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"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A : Any = {
'configuration_mobilevit': ['MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MobileViTConfig', 'MobileViTOnnxConfig'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[Any] = ['MobileViTFeatureExtractor']
A : Any = ['MobileViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : Optional[int] = [
'MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileViTForImageClassification',
'MobileViTForSemanticSegmentation',
'MobileViTModel',
'MobileViTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A : List[Any] = [
'TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileViTForImageClassification',
'TFMobileViTForSemanticSegmentation',
'TFMobileViTModel',
'TFMobileViTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilevit import MOBILEVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, MobileViTConfig, MobileViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_mobilevit import MobileViTFeatureExtractor
from .image_processing_mobilevit import MobileViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilevit import (
MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTModel,
MobileViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilevit import (
TF_MOBILEVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileViTForImageClassification,
TFMobileViTForSemanticSegmentation,
TFMobileViTModel,
TFMobileViTPreTrainedModel,
)
else:
import sys
A : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 636 |
'''simple docstring'''
import json
import os
from typing import Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__lowerCamelCase : Tuple = logging.get_logger(__name__)
__lowerCamelCase : Optional[Any] = {'vocab_file': 'vocab.json'}
__lowerCamelCase : Optional[Any] = {
'vocab_file': {
'mgp-str': 'https://huggingface.co/alibaba-damo/mgp-str-base/blob/main/vocab.json',
}
}
__lowerCamelCase : List[Any] = {'mgp-str': 27}
class UpperCAmelCase ( lowercase_):
"""simple docstring"""
lowerCAmelCase_ = VOCAB_FILES_NAMES
lowerCAmelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCAmelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Optional[int] , UpperCamelCase__ : str , UpperCamelCase__ : List[str]="[GO]" , UpperCamelCase__ : Optional[Any]="[GO]" , UpperCamelCase__ : int="[s]" , UpperCamelCase__ : Dict="[GO]" , **UpperCamelCase__ : List[Any] ) -> List[str]:
super().__init__(
unk_token=UpperCamelCase__ , bos_token=UpperCamelCase__ , eos_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , **UpperCamelCase__ , )
with open(UpperCamelCase__ , encoding='''utf-8''' ) as vocab_handle:
_UpperCamelCase =json.load(UpperCamelCase__ )
_UpperCamelCase ={v: k for k, v in self.vocab.items()}
@property
def UpperCamelCase__ ( self : Union[str, Any] ) -> Tuple:
return len(self.vocab )
def UpperCamelCase__ ( self : int ) -> Union[str, Any]:
return dict(self.vocab , **self.added_tokens_encoder )
def UpperCamelCase__ ( self : Optional[int] , UpperCamelCase__ : str ) -> List[str]:
_UpperCamelCase =[]
for s in text:
char_tokens.extend(UpperCamelCase__ )
return char_tokens
def UpperCamelCase__ ( self : List[Any] , UpperCamelCase__ : Optional[int] ) -> Dict:
return self.vocab.get(UpperCamelCase__ , self.vocab.get(self.unk_token ) )
def UpperCamelCase__ ( self : Optional[Any] , UpperCamelCase__ : Optional[int] ) -> Any:
return self.decoder.get(UpperCamelCase__ )
def UpperCamelCase__ ( self : str , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(UpperCamelCase__ ):
logger.error('''Vocabulary path ({}) should be a directory'''.format(UpperCamelCase__ ) )
return
_UpperCamelCase =os.path.join(
UpperCamelCase__ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
with open(UpperCamelCase__ , '''w''' , encoding='''utf-8''' ) as f:
f.write(json.dumps(self.vocab , indent=2 , sort_keys=UpperCamelCase__ , ensure_ascii=UpperCamelCase__ ) + '''\n''' )
return (vocab_file,)
| 404 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class __A :
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[int]=13 , UpperCAmelCase_ : str=32 , UpperCAmelCase_ : List[Any]=3 , UpperCAmelCase_ : Optional[Any]=4 , UpperCAmelCase_ : Dict=[10, 20, 30, 40] , UpperCAmelCase_ : List[Any]=[2, 2, 3, 2] , UpperCAmelCase_ : Tuple=True , UpperCAmelCase_ : Tuple=True , UpperCAmelCase_ : Any=37 , UpperCAmelCase_ : str="gelu" , UpperCAmelCase_ : Optional[int]=10 , UpperCAmelCase_ : Dict=0.02 , UpperCAmelCase_ : int=["stage2", "stage3", "stage4"] , UpperCAmelCase_ : Optional[int]=[2, 3, 4] , UpperCAmelCase_ : List[str]=None , ) ->Union[str, Any]:
"""simple docstring"""
snake_case_ = parent
snake_case_ = batch_size
snake_case_ = image_size
snake_case_ = num_channels
snake_case_ = num_stages
snake_case_ = hidden_sizes
snake_case_ = depths
snake_case_ = is_training
snake_case_ = use_labels
snake_case_ = intermediate_size
snake_case_ = hidden_act
snake_case_ = num_labels
snake_case_ = initializer_range
snake_case_ = out_features
snake_case_ = out_indices
snake_case_ = scope
def lowerCAmelCase ( self : List[str] ) ->str:
"""simple docstring"""
snake_case_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.num_labels )
snake_case_ = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase ( self : Dict ) ->Optional[int]:
"""simple docstring"""
return ConvNextVaConfig(
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=UpperCAmelCase_ , initializer_range=self.initializer_range , out_features=self.out_features , out_indices=self.out_indices , num_labels=self.num_labels , )
def lowerCAmelCase ( self : List[Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] ) ->List[Any]:
"""simple docstring"""
snake_case_ = ConvNextVaModel(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
snake_case_ = 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 : List[str] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[Any] ) ->Any:
"""simple docstring"""
snake_case_ = ConvNextVaForImageClassification(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
snake_case_ = model(UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase ( self : Optional[Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Union[str, Any] ) ->Tuple:
"""simple docstring"""
snake_case_ = ConvNextVaBackbone(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
snake_case_ = model(UpperCAmelCase_ )
# 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
snake_case_ = None
snake_case_ = ConvNextVaBackbone(config=UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
snake_case_ = model(UpperCAmelCase_ )
# 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 lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {"""pixel_values""": pixel_values}
return config, inputs_dict
def lowerCAmelCase ( self : List[str] ) ->Union[str, Any]:
"""simple docstring"""
snake_case_ = self.prepare_config_and_inputs()
snake_case_ , snake_case_ , snake_case_ = config_and_inputs
snake_case_ = {"""pixel_values""": pixel_values, """labels""": labels}
return config, inputs_dict
@require_torch
class __A (snake_case__ , snake_case__ , unittest.TestCase):
'''simple docstring'''
__lowercase: Optional[Any] = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
__lowercase: Union[str, Any] = (
{"""feature-extraction""": ConvNextVaModel, """image-classification""": ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
__lowercase: Union[str, Any] = False
__lowercase: Optional[Any] = False
__lowercase: Any = False
__lowercase: Union[str, Any] = False
__lowercase: Dict = False
def lowerCAmelCase ( self : Union[str, Any] ) ->Tuple:
"""simple docstring"""
snake_case_ = ConvNextVaModelTester(self )
snake_case_ = ConfigTester(self , config_class=UpperCAmelCase_ , has_text_modality=UpperCAmelCase_ , hidden_size=37 )
def lowerCAmelCase ( self : List[Any] ) ->Optional[Any]:
"""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 : str ) ->Optional[Any]:
"""simple docstring"""
return
@unittest.skip(reason="""ConvNextV2 does not use inputs_embeds""" )
def lowerCAmelCase ( self : Optional[int] ) ->Union[str, Any]:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNextV2 does not support input and output embeddings""" )
def lowerCAmelCase ( self : Optional[Any] ) ->List[str]:
"""simple docstring"""
pass
@unittest.skip(reason="""ConvNextV2 does not use feedforward chunking""" )
def lowerCAmelCase ( self : Optional[int] ) ->List[str]:
"""simple docstring"""
pass
def lowerCAmelCase ( self : Dict ) ->Optional[int]:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_with_labels()
snake_case_ = True
if model_class.__name__ in [
*get_values(UpperCAmelCase_ ),
*get_values(UpperCAmelCase_ ),
]:
continue
snake_case_ = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.train()
snake_case_ = self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ , return_labels=UpperCAmelCase_ )
snake_case_ = model(**UpperCAmelCase_ ).loss
loss.backward()
def lowerCAmelCase ( self : Optional[int] ) ->Any:
"""simple docstring"""
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_with_labels()
snake_case_ = False
snake_case_ = True
if (
model_class.__name__
in [*get_values(UpperCAmelCase_ ), *get_values(UpperCAmelCase_ )]
or not model_class.supports_gradient_checkpointing
):
continue
snake_case_ = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.gradient_checkpointing_enable()
model.train()
snake_case_ = self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ , return_labels=UpperCAmelCase_ )
snake_case_ = model(**UpperCAmelCase_ ).loss
loss.backward()
def lowerCAmelCase ( self : List[Any] ) ->Union[str, Any]:
"""simple docstring"""
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = model_class(UpperCAmelCase_ )
snake_case_ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case_ = [*signature.parameters.keys()]
snake_case_ = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , UpperCAmelCase_ )
def lowerCAmelCase ( self : Optional[int] ) ->Union[str, Any]:
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def lowerCAmelCase ( self : Optional[Any] ) ->Dict:
"""simple docstring"""
def check_hidden_states_output(UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : str ):
snake_case_ = model_class(UpperCAmelCase_ )
model.to(UpperCAmelCase_ )
model.eval()
with torch.no_grad():
snake_case_ = model(**self._prepare_for_class(UpperCAmelCase_ , UpperCAmelCase_ ) )
snake_case_ = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
snake_case_ = self.model_tester.num_stages
self.assertEqual(len(UpperCAmelCase_ ) , expected_num_stages + 1 )
# ConvNextV2'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] , )
snake_case_ , snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case_ = True
check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case_ = True
check_hidden_states_output(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )
def lowerCAmelCase ( self : Union[str, Any] ) ->Dict:
"""simple docstring"""
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase_ )
@slow
def lowerCAmelCase ( self : Tuple ) ->str:
"""simple docstring"""
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case_ = ConvNextVaModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
def _a ( ) -> str:
snake_case_ = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class __A (unittest.TestCase):
'''simple docstring'''
@cached_property
def lowerCAmelCase ( self : Union[str, Any] ) ->Optional[int]:
"""simple docstring"""
return AutoImageProcessor.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ) if is_vision_available() else None
@slow
def lowerCAmelCase ( self : Tuple ) ->int:
"""simple docstring"""
snake_case_ = ConvNextVaForImageClassification.from_pretrained("""facebook/convnextv2-tiny-1k-224""" ).to(UpperCAmelCase_ )
snake_case_ = self.default_image_processor
snake_case_ = prepare_img()
snake_case_ = preprocessor(images=UpperCAmelCase_ , return_tensors="""pt""" ).to(UpperCAmelCase_ )
# forward pass
with torch.no_grad():
snake_case_ = model(**UpperCAmelCase_ )
# verify the logits
snake_case_ = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , UpperCAmelCase_ )
snake_case_ = torch.tensor([0.9_996, 0.1_966, -0.4_386] ).to(UpperCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , UpperCAmelCase_ , atol=1E-4 ) )
| 2 |
"""simple docstring"""
import argparse
import json
import os
from pathlib import Path
import requests
import torch
from transformers import JukeboxConfig, JukeboxModel
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : Optional[int] = 'https://openaipublic.azureedge.net/jukebox/models/'
__SCREAMING_SNAKE_CASE : List[Any] = {
'jukebox-1b-lyrics': [
'5b/vqvae.pth.tar',
'5b/prior_level_0.pth.tar',
'5b/prior_level_1.pth.tar',
'1b_lyrics/prior_level_2.pth.tar',
],
'jukebox-5b-lyrics': [
'5b/vqvae.pth.tar',
'5b/prior_level_0.pth.tar',
'5b/prior_level_1.pth.tar',
'5b_lyrics/prior_level_2.pth.tar',
],
}
def _a ( _SCREAMING_SNAKE_CASE ) -> int:
if key.endswith(""".model.1.bias""" ) and len(key.split(""".""" ) ) > 10:
snake_case_ = key.replace(""".model.1.bias""" , """.conv1d_1.bias""" )
elif key.endswith(""".model.1.weight""" ) and len(key.split(""".""" ) ) > 10:
snake_case_ = key.replace(""".model.1.weight""" , """.conv1d_1.weight""" )
elif key.endswith(""".model.3.bias""" ) and len(key.split(""".""" ) ) > 10:
snake_case_ = key.replace(""".model.3.bias""" , """.conv1d_2.bias""" )
elif key.endswith(""".model.3.weight""" ) and len(key.split(""".""" ) ) > 10:
snake_case_ = key.replace(""".model.3.weight""" , """.conv1d_2.weight""" )
if "conditioner_blocks.0." in key:
snake_case_ = key.replace("""conditioner_blocks.0""" , """conditioner_blocks""" )
if "prime_prior" in key:
snake_case_ = key.replace("""prime_prior""" , """encoder""" )
if ".emb." in key and "total" not in key and "absolute" not in key and "relative" not in key:
snake_case_ = key.replace(""".emb.""" , """.""" )
if key.endswith("""k""" ): # replace vqvae.X.k with vqvae.X.codebook
return key.replace(""".k""" , """.codebook""" )
if "y_emb." in key:
return key.replace("""y_emb.""" , """metadata_embedding.""" )
if "x_emb.emb." in key:
snake_case_ = key.replace("""0.x_emb.emb""" , """embed_tokens""" )
if "prime_state_ln" in key:
return key.replace("""prime_state_ln""" , """encoder.final_layer_norm""" )
if ".ln" in key:
return key.replace(""".ln""" , """.layer_norm""" )
if "_ln" in key:
return key.replace("""_ln""" , """_layer_norm""" )
if "prime_state_proj" in key:
return key.replace("""prime_state_proj""" , """encoder.proj_in""" )
if "prime_x_out" in key:
return key.replace("""prime_x_out""" , """encoder.lm_head""" )
if "prior.x_out" in key:
return key.replace("""x_out""" , """fc_proj_out""" )
if "x_emb" in key:
return key.replace("""x_emb""" , """embed_tokens""" )
return key
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
snake_case_ = {}
import re
snake_case_ = re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
snake_case_ = re.compile(
r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
snake_case_ = re.compile(r"""encoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )
snake_case_ = re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).(bias|weight)""" )
snake_case_ = re.compile(
r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
snake_case_ = re.compile(r"""decoders.(\d*).level_blocks.(\d*).model.(\d*).(bias|weight)""" )
snake_case_ = re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).(bias|weight)""" )
snake_case_ = re.compile(
r"""conditioner_blocks.(\d*).cond.model.(\d*).(\d).model.(\d*).model.(\d*).(bias|weight)""" )
snake_case_ = re.compile(r"""conditioner_blocks.(\d*).cond.model.(\d*).(bias|weight)""" )
for original_key, value in state_dict.items():
# rename vqvae.encoder keys
if re_encoder_block_conv_in.fullmatch(_SCREAMING_SNAKE_CASE ):
snake_case_ = re_encoder_block_conv_in.match(_SCREAMING_SNAKE_CASE )
snake_case_ = regex_match.groups()
snake_case_ = int(groups[2] ) * 2 + int(groups[3] )
snake_case_ = f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}.{groups[-1]}"""
snake_case_ = re_encoder_block_conv_in.sub(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif re_encoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE ):
snake_case_ = re_encoder_block_resnet.match(_SCREAMING_SNAKE_CASE )
snake_case_ = regex_match.groups()
snake_case_ = int(groups[2] ) * 2 + int(groups[3] )
snake_case_ = {"""1""": 1, """3""": 2}[groups[-2]]
snake_case_ = f"""encoders.{groups[0]}.level_blocks.{groups[1]}.downsample_block.{block_index}."""
snake_case_ = f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
snake_case_ = prefix + resnet_block
snake_case_ = re_encoder_block_resnet.sub(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif re_encoder_block_proj_out.fullmatch(_SCREAMING_SNAKE_CASE ):
snake_case_ = re_encoder_block_proj_out.match(_SCREAMING_SNAKE_CASE )
snake_case_ = regex_match.groups()
snake_case_ = f"""encoders.{groups[0]}.level_blocks.{groups[1]}.proj_out.{groups[-1]}"""
snake_case_ = re_encoder_block_proj_out.sub(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# rename vqvae.decoder keys
elif re_decoder_block_conv_out.fullmatch(_SCREAMING_SNAKE_CASE ):
snake_case_ = re_decoder_block_conv_out.match(_SCREAMING_SNAKE_CASE )
snake_case_ = regex_match.groups()
snake_case_ = int(groups[2] ) * 2 + int(groups[3] ) - 2
snake_case_ = f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}.{groups[-1]}"""
snake_case_ = re_decoder_block_conv_out.sub(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif re_decoder_block_resnet.fullmatch(_SCREAMING_SNAKE_CASE ):
snake_case_ = re_decoder_block_resnet.match(_SCREAMING_SNAKE_CASE )
snake_case_ = regex_match.groups()
snake_case_ = int(groups[2] ) * 2 + int(groups[3] ) - 2
snake_case_ = {"""1""": 1, """3""": 2}[groups[-2]]
snake_case_ = f"""decoders.{groups[0]}.level_blocks.{groups[1]}.upsample_block.{block_index}."""
snake_case_ = f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
snake_case_ = prefix + resnet_block
snake_case_ = re_decoder_block_resnet.sub(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif re_decoder_block_proj_in.fullmatch(_SCREAMING_SNAKE_CASE ):
snake_case_ = re_decoder_block_proj_in.match(_SCREAMING_SNAKE_CASE )
snake_case_ = regex_match.groups()
snake_case_ = f"""decoders.{groups[0]}.level_blocks.{groups[1]}.proj_in.{groups[-1]}"""
snake_case_ = re_decoder_block_proj_in.sub(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# rename prior cond.model to upsampler.upsample_block and resnet
elif re_prior_cond_conv_out.fullmatch(_SCREAMING_SNAKE_CASE ):
snake_case_ = re_prior_cond_conv_out.match(_SCREAMING_SNAKE_CASE )
snake_case_ = regex_match.groups()
snake_case_ = int(groups[1] ) * 2 + int(groups[2] ) - 2
snake_case_ = f"""conditioner_blocks.upsampler.upsample_block.{block_index}.{groups[-1]}"""
snake_case_ = re_prior_cond_conv_out.sub(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif re_prior_cond_resnet.fullmatch(_SCREAMING_SNAKE_CASE ):
snake_case_ = re_prior_cond_resnet.match(_SCREAMING_SNAKE_CASE )
snake_case_ = regex_match.groups()
snake_case_ = int(groups[1] ) * 2 + int(groups[2] ) - 2
snake_case_ = {"""1""": 1, """3""": 2}[groups[-2]]
snake_case_ = f"""conditioner_blocks.upsampler.upsample_block.{block_index}."""
snake_case_ = f"""resnet_block.{groups[-3]}.conv1d_{conv_index}.{groups[-1]}"""
snake_case_ = prefix + resnet_block
snake_case_ = re_prior_cond_resnet.sub(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
elif re_prior_cond_proj_in.fullmatch(_SCREAMING_SNAKE_CASE ):
snake_case_ = re_prior_cond_proj_in.match(_SCREAMING_SNAKE_CASE )
snake_case_ = regex_match.groups()
snake_case_ = f"""conditioner_blocks.upsampler.proj_in.{groups[-1]}"""
snake_case_ = re_prior_cond_proj_in.sub(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# keep original key
else:
snake_case_ = original_key
snake_case_ = replace_key(_SCREAMING_SNAKE_CASE )
if f"""{key_prefix}.{key}""" not in model_state_dict or key is None:
print(f"""failed converting {original_key} to {key}, does not match""" )
# handle missmatched shape
elif value.shape != model_state_dict[f"""{key_prefix}.{key}"""].shape:
snake_case_ = model_state_dict[f"""{key_prefix}.{key}"""]
print(f"""{original_key}-> {key} : \nshape {val.shape} and { value.shape}, do not match""" )
snake_case_ = original_key
snake_case_ = original_key
snake_case_ = value
return new_dict
@torch.no_grad()
def _a ( _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Optional[int]:
for file in MODEL_MAPPING[model_name]:
if not os.path.isfile(f"""{pytorch_dump_folder_path}/{file.split("/" )[-1]}""" ):
snake_case_ = requests.get(f"""{PREFIX}{file}""" , allow_redirects=_SCREAMING_SNAKE_CASE )
os.makedirs(f"""{pytorch_dump_folder_path}/""" , exist_ok=_SCREAMING_SNAKE_CASE )
open(f"""{pytorch_dump_folder_path}/{file.split("/" )[-1]}""" , """wb""" ).write(r.content )
snake_case_ = MODEL_MAPPING[model_name.split("""/""" )[-1]]
snake_case_ = JukeboxConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
snake_case_ = JukeboxModel(_SCREAMING_SNAKE_CASE )
snake_case_ = []
snake_case_ = {}
for i, dict_name in enumerate(_SCREAMING_SNAKE_CASE ):
snake_case_ = torch.load(f"""{pytorch_dump_folder_path}/{dict_name.split("/" )[-1]}""" )["""model"""]
snake_case_ = {}
for k in old_dic.keys():
if k.endswith(""".b""" ):
snake_case_ = old_dic[k]
elif k.endswith(""".w""" ):
snake_case_ = old_dic[k]
elif "level_2" not in dict_name and "cond.model." in k:
snake_case_ = old_dic[k]
else:
snake_case_ = old_dic[k]
snake_case_ = """vqvae""" if i == 0 else f"""priors.{3 - i}"""
snake_case_ = fix_jukebox_keys(_SCREAMING_SNAKE_CASE , model.state_dict() , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
weight_dict.append(_SCREAMING_SNAKE_CASE )
snake_case_ = weight_dict.pop(0 )
model.vqvae.load_state_dict(_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
model.priors[i].load_state_dict(weight_dict[2 - i] )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
with open(f"""{pytorch_dump_folder_path}/mapping.json""" , """w""" ) as txtfile:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
return weight_dict
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='jukebox-5b-lyrics',
type=str,
help='Name of the model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='jukebox-5b-lyrics-converted',
type=str,
help='Path to the output PyTorch model directory.',
)
__SCREAMING_SNAKE_CASE : str = parser.parse_args()
convert_openai_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 2 | 1 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowerCAmelCase__ ( unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
__UpperCamelCase = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def _SCREAMING_SNAKE_CASE ( self : Tuple , lowercase_ : Dict , lowercase_ : Union[str, Any] , lowercase_ : str):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : int = TextaTextGenerationPipeline(model=lowercase_ , tokenizer=lowercase_)
return generator, ["Something to write", "Something else"]
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , lowercase_ : int , lowercase_ : Optional[Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Any = generator('''Something there''')
self.assertEqual(lowercase_ , [{'''generated_text''': ANY(lowercase_)}])
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]['''generated_text'''].startswith('''Something there'''))
SCREAMING_SNAKE_CASE_ : Tuple = generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=lowercase_)
self.assertEqual(
lowercase_ , [
[{'''generated_text''': ANY(lowercase_)}, {'''generated_text''': ANY(lowercase_)}],
[{'''generated_text''': ANY(lowercase_)}, {'''generated_text''': ANY(lowercase_)}],
] , )
SCREAMING_SNAKE_CASE_ : Any = generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=lowercase_)
self.assertEqual(
lowercase_ , [
[{'''generated_text''': ANY(lowercase_)}, {'''generated_text''': ANY(lowercase_)}],
[{'''generated_text''': ANY(lowercase_)}, {'''generated_text''': ANY(lowercase_)}],
] , )
with self.assertRaises(lowercase_):
generator(4)
@require_torch
def _SCREAMING_SNAKE_CASE ( self : Dict):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : str = pipeline('''text2text-generation''' , model='''patrickvonplaten/t5-tiny-random''' , framework='''pt''')
# do_sample=False necessary for reproducibility
SCREAMING_SNAKE_CASE_ : Union[str, Any] = generator('''Something there''' , do_sample=lowercase_)
self.assertEqual(lowercase_ , [{'''generated_text''': ''''''}])
SCREAMING_SNAKE_CASE_ : Optional[Any] = 3
SCREAMING_SNAKE_CASE_ : List[Any] = generator(
'''Something there''' , num_return_sequences=lowercase_ , num_beams=lowercase_ , )
SCREAMING_SNAKE_CASE_ : Any = [
{'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide Beide'''},
{'''generated_text''': '''Beide Beide Beide Beide Beide Beide Beide Beide'''},
{'''generated_text''': ''''''},
]
self.assertEqual(lowercase_ , lowercase_)
SCREAMING_SNAKE_CASE_ : str = generator('''This is a test''' , do_sample=lowercase_ , num_return_sequences=2 , return_tensors=lowercase_)
self.assertEqual(
lowercase_ , [
{'''generated_token_ids''': ANY(torch.Tensor)},
{'''generated_token_ids''': ANY(torch.Tensor)},
] , )
SCREAMING_SNAKE_CASE_ : int = generator.model.config.eos_token_id
SCREAMING_SNAKE_CASE_ : List[Any] = '''<pad>'''
SCREAMING_SNAKE_CASE_ : Dict = generator(
['''This is a test''', '''This is a second test'''] , do_sample=lowercase_ , num_return_sequences=2 , batch_size=2 , return_tensors=lowercase_ , )
self.assertEqual(
lowercase_ , [
[
{'''generated_token_ids''': ANY(torch.Tensor)},
{'''generated_token_ids''': ANY(torch.Tensor)},
],
[
{'''generated_token_ids''': ANY(torch.Tensor)},
{'''generated_token_ids''': ANY(torch.Tensor)},
],
] , )
@require_tf
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] = pipeline('''text2text-generation''' , model='''patrickvonplaten/t5-tiny-random''' , framework='''tf''')
# do_sample=False necessary for reproducibility
SCREAMING_SNAKE_CASE_ : int = generator('''Something there''' , do_sample=lowercase_)
self.assertEqual(lowercase_ , [{'''generated_text''': ''''''}])
| 512 |
"""simple docstring"""
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = 42
__UpperCamelCase = 42
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 512 | 1 |
'''simple docstring'''
import random
def UpperCAmelCase ( lowerCamelCase_ :Any , lowerCamelCase_ :List[Any] , lowerCamelCase_ :Dict ):
'''simple docstring'''
snake_case_ : int = a[left_index]
snake_case_ : Optional[Any] = left_index + 1
for j in range(left_index + 1 , lowerCamelCase_ ):
if a[j] < pivot:
snake_case_ , snake_case_ : int = a[i], a[j]
i += 1
snake_case_ , snake_case_ : Optional[int] = a[i - 1], a[left_index]
return i - 1
def UpperCAmelCase ( lowerCamelCase_ :List[Any] , lowerCamelCase_ :Union[str, Any] , lowerCamelCase_ :List[Any] ):
'''simple docstring'''
if left < right:
snake_case_ : List[Any] = random.randint(lowerCamelCase_ , right - 1 )
snake_case_ , snake_case_ : int = (
a[left],
a[pivot],
) # switches the pivot with the left most bound
snake_case_ : List[str] = partition(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
quick_sort_random(
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) # recursive quicksort to the left of the pivot point
quick_sort_random(
lowerCamelCase_ , pivot_index + 1 , lowerCamelCase_ ) # recursive quicksort to the right of the pivot point
def UpperCAmelCase ( ):
'''simple docstring'''
snake_case_ : Tuple = input("""Enter numbers separated by a comma:\n""" ).strip()
snake_case_ : List[str] = [int(lowerCamelCase_ ) for item in user_input.split(""",""" )]
quick_sort_random(lowerCamelCase_ , 0 , len(lowerCamelCase_ ) )
print(lowerCamelCase_ )
if __name__ == "__main__":
main() | 267 |
'''simple docstring'''
import math
def UpperCAmelCase ( lowerCamelCase_ :list , lowerCamelCase_ :int ):
'''simple docstring'''
snake_case_ : Union[str, Any] = len(lowerCamelCase_ )
snake_case_ : List[Any] = int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
snake_case_ : str = 0
while arr[min(lowerCamelCase_ , lowerCamelCase_ ) - 1] < x:
snake_case_ : Any = step
step += int(math.floor(math.sqrt(lowerCamelCase_ ) ) )
if prev >= n:
return -1
while arr[prev] < x:
snake_case_ : Optional[Any] = prev + 1
if prev == min(lowerCamelCase_ , lowerCamelCase_ ):
return -1
if arr[prev] == x:
return prev
return -1
if __name__ == "__main__":
__A : Any = input('Enter numbers separated by a comma:\n').strip()
__A : Union[str, Any] = [int(item) for item in user_input.split(',')]
__A : Optional[int] = int(input('Enter the number to be searched:\n'))
__A : int = jump_search(arr, x)
if res == -1:
print('Number not found!')
else:
print(F'Number {x} is at index {res}') | 267 | 1 |
"""simple docstring"""
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
_UpperCamelCase = random.Random()
def _a ( _snake_case , _snake_case=1.0 , _snake_case=None , _snake_case=None ):
"""simple docstring"""
if rng is None:
UpperCAmelCase = global_rng
UpperCAmelCase = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class lowerCamelCase__ ( unittest.TestCase ):
def __init__( self ,A ,A=7 ,A=400 ,A=2_000 ,A=10 ,A=160 ,A=8 ,A=0.0 ,A=4_000 ,A=False ,A=True ,):
UpperCAmelCase = parent
UpperCAmelCase = batch_size
UpperCAmelCase = min_seq_length
UpperCAmelCase = max_seq_length
UpperCAmelCase = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCAmelCase = padding_value
UpperCAmelCase = sampling_rate
UpperCAmelCase = return_attention_mask
UpperCAmelCase = do_normalize
UpperCAmelCase = feature_size
UpperCAmelCase = chunk_length
UpperCAmelCase = hop_length
def _UpperCamelCase ( self ):
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def _UpperCamelCase ( self ,A=False ,A=False ):
def _flatten(A ):
return list(itertools.chain(*SCREAMING_SNAKE_CASE__ ) )
if equal_length:
UpperCAmelCase = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
UpperCAmelCase = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff )
]
if numpify:
UpperCAmelCase = [np.asarray(SCREAMING_SNAKE_CASE__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowerCamelCase__ ( snake_case , unittest.TestCase ):
SCREAMING_SNAKE_CASE = WhisperFeatureExtractor if is_speech_available() else None
def _UpperCamelCase ( self ):
UpperCAmelCase = WhisperFeatureExtractionTester(self )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase = feat_extract_first.save_pretrained(SCREAMING_SNAKE_CASE__ )[0]
check_json_file_has_correct_format(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase = self.feature_extraction_class.from_pretrained(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase = feat_extract_first.to_dict()
UpperCAmelCase = feat_extract_second.to_dict()
UpperCAmelCase = feat_extract_first.mel_filters
UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase = os.path.join(SCREAMING_SNAKE_CASE__ ,"""feat_extract.json""" )
feat_extract_first.to_json_file(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase = self.feature_extraction_class.from_json_file(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase = feat_extract_first.to_dict()
UpperCAmelCase = feat_extract_second.to_dict()
UpperCAmelCase = feat_extract_first.mel_filters
UpperCAmelCase = feat_extract_second.mel_filters
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) )
self.assertEqual(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ )
def _UpperCamelCase ( self ):
# Tests that all call wrap to encode_plus and batch_encode_plus
UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
UpperCAmelCase = [floats_list((1, x) )[0] for x in range(800 ,1_400 ,200 )]
UpperCAmelCase = [np.asarray(SCREAMING_SNAKE_CASE__ ) for speech_input in speech_inputs]
# Test feature size
UpperCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE__ ,padding="""max_length""" ,return_tensors="""np""" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
UpperCAmelCase = feature_extractor(speech_inputs[0] ,return_tensors="""np""" ).input_features
UpperCAmelCase = feature_extractor(np_speech_inputs[0] ,return_tensors="""np""" ).input_features
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=1e-3 ) )
# Test batched
UpperCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE__ ,return_tensors="""np""" ).input_features
UpperCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE__ ,return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
UpperCAmelCase = [floats_list((1, x) )[0] for x in (800, 800, 800)]
UpperCAmelCase = np.asarray(SCREAMING_SNAKE_CASE__ )
UpperCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE__ ,return_tensors="""np""" ).input_features
UpperCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE__ ,return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=1e-3 ) )
# Test truncation required
UpperCAmelCase = [floats_list((1, x) )[0] for x in range(200 ,(feature_extractor.n_samples + 500) ,200 )]
UpperCAmelCase = [np.asarray(SCREAMING_SNAKE_CASE__ ) for speech_input in speech_inputs]
UpperCAmelCase = [x[: feature_extractor.n_samples] for x in speech_inputs]
UpperCAmelCase = [np.asarray(SCREAMING_SNAKE_CASE__ ) for speech_input in speech_inputs_truncated]
UpperCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE__ ,return_tensors="""np""" ).input_features
UpperCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE__ ,return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ):
self.assertTrue(np.allclose(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ,atol=1e-3 ) )
def _UpperCamelCase ( self ):
import torch
UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCAmelCase = np.random.rand(100 ,32 ).astype(np.floataa )
UpperCAmelCase = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
UpperCAmelCase = feature_extractor.pad([{"""input_features""": inputs}] ,return_tensors="""np""" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
UpperCAmelCase = feature_extractor.pad([{"""input_features""": inputs}] ,return_tensors="""pt""" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def _UpperCamelCase ( self ,A ):
UpperCAmelCase = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,"""clean""" ,split="""validation""" )
# automatic decoding with librispeech
UpperCAmelCase = ds.sort("""id""" ).select(range(SCREAMING_SNAKE_CASE__ ) )[:num_samples]['audio']
return [x["array"] for x in speech_samples]
def _UpperCamelCase ( self ):
# fmt: off
UpperCAmelCase = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
UpperCAmelCase = self._load_datasamples(1 )
UpperCAmelCase = WhisperFeatureExtractor()
UpperCAmelCase = feature_extractor(SCREAMING_SNAKE_CASE__ ,return_tensors="""pt""" ).input_features
self.assertEqual(input_features.shape ,(1, 80, 3_000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] ,SCREAMING_SNAKE_CASE__ ,atol=1e-4 ) )
def _UpperCamelCase ( self ):
UpperCAmelCase = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
UpperCAmelCase = self._load_datasamples(1 )[0]
UpperCAmelCase = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
UpperCAmelCase = feat_extract.zero_mean_unit_var_norm([audio] ,attention_mask=SCREAMING_SNAKE_CASE__ )[0]
self.assertTrue(np.all(np.mean(SCREAMING_SNAKE_CASE__ ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(SCREAMING_SNAKE_CASE__ ) - 1 ) < 1e-3 ) )
| 341 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="""%(message)s""")
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> np.ndarray:
return input_array.reshape((input_array.size, 1) )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> np.ndarray:
__lowerCamelCase : str = np.nan
for i in range(lowerCamelCase__ ):
__lowerCamelCase : int = features[:, labels == i]
__lowerCamelCase : Optional[int] = data.mean(1 )
# Centralize the data of class i
__lowerCamelCase : int = data - column_reshape(lowerCamelCase__ )
if i > 0:
# If covariance_sum is not None
covariance_sum += np.dot(lowerCamelCase__ , centered_data.T )
else:
# If covariance_sum is np.nan (i.e. first loop)
__lowerCamelCase : Union[str, Any] = np.dot(lowerCamelCase__ , centered_data.T )
return covariance_sum / features.shape[1]
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> np.ndarray:
__lowerCamelCase : Optional[Any] = features.mean(1 )
__lowerCamelCase : Union[str, Any] = np.nan
for i in range(lowerCamelCase__ ):
__lowerCamelCase : Optional[Any] = features[:, labels == i]
__lowerCamelCase : Union[str, Any] = data.shape[1]
__lowerCamelCase : Union[str, Any] = data.mean(1 )
if i > 0:
# If covariance_sum is not None
covariance_sum += device_data * np.dot(
column_reshape(lowerCamelCase__ ) - column_reshape(lowerCamelCase__ ) , (column_reshape(lowerCamelCase__ ) - column_reshape(lowerCamelCase__ )).T , )
else:
# If covariance_sum is np.nan (i.e. first loop)
__lowerCamelCase : List[str] = device_data * np.dot(
column_reshape(lowerCamelCase__ ) - column_reshape(lowerCamelCase__ ) , (column_reshape(lowerCamelCase__ ) - column_reshape(lowerCamelCase__ )).T , )
return covariance_sum / features.shape[1]
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> np.ndarray:
# Check if the features have been loaded
if features.any():
__lowerCamelCase : Tuple = features.mean(1 )
# Center the dataset
__lowerCamelCase : Any = features - np.reshape(lowerCamelCase__ , (data_mean.size, 1) )
__lowerCamelCase : Optional[int] = np.dot(lowerCamelCase__ , centered_data.T ) / features.shape[1]
__lowerCamelCase , __lowerCamelCase : List[Any] = np.linalg.eigh(lowerCamelCase__ )
# Take all the columns in the reverse order (-1), and then takes only the first
__lowerCamelCase : Dict = eigenvectors[:, ::-1][:, 0:dimensions]
# Project the database on the new space
__lowerCamelCase : int = np.dot(filtered_eigenvectors.T , lowerCamelCase__ )
logging.info('Principal Component Analysis computed' )
return projected_data
else:
logging.basicConfig(level=logging.ERROR , format='%(message)s' , force=lowerCamelCase__ )
logging.error('Dataset empty' )
raise AssertionError
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> np.ndarray:
assert classes > dimensions
# Check if features have been already loaded
if features.any:
__lowerCamelCase , __lowerCamelCase : Dict = eigh(
covariance_between_classes(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) , covariance_within_classes(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) , )
__lowerCamelCase : Union[str, Any] = eigenvectors[:, ::-1][:, :dimensions]
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase : str = np.linalg.svd(lowerCamelCase__ )
__lowerCamelCase : int = svd_matrix[:, 0:dimensions]
__lowerCamelCase : Optional[int] = np.dot(filtered_svd_matrix.T , lowerCamelCase__ )
logging.info('Linear Discriminant Analysis computed' )
return projected_data
else:
logging.basicConfig(level=logging.ERROR , format='%(message)s' , force=lowerCamelCase__ )
logging.error('Dataset empty' )
raise AssertionError
def SCREAMING_SNAKE_CASE__ ( ) -> None:
# Create dummy dataset with 2 classes and 3 features
__lowerCamelCase : Optional[int] = np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]] )
__lowerCamelCase : Optional[int] = np.array([0, 0, 0, 1, 1] )
__lowerCamelCase : Optional[Any] = 2
__lowerCamelCase : Tuple = 2
# Assert that the function raises an AssertionError if dimensions > classes
with pytest.raises(lowerCamelCase__ ) as error_info:
__lowerCamelCase : int = linear_discriminant_analysis(
lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
if isinstance(lowerCamelCase__ , np.ndarray ):
raise AssertionError(
'Did not raise AssertionError for dimensions > classes' )
assert error_info.type is AssertionError
def SCREAMING_SNAKE_CASE__ ( ) -> None:
__lowerCamelCase : Dict = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]] )
__lowerCamelCase : Dict = 2
__lowerCamelCase : int = np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]] )
with pytest.raises(lowerCamelCase__ ) as error_info:
__lowerCamelCase : Optional[Any] = principal_component_analysis(lowerCamelCase__ , lowerCamelCase__ )
if not np.allclose(lowerCamelCase__ , lowerCamelCase__ ):
raise AssertionError
assert error_info.type is AssertionError
if __name__ == "__main__":
import doctest
doctest.testmod()
| 652 | 0 |
'''simple docstring'''
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class A :
def __init__( self , snake_case_ , snake_case_=1_3 , snake_case_=3_0 , snake_case_=2 , snake_case_=3 , snake_case_=True , snake_case_=True , snake_case_=3_2 , snake_case_=5 , snake_case_=4 , snake_case_=3_7 , snake_case_="gelu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=1_0 , snake_case_=0.02 , snake_case_=3 , snake_case_=None , snake_case_=2 , ) -> Dict:
_a = parent
_a = batch_size
_a = image_size
_a = patch_size
_a = num_channels
_a = is_training
_a = use_labels
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = type_sequence_label_size
_a = initializer_range
_a = scope
_a = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
_a = (image_size // patch_size) ** 2
_a = num_patches + 2
def __lowerCAmelCase ( self ) -> List[Any]:
_a = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_a = None
if self.use_labels:
_a = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_a = self.get_config()
return config, pixel_values, labels
def __lowerCAmelCase ( self ) -> Optional[int]:
return DeiTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=snake_case_ , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ ) -> Optional[int]:
_a = DeiTModel(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_a = model(snake_case_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ ) -> List[Any]:
_a = DeiTForMaskedImageModeling(config=snake_case_ )
model.to(snake_case_ )
model.eval()
_a = model(snake_case_ )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
_a = 1
_a = DeiTForMaskedImageModeling(snake_case_ )
model.to(snake_case_ )
model.eval()
_a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_a = model(snake_case_ )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_ ) -> Any:
_a = self.type_sequence_label_size
_a = DeiTForImageClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
_a = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
_a = 1
_a = DeiTForImageClassification(snake_case_ )
model.to(snake_case_ )
model.eval()
_a = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
_a = model(snake_case_ , labels=snake_case_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def __lowerCAmelCase ( self ) -> Tuple:
_a = self.prepare_config_and_inputs()
(
(
_a
) , (
_a
) , (
_a
) ,
) = config_and_inputs
_a = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class A ( a , a , unittest.TestCase ):
__UpperCAmelCase : List[str] = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
__UpperCAmelCase : Tuple = (
{
"""feature-extraction""": DeiTModel,
"""image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
__UpperCAmelCase : Any = False
__UpperCAmelCase : int = False
__UpperCAmelCase : List[Any] = False
def __lowerCAmelCase ( self ) -> int:
_a = DeiTModelTester(self )
_a = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ , hidden_size=3_7 )
def __lowerCAmelCase ( self ) -> List[str]:
self.config_tester.run_common_tests()
@unittest.skip(reason="DeiT does not use inputs_embeds" )
def __lowerCAmelCase ( self ) -> Optional[Any]:
pass
def __lowerCAmelCase ( self ) -> Any:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(snake_case_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
_a = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case_ , nn.Linear ) )
def __lowerCAmelCase ( self ) -> int:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_a = model_class(snake_case_ )
_a = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_a = [*signature.parameters.keys()]
_a = ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case_ )
def __lowerCAmelCase ( self ) -> Optional[Any]:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case_ )
def __lowerCAmelCase ( self ) -> int:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*snake_case_ )
def __lowerCAmelCase ( self ) -> Dict:
_a = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case_ )
def __lowerCAmelCase ( self , snake_case_ , snake_case_ , snake_case_=False ) -> Dict:
_a = super()._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def __lowerCAmelCase ( self ) -> Union[str, Any]:
if not self.model_tester.is_training:
return
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(snake_case_ )
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
_a = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_a = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
_a = model(**snake_case_ ).loss
loss.backward()
def __lowerCAmelCase ( self ) -> List[Any]:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
_a = False
_a = True
for model_class in self.all_model_classes:
if model_class in get_values(snake_case_ ) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
_a = model_class(snake_case_ )
model.gradient_checkpointing_enable()
model.to(snake_case_ )
model.train()
_a = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
_a = model(**snake_case_ ).loss
loss.backward()
def __lowerCAmelCase ( self ) -> Optional[int]:
_a , _a = self.model_tester.prepare_config_and_inputs_for_common()
_a = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(snake_case_ ),
*get_values(snake_case_ ),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'''Testing {model_class} with {problem_type['title']}''' ):
_a = problem_type["title"]
_a = problem_type["num_labels"]
_a = model_class(snake_case_ )
model.to(snake_case_ )
model.train()
_a = self._prepare_for_class(snake_case_ , snake_case_ , return_labels=snake_case_ )
if problem_type["num_labels"] > 1:
_a = inputs["labels"].unsqueeze(1 ).repeat(1 , problem_type["num_labels"] )
_a = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=snake_case_ ) as warning_list:
_a = model(**snake_case_ ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
F'''Something is going wrong in the regression problem: intercepted {w.message}''' )
loss.backward()
@slow
def __lowerCAmelCase ( self ) -> str:
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_a = DeiTModel.from_pretrained(snake_case_ )
self.assertIsNotNone(snake_case_ )
def _lowercase ( ):
_a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class A ( unittest.TestCase ):
@cached_property
def __lowerCAmelCase ( self ) -> Union[str, Any]:
return (
DeiTImageProcessor.from_pretrained("facebook/deit-base-distilled-patch16-224" )
if is_vision_available()
else None
)
@slow
def __lowerCAmelCase ( self ) -> Dict:
_a = DeiTForImageClassificationWithTeacher.from_pretrained("facebook/deit-base-distilled-patch16-224" ).to(
snake_case_ )
_a = self.default_image_processor
_a = prepare_img()
_a = image_processor(images=snake_case_ , return_tensors="pt" ).to(snake_case_ )
# forward pass
with torch.no_grad():
_a = model(**snake_case_ )
# verify the logits
_a = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape , snake_case_ )
_a = torch.tensor([-1.0_266, 0.1_912, -1.2_861] ).to(snake_case_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , snake_case_ , atol=1E-4 ) )
@slow
@require_accelerate
@require_torch_gpu
def __lowerCAmelCase ( self ) -> Any:
_a = DeiTModel.from_pretrained(
"facebook/deit-base-distilled-patch16-224" , torch_dtype=torch.floataa , device_map="auto" )
_a = self.default_image_processor
_a = prepare_img()
_a = image_processor(images=snake_case_ , return_tensors="pt" )
_a = inputs.pixel_values.to(snake_case_ )
# forward pass to make sure inference works in fp16
with torch.no_grad():
_a = model(snake_case_ )
| 691 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import add_start_docstrings
__snake_case : Optional[int] = R"\n [`RagConfig`] stores the configuration of a *RagModel*. Configuration objects inherit from [`PretrainedConfig`] and\n can be used to control the model outputs. Read the documentation from [`PretrainedConfig`] for more information.\n\n Args:\n title_sep (`str`, *optional*, defaults to `\" / \"`):\n Separator inserted between the title and the text of the retrieved document when calling [`RagRetriever`].\n doc_sep (`str`, *optional*, defaults to `\" // \"`):\n Separator inserted between the text of the retrieved document and the original input when calling\n [`RagRetriever`].\n n_docs (`int`, *optional*, defaults to 5):\n Number of documents to retrieve.\n max_combined_length (`int`, *optional*, defaults to 300):\n Max length of contextualized input returned by [`~RagRetriever.__call__`].\n retrieval_vector_size (`int`, *optional*, defaults to 768):\n Dimensionality of the document embeddings indexed by [`RagRetriever`].\n retrieval_batch_size (`int`, *optional*, defaults to 8):\n Retrieval batch size, defined as the number of queries issues concurrently to the faiss index encapsulated\n [`RagRetriever`].\n dataset (`str`, *optional*, defaults to `\"wiki_dpr\"`):\n A dataset identifier of the indexed dataset in HuggingFace Datasets (list all available datasets and ids\n using `datasets.list_datasets()`).\n dataset_split (`str`, *optional*, defaults to `\"train\"`)\n Which split of the `dataset` to load.\n index_name (`str`, *optional*, defaults to `\"compressed\"`)\n The index name of the index associated with the `dataset`. One can choose between `\"legacy\"`, `\"exact\"` and\n `\"compressed\"`.\n index_path (`str`, *optional*)\n The path to the serialized faiss index on disk.\n passages_path (`str`, *optional*):\n A path to text passages compatible with the faiss index. Required if using\n [`~models.rag.retrieval_rag.LegacyIndex`]\n use_dummy_dataset (`bool`, *optional*, defaults to `False`)\n Whether to load a \"dummy\" variant of the dataset specified by `dataset`.\n label_smoothing (`float`, *optional*, defaults to 0.0):\n Only relevant if `return_loss` is set to `True`. Controls the `epsilon` parameter value for label smoothing\n in the loss calculation. If set to 0, no label smoothing is performed.\n do_marginalize (`bool`, *optional*, defaults to `False`):\n If `True`, the logits are marginalized over all documents by making use of\n `torch.nn.functional.log_softmax`.\n reduce_loss (`bool`, *optional*, defaults to `False`):\n Whether or not to reduce the NLL loss using the `torch.Tensor.sum` operation.\n do_deduplication (`bool`, *optional*, defaults to `True`):\n Whether or not to deduplicate the generations from different context documents for a given input. Has to be\n set to `False` if used while training with distributed backend.\n exclude_bos_score (`bool`, *optional*, defaults to `False`):\n Whether or not to disregard the BOS token when computing the loss.\n output_retrieved(`bool`, *optional*, defaults to `False`):\n If set to `True`, `retrieved_doc_embeds`, `retrieved_doc_ids`, `context_input_ids` and\n `context_attention_mask` are returned. See returned tensors for more detail.\n use_cache (`bool`, *optional*, defaults to `True`):\n Whether or not the model should return the last key/values attentions (not used by all models).\n forced_eos_token_id (`int`, *optional*):\n The id of the token to force as the last generated token when `max_length` is reached. Usually set to\n `eos_token_id`.\n"
@add_start_docstrings(a )
class A ( a ):
__UpperCAmelCase : Dict = """rag"""
__UpperCAmelCase : Dict = True
def __init__( self , snake_case_=None , snake_case_=True , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_=" / " , snake_case_=" // " , snake_case_=5 , snake_case_=3_0_0 , snake_case_=7_6_8 , snake_case_=8 , snake_case_="wiki_dpr" , snake_case_="train" , snake_case_="compressed" , snake_case_=None , snake_case_=None , snake_case_=False , snake_case_=False , snake_case_=0.0 , snake_case_=True , snake_case_=False , snake_case_=False , snake_case_=False , snake_case_=True , snake_case_=None , **snake_case_ , ) -> Optional[Any]:
super().__init__(
bos_token_id=snake_case_ , pad_token_id=snake_case_ , eos_token_id=snake_case_ , decoder_start_token_id=snake_case_ , forced_eos_token_id=snake_case_ , is_encoder_decoder=snake_case_ , prefix=snake_case_ , vocab_size=snake_case_ , **snake_case_ , )
assert (
"question_encoder" in kwargs and "generator" in kwargs
), "Config has to be initialized with question_encoder and generator config"
_a = kwargs.pop("question_encoder" )
_a = question_encoder_config.pop("model_type" )
_a = kwargs.pop("generator" )
_a = decoder_config.pop("model_type" )
from ..auto.configuration_auto import AutoConfig
_a = AutoConfig.for_model(snake_case_ , **snake_case_ )
_a = AutoConfig.for_model(snake_case_ , **snake_case_ )
_a = reduce_loss
_a = label_smoothing
_a = exclude_bos_score
_a = do_marginalize
_a = title_sep
_a = doc_sep
_a = n_docs
_a = max_combined_length
_a = dataset
_a = dataset_split
_a = index_name
_a = retrieval_vector_size
_a = retrieval_batch_size
_a = passages_path
_a = index_path
_a = use_dummy_dataset
_a = output_retrieved
_a = do_deduplication
_a = use_cache
if self.forced_eos_token_id is None:
_a = getattr(self.generator , "forced_eos_token_id" , snake_case_ )
@classmethod
def __lowerCAmelCase ( cls , snake_case_ , snake_case_ , **snake_case_ ) -> PretrainedConfig:
return cls(question_encoder=question_encoder_config.to_dict() , generator=generator_config.to_dict() , **snake_case_ )
def __lowerCAmelCase ( self ) -> Optional[int]:
_a = copy.deepcopy(self.__dict__ )
_a = self.question_encoder.to_dict()
_a = self.generator.to_dict()
_a = self.__class__.model_type
return output
| 691 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
SCREAMING_SNAKE_CASE_ : Optional[int] = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
'''tokenization_biogpt''': ['''BioGptTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ : List[str] = [
'''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''BioGptForCausalLM''',
'''BioGptForTokenClassification''',
'''BioGptForSequenceClassification''',
'''BioGptModel''',
'''BioGptPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
from .tokenization_biogpt import BioGptTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_biogpt import (
BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ : Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 375 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ : Dict = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {
'''RWKV/rwkv-4-169m-pile''': '''https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-430m-pile''': '''https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-1b5-pile''': '''https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-3b-pile''': '''https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-7b-pile''': '''https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json''',
'''RWKV/rwkv-4-14b-pile''': '''https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json''',
'''RWKV/rwkv-raven-1b5''': '''https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json''',
'''RWKV/rwkv-raven-3b''': '''https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json''',
'''RWKV/rwkv-raven-7b''': '''https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json''',
'''RWKV/rwkv-raven-14b''': '''https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json''',
}
class snake_case_ ( UpperCAmelCase_ ):
'''simple docstring'''
__UpperCamelCase = '''rwkv'''
__UpperCamelCase = {'''max_position_embeddings''': '''context_length'''}
def __init__( self : List[str] , __lowerCamelCase : Tuple=50_277 , __lowerCamelCase : List[str]=1_024 , __lowerCamelCase : List[Any]=4_096 , __lowerCamelCase : Tuple=32 , __lowerCamelCase : List[str]=None , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : Optional[Any]=1E-5 , __lowerCamelCase : Tuple=0 , __lowerCamelCase : int=0 , __lowerCamelCase : Dict=6 , __lowerCamelCase : List[Any]=False , __lowerCamelCase : Tuple=True , **__lowerCamelCase : Optional[int] , ) -> List[Any]:
'''simple docstring'''
__lowercase = vocab_size
__lowercase = context_length
__lowercase = hidden_size
__lowercase = num_hidden_layers
__lowercase = attention_hidden_size if attention_hidden_size is not None else hidden_size
__lowercase = intermediate_size if intermediate_size is not None else 4 * hidden_size
__lowercase = layer_norm_epsilon
__lowercase = rescale_every
__lowercase = use_cache
__lowercase = bos_token_id
__lowercase = eos_token_id
super().__init__(
tie_word_embeddings=__lowerCamelCase , bos_token_id=__lowerCamelCase , eos_token_id=__lowerCamelCase , **__lowerCamelCase )
| 375 | 1 |
"""simple docstring"""
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ )-> str:
"""simple docstring"""
def get_masked_lm_array(UpperCAmelCase_ ):
UpperCamelCase = F"masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"
UpperCamelCase = tf.train.load_variable(_lowerCamelCase , _lowerCamelCase )
if "kernel" in name:
UpperCamelCase = array.transpose()
return torch.from_numpy(_lowerCamelCase )
def get_encoder_array(UpperCAmelCase_ ):
UpperCamelCase = F"encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"
UpperCamelCase = tf.train.load_variable(_lowerCamelCase , _lowerCamelCase )
if "kernel" in name:
UpperCamelCase = array.transpose()
return torch.from_numpy(_lowerCamelCase )
def get_encoder_layer_array(UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCamelCase = F"encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"
UpperCamelCase = tf.train.load_variable(_lowerCamelCase , _lowerCamelCase )
if "kernel" in name:
UpperCamelCase = array.transpose()
return torch.from_numpy(_lowerCamelCase )
def get_encoder_attention_layer_array(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
UpperCamelCase = F"encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"
UpperCamelCase = tf.train.load_variable(_lowerCamelCase , _lowerCamelCase )
UpperCamelCase = array.reshape(_lowerCamelCase )
if "kernel" in name:
UpperCamelCase = array.transpose()
return torch.from_numpy(_lowerCamelCase )
print(F"Loading model based on config from {config_path}..." )
UpperCamelCase = BertConfig.from_json_file(_lowerCamelCase )
UpperCamelCase = BertForMaskedLM(_lowerCamelCase )
# Layers
for layer_index in range(0 , config.num_hidden_layers ):
UpperCamelCase = model.bert.encoder.layer[layer_index]
# Self-attention
UpperCamelCase = layer.attention.self
UpperCamelCase = get_encoder_attention_layer_array(
_lowerCamelCase , "_query_dense/kernel" , self_attn.query.weight.data.shape )
UpperCamelCase = get_encoder_attention_layer_array(
_lowerCamelCase , "_query_dense/bias" , self_attn.query.bias.data.shape )
UpperCamelCase = get_encoder_attention_layer_array(
_lowerCamelCase , "_key_dense/kernel" , self_attn.key.weight.data.shape )
UpperCamelCase = get_encoder_attention_layer_array(
_lowerCamelCase , "_key_dense/bias" , self_attn.key.bias.data.shape )
UpperCamelCase = get_encoder_attention_layer_array(
_lowerCamelCase , "_value_dense/kernel" , self_attn.value.weight.data.shape )
UpperCamelCase = get_encoder_attention_layer_array(
_lowerCamelCase , "_value_dense/bias" , self_attn.value.bias.data.shape )
# Self-attention Output
UpperCamelCase = layer.attention.output
UpperCamelCase = get_encoder_attention_layer_array(
_lowerCamelCase , "_output_dense/kernel" , self_output.dense.weight.data.shape )
UpperCamelCase = get_encoder_attention_layer_array(
_lowerCamelCase , "_output_dense/bias" , self_output.dense.bias.data.shape )
UpperCamelCase = get_encoder_layer_array(_lowerCamelCase , "_attention_layer_norm/gamma" )
UpperCamelCase = get_encoder_layer_array(_lowerCamelCase , "_attention_layer_norm/beta" )
# Intermediate
UpperCamelCase = layer.intermediate
UpperCamelCase = get_encoder_layer_array(_lowerCamelCase , "_intermediate_dense/kernel" )
UpperCamelCase = get_encoder_layer_array(_lowerCamelCase , "_intermediate_dense/bias" )
# Output
UpperCamelCase = layer.output
UpperCamelCase = get_encoder_layer_array(_lowerCamelCase , "_output_dense/kernel" )
UpperCamelCase = get_encoder_layer_array(_lowerCamelCase , "_output_dense/bias" )
UpperCamelCase = get_encoder_layer_array(_lowerCamelCase , "_output_layer_norm/gamma" )
UpperCamelCase = get_encoder_layer_array(_lowerCamelCase , "_output_layer_norm/beta" )
# Embeddings
UpperCamelCase = get_encoder_array("_position_embedding_layer/embeddings" )
UpperCamelCase = get_encoder_array("_type_embedding_layer/embeddings" )
UpperCamelCase = get_encoder_array("_embedding_norm_layer/gamma" )
UpperCamelCase = get_encoder_array("_embedding_norm_layer/beta" )
# LM Head
UpperCamelCase = model.cls.predictions.transform
UpperCamelCase = get_masked_lm_array("dense/kernel" )
UpperCamelCase = get_masked_lm_array("dense/bias" )
UpperCamelCase = get_masked_lm_array("layer_norm/gamma" )
UpperCamelCase = get_masked_lm_array("layer_norm/beta" )
UpperCamelCase = get_masked_lm_array("embedding_table" )
# Pooling
UpperCamelCase = BertPooler(config=_lowerCamelCase )
UpperCamelCase = get_encoder_array("_pooler_layer/kernel" )
UpperCamelCase = get_encoder_array("_pooler_layer/bias" )
# Export final model
model.save_pretrained(_lowerCamelCase )
# Integration test - should load without any errors ;)
UpperCamelCase = BertForMaskedLM.from_pretrained(_lowerCamelCase )
print(new_model.eval() )
print("Model conversion was done sucessfully!" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = argparse.ArgumentParser()
parser.add_argument(
"""--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow Token Dropping checkpoint path."""
