from_csv(
args.train_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
__snake_case = None
if args.validation_data:
self.logger.info(f'Loading validation dataset from {args.validation_data}' )
__snake_case = Processor.create_from_csv(
args.validation_data , column_label=args.column_label , column_text=args.column_text , column_id=args.column_id , skip_first_row=args.skip_first_row , )
__snake_case = args.validation_split
__snake_case = args.train_batch_size
__snake_case = args.valid_batch_size
__snake_case = args.learning_rate
__snake_case = args.adam_epsilon
def a ( self : Optional[int] ) -> int:
if self.framework == "tf":
return self.run_tf()
return self.run_torch()
def a ( self : Tuple ) -> List[str]:
raise NotImplementedError
def a ( self : Dict ) -> Optional[int]:
self.pipeline.fit(
self.train_dataset , validation_data=self.valid_dataset , validation_split=self.validation_split , learning_rate=self.learning_rate , adam_epsilon=self.adam_epsilon , train_batch_size=self.train_batch_size , valid_batch_size=self.valid_batch_size , )
# Save trained pipeline
self.pipeline.save_pretrained(self.output )
| 56 |
'''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 lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : List[str] = tempfile.mkdtemp()
# fmt: off
_SCREAMING_SNAKE_CASE : Any = ["""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
_SCREAMING_SNAKE_CASE : Union[str, Any] = dict(zip(__snake_case , range(len(__snake_case ) ) ) )
_SCREAMING_SNAKE_CASE : str = ["""#version: 0.2""", """l o""", """lo w</w>""", """e r</w>""", """"""]
_SCREAMING_SNAKE_CASE : Any = {"""unk_token""": """<unk>"""}
_SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
_SCREAMING_SNAKE_CASE : Optional[Any] = 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(__snake_case ) + """\n""" )
with open(self.merges_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(__snake_case ) )
_SCREAMING_SNAKE_CASE : Any = {
"""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],
}
_SCREAMING_SNAKE_CASE : List[str] = os.path.join(self.tmpdirname , __snake_case )
with open(self.image_processor_file , """w""" , encoding="""utf-8""" ) as fp:
json.dump(__snake_case , __snake_case )
def UpperCAmelCase_ ( self , **__snake_case ):
return CLIPTokenizer.from_pretrained(self.tmpdirname , **__snake_case )
def UpperCAmelCase_ ( self , **__snake_case ):
return CLIPTokenizerFast.from_pretrained(self.tmpdirname , **__snake_case )
def UpperCAmelCase_ ( self , **__snake_case ):
return ViTImageProcessor.from_pretrained(self.tmpdirname , **__snake_case )
def UpperCAmelCase_ ( self ):
shutil.rmtree(self.tmpdirname )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Optional[Any] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_SCREAMING_SNAKE_CASE : List[str] = [Image.fromarray(np.moveaxis(__snake_case , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : List[str] = self.get_tokenizer()
_SCREAMING_SNAKE_CASE : int = self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
_SCREAMING_SNAKE_CASE : str = CLIPSegProcessor(tokenizer=__snake_case , image_processor=__snake_case )
processor_slow.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE : List[str] = CLIPSegProcessor.from_pretrained(self.tmpdirname , use_fast=__snake_case )
_SCREAMING_SNAKE_CASE : Any = CLIPSegProcessor(tokenizer=__snake_case , image_processor=__snake_case )
processor_fast.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE : Union[str, Any] = 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 , __snake_case )
self.assertIsInstance(processor_fast.tokenizer , __snake_case )
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 , __snake_case )
self.assertIsInstance(processor_fast.image_processor , __snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : int = CLIPSegProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE : str = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
_SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor(do_normalize=__snake_case , padding_value=1.0 )
_SCREAMING_SNAKE_CASE : Tuple = CLIPSegProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=__snake_case , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , __snake_case )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , __snake_case )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Optional[Any] = self.get_image_processor()
_SCREAMING_SNAKE_CASE : Optional[int] = self.get_tokenizer()
_SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPSegProcessor(tokenizer=__snake_case , image_processor=__snake_case )
_SCREAMING_SNAKE_CASE : List[Any] = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE : List[Any] = image_processor(__snake_case , return_tensors="""np""" )
_SCREAMING_SNAKE_CASE : Union[str, Any] = processor(images=__snake_case , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1e-2 )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : List[str] = self.get_image_processor()
_SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
_SCREAMING_SNAKE_CASE : Optional[Any] = CLIPSegProcessor(tokenizer=__snake_case , image_processor=__snake_case )
_SCREAMING_SNAKE_CASE : str = """lower newer"""
_SCREAMING_SNAKE_CASE : Any = processor(text=__snake_case )
_SCREAMING_SNAKE_CASE : Tuple = tokenizer(__snake_case )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
_SCREAMING_SNAKE_CASE : List[Any] = self.get_tokenizer()
_SCREAMING_SNAKE_CASE : Dict = CLIPSegProcessor(tokenizer=__snake_case , image_processor=__snake_case )
_SCREAMING_SNAKE_CASE : Any = """lower newer"""
_SCREAMING_SNAKE_CASE : List[Any] = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE : List[str] = processor(text=__snake_case , images=__snake_case )
self.assertListEqual(list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(__snake_case ):
processor()
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Dict = self.get_image_processor()
_SCREAMING_SNAKE_CASE : Tuple = self.get_tokenizer()
_SCREAMING_SNAKE_CASE : List[Any] = CLIPSegProcessor(tokenizer=__snake_case , image_processor=__snake_case )
_SCREAMING_SNAKE_CASE : str = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE : Optional[int] = self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE : List[Any] = processor(images=__snake_case , visual_prompt=__snake_case )
self.assertListEqual(list(inputs.keys() ) , ["""pixel_values""", """conditional_pixel_values"""] )
# test if it raises when no input is passed
with pytest.raises(__snake_case ):
processor()
def UpperCAmelCase_ ( self ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = self.get_image_processor()
_SCREAMING_SNAKE_CASE : Dict = self.get_tokenizer()
_SCREAMING_SNAKE_CASE : Union[str, Any] = CLIPSegProcessor(tokenizer=__snake_case , image_processor=__snake_case )
_SCREAMING_SNAKE_CASE : Union[str, Any] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_SCREAMING_SNAKE_CASE : int = processor.batch_decode(__snake_case )
_SCREAMING_SNAKE_CASE : int = tokenizer.batch_decode(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
| 533 | 0 |
from ..utils import DummyObject, requires_backends
class snake_case ( metaclass=SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : Any = ["""speech"""]
def __init__( self : List[str] , *lowerCAmelCase : Optional[int] , **lowerCAmelCase : Optional[Any]) -> Tuple:
"""simple docstring"""
requires_backends(self , ["""speech"""])
class snake_case ( metaclass=SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : Union[str, Any] = ["""speech"""]
def __init__( self : Optional[int] , *lowerCAmelCase : Union[str, Any] , **lowerCAmelCase : Optional[int]) -> Optional[Any]:
"""simple docstring"""
requires_backends(self , ["""speech"""])
| 713 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
a__ = None
a__ = logging.get_logger(__name__)
a__ = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
a__ = {
"""vocab_file""": {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model""",
},
"""tokenizer_file""": {
"""camembert-base""": """https://huggingface.co/camembert-base/resolve/main/tokenizer.json""",
},
}
a__ = {
"""camembert-base""": 5_12,
}
a__ = """▁"""
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : List[str] = VOCAB_FILES_NAMES
snake_case_ : str = PRETRAINED_VOCAB_FILES_MAP
snake_case_ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ : List[Any] = ["""input_ids""", """attention_mask"""]
snake_case_ : int = CamembertTokenizer
def __init__( self : Optional[int] , lowerCAmelCase : Optional[Any]=None , lowerCAmelCase : int=None , lowerCAmelCase : List[Any]="<s>" , lowerCAmelCase : List[str]="</s>" , lowerCAmelCase : Union[str, Any]="</s>" , lowerCAmelCase : Dict="<s>" , lowerCAmelCase : Dict="<unk>" , lowerCAmelCase : Any="<pad>" , lowerCAmelCase : List[str]="<mask>" , lowerCAmelCase : List[str]=["<s>NOTUSED", "</s>NOTUSED"] , **lowerCAmelCase : Dict , ) -> Tuple:
"""simple docstring"""
_snake_case : Dict = AddedToken(lowerCAmelCase , lstrip=lowerCAmelCase , rstrip=lowerCAmelCase) if isinstance(lowerCAmelCase , lowerCAmelCase) else mask_token
super().__init__(
lowerCAmelCase , tokenizer_file=lowerCAmelCase , bos_token=lowerCAmelCase , eos_token=lowerCAmelCase , sep_token=lowerCAmelCase , cls_token=lowerCAmelCase , unk_token=lowerCAmelCase , pad_token=lowerCAmelCase , mask_token=lowerCAmelCase , additional_special_tokens=lowerCAmelCase , **lowerCAmelCase , )
_snake_case : List[str] = vocab_file
_snake_case : Optional[Any] = False if not self.vocab_file else True
def UpperCamelCase_ ( self : Optional[int] , lowerCAmelCase : List[int] , lowerCAmelCase : Optional[List[int]] = None) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_snake_case : Optional[Any] = [self.cls_token_id]
_snake_case : List[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def UpperCamelCase_ ( self : str , lowerCAmelCase : List[int] , lowerCAmelCase : Optional[List[int]] = None) -> List[int]:
"""simple docstring"""
_snake_case : str = [self.sep_token_id]
_snake_case : List[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 + sep + token_ids_a + sep) * [0]
def UpperCamelCase_ ( self : str , lowerCAmelCase : str , lowerCAmelCase : Optional[str] = None) -> Tuple[str]:
"""simple docstring"""
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""")
if not os.path.isdir(lowerCAmelCase):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''')
return
_snake_case : Optional[int] = os.path.join(
lowerCAmelCase , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""])
if os.path.abspath(self.vocab_file) != os.path.abspath(lowerCAmelCase):
copyfile(self.vocab_file , lowerCAmelCase)
return (out_vocab_file,)
| 198 | 0 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""microsoft/wavlm-base""": """https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json""",
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class UpperCAmelCase_ ( __UpperCamelCase ):
"""simple docstring"""
UpperCamelCase_ : Any ='wavlm'
def __init__( self , SCREAMING_SNAKE_CASE_=32 , SCREAMING_SNAKE_CASE_=768 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=12 , SCREAMING_SNAKE_CASE_=3072 , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=0.02 , SCREAMING_SNAKE_CASE_=1e-5 , SCREAMING_SNAKE_CASE_="group" , SCREAMING_SNAKE_CASE_="gelu" , SCREAMING_SNAKE_CASE_=(512, 512, 512, 512, 512, 512, 512) , SCREAMING_SNAKE_CASE_=(5, 2, 2, 2, 2, 2, 2) , SCREAMING_SNAKE_CASE_=(10, 3, 3, 3, 3, 2, 2) , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=128 , SCREAMING_SNAKE_CASE_=16 , SCREAMING_SNAKE_CASE_=320 , SCREAMING_SNAKE_CASE_=800 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=True , SCREAMING_SNAKE_CASE_=0.05 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.0 , SCREAMING_SNAKE_CASE_=10 , SCREAMING_SNAKE_CASE_=320 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=256 , SCREAMING_SNAKE_CASE_=256 , SCREAMING_SNAKE_CASE_=0.1 , SCREAMING_SNAKE_CASE_="mean" , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=256 , SCREAMING_SNAKE_CASE_=(512, 512, 512, 512, 1500) , SCREAMING_SNAKE_CASE_=(5, 3, 3, 1, 1) , SCREAMING_SNAKE_CASE_=(1, 2, 3, 1, 1) , SCREAMING_SNAKE_CASE_=512 , SCREAMING_SNAKE_CASE_=80 , SCREAMING_SNAKE_CASE_=0 , SCREAMING_SNAKE_CASE_=1 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=False , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=2 , SCREAMING_SNAKE_CASE_=3 , SCREAMING_SNAKE_CASE_=None , **SCREAMING_SNAKE_CASE_ , ) -> str:
super().__init__(**SCREAMING_SNAKE_CASE_ , pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ )
UpperCamelCase :str = hidden_size
UpperCamelCase :Dict = feat_extract_norm
UpperCamelCase :List[Any] = feat_extract_activation
UpperCamelCase :Dict = list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :List[str] = list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Union[str, Any] = list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :List[str] = conv_bias
UpperCamelCase :Union[str, Any] = num_buckets
UpperCamelCase :List[Any] = max_bucket_distance
UpperCamelCase :Tuple = num_conv_pos_embeddings
UpperCamelCase :Optional[int] = num_conv_pos_embedding_groups
UpperCamelCase :Optional[int] = len(self.conv_dim )
UpperCamelCase :List[str] = num_hidden_layers
UpperCamelCase :Optional[int] = intermediate_size
UpperCamelCase :Tuple = hidden_act
UpperCamelCase :Optional[Any] = num_attention_heads
UpperCamelCase :Dict = hidden_dropout
UpperCamelCase :List[Any] = attention_dropout
UpperCamelCase :Dict = activation_dropout
UpperCamelCase :Optional[Any] = feat_proj_dropout
UpperCamelCase :List[Any] = final_dropout
UpperCamelCase :List[str] = layerdrop
UpperCamelCase :str = layer_norm_eps
UpperCamelCase :Dict = initializer_range
UpperCamelCase :List[str] = num_ctc_classes
UpperCamelCase :Dict = vocab_size
UpperCamelCase :List[str] = do_stable_layer_norm
UpperCamelCase :Optional[Any] = use_weighted_layer_sum
UpperCamelCase :Tuple = classifier_proj_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
F''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
F''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
UpperCamelCase :Dict = apply_spec_augment
UpperCamelCase :Dict = mask_time_prob
UpperCamelCase :Dict = mask_time_length
UpperCamelCase :Optional[int] = mask_time_min_masks
UpperCamelCase :int = mask_feature_prob
UpperCamelCase :List[Any] = mask_feature_length
# parameters for pretraining with codevector quantized representations
UpperCamelCase :Optional[int] = num_codevectors_per_group
UpperCamelCase :List[Any] = num_codevector_groups
UpperCamelCase :Optional[Any] = contrastive_logits_temperature
UpperCamelCase :Optional[Any] = num_negatives
UpperCamelCase :Union[str, Any] = codevector_dim
UpperCamelCase :Optional[int] = proj_codevector_dim
UpperCamelCase :Tuple = diversity_loss_weight
# ctc loss
UpperCamelCase :Union[str, Any] = ctc_loss_reduction
UpperCamelCase :Optional[Any] = ctc_zero_infinity
# adapter
UpperCamelCase :List[str] = add_adapter
UpperCamelCase :List[Any] = adapter_kernel_size
UpperCamelCase :Tuple = adapter_stride
UpperCamelCase :str = num_adapter_layers
UpperCamelCase :Any = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
UpperCamelCase :Optional[int] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
UpperCamelCase :List[Any] = list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :List[Any] = list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :Union[str, Any] = list(SCREAMING_SNAKE_CASE_ )
UpperCamelCase :List[str] = xvector_output_dim
@property
def UpperCAmelCase ( self ) -> Any:
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 658 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class lowerCAmelCase :
def __init__( self : List[str] , UpperCAmelCase : List[Any] , UpperCAmelCase : str=14 , UpperCAmelCase : Any=7 , UpperCAmelCase : Dict=True , UpperCAmelCase : List[str]=True , UpperCAmelCase : Any=False , UpperCAmelCase : Tuple=True , UpperCAmelCase : int=99 , UpperCAmelCase : str=32 , UpperCAmelCase : int=4 , UpperCAmelCase : Optional[Any]=4 , UpperCAmelCase : Any=4 , UpperCAmelCase : Optional[int]=37 , UpperCAmelCase : Union[str, Any]="gelu" , UpperCAmelCase : List[Any]=0.1 , UpperCAmelCase : Dict=0.1 , UpperCAmelCase : Optional[int]=512 , UpperCAmelCase : List[Any]=0.0_2 , ) -> List[Any]:
lowerCamelCase__ : Tuple = parent
lowerCamelCase__ : Tuple = batch_size
lowerCamelCase__ : Optional[Any] = seq_length
lowerCamelCase__ : str = is_training
lowerCamelCase__ : Any = use_input_mask
lowerCamelCase__ : Optional[Any] = use_token_type_ids
lowerCamelCase__ : Optional[Any] = use_labels
lowerCamelCase__ : Optional[Any] = vocab_size
lowerCamelCase__ : Optional[int] = hidden_size
lowerCamelCase__ : Optional[Any] = rotary_dim
lowerCamelCase__ : Optional[Any] = num_hidden_layers
lowerCamelCase__ : Optional[Any] = num_attention_heads
lowerCamelCase__ : Tuple = intermediate_size
lowerCamelCase__ : Union[str, Any] = hidden_act
lowerCamelCase__ : List[Any] = hidden_dropout_prob
lowerCamelCase__ : int = attention_probs_dropout_prob
lowerCamelCase__ : Dict = max_position_embeddings
lowerCamelCase__ : Tuple = initializer_range
lowerCamelCase__ : Optional[int] = None
lowerCamelCase__ : int = vocab_size - 1
lowerCamelCase__ : str = vocab_size - 1
lowerCamelCase__ : str = vocab_size - 1
def A_ ( self : str ) -> int:
lowerCamelCase__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowerCamelCase__ : Optional[Any] = None
if self.use_input_mask:
lowerCamelCase__ : List[str] = random_attention_mask([self.batch_size, self.seq_length] )
lowerCamelCase__ : Tuple = GPTJConfig(
vocab_size=self.vocab_size , n_embd=self.hidden_size , n_layer=self.num_hidden_layers , n_head=self.num_attention_heads , n_positions=self.max_position_embeddings , use_cache=UpperCAmelCase , bos_token_id=self.bos_token_id , eos_token_id=self.eos_token_id , pad_token_id=self.pad_token_id , rotary_dim=self.rotary_dim , )
return (config, input_ids, input_mask)
def A_ ( self : Optional[Any] ) -> Union[str, Any]:
lowerCamelCase__ : Tuple = self.prepare_config_and_inputs()
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : List[Any] = config_and_inputs
lowerCamelCase__ : List[Any] = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
def A_ ( self : int , UpperCAmelCase : Optional[Any] , UpperCAmelCase : Any , UpperCAmelCase : List[str] , UpperCAmelCase : Optional[Any] ) -> Tuple:
lowerCamelCase__ : Tuple = 20
lowerCamelCase__ : Dict = model_class_name(UpperCAmelCase )
lowerCamelCase__ : Dict = model.init_cache(input_ids.shape[0] , UpperCAmelCase )
lowerCamelCase__ : int = jnp.ones((input_ids.shape[0], max_decoder_length) , dtype='i4' )
lowerCamelCase__ : List[str] = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
lowerCamelCase__ : Optional[int] = model(
input_ids[:, :-1] , attention_mask=UpperCAmelCase , past_key_values=UpperCAmelCase , position_ids=UpperCAmelCase , )
lowerCamelCase__ : List[str] = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
lowerCamelCase__ : List[str] = model(
input_ids[:, -1:] , attention_mask=UpperCAmelCase , past_key_values=outputs_cache.past_key_values , position_ids=UpperCAmelCase , )
lowerCamelCase__ : List[str] = model(UpperCAmelCase )
lowerCamelCase__ : Optional[Any] = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F"""Max diff is {diff}""" )
def A_ ( self : Union[str, Any] , UpperCAmelCase : Any , UpperCAmelCase : Any , UpperCAmelCase : List[Any] , UpperCAmelCase : List[str] ) -> Optional[Any]:
lowerCamelCase__ : Any = 20
lowerCamelCase__ : Any = model_class_name(UpperCAmelCase )
lowerCamelCase__ : List[str] = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] , axis=-1 , )
lowerCamelCase__ : str = model.init_cache(input_ids.shape[0] , UpperCAmelCase )
lowerCamelCase__ : Dict = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] , (input_ids.shape[0], input_ids.shape[-1] - 1) )
lowerCamelCase__ : List[Any] = model(
input_ids[:, :-1] , attention_mask=UpperCAmelCase , past_key_values=UpperCAmelCase , position_ids=UpperCAmelCase , )
lowerCamelCase__ : Optional[Any] = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] , dtype='i4' )
lowerCamelCase__ : List[Any] = model(
input_ids[:, -1:] , past_key_values=outputs_cache.past_key_values , attention_mask=UpperCAmelCase , position_ids=UpperCAmelCase , )
lowerCamelCase__ : List[Any] = model(UpperCAmelCase , attention_mask=UpperCAmelCase )
lowerCamelCase__ : Dict = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1e-3 , msg=F"""Max diff is {diff}""" )
@require_flax
class lowerCAmelCase ( __UpperCamelCase, __UpperCamelCase, unittest.TestCase ):
UpperCAmelCase__ = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
UpperCAmelCase__ = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def A_ ( self : Optional[Any] ) -> Tuple:
lowerCamelCase__ : Optional[Any] = FlaxGPTJModelTester(self )
def A_ ( self : Tuple ) -> Union[str, Any]:
for model_class_name in self.all_model_classes:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
def A_ ( self : Union[str, Any] ) -> Tuple:
for model_class_name in self.all_model_classes:
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
@tooslow
def A_ ( self : Dict ) -> Tuple:
lowerCamelCase__ : str = GPTaTokenizer.from_pretrained('gpt2' , pad_token='<|endoftext|>' , padding_side='left' )
lowerCamelCase__ : Dict = tokenizer(['Hello this is a long string', 'Hey'] , return_tensors='np' , padding=UpperCAmelCase , truncation=UpperCAmelCase )
lowerCamelCase__ : str = FlaxGPTJForCausalLM.from_pretrained('EleutherAI/gpt-j-6B' )
lowerCamelCase__ : Optional[int] = False
lowerCamelCase__ : Optional[int] = model.config.eos_token_id
lowerCamelCase__ : Union[str, Any] = jax.jit(model.generate )
lowerCamelCase__ : Optional[Any] = jit_generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , pad_token_id=tokenizer.pad_token_id ).sequences
lowerCamelCase__ : Optional[int] = tokenizer.batch_decode(UpperCAmelCase , skip_special_tokens=UpperCAmelCase )
lowerCamelCase__ : int = [
'Hello this is a long string of text.\n\nI\'m trying to get the text of the',
'Hey, I\'m a little late to the party. I\'m going to',
]
self.assertListEqual(UpperCAmelCase , UpperCAmelCase )
@is_pt_flax_cross_test
def A_ ( self : List[str] ) -> Union[str, Any]:
lowerCamelCase__ , lowerCamelCase__ : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
lowerCamelCase__ : Dict = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : Optional[int] = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
lowerCamelCase__ : Any = model_class.__name__[4:] # Skip the "Flax" at the beginning
lowerCamelCase__ : Union[str, Any] = getattr(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ , lowerCamelCase__ : Tuple = pt_inputs['input_ids'].shape
lowerCamelCase__ : Tuple = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCAmelCase ):
lowerCamelCase__ : List[str] = 0
lowerCamelCase__ : Optional[int] = 1
lowerCamelCase__ : Any = 0
lowerCamelCase__ : Any = 1
lowerCamelCase__ : Dict = pt_model_class(UpperCAmelCase ).eval()
lowerCamelCase__ : str = model_class(UpperCAmelCase , dtype=jnp.floataa )
lowerCamelCase__ : str = convert_pytorch_state_dict_to_flax(pt_model.state_dict() , UpperCAmelCase )
lowerCamelCase__ : Tuple = fx_state
with torch.no_grad():
lowerCamelCase__ : List[str] = pt_model(**UpperCAmelCase ).to_tuple()
lowerCamelCase__ : Dict = fx_model(**UpperCAmelCase ).to_tuple()
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(UpperCAmelCase , UpperCAmelCase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(UpperCAmelCase )
lowerCamelCase__ : Tuple = model_class.from_pretrained(UpperCAmelCase , from_pt=UpperCAmelCase )
lowerCamelCase__ : int = fx_model_loaded(**UpperCAmelCase ).to_tuple()
self.assertEqual(
len(UpperCAmelCase ) , len(UpperCAmelCase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output_loaded, pt_output in zip(UpperCAmelCase , UpperCAmelCase ):
self.assert_almost_equals(fx_output_loaded[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@is_pt_flax_cross_test
def A_ ( self : Union[str, Any] ) -> int:
lowerCamelCase__ , lowerCamelCase__ : Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
lowerCamelCase__ : Tuple = self._prepare_for_class(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : Tuple = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
lowerCamelCase__ : Union[str, Any] = model_class.__name__[4:] # Skip the "Flax" at the beginning
lowerCamelCase__ : int = getattr(UpperCAmelCase , UpperCAmelCase )
lowerCamelCase__ : List[Any] = pt_model_class(UpperCAmelCase ).eval()
lowerCamelCase__ : str = model_class(UpperCAmelCase , dtype=jnp.floataa )
lowerCamelCase__ : List[str] = load_flax_weights_in_pytorch_model(UpperCAmelCase , fx_model.params )
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = pt_inputs['input_ids'].shape
lowerCamelCase__ : Dict = np.random.randint(0 , seq_length - 1 , size=(batch_size,) )
for batch_idx, start_index in enumerate(UpperCAmelCase ):
lowerCamelCase__ : List[Any] = 0
lowerCamelCase__ : Tuple = 1
lowerCamelCase__ : Optional[Any] = 0
lowerCamelCase__ : List[str] = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
lowerCamelCase__ : Any = pt_model(**UpperCAmelCase ).to_tuple()
lowerCamelCase__ : List[Any] = fx_model(**UpperCAmelCase ).to_tuple()
self.assertEqual(len(UpperCAmelCase ) , len(UpperCAmelCase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(UpperCAmelCase , UpperCAmelCase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(UpperCAmelCase )
lowerCamelCase__ : Tuple = pt_model_class.from_pretrained(UpperCAmelCase , from_flax=UpperCAmelCase )
with torch.no_grad():
lowerCamelCase__ : Optional[int] = pt_model_loaded(**UpperCAmelCase ).to_tuple()
self.assertEqual(
len(UpperCAmelCase ) , len(UpperCAmelCase ) , 'Output lengths differ between Flax and PyTorch' )
for fx_output, pt_output in zip(UpperCAmelCase , UpperCAmelCase ):
self.assert_almost_equals(fx_output[:, -1] , pt_output[:, -1].numpy() , 4e-2 )
@tooslow
def A_ ( self : List[str] ) -> List[Any]:
for model_class_name in self.all_model_classes:
lowerCamelCase__ : Union[str, Any] = model_class_name.from_pretrained('EleutherAI/gpt-j-6B' )
lowerCamelCase__ : Any = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCAmelCase )
| 295 | 0 |
'''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,
)
SCREAMING_SNAKE_CASE__ = {
'''configuration_albert''': ['''ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''AlbertConfig''', '''AlbertOnnxConfig'''],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['''AlbertTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['''AlbertTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AlbertForMaskedLM''',
'''AlbertForMultipleChoice''',
'''AlbertForPreTraining''',
'''AlbertForQuestionAnswering''',
'''AlbertForSequenceClassification''',
'''AlbertForTokenClassification''',
'''AlbertModel''',
'''AlbertPreTrainedModel''',
'''load_tf_weights_in_albert''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFAlbertForMaskedLM''',
'''TFAlbertForMultipleChoice''',
'''TFAlbertForPreTraining''',
'''TFAlbertForQuestionAnswering''',
'''TFAlbertForSequenceClassification''',
'''TFAlbertForTokenClassification''',
'''TFAlbertMainLayer''',
'''TFAlbertModel''',
'''TFAlbertPreTrainedModel''',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'''FlaxAlbertForMaskedLM''',
'''FlaxAlbertForMultipleChoice''',
'''FlaxAlbertForPreTraining''',
'''FlaxAlbertForQuestionAnswering''',
'''FlaxAlbertForSequenceClassification''',
'''FlaxAlbertForTokenClassification''',
'''FlaxAlbertModel''',
'''FlaxAlbertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 715 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
'configuration_swiftformer': [
'SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'SwiftFormerConfig',
'SwiftFormerOnnxConfig',
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
'SwiftFormerForImageClassification',
'SwiftFormerModel',
'SwiftFormerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_swiftformer import (
SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
SwiftFormerConfig,
SwiftFormerOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_swiftformer import (
SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
SwiftFormerForImageClassification,
SwiftFormerModel,
SwiftFormerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 35 | 0 |
"""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
a :int = logging.get_logger(__name__)
a :List[Any] = {
"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 (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Dict = """bloom"""
_SCREAMING_SNAKE_CASE :Optional[Any] = ["""past_key_values"""]
_SCREAMING_SNAKE_CASE :Union[str, Any] = {
"""num_hidden_layers""": """n_layer""",
"""num_attention_heads""": """n_head""",
}
def __init__( self , _a=250_880 , _a=64 , _a=2 , _a=8 , _a=1E-5 , _a=0.02 , _a=True , _a=1 , _a=2 , _a=False , _a=0.0 , _a=0.0 , _a=1 , _a=False , **_a , ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = vocab_size
# Backward compatibility with n_embed kwarg
SCREAMING_SNAKE_CASE__ : Union[str, Any] = kwargs.pop("""n_embed""" , _a )
SCREAMING_SNAKE_CASE__ : Tuple = hidden_size if n_embed is None else n_embed
SCREAMING_SNAKE_CASE__ : Optional[Any] = n_layer
SCREAMING_SNAKE_CASE__ : Any = n_head
SCREAMING_SNAKE_CASE__ : List[Any] = layer_norm_epsilon
SCREAMING_SNAKE_CASE__ : List[Any] = initializer_range
SCREAMING_SNAKE_CASE__ : Union[str, Any] = use_cache
SCREAMING_SNAKE_CASE__ : Optional[Any] = pretraining_tp
SCREAMING_SNAKE_CASE__ : List[str] = apply_residual_connection_post_layernorm
SCREAMING_SNAKE_CASE__ : List[Any] = hidden_dropout
SCREAMING_SNAKE_CASE__ : List[Any] = attention_dropout
SCREAMING_SNAKE_CASE__ : Optional[int] = bos_token_id
SCREAMING_SNAKE_CASE__ : Union[str, Any] = eos_token_id
SCREAMING_SNAKE_CASE__ : List[str] = slow_but_exact
super().__init__(bos_token_id=_a , eos_token_id=_a , **_a )
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = version.parse("""1.12""")
def __init__( self , _a , _a = "default" , _a = None , _a = False , ) -> Tuple:
"""simple docstring"""
super().__init__(_a , task=_a , patching_specs=_a , use_past=_a )
if not getattr(self._config , """pad_token_id""" , _a ):
# TODO: how to do that better?
SCREAMING_SNAKE_CASE__ : List[Any] = 0
@property
def _a ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : 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_(_a , direction="""inputs""" , inverted_values_shape=_a )
SCREAMING_SNAKE_CASE__ : List[str] = {0: """batch""", 1: """past_sequence + sequence"""}
else:
SCREAMING_SNAKE_CASE__ : Dict = {0: """batch""", 1: """sequence"""}
return common_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return self._config.n_layer
@property
def _a ( self ) -> int:
"""simple docstring"""
return self._config.n_head
@property
def _a ( self ) -> float:
"""simple docstring"""
return 1E-3
def _a ( self , _a , _a = -1 , _a = -1 , _a = False , _a = None , ) -> Mapping[str, Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = super(_a , self ).generate_dummy_inputs(
_a , batch_size=_a , seq_length=_a , is_pair=_a , framework=_a )
# We need to order the input in the way they appears in the forward()
SCREAMING_SNAKE_CASE__ : Optional[int] = 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
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : int = common_inputs["""input_ids"""].shape
# Not using the same length for past_key_values
SCREAMING_SNAKE_CASE__ : Tuple = seqlen + 2
SCREAMING_SNAKE_CASE__ : str = self._config.hidden_size // self.num_attention_heads
SCREAMING_SNAKE_CASE__ : List[Any] = (
batch * self.num_attention_heads,
head_dim,
past_key_values_length,
)
SCREAMING_SNAKE_CASE__ : List[Any] = (
batch * self.num_attention_heads,
past_key_values_length,
head_dim,
)
SCREAMING_SNAKE_CASE__ : Tuple = [
(torch.zeros(_a ), torch.zeros(_a )) for _ in range(self.num_layers )
]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = common_inputs["""attention_mask"""]
if self.use_past:
SCREAMING_SNAKE_CASE__ : Tuple = ordered_inputs["""attention_mask"""].dtype
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.cat(
[ordered_inputs["""attention_mask"""], torch.ones(_a , _a , dtype=_a )] , dim=1 )
return ordered_inputs
@property
def _a ( self ) -> int:
"""simple docstring"""
return 13
| 680 |
"""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 __a (UpperCamelCase_ , unittest.TestCase):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = LayoutLMTokenizer
_SCREAMING_SNAKE_CASE :Optional[int] = LayoutLMTokenizerFast
_SCREAMING_SNAKE_CASE :str = True
_SCREAMING_SNAKE_CASE :Optional[int] = True
def _a ( self ) -> Tuple:
"""simple docstring"""
super().setUp()
SCREAMING_SNAKE_CASE__ : List[str] = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE__ : int = 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 _a ( self , **_a ) -> Optional[int]:
"""simple docstring"""
return LayoutLMTokenizer.from_pretrained(self.tmpdirname , **_a )
def _a ( self , _a ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = """UNwant\u00E9d,running"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = """unwanted, running"""
return input_text, output_text
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.tokenizer_class(self.vocab_file )
SCREAMING_SNAKE_CASE__ : List[str] = tokenizer.tokenize("""UNwant\u00E9d,running""" )
self.assertListEqual(_a , ["""un""", """##want""", """##ed""", """,""", """runn""", """##ing"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [7, 4, 5, 10, 8, 9] )
def _a ( self ) -> Optional[int]:
"""simple docstring"""
pass
| 680 | 1 |
'''simple docstring'''
import enum
import shutil
import sys
UpperCamelCase__ , UpperCamelCase__: Any = shutil.get_terminal_size()
UpperCamelCase__: Optional[Any] = {"UP": "A", "DOWN": "B", "RIGHT": "C", "LEFT": "D"}
class SCREAMING_SNAKE_CASE( enum.Enum ):
"""simple docstring"""
lowerCamelCase__ = 0
lowerCamelCase__ = 1
def snake_case_ ( _lowerCAmelCase : Optional[int] , _lowerCAmelCase : Tuple="" ) -> List[str]:
sys.stdout.write(str(_lowerCAmelCase ) + end )
sys.stdout.flush()
def snake_case_ ( _lowerCAmelCase : Dict , _lowerCAmelCase : str , _lowerCAmelCase : Union[str, Any]="" ) -> List[str]:
forceWrite(f"""\u001b[{color}m{content}\u001b[0m""" , _lowerCAmelCase )
def snake_case_ ( ) -> int:
forceWrite('''\r''' )
def snake_case_ ( _lowerCAmelCase : int , _lowerCAmelCase : str ) -> Dict:
forceWrite(f"""\033[{num_lines}{CURSOR_TO_CHAR[direction.upper()]}""" )
def snake_case_ ( ) -> Optional[int]:
forceWrite(''' ''' * TERMINAL_WIDTH )
reset_cursor()
def snake_case_ ( ) -> List[Any]:
reset_cursor()
forceWrite('''-''' * TERMINAL_WIDTH )
| 528 |
'''simple docstring'''
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 snake_case_ ( _lowerCAmelCase : Dataset , _lowerCAmelCase : Dict[str, str] ) -> str:
UpperCAmelCase : Optional[int] = args.log_outputs
UpperCAmelCase : Any = '''_'''.join(args.dataset.split('''/''' ) + [args.config, args.split] )
# load metric
UpperCAmelCase : Union[str, Any] = load_metric('''wer''' )
UpperCAmelCase : Dict = load_metric('''cer''' )
# compute metrics
UpperCAmelCase : Any = wer.compute(references=result['''target'''] , predictions=result['''prediction'''] )
UpperCAmelCase : str = cer.compute(references=result['''target'''] , predictions=result['''prediction'''] )
# print & log results
UpperCAmelCase : Tuple = f"""WER: {wer_result}\nCER: {cer_result}"""
print(_lowerCAmelCase )
with open(f"""{dataset_id}_eval_results.txt""" , '''w''' ) as f:
f.write(_lowerCAmelCase )
# log all results in text file. Possibly interesting for analysis
if log_outputs is not None:
UpperCAmelCase : Union[str, Any] = f"""log_{dataset_id}_predictions.txt"""
UpperCAmelCase : Optional[Any] = f"""log_{dataset_id}_targets.txt"""
with open(_lowerCAmelCase , '''w''' ) as p, open(_lowerCAmelCase , '''w''' ) as t:
# mapping function to write output
def write_to_file(_lowerCAmelCase : int , _lowerCAmelCase : str ):
p.write(f"""{i}""" + '''\n''' )
p.write(batch['''prediction'''] + '''\n''' )
t.write(f"""{i}""" + '''\n''' )
t.write(batch['''target'''] + '''\n''' )
result.map(_lowerCAmelCase , with_indices=_lowerCAmelCase )
def snake_case_ ( _lowerCAmelCase : str ) -> str:
UpperCAmelCase : Optional[int] = '''[,?.!\-\;\:"“%‘”�—’…–]''' # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training
UpperCAmelCase : List[Any] = re.sub(_lowerCAmelCase , '''''' , text.lower() )
# In addition, we can normalize the target text, e.g. removing new lines characters etc...
# note that order is important here!
UpperCAmelCase : str = ['''\n\n''', '''\n''', ''' ''', ''' ''']
for t in token_sequences_to_ignore:
UpperCAmelCase : List[Any] = ''' '''.join(text.split(_lowerCAmelCase ) )
return text
def snake_case_ ( _lowerCAmelCase : Optional[Any] ) -> Union[str, Any]:
# load dataset
UpperCAmelCase : Optional[int] = load_dataset(args.dataset , args.config , split=args.split , use_auth_token=_lowerCAmelCase )
# for testing: only process the first two examples as a test
# dataset = dataset.select(range(10))
# load processor
UpperCAmelCase : Any = AutoFeatureExtractor.from_pretrained(args.model_id )
UpperCAmelCase : Optional[Any] = feature_extractor.sampling_rate
# resample audio
UpperCAmelCase : Tuple = dataset.cast_column('''audio''' , Audio(sampling_rate=_lowerCAmelCase ) )
# load eval pipeline
if args.device is None:
UpperCAmelCase : List[Any] = 0 if torch.cuda.is_available() else -1
UpperCAmelCase : List[Any] = pipeline('''automatic-speech-recognition''' , model=args.model_id , device=args.device )
# map function to decode audio
def map_to_pred(_lowerCAmelCase : List[str] ):
UpperCAmelCase : Optional[int] = asr(
batch['''audio''']['''array'''] , chunk_length_s=args.chunk_length_s , stride_length_s=args.stride_length_s )
UpperCAmelCase : str = prediction['''text''']
UpperCAmelCase : int = normalize_text(batch['''sentence'''] )
return batch
# run inference on all examples
UpperCAmelCase : Tuple = dataset.map(_lowerCAmelCase , remove_columns=dataset.column_names )
# compute and log_results
# do not change function below
log_results(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
UpperCamelCase__: List[Any] = 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__: List[str] = parser.parse_args()
main(args)
| 528 | 1 |
'''simple docstring'''
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_multi_gpu,
)
logging.basicConfig(level=logging.DEBUG)
_lowerCamelCase : List[Any] = logging.getLogger()
_lowerCamelCase : Optional[int] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class SCREAMING_SNAKE_CASE ( a__ ):
"""simple docstring"""
def A ( self : Optional[Any] , UpperCamelCase__ : Tuple ):
"""simple docstring"""
os.makedirs(UpperCamelCase__ , exist_ok=UpperCamelCase__ )
UpperCamelCase = {"source": "What is love ?", "target": "life"}
UpperCamelCase = {"train": 1_2, "val": 2, "test": 2}
for split in ["train", "test", "val"]:
for field in ["source", "target"]:
UpperCamelCase = "\n".join([contents[field]] * n_lines[split] )
with open(os.path.join(UpperCamelCase__ , f"""{split}.{field}""" ) , 'w' ) as f:
f.write(UpperCamelCase__ )
def A ( self : Any , UpperCamelCase__ : int , UpperCamelCase__ : str = "pytorch" ):
"""simple docstring"""
UpperCamelCase = self.get_auto_remove_tmp_dir()
UpperCamelCase = os.path.join(UpperCamelCase__ , 'output' )
UpperCamelCase = os.path.join(UpperCamelCase__ , 'data' )
self._create_dummy_data(data_dir=UpperCamelCase__ )
UpperCamelCase = f"""
--data_dir {data_dir} \
--output_dir {output_dir} \
--model_name_or_path facebook/rag-sequence-base \
--model_type rag_sequence \
--do_train \
--do_predict \
--n_val -1 \
--val_check_interval 1.0 \
--train_batch_size 2 \
--eval_batch_size 1 \
--max_source_length 25 \
--max_target_length 25 \
--val_max_target_length 25 \
--test_max_target_length 25 \
--label_smoothing 0.1 \
--dropout 0.1 \
--attention_dropout 0.1 \
--weight_decay 0.001 \
--adam_epsilon 1e-08 \
--max_grad_norm 0.1 \
--lr_scheduler polynomial \
--learning_rate 3e-04 \
--num_train_epochs 1 \
--warmup_steps 4 \
--gradient_accumulation_steps 1 \
--distributed-port 8787 \
--use_dummy_dataset 1 \
--distributed_retriever {distributed_retriever} \
""".split()
if gpus > 0:
testargs.append(f"""--gpus={gpus}""" )
if is_apex_available():
testargs.append('--fp16' )
else:
testargs.append('--gpus=0' )
testargs.append('--distributed_backend=ddp_cpu' )
testargs.append('--num_processes=2' )
UpperCamelCase = [sys.executable, str(Path(finetune_rag.__file__ ).resolve() )] + testargs
execute_subprocess_async(UpperCamelCase__ , env=self.get_env() )
UpperCamelCase = os.path.join(UpperCamelCase__ , 'metrics.json' )
with open(UpperCamelCase__ ) as f:
UpperCamelCase = json.load(UpperCamelCase__ )
return result
@require_torch_gpu
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = self._run_finetune(gpus=1 )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
@require_torch_multi_gpu
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = self._run_finetune(gpus=2 )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
@require_torch_gpu
@require_ray
def A ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = self._run_finetune(gpus=1 , distributed_retriever='ray' )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
@require_torch_multi_gpu
@require_ray
def A ( self : Any ):
"""simple docstring"""
UpperCamelCase = self._run_finetune(gpus=1 , distributed_retriever='ray' )
self.assertGreaterEqual(result['test'][0]['test_avg_em'] , 0.2 )
| 430 | """simple docstring"""
import math
def lowercase_ ( _lowerCamelCase: int ) -> list[int]:
'''simple docstring'''
__lowerCamelCase : Optional[int] = []
__lowerCamelCase : Tuple = 2
__lowerCamelCase : str = int(math.sqrt(_lowerCamelCase ) ) # Size of every segment
__lowerCamelCase : str = [True] * (end + 1)
__lowerCamelCase : int = []
while start <= end:
if temp[start] is True:
in_prime.append(_lowerCamelCase )
for i in range(start * start , end + 1 , _lowerCamelCase ):
__lowerCamelCase : List[str] = False
start += 1
prime += in_prime
__lowerCamelCase : Union[str, Any] = end + 1
__lowerCamelCase : Union[str, Any] = min(2 * end , _lowerCamelCase )
while low <= n:
__lowerCamelCase : List[Any] = [True] * (high - low + 1)
for each in in_prime:
__lowerCamelCase : int = math.floor(low / each ) * each
if t < low:
t += each
for j in range(_lowerCamelCase , high + 1 , _lowerCamelCase ):
__lowerCamelCase : Dict = False
for j in range(len(_lowerCamelCase ) ):
if temp[j] is True:
prime.append(j + low )
__lowerCamelCase : List[str] = high + 1
__lowerCamelCase : Union[str, Any] = min(high + end , _lowerCamelCase )
return prime
print(sieve(10**6)) | 646 | 0 |
"""simple docstring"""
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from .logging import get_logger
lowerCamelCase_ : Any = get_logger(__name__)
class UpperCamelCase_ :
def __init__( self : Tuple , lowerCAmelCase_ : Optional[str] = None ) -> str:
UpperCAmelCase_ : Optional[Any] = (
os.path.join(lowerCAmelCase_ , config.EXTRACTED_DATASETS_DIR ) if cache_dir else config.EXTRACTED_DATASETS_PATH
)
UpperCAmelCase_ : str = Extractor
def _SCREAMING_SNAKE_CASE ( self : Dict , lowerCAmelCase_ : str ) -> str:
from .file_utils import hash_url_to_filename
# Path where we extract compressed archives
# We extract in the cache dir, and get the extracted path name by hashing the original path"
UpperCAmelCase_ : Optional[Any] = os.path.abspath(lowerCAmelCase_ )
return os.path.join(self.extract_dir , hash_url_to_filename(lowerCAmelCase_ ) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowerCAmelCase_ : str , lowerCAmelCase_ : bool ) -> bool:
return force_extract or (
not os.path.isfile(lowerCAmelCase_ ) and not (os.path.isdir(lowerCAmelCase_ ) and os.listdir(lowerCAmelCase_ ))
)
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowerCAmelCase_ : str , lowerCAmelCase_ : bool = False ) -> str:
UpperCAmelCase_ : str = self.extractor.infer_extractor_format(lowerCAmelCase_ )
if not extractor_format:
return input_path
UpperCAmelCase_ : Tuple = self._get_output_path(lowerCAmelCase_ )
if self._do_extract(lowerCAmelCase_ , lowerCAmelCase_ ):
self.extractor.extract(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
return output_path
class UpperCamelCase_ (__A ):
@classmethod
@abstractmethod
def _SCREAMING_SNAKE_CASE ( cls : int , lowerCAmelCase_ : Union[Path, str] , **lowerCAmelCase_ : int ) -> bool:
...
@staticmethod
@abstractmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Union[Path, str] ) -> None:
...
class UpperCamelCase_ (__A , __A ):
__magic_name__ = []
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : int ) -> List[str]:
with open(lowerCAmelCase_ , "rb" ) as f:
return f.read(lowerCAmelCase_ )
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : int , lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : bytes = b"" ) -> bool:
if not magic_number:
UpperCAmelCase_ : List[Any] = max(len(lowerCAmelCase_ ) for cls_magic_number in cls.magic_numbers )
try:
UpperCAmelCase_ : str = cls.read_magic_number(lowerCAmelCase_ , lowerCAmelCase_ )
except OSError:
return False
return any(magic_number.startswith(lowerCAmelCase_ ) for cls_magic_number in cls.magic_numbers )
class UpperCamelCase_ (__A ):
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Dict , lowerCAmelCase_ : Union[Path, str] , **lowerCAmelCase_ : Any ) -> bool:
return tarfile.is_tarfile(lowerCAmelCase_ )
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Any , lowerCAmelCase_ : Any ) -> Optional[int]:
def resolved(lowerCAmelCase_ : str ) -> str:
return os.path.realpath(os.path.abspath(lowerCAmelCase_ ) )
def badpath(lowerCAmelCase_ : str , lowerCAmelCase_ : str ) -> bool:
# joinpath will ignore base if path is absolute
return not resolved(os.path.join(lowerCAmelCase_ , lowerCAmelCase_ ) ).startswith(lowerCAmelCase_ )
def badlink(lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : str ) -> bool:
# Links are interpreted relative to the directory containing the link
UpperCAmelCase_ : Any = resolved(os.path.join(lowerCAmelCase_ , os.path.dirname(info.name ) ) )
return badpath(info.linkname , base=lowerCAmelCase_ )
UpperCAmelCase_ : int = resolved(lowerCAmelCase_ )
for finfo in members:
if badpath(finfo.name , lowerCAmelCase_ ):
logger.error(f"""Extraction of {finfo.name} is blocked (illegal path)""" )
elif finfo.issym() and badlink(lowerCAmelCase_ , lowerCAmelCase_ ):
logger.error(f"""Extraction of {finfo.name} is blocked: Symlink to {finfo.linkname}""" )
elif finfo.islnk() and badlink(lowerCAmelCase_ , lowerCAmelCase_ ):
logger.error(f"""Extraction of {finfo.name} is blocked: Hard link to {finfo.linkname}""" )
else:
yield finfo
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Union[Path, str] ) -> None:
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
UpperCAmelCase_ : List[Any] = tarfile.open(lowerCAmelCase_ )
tar_file.extractall(lowerCAmelCase_ , members=TarExtractor.safemembers(lowerCAmelCase_ , lowerCAmelCase_ ) )
tar_file.close()
class UpperCamelCase_ (__A ):
__magic_name__ = [b'''\x1F\x8B''']
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Union[Path, str] ) -> None:
with gzip.open(lowerCAmelCase_ , "rb" ) as gzip_file:
with open(lowerCAmelCase_ , "wb" ) as extracted_file:
shutil.copyfileobj(lowerCAmelCase_ , lowerCAmelCase_ )
class UpperCamelCase_ (__A ):
__magic_name__ = [
b'''PK\x03\x04''',
b'''PK\x05\x06''', # empty archive
b'''PK\x07\x08''', # spanned archive
]
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Dict , lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : bytes = b"" ) -> bool:
if super().is_extractable(lowerCAmelCase_ , magic_number=lowerCAmelCase_ ):
return True
try:
# Alternative version of zipfile.is_zipfile that has less false positives, but misses executable zip archives.
# From: https://github.com/python/cpython/pull/5053
from zipfile import (
_CD_SIGNATURE,
_ECD_DISK_NUMBER,
_ECD_DISK_START,
_ECD_ENTRIES_TOTAL,
_ECD_OFFSET,
_ECD_SIZE,
_EndRecData,
sizeCentralDir,
stringCentralDir,
structCentralDir,
)
with open(lowerCAmelCase_ , "rb" ) as fp:
UpperCAmelCase_ : Optional[int] = _EndRecData(lowerCAmelCase_ )
if endrec:
if endrec[_ECD_ENTRIES_TOTAL] == 0 and endrec[_ECD_SIZE] == 0 and endrec[_ECD_OFFSET] == 0:
return True # Empty zipfiles are still zipfiles
elif endrec[_ECD_DISK_NUMBER] == endrec[_ECD_DISK_START]:
fp.seek(endrec[_ECD_OFFSET] ) # Central directory is on the same disk
if fp.tell() == endrec[_ECD_OFFSET] and endrec[_ECD_SIZE] >= sizeCentralDir:
UpperCAmelCase_ : str = fp.read(lowerCAmelCase_ ) # CD is where we expect it to be
if len(lowerCAmelCase_ ) == sizeCentralDir:
UpperCAmelCase_ : Optional[int] = struct.unpack(lowerCAmelCase_ , lowerCAmelCase_ ) # CD is the right size
if centdir[_CD_SIGNATURE] == stringCentralDir:
return True # First central directory entry has correct magic number
return False
except Exception: # catch all errors in case future python versions change the zipfile internals
return False
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Union[Path, str] ) -> None:
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
with zipfile.ZipFile(lowerCAmelCase_ , "r" ) as zip_file:
zip_file.extractall(lowerCAmelCase_ )
zip_file.close()
class UpperCamelCase_ (__A ):
__magic_name__ = [b'''\xFD\x37\x7A\x58\x5A\x00''']
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Union[Path, str] ) -> None:
with lzma.open(lowerCAmelCase_ ) as compressed_file:
with open(lowerCAmelCase_ , "wb" ) as extracted_file:
shutil.copyfileobj(lowerCAmelCase_ , lowerCAmelCase_ )
class UpperCamelCase_ (__A ):
__magic_name__ = [b'''Rar!\x1a\x07\x00''', b'''Rar!\x1a\x07\x01\x00'''] # RAR_ID # RAR5_ID
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Union[Path, str] ) -> None:
if not config.RARFILE_AVAILABLE:
raise ImportError("Please pip install rarfile" )
import rarfile
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
UpperCAmelCase_ : List[Any] = rarfile.RarFile(lowerCAmelCase_ )
rf.extractall(lowerCAmelCase_ )
rf.close()
class UpperCamelCase_ (__A ):
__magic_name__ = [b'''\x28\xb5\x2F\xFD''']
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Union[Path, str] ) -> None:
if not config.ZSTANDARD_AVAILABLE:
raise ImportError("Please pip install zstandard" )
import zstandard as zstd
UpperCAmelCase_ : Tuple = zstd.ZstdDecompressor()
with open(lowerCAmelCase_ , "rb" ) as ifh, open(lowerCAmelCase_ , "wb" ) as ofh:
dctx.copy_stream(lowerCAmelCase_ , lowerCAmelCase_ )
class UpperCamelCase_ (__A ):
__magic_name__ = [b'''\x42\x5A\x68''']
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Union[Path, str] ) -> None:
with bza.open(lowerCAmelCase_ , "rb" ) as compressed_file:
with open(lowerCAmelCase_ , "wb" ) as extracted_file:
shutil.copyfileobj(lowerCAmelCase_ , lowerCAmelCase_ )
class UpperCamelCase_ (__A ):
__magic_name__ = [b'''\x37\x7A\xBC\xAF\x27\x1C''']
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Union[Path, str] ) -> None:
if not config.PY7ZR_AVAILABLE:
raise ImportError("Please pip install py7zr" )
import pyazr
os.makedirs(lowerCAmelCase_ , exist_ok=lowerCAmelCase_ )
with pyazr.SevenZipFile(lowerCAmelCase_ , "r" ) as archive:
archive.extractall(lowerCAmelCase_ )
class UpperCamelCase_ (__A ):
__magic_name__ = [b'''\x04\x22\x4D\x18''']
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Union[Path, str] ) -> None:
if not config.LZ4_AVAILABLE:
raise ImportError("Please pip install lz4" )
import lza.frame
with lza.frame.open(lowerCAmelCase_ , "rb" ) as compressed_file:
with open(lowerCAmelCase_ , "wb" ) as extracted_file:
shutil.copyfileobj(lowerCAmelCase_ , lowerCAmelCase_ )
class UpperCamelCase_ :
# Put zip file to the last, b/c it is possible wrongly detected as zip (I guess it means: as tar or gzip)
__magic_name__ = {
'''tar''': TarExtractor,
'''gzip''': GzipExtractor,
'''zip''': ZipExtractor,
'''xz''': XzExtractor,
'''rar''': RarExtractor,
'''zstd''': ZstdExtractor,
'''bz2''': BzipaExtractor,
'''7z''': SevenZipExtractor, # <Added version="2.4.0"/>
'''lz4''': LzaExtractor, # <Added version="2.4.0"/>
}
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : Optional[int] ) -> List[str]:
return max(
len(lowerCAmelCase_ )
for extractor in cls.extractors.values()
if issubclass(lowerCAmelCase_ , lowerCAmelCase_ )
for extractor_magic_number in extractor.magic_numbers )
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : int ) -> int:
try:
return MagicNumberBaseExtractor.read_magic_number(lowerCAmelCase_ , magic_number_length=lowerCAmelCase_ )
except OSError:
return b""
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : int , lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : bool = False ) -> bool:
warnings.warn(
"Method 'is_extractable' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'infer_extractor_format' instead." , category=lowerCAmelCase_ , )
UpperCAmelCase_ : Tuple = cls.infer_extractor_format(lowerCAmelCase_ )
if extractor_format:
return True if not return_extractor else (True, cls.extractors[extractor_format])
return False if not return_extractor else (False, None)
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : str , lowerCAmelCase_ : Union[Path, str] ) -> str: # <Added version="2.4.0"/>
UpperCAmelCase_ : Any = cls._get_magic_number_max_length()
UpperCAmelCase_ : Optional[Any] = cls._read_magic_number(lowerCAmelCase_ , lowerCAmelCase_ )
for extractor_format, extractor in cls.extractors.items():
if extractor.is_extractable(lowerCAmelCase_ , magic_number=lowerCAmelCase_ ):
return extractor_format
@classmethod
def _SCREAMING_SNAKE_CASE ( cls : int , lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Union[Path, str] , lowerCAmelCase_ : Optional[str] = None , lowerCAmelCase_ : Optional[BaseExtractor] = "deprecated" , ) -> None:
os.makedirs(os.path.dirname(lowerCAmelCase_ ) , exist_ok=lowerCAmelCase_ )
# Prevent parallel extractions
UpperCAmelCase_ : List[str] = str(Path(lowerCAmelCase_ ).with_suffix(".lock" ) )
with FileLock(lowerCAmelCase_ ):
shutil.rmtree(lowerCAmelCase_ , ignore_errors=lowerCAmelCase_ )
if extractor_format or extractor != "deprecated":
if extractor != "deprecated" or not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ): # passed as positional arg
warnings.warn(
"Parameter 'extractor' was deprecated in version 2.4.0 and will be removed in 3.0.0. "
"Use 'extractor_format' instead." , category=lowerCAmelCase_ , )
UpperCAmelCase_ : Any = extractor if extractor != "deprecated" else extractor_format
else:
UpperCAmelCase_ : int = cls.extractors[extractor_format]
return extractor.extract(lowerCAmelCase_ , lowerCAmelCase_ )
else:
warnings.warn(
"Parameter 'extractor_format' was made required in version 2.4.0 and not passing it will raise an "
"exception in 3.0.0." , category=lowerCAmelCase_ , )
for extractor in cls.extractors.values():
if extractor.is_extractable(lowerCAmelCase_ ):
return extractor.extract(lowerCAmelCase_ , lowerCAmelCase_ )
| 701 |
"""simple docstring"""
lowerCamelCase_ = {
'''a''': '''AAAAA''',
'''b''': '''AAAAB''',
'''c''': '''AAABA''',
'''d''': '''AAABB''',
'''e''': '''AABAA''',
'''f''': '''AABAB''',
'''g''': '''AABBA''',
'''h''': '''AABBB''',
'''i''': '''ABAAA''',
'''j''': '''BBBAA''',
'''k''': '''ABAAB''',
'''l''': '''ABABA''',
'''m''': '''ABABB''',
'''n''': '''ABBAA''',
'''o''': '''ABBAB''',
'''p''': '''ABBBA''',
'''q''': '''ABBBB''',
'''r''': '''BAAAA''',
'''s''': '''BAAAB''',
'''t''': '''BAABA''',
'''u''': '''BAABB''',
'''v''': '''BBBAB''',
'''w''': '''BABAA''',
'''x''': '''BABAB''',
'''y''': '''BABBA''',
'''z''': '''BABBB''',
''' ''': ''' ''',
}
lowerCamelCase_ = {value: key for key, value in encode_dict.items()}
def snake_case ( A__ ):
UpperCAmelCase_ : Union[str, Any] = ""
for letter in word.lower():
if letter.isalpha() or letter == " ":
encoded += encode_dict[letter]
else:
raise Exception("encode() accepts only letters of the alphabet and spaces" )
return encoded
def snake_case ( A__ ):
if set(A__ ) - {"A", "B", " "} != set():
raise Exception("decode() accepts only 'A', 'B' and spaces" )
UpperCAmelCase_ : Dict = ""
for word in coded.split():
while len(A__ ) != 0:
decoded += decode_dict[word[:5]]
UpperCAmelCase_ : str = word[5:]
decoded += " "
return decoded.strip()
if __name__ == "__main__":
from doctest import testmod
testmod()
| 463 | 0 |
import requests
A : str = 'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='
def UpperCamelCase ( __magic_name__ : str ) -> None:
"""simple docstring"""
lowercase__ = requests.get(_NEWS_API + bbc_news_api_key ).json()
# each article in the list is a dict
for i, article in enumerate(bbc_news_page["""articles"""] , 1 ):
print(f'''{i}.) {article["title"]}''' )
if __name__ == "__main__":
fetch_bbc_news(bbc_news_api_key='<Your BBC News API key goes here>')
| 15 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
from accelerate.test_utils import execute_subprocess_async
def _lowerCamelCase ( lowerCamelCase_: str=None ):
'''simple docstring'''
if subparsers is not None:
A : Tuple = subparsers.add_parser('''test''' )
else:
A : List[str] = argparse.ArgumentParser('''Accelerate test command''' )
parser.add_argument(
'''--config_file''' , default=lowerCamelCase_ , help=(
'''The path to use to store the config file. Will default to a file named default_config.yaml in the cache '''
'''location, which is the content of the environment `HF_HOME` suffixed with \'accelerate\', or if you don\'t have '''
'''such an environment variable, your cache directory (\'~/.cache\' or the content of `XDG_CACHE_HOME`) suffixed '''
'''with \'huggingface\'.'''
) , )
if subparsers is not None:
parser.set_defaults(func=lowerCamelCase_ )
return parser
def _lowerCamelCase ( lowerCamelCase_: str ):
'''simple docstring'''
A : Dict = os.path.sep.join(__file__.split(os.path.sep )[:-2] + ['''test_utils''', '''scripts''', '''test_script.py'''] )
if args.config_file is None:
A : Any = script_name
else:
A : str = f"""--config_file={args.config_file} {script_name}"""
A : Tuple = ['''accelerate-launch'''] + test_args.split()
A : List[Any] = execute_subprocess_async(lowerCamelCase_ , env=os.environ.copy() )
if result.returncode == 0:
print('''Test is a success! You are ready for your distributed training!''' )
def _lowerCamelCase ( ):
'''simple docstring'''
A : List[str] = test_command_parser()
A : Any = parser.parse_args()
test_command(lowerCamelCase_ )
if __name__ == "__main__":
main() | 256 | 0 |
from __future__ import annotations
def __a ( __lowerCAmelCase ) -> bool:
return len(set(__lowerCAmelCase ) ) == len(__lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod() | 308 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowerCamelCase : List[Any] = logging.get_logger(__name__)
_lowerCamelCase : Any = {
"""facebook/levit-128S""": """https://huggingface.co/facebook/levit-128S/resolve/main/config.json""",
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class lowercase ( SCREAMING_SNAKE_CASE_):
'''simple docstring'''
UpperCAmelCase : Dict = 'levit'
def __init__( self : List[str] , snake_case : int=224 , snake_case : Dict=3 , snake_case : List[str]=3 , snake_case : Optional[int]=2 , snake_case : int=1 , snake_case : List[str]=16 , snake_case : Optional[Any]=[128, 256, 384] , snake_case : Optional[int]=[4, 8, 12] , snake_case : Optional[int]=[4, 4, 4] , snake_case : Union[str, Any]=[16, 16, 16] , snake_case : List[str]=0 , snake_case : Any=[2, 2, 2] , snake_case : Any=[2, 2, 2] , snake_case : int=0.02 , **snake_case : Dict , ):
'''simple docstring'''
super().__init__(**snake_case )
SCREAMING_SNAKE_CASE : Tuple = image_size
SCREAMING_SNAKE_CASE : List[Any] = num_channels
SCREAMING_SNAKE_CASE : Dict = kernel_size
SCREAMING_SNAKE_CASE : Optional[Any] = stride
SCREAMING_SNAKE_CASE : int = padding
SCREAMING_SNAKE_CASE : Optional[int] = hidden_sizes
SCREAMING_SNAKE_CASE : Optional[Any] = num_attention_heads
SCREAMING_SNAKE_CASE : List[str] = depths
SCREAMING_SNAKE_CASE : Any = key_dim
SCREAMING_SNAKE_CASE : List[Any] = drop_path_rate
SCREAMING_SNAKE_CASE : Optional[Any] = patch_size
SCREAMING_SNAKE_CASE : Any = attention_ratio
SCREAMING_SNAKE_CASE : Dict = mlp_ratio
SCREAMING_SNAKE_CASE : int = initializer_range
SCREAMING_SNAKE_CASE : Tuple = [
['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class lowercase ( SCREAMING_SNAKE_CASE_):
'''simple docstring'''
UpperCAmelCase : List[str] = version.parse('1.11')
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
return 1E-4 | 308 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return x if y == 0 else greatest_common_divisor(A , x % y )
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
return (x * y) // greatest_common_divisor(A , A )
def UpperCAmelCase_ ( A = 2_0 ):
'''simple docstring'''
_a : List[Any] = 1
for i in range(1 , n + 1 ):
_a : Union[str, Any] = lcm(A , A )
return g
if __name__ == "__main__":
print(f'''{solution() = }''')
| 120 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"google/vivit-b-16x2-kinetics400": (
"https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json"
),
# See all Vivit models at https://huggingface.co/models?filter=vivit
}
class a ( snake_case__ ):
'''simple docstring'''
__lowerCAmelCase : str = """vivit"""
def __init__( self , lowerCamelCase_=2_2_4 , lowerCamelCase_=3_2 , lowerCamelCase_=[2, 1_6, 1_6] , lowerCamelCase_=3 , lowerCamelCase_=7_6_8 , lowerCamelCase_=1_2 , lowerCamelCase_=1_2 , lowerCamelCase_=3_0_7_2 , lowerCamelCase_="gelu_fast" , lowerCamelCase_=0.0 , lowerCamelCase_=0.0 , lowerCamelCase_=0.02 , lowerCamelCase_=1e-06 , lowerCamelCase_=True , **lowerCamelCase_ , ) -> int:
_a : Tuple = hidden_size
_a : Dict = num_hidden_layers
_a : List[str] = num_attention_heads
_a : int = intermediate_size
_a : Optional[int] = hidden_act
_a : Dict = hidden_dropout_prob
_a : List[str] = attention_probs_dropout_prob
_a : List[str] = initializer_range
_a : List[str] = layer_norm_eps
_a : Any = image_size
_a : Optional[Any] = num_frames
_a : Dict = tubelet_size
_a : Union[str, Any] = num_channels
_a : Optional[int] = qkv_bias
super().__init__(**lowerCamelCase_ )
| 120 | 1 |
'''simple docstring'''
class __UpperCamelCase :
def __init__( self :List[Any] ,_UpperCamelCase :Optional[Any] ,_UpperCamelCase :List[str] ):
snake_case_ : int = name
snake_case_ : Tuple = val
def __str__( self :Optional[int] ):
return F'''{self.__class__.__name__}({self.name}, {self.val})'''
def __lt__( self :int ,_UpperCamelCase :List[Any] ):
return self.val < other.val
class __UpperCamelCase :
def __init__( self :List[str] ,_UpperCamelCase :Any ):
snake_case_ : Union[str, Any] = {}
snake_case_ : Union[str, Any] = {}
snake_case_ : List[str] = self.build_heap(_UpperCamelCase )
def __getitem__( self :Dict ,_UpperCamelCase :Dict ):
return self.get_value(_UpperCamelCase )
def a__ ( self :Dict ,_UpperCamelCase :str ):
return (idx - 1) // 2
def a__ ( self :List[Any] ,_UpperCamelCase :Dict ):
return idx * 2 + 1
def a__ ( self :List[str] ,_UpperCamelCase :Tuple ):
return idx * 2 + 2
def a__ ( self :Union[str, Any] ,_UpperCamelCase :Any ):
return self.heap_dict[key]
def a__ ( self :Optional[Any] ,_UpperCamelCase :Tuple ):
snake_case_ : Tuple = len(_UpperCamelCase ) - 1
snake_case_ : int = self.get_parent_idx(_UpperCamelCase )
for idx, i in enumerate(_UpperCamelCase ):
snake_case_ : Optional[int] = idx
snake_case_ : List[Any] = i.val
for i in range(_UpperCamelCase ,-1 ,-1 ):
self.sift_down(_UpperCamelCase ,_UpperCamelCase )
return array
def a__ ( self :List[Any] ,_UpperCamelCase :Tuple ,_UpperCamelCase :Optional[int] ):
while True:
snake_case_ : Union[str, Any] = self.get_left_child_idx(_UpperCamelCase ) # noqa: E741
snake_case_ : List[str] = self.get_right_child_idx(_UpperCamelCase )
snake_case_ : Tuple = idx
if l < len(_UpperCamelCase ) and array[l] < array[idx]:
snake_case_ : List[Any] = l
if r < len(_UpperCamelCase ) and array[r] < array[smallest]:
snake_case_ : str = r
if smallest != idx:
snake_case_ : List[Any] = array[smallest], array[idx]
(
snake_case_
) : Optional[int] = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
snake_case_ : str = smallest
else:
break
def a__ ( self :Tuple ,_UpperCamelCase :Any ):
snake_case_ : List[str] = self.get_parent_idx(_UpperCamelCase )
while p >= 0 and self.heap[p] > self.heap[idx]:
snake_case_ : Union[str, Any] = self.heap[idx], self.heap[p]
snake_case_ : Any = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
snake_case_ : Dict = p
snake_case_ : int = self.get_parent_idx(_UpperCamelCase )
def a__ ( self :Optional[int] ):
return self.heap[0]
def a__ ( self :int ):
snake_case_ : Tuple = self.heap[-1], self.heap[0]
snake_case_ : Union[str, Any] = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
snake_case_ : List[Any] = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 ,self.heap )
return x
def a__ ( self :int ,_UpperCamelCase :List[Any] ):
self.heap.append(_UpperCamelCase )
snake_case_ : List[str] = len(self.heap ) - 1
snake_case_ : List[Any] = node.val
self.sift_up(len(self.heap ) - 1 )
def a__ ( self :Union[str, Any] ):
return len(self.heap ) == 0
def a__ ( self :List[str] ,_UpperCamelCase :Optional[int] ,_UpperCamelCase :str ):
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
snake_case_ : Dict = new_value
snake_case_ : List[Any] = new_value
self.sift_up(self.idx_of_element[node] )
__A : Optional[int] = Node('R', -1)
__A : int = Node('B', 6)
__A : List[Any] = Node('A', 3)
__A : Tuple = Node('X', 1)
__A : int = Node('E', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
__A : Any = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
print(i)
print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod() | 710 |
'''simple docstring'''
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__A : str = logging.get_logger(__name__)
__A : Dict = {
'google/pix2struct-textcaps-base': (
'https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json'
),
}
class __UpperCamelCase ( lowercase__ ):
lowercase : Tuple = 'pix2struct_text_model'
lowercase : int = ['past_key_values']
lowercase : int = {
'hidden_size': 'hidden_size',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self :int ,_UpperCamelCase :List[str]=5_0_2_4_4 ,_UpperCamelCase :Optional[Any]=7_6_8 ,_UpperCamelCase :Dict=6_4 ,_UpperCamelCase :Dict=2_0_4_8 ,_UpperCamelCase :Optional[int]=1_2 ,_UpperCamelCase :Union[str, Any]=1_2 ,_UpperCamelCase :List[str]=3_2 ,_UpperCamelCase :Union[str, Any]=1_2_8 ,_UpperCamelCase :Tuple=0.1 ,_UpperCamelCase :List[str]=1E-6 ,_UpperCamelCase :List[Any]=1.0 ,_UpperCamelCase :Optional[int]="gelu_new" ,_UpperCamelCase :Dict=0 ,_UpperCamelCase :List[str]=False ,_UpperCamelCase :Optional[int]=0 ,_UpperCamelCase :Dict=1 ,_UpperCamelCase :List[Any]=False ,_UpperCamelCase :Tuple=True ,**_UpperCamelCase :List[Any] ,):
snake_case_ : List[str] = vocab_size
snake_case_ : Any = hidden_size
snake_case_ : Any = d_kv
snake_case_ : List[Any] = d_ff
snake_case_ : Union[str, Any] = num_layers
snake_case_ : Union[str, Any] = num_heads
snake_case_ : str = relative_attention_num_buckets
snake_case_ : Optional[int] = relative_attention_max_distance
snake_case_ : Tuple = dropout_rate
snake_case_ : Tuple = layer_norm_epsilon
snake_case_ : Any = initializer_factor
snake_case_ : List[Any] = use_cache
snake_case_ : Optional[int] = eos_token_id
snake_case_ : List[Any] = decoder_start_token_id
# for backwards compatibility
snake_case_ : List[str] = dense_act_fn
super().__init__(
pad_token_id=_UpperCamelCase ,eos_token_id=_UpperCamelCase ,decoder_start_token_id=_UpperCamelCase ,tie_word_embeddings=_UpperCamelCase ,is_decoder=_UpperCamelCase ,**_UpperCamelCase ,)
@classmethod
def a__ ( cls :Optional[int] ,_UpperCamelCase :Union[str, os.PathLike] ,**_UpperCamelCase :Optional[int] ):
cls._set_token_in_kwargs(_UpperCamelCase )
snake_case_ , snake_case_ : Optional[Any] = cls.get_config_dict(_UpperCamelCase ,**_UpperCamelCase )
# get the text config dict if we are loading from Pix2StructConfig
if config_dict.get("""model_type""" ) == "pix2struct":
snake_case_ : Optional[int] = 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(_UpperCamelCase ,**_UpperCamelCase )
class __UpperCamelCase ( lowercase__ ):
lowercase : Dict = 'pix2struct_vision_model'
def __init__( self :List[str] ,_UpperCamelCase :Any=7_6_8 ,_UpperCamelCase :List[str]=7_6_8 ,_UpperCamelCase :List[Any]=2_0_4_8 ,_UpperCamelCase :Union[str, Any]=6_4 ,_UpperCamelCase :int=1_2 ,_UpperCamelCase :int=1_2 ,_UpperCamelCase :Any="gelu_new" ,_UpperCamelCase :Optional[int]=1E-6 ,_UpperCamelCase :List[Any]=0.0 ,_UpperCamelCase :Union[str, Any]=0.0 ,_UpperCamelCase :int=1E-1_0 ,_UpperCamelCase :str=1.0 ,_UpperCamelCase :Optional[int]=4_0_9_6 ,_UpperCamelCase :str=3_2 ,_UpperCamelCase :Union[str, Any]=1_2_8 ,**_UpperCamelCase :Union[str, Any] ,):
super().__init__(**_UpperCamelCase )
snake_case_ : str = hidden_size
snake_case_ : Tuple = patch_embed_hidden_size
snake_case_ : Optional[int] = d_ff
snake_case_ : Dict = dropout_rate
snake_case_ : Optional[int] = num_hidden_layers
snake_case_ : Dict = num_attention_heads
snake_case_ : Union[str, Any] = initializer_range
snake_case_ : Optional[Any] = initializer_factor
snake_case_ : List[str] = attention_dropout
snake_case_ : List[str] = layer_norm_eps
snake_case_ : List[str] = dense_act_fn
snake_case_ : int = seq_len
snake_case_ : str = relative_attention_num_buckets
snake_case_ : Tuple = relative_attention_max_distance
snake_case_ : List[str] = d_kv
@classmethod
def a__ ( cls :Optional[Any] ,_UpperCamelCase :Union[str, os.PathLike] ,**_UpperCamelCase :List[str] ):
cls._set_token_in_kwargs(_UpperCamelCase )
snake_case_ , snake_case_ : Tuple = cls.get_config_dict(_UpperCamelCase ,**_UpperCamelCase )
# get the vision config dict if we are loading from Pix2StructConfig
if config_dict.get("""model_type""" ) == "pix2struct":
snake_case_ : Dict = 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(_UpperCamelCase ,**_UpperCamelCase )
class __UpperCamelCase ( lowercase__ ):
lowercase : Union[str, Any] = 'pix2struct'
lowercase : int = True
def __init__( self :int ,_UpperCamelCase :Tuple=None ,_UpperCamelCase :Optional[int]=None ,_UpperCamelCase :Dict=1.0 ,_UpperCamelCase :Optional[int]=0.02 ,_UpperCamelCase :Tuple=False ,_UpperCamelCase :List[str]=False ,_UpperCamelCase :Dict=True ,**_UpperCamelCase :Union[str, Any] ,):
super().__init__(tie_word_embeddings=_UpperCamelCase ,is_encoder_decoder=_UpperCamelCase ,**_UpperCamelCase )
if text_config is None:
snake_case_ : Optional[int] = {}
logger.info("""text_config is None. Initializing the Pix2StructTextConfig with default values.""" )
if vision_config is None:
snake_case_ : List[str] = {}
logger.info("""vision_config is None. Initializing the Pix2StructVisionConfig with default values.""" )
snake_case_ : Optional[int] = PixaStructTextConfig(**_UpperCamelCase )
snake_case_ : List[str] = PixaStructVisionConfig(**_UpperCamelCase )
snake_case_ : Any = self.text_config.decoder_start_token_id
snake_case_ : List[Any] = self.text_config.pad_token_id
snake_case_ : Optional[int] = self.text_config.eos_token_id
snake_case_ : Optional[int] = initializer_factor
snake_case_ : List[str] = initializer_range
snake_case_ : Union[str, Any] = self.initializer_range
snake_case_ : str = self.initializer_range
snake_case_ : Dict = is_vqa
@classmethod
def a__ ( cls :Any ,_UpperCamelCase :PixaStructTextConfig ,_UpperCamelCase :PixaStructVisionConfig ,**_UpperCamelCase :Any ):
return cls(text_config=text_config.to_dict() ,vision_config=vision_config.to_dict() ,**_UpperCamelCase )
def a__ ( self :Optional[int] ):
snake_case_ : int = copy.deepcopy(self.__dict__ )
snake_case_ : str = self.text_config.to_dict()
snake_case_ : Tuple = self.vision_config.to_dict()
snake_case_ : Any = self.__class__.model_type
return output | 267 | 0 |
'''simple docstring'''
import os
def lowercase ( ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = os.path.dirname(os.path.realpath(__magic_name__ ) )
UpperCAmelCase : Any = os.path.join(__magic_name__ , "triangle.txt" )
with open(__magic_name__ ) as f:
UpperCAmelCase : str = f.readlines()
UpperCAmelCase : Optional[int] = []
for line in triangle:
UpperCAmelCase : List[str] = []
for number in line.strip().split(" " ):
numbers_from_line.append(int(__magic_name__ ) )
a.append(__magic_name__ )
for i in range(1 , len(__magic_name__ ) ):
for j in range(len(a[i] ) ):
UpperCAmelCase : Union[str, Any] = a[i - 1][j] if j != len(a[i - 1] ) else 0
UpperCAmelCase : List[str] = a[i - 1][j - 1] if j > 0 else 0
a[i][j] += max(__magic_name__ , __magic_name__ )
return max(a[-1] )
if __name__ == "__main__":
print(solution())
| 679 |
'''simple docstring'''
import os
import tempfile
import unittest
import uuid
from pathlib import Path
from transformers.testing_utils import get_tests_dir, require_soundfile, require_torch, require_vision
from transformers.tools.agent_types import AgentAudio, AgentImage, AgentText
from transformers.utils import is_soundfile_availble, is_torch_available, is_vision_available
if is_torch_available():
import torch
if is_soundfile_availble():
import soundfile as sf
if is_vision_available():
from PIL import Image
def lowercase ( __magic_name__="" ):
'''simple docstring'''
UpperCAmelCase : Dict = tempfile.mkdtemp()
return os.path.join(__magic_name__ , str(uuid.uuida() ) + suffix )
@require_soundfile
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Any = torch.rand(1_2 , dtype=torch.floataa ) - 0.5
UpperCAmelCase : int = AgentAudio(snake_case )
UpperCAmelCase : str = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(snake_case , agent_type.to_raw() , atol=1e-4 ) )
del agent_type
# Ensure the path remains even after the object deletion
self.assertTrue(os.path.exists(snake_case ) )
# Ensure that the file contains the same value as the original tensor
UpperCAmelCase , UpperCAmelCase : str = sf.read(snake_case )
self.assertTrue(torch.allclose(snake_case , torch.tensor(snake_case ) , atol=1e-4 ) )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = torch.rand(1_2 , dtype=torch.floataa ) - 0.5
UpperCAmelCase : Any = get_new_path(suffix=".wav" )
sf.write(snake_case , snake_case , 1_6_0_0_0 )
UpperCAmelCase : Optional[Any] = AgentAudio(snake_case )
self.assertTrue(torch.allclose(snake_case , agent_type.to_raw() , atol=1e-4 ) )
self.assertEqual(agent_type.to_string() , snake_case )
@require_vision
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = torch.randint(0 , 2_5_6 , (6_4, 6_4, 3) )
UpperCAmelCase : Tuple = AgentImage(snake_case )
UpperCAmelCase : Tuple = str(agent_type.to_string() )
# Ensure that the tensor and the agent_type's tensor are the same
self.assertTrue(torch.allclose(snake_case , agent_type._tensor , atol=1e-4 ) )
self.assertIsInstance(agent_type.to_raw() , Image.Image )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(snake_case ) )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
UpperCAmelCase : Any = Image.open(snake_case )
UpperCAmelCase : List[str] = AgentImage(snake_case )
self.assertTrue(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(snake_case ) )
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = Path(get_tests_dir("fixtures/tests_samples/COCO" ) ) / "000000039769.png"
UpperCAmelCase : Dict = Image.open(snake_case )
UpperCAmelCase : int = AgentImage(snake_case )
self.assertFalse(path.samefile(agent_type.to_string() ) )
self.assertTrue(image == agent_type.to_raw() )
# Ensure the path remains even after the object deletion
del agent_type
self.assertTrue(os.path.exists(snake_case ) )
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def A_ ( self ):
'''simple docstring'''
UpperCAmelCase : Any = "Hey!"
UpperCAmelCase : Tuple = AgentText(snake_case )
self.assertEqual(snake_case , agent_type.to_string() )
self.assertEqual(snake_case , agent_type.to_raw() )
self.assertEqual(snake_case , snake_case )
| 679 | 1 |
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
__A : Optional[int] = logging.get_logger(__name__)
__A : Any = "T5Config"
def lowercase ( UpperCamelCase : jnp.array , UpperCamelCase : int , UpperCamelCase : int ):
"""simple docstring"""
A__ : Dict =jnp.zeros_like(UpperCamelCase )
A__ : List[str] =shifted_input_ids.at[:, 1:].set(input_ids[:, :-1] )
A__ : Union[str, Any] =shifted_input_ids.at[:, 0].set(UpperCamelCase )
A__ : Optional[Any] =jnp.where(shifted_input_ids == -100 , UpperCamelCase , UpperCamelCase )
return shifted_input_ids
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''
__magic_name__ : List[Any] = """mt5"""
__magic_name__ : Optional[Any] = MTaConfig
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''
__magic_name__ : Dict = """mt5"""
__magic_name__ : List[Any] = MTaConfig
class __lowerCAmelCase ( _UpperCamelCase):
'''simple docstring'''
__magic_name__ : Tuple = """mt5"""
__magic_name__ : Dict = MTaConfig
| 595 | """simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__A : Dict = {
"configuration_rembert": ["REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "RemBertConfig", "RemBertOnnxConfig"]
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : str = ["RemBertTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Tuple = ["RemBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
"REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"RemBertForCausalLM",
"RemBertForMaskedLM",
"RemBertForMultipleChoice",
"RemBertForQuestionAnswering",
"RemBertForSequenceClassification",
"RemBertForTokenClassification",
"RemBertLayer",
"RemBertModel",
"RemBertPreTrainedModel",
"load_tf_weights_in_rembert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : List[str] = [
"TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFRemBertForCausalLM",
"TFRemBertForMaskedLM",
"TFRemBertForMultipleChoice",
"TFRemBertForQuestionAnswering",
"TFRemBertForSequenceClassification",
"TFRemBertForTokenClassification",
"TFRemBertLayer",
"TFRemBertModel",
"TFRemBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_rembert import REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RemBertConfig, RemBertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert import RemBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_rembert_fast import RemBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_rembert import (
REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RemBertForCausalLM,
RemBertForMaskedLM,
RemBertForMultipleChoice,
RemBertForQuestionAnswering,
RemBertForSequenceClassification,
RemBertForTokenClassification,
RemBertLayer,
RemBertModel,
RemBertPreTrainedModel,
load_tf_weights_in_rembert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_rembert import (
TF_REMBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRemBertForCausalLM,
TFRemBertForMaskedLM,
TFRemBertForMultipleChoice,
TFRemBertForQuestionAnswering,
TFRemBertForSequenceClassification,
TFRemBertForTokenClassification,
TFRemBertLayer,
TFRemBertModel,
TFRemBertPreTrainedModel,
)
else:
import sys
__A : Union[str, Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 595 | 1 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" , """False""" ) ) is not True , reason="""Skipping test because should only be run when releasing minor transformers version""" , )
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.p3.16xlarge""",
"""results""": {"""train_runtime""": 6_50, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
},
{
"""framework""": """pytorch""",
"""script""": """run_ddp.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.p3.16xlarge""",
"""results""": {"""train_runtime""": 6_00, """eval_accuracy""": 0.7, """eval_loss""": 0.6},
},
{
"""framework""": """tensorflow""",
"""script""": """run_tf_dist.py""",
"""model_name_or_path""": """distilbert-base-cased""",
"""instance_type""": """ml.p3.16xlarge""",
"""results""": {"""train_runtime""": 6_00, """eval_accuracy""": 0.6, """eval_loss""": 0.7},
},
] )
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
def UpperCamelCase__( self ):
'''simple docstring'''
if self.framework == "pytorch":
subprocess.run(
F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding='''utf-8''' , check=__lowerCamelCase , )
assert hasattr(self , '''env''' )
def UpperCamelCase__( self , __lowerCamelCase ):
'''simple docstring'''
__A : Dict = F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
# distributed data settings
__A : List[Any] = {'''smdistributed''': {'''dataparallel''': {'''enabled''': True}}} if self.script != '''run_ddp.py''' else None
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=__lowerCamelCase , instance_count=__lowerCamelCase , instance_type=self.instance_type , debugger_hook_config=__lowerCamelCase , hyperparameters={**self.env.distributed_hyperparameters, '''model_name_or_path''': self.model_name_or_path} , metric_definitions=self.env.metric_definitions , distribution=__lowerCamelCase , py_version='''py36''' , )
def UpperCamelCase__( self , __lowerCamelCase ):
'''simple docstring'''
TrainingJobAnalytics(__lowerCamelCase ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(2,)] )
def UpperCamelCase__( self , __lowerCamelCase ):
'''simple docstring'''
__A : Optional[Any] = self.create_estimator(__lowerCamelCase )
# run training
estimator.fit()
# result dataframe
__A : Union[str, Any] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
__A : List[str] = list(result_metrics_df[result_metrics_df.metric_name == '''eval_accuracy''']['''value'''] )
__A : Any = list(result_metrics_df[result_metrics_df.metric_name == '''eval_loss''']['''value'''] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
__A : List[Any] = (
Session().describe_training_job(estimator.latest_training_job.name ).get('''TrainingTimeInSeconds''' , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results['''eval_accuracy'''] for t in eval_accuracy )
assert all(t <= self.results['''eval_loss'''] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F"""{estimator.latest_training_job.name}.json""" , '''w''' ) as outfile:
json.dump({'''train_time''': train_runtime, '''eval_accuracy''': eval_accuracy, '''eval_loss''': eval_loss} , __lowerCamelCase )
| 177 |
"""simple docstring"""
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
if is_torch_available():
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
@require_torch
@require_sentencepiece
@require_tokenizers
class __snake_case ( unittest.TestCase ):
"""simple docstring"""
@slow
def UpperCamelCase__( self ):
'''simple docstring'''
__A : Any = AutoModelForSeqaSeqLM.from_pretrained('''google/mt5-small''' , return_dict=__lowerCamelCase ).to(__lowerCamelCase )
__A : Optional[Any] = AutoTokenizer.from_pretrained('''google/mt5-small''' )
__A : Optional[int] = tokenizer('''Hello there''' , return_tensors='''pt''' ).input_ids
__A : str = tokenizer('''Hi I am''' , return_tensors='''pt''' ).input_ids
__A : Any = model(input_ids.to(__lowerCamelCase ) , labels=labels.to(__lowerCamelCase ) ).loss
__A : Tuple = -(labels.shape[-1] * loss.item())
__A : int = -8_4.9_1_2_7
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 177 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__A : List[str] = {"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 : List[str] = [
"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 : Tuple = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 707 |
"""simple docstring"""
from scipy.stats import spearmanr
import datasets
__A : Optional[int] = '\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correlations imply that as data in dataset x increases, so\ndoes data in dataset y. Negative correlations imply that as x increases,\ny decreases. Correlations of -1 or +1 imply an exact monotonic relationship.\n\nUnlike the Pearson correlation, the Spearman correlation does not\nassume that both datasets are normally distributed.\n\nThe p-value roughly indicates the probability of an uncorrelated system\nproducing datasets that have a Spearman correlation at least as extreme\nas the one computed from these datasets. The p-values are not entirely\nreliable but are probably reasonable for datasets larger than 500 or so.\n'
__A : List[Any] = '\nArgs:\n predictions (`List[float]`): Predicted labels, as returned by a model.\n references (`List[float]`): Ground truth labels.\n return_pvalue (`bool`): If `True`, returns the p-value. If `False`, returns\n only the spearmanr score. Defaults to `False`.\nReturns:\n spearmanr (`float`): Spearman correlation coefficient.\n p-value (`float`): p-value. **Note**: is only returned if `return_pvalue=True` is input.\nExamples:\n Example 1:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5], predictions=[10, 9, 2.5, 6, 4])\n >>> print(results)\n {\'spearmanr\': -0.7}\n\n Example 2:\n >>> spearmanr_metric = datasets.load_metric("spearmanr")\n >>> results = spearmanr_metric.compute(references=[1, 2, 3, 4, 5],\n ... predictions=[10, 9, 2.5, 6, 4],\n ... return_pvalue=True)\n >>> print(results[\'spearmanr\'])\n -0.7\n >>> print(round(results[\'spearmanr_pvalue\'], 2))\n 0.19\n'
__A : Optional[int] = R'\\n@book{kokoska2000crc,\n title={CRC standard probability and statistics tables and formulae},\n author={Kokoska, Stephen and Zwillinger, Daniel},\n year={2000},\n publisher={Crc Press}\n}\n@article{2020SciPy-NMeth,\n author = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and\n Haberland, Matt and Reddy, Tyler and Cournapeau, David and\n Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and\n Bright, Jonathan and {van der Walt}, St{\'e}fan J. and\n Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and\n Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and\n Kern, Robert and Larson, Eric and Carey, C J and\n Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and\n {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and\n Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and\n Harris, Charles R. and Archibald, Anne M. and\n Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and\n {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},\n title = {{{SciPy} 1.0: Fundamental Algorithms for Scientific\n Computing in Python}},\n journal = {Nature Methods},\n year = {2020},\n volume = {17},\n pages = {261--272},\n adsurl = {https://rdcu.be/b08Wh},\n doi = {10.1038/s41592-019-0686-2},\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCAmelCase__ ( datasets.Metric ):
"""simple docstring"""
def snake_case ( self : List[str] ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("float" ),
"references": datasets.Value("float" ),
} ) , reference_urls=["https://docs.scipy.org/doc/scipy/reference/generated/scipy.stats.spearmanr.html"] , )
def snake_case ( self : Optional[int] , lowercase__ : str , lowercase__ : Any , lowercase__ : int=False ):
__lowercase : Optional[int] = spearmanr(lowercase__ , lowercase__ )
if return_pvalue:
return {"spearmanr": results[0], "spearmanr_pvalue": results[1]}
else:
return {"spearmanr": results[0]}
| 281 | 0 |
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__UpperCamelCase : Tuple = 16
__UpperCamelCase : List[str] = 32
def _UpperCAmelCase ( UpperCAmelCase : Accelerator , UpperCAmelCase : int = 16 ):
"""simple docstring"""
__lowerCamelCase : List[str] = AutoTokenizer.from_pretrained("""bert-base-cased""" )
__lowerCamelCase : Tuple = load_dataset("""glue""" , """mrpc""" )
def tokenize_function(UpperCAmelCase : Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
__lowerCamelCase : str = tokenizer(examples["""sentence1"""] , examples["""sentence2"""] , truncation=UpperCAmelCase , max_length=UpperCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
__lowerCamelCase : Dict = datasets.map(
UpperCAmelCase , batched=UpperCAmelCase , remove_columns=["""idx""", """sentence1""", """sentence2"""] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
__lowerCamelCase : Optional[Any] = tokenized_datasets.rename_column("""label""" , """labels""" )
def collate_fn(UpperCAmelCase : Optional[int] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
__lowerCamelCase : Optional[Any] = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
__lowerCamelCase : int = 16
elif accelerator.mixed_precision != "no":
__lowerCamelCase : List[str] = 8
else:
__lowerCamelCase : Any = None
return tokenizer.pad(
UpperCAmelCase , padding="""longest""" , max_length=UpperCAmelCase , pad_to_multiple_of=UpperCAmelCase , return_tensors="""pt""" , )
# Instantiate dataloaders.
__lowerCamelCase : Optional[Any] = DataLoader(
tokenized_datasets["""train"""] , shuffle=UpperCAmelCase , collate_fn=UpperCAmelCase , batch_size=UpperCAmelCase , drop_last=UpperCAmelCase )
__lowerCamelCase : Optional[int] = DataLoader(
tokenized_datasets["""validation"""] , shuffle=UpperCAmelCase , collate_fn=UpperCAmelCase , batch_size=UpperCAmelCase , drop_last=(accelerator.mixed_precision == """fp8""") , )
return train_dataloader, eval_dataloader
def _UpperCAmelCase ( UpperCAmelCase : Tuple , UpperCAmelCase : Tuple ):
"""simple docstring"""
__lowerCamelCase : Any = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
__lowerCamelCase : List[str] = config["""lr"""]
__lowerCamelCase : int = int(config["""num_epochs"""] )
__lowerCamelCase : Optional[Any] = int(config["""seed"""] )
__lowerCamelCase : Optional[int] = int(config["""batch_size"""] )
__lowerCamelCase : List[str] = evaluate.load("""glue""" , """mrpc""" )
# If the batch size is too big we use gradient accumulation
__lowerCamelCase : Any = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
__lowerCamelCase : int = batch_size // MAX_GPU_BATCH_SIZE
__lowerCamelCase : List[str] = MAX_GPU_BATCH_SIZE
set_seed(UpperCAmelCase )
__lowerCamelCase , __lowerCamelCase : Union[str, Any] = get_dataloaders(UpperCAmelCase , UpperCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
__lowerCamelCase : Union[str, Any] = AutoModelForSequenceClassification.from_pretrained("""bert-base-cased""" , return_dict=UpperCAmelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
__lowerCamelCase : str = model.to(accelerator.device )
# Instantiate optimizer
__lowerCamelCase : Tuple = AdamW(params=model.parameters() , lr=UpperCAmelCase )
# Instantiate scheduler
__lowerCamelCase : int = get_linear_schedule_with_warmup(
optimizer=UpperCAmelCase , num_warmup_steps=100 , num_training_steps=(len(UpperCAmelCase ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase : List[str] = accelerator.prepare(
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# Now we train the model
for epoch in range(UpperCAmelCase ):
model.train()
for step, batch in enumerate(UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
__lowerCamelCase : int = model(**UpperCAmelCase )
__lowerCamelCase : Optional[int] = outputs.loss
__lowerCamelCase : Optional[int] = loss / gradient_accumulation_steps
accelerator.backward(UpperCAmelCase )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
__lowerCamelCase : Dict = model(**UpperCAmelCase )
__lowerCamelCase : List[str] = outputs.logits.argmax(dim=-1 )
__lowerCamelCase , __lowerCamelCase : str = accelerator.gather_for_metrics((predictions, batch["""labels"""]) )
metric.add_batch(
predictions=UpperCAmelCase , references=UpperCAmelCase , )
__lowerCamelCase : List[str] = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""" , UpperCAmelCase )
def _UpperCAmelCase ( ):
"""simple docstring"""
__lowerCamelCase : List[Any] = argparse.ArgumentParser(description="""Simple example of training script.""" )
parser.add_argument(
"""--mixed_precision""" , type=UpperCAmelCase , default=UpperCAmelCase , choices=["""no""", """fp16""", """bf16""", """fp8"""] , help="""Whether to use mixed precision. Choose"""
"""between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."""
"""and an Nvidia Ampere GPU.""" , )
parser.add_argument("""--cpu""" , action="""store_true""" , help="""If passed, will train on the CPU.""" )
__lowerCamelCase : Dict = parser.parse_args()
__lowerCamelCase : Any = {"""lr""": 2e-5, """num_epochs""": 3, """seed""": 42, """batch_size""": 16}
training_function(UpperCAmelCase , UpperCAmelCase )
if __name__ == "__main__":
main()
| 519 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
__UpperCamelCase : Tuple = {
'E': 12.70,
'T': 9.06,
'A': 8.17,
'O': 7.51,
'I': 6.97,
'N': 6.75,
'S': 6.33,
'H': 6.09,
'R': 5.99,
'D': 4.25,
'L': 4.03,
'C': 2.78,
'U': 2.76,
'M': 2.41,
'W': 2.36,
'F': 2.23,
'G': 2.02,
'Y': 1.97,
'P': 1.93,
'B': 1.29,
'V': 0.98,
'K': 0.77,
'J': 0.15,
'X': 0.15,
'Q': 0.10,
'Z': 0.07,
}
__UpperCamelCase : Dict = 'ETAOINSHRDLCUMWFGYPBVKJXQZ'
__UpperCamelCase : List[Any] = 'ABCDEFGHIJKLMNOPQRSTUVWXYZ'
def _UpperCAmelCase ( UpperCAmelCase : str ):
"""simple docstring"""
__lowerCamelCase : str = {letter: 0 for letter in string.ascii_uppercase}
for letter in message.upper():
if letter in LETTERS:
letter_count[letter] += 1
return letter_count
def _UpperCAmelCase ( UpperCAmelCase : tuple ):
"""simple docstring"""
return x[0]
def _UpperCAmelCase ( UpperCAmelCase : str ):
"""simple docstring"""
__lowerCamelCase : Tuple = get_letter_count(UpperCAmelCase )
__lowerCamelCase : dict[int, list[str]] = {
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(UpperCAmelCase )
__lowerCamelCase : dict[int, str] = {}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=UpperCAmelCase )
__lowerCamelCase : Any = """""".join(freq_to_letter[freq] )
__lowerCamelCase : Optional[Any] = list(freq_to_letter_str.items() )
freq_pairs.sort(key=UpperCAmelCase , reverse=UpperCAmelCase )
__lowerCamelCase : list[str] = [freq_pair[1] for freq_pair in freq_pairs]
return "".join(UpperCAmelCase )
def _UpperCAmelCase ( UpperCAmelCase : str ):
"""simple docstring"""
__lowerCamelCase : List[str] = get_frequency_order(UpperCAmelCase )
__lowerCamelCase : Any = 0
for common_letter in ETAOIN[:6]:
if common_letter in freq_order[:6]:
match_score += 1
for uncommon_letter in ETAOIN[-6:]:
if uncommon_letter in freq_order[-6:]:
match_score += 1
return match_score
if __name__ == "__main__":
import doctest
doctest.testmod()
| 519 | 1 |
'''simple docstring'''
def _A ( A__ = 10 ):
"""simple docstring"""
if not isinstance(A__ , A__ ) or n < 0:
raise ValueError('''Invalid input''' )
__lowercase = 10**n
__lowercase = 28433 * (pow(2 , 7830457 , A__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(f'{solution(10) = }')
| 624 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCAmelCase__ = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ViTMAEForPreTraining''',
'''ViTMAELayer''',
'''ViTMAEModel''',
'''ViTMAEPreTrainedModel''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
'''TFViTMAEForPreTraining''',
'''TFViTMAEModel''',
'''TFViTMAEPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_vit_mae import VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTMAEConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit_mae import (
VIT_MAE_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTMAEForPreTraining,
ViTMAELayer,
ViTMAEModel,
ViTMAEPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit_mae import TFViTMAEForPreTraining, TFViTMAEModel, TFViTMAEPreTrainedModel
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 624 | 1 |
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def lowerCamelCase( a__ ,a__ ,a__=1e-12):
_SCREAMING_SNAKE_CASE =jnp.divide(emb_a.T ,jnp.clip(jnp.linalg.norm(a__ ,axis=1) ,a_min=a__)).T
_SCREAMING_SNAKE_CASE =jnp.divide(emb_a.T ,jnp.clip(jnp.linalg.norm(a__ ,axis=1) ,a_min=a__)).T
return jnp.matmul(a__ ,norm_emb_a.T)
class A__ ( nn.Module ):
UpperCAmelCase = 42
UpperCAmelCase = jnp.floataa
def __UpperCamelCase ( self : List[str] ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =FlaxCLIPVisionModule(self.config.vision_config )
_SCREAMING_SNAKE_CASE =nn.Dense(self.config.projection_dim , use_bias=_a , dtype=self.dtype )
_SCREAMING_SNAKE_CASE =self.param('''concept_embeds''' , jax.nn.initializers.ones , (17, self.config.projection_dim) )
_SCREAMING_SNAKE_CASE =self.param(
'''special_care_embeds''' , jax.nn.initializers.ones , (3, self.config.projection_dim) )
_SCREAMING_SNAKE_CASE =self.param('''concept_embeds_weights''' , jax.nn.initializers.ones , (17,) )
_SCREAMING_SNAKE_CASE =self.param('''special_care_embeds_weights''' , jax.nn.initializers.ones , (3,) )
def __call__( self : Optional[Any] , _a : Dict ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.vision_model(_a )[1]
_SCREAMING_SNAKE_CASE =self.visual_projection(_a )
_SCREAMING_SNAKE_CASE =jax_cosine_distance(_a , self.special_care_embeds )
_SCREAMING_SNAKE_CASE =jax_cosine_distance(_a , self.concept_embeds )
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
_SCREAMING_SNAKE_CASE =0.0
_SCREAMING_SNAKE_CASE =special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
_SCREAMING_SNAKE_CASE =jnp.round(_a , 3 )
_SCREAMING_SNAKE_CASE =jnp.any(special_scores > 0 , axis=1 , keepdims=_a )
# Use a lower threshold if an image has any special care concept
_SCREAMING_SNAKE_CASE =is_special_care * 0.01
_SCREAMING_SNAKE_CASE =cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
_SCREAMING_SNAKE_CASE =jnp.round(_a , 3 )
_SCREAMING_SNAKE_CASE =jnp.any(concept_scores > 0 , axis=1 )
return has_nsfw_concepts
class A__ ( UpperCamelCase__ ):
UpperCAmelCase = CLIPConfig
UpperCAmelCase = "clip_input"
UpperCAmelCase = FlaxStableDiffusionSafetyCheckerModule
def __init__( self : Tuple , _a : CLIPConfig , _a : Optional[Tuple] = None , _a : int = 0 , _a : jnp.dtype = jnp.floataa , _a : bool = True , **_a : Any , ) -> str:
"""simple docstring"""
if input_shape is None:
_SCREAMING_SNAKE_CASE =(1, 224, 224, 3)
_SCREAMING_SNAKE_CASE =self.module_class(config=_a , dtype=_a , **_a )
super().__init__(_a , _a , input_shape=_a , seed=_a , dtype=_a , _do_init=_do_init )
def __UpperCamelCase ( self : List[Any] , _a : jax.random.KeyArray , _a : Tuple , _a : FrozenDict = None ) -> FrozenDict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =jax.random.normal(_a , _a )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE =jax.random.split(_a )
_SCREAMING_SNAKE_CASE ={'''params''': params_rng, '''dropout''': dropout_rng}
_SCREAMING_SNAKE_CASE =self.module.init(_a , _a )['''params''']
return random_params
def __call__( self : str , _a : Union[str, Any] , _a : dict = None , ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =jnp.transpose(_a , (0, 2, 3, 1) )
return self.module.apply(
{'''params''': params or self.params} , jnp.array(_a , dtype=jnp.floataa ) , rngs={} , ) | 691 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor, ChineseCLIPProcessor
@require_vision
class A__ ( unittest.TestCase ):
def __UpperCamelCase ( self : int ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =tempfile.mkdtemp()
_SCREAMING_SNAKE_CASE =[
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''的''',
'''价''',
'''格''',
'''是''',
'''15''',
'''便''',
'''alex''',
'''##andra''',
'''，''',
'''。''',
'''-''',
'''t''',
'''shirt''',
]
_SCREAMING_SNAKE_CASE =os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as vocab_writer:
vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )
_SCREAMING_SNAKE_CASE ={
'''do_resize''': True,
'''size''': {'''height''': 224, '''width''': 224},
'''do_center_crop''': True,
'''crop_size''': {'''height''': 18, '''width''': 18},
'''do_normalize''': True,
'''image_mean''': [0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73],
'''image_std''': [0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11],
'''do_convert_rgb''': True,
}
_SCREAMING_SNAKE_CASE =os.path.join(self.tmpdirname , _a )
with open(self.image_processor_file , '''w''' , encoding='''utf-8''' ) as fp:
json.dump(_a , _a )
def __UpperCamelCase ( self : Optional[int] , **_a : str ) -> List[str]:
"""simple docstring"""
return BertTokenizer.from_pretrained(self.tmpdirname , **_a )
def __UpperCamelCase ( self : List[Any] , **_a : Any ) -> Dict:
"""simple docstring"""
return BertTokenizerFast.from_pretrained(self.tmpdirname , **_a )
def __UpperCamelCase ( self : int , **_a : Optional[Any] ) -> Any:
"""simple docstring"""
return ChineseCLIPImageProcessor.from_pretrained(self.tmpdirname , **_a )
def __UpperCamelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
shutil.rmtree(self.tmpdirname )
def __UpperCamelCase ( self : int ) -> Optional[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =[np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
_SCREAMING_SNAKE_CASE =[Image.fromarray(np.moveaxis(_a , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def __UpperCamelCase ( self : Any ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =self.get_rust_tokenizer()
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =ChineseCLIPProcessor(tokenizer=_a , image_processor=_a )
processor_slow.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE =ChineseCLIPProcessor.from_pretrained(self.tmpdirname , use_fast=_a )
_SCREAMING_SNAKE_CASE =ChineseCLIPProcessor(tokenizer=_a , image_processor=_a )
processor_fast.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE =ChineseCLIPProcessor.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 , _a )
self.assertIsInstance(processor_fast.tokenizer , _a )
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 , _a )
self.assertIsInstance(processor_fast.image_processor , _a )
def __UpperCamelCase ( self : str ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =ChineseCLIPProcessor(tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
_SCREAMING_SNAKE_CASE =self.get_tokenizer(cls_token='''(CLS)''' , sep_token='''(SEP)''' )
_SCREAMING_SNAKE_CASE =self.get_image_processor(do_normalize=_a )
_SCREAMING_SNAKE_CASE =ChineseCLIPProcessor.from_pretrained(
self.tmpdirname , cls_token='''(CLS)''' , sep_token='''(SEP)''' , do_normalize=_a )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , _a )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , _a )
def __UpperCamelCase ( self : List[Any] ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =ChineseCLIPProcessor(tokenizer=_a , image_processor=_a )
_SCREAMING_SNAKE_CASE =self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE =image_processor(_a , return_tensors='''np''' )
_SCREAMING_SNAKE_CASE =processor(images=_a , return_tensors='''np''' )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def __UpperCamelCase ( self : Union[str, Any] ) -> Any:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =ChineseCLIPProcessor(tokenizer=_a , image_processor=_a )
_SCREAMING_SNAKE_CASE ='''Alexandra，T-shirt的价格是15便士。'''
_SCREAMING_SNAKE_CASE =processor(text=_a )
_SCREAMING_SNAKE_CASE =tokenizer(_a )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key] )
def __UpperCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =ChineseCLIPProcessor(tokenizer=_a , image_processor=_a )
_SCREAMING_SNAKE_CASE ='''Alexandra，T-shirt的价格是15便士。'''
_SCREAMING_SNAKE_CASE =self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE =processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , ['''input_ids''', '''token_type_ids''', '''attention_mask''', '''pixel_values'''] )
# test if it raises when no input is passed
with pytest.raises(_a ):
processor()
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =ChineseCLIPProcessor(tokenizer=_a , image_processor=_a )
_SCREAMING_SNAKE_CASE =[[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
_SCREAMING_SNAKE_CASE =processor.batch_decode(_a )
_SCREAMING_SNAKE_CASE =tokenizer.batch_decode(_a )
self.assertListEqual(_a , _a )
def __UpperCamelCase ( self : Any ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.get_image_processor()
_SCREAMING_SNAKE_CASE =self.get_tokenizer()
_SCREAMING_SNAKE_CASE =ChineseCLIPProcessor(tokenizer=_a , image_processor=_a )
_SCREAMING_SNAKE_CASE ='''Alexandra，T-shirt的价格是15便士。'''
_SCREAMING_SNAKE_CASE =self.prepare_image_inputs()
_SCREAMING_SNAKE_CASE =processor(text=_a , images=_a )
self.assertListEqual(list(inputs.keys() ) , processor.model_input_names ) | 691 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
SCREAMING_SNAKE_CASE__ = False
class a_ ( unittest.TestCase ):
pass
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
def A__ ( self ) -> str:
"""simple docstring"""
UpperCamelCase = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(_a )
pipe.set_progress_bar_config(disable=_a )
UpperCamelCase = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
UpperCamelCase = torch.manual_seed(0 )
UpperCamelCase = pipe(
image=_a , generator=_a , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
UpperCamelCase = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
UpperCamelCase = np.array([0.0_4_4_1, 0.0_4_6_9, 0.0_5_0_7, 0.0_5_7_5, 0.0_6_3_2, 0.0_6_5_0, 0.0_8_6_5, 0.0_9_0_9, 0.0_9_4_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 709 |
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = 8.31_44_62 # Unit - J mol-1 K-1
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def lowercase__ ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase )-> float:
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()
| 35 | 0 |
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = name
snake_case_ = val
def __str__( self ):
"""simple docstring"""
return f"""{self.__class__.__name__}({self.name}, {self.val})"""
def __lt__( self , __UpperCamelCase ):
"""simple docstring"""
return self.val < other.val
class SCREAMING_SNAKE_CASE :
"""simple docstring"""
def __init__( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = {}
snake_case_ = {}
snake_case_ = self.build_heap(__UpperCamelCase )
def __getitem__( self , __UpperCamelCase ):
"""simple docstring"""
return self.get_value(__UpperCamelCase )
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return (idx - 1) // 2
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return idx * 2 + 1
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return idx * 2 + 2
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
return self.heap_dict[key]
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = len(__UpperCamelCase ) - 1
snake_case_ = self.get_parent_idx(__UpperCamelCase )
for idx, i in enumerate(__UpperCamelCase ):
snake_case_ = idx
snake_case_ = i.val
for i in range(__UpperCamelCase , -1 , -1 ):
self.sift_down(__UpperCamelCase , __UpperCamelCase )
return array
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
while True:
snake_case_ = self.get_left_child_idx(__UpperCamelCase ) # noqa: E741
snake_case_ = self.get_right_child_idx(__UpperCamelCase )
snake_case_ = idx
if l < len(__UpperCamelCase ) and array[l] < array[idx]:
snake_case_ = l
if r < len(__UpperCamelCase ) and array[r] < array[smallest]:
snake_case_ = r
if smallest != idx:
snake_case_ , snake_case_ = array[smallest], array[idx]
(
(
snake_case_
) , (
snake_case_
) ,
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
snake_case_ = smallest
else:
break
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
snake_case_ = self.get_parent_idx(__UpperCamelCase )
while p >= 0 and self.heap[p] > self.heap[idx]:
snake_case_ , snake_case_ = self.heap[idx], self.heap[p]
snake_case_ , snake_case_ = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
snake_case_ = p
snake_case_ = self.get_parent_idx(__UpperCamelCase )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return self.heap[0]
def __lowerCAmelCase ( self ):
"""simple docstring"""
snake_case_ , snake_case_ = self.heap[-1], self.heap[0]
snake_case_ , snake_case_ = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
snake_case_ = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def __lowerCAmelCase ( self , __UpperCamelCase ):
"""simple docstring"""
self.heap.append(__UpperCamelCase )
snake_case_ = len(self.heap ) - 1
snake_case_ = node.val
self.sift_up(len(self.heap ) - 1 )
def __lowerCAmelCase ( self ):
"""simple docstring"""
return len(self.heap ) == 0
def __lowerCAmelCase ( self , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
snake_case_ = new_value
snake_case_ = new_value
self.sift_up(self.idx_of_element[node] )
A = Node('R', -1)
A = Node('B', 6)
A = Node('A', 3)
A = Node('X', 1)
A = Node('E', 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
A = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print('Min Heap - before decrease key')
for i in my_min_heap.heap:
print(i)
print('Min Heap - After decrease key of node [B -> -17]')
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 187 |
import functools
def a(lowercase__ , lowercase__ ):
'''simple docstring'''
# Validation
if not isinstance(lowercase__ , lowercase__ ) or not all(isinstance(lowercase__ , lowercase__ ) for day in days ):
raise ValueError('The parameter days should be a list of integers' )
if len(lowercase__ ) != 3 or not all(isinstance(lowercase__ , lowercase__ ) for cost in costs ):
raise ValueError('The parameter costs should be a list of three integers' )
if len(lowercase__ ) == 0:
return 0
if min(lowercase__ ) <= 0:
raise ValueError('All days elements should be greater than 0' )
if max(lowercase__ ) >= 366:
raise ValueError('All days elements should be less than 366' )
snake_case_ = set(lowercase__ )
@functools.cache
def dynamic_programming(lowercase__ ) -> int:
if index > 365:
return 0
if index not in days_set:
return dynamic_programming(index + 1 )
return min(
costs[0] + dynamic_programming(index + 1 ) , costs[1] + dynamic_programming(index + 7 ) , costs[2] + dynamic_programming(index + 30 ) , )
return dynamic_programming(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 187 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class lowercase__ ( _UpperCAmelCase ):
A__ : Tuple ="""open-llama"""
def __init__( self : Tuple , UpperCAmelCase_ : str=100000 , UpperCAmelCase_ : Dict=4096 , UpperCAmelCase_ : Optional[int]=11008 , UpperCAmelCase_ : Optional[int]=32 , UpperCAmelCase_ : Tuple=32 , UpperCAmelCase_ : Optional[int]="silu" , UpperCAmelCase_ : Tuple=2048 , UpperCAmelCase_ : Any=0.02 , UpperCAmelCase_ : List[Any]=1e-6 , UpperCAmelCase_ : Tuple=True , UpperCAmelCase_ : int=0 , UpperCAmelCase_ : Optional[int]=1 , UpperCAmelCase_ : Any=2 , UpperCAmelCase_ : Dict=False , UpperCAmelCase_ : int=True , UpperCAmelCase_ : List[str]=0.1 , UpperCAmelCase_ : int=0.1 , UpperCAmelCase_ : Union[str, Any]=True , UpperCAmelCase_ : List[str]=True , UpperCAmelCase_ : Dict=None , **UpperCAmelCase_ : Dict , ):
SCREAMING_SNAKE_CASE__ = vocab_size
SCREAMING_SNAKE_CASE__ = max_position_embeddings
SCREAMING_SNAKE_CASE__ = hidden_size
SCREAMING_SNAKE_CASE__ = intermediate_size
SCREAMING_SNAKE_CASE__ = num_hidden_layers
SCREAMING_SNAKE_CASE__ = num_attention_heads
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = rms_norm_eps
SCREAMING_SNAKE_CASE__ = use_cache
SCREAMING_SNAKE_CASE__ = kwargs.pop(
'use_memorry_efficient_attention' , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_dropout_prob
SCREAMING_SNAKE_CASE__ = use_stable_embedding
SCREAMING_SNAKE_CASE__ = shared_input_output_embedding
SCREAMING_SNAKE_CASE__ = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=UpperCAmelCase_ , bos_token_id=UpperCAmelCase_ , eos_token_id=UpperCAmelCase_ , tie_word_embeddings=UpperCAmelCase_ , **UpperCAmelCase_ , )
def A_ ( self : str ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , UpperCAmelCase_ ) or len(self.rope_scaling ) != 2:
raise ValueError(
'`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
F'got {self.rope_scaling}' )
SCREAMING_SNAKE_CASE__ = self.rope_scaling.get('type' , UpperCAmelCase_ )
SCREAMING_SNAKE_CASE__ = self.rope_scaling.get('factor' , UpperCAmelCase_ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(UpperCAmelCase_ , UpperCAmelCase_ ) or rope_scaling_factor <= 1.0:
raise ValueError(F'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 400 |
from collections.abc import Callable
def _lowercase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ) -> float:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = a
SCREAMING_SNAKE_CASE__ = b
if function(UpperCamelCase_ ) == 0: # one of the a or b is a root for the function
return a
elif function(UpperCamelCase_ ) == 0:
return b
elif (
function(UpperCamelCase_ ) * function(UpperCamelCase_ ) > 0
): # if none of these are root and they are both positive or negative,
# then this algorithm can't find the root
raise ValueError('could not find root in given interval.' )
else:
SCREAMING_SNAKE_CASE__ = start + (end - start) / 2.0
while abs(start - mid ) > 10**-7: # until precisely equals to 10^-7
if function(UpperCamelCase_ ) == 0:
return mid
elif function(UpperCamelCase_ ) * function(UpperCamelCase_ ) < 0:
SCREAMING_SNAKE_CASE__ = mid
else:
SCREAMING_SNAKE_CASE__ = mid
SCREAMING_SNAKE_CASE__ = start + (end - start) / 2.0
return mid
def _lowercase ( UpperCamelCase_ ) -> float:
'''simple docstring'''
return x**3 - 2 * x - 5
if __name__ == "__main__":
print(bisection(f, 1, 10_00))
import doctest
doctest.testmod()
| 400 | 1 |
"""simple docstring"""
def _lowerCAmelCase(a : int , a : int ) -> str:
if a < 0 or b < 0:
raise ValueError('''the value of both inputs must be positive''' )
_SCREAMING_SNAKE_CASE =str(bin(SCREAMING_SNAKE_CASE__ ) )[2:] # remove the leading "0b"
_SCREAMING_SNAKE_CASE =str(bin(SCREAMING_SNAKE_CASE__ ) )[2:] # remove the leading "0b"
_SCREAMING_SNAKE_CASE =max(len(SCREAMING_SNAKE_CASE__ ) , len(SCREAMING_SNAKE_CASE__ ) )
return "0b" + "".join(
str(int(char_a == '''1''' and char_b == '''1''' ) )
for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE__ ) , b_binary.zfill(SCREAMING_SNAKE_CASE__ ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 255 |
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class snake_case ( SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
snake_case_ : Optional[int] = ["""image_processor""", """tokenizer"""]
snake_case_ : Optional[int] = """LayoutLMv2ImageProcessor"""
snake_case_ : Dict = ("""LayoutXLMTokenizer""", """LayoutXLMTokenizerFast""")
def __init__( self : Optional[int] , lowerCAmelCase : List[str]=None , lowerCAmelCase : List[Any]=None , **lowerCAmelCase : List[str]) -> Optional[Any]:
"""simple docstring"""
if "feature_extractor" in kwargs:
warnings.warn(
"""The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"""
""" instead.""" , lowerCAmelCase , )
_snake_case : Tuple = kwargs.pop("""feature_extractor""")
_snake_case : List[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__(lowerCAmelCase , lowerCAmelCase)
def __call__( self : List[str] , lowerCAmelCase : Optional[Any] , lowerCAmelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , lowerCAmelCase : Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None , lowerCAmelCase : Union[List[List[int]], List[List[List[int]]]] = None , lowerCAmelCase : Optional[Union[List[int], List[List[int]]]] = None , lowerCAmelCase : bool = True , lowerCAmelCase : Union[bool, str, PaddingStrategy] = False , lowerCAmelCase : Union[bool, str, TruncationStrategy] = None , lowerCAmelCase : Optional[int] = None , lowerCAmelCase : int = 0 , lowerCAmelCase : Optional[int] = None , lowerCAmelCase : Optional[bool] = None , lowerCAmelCase : Optional[bool] = None , lowerCAmelCase : bool = False , lowerCAmelCase : bool = False , lowerCAmelCase : bool = False , lowerCAmelCase : bool = False , lowerCAmelCase : bool = True , lowerCAmelCase : Optional[Union[str, TensorType]] = None , **lowerCAmelCase : Optional[int] , ) -> BatchEncoding:
"""simple docstring"""
if self.image_processor.apply_ocr and (boxes is not None):
raise ValueError(
"""You cannot provide bounding boxes """
"""if you initialized the image processor with apply_ocr set to True.""")
if self.image_processor.apply_ocr and (word_labels is not None):
raise ValueError(
"""You cannot provide word labels if you initialized the image processor with apply_ocr set to True.""")
if return_overflowing_tokens is True and return_offsets_mapping is False:
raise ValueError("""You cannot return overflowing tokens without returning the offsets mapping.""")