)
parser.add_argument(
"""--bert_config_file""",
type=str,
required=True,
help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
)
parser.add_argument(
"""--pytorch_dump_path""",
type=str,
required=True,
help="""Path to the output PyTorch model.""",
)
SCREAMING_SNAKE_CASE = parser.parse_args()
convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 719 |
"""simple docstring"""
def lowerCamelCase__ ( UpperCAmelCase_ )-> list:
"""simple docstring"""
if len(UpperCAmelCase_ ) <= 1:
return [tuple(UpperCAmelCase_ )]
UpperCamelCase = []
def generate(UpperCAmelCase_ , UpperCAmelCase_ ):
if k == 1:
res.append(tuple(arr[:] ) )
return
generate(k - 1 , UpperCAmelCase_ )
for i in range(k - 1 ):
if k % 2 == 0: # k is even
UpperCamelCase , UpperCamelCase = arr[k - 1], arr[i]
else: # k is odd
UpperCamelCase , UpperCamelCase = arr[k - 1], arr[0]
generate(k - 1 , UpperCAmelCase_ )
generate(len(UpperCAmelCase_ ) , UpperCAmelCase_ )
return res
if __name__ == "__main__":
SCREAMING_SNAKE_CASE = input("""Enter numbers separated by a comma:\n""").strip()
SCREAMING_SNAKE_CASE = [int(item) for item in user_input.split(""",""")]
print(heaps(arr))
| 556 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A = {'tokenization_wav2vec2_phoneme': ['Wav2Vec2PhonemeCTCTokenizer']}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
import sys
A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 320 |
'''simple docstring'''
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __snake_case ( a__):
_lowerCAmelCase = (UnCLIPScheduler,)
def UpperCAmelCase_ ( self, **A ):
"""simple docstring"""
lowerCamelCase : Tuple = {
'num_train_timesteps': 1000,
'variance_type': 'fixed_small_log',
'clip_sample': True,
'clip_sample_range': 1.0,
'prediction_type': 'epsilon',
}
config.update(**A )
return config
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for timesteps in [1, 5, 100, 1000]:
self.check_over_configs(num_train_timesteps=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for variance in ["fixed_small_log", "learned_range"]:
self.check_over_configs(variance_type=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for clip_sample_range in [1, 5, 10, 20]:
self.check_over_configs(clip_sample_range=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "sample"]:
self.check_over_configs(prediction_type=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for time_step in [0, 500, 999]:
for prev_timestep in [None, 5, 100, 250, 500, 750]:
if prev_timestep is not None and prev_timestep >= time_step:
continue
self.check_over_forward(time_step=A, prev_timestep=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : List[str] = self.scheduler_classes[0]
lowerCamelCase : Tuple = self.get_scheduler_config(variance_type='fixed_small_log' )
lowerCamelCase : Dict = scheduler_class(**A )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 1.00_00e-10 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.054_9625 ) ) < 1e-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.999_4987 ) ) < 1e-5
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = self.scheduler_classes[0]
lowerCamelCase : Optional[Any] = self.get_scheduler_config(variance_type='learned_range' )
lowerCamelCase : Optional[Any] = scheduler_class(**A )
lowerCamelCase : List[Any] = 0.5
assert scheduler._get_variance(1, predicted_variance=A ) - -10.171_2790 < 1e-5
assert scheduler._get_variance(487, predicted_variance=A ) - -5.799_8052 < 1e-5
assert scheduler._get_variance(999, predicted_variance=A ) - -0.001_0011 < 1e-5
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = self.scheduler_classes[0]
lowerCamelCase : Dict = self.get_scheduler_config()
lowerCamelCase : Optional[Any] = scheduler_class(**A )
lowerCamelCase : Optional[int] = scheduler.timesteps
lowerCamelCase : Optional[Any] = self.dummy_model()
lowerCamelCase : int = self.dummy_sample_deter
lowerCamelCase : str = torch.manual_seed(0 )
for i, t in enumerate(A ):
# 1. predict noise residual
lowerCamelCase : List[Any] = model(A, A )
# 2. predict previous mean of sample x_t-1
lowerCamelCase : int = scheduler.step(A, A, A, generator=A ).prev_sample
lowerCamelCase : List[Any] = pred_prev_sample
lowerCamelCase : Tuple = torch.sum(torch.abs(A ) )
lowerCamelCase : List[str] = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 252.268_2495 ) < 1e-2
assert abs(result_mean.item() - 0.328_4743 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Optional[int] = self.scheduler_classes[0]
lowerCamelCase : Union[str, Any] = self.get_scheduler_config()
lowerCamelCase : Optional[int] = scheduler_class(**A )
scheduler.set_timesteps(25 )
lowerCamelCase : str = scheduler.timesteps
lowerCamelCase : Optional[int] = self.dummy_model()
lowerCamelCase : Tuple = self.dummy_sample_deter
lowerCamelCase : int = torch.manual_seed(0 )
for i, t in enumerate(A ):
# 1. predict noise residual
lowerCamelCase : Union[str, Any] = model(A, A )
if i + 1 == timesteps.shape[0]:
lowerCamelCase : Dict = None
else:
lowerCamelCase : Optional[int] = timesteps[i + 1]
# 2. predict previous mean of sample x_t-1
lowerCamelCase : Optional[int] = scheduler.step(
A, A, A, prev_timestep=A, generator=A ).prev_sample
lowerCamelCase : str = pred_prev_sample
lowerCamelCase : Any = torch.sum(torch.abs(A ) )
lowerCamelCase : Union[str, Any] = torch.mean(torch.abs(A ) )
assert abs(result_sum.item() - 258.204_4983 ) < 1e-2
assert abs(result_mean.item() - 0.336_2038 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
pass
def UpperCAmelCase_ ( self ):
"""simple docstring"""
pass
| 320 | 1 |
import unittest
import torch
from torch import nn
from accelerate.test_utils import require_cuda
from accelerate.utils.memory import find_executable_batch_size, release_memory
def UpperCAmelCase_ ( ):
raise RuntimeError("""CUDA out of memory.""" )
class UpperCamelCase__ ( nn.Module ):
def __init__( self : str ):
'''simple docstring'''
super().__init__()
lowercase_ = nn.Linear(3 , 4 )
lowercase_ = nn.BatchNormad(4 )
lowercase_ = nn.Linear(4 , 5 )
def UpperCAmelCase__ ( self : Dict , UpperCamelCase__ : Any ):
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(UpperCamelCase__ ) ) )
class UpperCamelCase__ ( unittest.TestCase ):
def UpperCAmelCase__ ( self : int ):
'''simple docstring'''
lowercase_ = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(UpperCamelCase__ : Optional[Any] ):
nonlocal batch_sizes
batch_sizes.append(UpperCamelCase__ )
if batch_size != 8:
raise_fake_out_of_memory()
mock_training_loop_function()
self.assertListEqual(UpperCamelCase__ , [128, 64, 32, 16, 8] )
def UpperCAmelCase__ ( self : Union[str, Any] ):
'''simple docstring'''
lowercase_ = []
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(UpperCamelCase__ : int , UpperCamelCase__ : Tuple ):
nonlocal batch_sizes
batch_sizes.append(UpperCamelCase__ )
if batch_size != 8:
raise_fake_out_of_memory()
return batch_size, arga
lowercase_ , lowercase_ = mock_training_loop_function("""hello""" )
self.assertListEqual(UpperCamelCase__ , [128, 64, 32, 16, 8] )
self.assertListEqual([bs, arga] , [8, """hello"""] )
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=0 )
def mock_training_loop_function(UpperCamelCase__ : List[str] ):
pass
with self.assertRaises(UpperCamelCase__ ) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0] )
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(UpperCamelCase__ : List[Any] ):
if batch_size > 0:
raise_fake_out_of_memory()
pass
with self.assertRaises(UpperCamelCase__ ) as cm:
mock_training_loop_function()
self.assertIn("""No executable batch size found, reached zero.""" , cm.exception.args[0] )
def UpperCAmelCase__ ( self : int ):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=128 )
def mock_training_loop_function(UpperCamelCase__ : Optional[int] , UpperCamelCase__ : Any , UpperCamelCase__ : int ):
if batch_size != 8:
raise raise_fake_out_of_memory()
with self.assertRaises(UpperCamelCase__ ) as cm:
mock_training_loop_function(128 , """hello""" , """world""" )
self.assertIn("""Batch size was passed into `f`""" , cm.exception.args[0] )
self.assertIn("""`f(arg1='hello', arg2='world')""" , cm.exception.args[0] )
def UpperCAmelCase__ ( self : Any ):
'''simple docstring'''
@find_executable_batch_size(starting_batch_size=16 )
def mock_training_loop_function(UpperCamelCase__ : Any ):
raise ValueError("""Oops, we had an error!""" )
with self.assertRaises(UpperCamelCase__ ) as cm:
mock_training_loop_function()
self.assertIn("""Oops, we had an error!""" , cm.exception.args[0] )
@require_cuda
def UpperCAmelCase__ ( self : Optional[int] ):
'''simple docstring'''
lowercase_ = torch.cuda.memory_allocated()
lowercase_ = ModelForTest()
model.cuda()
self.assertGreater(torch.cuda.memory_allocated() , UpperCamelCase__ )
lowercase_ = release_memory(UpperCamelCase__ )
self.assertEqual(torch.cuda.memory_allocated() , UpperCamelCase__ )
| 650 |
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta import TaTokenizer
else:
a = None
a = logging.get_logger(__name__)
a = {'vocab_file': 'spiece.model', 'tokenizer_file': 'tokenizer.json'}
a = {
'vocab_file': {
't5-small': 'https://huggingface.co/t5-small/resolve/main/spiece.model',
't5-base': 'https://huggingface.co/t5-base/resolve/main/spiece.model',
't5-large': 'https://huggingface.co/t5-large/resolve/main/spiece.model',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/spiece.model',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/spiece.model',
},
'tokenizer_file': {
't5-small': 'https://huggingface.co/t5-small/resolve/main/tokenizer.json',
't5-base': 'https://huggingface.co/t5-base/resolve/main/tokenizer.json',
't5-large': 'https://huggingface.co/t5-large/resolve/main/tokenizer.json',
't5-3b': 'https://huggingface.co/t5-3b/resolve/main/tokenizer.json',
't5-11b': 'https://huggingface.co/t5-11b/resolve/main/tokenizer.json',
},
}
# TODO(PVP) - this should be removed in Transformers v5
a = {
't5-small': 5_1_2,
't5-base': 5_1_2,
't5-large': 5_1_2,
't5-3b': 5_1_2,
't5-11b': 5_1_2,
}
class UpperCamelCase__ ( __magic_name__ ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = VOCAB_FILES_NAMES
__SCREAMING_SNAKE_CASE : int = PRETRAINED_VOCAB_FILES_MAP
__SCREAMING_SNAKE_CASE : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__SCREAMING_SNAKE_CASE : str = ['input_ids', 'attention_mask']
__SCREAMING_SNAKE_CASE : Dict = TaTokenizer
__SCREAMING_SNAKE_CASE : List[int] = []
def __init__( self : int , UpperCamelCase__ : Union[str, Any]=None , UpperCamelCase__ : Any=None , UpperCamelCase__ : Dict="</s>" , UpperCamelCase__ : Tuple="<unk>" , UpperCamelCase__ : Optional[Any]="<pad>" , UpperCamelCase__ : Union[str, Any]=100 , UpperCamelCase__ : Optional[Any]=None , **UpperCamelCase__ : List[str] , ):
'''simple docstring'''
if extra_ids > 0 and additional_special_tokens is None:
lowercase_ = [F'''<extra_id_{i}>''' for i in range(UpperCamelCase__ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra special tokens
lowercase_ = len(set(filter(lambda UpperCamelCase__ : bool("""extra_id_""" in str(UpperCamelCase__ ) ) , UpperCamelCase__ ) ) )
if extra_tokens != extra_ids:
raise ValueError(
F'''Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'''
""" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"""
""" tokens""" )
super().__init__(
UpperCamelCase__ , tokenizer_file=UpperCamelCase__ , eos_token=UpperCamelCase__ , unk_token=UpperCamelCase__ , pad_token=UpperCamelCase__ , extra_ids=UpperCamelCase__ , additional_special_tokens=UpperCamelCase__ , **UpperCamelCase__ , )
lowercase_ = vocab_file
lowercase_ = False if not self.vocab_file else True
lowercase_ = extra_ids
@staticmethod
def UpperCAmelCase__ ( UpperCamelCase__ : Tuple , UpperCamelCase__ : str , UpperCamelCase__ : int ):
'''simple docstring'''
if pretrained_model_name_or_path in TaTokenizerFast.max_model_input_sizes:
lowercase_ = TaTokenizerFast.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"""This tokenizer was incorrectly instantiated with a model max length of"""
F''' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'''
""" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"""
""" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"""
F''' {pretrained_model_name_or_path} automatically truncating your input to'''
F''' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'''
F''' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'''
""" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"""
""" instantiate this tokenizer with `model_max_length` set to your preferred value.""" , UpperCamelCase__ , )
return max_model_length
def UpperCAmelCase__ ( self : int , UpperCamelCase__ : str , UpperCamelCase__ : Optional[str] = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(UpperCamelCase__ ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowercase_ = os.path.join(
UpperCamelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase__ ):
copyfile(self.vocab_file , UpperCamelCase__ )
logger.info(F'''Copy vocab file to {out_vocab_file}''' )
return (out_vocab_file,)
def UpperCAmelCase__ ( self : int , UpperCamelCase__ : List[int] , UpperCamelCase__ : Optional[List[int]] = None ):
'''simple docstring'''
lowercase_ = token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return self.prefix_tokens + token_ids_a
else:
lowercase_ = token_ids_a + [self.eos_token_id]
return self.prefix_tokens + token_ids_a + token_ids_a
def UpperCAmelCase__ ( self : str , UpperCamelCase__ : List[int] , UpperCamelCase__ : Optional[List[int]] = None ):
'''simple docstring'''
lowercase_ = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def UpperCAmelCase__ ( self : Dict ):
'''simple docstring'''
return list(
set(filter(lambda UpperCamelCase__ : bool(re.search(R"""<extra_id_\d+>""" , UpperCamelCase__ ) ) is not None , self.additional_special_tokens ) ) )
def UpperCAmelCase__ ( self : str ):
'''simple docstring'''
return [self.convert_tokens_to_ids(UpperCamelCase__ ) for token in self.get_sentinel_tokens()]
| 650 | 1 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _UpperCAmelCase ( A__ ):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ['''image_processor''', '''tokenizer''']
SCREAMING_SNAKE_CASE_ : str = '''ViTImageProcessor'''
SCREAMING_SNAKE_CASE_ : int = ('''CLIPTokenizer''', '''CLIPTokenizerFast''')
def __init__( self : List[str] , A : Any=None , A : Optional[Any]=None , **A : List[Any] ) -> Any:
lowercase_ : int = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , lowerCAmelCase__ , )
lowercase_ : str = kwargs.pop('''feature_extractor''' )
lowercase_ : Optional[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 : Tuple , A : int=None , A : Tuple=None , A : Any=None , A : str=None , **A : str ) -> Any:
if text is None and visual_prompt is None and images is None:
raise ValueError('''You have to specify either text, visual prompt or images.''' )
if text is not None and visual_prompt is not None:
raise ValueError('''You have to specify exactly one type of prompt. Either text or visual prompt.''' )
if text is not None:
lowercase_ : Tuple = self.tokenizer(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
if visual_prompt is not None:
lowercase_ : Any = self.image_processor(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
if images is not None:
lowercase_ : str = self.image_processor(lowerCAmelCase__ , return_tensors=lowerCAmelCase__ , **lowerCAmelCase__ )
if visual_prompt is not None and images is not None:
lowercase_ : Optional[Any] = {
'''pixel_values''': image_features.pixel_values,
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
elif text is not None and images is not None:
lowercase_ : Any = image_features.pixel_values
return encoding
elif text is not None:
return encoding
elif visual_prompt is not None:
lowercase_ : Union[str, Any] = {
'''conditional_pixel_values''': prompt_features.pixel_values,
}
return encoding
else:
return BatchEncoding(data=dict(**lowerCAmelCase__ ) , tensor_type=lowerCAmelCase__ )
def A ( self : Any , *A : Any , **A : int ) -> Any:
return self.tokenizer.batch_decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
def A ( self : List[str] , *A : Dict , **A : Union[str, Any] ) -> Union[str, Any]:
return self.tokenizer.decode(*lowerCAmelCase__ , **lowerCAmelCase__ )
@property
def A ( self : Optional[int] ) -> Optional[Any]:
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 A ( self : Optional[int] ) -> Union[str, Any]:
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , lowerCAmelCase__ , )
return self.image_processor
| 231 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=A__ )
class _lowercase ( A__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : str = field(default='''summarization''' , metadata={'''include_in_asdict_even_if_is_default''': True} )
SCREAMING_SNAKE_CASE__ : ClassVar[Features] = Features({'''text''': Value('''string''' )} )
SCREAMING_SNAKE_CASE__ : ClassVar[Features] = Features({'''summary''': Value('''string''' )} )
SCREAMING_SNAKE_CASE__ : str = "text"
SCREAMING_SNAKE_CASE__ : str = "summary"
@property
def __magic_name__( self :Union[str, Any] ) -> Dict[str, str]:
return {self.text_column: "text", self.summary_column: "summary"}
| 696 | 0 |
from numpy import exp, pi, sqrt
def __a ( __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : float = 0.0 , __UpperCAmelCase : float = 1.0 ) -> int:
"""simple docstring"""
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 701 |
# 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 __a ( __UpperCAmelCase : int , __UpperCAmelCase : str , __UpperCAmelCase : Optional[Any]=0 ) -> Tuple:
"""simple docstring"""
if name is None:
lowerCamelCase_ : Dict = None
else:
lowerCamelCase_ : Any = "." * max(0 , spaces - 2 ) + "# {:" + str(50 - spaces ) + "s}"
lowerCamelCase_ : Dict = 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 __a ( __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Dict ) -> Optional[Any]:
"""simple docstring"""
lowerCamelCase_ : Tuple = param.size()
if checkpoint_version == 1.0:
# version 1.0 stores [num_heads * hidden_size * num_splits, :]
lowerCamelCase_ : Dict = (num_heads, hidden_size, num_splits) + input_shape[1:]
lowerCamelCase_ : Optional[Any] = param.view(*__UpperCAmelCase )
lowerCamelCase_ : Optional[Any] = param.transpose(0 , 2 )
lowerCamelCase_ : Any = param.transpose(1 , 2 ).contiguous()
elif checkpoint_version >= 2.0:
# other versions store [num_heads * num_splits * hidden_size, :]
lowerCamelCase_ : Optional[int] = (num_heads, num_splits, hidden_size) + input_shape[1:]
lowerCamelCase_ : Optional[Any] = param.view(*__UpperCAmelCase )
lowerCamelCase_ : Optional[int] = param.transpose(0 , 1 ).contiguous()
lowerCamelCase_ : Union[str, Any] = param.view(*__UpperCAmelCase )
return param
def __a ( __UpperCAmelCase : int , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : Union[str, Any] ) -> str:
"""simple docstring"""
lowerCamelCase_ : Tuple = {}
# old versions did not store training args
lowerCamelCase_ : Optional[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))
lowerCamelCase_ : List[str] = ds_args.padded_vocab_size
lowerCamelCase_ : Optional[int] = ds_args.max_position_embeddings
lowerCamelCase_ : Union[str, Any] = ds_args.hidden_size
lowerCamelCase_ : Tuple = ds_args.num_layers
lowerCamelCase_ : List[str] = ds_args.num_attention_heads
lowerCamelCase_ : List[str] = ds_args.ffn_hidden_size
# pprint(config)
# The number of heads.
lowerCamelCase_ : List[Any] = config.n_head
# The hidden_size per head.
lowerCamelCase_ : Tuple = config.n_embd // config.n_head
# Megatron-LM checkpoint version
if "checkpoint_version" in input_state_dict.keys():
lowerCamelCase_ : Any = input_state_dict["checkpoint_version"]
else:
lowerCamelCase_ : int = 0.0
# The model.
lowerCamelCase_ : int = input_state_dict["model"]
# The language model.
lowerCamelCase_ : Dict = model["language_model"]
# The embeddings.
lowerCamelCase_ : Optional[int] = lm["embedding"]
# The word embeddings.
lowerCamelCase_ : Union[str, Any] = embeddings["word_embeddings"]["weight"]
# Truncate the embedding table to vocab_size rows.
lowerCamelCase_ : int = word_embeddings[: config.vocab_size, :]
lowerCamelCase_ : int = word_embeddings
# The position embeddings.
lowerCamelCase_ : List[str] = embeddings["position_embeddings"]["weight"]
# Read the causal mask dimension (seqlen). [max_sequence_length, hidden_size]
lowerCamelCase_ : List[Any] = pos_embeddings.size(0 )
if n_positions != config.n_positions:
raise ValueError(
f"pos_embeddings.max_sequence_length={n_positions} and config.n_positions={config.n_positions} don't match" )
# Store the position embeddings.
lowerCamelCase_ : Optional[int] = pos_embeddings
# The transformer.
lowerCamelCase_ : List[str] = lm["transformer"] if "transformer" in lm.keys() else lm["encoder"]
# The regex to extract layer names.
lowerCamelCase_ : Optional[int] = re.compile(R"layers\.(\d+)\.([a-z0-9_.]+)\.([a-z]+)" )
# The simple map of names for "automated" rules.
lowerCamelCase_ : Optional[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.
lowerCamelCase_ : Optional[int] = layer_re.match(__UpperCAmelCase )
# Stop if that's not a layer
if m is None:
break
# The index of the layer.
lowerCamelCase_ : str = int(m.group(1 ) )
# The name of the operation.
lowerCamelCase_ : Optional[int] = m.group(2 )
# Is it a weight or a bias?
lowerCamelCase_ : int = m.group(3 )
# The name of the layer.
lowerCamelCase_ : Optional[Any] = f"transformer.h.{layer_idx}"
# For layernorm(s), simply store the layer norm.
if op_name.endswith("layernorm" ):
lowerCamelCase_ : Optional[int] = "ln_1" if op_name.startswith("input" ) else "ln_2"
lowerCamelCase_ : int = 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.
lowerCamelCase_ : Union[str, Any] = torch.tril(torch.ones((n_positions, n_positions) , dtype=torch.floataa ) ).view(
1 , 1 , __UpperCAmelCase , __UpperCAmelCase )
lowerCamelCase_ : str = causal_mask
# Insert a "dummy" tensor for masked_bias.
lowerCamelCase_ : Any = torch.tensor(-1e4 , dtype=torch.floataa )
lowerCamelCase_ : Union[str, Any] = masked_bias
lowerCamelCase_ : 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.
lowerCamelCase_ : Dict = out_val.transpose(0 , 1 ).contiguous()
# Store.
lowerCamelCase_ : Tuple = 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":
lowerCamelCase_ : Union[str, Any] = fix_query_key_value_ordering(__UpperCAmelCase , __UpperCAmelCase , 3 , __UpperCAmelCase , __UpperCAmelCase )
# Store. No change of shape.
lowerCamelCase_ : Dict = out_val
# Transpose the weights.
elif weight_or_bias == "weight":
lowerCamelCase_ : Union[str, Any] = megatron_to_transformers[op_name]
lowerCamelCase_ : int = val.transpose(0 , 1 )
# Copy the bias.
elif weight_or_bias == "bias":
lowerCamelCase_ : Optional[int] = megatron_to_transformers[op_name]
lowerCamelCase_ : Dict = val
# DEBUG.
assert config.n_layer == layer_idx + 1
# The final layernorm.
lowerCamelCase_ : List[Any] = transformer["final_layernorm.weight"]
lowerCamelCase_ : List[Any] = transformer["final_layernorm.bias"]
# For LM head, transformers' wants the matrix to weight embeddings.
lowerCamelCase_ : Union[str, Any] = word_embeddings
# It should be done!
return output_state_dict
def __a ( ) -> int:
"""simple docstring"""
lowerCamelCase_ : Optional[int] = 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." , )
lowerCamelCase_ : str = parser.parse_args()
# Extract the basename.
lowerCamelCase_ : Tuple = 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:
lowerCamelCase_ : int = torch.load(__UpperCAmelCase , map_location="cpu" )
else:
lowerCamelCase_ : int = torch.load(args.path_to_checkpoint , map_location="cpu" )
lowerCamelCase_ : Any = 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:
lowerCamelCase_ : Optional[int] = "gelu_fast"
elif ds_args.openai_gelu:
lowerCamelCase_ : List[str] = "gelu_new"
else:
lowerCamelCase_ : int = "gelu"
else:
# in the very early days this used to be "gelu_new"
lowerCamelCase_ : Any = "gelu_new"
# Spell out all parameters in case the defaults change.
lowerCamelCase_ : int = GPTaConfig(
vocab_size=50257 , n_positions=1024 , n_embd=1024 , n_layer=24 , n_head=16 , n_inner=4096 , activation_function=__UpperCAmelCase , resid_pdrop=0.1 , embd_pdrop=0.1 , attn_pdrop=0.1 , layer_norm_epsilon=1e-5 , initializer_range=0.0_2 , 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=50256 , eos_token_id=50256 , )
else:
lowerCamelCase_ : Dict = GPTaConfig.from_json_file(args.config_file )
lowerCamelCase_ : Tuple = ["GPT2LMHeadModel"]
# Convert.
print("Converting" )
lowerCamelCase_ : Dict = 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:
lowerCamelCase_ : List[Any] = ds_args.tokenizer_type
if tokenizer_type == "GPT2BPETokenizer":
lowerCamelCase_ : Union[str, Any] = "gpt2"
elif tokenizer_type == "PretrainedFromHF":
lowerCamelCase_ : Union[str, Any] = ds_args.tokenizer_name_or_path
else:
raise ValueError(f"Unrecognized tokenizer_type {tokenizer_type}" )
else:
lowerCamelCase_ : List[Any] = "gpt2"
lowerCamelCase_ : Optional[Any] = AutoTokenizer.from_pretrained(__UpperCAmelCase )
lowerCamelCase_ : Union[str, Any] = type(__UpperCAmelCase ).__name__
lowerCamelCase_ : Dict = 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.
lowerCamelCase_ : List[str] = os.path.join(__UpperCAmelCase , "pytorch_model.bin" )
print(f"Saving checkpoint to \"{output_checkpoint_file}\"" )
torch.save(__UpperCAmelCase , __UpperCAmelCase )
####################################################################################################
if __name__ == "__main__":
main()
####################################################################################################
| 253 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class SCREAMING_SNAKE_CASE ( metaclass=_a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""transformers""", """torch""", """note_seq"""]
def __init__( self : Dict , *UpperCamelCase__ : str , **UpperCamelCase__ : Dict ):
"""simple docstring"""
requires_backends(self , ['transformers', 'torch', 'note_seq'] )
@classmethod
def A ( cls : Tuple , *UpperCamelCase__ : Optional[int] , **UpperCamelCase__ : Union[str, Any] ):
"""simple docstring"""
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
@classmethod
def A ( cls : Dict , *UpperCamelCase__ : Dict , **UpperCamelCase__ : str ):
"""simple docstring"""
requires_backends(cls , ['transformers', 'torch', 'note_seq'] )
| 430 |
'''simple docstring'''
from math import factorial
def __lowerCamelCase ( A__ , A__ , A__ ) -> float:
"""simple docstring"""
if successes > trials:
raise ValueError('successes must be lower or equal to trials' )
if trials < 0 or successes < 0:
raise ValueError('the function is defined for non-negative integers' )
if not isinstance(A__ , A__ ) or not isinstance(A__ , A__ ):
raise ValueError('the function is defined for non-negative integers' )
if not 0 < prob < 1:
raise ValueError('prob has to be in range of 1 - 0' )
UpperCamelCase = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
UpperCamelCase = float(factorial(A__ ) )
coefficient /= factorial(A__ ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print("Probability of 2 successes out of 4 trails")
print("with probability of 0.75 is:", end=" ")
print(binomial_distribution(2, 4, 0.75))
| 430 | 1 |
from itertools import count
def lowerCamelCase__ ( _lowerCamelCase = 50 ) ->int:
_UpperCAmelCase =[1] * min_block_length
for n in count(_lowerCamelCase ):
fill_count_functions.append(1 )
for block_length in range(_lowerCamelCase , n + 1 ):
for block_start in range(n - block_length ):
fill_count_functions[n] += fill_count_functions[
n - block_start - block_length - 1
]
fill_count_functions[n] += 1
if fill_count_functions[n] > 100_0000:
break
return n
if __name__ == "__main__":
print(F"""{solution() = }""")
| 592 |
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class _a ( unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase ="ylacombe/bark-small"
_UpperCAmelCase =tempfile.mkdtemp()
_UpperCAmelCase ="en_speaker_1"
_UpperCAmelCase ="This is a test string"
_UpperCAmelCase ="speaker_embeddings_path.json"
_UpperCAmelCase ="speaker_embeddings"
def SCREAMING_SNAKE_CASE ( self , **_snake_case ):
return AutoTokenizer.from_pretrained(self.checkpoint , **_snake_case )
def SCREAMING_SNAKE_CASE ( self ):
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =self.get_tokenizer()
_UpperCAmelCase =BarkProcessor(tokenizer=_snake_case )
processor.save_pretrained(self.tmpdirname )
_UpperCAmelCase =BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
_UpperCAmelCase =self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
_UpperCAmelCase =BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="(BOS)" , eos_token="(EOS)" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
_UpperCAmelCase =35
_UpperCAmelCase =2
_UpperCAmelCase =8
_UpperCAmelCase ={
"semantic_prompt": np.ones(_snake_case ),
"coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
"fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
_UpperCAmelCase =processor(text=self.input_string , voice_preset=_snake_case )
_UpperCAmelCase =inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_snake_case , np.array([] ) ).tolist() )
# test loading voice preset from npz file
_UpperCAmelCase =os.path.join(self.tmpdirname , "file.npz" )
np.savez(_snake_case , **_snake_case )
_UpperCAmelCase =processor(text=self.input_string , voice_preset=_snake_case )
_UpperCAmelCase =inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(_snake_case , np.array([] ) ).tolist() )
# test loading voice preset from the hub
_UpperCAmelCase =processor(text=self.input_string , voice_preset=self.voice_preset )
def SCREAMING_SNAKE_CASE ( self ):
_UpperCAmelCase =self.get_tokenizer()
_UpperCAmelCase =BarkProcessor(tokenizer=_snake_case )
_UpperCAmelCase =processor(text=self.input_string )
_UpperCAmelCase =tokenizer(
self.input_string , padding="max_length" , max_length=256 , add_special_tokens=_snake_case , return_attention_mask=_snake_case , return_token_type_ids=_snake_case , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 592 | 1 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxCrossAttnUpBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
FlaxUpBlockaD,
)
@flax.struct.dataclass
class __lowercase ( __snake_case ):
UpperCamelCase = 42
@flax_register_to_config
class __lowercase ( nn.Module , __snake_case , __snake_case ):
UpperCamelCase = 32
UpperCamelCase = 4
UpperCamelCase = 4
UpperCamelCase = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
UpperCamelCase = ("UpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D", "CrossAttnUpBlock2D")
UpperCamelCase = False
UpperCamelCase = (3_20, 6_40, 12_80, 12_80)
UpperCamelCase = 2
UpperCamelCase = 8
UpperCamelCase = None
UpperCamelCase = 12_80
UpperCamelCase = 0.0
UpperCamelCase = False
UpperCamelCase = jnp.floataa
UpperCamelCase = True
UpperCamelCase = 0
UpperCamelCase = False
def _lowercase ( self : Dict , __lowerCamelCase : jax.random.KeyArray ) -> FrozenDict:
"""simple docstring"""
UpperCAmelCase = (1, self.in_channels, self.sample_size, self.sample_size)
UpperCAmelCase = jnp.zeros(__lowerCamelCase , dtype=jnp.floataa )
UpperCAmelCase = jnp.ones((1,) , dtype=jnp.intaa )
UpperCAmelCase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
UpperCAmelCase , UpperCAmelCase = jax.random.split(__lowerCamelCase )
UpperCAmelCase = {"""params""": params_rng, """dropout""": dropout_rng}
return self.init(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )["params"]
def _lowercase ( self : str ) -> Tuple:
"""simple docstring"""
UpperCAmelCase = self.block_out_channels
UpperCAmelCase = block_out_channels[0] * 4
if self.num_attention_heads is not None:
raise ValueError(
"""At the moment it is not possible to define the number of attention heads via `num_attention_heads` because of a naming issue as described in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131. Passing `num_attention_heads` will only be supported in diffusers v0.19.""" )
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
UpperCAmelCase = self.num_attention_heads or self.attention_head_dim
# input
UpperCAmelCase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
UpperCAmelCase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
UpperCAmelCase = FlaxTimestepEmbedding(__lowerCamelCase , dtype=self.dtype )
UpperCAmelCase = self.only_cross_attention
if isinstance(__lowerCamelCase , __lowerCamelCase ):
UpperCAmelCase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(__lowerCamelCase , __lowerCamelCase ):
UpperCAmelCase = (num_attention_heads,) * len(self.down_block_types )
# down
UpperCAmelCase = []
UpperCAmelCase = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
UpperCAmelCase = output_channel
UpperCAmelCase = block_out_channels[i]
UpperCAmelCase = i == len(__lowerCamelCase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
UpperCAmelCase = FlaxCrossAttnDownBlockaD(
in_channels=__lowerCamelCase , out_channels=__lowerCamelCase , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
UpperCAmelCase = FlaxDownBlockaD(
in_channels=__lowerCamelCase , out_channels=__lowerCamelCase , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(__lowerCamelCase )
UpperCAmelCase = down_blocks
# mid
UpperCAmelCase = FlaxUNetMidBlockaDCrossAttn(
in_channels=block_out_channels[-1] , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
# up
UpperCAmelCase = []
UpperCAmelCase = list(reversed(__lowerCamelCase ) )
UpperCAmelCase = list(reversed(__lowerCamelCase ) )
UpperCAmelCase = list(reversed(__lowerCamelCase ) )
UpperCAmelCase = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
UpperCAmelCase = output_channel
UpperCAmelCase = reversed_block_out_channels[i]
UpperCAmelCase = reversed_block_out_channels[min(i + 1 , len(__lowerCamelCase ) - 1 )]
UpperCAmelCase = i == len(__lowerCamelCase ) - 1
if up_block_type == "CrossAttnUpBlock2D":
UpperCAmelCase = FlaxCrossAttnUpBlockaD(
in_channels=__lowerCamelCase , out_channels=__lowerCamelCase , prev_output_channel=__lowerCamelCase , num_layers=self.layers_per_block + 1 , num_attention_heads=reversed_num_attention_heads[i] , add_upsample=not is_final_block , dropout=self.dropout , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
else:
UpperCAmelCase = FlaxUpBlockaD(
in_channels=__lowerCamelCase , out_channels=__lowerCamelCase , prev_output_channel=__lowerCamelCase , num_layers=self.layers_per_block + 1 , add_upsample=not is_final_block , dropout=self.dropout , dtype=self.dtype , )
up_blocks.append(__lowerCamelCase )
UpperCAmelCase = output_channel
UpperCAmelCase = up_blocks
# out
UpperCAmelCase = nn.GroupNorm(num_groups=3_2 , epsilon=1e-5 )
UpperCAmelCase = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Optional[Any] , __lowerCamelCase : Optional[Any] , __lowerCamelCase : Dict , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any]=None , __lowerCamelCase : Optional[int]=None , __lowerCamelCase : bool = True , __lowerCamelCase : bool = False , ) -> Union[FlaxUNetaDConditionOutput, Tuple]:
"""simple docstring"""
if not isinstance(__lowerCamelCase , jnp.ndarray ):
UpperCAmelCase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(__lowerCamelCase , jnp.ndarray ) and len(timesteps.shape ) == 0:
UpperCAmelCase = timesteps.astype(dtype=jnp.floataa )
UpperCAmelCase = jnp.expand_dims(__lowerCamelCase , 0 )
UpperCAmelCase = self.time_proj(__lowerCamelCase )
UpperCAmelCase = self.time_embedding(__lowerCamelCase )
# 2. pre-process
UpperCAmelCase = jnp.transpose(__lowerCamelCase , (0, 2, 3, 1) )
UpperCAmelCase = self.conv_in(__lowerCamelCase )
# 3. down
UpperCAmelCase = (sample,)
for down_block in self.down_blocks:
if isinstance(__lowerCamelCase , __lowerCamelCase ):
UpperCAmelCase , UpperCAmelCase = down_block(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , deterministic=not train )
else:
UpperCAmelCase , UpperCAmelCase = down_block(__lowerCamelCase , __lowerCamelCase , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
UpperCAmelCase = ()
for down_block_res_sample, down_block_additional_residual in zip(
__lowerCamelCase , __lowerCamelCase ):
down_block_res_sample += down_block_additional_residual
new_down_block_res_samples += (down_block_res_sample,)
UpperCAmelCase = new_down_block_res_samples
# 4. mid
UpperCAmelCase = self.mid_block(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , deterministic=not train )
if mid_block_additional_residual is not None:
sample += mid_block_additional_residual
# 5. up
for up_block in self.up_blocks:
UpperCAmelCase = down_block_res_samples[-(self.layers_per_block + 1) :]
UpperCAmelCase = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(__lowerCamelCase , __lowerCamelCase ):
UpperCAmelCase = up_block(
__lowerCamelCase , temb=__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , res_hidden_states_tuple=__lowerCamelCase , deterministic=not train , )
else:
UpperCAmelCase = up_block(__lowerCamelCase , temb=__lowerCamelCase , res_hidden_states_tuple=__lowerCamelCase , deterministic=not train )
# 6. post-process
UpperCAmelCase = self.conv_norm_out(__lowerCamelCase )
UpperCAmelCase = nn.silu(__lowerCamelCase )
UpperCAmelCase = self.conv_out(__lowerCamelCase )
UpperCAmelCase = jnp.transpose(__lowerCamelCase , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=__lowerCamelCase )
| 377 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __lowercase ( __snake_case ):
def __init__( self : Dict , *__lowerCamelCase : Dict , __lowerCamelCase : Union[str, Any]=None , __lowerCamelCase : str=None , **__lowerCamelCase : Optional[int] ) -> Optional[int]:
"""simple docstring"""
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
UpperCAmelCase = eval_examples
UpperCAmelCase = post_process_function
def _lowercase ( self : Any , __lowerCamelCase : int=None , __lowerCamelCase : int=None , __lowerCamelCase : Tuple=None , __lowerCamelCase : str = "eval" ) -> List[str]:
"""simple docstring"""
UpperCAmelCase = self.eval_dataset if eval_dataset is None else eval_dataset
UpperCAmelCase = self.get_eval_dataloader(__lowerCamelCase )
UpperCAmelCase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
UpperCAmelCase = self.compute_metrics
UpperCAmelCase = None
UpperCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
UpperCAmelCase = time.time()
try:
UpperCAmelCase = eval_loop(
__lowerCamelCase , description="""Evaluation""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__lowerCamelCase , metric_key_prefix=__lowerCamelCase , )
finally:
UpperCAmelCase = compute_metrics
UpperCAmelCase = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
__lowerCamelCase , __lowerCamelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
UpperCAmelCase = self.post_process_function(__lowerCamelCase , __lowerCamelCase , output.predictions )
UpperCAmelCase = self.compute_metrics(__lowerCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
UpperCAmelCase = metrics.pop(__lowerCamelCase )
metrics.update(output.metrics )
else:
UpperCAmelCase = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(__lowerCamelCase )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
UpperCAmelCase = self.callback_handler.on_evaluate(self.args , self.state , self.control , __lowerCamelCase )
return metrics
def _lowercase ( self : Optional[Any] , __lowerCamelCase : Union[str, Any] , __lowerCamelCase : Any , __lowerCamelCase : Dict=None , __lowerCamelCase : str = "test" ) -> Dict:
"""simple docstring"""
UpperCAmelCase = self.get_test_dataloader(__lowerCamelCase )