# first, apply the image processor
_snake_case : str = self.image_processor(images=lowerCAmelCase , return_tensors=lowerCAmelCase)
# second, apply the tokenizer
if text is not None and self.image_processor.apply_ocr and text_pair is None:
if isinstance(lowerCAmelCase , lowerCAmelCase):
_snake_case : Optional[Any] = [text] # add batch dimension (as the image processor always adds a batch dimension)
_snake_case : Optional[Any] = features["""words"""]
_snake_case : Tuple = self.tokenizer(
text=text if text is not None else features["""words"""] , text_pair=text_pair if text_pair is not None else None , boxes=boxes if boxes is not None else features["""boxes"""] , word_labels=lowerCAmelCase , add_special_tokens=lowerCAmelCase , padding=lowerCAmelCase , truncation=lowerCAmelCase , max_length=lowerCAmelCase , stride=lowerCAmelCase , pad_to_multiple_of=lowerCAmelCase , return_token_type_ids=lowerCAmelCase , return_attention_mask=lowerCAmelCase , return_overflowing_tokens=lowerCAmelCase , return_special_tokens_mask=lowerCAmelCase , return_offsets_mapping=lowerCAmelCase , return_length=lowerCAmelCase , verbose=lowerCAmelCase , return_tensors=lowerCAmelCase , **lowerCAmelCase , )
# add pixel values
_snake_case : Optional[int] = features.pop("""pixel_values""")
if return_overflowing_tokens is True:
_snake_case : Tuple = self.get_overflowing_images(lowerCAmelCase , encoded_inputs["""overflow_to_sample_mapping"""])
_snake_case : int = images
return encoded_inputs
def UpperCamelCase_ ( self : Tuple , lowerCAmelCase : str , lowerCAmelCase : Tuple) -> Tuple:
"""simple docstring"""
_snake_case : int = []
for sample_idx in overflow_to_sample_mapping:
images_with_overflow.append(images[sample_idx])
if len(lowerCAmelCase) != len(lowerCAmelCase):
raise ValueError(
"""Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"""
F''' {len(lowerCAmelCase)} and {len(lowerCAmelCase)}''')
return images_with_overflow
def UpperCamelCase_ ( self : List[Any] , *lowerCAmelCase : Union[str, Any] , **lowerCAmelCase : Optional[int]) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*lowerCAmelCase , **lowerCAmelCase)
def UpperCamelCase_ ( self : Dict , *lowerCAmelCase : Any , **lowerCAmelCase : int) -> List[str]:
"""simple docstring"""
return self.tokenizer.decode(*lowerCAmelCase , **lowerCAmelCase)
@property
def UpperCamelCase_ ( self : Tuple) -> Any:
"""simple docstring"""
return ["input_ids", "bbox", "attention_mask", "image"]
@property
def UpperCamelCase_ ( self : Any) -> Optional[int]:
"""simple docstring"""
warnings.warn(
"""`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.""" , lowerCAmelCase , )
return self.image_processor_class
@property
def UpperCamelCase_ ( self : Optional[int]) -> List[Any]:
"""simple docstring"""
warnings.warn(
"""`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.""" , lowerCAmelCase , )
return self.image_processor
| 477 | 0 |
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , _A , _A ) -> int:
SCREAMING_SNAKE_CASE_ = name
SCREAMING_SNAKE_CASE_ = val
def __str__( self ) -> Optional[int]:
return F'''{self.__class__.__name__}({self.name}, {self.val})'''
def __lt__( self , _A ) -> Tuple:
return self.val < other.val
class UpperCamelCase__ :
"""simple docstring"""
def __init__( self , _A ) -> List[Any]:
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = self.build_heap(_A )
def __getitem__( self , _A ) -> List[Any]:
return self.get_value(_A )
def _UpperCamelCase ( self , _A ) -> Union[str, Any]:
return (idx - 1) // 2
def _UpperCamelCase ( self , _A ) -> Dict:
return idx * 2 + 1
def _UpperCamelCase ( self , _A ) -> int:
return idx * 2 + 2
def _UpperCamelCase ( self , _A ) -> Tuple:
return self.heap_dict[key]
def _UpperCamelCase ( self , _A ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = len(_A ) - 1
SCREAMING_SNAKE_CASE_ = self.get_parent_idx(_A )
for idx, i in enumerate(_A ):
SCREAMING_SNAKE_CASE_ = idx
SCREAMING_SNAKE_CASE_ = i.val
for i in range(_A , -1 , -1 ):
self.sift_down(_A , _A )
return array
def _UpperCamelCase ( self , _A , _A ) -> List[str]:
while True:
SCREAMING_SNAKE_CASE_ = self.get_left_child_idx(_A ) # noqa: E741
SCREAMING_SNAKE_CASE_ = self.get_right_child_idx(_A )
SCREAMING_SNAKE_CASE_ = idx
if l < len(_A ) and array[l] < array[idx]:
SCREAMING_SNAKE_CASE_ = l
if r < len(_A ) and array[r] < array[smallest]:
SCREAMING_SNAKE_CASE_ = r
if smallest != idx:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = array[smallest], array[idx]
(
(
SCREAMING_SNAKE_CASE_
) , (
SCREAMING_SNAKE_CASE_
) ,
) = (
self.idx_of_element[array[smallest]],
self.idx_of_element[array[idx]],
)
SCREAMING_SNAKE_CASE_ = smallest
else:
break
def _UpperCamelCase ( self , _A ) -> Tuple:
SCREAMING_SNAKE_CASE_ = self.get_parent_idx(_A )
while p >= 0 and self.heap[p] > self.heap[idx]:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.heap[idx], self.heap[p]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = (
self.idx_of_element[self.heap[idx]],
self.idx_of_element[self.heap[p]],
)
SCREAMING_SNAKE_CASE_ = p
SCREAMING_SNAKE_CASE_ = self.get_parent_idx(_A )
def _UpperCamelCase ( self ) -> Union[str, Any]:
return self.heap[0]
def _UpperCamelCase ( self ) -> Any:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = self.heap[-1], self.heap[0]
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = (
self.idx_of_element[self.heap[-1]],
self.idx_of_element[self.heap[0]],
)
SCREAMING_SNAKE_CASE_ = self.heap.pop()
del self.idx_of_element[x]
self.sift_down(0 , self.heap )
return x
def _UpperCamelCase ( self , _A ) -> Union[str, Any]:
self.heap.append(_A )
SCREAMING_SNAKE_CASE_ = len(self.heap ) - 1
SCREAMING_SNAKE_CASE_ = node.val
self.sift_up(len(self.heap ) - 1 )
def _UpperCamelCase ( self ) -> Optional[int]:
return len(self.heap ) == 0
def _UpperCamelCase ( self , _A , _A ) -> Optional[Any]:
assert (
self.heap[self.idx_of_element[node]].val > new_value
), "newValue must be less that current value"
SCREAMING_SNAKE_CASE_ = new_value
SCREAMING_SNAKE_CASE_ = new_value
self.sift_up(self.idx_of_element[node] )
__UpperCAmelCase = Node("R", -1)
__UpperCAmelCase = Node("B", 6)
__UpperCAmelCase = Node("A", 3)
__UpperCAmelCase = Node("X", 1)
__UpperCAmelCase = Node("E", 4)
# Use one of these two ways to generate Min-Heap
# Generating Min-Heap from array
__UpperCAmelCase = MinHeap([r, b, a, x, e])
# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)
# Before
print("Min Heap - before decrease key")
for i in my_min_heap.heap:
print(i)
print("Min Heap - After decrease key of node [B -> -17]")
my_min_heap.decrease_key(b, -17)
# After
for i in my_min_heap.heap:
print(i)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 597 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy
__UpperCAmelCase = logging.getLogger(__name__)
__UpperCAmelCase = "pytorch_model.bin"
@dataclasses.dataclass
class UpperCamelCase__ :
"""simple docstring"""
UpperCAmelCase_ =dataclasses.field(
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models."} )
UpperCAmelCase_ =dataclasses.field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co."} , )
@dataclasses.dataclass
class UpperCamelCase__ :
"""simple docstring"""
UpperCAmelCase_ =dataclasses.field(metadata={"help": "A csv or a json file containing the training data."} )
UpperCAmelCase_ =dataclasses.field(metadata={"help": "A csv or a json file containing the data to predict on."} )
UpperCAmelCase_ =dataclasses.field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "A csv or a json file containing the validation data."} )
UpperCAmelCase_ =dataclasses.field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "The name of the task to train on."} , )
UpperCAmelCase_ =dataclasses.field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "The list of labels for the task."} )
@dataclasses.dataclass
class UpperCamelCase__ :
"""simple docstring"""
UpperCAmelCase_ =dataclasses.field(
metadata={"help": "The output directory where the model predictions and checkpoints will be written."} )
UpperCAmelCase_ =dataclasses.field(
default="accuracy" , metadata={"help": "The evaluation metric used for the task."} )
UpperCAmelCase_ =dataclasses.field(
default="no" , metadata={
"help": "The evaluation strategy to adopt during training. Possible values are: [\"no\", \"step\", \"epoch]"
} , )
UpperCAmelCase_ =dataclasses.field(
default=10 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
UpperCAmelCase_ =dataclasses.field(
default=0.0 , metadata={
"help": "How much the specified evaluation metric must improve to satisfy early stopping conditions."
} , )
UpperCAmelCase_ =dataclasses.field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "Whether to filter the pseudo-labeled data based on the confidence score."} , )
UpperCAmelCase_ =dataclasses.field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "Whether to filter the pseudo-labeled data based on the validation performance."} , )
UpperCAmelCase_ =dataclasses.field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "Whether to fine-tune on labeled data after pseudo training."} , )
UpperCAmelCase_ =dataclasses.field(
default=0.0 , metadata={"help": "Confidence threshold for pseudo-labeled data filtering."} , )
UpperCAmelCase_ =dataclasses.field(
default=100 , metadata={"help": "Number of evaluation calls with no improvement after which training will be stopped."} , )
UpperCAmelCase_ =dataclasses.field(
default=__SCREAMING_SNAKE_CASE , metadata={"help": "Random seed for initialization."} , )
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = datasets.concatenate_datasets([infer_input, infer_output], axis=1 )
if args.do_filter_by_confidence:
SCREAMING_SNAKE_CASE_ = dataset.filter(lambda __lowerCamelCase : example["probability"] > args.confidence_threshold )
if args.do_filter_by_val_performance:
assert eval_result >= 0.0 and eval_result <= 1.0
SCREAMING_SNAKE_CASE_ = int(eval_result * len(__lowerCamelCase ) )
print(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = dataset.sort('''probability''', reverse=__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = dataset.select(range(__lowerCamelCase ) )
SCREAMING_SNAKE_CASE_ = dataset.remove_columns(['''label''', '''probability'''] )
SCREAMING_SNAKE_CASE_ = dataset.rename_column('''prediction''', '''label''' )
SCREAMING_SNAKE_CASE_ = dataset.map(lambda __lowerCamelCase : {"label": idalabel[example["label"]]} )
SCREAMING_SNAKE_CASE_ = dataset.shuffle(seed=args.seed )
SCREAMING_SNAKE_CASE_ = os.path.join(__lowerCamelCase, F'''train_pseudo.{args.data_file_extension}''' )
if args.data_file_extension == "csv":
dataset.to_csv(__lowerCamelCase, index=__lowerCamelCase )
else:
dataset.to_json(__lowerCamelCase )
def A__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, **__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = Accelerator()
# Make one log on every process with the configuration for debugging.
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=logging.INFO, )
logger.info(accelerator.state )
# Setup logging, we only want one process per machine to log things on the
# screen. accelerator.is_local_main_process is only True for one process per
# machine.
logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_info()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
SCREAMING_SNAKE_CASE_ = STModelArguments(model_name_or_path=__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = STDataArguments(train_file=__lowerCamelCase, infer_file=__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = STTrainingArguments(output_dir=__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = argparse.Namespace()
for arg_class in (model_args, data_args, training_args):
for key, value in vars(__lowerCamelCase ).items():
setattr(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
for key, value in kwargs.items():
if hasattr(__lowerCamelCase, __lowerCamelCase ):
setattr(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
# Sanity checks
SCREAMING_SNAKE_CASE_ = {}
SCREAMING_SNAKE_CASE_ = None
# You need to provide the training data and the data to predict on
assert args.train_file is not None
assert args.infer_file is not None
SCREAMING_SNAKE_CASE_ = args.train_file
SCREAMING_SNAKE_CASE_ = args.infer_file
if args.evaluation_strategy != IntervalStrategy.NO.value:
assert args.eval_file is not None
SCREAMING_SNAKE_CASE_ = args.eval_file
for key in data_files:
SCREAMING_SNAKE_CASE_ = data_files[key].split('''.''' )[-1]
assert extension in ["csv", "json"], F'''`{key}_file` should be a csv or a json file.'''
if args.data_file_extension is None:
SCREAMING_SNAKE_CASE_ = extension
else:
assert extension == args.data_file_extension, F'''`{key}_file` should be a {args.data_file_extension} file`.'''
assert (
args.eval_metric in datasets.list_metrics()
), F'''{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}.'''
# If passed along, set the training seed now.
if args.seed is not None:
set_seed(args.seed )
logger.info('''Creating the initial data directory for self-training...''' )
SCREAMING_SNAKE_CASE_ = F'''{args.output_dir}/self-train_iter-{{}}'''.format
SCREAMING_SNAKE_CASE_ = data_dir_format(0 )
if accelerator.is_main_process:
if args.output_dir is not None:
os.makedirs(args.output_dir, exist_ok=__lowerCamelCase )
os.makedirs(__lowerCamelCase, exist_ok=__lowerCamelCase )
accelerator.wait_for_everyone()
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = None
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = False
# Show the progress bar
SCREAMING_SNAKE_CASE_ = tqdm(range(args.max_selftrain_iterations ), disable=not accelerator.is_local_main_process )
# Self-train
for iteration in range(0, int(args.max_selftrain_iterations ) ):
SCREAMING_SNAKE_CASE_ = data_dir_format(__lowerCamelCase )
assert os.path.exists(__lowerCamelCase )
# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
# iteration > 0
SCREAMING_SNAKE_CASE_ = os.path.join(__lowerCamelCase, '''stage-1''' )
SCREAMING_SNAKE_CASE_ = {
'''accelerator''': accelerator,
'''model_name_or_path''': args.model_name_or_path,
'''cache_dir''': args.cache_dir,
'''do_train''': True,
'''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
'''do_eval''': True if args.eval_file is not None else False,
'''eval_file''': data_files['''eval'''],
'''do_predict''': True,
'''infer_file''': data_files['''infer'''],
'''task_name''': args.task_name,
'''label_list''': args.label_list,
'''output_dir''': current_output_dir,
'''eval_metric''': args.eval_metric,
'''evaluation_strategy''': args.evaluation_strategy,
'''early_stopping_patience''': args.early_stopping_patience,
'''early_stopping_threshold''': args.early_stopping_threshold,
'''seed''': args.seed,
}
# Add additional training arguments
for key, value in kwargs.items():
if key not in arguments_dict and not hasattr(__lowerCamelCase, __lowerCamelCase ):
arguments_dict.update({key: value} )
SCREAMING_SNAKE_CASE_ = os.path.join(__lowerCamelCase, '''best-checkpoint''', __lowerCamelCase )
if os.path.exists(__lowerCamelCase ):
logger.info(
'''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.''', __lowerCamelCase, __lowerCamelCase, )
else:
logger.info('''***** Running self-training: iteration: %d, stage: 1 *****''', __lowerCamelCase )
finetune(**__lowerCamelCase )
accelerator.wait_for_everyone()
assert os.path.exists(__lowerCamelCase )
logger.info('''Self-training job completed: iteration: %d, stage: 1.''', __lowerCamelCase )
if iteration > 0 and args.finetune_on_labeled_data:
# Stage 2 (optional): fine-tuning on the original labeled data
SCREAMING_SNAKE_CASE_ = os.path.join(__lowerCamelCase, '''best-checkpoint''' )
SCREAMING_SNAKE_CASE_ = os.path.join(__lowerCamelCase, '''stage-2''' )
# Update arguments_dict
SCREAMING_SNAKE_CASE_ = model_path
SCREAMING_SNAKE_CASE_ = data_files['''train''']
SCREAMING_SNAKE_CASE_ = current_output_dir
SCREAMING_SNAKE_CASE_ = os.path.join(__lowerCamelCase, '''best-checkpoint''', __lowerCamelCase )
if os.path.exists(__lowerCamelCase ):
logger.info(
'''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.''', __lowerCamelCase, __lowerCamelCase, )
else:
logger.info('''***** Running self-training: iteration: %d, stage: 2 *****''', __lowerCamelCase )
finetune(**__lowerCamelCase )
accelerator.wait_for_everyone()
assert os.path.exists(__lowerCamelCase )
logger.info('''Self-training job completed: iteration: %d, stage: 2.''', __lowerCamelCase )
SCREAMING_SNAKE_CASE_ = iteration
SCREAMING_SNAKE_CASE_ = data_dir_format(iteration + 1 )
SCREAMING_SNAKE_CASE_ = AutoConfig.from_pretrained(os.path.join(__lowerCamelCase, '''best-checkpoint''' ) )
SCREAMING_SNAKE_CASE_ = config.idalabel
SCREAMING_SNAKE_CASE_ = os.path.join(__lowerCamelCase, '''eval_results_best-checkpoint.json''' )
SCREAMING_SNAKE_CASE_ = os.path.join(__lowerCamelCase, '''test_results_best-checkpoint.json''' )
assert os.path.exists(__lowerCamelCase )
with open(__lowerCamelCase, '''r''' ) as f:
SCREAMING_SNAKE_CASE_ = float(json.load(__lowerCamelCase )[args.eval_metric] )
SCREAMING_SNAKE_CASE_ = os.path.join(__lowerCamelCase, '''infer_output_best-checkpoint.csv''' )
assert os.path.exists(__lowerCamelCase )
# Loading the dataset from local csv or json files.
SCREAMING_SNAKE_CASE_ = load_dataset(args.data_file_extension, data_files={'''data''': data_files['''infer''']} )['''data''']
SCREAMING_SNAKE_CASE_ = load_dataset('''csv''', data_files={'''data''': infer_output_file} )['''data''']
if accelerator.is_main_process:
os.makedirs(__lowerCamelCase, exist_ok=__lowerCamelCase )
shutil.copy(__lowerCamelCase, os.path.join(__lowerCamelCase, F'''eval_results_iter-{iteration}.json''' ) )
if os.path.exists(__lowerCamelCase ):
shutil.copy(__lowerCamelCase, os.path.join(__lowerCamelCase, F'''test_results_iter-{iteration}.json''' ) )
create_pseudo_labeled_data(__lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, __lowerCamelCase )
accelerator.wait_for_everyone()
SCREAMING_SNAKE_CASE_ = os.path.join(__lowerCamelCase, F'''train_pseudo.{args.data_file_extension}''' )
if args.evaluation_strategy != IntervalStrategy.NO.value:
SCREAMING_SNAKE_CASE_ = eval_result
if best_iteration is None:
SCREAMING_SNAKE_CASE_ = new_iteration
SCREAMING_SNAKE_CASE_ = new_eval_result
else:
if new_eval_result - best_eval_result > args.early_stopping_threshold:
SCREAMING_SNAKE_CASE_ = new_iteration
SCREAMING_SNAKE_CASE_ = new_eval_result
SCREAMING_SNAKE_CASE_ = 0
else:
if new_eval_result == best_eval_result:
SCREAMING_SNAKE_CASE_ = new_iteration
SCREAMING_SNAKE_CASE_ = new_eval_result
early_stopping_patience_counter += 1
if early_stopping_patience_counter >= args.early_stopping_patience:
SCREAMING_SNAKE_CASE_ = True
progress_bar.update(1 )
if should_training_stop:
break
if best_iteration is not None:
# Save the best iteration
logger.info('''Best iteration: %d''', __lowerCamelCase )
logger.info('''Best evaluation result: %s = %f''', args.eval_metric, __lowerCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__lowerCamelCase, F'''eval_results_iter-{iteration}.json''' ), os.path.join(__lowerCamelCase, '''eval_results_best-iteration.json''' ), )
else:
# Assume that the last iteration is the best
logger.info('''Best iteration: %d''', args.max_selftrain_iterations - 1 )
logger.info('''Best evaluation result: %s = %f''', args.eval_metric, __lowerCamelCase )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
shutil.copy(
os.path.join(__lowerCamelCase, F'''eval_results_iter-{args.max_selftrain_iterations - 1}.json''' ), os.path.join(__lowerCamelCase, '''eval_results_best-iteration.json''' ), )
| 597 | 1 |
"""simple docstring"""
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class __A ( datasets.BuilderConfig ):
UpperCAmelCase__ = None
class __A ( datasets.ArrowBasedBuilder ):
UpperCAmelCase__ = PandasConfig
def lowerCamelCase__ ( self : str ) -> List[str]:
return datasets.DatasetInfo(features=self.config.features )
def lowerCamelCase__ ( self : Union[str, Any] , __snake_case : Optional[Any] ) -> Optional[int]:
if not self.config.data_files:
raise ValueError(F'At least one data file must be specified, but got data_files={self.config.data_files}' )
__magic_name__: Optional[int] = dl_manager.download_and_extract(self.config.data_files )
if isinstance(__snake_case , (str, list, tuple) ):
__magic_name__: int = data_files
if isinstance(__snake_case , __snake_case ):
__magic_name__: Optional[Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
__magic_name__: Dict = [dl_manager.iter_files(__snake_case ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""files""": files} )]
__magic_name__: Any = []
for split_name, files in data_files.items():
if isinstance(__snake_case , __snake_case ):
__magic_name__: str = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
__magic_name__: Dict = [dl_manager.iter_files(__snake_case ) for file in files]
splits.append(datasets.SplitGenerator(name=__snake_case , gen_kwargs={"""files""": files} ) )
return splits
def lowerCamelCase__ ( self : List[str] , __snake_case : Tuple ) -> pa.Table:
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
__magic_name__: int = table_cast(__snake_case , self.config.features.arrow_schema )
return pa_table
def lowerCamelCase__ ( self : List[Any] , __snake_case : Any ) -> Any:
for i, file in enumerate(itertools.chain.from_iterable(__snake_case ) ):
with open(__snake_case , """rb""" ) as f:
__magic_name__: Dict = pa.Table.from_pandas(pd.read_pickle(__snake_case ) )
yield i, self._cast_table(__snake_case )
| 96 |
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 A_ ( A__ ) -> Dict:
a__ : Dict = 384
if "tiny" in model_name:
a__ : Any = [3, 3, 9, 3]
a__ : List[str] = [96, 192, 384, 768]
if "small" in model_name:
a__ : List[Any] = [3, 3, 27, 3]
a__ : Tuple = [96, 192, 384, 768]
if "base" in model_name:
a__ : Union[str, Any] = [3, 3, 27, 3]
a__ : List[Any] = [128, 256, 512, 1024]
a__ : int = 512
if "large" in model_name:
a__ : Dict = [3, 3, 27, 3]
a__ : Tuple = [192, 384, 768, 1536]
a__ : Any = 768
if "xlarge" in model_name:
a__ : Union[str, Any] = [3, 3, 27, 3]
a__ : Dict = [256, 512, 1024, 2048]
a__ : Optional[int] = 1024
# set label information
a__ : Optional[int] = 150
a__ : Optional[Any] = 'huggingface/label-files'
a__ : Union[str, Any] = 'ade20k-id2label.json'
a__ : Dict = json.load(open(hf_hub_download(A__ , A__ , repo_type='dataset' ) , 'r' ) )
a__ : str = {int(A__ ): v for k, v in idalabel.items()}
a__ : int = {v: k for k, v in idalabel.items()}
a__ : Tuple = ConvNextConfig(
depths=A__ , hidden_sizes=A__ , out_features=['stage1', 'stage2', 'stage3', 'stage4'] )
a__ : Dict = UperNetConfig(
backbone_config=A__ , auxiliary_in_channels=A__ , num_labels=A__ , idalabel=A__ , labelaid=A__ , )
return config
def A_ ( A__ ) -> Union[str, Any]:
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 A_ ( A__ , A__ , A__ ) -> Optional[Any]:
a__ : Union[str, Any] = dct.pop(A__ )
a__ : Tuple = val
def A_ ( A__ , A__ , A__ ) -> List[Any]:
a__ : Dict = {
'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__ : Union[str, Any] = model_name_to_url[model_name]
a__ : Optional[int] = torch.hub.load_state_dict_from_url(A__ , map_location='cpu' )['state_dict']
a__ : Dict = get_upernet_config(A__ )
a__ : Dict = UperNetForSemanticSegmentation(A__ )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
a__ : List[Any] = state_dict.pop(A__ )
if "bn" in key:
a__ : Optional[int] = key.replace('bn' , 'batch_norm' )
a__ : Any = val
# rename keys
a__ : List[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__ : List[Any] = 'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
a__ : int = Image.open(requests.get(A__ , stream=A__ ).raw ).convert('RGB' )
a__ : str = SegformerImageProcessor()
a__ : List[str] = processor(A__ , return_tensors='pt' ).pixel_values
with torch.no_grad():
a__ : List[Any] = model(A__ )
if model_name == "upernet-convnext-tiny":
a__ : int = torch.tensor(
[[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]] )
elif model_name == "upernet-convnext-small":
a__ : int = torch.tensor(
[[-8.82_36, -8.82_36, -8.67_71], [-8.82_36, -8.82_36, -8.67_71], [-8.76_38, -8.76_38, -8.62_40]] )
elif model_name == "upernet-convnext-base":
a__ : Tuple = torch.tensor(
[[-8.85_58, -8.85_58, -8.69_05], [-8.85_58, -8.85_58, -8.69_05], [-8.76_69, -8.76_69, -8.60_21]] )
elif model_name == "upernet-convnext-large":
a__ : str = torch.tensor(
[[-8.66_60, -8.66_60, -8.62_10], [-8.66_60, -8.66_60, -8.62_10], [-8.63_10, -8.63_10, -8.59_64]] )
elif model_name == "upernet-convnext-xlarge":
a__ : Union[str, Any] = torch.tensor(
[[-8.49_80, -8.49_80, -8.39_77], [-8.49_80, -8.49_80, -8.39_77], [-8.43_79, -8.43_79, -8.34_12]] )
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 : List[str] = 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 : Dict = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 302 | 0 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def A ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
torch.manual_seed(0 )
UpperCamelCase__ = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
UpperCamelCase__ = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
UpperCamelCase__ = UNetaDConditionModel(
sample_size=3_2 , layers_per_block=1 , block_out_channels=[3_2, 6_4] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=3 , out_channels=6 , cross_attention_dim=3_2 , encoder_hid_dim=3_2 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
UpperCamelCase__ = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=lowercase , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
UpperCamelCase__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def A ( self : List[Any] ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
UpperCamelCase__ = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
UpperCamelCase__ = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
UpperCamelCase__ = UNetaDConditionModel(
sample_size=3_2 , layers_per_block=[1, 2] , block_out_channels=[3_2, 6_4] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=6 , out_channels=6 , cross_attention_dim=3_2 , encoder_hid_dim=3_2 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , class_embed_type="""timestep""" , mid_block_scale_factor=1.4_1_4 , time_embedding_act_fn="""gelu""" , time_embedding_dim=3_2 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
UpperCamelCase__ = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=lowercase , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
UpperCamelCase__ = DDPMScheduler(
num_train_timesteps=1_0_0_0 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , )
torch.manual_seed(0 )
UpperCamelCase__ = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def A ( self : Any ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ = self.get_dummy_components()
UpperCamelCase__ = self.pipeline_class(**lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
UpperCamelCase__ = self.get_dummy_inputs(lowercase )
UpperCamelCase__ = inputs["""prompt"""]
UpperCamelCase__ = inputs["""generator"""]
UpperCamelCase__ = inputs["""num_inference_steps"""]
UpperCamelCase__ = inputs["""output_type"""]
if "image" in inputs:
UpperCamelCase__ = inputs["""image"""]
else:
UpperCamelCase__ = None
if "mask_image" in inputs:
UpperCamelCase__ = inputs["""mask_image"""]
else:
UpperCamelCase__ = None
if "original_image" in inputs:
UpperCamelCase__ = inputs["""original_image"""]
else:
UpperCamelCase__ = None
UpperCamelCase__ , UpperCamelCase__ = pipe.encode_prompt(lowercase )
# inputs with prompt converted to embeddings
UpperCamelCase__ = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
UpperCamelCase__ = image
if mask_image is not None:
UpperCamelCase__ = mask_image
if original_image is not None:
UpperCamelCase__ = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(lowercase , lowercase , lowercase )
UpperCamelCase__ = pipe(**lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowercase )
UpperCamelCase__ = self.pipeline_class.from_pretrained(lowercase )
pipe_loaded.to(lowercase )
pipe_loaded.set_progress_bar_config(disable=lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(lowercase , lowercase ) is None , f"`{optional_component}` did not stay set to None after loading." , )
UpperCamelCase__ = self.get_dummy_inputs(lowercase )
UpperCamelCase__ = inputs["""generator"""]
UpperCamelCase__ = inputs["""num_inference_steps"""]
UpperCamelCase__ = inputs["""output_type"""]
# inputs with prompt converted to embeddings
UpperCamelCase__ = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
UpperCamelCase__ = image
if mask_image is not None:
UpperCamelCase__ = mask_image
if original_image is not None:
UpperCamelCase__ = original_image
UpperCamelCase__ = pipe_loaded(**lowercase )[0]
UpperCamelCase__ = np.abs(to_np(lowercase ) - to_np(lowercase ) ).max()
self.assertLess(lowercase , 1e-4 )
def A ( self : List[Any] ) -> Any:
'''simple docstring'''
UpperCamelCase__ = self.get_dummy_components()
UpperCamelCase__ = self.pipeline_class(**lowercase )
pipe.to(lowercase )
pipe.set_progress_bar_config(disable=lowercase )
UpperCamelCase__ = self.get_dummy_inputs(lowercase )
UpperCamelCase__ = pipe(**lowercase )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(lowercase )
UpperCamelCase__ = self.pipeline_class.from_pretrained(lowercase )
pipe_loaded.to(lowercase )
pipe_loaded.set_progress_bar_config(disable=lowercase )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
UpperCamelCase__ = self.get_dummy_inputs(lowercase )
UpperCamelCase__ = pipe_loaded(**lowercase )[0]
UpperCamelCase__ = np.abs(to_np(lowercase ) - to_np(lowercase ) ).max()
self.assertLess(lowercase , 1e-4 )
| 719 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
lowerCamelCase_ : List[str] = TypeVar('''T''')
lowerCamelCase_ : Optional[int] = TypeVar('''U''')
class _SCREAMING_SNAKE_CASE ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Dict , lowercase : T | None , lowercase : U | None ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ = key
UpperCamelCase__ = val
UpperCamelCase__ = None
UpperCamelCase__ = None
def __repr__( self : List[Any] ) -> str:
'''simple docstring'''
return (
f"Node: key: {self.key}, val: {self.val}, "
f"has next: {bool(self.next )}, has prev: {bool(self.prev )}"
)
class _SCREAMING_SNAKE_CASE ( Generic[T, U] ):
'''simple docstring'''
def __init__( self : Union[str, Any] ) -> None:
'''simple docstring'''
UpperCamelCase__ = DoubleLinkedListNode(lowercase , lowercase )
UpperCamelCase__ = DoubleLinkedListNode(lowercase , lowercase )
UpperCamelCase__ , UpperCamelCase__ = self.rear, self.head
def __repr__( self : int ) -> str:
'''simple docstring'''
UpperCamelCase__ = ["""DoubleLinkedList"""]
UpperCamelCase__ = self.head
while node.next is not None:
rep.append(str(lowercase ) )
UpperCamelCase__ = node.next
rep.append(str(self.rear ) )
return ",\n ".join(lowercase )
def A ( self : str , lowercase : DoubleLinkedListNode[T, U] ) -> None:
'''simple docstring'''
UpperCamelCase__ = self.rear.prev
# All nodes other than self.head are guaranteed to have non-None previous
assert previous is not None
UpperCamelCase__ = node
UpperCamelCase__ = previous
UpperCamelCase__ = node
UpperCamelCase__ = self.rear
def A ( self : Any , lowercase : DoubleLinkedListNode[T, U] ) -> DoubleLinkedListNode[T, U] | None:
'''simple docstring'''
if node.prev is None or node.next is None:
return None
UpperCamelCase__ = node.next
UpperCamelCase__ = node.prev
UpperCamelCase__ = None
UpperCamelCase__ = None
return node
class _SCREAMING_SNAKE_CASE ( Generic[T, U] ):
'''simple docstring'''
__a : dict[Callable[[T], U], LRUCache[T, U]] = {}
def __init__( self : int , lowercase : int ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ = DoubleLinkedList()
UpperCamelCase__ = capacity
UpperCamelCase__ = 0
UpperCamelCase__ = 0
UpperCamelCase__ = 0
UpperCamelCase__ = {}
def __repr__( self : Any ) -> str:
'''simple docstring'''
return (
f"CacheInfo(hits={self.hits}, misses={self.miss}, "
f"capacity={self.capacity}, current size={self.num_keys})"
)
def __contains__( self : Any , lowercase : T ) -> bool:
'''simple docstring'''
return key in self.cache
def A ( self : Tuple , lowercase : T ) -> U | None:
'''simple docstring'''
if key in self.cache:
self.hits += 1
UpperCamelCase__ = self.cache[key]
UpperCamelCase__ = self.list.remove(self.cache[key] )
assert node == value_node
# node is guaranteed not None because it is in self.cache
assert node is not None
self.list.add(lowercase )
return node.val
self.miss += 1
return None
def A ( self : Dict , lowercase : T , lowercase : U ) -> None:
'''simple docstring'''
if key not in self.cache:
if self.num_keys >= self.capacity:
# delete first node (oldest) when over capacity
UpperCamelCase__ = self.list.head.next
# guaranteed to have a non-None first node when num_keys > 0
# explain to type checker via assertions
assert first_node is not None
assert first_node.key is not None
assert (
self.list.remove(lowercase ) is not None
) # node guaranteed to be in list assert node.key is not None
del self.cache[first_node.key]
self.num_keys -= 1
UpperCamelCase__ = DoubleLinkedListNode(lowercase , lowercase )
self.list.add(self.cache[key] )
self.num_keys += 1
else:
# bump node to the end of the list, update value
UpperCamelCase__ = self.list.remove(self.cache[key] )
assert node is not None # node guaranteed to be in list
UpperCamelCase__ = value
self.list.add(lowercase )
@classmethod
def A ( cls : Optional[int] , lowercase : int = 1_2_8 ) -> Callable[[Callable[[T], U]], Callable[..., U]]:
'''simple docstring'''
def cache_decorator_inner(lowercase : Callable[[T], U] ) -> Callable[..., U]:
def cache_decorator_wrapper(*lowercase : T ) -> U:
if func not in cls.decorator_function_to_instance_map:
UpperCamelCase__ = LRUCache(lowercase )
UpperCamelCase__ = cls.decorator_function_to_instance_map[func].get(args[0] )
if result is None:
UpperCamelCase__ = func(*lowercase )
cls.decorator_function_to_instance_map[func].put(args[0] , lowercase )
return result
def cache_info() -> LRUCache[T, U]:
return cls.decorator_function_to_instance_map[func]
setattr(lowercase , """cache_info""" , lowercase ) # noqa: B010
return cache_decorator_wrapper
return cache_decorator_inner
if __name__ == "__main__":
import doctest
doctest.testmod()
| 265 | 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 __magic_name__ ( _lowerCamelCase : Union[str, Any] , _lowerCamelCase : Any , _lowerCamelCase : Union[str, Any]=0 ):
# Format the message.
if name is None:
__a : Dict = None
else:
__a : List[Any] = "." * max(0 , spaces - 2 ) + "# {:" + str(5_0 - spaces ) + "s}"
__a : Optional[Any] = fmt.format(__UpperCamelCase )
# Print and recurse (if needed).
if isinstance(__UpperCamelCase , __UpperCamelCase ):
if msg is not None:
print(__UpperCamelCase )
for k in val.keys():
recursive_print(__UpperCamelCase , val[k] , spaces + 2 )
elif isinstance(__UpperCamelCase , torch.Tensor ):
print(__UpperCamelCase , """:""" , val.size() )
else:
print(__UpperCamelCase , """:""" , __UpperCamelCase )
def __magic_name__ ( _lowerCamelCase : List[Any] , _lowerCamelCase : List[Any] , _lowerCamelCase : Optional[Any] , _lowerCamelCase : Any , _lowerCamelCase : Optional[Any] ):
# Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
# for compatibility with later versions of NVIDIA Megatron-LM.
# The inverse operation is performed inside Megatron-LM to read checkpoints:
# https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
# If param is the weight tensor of the self-attention block, the returned tensor
# will have to be transposed one more time to be read by HuggingFace GPT2.
__a : Optional[int] = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
__a : Optional[int] = (num_heads, hidden_size, num_splits) + input_shape[1:]
__a : List[Any] = param.view(*__UpperCamelCase )
__a : Tuple = param.transpose(0 , 2 )
__a : List[str] = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
__a : Dict = (num_heads, num_splits, hidden_size) + input_shape[1:]
__a : Optional[int] = param.view(*__UpperCamelCase )
__a : Any = param.transpose(0 , 1 ).contiguous()
__a : Dict = param.view(*__UpperCamelCase )
return param
def __magic_name__ ( _lowerCamelCase : Any , _lowerCamelCase : List[Any] , _lowerCamelCase : Any ):
# The converted output model.
__a : Dict = {}
# old versions did not store training args
__a : Union[str, Any] = input_state_dict.get("""args""" , __UpperCamelCase )
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 : Dict = ds_args.padded_vocab_size
__a : Optional[int] = ds_args.max_position_embeddings
__a : Any = ds_args.hidden_size
__a : int = ds_args.num_layers
__a : int = ds_args.num_attention_heads
__a : Any = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
__a : Tuple = config.n_head
# The hidden_size per head.
__a : Dict = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
__a : Union[str, Any] = input_state_dict["checkpoint_version"]
else:
__a : Union[str, Any] = 0.0
# The model.
__a : List[Any] = input_state_dict["model"]
# The language model.
__a : str = model["language_model"]
# The embeddings.
__a : List[Any] = lm["embedding"]
# The word embeddings.
__a : int = embeddings["word_embeddings"]["weight"]
# Truncate the embedding table to vocab_size rows.
__a : List[str] = word_embeddings[: config.vocab_size, :]
__a : int = word_embeddings
# The position embeddings.
__a : Union[str, Any] = embeddings["position_embeddings"]["weight"]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
__a : int = 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 : Optional[Any] = pos_embeddings
# The transformer.
__a : Dict = lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]
# The regex to extract layer names.
__a : Any = re.compile(r"""layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)""" )
# The simple map of names for "automated" rules.
__a : Any = {
"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 : int = layer_re.match(__UpperCamelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
__a : Any = int(m.group(1 ) )
# The name of the operation.
__a : Optional[Any] = m.group(2 )
# Is it a weight or a bias?
__a : str = m.group(3 )
# The name of the layer.
__a : Optional[Any] = F'''transformer.h.{layer_idx}'''
# For layernorm(s), simply store the layer norm.
if op_name.endswith("""layernorm""" ):
__a : List[str] = "ln_1" if op_name.startswith("""input""" ) else "ln_2"
__a : Any = val
# Transpose the QKV matrix.
elif (
op_name == "attention.query_key_value" or op_name == "self_attention.query_key_value"
) and weight_or_bias == "weight":
# Insert a tensor of 1x1xDxD bias.
__a : List[str] = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __UpperCamelCase , __UpperCamelCase )
__a : Optional[Any] = causal_mask
# Insert a "dummy" tensor for masked_bias.
__a : List[Any] = torch.tensor(-1E4 , dtype=torch.floataa )
__a : Dict = masked_bias
__a : Union[str, Any] = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Megatron stores (3*D) x D but transformers-GPT2 expects D x 3*D.
__a : Optional[int] = out_val.transpose(0 , 1 ).contiguous()
# Store.
__a : List[str] = 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 : Dict = fix_query_key_value_ordering(__UpperCamelCase , __UpperCamelCase , 3 , __UpperCamelCase , __UpperCamelCase )
# Store. No change of shape.
__a : List[str] = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
__a : Dict = megatron_to_transformers[op_name]
__a : Dict = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
__a : str = megatron_to_transformers[op_name]
__a : Union[str, Any] = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
__a : str = transformer["final_layernorm.weight"]
__a : int = transformer["final_layernorm.bias"]
# For LM head, transformers' wants the matrix to weight embeddings.
__a : Any = word_embeddings
# It should be done!
return output_state_dict
def __magic_name__ ( ):
# Create the argument parser.
__a : List[str] = argparse.ArgumentParser()
parser.add_argument("""--print-checkpoint-structure""" , action="""store_true""" )
parser.add_argument(
"""path_to_checkpoint""" , type=__UpperCamelCase , help="""Path to the checkpoint file (.zip archive or direct .pt file)""" , )
parser.add_argument(
"""--config_file""" , default="""""" , type=__UpperCamelCase , help="""An optional config json file describing the pre-trained model.""" , )
__a : Any = parser.parse_args()
# Extract the basename.
__a : List[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 : Tuple = torch.load(__UpperCamelCase , map_location="""cpu""" )
else:
__a : Tuple = torch.load(args.path_to_checkpoint , map_location="""cpu""" )
__a : int = input_state_dict.get("""args""" , __UpperCamelCase )
# 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 : int = "gelu_fast"
elif ds_args.openai_gelu:
__a : int = "gelu_new"
else:
__a : Tuple = "gelu"
else:
# in the very early days this used to be "gelu_new"
__a : str = "gelu_new"
# Spell out all parameters in case the defaults change.
__a : Dict = GPTaConfig(
vocab_size=5_0_2_5_7 , n_positions=1_0_2_4 , n_embd=1_0_2_4 , n_layer=2_4 , n_head=1_6 , n_inner=4_0_9_6 , activation_function=__UpperCamelCase , 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=__UpperCamelCase , summary_activation=__UpperCamelCase , summary_proj_to_labels=__UpperCamelCase , summary_first_dropout=0.1 , scale_attn_weights=__UpperCamelCase , use_cache=__UpperCamelCase , bos_token_id=5_0_2_5_6 , eos_token_id=5_0_2_5_6 , )
else:
__a : Optional[Any] = GPTaConfig.from_json_file(args.config_file )
__a : str = ["GPT2LMHeadModel"]
# Convert.
print("""Converting""" )
__a : Optional[int] = convert_megatron_checkpoint(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# Print the structure of converted state dict.
if args.print_checkpoint_structure:
recursive_print(__UpperCamelCase , __UpperCamelCase )
# Add tokenizer class info to config
# see https://github.com/huggingface/transformers/issues/13906)
if ds_args is not None:
__a : Tuple = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
__a : Dict = "gpt2"
elif tokenizer_type == "PretrainedFromHF":
__a : Tuple = ds_args.tokenizer_name_or_path
else:
raise ValueError(F'''Unrecognized tokenizer_type {tokenizer_type}''' )
else:
__a : str = "gpt2"
__a : str = AutoTokenizer.from_pretrained(__UpperCamelCase )
__a : Union[str, Any] = type(__UpperCamelCase ).__name__
__a : Tuple = tokenizer_class
# Store the config to file.
print("""Saving config""" )
config.save_pretrained(__UpperCamelCase )
# Save tokenizer based on args
print(F'''Adding {tokenizer_class} tokenizer files''' )
tokenizer.save_pretrained(__UpperCamelCase )
# Store the state_dict to file.
__a : Tuple = os.path.join(__UpperCamelCase , """pytorch_model.bin""" )
print(F'''Saving checkpoint to "{output_checkpoint_file}"''' )
torch.save(__UpperCamelCase , __UpperCamelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 581 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase = {
"""Salesforce/codegen-350M-nl""": """https://huggingface.co/Salesforce/codegen-350M-nl/resolve/main/config.json""",
"""Salesforce/codegen-350M-multi""": """https://huggingface.co/Salesforce/codegen-350M-multi/resolve/main/config.json""",
"""Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/config.json""",
"""Salesforce/codegen-2B-nl""": """https://huggingface.co/Salesforce/codegen-2B-nl/resolve/main/config.json""",
"""Salesforce/codegen-2B-multi""": """https://huggingface.co/Salesforce/codegen-2B-multi/resolve/main/config.json""",
"""Salesforce/codegen-2B-mono""": """https://huggingface.co/Salesforce/codegen-2B-mono/resolve/main/config.json""",
"""Salesforce/codegen-6B-nl""": """https://huggingface.co/Salesforce/codegen-6B-nl/resolve/main/config.json""",
"""Salesforce/codegen-6B-multi""": """https://huggingface.co/Salesforce/codegen-6B-multi/resolve/main/config.json""",
"""Salesforce/codegen-6B-mono""": """https://huggingface.co/Salesforce/codegen-6B-mono/resolve/main/config.json""",
"""Salesforce/codegen-16B-nl""": """https://huggingface.co/Salesforce/codegen-16B-nl/resolve/main/config.json""",
"""Salesforce/codegen-16B-multi""": """https://huggingface.co/Salesforce/codegen-16B-multi/resolve/main/config.json""",
"""Salesforce/codegen-16B-mono""": """https://huggingface.co/Salesforce/codegen-16B-mono/resolve/main/config.json""",
}
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
A :str = "codegen"
A :Tuple = {
"max_position_embeddings": "n_positions",
"hidden_size": "n_embd",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , __UpperCAmelCase=5_0400 , __UpperCAmelCase=2048 , __UpperCAmelCase=2048 , __UpperCAmelCase=4096 , __UpperCAmelCase=28 , __UpperCAmelCase=16 , __UpperCAmelCase=64 , __UpperCAmelCase=None , __UpperCAmelCase="gelu_new" , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=0.0 , __UpperCAmelCase=1E-5 , __UpperCAmelCase=0.0_2 , __UpperCAmelCase=True , __UpperCAmelCase=5_0256 , __UpperCAmelCase=5_0256 , __UpperCAmelCase=False , **__UpperCAmelCase , ):
"""simple docstring"""
a__ : Dict = vocab_size
a__ : int = n_ctx
a__ : str = n_positions
a__ : Union[str, Any] = n_embd
a__ : Union[str, Any] = n_layer
a__ : Optional[int] = n_head
a__ : Dict = n_inner
a__ : Optional[Any] = rotary_dim
a__ : str = activation_function
a__ : List[Any] = resid_pdrop
a__ : str = embd_pdrop
a__ : List[str] = attn_pdrop
a__ : str = layer_norm_epsilon
a__ : Any = initializer_range
a__ : List[str] = use_cache
a__ : Tuple = bos_token_id
a__ : List[Any] = eos_token_id
super().__init__(
bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , tie_word_embeddings=__UpperCAmelCase , **__UpperCAmelCase )
class _a ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , __UpperCAmelCase , __UpperCAmelCase = "default" , __UpperCAmelCase = None , __UpperCAmelCase = False , ):
"""simple docstring"""
super().__init__(__UpperCAmelCase , task=__UpperCAmelCase , patching_specs=__UpperCAmelCase , use_past=__UpperCAmelCase )
if not getattr(self._config , "pad_token_id" , __UpperCAmelCase ):
# TODO: how to do that better?
a__ : Union[str, Any] = 0
@property
def _A ( self ):
"""simple docstring"""
a__ : Optional[Any] = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}} )
if self.use_past:
self.fill_with_past_key_values_(__UpperCAmelCase , direction="inputs" )
a__ : Any = {0: "batch", 1: "past_sequence + sequence"}
else:
a__ : List[Any] = {0: "batch", 1: "sequence"}
return common_inputs
@property
def _A ( self ):
"""simple docstring"""
return self._config.n_layer
@property
def _A ( self ):
"""simple docstring"""
return self._config.n_head
def _A ( self , __UpperCAmelCase , __UpperCAmelCase = -1 , __UpperCAmelCase = -1 , __UpperCAmelCase = False , __UpperCAmelCase = None , ):
"""simple docstring"""
a__ : Dict = super(__UpperCAmelCase , self ).generate_dummy_inputs(
__UpperCAmelCase , batch_size=__UpperCAmelCase , seq_length=__UpperCAmelCase , is_pair=__UpperCAmelCase , framework=__UpperCAmelCase )
# We need to order the input in the way they appears in the forward()
a__ : Union[str, Any] = 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
a__ , a__ : Union[str, Any] = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
a__ : Any = seqlen + 2
a__ : str = (
batch,
self.num_attention_heads,
past_key_values_length,
self._config.hidden_size // self.num_attention_heads,
)
a__ : str = [
(torch.zeros(__UpperCAmelCase ), torch.zeros(__UpperCAmelCase )) for _ in range(self.num_layers )
]
a__ : Union[str, Any] = common_inputs["attention_mask"]
if self.use_past:
a__ : str = ordered_inputs["attention_mask"].dtype
a__ : Union[str, Any] = torch.cat(
[ordered_inputs["attention_mask"], torch.ones(__UpperCAmelCase , __UpperCAmelCase , dtype=__UpperCAmelCase )] , dim=1 )
return ordered_inputs
@property
def _A ( self ):
"""simple docstring"""
return 13
| 191 | 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 (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCAmelCase : List[Any] = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
_lowerCAmelCase : int = []
for i in range(6):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', F'''encoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', F'''encoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.weight''', F'''encoder.layers.{i}.fc1.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear1.bias''', F'''encoder.layers.{i}.fc1.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.weight''', F'''encoder.layers.{i}.fc2.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.linear2.bias''', F'''encoder.layers.{i}.fc2.bias'''))
rename_keys.append(
(F'''transformer.encoder.layers.{i}.norm1.weight''', F'''encoder.layers.{i}.self_attn_layer_norm.weight''')
)
rename_keys.append((F'''transformer.encoder.layers.{i}.norm1.bias''', F'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.weight''', F'''encoder.layers.{i}.final_layer_norm.weight'''))
rename_keys.append((F'''transformer.encoder.layers.{i}.norm2.bias''', F'''encoder.layers.{i}.final_layer_norm.bias'''))
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', F'''decoder.layers.{i}.self_attn.out_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', F'''decoder.layers.{i}.self_attn.out_proj.bias''')
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_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'''))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"),
("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
]
)
def _A ( snake_case__ : Union[str, Any] , snake_case__ : List[str] , snake_case__ : int ):
snake_case__ : List[Any] = state_dict.pop(snake_case__ )
snake_case__ : List[str] = val
def _A ( snake_case__ : List[str] ):
snake_case__ : str = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
snake_case__ : str = key.replace('''backbone.0.body''' , '''backbone.conv_encoder.model''' )
snake_case__ : List[Any] = value
else:
snake_case__ : Any = value
return new_state_dict
def _A ( snake_case__ : int , snake_case__ : Any=False ):
snake_case__ : int = ''''''
if is_panoptic:
snake_case__ : Optional[Any] = '''conditional_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)
snake_case__ : int = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight''' )
snake_case__ : Optional[int] = state_dict.pop(f'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias''' )
# next, add query, keys and values (in that order) to the state dict
snake_case__ : Optional[int] = in_proj_weight[:2_56, :]
snake_case__ : str = in_proj_bias[:2_56]
snake_case__ : Dict = in_proj_weight[2_56:5_12, :]
snake_case__ : Tuple = in_proj_bias[2_56:5_12]
snake_case__ : int = in_proj_weight[-2_56:, :]
snake_case__ : Tuple = in_proj_bias[-2_56:]
def _A ( ):
snake_case__ : str = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
snake_case__ : int = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw )
return im
@torch.no_grad()
def _A ( snake_case__ : Optional[Any] , snake_case__ : Optional[Any] ):
snake_case__ : List[Any] = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
snake_case__ : Optional[int] = '''resnet101'''
if "dc5" in model_name:
snake_case__ : List[Any] = True
snake_case__ : Dict = '''panoptic''' in model_name
if is_panoptic:
snake_case__ : Union[str, Any] = 2_50
else:
snake_case__ : str = 91
snake_case__ : Tuple = '''huggingface/label-files'''
snake_case__ : Dict = '''coco-detection-id2label.json'''
snake_case__ : Any = json.load(open(hf_hub_download(snake_case__ , snake_case__ , repo_type='''dataset''' ) , '''r''' ) )
snake_case__ : Tuple = {int(snake_case__ ): v for k, v in idalabel.items()}
snake_case__ : Tuple = idalabel
snake_case__ : str = {v: k for k, v in idalabel.items()}
# load image processor
snake_case__ : Optional[int] = '''coco_panoptic''' if is_panoptic else '''coco_detection'''
snake_case__ : int = ConditionalDetrImageProcessor(format=snake_case__ )
# prepare image
snake_case__ : Dict = prepare_img()
snake_case__ : int = image_processor(images=snake_case__ , return_tensors='''pt''' )
snake_case__ : Any = encoding['''pixel_values''']
logger.info(f'''Converting model {model_name}...''' )
# load original model from torch hub
snake_case__ : Union[str, Any] = torch.hub.load('''DeppMeng/ConditionalDETR''' , snake_case__ , pretrained=snake_case__ ).eval()
snake_case__ : str = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
snake_case__ : Optional[Any] = '''conditional_detr.''' + src
rename_key(snake_case__ , snake_case__ , snake_case__ )
snake_case__ : Dict = rename_backbone_keys(snake_case__ )
# query, key and value matrices need special treatment
read_in_q_k_v(snake_case__ , is_panoptic=snake_case__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
snake_case__ : Tuple = '''conditional_detr.model.''' if is_panoptic else '''model.'''