# Temporarily disable metric computation, we will do it in the loop here.
UpperCAmelCase = self.compute_metrics
UpperCAmelCase = None
UpperCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
UpperCAmelCase = time.time()
try:
UpperCAmelCase = eval_loop(
__lowerCamelCase , description="""Prediction""" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=__lowerCamelCase , metric_key_prefix=__lowerCamelCase , )
finally:
UpperCAmelCase = compute_metrics
UpperCAmelCase = self.args.eval_batch_size * self.args.world_size
if F"""{metric_key_prefix}_jit_compilation_time""" in output.metrics:
start_time += output.metrics[F"""{metric_key_prefix}_jit_compilation_time"""]
output.metrics.update(
speed_metrics(
__lowerCamelCase , __lowerCamelCase , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
UpperCAmelCase = self.post_process_function(__lowerCamelCase , __lowerCamelCase , output.predictions , """predict""" )
UpperCAmelCase = self.compute_metrics(__lowerCamelCase )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(F"""{metric_key_prefix}_""" ):
UpperCAmelCase = metrics.pop(__lowerCamelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=__lowerCamelCase )
| 377 | 1 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin, filter_non_english
@require_tokenizers
class SCREAMING_SNAKE_CASE (UpperCAmelCase , unittest.TestCase ):
_UpperCamelCase : Tuple = BertTokenizer
_UpperCamelCase : Tuple = BertTokenizerFast
_UpperCamelCase : Dict = True
_UpperCamelCase : List[Any] = True
_UpperCamelCase : List[Any] = filter_non_english
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
super().setUp()
lowercase__ = [
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
lowercase__ = 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 SCREAMING_SNAKE_CASE_ ( self : str , a : List[str] )-> Union[str, Any]:
"""simple docstring"""
lowercase__ = 'UNwant\u00E9d,running'
lowercase__ = 'unwanted, running'
return input_text, output_text
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] )-> Optional[int]:
"""simple docstring"""
lowercase__ = self.tokenizer_class(self.vocab_file )
lowercase__ = tokenizer.tokenize('UNwant\u00E9d,running' )
self.assertListEqual(a , ['un', '##want', '##ed', ',', 'runn', '##ing'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(a ) , [9, 6, 7, 12, 10, 11] )
def SCREAMING_SNAKE_CASE_ ( self : Any )-> Optional[int]:
"""simple docstring"""
if not self.test_rust_tokenizer:
return
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
lowercase__ = 'UNwant\u00E9d,running'
lowercase__ = tokenizer.tokenize(a )
lowercase__ = rust_tokenizer.tokenize(a )
self.assertListEqual(a , a )
lowercase__ = tokenizer.encode(a , add_special_tokens=a )
lowercase__ = rust_tokenizer.encode(a , add_special_tokens=a )
self.assertListEqual(a , a )
lowercase__ = self.get_rust_tokenizer()
lowercase__ = tokenizer.encode(a )
lowercase__ = rust_tokenizer.encode(a )
self.assertListEqual(a , a )
# With lower casing
lowercase__ = self.get_tokenizer(do_lower_case=a )
lowercase__ = self.get_rust_tokenizer(do_lower_case=a )
lowercase__ = 'UNwant\u00E9d,running'
lowercase__ = tokenizer.tokenize(a )
lowercase__ = rust_tokenizer.tokenize(a )
self.assertListEqual(a , a )
lowercase__ = tokenizer.encode(a , add_special_tokens=a )
lowercase__ = rust_tokenizer.encode(a , add_special_tokens=a )
self.assertListEqual(a , a )
lowercase__ = self.get_rust_tokenizer()
lowercase__ = tokenizer.encode(a )
lowercase__ = rust_tokenizer.encode(a )
self.assertListEqual(a , a )
def SCREAMING_SNAKE_CASE_ ( self : int )-> Optional[int]:
"""simple docstring"""
lowercase__ = BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('ah\u535A\u63A8zz' ) , ['ah', '\u535A', '\u63A8', 'zz'] )
def SCREAMING_SNAKE_CASE_ ( self : Any )-> Union[str, Any]:
"""simple docstring"""
lowercase__ = BasicTokenizer(do_lower_case=a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['hello', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def SCREAMING_SNAKE_CASE_ ( self : List[str] )-> Optional[Any]:
"""simple docstring"""
lowercase__ = BasicTokenizer(do_lower_case=a , strip_accents=a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hällo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['h\u00E9llo'] )
def SCREAMING_SNAKE_CASE_ ( self : Tuple )-> Optional[Any]:
"""simple docstring"""
lowercase__ = BasicTokenizer(do_lower_case=a , strip_accents=a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] )-> Tuple:
"""simple docstring"""
lowercase__ = BasicTokenizer(do_lower_case=a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['hallo', '!', 'how', 'are', 'you', '?'] )
self.assertListEqual(tokenizer.tokenize('H\u00E9llo' ) , ['hello'] )
def SCREAMING_SNAKE_CASE_ ( self : str )-> Optional[Any]:
"""simple docstring"""
lowercase__ = BasicTokenizer(do_lower_case=a )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? ' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?'] )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] )-> List[Any]:
"""simple docstring"""
lowercase__ = BasicTokenizer(do_lower_case=a , strip_accents=a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HäLLo', '!', 'how', 'Are', 'yoU', '?'] )
def SCREAMING_SNAKE_CASE_ ( self : List[str] )-> List[str]:
"""simple docstring"""
lowercase__ = BasicTokenizer(do_lower_case=a , strip_accents=a )
self.assertListEqual(
tokenizer.tokenize(' \tHäLLo!how \n Are yoU? ' ) , ['HaLLo', '!', 'how', 'Are', 'yoU', '?'] )
def SCREAMING_SNAKE_CASE_ ( self : int )-> Dict:
"""simple docstring"""
lowercase__ = BasicTokenizer(do_lower_case=a , never_split=['[UNK]'] )
self.assertListEqual(
tokenizer.tokenize(' \tHeLLo!how \n Are yoU? [UNK]' ) , ['HeLLo', '!', 'how', 'Are', 'yoU', '?', '[UNK]'] )
def SCREAMING_SNAKE_CASE_ ( self : Any )-> Optional[Any]:
"""simple docstring"""
lowercase__ = BasicTokenizer()
lowercase__ = 'a\n\'ll !!to?\'d of, can\'t.'
lowercase__ = ['a', '\'', 'll', '!', '!', 'to', '?', '\'', 'd', 'of', ',', 'can', '\'', 't', '.']
self.assertListEqual(tokenizer.tokenize(a ) , a )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
lowercase__ = ['[UNK]', '[CLS]', '[SEP]', 'want', '##want', '##ed', 'wa', 'un', 'runn', '##ing']
lowercase__ = {}
for i, token in enumerate(a ):
lowercase__ = i
lowercase__ = WordpieceTokenizer(vocab=a , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('unwanted running' ) , ['un', '##want', '##ed', 'runn', '##ing'] )
self.assertListEqual(tokenizer.tokenize('unwantedX running' ) , ['[UNK]', 'runn', '##ing'] )
def SCREAMING_SNAKE_CASE_ ( self : Any )-> int:
"""simple docstring"""
self.assertTrue(_is_whitespace(' ' ) )
self.assertTrue(_is_whitespace('\t' ) )
self.assertTrue(_is_whitespace('\r' ) )
self.assertTrue(_is_whitespace('\n' ) )
self.assertTrue(_is_whitespace('\u00A0' ) )
self.assertFalse(_is_whitespace('A' ) )
self.assertFalse(_is_whitespace('-' ) )
def SCREAMING_SNAKE_CASE_ ( self : Any )-> Dict:
"""simple docstring"""
self.assertTrue(_is_control('\u0005' ) )
self.assertFalse(_is_control('A' ) )
self.assertFalse(_is_control(' ' ) )
self.assertFalse(_is_control('\t' ) )
self.assertFalse(_is_control('\r' ) )
def SCREAMING_SNAKE_CASE_ ( self : Dict )-> Union[str, Any]:
"""simple docstring"""
self.assertTrue(_is_punctuation('-' ) )
self.assertTrue(_is_punctuation('$' ) )
self.assertTrue(_is_punctuation('`' ) )
self.assertTrue(_is_punctuation('.' ) )
self.assertFalse(_is_punctuation('A' ) )
self.assertFalse(_is_punctuation(' ' ) )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] )-> Union[str, Any]:
"""simple docstring"""
lowercase__ = self.get_tokenizer()
lowercase__ = self.get_rust_tokenizer()
# Example taken from the issue https://github.com/huggingface/tokenizers/issues/340
self.assertListEqual([tokenizer.tokenize(a ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
self.assertListEqual(
[rust_tokenizer.tokenize(a ) for t in ['Test', '\xad', 'test']] , [['[UNK]'], [], ['[UNK]']] )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] )-> Optional[Any]:
"""simple docstring"""
lowercase__ = self.tokenizer_class.from_pretrained('bert-base-uncased' )
lowercase__ = tokenizer.encode('sequence builders' , add_special_tokens=a )
lowercase__ = tokenizer.encode('multi-sequence build' , add_special_tokens=a )
lowercase__ = tokenizer.build_inputs_with_special_tokens(a )
lowercase__ = tokenizer.build_inputs_with_special_tokens(a , a )
assert encoded_sentence == [101] + text + [102]
assert encoded_pair == [101] + text + [102] + text_a + [102]
def SCREAMING_SNAKE_CASE_ ( self : Tuple )-> Any:
"""simple docstring"""
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowercase__ = self.rust_tokenizer_class.from_pretrained(a , **a )
lowercase__ = f"""A, naïve {tokenizer_r.mask_token} AllenNLP sentence."""
lowercase__ = tokenizer_r.encode_plus(
a , return_attention_mask=a , return_token_type_ids=a , return_offsets_mapping=a , add_special_tokens=a , )
lowercase__ = tokenizer_r.do_lower_case if hasattr(a , 'do_lower_case' ) else False
lowercase__ = (
[
((0, 0), tokenizer_r.cls_token),
((0, 1), 'A'),
((1, 2), ','),
((3, 5), 'na'),
((5, 6), '##ï'),
((6, 8), '##ve'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'Allen'),
((21, 23), '##NL'),
((23, 24), '##P'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
if not do_lower_case
else [
((0, 0), tokenizer_r.cls_token),
((0, 1), 'a'),
((1, 2), ','),
((3, 8), 'naive'),
((9, 15), tokenizer_r.mask_token),
((16, 21), 'allen'),
((21, 23), '##nl'),
((23, 24), '##p'),
((25, 33), 'sentence'),
((33, 34), '.'),
((0, 0), tokenizer_r.sep_token),
]
)
self.assertEqual(
[e[1] for e in expected_results] , tokenizer_r.convert_ids_to_tokens(tokens['input_ids'] ) )
self.assertEqual([e[0] for e in expected_results] , tokens['offset_mapping'] )
def SCREAMING_SNAKE_CASE_ ( self : Tuple )-> Dict:
"""simple docstring"""
lowercase__ = ['的', '人', '有']
lowercase__ = ''.join(a )
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowercase__ = True
lowercase__ = self.tokenizer_class.from_pretrained(a , **a )
lowercase__ = self.rust_tokenizer_class.from_pretrained(a , **a )
lowercase__ = tokenizer_p.encode(a , add_special_tokens=a )
lowercase__ = tokenizer_r.encode(a , add_special_tokens=a )
lowercase__ = tokenizer_r.convert_ids_to_tokens(a )
lowercase__ = tokenizer_p.convert_ids_to_tokens(a )
# it is expected that each Chinese character is not preceded by "##"
self.assertListEqual(a , a )
self.assertListEqual(a , a )
lowercase__ = False
lowercase__ = self.rust_tokenizer_class.from_pretrained(a , **a )
lowercase__ = self.tokenizer_class.from_pretrained(a , **a )
lowercase__ = tokenizer_r.encode(a , add_special_tokens=a )
lowercase__ = tokenizer_p.encode(a , add_special_tokens=a )
lowercase__ = tokenizer_r.convert_ids_to_tokens(a )
lowercase__ = tokenizer_p.convert_ids_to_tokens(a )
# it is expected that only the first Chinese character is not preceded by "##".
lowercase__ = [
f"""##{token}""" if idx != 0 else token for idx, token in enumerate(a )
]
self.assertListEqual(a , a )
self.assertListEqual(a , a )
| 45 |
def __UpperCamelCase (_SCREAMING_SNAKE_CASE ) -> List[Any]:
stooge(_SCREAMING_SNAKE_CASE , 0 , len(_SCREAMING_SNAKE_CASE ) - 1 )
return arr
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
lowercase__ , lowercase__ = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
lowercase__ = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , (h - t) )
# Recursively sort last 2/3 elements
stooge(_SCREAMING_SNAKE_CASE , i + t , (_SCREAMING_SNAKE_CASE) )
# Recursively sort first 2/3 elements
stooge(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , (h - t) )
if __name__ == "__main__":
lowercase_ = input("""Enter numbers separated by a comma:\n""").strip()
lowercase_ = [int(item) for item in user_input.split(""",""")]
print(stooge_sort(unsorted))
| 45 | 1 |
# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase__ = {
'''configuration_cpmant''': ['''CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CpmAntConfig'''],
'''tokenization_cpmant''': ['''CpmAntTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase__ = [
'''CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CpmAntForCausalLM''',
'''CpmAntModel''',
'''CpmAntPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
from .tokenization_cpmant import CpmAntTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_cpmant import (
CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
CpmAntForCausalLM,
CpmAntModel,
CpmAntPreTrainedModel,
)
else:
import sys
lowerCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 547 | import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBertTokenizer,
GPTaConfig,
GPTaLMHeadModel,
GPTaTokenizer,
RobertaConfig,
RobertaForMaskedLM,
RobertaTokenizer,
)
from utils import git_log, init_gpu_params, logger, set_seed
lowerCamelCase__ = {
'''distilbert''': (DistilBertConfig, DistilBertForMaskedLM, DistilBertTokenizer),
'''roberta''': (RobertaConfig, RobertaForMaskedLM, RobertaTokenizer),
'''bert''': (BertConfig, BertForMaskedLM, BertTokenizer),
'''gpt2''': (GPTaConfig, GPTaLMHeadModel, GPTaTokenizer),
}
def lowerCAmelCase__ ( a__ ) ->Optional[Any]:
'''simple docstring'''
assert (args.mlm and args.alpha_mlm > 0.0) or (not args.mlm and args.alpha_mlm == 0.0)
assert (args.alpha_mlm > 0.0 and args.alpha_clm == 0.0) or (args.alpha_mlm == 0.0 and args.alpha_clm > 0.0)
if args.mlm:
assert os.path.isfile(args.token_counts )
assert (args.student_type in ["roberta", "distilbert"]) and (args.teacher_type in ["roberta", "bert"])
else:
assert (args.student_type in ["gpt2"]) and (args.teacher_type in ["gpt2"])
assert args.teacher_type == args.student_type or (
args.student_type == "distilbert" and args.teacher_type == "bert"
)
assert os.path.isfile(args.student_config )
if args.student_pretrained_weights is not None:
assert os.path.isfile(args.student_pretrained_weights )
if args.freeze_token_type_embds:
assert args.student_type in ["roberta"]
assert args.alpha_ce >= 0.0
assert args.alpha_mlm >= 0.0
assert args.alpha_clm >= 0.0
assert args.alpha_mse >= 0.0
assert args.alpha_cos >= 0.0
assert args.alpha_ce + args.alpha_mlm + args.alpha_clm + args.alpha_mse + args.alpha_cos > 0.0
def lowerCAmelCase__ ( a__ , a__ ) ->List[Any]:
'''simple docstring'''
if args.student_type == "roberta":
_UpperCamelCase = False
elif args.student_type == "gpt2":
_UpperCamelCase = False
def lowerCAmelCase__ ( a__ , a__ ) ->List[str]:
'''simple docstring'''
if args.student_type == "roberta":
_UpperCamelCase = False
def lowerCAmelCase__ ( ) ->Any:
'''simple docstring'''
_UpperCamelCase = argparse.ArgumentParser(description="Training" )
parser.add_argument("--force" , action="store_true" , help="Overwrite dump_path if it already exists." )
parser.add_argument(
"--dump_path" , type=a__ , required=a__ , help="The output directory (log, checkpoints, parameters, etc.)" )
parser.add_argument(
"--data_file" , type=a__ , required=a__ , help="The binarized file (tokenized + tokens_to_ids) and grouped by sequence." , )
parser.add_argument(
"--student_type" , type=a__ , choices=["distilbert", "roberta", "gpt2"] , required=a__ , help="The student type (DistilBERT, RoBERTa)." , )
parser.add_argument("--student_config" , type=a__ , required=a__ , help="Path to the student configuration." )
parser.add_argument(
"--student_pretrained_weights" , default=a__ , type=a__ , help="Load student initialization checkpoint." )
parser.add_argument(
"--teacher_type" , choices=["bert", "roberta", "gpt2"] , required=a__ , help="Teacher type (BERT, RoBERTa)." )
parser.add_argument("--teacher_name" , type=a__ , required=a__ , help="The teacher model." )
parser.add_argument("--temperature" , default=2.0 , type=a__ , help="Temperature for the softmax temperature." )
parser.add_argument(
"--alpha_ce" , default=0.5 , type=a__ , help="Linear weight for the distillation loss. Must be >=0." )
parser.add_argument(
"--alpha_mlm" , default=0.0 , type=a__ , help="Linear weight for the MLM loss. Must be >=0. Should be used in conjunction with `mlm` flag." , )
parser.add_argument("--alpha_clm" , default=0.5 , type=a__ , help="Linear weight for the CLM loss. Must be >=0." )
parser.add_argument("--alpha_mse" , default=0.0 , type=a__ , help="Linear weight of the MSE loss. Must be >=0." )
parser.add_argument(
"--alpha_cos" , default=0.0 , type=a__ , help="Linear weight of the cosine embedding loss. Must be >=0." )
parser.add_argument(
"--mlm" , action="store_true" , help="The LM step: MLM or CLM. If `mlm` is True, the MLM is used over CLM." )
parser.add_argument(
"--mlm_mask_prop" , default=0.15 , type=a__ , help="Proportion of tokens for which we need to make a prediction." , )
parser.add_argument("--word_mask" , default=0.8 , type=a__ , help="Proportion of tokens to mask out." )
parser.add_argument("--word_keep" , default=0.1 , type=a__ , help="Proportion of tokens to keep." )
parser.add_argument("--word_rand" , default=0.1 , type=a__ , help="Proportion of tokens to randomly replace." )
parser.add_argument(
"--mlm_smoothing" , default=0.7 , type=a__ , help="Smoothing parameter to emphasize more rare tokens (see XLM, similar to word2vec)." , )
parser.add_argument("--token_counts" , type=a__ , help="The token counts in the data_file for MLM." )
parser.add_argument(
"--restrict_ce_to_mask" , action="store_true" , help="If true, compute the distillation loss only the [MLM] prediction distribution." , )
parser.add_argument(
"--freeze_pos_embs" , action="store_true" , help="Freeze positional embeddings during distillation. For student_type in ['roberta', 'gpt2'] only." , )
parser.add_argument(
"--freeze_token_type_embds" , action="store_true" , help="Freeze token type embeddings during distillation if existent. For student_type in ['roberta'] only." , )
parser.add_argument("--n_epoch" , type=a__ , default=3 , help="Number of pass on the whole dataset." )
parser.add_argument("--batch_size" , type=a__ , default=5 , help="Batch size (for each process)." )
parser.add_argument(
"--group_by_size" , action="store_false" , help="If true, group sequences that have similar length into the same batch. Default is true." , )
parser.add_argument(
"--gradient_accumulation_steps" , type=a__ , default=50 , help="Gradient accumulation for larger training batches." , )
parser.add_argument("--warmup_prop" , default=0.05 , type=a__ , help="Linear warmup proportion." )
parser.add_argument("--weight_decay" , default=0.0 , type=a__ , help="Weight decay if we apply some." )
parser.add_argument("--learning_rate" , default=5e-4 , type=a__ , help="The initial learning rate for Adam." )
parser.add_argument("--adam_epsilon" , default=1e-6 , type=a__ , help="Epsilon for Adam optimizer." )
parser.add_argument("--max_grad_norm" , default=5.0 , type=a__ , help="Max gradient norm." )
parser.add_argument("--initializer_range" , default=0.02 , type=a__ , help="Random initialization range." )
parser.add_argument(
"--fp16" , action="store_true" , help="Whether to use 16-bit (mixed) precision (through NVIDIA apex) instead of 32-bit" , )
parser.add_argument(
"--fp16_opt_level" , type=a__ , default="O1" , help=(
"For fp16: Apex AMP optimization level selected in ['O0', 'O1', 'O2', and 'O3']."
"See details at https://nvidia.github.io/apex/amp.html"
) , )
parser.add_argument("--n_gpu" , type=a__ , default=1 , help="Number of GPUs in the node." )
parser.add_argument("--local_rank" , type=a__ , default=-1 , help="Distributed training - Local rank" )
parser.add_argument("--seed" , type=a__ , default=56 , help="Random seed" )
parser.add_argument("--log_interval" , type=a__ , default=500 , help="Tensorboard logging interval." )
parser.add_argument("--checkpoint_interval" , type=a__ , default=4_000 , help="Checkpoint interval." )
_UpperCamelCase = parser.parse_args()
sanity_checks(a__ )
# ARGS #
init_gpu_params(a__ )
set_seed(a__ )
if args.is_master:
if os.path.exists(args.dump_path ):
if not args.force:
raise ValueError(
f'Serialization dir {args.dump_path} already exists, but you have not precised wheter to overwrite'
" itUse `--force` if you want to overwrite it" )
else:
shutil.rmtree(args.dump_path )
if not os.path.exists(args.dump_path ):
os.makedirs(args.dump_path )
logger.info(f'Experiment will be dumped and logged in {args.dump_path}' )
# SAVE PARAMS #
logger.info(f'Param: {args}' )
with open(os.path.join(args.dump_path , "parameters.json" ) , "w" ) as f:
json.dump(vars(a__ ) , a__ , indent=4 )
git_log(args.dump_path )
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = MODEL_CLASSES[args.student_type]
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase = MODEL_CLASSES[args.teacher_type]
# TOKENIZER #
_UpperCamelCase = teacher_tokenizer_class.from_pretrained(args.teacher_name )
_UpperCamelCase = {}
for tok_name, tok_symbol in tokenizer.special_tokens_map.items():
_UpperCamelCase = tokenizer.all_special_tokens.index(a__ )
_UpperCamelCase = tokenizer.all_special_ids[idx]
logger.info(f'Special tokens {special_tok_ids}' )
_UpperCamelCase = special_tok_ids
_UpperCamelCase = tokenizer.max_model_input_sizes[args.teacher_name]
# DATA LOADER #
logger.info(f'Loading data from {args.data_file}' )
with open(args.data_file , "rb" ) as fp:
_UpperCamelCase = pickle.load(a__ )
if args.mlm:
logger.info(f'Loading token counts from {args.token_counts} (already pre-computed)' )
with open(args.token_counts , "rb" ) as fp:
_UpperCamelCase = pickle.load(a__ )
_UpperCamelCase = np.maximum(a__ , 1 ) ** -args.mlm_smoothing
for idx in special_tok_ids.values():
_UpperCamelCase = 0.0 # do not predict special tokens
_UpperCamelCase = torch.from_numpy(a__ )
else:
_UpperCamelCase = None
_UpperCamelCase = LmSeqsDataset(params=a__ , data=a__ )
logger.info("Data loader created." )
# STUDENT #
logger.info(f'Loading student config from {args.student_config}' )
_UpperCamelCase = student_config_class.from_pretrained(args.student_config )
_UpperCamelCase = True
if args.student_pretrained_weights is not None:
logger.info(f'Loading pretrained weights from {args.student_pretrained_weights}' )
_UpperCamelCase = student_model_class.from_pretrained(args.student_pretrained_weights , config=a__ )
else:
_UpperCamelCase = student_model_class(a__ )
if args.n_gpu > 0:
student.to(f'cuda:{args.local_rank}' )
logger.info("Student loaded." )
# TEACHER #
_UpperCamelCase = teacher_model_class.from_pretrained(args.teacher_name , output_hidden_states=a__ )
if args.n_gpu > 0:
teacher.to(f'cuda:{args.local_rank}' )
logger.info(f'Teacher loaded from {args.teacher_name}.' )
# FREEZING #
if args.freeze_pos_embs:
freeze_pos_embeddings(a__ , a__ )
if args.freeze_token_type_embds:
freeze_token_type_embeddings(a__ , a__ )
# SANITY CHECKS #
assert student.config.vocab_size == teacher.config.vocab_size
assert student.config.hidden_size == teacher.config.hidden_size
assert student.config.max_position_embeddings == teacher.config.max_position_embeddings
if args.mlm:
assert token_probs.size(0 ) == stu_architecture_config.vocab_size
# DISTILLER #
torch.cuda.empty_cache()
_UpperCamelCase = Distiller(
params=a__ , dataset=a__ , token_probs=a__ , student=a__ , teacher=a__ )
distiller.train()
logger.info("Let's go get some drinks." )
if __name__ == "__main__":
main()
| 547 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
import numpy as np
from transformers import LayoutLMConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.layoutlm.modeling_tf_layoutlm import (
TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLayoutLMForMaskedLM,
TFLayoutLMForQuestionAnswering,
TFLayoutLMForSequenceClassification,
TFLayoutLMForTokenClassification,
TFLayoutLMModel,
)
class __a :
def __init__( self : Dict , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Union[str, Any]=13 , UpperCAmelCase_ : Optional[Any]=7 , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : Tuple=True , UpperCAmelCase_ : Optional[Any]=True , UpperCAmelCase_ : int=True , UpperCAmelCase_ : Optional[Any]=99 , UpperCAmelCase_ : Tuple=32 , UpperCAmelCase_ : List[str]=2 , UpperCAmelCase_ : List[str]=4 , UpperCAmelCase_ : Dict=37 , UpperCAmelCase_ : List[str]="gelu" , UpperCAmelCase_ : List[Any]=0.1 , UpperCAmelCase_ : List[Any]=0.1 , UpperCAmelCase_ : Optional[int]=512 , UpperCAmelCase_ : List[Any]=16 , UpperCAmelCase_ : Tuple=2 , UpperCAmelCase_ : List[str]=0.02 , UpperCAmelCase_ : int=3 , UpperCAmelCase_ : str=4 , UpperCAmelCase_ : str=None , UpperCAmelCase_ : Optional[int]=1_000 , )-> List[Any]:
"""simple docstring"""
UpperCamelCase = parent
UpperCamelCase = batch_size
UpperCamelCase = seq_length
UpperCamelCase = is_training
UpperCamelCase = use_input_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_labels
UpperCamelCase = num_choices
UpperCamelCase = scope
UpperCamelCase = range_bbox
def _SCREAMING_SNAKE_CASE ( self : Any )-> List[Any]:
"""simple docstring"""
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
# convert bbox to numpy since TF does not support item assignment
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length, 4] , self.range_bbox ).numpy()
# Ensure that bbox is legal
for i in range(bbox.shape[0] ):
for j in range(bbox.shape[1] ):
if bbox[i, j, 3] < bbox[i, j, 1]:
UpperCamelCase = bbox[i, j, 3]
UpperCamelCase = bbox[i, j, 1]
UpperCamelCase = t
if bbox[i, j, 2] < bbox[i, j, 0]:
UpperCamelCase = bbox[i, j, 2]
UpperCamelCase = bbox[i, j, 0]
UpperCamelCase = t
UpperCamelCase = tf.convert_to_tensor(UpperCAmelCase_ )
UpperCamelCase = None
if self.use_input_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 = None
UpperCamelCase = None
UpperCamelCase = None
if self.use_labels:
UpperCamelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
UpperCamelCase = ids_tensor([self.batch_size] , self.num_choices )
UpperCamelCase = LayoutLMConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _SCREAMING_SNAKE_CASE ( self : List[str] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : List[Any] )-> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = TFLayoutLMModel(config=UpperCAmelCase_ )
UpperCamelCase = model(UpperCAmelCase_ , UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ )
UpperCamelCase = model(UpperCAmelCase_ , UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ )
UpperCamelCase = model(UpperCAmelCase_ , UpperCAmelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def _SCREAMING_SNAKE_CASE ( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : int , UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : Union[str, Any] )-> str:
"""simple docstring"""
UpperCamelCase = TFLayoutLMForMaskedLM(config=UpperCAmelCase_ )
UpperCamelCase = model(UpperCAmelCase_ , UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _SCREAMING_SNAKE_CASE ( self : List[str] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : Dict )-> str:
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = TFLayoutLMForSequenceClassification(config=UpperCAmelCase_ )
UpperCamelCase = model(UpperCAmelCase_ , UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _SCREAMING_SNAKE_CASE ( self : Any , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Dict )-> Union[str, Any]:
"""simple docstring"""
UpperCamelCase = self.num_labels
UpperCamelCase = TFLayoutLMForTokenClassification(config=UpperCAmelCase_ )
UpperCamelCase = model(UpperCAmelCase_ , UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , labels=UpperCAmelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Dict , UpperCAmelCase_ : int )-> Optional[Any]:
"""simple docstring"""
UpperCamelCase = TFLayoutLMForQuestionAnswering(config=UpperCAmelCase_ )
UpperCamelCase = model(UpperCAmelCase_ , UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] )-> List[Any]:
"""simple docstring"""
UpperCamelCase = self.prepare_config_and_inputs()
(
(
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) , (
UpperCamelCase
) ,
) = config_and_inputs
UpperCamelCase = {
"input_ids": input_ids,
"bbox": bbox,
"token_type_ids": token_type_ids,
"attention_mask": input_mask,
}
return config, inputs_dict
@require_tf
class __a ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
UpperCamelCase_ : Union[str, Any] = (
(
TFLayoutLMModel,
TFLayoutLMForMaskedLM,
TFLayoutLMForTokenClassification,
TFLayoutLMForSequenceClassification,
TFLayoutLMForQuestionAnswering,
)
if is_tf_available()
else ()
)
UpperCamelCase_ : str = (
{
'''feature-extraction''': TFLayoutLMModel,
'''fill-mask''': TFLayoutLMForMaskedLM,
'''text-classification''': TFLayoutLMForSequenceClassification,
'''token-classification''': TFLayoutLMForTokenClassification,
'''zero-shot''': TFLayoutLMForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCamelCase_ : Union[str, Any] = False
UpperCamelCase_ : List[Any] = True
UpperCamelCase_ : str = 10
def _SCREAMING_SNAKE_CASE ( self : List[str] )-> int:
"""simple docstring"""
UpperCamelCase = TFLayoutLMModelTester(self )
UpperCamelCase = ConfigTester(self , config_class=UpperCAmelCase_ , hidden_size=37 )
def _SCREAMING_SNAKE_CASE ( self : int )-> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def _SCREAMING_SNAKE_CASE ( self : Dict )-> int:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] )-> Any:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] )-> Any:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] )-> Dict:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*UpperCAmelCase_ )
def _SCREAMING_SNAKE_CASE ( self : Any )-> Dict:
"""simple docstring"""
UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*UpperCAmelCase_ )
@slow
def _SCREAMING_SNAKE_CASE ( self : str )-> List[Any]:
"""simple docstring"""
for model_name in TF_LAYOUTLM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCamelCase = TFLayoutLMModel.from_pretrained(UpperCAmelCase_ )
self.assertIsNotNone(UpperCAmelCase_ )
@unittest.skip("Onnx compliancy broke with TF 2.10" )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] )-> str:
"""simple docstring"""
pass
def lowerCamelCase__ ( )-> Tuple:
"""simple docstring"""
# Here we prepare a batch of 2 sequences to test a LayoutLM forward pass on:
# fmt: off
UpperCamelCase = tf.convert_to_tensor([[1_01,10_19,10_14,10_16,10_37,1_28_49,47_47,10_04,1_42_46,22_78,54_39,45_24,50_02,29_30,21_93,29_30,43_41,32_08,10_05,10_55,21_71,28_48,1_13_00,35_31,1_02],[1_01,40_70,40_34,70_20,10_24,30_58,10_15,10_13,28_61,10_13,60_70,1_92_74,27_72,62_05,2_78_14,1_61_47,1_61_47,43_43,20_47,1_02_83,1_09_69,1_43_89,10_12,23_38,1_02]] ) # noqa: E231
UpperCamelCase = tf.convert_to_tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],] ) # noqa: E231
UpperCamelCase = tf.convert_to_tensor([[[0,0,0,0],[4_23,2_37,4_40,2_51],[4_27,2_72,4_41,2_87],[4_19,1_15,4_37,1_29],[9_61,8_85,9_92,9_12],[2_56,38,3_30,58],[2_56,38,3_30,58],[3_36,42,3_53,57],[3_60,39,4_01,56],[3_60,39,4_01,56],[4_11,39,4_71,59],[4_79,41,5_28,59],[5_33,39,6_30,60],[67,1_13,1_34,1_31],[1_41,1_15,2_09,1_32],[68,1_49,1_33,1_66],[1_41,1_49,1_87,1_64],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[1_95,1_48,2_87,1_65],[2_95,1_48,3_49,1_65],[4_41,1_49,4_92,1_66],[4_97,1_49,5_46,1_64],[64,2_01,1_25,2_18],[10_00,10_00,10_00,10_00]],[[0,0,0,0],[6_62,1_50,7_54,1_66],[6_65,1_99,7_42,2_11],[5_19,2_13,5_54,2_28],[5_19,2_13,5_54,2_28],[1_34,4_33,1_87,4_54],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[1_30,4_67,2_04,4_80],[3_14,4_69,3_76,4_82],[5_04,6_84,5_82,7_06],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[9_41,8_25,9_73,9_00],[6_10,7_49,6_52,7_65],[1_30,6_59,1_68,6_72],[1_76,6_57,2_37,6_72],[2_38,6_57,3_12,6_72],[4_43,6_53,6_28,6_72],[4_43,6_53,6_28,6_72],[7_16,3_01,8_25,3_17],[10_00,10_00,10_00,10_00]]] ) # noqa: E231
UpperCamelCase = tf.convert_to_tensor([[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]] ) # noqa: E231
# these are sequence labels (i.e. at the token level)
UpperCamelCase = tf.convert_to_tensor([[-1_00,10,10,10,9,1,-1_00,7,7,-1_00,7,7,4,2,5,2,8,8,-1_00,-1_00,5,0,3,2,-1_00],[-1_00,12,12,12,-1_00,12,10,-1_00,-1_00,-1_00,-1_00,10,12,9,-1_00,-1_00,-1_00,10,10,10,9,12,-1_00,10,-1_00]] ) # noqa: E231
# fmt: on
return input_ids, attention_mask, bbox, token_type_ids, labels
@require_tf
class __a ( unittest.TestCase ):
@slow
def _SCREAMING_SNAKE_CASE ( self : str )-> Optional[int]:
"""simple docstring"""
UpperCamelCase = TFLayoutLMModel.from_pretrained("microsoft/layoutlm-base-uncased" )
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = prepare_layoutlm_batch_inputs()
# forward pass
UpperCamelCase = model(input_ids=UpperCAmelCase_ , bbox=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ )
# test the sequence output on [0, :3, :3]
UpperCamelCase = tf.convert_to_tensor(
[[0.1785, -0.1947, -0.0425], [-0.3254, -0.2807, 0.2553], [-0.5391, -0.3322, 0.3364]] , )
self.assertTrue(np.allclose(outputs.last_hidden_state[0, :3, :3] , UpperCAmelCase_ , atol=1e-3 ) )
# test the pooled output on [1, :3]
UpperCamelCase = tf.convert_to_tensor([-0.6580, -0.0214, 0.8552] )
self.assertTrue(np.allclose(outputs.pooler_output[1, :3] , UpperCAmelCase_ , atol=1e-3 ) )
@slow
def _SCREAMING_SNAKE_CASE ( self : Tuple )-> Any:
"""simple docstring"""
# initialize model with randomly initialized sequence classification head
UpperCamelCase = TFLayoutLMForSequenceClassification.from_pretrained("microsoft/layoutlm-base-uncased" , num_labels=2 )
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = prepare_layoutlm_batch_inputs()
# forward pass
UpperCamelCase = model(
input_ids=UpperCAmelCase_ , bbox=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , labels=tf.convert_to_tensor([1, 1] ) , )
# test whether we get a loss as a scalar
UpperCamelCase = outputs.loss
UpperCamelCase = (2,)
self.assertEqual(loss.shape , UpperCAmelCase_ )
# test the shape of the logits
UpperCamelCase = outputs.logits
UpperCamelCase = (2, 2)
self.assertEqual(logits.shape , UpperCAmelCase_ )
@slow
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] )-> Union[str, Any]:
"""simple docstring"""
# initialize model with randomly initialized token classification head
UpperCamelCase = TFLayoutLMForTokenClassification.from_pretrained("microsoft/layoutlm-base-uncased" , num_labels=13 )
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = prepare_layoutlm_batch_inputs()
# forward pass
UpperCamelCase = model(
input_ids=UpperCAmelCase_ , bbox=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ , labels=UpperCAmelCase_ )
# test the shape of the logits
UpperCamelCase = outputs.logits
UpperCamelCase = tf.convert_to_tensor((2, 25, 13) )
self.assertEqual(logits.shape , UpperCAmelCase_ )
@slow
def _SCREAMING_SNAKE_CASE ( self : Dict )-> Optional[Any]:
"""simple docstring"""
# initialize model with randomly initialized token classification head
UpperCamelCase = TFLayoutLMForQuestionAnswering.from_pretrained("microsoft/layoutlm-base-uncased" )
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = prepare_layoutlm_batch_inputs()
# forward pass
UpperCamelCase = model(input_ids=UpperCAmelCase_ , bbox=UpperCAmelCase_ , attention_mask=UpperCAmelCase_ , token_type_ids=UpperCAmelCase_ )
# test the shape of the logits
UpperCamelCase = tf.convert_to_tensor((2, 25) )
self.assertEqual(outputs.start_logits.shape , UpperCAmelCase_ )
self.assertEqual(outputs.end_logits.shape , UpperCAmelCase_ )
| 556 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class __a ( _lowerCAmelCase ):
UpperCamelCase_ : Union[List[PIL.Image.Image], np.ndarray]
UpperCamelCase_ : Optional[List[bool]]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPixaPixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version(""">=""", """0.0.12""")
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class __a ( _lowerCAmelCase ):
UpperCamelCase_ : np.ndarray
UpperCamelCase_ : List[bool]
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 556 | 1 |
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class lowerCamelCase__ :
"""simple docstring"""
_A = 42
_A = None
# Automatically constructed
_A = "dict"
_A = None
_A = field(default='Translation' , init=UpperCAmelCase_ , repr=UpperCAmelCase_)
def __call__(self ):
'''simple docstring'''
return pa.struct({lang: pa.string() for lang in sorted(self.languages )} )
def _a (self ):
'''simple docstring'''
from .features import Value
return {k: Value("string" ) for k in sorted(self.languages )}
@dataclass
class lowerCamelCase__ :
"""simple docstring"""
_A = None
_A = None
_A = None
# Automatically constructed
_A = "dict"
_A = None
_A = field(default='TranslationVariableLanguages' , init=UpperCAmelCase_ , repr=UpperCAmelCase_)
def _a (self ):
'''simple docstring'''
lowerCamelCase = sorted(set(self.languages ) ) if self.languages else None
lowerCamelCase = len(self.languages ) if self.languages else None
def __call__(self ):
'''simple docstring'''
return pa.struct({"language": pa.list_(pa.string() ), "translation": pa.list_(pa.string() )} )
def _a (self , __a ):
'''simple docstring'''
lowerCamelCase = set(self.languages )
if self.languages and set(__a ) - lang_set:
raise ValueError(
F"""Some languages in example ({", ".join(sorted(set(__a ) - lang_set ) )}) are not in valid set ({", ".join(__a )}).""" )
# Convert dictionary into tuples, splitting out cases where there are
# multiple translations for a single language.
lowerCamelCase = []
for lang, text in translation_dict.items():
if isinstance(__a , __a ):
translation_tuples.append((lang, text) )
else:
translation_tuples.extend([(lang, el) for el in text] )