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith('''conditional_detr''' )
and not key.startswith('''class_labels_classifier''' )
and not key.startswith('''bbox_predictor''' )
):
snake_case__ : int = state_dict.pop(snake_case__ )
snake_case__ : str = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
snake_case__ : str = state_dict.pop(snake_case__ )
snake_case__ : List[str] = val
elif key.startswith('''bbox_attention''' ) or key.startswith('''mask_head''' ):
continue
else:
snake_case__ : Any = state_dict.pop(snake_case__ )
snake_case__ : Union[str, Any] = val
else:
if not key.startswith('''class_labels_classifier''' ) and not key.startswith('''bbox_predictor''' ):
snake_case__ : Dict = state_dict.pop(snake_case__ )
snake_case__ : Optional[Any] = val
# finally, create HuggingFace model and load state dict
snake_case__ : int = ConditionalDetrForSegmentation(snake_case__ ) if is_panoptic else ConditionalDetrForObjectDetection(snake_case__ )
model.load_state_dict(snake_case__ )
model.eval()
model.push_to_hub(repo_id=snake_case__ , organization='''DepuMeng''' , commit_message='''Add model''' )
# verify our conversion
snake_case__ : Optional[Any] = conditional_detr(snake_case__ )
snake_case__ : List[Any] = model(snake_case__ )
assert torch.allclose(outputs.logits , original_outputs['''pred_logits'''] , atol=1E-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs['''pred_boxes'''] , atol=1E-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs['''pred_masks'''] , atol=1E-4 )
# Save model and image processor
logger.info(f'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...''' )
Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
model.save_pretrained(snake_case__ )
image_processor.save_pretrained(snake_case__ )
if __name__ == "__main__":
_lowerCAmelCase : int = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="conditional_detr_resnet50",
type=str,
help="Name of the CONDITIONAL_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."
)
_lowerCAmelCase : List[str] = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 712 |
'''simple docstring'''
from __future__ import annotations
def _A ( snake_case__ : list[float] , snake_case__ : list[float] ):
snake_case__ : Dict = sorted(numsa + numsa )
snake_case__ ,snake_case__ : Tuple = divmod(len(snake_case__ ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowerCAmelCase : Tuple = [float(x) for x in input("Enter the elements of first array: ").split()]
_lowerCAmelCase : List[str] = [float(x) for x in input("Enter the elements of second array: ").split()]
print(F'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')
| 694 | 0 |
from math import sqrt
def a ( A__ ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(sqrt(A__ ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def a ( A__ = 1_0_0_0_1 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Union[str, Any] = 0
SCREAMING_SNAKE_CASE__ : List[str] = 1
while count != nth and number < 3:
number += 1
if is_prime(A__ ):
count += 1
while count != nth:
number += 2
if is_prime(A__ ):
count += 1
return number
if __name__ == "__main__":
print(F'''{solution() = }''')
| 35 |
"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCAmelCase_ ( __a : dict ):
'''simple docstring'''
return (data["data"], data["target"])
def UpperCAmelCase_ ( __a : np.ndarray , __a : np.ndarray ):
'''simple docstring'''
_lowerCamelCase : Dict = XGBClassifier()
classifier.fit(__a , __a )
return classifier
def UpperCAmelCase_ ( ):
'''simple docstring'''
_lowerCamelCase : List[str] = load_iris()
_lowerCamelCase , _lowerCamelCase : Optional[Any] = data_handling(__a )
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase : int = train_test_split(
__a , __a , test_size=0.2_5 )
_lowerCamelCase : Optional[Any] = iris['target_names']
# Create an XGBoost Classifier from the training data
_lowerCamelCase : Tuple = xgboost(__a , __a )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
__a , __a , __a , display_labels=__a , cmap='Blues' , normalize='true' , )
plt.title('Normalized Confusion Matrix - IRIS Dataset' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 437 | 0 |
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def lowerCAmelCase__ ( 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).' )
lowerCAmelCase__ : Any = QuantumRegister(SCREAMING_SNAKE_CASE_ , 'qr' )
lowerCAmelCase__ : List[Any] = ClassicalRegister(SCREAMING_SNAKE_CASE_ , 'cr' )
lowerCAmelCase__ : Optional[int] = QuantumCircuit(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Optional[Any] = 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
lowerCAmelCase__ : str = Aer.get_backend('qasm_simulator' )
lowerCAmelCase__ : List[Any] = 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)}"""
) | 713 |
from numpy import exp, pi, sqrt
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 0.0 , SCREAMING_SNAKE_CASE_ = 1.0 ) -> int:
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 69 | 0 |
'''simple docstring'''
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
__UpperCAmelCase = get_tests_dir("fixtures")
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = mock.Mock()
snake_case: List[Any] = 5_00
snake_case: Any = {}
snake_case: Optional[Any] = HTTPError
snake_case: Tuple = {}
# Download this model to make sure it's in the cache.
snake_case: List[str] = WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=SCREAMING_SNAKE_CASE__ ) as mock_head:
snake_case: int = WavaVecaFeatureExtractor.from_pretrained('hf-internal-testing/tiny-random-wav2vec2' )
# This check we did call the fake head request
mock_head.assert_called()
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = WavaVecaFeatureExtractor.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-wav2vec2/resolve/main/preprocessor_config.json' )
@is_staging_test
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def _UpperCamelCase ( cls ):
'''simple docstring'''
snake_case: List[str] = TOKEN
HfFolder.save_token(SCREAMING_SNAKE_CASE__ )
@classmethod
def _UpperCamelCase ( cls ):
'''simple docstring'''
try:
delete_repo(token=cls._token , repo_id='test-feature-extractor' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-feature-extractor-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-feature-extractor' )
except HTTPError:
pass
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ )
feature_extractor.push_to_hub('test-feature-extractor' , use_auth_token=self._token )
snake_case: Tuple = WavaVecaFeatureExtractor.from_pretrained(F"""{USER}/test-feature-extractor""" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(SCREAMING_SNAKE_CASE__ , getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-feature-extractor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
SCREAMING_SNAKE_CASE__ , repo_id='test-feature-extractor' , push_to_hub=SCREAMING_SNAKE_CASE__ , use_auth_token=self._token )
snake_case: List[str] = WavaVecaFeatureExtractor.from_pretrained(F"""{USER}/test-feature-extractor""" )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(SCREAMING_SNAKE_CASE__ , getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[Any] = WavaVecaFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ )
feature_extractor.push_to_hub('valid_org/test-feature-extractor' , use_auth_token=self._token )
snake_case: List[Any] = WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(SCREAMING_SNAKE_CASE__ , getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-feature-extractor' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
feature_extractor.save_pretrained(
SCREAMING_SNAKE_CASE__ , repo_id='valid_org/test-feature-extractor-org' , push_to_hub=SCREAMING_SNAKE_CASE__ , use_auth_token=self._token )
snake_case: str = WavaVecaFeatureExtractor.from_pretrained('valid_org/test-feature-extractor-org' )
for k, v in feature_extractor.__dict__.items():
self.assertEqual(SCREAMING_SNAKE_CASE__ , getattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
def _UpperCamelCase ( self ):
'''simple docstring'''
CustomFeatureExtractor.register_for_auto_class()
snake_case: Any = CustomFeatureExtractor.from_pretrained(SCREAMING_SNAKE_CASE__ )
feature_extractor.push_to_hub('test-dynamic-feature-extractor' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(
feature_extractor.auto_map , {'AutoFeatureExtractor': 'custom_feature_extraction.CustomFeatureExtractor'} , )
snake_case: Optional[int] = AutoFeatureExtractor.from_pretrained(
F"""{USER}/test-dynamic-feature-extractor""" , trust_remote_code=SCREAMING_SNAKE_CASE__ )
# Can't make an isinstance check because the new_feature_extractor is from the CustomFeatureExtractor class of a dynamic module
self.assertEqual(new_feature_extractor.__class__.__name__ , 'CustomFeatureExtractor' ) | 329 |
'''simple docstring'''
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
return self.get_dummy_input()
@property
def _UpperCamelCase ( self ):
'''simple docstring'''
if self.block_type == "down":
return (4, 32, 16, 16)
elif self.block_type == "mid":
return (4, 32, 32, 32)
elif self.block_type == "up":
return (4, 32, 64, 64)
raise ValueError(F"""'{self.block_type}' is not a supported block_type. Set it to 'up', 'mid', or 'down'.""" )
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__=True , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , SCREAMING_SNAKE_CASE__=False , ):
'''simple docstring'''
snake_case: List[Any] = 4
snake_case: Any = 32
snake_case: Dict = (32, 32)
snake_case: str = torch.manual_seed(0 )
snake_case: List[Any] = torch.device(SCREAMING_SNAKE_CASE__ )
snake_case: Optional[Any] = (batch_size, num_channels) + sizes
snake_case: Optional[Any] = randn_tensor(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = {'hidden_states': hidden_states}
if include_temb:
snake_case: List[str] = 1_28
snake_case: str = randn_tensor((batch_size, temb_channels) , generator=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
if include_res_hidden_states_tuple:
snake_case: int = torch.manual_seed(1 )
snake_case: int = (randn_tensor(SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ ),)
if include_encoder_hidden_states:
snake_case: List[Any] = floats_tensor((batch_size, 32, 32) ).to(SCREAMING_SNAKE_CASE__ )
if include_skip_sample:
snake_case: Dict = randn_tensor(((batch_size, 3) + sizes) , generator=SCREAMING_SNAKE_CASE__ , device=SCREAMING_SNAKE_CASE__ )
return dummy_input
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = {
'in_channels': 32,
'out_channels': 32,
'temb_channels': 1_28,
}
if self.block_type == "up":
snake_case: int = 32
if self.block_type == "mid":
init_dict.pop('out_channels' )
snake_case: Optional[Any] = self.dummy_input
return init_dict, inputs_dict
def _UpperCamelCase ( self , SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case , snake_case: Optional[Any] = self.prepare_init_args_and_inputs_for_common()
snake_case: str = self.block_class(**SCREAMING_SNAKE_CASE__ )
unet_block.to(SCREAMING_SNAKE_CASE__ )
unet_block.eval()
with torch.no_grad():
snake_case: int = unet_block(**SCREAMING_SNAKE_CASE__ )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case: Tuple = output[0]
self.assertEqual(output.shape , self.output_shape )
snake_case: List[Any] = output[0, -1, -3:, -3:]
snake_case: Any = torch.tensor(SCREAMING_SNAKE_CASE__ ).to(SCREAMING_SNAKE_CASE__ )
assert torch_all_close(output_slice.flatten() , SCREAMING_SNAKE_CASE__ , atol=5E-3 )
@unittest.skipIf(torch_device == 'mps' , 'Training is not supported in mps' )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case , snake_case: Dict = self.prepare_init_args_and_inputs_for_common()
snake_case: Optional[Any] = self.block_class(**SCREAMING_SNAKE_CASE__ )
model.to(SCREAMING_SNAKE_CASE__ )
model.train()
snake_case: List[Any] = model(**SCREAMING_SNAKE_CASE__ )
if isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
snake_case: str = output[0]
snake_case: Optional[Any] = torch.device(SCREAMING_SNAKE_CASE__ )
snake_case: List[Any] = randn_tensor(output.shape , device=SCREAMING_SNAKE_CASE__ )
snake_case: Optional[int] = torch.nn.functional.mse_loss(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
loss.backward() | 329 | 1 |
"""simple docstring"""
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
__A : List[Any] = Mapping[str, np.ndarray]
__A : int = Mapping[str, Any] # Is a nested dict.
__A : List[Any] = 0.01
@dataclasses.dataclass(frozen=lowerCAmelCase_ )
class lowerCAmelCase__ :
"""simple docstring"""
__UpperCAmelCase : np.ndarray # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
__UpperCAmelCase : np.ndarray # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
__UpperCAmelCase : np.ndarray # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
__UpperCAmelCase : np.ndarray # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
__UpperCAmelCase : np.ndarray # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
__UpperCAmelCase : Optional[np.ndarray] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
__UpperCAmelCase : Optional[str] = None
# Templates used to generate this protein (prediction-only)
__UpperCAmelCase : Optional[Sequence[str]] = None
# Chain corresponding to each parent
__UpperCAmelCase : Optional[Sequence[int]] = None
def snake_case__ ( _lowerCamelCase ) ->Protein:
"""simple docstring"""
__lowercase : str = R"(\[[A-Z]+\]\n)"
__lowercase : List[str] = [tag.strip() for tag in re.split(_lowerCamelCase, _lowerCamelCase ) if len(_lowerCamelCase ) > 0]
__lowercase : Iterator[Tuple[str, List[str]]] = zip(tags[0::2], [l.split("\n" ) for l in tags[1::2]] )
__lowercase : List[str] = ["N", "CA", "C"]
__lowercase : Optional[Any] = None
__lowercase : Union[str, Any] = None
__lowercase : Dict = None
for g in groups:
if "[PRIMARY]" == g[0]:
__lowercase : Tuple = g[1][0].strip()
for i in range(len(_lowerCamelCase ) ):
if seq[i] not in residue_constants.restypes:
__lowercase : Optional[int] = "X" # FIXME: strings are immutable
__lowercase : Union[str, Any] = np.array(
[residue_constants.restype_order.get(_lowerCamelCase, residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
__lowercase : List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(_lowerCamelCase, g[1][axis].split() ) ) )
__lowercase : int = np.array(_lowerCamelCase )
__lowercase : Tuple = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(_lowerCamelCase ):
__lowercase : Optional[Any] = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
__lowercase : Any = np.array(list(map({"-": 0, "+": 1}.get, g[1][0].strip() ) ) )
__lowercase : Optional[int] = np.zeros(
(
len(_lowerCamelCase ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(_lowerCamelCase ):
__lowercase : Any = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=_lowerCamelCase, atom_mask=_lowerCamelCase, aatype=_lowerCamelCase, residue_index=np.arange(len(_lowerCamelCase ) ), b_factors=_lowerCamelCase, )
def snake_case__ ( _lowerCamelCase, _lowerCamelCase = 0 ) ->List[str]:
"""simple docstring"""
__lowercase : List[str] = []
__lowercase : Tuple = prot.remark
if remark is not None:
pdb_headers.append(F'REMARK {remark}' )
__lowercase : str = prot.parents
__lowercase : List[str] = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
__lowercase : Any = [p for i, p in zip(_lowerCamelCase, _lowerCamelCase ) if i == chain_id]
if parents is None or len(_lowerCamelCase ) == 0:
__lowercase : Dict = ["N/A"]
pdb_headers.append(F'PARENT {" ".join(_lowerCamelCase )}' )
return pdb_headers
def snake_case__ ( _lowerCamelCase, _lowerCamelCase ) ->str:
"""simple docstring"""
__lowercase : List[str] = []
__lowercase : List[str] = pdb_str.split("\n" )
__lowercase : Optional[int] = prot.remark
if remark is not None:
out_pdb_lines.append(F'REMARK {remark}' )
__lowercase : List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
__lowercase : Union[str, Any] = []
if prot.parents_chain_index is not None:
__lowercase : Dict[str, List[str]] = {}
for p, i in zip(prot.parents, prot.parents_chain_index ):
parent_dict.setdefault(str(_lowerCamelCase ), [] )
parent_dict[str(_lowerCamelCase )].append(_lowerCamelCase )
__lowercase : Dict = max([int(_lowerCamelCase ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
__lowercase : int = parent_dict.get(str(_lowerCamelCase ), ["N/A"] )
parents_per_chain.append(_lowerCamelCase )
else:
parents_per_chain.append(list(prot.parents ) )
else:
__lowercase : Optional[Any] = [["N/A"]]
def make_parent_line(_lowerCamelCase ) -> str:
return F'PARENT {" ".join(_lowerCamelCase )}'
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
__lowercase : Union[str, Any] = 0
for i, l in enumerate(_lowerCamelCase ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(_lowerCamelCase )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(_lowerCamelCase ):
__lowercase : Tuple = parents_per_chain[chain_counter]
else:
__lowercase : Tuple = ["N/A"]
out_pdb_lines.append(make_parent_line(_lowerCamelCase ) )
return "\n".join(_lowerCamelCase )
def snake_case__ ( _lowerCamelCase ) ->str:
"""simple docstring"""
__lowercase : Optional[Any] = residue_constants.restypes + ["X"]
def res_atoa(_lowerCamelCase ) -> str:
return residue_constants.restype_atoa.get(restypes[r], "UNK" )
__lowercase : int = residue_constants.atom_types
__lowercase : List[str] = []
__lowercase : Optional[int] = prot.atom_mask
__lowercase : List[Any] = prot.aatype
__lowercase : List[str] = prot.atom_positions
__lowercase : int = prot.residue_index.astype(np.intaa )
__lowercase : List[Any] = prot.b_factors
__lowercase : str = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("Invalid aatypes." )
__lowercase : int = get_pdb_headers(_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
pdb_lines.extend(_lowerCamelCase )
__lowercase : Union[str, Any] = aatype.shape[0]
__lowercase : Union[str, Any] = 1
__lowercase : Optional[Any] = 0
__lowercase : Any = string.ascii_uppercase
__lowercase : List[str] = None
# Add all atom sites.
for i in range(_lowerCamelCase ):
__lowercase : List[Any] = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(_lowerCamelCase, atom_positions[i], atom_mask[i], b_factors[i] ):
if mask < 0.5:
continue
__lowercase : Tuple = "ATOM"
__lowercase : Optional[int] = atom_name if len(_lowerCamelCase ) == 4 else F' {atom_name}'
__lowercase : Union[str, Any] = ""
__lowercase : Union[str, Any] = ""
__lowercase : Optional[Any] = 1.0_0
__lowercase : Optional[int] = atom_name[0] # Protein supports only C, N, O, S, this works.
__lowercase : Union[str, Any] = ""
__lowercase : Dict = "A"
if chain_index is not None:
__lowercase : Optional[int] = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
__lowercase : Optional[int] = (
F'{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}'
F'{res_name_a:>3} {chain_tag:>1}'
F'{residue_index[i]:>4}{insertion_code:>1} '
F'{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}'
F'{occupancy:>6.2f}{b_factor:>6.2f} '
F'{element:>2}{charge:>2}'
)
pdb_lines.append(_lowerCamelCase )
atom_index += 1
__lowercase : int = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
__lowercase : Dict = True
__lowercase : List[Any] = chain_index[i + 1]
if should_terminate:
# Close the chain.
__lowercase : Dict = "TER"
__lowercase : List[Any] = (
F'{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}'
)
pdb_lines.append(_lowerCamelCase )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(_lowerCamelCase, _lowerCamelCase ) )
pdb_lines.append("END" )
pdb_lines.append("" )
return "\n".join(_lowerCamelCase )
def snake_case__ ( _lowerCamelCase ) ->np.ndarray:
"""simple docstring"""
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def snake_case__ ( _lowerCamelCase, _lowerCamelCase, _lowerCamelCase = None, _lowerCamelCase = None, _lowerCamelCase = None, _lowerCamelCase = None, _lowerCamelCase = None, ) ->Protein:
"""simple docstring"""
return Protein(
aatype=features["aatype"], atom_positions=result["final_atom_positions"], atom_mask=result["final_atom_mask"], residue_index=features["residue_index"] + 1, b_factors=b_factors if b_factors is not None else np.zeros_like(result["final_atom_mask"] ), chain_index=_lowerCamelCase, remark=_lowerCamelCase, parents=_lowerCamelCase, parents_chain_index=_lowerCamelCase, )
| 281 |
"""simple docstring"""
def snake_case__ ( _lowerCamelCase, _lowerCamelCase ) ->int:
"""simple docstring"""
return abs(_lowerCamelCase ) if a == 0 else greatest_common_divisor(b % a, _lowerCamelCase )
def snake_case__ ( _lowerCamelCase, _lowerCamelCase ) ->int:
"""simple docstring"""
while y: # --> when y=0 then loop will terminate and return x as final GCD.
__lowercase ,__lowercase : Any = y, x % y
return abs(_lowerCamelCase )
def snake_case__ ( ) ->Optional[int]:
"""simple docstring"""
try:
__lowercase : Optional[int] = input("Enter two integers separated by comma (,): " ).split("," )
__lowercase : Optional[Any] = int(nums[0] )
__lowercase : str = int(nums[1] )
print(
F'greatest_common_divisor({num_a}, {num_a}) = '
F'{greatest_common_divisor(_lowerCamelCase, _lowerCamelCase )}' )
print(F'By iterative gcd({num_a}, {num_a}) = {gcd_by_iterative(_lowerCamelCase, _lowerCamelCase )}' )
except (IndexError, UnboundLocalError, ValueError):
print("Wrong input" )
if __name__ == "__main__":
main()
| 281 | 1 |
from abc import ABC, abstractmethod
from typing import List, Optional
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : int ) ->Optional[Any]:
# test for the above condition
self.test()
def snake_case__( self : int ) ->str:
snake_case_ = 0
snake_case_ = False
while not completed:
if counter == 1:
self.reset()
snake_case_ = self.advance()
if not self.does_advance(_UpperCamelCase ):
raise Exception(
'''Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true.''' )
snake_case_, snake_case_, snake_case_ = self.update(_UpperCamelCase )
counter += 1
if counter > 1_0_0_0_0:
raise Exception('''update() does not fulfill the constraint.''' )
if self.remaining() != 0:
raise Exception('''Custom Constraint is not defined correctly.''' )
@abstractmethod
def snake_case__( self : List[Any] ) ->Union[str, Any]:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def snake_case__( self : int , _UpperCamelCase : int ) ->List[str]:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def snake_case__( self : Union[str, Any] , _UpperCamelCase : int ) ->int:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def snake_case__( self : int ) ->str:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def snake_case__( self : int ) ->str:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
@abstractmethod
def snake_case__( self : List[str] , _UpperCamelCase : List[Any]=False ) ->List[Any]:
raise NotImplementedError(
f'''{self.__class__} is an abstract class. Only classes inheriting this class can be called.''' )
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : Optional[Any] , _UpperCamelCase : List[int] ) ->Dict:
super(_UpperCamelCase , self ).__init__()
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or len(_UpperCamelCase ) == 0:
raise ValueError(f'''`token_ids` has to be a non-empty list, but is {token_ids}.''' )
if any((not isinstance(_UpperCamelCase , _UpperCamelCase ) or token_id < 0) for token_id in token_ids ):
raise ValueError(f'''Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.''' )
snake_case_ = token_ids
snake_case_ = len(self.token_ids )
snake_case_ = -1 # the index of the currently fulfilled step
snake_case_ = False
def snake_case__( self : Dict ) ->Dict:
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def snake_case__( self : Union[str, Any] , _UpperCamelCase : int ) ->Optional[Any]:
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCamelCase )}''' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def snake_case__( self : Union[str, Any] , _UpperCamelCase : int ) ->int:
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError(f'''`token_id` has to be an `int`, but is {token_id} of type {type(_UpperCamelCase )}''' )
snake_case_ = False
snake_case_ = False
snake_case_ = False
if self.does_advance(_UpperCamelCase ):
self.fulfilled_idx += 1
snake_case_ = True
if self.fulfilled_idx == (self.seqlen - 1):
snake_case_ = True
snake_case_ = completed
else:
# failed to make progress.
snake_case_ = True
self.reset()
return stepped, completed, reset
def snake_case__( self : Any ) ->Union[str, Any]:
snake_case_ = False
snake_case_ = 0
def snake_case__( self : Union[str, Any] ) ->int:
return self.seqlen - (self.fulfilled_idx + 1)
def snake_case__( self : str , _UpperCamelCase : Union[str, Any]=False ) ->int:
snake_case_ = PhrasalConstraint(self.token_ids )
if stateful:
snake_case_ = self.seqlen
snake_case_ = self.fulfilled_idx
snake_case_ = self.completed
return new_constraint
class snake_case_ :
'''simple docstring'''
def __init__( self : List[str] , _UpperCamelCase : List[List[int]] , _UpperCamelCase : List[Any]=True ) ->str:
snake_case_ = max([len(_UpperCamelCase ) for one in nested_token_ids] )
snake_case_ = {}
for token_ids in nested_token_ids:
snake_case_ = root
for tidx, token_id in enumerate(_UpperCamelCase ):
if token_id not in level:
snake_case_ = {}
snake_case_ = level[token_id]
if no_subsets and self.has_subsets(_UpperCamelCase , _UpperCamelCase ):
raise ValueError(
'''Each list in `nested_token_ids` can\'t be a complete subset of another list, but is'''
f''' {nested_token_ids}.''' )
snake_case_ = root
def snake_case__( self : Any , _UpperCamelCase : List[Any] ) ->Optional[Any]:
snake_case_ = self.trie
for current_token in current_seq:
snake_case_ = start[current_token]
snake_case_ = list(start.keys() )
return next_tokens
def snake_case__( self : Optional[int] , _UpperCamelCase : int ) ->Optional[int]:
snake_case_ = self.next_tokens(_UpperCamelCase )
return len(_UpperCamelCase ) == 0
def snake_case__( self : List[Any] , _UpperCamelCase : List[Any] ) ->Dict:
snake_case_ = list(root.values() )
if len(_UpperCamelCase ) == 0:
return 1
else:
return sum([self.count_leaves(_UpperCamelCase ) for nn in next_nodes] )
def snake_case__( self : Union[str, Any] , _UpperCamelCase : Dict , _UpperCamelCase : Union[str, Any] ) ->int:
snake_case_ = self.count_leaves(_UpperCamelCase )
return len(_UpperCamelCase ) != leaf_count
class snake_case_ ( __A ):
'''simple docstring'''
def __init__( self : Optional[int] , _UpperCamelCase : List[List[int]] ) ->Any:
super(_UpperCamelCase , self ).__init__()
if not isinstance(_UpperCamelCase , _UpperCamelCase ) or len(_UpperCamelCase ) == 0:
raise ValueError(f'''`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.''' )
if any(not isinstance(_UpperCamelCase , _UpperCamelCase ) for token_ids in nested_token_ids ):
raise ValueError(f'''`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.''' )
if any(
any((not isinstance(_UpperCamelCase , _UpperCamelCase ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
f'''Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.''' )
snake_case_ = DisjunctiveTrie(_UpperCamelCase )
snake_case_ = nested_token_ids
snake_case_ = self.trie.max_height
snake_case_ = []
snake_case_ = False
def snake_case__( self : Optional[int] ) ->Optional[int]:
snake_case_ = self.trie.next_tokens(self.current_seq )
if len(_UpperCamelCase ) == 0:
return None
else:
return token_list
def snake_case__( self : Dict , _UpperCamelCase : int ) ->Dict:
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCamelCase )}''' )
snake_case_ = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def snake_case__( self : Tuple , _UpperCamelCase : int ) ->Optional[Any]:
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError(f'''`token_id` is supposed to be type `int`, but is {token_id} of type {type(_UpperCamelCase )}''' )
snake_case_ = False
snake_case_ = False
snake_case_ = False
if self.does_advance(_UpperCamelCase ):
self.current_seq.append(_UpperCamelCase )
snake_case_ = True
else:
snake_case_ = True
self.reset()
snake_case_ = self.trie.reached_leaf(self.current_seq )
snake_case_ = completed
return stepped, completed, reset
def snake_case__( self : List[Any] ) ->str:
snake_case_ = False
snake_case_ = []
def snake_case__( self : Tuple ) ->Dict:
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def snake_case__( self : Union[str, Any] , _UpperCamelCase : List[Any]=False ) ->Optional[int]:
snake_case_ = DisjunctiveConstraint(self.token_ids )
if stateful:
snake_case_ = self.seqlen
snake_case_ = self.current_seq
snake_case_ = self.completed
return new_constraint
class snake_case_ :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : List[Constraint] ) ->str:
snake_case_ = constraints
# max # of steps required to fulfill a given constraint
snake_case_ = max([c.seqlen for c in constraints] )
snake_case_ = len(_UpperCamelCase )
snake_case_ = False
self.init_state()
def snake_case__( self : Tuple ) ->Dict:
snake_case_ = []
snake_case_ = None
snake_case_ = [constraint.copy(stateful=_UpperCamelCase ) for constraint in self.constraints]
def snake_case__( self : Tuple ) ->int:
snake_case_ = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def snake_case__( self : Optional[Any] ) ->Optional[Any]:
snake_case_ = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
snake_case_ = constraint.advance()
if isinstance(_UpperCamelCase , _UpperCamelCase ):
token_list.append(_UpperCamelCase )
elif isinstance(_UpperCamelCase , _UpperCamelCase ):
token_list.extend(_UpperCamelCase )
else:
snake_case_ = self.inprogress_constraint.advance()
if isinstance(_UpperCamelCase , _UpperCamelCase ):
token_list.append(_UpperCamelCase )
elif isinstance(_UpperCamelCase , _UpperCamelCase ):
token_list.extend(_UpperCamelCase )
if len(_UpperCamelCase ) == 0:
return None
else:
return token_list
def snake_case__( self : Dict , _UpperCamelCase : Optional[List[int]] ) ->List[Any]:
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
snake_case_, snake_case_ = self.add(_UpperCamelCase )
# the entire list of constraints are fulfilled
if self.completed:
break
def snake_case__( self : Optional[int] , _UpperCamelCase : int ) ->List[Any]:
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
raise ValueError(f'''`token_id` should be an `int`, but is `{token_id}`.''' )
snake_case_, snake_case_ = False, False
if self.completed:
snake_case_ = True
snake_case_ = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
snake_case_, snake_case_, snake_case_ = self.inprogress_constraint.update(_UpperCamelCase )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=_UpperCamelCase ) )
snake_case_ = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
snake_case_ = None
if len(self.pending_constraints ) == 0:
# we're done!
snake_case_ = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(_UpperCamelCase ):
snake_case_, snake_case_, snake_case_ = pending_constraint.update(_UpperCamelCase )
if not stepped:
raise Exception(
'''`constraint.update(token_id)` is not yielding incremental progress, '''
'''even though `constraint.does_advance(token_id)` is true.''' )
if complete:
self.complete_constraints.append(_UpperCamelCase )
snake_case_ = None
if not complete and stepped:
snake_case_ = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
snake_case_ = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
snake_case_ = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def snake_case__( self : int , _UpperCamelCase : List[str]=True ) ->Optional[Any]:
snake_case_ = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
snake_case_ = [
constraint.copy(stateful=_UpperCamelCase ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
snake_case_ = self.inprogress_constraint.copy(stateful=_UpperCamelCase )
snake_case_ = [constraint.copy() for constraint in self.pending_constraints]
return new_state | 39 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__SCREAMING_SNAKE_CASE = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE = [
"""MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MegatronBertForCausalLM""",
"""MegatronBertForMaskedLM""",
"""MegatronBertForMultipleChoice""",
"""MegatronBertForNextSentencePrediction""",
"""MegatronBertForPreTraining""",
"""MegatronBertForQuestionAnswering""",
"""MegatronBertForSequenceClassification""",
"""MegatronBertForTokenClassification""",
"""MegatronBertModel""",
"""MegatronBertPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 388 | 0 |
import json
import logging
import os
import socket
import git
import numpy as np
import torch
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - PID: %(process)d - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
a_ = logging.getLogger(__name__)
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Dict:
"""simple docstring"""
__UpperCamelCase = git.Repo(search_parent_directories=lowercase_ )
__UpperCamelCase = {
'''repo_id''': str(lowercase_ ),
'''repo_sha''': str(repo.head.object.hexsha ),
'''repo_branch''': str(repo.active_branch ),
}
with open(os.path.join(lowercase_ , '''git_log.json''' ) , '''w''' ) as f:
json.dump(lowercase_ , lowercase_ , indent=4 )
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Optional[int]:
"""simple docstring"""
if params.n_gpu <= 0:
__UpperCamelCase = 0
__UpperCamelCase = -1
__UpperCamelCase = True
__UpperCamelCase = False
return
assert torch.cuda.is_available()
logger.info('''Initializing GPUs''' )
if params.n_gpu > 1:
assert params.local_rank != -1
__UpperCamelCase = int(os.environ['''WORLD_SIZE'''] )
__UpperCamelCase = int(os.environ['''N_GPU_NODE'''] )
__UpperCamelCase = int(os.environ['''RANK'''] )
# number of nodes / node ID
__UpperCamelCase = params.world_size // params.n_gpu_per_node
__UpperCamelCase = params.global_rank // params.n_gpu_per_node
__UpperCamelCase = True
assert params.n_nodes == int(os.environ['''N_NODES'''] )
assert params.node_id == int(os.environ['''NODE_RANK'''] )
# local job (single GPU)
else:
assert params.local_rank == -1
__UpperCamelCase = 1
__UpperCamelCase = 0
__UpperCamelCase = 0
__UpperCamelCase = 0
__UpperCamelCase = 1
__UpperCamelCase = 1
__UpperCamelCase = False
# sanity checks
assert params.n_nodes >= 1
assert 0 <= params.node_id < params.n_nodes
assert 0 <= params.local_rank <= params.global_rank < params.world_size
assert params.world_size == params.n_nodes * params.n_gpu_per_node
# define whether this is the master process / if we are in multi-node distributed mode
__UpperCamelCase = params.node_id == 0 and params.local_rank == 0
__UpperCamelCase = params.n_nodes > 1
# summary
__UpperCamelCase = F"--- Global rank: {params.global_rank} - "
logger.info(PREFIX + '''Number of nodes: %i''' % params.n_nodes )
logger.info(PREFIX + '''Node ID : %i''' % params.node_id )
logger.info(PREFIX + '''Local rank : %i''' % params.local_rank )
logger.info(PREFIX + '''World size : %i''' % params.world_size )
logger.info(PREFIX + '''GPUs per node : %i''' % params.n_gpu_per_node )
logger.info(PREFIX + '''Master : %s''' % str(params.is_master ) )
logger.info(PREFIX + '''Multi-node : %s''' % str(params.multi_node ) )
logger.info(PREFIX + '''Multi-GPU : %s''' % str(params.multi_gpu ) )
logger.info(PREFIX + '''Hostname : %s''' % socket.gethostname() )
# set GPU device
torch.cuda.set_device(params.local_rank )
# initialize multi-GPU
if params.multi_gpu:
logger.info('''Initializing PyTorch distributed''' )
torch.distributed.init_process_group(
init_method='''env://''' , backend='''nccl''' , )
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> Optional[int]:
"""simple docstring"""
np.random.seed(args.seed )
torch.manual_seed(args.seed )
if args.n_gpu > 0:
torch.cuda.manual_seed_all(args.seed )
| 375 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class _lowerCamelCase ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowerCAmelCase__ : Any = ""
lowerCAmelCase__ : Any = "hf-legacy" # "hf://"" is reserved for hffs
def __init__( self : str , snake_case : Optional[DatasetInfo] = None , snake_case : Optional[str] = None , **snake_case : List[Any] , ):
super().__init__(self , **snake_case )
__UpperCamelCase = repo_info
__UpperCamelCase = token
__UpperCamelCase = None
def snake_case ( self : List[Any] ):
if self.dir_cache is None:
__UpperCamelCase = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
__UpperCamelCase = {
'''name''': hf_file.rfilename,
'''size''': None,
'''type''': '''file''',
}
self.dir_cache.update(
{
str(snake_case ): {'''name''': str(snake_case ), '''size''': None, '''type''': '''directory'''}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def snake_case ( self : Dict , snake_case : str , snake_case : str = "rb" , **snake_case : Union[str, Any] , ):
if not isinstance(self.repo_info , snake_case ):
raise NotImplementedError(F"Open is only implemented for dataset repositories, but got {self.repo_info}" )
__UpperCamelCase = hf_hub_url(self.repo_info.id , snake_case , revision=self.repo_info.sha )
return fsspec.open(
snake_case , mode=snake_case , headers=get_authentication_headers_for_url(snake_case , use_auth_token=self.token ) , client_kwargs={'''trust_env''': True} , ).open()
def snake_case ( self : Optional[Any] , snake_case : Tuple , **snake_case : List[Any] ):
self._get_dirs()
__UpperCamelCase = self._strip_protocol(snake_case )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(snake_case )
def snake_case ( self : List[str] , snake_case : int , snake_case : Tuple=False , **snake_case : Dict ):
self._get_dirs()
__UpperCamelCase = PurePosixPath(path.strip('''/''' ) )
__UpperCamelCase = {}
for p, f in self.dir_cache.items():
__UpperCamelCase = PurePosixPath(p.strip('''/''' ) )
__UpperCamelCase = p.parent
if root == path:
__UpperCamelCase = f
__UpperCamelCase = list(paths.values() )
if detail:
return out
else:
return sorted(f['''name'''] for f in out )
| 375 | 1 |
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case (__lowercase):
UpperCamelCase_ = FileLock(str(tmpdir / 'foo.lock'))
UpperCamelCase_ = FileLock(str(tmpdir / 'foo.lock'))
UpperCamelCase_ = 0.01
with locka.acquire():
with pytest.raises(__lowercase):
UpperCamelCase_ = time.time()
locka.acquire(__lowercase)
assert time.time() - _start > timeout
def _snake_case (__lowercase):
UpperCamelCase_ = 'a' * 1000 + '.lock'
UpperCamelCase_ = FileLock(str(tmpdir / filename))
assert locka._lock_file.endswith('.lock')
assert not locka._lock_file.endswith(__lowercase)
assert len(os.path.basename(locka._lock_file)) <= 255
UpperCamelCase_ = FileLock(tmpdir / filename)
with locka.acquire():
with pytest.raises(__lowercase):
locka.acquire(0)
| 23 |
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 _SCREAMING_SNAKE_CASE ( a ) -> Tuple:
__A : str = 3_84
if "tiny" in model_name:
__A : Union[str, Any] = [3, 3, 9, 3]
__A : Any = [96, 1_92, 3_84, 7_68]
if "small" in model_name:
__A : str = [3, 3, 27, 3]
__A : Dict = [96, 1_92, 3_84, 7_68]
if "base" in model_name:
__A : Any = [3, 3, 27, 3]
__A : str = [1_28, 2_56, 5_12, 10_24]
__A : Optional[Any] = 5_12
if "large" in model_name:
__A : Dict = [3, 3, 27, 3]
__A : Any = [1_92, 3_84, 7_68, 15_36]
__A : str = 7_68
if "xlarge" in model_name:
__A : int = [3, 3, 27, 3]
__A : Optional[Any] = [2_56, 5_12, 10_24, 20_48]
__A : Optional[Any] = 10_24
# set label information
__A : int = 1_50
__A : int = 'huggingface/label-files'
__A : Any = 'ade20k-id2label.json'
__A : int = json.load(open(hf_hub_download(a , a , repo_type='dataset' ) , 'r' ) )
__A : List[Any] = {int(a ): v for k, v in idalabel.items()}
__A : List[Any] = {v: k for k, v in idalabel.items()}
__A : 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 _SCREAMING_SNAKE_CASE ( a ) -> Dict:
__A : str = []
# 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 _SCREAMING_SNAKE_CASE ( a , a , a ) -> Tuple:
__A : int = dct.pop(a )
__A : int = val
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> Any:
__A : List[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 : List[str] = model_name_to_url[model_name]
__A : Tuple = torch.hub.load_state_dict_from_url(a , map_location='cpu' )['state_dict']
__A : List[str] = get_upernet_config(a )
__A : Dict = UperNetForSemanticSegmentation(a )
model.eval()
# replace "bn" => "batch_norm"
for key in state_dict.copy().keys():
__A : str = state_dict.pop(a )
if "bn" in key:
__A : str = key.replace('bn' , 'batch_norm' )
__A : Optional[int] = val
# rename keys
__A : str = create_rename_keys(a )
for src, dest in rename_keys:
rename_key(a , a , a )
model.load_state_dict(a )
# verify on image
__A : Union[str, Any] = 'https://huggingface.co/datasets/hf-internal-testing/fixtures_ade20k/resolve/main/ADE_val_00000001.jpg'
__A : str = Image.open(requests.get(a , stream=a ).raw ).convert('RGB' )
__A : List[Any] = SegformerImageProcessor()
__A : str = processor(a , return_tensors='pt' ).pixel_values
with torch.no_grad():
__A : Tuple = model(a )
if model_name == "upernet-convnext-tiny":
__A : Optional[Any] = torch.tensor(
[[-8.8_110, -8.8_110, -8.6_521], [-8.8_110, -8.8_110, -8.6_521], [-8.7_746, -8.7_746, -8.6_130]] )
elif model_name == "upernet-convnext-small":
__A : Dict = torch.tensor(
[[-8.8_236, -8.8_236, -8.6_771], [-8.8_236, -8.8_236, -8.6_771], [-8.7_638, -8.7_638, -8.6_240]] )
elif model_name == "upernet-convnext-base":
__A : List[Any] = torch.tensor(
[[-8.8_558, -8.8_558, -8.6_905], [-8.8_558, -8.8_558, -8.6_905], [-8.7_669, -8.7_669, -8.6_021]] )
elif model_name == "upernet-convnext-large":
__A : Union[str, Any] = torch.tensor(
[[-8.6_660, -8.6_660, -8.6_210], [-8.6_660, -8.6_660, -8.6_210], [-8.6_310, -8.6_310, -8.5_964]] )
elif model_name == "upernet-convnext-xlarge":
__A : List[Any] = torch.tensor(
[[-8.4_980, -8.4_980, -8.3_977], [-8.4_980, -8.4_980, -8.3_977], [-8.4_379, -8.4_379, -8.3_412]] )
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__":
UpperCAmelCase : List[str] = 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.'''
)
UpperCAmelCase : Optional[int] = parser.parse_args()
convert_upernet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 239 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase = {'''configuration_wavlm''': ['''WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''WavLMConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = [
'''WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''WavLMForAudioFrameClassification''',
'''WavLMForCTC''',
'''WavLMForSequenceClassification''',
'''WavLMForXVector''',
'''WavLMModel''',
'''WavLMPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_wavlm import WAVLM_PRETRAINED_CONFIG_ARCHIVE_MAP, WavLMConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_wavlm import (
WAVLM_PRETRAINED_MODEL_ARCHIVE_LIST,
WavLMForAudioFrameClassification,
WavLMForCTC,
WavLMForSequenceClassification,
WavLMForXVector,
WavLMModel,
WavLMPreTrainedModel,
)
else:
import sys
lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 102 |
def __lowercase ( UpperCAmelCase__ = 10 , UpperCAmelCase__ = 1_000 , UpperCAmelCase__ = True ):
"""simple docstring"""
assert (
isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
and isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
and isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
), "Invalid type of value(s) specified to function!"
if min_val > max_val:
raise ValueError('Invalid value for min_val or max_val (min_value < max_value)' )
return min_val if option else max_val
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
return int((number_a + number_a) / 2 )
def __lowercase ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
"""simple docstring"""
assert (
isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and isinstance(UpperCAmelCase__ , UpperCAmelCase__ )
), 'argument values must be type of "int"'
if lower > higher:
raise ValueError('argument value for lower and higher must be(lower > higher)' )
if not lower < to_guess < higher:
raise ValueError(
'guess value must be within the range of lower and higher value' )
def answer(UpperCAmelCase__ ) -> str:
if number > to_guess:
return "high"
elif number < to_guess:
return "low"
else:
return "same"
print('started...' )
__lowerCAmelCase = lower
__lowerCAmelCase = higher
__lowerCAmelCase = []
while True:
__lowerCAmelCase = get_avg(UpperCAmelCase__ , UpperCAmelCase__ )
last_numbers.append(UpperCAmelCase__ )
if answer(UpperCAmelCase__ ) == "low":
__lowerCAmelCase = number
elif answer(UpperCAmelCase__ ) == "high":
__lowerCAmelCase = number
else:
break
print(F"""guess the number : {last_numbers[-1]}""" )
print(F"""details : {last_numbers!s}""" )
def __lowercase ( ):
"""simple docstring"""
__lowerCAmelCase = int(input('Enter lower value : ' ).strip() )
__lowerCAmelCase = int(input('Enter high value : ' ).strip() )
__lowerCAmelCase = int(input('Enter value to guess : ' ).strip() )
guess_the_number(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 102 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCAmelCase : int = {
"google/bigbird-roberta-base": "https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json",
"google/bigbird-roberta-large": "https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json",
"google/bigbird-base-trivia-itc": "https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json",
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class _snake_case ( _A ):
_A = 'big_bird'
def __init__( self ,UpperCamelCase=50_358 ,UpperCamelCase=768 ,UpperCamelCase=12 ,UpperCamelCase=12 ,UpperCamelCase=3_072 ,UpperCamelCase="gelu_new" ,UpperCamelCase=0.1 ,UpperCamelCase=0.1 ,UpperCamelCase=4_096 ,UpperCamelCase=2 ,UpperCamelCase=0.02 ,UpperCamelCase=1E-12 ,UpperCamelCase=True ,UpperCamelCase=0 ,UpperCamelCase=1 ,UpperCamelCase=2 ,UpperCamelCase=66 ,UpperCamelCase="block_sparse" ,UpperCamelCase=True ,UpperCamelCase=False ,UpperCamelCase=64 ,UpperCamelCase=3 ,UpperCamelCase=None ,**UpperCamelCase ,) -> Tuple:
super().__init__(
pad_token_id=UpperCamelCase ,bos_token_id=UpperCamelCase ,eos_token_id=UpperCamelCase ,sep_token_id=UpperCamelCase ,**UpperCamelCase ,)
snake_case__ :Union[str, Any] = vocab_size
snake_case__ :Dict = max_position_embeddings
snake_case__ :Tuple = hidden_size
snake_case__ :int = num_hidden_layers
snake_case__ :Optional[Any] = num_attention_heads
snake_case__ :Optional[Any] = intermediate_size
snake_case__ :List[str] = hidden_act
snake_case__ :List[Any] = hidden_dropout_prob
snake_case__ :Union[str, Any] = attention_probs_dropout_prob
snake_case__ :Dict = initializer_range
snake_case__ :Optional[Any] = type_vocab_size
snake_case__ :Any = layer_norm_eps
snake_case__ :List[Any] = use_cache
snake_case__ :Union[str, Any] = rescale_embeddings
snake_case__ :Tuple = attention_type
snake_case__ :str = use_bias
snake_case__ :Dict = block_size
snake_case__ :Optional[Any] = num_random_blocks
snake_case__ :str = classifier_dropout
class _snake_case ( _A ):
@property
def lowerCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
snake_case__ :Tuple = {0: "batch", 1: "choice", 2: "sequence"}
else:
snake_case__ :Optional[int] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] ) | 241 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowercase_ ( __snake_case : Optional[Any] ) -> List[Any]:
'''simple docstring'''
if (
(cp >= 0X4_e00 and cp <= 0X9_fff)
or (cp >= 0X3_400 and cp <= 0X4_dbf) #
or (cp >= 0X20_000 and cp <= 0X2a_6df) #
or (cp >= 0X2a_700 and cp <= 0X2b_73f) #
or (cp >= 0X2b_740 and cp <= 0X2b_81f) #
or (cp >= 0X2b_820 and cp <= 0X2c_eaf) #
or (cp >= 0Xf_900 and cp <= 0Xf_aff)
or (cp >= 0X2f_800 and cp <= 0X2f_a1f) #
): #
return True
return False
def lowercase_ ( __snake_case : str ) -> Tuple:
'''simple docstring'''
for char in word:
snake_case__ :Dict = ord(__snake_case )
if not _is_chinese_char(__snake_case ):
return 0
return 1
def lowercase_ ( __snake_case : List[str] ) -> Any:
'''simple docstring'''
snake_case__ :Optional[int] = set()
for token in tokens:
snake_case__ :Dict = len(__snake_case ) > 1 and is_chinese(__snake_case )
if chinese_word:
word_set.add(__snake_case )
snake_case__ :Tuple = list(__snake_case )
return word_list
def lowercase_ ( __snake_case : List[str] , __snake_case : set() ) -> int:
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
snake_case__ :List[str] = max([len(__snake_case ) for w in chinese_word_set] )
snake_case__ :str = bert_tokens
snake_case__ , snake_case__ :Dict = 0, len(__snake_case )
while start < end:
snake_case__ :Any = True
if is_chinese(bert_word[start] ):
snake_case__ :Union[str, Any] = min(end - start , __snake_case )
for i in range(__snake_case , 1 , -1 ):
snake_case__ :str = "".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
snake_case__ :int = "##" + bert_word[j]
snake_case__ :str = start + i
snake_case__ :Union[str, Any] = False
break
if single_word:
start += 1
return bert_word
def lowercase_ ( __snake_case : List[str] , __snake_case : LTP , __snake_case : BertTokenizer ) -> List[Any]:
'''simple docstring'''
snake_case__ :Union[str, Any] = []
for i in range(0 , len(__snake_case ) , 1_00 ):
snake_case__ :Any = ltp_tokenizer.pipeline(lines[i : i + 1_00] , tasks=["cws"] ).cws
snake_case__ :Optional[Any] = [get_chinese_word(__snake_case ) for r in res]
ltp_res.extend(__snake_case )
assert len(__snake_case ) == len(__snake_case )
snake_case__ :int = []
for i in range(0 , len(__snake_case ) , 1_00 ):
snake_case__ :str = bert_tokenizer(lines[i : i + 1_00] , add_special_tokens=__snake_case , truncation=__snake_case , max_length=5_12 )
bert_res.extend(res["input_ids"] )
assert len(__snake_case ) == len(__snake_case )
snake_case__ :Union[str, Any] = []
for input_ids, chinese_word in zip(__snake_case , __snake_case ):
snake_case__ :Dict = []
for id in input_ids:
snake_case__ :Tuple = bert_tokenizer._convert_id_to_token(__snake_case )
input_tokens.append(__snake_case )
snake_case__ :Tuple = add_sub_symbol(__snake_case , __snake_case )
snake_case__ :Dict = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__snake_case ):
if token[:2] == "##":
snake_case__ :Optional[Any] = token[2:]
# save chinese tokens' pos
if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ):
ref_id.append(__snake_case )
ref_ids.append(__snake_case )
assert len(__snake_case ) == len(__snake_case )
return ref_ids
def lowercase_ ( __snake_case : List[str] ) -> Optional[Any]:
'''simple docstring'''
with open(args.file_name , "r" , encoding="utf-8" ) as f:
snake_case__ :Optional[int] = f.readlines()
snake_case__ :Union[str, Any] = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
snake_case__ :Optional[int] = LTP(args.ltp ) # faster in GPU device
snake_case__ :Optional[int] = BertTokenizer.from_pretrained(args.bert )
snake_case__ :str = prepare_ref(__snake_case , __snake_case , __snake_case )
with open(args.save_path , "w" , encoding="utf-8" ) as f:
snake_case__ :List[str] = [json.dumps(__snake_case ) + "\n" for ref in ref_ids]
f.writelines(__snake_case )
if __name__ == "__main__":
__UpperCAmelCase : Optional[int] = argparse.ArgumentParser(description="prepare_chinese_ref")
parser.add_argument(
"--file_name",
required=False,
type=str,
default="./resources/chinese-demo.txt",
help="file need process, same as training data in lm",
)
parser.add_argument(
"--ltp",
required=False,
type=str,
default="./resources/ltp",
help="resources for LTP tokenizer, usually a path",
)
parser.add_argument(
"--bert",
required=False,
type=str,
default="./resources/robert",
help="resources for Bert tokenizer",
)
parser.add_argument(
"--save_path",
required=False,
type=str,
default="./resources/ref.txt",
help="path to save res",
)
__UpperCAmelCase : str = parser.parse_args()
main(args) | 241 | 1 |
"""simple docstring"""
import numpy as np
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ = 1E-1_2 ,lowerCAmelCase__ = 100 ,):
assert np.shape(lowerCAmelCase__ )[0] == np.shape(lowerCAmelCase__ )[1]
# Ensure proper dimensionality.
assert np.shape(lowerCAmelCase__ )[0] == np.shape(lowerCAmelCase__ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(lowerCAmelCase__ ) == np.iscomplexobj(lowerCAmelCase__ )
A__ = np.iscomplexobj(lowerCAmelCase__ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(lowerCAmelCase__ ,input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
A__ = False
A__ = 0
A__ = 0
A__ = 1E1_2
while not convergence:
# Multiple matrix by the vector.
A__ = np.dot(lowerCAmelCase__ ,lowerCAmelCase__ )
# Normalize the resulting output vector.
A__ = w / np.linalg.norm(lowerCAmelCase__ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
A__ = vector.conj().T if is_complex else vector.T
A__ = np.dot(lowerCAmelCase__ ,np.dot(lowerCAmelCase__ ,lowerCAmelCase__ ) )
# Check convergence.
A__ = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
A__ = True
A__ = lambda_
if is_complex:
A__ = np.real(lambda_ )
return lambda_, vector
def __lowerCamelCase ( ):
A__ = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
A__ = np.array([41, 4, 20] )
A__ = real_input_matrix.astype(np.complexaaa )
A__ = np.triu(1j * complex_input_matrix ,1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
A__ = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
A__ = real_input_matrix
A__ = real_vector
elif problem_type == "complex":
A__ = complex_input_matrix
A__ = complex_vector
# Our implementation.
A__ , A__ = power_iteration(lowerCAmelCase__ ,lowerCAmelCase__ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
A__ , A__ = np.linalg.eigh(lowerCAmelCase__ )
# Last eigenvalue is the maximum one.
A__ = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
A__ = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1E-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(lowerCAmelCase__ ) - np.abs(lowerCAmelCase__ ) ) <= 1E-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 554 |
"""simple docstring"""
from __future__ import annotations
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ = None ):
A__ = word_bank or []
# create a table
A__ = len(lowerCAmelCase__ ) + 1
A__ = []
for _ in range(lowerCAmelCase__ ):
table.append([] )
# seed value
A__ = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(lowerCAmelCase__ ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(lowerCAmelCase__ )] == word:
A__ = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(lowerCAmelCase__ )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(lowerCAmelCase__ )]:
combination.reverse()
return table[len(lowerCAmelCase__ )]
if __name__ == "__main__":
print(all_construct('''jwajalapa''', ['''jwa''', '''j''', '''w''', '''a''', '''la''', '''lapa''']))
print(all_construct('''rajamati''', ['''s''', '''raj''', '''amat''', '''raja''', '''ma''', '''i''', '''t''']))
print(
all_construct(
'''hexagonosaurus''',
['''h''', '''ex''', '''hex''', '''ag''', '''ago''', '''ru''', '''auru''', '''rus''', '''go''', '''no''', '''o''', '''s'''],
)
)
| 554 | 1 |
def _lowercase( __a : list ):
if len(_UpperCAmelCase ) < 2:
return collection
def circle_sort_util(__a : list , __a : int , __a : int ) -> bool:
a__ =False
if low == high:
return swapped
a__ =low
a__ =high
while left < right:
if collection[left] > collection[right]:
a__ , a__ =(
collection[right],
collection[left],
)
a__ =True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
a__ , a__ =(
collection[right + 1],
collection[left],
)
a__ =True
a__ =low + int((high - low) / 2 )
a__ =circle_sort_util(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
a__ =circle_sort_util(_UpperCAmelCase , mid + 1 , _UpperCAmelCase )
return swapped or left_swap or right_swap
a__ =True
while is_not_sorted is True:
a__ =circle_sort_util(_UpperCAmelCase , 0 , len(_UpperCAmelCase ) - 1 )
return collection
if __name__ == "__main__":
_lowerCAmelCase: Optional[Any] = input('Enter numbers separated by a comma:\n').strip()
_lowerCAmelCase: List[str] = [int(item) for item in user_input.split(',')]
print(circle_sort(unsorted))
| 20 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
a__ : Any = 16
a__ : str = 32
def UpperCAmelCase_ ( _UpperCAmelCase :Accelerator , _UpperCAmelCase :int = 16 ) -> int:
'''simple docstring'''
A_ = AutoTokenizer.from_pretrained('''bert-base-cased''' )
A_ = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(_UpperCAmelCase :Optional[Any] ):
# max_length=None => use the model max length (it's actually the default)
A_ = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=_UpperCAmelCase , max_length=_UpperCAmelCase )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A_ = datasets.map(
_UpperCAmelCase , batched=_UpperCAmelCase , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A_ = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(_UpperCAmelCase :List[Any] ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A_ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A_ = 16
elif accelerator.mixed_precision != "no":
A_ = 8
else:
A_ = None
return tokenizer.pad(
_UpperCAmelCase , padding='''longest''' , max_length=_UpperCAmelCase , pad_to_multiple_of=_UpperCAmelCase , return_tensors='''pt''' , )