# Ensure translations are in ascending order by language code.
lowerCamelCase , lowerCamelCase = zip(*sorted(__a ) )
return {"language": languages, "translation": translations}
def _a (self ):
'''simple docstring'''
from .features import Sequence, Value
return {
"language": Sequence(Value("string" ) ),
"translation": Sequence(Value("string" ) ),
} | 623 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
)
from ...test_tokenization_common import TokenizerTesterMixin
a_ : Tuple = get_tests_dir('fixtures/test_sentencepiece.model')
if is_torch_available():
from transformers.models.mbart.modeling_mbart import shift_tokens_right
a_ : Union[str, Any] = 2_5_0_0_0_4
a_ : int = 2_5_0_0_2_0
@require_sentencepiece
@require_tokenizers
class lowerCamelCase__ ( UpperCAmelCase_ , unittest.TestCase):
"""simple docstring"""
_A = MBartaaTokenizer
_A = MBartaaTokenizerFast
_A = True
_A = True
def _a (self ):
'''simple docstring'''
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase = MBartaaTokenizer(__a , src_lang="en_XX" , tgt_lang="ro_RO" , keep_accents=__a )
tokenizer.save_pretrained(self.tmpdirname )
def _a (self ):
'''simple docstring'''
lowerCamelCase = "<s>"
lowerCamelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__a ) , __a )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__a ) , __a )
def _a (self ):
'''simple docstring'''
lowerCamelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(__a ) , 10_54 )
def _a (self ):
'''simple docstring'''
self.assertEqual(self.get_tokenizer().vocab_size , 10_54 )
def _a (self ):
'''simple docstring'''
lowerCamelCase = MBartaaTokenizer(__a , src_lang="en_XX" , tgt_lang="ro_RO" , keep_accents=__a )
lowerCamelCase = tokenizer.tokenize("This is a test" )
self.assertListEqual(__a , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__a ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
lowerCamelCase = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
__a , [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "9", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "é", "."] , )
lowerCamelCase = tokenizer.convert_tokens_to_ids(__a )
self.assertListEqual(
__a , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
] , )
lowerCamelCase = tokenizer.convert_ids_to_tokens(__a )
self.assertListEqual(
__a , [SPIECE_UNDERLINE + "I", SPIECE_UNDERLINE + "was", SPIECE_UNDERLINE + "b", "or", "n", SPIECE_UNDERLINE + "in", SPIECE_UNDERLINE + "", "<unk>", "2", "0", "0", "0", ",", SPIECE_UNDERLINE + "and", SPIECE_UNDERLINE + "this", SPIECE_UNDERLINE + "is", SPIECE_UNDERLINE + "f", "al", "s", "<unk>", "."] , )
@slow
def _a (self ):
'''simple docstring'''
lowerCamelCase = {"input_ids": [[25_00_04, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [25_00_04, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [25_00_04, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=__a , model_name="facebook/mbart-large-50" , revision="d3913889c59cd5c9e456b269c376325eabad57e2" , )
def _a (self ):
'''simple docstring'''
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCamelCase = (self.rust_tokenizer_class, "hf-internal-testing/tiny-random-mbart50", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
lowerCamelCase = self.rust_tokenizer_class.from_pretrained(__a , **__a )
lowerCamelCase = self.tokenizer_class.from_pretrained(__a , **__a )
lowerCamelCase = tempfile.mkdtemp()
lowerCamelCase = tokenizer_r.save_pretrained(__a )
lowerCamelCase = tokenizer_p.save_pretrained(__a )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
lowerCamelCase = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(__a , __a )
# Checks everything loads correctly in the same way
lowerCamelCase = tokenizer_r.from_pretrained(__a )
lowerCamelCase = tokenizer_p.from_pretrained(__a )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__a , __a ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(__a )
# Save tokenizer rust, legacy_format=True
lowerCamelCase = tempfile.mkdtemp()
lowerCamelCase = tokenizer_r.save_pretrained(__a , legacy_format=__a )
lowerCamelCase = tokenizer_p.save_pretrained(__a )
# Checks it save with the same files
self.assertSequenceEqual(__a , __a )
# Checks everything loads correctly in the same way
lowerCamelCase = tokenizer_r.from_pretrained(__a )
lowerCamelCase = tokenizer_p.from_pretrained(__a )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__a , __a ) )
shutil.rmtree(__a )
# Save tokenizer rust, legacy_format=False
lowerCamelCase = tempfile.mkdtemp()
lowerCamelCase = tokenizer_r.save_pretrained(__a , legacy_format=__a )
lowerCamelCase = tokenizer_p.save_pretrained(__a )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCamelCase = tokenizer_r.from_pretrained(__a )
lowerCamelCase = tokenizer_p.from_pretrained(__a )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(__a , __a ) )
shutil.rmtree(__a )
@require_torch
@require_sentencepiece
@require_tokenizers
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
_A = 'facebook/mbart-large-50-one-to-many-mmt'
_A = [
' UN Chief Says There Is No Military Solution in Syria',
' Secretary-General Ban Ki-moon says his response to Russia\'s stepped up military support for Syria is that "there is no military solution" to the nearly five-year conflict and more weapons will only worsen the violence and misery for millions of people.',
]
_A = [
'Şeful ONU declară că nu există o soluţie militară în Siria',
'Secretarul General Ban Ki-moon declară că răspunsul său la intensificarea sprijinului militar al Rusiei'
' pentru Siria este că "nu există o soluţie militară" la conflictul de aproape cinci ani şi că noi arme nu vor'
' face decât să înrăutăţească violenţele şi mizeria pentru milioane de oameni.',
]
_A = [EN_CODE, 82_74, 12_78_73, 2_59_16, 7, 86_22, 20_71, 4_38, 6_74_85, 53, 18_78_95, 23, 5_17_12, 2]
@classmethod
def _a (cls ):
'''simple docstring'''
lowerCamelCase = MBartaaTokenizer.from_pretrained(
cls.checkpoint_name , src_lang="en_XX" , tgt_lang="ro_RO" )
lowerCamelCase = 1
return cls
def _a (self ):
'''simple docstring'''
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ar_AR"] , 25_00_01 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["en_EN"] , 25_00_04 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["ro_RO"] , 25_00_20 )
self.assertEqual(self.tokenizer.fairseq_tokens_to_ids["mr_IN"] , 25_00_38 )
def _a (self ):
'''simple docstring'''
lowerCamelCase = self.tokenizer.batch_encode_plus(self.src_text ).input_ids[0]
self.assertListEqual(self.expected_src_tokens , __a )
def _a (self ):
'''simple docstring'''
self.assertIn(__a , self.tokenizer.all_special_ids )
lowerCamelCase = [RO_CODE, 8_84, 90_19, 96, 9, 9_16, 8_67_92, 36, 1_87_43, 1_55_96, 5, 2]
lowerCamelCase = self.tokenizer.decode(__a , skip_special_tokens=__a )
lowerCamelCase = self.tokenizer.decode(generated_ids[1:] , skip_special_tokens=__a )
self.assertEqual(__a , __a )
self.assertNotIn(self.tokenizer.eos_token , __a )
def _a (self ):
'''simple docstring'''
lowerCamelCase = ["this is gunna be a long sentence " * 20]
assert isinstance(src_text[0] , __a )
lowerCamelCase = 10
lowerCamelCase = self.tokenizer(__a , max_length=__a , truncation=__a ).input_ids[0]
self.assertEqual(ids[0] , __a )
self.assertEqual(ids[-1] , 2 )
self.assertEqual(len(__a ) , __a )
def _a (self ):
'''simple docstring'''
self.assertListEqual(self.tokenizer.convert_tokens_to_ids(["<mask>", "ar_AR"] ) , [25_00_53, 25_00_01] )
def _a (self ):
'''simple docstring'''
lowerCamelCase = tempfile.mkdtemp()
lowerCamelCase = self.tokenizer.fairseq_tokens_to_ids
self.tokenizer.save_pretrained(__a )
lowerCamelCase = MBartaaTokenizer.from_pretrained(__a )
self.assertDictEqual(new_tok.fairseq_tokens_to_ids , __a )
@require_torch
def _a (self ):
'''simple docstring'''
lowerCamelCase = self.tokenizer(self.src_text , text_target=self.tgt_text , padding=__a , return_tensors="pt" )
lowerCamelCase = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
# fairseq batch: https://gist.github.com/sshleifer/cba08bc2109361a74ac3760a7e30e4f4
assert batch.input_ids[1][0] == EN_CODE
assert batch.input_ids[1][-1] == 2
assert batch.labels[1][0] == RO_CODE
assert batch.labels[1][-1] == 2
assert batch.decoder_input_ids[1][:2].tolist() == [2, RO_CODE]
@require_torch
def _a (self ):
'''simple docstring'''
lowerCamelCase = self.tokenizer(
self.src_text , text_target=self.tgt_text , padding=__a , truncation=__a , max_length=len(self.expected_src_tokens ) , return_tensors="pt" , )
lowerCamelCase = shift_tokens_right(batch["labels"] , self.tokenizer.pad_token_id )
self.assertIsInstance(__a , __a )
self.assertEqual((2, 14) , batch.input_ids.shape )
self.assertEqual((2, 14) , batch.attention_mask.shape )
lowerCamelCase = batch.input_ids.tolist()[0]
self.assertListEqual(self.expected_src_tokens , __a )
self.assertEqual(2 , batch.decoder_input_ids[0, 0] ) # decoder_start_token_id
# Test that special tokens are reset
self.assertEqual(self.tokenizer.prefix_tokens , [EN_CODE] )
self.assertEqual(self.tokenizer.suffix_tokens , [self.tokenizer.eos_token_id] )
def _a (self ):
'''simple docstring'''
lowerCamelCase = self.tokenizer(self.src_text , padding=__a , truncation=__a , max_length=3 , return_tensors="pt" )
lowerCamelCase = self.tokenizer(
text_target=self.tgt_text , padding=__a , truncation=__a , max_length=10 , return_tensors="pt" )
lowerCamelCase = targets["input_ids"]
lowerCamelCase = shift_tokens_right(__a , self.tokenizer.pad_token_id )
self.assertEqual(batch.input_ids.shape[1] , 3 )
self.assertEqual(batch.decoder_input_ids.shape[1] , 10 )
@require_torch
def _a (self ):
'''simple docstring'''
lowerCamelCase = self.tokenizer._build_translation_inputs(
"A test" , return_tensors="pt" , src_lang="en_XX" , tgt_lang="ar_AR" )
self.assertEqual(
nested_simplify(__a ) , {
# en_XX, A, test, EOS
"input_ids": [[25_00_04, 62, 30_34, 2]],
"attention_mask": [[1, 1, 1, 1]],
# ar_AR
"forced_bos_token_id": 25_00_01,
} , ) | 623 | 1 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format="%(message)s")
def _lowerCAmelCase ( A__: np.ndarray ):
'''simple docstring'''
return input_array.reshape((input_array.size, 1) )
def _lowerCAmelCase ( A__: np.ndarray , A__: np.ndarray , A__: int ):
'''simple docstring'''
UpperCAmelCase = np.nan
for i in range(A__ ):
UpperCAmelCase = features[:, labels == i]
UpperCAmelCase = data.mean(1 )
# Centralize the data of class i
UpperCAmelCase = data - column_reshape(A__ )
if i > 0:
# If covariance_sum is not None
covariance_sum += np.dot(A__ , centered_data.T )
else:
# If covariance_sum is np.nan (i.e. first loop)
UpperCAmelCase = np.dot(A__ , centered_data.T )
return covariance_sum / features.shape[1]
def _lowerCAmelCase ( A__: np.ndarray , A__: np.ndarray , A__: int ):
'''simple docstring'''
UpperCAmelCase = features.mean(1 )
UpperCAmelCase = np.nan
for i in range(A__ ):
UpperCAmelCase = features[:, labels == i]
UpperCAmelCase = data.shape[1]
UpperCAmelCase = data.mean(1 )
if i > 0:
# If covariance_sum is not None
covariance_sum += device_data * np.dot(
column_reshape(A__ ) - column_reshape(A__ ) , (column_reshape(A__ ) - column_reshape(A__ )).T , )
else:
# If covariance_sum is np.nan (i.e. first loop)
UpperCAmelCase = device_data * np.dot(
column_reshape(A__ ) - column_reshape(A__ ) , (column_reshape(A__ ) - column_reshape(A__ )).T , )
return covariance_sum / features.shape[1]
def _lowerCAmelCase ( A__: np.ndarray , A__: int ):
'''simple docstring'''
if features.any():
UpperCAmelCase = features.mean(1 )
# Center the dataset
UpperCAmelCase = features - np.reshape(A__ , (data_mean.size, 1) )
UpperCAmelCase = np.dot(A__ , centered_data.T ) / features.shape[1]
UpperCAmelCase , UpperCAmelCase = np.linalg.eigh(A__ )
# Take all the columns in the reverse order (-1), and then takes only the first
UpperCAmelCase = eigenvectors[:, ::-1][:, 0:dimensions]
# Project the database on the new space
UpperCAmelCase = np.dot(filtered_eigenvectors.T , A__ )
logging.info('''Principal Component Analysis computed''' )
return projected_data
else:
logging.basicConfig(level=logging.ERROR , format='''%(message)s''' , force=A__ )
logging.error('''Dataset empty''' )
raise AssertionError
def _lowerCAmelCase ( A__: np.ndarray , A__: np.ndarray , A__: int , A__: int ):
'''simple docstring'''
assert classes > dimensions
# Check if features have been already loaded
if features.any:
UpperCAmelCase , UpperCAmelCase = eigh(
covariance_between_classes(A__ , A__ , A__ ) , covariance_within_classes(A__ , A__ , A__ ) , )
UpperCAmelCase = eigenvectors[:, ::-1][:, :dimensions]
UpperCAmelCase , UpperCAmelCase , UpperCAmelCase = np.linalg.svd(A__ )
UpperCAmelCase = svd_matrix[:, 0:dimensions]
UpperCAmelCase = np.dot(filtered_svd_matrix.T , A__ )
logging.info('''Linear Discriminant Analysis computed''' )
return projected_data
else:
logging.basicConfig(level=logging.ERROR , format='''%(message)s''' , force=A__ )
logging.error('''Dataset empty''' )
raise AssertionError
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase = np.array([[1, 2, 3, 4, 5], [2, 3, 4, 5, 6], [3, 4, 5, 6, 7]] )
UpperCAmelCase = np.array([0, 0, 0, 1, 1] )
UpperCAmelCase = 2
UpperCAmelCase = 2
# Assert that the function raises an AssertionError if dimensions > classes
with pytest.raises(A__ ) as error_info:
UpperCAmelCase = linear_discriminant_analysis(
A__ , A__ , A__ , A__ )
if isinstance(A__ , np.ndarray ):
raise AssertionError(
'''Did not raise AssertionError for dimensions > classes''' )
assert error_info.type is AssertionError
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]] )
UpperCAmelCase = 2
UpperCAmelCase = np.array([[6.9282_0323, 8.6602_5404, 10.3923_0485], [3.0, 3.0, 3.0]] )
with pytest.raises(A__ ) as error_info:
UpperCAmelCase = principal_component_analysis(A__ , A__ )
if not np.allclose(A__ , A__ ):
raise AssertionError
assert error_info.type is AssertionError
if __name__ == "__main__":
import doctest
doctest.testmod()
| 707 |
def _lowerCAmelCase ( A__: int , A__: int ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
UpperCAmelCase = str(bin(A__ ) )
binary_number += "0" * shift_amount
return binary_number
def _lowerCAmelCase ( A__: int , A__: int ):
'''simple docstring'''
if number < 0 or shift_amount < 0:
raise ValueError('''both inputs must be positive integers''' )
UpperCAmelCase = str(bin(A__ ) )[2:]
if shift_amount >= len(A__ ):
return "0b0"
UpperCAmelCase = binary_number[: len(A__ ) - shift_amount]
return "0b" + shifted_binary_number
def _lowerCAmelCase ( A__: int , A__: int ):
'''simple docstring'''
if number >= 0: # Get binary representation of positive number
UpperCAmelCase = '''0''' + str(bin(A__ ) ).strip('''-''' )[2:]
else: # Get binary (2's complement) representation of negative number
UpperCAmelCase = len(bin(A__ )[3:] ) # Find 2's complement of number
UpperCAmelCase = bin(abs(A__ ) - (1 << binary_number_length) )[3:]
UpperCAmelCase = (
'''1''' + '''0''' * (binary_number_length - len(A__ )) + binary_number
)
if shift_amount >= len(A__ ):
return "0b" + binary_number[0] * len(A__ )
return (
"0b"
+ binary_number[0] * shift_amount
+ binary_number[: len(A__ ) - shift_amount]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 391 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_barthez import BarthezTokenizer
else:
lowerCAmelCase__ : Any = None
lowerCAmelCase__ : str = logging.get_logger(__name__)
lowerCAmelCase__ : int = {"""vocab_file""": """sentencepiece.bpe.model""", """tokenizer_file""": """tokenizer.json"""}
lowerCAmelCase__ : Union[str, Any] = {
"""vocab_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/sentencepiece.bpe.model""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/sentencepiece.bpe.model"""
),
},
"""tokenizer_file""": {
"""moussaKam/mbarthez""": """https://huggingface.co/moussaKam/mbarthez/resolve/main/tokenizer.json""",
"""moussaKam/barthez""": """https://huggingface.co/moussaKam/barthez/resolve/main/tokenizer.json""",
"""moussaKam/barthez-orangesum-title""": (
"""https://huggingface.co/moussaKam/barthez-orangesum-title/resolve/main/tokenizer.json"""
),
},
}
lowerCAmelCase__ : int = {
"""moussaKam/mbarthez""": 1024,
"""moussaKam/barthez""": 1024,
"""moussaKam/barthez-orangesum-title""": 1024,
}
lowerCAmelCase__ : int = """▁"""
class a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ["""input_ids""", """attention_mask"""]
__UpperCAmelCase = BarthezTokenizer
def __init__( self : Optional[int] , snake_case_ : Any=None , snake_case_ : Union[str, Any]=None , snake_case_ : List[Any]="<s>" , snake_case_ : Tuple="</s>" , snake_case_ : List[Any]="</s>" , snake_case_ : List[Any]="<s>" , snake_case_ : str="<unk>" , snake_case_ : List[Any]="<pad>" , snake_case_ : Any="<mask>" , **snake_case_ : Dict , ):
'''simple docstring'''
snake_case__ : str = AddedToken(snake_case_ , lstrip=snake_case_ , rstrip=snake_case_ ) if isinstance(snake_case_ , snake_case_ ) else mask_token
super().__init__(
snake_case_ , tokenizer_file=snake_case_ , bos_token=snake_case_ , eos_token=snake_case_ , unk_token=snake_case_ , sep_token=snake_case_ , cls_token=snake_case_ , pad_token=snake_case_ , mask_token=snake_case_ , **snake_case_ , )
snake_case__ : str = vocab_file
snake_case__ : Dict = False if not self.vocab_file else True
def __magic_name__ ( self : List[str] , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case__ : Any = [self.cls_token_id]
snake_case__ : Tuple = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def __magic_name__ ( self : Dict , snake_case_ : List[int] , snake_case_ : Optional[List[int]] = None ):
'''simple docstring'''
snake_case__ : List[str] = [self.sep_token_id]
snake_case__ : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __magic_name__ ( self : int , snake_case_ : str , snake_case_ : Optional[str] = None ):
'''simple docstring'''
if not self.can_save_slow_tokenizer:
raise ValueError(
'''Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '''
'''tokenizer.''' )
if not os.path.isdir(snake_case_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
snake_case__ : Optional[int] = os.path.join(
snake_case_ , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case_ ):
copyfile(self.vocab_file , snake_case_ )
return (out_vocab_file,)
| 347 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class a ( metaclass=SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__UpperCAmelCase = ["""transformers""", """torch""", """note_seq"""]
def __init__( self : Dict , *snake_case_ : Any , **snake_case_ : List[Any] ):
'''simple docstring'''
requires_backends(self , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def __magic_name__ ( cls : Optional[int] , *snake_case_ : Union[str, Any] , **snake_case_ : List[Any] ):
'''simple docstring'''
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
@classmethod
def __magic_name__ ( cls : List[Any] , *snake_case_ : Any , **snake_case_ : int ):
'''simple docstring'''
requires_backends(cls , ['''transformers''', '''torch''', '''note_seq'''] )
| 347 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
UpperCAmelCase__ =logging.get_logger(__name__)
class lowerCamelCase__ ( lowercase__ ):
def __init__( self : Tuple , *A_ : List[Any] , **A_ : Union[str, Any] ):
'''simple docstring'''
warnings.warn(
"""The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DeiTImageProcessor instead.""" , UpperCAmelCase__ , )
super().__init__(*UpperCAmelCase__ , **UpperCAmelCase__ )
| 700 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", datefmt="%m/%d/%Y %H:%M:%S", level=logging.INFO
)
UpperCAmelCase__ =logging.getLogger(__name__)
if __name__ == "__main__":
UpperCAmelCase__ =argparse.ArgumentParser(
description="Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"
)
parser.add_argument(
"--data_file", type=str, default="data/dump.bert-base-uncased.pickle", help="The binarized dataset."
)
parser.add_argument(
"--token_counts_dump", type=str, default="data/token_counts.bert-base-uncased.pickle", help="The dump file."
)
parser.add_argument("--vocab_size", default=3_0522, type=int)
UpperCAmelCase__ =parser.parse_args()
logger.info(f"""Loading data from {args.data_file}""")
with open(args.data_file, "rb") as fp:
UpperCAmelCase__ =pickle.load(fp)
logger.info("Counting occurrences for MLM.")
UpperCAmelCase__ =Counter()
for tk_ids in data:
counter.update(tk_ids)
UpperCAmelCase__ =[0] * args.vocab_size
for k, v in counter.items():
UpperCAmelCase__ =v
logger.info(f"""Dump to {args.token_counts_dump}""")
with open(args.token_counts_dump, "wb") as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 442 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( _lowercase , _lowercase = None ):
'''simple docstring'''
UpperCAmelCase_ : int = word_bank or []
# create a table
UpperCAmelCase_ : int = len(SCREAMING_SNAKE_CASE_ ) + 1
UpperCAmelCase_ : List[str] = []
for _ in range(SCREAMING_SNAKE_CASE_ ):
table.append([] )
# seed value
UpperCAmelCase_ : List[str] = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(SCREAMING_SNAKE_CASE_ ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(SCREAMING_SNAKE_CASE_ )] == word:
UpperCAmelCase_ : Tuple = [
[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(SCREAMING_SNAKE_CASE_ )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(SCREAMING_SNAKE_CASE_ )]:
combination.reverse()
return table[len(SCREAMING_SNAKE_CASE_ )]
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'],
)
) | 30 |
import hashlib
import unittest
from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
else:
class UpperCAmelCase :
@staticmethod
def lowerCamelCase_ ( *__magic_name__ : Optional[Any] , **__magic_name__ : List[Any] ):
"""simple docstring"""
pass
def _lowercase ( SCREAMING_SNAKE_CASE_ : Image ):
"""simple docstring"""
UpperCamelCase = hashlib.mda(image.tobytes() )
return m.hexdigest()
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class UpperCAmelCase ( unittest.TestCase ):
lowercase = MODEL_FOR_DEPTH_ESTIMATION_MAPPING
def lowerCamelCase_ ( self : Tuple , __magic_name__ : Any , __magic_name__ : Optional[int] , __magic_name__ : Any ):
"""simple docstring"""
UpperCamelCase = DepthEstimationPipeline(model=__magic_name__ , image_processor=__magic_name__ )
return depth_estimator, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def lowerCamelCase_ ( self : Optional[int] , __magic_name__ : List[Any] , __magic_name__ : Optional[int] ):
"""simple docstring"""
UpperCamelCase = depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
self.assertEqual({"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )} , __magic_name__ )
import datasets
UpperCamelCase = datasets.load_dataset("""hf-internal-testing/fixtures_image_utils""" , """image""" , split="""test""" )
UpperCamelCase = depth_estimator(
[
Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" ),
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
# RGBA
dataset[0]["""file"""],
# LA
dataset[1]["""file"""],
# L
dataset[2]["""file"""],
] )
self.assertEqual(
[
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
{"""predicted_depth""": ANY(torch.Tensor ), """depth""": ANY(Image.Image )},
] , __magic_name__ , )
@require_tf
@unittest.skip("""Depth estimation is not implemented in TF""" )
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
pass
@slow
@require_torch
def lowerCamelCase_ ( self : Dict ):
"""simple docstring"""
UpperCamelCase = """Intel/dpt-large"""
UpperCamelCase = pipeline("""depth-estimation""" , model=__magic_name__ )
UpperCamelCase = depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
UpperCamelCase = hashimage(outputs["""depth"""] )
# This seems flaky.
# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
self.assertEqual(nested_simplify(outputs["""predicted_depth"""].max().item() ) , 29.304 )
self.assertEqual(nested_simplify(outputs["""predicted_depth"""].min().item() ) , 2.662 )
@require_torch
def lowerCamelCase_ ( self : Optional[int] ):
"""simple docstring"""
self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT""" )
| 386 | 0 |
# flake8: noqa
# Lint as: python3
from typing import Dict, List, Optional, Type
from .. import config
from ..utils import logging
from .formatting import (
ArrowFormatter,
CustomFormatter,
Formatter,
PandasFormatter,
PythonFormatter,
TensorFormatter,
format_table,
query_table,
)
from .np_formatter import NumpyFormatter
_a = logging.get_logger(__name__)
_a = {}
_a = {}
_a = {}
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case = None ,) -> Union[str, Any]:
'''simple docstring'''
lowerCamelCase__ = aliases if aliases is not None else []
if format_type in _FORMAT_TYPES:
logger.warning(
F'Overwriting format type \'{format_type}\' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})' )
lowerCamelCase__ = formatter_cls
for alias in set(aliases + [format_type] ):
if alias in _FORMAT_TYPES_ALIASES:
logger.warning(
F'Overwriting format type alias \'{alias}\' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})' )
lowerCamelCase__ = format_type
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case = None ) -> List[str]:
'''simple docstring'''
lowerCamelCase__ = aliases if aliases is not None else []
for alias in set(aliases + [format_type] ):
lowerCamelCase__ = unavailable_error
# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["python"])
_register_formatter(ArrowFormatter, "arrow", aliases=["pa", "pyarrow"])
_register_formatter(NumpyFormatter, "numpy", aliases=["np"])
_register_formatter(PandasFormatter, "pandas", aliases=["pd"])
_register_formatter(CustomFormatter, "custom")
if config.TORCH_AVAILABLE:
from .torch_formatter import TorchFormatter
_register_formatter(TorchFormatter, "torch", aliases=["pt", "pytorch"])
else:
_a = ValueError("PyTorch needs to be installed to be able to return PyTorch tensors.")
_register_unavailable_formatter(_torch_error, "torch", aliases=["pt", "pytorch"])
if config.TF_AVAILABLE:
from .tf_formatter import TFFormatter
_register_formatter(TFFormatter, "tensorflow", aliases=["tf"])
else:
_a = ValueError("Tensorflow needs to be installed to be able to return Tensorflow tensors.")
_register_unavailable_formatter(_tf_error, "tensorflow", aliases=["tf"])
if config.JAX_AVAILABLE:
from .jax_formatter import JaxFormatter
_register_formatter(JaxFormatter, "jax", aliases=[])
else:
_a = ValueError("JAX needs to be installed to be able to return JAX arrays.")
_register_unavailable_formatter(_jax_error, "jax", aliases=[])
def lowerCAmelCase__(__snake_case ) -> Optional[str]:
'''simple docstring'''
if format_type in _FORMAT_TYPES_ALIASES:
return _FORMAT_TYPES_ALIASES[format_type]
else:
return format_type
def lowerCAmelCase__(__snake_case ,**__snake_case ) -> Formatter:
'''simple docstring'''
lowerCamelCase__ = get_format_type_from_alias(__snake_case )
if format_type in _FORMAT_TYPES:
return _FORMAT_TYPES[format_type](**__snake_case )
if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
else:
raise ValueError(
F'Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None )}, but got \'{format_type}\'' )
| 714 |
import contextlib
from multiprocessing import Pool, RLock
from tqdm.auto import tqdm
from ..utils import experimental, logging
_a = logging.get_logger(__name__)
class __A :
'''simple docstring'''
lowerCAmelCase_ = None
@experimental
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> Tuple:
'''simple docstring'''
if ParallelBackendConfig.backend_name is None:
return _map_with_multiprocessing_pool(
__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
return _map_with_joblib(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case )
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = num_proc if num_proc <= len(__snake_case ) else len(__snake_case )
lowerCamelCase__ = [] # We organize the splits ourselve (contiguous splits)
for index in range(__snake_case ):
lowerCamelCase__ = len(__snake_case ) // num_proc
lowerCamelCase__ = len(__snake_case ) % num_proc
lowerCamelCase__ = div * index + min(__snake_case ,__snake_case )
lowerCamelCase__ = start + div + (1 if index < mod else 0)
split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc) )
if len(__snake_case ) != sum(len(i[1] ) for i in split_kwds ):
raise ValueError(
F'Error dividing inputs iterable among processes. '
F'Total number of objects {len(__snake_case )}, '
F'length: {sum(len(i[1] ) for i in split_kwds )}' )
logger.info(
F'Spawning {num_proc} processes for {len(__snake_case )} objects in slices of {[len(i[1] ) for i in split_kwds]}' )
lowerCamelCase__ , lowerCamelCase__ = None, None
if not disable_tqdm:
lowerCamelCase__ , lowerCamelCase__ = (RLock(),), tqdm.set_lock
with Pool(__snake_case ,initargs=__snake_case ,initializer=__snake_case ) as pool:
lowerCamelCase__ = pool.map(__snake_case ,__snake_case )
logger.info(F'Finished {num_proc} processes' )
lowerCamelCase__ = [obj for proc_res in mapped for obj in proc_res]
logger.info(F'Unpacked {len(__snake_case )} objects' )
return mapped
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> List[str]:
'''simple docstring'''
import joblib
with joblib.parallel_backend(ParallelBackendConfig.backend_name ,n_jobs=__snake_case ):
return joblib.Parallel()(
joblib.delayed(__snake_case )((function, obj, types, None, True, None) ) for obj in iterable )
@experimental
@contextlib.contextmanager
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
lowerCamelCase__ = backend_name
if backend_name == "spark":
from joblibspark import register_spark
register_spark()
# TODO: call create_cache_and_write_probe if "download" in steps
# TODO: raise NotImplementedError when Dataset.map etc is called
try:
yield
finally:
lowerCamelCase__ = None
| 29 | 0 |
import asyncio
import os
import re
import sys
import tempfile
import unittest
from contextlib import contextmanager
from copy import deepcopy
from distutils.util import strtobool
from enum import Enum
from importlib.util import find_spec
from pathlib import Path
from unittest.mock import patch
import pyarrow as pa
import pytest
import requests
from packaging import version
from datasets import config
if config.PY_VERSION < version.parse('''3.8'''):
import importlib_metadata
else:
import importlib.metadata as importlib_metadata
def lowerCamelCase__ ( __A :Optional[Any] ,__A :List[str]=False ):
"""simple docstring"""
try:
__snake_case = os.environ[key]
except KeyError:
# KEY isn't set, default to `default`.
__snake_case = default
else:
# KEY is set, convert it to True or False.
try:
__snake_case = strtobool(__A )
except ValueError:
# More values are supported, but let's keep the message simple.
raise ValueError(F'If set, {key} must be yes or no.' )
return _value
UpperCamelCase__ = parse_flag_from_env('''RUN_SLOW''', default=False)
UpperCamelCase__ = parse_flag_from_env('''RUN_REMOTE''', default=False)
UpperCamelCase__ = parse_flag_from_env('''RUN_LOCAL''', default=True)
UpperCamelCase__ = parse_flag_from_env('''RUN_PACKAGED''', default=True)
# Compression
UpperCamelCase__ = pytest.mark.skipif(not config.LZ4_AVAILABLE, reason='''test requires lz4''')
UpperCamelCase__ = pytest.mark.skipif(not config.PY7ZR_AVAILABLE, reason='''test requires py7zr''')
UpperCamelCase__ = pytest.mark.skipif(not config.ZSTANDARD_AVAILABLE, reason='''test requires zstandard''')
# Audio
UpperCamelCase__ = pytest.mark.skipif(
# On Windows and OS X, soundfile installs sndfile
find_spec('''soundfile''') is None or version.parse(importlib_metadata.version('''soundfile''')) < version.parse('''0.12.0'''),
reason='''test requires sndfile>=0.12.1: \'pip install \"soundfile>=0.12.1\"\'; ''',
)
# Beam
UpperCamelCase__ = pytest.mark.skipif(
not config.BEAM_AVAILABLE or config.DILL_VERSION >= version.parse('''0.3.2'''),
reason='''test requires apache-beam and a compatible dill version''',
)
# Dill-cloudpickle compatibility
UpperCamelCase__ = pytest.mark.skipif(
config.DILL_VERSION <= version.parse('''0.3.2'''),
reason='''test requires dill>0.3.2 for cloudpickle compatibility''',
)
# Windows
UpperCamelCase__ = pytest.mark.skipif(
sys.platform == '''win32''',
reason='''test should not be run on Windows''',
)
def lowerCamelCase__ ( __A :Optional[int] ):
"""simple docstring"""
try:
import faiss # noqa
except ImportError:
__snake_case = unittest.skip("""test requires faiss""" )(__A )
return test_case
def lowerCamelCase__ ( __A :Tuple ):
"""simple docstring"""
try:
import regex # noqa
except ImportError:
__snake_case = unittest.skip("""test requires regex""" )(__A )
return test_case
def lowerCamelCase__ ( __A :Optional[int] ):
"""simple docstring"""
try:
import elasticsearch # noqa
except ImportError:
__snake_case = unittest.skip("""test requires elasticsearch""" )(__A )
return test_case
def lowerCamelCase__ ( __A :Union[str, Any] ):
"""simple docstring"""
try:
import sqlalchemy # noqa
except ImportError:
__snake_case = unittest.skip("""test requires sqlalchemy""" )(__A )
return test_case
def lowerCamelCase__ ( __A :Dict ):
"""simple docstring"""
if not config.TORCH_AVAILABLE:
__snake_case = unittest.skip("""test requires PyTorch""" )(__A )
return test_case
def lowerCamelCase__ ( __A :str ):
"""simple docstring"""
if not config.TF_AVAILABLE:
__snake_case = unittest.skip("""test requires TensorFlow""" )(__A )
return test_case
def lowerCamelCase__ ( __A :List[str] ):
"""simple docstring"""
if not config.JAX_AVAILABLE:
__snake_case = unittest.skip("""test requires JAX""" )(__A )
return test_case
def lowerCamelCase__ ( __A :Dict ):
"""simple docstring"""
if not config.PIL_AVAILABLE:
__snake_case = unittest.skip("""test requires Pillow""" )(__A )
return test_case
def lowerCamelCase__ ( __A :str ):
"""simple docstring"""
try:
import transformers # noqa F401
except ImportError:
return unittest.skip("""test requires transformers""" )(__A )
else:
return test_case
def lowerCamelCase__ ( __A :Tuple ):
"""simple docstring"""
try:
import tiktoken # noqa F401
except ImportError:
return unittest.skip("""test requires tiktoken""" )(__A )
else:
return test_case
def lowerCamelCase__ ( __A :List[Any] ):
"""simple docstring"""
try:
import spacy # noqa F401
except ImportError:
return unittest.skip("""test requires spacy""" )(__A )
else:
return test_case
def lowerCamelCase__ ( __A :Optional[int] ):
"""simple docstring"""
def _require_spacy_model(__A :List[str] ):
try:
import spacy # noqa F401
spacy.load(__A )
except ImportError:
return unittest.skip("""test requires spacy""" )(__A )
except OSError:
return unittest.skip("""test requires spacy model '{}'""".format(__A ) )(__A )
else:
return test_case
return _require_spacy_model
def lowerCamelCase__ ( __A :int ):
"""simple docstring"""
try:
import pyspark # noqa F401
except ImportError:
return unittest.skip("""test requires pyspark""" )(__A )
else:
return test_case
def lowerCamelCase__ ( __A :Any ):
"""simple docstring"""
try:
import joblibspark # noqa F401
except ImportError:
return unittest.skip("""test requires joblibspark""" )(__A )
else:
return test_case
def lowerCamelCase__ ( __A :Optional[Any] ):
"""simple docstring"""
if not _run_slow_tests or _run_slow_tests == 0:
__snake_case = unittest.skip("""test is slow""" )(__A )
return test_case
def lowerCamelCase__ ( __A :Tuple ):
"""simple docstring"""
if not _run_local_tests or _run_local_tests == 0:
__snake_case = unittest.skip("""test is local""" )(__A )
return test_case
def lowerCamelCase__ ( __A :List[Any] ):
"""simple docstring"""
if not _run_packaged_tests or _run_packaged_tests == 0:
__snake_case = unittest.skip("""test is packaged""" )(__A )
return test_case
def lowerCamelCase__ ( __A :Union[str, Any] ):
"""simple docstring"""
if not _run_remote_tests or _run_remote_tests == 0:
__snake_case = unittest.skip("""test requires remote""" )(__A )
return test_case
def lowerCamelCase__ ( *__A :Any ):
"""simple docstring"""
def decorate(cls :Union[str, Any] ):
for name, fn in cls.__dict__.items():
if callable(__A ) and name.startswith("""test""" ):
for decorator in decorators:
__snake_case = decorator(__A )
setattr(cls ,__A ,__A )
return cls
return decorate
class __snake_case ( snake_case__ ):
"""simple docstring"""
pass
class __snake_case ( snake_case__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 2
@contextmanager
def lowerCamelCase__ ( __A :Tuple=OfflineSimulationMode.CONNECTION_FAILS ,__A :int=1e-1_6 ):
"""simple docstring"""
__snake_case = requests.Session().request
def timeout_request(__A :List[str] ,__A :Tuple ,__A :List[str] ,**__A :str ):
# Change the url to an invalid url so that the connection hangs
__snake_case = """https://10.255.255.1"""
if kwargs.get("""timeout""" ) is None:
raise RequestWouldHangIndefinitelyError(
F'Tried a call to {url} in offline mode with no timeout set. Please set a timeout.' )
__snake_case = timeout
try:
return online_request(__A ,__A ,**__A )
except Exception as e:
# The following changes in the error are just here to make the offline timeout error prettier
__snake_case = url
__snake_case = e.args[0]
__snake_case = (max_retry_error.args[0].replace("""10.255.255.1""" ,F'OfflineMock[{url}]' ),)
__snake_case = (max_retry_error,)
raise
def raise_connection_error(__A :Dict ,__A :List[str] ,**__A :List[str] ):
raise requests.ConnectionError("""Offline mode is enabled.""" ,request=__A )
if mode is OfflineSimulationMode.CONNECTION_FAILS:
with patch("""requests.Session.send""" ,__A ):
yield
elif mode is OfflineSimulationMode.CONNECTION_TIMES_OUT:
# inspired from https://stackoverflow.com/a/904609
with patch("""requests.Session.request""" ,__A ):
yield
elif mode is OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1:
with patch("""datasets.config.HF_DATASETS_OFFLINE""" ,__A ):
yield
else:
raise ValueError("""Please use a value from the OfflineSimulationMode enum.""" )
@contextmanager
def lowerCamelCase__ ( *__A :Any ,**__A :Optional[int] ):
"""simple docstring"""
__snake_case = str(Path().resolve() )
with tempfile.TemporaryDirectory(*__A ,**__A ) as tmp_dir:
try:
os.chdir(__A )
yield
finally:
os.chdir(__A )
@contextmanager
def lowerCamelCase__ ( ):
"""simple docstring"""
import gc
gc.collect()
__snake_case = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory > 0, "Arrow memory didn't increase."
@contextmanager
def lowerCamelCase__ ( ):
"""simple docstring"""
import gc
gc.collect()
__snake_case = pa.total_allocated_bytes()
yield
assert pa.total_allocated_bytes() - previous_allocated_memory <= 0, "Arrow memory wasn't expected to increase."