# Instantiate dataloaders.
A_ = DataLoader(
tokenized_datasets['''train'''] , shuffle=_UpperCAmelCase , collate_fn=_UpperCAmelCase , batch_size=_UpperCAmelCase )
A_ = DataLoader(
tokenized_datasets['''validation'''] , shuffle=_UpperCAmelCase , collate_fn=_UpperCAmelCase , batch_size=_UpperCAmelCase )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
a__ : Optional[Any] = mocked_dataloaders # noqa: F811
def UpperCAmelCase_ ( _UpperCAmelCase :List[str] , _UpperCAmelCase :Dict ) -> Dict:
'''simple docstring'''
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , _UpperCAmelCase ) == "1":
A_ = 2
# New Code #
A_ = int(args.gradient_accumulation_steps )
A_ = int(args.local_sgd_steps )
# Initialize accelerator
A_ = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=_UpperCAmelCase )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A_ = config['''lr''']
A_ = int(config['''num_epochs'''] )
A_ = int(config['''seed'''] )
A_ = int(config['''batch_size'''] )
A_ = evaluate.load('''glue''' , '''mrpc''' )
set_seed(_UpperCAmelCase )
A_ , A_ = get_dataloaders(_UpperCAmelCase , _UpperCAmelCase )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A_ = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=_UpperCAmelCase )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A_ = model.to(accelerator.device )
# Instantiate optimizer
A_ = AdamW(params=model.parameters() , lr=_UpperCAmelCase )
# Instantiate scheduler
A_ = get_linear_schedule_with_warmup(
optimizer=_UpperCAmelCase , num_warmup_steps=100 , num_training_steps=(len(_UpperCAmelCase ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A_ , A_ , A_ , A_ , A_ = accelerator.prepare(
_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# Now we train the model
for epoch in range(_UpperCAmelCase ):
model.train()
with LocalSGD(
accelerator=_UpperCAmelCase , model=_UpperCAmelCase , local_sgd_steps=_UpperCAmelCase , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(_UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(_UpperCAmelCase ):
A_ = model(**_UpperCAmelCase )
A_ = output.loss
accelerator.backward(_UpperCAmelCase )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(_UpperCAmelCase ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A_ = model(**_UpperCAmelCase )
A_ = outputs.logits.argmax(dim=-1 )
A_ , A_ = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=_UpperCAmelCase , references=_UpperCAmelCase , )
A_ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'epoch {epoch}:' , _UpperCAmelCase )
def UpperCAmelCase_ ( ) -> Union[str, Any]:
'''simple docstring'''
A_ = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=_UpperCAmelCase , default=_UpperCAmelCase , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
# New Code #
parser.add_argument(
'''--gradient_accumulation_steps''' , type=_UpperCAmelCase , default=1 , help='''The number of minibatches to be ran before gradients are accumulated.''' , )
parser.add_argument(
'''--local_sgd_steps''' , type=_UpperCAmelCase , default=8 , help='''Number of local SGD steps or None to disable local SGD''' )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
A_ = parser.parse_args()
A_ = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(_UpperCAmelCase , _UpperCAmelCase )
if __name__ == "__main__":
main()
| 188 | 0 |
def lowercase_ ( SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
snake_case__ : Dict =[0 for i in range(len(_A ) )]
# initialize interval's left pointer and right pointer
snake_case__ : List[str] =0, 0
for i in range(1 , len(_A ) ):
# case when current index is inside the interval
if i <= right_pointer:
snake_case__ : Dict =min(right_pointer - i + 1 , z_result[i - left_pointer] )
snake_case__ : Tuple =min_edge
while go_next(_A , _A , _A ):
z_result[i] += 1
# if new index's result gives us more right interval,
# we've to update left_pointer and right_pointer
if i + z_result[i] - 1 > right_pointer:
snake_case__ : Any =i, i + z_result[i] - 1
return z_result
def lowercase_ ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : list[int] , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
return i + z_result[i] < len(_A ) and s[z_result[i]] == s[i + z_result[i]]
def lowercase_ ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
snake_case__ : int =0
# concatenate 'pattern' and 'input_str' and call z_function
# with concatenated string
snake_case__ : Optional[int] =z_function(pattern + input_str )
for val in z_result:
# if value is greater then length of the pattern string
# that means this index is starting position of substring
# which is equal to pattern string
if val >= len(_A ):
answer += 1
return answer
if __name__ == "__main__":
import doctest
doctest.testmod()
| 705 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
'''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
'''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
'''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class _lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ ='''mobilenet_v2'''
def __init__( self , __SCREAMING_SNAKE_CASE=3 , __SCREAMING_SNAKE_CASE=224 , __SCREAMING_SNAKE_CASE=1.0 , __SCREAMING_SNAKE_CASE=8 , __SCREAMING_SNAKE_CASE=8 , __SCREAMING_SNAKE_CASE=6 , __SCREAMING_SNAKE_CASE=32 , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE="relu6" , __SCREAMING_SNAKE_CASE=True , __SCREAMING_SNAKE_CASE=0.8 , __SCREAMING_SNAKE_CASE=0.02 , __SCREAMING_SNAKE_CASE=0.001 , __SCREAMING_SNAKE_CASE=255 , **__SCREAMING_SNAKE_CASE , ) -> List[str]:
"""simple docstring"""
super().__init__(**__SCREAMING_SNAKE_CASE )
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''' )
snake_case__ : Optional[int] =num_channels
snake_case__ : Optional[int] =image_size
snake_case__ : int =depth_multiplier
snake_case__ : Optional[Any] =depth_divisible_by
snake_case__ : Any =min_depth
snake_case__ : Tuple =expand_ratio
snake_case__ : int =output_stride
snake_case__ : List[Any] =first_layer_is_expansion
snake_case__ : Union[str, Any] =finegrained_output
snake_case__ : int =hidden_act
snake_case__ : Tuple =tf_padding
snake_case__ : List[Any] =classifier_dropout_prob
snake_case__ : Dict =initializer_range
snake_case__ : List[str] =layer_norm_eps
snake_case__ : str =semantic_loss_ignore_index
class _lowerCAmelCase ( __UpperCamelCase ):
"""simple docstring"""
lowerCAmelCase__ =version.parse('''1.11''' )
@property
def UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict([('''pixel_values''', {0: '''batch'''})] )
@property
def UpperCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
"""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 UpperCAmelCase ( self ) -> float:
"""simple docstring"""
return 1e-4
| 408 | 0 |
from collections import defaultdict
from typing import Optional
from ..image_utils import load_image
from ..utils import (
add_end_docstrings,
is_torch_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
@add_end_docstrings(lowercase__ )
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self , **lowerCAmelCase ):
super().__init__(**lowerCAmelCase )
requires_backends(self , "vision" )
requires_backends(self , "torch" )
if self.framework != "pt":
raise ValueError(f'''The {self.__class__} is only available in PyTorch.''' )
self.check_model_type(lowerCAmelCase )
def A__ ( self , **lowerCAmelCase ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = {}
UpperCAmelCase_ = {}
# preprocess args
if "points_per_batch" in kwargs:
UpperCAmelCase_ = kwargs["points_per_batch"]
if "points_per_crop" in kwargs:
UpperCAmelCase_ = kwargs["points_per_crop"]
if "crops_n_layers" in kwargs:
UpperCAmelCase_ = kwargs["crops_n_layers"]
if "crop_overlap_ratio" in kwargs:
UpperCAmelCase_ = kwargs["crop_overlap_ratio"]
if "crop_n_points_downscale_factor" in kwargs:
UpperCAmelCase_ = kwargs["crop_n_points_downscale_factor"]
# postprocess args
if "pred_iou_thresh" in kwargs:
UpperCAmelCase_ = kwargs["pred_iou_thresh"]
if "stability_score_offset" in kwargs:
UpperCAmelCase_ = kwargs["stability_score_offset"]
if "mask_threshold" in kwargs:
UpperCAmelCase_ = kwargs["mask_threshold"]
if "stability_score_thresh" in kwargs:
UpperCAmelCase_ = kwargs["stability_score_thresh"]
if "crops_nms_thresh" in kwargs:
UpperCAmelCase_ = kwargs["crops_nms_thresh"]
if "output_rle_mask" in kwargs:
UpperCAmelCase_ = kwargs["output_rle_mask"]
if "output_bboxes_mask" in kwargs:
UpperCAmelCase_ = kwargs["output_bboxes_mask"]
return preprocess_kwargs, forward_params, postprocess_kwargs
def __call__( self , lowerCAmelCase , *lowerCAmelCase , lowerCAmelCase=None , lowerCAmelCase=None , **lowerCAmelCase ):
return super().__call__(lowerCAmelCase , *lowerCAmelCase , num_workers=lowerCAmelCase , batch_size=lowerCAmelCase , **lowerCAmelCase )
def A__ ( self , lowerCAmelCase , lowerCAmelCase=64 , lowerCAmelCase = 0 , lowerCAmelCase = 512 / 1500 , lowerCAmelCase = 32 , lowerCAmelCase = 1 , ):
UpperCAmelCase_ = load_image(lowerCAmelCase )
UpperCAmelCase_ = self.image_processor.size["longest_edge"]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.image_processor.generate_crop_boxes(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase_ = self.image_processor(images=lowerCAmelCase , return_tensors="pt" )
with self.device_placement():
if self.framework == "pt":
UpperCAmelCase_ = self.get_inference_context()
with inference_context():
UpperCAmelCase_ = self._ensure_tensor_on_device(lowerCAmelCase , device=self.device )
UpperCAmelCase_ = self.model.get_image_embeddings(model_inputs.pop("pixel_values" ) )
UpperCAmelCase_ = image_embeddings
UpperCAmelCase_ = grid_points.shape[1]
UpperCAmelCase_ = points_per_batch if points_per_batch is not None else n_points
if points_per_batch <= 0:
raise ValueError(
"Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
"To return all points at once, set points_per_batch to None" )
for i in range(0 , lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase_ = grid_points[:, i : i + points_per_batch, :, :]
UpperCAmelCase_ = input_labels[:, i : i + points_per_batch]
UpperCAmelCase_ = i == n_points - points_per_batch
yield {
"input_points": batched_points,
"input_labels": labels,
"input_boxes": crop_boxes,
"is_last": is_last,
**model_inputs,
}
def A__ ( self , lowerCAmelCase , lowerCAmelCase=0.88 , lowerCAmelCase=0.95 , lowerCAmelCase=0 , lowerCAmelCase=1 , ):
UpperCAmelCase_ = model_inputs.pop("input_boxes" )
UpperCAmelCase_ = model_inputs.pop("is_last" )
UpperCAmelCase_ = model_inputs.pop("original_sizes" ).tolist()
UpperCAmelCase_ = model_inputs.pop("reshaped_input_sizes" ).tolist()
UpperCAmelCase_ = self.model(**lowerCAmelCase )
# post processing happens here in order to avoid CPU GPU copies of ALL the masks
UpperCAmelCase_ = model_outputs["pred_masks"]
UpperCAmelCase_ = self.image_processor.post_process_masks(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , binarize=lowerCAmelCase )
UpperCAmelCase_ = model_outputs["iou_scores"]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.image_processor.filter_masks(
masks[0] , iou_scores[0] , original_sizes[0] , input_boxes[0] , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , )
return {
"masks": masks,
"is_last": is_last,
"boxes": boxes,
"iou_scores": iou_scores,
}
def A__ ( self , lowerCAmelCase , lowerCAmelCase=False , lowerCAmelCase=False , lowerCAmelCase=0.7 , ):
UpperCAmelCase_ = []
UpperCAmelCase_ = []
UpperCAmelCase_ = []
for model_output in model_outputs:
all_scores.append(model_output.pop("iou_scores" ) )
all_masks.extend(model_output.pop("masks" ) )
all_boxes.append(model_output.pop("boxes" ) )
UpperCAmelCase_ = torch.cat(lowerCAmelCase )
UpperCAmelCase_ = torch.cat(lowerCAmelCase )
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = self.image_processor.post_process_for_mask_generation(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase_ = defaultdict(lowerCAmelCase )
for output in model_outputs:
for k, v in output.items():
extra[k].append(lowerCAmelCase )
UpperCAmelCase_ = {}
if output_rle_mask:
UpperCAmelCase_ = rle_mask
if output_bboxes_mask:
UpperCAmelCase_ = bounding_boxes
return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
| 579 |
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class lowerCamelCase :
'''simple docstring'''
def __init__( self , lowerCAmelCase = None , lowerCAmelCase = None , lowerCAmelCase=None , lowerCAmelCase=None ):
if not conversation_id:
UpperCAmelCase_ = uuid.uuida()
if past_user_inputs is None:
UpperCAmelCase_ = []
if generated_responses is None:
UpperCAmelCase_ = []
UpperCAmelCase_ = conversation_id
UpperCAmelCase_ = past_user_inputs
UpperCAmelCase_ = generated_responses
UpperCAmelCase_ = text
def __eq__( self , lowerCAmelCase ):
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
return False
if self.uuid == other.uuid:
return True
return (
self.new_user_input == other.new_user_input
and self.past_user_inputs == other.past_user_inputs
and self.generated_responses == other.generated_responses
)
def A__ ( self , lowerCAmelCase , lowerCAmelCase = False ):
if self.new_user_input:
if overwrite:
logger.warning(
f'''User input added while unprocessed input was existing: "{self.new_user_input}" was overwritten '''
f'''with: "{text}".''' )
UpperCAmelCase_ = text
else:
logger.warning(
f'''User input added while unprocessed input was existing: "{self.new_user_input}" new input '''
f'''ignored: "{text}". Set `overwrite` to True to overwrite unprocessed user input''' )
else:
UpperCAmelCase_ = text
def A__ ( self ):
if self.new_user_input:
self.past_user_inputs.append(self.new_user_input )
UpperCAmelCase_ = None
def A__ ( self , lowerCAmelCase ):
self.generated_responses.append(lowerCAmelCase )
def A__ ( self ):
for user_input, generated_response in zip(self.past_user_inputs , self.generated_responses ):
yield True, user_input
yield False, generated_response
if self.new_user_input:
yield True, self.new_user_input
def __repr__( self ):
UpperCAmelCase_ = f'''Conversation id: {self.uuid} \n'''
for is_user, text in self.iter_texts():
UpperCAmelCase_ = "user" if is_user else "bot"
output += f'''{name} >> {text} \n'''
return output
@add_end_docstrings(
lowercase__, r'\n min_length_for_response (`int`, *optional*, defaults to 32):\n The minimum length (in number of tokens) for a response.\n minimum_tokens (`int`, *optional*, defaults to 10):\n The minimum length of tokens to leave for a response.\n ', )
class lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self , *lowerCAmelCase , **lowerCAmelCase ):
super().__init__(*lowerCAmelCase , **lowerCAmelCase )
if self.tokenizer.pad_token_id is None:
UpperCAmelCase_ = self.tokenizer.eos_token
def A__ ( self , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=None , **lowerCAmelCase ):
UpperCAmelCase_ = {}
UpperCAmelCase_ = {}
UpperCAmelCase_ = {}
if min_length_for_response is not None:
UpperCAmelCase_ = min_length_for_response
if minimum_tokens is not None:
UpperCAmelCase_ = minimum_tokens
if "max_length" in generate_kwargs:
UpperCAmelCase_ = generate_kwargs["max_length"]
# self.max_length = generate_kwargs.get("max_length", self.model.config.max_length)
if clean_up_tokenization_spaces is not None:
UpperCAmelCase_ = clean_up_tokenization_spaces
if generate_kwargs:
forward_params.update(lowerCAmelCase )
return preprocess_params, forward_params, postprocess_params
def __call__( self , lowerCAmelCase , lowerCAmelCase=0 , **lowerCAmelCase ):
UpperCAmelCase_ = super().__call__(lowerCAmelCase , num_workers=lowerCAmelCase , **lowerCAmelCase )
if isinstance(lowerCAmelCase , lowerCAmelCase ) and len(lowerCAmelCase ) == 1:
return outputs[0]
return outputs
def A__ ( self , lowerCAmelCase , lowerCAmelCase=32 ):
if not isinstance(lowerCAmelCase , lowerCAmelCase ):
raise ValueError("ConversationalPipeline, expects Conversation as inputs" )
if conversation.new_user_input is None:
raise ValueError(
f'''Conversation with UUID {type(conversation.uuid )} does not contain new user input to process. '''
"Add user inputs with the conversation's `add_user_input` method" )
if hasattr(self.tokenizer , "_build_conversation_input_ids" ):
UpperCAmelCase_ = self.tokenizer._build_conversation_input_ids(lowerCAmelCase )
else:
# If the tokenizer cannot handle conversations, we default to only the old version
UpperCAmelCase_ = self._legacy_parse_and_tokenize(lowerCAmelCase )
if self.framework == "pt":
UpperCAmelCase_ = torch.LongTensor([input_ids] )
elif self.framework == "tf":
UpperCAmelCase_ = tf.constant([input_ids] )
return {"input_ids": input_ids, "conversation": conversation}
def A__ ( self , lowerCAmelCase , lowerCAmelCase=10 , **lowerCAmelCase ):
UpperCAmelCase_ = generate_kwargs.get("max_length" , self.model.config.max_length )
UpperCAmelCase_ = model_inputs["input_ids"].shape[1]
if max_length - minimum_tokens < n:
logger.warning(f'''Conversation input is to long ({n}), trimming it to ({max_length} - {minimum_tokens})''' )
UpperCAmelCase_ = max_length - minimum_tokens
UpperCAmelCase_ = model_inputs["input_ids"][:, -trim:]
if "attention_mask" in model_inputs:
UpperCAmelCase_ = model_inputs["attention_mask"][:, -trim:]
UpperCAmelCase_ = model_inputs.pop("conversation" )
UpperCAmelCase_ = max_length
UpperCAmelCase_ = self.model.generate(**lowerCAmelCase , **lowerCAmelCase )
if self.model.config.is_encoder_decoder:
UpperCAmelCase_ = 1
else:
UpperCAmelCase_ = n
return {"output_ids": output_ids[:, start_position:], "conversation": conversation}
def A__ ( self , lowerCAmelCase , lowerCAmelCase=True ):
UpperCAmelCase_ = model_outputs["output_ids"]
UpperCAmelCase_ = self.tokenizer.decode(
output_ids[0] , skip_special_tokens=lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase , )
UpperCAmelCase_ = model_outputs["conversation"]
conversation.mark_processed()
conversation.append_response(lowerCAmelCase )
return conversation
def A__ ( self , lowerCAmelCase ):
UpperCAmelCase_ = self.tokenizer.eos_token_id
UpperCAmelCase_ = []
for is_user, text in conversation.iter_texts():
if eos_token_id is not None:
input_ids.extend(self.tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) + [eos_token_id] )
else:
input_ids.extend(self.tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase ) )
if len(lowerCAmelCase ) > self.tokenizer.model_max_length:
UpperCAmelCase_ = input_ids[-self.tokenizer.model_max_length :]
return input_ids
| 579 | 1 |
'''simple docstring'''
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModule
def _SCREAMING_SNAKE_CASE ( A : List[Any] , A : Any , A : int=1e-12 ) -> str:
"""simple docstring"""
__snake_case : int = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(snake_case__ , axis=1 ) , a_min=snake_case__ ) ).T
__snake_case : Optional[Any] = jnp.divide(emb_a.T , jnp.clip(jnp.linalg.norm(snake_case__ , axis=1 ) , a_min=snake_case__ ) ).T
return jnp.matmul(snake_case__ , norm_emb_a.T )
class a_ ( nn.Module ):
_snake_case = 42
_snake_case = jnp.floataa
def SCREAMING_SNAKE_CASE__ (self) -> Dict:
"""simple docstring"""
__snake_case : List[Any] = FlaxCLIPVisionModule(self.config.vision_config)
__snake_case : List[str] = nn.Dense(self.config.projection_dim , use_bias=A_ , dtype=self.dtype)
__snake_case : Tuple = self.param('concept_embeds' , jax.nn.initializers.ones , (1_7, self.config.projection_dim))
__snake_case : str = self.param(
'special_care_embeds' , jax.nn.initializers.ones , (3, self.config.projection_dim))
__snake_case : Tuple = self.param('concept_embeds_weights' , jax.nn.initializers.ones , (1_7,))
__snake_case : str = self.param('special_care_embeds_weights' , jax.nn.initializers.ones , (3,))
def __call__(self , __a) -> Optional[Any]:
"""simple docstring"""
__snake_case : Tuple = self.vision_model(A_)[1]
__snake_case : Tuple = self.visual_projection(A_)
__snake_case : Dict = jax_cosine_distance(A_ , self.special_care_embeds)
__snake_case : Optional[Any] = jax_cosine_distance(A_ , self.concept_embeds)
# increase this value to create a stronger `nfsw` filter
# at the cost of increasing the possibility of filtering benign image inputs
__snake_case : Optional[int] = 0.0
__snake_case : List[Any] = special_cos_dist - self.special_care_embeds_weights[None, :] + adjustment
__snake_case : Dict = jnp.round(A_ , 3)
__snake_case : str = jnp.any(special_scores > 0 , axis=1 , keepdims=A_)
# Use a lower threshold if an image has any special care concept
__snake_case : List[str] = is_special_care * 0.01
__snake_case : Optional[Any] = cos_dist - self.concept_embeds_weights[None, :] + special_adjustment
__snake_case : Tuple = jnp.round(A_ , 3)
__snake_case : Dict = jnp.any(concept_scores > 0 , axis=1)
return has_nsfw_concepts
class a_ ( _lowercase ):
_snake_case = CLIPConfig
_snake_case = '''clip_input'''
_snake_case = FlaxStableDiffusionSafetyCheckerModule
def __init__(self , __a , __a = None , __a = 0 , __a = jnp.floataa , __a = True , **__a , ) -> int:
"""simple docstring"""
if input_shape is None:
__snake_case : Any = (1, 2_2_4, 2_2_4, 3)
__snake_case : str = self.module_class(config=A_ , dtype=A_ , **A_)
super().__init__(A_ , A_ , input_shape=A_ , seed=A_ , dtype=A_ , _do_init=_do_init)
def SCREAMING_SNAKE_CASE__ (self , __a , __a , __a = None) -> FrozenDict:
"""simple docstring"""
__snake_case : Optional[int] = jax.random.normal(A_ , A_)
__snake_case ,__snake_case : Union[str, Any] = jax.random.split(A_)
__snake_case : List[Any] = {'params': params_rng, 'dropout': dropout_rng}
__snake_case : Optional[int] = self.module.init(A_ , A_)['params']
return random_params
def __call__(self , __a , __a = None , ) -> List[str]:
"""simple docstring"""
__snake_case : int = jnp.transpose(A_ , (0, 2, 3, 1))
return self.module.apply(
{'params': params or self.params} , jnp.array(A_ , dtype=jnp.floataa) , rngs={} , ) | 700 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ChineseCLIPImageProcessor
class a_ ( unittest.TestCase ):
def __init__(self , __a , __a=7 , __a=3 , __a=1_8 , __a=3_0 , __a=4_0_0 , __a=True , __a=None , __a=True , __a=None , __a=True , __a=[0.48_145_466, 0.4_578_275, 0.40_821_073] , __a=[0.26_862_954, 0.26_130_258, 0.27_577_711] , __a=True , ) -> List[Any]:
"""simple docstring"""
__snake_case : Tuple = size if size is not None else {'height': 2_2_4, 'width': 2_2_4}
__snake_case : Any = crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}
__snake_case : Optional[int] = parent
__snake_case : Dict = batch_size
__snake_case : str = num_channels
__snake_case : Optional[Any] = image_size
__snake_case : Optional[int] = min_resolution
__snake_case : Tuple = max_resolution
__snake_case : Optional[int] = do_resize
__snake_case : Optional[int] = size
__snake_case : Union[str, Any] = do_center_crop
__snake_case : List[Any] = crop_size
__snake_case : int = do_normalize
__snake_case : Optional[Any] = image_mean
__snake_case : str = image_std
__snake_case : Optional[Any] = do_convert_rgb
def SCREAMING_SNAKE_CASE__ (self) -> Optional[int]:
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_convert_rgb": self.do_convert_rgb,
}
def SCREAMING_SNAKE_CASE__ (self , __a=False , __a=False , __a=False) -> List[str]:
"""simple docstring"""
assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"
if equal_resolution:
__snake_case : Optional[int] = []
for i in range(self.batch_size):
image_inputs.append(
np.random.randint(
2_5_5 , size=(self.num_channels, self.max_resolution, self.max_resolution) , dtype=np.uinta))
else:
__snake_case : Dict = []
for i in range(self.batch_size):
__snake_case ,__snake_case : Optional[Any] = np.random.choice(np.arange(self.min_resolution , self.max_resolution) , 2)
image_inputs.append(np.random.randint(2_5_5 , size=(self.num_channels, width, height) , dtype=np.uinta))
if not numpify and not torchify:
# PIL expects the channel dimension as last dimension
__snake_case : int = [Image.fromarray(np.moveaxis(__a , 0 , -1)) for x in image_inputs]
if torchify:
__snake_case : List[Any] = [torch.from_numpy(__a) for x in image_inputs]
return image_inputs
@require_torch
@require_vision
class a_ ( UpperCamelCase_ , unittest.TestCase ):
_snake_case = ChineseCLIPImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ (self) -> int:
"""simple docstring"""
__snake_case : Union[str, Any] = ChineseCLIPImageProcessingTester(self , do_center_crop=__a)
@property
def SCREAMING_SNAKE_CASE__ (self) -> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ (self) -> Dict:
"""simple docstring"""
__snake_case : int = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(__a , 'do_resize'))
self.assertTrue(hasattr(__a , 'size'))
self.assertTrue(hasattr(__a , 'do_center_crop'))
self.assertTrue(hasattr(__a , 'center_crop'))
self.assertTrue(hasattr(__a , 'do_normalize'))
self.assertTrue(hasattr(__a , 'image_mean'))
self.assertTrue(hasattr(__a , 'image_std'))
self.assertTrue(hasattr(__a , 'do_convert_rgb'))
def SCREAMING_SNAKE_CASE__ (self) -> int:
"""simple docstring"""
__snake_case : Optional[int] = self.image_processing_class.from_dict(self.image_processor_dict)
self.assertEqual(image_processor.size , {'height': 2_2_4, 'width': 2_2_4})
self.assertEqual(image_processor.crop_size , {'height': 1_8, 'width': 1_8})
__snake_case : List[str] = self.image_processing_class.from_dict(self.image_processor_dict , size=4_2 , crop_size=8_4)
self.assertEqual(image_processor.size , {'shortest_edge': 4_2})
self.assertEqual(image_processor.crop_size , {'height': 8_4, 'width': 8_4})
def SCREAMING_SNAKE_CASE__ (self) -> Union[str, Any]:
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ (self) -> List[Any]:
"""simple docstring"""
__snake_case : List[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
__snake_case : Optional[Any] = self.image_processor_tester.prepare_inputs(equal_resolution=__a)
for image in image_inputs:
self.assertIsInstance(__a , Image.Image)
# Test not batched input
__snake_case : int = image_processing(image_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__snake_case : List[Any] = image_processing(__a , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def SCREAMING_SNAKE_CASE__ (self) -> List[Any]:
"""simple docstring"""
__snake_case : Union[str, Any] = self.image_processing_class(**self.image_processor_dict)
# create random numpy tensors
__snake_case : Optional[int] = self.image_processor_tester.prepare_inputs(equal_resolution=__a , numpify=__a)
for image in image_inputs:
self.assertIsInstance(__a , np.ndarray)
# Test not batched input
__snake_case : List[Any] = image_processing(image_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__snake_case : int = image_processing(__a , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
def SCREAMING_SNAKE_CASE__ (self) -> Dict:
"""simple docstring"""
__snake_case : Any = self.image_processing_class(**self.image_processor_dict)
# create random PyTorch tensors
__snake_case : Tuple = self.image_processor_tester.prepare_inputs(equal_resolution=__a , torchify=__a)
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor)
# Test not batched input
__snake_case : Any = image_processing(image_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__snake_case : Union[str, Any] = image_processing(__a , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
@require_torch
@require_vision
class a_ ( UpperCamelCase_ , unittest.TestCase ):
_snake_case = ChineseCLIPImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ (self) -> Optional[int]:
"""simple docstring"""
__snake_case : Optional[Any] = ChineseCLIPImageProcessingTester(self , num_channels=4 , do_center_crop=__a)
__snake_case : List[Any] = 3
@property
def SCREAMING_SNAKE_CASE__ (self) -> List[str]:
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ (self) -> Dict:
"""simple docstring"""
__snake_case : Any = self.image_processing_class(**self.image_processor_dict)
self.assertTrue(hasattr(__a , 'do_resize'))
self.assertTrue(hasattr(__a , 'size'))
self.assertTrue(hasattr(__a , 'do_center_crop'))
self.assertTrue(hasattr(__a , 'center_crop'))
self.assertTrue(hasattr(__a , 'do_normalize'))
self.assertTrue(hasattr(__a , 'image_mean'))
self.assertTrue(hasattr(__a , 'image_std'))
self.assertTrue(hasattr(__a , 'do_convert_rgb'))
def SCREAMING_SNAKE_CASE__ (self) -> Tuple:
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__ (self) -> int:
"""simple docstring"""
__snake_case : List[Any] = self.image_processing_class(**self.image_processor_dict)
# create random PIL images
__snake_case : Union[str, Any] = self.image_processor_tester.prepare_inputs(equal_resolution=__a)
for image in image_inputs:
self.assertIsInstance(__a , Image.Image)
# Test not batched input
__snake_case : Optional[int] = image_processing(image_inputs[0] , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , )
# Test batched
__snake_case : Optional[int] = image_processing(__a , return_tensors='pt').pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.expected_encoded_image_num_channels,
self.image_processor_tester.crop_size['height'],
self.image_processor_tester.crop_size['width'],
) , ) | 61 | 0 |
'''simple docstring'''
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(".")
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Optional[int] = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
'`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
f'''{test_file} instead.''' )
_a : Tuple = components[-1]
if not test_fn.endswith('py' ):
raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.''' )
if not test_fn.startswith('test_modeling_' ):
raise ValueError(
f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.''' )
_a : Optional[int] = components[:-1] + [test_fn.replace('.py' , '' )]
_a : Optional[int] = '.'.join(_snake_case )
return test_module_path
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : List[Any] = get_module_path(_snake_case )
_a : str = importlib.import_module(_snake_case )
return test_module
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Any = []
_a : Union[str, Any] = get_test_module(_snake_case )
for attr in dir(_snake_case ):
if attr.endswith('ModelTester' ):
tester_classes.append(getattr(_snake_case , _snake_case ) )
# sort with class names
return sorted(_snake_case , key=lambda A : x.__name__ )
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Any = []
_a : Union[str, Any] = get_test_module(_snake_case )
for attr in dir(_snake_case ):
_a : List[Any] = getattr(_snake_case , _snake_case )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
_a : Tuple = getattr(_snake_case , 'all_model_classes' , [] )
if len(_snake_case ) > 0:
test_classes.append(_snake_case )
# sort with class names
return sorted(_snake_case , key=lambda A : x.__name__ )
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : List[str] = get_test_classes(_snake_case )
_a : Any = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(_snake_case , key=lambda A : x.__name__ )
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : List[str] = test_class()
if hasattr(_snake_case , 'setUp' ):
test.setUp()
_a : Any = None
if hasattr(_snake_case , 'model_tester' ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
_a : Optional[int] = test.model_tester.__class__
return model_tester
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
_a : Tuple = get_test_classes(_snake_case )
_a : Dict = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(_snake_case )
# sort with class names
return sorted(_snake_case , key=lambda A : x.__name__ )
def UpperCAmelCase_ ( A , A ):
'''simple docstring'''
_a : Union[str, Any] = get_test_classes_for_model(_snake_case , _snake_case )
_a : str = []
for test_class in test_classes:
_a : int = get_model_tester_from_test_class(_snake_case )
if tester_class is not None:
tester_classes.append(_snake_case )
# sort with class names
return sorted(_snake_case , key=lambda A : x.__name__ )
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Optional[int] = get_test_classes(_snake_case )
_a : Optional[int] = {test_class: get_model_tester_from_test_class(_snake_case ) for test_class in test_classes}
return test_tester_mapping
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Union[str, Any] = get_model_classes(_snake_case )
_a : List[Any] = {
model_class: get_test_classes_for_model(_snake_case , _snake_case ) for model_class in model_classes
}
return model_test_mapping
def UpperCAmelCase_ ( A ):
'''simple docstring'''
_a : Optional[Any] = get_model_classes(_snake_case )
_a : Optional[int] = {
model_class: get_tester_classes_for_model(_snake_case , _snake_case ) for model_class in model_classes
}
return model_to_tester_mapping
def UpperCAmelCase_ ( A ):
'''simple docstring'''
if isinstance(_snake_case , _snake_case ):
return o
elif isinstance(_snake_case , _snake_case ):
return o.__name__
elif isinstance(_snake_case , (list, tuple) ):
return [to_json(_snake_case ) for x in o]
elif isinstance(_snake_case , _snake_case ):
return {to_json(_snake_case ): to_json(_snake_case ) for k, v in o.items()}
else:
return o
| 120 |
"""simple docstring"""
import operator as op
def _a ( _snake_case ):
"""simple docstring"""
UpperCAmelCase = []
UpperCAmelCase = lambda _snake_case , _snake_case : int(x / y ) # noqa: E731 integer division operation
UpperCAmelCase = {
"""^""": op.pow,
"""*""": op.mul,
"""/""": div,
"""+""": op.add,
"""-""": op.sub,
} # operators & their respective operation
# print table header
print("""Symbol""".center(8 ) , """Action""".center(12 ) , """Stack""" , sep=""" | """ )
print("""-""" * (30 + len(_snake_case )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(_snake_case ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ("""push(""" + x + """)""").ljust(12 ) , """,""".join(_snake_case ) , sep=""" | """ )
else:
UpperCAmelCase = stack.pop() # pop stack
# output in tabular format
print("""""".rjust(8 ) , ("""pop(""" + b + """)""").ljust(12 ) , """,""".join(_snake_case ) , sep=""" | """ )
UpperCAmelCase = stack.pop() # pop stack
# output in tabular format
print("""""".rjust(8 ) , ("""pop(""" + a + """)""").ljust(12 ) , """,""".join(_snake_case ) , sep=""" | """ )
stack.append(
str(opr[x](int(_snake_case ) , int(_snake_case ) ) ) ) # evaluate the 2 values popped from stack & push result to stack
# output in tabular format
print(
x.rjust(8 ) , ("""push(""" + a + x + b + """)""").ljust(12 ) , """,""".join(_snake_case ) , sep=""" | """ , )
return int(stack[0] )
if __name__ == "__main__":
_UpperCamelCase = input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
print("""\n\tResult = """, solve(Postfix))
| 341 | 0 |
'''simple docstring'''
import random
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : float , UpperCAmelCase__ : bool = False ) -> dict:
lowercase_ : dict = {i: [] for i in range(UpperCAmelCase__ )}
# if probability is greater or equal than 1, then generate a complete graph
if probability >= 1:
return complete_graph(UpperCAmelCase__ )
# if probability is lower or equal than 0, then return a graph without edges
if probability <= 0:
return graph
# for each couple of nodes, add an edge from u to v
# if the number randomly generated is greater than probability probability
for i in range(UpperCAmelCase__ ):
for j in range(i + 1 , UpperCAmelCase__ ):
if random.random() < probability:
graph[i].append(UpperCAmelCase__ )
if not directed:
# if the graph is undirected, add an edge in from j to i, either
graph[j].append(UpperCAmelCase__ )
return graph
def lowerCamelCase ( UpperCAmelCase__ : int ) -> dict:
return {
i: [j for j in range(UpperCAmelCase__ ) if i != j] for i in range(UpperCAmelCase__ )
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 30 | '''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=_UpperCAmelCase)
class __magic_name__ ( _UpperCAmelCase):
UpperCamelCase__ = field(default='''image-classification''', metadata={'''include_in_asdict_even_if_is_default''': True})
UpperCamelCase__ = Features({'''image''': Image()})
UpperCamelCase__ = Features({'''labels''': ClassLabel})
UpperCamelCase__ = "image"
UpperCamelCase__ = "labels"
def SCREAMING_SNAKE_CASE_ ( self : Tuple , lowercase_ : str ):
if self.label_column not in features:
raise ValueError(f'''Column {self.label_column} is not present in features.''' )
if not isinstance(features[self.label_column] , lowercase_ ):
raise ValueError(f'''Column {self.label_column} is not a ClassLabel.''' )
lowercase_ : List[str] = copy.deepcopy(self )
lowercase_ : List[str] = self.label_schema.copy()
lowercase_ : List[Any] = features[self.label_column]
lowercase_ : Optional[Any] = label_schema
return task_template
@property
def SCREAMING_SNAKE_CASE_ ( self : int ):
return {
self.image_column: "image",
self.label_column: "labels",
}
| 30 | 1 |
'''simple docstring'''
from maths.prime_check import is_prime
def a__ ( _SCREAMING_SNAKE_CASE : int ) -> int:
"""simple docstring"""
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
UpperCAmelCase_ : Any = F'''Input value of [number={number}] must be an integer'''
raise TypeError(_SCREAMING_SNAKE_CASE )
if is_prime(_SCREAMING_SNAKE_CASE ) and is_prime(number + 2 ):
return number + 2
else:
return -1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 71 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer
UpperCamelCase__ = logging.get_logger(__name__)
UpperCamelCase__ = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
UpperCamelCase__ = {
'vocab_file': {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
),
}
}
UpperCamelCase__ = {
'junnyu/roformer_chinese_small': 15_36,
'junnyu/roformer_chinese_base': 15_36,
'junnyu/roformer_chinese_char_small': 5_12,
'junnyu/roformer_chinese_char_base': 5_12,
'junnyu/roformer_small_discriminator': 1_28,
'junnyu/roformer_small_generator': 1_28,
}
UpperCamelCase__ = {
'junnyu/roformer_chinese_small': {'do_lower_case': True},
'junnyu/roformer_chinese_base': {'do_lower_case': True},
'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
'junnyu/roformer_small_discriminator': {'do_lower_case': True},
'junnyu/roformer_small_generator': {'do_lower_case': True},
}
class a ( lowercase ):
UpperCamelCase : int = VOCAB_FILES_NAMES
UpperCamelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase : Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase : Tuple = PRETRAINED_INIT_CONFIGURATION
UpperCamelCase : Optional[int] = RoFormerTokenizer
def __init__( self , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=True , UpperCamelCase_="[UNK]" , UpperCamelCase_="[SEP]" , UpperCamelCase_="[PAD]" , UpperCamelCase_="[CLS]" , UpperCamelCase_="[MASK]" , UpperCamelCase_=True , UpperCamelCase_=None , **UpperCamelCase_ , ):
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_ , )
UpperCAmelCase__ : Union[str, Any] = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
pre_tok_state.get('lowercase' , UpperCamelCase_ ) != do_lower_case
or pre_tok_state.get('strip_accents' , UpperCamelCase_ ) != strip_accents
):
UpperCAmelCase__ : Any = getattr(UpperCamelCase_ , pre_tok_state.pop('type' ) )
UpperCAmelCase__ : str = do_lower_case
UpperCAmelCase__ : Union[str, Any] = strip_accents
UpperCAmelCase__ : Dict = pre_tok_class(**UpperCamelCase_ )
UpperCAmelCase__ : Union[str, Any] = do_lower_case
def __getstate__( self ):
UpperCAmelCase__ : int = self.__dict__.copy()
UpperCAmelCase__ : int = BertPreTokenizer()
return state
def __setstate__( self , UpperCamelCase_ ):
UpperCAmelCase__ : Union[str, Any] = d
UpperCAmelCase__ : List[str] = self.__dict__['_tokenizer'].get_vocab()
UpperCAmelCase__ : List[Any] = PreTokenizer.custom(JiebaPreTokenizer(UpperCamelCase_ ) )
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_=None ):
UpperCAmelCase__ : List[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 __snake_case ( self , UpperCamelCase_ , UpperCamelCase_ = None ):
UpperCAmelCase__ : int = [self.sep_token_id]
UpperCAmelCase__ : Dict = [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 ):
UpperCAmelCase__ : Any = self._tokenizer.model.save(UpperCamelCase_ , name=UpperCamelCase_ )
return tuple(UpperCamelCase_ )
def __snake_case ( self , UpperCamelCase_ , UpperCamelCase_=None , UpperCamelCase_=None , UpperCamelCase_=False , **UpperCamelCase_ , ):
UpperCAmelCase__ : int = BertPreTokenizer()
return super().save_pretrained(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , **UpperCamelCase_ )
| 110 | 0 |
import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_UpperCAmelCase : Tuple = [
# tf -> hf
("/", "."),
("layer_", "layers."),
("kernel", "weight"),
("beta", "bias"),
("gamma", "weight"),
("pegasus", "model"),
]
_UpperCAmelCase : Any = [
(".output.dense", ".fc2"),
("intermediate.LayerNorm", "final_layer_norm"),
("intermediate.dense", "fc1"),
]
_UpperCAmelCase : str = (
INIT_COMMON
+ [
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.out_proj"),
("attention.self", "self_attn"),
("attention.encdec.LayerNorm", "encoder_attn_layer_norm"),
("attention.encdec_output.dense", "encoder_attn.out_proj"),
("attention.encdec", "encoder_attn"),
("key", "k_proj"),
("value", "v_proj"),
("query", "q_proj"),
("decoder.LayerNorm", "decoder.layernorm_embedding"),
]
+ END_COMMON
)
_UpperCAmelCase : Tuple = (
INIT_COMMON
+ [
("embeddings.word_embeddings", "shared.weight"),
("embeddings.position_embeddings", "embed_positions.weight"),
("attention.self.LayerNorm", "self_attn_layer_norm"),
("attention.output.dense", "self_attn.output"),
("attention.self", "self_attn.self"),
("encoder.LayerNorm", "encoder.layernorm_embedding"),
]
+ END_COMMON
)
_UpperCAmelCase : List[Any] = [
"encdec/key/bias",
"encdec/query/bias",
"encdec/value/bias",
"self/key/bias",
"self/query/bias",
"self/value/bias",
"encdec_output/dense/bias",
"attention/output/dense/bias",
]
def A ( lowercase , lowercase ) -> Optional[int]:
'''simple docstring'''
for tf_name, hf_name in patterns:
UpperCamelCase = k.replace(lowercase , lowercase )
return k
def A ( lowercase , lowercase ) -> BigBirdPegasusForConditionalGeneration:
'''simple docstring'''
UpperCamelCase = BigBirdPegasusConfig(**lowercase )
UpperCamelCase = BigBirdPegasusForConditionalGeneration(lowercase )
UpperCamelCase = torch_model.state_dict()
UpperCamelCase = {}
# separating decoder weights
UpperCamelCase = {k: tf_weights[k] for k in tf_weights if k.startswith('pegasus/decoder' )}
UpperCamelCase = {k: tf_weights[k] for k in tf_weights if not k.startswith('pegasus/decoder' )}
for k, v in tqdm(decoder_weights.items() , 'tf -> hf conversion' ):
UpperCamelCase = [k.endswith(lowercase ) for ending in KEYS_TO_IGNORE]
if any(lowercase ):
continue
UpperCamelCase = DECODER_PATTERNS
UpperCamelCase = rename_state_dict_key(lowercase , lowercase )
if new_k not in state_dict:
raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})''' )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
UpperCamelCase = v.T
UpperCamelCase = torch.from_numpy(lowercase )
assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''
for k, v in tqdm(remaining_weights.items() , 'tf -> hf conversion' ):
UpperCamelCase = [k.endswith(lowercase ) for ending in KEYS_TO_IGNORE]
if any(lowercase ):
continue
UpperCamelCase = REMAINING_PATTERNS
UpperCamelCase = rename_state_dict_key(lowercase , lowercase )
if new_k not in state_dict and k != "pegasus/embeddings/position_embeddings":
raise ValueError(f'''could not find new key {new_k} in state dict. (converted from {k})''' )
if any(True if i in k else False for i in ['dense', 'query', 'key', 'value'] ):
UpperCamelCase = v.T
UpperCamelCase = torch.from_numpy(lowercase )
if k != "pegasus/embeddings/position_embeddings":
assert v.shape == state_dict[new_k].shape, f'''{new_k}, {k}, {v.shape}, {state_dict[new_k].shape}'''
UpperCamelCase = mapping['model.embed_positions.weight']
UpperCamelCase = mapping.pop('model.embed_positions.weight' )
UpperCamelCase , UpperCamelCase = torch_model.load_state_dict(lowercase , strict=lowercase )
UpperCamelCase = [
k
for k in missing
if k
not in [
'final_logits_bias',
'model.encoder.embed_tokens.weight',
'model.decoder.embed_tokens.weight',
'lm_head.weight',
]
]
assert unexpected_missing == [], f'''no matches found for the following torch keys {unexpected_missing}'''
assert extra == [], f'''no matches found for the following tf keys {extra}'''
return torch_model
def A ( lowercase ) -> Dict:
'''simple docstring'''
UpperCamelCase = tf.train.list_variables(lowercase )
UpperCamelCase = {}
UpperCamelCase = ['global_step']
for name, shape in tqdm(lowercase , desc='converting tf checkpoint to dict' ):
UpperCamelCase = any(pat in name for pat in ignore_name )
if skip_key:
continue
UpperCamelCase = tf.train.load_variable(lowercase , lowercase )
UpperCamelCase = array
return tf_weights
def A ( lowercase , lowercase , lowercase ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase = get_tf_weights_as_numpy(lowercase )
UpperCamelCase = convert_bigbird_pegasus(lowercase , lowercase )
torch_model.save_pretrained(lowercase )
if __name__ == "__main__":
_UpperCAmelCase : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--tf_ckpt_path", type=str, help="passed to tf.train.list_variables")
parser.add_argument("--save_dir", default=None, type=str, help="Path to the output PyTorch model.")
_UpperCAmelCase : str = parser.parse_args()
_UpperCAmelCase : Optional[Any] = {}
convert_bigbird_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir, config_update=config_update)
| 3 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : Tuple = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
"self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
"self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
"self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
"self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
"self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
"self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
"self_attn.rotary_emb": "encoder.embed_positions",
"self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
"conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
"conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
"conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
"conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
"conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
"ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
"ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
"ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
"ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
"ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
"ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
_UpperCAmelCase : Any = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def A ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Dict:
'''simple docstring'''
for attribute in key.split('.' ):
UpperCamelCase = getattr(lowercase , lowercase )
if weight_type is not None:
UpperCamelCase = getattr(lowercase , lowercase ).shape
else:
UpperCamelCase = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
f'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}''' )
if weight_type == "weight":
UpperCamelCase = value
elif weight_type == "weight_g":
UpperCamelCase = value
elif weight_type == "weight_v":
UpperCamelCase = value
elif weight_type == "bias":
UpperCamelCase = value
elif weight_type == "running_mean":
UpperCamelCase = value
elif weight_type == "running_var":
UpperCamelCase = value
elif weight_type == "num_batches_tracked":
UpperCamelCase = value
elif weight_type == "inv_freq":
UpperCamelCase = value
else:
UpperCamelCase = value
logger.info(f'''{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.''' )
def A ( lowercase , lowercase , lowercase ) -> Any:
'''simple docstring'''
UpperCamelCase = []
UpperCamelCase = fairseq_model.state_dict()
UpperCamelCase = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
UpperCamelCase = False
if "conv_layers" in name:
load_conv_layer(
lowercase , lowercase , lowercase , lowercase , hf_model.config.feat_extract_norm == 'group' , )
UpperCamelCase = True
else:
for key, mapped_key in MAPPING.items():
UpperCamelCase = 'wav2vec2_conformer.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split('w2v_model.' )[-1] == name.split('.' )[0]:
UpperCamelCase = True
if "*" in mapped_key:
UpperCamelCase = name.split(lowercase )[0].split('.' )[-2]
UpperCamelCase = mapped_key.replace('*' , lowercase )
if "pos_bias_u" in name:
UpperCamelCase = None
elif "pos_bias_v" in name:
UpperCamelCase = None
elif "weight_g" in name:
UpperCamelCase = 'weight_g'
elif "weight_v" in name:
UpperCamelCase = 'weight_v'
elif "bias" in name:
UpperCamelCase = 'bias'
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
UpperCamelCase = 'weight'
elif "running_mean" in name:
UpperCamelCase = 'running_mean'
elif "inv_freq" in name:
UpperCamelCase = 'inv_freq'
elif "running_var" in name:
UpperCamelCase = 'running_var'
elif "num_batches_tracked" in name:
UpperCamelCase = 'num_batches_tracked'
else:
UpperCamelCase = None
set_recursively(lowercase , lowercase , lowercase , lowercase , lowercase )
continue
if not is_used:
unused_weights.append(lowercase )
logger.warning(f'''Unused weights: {unused_weights}''' )
def A ( lowercase , lowercase , lowercase , lowercase , lowercase ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase = full_name.split('conv_layers.' )[-1]
UpperCamelCase = name.split('.' )
UpperCamelCase = int(items[0] )
UpperCamelCase = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.''' )
UpperCamelCase = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.''' )
UpperCamelCase = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.''' )
UpperCamelCase = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.''' )
UpperCamelCase = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(lowercase )
@torch.no_grad()
def A ( lowercase , lowercase , lowercase=None , lowercase=None , lowercase=True ) -> int:
'''simple docstring'''
if config_path is not None:
UpperCamelCase = WavaVecaConformerConfig.from_pretrained(lowercase , hidden_act='swish' )
else:
UpperCamelCase = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
UpperCamelCase = 'rotary'
if is_finetuned:
if dict_path:
UpperCamelCase = Dictionary.load(lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
UpperCamelCase = target_dict.pad_index
UpperCamelCase = target_dict.bos_index
UpperCamelCase = target_dict.eos_index
UpperCamelCase = len(target_dict.symbols )
UpperCamelCase = os.path.join(lowercase , 'vocab.json' )
if not os.path.isdir(lowercase ):
logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(lowercase ) )
return
os.makedirs(lowercase , exist_ok=lowercase )
UpperCamelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
UpperCamelCase = 0
UpperCamelCase = 1
with open(lowercase , 'w' , encoding='utf-8' ) as vocab_handle:
json.dump(lowercase , lowercase )
UpperCamelCase = WavaVecaCTCTokenizer(
lowercase , 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=lowercase , )
UpperCamelCase = True if config.feat_extract_norm == 'layer' else False
UpperCamelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=16_000 , padding_value=0 , do_normalize=lowercase , return_attention_mask=lowercase , )
UpperCamelCase = WavaVecaProcessor(feature_extractor=lowercase , tokenizer=lowercase )
processor.save_pretrained(lowercase )
UpperCamelCase = WavaVecaConformerForCTC(lowercase )
else:
UpperCamelCase = WavaVecaConformerForPreTraining(lowercase )
if is_finetuned:
UpperCamelCase , UpperCamelCase , UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={'data': '/'.join(dict_path.split('/' )[:-1] )} )
else:
UpperCamelCase = argparse.Namespace(task='audio_pretraining' )
UpperCamelCase = fairseq.tasks.setup_task(lowercase )
UpperCamelCase , UpperCamelCase , UpperCamelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=lowercase )
UpperCamelCase = model[0].eval()
recursively_load_weights(lowercase , lowercase , not is_finetuned )
hf_wavavec.save_pretrained(lowercase )
if __name__ == "__main__":
_UpperCAmelCase : Tuple = argparse.ArgumentParser()
parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
parser.add_argument(
"--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
)
_UpperCAmelCase : Dict = parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 3 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( lowerCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
lowerCAmelCase_ = MgpstrTokenizer
lowerCAmelCase_ = False
lowerCAmelCase_ = {}
lowerCAmelCase_ = False
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
super().setUp()
# fmt: off
__SCREAMING_SNAKE_CASE : Optional[int] = ['''[GO]''', '''[s]''', '''0''', '''1''', '''2''', '''3''', '''4''', '''5''', '''6''', '''7''', '''8''', '''9''', '''a''', '''b''', '''c''', '''d''', '''e''', '''f''', '''g''', '''h''', '''i''', '''j''', '''k''', '''l''', '''m''', '''n''', '''o''', '''p''', '''q''', '''r''', '''s''', '''t''', '''u''', '''v''', '''w''', '''x''', '''y''', '''z''']
# fmt: on
__SCREAMING_SNAKE_CASE : Optional[int] = dict(zip(_A , range(len(_A ) ) ) )
__SCREAMING_SNAKE_CASE : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
with open(self.vocab_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write(json.dumps(_A ) + '''\n''' )
def UpperCAmelCase__ ( self : Tuple , **_A : List[str] ):
"""simple docstring"""
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **_A )
def UpperCAmelCase__ ( self : int , _A : Optional[int] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Dict = '''tester'''
__SCREAMING_SNAKE_CASE : str = '''tester'''
return input_text, output_text
@unittest.skip('''MGP-STR always lower cases letters.''' )
def UpperCAmelCase__ ( self : Optional[Any] ):
"""simple docstring"""
pass
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Any = self.get_tokenizers(do_lower_case=_A )
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = '''[SPECIAL_TOKEN]'''
tokenizer.add_special_tokens({'''cls_token''': special_token} )
__SCREAMING_SNAKE_CASE : List[Any] = tokenizer.encode([special_token] , add_special_tokens=_A )
self.assertEqual(len(_A ) , 1 )
__SCREAMING_SNAKE_CASE : Tuple = tokenizer.decode(_A , skip_special_tokens=_A )
self.assertTrue(special_token not in decoded )
def UpperCAmelCase__ ( self : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : str = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'''{tokenizer.__class__.__name__}''' ):
__SCREAMING_SNAKE_CASE, __SCREAMING_SNAKE_CASE : Dict = self.get_input_output_texts(_A )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.tokenize(_A )
__SCREAMING_SNAKE_CASE : Union[str, Any] = tokenizer.convert_tokens_to_ids(_A )
__SCREAMING_SNAKE_CASE : str = tokenizer.encode(_A , add_special_tokens=_A )
self.assertListEqual(_A , _A )
__SCREAMING_SNAKE_CASE : List[str] = tokenizer.convert_ids_to_tokens(_A )
self.assertNotEqual(len(_A ) , 0 )
__SCREAMING_SNAKE_CASE : Tuple = tokenizer.decode(_A )
self.assertIsInstance(_A , _A )
self.assertEqual(text_a.replace(''' ''' , '''''' ) , _A )
@unittest.skip('''MGP-STR tokenizer only handles one sequence.''' )
def UpperCAmelCase__ ( self : Dict ):
"""simple docstring"""
pass
@unittest.skip('''inputs cannot be pretokenized in MgpstrTokenizer''' )
def UpperCAmelCase__ ( self : Union[str, Any] ):
"""simple docstring"""
pass
| 74 |
"""simple docstring"""
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class a__ ( A__ ):
UpperCAmelCase__ = ''''''
UpperCAmelCase__ = '''hf-legacy''' # "hf://"" is reserved for hffs
def __init__( self :Dict , _lowerCamelCase :Optional[DatasetInfo] = None , _lowerCamelCase :Optional[str] = None , **_lowerCamelCase :Tuple , ):
'''simple docstring'''
super().__init__(self , **_lowerCamelCase )
UpperCamelCase_ : List[str] =repo_info
UpperCamelCase_ : Any =token
UpperCamelCase_ : Tuple =None
def lowerCamelCase_ ( self :Dict ):
'''simple docstring'''
if self.dir_cache is None:
UpperCamelCase_ : Any ={}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
UpperCamelCase_ : Optional[Any] ={
'name': hf_file.rfilename,
'size': None,
'type': 'file',
}
self.dir_cache.update(
{
str(_lowerCamelCase ): {'name': str(_lowerCamelCase ), 'size': None, 'type': 'directory'}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def lowerCamelCase_ ( self :Union[str, Any] , _lowerCamelCase :str , _lowerCamelCase :str = "rb" , **_lowerCamelCase :str , ):
'''simple docstring'''
if not isinstance(self.repo_info , _lowerCamelCase ):
raise NotImplementedError(f'''Open is only implemented for dataset repositories, but got {self.repo_info}''' )
UpperCamelCase_ : List[Any] =hf_hub_url(self.repo_info.id , _lowerCamelCase , revision=self.repo_info.sha )
return fsspec.open(
_lowerCamelCase , mode=_lowerCamelCase , headers=get_authentication_headers_for_url(_lowerCamelCase , use_auth_token=self.token ) , client_kwargs={'trust_env': True} , ).open()
def lowerCamelCase_ ( self :Optional[Any] , _lowerCamelCase :Tuple , **_lowerCamelCase :Any ):
'''simple docstring'''
self._get_dirs()
UpperCamelCase_ : Tuple =self._strip_protocol(_lowerCamelCase )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(_lowerCamelCase )
def lowerCamelCase_ ( self :Optional[Any] , _lowerCamelCase :List[str] , _lowerCamelCase :List[Any]=False , **_lowerCamelCase :Any ):
'''simple docstring'''
self._get_dirs()
UpperCamelCase_ : str =PurePosixPath(path.strip('/' ) )
UpperCamelCase_ : List[str] ={}
for p, f in self.dir_cache.items():
UpperCamelCase_ : List[Any] =PurePosixPath(p.strip('/' ) )
UpperCamelCase_ : Tuple =p.parent
if root == path:
UpperCamelCase_ : int =f
UpperCamelCase_ : Optional[int] =list(paths.values() )
if detail:
return out
else:
return sorted(f['name'] for f in out )
| 357 | 0 |
"""simple docstring"""
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
SCREAMING_SNAKE_CASE_ : Any = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : Tuple = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
SCREAMING_SNAKE_CASE_ : List[str] = {
'''vocab_file''': {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-ctx_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-ctx_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json'''
),
},
}
SCREAMING_SNAKE_CASE_ : Any = {
'''vocab_file''': {
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-question_encoder-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-question_encoder-multiset-base''': (
'''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json'''
),
},
}
SCREAMING_SNAKE_CASE_ : Dict = {
'''vocab_file''': {
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''facebook/dpr-reader-single-nq-base''': (
'''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json'''
),
'''facebook/dpr-reader-multiset-base''': (
'''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json'''
),
},
}
SCREAMING_SNAKE_CASE_ : Any = {
'''facebook/dpr-ctx_encoder-single-nq-base''': 512,
'''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
SCREAMING_SNAKE_CASE_ : int = {
'''facebook/dpr-question_encoder-single-nq-base''': 512,
'''facebook/dpr-question_encoder-multiset-base''': 512,
}
SCREAMING_SNAKE_CASE_ : Optional[Any] = {
'''facebook/dpr-reader-single-nq-base''': 512,
'''facebook/dpr-reader-multiset-base''': 512,
}
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {
'''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
SCREAMING_SNAKE_CASE_ : List[str] = {
'''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
SCREAMING_SNAKE_CASE_ : str = {
'''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class _A ( __a ):
__a = VOCAB_FILES_NAMES
__a = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__a = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class _A ( __a ):
__a = VOCAB_FILES_NAMES
__a = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
__a = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
SCREAMING_SNAKE_CASE_ : Optional[Any] = collections.namedtuple(
'''DPRSpanPrediction''', ['''span_score''', '''relevance_score''', '''doc_id''', '''start_index''', '''end_index''', '''text''']
)
SCREAMING_SNAKE_CASE_ : Optional[int] = collections.namedtuple('''DPRReaderOutput''', ['''start_logits''', '''end_logits''', '''relevance_logits'''])
SCREAMING_SNAKE_CASE_ : Optional[Any] = r'''
Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.
It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),
using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`
with the format:
```
[CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>
```
Args:
questions (`str` or `List[str]`):
The questions to be encoded. You can specify one question for many passages. In this case, the question
will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in
`titles` or `texts`.
titles (`str` or `List[str]`):
The passages titles to be encoded. This can be a string or a list of strings if there are several passages.
texts (`str` or `List[str]`):
The passages texts to be encoded. This can be a string or a list of strings if there are several passages.
padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
Activates and controls padding. Accepts the following values:
- `True` or `\'longest\'`: Pad to the longest sequence in the batch (or no padding if only a single sequence
if provided).
- `\'max_length\'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided.
- `False` or `\'do_not_pad\'` (default): No padding (i.e., can output a batch with sequences of different
lengths).
truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):
Activates and controls truncation. Accepts the following values:
- `True` or `\'longest_first\'`: Truncate to a maximum length specified with the argument `max_length` or to
the maximum acceptable input length for the model if that argument is not provided. This will truncate
token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch
of pairs) is provided.
- `\'only_first\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the first
sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `\'only_second\'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum
acceptable input length for the model if that argument is not provided. This will only truncate the
second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.
- `False` or `\'do_not_truncate\'` (default): No truncation (i.e., can output batch with sequence lengths
greater than the model maximum admissible input size).
max_length (`int`, *optional*):
Controls the maximum length to use by one of the truncation/padding parameters.