def lowerCamelCase__ ( __A :List[str] ,__A :str ):
"""simple docstring"""
return deepcopy(__A ).integers(0 ,1_0_0 ,1_0 ).tolist() == deepcopy(__A ).integers(0 ,1_0_0 ,1_0 ).tolist()
def lowerCamelCase__ ( __A :List[str] ):
"""simple docstring"""
import decorator
from requests.exceptions import HTTPError
def _wrapper(__A :str ,*__A :Optional[int] ,**__A :Any ):
try:
return func(*__A ,**__A )
except HTTPError as err:
if str(__A ).startswith("""500""" ) or str(__A ).startswith("""502""" ):
pytest.xfail(str(__A ) )
raise err
return decorator.decorator(_wrapper ,__A )
class __snake_case :
"""simple docstring"""
def __init__( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> Any:
"""simple docstring"""
__snake_case = returncode
__snake_case = stdout
__snake_case = stderr
async def lowerCamelCase__ ( __A :Dict ,__A :Optional[Any] ):
"""simple docstring"""
while True:
__snake_case = await stream.readline()
if line:
callback(__A )
else:
break
async def lowerCamelCase__ ( __A :Any ,__A :Tuple=None ,__A :Tuple=None ,__A :Optional[int]=None ,__A :Optional[int]=False ,__A :int=False ):
"""simple docstring"""
if echo:
print("""\nRunning: """ ,""" """.join(__A ) )
__snake_case = await asyncio.create_subprocess_exec(
cmd[0] ,*cmd[1:] ,stdin=__A ,stdout=asyncio.subprocess.PIPE ,stderr=asyncio.subprocess.PIPE ,env=__A ,)
# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
#
# If it starts hanging, will need to switch to the following code. The problem is that no data
# will be seen until it's done and if it hangs for example there will be no debug info.
# out, err = await p.communicate()
# return _RunOutput(p.returncode, out, err)
__snake_case = []
__snake_case = []
def tee(__A :Any ,__A :Any ,__A :Optional[int] ,__A :Optional[Any]="" ):
__snake_case = line.decode("""utf-8""" ).rstrip()
sink.append(__A )
if not quiet:
print(__A ,__A ,file=__A )
# XXX: the timeout doesn't seem to make any difference here
await asyncio.wait(
[
_read_stream(p.stdout ,lambda __A : tee(__A ,__A ,sys.stdout ,label="""stdout:""" ) ),
_read_stream(p.stderr ,lambda __A : tee(__A ,__A ,sys.stderr ,label="""stderr:""" ) ),
] ,timeout=__A ,)
return _RunOutput(await p.wait() ,__A ,__A )
def lowerCamelCase__ ( __A :Union[str, Any] ,__A :str=None ,__A :Any=None ,__A :Tuple=1_8_0 ,__A :List[Any]=False ,__A :str=True ):
"""simple docstring"""
__snake_case = asyncio.get_event_loop()
__snake_case = loop.run_until_complete(
_stream_subprocess(__A ,env=__A ,stdin=__A ,timeout=__A ,quiet=__A ,echo=__A ) )
__snake_case = """ """.join(__A )
if result.returncode > 0:
__snake_case = """\n""".join(result.stderr )
raise RuntimeError(
F'\'{cmd_str}\' failed with returncode {result.returncode}\n\n'
F'The combined stderr from workers follows:\n{stderr}' )
# check that the subprocess actually did run and produced some output, should the test rely on
# the remote side to do the testing
if not result.stdout and not result.stderr:
raise RuntimeError(F'\'{cmd_str}\' produced no output.' )
return result
def lowerCamelCase__ ( ):
"""simple docstring"""
__snake_case = os.environ.get("""PYTEST_XDIST_WORKER""" ,"""gw0""" )
__snake_case = re.sub(r"""^gw""" ,"""""" ,__A ,0 ,re.M )
return int(__A )
def lowerCamelCase__ ( ):
"""simple docstring"""
__snake_case = 2_9_5_0_0
__snake_case = pytest_xdist_worker_id()
return port + uniq_delta
| 268 |
from __future__ import annotations
def lowerCamelCase__ ( __A :list[float] ,__A :Union[str, Any] ):
"""simple docstring"""
print(F'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(__A ):
print(F'{i}\t\t{d}' )
def lowerCamelCase__ ( __A :list[dict[str, int]] ,__A :list[float] ,__A :int ):
"""simple docstring"""
for j in range(__A ):
__snake_case , __snake_case , __snake_case = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
return True
return False
def lowerCamelCase__ ( __A :list[dict[str, int]] ,__A :int ,__A :int ,__A :int ):
"""simple docstring"""
__snake_case = [float("""inf""" )] * vertex_count
__snake_case = 0.0
for _ in range(vertex_count - 1 ):
for j in range(__A ):
__snake_case , __snake_case , __snake_case = (graph[j][k] for k in ["""src""", """dst""", """weight"""])
if distance[u] != float("""inf""" ) and distance[u] + w < distance[v]:
__snake_case = distance[u] + w
__snake_case = check_negative_cycle(__A ,__A ,__A )
if negative_cycle_exists:
raise Exception("""Negative cycle found""" )
return distance
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase__ = int(input('''Enter number of vertices: ''').strip())
UpperCamelCase__ = int(input('''Enter number of edges: ''').strip())
UpperCamelCase__ = [{} for _ in range(E)]
for i in range(E):
print('''Edge ''', i + 1)
UpperCamelCase__ ,UpperCamelCase__ ,UpperCamelCase__ = (
int(x)
for x in input('''Enter source, destination, weight: ''').strip().split(''' ''')
)
UpperCamelCase__ = {'''src''': src, '''dst''': dest, '''weight''': weight}
UpperCamelCase__ = int(input('''\nEnter shortest path source:''').strip())
UpperCamelCase__ = bellman_ford(graph, V, E, source)
print_distance(shortest_distance, 0)
| 268 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
snake_case_ : Optional[Any] = subprocess.check_output('''git merge-base main HEAD'''.split()).decode('''utf-8''')
snake_case_ : List[str] = subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode('''utf-8''').split()
snake_case_ : Tuple = '''|'''.join(sys.argv[1:])
snake_case_ : List[str] = re.compile(Rf"""^({joined_dirs}).*?\.py$""")
snake_case_ : Optional[Any] = [x for x in modified_files if regex.match(x)]
print(''' '''.join(relevant_modified_files), end='''''') | 718 |
import os
import unittest
from transformers import BatchEncoding
from transformers.models.bert.tokenization_bert import (
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.models.prophetnet.tokenization_prophetnet import VOCAB_FILES_NAMES, ProphetNetTokenizer
from transformers.testing_utils import require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
class A__ ( UpperCamelCase__ , unittest.TestCase ):
UpperCAmelCase = ProphetNetTokenizer
UpperCAmelCase = False
def __UpperCamelCase ( self : List[Any] ) -> int:
"""simple docstring"""
super().setUp()
_SCREAMING_SNAKE_CASE =[
'''[UNK]''',
'''[CLS]''',
'''[SEP]''',
'''[PAD]''',
'''[MASK]''',
'''want''',
'''##want''',
'''##ed''',
'''wa''',
'''un''',
'''runn''',
'''##ing''',
''',''',
'''low''',
'''lowest''',
]
_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] ) )
def __UpperCamelCase ( self : str , _a : Union[str, Any] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='''UNwant\u00E9d,running'''
_SCREAMING_SNAKE_CASE ='''unwanted, running'''
return input_text, output_text
def __UpperCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.tokenizer_class(self.vocab_file )
_SCREAMING_SNAKE_CASE =tokenizer.tokenize('''UNwant\u00E9d,running''' )
self.assertListEqual(_a , ['''un''', '''##want''', '''##ed''', ''',''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_a ) , [9, 6, 7, 12, 10, 11] )
def __UpperCamelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =BasicTokenizer()
self.assertListEqual(tokenizer.tokenize('''ah\u535A\u63A8zz''' ) , ['''ah''', '''\u535A''', '''\u63A8''', '''zz'''] )
def __UpperCamelCase ( self : Dict ) -> List[Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =BasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''hello''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __UpperCamelCase ( self : Dict ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hällo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''h\u00E9llo'''] )
def __UpperCamelCase ( self : List[str] ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __UpperCamelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =BasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''hallo''', '''!''', '''how''', '''are''', '''you''', '''?'''] )
self.assertListEqual(tokenizer.tokenize('''H\u00E9llo''' ) , ['''hello'''] )
def __UpperCamelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =BasicTokenizer(do_lower_case=_a )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? ''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __UpperCamelCase ( self : Dict ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HäLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __UpperCamelCase ( self : Optional[Any] ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =BasicTokenizer(do_lower_case=_a , strip_accents=_a )
self.assertListEqual(
tokenizer.tokenize(''' \tHäLLo!how \n Are yoU? ''' ) , ['''HaLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?'''] )
def __UpperCamelCase ( self : List[str] ) -> Dict:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =BasicTokenizer(do_lower_case=_a , never_split=['''[UNK]'''] )
self.assertListEqual(
tokenizer.tokenize(''' \tHeLLo!how \n Are yoU? [UNK]''' ) , ['''HeLLo''', '''!''', '''how''', '''Are''', '''yoU''', '''?''', '''[UNK]'''] )
def __UpperCamelCase ( self : List[Any] ) -> List[str]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =['''[UNK]''', '''[CLS]''', '''[SEP]''', '''want''', '''##want''', '''##ed''', '''wa''', '''un''', '''runn''', '''##ing''']
_SCREAMING_SNAKE_CASE ={}
for i, token in enumerate(_a ):
_SCREAMING_SNAKE_CASE =i
_SCREAMING_SNAKE_CASE =WordpieceTokenizer(vocab=_a , unk_token='''[UNK]''' )
self.assertListEqual(tokenizer.tokenize('''''' ) , [] )
self.assertListEqual(tokenizer.tokenize('''unwanted running''' ) , ['''un''', '''##want''', '''##ed''', '''runn''', '''##ing'''] )
self.assertListEqual(tokenizer.tokenize('''unwantedX running''' ) , ['''[UNK]''', '''runn''', '''##ing'''] )
@require_torch
def __UpperCamelCase ( self : Any ) -> Union[str, Any]:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased''' )
_SCREAMING_SNAKE_CASE =['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
_SCREAMING_SNAKE_CASE =[1037, 2146, 2_0423, 2005, 7680, 7849, 3989, 1012, 102]
_SCREAMING_SNAKE_CASE =tokenizer(_a , padding=_a , return_tensors='''pt''' )
self.assertIsInstance(_a , _a )
_SCREAMING_SNAKE_CASE =list(batch.input_ids.numpy()[0] )
self.assertListEqual(_a , _a )
self.assertEqual((2, 9) , batch.input_ids.shape )
self.assertEqual((2, 9) , batch.attention_mask.shape )
def __UpperCamelCase ( self : List[str] ) -> Optional[int]:
"""simple docstring"""
self.assertTrue(_is_whitespace(''' ''' ) )
self.assertTrue(_is_whitespace('''\t''' ) )
self.assertTrue(_is_whitespace('''\r''' ) )
self.assertTrue(_is_whitespace('''\n''' ) )
self.assertTrue(_is_whitespace('''\u00A0''' ) )
self.assertFalse(_is_whitespace('''A''' ) )
self.assertFalse(_is_whitespace('''-''' ) )
def __UpperCamelCase ( self : int ) -> Dict:
"""simple docstring"""
self.assertTrue(_is_control('''\u0005''' ) )
self.assertFalse(_is_control('''A''' ) )
self.assertFalse(_is_control(''' ''' ) )
self.assertFalse(_is_control('''\t''' ) )
self.assertFalse(_is_control('''\r''' ) )
def __UpperCamelCase ( self : List[str] ) -> List[str]:
"""simple docstring"""
self.assertTrue(_is_punctuation('''-''' ) )
self.assertTrue(_is_punctuation('''$''' ) )
self.assertTrue(_is_punctuation('''`''' ) )
self.assertTrue(_is_punctuation('''.''' ) )
self.assertFalse(_is_punctuation('''A''' ) )
self.assertFalse(_is_punctuation(''' ''' ) )
@slow
def __UpperCamelCase ( self : Tuple ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =self.tokenizer_class.from_pretrained('''microsoft/prophetnet-large-uncased''' )
_SCREAMING_SNAKE_CASE =tokenizer.encode('''sequence builders''' , add_special_tokens=_a )
_SCREAMING_SNAKE_CASE =tokenizer.encode('''multi-sequence build''' , add_special_tokens=_a )
_SCREAMING_SNAKE_CASE =tokenizer.build_inputs_with_special_tokens(_a )
_SCREAMING_SNAKE_CASE =tokenizer.build_inputs_with_special_tokens(_a , _a )
assert encoded_sentence == text + [102]
assert encoded_pair == text + [102] + text_a + [102] | 191 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowercase : int = logging.get_logger(__name__)
_lowercase : Optional[Any] = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
# See all Donut models at https://huggingface.co/models?filter=donut-swin
}
class _UpperCAmelCase ( _lowerCAmelCase ):
a__ : Dict = "donut-swin"
a__ : str = {
"num_attention_heads": "num_heads",
"num_hidden_layers": "num_layers",
}
def __init__( self : Dict , _lowercase : Union[str, Any]=2_24 , _lowercase : List[Any]=4 , _lowercase : Tuple=3 , _lowercase : Union[str, Any]=96 , _lowercase : Dict=[2, 2, 6, 2] , _lowercase : Optional[int]=[3, 6, 12, 24] , _lowercase : Union[str, Any]=7 , _lowercase : List[str]=4.0 , _lowercase : List[Any]=True , _lowercase : Dict=0.0 , _lowercase : str=0.0 , _lowercase : Union[str, Any]=0.1 , _lowercase : Optional[Any]="gelu" , _lowercase : Optional[Any]=False , _lowercase : List[str]=0.02 , _lowercase : Union[str, Any]=1E-5 , **_lowercase : int , ):
super().__init__(**_lowercase )
__UpperCAmelCase = image_size
__UpperCAmelCase = patch_size
__UpperCAmelCase = num_channels
__UpperCAmelCase = embed_dim
__UpperCAmelCase = depths
__UpperCAmelCase = len(_lowercase )
__UpperCAmelCase = num_heads
__UpperCAmelCase = window_size
__UpperCAmelCase = mlp_ratio
__UpperCAmelCase = qkv_bias
__UpperCAmelCase = hidden_dropout_prob
__UpperCAmelCase = attention_probs_dropout_prob
__UpperCAmelCase = drop_path_rate
__UpperCAmelCase = hidden_act
__UpperCAmelCase = use_absolute_embeddings
__UpperCAmelCase = layer_norm_eps
__UpperCAmelCase = initializer_range
# we set the hidden_size attribute in order to make Swin work with VisionEncoderDecoderModel
# this indicates the channel dimension after the last stage of the model
__UpperCAmelCase = int(embed_dim * 2 ** (len(_lowercase ) - 1) )
| 49 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DiffusionPipeline,
EulerDiscreteScheduler,
StableDiffusionXLImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class snake_case__ (A__ , A__ , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase :Optional[Any] = StableDiffusionXLImgaImgPipeline
__lowerCAmelCase :Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"height", "width"}
__lowerCAmelCase :Optional[Any] = PipelineTesterMixin.required_optional_params - {"latents"}
__lowerCAmelCase :Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
__lowerCAmelCase :Optional[int] = IMAGE_TO_IMAGE_IMAGE_PARAMS
__lowerCAmelCase :Optional[int] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def SCREAMING_SNAKE_CASE__( self ) -> str:
"""simple docstring"""
torch.manual_seed(0 )
a__ : Optional[Any] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") , up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") , attention_head_dim=(2, 4) , use_linear_projection=__lowercase , addition_embed_type="""text_time""" , addition_time_embed_dim=8 , transformer_layers_per_block=(1, 2) , projection_class_embeddings_input_dim=8_0 , cross_attention_dim=6_4 , )
a__ : List[Any] = EulerDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , steps_offset=1 , beta_schedule="""scaled_linear""" , timestep_spacing="""leading""" , )
torch.manual_seed(0 )
a__ : Tuple = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] , up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
a__ : int = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act="""gelu""" , projection_dim=3_2 , )
a__ : Optional[int] = CLIPTextModel(__lowercase )
a__ : List[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=__lowercase )
a__ : Union[str, Any] = CLIPTextModelWithProjection(__lowercase )
a__ : Optional[Any] = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" , local_files_only=__lowercase )
a__ : List[str] = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""text_encoder_2""": text_encoder_a,
"""tokenizer_2""": tokenizer_a,
# "safety_checker": None,
# "feature_extractor": None,
}
return components
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase=0 ) -> Tuple:
"""simple docstring"""
a__ : Any = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(__lowercase ) ).to(__lowercase )
a__ : Union[str, Any] = image / 2 + 0.5
if str(__lowercase ).startswith("""mps""" ):
a__ : Dict = torch.manual_seed(__lowercase )
else:
a__ : List[str] = torch.Generator(device=__lowercase ).manual_seed(__lowercase )
a__ : Optional[Any] = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 5.0,
"""output_type""": """numpy""",
"""strength""": 0.7_5,
}
return inputs
def SCREAMING_SNAKE_CASE__( self ) -> Tuple:
"""simple docstring"""
a__ : int = """cpu""" # ensure determinism for the device-dependent torch.Generator
a__ : Any = self.get_dummy_components()
a__ : List[Any] = StableDiffusionXLImgaImgPipeline(**__lowercase )
a__ : List[Any] = sd_pipe.to(__lowercase )
sd_pipe.set_progress_bar_config(disable=__lowercase )
a__ : Dict = self.get_dummy_inputs(__lowercase )
a__ : str = sd_pipe(**__lowercase ).images
a__ : Dict = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
a__ : Union[str, Any] = np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def SCREAMING_SNAKE_CASE__( self ) -> int:
"""simple docstring"""
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def SCREAMING_SNAKE_CASE__( self ) -> Optional[int]:
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def SCREAMING_SNAKE_CASE__( self ) -> Optional[int]:
"""simple docstring"""
pass
def SCREAMING_SNAKE_CASE__( self ) -> Tuple:
"""simple docstring"""
a__ : Union[str, Any] = self.get_dummy_components()
a__ : List[str] = StableDiffusionXLImgaImgPipeline(**__lowercase )
a__ : Optional[int] = sd_pipe.to(__lowercase )
a__ : int = sd_pipe.to(__lowercase )
sd_pipe.set_progress_bar_config(disable=__lowercase )
# forward without prompt embeds
a__ : Any = self.get_dummy_inputs(__lowercase )
a__ : Optional[int] = 3 * ["""this is a negative prompt"""]
a__ : List[str] = negative_prompt
a__ : Any = 3 * [inputs["""prompt"""]]
a__ : Union[str, Any] = sd_pipe(**__lowercase )
a__ : Dict = output.images[0, -3:, -3:, -1]
# forward with prompt embeds
a__ : Optional[Any] = self.get_dummy_inputs(__lowercase )
a__ : Dict = 3 * ["""this is a negative prompt"""]
a__ : int = 3 * [inputs.pop("""prompt""" )]
(
(
a__
) , (
a__
) , (
a__
) , (
a__
) ,
) : List[Any] = sd_pipe.encode_prompt(__lowercase , negative_prompt=__lowercase )
a__ : Any = sd_pipe(
**__lowercase , prompt_embeds=__lowercase , negative_prompt_embeds=__lowercase , pooled_prompt_embeds=__lowercase , negative_pooled_prompt_embeds=__lowercase , )
a__ : Optional[int] = output.images[0, -3:, -3:, -1]
# make sure that it's equal
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4
@slow
@require_torch_gpu
class snake_case__ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE__( self ) -> Union[str, Any]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__( self , __lowercase , __lowercase="cpu" , __lowercase=torch.floataa , __lowercase=0 ) -> List[str]:
"""simple docstring"""
a__ : List[Any] = torch.Generator(device=__lowercase ).manual_seed(__lowercase )
a__ : List[Any] = np.random.RandomState(__lowercase ).standard_normal((1, 4, 6_4, 6_4) )
a__ : Dict = torch.from_numpy(__lowercase ).to(device=__lowercase , dtype=__lowercase )
a__ : List[Any] = {
"""prompt""": """a photograph of an astronaut riding a horse""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def SCREAMING_SNAKE_CASE__( self ) -> int:
"""simple docstring"""
a__ : Optional[int] = DiffusionPipeline.from_pretrained("""stabilityai/stable-diffusion-2-base""" )
pipe.to(__lowercase )
pipe.set_progress_bar_config(disable=__lowercase )
a__ : Any = self.get_inputs(__lowercase )
a__ : List[str] = pipe(**__lowercase ).images
a__ : List[str] = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
a__ : Any = np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6] )
assert np.abs(image_slice - expected_slice ).max() < 7E-3
| 136 | 0 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class A__ :
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=99 , A_=32 , A_=5 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=50 , A_=0.02 , A_=True , A_=None , ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = parent
UpperCamelCase : List[str] = batch_size
UpperCamelCase : Optional[int] = seq_length
UpperCamelCase : Union[str, Any] = is_training
UpperCamelCase : List[Any] = use_input_mask
UpperCamelCase : str = vocab_size
UpperCamelCase : Union[str, Any] = hidden_size
UpperCamelCase : Union[str, Any] = num_hidden_layers
UpperCamelCase : Union[str, Any] = num_attention_heads
UpperCamelCase : List[str] = intermediate_size
UpperCamelCase : int = hidden_act
UpperCamelCase : Union[str, Any] = hidden_dropout_prob
UpperCamelCase : List[str] = attention_probs_dropout_prob
UpperCamelCase : Tuple = max_position_embeddings
UpperCamelCase : List[Any] = initializer_range
UpperCamelCase : Union[str, Any] = use_labels
UpperCamelCase : str = scope
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : List[str] = None
if self.use_input_mask:
UpperCamelCase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
UpperCamelCase : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
UpperCamelCase : Optional[int] = self.get_config()
return config, input_ids, input_mask, token_labels
def __UpperCamelCase( self ):
'''simple docstring'''
return BertGenerationConfig(
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 , is_decoder=A_ , initializer_range=self.initializer_range , )
def __UpperCamelCase( self ):
'''simple docstring'''
(
UpperCamelCase
) : Dict = self.prepare_config_and_inputs()
UpperCamelCase : Optional[Any] = True
UpperCamelCase : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCamelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , **A_ , ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = BertGenerationEncoder(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : List[str] = model(A_ , attention_mask=A_ )
UpperCamelCase : int = model(A_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , **A_ , ):
'''simple docstring'''
UpperCamelCase : str = True
UpperCamelCase : Tuple = BertGenerationEncoder(config=A_ )
model.to(A_ )
model.eval()
UpperCamelCase : Dict = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , )
UpperCamelCase : Optional[int] = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , A_ , A_ , **A_ , ):
'''simple docstring'''
UpperCamelCase : Union[str, Any] = True
UpperCamelCase : Tuple = True
UpperCamelCase : Optional[int] = BertGenerationDecoder(config=A_ ).to(A_ ).eval()
# first forward pass
UpperCamelCase : str = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , use_cache=A_ , )
UpperCamelCase : Dict = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
UpperCamelCase : int = ids_tensor((self.batch_size, 3) , config.vocab_size )
UpperCamelCase : Dict = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
UpperCamelCase : Optional[int] = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCamelCase : Dict = torch.cat([input_mask, next_mask] , dim=-1 )
UpperCamelCase : List[str] = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , output_hidden_states=A_ , )["hidden_states"][0]
UpperCamelCase : Union[str, Any] = model(
A_ , attention_mask=A_ , encoder_hidden_states=A_ , encoder_attention_mask=A_ , past_key_values=A_ , output_hidden_states=A_ , )["hidden_states"][0]
# select random slice
UpperCamelCase : List[Any] = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCamelCase : Dict = output_from_no_past[:, -3:, random_slice_idx].detach()
UpperCamelCase : Optional[Any] = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(A_ , A_ , atol=1e-3 ) )
def __UpperCamelCase( self , A_ , A_ , A_ , A_ , *A_ , ):
'''simple docstring'''
UpperCamelCase : Any = BertGenerationDecoder(A_ )
model.to(A_ )
model.eval()
UpperCamelCase : Union[str, Any] = model(A_ , attention_mask=A_ , labels=A_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[Any] = self.prepare_config_and_inputs()
UpperCamelCase : Dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class A__ ( __snake_case , __snake_case , __snake_case , unittest.TestCase ):
_UpperCAmelCase :str = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
_UpperCAmelCase :Optional[int] = (BertGenerationDecoder,) if is_torch_available() else ()
_UpperCAmelCase :List[str] = (
{'feature-extraction': BertGenerationEncoder, 'text-generation': BertGenerationDecoder}
if is_torch_available()
else {}
)
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = BertGenerationEncoderTester(self )
UpperCamelCase : Optional[Any] = ConfigTester(self , config_class=A_ , hidden_size=37 )
def __UpperCamelCase( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
UpperCamelCase : Dict = "bert"
self.model_tester.create_and_check_model(A_ , A_ , A_ , A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : str = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*A_ )
def __UpperCamelCase( self ):
'''simple docstring'''
(
UpperCamelCase
) : Tuple = self.model_tester.prepare_config_and_inputs_for_decoder()
UpperCamelCase : List[str] = None
self.model_tester.create_and_check_model_as_decoder(
A_ , A_ , A_ , A_ , A_ , A_ , )
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*A_ )
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[Any] = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
self.assertIsNotNone(A_ )
@require_torch
class A__ ( unittest.TestCase ):
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : Optional[int] = BertGenerationEncoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
UpperCamelCase : Tuple = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
UpperCamelCase : Union[str, Any] = model(A_ )[0]
UpperCamelCase : List[str] = torch.Size([1, 8, 1024] )
self.assertEqual(output.shape , A_ )
UpperCamelCase : int = torch.tensor(
[[[0.17_75, 0.00_83, -0.03_21], [1.60_02, 0.12_87, 0.39_12], [2.14_73, 0.57_91, 0.60_66]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A_ , atol=1e-4 ) )
@require_torch
class A__ ( unittest.TestCase ):
@slow
def __UpperCamelCase( self ):
'''simple docstring'''
UpperCamelCase : List[str] = BertGenerationDecoder.from_pretrained("google/bert_for_seq_generation_L-24_bbc_encoder" )
UpperCamelCase : List[Any] = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 1_0140, 102]] )
with torch.no_grad():
UpperCamelCase : int = model(A_ )[0]
UpperCamelCase : Optional[int] = torch.Size([1, 8, 5_0358] )
self.assertEqual(output.shape , A_ )
UpperCamelCase : List[Any] = torch.tensor(
[[[-0.57_88, -2.59_94, -3.70_54], [0.04_38, 4.79_97, 1.87_95], [1.58_62, 6.64_09, 4.46_38]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , A_ , atol=1e-4 ) )
| 706 |
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = None , _lowerCAmelCase = None , ) -> str:
if config_name_or_path is None:
UpperCamelCase : Dict = "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"
if generator_tokenizer_name_or_path is None:
UpperCamelCase : Tuple = generator_name_or_path
if question_encoder_tokenizer_name_or_path is None:
UpperCamelCase : Tuple = question_encoder_name_or_path
UpperCamelCase : Any = RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration
# Save model.
UpperCamelCase : Optional[Any] = RagConfig.from_pretrained(_lowerCAmelCase )
UpperCamelCase : Union[str, Any] = AutoConfig.from_pretrained(_lowerCAmelCase )
UpperCamelCase : Tuple = AutoConfig.from_pretrained(_lowerCAmelCase )
UpperCamelCase : int = gen_config
UpperCamelCase : Dict = question_encoder_config
UpperCamelCase : Tuple = model_class.from_pretrained_question_encoder_generator(
_lowerCAmelCase , _lowerCAmelCase , config=_lowerCAmelCase )
rag_model.save_pretrained(_lowerCAmelCase )
# Sanity check.
model_class.from_pretrained(_lowerCAmelCase )
# Save tokenizers.
UpperCamelCase : Optional[Any] = AutoTokenizer.from_pretrained(_lowerCAmelCase )
gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/" )
UpperCamelCase : Union[str, Any] = AutoTokenizer.from_pretrained(_lowerCAmelCase )
question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/" )
if __name__ == "__main__":
__lowerCamelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"""--model_type""",
choices=["""rag_sequence""", """rag_token"""],
required=True,
type=str,
help="""RAG model type: rag_sequence, rag_token""",
)
parser.add_argument("""--dest""", type=str, required=True, help="""Path to the output checkpoint directory.""")
parser.add_argument("""--generator_name_or_path""", type=str, required=True, help="""Generator model identifier""")
parser.add_argument(
"""--question_encoder_name_or_path""", type=str, required=True, help="""Question encoder model identifier"""
)
parser.add_argument(
"""--generator_tokenizer_name_or_path""",
type=str,
help="""Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``""",
)
parser.add_argument(
"""--question_encoder_tokenizer_name_or_path""",
type=str,
help="""Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``""",
)
parser.add_argument(
"""--config_name_or_path""",
type=str,
help=(
"""Identifier of the model config to use, if not provided, resolves to a base config for a given"""
""" ``model_type``"""
),
)
__lowerCamelCase : Dict = parser.parse_args()
__lowerCamelCase : Dict = Path(args.dest)
dest_dir.mkdir(exist_ok=True)
consolidate(
args.model_type,
args.generator_name_or_path,
args.question_encoder_name_or_path,
dest_dir,
args.config_name_or_path,
args.generator_tokenizer_name_or_path,
args.question_encoder_tokenizer_name_or_path,
)
| 38 | 0 |
"""simple docstring"""
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
lowerCAmelCase_ = {
'tiny.en': 'https://openaipublic.azureedge.net/main/whisper/models/d3dd57d32accea0b295c96e26691aa14d8822fac7d9d27d5dc00b4ca2826dd03/tiny.en.pt',
'tiny': 'https://openaipublic.azureedge.net/main/whisper/models/65147644a518d12f04e32d6f3b26facc3f8dd46e5390956a9424a650c0ce22b9/tiny.pt',
'base.en': 'https://openaipublic.azureedge.net/main/whisper/models/25a8566e1d0c1e2231d1c762132cd20e0f96a85d16145c3a00adf5d1ac670ead/base.en.pt',
'base': 'https://openaipublic.azureedge.net/main/whisper/models/ed3a0b6b1c0edf879ad9b11b1af5a0e6ab5db9205f891f668f8b0e6c6326e34e/base.pt',
'small.en': 'https://openaipublic.azureedge.net/main/whisper/models/f953ad0fd29cacd07d5a9eda5624af0f6bcf2258be67c92b79389873d91e0872/small.en.pt',
'small': 'https://openaipublic.azureedge.net/main/whisper/models/9ecf779972d90ba49c06d968637d720dd632c55bbf19d441fb42bf17a411e794/small.pt',
'medium.en': 'https://openaipublic.azureedge.net/main/whisper/models/d7440d1dc186f76616474e0ff0b3b6b879abc9d1a4926b7adfa41db2d497ab4f/medium.en.pt',
'medium': 'https://openaipublic.azureedge.net/main/whisper/models/345ae4da62f9b3d59415adc60127b97c714f32e89e936602e85993674d08dcb1/medium.pt',
'large': 'https://openaipublic.azureedge.net/main/whisper/models/e4b87e7e0bf463eb8e6956e646f1e277e901512310def2c24bf0e11bd3c28e9a/large.pt',
'large-v2': 'https://openaipublic.azureedge.net/main/whisper/models/81f7c96c852ee8fc832187b0132e569d6c3065a3252ed18e56effd0b6a73e524/large-v2.pt',
}
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
lowercase__ : Union[str, Any] = ['''layers''', '''blocks''']
for k in ignore_keys:
state_dict.pop(__lowerCamelCase , __lowerCamelCase )
lowerCAmelCase_ = {
'blocks': 'layers',
'mlp.0': 'fc1',
'mlp.2': 'fc2',
'mlp_ln': 'final_layer_norm',
'.attn.query': '.self_attn.q_proj',
'.attn.key': '.self_attn.k_proj',
'.attn.value': '.self_attn.v_proj',
'.attn_ln': '.self_attn_layer_norm',
'.attn.out': '.self_attn.out_proj',
'.cross_attn.query': '.encoder_attn.q_proj',
'.cross_attn.key': '.encoder_attn.k_proj',
'.cross_attn.value': '.encoder_attn.v_proj',
'.cross_attn_ln': '.encoder_attn_layer_norm',
'.cross_attn.out': '.encoder_attn.out_proj',
'decoder.ln.': 'decoder.layer_norm.',
'encoder.ln.': 'encoder.layer_norm.',
'token_embedding': 'embed_tokens',
'encoder.positional_embedding': 'encoder.embed_positions.weight',
'decoder.positional_embedding': 'decoder.embed_positions.weight',
'ln_post': 'layer_norm',
}
def __UpperCAmelCase ( __lowerCamelCase ) -> Any:
lowercase__ : List[Any] = list(s_dict.keys() )
for key in keys:
lowercase__ : Tuple = key
for k, v in WHISPER_MAPPING.items():
if k in key:
lowercase__ : List[Any] = new_key.replace(__lowerCamelCase , __lowerCamelCase )
print(f"""{key} -> {new_key}""" )
lowercase__ : int = s_dict.pop(__lowerCamelCase )
return s_dict
def __UpperCAmelCase ( __lowerCamelCase ) -> str:
lowercase__ , lowercase__ : Optional[Any] = emb.weight.shape
lowercase__ : Any = nn.Linear(__lowerCamelCase , __lowerCamelCase , bias=__lowerCamelCase )
lowercase__ : Any = emb.weight.data
return lin_layer
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> bytes:
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
lowercase__ : str = os.path.basename(__lowerCamelCase )
lowercase__ : Tuple = url.split('''/''' )[-2]
lowercase__ : List[str] = os.path.join(__lowerCamelCase , __lowerCamelCase )
if os.path.exists(__lowerCamelCase ) and not os.path.isfile(__lowerCamelCase ):
raise RuntimeError(f"""{download_target} exists and is not a regular file""" )
if os.path.isfile(__lowerCamelCase ):
lowercase__ : Dict = open(__lowerCamelCase , '''rb''' ).read()
if hashlib.shaaaa(__lowerCamelCase ).hexdigest() == expected_shaaaa:
return model_bytes
else:
warnings.warn(f"""{download_target} exists, but the SHA256 checksum does not match; re-downloading the file""" )
with urllib.request.urlopen(__lowerCamelCase ) as source, open(__lowerCamelCase , '''wb''' ) as output:
with tqdm(
total=int(source.info().get('''Content-Length''' ) ) , ncols=80 , unit='''iB''' , unit_scale=__lowerCamelCase , unit_divisor=10_24 ) as loop:
while True:
lowercase__ : Optional[int] = source.read(81_92 )
if not buffer:
break
output.write(__lowerCamelCase )
loop.update(len(__lowerCamelCase ) )
lowercase__ : List[Any] = open(__lowerCamelCase , '''rb''' ).read()
if hashlib.shaaaa(__lowerCamelCase ).hexdigest() != expected_shaaaa:
raise RuntimeError(
'''Model has been downloaded but the SHA256 checksum does not not match. Please retry loading the model.''' )
return model_bytes
def __UpperCAmelCase ( __lowerCamelCase , __lowerCamelCase ) -> int:
if ".pt" not in checkpoint_path:
lowercase__ : Tuple = _download(_MODELS[checkpoint_path] )
else:
lowercase__ : Dict = torch.load(__lowerCamelCase , map_location='''cpu''' )
lowercase__ : Tuple = original_checkpoint['''dims''']
lowercase__ : List[str] = original_checkpoint['''model_state_dict''']
lowercase__ : Union[str, Any] = state_dict['''decoder.token_embedding.weight''']
remove_ignore_keys_(__lowerCamelCase )
rename_keys(__lowerCamelCase )
lowercase__ : str = True
lowercase__ : Optional[Any] = state_dict['''decoder.layers.0.fc1.weight'''].shape[0]
lowercase__ : Optional[int] = WhisperConfig(
vocab_size=dimensions['''n_vocab'''] , encoder_ffn_dim=__lowerCamelCase , decoder_ffn_dim=__lowerCamelCase , num_mel_bins=dimensions['''n_mels'''] , d_model=dimensions['''n_audio_state'''] , max_target_positions=dimensions['''n_text_ctx'''] , encoder_layers=dimensions['''n_audio_layer'''] , encoder_attention_heads=dimensions['''n_audio_head'''] , decoder_layers=dimensions['''n_text_layer'''] , decoder_attention_heads=dimensions['''n_text_state'''] , max_source_positions=dimensions['''n_audio_ctx'''] , )
lowercase__ : List[str] = WhisperForConditionalGeneration(__lowerCamelCase )
lowercase__ , lowercase__ : List[Any] = model.model.load_state_dict(__lowerCamelCase , strict=__lowerCamelCase )
if len(__lowerCamelCase ) > 0 and not set(__lowerCamelCase ) <= {
"encoder.embed_positions.weights",
"decoder.embed_positions.weights",
}:
raise ValueError(
'''Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights` are allowed to be missing,'''
f""" but all the following weights are missing {missing}""" )
if tie_embeds:
lowercase__ : Any = make_linear_from_emb(model.model.decoder.embed_tokens )
else:
lowercase__ : Any = proj_out_weights
model.save_pretrained(__lowerCamelCase )
if __name__ == "__main__":
lowerCAmelCase_ = argparse.ArgumentParser()
# # Required parameters
parser.add_argument('--checkpoint_path', type=str, help='Patht to the downloaded checkpoints')
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
lowerCAmelCase_ = parser.parse_args()
convert_openai_whisper_to_tfms(args.checkpoint_path, args.pytorch_dump_folder_path)
| 560 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
'junnyu/roformer_chinese_char_small': (
'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
),
'junnyu/roformer_chinese_char_base': (
'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
),
'junnyu/roformer_small_discriminator': (
'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
),
'junnyu/roformer_small_generator': (
'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class __A ( A_ ):
'''simple docstring'''
lowerCAmelCase : List[str] = "roformer"
def __init__( self : Any ,_snake_case : str=50_000 ,_snake_case : int=None ,_snake_case : int=768 ,_snake_case : Tuple=12 ,_snake_case : Dict=12 ,_snake_case : Dict=3_072 ,_snake_case : Tuple="gelu" ,_snake_case : List[Any]=0.1 ,_snake_case : List[Any]=0.1 ,_snake_case : Optional[Any]=1_536 ,_snake_case : Dict=2 ,_snake_case : Union[str, Any]=0.02 ,_snake_case : Optional[Any]=1e-12 ,_snake_case : Optional[Any]=0 ,_snake_case : Tuple=False ,_snake_case : Optional[int]=True ,**_snake_case : Optional[int] ,) -> Tuple:
"""simple docstring"""
super().__init__(pad_token_id=_snake_case ,**_snake_case )
lowercase__ : Optional[int] = vocab_size
lowercase__ : int = hidden_size if embedding_size is None else embedding_size
lowercase__ : Union[str, Any] = hidden_size
lowercase__ : Any = num_hidden_layers
lowercase__ : Union[str, Any] = num_attention_heads
lowercase__ : str = hidden_act
lowercase__ : Union[str, Any] = intermediate_size
lowercase__ : Dict = hidden_dropout_prob
lowercase__ : Optional[Any] = attention_probs_dropout_prob
lowercase__ : List[Any] = max_position_embeddings
lowercase__ : List[str] = type_vocab_size
lowercase__ : Optional[int] = initializer_range
lowercase__ : List[Any] = layer_norm_eps
lowercase__ : Optional[Any] = rotary_value
lowercase__ : Optional[int] = use_cache
class __A ( A_ ):
'''simple docstring'''
@property
def UpperCAmelCase ( self : Any ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
if self.task == "multiple-choice":
lowercase__ : Union[str, Any] = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
lowercase__ : List[Any] = {0: '''batch''', 1: '''sequence'''}
lowercase__ : Optional[Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 560 | 1 |
'''simple docstring'''
def __lowercase ( __SCREAMING_SNAKE_CASE ) -> set:
"""simple docstring"""
__a = set()
# edges = list of graph's edges
__a = get_edges(__SCREAMING_SNAKE_CASE )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to chosen_vertices and then
# remove all arcs adjacent to the from_node and to_node
while edges:
__a , __a = edges.pop()
chosen_vertices.add(__SCREAMING_SNAKE_CASE )
chosen_vertices.add(__SCREAMING_SNAKE_CASE )
for edge in edges.copy():
if from_node in edge or to_node in edge:
edges.discard(__SCREAMING_SNAKE_CASE )
return chosen_vertices
def __lowercase ( __SCREAMING_SNAKE_CASE ) -> set:
"""simple docstring"""
__a = set()
for from_node, to_nodes in graph.items():
for to_node in to_nodes:
edges.add((from_node, to_node) )
return edges
if __name__ == "__main__":
import doctest
doctest.testmod()
# graph = {0: [1, 3], 1: [0, 3], 2: [0, 3, 4], 3: [0, 1, 2], 4: [2, 3]}
# print(f"Matching vertex cover:\n{matching_min_vertex_cover(graph)}")
| 201 |
'''simple docstring'''
from ...processing_utils import ProcessorMixin
class lowerCAmelCase_ ( snake_case__ ):
"""simple docstring"""
a_ :Dict =["""image_processor""", """feature_extractor"""]
a_ :str ="""TvltImageProcessor"""
a_ :str ="""TvltFeatureExtractor"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : Optional[Any] , SCREAMING_SNAKE_CASE__ : Union[str, Any] ):
'''simple docstring'''
super().__init__(image_processor=SCREAMING_SNAKE_CASE__ , feature_extractor=SCREAMING_SNAKE_CASE__ )
__a = image_processor
__a = feature_extractor
def __call__( self : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str=None , SCREAMING_SNAKE_CASE__ : Optional[Any]=None , SCREAMING_SNAKE_CASE__ : Tuple=None , SCREAMING_SNAKE_CASE__ : List[Any]=None , SCREAMING_SNAKE_CASE__ : Any=False , SCREAMING_SNAKE_CASE__ : List[str]=False , *SCREAMING_SNAKE_CASE__ : str , **SCREAMING_SNAKE_CASE__ : Optional[int] , ):
'''simple docstring'''
if images is None and audio is None:
raise ValueError("""You need to specify either an `images` or `audio` input to process.""" )
__a = None
if images is not None:
__a = self.image_processor(SCREAMING_SNAKE_CASE__ , mask_pixel=SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
if images_mixed is not None:
__a = self.image_processor(SCREAMING_SNAKE_CASE__ , is_mixed=SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
if audio is not None:
__a = self.feature_extractor(
SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , sampling_rate=SCREAMING_SNAKE_CASE__ , mask_audio=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
__a = {}
if audio is not None:
output_dict.update(SCREAMING_SNAKE_CASE__ )
if images is not None:
output_dict.update(SCREAMING_SNAKE_CASE__ )
if images_mixed_dict is not None:
output_dict.update(SCREAMING_SNAKE_CASE__ )
return output_dict
@property
def __a ( self : List[str] ):
'''simple docstring'''
__a = self.image_processor.model_input_names
__a = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names ) )
| 201 | 1 |
import qiskit
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
__UpperCAmelCase : Optional[int] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__UpperCAmelCase : Optional[Any] = qiskit.QuantumCircuit(__lowerCamelCase , __lowerCamelCase )
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0 )
circuit.x(1 )
# Map the quantum measurement to the classical bits
circuit.measure([0, 1] , [0, 1] )
# Execute the circuit on the qasm simulator
__UpperCAmelCase : str = qiskit.execute(__lowerCamelCase , __lowerCamelCase , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(__lowerCamelCase )
if __name__ == "__main__":
a : List[str] = single_qubit_measure(2, 2)
print(f"""Total count for various states are: {counts}""")
| 63 |
"""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
_snake_case = 'base_with_context'
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Any = nn.Parameter(torch.FloatTensor(weights["""token_embedder"""]["""embedding"""] ) )
_a : Tuple = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=UpperCamelCase__ )
for lyr_num, lyr in enumerate(model.encoders ):
_a : str = weights[F"""layers_{lyr_num}"""]
_a : Optional[Any] = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""] ) )
_a : List[str] = ly_weight["""attention"""]
_a : Any = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_a : int = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_a : str = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_a : Optional[int] = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_a : List[str] = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_a : Any = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_a : Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_a : Optional[int] = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_a : str = nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )
return model
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = nn.Parameter(torch.FloatTensor(weights["""input_proj"""]["""kernel"""].T ) )
_a : List[str] = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=UpperCamelCase__ )
for lyr_num, lyr in enumerate(model.encoders ):
_a : Union[str, Any] = weights[F"""layers_{lyr_num}"""]
_a : Optional[Any] = ly_weight["""attention"""]
_a : Tuple = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_a : List[str] = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_a : str = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_a : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_a : List[Any] = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_attention_layer_norm"""]["""scale"""] ) )
_a : List[str] = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_a : int = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_a : Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_a : Optional[Any] = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_a : Any = nn.Parameter(torch.FloatTensor(weights["""encoder_norm"""]["""scale"""] ) )
return model
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
_a : Dict = nn.Parameter(torch.FloatTensor(weights["""time_emb_dense0"""]["""kernel"""].T ) )
_a : int = nn.Parameter(torch.FloatTensor(weights["""time_emb_dense1"""]["""kernel"""].T ) )
_a : Optional[int] = nn.Parameter(
torch.FloatTensor(weights["""Embed_0"""]["""embedding"""] ) , requires_grad=UpperCamelCase__ )
_a : str = nn.Parameter(
torch.FloatTensor(weights["""continuous_inputs_projection"""]["""kernel"""].T ) )
for lyr_num, lyr in enumerate(model.decoders ):
_a : Tuple = weights[F"""layers_{lyr_num}"""]
_a : Tuple = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_self_attention_layer_norm"""]["""scale"""] ) )
_a : Tuple = nn.Parameter(
torch.FloatTensor(ly_weight["""FiLMLayer_0"""]["""DenseGeneral_0"""]["""kernel"""].T ) )
_a : Tuple = ly_weight["""self_attention"""]
_a : Any = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_a : Any = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_a : Tuple = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_a : List[str] = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_a : Optional[int] = ly_weight["""MultiHeadDotProductAttention_0"""]
_a : Union[str, Any] = nn.Parameter(torch.FloatTensor(attention_weights["""query"""]["""kernel"""].T ) )
_a : Tuple = nn.Parameter(torch.FloatTensor(attention_weights["""key"""]["""kernel"""].T ) )
_a : Optional[Any] = nn.Parameter(torch.FloatTensor(attention_weights["""value"""]["""kernel"""].T ) )
_a : Optional[int] = nn.Parameter(torch.FloatTensor(attention_weights["""out"""]["""kernel"""].T ) )
_a : str = nn.Parameter(
torch.FloatTensor(ly_weight["""pre_cross_attention_layer_norm"""]["""scale"""] ) )
_a : Optional[int] = nn.Parameter(torch.FloatTensor(ly_weight["""pre_mlp_layer_norm"""]["""scale"""] ) )
_a : List[Any] = nn.Parameter(
torch.FloatTensor(ly_weight["""FiLMLayer_1"""]["""DenseGeneral_0"""]["""kernel"""].T ) )
_a : List[str] = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_0"""]["""kernel"""].T ) )
_a : str = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wi_1"""]["""kernel"""].T ) )
_a : List[str] = nn.Parameter(torch.FloatTensor(ly_weight["""mlp"""]["""wo"""]["""kernel"""].T ) )
_a : Tuple = nn.Parameter(torch.FloatTensor(weights["""decoder_norm"""]["""scale"""] ) )
_a : List[Any] = nn.Parameter(torch.FloatTensor(weights["""spec_out_dense"""]["""kernel"""].T ) )
return model
def lowerCAmelCase__ ( UpperCamelCase__ ):
'''simple docstring'''
_a : Union[str, Any] = checkpoints.load_tax_checkpoint(args.checkpoint_path )
_a : str = jnp.tree_util.tree_map(onp.array , UpperCamelCase__ )
_a : Optional[Any] = [
"""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()""",
]
_a : Any = os.path.join(args.checkpoint_path , """..""" , """config.gin""" )
_a : List[Any] = inference.parse_training_gin_file(UpperCamelCase__ , UpperCamelCase__ )
_a : List[Any] = inference.InferenceModel(args.checkpoint_path , UpperCamelCase__ )
_a : Optional[Any] = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" , variance_type="""fixed_large""" )
_a : Dict = 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""" , )
_a : List[str] = 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""" , )
_a : List[str] = 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 , )
_a : Optional[int] = load_notes_encoder(ta_checkpoint["""target"""]["""token_encoder"""] , UpperCamelCase__ )
_a : Tuple = load_continuous_encoder(ta_checkpoint["""target"""]["""continuous_encoder"""] , UpperCamelCase__ )
_a : Any = load_decoder(ta_checkpoint["""target"""]["""decoder"""] , UpperCamelCase__ )
_a : Dict = OnnxRuntimeModel.from_pretrained("""kashif/soundstream_mel_decoder""" )
_a : List[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__":
_snake_case = 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.',
)
_snake_case = parser.parse_args()
main(args)
| 389 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Value
from .base import TaskTemplate
@dataclass(frozen=__lowerCamelCase )
class a ( __lowerCamelCase ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
__lowerCAmelCase : str = field(default="""summarization""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
__lowerCAmelCase : ClassVar[Features] = Features({"""text""": Value("""string""" )} )
__lowerCAmelCase : ClassVar[Features] = Features({"""summary""": Value("""string""" )} )
__lowerCAmelCase : str = "text"
__lowerCAmelCase : str = "summary"
@property
def __lowerCamelCase ( self :Dict ):
return {self.text_column: "text", self.summary_column: "summary"}
| 219 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
A__ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class a ( __lowerCamelCase ):
__lowerCAmelCase : Optional[int] = ["""pixel_values"""]
def __init__( self :Union[str, Any] ,__lowercase :bool = True ,__lowercase :Dict[str, int] = None ,__lowercase :PILImageResampling = PILImageResampling.BICUBIC ,__lowercase :bool = True ,__lowercase :Dict[str, int] = None ,__lowercase :bool = True ,__lowercase :Union[int, float] = 1 / 2_5_5 ,__lowercase :bool = True ,__lowercase :Optional[Union[float, List[float]]] = None ,__lowercase :Optional[Union[float, List[float]]] = None ,__lowercase :bool = True ,**__lowercase :Tuple ,):
super().__init__(**__lowercase )
snake_case__ : Optional[int] = size if size is not None else {'''shortest_edge''': 2_2_4}
snake_case__ : Dict = get_size_dict(__lowercase ,default_to_square=__lowercase )
snake_case__ : List[Any] = crop_size if crop_size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
snake_case__ : str = get_size_dict(__lowercase ,default_to_square=__lowercase ,param_name='''crop_size''' )
snake_case__ : Dict = do_resize
snake_case__ : List[str] = size
snake_case__ : Union[str, Any] = resample
snake_case__ : str = do_center_crop
snake_case__ : List[str] = crop_size
snake_case__ : str = do_rescale
snake_case__ : Dict = rescale_factor
snake_case__ : Tuple = do_normalize
snake_case__ : List[str] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
snake_case__ : str = image_std if image_std is not None else OPENAI_CLIP_STD
snake_case__ : Optional[Any] = do_convert_rgb
def __lowerCamelCase ( self :List[Any] ,__lowercase :np.ndarray ,__lowercase :Dict[str, int] ,__lowercase :PILImageResampling = PILImageResampling.BICUBIC ,__lowercase :Optional[Union[str, ChannelDimension]] = None ,**__lowercase :Optional[int] ,):
snake_case__ : Optional[Any] = get_size_dict(__lowercase ,default_to_square=__lowercase )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
snake_case__ : Any = get_resize_output_image_size(__lowercase ,size=size['''shortest_edge'''] ,default_to_square=__lowercase )
return resize(__lowercase ,size=__lowercase ,resample=__lowercase ,data_format=__lowercase ,**__lowercase )
def __lowerCamelCase ( self :Tuple ,__lowercase :np.ndarray ,__lowercase :Dict[str, int] ,__lowercase :Optional[Union[str, ChannelDimension]] = None ,**__lowercase :Optional[Any] ,):
snake_case__ : Optional[Any] = get_size_dict(__lowercase )
if "height" not in size or "width" not in size:
raise ValueError(F"""The `size` parameter must contain the keys (height, width). Got {size.keys()}""" )
return center_crop(__lowercase ,size=(size['''height'''], size['''width''']) ,data_format=__lowercase ,**__lowercase )
def __lowerCamelCase ( self :Dict ,__lowercase :np.ndarray ,__lowercase :Union[int, float] ,__lowercase :Optional[Union[str, ChannelDimension]] = None ,**__lowercase :Dict ,):
return rescale(__lowercase ,scale=__lowercase ,data_format=__lowercase ,**__lowercase )
def __lowerCamelCase ( self :int ,__lowercase :np.ndarray ,__lowercase :Union[float, List[float]] ,__lowercase :Union[float, List[float]] ,__lowercase :Optional[Union[str, ChannelDimension]] = None ,**__lowercase :List[str] ,):
return normalize(__lowercase ,mean=__lowercase ,std=__lowercase ,data_format=__lowercase ,**__lowercase )
def __lowerCamelCase ( self :Dict ,__lowercase :ImageInput ,__lowercase :bool = None ,__lowercase :Dict[str, int] = None ,__lowercase :PILImageResampling = None ,__lowercase :bool = None ,__lowercase :int = None ,__lowercase :bool = None ,__lowercase :float = None ,__lowercase :bool = None ,__lowercase :Optional[Union[float, List[float]]] = None ,__lowercase :Optional[Union[float, List[float]]] = None ,__lowercase :bool = None ,__lowercase :Optional[Union[str, TensorType]] = None ,__lowercase :Optional[ChannelDimension] = ChannelDimension.FIRST ,**__lowercase :int ,):
snake_case__ : Dict = do_resize if do_resize is not None else self.do_resize
snake_case__ : Union[str, Any] = size if size is not None else self.size
snake_case__ : Any = get_size_dict(__lowercase ,param_name='''size''' ,default_to_square=__lowercase )
snake_case__ : int = resample if resample is not None else self.resample
snake_case__ : Tuple = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case__ : List[Any] = crop_size if crop_size is not None else self.crop_size
snake_case__ : Union[str, Any] = get_size_dict(__lowercase ,param_name='''crop_size''' ,default_to_square=__lowercase )
snake_case__ : Any = do_rescale if do_rescale is not None else self.do_rescale
snake_case__ : Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case__ : Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ : List[Any] = image_mean if image_mean is not None else self.image_mean
snake_case__ : Optional[int] = image_std if image_std is not None else self.image_std
snake_case__ : Optional[int] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
snake_case__ : Dict = make_list_of_images(__lowercase )
if not valid_images(__lowercase ):
raise ValueError(
'''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
'''torch.Tensor, tf.Tensor or jax.ndarray.''' )
if do_resize and size is None:
raise ValueError('''Size must be specified if do_resize is True.''' )
if do_center_crop and crop_size is None:
raise ValueError('''Crop size must be specified if do_center_crop is True.''' )
if do_rescale and rescale_factor is None:
raise ValueError('''Rescale factor must be specified if do_rescale is True.''' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('''Image mean and std must be specified if do_normalize is True.''' )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
snake_case__ : str = [convert_to_rgb(__lowercase ) for image in images]