If left unset or set to `None`, this will use the predefined model maximum length if a maximum length
is required by one of the truncation/padding parameters. If the model has no specific maximum input
length (like XLNet) truncation/padding to a maximum length will be deactivated.
return_tensors (`str` or [`~utils.TensorType`], *optional*):
If set, will return tensors instead of list of python integers. Acceptable values are:
- `\'tf\'`: Return TensorFlow `tf.constant` objects.
- `\'pt\'`: Return PyTorch `torch.Tensor` objects.
- `\'np\'`: Return Numpy `np.ndarray` objects.
return_attention_mask (`bool`, *optional*):
Whether or not to return the attention mask. If not set, will return the attention mask according to the
specific tokenizer\'s default, defined by the `return_outputs` attribute.
[What are attention masks?](../glossary#attention-mask)
Returns:
`Dict[str, List[List[int]]]`: A dictionary with the following keys:
- `input_ids`: List of token ids to be fed to a model.
- `attention_mask`: List of indices specifying which tokens should be attended to by the model.
'''
@add_start_docstrings(__a )
class _A :
def __call__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = False , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None , **SCREAMING_SNAKE_CASE__ , ) -> BatchEncoding:
if titles is None and texts is None:
return super().__call__(
SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
elif titles is None or texts is None:
lowerCamelCase__ = titles if texts is None else texts
return super().__call__(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ , return_attention_mask=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ , )
lowerCamelCase__ = titles if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else [titles]
lowerCamelCase__ = texts if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else [texts]
lowerCamelCase__ = len(SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ = questions if not isinstance(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) else [questions] * n_passages
if len(SCREAMING_SNAKE_CASE__ ) != len(SCREAMING_SNAKE_CASE__ ):
raise ValueError(
f'There should be as many titles than texts but got {len(SCREAMING_SNAKE_CASE__ )} titles and {len(SCREAMING_SNAKE_CASE__ )} texts.' )
lowerCamelCase__ = super().__call__(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ )["input_ids"]
lowerCamelCase__ = super().__call__(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , truncation=SCREAMING_SNAKE_CASE__ )["input_ids"]
lowerCamelCase__ = {
"input_ids": [
(encoded_question_and_title + encoded_text)[:max_length]
if max_length is not None and truncation
else encoded_question_and_title + encoded_text
for encoded_question_and_title, encoded_text in zip(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
]
}
if return_attention_mask is not False:
lowerCamelCase__ = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
lowerCamelCase__ = attention_mask
return self.pad(SCREAMING_SNAKE_CASE__ , padding=SCREAMING_SNAKE_CASE__ , max_length=SCREAMING_SNAKE_CASE__ , return_tensors=SCREAMING_SNAKE_CASE__ )
def _lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 16 , SCREAMING_SNAKE_CASE__ = 64 , SCREAMING_SNAKE_CASE__ = 4 , ) -> List[DPRSpanPrediction]:
lowerCamelCase__ = reader_input["input_ids"]
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = reader_output[:3]
lowerCamelCase__ = len(SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ = sorted(range(SCREAMING_SNAKE_CASE__ ) , reverse=SCREAMING_SNAKE_CASE__ , key=relevance_logits.__getitem__ )
lowerCamelCase__ = []
for doc_id in sorted_docs:
lowerCamelCase__ = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
lowerCamelCase__ = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
lowerCamelCase__ = sequence_ids.index(self.pad_token_id )
else:
lowerCamelCase__ = len(SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ = self._get_best_spans(
start_logits=start_logits[doc_id][passage_offset:sequence_len] , end_logits=end_logits[doc_id][passage_offset:sequence_len] , max_answer_length=SCREAMING_SNAKE_CASE__ , top_spans=SCREAMING_SNAKE_CASE__ , )
for start_index, end_index in best_spans:
start_index += passage_offset
end_index += passage_offset
nbest_spans_predictions.append(
DPRSpanPrediction(
span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] , relevance_score=relevance_logits[doc_id] , doc_id=SCREAMING_SNAKE_CASE__ , start_index=SCREAMING_SNAKE_CASE__ , end_index=SCREAMING_SNAKE_CASE__ , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(SCREAMING_SNAKE_CASE__ ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def _lowerCamelCase ( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , ) -> List[DPRSpanPrediction]:
lowerCamelCase__ = []
for start_index, start_score in enumerate(SCREAMING_SNAKE_CASE__ ):
for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length] ):
scores.append(((start_index, start_index + answer_length), start_score + end_score) )
lowerCamelCase__ = sorted(SCREAMING_SNAKE_CASE__ , key=lambda SCREAMING_SNAKE_CASE__ : x[1] , reverse=SCREAMING_SNAKE_CASE__ )
lowerCamelCase__ = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(f'Wrong span indices: [{start_index}:{end_index}]' )
lowerCamelCase__ = end_index - start_index + 1
if length > max_answer_length:
raise ValueError(f'Span is too long: {length} > {max_answer_length}' )
if any(
start_index <= prev_start_index <= prev_end_index <= end_index
or prev_start_index <= start_index <= end_index <= prev_end_index
for (prev_start_index, prev_end_index) in chosen_span_intervals ):
continue
chosen_span_intervals.append((start_index, end_index) )
if len(SCREAMING_SNAKE_CASE__ ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(__a )
class _A ( __a , __a ):
__a = VOCAB_FILES_NAMES
__a = READER_PRETRAINED_VOCAB_FILES_MAP
__a = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__a = READER_PRETRAINED_INIT_CONFIGURATION
__a = ['input_ids', 'attention_mask']
| 274 |
"""simple docstring"""
def UpperCAmelCase__ ( A__ ) -> list[int]:
"""simple docstring"""
if num <= 0:
raise ValueError("Input must be a positive integer" )
lowerCamelCase__ = [True] * (num + 1)
lowerCamelCase__ = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , num + 1 , A__ ):
lowerCamelCase__ = False
p += 1
return [prime for prime in range(2 , num + 1 ) if primes[prime]]
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE_ : Optional[Any] = int(input('''Enter a positive integer: ''').strip())
print(prime_sieve_eratosthenes(user_num))
| 274 | 1 |
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
snake_case : List[str] = get_tests_dir('''fixtures/test_sentencepiece.model''')
snake_case : Union[str, Any] = get_tests_dir('''fixtures/test_sentencepiece_bpe.model''')
snake_case : str = '''pt''' if is_torch_available() else '''tf'''
@require_sentencepiece
@require_tokenizers
class snake_case_ (lowerCamelCase_ , unittest.TestCase ):
UpperCAmelCase__ : Union[str, Any] = CamembertTokenizer
UpperCAmelCase__ : int = CamembertTokenizerFast
UpperCAmelCase__ : str = True
UpperCAmelCase__ : Dict = True
def lowerCamelCase__( self :Optional[int] ) -> Dict:
super().setUp()
# We have a SentencePiece fixture for testing
a__ = CamembertTokenizer(__snake_case )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCamelCase__( self :Any ) -> Dict:
a__ = '<pad>'
a__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case ) ,__snake_case )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case ) ,__snake_case )
def lowerCamelCase__( self :Any ) -> str:
a__ = 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(__snake_case ) ,10_04 )
def lowerCamelCase__( self :List[Any] ) -> Tuple:
self.assertEqual(self.get_tokenizer().vocab_size ,10_05 )
def lowerCamelCase__( self :int ) -> str:
a__ = CamembertTokenizer(__snake_case )
tokenizer.save_pretrained(self.tmpdirname )
a__ = CamembertTokenizerFast.from_pretrained(self.tmpdirname )
a__ = 'I was born in 92000, and this is falsé.'
a__ = tokenizer.encode(__snake_case )
a__ = rust_tokenizer.encode(__snake_case )
self.assertListEqual(__snake_case ,__snake_case )
a__ = tokenizer.encode(__snake_case ,add_special_tokens=__snake_case )
a__ = rust_tokenizer.encode(__snake_case ,add_special_tokens=__snake_case )
self.assertListEqual(__snake_case ,__snake_case )
# <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)
a__ = tokenizer.convert_ids_to_tokens(__snake_case )
a__ = rust_tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case ,__snake_case )
def lowerCamelCase__( self :List[Any] ) -> Any:
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(__snake_case )
a__ = rust_tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case ,__snake_case )
a__ = tokenizer.encode(__snake_case ,add_special_tokens=__snake_case )
a__ = rust_tokenizer.encode(__snake_case ,add_special_tokens=__snake_case )
self.assertListEqual(__snake_case ,__snake_case )
a__ = self.get_rust_tokenizer()
a__ = tokenizer.encode(__snake_case )
a__ = rust_tokenizer.encode(__snake_case )
self.assertListEqual(__snake_case ,__snake_case )
@slow
def lowerCamelCase__( self :Tuple ) -> Optional[Any]:
# fmt: off
a__ = {'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.
a__ = [
'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=__snake_case ,model_name='camembert-base' ,revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf' ,sequences=__snake_case ,)
| 335 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
snake_case : int = logging.get_logger(__name__)
snake_case : List[Any] = {
'''microsoft/layoutlmv3-base''': '''https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json''',
}
class snake_case_ (lowerCamelCase_ ):
UpperCAmelCase__ : Optional[Any] = '''layoutlmv3'''
def __init__( self :Optional[Any] ,__snake_case :Dict=5_02_65 ,__snake_case :Union[str, Any]=7_68 ,__snake_case :Dict=12 ,__snake_case :List[str]=12 ,__snake_case :Any=30_72 ,__snake_case :int="gelu" ,__snake_case :List[str]=0.1 ,__snake_case :Optional[Any]=0.1 ,__snake_case :List[Any]=5_12 ,__snake_case :Any=2 ,__snake_case :Dict=0.02 ,__snake_case :Dict=1E-5 ,__snake_case :Tuple=1 ,__snake_case :Optional[int]=0 ,__snake_case :List[Any]=2 ,__snake_case :Optional[Any]=10_24 ,__snake_case :List[str]=1_28 ,__snake_case :List[str]=1_28 ,__snake_case :str=True ,__snake_case :Any=32 ,__snake_case :Union[str, Any]=1_28 ,__snake_case :Optional[Any]=64 ,__snake_case :List[Any]=2_56 ,__snake_case :Any=True ,__snake_case :Optional[int]=True ,__snake_case :List[str]=True ,__snake_case :Any=2_24 ,__snake_case :Union[str, Any]=3 ,__snake_case :int=16 ,__snake_case :Any=None ,**__snake_case :Dict ,) -> Any:
super().__init__(
vocab_size=__snake_case ,hidden_size=__snake_case ,num_hidden_layers=__snake_case ,num_attention_heads=__snake_case ,intermediate_size=__snake_case ,hidden_act=__snake_case ,hidden_dropout_prob=__snake_case ,attention_probs_dropout_prob=__snake_case ,max_position_embeddings=__snake_case ,type_vocab_size=__snake_case ,initializer_range=__snake_case ,layer_norm_eps=__snake_case ,pad_token_id=__snake_case ,bos_token_id=__snake_case ,eos_token_id=__snake_case ,**__snake_case ,)
a__ = max_ad_position_embeddings
a__ = coordinate_size
a__ = shape_size
a__ = has_relative_attention_bias
a__ = rel_pos_bins
a__ = max_rel_pos
a__ = has_spatial_attention_bias
a__ = rel_ad_pos_bins
a__ = max_rel_ad_pos
a__ = text_embed
a__ = visual_embed
a__ = input_size
a__ = num_channels
a__ = patch_size
a__ = classifier_dropout
class snake_case_ (lowerCamelCase_ ):
UpperCAmelCase__ : Tuple = version.parse('''1.12''' )
@property
def lowerCamelCase__( self :Optional[int] ) -> Mapping[str, Mapping[int, str]]:
# The order of inputs is different for question answering and sequence classification
if self.task in ["question-answering", "sequence-classification"]:
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
('attention_mask', {0: 'batch', 1: 'sequence'}),
('bbox', {0: 'batch', 1: 'sequence'}),
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
else:
return OrderedDict(
[
('input_ids', {0: 'batch', 1: 'sequence'}),
('bbox', {0: 'batch', 1: 'sequence'}),
('attention_mask', {0: 'batch', 1: 'sequence'}),
('pixel_values', {0: 'batch', 1: 'num_channels'}),
] )
@property
def lowerCamelCase__( self :Tuple ) -> float:
return 1E-5
@property
def lowerCamelCase__( self :Any ) -> int:
return 12
def lowerCamelCase__( self :Tuple ,__snake_case :"ProcessorMixin" ,__snake_case :int = -1 ,__snake_case :int = -1 ,__snake_case :bool = False ,__snake_case :Optional["TensorType"] = None ,__snake_case :int = 3 ,__snake_case :int = 40 ,__snake_case :int = 40 ,) -> Mapping[str, Any]:
setattr(processor.image_processor ,'apply_ocr' ,__snake_case )
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
a__ = compute_effective_axis_dimension(
__snake_case ,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
a__ = processor.tokenizer.num_special_tokens_to_add(__snake_case )
a__ = compute_effective_axis_dimension(
__snake_case ,fixed_dimension=OnnxConfig.default_fixed_sequence ,num_token_to_add=__snake_case )
# Generate dummy inputs according to compute batch and sequence
a__ = [[' '.join([processor.tokenizer.unk_token] ) * seq_length]] * batch_size
# Generate dummy bounding boxes
a__ = [[[48, 84, 73, 1_28]]] * batch_size
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
a__ = self._generate_dummy_images(__snake_case ,__snake_case ,__snake_case ,__snake_case )
a__ = dict(
processor(
__snake_case ,text=__snake_case ,boxes=__snake_case ,return_tensors=__snake_case ,) )
return inputs
| 335 | 1 |
snake_case_ = frozenset(
[
"""prompt""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
snake_case_ = frozenset(["""prompt""", """negative_prompt"""])
snake_case_ = frozenset([])
snake_case_ = frozenset(["""image"""])
snake_case_ = frozenset(
[
"""image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
snake_case_ = frozenset(["""image"""])
snake_case_ = frozenset(
[
"""prompt""",
"""image""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
]
)
snake_case_ = frozenset(["""prompt""", """image""", """negative_prompt"""])
snake_case_ = frozenset(
[
# Text guided image variation with an image mask
"""prompt""",
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
]
)
snake_case_ = frozenset(["""prompt""", """image""", """mask_image""", """negative_prompt"""])
snake_case_ = frozenset(
[
# image variation with an image mask
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
snake_case_ = frozenset(["""image""", """mask_image"""])
snake_case_ = frozenset(
[
"""example_image""",
"""image""",
"""mask_image""",
"""height""",
"""width""",
"""guidance_scale""",
]
)
snake_case_ = frozenset(["""example_image""", """image""", """mask_image"""])
snake_case_ = frozenset(["""class_labels"""])
snake_case_ = frozenset(["""class_labels"""])
snake_case_ = frozenset(["""batch_size"""])
snake_case_ = frozenset([])
snake_case_ = frozenset(["""batch_size"""])
snake_case_ = frozenset([])
snake_case_ = frozenset(
[
"""prompt""",
"""audio_length_in_s""",
"""guidance_scale""",
"""negative_prompt""",
"""prompt_embeds""",
"""negative_prompt_embeds""",
"""cross_attention_kwargs""",
]
)
snake_case_ = frozenset(["""prompt""", """negative_prompt"""])
snake_case_ = frozenset(["""input_tokens"""])
snake_case_ = frozenset(["""input_tokens"""])
| 703 |
'''simple docstring'''
snake_case_ = [
9_99,
8_00,
7_99,
6_00,
5_99,
5_00,
4_00,
3_99,
3_77,
3_55,
3_33,
3_11,
2_88,
2_66,
2_44,
2_22,
2_00,
1_99,
1_77,
1_55,
1_33,
1_11,
88,
66,
44,
22,
0,
]
snake_case_ = [
9_99,
9_76,
9_52,
9_28,
9_05,
8_82,
8_58,
8_57,
8_10,
7_62,
7_15,
7_14,
5_72,
4_29,
4_28,
2_86,
2_85,
2_38,
1_90,
1_43,
1_42,
1_18,
95,
71,
47,
24,
0,
]
snake_case_ = [
9_99,
9_88,
9_77,
9_66,
9_55,
9_44,
9_33,
9_22,
9_11,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_50,
3_00,
2_99,
2_66,
2_33,
2_00,
1_99,
1_79,
1_59,
1_40,
1_20,
1_00,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
snake_case_ = [
9_99,
9_95,
9_92,
9_89,
9_85,
9_81,
9_78,
9_75,
9_71,
9_67,
9_64,
9_61,
9_57,
9_56,
9_51,
9_47,
9_42,
9_37,
9_33,
9_28,
9_23,
9_19,
9_14,
9_13,
9_08,
9_03,
8_97,
8_92,
8_87,
8_81,
8_76,
8_71,
8_70,
8_64,
8_58,
8_52,
8_46,
8_40,
8_34,
8_28,
8_27,
8_20,
8_13,
8_06,
7_99,
7_92,
7_85,
7_84,
7_77,
7_70,
7_63,
7_56,
7_49,
7_42,
7_41,
7_33,
7_24,
7_16,
7_07,
6_99,
6_98,
6_88,
6_77,
6_66,
6_56,
6_55,
6_45,
6_34,
6_23,
6_13,
6_12,
5_98,
5_84,
5_70,
5_69,
5_55,
5_41,
5_27,
5_26,
5_05,
4_84,
4_83,
4_62,
4_40,
4_39,
3_96,
3_95,
3_52,
3_51,
3_08,
3_07,
2_64,
2_63,
2_20,
2_19,
1_76,
1_32,
88,
44,
0,
]
snake_case_ = [
9_99,
9_97,
9_95,
9_92,
9_90,
9_88,
9_86,
9_84,
9_81,
9_79,
9_77,
9_75,
9_72,
9_70,
9_68,
9_66,
9_64,
9_61,
9_59,
9_57,
9_56,
9_54,
9_51,
9_49,
9_46,
9_44,
9_41,
9_39,
9_36,
9_34,
9_31,
9_29,
9_26,
9_24,
9_21,
9_19,
9_16,
9_14,
9_13,
9_10,
9_07,
9_05,
9_02,
8_99,
8_96,
8_93,
8_91,
8_88,
8_85,
8_82,
8_79,
8_77,
8_74,
8_71,
8_70,
8_67,
8_64,
8_61,
8_58,
8_55,
8_52,
8_49,
8_46,
8_43,
8_40,
8_37,
8_34,
8_31,
8_28,
8_27,
8_24,
8_21,
8_17,
8_14,
8_11,
8_08,
8_04,
8_01,
7_98,
7_95,
7_91,
7_88,
7_85,
7_84,
7_80,
7_77,
7_74,
7_70,
7_66,
7_63,
7_60,
7_56,
7_52,
7_49,
7_46,
7_42,
7_41,
7_37,
7_33,
7_30,
7_26,
7_22,
7_18,
7_14,
7_10,
7_07,
7_03,
6_99,
6_98,
6_94,
6_90,
6_85,
6_81,
6_77,
6_73,
6_69,
6_64,
6_60,
6_56,
6_55,
6_50,
6_46,
6_41,
6_36,
6_32,
6_27,
6_22,
6_18,
6_13,
6_12,
6_07,
6_02,
5_96,
5_91,
5_86,
5_80,
5_75,
5_70,
5_69,
5_63,
5_57,
5_51,
5_45,
5_39,
5_33,
5_27,
5_26,
5_19,
5_12,
5_05,
4_98,
4_91,
4_84,
4_83,
4_74,
4_66,
4_57,
4_49,
4_40,
4_39,
4_28,
4_18,
4_07,
3_96,
3_95,
3_81,
3_66,
3_52,
3_51,
3_30,
3_08,
3_07,
2_86,
2_64,
2_63,
2_42,
2_20,
2_19,
1_76,
1_75,
1_32,
1_31,
88,
44,
0,
]
snake_case_ = [
9_99,
9_91,
9_82,
9_74,
9_66,
9_58,
9_50,
9_41,
9_33,
9_25,
9_16,
9_08,
9_00,
8_99,
8_74,
8_50,
8_25,
8_00,
7_99,
7_00,
6_00,
5_00,
4_00,
3_00,
2_00,
1_00,
0,
]
snake_case_ = [
9_99,
9_92,
9_85,
9_78,
9_71,
9_64,
9_57,
9_49,
9_42,
9_35,
9_28,
9_21,
9_14,
9_07,
9_00,
8_99,
8_79,
8_59,
8_40,
8_20,
8_00,
7_99,
7_66,
7_33,
7_00,
6_99,
6_50,
6_00,
5_99,
5_00,
4_99,
4_00,
3_99,
3_00,
2_99,
2_00,
1_99,
1_00,
99,
0,
]
snake_case_ = [
9_99,
9_96,
9_92,
9_89,
9_85,
9_82,
9_79,
9_75,
9_72,
9_68,
9_65,
9_61,
9_58,
9_55,
9_51,
9_48,
9_44,
9_41,
9_38,
9_34,
9_31,
9_27,
9_24,
9_20,
9_17,
9_14,
9_10,
9_07,
9_03,
9_00,
8_99,
8_91,
8_84,
8_76,
8_69,
8_61,
8_53,
8_46,
8_38,
8_30,
8_23,
8_15,
8_08,
8_00,
7_99,
7_88,
7_77,
7_66,
7_55,
7_44,
7_33,
7_22,
7_11,
7_00,
6_99,
6_88,
6_77,
6_66,
6_55,
6_44,
6_33,
6_22,
6_11,
6_00,
5_99,
5_85,
5_71,
5_57,
5_42,
5_28,
5_14,
5_00,
4_99,
4_85,
4_71,
4_57,
4_42,
4_28,
4_14,
4_00,
3_99,
3_79,
3_59,
3_40,
3_20,
3_00,
2_99,
2_79,
2_59,
2_40,
2_20,
2_00,
1_99,
1_66,
1_33,
1_00,
99,
66,
33,
0,
]
| 537 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case_ : Tuple = {
'configuration_instructblip': [
'INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
'InstructBlipConfig',
'InstructBlipQFormerConfig',
'InstructBlipVisionConfig',
],
'processing_instructblip': ['InstructBlipProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case_ : Tuple = [
'INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
'InstructBlipQFormerModel',
'InstructBlipPreTrainedModel',
'InstructBlipForConditionalGeneration',
'InstructBlipVisionModel',
]
if TYPE_CHECKING:
from .configuration_instructblip import (
INSTRUCTBLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
InstructBlipConfig,
InstructBlipQFormerConfig,
InstructBlipVisionConfig,
)
from .processing_instructblip import InstructBlipProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_instructblip import (
INSTRUCTBLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
InstructBlipForConditionalGeneration,
InstructBlipPreTrainedModel,
InstructBlipQFormerModel,
InstructBlipVisionModel,
)
else:
import sys
snake_case_ : Any = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 195 |
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class UpperCAmelCase_ ( a):
lowerCamelCase__ = (UniPCMultistepScheduler,)
lowerCamelCase__ = (('num_inference_steps', 25),)
def snake_case__ ( self, **__a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = {
"num_train_timesteps": 1000,
"beta_start": 0.0_001,
"beta_end": 0.02,
"beta_schedule": "linear",
"solver_order": 2,
"solver_type": "bh2",
}
config.update(**__a)
return config
def snake_case__ ( self, __a=0, **__a):
'''simple docstring'''
_lowerCAmelCase : str = dict(self.forward_default_kwargs)
_lowerCAmelCase : Union[str, Any] = kwargs.pop("num_inference_steps", __a)
_lowerCAmelCase : List[str] = self.dummy_sample
_lowerCAmelCase : List[Any] = 0.1 * sample
_lowerCAmelCase : Optional[Any] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase : List[str] = self.get_scheduler_config(**__a)
_lowerCAmelCase : List[str] = scheduler_class(**__a)
scheduler.set_timesteps(__a)
# copy over dummy past residuals
_lowerCAmelCase : List[str] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a)
_lowerCAmelCase : Optional[Any] = scheduler_class.from_pretrained(__a)
new_scheduler.set_timesteps(__a)
# copy over dummy past residuals
_lowerCAmelCase : List[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCAmelCase , _lowerCAmelCase : Dict = sample, sample
for t in range(__a, time_step + scheduler.config.solver_order + 1):
_lowerCAmelCase : Dict = scheduler.step(__a, __a, __a, **__a).prev_sample
_lowerCAmelCase : int = new_scheduler.step(__a, __a, __a, **__a).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def snake_case__ ( self, __a=0, **__a):
'''simple docstring'''
_lowerCAmelCase : List[Any] = dict(self.forward_default_kwargs)
_lowerCAmelCase : List[str] = kwargs.pop("num_inference_steps", __a)
_lowerCAmelCase : Dict = self.dummy_sample
_lowerCAmelCase : List[str] = 0.1 * sample
_lowerCAmelCase : str = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase : str = self.get_scheduler_config()
_lowerCAmelCase : str = scheduler_class(**__a)
scheduler.set_timesteps(__a)
# copy over dummy past residuals (must be after setting timesteps)
_lowerCAmelCase : List[str] = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(__a)
_lowerCAmelCase : Tuple = scheduler_class.from_pretrained(__a)
# copy over dummy past residuals
new_scheduler.set_timesteps(__a)
# copy over dummy past residual (must be after setting timesteps)
_lowerCAmelCase : Any = dummy_past_residuals[: new_scheduler.config.solver_order]
_lowerCAmelCase : List[Any] = scheduler.step(__a, __a, __a, **__a).prev_sample
_lowerCAmelCase : Optional[Any] = new_scheduler.step(__a, __a, __a, **__a).prev_sample
assert torch.sum(torch.abs(output - new_output)) < 1E-5, "Scheduler outputs are not identical"
def snake_case__ ( self, __a=None, **__a):
'''simple docstring'''
if scheduler is None:
_lowerCAmelCase : List[Any] = self.scheduler_classes[0]
_lowerCAmelCase : List[Any] = self.get_scheduler_config(**__a)
_lowerCAmelCase : str = scheduler_class(**__a)
_lowerCAmelCase : str = self.scheduler_classes[0]
_lowerCAmelCase : str = self.get_scheduler_config(**__a)
_lowerCAmelCase : Optional[Any] = scheduler_class(**__a)
_lowerCAmelCase : int = 10
_lowerCAmelCase : str = self.dummy_model()
_lowerCAmelCase : List[str] = self.dummy_sample_deter
scheduler.set_timesteps(__a)
for i, t in enumerate(scheduler.timesteps):
_lowerCAmelCase : Tuple = model(__a, __a)
_lowerCAmelCase : Union[str, Any] = scheduler.step(__a, __a, __a).prev_sample
return sample
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = dict(self.forward_default_kwargs)
_lowerCAmelCase : List[str] = kwargs.pop("num_inference_steps", __a)
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase : int = self.get_scheduler_config()
_lowerCAmelCase : str = scheduler_class(**__a)
_lowerCAmelCase : List[str] = self.dummy_sample
_lowerCAmelCase : str = 0.1 * sample
if num_inference_steps is not None and hasattr(__a, "set_timesteps"):
scheduler.set_timesteps(__a)
elif num_inference_steps is not None and not hasattr(__a, "set_timesteps"):
_lowerCAmelCase : str = num_inference_steps
# copy over dummy past residuals (must be done after set_timesteps)
_lowerCAmelCase : Tuple = [residual + 0.2, residual + 0.15, residual + 0.10]
_lowerCAmelCase : Dict = dummy_past_residuals[: scheduler.config.solver_order]
_lowerCAmelCase : Optional[int] = scheduler.timesteps[5]
_lowerCAmelCase : Tuple = scheduler.timesteps[6]
_lowerCAmelCase : int = scheduler.step(__a, __a, __a, **__a).prev_sample
_lowerCAmelCase : Optional[Any] = scheduler.step(__a, __a, __a, **__a).prev_sample
self.assertEqual(output_a.shape, sample.shape)
self.assertEqual(output_a.shape, output_a.shape)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[Any] = UniPCMultistepScheduler(**self.get_scheduler_config())
_lowerCAmelCase : Optional[Any] = self.full_loop(scheduler=__a)
_lowerCAmelCase : List[str] = torch.mean(torch.abs(__a))
assert abs(result_mean.item() - 0.2_464) < 1E-3
_lowerCAmelCase : Tuple = DPMSolverSinglestepScheduler.from_config(scheduler.config)
_lowerCAmelCase : Any = DEISMultistepScheduler.from_config(scheduler.config)
_lowerCAmelCase : Any = DPMSolverMultistepScheduler.from_config(scheduler.config)
_lowerCAmelCase : Optional[Any] = UniPCMultistepScheduler.from_config(scheduler.config)
_lowerCAmelCase : Any = self.full_loop(scheduler=__a)
_lowerCAmelCase : Any = torch.mean(torch.abs(__a))
assert abs(result_mean.item() - 0.2_464) < 1E-3
def snake_case__ ( self):
'''simple docstring'''
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=__a)
def snake_case__ ( self):
'''simple docstring'''
self.check_over_configs(thresholding=__a)
for order in [1, 2, 3]:
for solver_type in ["bh1", "bh2"]:
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
thresholding=__a, prediction_type=__a, sample_max_value=__a, solver_order=__a, solver_type=__a, )
def snake_case__ ( self):
'''simple docstring'''
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=__a)
def snake_case__ ( self):
'''simple docstring'''
for solver_type in ["bh1", "bh2"]:
for order in [1, 2, 3]:
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(
solver_order=__a, solver_type=__a, prediction_type=__a, )
_lowerCAmelCase : Optional[int] = self.full_loop(
solver_order=__a, solver_type=__a, prediction_type=__a, )
assert not torch.isnan(__a).any(), "Samples have nan numbers"
def snake_case__ ( self):
'''simple docstring'''
self.check_over_configs(lower_order_final=__a)
self.check_over_configs(lower_order_final=__a)
def snake_case__ ( self):
'''simple docstring'''
for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
self.check_over_forward(num_inference_steps=__a, time_step=0)
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : int = self.full_loop()
_lowerCAmelCase : str = torch.mean(torch.abs(__a))
assert abs(result_mean.item() - 0.2_464) < 1E-3
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.full_loop(prediction_type="v_prediction")
_lowerCAmelCase : Union[str, Any] = torch.mean(torch.abs(__a))
assert abs(result_mean.item() - 0.1_014) < 1E-3
def snake_case__ ( self):
'''simple docstring'''
_lowerCAmelCase : Optional[int] = self.scheduler_classes[0]
_lowerCAmelCase : Union[str, Any] = self.get_scheduler_config(thresholding=__a, dynamic_thresholding_ratio=0)
_lowerCAmelCase : Optional[int] = scheduler_class(**__a)
_lowerCAmelCase : str = 10
_lowerCAmelCase : List[str] = self.dummy_model()
_lowerCAmelCase : Tuple = self.dummy_sample_deter.half()
scheduler.set_timesteps(__a)
for i, t in enumerate(scheduler.timesteps):
_lowerCAmelCase : Dict = model(__a, __a)
_lowerCAmelCase : List[str] = scheduler.step(__a, __a, __a).prev_sample
assert sample.dtype == torch.floataa
def snake_case__ ( self, **__a):
'''simple docstring'''
for scheduler_class in self.scheduler_classes:
_lowerCAmelCase : Any = self.get_scheduler_config(**__a)
_lowerCAmelCase : List[Any] = scheduler_class(**__a)
scheduler.set_timesteps(scheduler.config.num_train_timesteps)
assert len(scheduler.timesteps.unique()) == scheduler.num_inference_steps
| 500 | 0 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
SCREAMING_SNAKE_CASE__ : str = HfApi()
SCREAMING_SNAKE_CASE__ : List[str] = {}
# fmt: off
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
SCREAMING_SNAKE_CASE__ : Dict = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
SCREAMING_SNAKE_CASE__ : str = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
SCREAMING_SNAKE_CASE__ : List[Any] = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
SCREAMING_SNAKE_CASE__ : Tuple = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
SCREAMING_SNAKE_CASE__ : Any = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
SCREAMING_SNAKE_CASE__ : Tuple = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
SCREAMING_SNAKE_CASE__ : Dict = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
SCREAMING_SNAKE_CASE__ : Tuple = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
SCREAMING_SNAKE_CASE__ : List[str] = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
SCREAMING_SNAKE_CASE__ : Any = api.list_models(filter='''diffusers''')
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
SCREAMING_SNAKE_CASE__ : Union[str, Any] = '''/home/patrick/google_checkpoints/''' + mod.modelId.split('''/''')[-1]
print(f"""Started running {mod.modelId}!!!""")
if mod.modelId.startswith('''CompVis'''):
SCREAMING_SNAKE_CASE__ : List[str] = UNetaDModel.from_pretrained(local_checkpoint, subfolder='''unet''')
else:
SCREAMING_SNAKE_CASE__ : List[str] = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
SCREAMING_SNAKE_CASE__ : int = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
SCREAMING_SNAKE_CASE__ : Optional[int] = torch.tensor([1_0] * noise.shape[0])
with torch.no_grad():
SCREAMING_SNAKE_CASE__ : Any = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :3_0], results['''_'''.join('''_'''.join(mod.modelId.split('''/''')).split('''-'''))], atol=1e-3
)
print(f"""{mod.modelId} has passed successfully!!!""")
| 711 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'''configuration_transfo_xl''': ['''TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TransfoXLConfig'''],
'''tokenization_transfo_xl''': ['''TransfoXLCorpus''', '''TransfoXLTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : List[Any] = [
'''TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''AdaptiveEmbedding''',
'''TransfoXLForSequenceClassification''',
'''TransfoXLLMHeadModel''',
'''TransfoXLModel''',
'''TransfoXLPreTrainedModel''',
'''load_tf_weights_in_transfo_xl''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ : str = [
'''TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFAdaptiveEmbedding''',
'''TFTransfoXLForSequenceClassification''',
'''TFTransfoXLLMHeadModel''',
'''TFTransfoXLMainLayer''',
'''TFTransfoXLModel''',
'''TFTransfoXLPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_transfo_xl import TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP, TransfoXLConfig
from .tokenization_transfo_xl import TransfoXLCorpus, TransfoXLTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_transfo_xl import (
TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
AdaptiveEmbedding,
TransfoXLForSequenceClassification,
TransfoXLLMHeadModel,
TransfoXLModel,
TransfoXLPreTrainedModel,
load_tf_weights_in_transfo_xl,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_transfo_xl import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAdaptiveEmbedding,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLMainLayer,
TFTransfoXLModel,
TFTransfoXLPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ : Union[str, Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 581 | 0 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import RegNetForImageClassification, RegNetModel
from transformers.models.regnet.modeling_regnet import REGNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase :
"""simple docstring"""
def __init__( self , UpperCamelCase_ , UpperCamelCase_=3 , UpperCamelCase_=32 , UpperCamelCase_=3 , UpperCamelCase_=10 , UpperCamelCase_=[10, 20, 30, 40] , UpperCamelCase_=[1, 1, 2, 1] , UpperCamelCase_=True , UpperCamelCase_=True , UpperCamelCase_="relu" , UpperCamelCase_=3 , UpperCamelCase_=None , ):
'''simple docstring'''
UpperCamelCase__ :Optional[int] = parent
UpperCamelCase__ :List[str] = batch_size
UpperCamelCase__ :int = image_size
UpperCamelCase__ :Any = num_channels
UpperCamelCase__ :Optional[int] = embeddings_size
UpperCamelCase__ :Dict = hidden_sizes
UpperCamelCase__ :Optional[int] = depths
UpperCamelCase__ :int = is_training
UpperCamelCase__ :List[Any] = use_labels
UpperCamelCase__ :Union[str, Any] = hidden_act
UpperCamelCase__ :Dict = num_labels
UpperCamelCase__ :Union[str, Any] = scope
UpperCamelCase__ :Optional[int] = len(UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase__ :List[Any] = None
if self.use_labels:
UpperCamelCase__ :Dict = ids_tensor([self.batch_size] , self.num_labels )
UpperCamelCase__ :Optional[int] = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels , embeddings_size=self.embeddings_size , hidden_sizes=self.hidden_sizes , depths=self.depths , hidden_act=self.hidden_act , num_labels=self.num_labels , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :int = RegNetModel(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
UpperCamelCase__ :Tuple = model(UpperCamelCase_ )
# expected last hidden states: B, C, H // 32, W // 32
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) , )
def lowerCAmelCase__ ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
'''simple docstring'''
UpperCamelCase__ :int = self.num_labels
UpperCamelCase__ :Union[str, Any] = RegNetForImageClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
UpperCamelCase__ :Union[str, Any] = model(UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :str = self.prepare_config_and_inputs()
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ :List[str] = config_and_inputs
UpperCamelCase__ :Union[str, Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( A__ , A__ , unittest.TestCase ):
"""simple docstring"""
_a = (RegNetModel, RegNetForImageClassification) if is_torch_available() else ()
_a = (
{'feature-extraction': RegNetModel, 'image-classification': RegNetForImageClassification}
if is_torch_available()
else {}
)
_a = False
_a = False
_a = False
_a = False
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = RegNetModelTester(self )
UpperCamelCase__ :Union[str, Any] = ConfigTester(self , config_class=UpperCamelCase_ , has_text_modality=UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return
@unittest.skip(reason='''RegNet does not use inputs_embeds''' )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
pass
@unittest.skip(reason='''RegNet does not support input and output embeddings''' )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
pass
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ :Tuple = model_class(UpperCamelCase_ )
UpperCamelCase__ :str = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase__ :Optional[Any] = [*signature.parameters.keys()]
UpperCamelCase__ :Union[str, Any] = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :Tuple = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ :Optional[Any] = model_class(config=UpperCamelCase_ )
for name, module in model.named_modules():
if isinstance(UpperCamelCase_ , (nn.BatchNormad, nn.GroupNorm) ):
self.assertTrue(
torch.all(module.weight == 1 ) , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
self.assertTrue(
torch.all(module.bias == 0 ) , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
def check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
UpperCamelCase__ :Tuple = model_class(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
with torch.no_grad():
UpperCamelCase__ :Optional[Any] = model(**self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ ) )
UpperCamelCase__ :List[str] = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
UpperCamelCase__ :int = self.model_tester.num_stages
self.assertEqual(len(UpperCamelCase_ ) , expected_num_stages + 1 )
# RegNet's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 2, self.model_tester.image_size // 2] , )
UpperCamelCase__ , UpperCamelCase__ :str = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ :Union[str, Any] = ['''basic''', '''bottleneck''']
for model_class in self.all_model_classes:
for layer_type in layers_type:
UpperCamelCase__ :Optional[int] = layer_type
UpperCamelCase__ :Any = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCamelCase__ :Union[str, Any] = True
check_hidden_states_output(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_ )
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
for model_name in REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase__ :Union[str, Any] = RegNetModel.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
def a ( ) -> str:
'''simple docstring'''
UpperCamelCase__ :int = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowercase ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def lowerCAmelCase__ ( self ):
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
if is_vision_available()
else None
)
@slow
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Tuple = RegNetForImageClassification.from_pretrained(REGNET_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(UpperCamelCase_ )
UpperCamelCase__ :Tuple = self.default_image_processor
UpperCamelCase__ :List[Any] = prepare_img()
UpperCamelCase__ :Optional[Any] = image_processor(images=UpperCamelCase_ , return_tensors='''pt''' ).to(UpperCamelCase_ )
# forward pass
with torch.no_grad():
UpperCamelCase__ :Tuple = model(**UpperCamelCase_ )
# verify the logits
UpperCamelCase__ :Union[str, Any] = torch.Size((1, 1000) )
self.assertEqual(outputs.logits.shape , UpperCamelCase_ )
UpperCamelCase__ :Union[str, Any] = torch.tensor([-0.4180, -1.5051, -3.4836] ).to(UpperCamelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCamelCase_ , atol=1e-4 ) ) | 189 |
'''simple docstring'''
from __future__ import annotations
from decimal import Decimal
from numpy import array
def a ( __a ) -> list[list[float]]:
'''simple docstring'''
UpperCamelCase__ :int = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementation only works for 2x2 matrices
if len(__a ) == 2 and len(matrix[0] ) == 2 and len(matrix[1] ) == 2:
# Calculate the determinant of the matrix
UpperCamelCase__ :str = float(
d(matrix[0][0] ) * d(matrix[1][1] ) - d(matrix[1][0] ) * d(matrix[0][1] ) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creates a copy of the matrix with swapped positions of the elements
UpperCamelCase__ :List[Any] = [[0.0, 0.0], [0.0, 0.0]]
UpperCamelCase__ , UpperCamelCase__ :Union[str, Any] = matrix[1][1], matrix[0][0]
UpperCamelCase__ , UpperCamelCase__ :Dict = -matrix[1][0], -matrix[0][1]
# Calculate the inverse of the matrix
return [
[(float(d(__a ) ) / determinant) or 0.0 for n in row] for row in swapped_matrix
]
elif (
len(__a ) == 3
and len(matrix[0] ) == 3
and len(matrix[1] ) == 3
and len(matrix[2] ) == 3
):
# Calculate the determinant of the matrix using Sarrus rule
UpperCamelCase__ :int = float(
(
(d(matrix[0][0] ) * d(matrix[1][1] ) * d(matrix[2][2] ))
+ (d(matrix[0][1] ) * d(matrix[1][2] ) * d(matrix[2][0] ))
+ (d(matrix[0][2] ) * d(matrix[1][0] ) * d(matrix[2][1] ))
)
- (
(d(matrix[0][2] ) * d(matrix[1][1] ) * d(matrix[2][0] ))
+ (d(matrix[0][1] ) * d(matrix[1][0] ) * d(matrix[2][2] ))
+ (d(matrix[0][0] ) * d(matrix[1][2] ) * d(matrix[2][1] ))
) )
if determinant == 0:
raise ValueError('''This matrix has no inverse.''' )
# Creating cofactor matrix
UpperCamelCase__ :List[Any] = [
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
[d(0.0 ), d(0.0 ), d(0.0 )],
]
UpperCamelCase__ :Tuple = (d(matrix[1][1] ) * d(matrix[2][2] )) - (
d(matrix[1][2] ) * d(matrix[2][1] )
)
UpperCamelCase__ :List[str] = -(
(d(matrix[1][0] ) * d(matrix[2][2] )) - (d(matrix[1][2] ) * d(matrix[2][0] ))
)
UpperCamelCase__ :Union[str, Any] = (d(matrix[1][0] ) * d(matrix[2][1] )) - (
d(matrix[1][1] ) * d(matrix[2][0] )
)
UpperCamelCase__ :List[Any] = -(
(d(matrix[0][1] ) * d(matrix[2][2] )) - (d(matrix[0][2] ) * d(matrix[2][1] ))
)
UpperCamelCase__ :Optional[int] = (d(matrix[0][0] ) * d(matrix[2][2] )) - (
d(matrix[0][2] ) * d(matrix[2][0] )
)
UpperCamelCase__ :List[Any] = -(
(d(matrix[0][0] ) * d(matrix[2][1] )) - (d(matrix[0][1] ) * d(matrix[2][0] ))
)
UpperCamelCase__ :List[Any] = (d(matrix[0][1] ) * d(matrix[1][2] )) - (
d(matrix[0][2] ) * d(matrix[1][1] )
)
UpperCamelCase__ :List[str] = -(
(d(matrix[0][0] ) * d(matrix[1][2] )) - (d(matrix[0][2] ) * d(matrix[1][0] ))
)
UpperCamelCase__ :Dict = (d(matrix[0][0] ) * d(matrix[1][1] )) - (
d(matrix[0][1] ) * d(matrix[1][0] )
)
# Transpose the cofactor matrix (Adjoint matrix)
UpperCamelCase__ :Optional[Any] = array(__a )
for i in range(3 ):
for j in range(3 ):
UpperCamelCase__ :Optional[Any] = cofactor_matrix[j][i]
# Inverse of the matrix using the formula (1/determinant) * adjoint matrix
UpperCamelCase__ :Any = array(__a )
for i in range(3 ):
for j in range(3 ):
inverse_matrix[i][j] /= d(__a )
# Calculate the inverse of the matrix
return [[float(d(__a ) ) or 0.0 for n in row] for row in inverse_matrix]
raise ValueError('''Please provide a matrix of size 2x2 or 3x3.''' ) | 189 | 1 |
'''simple docstring'''
import heapq
def A__ ( lowercase: dict ) -> set[int]:
A : list[list] =[]
# for each node and his adjacency list add them and the rank of the node to queue
# using heapq module the queue will be filled like a Priority Queue
# heapq works with a min priority queue, so I used -1*len(v) to build it
for key, value in graph.items():
# O(log(n))
heapq.heappush(lowercase, [-1 * len(lowercase ), (key, value)] )
# chosen_vertices = set of chosen vertices
A : Dict =set()
# while queue isn't empty and there are still edges
# (queue[0][0] is the rank of the node with max rank)
while queue and queue[0][0] != 0:
# extract vertex with max rank from queue and add it to chosen_vertices
A : List[str] =heapq.heappop(lowercase )[1][0]
chosen_vertices.add(lowercase )
# Remove all arcs adjacent to argmax
for elem in queue:
# if v haven't adjacent node, skip
if elem[0] == 0:
continue
# if argmax is reachable from elem
# remove argmax from elem's adjacent list and update his rank
if argmax in elem[1][1]:
A : str =elem[1][1].index(lowercase )
del elem[1][1][index]
elem[0] += 1
# re-order the queue
heapq.heapify(lowercase )
return chosen_vertices
if __name__ == "__main__":
import doctest
doctest.testmod()
_lowercase : List[Any] ={0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
print(f'''Minimum vertex cover:\n{greedy_min_vertex_cover(graph)}''')
| 720 | 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 SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Union[str, Any]:
A : Dict =tempfile.mkdtemp()
A : int =SamImageProcessor()
A : Union[str, Any] =SamProcessor(SCREAMING_SNAKE_CASE__ )
processor.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , **SCREAMING_SNAKE_CASE__ : Optional[Any] ) -> Optional[int]:
return AutoProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ ).image_processor
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Any:
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Optional[int]:
A : str =[np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
A : Optional[int] =[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self : str ) -> Tuple:
A : Optional[int] =SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A : str =self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__ , padding_value=1.0 )
A : Union[str, Any] =SamProcessor.from_pretrained(self.tmpdirname , do_normalize=SCREAMING_SNAKE_CASE__ , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Optional[int]:
A : Optional[Any] =self.get_image_processor()
A : Optional[Any] =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__ )
A : Dict =self.prepare_image_inputs()
A : Optional[int] =image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='np' )
A : Optional[Any] =processor(images=SCREAMING_SNAKE_CASE__ , 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 SCREAMING_SNAKE_CASE_ ( self : int ) -> Any:
A : str =self.get_image_processor()
A : Union[str, Any] =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__ )
A : str =[torch.ones((1, 3, 5, 5) )]
A : Optional[Any] =[[17_64, 26_46]]
A : List[Any] =[[6_83, 10_24]]
A : Union[str, Any] =processor.post_process_masks(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
A : Any =processor.post_process_masks(
SCREAMING_SNAKE_CASE__ , torch.tensor(SCREAMING_SNAKE_CASE__ ) , torch.tensor(SCREAMING_SNAKE_CASE__ ) )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
# should also work with np
A : str =[np.ones((1, 3, 5, 5) )]
A : int =processor.post_process_masks(SCREAMING_SNAKE_CASE__ , np.array(SCREAMING_SNAKE_CASE__ ) , np.array(SCREAMING_SNAKE_CASE__ ) )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
A : Any =[[1, 0], [0, 1]]
with self.assertRaises(SCREAMING_SNAKE_CASE__ ):
A : Any =processor.post_process_masks(SCREAMING_SNAKE_CASE__ , np.array(SCREAMING_SNAKE_CASE__ ) , np.array(SCREAMING_SNAKE_CASE__ ) )
@require_vision
@require_tf
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( self : str ) -> str:
A : Tuple =tempfile.mkdtemp()
A : Union[str, Any] =SamImageProcessor()
A : Union[str, Any] =SamProcessor(SCREAMING_SNAKE_CASE__ )
processor.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : int , **SCREAMING_SNAKE_CASE__ : str ) -> Union[str, Any]:
return AutoProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ ).image_processor
def SCREAMING_SNAKE_CASE_ ( self : str ) -> List[str]:
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> Tuple:
A : Optional[Any] =[np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
A : Any =[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> List[str]:
A : Optional[Any] =SamProcessor(image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
A : Optional[Any] =self.get_image_processor(do_normalize=SCREAMING_SNAKE_CASE__ , padding_value=1.0 )
A : Dict =SamProcessor.from_pretrained(self.tmpdirname , do_normalize=SCREAMING_SNAKE_CASE__ , padding_value=1.0 )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Any:
A : Any =self.get_image_processor()
A : Any =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__ )
A : int =self.prepare_image_inputs()
A : Tuple =image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='np' )
A : List[Any] =processor(images=SCREAMING_SNAKE_CASE__ , 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 SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Tuple:
A : int =self.get_image_processor()
A : Any =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__ )
A : Optional[int] =[tf.ones((1, 3, 5, 5) )]
A : Tuple =[[17_64, 26_46]]
A : Union[str, Any] =[[6_83, 10_24]]
A : int =processor.post_process_masks(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , return_tensors='tf' )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
A : List[Any] =processor.post_process_masks(
SCREAMING_SNAKE_CASE__ , tf.convert_to_tensor(SCREAMING_SNAKE_CASE__ ) , tf.convert_to_tensor(SCREAMING_SNAKE_CASE__ ) , return_tensors='tf' , )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
# should also work with np
A : Any =[np.ones((1, 3, 5, 5) )]
A : Optional[Any] =processor.post_process_masks(
SCREAMING_SNAKE_CASE__ , np.array(SCREAMING_SNAKE_CASE__ ) , np.array(SCREAMING_SNAKE_CASE__ ) , return_tensors='tf' )
self.assertEqual(masks[0].shape , (1, 3, 17_64, 26_46) )
A : Any =[[1, 0], [0, 1]]
with self.assertRaises(tf.errors.InvalidArgumentError ):
A : List[str] =processor.post_process_masks(
SCREAMING_SNAKE_CASE__ , np.array(SCREAMING_SNAKE_CASE__ ) , np.array(SCREAMING_SNAKE_CASE__ ) , return_tensors='tf' )
@require_vision
@require_torchvision
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Union[str, Any]:
A : Optional[int] =tempfile.mkdtemp()
A : Union[str, Any] =SamImageProcessor()
A : Dict =SamProcessor(SCREAMING_SNAKE_CASE__ )
processor.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : int , **SCREAMING_SNAKE_CASE__ : List[str] ) -> Any:
return AutoProcessor.from_pretrained(self.tmpdirname , **SCREAMING_SNAKE_CASE__ ).image_processor
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Any:
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Tuple:
A : Any =[np.random.randint(2_55 , size=(3, 30, 4_00) , dtype=np.uinta )]
A : Tuple =[Image.fromarray(np.moveaxis(SCREAMING_SNAKE_CASE__ , 0 , -1 ) ) for x in image_inputs]
return image_inputs
@is_pt_tf_cross_test
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> List[str]:
A : Optional[Any] =self.get_image_processor()
A : Dict =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__ )
A : Optional[int] =np.random.randint(0 , 2 , size=(1, 3, 5, 5) ).astype(np.floataa )
A : Optional[int] =[tf.convert_to_tensor(SCREAMING_SNAKE_CASE__ )]
A : Union[str, Any] =[torch.tensor(SCREAMING_SNAKE_CASE__ )]
A : int =[[17_64, 26_46]]
A : int =[[6_83, 10_24]]
A : Dict =processor.post_process_masks(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , return_tensors='tf' )
A : Optional[Any] =processor.post_process_masks(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , return_tensors='pt' )
self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy() ) )
@is_pt_tf_cross_test
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] ) -> Any:
A : Union[str, Any] =self.get_image_processor()
A : int =SamProcessor(image_processor=SCREAMING_SNAKE_CASE__ )
A : int =self.prepare_image_inputs()
A : List[Any] =image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='pt' )['pixel_values'].numpy()
A : Tuple =processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='pt' )['pixel_values'].numpy()
A : Optional[int] =image_processor(SCREAMING_SNAKE_CASE__ , return_tensors='tf' )['pixel_values'].numpy()
A : Dict =processor(images=SCREAMING_SNAKE_CASE__ , return_tensors='tf' )['pixel_values'].numpy()
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
| 661 | 0 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def UpperCAmelCase ( UpperCAmelCase=32 ,UpperCAmelCase=10 ,UpperCAmelCase=100 ,UpperCAmelCase=1026 ,UpperCAmelCase=True ,UpperCAmelCase="data/tokenized_stories_train_wikitext103.jbl" ,UpperCAmelCase="igf_context_pairs.jbl" ,)-> List[str]:
'''simple docstring'''
set_seed(3 )
# generate train_data and objective_set
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = generate_datasets(
_UpperCamelCase ,_UpperCamelCase ,number=_UpperCamelCase ,min_len=1026 ,trim=_UpperCamelCase )
# keeps model same across runs
set_seed(4 )
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
SCREAMING_SNAKE_CASE_ = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
# load pretrained model
SCREAMING_SNAKE_CASE_ = load_gpta('''gpt2''' ).to(_UpperCamelCase )
print('''computing perplexity on objective set''' )
SCREAMING_SNAKE_CASE_ = compute_perplexity(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ).item()
print('''perplexity on objective set:''' ,_UpperCamelCase )
# collect igf pairs and save to file demo.jbl
collect_objective_set(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase=15 ,UpperCAmelCase=128 ,UpperCAmelCase=100 ,UpperCAmelCase="igf_model.pt" ,)-> int:
'''simple docstring'''
set_seed(42 )
# Load pre-trained model
SCREAMING_SNAKE_CASE_ = GPTaLMHeadModel.from_pretrained('''gpt2''' )
# Initialize secondary learner to use embedding weights of model
SCREAMING_SNAKE_CASE_ = SecondaryLearner(_UpperCamelCase )
# Train secondary learner
SCREAMING_SNAKE_CASE_ = train_secondary_learner(
_UpperCamelCase ,_UpperCamelCase ,max_epochs=_UpperCamelCase ,batch_size=_UpperCamelCase ,eval_freq=100 ,igf_model_path=_UpperCamelCase ,)
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def UpperCAmelCase ( UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase ,UpperCAmelCase=32 ,UpperCAmelCase=1000 ,UpperCAmelCase=16 ,UpperCAmelCase=1.0 ,UpperCAmelCase=recopy_gpta ,UpperCAmelCase=None ,UpperCAmelCase=10 ,UpperCAmelCase="gpt2_finetuned.pt" ,)-> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = torch.device('''cuda:0''' if torch.cuda.is_available() else '''cpu''' )
SCREAMING_SNAKE_CASE_ = RandomSampler(_UpperCamelCase )
SCREAMING_SNAKE_CASE_ = DataLoader(_UpperCamelCase ,sampler=_UpperCamelCase )
SCREAMING_SNAKE_CASE_ = max_steps // (len(_UpperCamelCase )) + 1
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = torch.zeros((1, context_len) ,dtype=torch.long ,device=_UpperCamelCase )
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = recopy_model(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
model.train()
if secondary_learner is not None:
secondary_learner.to(_UpperCamelCase )
secondary_learner.eval()
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
# Compute the performance of the transformer model at the beginning
SCREAMING_SNAKE_CASE_ = compute_perplexity(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
test_perps.append(_UpperCamelCase )
print('''Test perplexity, step''' ,_UpperCamelCase ,''':''' ,_UpperCamelCase )
for epoch in range(int(_UpperCamelCase ) ):
for step, example in enumerate(_UpperCamelCase ):
torch.cuda.empty_cache()
SCREAMING_SNAKE_CASE_ = random.randint(0 ,example.size(2 ) - context_len - 1 )
SCREAMING_SNAKE_CASE_ = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
SCREAMING_SNAKE_CASE_ = model(_UpperCamelCase ,labels=_UpperCamelCase )
SCREAMING_SNAKE_CASE_ = True
if secondary_learner is not None:
SCREAMING_SNAKE_CASE_ = secondary_learner.forward(
torch.tensor(_UpperCamelCase ,dtype=torch.long ,device=_UpperCamelCase ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(_UpperCamelCase ) )
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
SCREAMING_SNAKE_CASE_ = -1
if predicted_q < threshold:
SCREAMING_SNAKE_CASE_ = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
SCREAMING_SNAKE_CASE_ = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
SCREAMING_SNAKE_CASE_ = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters() ,3.0 )
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
SCREAMING_SNAKE_CASE_ = compute_perplexity(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
test_perps.append(_UpperCamelCase )
print('''Test perplexity, step''' ,_UpperCamelCase ,''':''' ,_UpperCamelCase )
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict() ,_UpperCamelCase )
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def UpperCAmelCase ( )-> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser(description='''Fine-tune a transformer model with IGF on a language modeling task''' )
# Required parameters
parser.add_argument(
'''--data_dir''' ,default=_UpperCamelCase ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''The input data dir. Should contain data files for WikiText.''' ,)
parser.add_argument(
'''--model_name_or_path''' ,default=_UpperCamelCase ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''Path to pretrained model or model identifier from huggingface.co/models''' ,)
parser.add_argument(
'''--data_file''' ,type=_UpperCamelCase ,default=_UpperCamelCase ,help=(
'''A jbl file containing tokenized data which can be split as objective dataset, '''
'''train_dataset and test_dataset.'''