# All transformations expect numpy arrays.
snake_case__ : Tuple = [to_numpy_array(__lowercase ) for image in images]
if do_resize:
snake_case__ : List[str] = [self.resize(image=__lowercase ,size=__lowercase ,resample=__lowercase ) for image in images]
if do_center_crop:
snake_case__ : str = [self.center_crop(image=__lowercase ,size=__lowercase ) for image in images]
if do_rescale:
snake_case__ : List[Any] = [self.rescale(image=__lowercase ,scale=__lowercase ) for image in images]
if do_normalize:
snake_case__ : Tuple = [self.normalize(image=__lowercase ,mean=__lowercase ,std=__lowercase ) for image in images]
snake_case__ : Dict = [to_channel_dimension_format(__lowercase ,__lowercase ) for image in images]
snake_case__ : List[Any] = {'''pixel_values''': images}
return BatchFeature(data=__lowercase ,tensor_type=__lowercase )
| 219 | 1 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class _UpperCAmelCase ( lowerCAmelCase__ ):
"""simple docstring"""
def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
a_ : Any = params
a_ : Dict = np.array(lowerCAmelCase_ )
a_ : str = np.array([len(lowerCAmelCase_ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , lowerCAmelCase_ ):
'''simple docstring'''
return (self.token_ids[index], self.lengths[index])
def __len__( self ):
'''simple docstring'''
return len(self.lengths )
def _lowerCAmelCase ( self ):
'''simple docstring'''
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def _lowerCAmelCase ( self ):
'''simple docstring'''
a_ : Union[str, Any] = self.params.max_model_input_size
a_ : Optional[int] = self.lengths > max_len
logger.info(f'''Splitting {sum(lowerCAmelCase_ )} too long sequences.''' )
def divide_chunks(lowerCAmelCase_ , lowerCAmelCase_ ):
return [l[i : i + n] for i in range(0 , len(lowerCAmelCase_ ) , lowerCAmelCase_ )]
a_ : List[Any] = []
a_ : Optional[Any] = []
if self.params.mlm:
a_ , a_ : str = self.params.special_tok_ids["""cls_token"""], self.params.special_tok_ids["""sep_token"""]
else:
a_ , a_ : Dict = self.params.special_tok_ids["""bos_token"""], self.params.special_tok_ids["""eos_token"""]
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
a_ : Any = []
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
a_ : Tuple = np.insert(lowerCAmelCase_ , 0 , lowerCAmelCase_ )
if sub_s[-1] != sep_id:
a_ : Union[str, Any] = np.insert(lowerCAmelCase_ , len(lowerCAmelCase_ ) , lowerCAmelCase_ )
assert len(lowerCAmelCase_ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(lowerCAmelCase_ )
new_tok_ids.extend(lowerCAmelCase_ )
new_lengths.extend([len(lowerCAmelCase_ ) for l in sub_seqs] )
a_ : Tuple = np.array(lowerCAmelCase_ )
a_ : str = np.array(lowerCAmelCase_ )
def _lowerCAmelCase ( self ):
'''simple docstring'''
a_ : Any = len(self )
a_ : Dict = self.lengths > 11
a_ : Union[str, Any] = self.token_ids[indices]
a_ : Any = self.lengths[indices]
a_ : Union[str, Any] = len(self )
logger.info(f'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''' )
def _lowerCAmelCase ( self ):
'''simple docstring'''
if "unk_token" not in self.params.special_tok_ids:
return
else:
a_ : Optional[int] = self.params.special_tok_ids["""unk_token"""]
a_ : List[str] = len(self )
a_ : List[str] = np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
a_ : Dict = (unk_occs / self.lengths) < 0.5
a_ : Optional[int] = self.token_ids[indices]
a_ : Union[str, Any] = self.lengths[indices]
a_ : Union[str, Any] = len(self )
logger.info(f'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''' )
def _lowerCAmelCase ( self ):
'''simple docstring'''
if not self.params.is_master:
return
logger.info(f'''{len(self )} sequences''' )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def _lowerCAmelCase ( self , lowerCAmelCase_ ):
'''simple docstring'''
a_ : Tuple = [t[0] for t in batch]
a_ : Dict = [t[1] for t in batch]
assert len(lowerCAmelCase_ ) == len(lowerCAmelCase_ )
# Max for paddings
a_ : int = max(lowerCAmelCase_ )
# Pad token ids
if self.params.mlm:
a_ : Union[str, Any] = self.params.special_tok_ids["""pad_token"""]
else:
a_ : int = self.params.special_tok_ids["""unk_token"""]
a_ : List[Any] = [list(t.astype(lowerCAmelCase_ ) ) + [pad_idx] * (max_seq_len_ - len(lowerCAmelCase_ )) for t in token_ids]
assert len(tk_ ) == len(lowerCAmelCase_ )
assert all(len(lowerCAmelCase_ ) == max_seq_len_ for t in tk_ )
a_ : Any = torch.tensor(tk_ ) # (bs, max_seq_len_)
a_ : Any = torch.tensor(lowerCAmelCase_ ) # (bs)
return tk_t, lg_t
| 577 |
'''simple docstring'''
def _snake_case ( A_ : Optional[int] ):
"""simple docstring"""
a_ : str = len(A_ )
for i in range(length - 1 ):
a_ : List[Any] = i
for k in range(i + 1 , A_ ):
if collection[k] < collection[least]:
a_ : Union[str, Any] = k
if least != i:
a_ , a_ : int = (collection[i], collection[least])
return collection
if __name__ == "__main__":
__snake_case: Union[str, Any] = input("Enter numbers separated by a comma:\n").strip()
__snake_case: Any = [int(item) for item in user_input.split(",")]
print(selection_sort(unsorted))
| 577 | 1 |
import os
import unittest
from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow
from ...test_tokenization_common import TokenizerTesterMixin
@require_jieba
class lowerCAmelCase_ ( A_ ,unittest.TestCase ):
'''simple docstring'''
A_ : Tuple = CpmAntTokenizer
A_ : Optional[int] = False
def _A ( self ):
'''simple docstring'''
super().setUp()
a__ = [
"""<d>""",
"""</d>""",
"""<s>""",
"""</s>""",
"""</_>""",
"""<unk>""",
"""<pad>""",
"""</n>""",
"""我""",
"""是""",
"""C""",
"""P""",
"""M""",
"""A""",
"""n""",
"""t""",
]
a__ = 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] ) )
@tooslow
def _A ( self ):
'''simple docstring'''
a__ = CpmAntTokenizer.from_pretrained("""openbmb/cpm-ant-10b""" )
a__ = """今天天气真好！"""
a__ = ["""今天""", """天气""", """真""", """好""", """！"""]
a__ = tokenizer.tokenize(lowerCamelCase )
self.assertListEqual(lowerCamelCase , lowerCamelCase )
a__ = """今天天气真好！"""
a__ = [tokenizer.bos_token] + tokens
a__ = [6, 9802, 1_4962, 2082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase ) , lowerCamelCase )
a__ = tokenizer.decode(lowerCamelCase )
self.assertEqual(lowerCamelCase , lowerCamelCase )
| 412 |
import operator as op
def UpperCAmelCase ( lowercase__ : str ):
'''simple docstring'''
a__ = []
a__ = lambda lowercase__ , lowercase__ : int(x / y ) # noqa: E731 integer division operation
a__ = {
"""^""": 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(lowercase__ )) )
for x in post_fix:
if x.isdigit(): # if x in digit
stack.append(lowercase__ ) # append x to stack
# output in tabular format
print(x.rjust(8 ) , ("""push(""" + x + """)""").ljust(12 ) , """,""".join(lowercase__ ) , sep=""" | """ )
else:
a__ = stack.pop() # pop stack
# output in tabular format
print("""""".rjust(8 ) , ("""pop(""" + b + """)""").ljust(12 ) , """,""".join(lowercase__ ) , sep=""" | """ )
a__ = stack.pop() # pop stack
# output in tabular format
print("""""".rjust(8 ) , ("""pop(""" + a + """)""").ljust(12 ) , """,""".join(lowercase__ ) , sep=""" | """ )
stack.append(
str(opr[x](int(lowercase__ ) , int(lowercase__ ) ) ) ) # 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(lowercase__ ) , sep=""" | """ , )
return int(stack[0] )
if __name__ == "__main__":
_lowercase : int =input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
print("""\n\tResult = """, solve(Postfix))
| 412 | 1 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_albert import AlbertTokenizer
else:
__magic_name__ = None
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {"""vocab_file""": """spiece.model""", """tokenizer_file""": """tokenizer.json"""}
__magic_name__ = {
"""vocab_file""": {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/spiece.model""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/spiece.model""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/spiece.model""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/spiece.model""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model""",
},
"""tokenizer_file""": {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/tokenizer.json""",
"""albert-large-v1""": """https://huggingface.co/albert-large-v1/resolve/main/tokenizer.json""",
"""albert-xlarge-v1""": """https://huggingface.co/albert-xlarge-v1/resolve/main/tokenizer.json""",
"""albert-xxlarge-v1""": """https://huggingface.co/albert-xxlarge-v1/resolve/main/tokenizer.json""",
"""albert-base-v2""": """https://huggingface.co/albert-base-v2/resolve/main/tokenizer.json""",
"""albert-large-v2""": """https://huggingface.co/albert-large-v2/resolve/main/tokenizer.json""",
"""albert-xlarge-v2""": """https://huggingface.co/albert-xlarge-v2/resolve/main/tokenizer.json""",
"""albert-xxlarge-v2""": """https://huggingface.co/albert-xxlarge-v2/resolve/main/tokenizer.json""",
},
}
__magic_name__ = {
"""albert-base-v1""": 5_1_2,
"""albert-large-v1""": 5_1_2,
"""albert-xlarge-v1""": 5_1_2,
"""albert-xxlarge-v1""": 5_1_2,
"""albert-base-v2""": 5_1_2,
"""albert-large-v2""": 5_1_2,
"""albert-xlarge-v2""": 5_1_2,
"""albert-xxlarge-v2""": 5_1_2,
}
__magic_name__ = """▁"""
class lowerCAmelCase__ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
__UpperCAmelCase : str = VOCAB_FILES_NAMES
__UpperCAmelCase : Dict = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase : List[str] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase : Union[str, Any] = AlbertTokenizer
def __init__( self , a_=None , a_=None , a_=True , a_=True , a_=False , a_="[CLS]" , a_="[SEP]" , a_="<unk>" , a_="[SEP]" , a_="<pad>" , a_="[CLS]" , a_="[MASK]" , **a_ , ):
lowerCamelCase_ : Optional[Any] = (
AddedToken(snake_case__ , lstrip=snake_case__ , rstrip=snake_case__ , normalized=snake_case__ )
if isinstance(snake_case__ , snake_case__ )
else mask_token
)
super().__init__(
snake_case__ , tokenizer_file=snake_case__ , do_lower_case=snake_case__ , remove_space=snake_case__ , keep_accents=snake_case__ , bos_token=snake_case__ , eos_token=snake_case__ , unk_token=snake_case__ , sep_token=snake_case__ , pad_token=snake_case__ , cls_token=snake_case__ , mask_token=snake_case__ , **snake_case__ , )
lowerCamelCase_ : Dict = do_lower_case
lowerCamelCase_ : int = remove_space
lowerCamelCase_ : int = keep_accents
lowerCamelCase_ : Any = vocab_file
lowerCamelCase_ : str = False if not self.vocab_file else True
def _UpperCamelCase ( self , a_ , a_ = None ):
lowerCamelCase_ : Tuple = [self.sep_token_id]
lowerCamelCase_ : Optional[int] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def _UpperCamelCase ( self , a_ , a_ = None ):
lowerCamelCase_ : Tuple = [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 _UpperCamelCase ( self , a_ , a_ = None ):
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(snake_case__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
lowerCamelCase_ : Optional[Any] = os.path.join(
snake_case__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(snake_case__ ):
copyfile(self.vocab_file , snake_case__ )
return (out_vocab_file,)
| 250 |
"""simple docstring"""
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
a : Dict = logging.getLogger()
@unittest.skip("Temporarily disable the doc tests." )
@require_torch
@require_tf
@slow
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ = None , snake_case__ = None , snake_case__ = None , snake_case__ = True , ):
'''simple docstring'''
lowercase__ : List[Any]= [file for file in os.listdir(snake_case__ ) if os.path.isfile(os.path.join(snake_case__ , snake_case__ ) )]
if identifier is not None:
lowercase__ : str= [file for file in files if identifier in file]
if n_identifier is not None:
if isinstance(snake_case__ , snake_case__ ):
for n_ in n_identifier:
lowercase__ : List[Any]= [file for file in files if n_ not in file]
else:
lowercase__ : int= [file for file in files if n_identifier not in file]
lowercase__ : List[Any]= ignore_files or []
ignore_files.append("__init__.py" )
lowercase__ : Union[str, Any]= [file for file in files if file not in ignore_files]
for file in files:
# Open all files
print("Testing" , snake_case__ )
if only_modules:
lowercase__ : Dict= file.split("." )[0]
try:
lowercase__ : int= getattr(snake_case__ , snake_case__ )
lowercase__ : Union[str, Any]= doctest.DocTestSuite(snake_case__ )
lowercase__ : Union[str, Any]= unittest.TextTestRunner().run(snake_case__ )
self.assertIs(len(result.failures ) , 0 )
except AttributeError:
logger.info(F'''{module_identifier} is not a module.''' )
else:
lowercase__ : List[str]= doctest.testfile(str(".." / directory / file ) , optionflags=doctest.ELLIPSIS )
self.assertIs(result.failed , 0 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[Any]= Path("src/transformers" )
lowercase__ : str= "modeling"
lowercase__ : Dict= [
"modeling_ctrl.py",
"modeling_tf_ctrl.py",
]
self.analyze_directory(snake_case__ , identifier=snake_case__ , ignore_files=snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[int]= Path("src/transformers" )
lowercase__ : Tuple= "tokenization"
self.analyze_directory(snake_case__ , identifier=snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[int]= Path("src/transformers" )
lowercase__ : int= "configuration"
self.analyze_directory(snake_case__ , identifier=snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[int]= Path("src/transformers" )
lowercase__ : List[str]= ["configuration", "modeling", "tokenization"]
self.analyze_directory(snake_case__ , n_identifier=snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= Path("docs/source" )
lowercase__ : Any= ["favicon.ico"]
self.analyze_directory(snake_case__ , ignore_files=snake_case__ , only_modules=snake_case__ )
| 218 | 0 |
class UpperCamelCase:
def __init__( self : int , SCREAMING_SNAKE_CASE : Dict ) -> int:
'''simple docstring'''
__snake_case = arr.split("," )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
__snake_case = [int(self.array[0] )] * len(self.array )
__snake_case = [int(self.array[0] )] * len(self.array )
for i in range(1 , len(self.array ) ):
__snake_case = max(
int(self.array[i] ) + sum_value[i - 1] , int(self.array[i] ) )
__snake_case = max(sum_value[i] , rear[i - 1] )
return rear[len(self.array ) - 1]
if __name__ == "__main__":
A : Tuple = input('please input some numbers:')
A : List[str] = SubArray(whole_array)
A : str = array.solve_sub_array()
print(('the results is:', re))
| 473 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def _lowerCAmelCase ( _lowerCAmelCase = "" ) -> dict[str, float]:
'''simple docstring'''
__snake_case = url or "https://www.imdb.com/chart/top/?ref_=nv_mv_250"
__snake_case = BeautifulSoup(requests.get(_lowerCAmelCase ).text , "html.parser" )
__snake_case = soup.find_all("td" , attrs="titleColumn" )
__snake_case = soup.find_all("td" , class_="ratingColumn imdbRating" )
return {
title.a.text: float(rating.strong.text )
for title, rating in zip(_lowerCAmelCase , _lowerCAmelCase )
}
def _lowerCAmelCase ( _lowerCAmelCase = "IMDb_Top_250_Movies.csv" ) -> None:
'''simple docstring'''
__snake_case = get_imdb_top_aaa_movies()
with open(_lowerCAmelCase , "w" , newline="" ) as out_file:
__snake_case = csv.writer(_lowerCAmelCase )
writer.writerow(["Movie title", "IMDb rating"] )
for title, rating in movies.items():
writer.writerow([title, rating] )
if __name__ == "__main__":
write_movies()
| 473 | 1 |
'''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
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : List[str] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
lowerCAmelCase_ : Any = {
'''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'''
),
},
}
lowerCAmelCase_ : List[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'''
),
},
}
lowerCAmelCase_ : Optional[Any] = {
'''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'''
),
},
}
lowerCAmelCase_ : Tuple = {
'''facebook/dpr-ctx_encoder-single-nq-base''': 512,
'''facebook/dpr-ctx_encoder-multiset-base''': 512,
}
lowerCAmelCase_ : Any = {
'''facebook/dpr-question_encoder-single-nq-base''': 512,
'''facebook/dpr-question_encoder-multiset-base''': 512,
}
lowerCAmelCase_ : Union[str, Any] = {
'''facebook/dpr-reader-single-nq-base''': 512,
'''facebook/dpr-reader-multiset-base''': 512,
}
lowerCAmelCase_ : Any = {
'''facebook/dpr-ctx_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-ctx_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCAmelCase_ : Optional[Any] = {
'''facebook/dpr-question_encoder-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-question_encoder-multiset-base''': {'''do_lower_case''': True},
}
lowerCAmelCase_ : str = {
'''facebook/dpr-reader-single-nq-base''': {'''do_lower_case''': True},
'''facebook/dpr-reader-multiset-base''': {'''do_lower_case''': True},
}
class lowerCamelCase_ ( UpperCamelCase__ ):
_lowerCAmelCase : Tuple = VOCAB_FILES_NAMES
_lowerCAmelCase : Tuple = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : str = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : Union[str, Any] = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
class lowerCamelCase_ ( UpperCamelCase__ ):
_lowerCAmelCase : List[Any] = VOCAB_FILES_NAMES
_lowerCAmelCase : Optional[int] = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : Tuple = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : Tuple = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
lowerCAmelCase_ : Any = collections.namedtuple(
'DPRSpanPrediction', ['span_score', 'relevance_score', 'doc_id', 'start_index', 'end_index', 'text']
)
lowerCAmelCase_ : Tuple = collections.namedtuple('DPRReaderOutput', ['start_logits', 'end_logits', 'relevance_logits'])
lowerCAmelCase_ : 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(UpperCamelCase__ )
class lowerCamelCase_ :
def __call__( self : int , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Optional[str] = None , lowerCAmelCase__ : Optional[str] = None , lowerCAmelCase__ : Union[bool, str] = False , lowerCAmelCase__ : Union[bool, str] = False , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[Union[str, TensorType]] = None , lowerCAmelCase__ : Optional[bool] = None , **lowerCAmelCase__ : Dict , ):
"""simple docstring"""
if titles is None and texts is None:
return super().__call__(
_a , padding=_a , truncation=_a , max_length=_a , return_tensors=_a , return_attention_mask=_a , **_a , )
elif titles is None or texts is None:
SCREAMING_SNAKE_CASE : Union[str, Any] = titles if texts is None else texts
return super().__call__(
_a , _a , padding=_a , truncation=_a , max_length=_a , return_tensors=_a , return_attention_mask=_a , **_a , )
SCREAMING_SNAKE_CASE : Union[str, Any] = titles if not isinstance(_a , _a ) else [titles]
SCREAMING_SNAKE_CASE : List[Any] = texts if not isinstance(_a , _a ) else [texts]
SCREAMING_SNAKE_CASE : Union[str, Any] = len(_a )
SCREAMING_SNAKE_CASE : Optional[Any] = questions if not isinstance(_a , _a ) else [questions] * n_passages
if len(_a ) != len(_a ):
raise ValueError(
F"""There should be as many titles than texts but got {len(_a )} titles and {len(_a )} texts.""" )
SCREAMING_SNAKE_CASE : str = super().__call__(_a , _a , padding=_a , truncation=_a )['''input_ids''']
SCREAMING_SNAKE_CASE : Dict = super().__call__(_a , add_special_tokens=_a , padding=_a , truncation=_a )['''input_ids''']
SCREAMING_SNAKE_CASE : Optional[int] = {
'''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(_a , _a )
]
}
if return_attention_mask is not False:
SCREAMING_SNAKE_CASE : Dict = []
for input_ids in encoded_inputs["input_ids"]:
attention_mask.append([int(input_id != self.pad_token_id ) for input_id in input_ids] )
SCREAMING_SNAKE_CASE : str = attention_mask
return self.pad(_a , padding=_a , max_length=_a , return_tensors=_a )
def __lowercase ( self : int , lowerCAmelCase__ : BatchEncoding , lowerCAmelCase__ : DPRReaderOutput , lowerCAmelCase__ : int = 16 , lowerCAmelCase__ : int = 64 , lowerCAmelCase__ : int = 4 , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = reader_input['''input_ids''']
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : Union[str, Any] = reader_output[:3]
SCREAMING_SNAKE_CASE : Any = len(_a )
SCREAMING_SNAKE_CASE : int = sorted(range(_a ) , reverse=_a , key=relevance_logits.__getitem__ )
SCREAMING_SNAKE_CASE : List[str] = []
for doc_id in sorted_docs:
SCREAMING_SNAKE_CASE : Any = list(input_ids[doc_id] )
# assuming question & title information is at the beginning of the sequence
SCREAMING_SNAKE_CASE : Optional[int] = sequence_ids.index(self.sep_token_id , 2 ) + 1 # second sep id
if sequence_ids[-1] == self.pad_token_id:
SCREAMING_SNAKE_CASE : Any = sequence_ids.index(self.pad_token_id )
else:
SCREAMING_SNAKE_CASE : str = len(_a )
SCREAMING_SNAKE_CASE : List[Any] = 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=_a , top_spans=_a , )
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=_a , start_index=_a , end_index=_a , text=self.decode(sequence_ids[start_index : end_index + 1] ) , ) )
if len(_a ) >= num_spans:
break
return nbest_spans_predictions[:num_spans]
def __lowercase ( self : str , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : List[int] , lowerCAmelCase__ : int , lowerCAmelCase__ : int , ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = []
for start_index, start_score in enumerate(_a ):
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) )
SCREAMING_SNAKE_CASE : int = sorted(_a , key=lambda lowerCAmelCase__ : x[1] , reverse=_a )
SCREAMING_SNAKE_CASE : Optional[Any] = []
for (start_index, end_index), score in scores:
if start_index > end_index:
raise ValueError(F"""Wrong span indices: [{start_index}:{end_index}]""" )
SCREAMING_SNAKE_CASE : int = 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(_a ) == top_spans:
break
return chosen_span_intervals
@add_end_docstrings(UpperCamelCase__ )
class lowerCamelCase_ ( UpperCamelCase__ , UpperCamelCase__ ):
_lowerCAmelCase : Optional[Any] = VOCAB_FILES_NAMES
_lowerCAmelCase : Dict = READER_PRETRAINED_VOCAB_FILES_MAP
_lowerCAmelCase : str = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_lowerCAmelCase : int = READER_PRETRAINED_INIT_CONFIGURATION
_lowerCAmelCase : List[Any] = ['input_ids', 'attention_mask']
| 527 |
import requests
from bsa import BeautifulSoup
def lowerCamelCase( a__ = "https://www.worldometers.info/coronavirus"):
_SCREAMING_SNAKE_CASE =BeautifulSoup(requests.get(a__).text ,'''html.parser''')
_SCREAMING_SNAKE_CASE =soup.findAll('''h1''')
_SCREAMING_SNAKE_CASE =soup.findAll('''div''' ,{'''class''': '''maincounter-number'''})
keys += soup.findAll('''span''' ,{'''class''': '''panel-title'''})
values += soup.findAll('''div''' ,{'''class''': '''number-table-main'''})
return {key.text.strip(): value.text.strip() for key, value in zip(a__ ,a__)}
if __name__ == "__main__":
print('''\033[1m''' + '''COVID-19 Status of the World''' + '''\033[0m\n''')
for key, value in world_covidaa_stats().items():
print(f"""{key}\n{value}\n""") | 691 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
_SCREAMING_SNAKE_CASE = {
'''configuration_tapas''': ['''TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TapasConfig'''],
'''tokenization_tapas''': ['''TapasTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'''TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TapasForMaskedLM''',
'''TapasForQuestionAnswering''',
'''TapasForSequenceClassification''',
'''TapasModel''',
'''TapasPreTrainedModel''',
'''load_tf_weights_in_tapas''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE = [
'''TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFTapasForMaskedLM''',
'''TFTapasForQuestionAnswering''',
'''TFTapasForSequenceClassification''',
'''TFTapasModel''',
'''TFTapasPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_tapas import TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP, TapasConfig
from .tokenization_tapas import TapasTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tapas import (
TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasPreTrainedModel,
load_tf_weights_in_tapas,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_tapas import (
TF_TAPAS_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTapasForMaskedLM,
TFTapasForQuestionAnswering,
TFTapasForSequenceClassification,
TFTapasModel,
TFTapasPreTrainedModel,
)
else:
import sys
_SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 56 |
'''simple docstring'''
import math
def _lowerCAmelCase ( lowerCamelCase_ : int ):
assert isinstance(lowerCamelCase_ , lowerCamelCase_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
__lowercase = range(3 , int(math.sqrt(lowerCamelCase_ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _lowerCAmelCase ( lowerCamelCase_ : Dict , lowerCamelCase_ : Any=1 , **lowerCamelCase_ : Tuple ):
__lowercase = factor * value
__lowercase = value
while not is_prime(lowerCamelCase_ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **lowerCamelCase_ )
return value
| 56 | 1 |
'''simple docstring'''
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
lowerCAmelCase = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase = direct_transformers_import(TRANSFORMERS_PATH)
# Regexes that match TF/Flax/PT model names.
lowerCAmelCase = re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
lowerCAmelCase = re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
lowerCAmelCase = re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
lowerCAmelCase = [
("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
(
"""zero-shot-object-detection""",
"""MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
"""AutoModelForZeroShotObjectDetection""",
),
("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
(
"""table-question-answering""",
"""MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
"""AutoModelForTableQuestionAnswering""",
),
("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
(
"""next-sentence-prediction""",
"""MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
"""AutoModelForNextSentencePrediction""",
),
(
"""audio-frame-classification""",
"""MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
"""AutoModelForAudioFrameClassification""",
),
("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
(
"""document-question-answering""",
"""MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
"""AutoModelForDocumentQuestionAnswering""",
),
(
"""visual-question-answering""",
"""MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
"""AutoModelForVisualQuestionAnswering""",
),
("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
(
"""zero-shot-image-classification""",
"""MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
"""AutoModelForZeroShotImageClassification""",
),
("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]
def __A ( a_ : Union[str, Any] ):
lowerCAmelCase : List[str] = re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)" ,a_ )
return [m.group(0 ) for m in matches]
def __A ( ):
lowerCAmelCase : List[Any] = transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
lowerCAmelCase : Any = {
config.replace("Config" ,"" ): model_type for model_type, config in config_maping_names.items()
}
# Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
lowerCAmelCase : Any = collections.defaultdict(a_ )
lowerCAmelCase : Dict = collections.defaultdict(a_ )
lowerCAmelCase : Any = collections.defaultdict(a_ )
# Let's lookup through all transformers object (once) and find if models are supported by a given backend.
for attr_name in dir(a_ ):
lowerCAmelCase : str = None
if _re_tf_models.match(a_ ) is not None:
lowerCAmelCase : Union[str, Any] = tf_models
lowerCAmelCase : Any = _re_tf_models.match(a_ ).groups()[0]
elif _re_flax_models.match(a_ ) is not None:
lowerCAmelCase : Dict = flax_models
lowerCAmelCase : Optional[Any] = _re_flax_models.match(a_ ).groups()[0]
elif _re_pt_models.match(a_ ) is not None:
lowerCAmelCase : int = pt_models
lowerCAmelCase : int = _re_pt_models.match(a_ ).groups()[0]
if lookup_dict is not None:
while len(a_ ) > 0:
if attr_name in model_prefix_to_model_type:
lowerCAmelCase : Optional[int] = True
break
# Try again after removing the last word in the name
lowerCAmelCase : str = "".join(camel_case_split(a_ )[:-1] )
lowerCAmelCase : List[str] = set(list(pt_models.keys() ) + list(tf_models.keys() ) + list(flax_models.keys() ) )
lowerCAmelCase : List[Any] = list(a_ )
all_models.sort()
lowerCAmelCase : Union[str, Any] = {"model_type": all_models}
lowerCAmelCase : str = [pt_models[t] for t in all_models]
lowerCAmelCase : Tuple = [tf_models[t] for t in all_models]
lowerCAmelCase : Any = [flax_models[t] for t in all_models]
# Now let's use the auto-mapping names to make sure
lowerCAmelCase : List[str] = {}
for t in all_models:
if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
lowerCAmelCase : Optional[int] = "AutoProcessor"
elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
lowerCAmelCase : int = "AutoTokenizer"
elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
lowerCAmelCase : Union[str, Any] = "AutoFeatureExtractor"
else:
# Default to AutoTokenizer if a model has nothing, for backward compatibility.
lowerCAmelCase : Optional[int] = "AutoTokenizer"
lowerCAmelCase : Union[str, Any] = [processors[t] for t in all_models]
return pd.DataFrame(a_ )
def __A ( a_ : int ):
lowerCAmelCase : str = [
transformers_module.models.auto.modeling_auto,
transformers_module.models.auto.modeling_tf_auto,
transformers_module.models.auto.modeling_flax_auto,
]
for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
lowerCAmelCase : Dict = [model_mapping, f'''TF_{model_mapping}''', f'''FLAX_{model_mapping}''']
lowerCAmelCase : List[Any] = [auto_class, f'''TF_{auto_class}''', f'''Flax_{auto_class}''']
# Loop through all three frameworks
for module, cls, mapping in zip(a_ ,a_ ,a_ ):
# The type of pipeline may not exist in this framework
if not hasattr(a_ ,a_ ):
continue
# First extract all model_names
lowerCAmelCase : Optional[Any] = []
for name in getattr(a_ ,a_ ).values():
if isinstance(a_ ,a_ ):
model_names.append(a_ )
else:
model_names.extend(list(a_ ) )
# Add pipeline tag and auto model class for those models
table.update({model_name: (pipeline_tag, cls) for model_name in model_names} )
return table
def __A ( a_ : str ,a_ : Any ):
lowerCAmelCase : str = get_frameworks_table()
lowerCAmelCase : str = Dataset.from_pandas(a_ )
lowerCAmelCase : Optional[Any] = hf_hub_download(
"huggingface/transformers-metadata" ,"pipeline_tags.json" ,repo_type="dataset" ,token=a_ )
lowerCAmelCase : Dict = Dataset.from_json(a_ )
lowerCAmelCase : Dict = {
tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
for i in range(len(a_ ) )
}
lowerCAmelCase : List[Any] = update_pipeline_and_auto_class_table(a_ )
# Sort the model classes to avoid some nondeterministic updates to create false update commits.
lowerCAmelCase : Tuple = sorted(table.keys() )
lowerCAmelCase : Tuple = pd.DataFrame(
{
"model_class": model_classes,
"pipeline_tag": [table[m][0] for m in model_classes],
"auto_class": [table[m][1] for m in model_classes],
} )
lowerCAmelCase : List[str] = Dataset.from_pandas(a_ )
with tempfile.TemporaryDirectory() as tmp_dir:
frameworks_dataset.to_json(os.path.join(a_ ,"frameworks.json" ) )
tags_dataset.to_json(os.path.join(a_ ,"pipeline_tags.json" ) )
if commit_sha is not None:
lowerCAmelCase : Dict = (
f'''Update with commit {commit_sha}\n\nSee: '''
f'''https://github.com/huggingface/transformers/commit/{commit_sha}'''
)
else:
lowerCAmelCase : Optional[int] = "Update"
upload_folder(
repo_id="huggingface/transformers-metadata" ,folder_path=a_ ,repo_type="dataset" ,token=a_ ,commit_message=a_ ,)
def __A ( ):
lowerCAmelCase : Union[str, Any] = {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
lowerCAmelCase : List[Any] = transformers_module.pipelines.SUPPORTED_TASKS
lowerCAmelCase : List[Any] = []
for key in pipeline_tasks:
if key not in in_table:
lowerCAmelCase : Optional[int] = pipeline_tasks[key]["pt"]
if isinstance(a_ ,(list, tuple) ):
lowerCAmelCase : Union[str, Any] = model[0]
lowerCAmelCase : List[Any] = model.__name__
if model not in in_table.values():
missing.append(a_ )
if len(a_ ) > 0:
lowerCAmelCase : Tuple = ", ".join(a_ )
raise ValueError(
"The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
f'''`utils/update_metadata.py`: {msg}. Please add them!''' )
if __name__ == "__main__":
lowerCAmelCase = argparse.ArgumentParser()
parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
lowerCAmelCase = parser.parse_args()
if args.check_only:
check_pipeline_tags()
else:
update_metadata(args.token, args.commit_sha)
| 525 |
'''simple docstring'''
import math
import tensorflow as tf
from packaging import version
def __A ( a_ : List[Any] ):
lowerCAmelCase : Any = tf.convert_to_tensor(a_ )
lowerCAmelCase : List[Any] = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) ,x.dtype ) ))
return x * cdf
def __A ( a_ : int ):
lowerCAmelCase : Dict = tf.convert_to_tensor(a_ )
lowerCAmelCase : int = tf.cast(math.pi ,x.dtype )
lowerCAmelCase : Dict = tf.cast(0.0_4_4_7_1_5 ,x.dtype )
lowerCAmelCase : Dict = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(a_ ,3 )) ))
return x * cdf
def __A ( a_ : Union[str, Any] ):
lowerCAmelCase : Any = tf.convert_to_tensor(a_ )
return x * tf.tanh(tf.math.softplus(a_ ) )
def __A ( a_ : List[str] ):
lowerCAmelCase : Dict = tf.convert_to_tensor(a_ )
lowerCAmelCase : Any = tf.cast(0.0_4_4_7_1_5 ,x.dtype )
lowerCAmelCase : Optional[int] = tf.cast(0.7_9_7_8_8_4_5_6_0_8 ,x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def __A ( a_ : Union[str, Any] ):
lowerCAmelCase : Optional[int] = tf.convert_to_tensor(a_ )
lowerCAmelCase : List[Any] = tf.cast(1.7_0_2 ,x.dtype )
return x * tf.math.sigmoid(coeff * x )
def __A ( a_ : List[Any] ):
return tf.clip_by_value(_gelu(a_ ) ,-1_0 ,1_0 )
def __A ( a_ : List[Any] ,a_ : List[Any]=-1 ):
lowerCAmelCase , lowerCAmelCase : Optional[int] = tf.split(a_ ,2 ,axis=a_ )
return a * tf.math.sigmoid(a_ )
if version.parse(tf.version.VERSION) >= version.parse("""2.4"""):
def __A ( a_ : Optional[Any] ):
return tf.keras.activations.gelu(a_ ,approximate=a_ )
lowerCAmelCase = tf.keras.activations.gelu
lowerCAmelCase = approximate_gelu_wrap
else:
lowerCAmelCase = _gelu
lowerCAmelCase = _gelu_new
lowerCAmelCase = {
"""gelu""": gelu,
"""gelu_10""": gelu_aa,
"""gelu_fast""": gelu_fast,
"""gelu_new""": gelu_new,
"""glu""": glu,
"""mish""": mish,
"""quick_gelu""": quick_gelu,
"""relu""": tf.keras.activations.relu,
"""sigmoid""": tf.keras.activations.sigmoid,
"""silu""": tf.keras.activations.swish,
"""swish""": tf.keras.activations.swish,
"""tanh""": tf.keras.activations.tanh,
}
def __A ( a_ : int ):
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f'''function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}''' )
| 525 | 1 |
import os
from math import logaa
def __lowerCamelCase ( snake_case__ = "base_exp.txt" ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(snake_case__ ) ,snake_case__ ) ) ):
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = list(map(snake_case__ ,line.split(""",""" ) ) )
if x * logaa(snake_case__ ) > largest:
_SCREAMING_SNAKE_CASE = x * logaa(snake_case__ )
_SCREAMING_SNAKE_CASE = i + 1
return result
if __name__ == "__main__":
print(solution()) | 706 |
from cva import destroyAllWindows, imread, imshow, waitKey
def __lowerCamelCase ( snake_case__ ) -> str:
"""simple docstring"""
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = img.shape[0], img.shape[1]
# converting each pixel's color to its negative
for i in range(snake_case__ ):
for j in range(snake_case__ ):
_SCREAMING_SNAKE_CASE = [2_55, 2_55, 2_55] - img[i][j]
return img
if __name__ == "__main__":
# read original image
UpperCamelCase = imread('''image_data/lena.jpg''', 1)
# convert to its negative
UpperCamelCase = convert_to_negative(img)
# show result image
imshow('''negative of original image''', img)
waitKey(0)
destroyAllWindows()
| 569 | 0 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
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 ConvNextVaBackbone, ConvNextVaForImageClassification, ConvNextVaModel
from transformers.models.convnextva.modeling_convnextva import CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCamelCase__ :
"""simple docstring"""
def __init__( self : Optional[int] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Tuple=13 , __lowerCAmelCase : List[str]=32 , __lowerCAmelCase : Optional[int]=3 , __lowerCAmelCase : Optional[int]=4 , __lowerCAmelCase : List[Any]=[10, 20, 30, 40] , __lowerCAmelCase : str=[2, 2, 3, 2] , __lowerCAmelCase : Optional[Any]=True , __lowerCAmelCase : str=True , __lowerCAmelCase : Optional[Any]=37 , __lowerCAmelCase : Dict="gelu" , __lowerCAmelCase : List[Any]=10 , __lowerCAmelCase : int=0.02 , __lowerCAmelCase : Any=["stage2", "stage3", "stage4"] , __lowerCAmelCase : List[Any]=[2, 3, 4] , __lowerCAmelCase : List[Any]=None , ) -> Optional[int]:
_A = parent
_A = batch_size
_A = image_size
_A = num_channels
_A = num_stages
_A = hidden_sizes
_A = depths
_A = is_training
_A = use_labels
_A = intermediate_size
_A = hidden_act
_A = num_labels
_A = initializer_range
_A = out_features
_A = out_indices
_A = scope
def snake_case_ ( self : Any ) -> List[Any]:
_A = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
_A = None
if self.use_labels:
_A = ids_tensor([self.batch_size] , self.num_labels )
_A = self.get_config()
return config, pixel_values, labels
def snake_case_ ( self : Tuple ) -> List[str]:
return ConvNextVaConfig(
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 snake_case_ ( self : str , __lowerCAmelCase : Any , __lowerCAmelCase : int , __lowerCAmelCase : int ) -> List[str]:
_A = ConvNextVaModel(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_A = 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 snake_case_ ( self : List[Any] , __lowerCAmelCase : str , __lowerCAmelCase : int , __lowerCAmelCase : Any ) -> Optional[Any]:
_A = ConvNextVaForImageClassification(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_A = model(__lowerCAmelCase , labels=__lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case_ ( self : str , __lowerCAmelCase : int , __lowerCAmelCase : List[str] , __lowerCAmelCase : Dict ) -> int:
_A = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_A = 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
_A = None
_A = ConvNextVaBackbone(config=__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
_A = 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 snake_case_ ( self : Any ) -> Union[str, Any]:
_A = self.prepare_config_and_inputs()
_A , _A , _A = config_and_inputs
_A = {'''pixel_values''': pixel_values}
return config, inputs_dict
def snake_case_ ( self : Optional[int] ) -> Optional[Any]:
_A = self.prepare_config_and_inputs()
_A , _A , _A = config_and_inputs
_A = {'''pixel_values''': pixel_values, '''labels''': labels}
return config, inputs_dict
@require_torch
class lowerCamelCase__ ( _A , _A , unittest.TestCase):
"""simple docstring"""
a__ : Optional[int] = (
(
ConvNextVaModel,
ConvNextVaForImageClassification,
ConvNextVaBackbone,
)
if is_torch_available()
else ()
)
a__ : Tuple = (
{"feature-extraction": ConvNextVaModel, "image-classification": ConvNextVaForImageClassification}
if is_torch_available()
else {}
)
a__ : Optional[Any] = False
a__ : Any = False
a__ : Dict = False
a__ : Optional[Any] = False
a__ : Optional[Any] = False
def snake_case_ ( self : Optional[int] ) -> List[str]:
_A = ConvNextVaModelTester(self )
_A = ConfigTester(self , config_class=__lowerCAmelCase , has_text_modality=__lowerCAmelCase , hidden_size=37 )
def snake_case_ ( self : Optional[Any] ) -> List[Any]:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def snake_case_ ( self : Any ) -> str:
return
@unittest.skip(reason='''ConvNextV2 does not use inputs_embeds''' )
def snake_case_ ( self : Any ) -> Any:
pass
@unittest.skip(reason='''ConvNextV2 does not support input and output embeddings''' )
def snake_case_ ( self : List[Any] ) -> Optional[int]:
pass
@unittest.skip(reason='''ConvNextV2 does not use feedforward chunking''' )
def snake_case_ ( self : Union[str, Any] ) -> Tuple:
pass
def snake_case_ ( self : int ) -> List[Any]:
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
_A , _A = self.model_tester.prepare_config_and_inputs_with_labels()
_A = True
if model_class.__name__ in [
*get_values(__lowerCAmelCase ),
*get_values(__lowerCAmelCase ),
]:
continue
_A = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.train()
_A = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
_A = model(**__lowerCAmelCase ).loss
loss.backward()
def snake_case_ ( self : List[str] ) -> Any:
if not self.model_tester.is_training:
return
for model_class in self.all_model_classes:
_A , _A = self.model_tester.prepare_config_and_inputs_with_labels()
_A = False
_A = True
if (
model_class.__name__
in [*get_values(__lowerCAmelCase ), *get_values(__lowerCAmelCase )]
or not model_class.supports_gradient_checkpointing
):
continue
_A = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.gradient_checkpointing_enable()
model.train()
_A = self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase , return_labels=__lowerCAmelCase )
_A = model(**__lowerCAmelCase ).loss
loss.backward()
def snake_case_ ( self : Union[str, Any] ) -> str:
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = model_class(__lowerCAmelCase )
_A = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_A = [*signature.parameters.keys()]
_A = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __lowerCAmelCase )
def snake_case_ ( self : str ) -> List[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__lowerCAmelCase )
def snake_case_ ( self : Tuple ) -> Any:
def check_hidden_states_output(__lowerCAmelCase : Tuple , __lowerCAmelCase : int , __lowerCAmelCase : List[str] ):
_A = model_class(__lowerCAmelCase )
model.to(__lowerCAmelCase )
model.eval()
with torch.no_grad():
_A = model(**self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase ) )
_A = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
_A = self.model_tester.num_stages
self.assertEqual(len(__lowerCAmelCase ) , expected_num_stages + 1 )
# ConvNextV2'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] , )
_A , _A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_A = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
_A = True
check_hidden_states_output(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
def snake_case_ ( self : Optional[int] ) -> List[Any]:
_A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__lowerCAmelCase )
@slow
def snake_case_ ( self : List[str] ) -> Union[str, Any]:
for model_name in CONVNEXTV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_A = ConvNextVaModel.from_pretrained(__lowerCAmelCase )
self.assertIsNotNone(__lowerCAmelCase )
def SCREAMING_SNAKE_CASE_ ( ) -> Tuple:
_A = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
@cached_property
def snake_case_ ( self : List[str] ) -> Dict:
return AutoImageProcessor.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ) if is_vision_available() else None
@slow
def snake_case_ ( self : Tuple ) -> int:
_A = ConvNextVaForImageClassification.from_pretrained('''facebook/convnextv2-tiny-1k-224''' ).to(__lowerCAmelCase )
_A = self.default_image_processor
_A = prepare_img()
_A = preprocessor(images=__lowerCAmelCase , return_tensors='''pt''' ).to(__lowerCAmelCase )
# forward pass
with torch.no_grad():
_A = model(**__lowerCAmelCase )
# verify the logits
_A = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , __lowerCAmelCase )
_A = torch.tensor([0.9996, 0.1966, -0.4386] ).to(__lowerCAmelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , __lowerCAmelCase , atol=1E-4 ) )
| 2 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
UpperCAmelCase_ = logging.get_logger(__name__)
UpperCAmelCase_ = """▁"""
UpperCAmelCase_ = {"""vocab_file""": """sentencepiece.bpe.model""", """monolingual_vocab_file""": """dict.txt"""}
UpperCAmelCase_ = {
"""vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model""",
},
"""monolingual_vocab_file""": {
"""vinai/bartpho-syllable""": """https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt""",
},
}
UpperCAmelCase_ = {"""vinai/bartpho-syllable""": 1_0_2_4}
class lowerCamelCase__ ( _A):
"""simple docstring"""
a__ : int = VOCAB_FILES_NAMES
a__ : Tuple = PRETRAINED_VOCAB_FILES_MAP
a__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
a__ : Tuple = ["input_ids", "attention_mask"]
def __init__( self : Union[str, Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : List[str] , __lowerCAmelCase : Union[str, Any]="<s>" , __lowerCAmelCase : Dict="</s>" , __lowerCAmelCase : List[Any]="</s>" , __lowerCAmelCase : Optional[Any]="<s>" , __lowerCAmelCase : Tuple="<unk>" , __lowerCAmelCase : int="<pad>" , __lowerCAmelCase : Optional[Any]="<mask>" , __lowerCAmelCase : Optional[Dict[str, Any]] = None , **__lowerCAmelCase : Tuple , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it
_A = AddedToken(__lowerCAmelCase , lstrip=__lowerCAmelCase , rstrip=__lowerCAmelCase ) if isinstance(__lowerCAmelCase , __lowerCAmelCase ) else mask_token
_A = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=__lowerCAmelCase , eos_token=__lowerCAmelCase , unk_token=__lowerCAmelCase , sep_token=__lowerCAmelCase , cls_token=__lowerCAmelCase , pad_token=__lowerCAmelCase , mask_token=__lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **__lowerCAmelCase , )
_A = vocab_file
_A = monolingual_vocab_file
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(__lowerCAmelCase ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
_A = {}
_A = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = cnt
cnt += 1
with open(__lowerCAmelCase , '''r''' , encoding='''utf-8''' ) as f:
for line in f.readlines():
_A = line.strip().split()[0]
_A = len(self.fairseq_tokens_to_ids )
if str(__lowerCAmelCase ) not in self.fairseq_tokens_to_ids:
_A = len(self.fairseq_tokens_to_ids )
_A = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self : Any ) -> List[Any]:
_A = self.__dict__.copy()
_A = None
_A = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Union[str, Any] , __lowerCAmelCase : Dict ) -> List[Any]:
_A = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
_A = {}
_A = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
_A = [self.cls_token_id]
_A = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def snake_case_ ( self : List[Any] , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None , __lowerCAmelCase : bool = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=__lowerCAmelCase , token_ids_a=__lowerCAmelCase , already_has_special_tokens=__lowerCAmelCase )
if token_ids_a is None:
return [1] + ([0] * len(__lowerCAmelCase )) + [1]
return [1] + ([0] * len(__lowerCAmelCase )) + [1, 1] + ([0] * len(__lowerCAmelCase )) + [1]
def snake_case_ ( self : Any , __lowerCAmelCase : List[int] , __lowerCAmelCase : Optional[List[int]] = None ) -> List[int]:
_A = [self.sep_token_id]
_A = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def snake_case_ ( self : Optional[int] ) -> Union[str, Any]:
return len(self.fairseq_ids_to_tokens )
def snake_case_ ( self : Dict ) -> Optional[Any]:
_A = {self.convert_ids_to_tokens(__lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def snake_case_ ( self : List[str] , __lowerCAmelCase : str ) -> List[str]:
return self.sp_model.encode(__lowerCAmelCase , out_type=__lowerCAmelCase )
def snake_case_ ( self : str , __lowerCAmelCase : Optional[Any] ) -> Dict:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def snake_case_ ( self : int , __lowerCAmelCase : Optional[int] ) -> List[str]:
return self.fairseq_ids_to_tokens[index]
def snake_case_ ( self : List[str] , __lowerCAmelCase : Union[str, Any] ) -> Tuple:
_A = ''''''.join(__lowerCAmelCase ).replace(__lowerCAmelCase , ''' ''' ).strip()
return out_string
def snake_case_ ( self : str , __lowerCAmelCase : str , __lowerCAmelCase : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(__lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
_A = os.path.join(
__lowerCAmelCase , (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''monolingual_vocab_file'''] , )
if os.path.abspath(self.vocab_file ) != os.path.abspath(__lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(__lowerCAmelCase , '''wb''' ) as fi:
_A = self.sp_model.serialized_model_proto()
fi.write(__lowerCAmelCase )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
__lowerCAmelCase ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , __lowerCAmelCase )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(__lowerCAmelCase , '''w''' , encoding='''utf-8''' ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f'''{str(__lowerCAmelCase )} \n''' )
return out_vocab_file, out_monolingual_vocab_file
| 2 | 1 |
def A__ ( SCREAMING_SNAKE_CASE__) -> List[Any]:
__snake_case: List[Any] = len(SCREAMING_SNAKE_CASE__)
for i in range(length - 1):
__snake_case: Tuple = i
for k in range(i + 1 , SCREAMING_SNAKE_CASE__):
if collection[k] < collection[least]:
__snake_case: str = k
if least != i:
__snake_case , __snake_case: Optional[Any] = (collection[i], collection[least])
return collection
if __name__ == "__main__":
__UpperCAmelCase : Any = input("Enter numbers separated by a comma:\n").strip()
__UpperCAmelCase : List[str] = [int(item) for item in user_input.split(",")]
print(selection_sort(unsorted))
| 155 |
def A__ ( SCREAMING_SNAKE_CASE__) -> list:
__snake_case: List[str] = int(SCREAMING_SNAKE_CASE__)
if n_element < 1:
__snake_case: Any = ValueError("""a should be a positive number""")
raise my_error
__snake_case: str = [1]
__snake_case , __snake_case , __snake_case: Union[str, Any] = (0, 0, 0)
__snake_case: Optional[Any] = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5))
index += 1
return hamming_list
if __name__ == "__main__":
__UpperCAmelCase : int = input("Enter the last number (nth term) of the Hamming Number Series: ")
print("Formula of Hamming Number Series => 2^i * 3^j * 5^k")
__UpperCAmelCase : Union[str, Any] = hamming(int(n))
print("-----------------------------------------------------")
print(f'The list with nth numbers is: {hamming_numbers}')
print("-----------------------------------------------------")
| 155 | 1 |
from math import isqrt, loga
def A__ ( SCREAMING_SNAKE_CASE_ : int ) -> list[int]:
"""simple docstring"""
_UpperCAmelCase = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
_UpperCAmelCase = False
return [i for i in range(2 , SCREAMING_SNAKE_CASE_ ) if is_prime[i]]
def A__ ( SCREAMING_SNAKE_CASE_ : int = 80_08_00 , SCREAMING_SNAKE_CASE_ : int = 80_08_00 ) -> int:
"""simple docstring"""
_UpperCAmelCase = degree * loga(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = int(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = calculate_prime_numbers(SCREAMING_SNAKE_CASE_ )
_UpperCAmelCase = 0
_UpperCAmelCase = 0
_UpperCAmelCase = len(SCREAMING_SNAKE_CASE_ ) - 1
while left < right:
while (
prime_numbers[right] * loga(prime_numbers[left] )
+ prime_numbers[left] * loga(prime_numbers[right] )
> upper_bound
):
right -= 1
hybrid_integers_count += right - left
left += 1
return hybrid_integers_count
if __name__ == "__main__":
print(f'''{solution() = }''') | 32 |
"""simple docstring"""
# Lint as: python3
import itertools
import os
import re
a_ = re.compile(r"""([A-Z]+)([A-Z][a-z])""")
a_ = re.compile(r"""([a-z\d])([A-Z])""")
a_ = re.compile(r"""(?<!_)_(?!_)""")
a_ = re.compile(r"""(_{2,})""")
a_ = r"""^\w+(\.\w+)*$"""
a_ = r"""<>:/\|?*"""
def UpperCAmelCase_ ( __a : Optional[int] ):
'''simple docstring'''
_lowerCamelCase : str = _uppercase_uppercase_re.sub(r'\1_\2' , __a )
_lowerCamelCase : Tuple = _lowercase_uppercase_re.sub(r'\1_\2' , __a )
return name.lower()
def UpperCAmelCase_ ( __a : Optional[int] ):
'''simple docstring'''
_lowerCamelCase : Dict = _single_underscore_re.split(__a )
_lowerCamelCase : Tuple = [_multiple_underscores_re.split(__a ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(__a ) if n != '' )
def UpperCAmelCase_ ( __a : List[Any] ):
'''simple docstring'''
if os.path.basename(__a ) != name:
raise ValueError(f"Should be a dataset name, not a path: {name}" )
return camelcase_to_snakecase(__a )
def UpperCAmelCase_ ( __a : Union[str, Any] , __a : Optional[int] ):
'''simple docstring'''
if os.path.basename(__a ) != name:
raise ValueError(f"Should be a dataset name, not a path: {name}" )
if not re.match(_split_re , __a ):
raise ValueError(f"Split name should match '{_split_re}'' but got '{split}'." )
return f"{filename_prefix_for_name(__a )}-{split}"
def UpperCAmelCase_ ( __a : Any , __a : Union[str, Any] , __a : List[Any] , __a : List[str]=None ):
'''simple docstring'''
_lowerCamelCase : List[Any] = filename_prefix_for_split(__a , __a )
if filetype_suffix:
prefix += f".{filetype_suffix}"
_lowerCamelCase : List[str] = os.path.join(__a , __a )
return f"{filepath}*"
def UpperCAmelCase_ ( __a : str , __a : List[Any] , __a : List[str] , __a : Tuple=None , __a : Tuple=None ):
'''simple docstring'''
_lowerCamelCase : Tuple = filename_prefix_for_split(__a , __a )
_lowerCamelCase : List[str] = os.path.join(__a , __a )
if shard_lengths:
_lowerCamelCase : Union[str, Any] = len(__a )
_lowerCamelCase : str = [f"{prefix}-{shard_id:05d}-of-{num_shards:05d}" for shard_id in range(__a )]
if filetype_suffix:
_lowerCamelCase : int = [filename + f".{filetype_suffix}" for filename in filenames]
return filenames
else:
_lowerCamelCase : int = prefix
if filetype_suffix:
filename += f".{filetype_suffix}"
return [filename]
| 437 | 0 |
"""simple docstring"""
import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple
from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available
from ...test_tokenization_common import TokenizerTesterMixin
if is_torch_available():
A = """pt"""
elif is_tf_available():
A = """tf"""
else:
A = """jax"""
class a__ ( __magic_name__ , unittest.TestCase ):
lowercase_ = PerceiverTokenizer
lowercase_ = False
def a_ ( self : Optional[int]):
"""simple docstring"""
super().setUp()
__UpperCAmelCase : Dict = PerceiverTokenizer()
tokenizer.save_pretrained(self.tmpdirname)
@cached_property
def a_ ( self : int):
"""simple docstring"""
return PerceiverTokenizer.from_pretrained("deepmind/language-perceiver")
def a_ ( self : Any , **UpperCamelCase_ : Optional[int]):
"""simple docstring"""
return self.tokenizer_class.from_pretrained(self.tmpdirname , **UpperCamelCase_)
def a_ ( self : Optional[Any] , UpperCamelCase_ : List[Any] , UpperCamelCase_ : Any=False , UpperCamelCase_ : Dict=20 , UpperCamelCase_ : Union[str, Any]=5):
"""simple docstring"""
__UpperCAmelCase : Optional[int] = []
for i in range(len(UpperCamelCase_)):
try:
__UpperCAmelCase : Union[str, Any] = tokenizer.decode([i] , clean_up_tokenization_spaces=UpperCamelCase_)
except UnicodeDecodeError:
pass
toks.append((i, tok))
__UpperCAmelCase : Union[str, Any] = list(filter(lambda UpperCamelCase_: re.match(r"^[ a-zA-Z]+$" , t[1]) , UpperCamelCase_))
__UpperCAmelCase : Dict = list(filter(lambda UpperCamelCase_: [t[0]] == tokenizer.encode(t[1] , add_special_tokens=UpperCamelCase_) , UpperCamelCase_))
if max_length is not None and len(UpperCamelCase_) > max_length:
__UpperCAmelCase : Dict = toks[:max_length]
if min_length is not None and len(UpperCamelCase_) < min_length and len(UpperCamelCase_) > 0:
while len(UpperCamelCase_) < min_length:
__UpperCAmelCase : int = toks + toks
# toks_str = [t[1] for t in toks]
__UpperCAmelCase : Dict = [t[0] for t in toks]
# Ensure consistency
__UpperCAmelCase : List[Any] = tokenizer.decode(UpperCamelCase_ , clean_up_tokenization_spaces=UpperCamelCase_)
if " " not in output_txt and len(UpperCamelCase_) > 1:
__UpperCAmelCase : Optional[int] = (
tokenizer.decode([toks_ids[0]] , clean_up_tokenization_spaces=UpperCamelCase_)
+ " "
+ tokenizer.decode(toks_ids[1:] , clean_up_tokenization_spaces=UpperCamelCase_)
)
if with_prefix_space:
__UpperCAmelCase : int = " " + output_txt
__UpperCAmelCase : Optional[Any] = tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_)
return output_txt, output_ids
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : int = self.perceiver_tokenizer
__UpperCAmelCase : Any = "Unicode €."
__UpperCAmelCase : Union[str, Any] = tokenizer(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
self.assertEqual(encoded["input_ids"] , UpperCamelCase_)
# decoding
__UpperCAmelCase : List[Any] = tokenizer.decode(UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , "[CLS]Unicode €.[SEP]")
__UpperCAmelCase : Tuple = tokenizer("e è é ê ë")
__UpperCAmelCase : Dict = [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
self.assertEqual(encoded["input_ids"] , UpperCamelCase_)
# decoding
__UpperCAmelCase : Dict = tokenizer.decode(UpperCamelCase_)
self.assertEqual(UpperCamelCase_ , "[CLS]e è é ê ë[SEP]")
# encode/decode, but with `encode` instead of `__call__`
self.assertEqual(tokenizer.decode(tokenizer.encode("e è é ê ë")) , "[CLS]e è é ê ë[SEP]")
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = self.perceiver_tokenizer
__UpperCAmelCase : int = ["A long paragraph for summarization.", "Another paragraph for summarization."]
# fmt: off
__UpperCAmelCase : List[str] = [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
# fmt: on
__UpperCAmelCase : str = tokenizer(UpperCamelCase_ , padding=UpperCamelCase_ , return_tensors=UpperCamelCase_)
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_)
if FRAMEWORK != "jax":
__UpperCAmelCase : Dict = list(batch.input_ids.numpy()[0])
else:
__UpperCAmelCase : Tuple = list(batch.input_ids.tolist()[0])
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_)
self.assertEqual((2, 38) , batch.input_ids.shape)
self.assertEqual((2, 38) , batch.attention_mask.shape)
def a_ ( self : str):
"""simple docstring"""
__UpperCAmelCase : int = self.perceiver_tokenizer
__UpperCAmelCase : Union[str, Any] = ["A long paragraph for summarization.", "Another paragraph for summarization."]
__UpperCAmelCase : Union[str, Any] = tokenizer(UpperCamelCase_ , padding=UpperCamelCase_ , return_tensors=UpperCamelCase_)
# check if input_ids are returned and no decoder_input_ids
self.assertIn("input_ids" , UpperCamelCase_)
self.assertIn("attention_mask" , UpperCamelCase_)
self.assertNotIn("decoder_input_ids" , UpperCamelCase_)
self.assertNotIn("decoder_attention_mask" , UpperCamelCase_)
def a_ ( self : Tuple):
"""simple docstring"""
__UpperCAmelCase : Tuple = self.perceiver_tokenizer
__UpperCAmelCase : Dict = [
"Summary of the text.",
"Another summary.",
]
__UpperCAmelCase : Optional[int] = tokenizer(
text_target=UpperCamelCase_ , max_length=32 , padding="max_length" , truncation=UpperCamelCase_ , return_tensors=UpperCamelCase_)
self.assertEqual(32 , targets["input_ids"].shape[1])
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Union[str, Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}"):
self.assertNotEqual(tokenizer.model_max_length , 42)
# Now let's start the test
__UpperCAmelCase : Optional[Any] = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
__UpperCAmelCase : Tuple = tempfile.mkdtemp()
__UpperCAmelCase : List[str] = " He is very happy, UNwant\u00E9d,running"
__UpperCAmelCase : str = tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_)
tokenizer.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : List[str] = tokenizer.__class__.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : Optional[int] = after_tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_)
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_)
shutil.rmtree(UpperCamelCase_)
__UpperCAmelCase : List[str] = self.get_tokenizers(model_max_length=42)
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}"):
# Isolate this from the other tests because we save additional tokens/etc
__UpperCAmelCase : str = tempfile.mkdtemp()
__UpperCAmelCase : Optional[Any] = " He is very happy, UNwant\u00E9d,running"
tokenizer.add_tokens(["bim", "bambam"])
__UpperCAmelCase : str = tokenizer.additional_special_tokens
additional_special_tokens.append("new_additional_special_token")
tokenizer.add_special_tokens({"additional_special_tokens": additional_special_tokens})
__UpperCAmelCase : Tuple = tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_)
tokenizer.save_pretrained(UpperCamelCase_)
__UpperCAmelCase : Optional[Any] = tokenizer.__class__.from_pretrained(UpperCamelCase_)
__UpperCAmelCase : str = after_tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_)
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_)
self.assertIn("new_additional_special_token" , after_tokenizer.additional_special_tokens)
self.assertEqual(after_tokenizer.model_max_length , 42)
__UpperCAmelCase : Union[str, Any] = tokenizer.__class__.from_pretrained(UpperCamelCase_ , model_max_length=43)
self.assertEqual(tokenizer.model_max_length , 43)
shutil.rmtree(UpperCamelCase_)
def a_ ( self : Optional[Any]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = []
if self.test_slow_tokenizer:
tokenizer_list.append((self.tokenizer_class, self.get_tokenizer()))
if self.test_rust_tokenizer:
tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer()))
for tokenizer_class, tokenizer_utils in tokenizer_list:
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer_utils.save_pretrained(UpperCamelCase_)
with open(os.path.join(UpperCamelCase_ , "special_tokens_map.json") , encoding="utf-8") as json_file:
__UpperCAmelCase : int = json.load(UpperCamelCase_)
with open(os.path.join(UpperCamelCase_ , "tokenizer_config.json") , encoding="utf-8") as json_file:
__UpperCAmelCase : Tuple = json.load(UpperCamelCase_)
__UpperCAmelCase : str = [F"<extra_id_{i}>" for i in range(125)]
__UpperCAmelCase : List[Any] = added_tokens_extra_ids + [
"an_additional_special_token"
]
__UpperCAmelCase : Optional[int] = added_tokens_extra_ids + [
"an_additional_special_token"
]
with open(os.path.join(UpperCamelCase_ , "special_tokens_map.json") , "w" , encoding="utf-8") as outfile:
json.dump(UpperCamelCase_ , UpperCamelCase_)
with open(os.path.join(UpperCamelCase_ , "tokenizer_config.json") , "w" , encoding="utf-8") as outfile:
json.dump(UpperCamelCase_ , UpperCamelCase_)
# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
# "special_tokens_map.json" files
__UpperCAmelCase : Tuple = tokenizer_class.from_pretrained(
UpperCamelCase_ , )
self.assertIn(
"an_additional_special_token" , tokenizer_without_change_in_init.additional_special_tokens)
self.assertEqual(
["an_additional_special_token"] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids(["an_additional_special_token"])) , )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
__UpperCAmelCase : int = added_tokens_extra_ids + [AddedToken("a_new_additional_special_token" , lstrip=UpperCamelCase_)]
__UpperCAmelCase : Union[str, Any] = tokenizer_class.from_pretrained(
UpperCamelCase_ , additional_special_tokens=UpperCamelCase_ , )
self.assertIn("a_new_additional_special_token" , tokenizer.additional_special_tokens)
self.assertEqual(
["a_new_additional_special_token"] , tokenizer.convert_ids_to_tokens(
tokenizer.convert_tokens_to_ids(["a_new_additional_special_token"])) , )
def a_ ( self : Union[str, Any]):
"""simple docstring"""
__UpperCAmelCase : List[Any] = self.perceiver_tokenizer
self.assertEqual(tokenizer.decode([178]) , "�")
def a_ ( self : Dict):
"""simple docstring"""
pass
def a_ ( self : str):
"""simple docstring"""
pass
def a_ ( self : int):
"""simple docstring"""
pass
def a_ ( self : Tuple):
"""simple docstring"""
pass
def a_ ( self : Any):
"""simple docstring"""
__UpperCAmelCase : Dict = self.get_tokenizers(fast=UpperCamelCase_ , do_lower_case=UpperCamelCase_)
for tokenizer in tokenizers:
with self.subTest(F"{tokenizer.__class__.__name__}"):
__UpperCAmelCase : Any = ["[CLS]", "t", "h", "i", "s", " ", "i", "s", " ", "a", " ", "t", "e", "s", "t", "[SEP]"]
__UpperCAmelCase : Optional[Any] = tokenizer.convert_tokens_to_string(UpperCamelCase_)
self.assertIsInstance(UpperCamelCase_ , UpperCamelCase_)
| 487 |
"""simple docstring"""
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
__UpperCAmelCase : List[str] = tau * frequency / samplerate
__UpperCAmelCase : Optional[int] = sin(UpperCamelCase )
__UpperCAmelCase : Any = cos(UpperCamelCase )
__UpperCAmelCase : Tuple = _sin / (2 * q_factor)
__UpperCAmelCase : Optional[Any] = (1 - _cos) / 2
__UpperCAmelCase : Any = 1 - _cos
__UpperCAmelCase : List[str] = 1 + alpha
__UpperCAmelCase : List[Any] = -2 * _cos
__UpperCAmelCase : Dict = 1 - alpha
__UpperCAmelCase : str = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
__UpperCAmelCase : int = tau * frequency / samplerate
__UpperCAmelCase : Optional[Any] = sin(UpperCamelCase )
__UpperCAmelCase : Dict = cos(UpperCamelCase )
__UpperCAmelCase : Tuple = _sin / (2 * q_factor)
__UpperCAmelCase : Dict = (1 + _cos) / 2
__UpperCAmelCase : Tuple = -1 - _cos
__UpperCAmelCase : Any = 1 + alpha
__UpperCAmelCase : int = -2 * _cos
__UpperCAmelCase : int = 1 - alpha
__UpperCAmelCase : Union[str, Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
__UpperCAmelCase : Tuple = tau * frequency / samplerate
__UpperCAmelCase : Tuple = sin(UpperCamelCase )
__UpperCAmelCase : List[str] = cos(UpperCamelCase )
__UpperCAmelCase : List[Any] = _sin / (2 * q_factor)
__UpperCAmelCase : Any = _sin / 2
__UpperCAmelCase : Optional[int] = 0
__UpperCAmelCase : int = -ba
__UpperCAmelCase : Dict = 1 + alpha
__UpperCAmelCase : List[str] = -2 * _cos
__UpperCAmelCase : int = 1 - alpha
__UpperCAmelCase : int = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase = 1 / sqrt(2 ) ) -> IIRFilter:
"""simple docstring"""
__UpperCAmelCase : str = tau * frequency / samplerate
__UpperCAmelCase : List[Any] = sin(UpperCamelCase )
__UpperCAmelCase : Any = cos(UpperCamelCase )
__UpperCAmelCase : List[str] = _sin / (2 * q_factor)
__UpperCAmelCase : Optional[Any] = 1 - alpha
__UpperCAmelCase : Tuple = -2 * _cos
__UpperCAmelCase : List[Any] = 1 + alpha
__UpperCAmelCase : str = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
__UpperCAmelCase : Dict = tau * frequency / samplerate
__UpperCAmelCase : Optional[int] = sin(UpperCamelCase )
__UpperCAmelCase : Optional[Any] = cos(UpperCamelCase )
__UpperCAmelCase : List[Any] = _sin / (2 * q_factor)
__UpperCAmelCase : List[Any] = 10 ** (gain_db / 40)
__UpperCAmelCase : List[str] = 1 + alpha * big_a
__UpperCAmelCase : Optional[Any] = -2 * _cos
__UpperCAmelCase : int = 1 - alpha * big_a
__UpperCAmelCase : Optional[int] = 1 + alpha / big_a
__UpperCAmelCase : Union[str, Any] = -2 * _cos
__UpperCAmelCase : Dict = 1 - alpha / big_a
__UpperCAmelCase : Dict = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
__UpperCAmelCase : str = tau * frequency / samplerate
__UpperCAmelCase : int = sin(UpperCamelCase )
__UpperCAmelCase : Optional[Any] = cos(UpperCamelCase )
__UpperCAmelCase : str = _sin / (2 * q_factor)
__UpperCAmelCase : str = 10 ** (gain_db / 40)
__UpperCAmelCase : Union[str, Any] = (big_a + 1) - (big_a - 1) * _cos
__UpperCAmelCase : Optional[int] = (big_a + 1) + (big_a - 1) * _cos
__UpperCAmelCase : List[str] = (big_a - 1) - (big_a + 1) * _cos
__UpperCAmelCase : List[Any] = (big_a - 1) + (big_a + 1) * _cos
__UpperCAmelCase : Optional[Any] = 2 * sqrt(UpperCamelCase ) * alpha
__UpperCAmelCase : Tuple = big_a * (pmc + aaa)
__UpperCAmelCase : Union[str, Any] = 2 * big_a * mpc
__UpperCAmelCase : Optional[int] = big_a * (pmc - aaa)
__UpperCAmelCase : Any = ppmc + aaa
__UpperCAmelCase : Dict = -2 * pmpc
__UpperCAmelCase : Any = ppmc - aaa
__UpperCAmelCase : str = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = 1 / sqrt(2 ) , ) -> IIRFilter:
"""simple docstring"""
__UpperCAmelCase : Dict = tau * frequency / samplerate
__UpperCAmelCase : Optional[int] = sin(UpperCamelCase )
__UpperCAmelCase : Dict = cos(UpperCamelCase )
__UpperCAmelCase : str = _sin / (2 * q_factor)
__UpperCAmelCase : int = 10 ** (gain_db / 40)
__UpperCAmelCase : Optional[Any] = (big_a + 1) - (big_a - 1) * _cos
__UpperCAmelCase : Optional[int] = (big_a + 1) + (big_a - 1) * _cos
__UpperCAmelCase : Union[str, Any] = (big_a - 1) - (big_a + 1) * _cos
__UpperCAmelCase : Tuple = (big_a - 1) + (big_a + 1) * _cos
__UpperCAmelCase : Optional[Any] = 2 * sqrt(UpperCamelCase ) * alpha
__UpperCAmelCase : Tuple = big_a * (ppmc + aaa)
__UpperCAmelCase : Any = -2 * big_a * pmpc
__UpperCAmelCase : int = big_a * (ppmc - aaa)
__UpperCAmelCase : int = pmc + aaa
__UpperCAmelCase : Tuple = 2 * mpc
__UpperCAmelCase : Union[str, Any] = pmc - aaa
__UpperCAmelCase : Any = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 487 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_imagegpt import ImageGPTImageProcessor
__UpperCAmelCase = logging.get_logger(__name__)
class a__ ( a__ ):
'''simple docstring'''
def __init__( self , *lowerCamelCase_ , **lowerCamelCase_ ) -> None:
warnings.warn(
'''The class ImageGPTFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'''
''' Please use ImageGPTImageProcessor instead.''' , lowerCamelCase_ , )
super().__init__(*lowerCamelCase_ , **lowerCamelCase_ ) | 90 | import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
__a : Union[str, Any] = logging.get_logger(__name__)
def UpperCAmelCase ( lowercase ):
"""simple docstring"""
__lowercase = torch.load(lowercase , map_location='''cpu''' )
if "model" in sd.keys():
__lowercase = torch.load(lowercase , map_location='''cpu''' )['''model''']
# pop unnecessary weights
__lowercase = [
'''decoder.version''',
'''decoder.output_projection.weight''',
]
for key in keys_to_delete:
if key in sd:
sd.pop(lowercase )
__lowercase = {
'''decoder.project_in_dim.weight''': '''decoder.project_in.weight''',
'''decoder.project_out_dim.weight''': '''decoder.project_out.weight''',
'''decoder.layer_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.layer_norm.bias''': '''decoder.final_layer_norm.bias''',
}
for old_key, new_key in keys_to_rename.items():
if old_key in sd:
__lowercase = sd.pop(lowercase )
__lowercase = list(sd.keys() )
for key in keys:
if ".qkv_proj." in key:
__lowercase = sd[key]
# We split QKV in separate Q,K,V
__lowercase = key.replace('''.qkv_proj.''' , '''.q_proj.''' )
__lowercase = key.replace('''.qkv_proj.''' , '''.k_proj.''' )
__lowercase = key.replace('''.qkv_proj.''' , '''.v_proj.''' )
__lowercase = value.shape[0]
assert depth % 3 == 0
# `SequeuceParallelTransformerBlock` has QKV weight is separated in K,V,Q despite the naming:
# https://cs.github.com/facebookresearch/metaseq/blob/51871bd73cd04c038f239ea2a26db1d7f6b37927/metaseq/modules/sequence_parallel_transformer_layer.py#L97
__lowercase , __lowercase , __lowercase = torch.split(lowercase , depth // 3 , dim=0 )
__lowercase = q
__lowercase = k
__lowercase = v
del sd[key]
return sd
@torch.no_grad()
def UpperCAmelCase ( lowercase , lowercase , lowercase=None ):
"""simple docstring"""
__lowercase = load_checkpoint(lowercase )
if config is not None:
__lowercase = OPTConfig.from_pretrained(lowercase )
else:
__lowercase = OPTConfig()
__lowercase = OPTModel(lowercase ).half().eval()
model.load_state_dict(lowercase )
# Check results
Path(lowercase ).mkdir(exist_ok=lowercase )
model.save_pretrained(lowercase )
if __name__ == "__main__":
__a : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--fairseq_path""",
type=str,
help=(
"""path to fairseq checkpoint in correct format. You can find all checkpoints in the correct format here:"""
""" https://huggingface.co/models?other=opt_metasq"""
),
)
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--hf_config""", default=None, type=str, help="""Define HF config.""")
__a : Any = parser.parse_args()
convert_opt_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, config=args.hf_config) | 534 | 0 |
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
lowercase = "https://www.google.com/search?q=" + " ".join(sys.argv[1:])
lowercase = requests.get(url, headers={"UserAgent": UserAgent().random})
# res.raise_for_status()
with open("project1a.html", "wb") as out_file: # only for knowing the class
for data in res.iter_content(10000):
out_file.write(data)
lowercase = BeautifulSoup(res.text, "html.parser")
lowercase = list(soup.select(".eZt8xd"))[:5]
print(len(links))
for link in links:
if link.text == "Maps":
webbrowser.open(link.get("href"))
else:
webbrowser.open(f'https://google.com{link.get("href")}')
| 607 |
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self , a ) -> None:
snake_case_ = set_counts
snake_case_ = max(a )
snake_case_ = len(a )
snake_case_ = [1] * num_sets
snake_case_ = list(range(a ) )
def _UpperCamelCase ( self , a , a ) -> bool:
snake_case_ = self.get_parent(a )
snake_case_ = self.get_parent(a )
if src_parent == dst_parent:
return False
if self.ranks[dst_parent] >= self.ranks[src_parent]:
self.set_counts[dst_parent] += self.set_counts[src_parent]
snake_case_ = 0
snake_case_ = dst_parent
if self.ranks[dst_parent] == self.ranks[src_parent]:
self.ranks[dst_parent] += 1
snake_case_ = self.set_counts[dst_parent]
else:
self.set_counts[src_parent] += self.set_counts[dst_parent]
snake_case_ = 0
snake_case_ = src_parent
snake_case_ = self.set_counts[src_parent]
snake_case_ = max(self.max_set , a )
return True
def _UpperCamelCase ( self , a ) -> int:
if self.parents[disj_set] == disj_set:
return disj_set
snake_case_ = self.get_parent(self.parents[disj_set] )
return self.parents[disj_set]
| 607 | 1 |
"""simple docstring"""
def lowercase_ ( __UpperCAmelCase = 1000 ) -> int:
return sum(e for e in range(3 , __UpperCAmelCase ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f"""{solution() = }""")
| 299 |
"""simple docstring"""
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class _lowerCamelCase ( nn.Module ):
def __init__( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
super().__init__()
lowerCAmelCase__ : Optional[int] = nn.Linear(3 , 4 )
lowerCAmelCase__ : int = nn.BatchNormad(4 )
lowerCAmelCase__ : Optional[Any] = nn.Linear(4 , 5 )
def _lowerCAmelCase ( self : List[str] , UpperCamelCase : List[Any] ) -> Tuple:
"""simple docstring"""
return self.lineara(self.batchnorm(self.lineara(UpperCamelCase ) ) )
class _lowerCamelCase ( unittest.TestCase ):
def _lowerCAmelCase ( self : Any ) -> Optional[int]:
"""simple docstring"""
lowerCAmelCase__ : Any = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase , model.state_dict() )
lowerCAmelCase__ : List[str] = os.path.join(UpperCamelCase , """index.json""" )
self.assertTrue(os.path.isfile(UpperCamelCase ) )
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
lowerCAmelCase__ : Tuple = os.path.join(UpperCamelCase , f"""{key}.dat""" )
self.assertTrue(os.path.isfile(UpperCamelCase ) )
# TODO: add tests on the fact weights are properly loaded
def _lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
lowerCAmelCase__ : List[str] = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
lowerCAmelCase__ : Union[str, Any] = torch.randn(2 , 3 , dtype=UpperCamelCase )
with TemporaryDirectory() as tmp_dir:
lowerCAmelCase__ : Optional[Any] = offload_weight(UpperCamelCase , """weight""" , UpperCamelCase , {} )
lowerCAmelCase__ : Dict = os.path.join(UpperCamelCase , """weight.dat""" )
self.assertTrue(os.path.isfile(UpperCamelCase ) )
self.assertDictEqual(UpperCamelCase , {"""weight""": {"""shape""": [2, 3], """dtype""": str(UpperCamelCase ).split(""".""" )[1]}} )
lowerCAmelCase__ : Any = load_offloaded_weight(UpperCamelCase , index["""weight"""] )
self.assertTrue(torch.equal(UpperCamelCase , UpperCamelCase ) )
def _lowerCAmelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
lowerCAmelCase__ : Union[str, Any] = ModelForTest()
lowerCAmelCase__ : Optional[Any] = model.state_dict()
lowerCAmelCase__ : Tuple = {k: v for k, v in state_dict.items() if """linear2""" not in k}
lowerCAmelCase__ : Any = {k: v for k, v in state_dict.items() if """linear2""" in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : str = OffloadedWeightsLoader(state_dict=UpperCamelCase , save_folder=UpperCamelCase )
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase , weight_map[key] ) )
lowerCAmelCase__ : str = {k: v for k, v in state_dict.items() if """weight""" in k}
lowerCAmelCase__ : str = {k: v for k, v in state_dict.items() if """weight""" not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase , UpperCamelCase )
lowerCAmelCase__ : Any = OffloadedWeightsLoader(state_dict=UpperCamelCase , save_folder=UpperCamelCase )
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase , weight_map[key] ) )
with TemporaryDirectory() as tmp_dir:
offload_state_dict(UpperCamelCase , UpperCamelCase )
# Duplicates are removed
lowerCAmelCase__ : List[str] = OffloadedWeightsLoader(state_dict=UpperCamelCase , save_folder=UpperCamelCase )
# Every key is there with the right value
self.assertEqual(sorted(UpperCamelCase ) , sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(UpperCamelCase , weight_map[key] ) )
def _lowerCAmelCase ( self : Dict ) -> Any:
"""simple docstring"""
lowerCAmelCase__ : List[str] = {"""a.1""": 0, """a.10""": 1, """a.2""": 2}
lowerCAmelCase__ : Any = extract_submodules_state_dict(UpperCamelCase , ["""a.1""", """a.2"""] )
self.assertDictEqual(UpperCamelCase , {"""a.1""": 0, """a.2""": 2} )
lowerCAmelCase__ : str = {"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
lowerCAmelCase__ : Union[str, Any] = extract_submodules_state_dict(UpperCamelCase , ["""a.1""", """a.2"""] )
self.assertDictEqual(UpperCamelCase , {"""a.1.a""": 0, """a.2.a""": 2} )
| 299 | 1 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : List[str] = ["""image_processor""", """tokenizer"""]
_UpperCamelCase : List[Any] = """CLIPImageProcessor"""
_UpperCamelCase : Optional[int] = ("""CLIPTokenizer""", """CLIPTokenizerFast""")
def __init__( self , snake_case=None , snake_case=None , **snake_case ):
lowercase = None
if "feature_extractor" in kwargs:
warnings.warn(
'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
' instead.' , lowercase_ , )
lowercase = kwargs.pop('feature_extractor' )
lowercase = 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__(lowercase_ , lowercase_ )
def __call__( self , snake_case=None , snake_case=None , snake_case=None , **snake_case ):
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:
lowercase = self.tokenizer(lowercase_ , return_tensors=lowercase_ , **lowercase_ )
if images is not None:
lowercase = self.image_processor(lowercase_ , return_tensors=lowercase_ , **lowercase_ )
if text is not None and images is not None:
lowercase = image_features.pixel_values
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**lowercase_ ) , tensor_type=lowercase_ )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.batch_decode(*lowercase_ , **lowercase_ )
def SCREAMING_SNAKE_CASE__ ( self , *snake_case , **snake_case ):
return self.tokenizer.decode(*lowercase_ , **lowercase_ )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.tokenizer.model_input_names
lowercase = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def SCREAMING_SNAKE_CASE__ ( self ):
warnings.warn(
'`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.' , lowercase_ , )
return self.image_processor_class
@property
def SCREAMING_SNAKE_CASE__ ( self ):
warnings.warn(
'`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.' , lowercase_ , )
return self.image_processor
| 716 |
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : jnp.ndarray
_UpperCamelCase : jnp.ndarray
class A_ ( nn.Module ):
'''simple docstring'''
_UpperCamelCase : int
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
_UpperCamelCase : jnp.dtype = jnp.floataa
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowercase = []
for i in range(len(self.block_out_channels ) - 1 ):
lowercase = self.block_out_channels[i]
lowercase = self.block_out_channels[i + 1]
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = nn.Conv(
snake_case , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case )
lowercase = blocks
lowercase = nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case ):
lowercase = self.conv_in(snake_case )
lowercase = nn.silu(snake_case )
for block in self.blocks:
lowercase = block(snake_case )
lowercase = nn.silu(snake_case )
lowercase = self.conv_out(snake_case )
return embedding
@flax_register_to_config
class A_ ( nn.Module , __lowerCamelCase , __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : int = 32
_UpperCamelCase : int = 4
_UpperCamelCase : Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCamelCase : Union[bool, Tuple[bool]] = False
_UpperCamelCase : Tuple[int] = (320, 640, 1280, 1280)
_UpperCamelCase : int = 2
_UpperCamelCase : Union[int, Tuple[int]] = 8
_UpperCamelCase : Optional[Union[int, Tuple[int]]] = None
_UpperCamelCase : int = 1280
_UpperCamelCase : float = 0.0
_UpperCamelCase : bool = False
_UpperCamelCase : jnp.dtype = jnp.floataa
_UpperCamelCase : bool = True
_UpperCamelCase : int = 0
_UpperCamelCase : str = "rgb"
_UpperCamelCase : Tuple[int] = (16, 32, 96, 256)
def SCREAMING_SNAKE_CASE__ ( self , snake_case ):
# init input tensors
lowercase = (1, self.in_channels, self.sample_size, self.sample_size)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase = jnp.ones((1,) , dtype=jnp.intaa )
lowercase = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowercase = (1, 3, self.sample_size * 8, self.sample_size * 8)
lowercase = jnp.zeros(snake_case , dtype=jnp.floataa )
lowercase , lowercase = jax.random.split(snake_case )
lowercase = {'params': params_rng, 'dropout': dropout_rng}
return self.init(snake_case , snake_case , snake_case , snake_case , snake_case )["params"]
def SCREAMING_SNAKE_CASE__ ( self ):
lowercase = self.block_out_channels
lowercase = block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowercase = self.num_attention_heads or self.attention_head_dim
# input
lowercase = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowercase = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowercase = FlaxTimestepEmbedding(snake_case , dtype=self.dtype )
lowercase = FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowercase = self.only_cross_attention
if isinstance(snake_case , snake_case ):
lowercase = (only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case , snake_case ):
lowercase = (num_attention_heads,) * len(self.down_block_types )
# down
lowercase = []
lowercase = []
lowercase = block_out_channels[0]
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
for i, down_block_type in enumerate(self.down_block_types ):
lowercase = output_channel
lowercase = block_out_channels[i]
lowercase = i == len(snake_case ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowercase = FlaxCrossAttnDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowercase = FlaxDownBlockaD(
in_channels=snake_case , out_channels=snake_case , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case )
for _ in range(self.layers_per_block ):
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
if not is_final_block:
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case )
lowercase = down_blocks
lowercase = controlnet_down_blocks
# mid
lowercase = block_out_channels[-1]
lowercase = FlaxUNetMidBlockaDCrossAttn(
in_channels=snake_case , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowercase = nn.Conv(
snake_case , kernel_size=(1, 1) , padding='VALID' , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self , snake_case , snake_case , snake_case , snake_case , snake_case = 1.0 , snake_case = True , snake_case = False , ):
lowercase = self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowercase = jnp.flip(snake_case , axis=1 )
# 1. time
if not isinstance(snake_case , jnp.ndarray ):
lowercase = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowercase = timesteps.astype(dtype=jnp.floataa )
lowercase = jnp.expand_dims(snake_case , 0 )
lowercase = self.time_proj(snake_case )
lowercase = self.time_embedding(snake_case )
# 2. pre-process
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.conv_in(snake_case )
lowercase = jnp.transpose(snake_case , (0, 2, 3, 1) )
lowercase = self.controlnet_cond_embedding(snake_case )
sample += controlnet_cond
# 3. down
lowercase = (sample,)
for down_block in self.down_blocks:
if isinstance(snake_case , snake_case ):
lowercase , lowercase = down_block(snake_case , snake_case , snake_case , deterministic=not train )
else:
lowercase , lowercase = down_block(snake_case , snake_case , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowercase = self.mid_block(snake_case , snake_case , snake_case , deterministic=not train )
# 5. contronet blocks
lowercase = ()
for down_block_res_sample, controlnet_block in zip(snake_case , self.controlnet_down_blocks ):
lowercase = controlnet_block(snake_case )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowercase = controlnet_down_block_res_samples
lowercase = self.controlnet_mid_block(snake_case )
# 6. scaling
lowercase = [sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=snake_case , mid_block_res_sample=snake_case )
| 565 | 0 |
'''simple docstring'''
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class lowerCAmelCase__ ( UpperCAmelCase_ ):
'''simple docstring'''
def __init__( self : Optional[int] , *a__ : List[Any] , a__ : Tuple=None , a__ : List[str]=None , **a__ : Any ):
super().__init__(*a__ , **a__ )
UpperCAmelCase = eval_examples
UpperCAmelCase = post_process_function
def __snake_case ( self : Any , a__ : Any=None , a__ : Optional[Any]=None , a__ : Dict=None , a__ : str = "eval" ):
UpperCAmelCase = self.eval_dataset if eval_dataset is None else eval_dataset
UpperCAmelCase = self.get_eval_dataloader(a__ )
UpperCAmelCase = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
UpperCAmelCase = self.compute_metrics
UpperCAmelCase = None
UpperCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
UpperCAmelCase = time.time()
try:
UpperCAmelCase = eval_loop(
a__ , description='''Evaluation''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=a__ , metric_key_prefix=a__ , )
finally:
UpperCAmelCase = compute_metrics
UpperCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
a__ , a__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
# Only the main node write the results by default
UpperCAmelCase = self.post_process_function(a__ , a__ , output.predictions )
UpperCAmelCase = self.compute_metrics(a__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"{metric_key_prefix}_" ):
UpperCAmelCase = metrics.pop(a__ )
metrics.update(output.metrics )
else:
UpperCAmelCase = output.metrics
if self.args.should_log:
# Only the main node log the results by default
self.log(a__ )
if self.args.tpu_metrics_debug or self.args.debug:
# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
xm.master_print(met.metrics_report() )
UpperCAmelCase = self.callback_handler.on_evaluate(self.args , self.state , self.control , a__ )
return metrics
def __snake_case ( self : str , a__ : Optional[int] , a__ : List[Any] , a__ : List[str]=None , a__ : str = "test" ):
UpperCAmelCase = self.get_test_dataloader(a__ )
# Temporarily disable metric computation, we will do it in the loop here.
UpperCAmelCase = self.compute_metrics
UpperCAmelCase = None
UpperCAmelCase = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
UpperCAmelCase = time.time()
try:
UpperCAmelCase = eval_loop(
a__ , description='''Prediction''' , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=a__ , metric_key_prefix=a__ , )
finally:
UpperCAmelCase = compute_metrics
UpperCAmelCase = self.args.eval_batch_size * self.args.world_size
if f"{metric_key_prefix}_jit_compilation_time" in output.metrics:
start_time += output.metrics[f"{metric_key_prefix}_jit_compilation_time"]
output.metrics.update(
speed_metrics(
a__ , a__ , num_samples=output.num_samples , num_steps=math.ceil(output.num_samples / total_batch_size ) , ) )
if self.post_process_function is None or self.compute_metrics is None:
return output
UpperCAmelCase = self.post_process_function(a__ , a__ , output.predictions , '''predict''' )
UpperCAmelCase = self.compute_metrics(a__ )
# Prefix all keys with metric_key_prefix + '_'
for key in list(metrics.keys() ):
if not key.startswith(f"{metric_key_prefix}_" ):
UpperCAmelCase = metrics.pop(a__ )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=a__ )
| 51 |
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig
a =logging.get_logger(__name__)
# General docstring
a ="""MobileNetV1Config"""
# Base docstring
a ="""google/mobilenet_v1_1.0_224"""
a =[1, 1024, 7, 7]
# Image classification docstring
a ="""google/mobilenet_v1_1.0_224"""
a ="""tabby, tabby cat"""
a =[
"""google/mobilenet_v1_1.0_224""",
"""google/mobilenet_v1_0.75_192""",
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=None ) -> str:
__lowerCamelCase : str = {}
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase : int = model.mobilenet_va
else:
__lowerCamelCase : List[str] = model
__lowerCamelCase : List[Any] = 'MobilenetV1/Conv2d_0/'
__lowerCamelCase : List[Any] = backbone.conv_stem.convolution.weight
__lowerCamelCase : List[str] = backbone.conv_stem.normalization.bias
__lowerCamelCase : Tuple = backbone.conv_stem.normalization.weight
__lowerCamelCase : Union[str, Any] = backbone.conv_stem.normalization.running_mean
__lowerCamelCase : Optional[int] = backbone.conv_stem.normalization.running_var
for i in range(1_3 ):
__lowerCamelCase : Any = i + 1
__lowerCamelCase : Union[str, Any] = i * 2
__lowerCamelCase : Optional[Any] = backbone.layer[pt_index]
__lowerCamelCase : Optional[int] = F"MobilenetV1/Conv2d_{tf_index}_depthwise/"
__lowerCamelCase : Tuple = pointer.convolution.weight
__lowerCamelCase : Optional[Any] = pointer.normalization.bias
__lowerCamelCase : Union[str, Any] = pointer.normalization.weight
__lowerCamelCase : List[str] = pointer.normalization.running_mean
__lowerCamelCase : Union[str, Any] = pointer.normalization.running_var
__lowerCamelCase : int = backbone.layer[pt_index + 1]
__lowerCamelCase : Union[str, Any] = F"MobilenetV1/Conv2d_{tf_index}_pointwise/"
__lowerCamelCase : Optional[Any] = pointer.convolution.weight
__lowerCamelCase : Any = pointer.normalization.bias
__lowerCamelCase : str = pointer.normalization.weight
__lowerCamelCase : Dict = pointer.normalization.running_mean
__lowerCamelCase : List[str] = pointer.normalization.running_var
if isinstance(lowerCamelCase__ , lowerCamelCase__ ):
__lowerCamelCase : Union[str, Any] = 'MobilenetV1/Logits/Conv2d_1c_1x1/'
__lowerCamelCase : Any = model.classifier.weight
__lowerCamelCase : int = model.classifier.bias
return tf_to_pt_map
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[Any]:
try:
import numpy as np
import tensorflow as tf
except ImportError:
logger.error(
'Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see '
'https://www.tensorflow.org/install/ for installation instructions.' )
raise
# Load weights from TF model
__lowerCamelCase : List[str] = tf.train.list_variables(lowerCamelCase__ )
__lowerCamelCase : List[str] = {}
for name, shape in init_vars:
logger.info(F"Loading TF weight {name} with shape {shape}" )
__lowerCamelCase : Any = tf.train.load_variable(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : List[Any] = array
# Build TF to PyTorch weights loading map
__lowerCamelCase : Tuple = _build_tf_to_pytorch_map(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ )
for name, pointer in tf_to_pt_map.items():
logger.info(F"Importing {name}" )
if name not in tf_weights:
logger.info(F"{name} not in tf pre-trained weights, skipping" )
continue
__lowerCamelCase : Optional[int] = tf_weights[name]
if "depthwise_weights" in name:
logger.info('Transposing depthwise' )
__lowerCamelCase : List[str] = np.transpose(lowerCamelCase__ , (2, 3, 0, 1) )
elif "weights" in name:
logger.info('Transposing' )
if len(pointer.shape ) == 2: # copying into linear layer
__lowerCamelCase : Any = array.squeeze().transpose()
else:
__lowerCamelCase : Tuple = np.transpose(lowerCamelCase__ , (3, 2, 0, 1) )
if pointer.shape != array.shape:
raise ValueError(F"Pointer shape {pointer.shape} and array shape {array.shape} mismatched" )
logger.info(F"Initialize PyTorch weight {name} {array.shape}" )
__lowerCamelCase : Optional[Any] = torch.from_numpy(lowerCamelCase__ )
tf_weights.pop(lowerCamelCase__ , lowerCamelCase__ )
tf_weights.pop(name + '/RMSProp' , lowerCamelCase__ )
tf_weights.pop(name + '/RMSProp_1' , lowerCamelCase__ )
tf_weights.pop(name + '/ExponentialMovingAverage' , lowerCamelCase__ )
logger.info(F"Weights not copied to PyTorch model: {', '.join(tf_weights.keys() )}" )
return model
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> torch.Tensor:
__lowerCamelCase , __lowerCamelCase : int = features.shape[-2:]
__lowerCamelCase , __lowerCamelCase : List[str] = conv_layer.stride
__lowerCamelCase , __lowerCamelCase : str = conv_layer.kernel_size
if in_height % stride_height == 0:
__lowerCamelCase : Optional[int] = max(kernel_height - stride_height , 0 )
else:
__lowerCamelCase : Union[str, Any] = max(kernel_height - (in_height % stride_height) , 0 )
if in_width % stride_width == 0:
__lowerCamelCase : List[str] = max(kernel_width - stride_width , 0 )
else:
__lowerCamelCase : List[str] = max(kernel_width - (in_width % stride_width) , 0 )
__lowerCamelCase : List[str] = pad_along_width // 2
__lowerCamelCase : Optional[int] = pad_along_width - pad_left
__lowerCamelCase : Any = pad_along_height // 2
__lowerCamelCase : List[Any] = pad_along_height - pad_top
__lowerCamelCase : Union[str, Any] = (pad_left, pad_right, pad_top, pad_bottom)
return nn.functional.pad(lowerCamelCase__ , lowerCamelCase__ , 'constant' , 0.0 )
class A_ ( nn.Module ):
def __init__( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : MobileNetVaConfig ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : int ,SCREAMING_SNAKE_CASE__ : Optional[int] = 1 ,SCREAMING_SNAKE_CASE__ : Optional[int] = 1 ,SCREAMING_SNAKE_CASE__ : bool = False ,SCREAMING_SNAKE_CASE__ : Optional[bool] = True ,SCREAMING_SNAKE_CASE__ : Optional[bool or str] = True ,):
super().__init__()
__lowerCamelCase : Dict = config
if in_channels % groups != 0:
raise ValueError(F"Input channels ({in_channels}) are not divisible by {groups} groups.")
if out_channels % groups != 0:
raise ValueError(F"Output channels ({out_channels}) are not divisible by {groups} groups.")