) ,)
parser.add_argument(
'''--igf_data_file''' ,type=_UpperCamelCase ,default=_UpperCamelCase ,help='''A jbl file containing the context and information gain pairs to train secondary learner.''' ,)
parser.add_argument(
'''--output_dir''' ,default=_UpperCamelCase ,type=_UpperCamelCase ,required=_UpperCamelCase ,help='''The output directory where the final fine-tuned model is stored.''' ,)
parser.add_argument(
'''--tokenizer_name''' ,default=_UpperCamelCase ,type=_UpperCamelCase ,help='''Pretrained tokenizer name or path if not the same as model_name''' ,)
parser.add_argument('''--seed''' ,type=_UpperCamelCase ,default=_UpperCamelCase ,help='''A seed for reproducible training.''' )
parser.add_argument(
'''--context_len''' ,default=32 ,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(
'''--size_objective_set''' ,default=100 ,type=_UpperCamelCase ,help='''number of articles that are long enough to be used as our objective set''' ,)
parser.add_argument(
'''--eval_freq''' ,default=100 ,type=_UpperCamelCase ,help='''secondary model evaluation is triggered at eval_freq''' )
parser.add_argument('''--max_steps''' ,default=1000 ,type=_UpperCamelCase ,help='''To calculate training epochs''' )
parser.add_argument(
'''--secondary_learner_batch_size''' ,default=128 ,type=_UpperCamelCase ,help='''batch size of training data for secondary learner''' ,)
parser.add_argument(
'''--batch_size''' ,default=16 ,type=_UpperCamelCase ,help='''batch size of training data of language model(gpt2) ''' )
parser.add_argument(
'''--eval_interval''' ,default=10 ,type=_UpperCamelCase ,help=(
'''decay the selectivity of our secondary learner filter from'''
'''1 standard deviation above average to 1 below average after 10 batches'''
) ,)
parser.add_argument(
'''--number''' ,default=100 ,type=_UpperCamelCase ,help='''The number of examples split to be used as objective_set/test_data''' )
parser.add_argument(
'''--min_len''' ,default=1026 ,type=_UpperCamelCase ,help='''The minimum length of the article to be used as objective set''' )
parser.add_argument(
'''--secondary_learner_max_epochs''' ,default=15 ,type=_UpperCamelCase ,help='''number of epochs to train secondary learner''' )
parser.add_argument('''--trim''' ,default=_UpperCamelCase ,type=_UpperCamelCase ,help='''truncate the example if it exceeds context length''' )
parser.add_argument(
'''--threshold''' ,default=1.0 ,type=_UpperCamelCase ,help=(
'''The threshold value used by secondary learner to filter the train_data and allow only'''
''' informative data as input to the model'''
) ,)
parser.add_argument('''--finetuned_model_name''' ,default='''gpt2_finetuned.pt''' ,type=_UpperCamelCase ,help='''finetuned_model_name''' )
parser.add_argument(
'''--recopy_model''' ,default=_UpperCamelCase ,type=_UpperCamelCase ,help='''Reset the model to the original pretrained GPT-2 weights after each iteration''' ,)
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32 ,max_steps=10 ,size_objective_set=100 ,min_len=1026 ,trim=_UpperCamelCase ,data_file='''data/tokenized_stories_train_wikitext103.jbl''' ,igf_data_file='''igf_context_pairs.jbl''' ,)
# Load train data for secondary learner
SCREAMING_SNAKE_CASE_ = joblib.load('''data/IGF_values.jbl''' )
# Train secondary learner
SCREAMING_SNAKE_CASE_ = training_secondary_learner(
_UpperCamelCase ,secondary_learner_max_epochs=15 ,secondary_learner_batch_size=128 ,eval_freq=100 ,igf_model_path='''igf_model.pt''' ,)
# load pretrained gpt2 model
SCREAMING_SNAKE_CASE_ = GPTaLMHeadModel.from_pretrained('''gpt2''' )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ = generate_datasets(
context_len=32 ,file='''data/tokenized_stories_train_wikitext103.jbl''' ,number=100 ,min_len=1026 ,trim=_UpperCamelCase )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase ,context_len=32 ,max_steps=1000 ,batch_size=16 ,threshold=1.0 ,recopy_model=_UpperCamelCase ,secondary_learner=_UpperCamelCase ,eval_interval=10 ,finetuned_model_name='''gpt2_finetuned.pt''' ,)
if __name__ == "__main__":
main()
| 393 |
from jiwer import compute_measures
import datasets
lowerCamelCase :Any = '\\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 :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 :List[Any] = '\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 UpperCAmelCase ( datasets.Metric ):
def _A ( self: int ):
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 _A ( self: Optional[Any] , __UpperCamelCase: Any=None , __UpperCamelCase: Dict=None , __UpperCamelCase: Tuple=False ):
if concatenate_texts:
return compute_measures(__UpperCamelCase , __UpperCamelCase )["wer"]
else:
_a = 0
_a = 0
for prediction, reference in zip(__UpperCamelCase , __UpperCamelCase ):
_a = compute_measures(__UpperCamelCase , __UpperCamelCase )
incorrect += measures["substitutions"] + measures["deletions"] + measures["insertions"]
total += measures["substitutions"] + measures["deletions"] + measures["hits"]
return incorrect / total
| 487 | 0 |
"""simple docstring"""
from __future__ import annotations
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
if not nums:
return 0
_lowercase : Optional[int] = nums[0]
_lowercase : Any = 0
for num in nums[1:]:
_lowercase : Tuple = (
max_excluding + num,
max(_lowerCAmelCase , _lowerCAmelCase ),
)
return max(_lowerCAmelCase , _lowerCAmelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 716 |
"""simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipelines.spectrogram_diffusion import SpectrogramContEncoder, SpectrogramNotesEncoder, TaFilmDecoder
UpperCAmelCase: str = """base_with_context"""
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Dict = nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""] ) )
_lowercase : Any = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=__UpperCAmelCase )
for lyr_num, lyr in enumerate(model.encoders ):
_lowercase : Optional[Any] = weights[F"""layers_{lyr_num}"""]
_lowercase : str = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""] ) )
_lowercase : Optional[Any] = ly_weight["""attention"""]
_lowercase : Dict = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_lowercase : List[Any] = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_lowercase : List[str] = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_lowercase : Tuple = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_lowercase : int = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_lowercase : Optional[int] = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_lowercase : Tuple = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_lowercase : List[Any] = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_lowercase : Optional[int] = nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )
return model
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : int = nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T ) )
_lowercase : Optional[int] = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=__UpperCAmelCase )
for lyr_num, lyr in enumerate(model.encoders ):
_lowercase : int = weights[F"""layers_{lyr_num}"""]
_lowercase : Any = ly_weight["""attention"""]
_lowercase : Tuple = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_lowercase : str = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_lowercase : Any = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_lowercase : Any = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_lowercase : Optional[Any] = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""] ) )
_lowercase : Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_lowercase : List[Any] = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_lowercase : str = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_lowercase : List[Any] = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_lowercase : Union[str, Any] = nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )
return model
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase ):
_lowercase : Dict = nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T ) )
_lowercase : Optional[int] = nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T ) )
_lowercase : Optional[int] = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=__UpperCAmelCase )
_lowercase : Optional[Any] = nn.Parameter(
torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
_lowercase : List[Any] = weights[F"""layers_{lyr_num}"""]
_lowercase : Dict = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""] ) )
_lowercase : int = nn.Parameter(
torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T ) )
_lowercase : List[Any] = ly_weight["""self_attention"""]
_lowercase : Union[str, Any] = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_lowercase : List[str] = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_lowercase : Union[str, Any] = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_lowercase : List[Any] = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_lowercase : Union[str, Any] = ly_weight["""MultiHeadDotProductAttention_0"""]
_lowercase : Tuple = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_lowercase : List[Any] = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_lowercase : Any = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_lowercase : Optional[int] = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_lowercase : List[str] = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""] ) )
_lowercase : Optional[int] = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_lowercase : Any = nn.Parameter(
torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T ) )
_lowercase : str = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_lowercase : Dict = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_lowercase : List[str] = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_lowercase : str = nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""] ) )
_lowercase : Any = nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T ) )
return model
def __SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
_lowercase : Tuple = checkpoints.load_tax_checkpoint(args.checkpoint_path )
_lowercase : List[Any] = jnp.tree_util.tree_map(onp.array , __UpperCAmelCase )
_lowercase : int = [
"""from __gin__ import dynamic_registration""",
"""from music_spectrogram_diffusion.models.diffusion import diffusion_utils""",
"""diffusion_utils.ClassifierFreeGuidanceConfig.eval_condition_weight = 2.0""",
"""diffusion_utils.DiffusionConfig.classifier_free_guidance = @diffusion_utils.ClassifierFreeGuidanceConfig()""",
]
_lowercase : List[Any] = os.path.join(args.checkpoint_path , """..""" , """config.gin""" )
_lowercase : Optional[int] = inference.parse_training_gin_file(__UpperCAmelCase , __UpperCAmelCase )
_lowercase : Optional[Any] = inference.InferenceModel(args.checkpoint_path , __UpperCAmelCase )
_lowercase : List[Any] = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" , variance_type="""fixed_large""" )
_lowercase : List[str] = SpectrogramNotesEncoder(
max_length=synth_model.sequence_length["""inputs"""] , vocab_size=synth_model.model.module.config.vocab_size , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="""gated-gelu""" , )
_lowercase : Dict = SpectrogramContEncoder(
input_dims=synth_model.audio_codec.n_dims , targets_context_length=synth_model.sequence_length["""targets_context"""] , d_model=synth_model.model.module.config.emb_dim , dropout_rate=synth_model.model.module.config.dropout_rate , num_layers=synth_model.model.module.config.num_encoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , feed_forward_proj="""gated-gelu""" , )
_lowercase : int = TaFilmDecoder(
input_dims=synth_model.audio_codec.n_dims , targets_length=synth_model.sequence_length["""targets_context"""] , max_decoder_noise_time=synth_model.model.module.config.max_decoder_noise_time , d_model=synth_model.model.module.config.emb_dim , num_layers=synth_model.model.module.config.num_decoder_layers , num_heads=synth_model.model.module.config.num_heads , d_kv=synth_model.model.module.config.head_dim , d_ff=synth_model.model.module.config.mlp_dim , dropout_rate=synth_model.model.module.config.dropout_rate , )
_lowercase : str = load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""] , __UpperCAmelCase )
_lowercase : Dict = load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""] , __UpperCAmelCase )
_lowercase : List[str] = load_decoder(ta_checkpoint["""target"""]["""decoder"""] , __UpperCAmelCase )
_lowercase : Any = OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder""" )
_lowercase : str = SpectrogramDiffusionPipeline(
notes_encoder=__UpperCAmelCase , continuous_encoder=__UpperCAmelCase , decoder=__UpperCAmelCase , scheduler=__UpperCAmelCase , melgan=__UpperCAmelCase , )
if args.save:
pipe.save_pretrained(args.output_path )
if __name__ == "__main__":
UpperCAmelCase: Any = argparse.ArgumentParser()
parser.add_argument("""--output_path""", default=None, type=str, required=True, help="""Path to the converted model.""")
parser.add_argument(
"""--save""", default=True, type=bool, required=False, help="""Whether to save the converted model or not."""
)
parser.add_argument(
"""--checkpoint_path""",
default=F'{MODEL}/checkpoint_500000',
type=str,
required=False,
help="""Path to the original jax model checkpoint.""",
)
UpperCAmelCase: Optional[Any] = parser.parse_args()
main(args)
| 600 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __init__( self , lowercase__ , lowercase__=7 , lowercase__=3 , lowercase__=18 , lowercase__=30 , lowercase__=400 , lowercase__=True , lowercase__=None , lowercase__=True , lowercase__=None , lowercase__=True , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = size if size is not None else {"shortest_edge": 20}
SCREAMING_SNAKE_CASE_ : Dict = crop_size if crop_size is not None else {"height": 18, "width": 18}
SCREAMING_SNAKE_CASE_ : int = parent
SCREAMING_SNAKE_CASE_ : Tuple = batch_size
SCREAMING_SNAKE_CASE_ : Any = num_channels
SCREAMING_SNAKE_CASE_ : Any = image_size
SCREAMING_SNAKE_CASE_ : Optional[int] = min_resolution
SCREAMING_SNAKE_CASE_ : Optional[Any] = max_resolution
SCREAMING_SNAKE_CASE_ : Optional[int] = do_resize
SCREAMING_SNAKE_CASE_ : Optional[int] = size
SCREAMING_SNAKE_CASE_ : Union[str, Any] = do_center_crop
SCREAMING_SNAKE_CASE_ : List[Any] = crop_size
SCREAMING_SNAKE_CASE_ : List[Any] = do_flip_channel_order
def __lowerCamelCase ( self ):
"""simple docstring"""
return {
"do_resize": self.do_resize,
"size": self.size,
"do_center_crop": self.do_center_crop,
"crop_size": self.crop_size,
"do_flip_channel_order": self.do_flip_channel_order,
}
@require_torch
@require_vision
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase,unittest.TestCase ):
_A = MobileViTImageProcessor if is_vision_available() else None
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = MobileViTImageProcessingTester(self )
@property
def __lowerCamelCase ( self ):
"""simple docstring"""
return self.image_processor_tester.prepare_image_processor_dict()
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowercase__ , "do_resize" ) )
self.assertTrue(hasattr(lowercase__ , "size" ) )
self.assertTrue(hasattr(lowercase__ , "do_center_crop" ) )
self.assertTrue(hasattr(lowercase__ , "center_crop" ) )
self.assertTrue(hasattr(lowercase__ , "do_flip_channel_order" ) )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 20} )
self.assertEqual(image_processor.crop_size , {"height": 18, "width": 18} )
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.image_processing_class.from_dict(self.image_processor_dict , size=42 , crop_size=84 )
self.assertEqual(image_processor.size , {"shortest_edge": 42} )
self.assertEqual(image_processor.crop_size , {"height": 84, "width": 84} )
def __lowerCamelCase ( self ):
"""simple docstring"""
pass
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE_ : Optional[Any] = prepare_image_inputs(self.image_processor_tester , equal_resolution=lowercase__ )
for image in image_inputs:
self.assertIsInstance(lowercase__ , Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE_ : Optional[int] = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
SCREAMING_SNAKE_CASE_ : int = image_processing(lowercase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE_ : Tuple = 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
SCREAMING_SNAKE_CASE_ : Dict = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
SCREAMING_SNAKE_CASE_ : str = image_processing(lowercase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
def __lowerCamelCase ( self ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : str = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE_ : Tuple = 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
SCREAMING_SNAKE_CASE_ : Dict = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
# Test batched
SCREAMING_SNAKE_CASE_ : List[str] = image_processing(lowercase__ , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["height"],
self.image_processor_tester.crop_size["width"],
) , )
| 421 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
snake_case_ = {'tokenization_byt5': ['ByT5Tokenizer']}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
snake_case_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 421 | 1 |
'''simple docstring'''
import json
import logging
import os
import sys
from time import time
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, require_torch_tpu
logging.basicConfig(level=logging.DEBUG)
_snake_case : Any = logging.getLogger()
def snake_case_ (UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
_a = {}
_a = os.path.join(UpperCamelCase , '''all_results.json''' )
if os.path.exists(UpperCamelCase ):
with open(UpperCamelCase , '''r''' ) as f:
_a = json.load(UpperCamelCase )
else:
raise ValueError(f'can\'t find {path}' )
return results
_snake_case : Optional[int] = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
@require_torch_tpu
class A ( _a ):
def __lowerCAmelCase ( self : Any ) -> Dict:
"""simple docstring"""
import xla_spawn
_a = self.get_auto_remove_tmp_dir()
_a = F'\n ./examples/pytorch/text-classification/run_glue.py\n --num_cores=8\n ./examples/pytorch/text-classification/run_glue.py\n --model_name_or_path distilbert-base-uncased\n --output_dir {tmp_dir}\n --overwrite_output_dir\n --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n --do_train\n --do_eval\n --debug tpu_metrics_debug\n --per_device_train_batch_size=2\n --per_device_eval_batch_size=1\n --learning_rate=1e-4\n --max_steps=10\n --warmup_steps=2\n --seed=42\n --max_seq_length=128\n '.split()
with patch.object(lowerCAmelCase_ , '''argv''' , lowerCAmelCase_ ):
_a = time()
xla_spawn.main()
_a = time()
_a = get_results(lowerCAmelCase_ )
self.assertGreaterEqual(result['''eval_accuracy'''] , 0.7_5 )
# Assert that the script takes less than 500 seconds to make sure it doesn't hang.
self.assertLess(end - start , 5_00 )
def __lowerCAmelCase ( self : Dict ) -> Optional[Any]:
"""simple docstring"""
import xla_spawn
_a = '''
./tests/test_trainer_tpu.py
--num_cores=8
./tests/test_trainer_tpu.py
'''.split()
with patch.object(lowerCAmelCase_ , '''argv''' , lowerCAmelCase_ ):
xla_spawn.main()
| 377 |
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class A ( ctypes.Structure ):
# _fields is a specific attr expected by ctypes
lowercase_ = [('size', ctypes.c_int), ('visible', ctypes.c_byte)]
def snake_case_ ():
'''simple docstring'''
if os.name == "nt":
_a = CursorInfo()
_a = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCamelCase , ctypes.byref(UpperCamelCase ) )
_a = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCamelCase , ctypes.byref(UpperCamelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25l''' )
sys.stdout.flush()
def snake_case_ ():
'''simple docstring'''
if os.name == "nt":
_a = CursorInfo()
_a = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(UpperCamelCase , ctypes.byref(UpperCamelCase ) )
_a = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(UpperCamelCase , ctypes.byref(UpperCamelCase ) )
elif os.name == "posix":
sys.stdout.write('''\033[?25h''' )
sys.stdout.flush()
@contextmanager
def snake_case_ ():
'''simple docstring'''
try:
hide_cursor()
yield
finally:
show_cursor()
| 377 | 1 |
'''simple docstring'''
A__ : List[str] = [0, 2, 4, 6, 8]
A__ : Union[str, Any] = [1, 3, 5, 7, 9]
def UpperCAmelCase__ ( UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : list[int] , UpperCAmelCase_ : int ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
__lowerCamelCase : Optional[Any] = 0
for digit in range(10 ):
__lowerCamelCase : Optional[int] = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , UpperCAmelCase_ , UpperCAmelCase_ )
return result
__lowerCamelCase : Optional[int] = 0
for digita in range(10 ):
__lowerCamelCase : List[Any] = digita
if (remainder + digita) % 2 == 0:
__lowerCamelCase : int = ODD_DIGITS
else:
__lowerCamelCase : Union[str, Any] = EVEN_DIGITS
for digita in other_parity_digits:
__lowerCamelCase : Optional[int] = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , UpperCAmelCase_ , UpperCAmelCase_ , )
return result
def UpperCAmelCase__ ( UpperCAmelCase_ : int = 9 ) -> int:
__lowerCamelCase : List[Any] = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(UpperCAmelCase_ , 0 , [0] * length , UpperCAmelCase_ )
return result
if __name__ == "__main__":
print(f'''{solution() = }''')
| 13 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCAmelCase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = ['transformers', 'torch', 'note_seq']
def __init__( self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Union[str, Any]:
'''simple docstring'''
requires_backends(self , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
@classmethod
def UpperCamelCase( cls , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> List[str]:
'''simple docstring'''
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
| 42 | 0 |
lowerCAmelCase__ = [
[0, 1_6, 1_3, 0, 0, 0],
[0, 0, 1_0, 1_2, 0, 0],
[0, 4, 0, 0, 1_4, 0],
[0, 0, 9, 0, 0, 2_0],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __lowerCamelCase ( __a : Optional[Any] , __a : List[Any] , __a : Tuple , __a : Tuple ) -> Optional[Any]:
# Return True if there is node that has not iterated.
_lowercase =[False] * len(__a )
_lowercase =[s]
_lowercase =True
while queue:
_lowercase =queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(__a )
_lowercase =True
_lowercase =u
return visited[t]
def __lowerCamelCase ( __a : Union[str, Any] , __a : Union[str, Any] , __a : Union[str, Any] ) -> int:
_lowercase =[-1] * (len(__a ))
_lowercase =0
_lowercase =[]
_lowercase =[i[:] for i in graph] # Record original cut, copy.
while bfs(__a , __a , __a , __a ):
_lowercase =float("Inf" )
_lowercase =sink
while s != source:
# Find the minimum value in select path
_lowercase =min(__a , graph[parent[s]][s] )
_lowercase =parent[s]
max_flow += path_flow
_lowercase =sink
while v != source:
_lowercase =parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
_lowercase =parent[v]
for i in range(len(__a ) ):
for j in range(len(graph[0] ) ):
if graph[i][j] == 0 and temp[i][j] > 0:
res.append((i, j) )
return res
if __name__ == "__main__":
print(mincut(test_graph, source=0, sink=5))
| 709 | import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict
if TYPE_CHECKING:
import PIL.Image
from .features import FeatureType
lowerCAmelCase__ = None
lowerCAmelCase__ = "<" if sys.byteorder == "little" else ">"
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
lowerCAmelCase__ = [
np.dtype("|b1"),
np.dtype("|u1"),
np.dtype("<u2"),
np.dtype(">u2"),
np.dtype("<i2"),
np.dtype(">i2"),
np.dtype("<u4"),
np.dtype(">u4"),
np.dtype("<i4"),
np.dtype(">i4"),
np.dtype("<f4"),
np.dtype(">f4"),
np.dtype("<f8"),
np.dtype(">f8"),
]
@dataclass
class _a :
"""simple docstring"""
__SCREAMING_SNAKE_CASE = True
__SCREAMING_SNAKE_CASE = None
# Automatically constructed
__SCREAMING_SNAKE_CASE = "PIL.Image.Image"
__SCREAMING_SNAKE_CASE = pa.struct({'bytes': pa.binary(), 'path': pa.string()} )
__SCREAMING_SNAKE_CASE = field(default='Image' , init=lowerCamelCase_ , repr=lowerCamelCase_ )
def __call__( self ):
return self.pa_type
def __lowerCAmelCase ( self , lowerCAmelCase_ ):
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
_lowercase =np.array(lowerCAmelCase_ )
if isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return {"path": value, "bytes": None}
elif isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
return {"path": None, "bytes": value}
elif isinstance(lowerCAmelCase_ , np.ndarray ):
# convert the image array to PNG/TIFF bytes
return encode_np_array(lowerCAmelCase_ )
elif isinstance(lowerCAmelCase_ , PIL.Image.Image ):
# convert the PIL image to bytes (default format is PNG/TIFF)
return encode_pil_image(lowerCAmelCase_ )
elif value.get("path" ) is not None and os.path.isfile(value["path"] ):
# we set "bytes": None to not duplicate the data if they're already available locally
return {"bytes": None, "path": value.get("path" )}
elif value.get("bytes" ) is not None or value.get("path" ) is not None:
# store the image bytes, and path is used to infer the image format using the file extension
return {"bytes": value.get("bytes" ), "path": value.get("path" )}
else:
raise ValueError(
F'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.''' )
def __lowerCAmelCase ( self , lowerCAmelCase_ , lowerCAmelCase_=None ):
if not self.decode:
raise RuntimeError("Decoding is disabled for this feature. Please use Image(decode=True) instead." )
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support decoding images, please install 'Pillow'." )
if token_per_repo_id is None:
_lowercase ={}
_lowercase , _lowercase =value["path"], value["bytes"]
if bytes_ is None:
if path is None:
raise ValueError(F'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.''' )
else:
if is_local_path(lowerCAmelCase_ ):
_lowercase =PIL.Image.open(lowerCAmelCase_ )
else:
_lowercase =path.split("::" )[-1]
try:
_lowercase =string_to_dict(lowerCAmelCase_ , config.HUB_DATASETS_URL )["repo_id"]
_lowercase =token_per_repo_id.get(lowerCAmelCase_ )
except ValueError:
_lowercase =None
with xopen(lowerCAmelCase_ , "rb" , use_auth_token=lowerCAmelCase_ ) as f:
_lowercase =BytesIO(f.read() )
_lowercase =PIL.Image.open(bytes_ )
else:
_lowercase =PIL.Image.open(BytesIO(bytes_ ) )
image.load() # to avoid "Too many open files" errors
return image
def __lowerCAmelCase ( self ):
from .features import Value
return (
self
if self.decode
else {
"bytes": Value("binary" ),
"path": Value("string" ),
}
)
def __lowerCAmelCase ( self , lowerCAmelCase_ ):
if pa.types.is_string(storage.type ):
_lowercase =pa.array([None] * len(lowerCAmelCase_ ) , type=pa.binary() )
_lowercase =pa.StructArray.from_arrays([bytes_array, storage] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_binary(storage.type ):
_lowercase =pa.array([None] * len(lowerCAmelCase_ ) , type=pa.string() )
_lowercase =pa.StructArray.from_arrays([storage, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_struct(storage.type ):
if storage.type.get_field_index("bytes" ) >= 0:
_lowercase =storage.field("bytes" )
else:
_lowercase =pa.array([None] * len(lowerCAmelCase_ ) , type=pa.binary() )
if storage.type.get_field_index("path" ) >= 0:
_lowercase =storage.field("path" )
else:
_lowercase =pa.array([None] * len(lowerCAmelCase_ ) , type=pa.string() )
_lowercase =pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=storage.is_null() )
elif pa.types.is_list(storage.type ):
_lowercase =pa.array(
[encode_np_array(np.array(lowerCAmelCase_ ) )["bytes"] if arr is not None else None for arr in storage.to_pylist()] , type=pa.binary() , )
_lowercase =pa.array([None] * len(lowerCAmelCase_ ) , type=pa.string() )
_lowercase =pa.StructArray.from_arrays(
[bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(lowerCAmelCase_ , self.pa_type )
def __lowerCAmelCase ( self , lowerCAmelCase_ ):
@no_op_if_value_is_null
def path_to_bytes(lowerCAmelCase_ ):
with xopen(lowerCAmelCase_ , "rb" ) as f:
_lowercase =f.read()
return bytes_
_lowercase =pa.array(
[
(path_to_bytes(x["path"] ) if x["bytes"] is None else x["bytes"]) if x is not None else None
for x in storage.to_pylist()
] , type=pa.binary() , )
_lowercase =pa.array(
[os.path.basename(lowerCAmelCase_ ) if path is not None else None for path in storage.field("path" ).to_pylist()] , type=pa.string() , )
_lowercase =pa.StructArray.from_arrays([bytes_array, path_array] , ["bytes", "path"] , mask=bytes_array.is_null() )
return array_cast(lowerCAmelCase_ , self.pa_type )
def __lowerCamelCase ( ) -> List[str]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
global _IMAGE_COMPRESSION_FORMATS
if _IMAGE_COMPRESSION_FORMATS is None:
PIL.Image.init()
_lowercase =list(set(PIL.Image.OPEN.keys() ) & set(PIL.Image.SAVE.keys() ) )
return _IMAGE_COMPRESSION_FORMATS
def __lowerCamelCase ( __a : "PIL.Image.Image" ) -> bytes:
_lowercase =BytesIO()
if image.format in list_image_compression_formats():
_lowercase =image.format
else:
_lowercase ="PNG" if image.mode in ["1", "L", "LA", "RGB", "RGBA"] else "TIFF"
image.save(__a , format=__a )
return buffer.getvalue()
def __lowerCamelCase ( __a : "PIL.Image.Image" ) -> dict:
if hasattr(__a , "filename" ) and image.filename != "":
return {"path": image.filename, "bytes": None}
else:
return {"path": None, "bytes": image_to_bytes(__a )}
def __lowerCamelCase ( __a : np.ndarray ) -> dict:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
_lowercase =array.dtype
_lowercase =dtype.byteorder if dtype.byteorder != "=" else _NATIVE_BYTEORDER
_lowercase =dtype.kind
_lowercase =dtype.itemsize
_lowercase =None
# Multi-channel array case (only np.dtype("|u1") is allowed)
if array.shape[2:]:
_lowercase =np.dtype("|u1" )
if dtype_kind not in ["u", "i"]:
raise TypeError(
f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.''' )
if dtype is not dest_dtype:
warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
# Exact match
elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
_lowercase =dtype
else: # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
while dtype_itemsize >= 1:
_lowercase =dtype_byteorder + dtype_kind + str(__a )
_lowercase =np.dtype(__a )
if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\'''' )
break
else:
dtype_itemsize //= 2
if dest_dtype is None:
raise TypeError(
f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}''' )
_lowercase =PIL.Image.fromarray(array.astype(__a ) )
return {"path": None, "bytes": image_to_bytes(__a )}
def __lowerCamelCase ( __a : Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]] ) -> List[dict]:
if config.PIL_AVAILABLE:
import PIL.Image
else:
raise ImportError("To support encoding images, please install 'Pillow'." )
if objs:
_lowercase , _lowercase =first_non_null_value(__a )
if isinstance(__a , __a ):
return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
if isinstance(__a , np.ndarray ):
_lowercase =no_op_if_value_is_null(__a )
return [obj_to_image_dict_func(__a ) for obj in objs]
elif isinstance(__a , PIL.Image.Image ):
_lowercase =no_op_if_value_is_null(__a )
return [obj_to_image_dict_func(__a ) for obj in objs]
else:
return objs
else:
return objs
| 594 | 0 |
import argparse
import torch
from transformers import (
UniSpeechSatConfig,
UniSpeechSatForAudioFrameClassification,
UniSpeechSatForSequenceClassification,
UniSpeechSatForXVector,
WavaVecaFeatureExtractor,
logging,
)
logging.set_verbosity_info()
__lowerCamelCase : List[str] = logging.get_logger(__name__)
def _snake_case ( lowerCAmelCase : Optional[Any] , lowerCAmelCase : Tuple , lowerCAmelCase : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = UniSpeechSatForSequenceClassification.from_pretrained(lowerCAmelCase , config=lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Any = downstream_dict["projector.weight"]
SCREAMING_SNAKE_CASE_ : Optional[Any] = downstream_dict["projector.bias"]
SCREAMING_SNAKE_CASE_ : str = downstream_dict["model.post_net.linear.weight"]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = downstream_dict["model.post_net.linear.bias"]
return model
def _snake_case ( lowerCAmelCase : Tuple , lowerCAmelCase : Any , lowerCAmelCase : List[Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Union[str, Any] = UniSpeechSatForAudioFrameClassification.from_pretrained(lowerCAmelCase , config=lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Optional[Any] = downstream_dict["model.linear.weight"]
SCREAMING_SNAKE_CASE_ : Optional[int] = downstream_dict["model.linear.bias"]
return model
def _snake_case ( lowerCAmelCase : Tuple , lowerCAmelCase : Optional[int] , lowerCAmelCase : Dict ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = UniSpeechSatForXVector.from_pretrained(lowerCAmelCase , config=lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Union[str, Any] = downstream_dict["connector.weight"]
SCREAMING_SNAKE_CASE_ : Tuple = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
SCREAMING_SNAKE_CASE_ : str = downstream_dict[
f'model.framelevel_feature_extractor.module.{i}.kernel.weight'
]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = downstream_dict[f'model.framelevel_feature_extractor.module.{i}.kernel.bias']
SCREAMING_SNAKE_CASE_ : Dict = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
SCREAMING_SNAKE_CASE_ : Any = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
SCREAMING_SNAKE_CASE_ : int = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
SCREAMING_SNAKE_CASE_ : Any = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
SCREAMING_SNAKE_CASE_ : Optional[int] = downstream_dict["objective.W"]
return model
@torch.no_grad()
def _snake_case ( lowerCAmelCase : List[str] , lowerCAmelCase : Any , lowerCAmelCase : int , lowerCAmelCase : List[str] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = torch.load(lowerCAmelCase , map_location="cpu" )
SCREAMING_SNAKE_CASE_ : str = checkpoint["Downstream"]
SCREAMING_SNAKE_CASE_ : Optional[int] = UniSpeechSatConfig.from_pretrained(lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : Dict = WavaVecaFeatureExtractor.from_pretrained(
lowerCAmelCase , return_attention_mask=lowerCAmelCase , do_normalize=lowerCAmelCase )
SCREAMING_SNAKE_CASE_ : int = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
SCREAMING_SNAKE_CASE_ : Optional[int] = convert_classification(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
elif arch.endswith("ForAudioFrameClassification" ):
SCREAMING_SNAKE_CASE_ : Any = convert_diarization(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
elif arch.endswith("ForXVector" ):
SCREAMING_SNAKE_CASE_ : int = convert_xvector(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
else:
raise NotImplementedError(f'S3PRL weights conversion is not supported for {arch}' )
if hf_config.use_weighted_layer_sum:
SCREAMING_SNAKE_CASE_ : int = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(lowerCAmelCase )
hf_model.save_pretrained(lowerCAmelCase )
if __name__ == "__main__":
__lowerCamelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
'''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
)
parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
__lowerCamelCase : Optional[int] = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 216 | from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
__lowerCamelCase : Any = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : str = ['''DPTFeatureExtractor''']
__lowerCamelCase : List[Any] = ['''DPTImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : int = [
'''DPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''DPTForDepthEstimation''',
'''DPTForSemanticSegmentation''',
'''DPTModel''',
'''DPTPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_dpt import DPT_PRETRAINED_CONFIG_ARCHIVE_MAP, DPTConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_dpt import DPTFeatureExtractor
from .image_processing_dpt import DPTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dpt import (
DPT_PRETRAINED_MODEL_ARCHIVE_LIST,
DPTForDepthEstimation,
DPTForSemanticSegmentation,
DPTModel,
DPTPreTrainedModel,
)
else:
import sys
__lowerCamelCase : List[Any] = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 216 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ : Union[str, Any] = {
'configuration_llama': ['LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LlamaConfig'],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : int = ['LlamaTokenizer']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : str = ['LlamaTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ : Optional[Any] = [
'LlamaForCausalLM',
'LlamaModel',
'LlamaPreTrainedModel',
'LlamaForSequenceClassification',
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
lowerCAmelCase__ : str = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 632 | """simple docstring"""
lowerCAmelCase__ : Tuple = range(2, 20 + 1)
lowerCAmelCase__ : Optional[Any] = [10**k for k in range(ks[-1] + 1)]
lowerCAmelCase__ : dict[int, dict[int, list[list[int]]]] = {}
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = sum(a_i[j] for j in range(lowerCamelCase , len(lowerCamelCase ) ) )
UpperCAmelCase__ = sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase ) , lowerCamelCase ) ) )
UpperCAmelCase__ , UpperCAmelCase__ = 0, 0
UpperCAmelCase__ = n - i
UpperCAmelCase__ = memo.get(lowerCamelCase )
if sub_memo is not None:
UpperCAmelCase__ = sub_memo.get(lowerCamelCase )
if jumps is not None and len(lowerCamelCase ) > 0:
# find and make the largest jump without going over
UpperCAmelCase__ = -1
for _k in range(len(lowerCamelCase ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
UpperCAmelCase__ = _k
break
if max_jump >= 0:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = jumps[max_jump]
# since the difference between jumps is cached, add c
UpperCAmelCase__ = diff + c
for j in range(min(lowerCamelCase , len(lowerCamelCase ) ) ):
UpperCAmelCase__ , UpperCAmelCase__ = divmod(lowerCamelCase , 1_0 )
if new_c > 0:
add(lowerCamelCase , lowerCamelCase , lowerCamelCase )
else:
UpperCAmelCase__ = []
else:
UpperCAmelCase__ = {c: []}
UpperCAmelCase__ = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
UpperCAmelCase__ , UpperCAmelCase__ = next_term(lowerCamelCase , k - 1 , i + dn , lowerCamelCase )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
UpperCAmelCase__ , UpperCAmelCase__ = compute(lowerCamelCase , lowerCamelCase , i + dn , lowerCamelCase )
diff += _diff
dn += terms_jumped
UpperCAmelCase__ = sub_memo[c]
# keep jumps sorted by # of terms skipped
UpperCAmelCase__ = 0
while j < len(lowerCamelCase ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(lowerCamelCase , (diff, dn, k) )
return (diff, dn)
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
if i >= n:
return 0, i
if k > len(lowerCamelCase ):
a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
UpperCAmelCase__ = i
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ = 0, 0, 0
for j in range(len(lowerCamelCase ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
UpperCAmelCase__ = ds_c + ds_b
diff += addend
UpperCAmelCase__ = 0
for j in range(lowerCamelCase ):
UpperCAmelCase__ = a_i[j] + addend
UpperCAmelCase__ , UpperCAmelCase__ = divmod(lowerCamelCase , 1_0 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return diff, i - start_i
def a_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
for j in range(lowerCamelCase , len(lowerCamelCase ) ):
UpperCAmelCase__ = digits[j] + addend
if s >= 1_0:
UpperCAmelCase__ , UpperCAmelCase__ = divmod(lowerCamelCase , 1_0 )
UpperCAmelCase__ = addend // 1_0 + quotient
else:
UpperCAmelCase__ = s
UpperCAmelCase__ = addend // 1_0
if addend == 0:
break
while addend > 0:
UpperCAmelCase__ , UpperCAmelCase__ = divmod(lowerCamelCase , 1_0 )
digits.append(lowerCamelCase )
def a_ ( lowerCamelCase = 1_0**1_5 ):
UpperCAmelCase__ = [1]
UpperCAmelCase__ = 1
UpperCAmelCase__ = 0
while True:
UpperCAmelCase__ , UpperCAmelCase__ = next_term(lowerCamelCase , 2_0 , i + dn , lowerCamelCase )
dn += terms_jumped
if dn == n - i:
break
UpperCAmelCase__ = 0
for j in range(len(lowerCamelCase ) ):
a_n += digits[j] * 1_0**j
return a_n
if __name__ == "__main__":
print(F"""{solution() = }""")
| 632 | 1 |
"""simple docstring"""
__magic_name__ : Optional[Any] = {}
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
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
UpperCamelCase : List[Any] = (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
UpperCamelCase : int = _calculate(days - 1 , lowercase__ , late + 1 )
# 2) if we are absent, the "absent" counter increases by 1, and the
# "late" counter resets to 0
UpperCamelCase : Optional[int] = _calculate(days - 1 , absent + 1 , 0 )
# 3) if we are on time, this resets the "late" counter and keeps the
# absent counter
UpperCamelCase : Any = _calculate(days - 1 , lowercase__ , 0 )
UpperCamelCase : int = state_late + state_absent + state_ontime
UpperCamelCase : str = prizestrings
return prizestrings
def UpperCamelCase (SCREAMING_SNAKE_CASE = 30 ):
return _calculate(lowercase__ , absent=0 , late=0 )
if __name__ == "__main__":
print(solution())
| 102 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"""microsoft/beit-base-patch16-224-pt22k""": (
"""https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json"""
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class __a ( __magic_name__ ):
"""simple docstring"""
__UpperCamelCase : Union[str, Any] = 'beit'
def __init__( self , snake_case=8_192 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3_072 , snake_case="gelu" , snake_case=0.0 , snake_case=0.0 , snake_case=0.02 , snake_case=1e-12 , snake_case=224 , snake_case=16 , snake_case=3 , snake_case=False , snake_case=False , snake_case=False , snake_case=False , snake_case=0.1 , snake_case=0.1 , snake_case=True , snake_case=[3, 5, 7, 11] , snake_case=[1, 2, 3, 6] , snake_case=True , snake_case=0.4 , snake_case=256 , snake_case=1 , snake_case=False , snake_case=255 , **snake_case , ):
"""simple docstring"""
super().__init__(**snake_case )
lowerCAmelCase__ : Union[str, Any] = vocab_size
lowerCAmelCase__ : int = hidden_size
lowerCAmelCase__ : str = num_hidden_layers
lowerCAmelCase__ : Any = num_attention_heads
lowerCAmelCase__ : List[str] = intermediate_size
lowerCAmelCase__ : int = hidden_act
lowerCAmelCase__ : Optional[Any] = hidden_dropout_prob
lowerCAmelCase__ : str = attention_probs_dropout_prob
lowerCAmelCase__ : Optional[int] = initializer_range
lowerCAmelCase__ : Dict = layer_norm_eps
lowerCAmelCase__ : int = image_size
lowerCAmelCase__ : Union[str, Any] = patch_size
lowerCAmelCase__ : Dict = num_channels
lowerCAmelCase__ : Optional[Any] = use_mask_token
lowerCAmelCase__ : Dict = use_absolute_position_embeddings
lowerCAmelCase__ : Any = use_relative_position_bias
lowerCAmelCase__ : List[Any] = use_shared_relative_position_bias
lowerCAmelCase__ : Dict = layer_scale_init_value
lowerCAmelCase__ : Optional[int] = drop_path_rate
lowerCAmelCase__ : Optional[Any] = use_mean_pooling
# decode head attributes (semantic segmentation)
lowerCAmelCase__ : Optional[int] = out_indices
lowerCAmelCase__ : List[Any] = pool_scales
# auxiliary head attributes (semantic segmentation)
lowerCAmelCase__ : List[Any] = use_auxiliary_head
lowerCAmelCase__ : Optional[int] = auxiliary_loss_weight
lowerCAmelCase__ : List[str] = auxiliary_channels
lowerCAmelCase__ : Optional[Any] = auxiliary_num_convs
lowerCAmelCase__ : Union[str, Any] = auxiliary_concat_input
lowerCAmelCase__ : List[str] = semantic_loss_ignore_index
class __a ( __magic_name__ ):
"""simple docstring"""
__UpperCamelCase : List[str] = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
] )
@property
def SCREAMING_SNAKE_CASE_ ( self ):
"""simple docstring"""
return 1e-4
| 453 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__ : Optional[int] = {
'configuration_perceiver': ['PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'PerceiverConfig', 'PerceiverOnnxConfig'],
'tokenization_perceiver': ['PerceiverTokenizer'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : List[Any] = ['PerceiverFeatureExtractor']
A__ : Optional[int] = ['PerceiverImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : List[str] = [
'PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST',
'PerceiverForImageClassificationConvProcessing',
'PerceiverForImageClassificationFourier',
'PerceiverForImageClassificationLearned',
'PerceiverForMaskedLM',
'PerceiverForMultimodalAutoencoding',
'PerceiverForOpticalFlow',
'PerceiverForSequenceClassification',
'PerceiverLayer',
'PerceiverModel',
'PerceiverPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_perceiver import PERCEIVER_PRETRAINED_CONFIG_ARCHIVE_MAP, PerceiverConfig, PerceiverOnnxConfig
from .tokenization_perceiver import PerceiverTokenizer
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_perceiver import PerceiverFeatureExtractor
from .image_processing_perceiver import PerceiverImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_perceiver import (
PERCEIVER_PRETRAINED_MODEL_ARCHIVE_LIST,
PerceiverForImageClassificationConvProcessing,
PerceiverForImageClassificationFourier,
PerceiverForImageClassificationLearned,
PerceiverForMaskedLM,
PerceiverForMultimodalAutoencoding,
PerceiverForOpticalFlow,
PerceiverForSequenceClassification,
PerceiverLayer,
PerceiverModel,
PerceiverPreTrainedModel,
)
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 671 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class _UpperCAmelCase :
"""simple docstring"""
lowercase__ = 42
lowercase__ = 42
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : str, lowerCamelCase : int ):
'''simple docstring'''
lowercase__ = [[] for _ in range(lowerCamelCase )]
lowercase__ = size
def __getitem__( self : Optional[Any], lowerCamelCase : int ):
'''simple docstring'''
return iter(self._graph[vertex] )
@property
def lowercase__ ( self : str ):
'''simple docstring'''
return self._size
def lowercase__ ( self : Union[str, Any], lowerCamelCase : int, lowerCamelCase : int, lowerCamelCase : int ):
'''simple docstring'''
if weight not in (0, 1):
raise ValueError('''Edge weight must be either 0 or 1.''' )
if to_vertex < 0 or to_vertex >= self.size:
raise ValueError('''Vertex indexes must be in [0; size).''' )
self._graph[from_vertex].append(Edge(lowerCamelCase, lowerCamelCase ) )
def lowercase__ ( self : Optional[int], lowerCamelCase : int, lowerCamelCase : int ):
'''simple docstring'''
lowercase__ = deque([start_vertex] )
lowercase__ = [None] * self.size
lowercase__ = 0
while queue:
lowercase__ = queue.popleft()
lowercase__ = distances[current_vertex]
if current_distance is None:
continue
for edge in self[current_vertex]:
lowercase__ = current_distance + edge.weight
lowercase__ = distances[edge.destination_vertex]
if (
isinstance(lowerCamelCase, lowerCamelCase )
and new_distance >= dest_vertex_distance
):
continue
lowercase__ = new_distance
if edge.weight == 0:
queue.appendleft(edge.destination_vertex )
else:
queue.append(edge.destination_vertex )
if distances[finish_vertex] is None:
raise ValueError('''No path from start_vertex to finish_vertex.''' )
return distances[finish_vertex]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 671 | 1 |
"""simple docstring"""
import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
A = logging.get_logger(__name__)
A = {
"""tensor(bool)""": np.bool_,
"""tensor(int8)""": np.inta,
"""tensor(uint8)""": np.uinta,
"""tensor(int16)""": np.intaa,
"""tensor(uint16)""": np.uintaa,
"""tensor(int32)""": np.intaa,
"""tensor(uint32)""": np.uintaa,
"""tensor(int64)""": np.intaa,
"""tensor(uint64)""": np.uintaa,
"""tensor(float16)""": np.floataa,
"""tensor(float)""": np.floataa,
"""tensor(double)""": np.floataa,
}
class a__ :
def __init__( self : Dict , UpperCamelCase_ : Union[str, Any]=None , **UpperCamelCase_ : Optional[Any]):
"""simple docstring"""
logger.info("`diffusers.OnnxRuntimeModel` is experimental and might change in the future.")
__UpperCAmelCase : List[Any] = model
__UpperCAmelCase : Optional[Any] = kwargs.get("model_save_dir" , UpperCamelCase_)
__UpperCAmelCase : Optional[int] = kwargs.get("latest_model_name" , UpperCamelCase_)
def __call__( self : Optional[int] , **UpperCamelCase_ : Any):
"""simple docstring"""
__UpperCAmelCase : List[str] = {k: np.array(UpperCamelCase_) for k, v in kwargs.items()}
return self.model.run(UpperCamelCase_ , UpperCamelCase_)
@staticmethod
def a_ ( UpperCamelCase_ : Union[str, Path] , UpperCamelCase_ : str=None , UpperCamelCase_ : str=None):
"""simple docstring"""
if provider is None:
logger.info("No onnxruntime provider specified, using CPUExecutionProvider")
__UpperCAmelCase : Any = "CPUExecutionProvider"
return ort.InferenceSession(UpperCamelCase_ , providers=[provider] , sess_options=UpperCamelCase_)
def a_ ( self : Dict , UpperCamelCase_ : Union[str, Path] , UpperCamelCase_ : Optional[str] = None , **UpperCamelCase_ : int):
"""simple docstring"""
__UpperCAmelCase : List[Any] = file_name if file_name is not None else ONNX_WEIGHTS_NAME
__UpperCAmelCase : Dict = self.model_save_dir.joinpath(self.latest_model_name)
__UpperCAmelCase : List[str] = Path(UpperCamelCase_).joinpath(UpperCamelCase_)
try:
shutil.copyfile(UpperCamelCase_ , UpperCamelCase_)
except shutil.SameFileError:
pass
# copy external weights (for models >2GB)
__UpperCAmelCase : Tuple = self.model_save_dir.joinpath(UpperCamelCase_)
if src_path.exists():
__UpperCAmelCase : int = Path(UpperCamelCase_).joinpath(UpperCamelCase_)
try:
shutil.copyfile(UpperCamelCase_ , UpperCamelCase_)
except shutil.SameFileError:
pass
def a_ ( self : Tuple , UpperCamelCase_ : Union[str, os.PathLike] , **UpperCamelCase_ : List[str] , ):
"""simple docstring"""
if os.path.isfile(UpperCamelCase_):
logger.error(F"Provided path ({save_directory}) should be a directory, not a file")
return
os.makedirs(UpperCamelCase_ , exist_ok=UpperCamelCase_)
# saving model weights/files
self._save_pretrained(UpperCamelCase_ , **UpperCamelCase_)
@classmethod
def a_ ( cls : Tuple , UpperCamelCase_ : Union[str, Path] , UpperCamelCase_ : Optional[Union[bool, str, None]] = None , UpperCamelCase_ : Optional[Union[str, None]] = None , UpperCamelCase_ : bool = False , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional["ort.SessionOptions"] = None , **UpperCamelCase_ : List[Any] , ):
"""simple docstring"""
__UpperCAmelCase : List[str] = file_name if file_name is not None else ONNX_WEIGHTS_NAME
# load model from local directory
if os.path.isdir(UpperCamelCase_):
__UpperCAmelCase : int = OnnxRuntimeModel.load_model(
os.path.join(UpperCamelCase_ , UpperCamelCase_) , provider=UpperCamelCase_ , sess_options=UpperCamelCase_)
__UpperCAmelCase : Dict = Path(UpperCamelCase_)
# load model from hub
else:
# download model
__UpperCAmelCase : Any = hf_hub_download(
repo_id=UpperCamelCase_ , filename=UpperCamelCase_ , use_auth_token=UpperCamelCase_ , revision=UpperCamelCase_ , cache_dir=UpperCamelCase_ , force_download=UpperCamelCase_ , )
__UpperCAmelCase : Optional[int] = Path(UpperCamelCase_).parent
__UpperCAmelCase : List[str] = Path(UpperCamelCase_).name
__UpperCAmelCase : Union[str, Any] = OnnxRuntimeModel.load_model(UpperCamelCase_ , provider=UpperCamelCase_ , sess_options=UpperCamelCase_)
return cls(model=UpperCamelCase_ , **UpperCamelCase_)
@classmethod
def a_ ( cls : List[Any] , UpperCamelCase_ : Union[str, Path] , UpperCamelCase_ : bool = True , UpperCamelCase_ : Optional[str] = None , UpperCamelCase_ : Optional[str] = None , **UpperCamelCase_ : Optional[int] , ):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = None
if len(str(UpperCamelCase_).split("@")) == 2:
__UpperCAmelCase , __UpperCAmelCase : Any = model_id.split("@")
return cls._from_pretrained(
model_id=UpperCamelCase_ , revision=UpperCamelCase_ , cache_dir=UpperCamelCase_ , force_download=UpperCamelCase_ , use_auth_token=UpperCamelCase_ , **UpperCamelCase_ , )
| 77 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self , lowerCamelCase__ , lowerCamelCase__=1_3 , lowerCamelCase__=7 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=9_9 , lowerCamelCase__=3_2 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=3_7 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=5_1_2 , lowerCamelCase__=1_6 , lowerCamelCase__=2 , lowerCamelCase__=0.02 , lowerCamelCase__=4 , ):
'''simple docstring'''
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 ):
'''simple docstring'''
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 = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCamelCase__ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def UpperCAmelCase ( self ):
'''simple docstring'''
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
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = self.prepare_config_and_inputs()
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = config_and_inputs
UpperCamelCase = True
UpperCamelCase = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class lowercase__ ( snake_case_, unittest.TestCase ):
'''simple docstring'''
_snake_case = True
_snake_case = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def UpperCAmelCase ( self ):
'''simple docstring'''
UpperCamelCase = FlaxRobertaModelTester(self )
@slow
def UpperCAmelCase ( self ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
UpperCamelCase = model_class_name.from_pretrained('''roberta-base''' , from_pt=lowerCamelCase__ )
UpperCamelCase = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
| 212 | 0 |
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
return " ".join(
"""""".join(word[::-1] ) if len(lowerCAmelCase ) > 4 else word for word in sentence.split() )
if __name__ == "__main__":
import doctest
doctest.testmod()
print(reverse_long_words('''Hey wollef sroirraw'''))
| 378 |
"""simple docstring"""
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class UpperCamelCase_ :
def __init__( self , snake_case__ , snake_case__=13 , snake_case__=2 , snake_case__=24 , snake_case__=16 , snake_case__=True , snake_case__=True , snake_case__=32 , snake_case__=5 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=10 , snake_case__=0.02 , snake_case__=None , snake_case__=2 , snake_case__=2 , ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = patch_size
UpperCAmelCase = max_length
UpperCAmelCase = num_mel_bins
UpperCAmelCase = is_training
UpperCAmelCase = use_labels
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = type_sequence_label_size
UpperCAmelCase = initializer_range
UpperCAmelCase = scope
UpperCAmelCase = frequency_stride
UpperCAmelCase = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
UpperCAmelCase = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
UpperCAmelCase = (self.max_length - self.patch_size) // self.time_stride + 1
UpperCAmelCase = frequency_out_dimension * time_out_dimension
UpperCAmelCase = num_patches + 2
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
UpperCAmelCase = None
if self.use_labels:
UpperCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCAmelCase = self.get_config()
return config, input_values, labels
def UpperCamelCase_ ( self ) -> int:
"""simple docstring"""
return ASTConfig(
patch_size=self.patch_size , max_length=self.max_length , num_mel_bins=self.num_mel_bins , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=snake_case__ , initializer_range=self.initializer_range , frequency_stride=self.frequency_stride , time_stride=self.time_stride , )
def UpperCamelCase_ ( self , snake_case__ , snake_case__ , snake_case__ ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = ASTModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
UpperCAmelCase = model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase_ ( self ) -> Optional[Any]:
"""simple docstring"""
UpperCAmelCase = self.prepare_config_and_inputs()
(
(
UpperCAmelCase
) , (
UpperCAmelCase
) , (
UpperCAmelCase
) ,
) = config_and_inputs
UpperCAmelCase = {"""input_values""": input_values}
return config, inputs_dict
@require_torch
class UpperCamelCase_ ( a_ , a_ , unittest.TestCase ):
_A : Any = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
_A : Union[str, Any] = (
{'audio-classification': ASTForAudioClassification, 'feature-extraction': ASTModel}
if is_torch_available()
else {}
)
_A : Any = False
_A : Dict = False
_A : Optional[Any] = False
_A : List[str] = False
def UpperCamelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ) -> Dict:
"""simple docstring"""
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def UpperCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = ASTModelTester(self )
UpperCAmelCase = ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ , hidden_size=37 )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""AST does not use inputs_embeds""" )
def UpperCamelCase_ ( self ) -> Tuple:
"""simple docstring"""
pass
def UpperCamelCase_ ( self ) -> Dict:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(snake_case__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
UpperCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case__ , nn.Linear ) )
def UpperCamelCase_ ( self ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase , UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase = model_class(snake_case__ )
UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase = [*signature.parameters.keys()]
UpperCAmelCase = ["""input_values"""]
self.assertListEqual(arg_names[:1] , snake_case__ )
def UpperCamelCase_ ( self ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
@slow
def UpperCamelCase_ ( self ) -> Union[str, Any]:
"""simple docstring"""
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase = ASTModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase = hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" )
UpperCAmelCase , UpperCAmelCase = torchaudio.load(lowerCAmelCase )
return audio, sampling_rate
@require_torch
@require_torchaudio
class UpperCamelCase_ ( unittest.TestCase ):
@cached_property
def UpperCamelCase_ ( self ) -> int:
"""simple docstring"""
return (
ASTFeatureExtractor.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" )
if is_torchaudio_available()
else None
)
@slow
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = self.default_feature_extractor
UpperCAmelCase = ASTForAudioClassification.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" ).to(snake_case__ )
UpperCAmelCase = self.default_feature_extractor
UpperCAmelCase , UpperCAmelCase = prepare_audio()
UpperCAmelCase = audio.squeeze().numpy()
UpperCAmelCase = feature_extractor(snake_case__ , sampling_rate=snake_case__ , return_tensors="""pt""" ).to(snake_case__ )
# forward pass
with torch.no_grad():
UpperCAmelCase = model(**snake_case__ )
# verify the logits
UpperCAmelCase = torch.Size((1, 5_27) )
self.assertEqual(outputs.logits.shape , snake_case__ )
UpperCAmelCase = torch.tensor([-0.8_760, -7.0_042, -8.6_602] ).to(snake_case__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case__ , atol=1e-4 ) )
| 378 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCAmelCase__ ( lowerCAmelCase_ ):
"""simple docstring"""
__UpperCamelCase = ["image_processor", "tokenizer"]
__UpperCamelCase = "CLIPImageProcessor"
__UpperCamelCase = ("CLIPTokenizer", "CLIPTokenizerFast")
def __init__( self : Optional[Any] , A__ : List[str]=None , A__ : Tuple=None , **A__ : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
a__ : Union[str, Any] = 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__ , )
a__ : Optional[int] = kwargs.pop('''feature_extractor''' )
a__ : int = 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 : List[str] , A__ : Tuple=None , A__ : Dict=None , A__ : Optional[int]=None , **A__ : int ) -> str:
'''simple docstring'''
if text is None and images is None:
raise ValueError('''You have to specify either text or images. Both cannot be none.''' )
if text is not None:
a__ : Optional[Any] = self.tokenizer(A__ , return_tensors=A__ , **A__ )
if images is not None:
a__ : List[str] = self.image_processor(A__ , return_tensors=A__ , **A__ )
if text is not None and images is not None:
a__ : Dict = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**A__ ) , tensor_type=A__ )
def __lowerCAmelCase ( self : Dict , *A__ : Optional[int] , **A__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
return self.tokenizer.batch_decode(*A__ , **A__ )
def __lowerCAmelCase ( self : Tuple , *A__ : int , **A__ : str ) -> List[str]:
'''simple docstring'''
return self.tokenizer.decode(*A__ , **A__ )
@property
def __lowerCAmelCase ( self : Any ) -> Union[str, Any]:
'''simple docstring'''
a__ : int = self.tokenizer.model_input_names
a__ : Optional[Any] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def __lowerCAmelCase ( self : str ) -> List[Any]:
'''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 __lowerCAmelCase ( self : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , A__ , )
return self.image_processor
| 688 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / 'utils'))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def __lowerCAmelCase ( self : Optional[int] ) -> int:
'''simple docstring'''
a__ : int = 0
def __lowerCAmelCase ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
a__ : Optional[int] = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(A__ , A__ )
def __lowerCAmelCase ( self : Dict ) -> int:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
a__ : List[Any] = Path(A__ ) / '''preprocessor_config.json'''
a__ : List[Any] = Path(A__ ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(A__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(A__ , '''w''' ) )
a__ : Any = AutoImageProcessor.from_pretrained(A__ )
self.assertIsInstance(A__ , A__ )
def __lowerCAmelCase ( self : str ) -> Union[str, Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
a__ : int = Path(A__ ) / '''preprocessor_config.json'''
a__ : Optional[Any] = Path(A__ ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(A__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(A__ , '''w''' ) )
a__ : Tuple = AutoImageProcessor.from_pretrained(A__ )
self.assertIsInstance(A__ , A__ )
def __lowerCAmelCase ( self : List[Any] ) -> List[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
a__ : Dict = CLIPConfig()
# Create a dummy config file with image_proceesor_type
a__ : int = Path(A__ ) / '''preprocessor_config.json'''
a__ : Optional[int] = Path(A__ ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(A__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(A__ , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
a__ : List[Any] = AutoImageProcessor.from_pretrained(A__ ).to_dict()
config_dict.pop('''image_processor_type''' )
a__ : Union[str, Any] = CLIPImageProcessor(**A__ )
# save in new folder
model_config.save_pretrained(A__ )
config.save_pretrained(A__ )
a__ : Union[str, Any] = AutoImageProcessor.from_pretrained(A__ )
# make sure private variable is not incorrectly saved
a__ : Optional[Any] = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(A__ , A__ )
def __lowerCAmelCase ( self : str ) -> List[Any]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdirname:
a__ : Optional[int] = Path(A__ ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(A__ , '''w''' ) , )
a__ : Any = AutoImageProcessor.from_pretrained(A__ )
self.assertIsInstance(A__ , A__ )
def __lowerCAmelCase ( self : str ) -> Optional[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
A__ , '''clip-base is not a local folder and is not a valid model identifier''' ):
a__ : str = AutoImageProcessor.from_pretrained('''clip-base''' )
def __lowerCAmelCase ( self : Optional[Any] ) -> int:
'''simple docstring'''
with self.assertRaisesRegex(
A__ , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
a__ : Tuple = AutoImageProcessor.from_pretrained(A__ , revision='''aaaaaa''' )
def __lowerCAmelCase ( self : str ) -> List[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
A__ , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
a__ : Union[str, Any] = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def __lowerCAmelCase ( self : List[Any] ) -> Tuple:
'''simple docstring'''
with self.assertRaises(A__ ):
a__ : str = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(A__ ):
a__ : Tuple = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=A__ )
a__ : Tuple = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=A__ )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(A__ )
a__ : str = AutoImageProcessor.from_pretrained(A__ , trust_remote_code=A__ )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def __lowerCAmelCase ( self : List[Any] ) -> Dict:
'''simple docstring'''
try:
AutoConfig.register('''custom''' , A__ )
AutoImageProcessor.register(A__ , A__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(A__ ):
AutoImageProcessor.register(A__ , A__ )
with tempfile.TemporaryDirectory() as tmpdirname:
a__ : Optional[int] = Path(A__ ) / '''preprocessor_config.json'''
a__ : List[str] = Path(A__ ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(A__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(A__ , '''w''' ) )
a__ : Tuple = CustomImageProcessor.from_pretrained(A__ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(A__ )
a__ : Tuple = AutoImageProcessor.from_pretrained(A__ )
self.assertIsInstance(A__ , A__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def __lowerCAmelCase ( self : List[Any] ) -> List[str]:
'''simple docstring'''
class lowerCAmelCase__ ( lowerCAmelCase_ ):
"""simple docstring"""
__UpperCamelCase = True
try:
AutoConfig.register('''custom''' , A__ )
AutoImageProcessor.register(A__ , A__ )
# If remote code is not set, the default is to use local
a__ : Dict = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
a__ : Optional[Any] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=A__ )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
a__ : Optional[int] = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=A__ )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(A__ , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 688 | 1 |
'''simple docstring'''
from math import isqrt
def _UpperCAmelCase ( __A : int ):
a_ : Dict = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , __A , __A ):
a_ : str = False
return [i for i in range(2 , __A ) if is_prime[i]]
def _UpperCAmelCase ( __A : int = 10**8 ):
a_ : Tuple = calculate_prime_numbers(max_number // 2 )
a_ : Union[str, Any] = 0
a_ : Any = 0
a_ : Optional[Any] = len(__A ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(F"""{solution() = }""")
| 666 |
'''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
snake_case__ = LongformerTokenizer
snake_case__ = True
snake_case__ = LongformerTokenizerFast
snake_case__ = True
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[str]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
a_ : Tuple = [
'''l''',
'''o''',
'''w''',
'''e''',
'''r''',
'''s''',
'''t''',
'''i''',
'''d''',
'''n''',
'''\u0120''',
'''\u0120l''',
'''\u0120n''',
'''\u0120lo''',
'''\u0120low''',
'''er''',
'''\u0120lowest''',
'''\u0120newer''',
'''\u0120wider''',
'''<unk>''',
]
a_ : Optional[Any] = dict(zip(__SCREAMING_SNAKE_CASE , range(len(__SCREAMING_SNAKE_CASE ) ) ) )
a_ : Union[str, Any] = ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
a_ : Any = {'''unk_token''': '''<unk>'''}
a_ : Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
a_ : Optional[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(__SCREAMING_SNAKE_CASE ) + '''\n''' )
with open(self.merges_file , '''w''' , encoding='''utf-8''' ) as fp:
fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE ) )
def SCREAMING_SNAKE_CASE ( self : Any , **__SCREAMING_SNAKE_CASE : Any ) -> int:
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] , **__SCREAMING_SNAKE_CASE : List[Any] ) -> List[str]:
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **__SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self : Dict , __SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
a_ : Union[str, Any] = '''lower newer'''
a_ : List[Any] = '''lower newer'''
return input_text, output_text
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[str]:
a_ : Optional[Any] = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
a_ : List[str] = '''lower newer'''
a_ : str = ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
a_ : Optional[int] = tokenizer.tokenize(__SCREAMING_SNAKE_CASE ) # , add_prefix_space=True)
self.assertListEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
a_ : Dict = tokens + [tokenizer.unk_token]
a_ : Any = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[Any]:
a_ : Union[str, Any] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode('''Hello world!''' , add_special_tokens=__SCREAMING_SNAKE_CASE ) , [0, 3_1414, 232, 328, 2] )
self.assertListEqual(
tokenizer.encode('''Hello world! cécé herlolip 418''' , add_special_tokens=__SCREAMING_SNAKE_CASE ) , [0, 3_1414, 232, 328, 740, 1140, 1_2695, 69, 4_6078, 1588, 2] , )
@slow
def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[Any]:
a_ : Dict = self.tokenizer_class.from_pretrained('''allenai/longformer-base-4096''' )
a_ : Tuple = tokenizer.encode('''sequence builders''' , add_special_tokens=__SCREAMING_SNAKE_CASE )
a_ : Union[str, Any] = tokenizer.encode('''multi-sequence build''' , add_special_tokens=__SCREAMING_SNAKE_CASE )
a_ : Any = tokenizer.encode(
'''sequence builders''' , add_special_tokens=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE )
a_ : Any = tokenizer.encode(
'''sequence builders''' , '''multi-sequence build''' , add_special_tokens=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE )
a_ : List[str] = tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE )
a_ : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def SCREAMING_SNAKE_CASE ( self : str ) -> int:
a_ : str = self.get_tokenizer()
a_ : int = '''Encode this sequence.'''
a_ : List[str] = tokenizer.byte_encoder[''' '''.encode('''utf-8''' )[0]]
# Testing encoder arguments
a_ : Dict = tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE )
a_ : Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
a_ : Dict = tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE )
a_ : Any = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
tokenizer.add_special_tokens({'''bos_token''': '''<s>'''} )
a_ : Dict = tokenizer.encode(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
a_ : Dict = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# Testing spaces after special tokens
a_ : Optional[Any] = '''<mask>'''
tokenizer.add_special_tokens(
{'''mask_token''': AddedToken(__SCREAMING_SNAKE_CASE , lstrip=__SCREAMING_SNAKE_CASE , rstrip=__SCREAMING_SNAKE_CASE )} ) # mask token has a left space
a_ : Optional[int] = tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE )
a_ : List[Any] = '''Encode <mask> sequence'''
a_ : List[str] = '''Encode <mask>sequence'''
a_ : int = tokenizer.encode(__SCREAMING_SNAKE_CASE )
a_ : Optional[int] = encoded.index(__SCREAMING_SNAKE_CASE )
a_ : Optional[int] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
a_ : Union[str, Any] = tokenizer.encode(__SCREAMING_SNAKE_CASE )
a_ : Optional[int] = encoded.index(__SCREAMING_SNAKE_CASE )
a_ : str = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Union[str, Any]:
pass
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
a_ : Any = self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
a_ : Any = self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
a_ : str = '''A, <mask> AllenNLP sentence.'''
a_ : List[Any] = tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , return_token_type_ids=__SCREAMING_SNAKE_CASE )
a_ : Dict = 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'''] ) , )
a_ : str = tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids'''] )
a_ : Tuple = 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, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2] )
self.assertSequenceEqual(tokens_r['''input_ids'''] , [0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 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>'''] )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> List[Any]:
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
a_ : Any = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE , trim_offsets=__SCREAMING_SNAKE_CASE )
a_ : Union[str, Any] = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
a_ : str = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state['''add_prefix_space'''] , __SCREAMING_SNAKE_CASE )
self.assertEqual(post_processor_state['''add_prefix_space'''] , __SCREAMING_SNAKE_CASE )
self.assertEqual(post_processor_state['''trim_offsets'''] , __SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self : int ) -> Optional[int]:
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
# `trim_offsets`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
a_ : Dict = '''hello''' # `hello` is a token in the vocabulary of `pretrained_name`
a_ : Union[str, Any] = f'{text_of_1_token} {text_of_1_token}'
a_ : Any = self.rust_tokenizer_class.from_pretrained(
__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE , trim_offsets=__SCREAMING_SNAKE_CASE )
a_ : Optional[int] = tokenizer_r(__SCREAMING_SNAKE_CASE , return_offsets_mapping=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__SCREAMING_SNAKE_CASE )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__SCREAMING_SNAKE_CASE ) + 1, len(__SCREAMING_SNAKE_CASE ) + 1 + len(__SCREAMING_SNAKE_CASE )) , )
a_ : Any = self.rust_tokenizer_class.from_pretrained(
__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE , trim_offsets=__SCREAMING_SNAKE_CASE )
a_ : str = tokenizer_r(__SCREAMING_SNAKE_CASE , return_offsets_mapping=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__SCREAMING_SNAKE_CASE )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__SCREAMING_SNAKE_CASE ) + 1, len(__SCREAMING_SNAKE_CASE ) + 1 + len(__SCREAMING_SNAKE_CASE )) , )
a_ : int = self.rust_tokenizer_class.from_pretrained(
__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE , trim_offsets=__SCREAMING_SNAKE_CASE )
a_ : Union[str, Any] = tokenizer_r(__SCREAMING_SNAKE_CASE , return_offsets_mapping=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__SCREAMING_SNAKE_CASE )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__SCREAMING_SNAKE_CASE ), len(__SCREAMING_SNAKE_CASE ) + 1 + len(__SCREAMING_SNAKE_CASE )) , )
a_ : Tuple = self.rust_tokenizer_class.from_pretrained(
__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE , trim_offsets=__SCREAMING_SNAKE_CASE )
a_ : Any = tokenizer_r(__SCREAMING_SNAKE_CASE , return_offsets_mapping=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertEqual(encoding.offset_mapping[0] , (0, len(__SCREAMING_SNAKE_CASE )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(__SCREAMING_SNAKE_CASE ), len(__SCREAMING_SNAKE_CASE ) + 1 + len(__SCREAMING_SNAKE_CASE )) , )
a_ : Union[str, Any] = f' {text}'
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
a_ : str = self.rust_tokenizer_class.from_pretrained(
__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE , trim_offsets=__SCREAMING_SNAKE_CASE )
a_ : int = tokenizer_r(__SCREAMING_SNAKE_CASE , return_offsets_mapping=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(__SCREAMING_SNAKE_CASE )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__SCREAMING_SNAKE_CASE ) + 1, 1 + len(__SCREAMING_SNAKE_CASE ) + 1 + len(__SCREAMING_SNAKE_CASE )) , )
a_ : int = self.rust_tokenizer_class.from_pretrained(
__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE , trim_offsets=__SCREAMING_SNAKE_CASE )
a_ : str = tokenizer_r(__SCREAMING_SNAKE_CASE , return_offsets_mapping=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__SCREAMING_SNAKE_CASE )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__SCREAMING_SNAKE_CASE ), 1 + len(__SCREAMING_SNAKE_CASE ) + 1 + len(__SCREAMING_SNAKE_CASE )) , )
a_ : Optional[int] = self.rust_tokenizer_class.from_pretrained(
__SCREAMING_SNAKE_CASE , use_fast=__SCREAMING_SNAKE_CASE , add_prefix_space=__SCREAMING_SNAKE_CASE , trim_offsets=__SCREAMING_SNAKE_CASE )
a_ : int = tokenizer_r(__SCREAMING_SNAKE_CASE , return_offsets_mapping=__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(__SCREAMING_SNAKE_CASE )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(__SCREAMING_SNAKE_CASE ), 1 + len(__SCREAMING_SNAKE_CASE ) + 1 + len(__SCREAMING_SNAKE_CASE )) , )
| 666 | 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__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = 42
__SCREAMING_SNAKE_CASE = None
def _lowercase ( __A ,__A=0.999 ,__A="cosine" ,):
'''simple docstring'''
if alpha_transform_type == "cosine":
def alpha_bar_fn(__A ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__A ):
return math.exp(t * -12.0 )
else:
raise ValueError(f"Unsupported alpha_tranform_type: {alpha_transform_type}" )
__UpperCamelCase = []
for i in range(__A ):
__UpperCamelCase = i / num_diffusion_timesteps
__UpperCamelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__A ) / alpha_bar_fn(__A ) ,__A ) )
return torch.tensor(__A ,dtype=torch.floataa )
class UpperCAmelCase__ ( UpperCAmelCase_ , UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = 1
@register_to_config
def __init__( self , lowercase = 1_0_0_0 , lowercase = 0.0_001 , lowercase = 0.02 , lowercase = "linear" , lowercase = None , lowercase = True , lowercase = True , lowercase = 0 , lowercase = "epsilon" , lowercase = 1.0 , **lowercase , ) -> Dict:
if kwargs.get("""set_alpha_to_one""" , lowercase ) is not None:
__UpperCamelCase = (
"""The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
)
deprecate("""set_alpha_to_one""" , """1.0.0""" , lowercase , standard_warn=lowercase )
__UpperCamelCase = kwargs["""set_alpha_to_one"""]
if trained_betas is not None:
__UpperCamelCase = torch.tensor(lowercase , dtype=torch.floataa )
elif beta_schedule == "linear":
__UpperCamelCase = torch.linspace(lowercase , lowercase , lowercase , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
__UpperCamelCase = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , lowercase , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
__UpperCamelCase = betas_for_alpha_bar(lowercase )
else:
raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}" )
__UpperCamelCase = 1.0 - self.betas
__UpperCamelCase = 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.
__UpperCamelCase = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
__UpperCamelCase = 1.0
# setable values
__UpperCamelCase = None
__UpperCamelCase = torch.from_numpy(np.arange(0 , lowercase ).copy().astype(np.intaa ) )
def __lowerCamelCase ( self , lowercase , lowercase = None ) -> torch.FloatTensor:
return sample
def __lowerCamelCase ( self , lowercase , lowercase = None ) -> Optional[int]:
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." )
__UpperCamelCase = num_inference_steps
__UpperCamelCase = 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
__UpperCamelCase = (np.arange(0 , lowercase ) * step_ratio).round().copy().astype(np.intaa )
__UpperCamelCase = torch.from_numpy(lowercase ).to(lowercase )
self.timesteps += self.config.steps_offset
def __lowerCamelCase ( self , lowercase , lowercase , lowercase , lowercase = 0.0 , lowercase = False , lowercase = None , lowercase = True , ) -> Union[DDIMSchedulerOutput, Tuple]:
# 1. get previous step value (=t+1)
__UpperCamelCase = 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
__UpperCamelCase = self.alphas_cumprod[timestep]
__UpperCamelCase = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
__UpperCamelCase = 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":
__UpperCamelCase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
__UpperCamelCase = model_output
elif self.config.prediction_type == "sample":
__UpperCamelCase = model_output
__UpperCamelCase = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
__UpperCamelCase = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
__UpperCamelCase = (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:
__UpperCamelCase = 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
__UpperCamelCase = (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
__UpperCamelCase = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=lowercase , pred_original_sample=lowercase )
def __len__( self ) -> int:
return self.config.num_train_timesteps
| 601 |
'''simple docstring'''
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase__ ( UpperCAmelCase_):
__SCREAMING_SNAKE_CASE = ['''image_processor''', '''tokenizer''']
__SCREAMING_SNAKE_CASE = '''Pix2StructImageProcessor'''
__SCREAMING_SNAKE_CASE = ('''T5Tokenizer''', '''T5TokenizerFast''')
def __init__( self , lowercase , lowercase ) -> Any:
__UpperCamelCase = False
super().__init__(lowercase , lowercase )
def __call__( self , lowercase=None , lowercase = None , lowercase = True , lowercase = False , lowercase = None , lowercase = None , lowercase = 2_0_4_8 , lowercase = 0 , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = True , lowercase = None , **lowercase , ) -> BatchEncoding:
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None and not self.image_processor.is_vqa:
__UpperCamelCase = self.tokenizer
__UpperCamelCase = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
return text_encoding
if not self.image_processor.is_vqa:
# add pixel_values
__UpperCamelCase = self.image_processor(
lowercase , return_tensors=lowercase , max_patches=lowercase , **lowercase )
else:
# add pixel_values and bbox
__UpperCamelCase = self.image_processor(
lowercase , return_tensors=lowercase , max_patches=lowercase , header_text=lowercase , **lowercase )
if text is not None and not self.image_processor.is_vqa:
__UpperCamelCase = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
if "attention_mask" in text_encoding:
__UpperCamelCase = text_encoding.pop("""attention_mask""" )
if "input_ids" in text_encoding:
__UpperCamelCase = text_encoding.pop("""input_ids""" )
else:
__UpperCamelCase = None
if text_encoding is not None:
encoding_image_processor.update(lowercase )
return encoding_image_processor
def __lowerCamelCase ( self , *lowercase , **lowercase ) -> Tuple:
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def __lowerCamelCase ( self , *lowercase , **lowercase ) -> List[str]:
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def __lowerCamelCase ( self ) -> Any:
__UpperCamelCase = self.tokenizer.model_input_names
__UpperCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
| 601 | 1 |
from __future__ import annotations
from collections.abc import MutableSequence
class A__ :
def __init__( self : int , a : int , a : MutableSequence[float] ):
'''simple docstring'''
if len(a ) != degree + 1:
raise ValueError(
'The number of coefficients should be equal to the degree + 1.' )
lowerCAmelCase__ : list[float] = list(a )
lowerCAmelCase__ : Tuple = degree
def __add__( self : Optional[int] , a : Polynomial ):
'''simple docstring'''
if self.degree > polynomial_a.degree:
lowerCAmelCase__ : List[str] = self.coefficients[:]
for i in range(polynomial_a.degree + 1 ):
coefficients[i] += polynomial_a.coefficients[i]
return Polynomial(self.degree , a )
else:
lowerCAmelCase__ : int = polynomial_a.coefficients[:]
for i in range(self.degree + 1 ):
coefficients[i] += self.coefficients[i]
return Polynomial(polynomial_a.degree , a )
def __sub__( self : Optional[Any] , a : Polynomial ):
'''simple docstring'''
return self + polynomial_a * Polynomial(0 , [-1] )
def __neg__( self : List[Any] ):
'''simple docstring'''
return Polynomial(self.degree , [-c for c in self.coefficients] )
def __mul__( self : Dict , a : Polynomial ):
'''simple docstring'''
lowerCAmelCase__ : list[float] = [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 _lowerCamelCase ( self : Optional[int] , a : int | float ):
'''simple docstring'''
lowerCAmelCase__ : int | float = 0
for i in range(self.degree + 1 ):
result += self.coefficients[i] * (substitution**i)
return result
def __str__( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : str = ''
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 ):
'''simple docstring'''
return self.__str__()
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : list[float] = [0] * self.degree
for i in range(self.degree ):
lowerCAmelCase__ : Dict = self.coefficients[i + 1] * (i + 1)
return Polynomial(self.degree - 1 , a )
def _lowerCamelCase ( self : Tuple , a : int | float = 0 ):
'''simple docstring'''
lowerCAmelCase__ : list[float] = [0] * (self.degree + 2)
lowerCAmelCase__ : Optional[Any] = constant
for i in range(self.degree + 1 ):
lowerCAmelCase__ : Tuple = self.coefficients[i] / (i + 1)
return Polynomial(self.degree + 1 , a )
def __eq__( self : Optional[Any] , a : object ):
'''simple docstring'''
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 : Optional[int] , a : object ):
'''simple docstring'''
return not self.__eq__(a ) | 69 |
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRContextEncoder, DPRContextEncoderTokenizerFast, HfArgumentParser
lowerCamelCase__ = logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase__ = """cuda""" if torch.cuda.is_available() else """cpu"""
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=100 , SCREAMING_SNAKE_CASE_=" " ) -> List[str]:
lowerCAmelCase__ : Optional[Any] = text.split(SCREAMING_SNAKE_CASE_ )
return [character.join(text[i : i + n] ).strip() for i in range(0 , len(SCREAMING_SNAKE_CASE_ ) , SCREAMING_SNAKE_CASE_ )]
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> dict:
lowerCAmelCase__ , lowerCAmelCase__ : int = [], []
for title, text in zip(documents['title'] , documents['text'] ):
if text is not None:
for passage in split_text(SCREAMING_SNAKE_CASE_ ):
titles.append(title if title is not None else '' )
texts.append(SCREAMING_SNAKE_CASE_ )
return {"title": titles, "text": texts}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> dict:
lowerCAmelCase__ : List[str] = ctx_tokenizer(
documents['title'] , documents['text'] , truncation=SCREAMING_SNAKE_CASE_ , padding='longest' , return_tensors='pt' )['input_ids']
lowerCAmelCase__ : Tuple = ctx_encoder(input_ids.to(device=SCREAMING_SNAKE_CASE_ ) , return_dict=SCREAMING_SNAKE_CASE_ ).pooler_output
return {"embeddings": embeddings.detach().cpu().numpy()}
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , ) -> Optional[Any]:
######################################
logger.info('Step 1 - Create the dataset' )
######################################
# The dataset needed for RAG must have three columns:
# - title (string): title of the document
# - text (string): text of a passage of the document
# - embeddings (array of dimension d): DPR representation of the passage
# Let's say you have documents in tab-separated csv files with columns "title" and "text"
assert os.path.isfile(rag_example_args.csv_path ), "Please provide a valid path to a csv file"
# You can load a Dataset object this way
lowerCAmelCase__ : str = load_dataset(
'csv' , data_files=[rag_example_args.csv_path] , split='train' , delimiter='\t' , column_names=['title', 'text'] )
# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files
# Then split the documents into passages of 100 words
lowerCAmelCase__ : Optional[Any] = dataset.map(SCREAMING_SNAKE_CASE_ , batched=SCREAMING_SNAKE_CASE_ , num_proc=processing_args.num_proc )
# And compute the embeddings
lowerCAmelCase__ : List[str] = DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name ).to(device=SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : str = DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name )
lowerCAmelCase__ : List[Any] = Features(
{'text': Value('string' ), 'title': Value('string' ), 'embeddings': Sequence(Value('float32' ) )} ) # optional, save as float32 instead of float64 to save space
lowerCAmelCase__ : List[Any] = dataset.map(
partial(SCREAMING_SNAKE_CASE_ , ctx_encoder=SCREAMING_SNAKE_CASE_ , ctx_tokenizer=SCREAMING_SNAKE_CASE_ ) , batched=SCREAMING_SNAKE_CASE_ , batch_size=processing_args.batch_size , features=SCREAMING_SNAKE_CASE_ , )
# And finally save your dataset
lowerCAmelCase__ : Optional[Any] = os.path.join(rag_example_args.output_dir , 'my_knowledge_dataset' )
dataset.save_to_disk(SCREAMING_SNAKE_CASE_ )
# from datasets import load_from_disk
# dataset = load_from_disk(passages_path) # to reload the dataset
######################################
logger.info('Step 2 - Index the dataset' )
######################################
# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
lowerCAmelCase__ : Optional[int] = faiss.IndexHNSWFlat(index_hnsw_args.d , index_hnsw_args.m , faiss.METRIC_INNER_PRODUCT )
dataset.add_faiss_index('embeddings' , custom_index=SCREAMING_SNAKE_CASE_ )
# And save the index
lowerCAmelCase__ : str = os.path.join(rag_example_args.output_dir , 'my_knowledge_dataset_hnsw_index.faiss' )
dataset.get_index('embeddings' ).save(SCREAMING_SNAKE_CASE_ )
# dataset.load_faiss_index("embeddings", index_path) # to reload the index
@dataclass
class A__ :
lowercase = field(
default=str(Path(__magic_name__ ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv' ) , metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''} , )
lowercase = field(
default=__magic_name__ , metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'} , )
lowercase = field(
default='facebook/rag-sequence-nq' , metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''} , )
lowercase = field(
default='facebook/dpr-ctx_encoder-multiset-base' , metadata={
'help': (
'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
' \'facebook/dpr-ctx_encoder-multiset-base\''
)
} , )
lowercase = field(
default=str(Path(__magic_name__ ).parent / 'test_run' / 'dummy-kb' ) , metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'} , )
@dataclass
class A__ :
lowercase = field(
default=__magic_name__ , metadata={
'help': 'The number of processes to use to split the documents into passages. Default is single process.'
} , )
lowercase = field(
default=16 , metadata={
'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
} , )
@dataclass
class A__ :
lowercase = field(
default=768 , metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'} , )
lowercase = field(
default=128 , metadata={
'help': (
'The number of bi-directional links created for every new element during the HNSW index construction.'
)
} , )
if __name__ == "__main__":
logging.basicConfig(level=logging.WARNING)
logger.setLevel(logging.INFO)
lowerCamelCase__ = HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = parser.parse_args_into_dataclasses()
with TemporaryDirectory() as tmp_dir:
lowerCamelCase__ = rag_example_args.output_dir or tmp_dir
main(rag_example_args, processing_args, index_hnsw_args) | 69 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {
"weiweishi/roc-bert-base-zh": "https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json",
}
class __UpperCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
_UpperCamelCase = """roc_bert"""
def __init__( self : int , _lowercase : Dict=30_522 , _lowercase : List[str]=768 , _lowercase : Union[str, Any]=12 , _lowercase : Tuple=12 , _lowercase : Union[str, Any]=3_072 , _lowercase : Union[str, Any]="gelu" , _lowercase : List[str]=0.1 , _lowercase : str=0.1 , _lowercase : Tuple=512 , _lowercase : List[Any]=2 , _lowercase : Optional[int]=0.02 , _lowercase : Any=1E-12 , _lowercase : Tuple=True , _lowercase : Optional[Any]=0 , _lowercase : Optional[Any]="absolute" , _lowercase : Any=None , _lowercase : List[str]=True , _lowercase : str=True , _lowercase : List[str]=768 , _lowercase : Tuple=910 , _lowercase : Tuple=512 , _lowercase : str=24_858 , _lowercase : Dict=True , **_lowercase : Union[str, Any] , ) -> Optional[int]:
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_ = use_cache
A_ = enable_pronunciation
A_ = enable_shape
A_ = pronunciation_embed_dim
A_ = pronunciation_vocab_size
A_ = shape_embed_dim
A_ = shape_vocab_size
A_ = concat_input
A_ = position_embedding_type
A_ = classifier_dropout
super().__init__(pad_token_id=_lowercase , **_lowercase)
| 366 |
'''simple docstring'''
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class __UpperCAmelCase ( lowerCAmelCase ,lowerCAmelCase ):
'''simple docstring'''
_UpperCamelCase = 1
@register_to_config
def __init__( self : str , _lowercase : int = 1_000 , _lowercase : Optional[Union[np.ndarray, List[float]]] = None) -> List[str]:
# set `betas`, `alphas`, `timesteps`
self.set_timesteps(_lowercase)
# standard deviation of the initial noise distribution
A_ = 1.0
# For now we only support F-PNDM, i.e. the runge-kutta method
# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
# mainly at formula (9), (12), (13) and the Algorithm 2.
A_ = 4
# running values
A_ = []
def __snake_case ( self : int , _lowercase : int , _lowercase : Union[str, torch.device] = None) -> Any:
A_ = num_inference_steps
A_ = torch.linspace(1 , 0 , num_inference_steps + 1)[:-1]
A_ = torch.cat([steps, torch.tensor([0.0])])
if self.config.trained_betas is not None:
A_ = torch.tensor(self.config.trained_betas , dtype=torch.floataa)
else:
A_ = torch.sin(steps * math.pi / 2) ** 2
A_ = (1.0 - self.betas**2) ** 0.5
A_ = (torch.atana(self.betas , self.alphas) / math.pi * 2)[:-1]
A_ = timesteps.to(_lowercase)
A_ = []
def __snake_case ( self : Dict , _lowercase : torch.FloatTensor , _lowercase : int , _lowercase : torch.FloatTensor , _lowercase : bool = True , ) -> Union[SchedulerOutput, Tuple]:
if self.num_inference_steps is None:
raise ValueError(
'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler')
A_ = (self.timesteps == timestep).nonzero().item()
A_ = timestep_index + 1
A_ = sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
self.ets.append(_lowercase)
if len(self.ets) == 1:
A_ = self.ets[-1]
elif len(self.ets) == 2:
A_ = (3 * self.ets[-1] - self.ets[-2]) / 2
elif len(self.ets) == 3:
A_ = (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
else:
A_ = (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])
A_ = self._get_prev_sample(_lowercase , _lowercase , _lowercase , _lowercase)
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=_lowercase)
def __snake_case ( self : Dict , _lowercase : torch.FloatTensor , *_lowercase : Optional[Any] , **_lowercase : int) -> torch.FloatTensor:
return sample
def __snake_case ( self : Optional[int] , _lowercase : Optional[Any] , _lowercase : Dict , _lowercase : Union[str, Any] , _lowercase : Union[str, Any]) -> Union[str, Any]:
A_ = self.alphas[timestep_index]
A_ = self.betas[timestep_index]
A_ = self.alphas[prev_timestep_index]
A_ = self.betas[prev_timestep_index]
A_ = (sample - sigma * ets) / max(_lowercase , 1E-8)
A_ = next_alpha * pred + ets * next_sigma
return prev_sample
def __len__( self : List[str]) -> Union[str, Any]:
return self.config.num_train_timesteps
| 366 | 1 |
def UpperCAmelCase__ ( _A , _A ):
"""simple docstring"""
if density <= 0:
raise ValueError('''Impossible fluid density''' )
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''' )
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doctest
doctest.testmod()
| 143 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
UpperCamelCase__ = (3, 9, -11, 0, 7, 5, 1, -1)
UpperCamelCase__ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class __lowercase :
_lowerCAmelCase = 42
_lowerCAmelCase = 42
class __lowercase :
def __init__( self : int , lowercase__ : Iterable[int] ):
a_ = None
for i in sorted(lowercase__ , reverse=lowercase__ ):
a_ = Node(lowercase__ , self.head )
def __iter__( self : str ):
a_ = self.head
while node:
yield node.data
a_ = node.next_node
def __len__( self : Optional[int] ):
return sum(1 for _ in self )
def __str__( self : Optional[Any] ):
return " -> ".join([str(lowercase__ ) for node in self] )
def UpperCAmelCase__ ( _A , _A ):
"""simple docstring"""
return SortedLinkedList(list(_A ) + list(_A ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ = SortedLinkedList
print(merge_lists(SSL(test_data_odd), SSL(test_data_even)))
| 143 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
UpperCamelCase_ : Dict = {'''configuration_plbart''': ['''PLBART_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''PLBartConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ : int = ['''PLBartTokenizer''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase_ : Optional[int] = [
'''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
UpperCamelCase_ : Optional[int] = _LazyModule(__name__, globals()['''__file__'''], _import_structure)
| 115 |
"""simple docstring"""
import random
import unittest
import numpy as np
import transformers
from transformers import is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax
if is_flax_available():
import os
import jax.numpy as jnp
from jax import jit
from transformers import AutoTokenizer, FlaxAutoModelForCausalLM
from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model
UpperCamelCase_ : List[Any] = '''0.12''' # assumed parallelism: 8
if is_torch_available():
import torch
def A_ (__a , __a , __a=None ):
'''simple docstring'''
if rng is None:
A_ = random.Random()
A_ = 1
for dim in shape:
total_dims *= dim
A_ = []
for _ in range(__a ):
values.append(rng.randint(0 , vocab_size - 1 ) )
A_ = np.array(__a , dtype=jnp.intaa ).reshape(__a )
return output
def A_ (__a , __a=None ):
'''simple docstring'''
A_ = ids_tensor(__a , vocab_size=2 , rng=__a )
# make sure that at least one token is attended to for each batch
A_ = 1
return attn_mask
@require_flax
class __lowerCAmelCase :
"""simple docstring"""
snake_case = None
snake_case = ()
def lowerCamelCase__ ( self : Any ) -> Tuple:
"""simple docstring"""
A_ , A_ = self.model_tester.prepare_config_and_inputs_for_common()
# cut to half length & take max batch_size 3
A_ = 2
A_ = inputs["input_ids"].shape[-1] // 2
A_ = inputs["input_ids"][:max_batch_size, :sequence_length]
A_ = jnp.ones_like(_snake_case )
A_ = attention_mask[:max_batch_size, :sequence_length]
# generate max 5 tokens
A_ = input_ids.shape[-1] + 5
if config.eos_token_id is not None and config.pad_token_id is None:
# hack to allow generate for models such as GPT2 as is done in `generate()`
A_ = config.eos_token_id
return config, input_ids, attention_mask, max_length
@is_pt_flax_cross_test
def lowerCamelCase__ ( self : List[Any] ) -> List[Any]:
"""simple docstring"""
A_ , A_ , A_ , A_ = self._get_input_ids_and_config()
A_ = False
A_ = max_length
A_ = 0
for model_class in self.all_generative_model_classes:
A_ = model_class(_snake_case )
A_ = model_class.__name__[4:] # Skip the "Flax" at the beginning
A_ = getattr(_snake_case , _snake_case )
A_ = pt_model_class(_snake_case ).eval()
A_ = load_flax_weights_in_pytorch_model(_snake_case , flax_model.params )
A_ = flax_model.generate(_snake_case ).sequences
A_ = pt_model.generate(torch.tensor(_snake_case , dtype=torch.long ) )
if flax_generation_outputs.shape[-1] > pt_generation_outputs.shape[-1]:
A_ = flax_generation_outputs[:, : pt_generation_outputs.shape[-1]]
self.assertListEqual(pt_generation_outputs.numpy().tolist() , flax_generation_outputs.tolist() )
def lowerCamelCase__ ( self : Any ) -> Optional[Any]:
"""simple docstring"""
A_ , A_ , A_ , A_ = self._get_input_ids_and_config()
A_ = False
A_ = max_length
for model_class in self.all_generative_model_classes:
A_ = model_class(_snake_case )
A_ = model.generate(_snake_case ).sequences
self.assertEqual(generation_outputs.shape[-1] , _snake_case )
A_ = jit(model.generate )
A_ = jit_generate(_snake_case ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : Tuple ) -> List[str]:
"""simple docstring"""
A_ , A_ , A_ , A_ = self._get_input_ids_and_config()
A_ = True
A_ = max_length
for model_class in self.all_generative_model_classes:
A_ = model_class(_snake_case )
A_ = model.generate(_snake_case ).sequences
self.assertEqual(generation_outputs.shape[-1] , _snake_case )
A_ = jit(model.generate )
A_ = jit_generate(_snake_case ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : Tuple ) -> Dict:
"""simple docstring"""
A_ , A_ , A_ , A_ = self._get_input_ids_and_config()
A_ = False
A_ = max_length
A_ = 2
for model_class in self.all_generative_model_classes:
A_ = model_class(_snake_case )
A_ = model.generate(_snake_case ).sequences
self.assertEqual(generation_outputs.shape[-1] , _snake_case )
A_ = jit(model.generate )
A_ = jit_generate(_snake_case ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : str ) -> Union[str, Any]:
"""simple docstring"""
A_ , A_ , A_ , A_ = self._get_input_ids_and_config()
A_ = False
A_ = max_length
A_ = 2
A_ = 2
for model_class in self.all_generative_model_classes:
A_ = model_class(_snake_case )
A_ = model.generate(_snake_case ).sequences
self.assertEqual(generation_outputs.shape[0] , input_ids.shape[0] * config.num_return_sequences )
def lowerCamelCase__ ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
A_ , A_ , A_ , A_ = self._get_input_ids_and_config()
A_ = True
A_ = max_length
A_ = 0.8
A_ = 10
A_ = 0.3
A_ = 1
A_ = 8
A_ = 9
for model_class in self.all_generative_model_classes:
A_ = model_class(_snake_case )
A_ = model.generate(_snake_case ).sequences
self.assertEqual(generation_outputs.shape[-1] , _snake_case )
A_ = jit(model.generate )
A_ = jit_generate(_snake_case ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : List[str] ) -> Any:
"""simple docstring"""
A_ , A_ , A_ , A_ = self._get_input_ids_and_config()
A_ = max_length
A_ = 1
A_ = 8
A_ = 9
for model_class in self.all_generative_model_classes:
A_ = model_class(_snake_case )
A_ = model.generate(_snake_case ).sequences
self.assertEqual(generation_outputs.shape[-1] , _snake_case )
A_ = jit(model.generate )
A_ = jit_generate(_snake_case ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : Optional[Any] ) -> Dict:
"""simple docstring"""
A_ , A_ , A_ , A_ = self._get_input_ids_and_config()
A_ = max_length
A_ = 2
A_ = 1
A_ = 8
A_ = 9
for model_class in self.all_generative_model_classes:
A_ = model_class(_snake_case )
A_ = model.generate(_snake_case ).sequences
self.assertEqual(generation_outputs.shape[-1] , _snake_case )
A_ = jit(model.generate )
A_ = jit_generate(_snake_case ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : Tuple ) -> Any:
"""simple docstring"""
A_ , A_ , A_ , A_ = self._get_input_ids_and_config()
# pad attention mask on the left
A_ = attention_mask.at[(0, 0)].set(0 )
A_ = False
A_ = max_length
for model_class in self.all_generative_model_classes:
A_ = model_class(_snake_case )
A_ = model.generate(_snake_case , attention_mask=_snake_case ).sequences
self.assertEqual(generation_outputs.shape[-1] , _snake_case )
A_ = jit(model.generate )
A_ = jit_generate(_snake_case , attention_mask=_snake_case ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : List[str] ) -> int:
"""simple docstring"""
A_ , A_ , A_ , A_ = self._get_input_ids_and_config()
# pad attention mask on the left
A_ = attention_mask.at[(0, 0)].set(0 )
A_ = True
A_ = max_length
for model_class in self.all_generative_model_classes:
A_ = model_class(_snake_case )
A_ = model.generate(_snake_case , attention_mask=_snake_case ).sequences
self.assertEqual(generation_outputs.shape[-1] , _snake_case )
A_ = jit(model.generate )
A_ = jit_generate(_snake_case , attention_mask=_snake_case ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
def lowerCamelCase__ ( self : Dict ) -> int:
"""simple docstring"""
A_ , A_ , A_ , A_ = self._get_input_ids_and_config()
# pad attention mask on the left
A_ = attention_mask.at[(0, 0)].set(0 )
A_ = 2
A_ = max_length
for model_class in self.all_generative_model_classes:
A_ = model_class(_snake_case )
A_ = model.generate(_snake_case , attention_mask=_snake_case ).sequences
self.assertEqual(generation_outputs.shape[-1] , _snake_case )
A_ = jit(model.generate )
A_ = jit_generate(_snake_case , attention_mask=_snake_case ).sequences
self.assertListEqual(generation_outputs.tolist() , jit_generation_outputs.tolist() )
@require_flax
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : Optional[Any] ) -> Any:
"""simple docstring"""
A_ = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-bert" )
A_ = FlaxAutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
A_ = "Hello world"
A_ = tokenizer(_snake_case , return_tensors="np" ).input_ids
# typos are quickly detected (the correct argument is `do_sample`)
with self.assertRaisesRegex(_snake_case , "do_samples" ):
model.generate(_snake_case , do_samples=_snake_case )
# arbitrary arguments that will not be used anywhere are also not accepted
with self.assertRaisesRegex(_snake_case , "foo" ):
A_ = {"foo": "bar"}
model.generate(_snake_case , **_snake_case )
| 115 | 1 |
"""simple docstring"""
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
__A = HUGGINGFACE_HUB_CACHE
__A = """config.json"""
__A = """diffusion_pytorch_model.bin"""
__A = """diffusion_flax_model.msgpack"""
__A = """model.onnx"""
__A = """diffusion_pytorch_model.safetensors"""
__A = """weights.pb"""
__A = """https://huggingface.co"""
__A = default_cache_path
__A = """diffusers_modules"""
__A = os.getenv('''HF_MODULES_CACHE''', os.path.join(hf_cache_home, '''modules'''))
__A = ["""fp16""", """non-ema"""]
__A = """.self_attn""" | 700 | """simple docstring"""
import math
from datetime import datetime, timedelta
def lowercase_ ( _lowerCamelCase: int ) -> datetime:
'''simple docstring'''
__lowerCamelCase : List[Any] = year % 19
__lowerCamelCase : List[str] = year % 4
__lowerCamelCase : Dict = year % 7
__lowerCamelCase : Optional[Any] = math.floor(year / 100 )
__lowerCamelCase : Tuple = math.floor((13 + 8 * leap_day_inhibits) / 25 )
__lowerCamelCase : List[str] = leap_day_inhibits / 4
__lowerCamelCase : Optional[Any] = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
__lowerCamelCase : Union[str, Any] = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
__lowerCamelCase : Union[str, Any] = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
__lowerCamelCase : List[Any] = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(_lowerCamelCase , 4 , 19 )
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(_lowerCamelCase , 4 , 18 )
else:
return datetime(_lowerCamelCase , 3 , 22 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (1994, 2000, 2010, 2021, 2023):
__A = '''will be''' if year > datetime.now().year else '''was'''
print(F"""Easter in {year} {tense} {gauss_easter(year)}""") | 366 | 0 |
'''simple docstring'''
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
'pipelines_utils',
'0.22.0',
'Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.',
standard_warn=False,
stacklevel=3,
)
| 596 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all feature extractors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...feature_extraction_utils import FeatureExtractionMixin
from ...utils import CONFIG_NAME, FEATURE_EXTRACTOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
UpperCAmelCase_ : int = logging.get_logger(__name__)
UpperCAmelCase_ : Dict = OrderedDict(
[
("""audio-spectrogram-transformer""", """ASTFeatureExtractor"""),
("""beit""", """BeitFeatureExtractor"""),
("""chinese_clip""", """ChineseCLIPFeatureExtractor"""),
("""clap""", """ClapFeatureExtractor"""),
("""clip""", """CLIPFeatureExtractor"""),
("""clipseg""", """ViTFeatureExtractor"""),
("""conditional_detr""", """ConditionalDetrFeatureExtractor"""),
("""convnext""", """ConvNextFeatureExtractor"""),
("""cvt""", """ConvNextFeatureExtractor"""),
("""data2vec-audio""", """Wav2Vec2FeatureExtractor"""),
("""data2vec-vision""", """BeitFeatureExtractor"""),
("""deformable_detr""", """DeformableDetrFeatureExtractor"""),
("""deit""", """DeiTFeatureExtractor"""),
("""detr""", """DetrFeatureExtractor"""),
("""dinat""", """ViTFeatureExtractor"""),
("""donut-swin""", """DonutFeatureExtractor"""),
("""dpt""", """DPTFeatureExtractor"""),
("""encodec""", """EncodecFeatureExtractor"""),
("""flava""", """FlavaFeatureExtractor"""),
("""glpn""", """GLPNFeatureExtractor"""),
("""groupvit""", """CLIPFeatureExtractor"""),
("""hubert""", """Wav2Vec2FeatureExtractor"""),
("""imagegpt""", """ImageGPTFeatureExtractor"""),
("""layoutlmv2""", """LayoutLMv2FeatureExtractor"""),
("""layoutlmv3""", """LayoutLMv3FeatureExtractor"""),
("""levit""", """LevitFeatureExtractor"""),
("""maskformer""", """MaskFormerFeatureExtractor"""),
("""mctct""", """MCTCTFeatureExtractor"""),
("""mobilenet_v1""", """MobileNetV1FeatureExtractor"""),
("""mobilenet_v2""", """MobileNetV2FeatureExtractor"""),
("""mobilevit""", """MobileViTFeatureExtractor"""),
("""nat""", """ViTFeatureExtractor"""),
("""owlvit""", """OwlViTFeatureExtractor"""),
("""perceiver""", """PerceiverFeatureExtractor"""),
("""poolformer""", """PoolFormerFeatureExtractor"""),
("""regnet""", """ConvNextFeatureExtractor"""),
("""resnet""", """ConvNextFeatureExtractor"""),
("""segformer""", """SegformerFeatureExtractor"""),
("""sew""", """Wav2Vec2FeatureExtractor"""),
("""sew-d""", """Wav2Vec2FeatureExtractor"""),
("""speech_to_text""", """Speech2TextFeatureExtractor"""),
("""speecht5""", """SpeechT5FeatureExtractor"""),
("""swiftformer""", """ViTFeatureExtractor"""),
("""swin""", """ViTFeatureExtractor"""),
("""swinv2""", """ViTFeatureExtractor"""),
("""table-transformer""", """DetrFeatureExtractor"""),
("""timesformer""", """VideoMAEFeatureExtractor"""),
("""tvlt""", """TvltFeatureExtractor"""),
("""unispeech""", """Wav2Vec2FeatureExtractor"""),
("""unispeech-sat""", """Wav2Vec2FeatureExtractor"""),
("""van""", """ConvNextFeatureExtractor"""),
("""videomae""", """VideoMAEFeatureExtractor"""),
("""vilt""", """ViltFeatureExtractor"""),
("""vit""", """ViTFeatureExtractor"""),
("""vit_mae""", """ViTFeatureExtractor"""),
("""vit_msn""", """ViTFeatureExtractor"""),
("""wav2vec2""", """Wav2Vec2FeatureExtractor"""),
("""wav2vec2-conformer""", """Wav2Vec2FeatureExtractor"""),
("""wavlm""", """Wav2Vec2FeatureExtractor"""),
("""whisper""", """WhisperFeatureExtractor"""),
("""xclip""", """CLIPFeatureExtractor"""),
("""yolos""", """YolosFeatureExtractor"""),
]
)
UpperCAmelCase_ : Tuple = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FEATURE_EXTRACTOR_MAPPING_NAMES)
def _A (__a ) -> str:
"""simple docstring"""
for module_name, extractors in FEATURE_EXTRACTOR_MAPPING_NAMES.items():
if class_name in extractors:
SCREAMING_SNAKE_CASE_ : List[str] = model_type_to_module_name(__a )
SCREAMING_SNAKE_CASE_ : Dict = importlib.import_module(f'.{module_name}' , '''transformers.models''' )
try:
return getattr(__a , __a )
except AttributeError:
continue
for _, extractor in FEATURE_EXTRACTOR_MAPPING._extra_content.items():
if getattr(__a , '''__name__''' , __a ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
SCREAMING_SNAKE_CASE_ : Any = importlib.import_module('''transformers''' )
if hasattr(__a , __a ):
return getattr(__a , __a )
return None
def _A (__a , __a = None , __a = False , __a = False , __a = None , __a = None , __a = None , __a = False , **__a , ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = get_file_from_repo(
__a , __a , cache_dir=__a , force_download=__a , resume_download=__a , proxies=__a , use_auth_token=__a , revision=__a , local_files_only=__a , )
if resolved_config_file is None:
logger.info(
'''Could not locate the feature extractor configuration file, will try to use the model config instead.''' )
return {}
with open(__a , encoding='''utf-8''' ) as reader:
return json.load(__a )
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self : int):
'''simple docstring'''
raise EnvironmentError(
'''AutoFeatureExtractor is designed to be instantiated '''
'''using the `AutoFeatureExtractor.from_pretrained(pretrained_model_name_or_path)` method.''')
@classmethod
@replace_list_option_in_docstrings(lowercase_)
def _SCREAMING_SNAKE_CASE ( cls : Union[str, Any] , lowercase_ : List[Any] , **lowercase_ : int):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Tuple = kwargs.pop('''config''' , lowercase_)
SCREAMING_SNAKE_CASE_ : Dict = kwargs.pop('''trust_remote_code''' , lowercase_)
SCREAMING_SNAKE_CASE_ : Any = True
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = FeatureExtractionMixin.get_feature_extractor_dict(lowercase_ , **lowercase_)
SCREAMING_SNAKE_CASE_ : Tuple = config_dict.get('''feature_extractor_type''' , lowercase_)
SCREAMING_SNAKE_CASE_ : str = None
if "AutoFeatureExtractor" in config_dict.get('''auto_map''' , {}):
SCREAMING_SNAKE_CASE_ : Optional[int] = config_dict['''auto_map''']['''AutoFeatureExtractor''']
# If we don't find the feature extractor class in the feature extractor config, let's try the model config.
if feature_extractor_class is None and feature_extractor_auto_map is None:
if not isinstance(lowercase_ , lowercase_):
SCREAMING_SNAKE_CASE_ : Any = AutoConfig.from_pretrained(lowercase_ , **lowercase_)
# It could be in `config.feature_extractor_type``
SCREAMING_SNAKE_CASE_ : Union[str, Any] = getattr(lowercase_ , '''feature_extractor_type''' , lowercase_)
if hasattr(lowercase_ , '''auto_map''') and "AutoFeatureExtractor" in config.auto_map:
SCREAMING_SNAKE_CASE_ : Dict = config.auto_map['''AutoFeatureExtractor''']
if feature_extractor_class is not None:
SCREAMING_SNAKE_CASE_ : Dict = feature_extractor_class_from_name(lowercase_)
SCREAMING_SNAKE_CASE_ : List[str] = feature_extractor_auto_map is not None
SCREAMING_SNAKE_CASE_ : Dict = feature_extractor_class is not None or type(lowercase_) in FEATURE_EXTRACTOR_MAPPING
SCREAMING_SNAKE_CASE_ : Optional[int] = resolve_trust_remote_code(
lowercase_ , lowercase_ , lowercase_ , lowercase_)
if has_remote_code and trust_remote_code:
SCREAMING_SNAKE_CASE_ : Dict = get_class_from_dynamic_module(
lowercase_ , lowercase_ , **lowercase_)
SCREAMING_SNAKE_CASE_ : Any = kwargs.pop('''code_revision''' , lowercase_)
if os.path.isdir(lowercase_):
feature_extractor_class.register_for_auto_class()
return feature_extractor_class.from_dict(lowercase_ , **lowercase_)
elif feature_extractor_class is not None:
return feature_extractor_class.from_dict(lowercase_ , **lowercase_)
# Last try: we use the FEATURE_EXTRACTOR_MAPPING.
elif type(lowercase_) in FEATURE_EXTRACTOR_MAPPING:
SCREAMING_SNAKE_CASE_ : Any = FEATURE_EXTRACTOR_MAPPING[type(lowercase_)]
return feature_extractor_class.from_dict(lowercase_ , **lowercase_)
raise ValueError(
F'Unrecognized feature extractor in {pretrained_model_name_or_path}. Should have a '
F'`feature_extractor_type` key in its {FEATURE_EXTRACTOR_NAME} of {CONFIG_NAME}, or one of the following '
F'`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in FEATURE_EXTRACTOR_MAPPING_NAMES.keys())}')
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowercase_ : Any , lowercase_ : List[Any]):
'''simple docstring'''
FEATURE_EXTRACTOR_MAPPING.register(lowercase_ , lowercase_)
| 512 | 0 |
import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_zstandard
@pytest.mark.parametrize(
"compression_format, is_archive" , [
("7z", True),
("bz2", False),
("gzip", False),
("lz4", False),
("tar", True),
("xz", False),
("zip", True),
("zstd", False),
] , )
def __lowercase ( lowerCamelCase_ : List[str] , lowerCamelCase_ : Any , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Tuple , lowerCamelCase_ : Dict , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : List[str] , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : str , lowerCamelCase_ : int , ):
SCREAMING_SNAKE_CASE__ = {
"7z": (seven_zip_file, SevenZipExtractor),
"bz2": (bza_file, BzipaExtractor),
"gzip": (gz_file, GzipExtractor),
"lz4": (lza_file, LzaExtractor),
"tar": (tar_file, TarExtractor),
"xz": (xz_file, XzExtractor),
"zip": (zip_file, ZipExtractor),
"zstd": (zstd_file, ZstdExtractor),
}
SCREAMING_SNAKE_CASE__ = input_paths_and_base_extractors[compression_format]
if input_path is None:
SCREAMING_SNAKE_CASE__ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_lowercase )
assert base_extractor.is_extractable(_lowercase )
SCREAMING_SNAKE_CASE__ = tmp_path / ("extracted" if is_archive else "extracted.txt")
base_extractor.extract(_lowercase , _lowercase )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
SCREAMING_SNAKE_CASE__ = file_path.read_text(encoding="utf-8" )
else:
SCREAMING_SNAKE_CASE__ = output_path.read_text(encoding="utf-8" )
SCREAMING_SNAKE_CASE__ = text_file.read_text(encoding="utf-8" )
assert extracted_file_content == expected_file_content
@pytest.mark.parametrize(
"compression_format, is_archive" , [
("7z", True),
("bz2", False),
("gzip", False),
("lz4", False),
("tar", True),
("xz", False),
("zip", True),
("zstd", False),
] , )
def __lowercase ( lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : Optional[Any] , lowerCamelCase_ : str , lowerCamelCase_ : Dict , lowerCamelCase_ : Union[str, Any] , lowerCamelCase_ : List[Any] , lowerCamelCase_ : str , lowerCamelCase_ : Any , lowerCamelCase_ : Optional[int] , lowerCamelCase_ : Any , lowerCamelCase_ : List[Any] , lowerCamelCase_ : Optional[int] , ):
SCREAMING_SNAKE_CASE__ = {
"7z": seven_zip_file,
"bz2": bza_file,
"gzip": gz_file,
"lz4": lza_file,
"tar": tar_file,
"xz": xz_file,
"zip": zip_file,
"zstd": zstd_file,
}
SCREAMING_SNAKE_CASE__ = input_paths[compression_format]
if input_path is None:
SCREAMING_SNAKE_CASE__ = F'''for \'{compression_format}\' compression_format, '''
if compression_format == "7z":
reason += require_pyazr.kwargs["reason"]
elif compression_format == "lz4":
reason += require_lza.kwargs["reason"]
elif compression_format == "zstd":
reason += require_zstandard.kwargs["reason"]
pytest.skip(_lowercase )
SCREAMING_SNAKE_CASE__ = Extractor.infer_extractor_format(_lowercase )
assert extractor_format is not None
SCREAMING_SNAKE_CASE__ = tmp_path / ("extracted" if is_archive else "extracted.txt")
Extractor.extract(_lowercase , _lowercase , _lowercase )
if is_archive:
assert output_path.is_dir()
for file_path in output_path.iterdir():
assert file_path.name == text_file.name
SCREAMING_SNAKE_CASE__ = file_path.read_text(encoding="utf-8" )
else:
SCREAMING_SNAKE_CASE__ = output_path.read_text(encoding="utf-8" )
SCREAMING_SNAKE_CASE__ = text_file.read_text(encoding="utf-8" )
assert extracted_file_content == expected_file_content
@pytest.fixture
def __lowercase ( lowerCamelCase_ : Tuple , lowerCamelCase_ : Optional[Any] ):
import tarfile
SCREAMING_SNAKE_CASE__ = tmp_path / "data_dot_dot"
directory.mkdir()
SCREAMING_SNAKE_CASE__ = directory / "tar_file_with_dot_dot.tar"
with tarfile.TarFile(_lowercase , "w" ) as f:
f.add(_lowercase , arcname=os.path.join(".." , text_file.name ) )
return path
@pytest.fixture
def __lowercase ( lowerCamelCase_ : List[str] ):
import tarfile
SCREAMING_SNAKE_CASE__ = tmp_path / "data_sym_link"
directory.mkdir()
SCREAMING_SNAKE_CASE__ = directory / "tar_file_with_sym_link.tar"
os.symlink(".." , directory / "subdir" , target_is_directory=_lowercase )
with tarfile.TarFile(_lowercase , "w" ) as f:
f.add(str(directory / "subdir" ) , arcname="subdir" ) # str required by os.readlink on Windows and Python < 3.8
return path
@pytest.mark.parametrize(
"insecure_tar_file, error_log" , [("tar_file_with_dot_dot", "illegal path"), ("tar_file_with_sym_link", "Symlink")] , )
def __lowercase ( lowerCamelCase_ : Any , lowerCamelCase_ : List[str] , lowerCamelCase_ : int , lowerCamelCase_ : int , lowerCamelCase_ : str , lowerCamelCase_ : int ):
SCREAMING_SNAKE_CASE__ = {
"tar_file_with_dot_dot": tar_file_with_dot_dot,
"tar_file_with_sym_link": tar_file_with_sym_link,
}
SCREAMING_SNAKE_CASE__ = insecure_tar_files[insecure_tar_file]
SCREAMING_SNAKE_CASE__ = tmp_path / "extracted"
TarExtractor.extract(_lowercase , _lowercase )
assert caplog.text
for record in caplog.records:
assert record.levelname == "ERROR"
assert error_log in record.msg
def __lowercase ( lowerCamelCase_ : Tuple ):
SCREAMING_SNAKE_CASE__ = tmpdir / "not_a_zip_file"
# From: https://github.com/python/cpython/pull/5053
SCREAMING_SNAKE_CASE__ = (
B"\x89PNG\r\n\x1a\n\x00\x00\x00\rIHDR\x00\x00\x00\x01\x00\x00"
B"\x00\x02\x08\x06\x00\x00\x00\x99\x81\xb6'\x00\x00\x00\x15I"
B"DATx\x01\x01\n\x00\xf5\xff\x00PK\x05\x06\x00PK\x06\x06\x07"
B"\xac\x01N\xc6|a\r\x00\x00\x00\x00IEND\xaeB`\x82"
)
with not_a_zip_file.open("wb" ) as f:
f.write(_lowercase )
assert zipfile.is_zipfile(str(_lowercase ) ) # is a false positive for `zipfile`
assert not ZipExtractor.is_extractable(_lowercase ) # but we're right
| 709 |
"""simple docstring"""
from math import pi
def __lowercase ( lowerCamelCase_ : int , lowerCamelCase_ : int ):
return 2 * pi * radius * (angle / 360)
if __name__ == "__main__":
print(arc_length(90, 10))
| 112 | 0 |
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