__lowerCamelCase : Optional[Any] = 0 if config.tf_padding else int((kernel_size - 1) / 2)
__lowerCamelCase : Optional[int] = nn.Convad(
in_channels=SCREAMING_SNAKE_CASE__ ,out_channels=SCREAMING_SNAKE_CASE__ ,kernel_size=SCREAMING_SNAKE_CASE__ ,stride=SCREAMING_SNAKE_CASE__ ,padding=SCREAMING_SNAKE_CASE__ ,groups=SCREAMING_SNAKE_CASE__ ,bias=SCREAMING_SNAKE_CASE__ ,padding_mode='zeros' ,)
if use_normalization:
__lowerCamelCase : Optional[int] = nn.BatchNormad(
num_features=SCREAMING_SNAKE_CASE__ ,eps=config.layer_norm_eps ,momentum=0.9997 ,affine=SCREAMING_SNAKE_CASE__ ,track_running_stats=SCREAMING_SNAKE_CASE__ ,)
else:
__lowerCamelCase : Dict = None
if use_activation:
if isinstance(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__):
__lowerCamelCase : Dict = ACTaFN[use_activation]
elif isinstance(config.hidden_act ,SCREAMING_SNAKE_CASE__):
__lowerCamelCase : str = ACTaFN[config.hidden_act]
else:
__lowerCamelCase : List[str] = config.hidden_act
else:
__lowerCamelCase : List[str] = None
def lowerCAmelCase ( self : List[str] ,SCREAMING_SNAKE_CASE__ : torch.Tensor):
if self.config.tf_padding:
__lowerCamelCase : Any = apply_tf_padding(SCREAMING_SNAKE_CASE__ ,self.convolution)
__lowerCamelCase : Optional[int] = self.convolution(SCREAMING_SNAKE_CASE__)
if self.normalization is not None:
__lowerCamelCase : Dict = self.normalization(SCREAMING_SNAKE_CASE__)
if self.activation is not None:
__lowerCamelCase : List[str] = self.activation(SCREAMING_SNAKE_CASE__)
return features
class A_ ( SCREAMING_SNAKE_CASE ):
_UpperCAmelCase : Union[str, Any] = MobileNetVaConfig
_UpperCAmelCase : List[str] = load_tf_weights_in_mobilenet_va
_UpperCAmelCase : List[str] = '''mobilenet_v1'''
_UpperCAmelCase : Any = '''pixel_values'''
_UpperCAmelCase : int = False
def lowerCAmelCase ( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : Union[nn.Linear, nn.Convad]):
if isinstance(SCREAMING_SNAKE_CASE__ ,(nn.Linear, nn.Convad)):
module.weight.data.normal_(mean=0.0 ,std=self.config.initializer_range)
if module.bias is not None:
module.bias.data.zero_()
elif isinstance(SCREAMING_SNAKE_CASE__ ,nn.BatchNormad):
module.bias.data.zero_()
module.weight.data.fill_(1.0)
a =r"""
This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.
Parameters:
config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""
a =r"""
Args:
pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
[`MobileNetV1ImageProcessor.__call__`] for details.
output_hidden_states (`bool`, *optional*):
Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for
more detail.
return_dict (`bool`, *optional*):
Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.
"""
@add_start_docstrings(
'''The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.''' , SCREAMING_SNAKE_CASE , )
class A_ ( SCREAMING_SNAKE_CASE ):
def __init__( self : int ,SCREAMING_SNAKE_CASE__ : MobileNetVaConfig ,SCREAMING_SNAKE_CASE__ : bool = True):
super().__init__(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[str] = config
__lowerCamelCase : Optional[int] = 3_2
__lowerCamelCase : List[str] = max(int(depth * config.depth_multiplier) ,config.min_depth)
__lowerCamelCase : Optional[Any] = MobileNetVaConvLayer(
SCREAMING_SNAKE_CASE__ ,in_channels=config.num_channels ,out_channels=SCREAMING_SNAKE_CASE__ ,kernel_size=3 ,stride=2 ,)
__lowerCamelCase : Any = [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]
__lowerCamelCase : str = nn.ModuleList()
for i in range(1_3):
__lowerCamelCase : str = out_channels
if strides[i] == 2 or i == 0:
depth *= 2
__lowerCamelCase : str = max(int(depth * config.depth_multiplier) ,config.min_depth)
self.layer.append(
MobileNetVaConvLayer(
SCREAMING_SNAKE_CASE__ ,in_channels=SCREAMING_SNAKE_CASE__ ,out_channels=SCREAMING_SNAKE_CASE__ ,kernel_size=3 ,stride=strides[i] ,groups=SCREAMING_SNAKE_CASE__ ,))
self.layer.append(
MobileNetVaConvLayer(
SCREAMING_SNAKE_CASE__ ,in_channels=SCREAMING_SNAKE_CASE__ ,out_channels=SCREAMING_SNAKE_CASE__ ,kernel_size=1 ,))
__lowerCamelCase : Optional[int] = nn.AdaptiveAvgPoolad((1, 1)) if add_pooling_layer else None
# Initialize weights and apply final processing
self.post_init()
def lowerCAmelCase ( self : List[Any] ,SCREAMING_SNAKE_CASE__ : Dict):
raise NotImplementedError
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__)
@add_code_sample_docstrings(
checkpoint=_CHECKPOINT_FOR_DOC ,output_type=SCREAMING_SNAKE_CASE__ ,config_class=_CONFIG_FOR_DOC ,modality='vision' ,expected_output=_EXPECTED_OUTPUT_SHAPE ,)
def lowerCAmelCase ( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : Optional[torch.Tensor] = None ,SCREAMING_SNAKE_CASE__ : Optional[bool] = None ,SCREAMING_SNAKE_CASE__ : Optional[bool] = None ,):
__lowerCamelCase : int = (
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
)
__lowerCamelCase : Optional[Any] = return_dict if return_dict is not None else self.config.use_return_dict
if pixel_values is None:
raise ValueError('You have to specify pixel_values')
__lowerCamelCase : Optional[Any] = self.conv_stem(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[Any] = () if output_hidden_states else None
for i, layer_module in enumerate(self.layer):
__lowerCamelCase : Dict = layer_module(SCREAMING_SNAKE_CASE__)
if output_hidden_states:
__lowerCamelCase : Any = all_hidden_states + (hidden_states,)
__lowerCamelCase : Optional[Any] = hidden_states
if self.pooler is not None:
__lowerCamelCase : Tuple = torch.flatten(self.pooler(SCREAMING_SNAKE_CASE__) ,start_dim=1)
else:
__lowerCamelCase : List[str] = None
if not return_dict:
return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None)
return BaseModelOutputWithPoolingAndNoAttention(
last_hidden_state=SCREAMING_SNAKE_CASE__ ,pooler_output=SCREAMING_SNAKE_CASE__ ,hidden_states=SCREAMING_SNAKE_CASE__ ,)
@add_start_docstrings(
'''
MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
ImageNet.
''' , SCREAMING_SNAKE_CASE , )
class A_ ( SCREAMING_SNAKE_CASE ):
def __init__( self : Any ,SCREAMING_SNAKE_CASE__ : MobileNetVaConfig):
super().__init__(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[Any] = config.num_labels
__lowerCamelCase : Optional[Any] = MobileNetVaModel(SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[str] = self.mobilenet_va.layer[-1].convolution.out_channels
# Classifier head
__lowerCamelCase : Any = nn.Dropout(config.classifier_dropout_prob ,inplace=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : Optional[Any] = nn.Linear(SCREAMING_SNAKE_CASE__ ,config.num_labels) if config.num_labels > 0 else nn.Identity()
# Initialize weights and apply final processing
self.post_init()
@add_start_docstrings_to_model_forward(SCREAMING_SNAKE_CASE__)
@add_code_sample_docstrings(
checkpoint=_IMAGE_CLASS_CHECKPOINT ,output_type=SCREAMING_SNAKE_CASE__ ,config_class=_CONFIG_FOR_DOC ,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,)
def lowerCAmelCase ( self : str ,SCREAMING_SNAKE_CASE__ : Optional[torch.Tensor] = None ,SCREAMING_SNAKE_CASE__ : Optional[bool] = None ,SCREAMING_SNAKE_CASE__ : Optional[torch.Tensor] = None ,SCREAMING_SNAKE_CASE__ : Optional[bool] = None ,):
__lowerCamelCase : Any = return_dict if return_dict is not None else self.config.use_return_dict
__lowerCamelCase : Optional[int] = self.mobilenet_va(SCREAMING_SNAKE_CASE__ ,output_hidden_states=SCREAMING_SNAKE_CASE__ ,return_dict=SCREAMING_SNAKE_CASE__)
__lowerCamelCase : List[Any] = outputs.pooler_output if return_dict else outputs[1]
__lowerCamelCase : List[str] = self.classifier(self.dropout(SCREAMING_SNAKE_CASE__))
__lowerCamelCase : List[str] = None
if labels is not None:
if self.config.problem_type is None:
if self.num_labels == 1:
__lowerCamelCase : Dict = 'regression'
elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
__lowerCamelCase : int = 'single_label_classification'
else:
__lowerCamelCase : Tuple = 'multi_label_classification'
if self.config.problem_type == "regression":
__lowerCamelCase : Tuple = MSELoss()
if self.num_labels == 1:
__lowerCamelCase : int = loss_fct(logits.squeeze() ,labels.squeeze())
else:
__lowerCamelCase : Union[str, Any] = loss_fct(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
elif self.config.problem_type == "single_label_classification":
__lowerCamelCase : List[str] = CrossEntropyLoss()
__lowerCamelCase : List[str] = loss_fct(logits.view(-1 ,self.num_labels) ,labels.view(-1))
elif self.config.problem_type == "multi_label_classification":
__lowerCamelCase : int = BCEWithLogitsLoss()
__lowerCamelCase : int = loss_fct(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
if not return_dict:
__lowerCamelCase : List[str] = (logits,) + outputs[2:]
return ((loss,) + output) if loss is not None else output
return ImageClassifierOutputWithNoAttention(
loss=SCREAMING_SNAKE_CASE__ ,logits=SCREAMING_SNAKE_CASE__ ,hidden_states=outputs.hidden_states ,)
| 652 | 0 |
'''simple docstring'''
from collections import defaultdict
from math import ceil, sqrt
def UpperCAmelCase ( UpperCAmelCase__ : int = 1_00_00_00 , UpperCAmelCase__ : int = 10):
lowerCamelCase : defaultdict = defaultdict(UpperCAmelCase__)
for outer_width in range(3 , (t_limit // 4) + 2):
if outer_width * outer_width > t_limit:
lowerCamelCase : List[str] = max(
ceil(sqrt(outer_width * outer_width - t_limit)) , 1)
else:
lowerCamelCase : int = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(UpperCAmelCase__ , outer_width - 1 , 2):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10)
if __name__ == "__main__":
print(f"""{solution() = }""")
| 449 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class __snake_case ( a__):
_lowerCAmelCase = (DPMSolverSinglestepScheduler,)
_lowerCAmelCase = (('''num_inference_steps''', 25),)
def UpperCAmelCase_ ( self, **A ):
"""simple docstring"""
lowerCamelCase : List[Any] = {
'num_train_timesteps': 1000,
'beta_start': 0.0001,
'beta_end': 0.02,
'beta_schedule': 'linear',
'solver_order': 2,
'prediction_type': 'epsilon',
'thresholding': False,
'sample_max_value': 1.0,
'algorithm_type': 'dpmsolver++',
'solver_type': 'midpoint',
'lambda_min_clipped': -float('inf' ),
'variance_type': None,
}
config.update(**A )
return config
def UpperCAmelCase_ ( self, A=0, **A ):
"""simple docstring"""
lowerCamelCase : List[str] = dict(self.forward_default_kwargs )
lowerCamelCase : Optional[Any] = kwargs.pop('num_inference_steps', A )
lowerCamelCase : Union[str, Any] = self.dummy_sample
lowerCamelCase : Dict = 0.1 * sample
lowerCamelCase : Dict = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowerCamelCase : Optional[Any] = self.get_scheduler_config(**A )
lowerCamelCase : Dict = scheduler_class(**A )
scheduler.set_timesteps(A )
# copy over dummy past residuals
lowerCamelCase : str = dummy_past_residuals[: scheduler.config.solver_order]
with tempfile.TemporaryDirectory() as tmpdirname:
scheduler.save_config(A )
lowerCamelCase : List[Any] = scheduler_class.from_pretrained(A )
new_scheduler.set_timesteps(A )
# copy over dummy past residuals
lowerCamelCase : Optional[int] = dummy_past_residuals[: new_scheduler.config.solver_order]
lowerCamelCase , lowerCamelCase : Optional[int] = 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 : Optional[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 UpperCAmelCase_ ( self ):
"""simple docstring"""
pass
def UpperCAmelCase_ ( self, A=0, **A ):
"""simple docstring"""
lowerCamelCase : List[str] = dict(self.forward_default_kwargs )
lowerCamelCase : str = kwargs.pop('num_inference_steps', A )
lowerCamelCase : Union[str, Any] = self.dummy_sample
lowerCamelCase : List[str] = 0.1 * sample
lowerCamelCase : List[str] = [residual + 0.2, residual + 0.15, residual + 0.10]
for scheduler_class in self.scheduler_classes:
lowerCamelCase : Tuple = self.get_scheduler_config()
lowerCamelCase : Optional[Any] = scheduler_class(**A )
scheduler.set_timesteps(A )
# copy over dummy past residuals (must be after setting timesteps)
lowerCamelCase : Any = 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 : List[Any] = dummy_past_residuals[: new_scheduler.config.solver_order]
lowerCamelCase : int = scheduler.step(A, A, A, **A ).prev_sample
lowerCamelCase : Dict = 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 UpperCAmelCase_ ( self, A=None, **A ):
"""simple docstring"""
if scheduler is None:
lowerCamelCase : Any = self.scheduler_classes[0]
lowerCamelCase : Optional[Any] = self.get_scheduler_config(**A )
lowerCamelCase : Optional[int] = scheduler_class(**A )
lowerCamelCase : List[Any] = self.scheduler_classes[0]
lowerCamelCase : Optional[Any] = self.get_scheduler_config(**A )
lowerCamelCase : Optional[int] = scheduler_class(**A )
lowerCamelCase : Any = 10
lowerCamelCase : Dict = self.dummy_model()
lowerCamelCase : Any = self.dummy_sample_deter
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
return sample
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Dict = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
lowerCamelCase : Dict = 50
lowerCamelCase : Tuple = self.dummy_model()
lowerCamelCase : Optional[int] = self.dummy_sample_deter
scheduler.set_timesteps(A )
# make sure that the first t is uneven
for i, t in enumerate(scheduler.timesteps[3:] ):
lowerCamelCase : Any = model(A, A )
lowerCamelCase : Optional[int] = scheduler.step(A, A, A ).prev_sample
lowerCamelCase : Any = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2574 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for timesteps in [25, 50, 100, 999, 1000]:
self.check_over_configs(num_train_timesteps=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Dict = DPMSolverSinglestepScheduler(**self.get_scheduler_config() )
lowerCamelCase : str = self.full_loop(scheduler=A )
lowerCamelCase : Optional[int] = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2791 ) < 1e-3
lowerCamelCase : Dict = DEISMultistepScheduler.from_config(scheduler.config )
lowerCamelCase : Optional[int] = DPMSolverMultistepScheduler.from_config(scheduler.config )
lowerCamelCase : Any = UniPCMultistepScheduler.from_config(scheduler.config )
lowerCamelCase : Optional[Any] = DPMSolverSinglestepScheduler.from_config(scheduler.config )
lowerCamelCase : str = self.full_loop(scheduler=A )
lowerCamelCase : Optional[int] = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2791 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
self.check_over_configs(thresholding=A )
for order in [1, 2, 3]:
for solver_type in ["midpoint", "heun"]:
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, algorithm_type='dpmsolver++', solver_order=A, solver_type=A, )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
for algorithm_type in ["dpmsolver", "dpmsolver++"]:
for solver_type in ["midpoint", "heun"]:
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, algorithm_type=A, )
lowerCamelCase : Optional[Any] = self.full_loop(
solver_order=A, solver_type=A, prediction_type=A, algorithm_type=A, )
assert not torch.isnan(A ).any(), "Samples have nan numbers"
def UpperCAmelCase_ ( self ):
"""simple docstring"""
self.check_over_configs(lower_order_final=A )
self.check_over_configs(lower_order_final=A )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
self.check_over_configs(lambda_min_clipped=-float('inf' ) )
self.check_over_configs(lambda_min_clipped=-5.1 )
def UpperCAmelCase_ ( self ):
"""simple docstring"""
self.check_over_configs(variance_type=A )
self.check_over_configs(variance_type='learned_range' )
def UpperCAmelCase_ ( 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 UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = self.full_loop()
lowerCamelCase : str = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2791 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Union[str, Any] = self.full_loop(use_karras_sigmas=A )
lowerCamelCase : Tuple = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.2248 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : List[Any] = self.full_loop(prediction_type='v_prediction' )
lowerCamelCase : Dict = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.1453 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : List[Any] = self.full_loop(prediction_type='v_prediction', use_karras_sigmas=A )
lowerCamelCase : Optional[Any] = torch.mean(torch.abs(A ) )
assert abs(result_mean.item() - 0.0649 ) < 1e-3
def UpperCAmelCase_ ( self ):
"""simple docstring"""
lowerCamelCase : Optional[Any] = self.scheduler_classes[0]
lowerCamelCase : Dict = self.get_scheduler_config(thresholding=A, dynamic_thresholding_ratio=0 )
lowerCamelCase : str = scheduler_class(**A )
lowerCamelCase : List[Any] = 10
lowerCamelCase : List[str] = self.dummy_model()
lowerCamelCase : int = self.dummy_sample_deter.half()
scheduler.set_timesteps(A )
for i, t in enumerate(scheduler.timesteps ):
lowerCamelCase : str = model(A, A )
lowerCamelCase : Tuple = scheduler.step(A, A, A ).prev_sample
assert sample.dtype == torch.floataa
| 449 | 1 |
'''simple docstring'''
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
A_ : str ={'''UserAgent''': UserAgent().random}
def snake_case_ ( __snake_case : List[Any]) -> dict:
lowerCAmelCase_ = script.contents[0]
lowerCAmelCase_ = json.loads(data[data.find('''{"config"''') : -1])
return info["entry_data"]["ProfilePage"][0]["graphql"]["user"]
class __UpperCAmelCase :
def __init__( self , _lowerCamelCase ):
lowerCAmelCase_ = F'''https://www.instagram.com/{username}/'''
lowerCAmelCase_ = self.get_json()
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = requests.get(self.url , headers=_lowerCamelCase ).text
lowerCAmelCase_ = BeautifulSoup(_lowerCamelCase , '''html.parser''' ).find_all('''script''' )
try:
return extract_user_profile(scripts[4] )
except (json.decoder.JSONDecodeError, KeyError):
return extract_user_profile(scripts[3] )
def __repr__( self ):
return F'''{self.__class__.__name__}(\'{self.username}\')'''
def __str__( self ):
return F'''{self.fullname} ({self.username}) is {self.biography}'''
@property
def UpperCAmelCase_ ( self ):
return self.user_data["username"]
@property
def UpperCAmelCase_ ( self ):
return self.user_data["full_name"]
@property
def UpperCAmelCase_ ( self ):
return self.user_data["biography"]
@property
def UpperCAmelCase_ ( self ):
return self.user_data["business_email"]
@property
def UpperCAmelCase_ ( self ):
return self.user_data["external_url"]
@property
def UpperCAmelCase_ ( self ):
return self.user_data["edge_followed_by"]["count"]
@property
def UpperCAmelCase_ ( self ):
return self.user_data["edge_follow"]["count"]
@property
def UpperCAmelCase_ ( self ):
return self.user_data["edge_owner_to_timeline_media"]["count"]
@property
def UpperCAmelCase_ ( self ):
return self.user_data["profile_pic_url_hd"]
@property
def UpperCAmelCase_ ( self ):
return self.user_data["is_verified"]
@property
def UpperCAmelCase_ ( self ):
return self.user_data["is_private"]
def snake_case_ ( __snake_case : str = "github") -> None:
import os
if os.environ.get('''CI'''):
return # test failing on GitHub Actions
lowerCAmelCase_ = InstagramUser(__snake_case)
assert instagram_user.user_data
assert isinstance(instagram_user.user_data , __snake_case)
assert instagram_user.username == username
if username != "github":
return
assert instagram_user.fullname == "GitHub"
assert instagram_user.biography == "Built for developers."
assert instagram_user.number_of_posts > 150
assert instagram_user.number_of_followers > 120000
assert instagram_user.number_of_followings > 15
assert instagram_user.email == "support@github.com"
assert instagram_user.website == "https://github.com/readme"
assert instagram_user.profile_picture_url.startswith('''https://instagram.''')
assert instagram_user.is_verified is True
assert instagram_user.is_private is False
if __name__ == "__main__":
import doctest
doctest.testmod()
A_ : Optional[Any] =InstagramUser('''github''')
print(instagram_user)
print(f'''{instagram_user.number_of_posts = }''')
print(f'''{instagram_user.number_of_followers = }''')
print(f'''{instagram_user.number_of_followings = }''')
print(f'''{instagram_user.email = }''')
print(f'''{instagram_user.website = }''')
print(f'''{instagram_user.profile_picture_url = }''')
print(f'''{instagram_user.is_verified = }''')
print(f'''{instagram_user.is_private = }''')
| 274 | '''simple docstring'''
import gc
import threading
import time
import psutil
import torch
class __UpperCAmelCase :
def __init__( self ):
lowerCAmelCase_ = psutil.Process()
lowerCAmelCase_ = False
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = -1
while True:
lowerCAmelCase_ = max(self.process.memory_info().rss , self.cpu_memory_peak )
# can't sleep or will not catch the peak right (this comment is here on purpose)
if not self.peak_monitoring:
break
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = True
lowerCAmelCase_ = threading.Thread(target=self.peak_monitor )
lowerCAmelCase_ = True
self.thread.start()
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = False
self.thread.join()
return self.cpu_memory_peak
A_ : List[str] =PeakCPUMemory()
def snake_case_ ( ) -> Tuple:
# Time
lowerCAmelCase_ = {'''time''': time.time()}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
lowerCAmelCase_ = psutil.Process().memory_info().rss
cpu_peak_tracker.start()
# GPU mem
for i in range(torch.cuda.device_count()):
lowerCAmelCase_ = torch.cuda.memory_allocated(__snake_case)
torch.cuda.reset_peak_memory_stats()
return measures
def snake_case_ ( __snake_case : Any) -> List[str]:
# Time
lowerCAmelCase_ = {'''time''': time.time() - start_measures['''time''']}
gc.collect()
torch.cuda.empty_cache()
# CPU mem
lowerCAmelCase_ = (psutil.Process().memory_info().rss - start_measures['''cpu''']) / 2**20
lowerCAmelCase_ = (cpu_peak_tracker.stop() - start_measures['''cpu''']) / 2**20
# GPU mem
for i in range(torch.cuda.device_count()):
lowerCAmelCase_ = (torch.cuda.memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20
lowerCAmelCase_ = (torch.cuda.max_memory_allocated(__snake_case) - start_measures[str(__snake_case)]) / 2**20
return measures
def snake_case_ ( __snake_case : Dict , __snake_case : Optional[int]) -> Dict:
print(F'''{description}:''')
print(F'''- Time: {measures['time']:.2f}s''')
for i in range(torch.cuda.device_count()):
print(F'''- GPU {i} allocated: {measures[str(__snake_case)]:.2f}MiB''')
lowerCAmelCase_ = measures[F'''{i}-peak''']
print(F'''- GPU {i} peak: {peak:.2f}MiB''')
print(F'''- CPU RAM allocated: {measures['cpu']:.2f}MiB''')
print(F'''- CPU RAM peak: {measures['cpu-peak']:.2f}MiB''')
| 274 | 1 |
"""simple docstring"""
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class snake_case ( _lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
A_ : Dict = RoFormerTokenizer
A_ : Optional[Any] = RoFormerTokenizerFast
A_ : Optional[Any] = True
A_ : List[str] = True
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
'''simple docstring'''
super().setUp()
def _SCREAMING_SNAKE_CASE ( self : Optional[int], **_lowerCamelCase : Any ):
'''simple docstring'''
return self.tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''', **_lowerCamelCase )
def _SCREAMING_SNAKE_CASE ( self : Dict, **_lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained('''junnyu/roformer_chinese_base''', **_lowerCamelCase )
def _SCREAMING_SNAKE_CASE ( self : Tuple ):
'''simple docstring'''
__A = '''永和服装饰品有限公司,今天天气非常好'''
__A = '''永和 服装 饰品 有限公司 , 今 天 天 气 非常 好'''
return input_text, output_text
def _SCREAMING_SNAKE_CASE ( self : str ):
'''simple docstring'''
__A = self.get_tokenizer()
__A , __A = self.get_chinese_input_output_texts()
__A = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase, output_text.split() )
__A = tokens + [tokenizer.unk_token]
__A = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ), _lowerCamelCase )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
'''simple docstring'''
__A = self.get_rust_tokenizer()
__A , __A = self.get_chinese_input_output_texts()
__A = tokenizer.tokenize(_lowerCamelCase )
self.assertListEqual(_lowerCamelCase, output_text.split() )
__A = tokens + [tokenizer.unk_token]
__A = [2_29_43, 2_13_32, 3_44_31, 4_59_04, 1_17, 3_06, 12_31, 12_31, 26_53, 3_39_94, 12_66, 1_00]
self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowerCamelCase ), _lowerCamelCase )
def _SCREAMING_SNAKE_CASE ( self : List[str] ):
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
'''simple docstring'''
pass
def _SCREAMING_SNAKE_CASE ( self : Dict ):
'''simple docstring'''
pass
| 701 |
"""simple docstring"""
import argparse
import json
import os
from tensorflow.core.protobuf.saved_model_pba import SavedModel
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
lowercase_ = '.'
# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
lowercase_ = [
'Assert',
'AssignVariableOp',
'EmptyTensorList',
'MergeV2Checkpoints',
'ReadVariableOp',
'ResourceGather',
'RestoreV2',
'SaveV2',
'ShardedFilename',
'StatefulPartitionedCall',
'StaticRegexFullMatch',
'VarHandleOp',
]
def lowerCAmelCase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ):
"""simple docstring"""
__A = SavedModel()
__A = []
with open(os.path.join(__UpperCamelCase , '''utils''' , '''tf_ops''' , '''onnx.json''' ) ) as f:
__A = json.load(__UpperCamelCase )['''opsets''']
for i in range(1 , opset + 1 ):
onnx_ops.extend(onnx_opsets[str(__UpperCamelCase )] )
with open(__UpperCamelCase , '''rb''' ) as f:
saved_model.ParseFromString(f.read() )
__A = set()
# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
for meta_graph in saved_model.meta_graphs:
# Add operations in the graph definition
model_op_names.update(node.op for node in meta_graph.graph_def.node )
# Go through the functions in the graph definition
for func in meta_graph.graph_def.library.function:
# Add operations in each function
model_op_names.update(node.op for node in func.node_def )
# Convert to list, sorted if you want
__A = sorted(__UpperCamelCase )
__A = []
for op in model_op_names:
if op not in onnx_ops and op not in INTERNAL_OPS:
incompatible_ops.append(__UpperCamelCase )
if strict and len(__UpperCamelCase ) > 0:
raise Exception(f'Found the following incompatible ops for the opset {opset}:\n' + incompatible_ops )
elif len(__UpperCamelCase ) > 0:
print(f'Found the following incompatible ops for the opset {opset}:' )
print(*__UpperCamelCase , sep='''\n''' )
else:
print(f'The saved model {saved_model_path} can properly be converted with ONNX.' )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
parser.add_argument('--saved_model_path', help='Path of the saved model to check (the .pb file).')
parser.add_argument(
'--opset', default=12, type=int, help='The ONNX opset against which the model has to be tested.'
)
parser.add_argument(
'--framework', choices=['onnx'], default='onnx', help='Frameworks against which to test the saved model.'
)
parser.add_argument(
'--strict', action='store_true', help='Whether make the checking strict (raise errors) or not (raise warnings)'
)
lowercase_ = parser.parse_args()
if args.framework == "onnx":
onnx_compliancy(args.saved_model_path, args.strict, args.opset)
| 215 | 0 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class A__ ( snake_case__ ):
"""simple docstring"""
__magic_name__ = (DDPMScheduler,)
def a_ ( self , **__snake_case ):
snake_case = {
'''num_train_timesteps''': 1_0_0_0,
'''beta_start''': 0.0001,
'''beta_end''': 0.02,
'''beta_schedule''': '''linear''',
'''variance_type''': '''fixed_small''',
'''clip_sample''': True,
}
config.update(**__snake_case )
return config
def a_ ( self ):
for timesteps in [1, 5, 1_0_0, 1_0_0_0]:
self.check_over_configs(num_train_timesteps=__snake_case )
def a_ ( self ):
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] , [0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=__snake_case , beta_end=__snake_case )
def a_ ( self ):
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=__snake_case )
def a_ ( self ):
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=__snake_case )
def a_ ( self ):
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=__snake_case )
def a_ ( self ):
self.check_over_configs(thresholding=__snake_case )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=__snake_case , prediction_type=__snake_case , sample_max_value=__snake_case , )
def a_ ( self ):
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=__snake_case )
def a_ ( self ):
for t in [0, 5_0_0, 9_9_9]:
self.check_over_forward(time_step=__snake_case )
def a_ ( self ):
snake_case = self.scheduler_classes[0]
snake_case = self.get_scheduler_config()
snake_case = scheduler_class(**__snake_case )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_8_7 ) - 0.0_0979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_9_9 ) - 0.02 ) ) < 1E-5
def a_ ( self ):
snake_case = self.scheduler_classes[0]
snake_case = self.get_scheduler_config()
snake_case = scheduler_class(**__snake_case )
snake_case = len(__snake_case )
snake_case = self.dummy_model()
snake_case = self.dummy_sample_deter
snake_case = torch.manual_seed(0 )
for t in reversed(range(__snake_case ) ):
# 1. predict noise residual
snake_case = model(__snake_case , __snake_case )
# 2. predict previous mean of sample x_t-1
snake_case = scheduler.step(__snake_case , __snake_case , __snake_case , generator=__snake_case ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
snake_case = pred_prev_sample
snake_case = torch.sum(torch.abs(__snake_case ) )
snake_case = torch.mean(torch.abs(__snake_case ) )
assert abs(result_sum.item() - 258.9606 ) < 1E-2
assert abs(result_mean.item() - 0.3372 ) < 1E-3
def a_ ( self ):
snake_case = self.scheduler_classes[0]
snake_case = self.get_scheduler_config(prediction_type='''v_prediction''' )
snake_case = scheduler_class(**__snake_case )
snake_case = len(__snake_case )
snake_case = self.dummy_model()
snake_case = self.dummy_sample_deter
snake_case = torch.manual_seed(0 )
for t in reversed(range(__snake_case ) ):
# 1. predict noise residual
snake_case = model(__snake_case , __snake_case )
# 2. predict previous mean of sample x_t-1
snake_case = scheduler.step(__snake_case , __snake_case , __snake_case , generator=__snake_case ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
snake_case = pred_prev_sample
snake_case = torch.sum(torch.abs(__snake_case ) )
snake_case = torch.mean(torch.abs(__snake_case ) )
assert abs(result_sum.item() - 202.0296 ) < 1E-2
assert abs(result_mean.item() - 0.2631 ) < 1E-3
def a_ ( self ):
snake_case = self.scheduler_classes[0]
snake_case = self.get_scheduler_config()
snake_case = scheduler_class(**__snake_case )
snake_case = [1_0_0, 8_7, 5_0, 1, 0]
scheduler.set_timesteps(timesteps=__snake_case )
snake_case = scheduler.timesteps
for i, timestep in enumerate(__snake_case ):
if i == len(__snake_case ) - 1:
snake_case = -1
else:
snake_case = timesteps[i + 1]
snake_case = scheduler.previous_timestep(__snake_case )
snake_case = prev_t.item()
self.assertEqual(__snake_case , __snake_case )
def a_ ( self ):
snake_case = self.scheduler_classes[0]
snake_case = self.get_scheduler_config()
snake_case = scheduler_class(**__snake_case )
snake_case = [1_0_0, 8_7, 5_0, 5_1, 0]
with self.assertRaises(__snake_case , msg='''`custom_timesteps` must be in descending order.''' ):
scheduler.set_timesteps(timesteps=__snake_case )
def a_ ( self ):
snake_case = self.scheduler_classes[0]
snake_case = self.get_scheduler_config()
snake_case = scheduler_class(**__snake_case )
snake_case = [1_0_0, 8_7, 5_0, 1, 0]
snake_case = len(__snake_case )
with self.assertRaises(__snake_case , msg='''Can only pass one of `num_inference_steps` or `custom_timesteps`.''' ):
scheduler.set_timesteps(num_inference_steps=__snake_case , timesteps=__snake_case )
def a_ ( self ):
snake_case = self.scheduler_classes[0]
snake_case = self.get_scheduler_config()
snake_case = scheduler_class(**__snake_case )
snake_case = [scheduler.config.num_train_timesteps]
with self.assertRaises(
__snake_case , msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}''' , ):
scheduler.set_timesteps(timesteps=__snake_case )
| 550 |
import inspect
import unittest
import numpy as np
from transformers import ViTConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
from transformers.models.vit.modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel
class A__ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , __snake_case , __snake_case=1_3 , __snake_case=3_0 , __snake_case=2 , __snake_case=3 , __snake_case=True , __snake_case=True , __snake_case=3_2 , __snake_case=5 , __snake_case=4 , __snake_case=3_7 , __snake_case="gelu" , __snake_case=0.1 , __snake_case=0.1 , __snake_case=1_0 , __snake_case=0.02 , ):
snake_case = parent
snake_case = batch_size
snake_case = image_size
snake_case = patch_size
snake_case = num_channels
snake_case = is_training
snake_case = use_labels
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 = type_sequence_label_size
snake_case = initializer_range
# in ViT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
snake_case = (image_size // patch_size) ** 2
snake_case = num_patches + 1
def a_ ( self ):
snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case = ViTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=__snake_case , initializer_range=self.initializer_range , )
return config, pixel_values
def a_ ( self , __snake_case , __snake_case ):
snake_case = FlaxViTModel(config=__snake_case )
snake_case = model(__snake_case )
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token)
snake_case = (self.image_size, self.image_size)
snake_case = (self.patch_size, self.patch_size)
snake_case = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, num_patches + 1, self.hidden_size) )
def a_ ( self , __snake_case , __snake_case ):
snake_case = self.type_sequence_label_size
snake_case = FlaxViTForImageClassification(config=__snake_case )
snake_case = model(__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case = 1
snake_case = FlaxViTForImageClassification(__snake_case )
snake_case = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case = model(__snake_case )
def a_ ( self ):
snake_case = self.prepare_config_and_inputs()
(
(
snake_case
) , (
snake_case
) ,
) = config_and_inputs
snake_case = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_flax
class A__ ( snake_case__ , unittest.TestCase ):
"""simple docstring"""
__magic_name__ = (FlaxViTModel, FlaxViTForImageClassification) if is_flax_available() else ()
def a_ ( self ):
snake_case = FlaxViTModelTester(self )
snake_case = ConfigTester(self , config_class=__snake_case , has_text_modality=__snake_case , hidden_size=3_7 )
def a_ ( self ):
self.config_tester.run_common_tests()
def a_ ( self ):
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__snake_case )
def a_ ( self ):
snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__snake_case )
def a_ ( self ):
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case = model_class(__snake_case )
snake_case = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case = [*signature.parameters.keys()]
snake_case = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , __snake_case )
def a_ ( self ):
snake_case , snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
snake_case = self._prepare_for_class(__snake_case , __snake_case )
snake_case = model_class(__snake_case )
@jax.jit
def model_jitted(__snake_case , **__snake_case ):
return model(pixel_values=__snake_case , **__snake_case )
with self.subTest('''JIT Enabled''' ):
snake_case = model_jitted(**__snake_case ).to_tuple()
with self.subTest('''JIT Disabled''' ):
with jax.disable_jit():
snake_case = model_jitted(**__snake_case ).to_tuple()
self.assertEqual(len(__snake_case ) , len(__snake_case ) )
for jitted_output, output in zip(__snake_case , __snake_case ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def a_ ( self ):
for model_class_name in self.all_model_classes:
snake_case = model_class_name.from_pretrained('''google/vit-base-patch16-224''' )
snake_case = model(np.ones((1, 3, 2_2_4, 2_2_4) ) )
self.assertIsNotNone(__snake_case )
| 550 | 1 |
"""simple docstring"""
import copy
import os
from typing import TYPE_CHECKING, List, Union
if TYPE_CHECKING:
pass
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : int = logging.get_logger(__name__)
lowerCAmelCase_ : Dict = {
'''kakaobrain/align-base''': '''https://huggingface.co/kakaobrain/align-base/resolve/main/config.json''',
}
class UpperCamelCase_ ( a_ ):
_A : Optional[Any] = 'align_text_model'
def __init__( self , snake_case__=3_05_22 , snake_case__=7_68 , snake_case__=12 , snake_case__=12 , snake_case__=30_72 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=5_12 , snake_case__=2 , snake_case__=0.02 , snake_case__=1e-12 , snake_case__=0 , snake_case__="absolute" , snake_case__=True , **snake_case__ , ) -> Union[str, Any]:
"""simple docstring"""
super().__init__(**snake_case__ )
UpperCAmelCase = vocab_size
UpperCAmelCase = hidden_size
UpperCAmelCase = num_hidden_layers
UpperCAmelCase = num_attention_heads
UpperCAmelCase = hidden_act
UpperCAmelCase = intermediate_size
UpperCAmelCase = hidden_dropout_prob
UpperCAmelCase = attention_probs_dropout_prob
UpperCAmelCase = max_position_embeddings
UpperCAmelCase = type_vocab_size
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = position_embedding_type
UpperCAmelCase = use_cache
UpperCAmelCase = pad_token_id
@classmethod
def UpperCamelCase_ ( cls , snake_case__ , **snake_case__ ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(snake_case__ )
UpperCAmelCase , UpperCAmelCase = cls.get_config_dict(snake_case__ , **snake_case__ )
# get the text config dict if we are loading from AlignConfig
if config_dict.get("""model_type""" ) == "align":
UpperCAmelCase = config_dict["""text_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(snake_case__ , **snake_case__ )
class UpperCamelCase_ ( a_ ):
_A : int = 'align_vision_model'
def __init__( self , snake_case__ = 3 , snake_case__ = 6_00 , snake_case__ = 2.0 , snake_case__ = 3.1 , snake_case__ = 8 , snake_case__ = [3, 3, 5, 3, 5, 5, 3] , snake_case__ = [32, 16, 24, 40, 80, 1_12, 1_92] , snake_case__ = [16, 24, 40, 80, 1_12, 1_92, 3_20] , snake_case__ = [] , snake_case__ = [1, 2, 2, 2, 1, 2, 1] , snake_case__ = [1, 2, 2, 3, 3, 4, 1] , snake_case__ = [1, 6, 6, 6, 6, 6, 6] , snake_case__ = 0.25 , snake_case__ = "swish" , snake_case__ = 25_60 , snake_case__ = "mean" , snake_case__ = 0.02 , snake_case__ = 0.001 , snake_case__ = 0.99 , snake_case__ = 0.2 , **snake_case__ , ) -> Dict:
"""simple docstring"""
super().__init__(**snake_case__ )
UpperCAmelCase = num_channels
UpperCAmelCase = image_size
UpperCAmelCase = width_coefficient
UpperCAmelCase = depth_coefficient
UpperCAmelCase = depth_divisor
UpperCAmelCase = kernel_sizes
UpperCAmelCase = in_channels
UpperCAmelCase = out_channels
UpperCAmelCase = depthwise_padding
UpperCAmelCase = strides
UpperCAmelCase = num_block_repeats
UpperCAmelCase = expand_ratios
UpperCAmelCase = squeeze_expansion_ratio
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dim
UpperCAmelCase = pooling_type
UpperCAmelCase = initializer_range
UpperCAmelCase = batch_norm_eps
UpperCAmelCase = batch_norm_momentum
UpperCAmelCase = drop_connect_rate
UpperCAmelCase = sum(snake_case__ ) * 4
@classmethod
def UpperCamelCase_ ( cls , snake_case__ , **snake_case__ ) -> "PretrainedConfig":
"""simple docstring"""
cls._set_token_in_kwargs(snake_case__ )
UpperCAmelCase , UpperCAmelCase = cls.get_config_dict(snake_case__ , **snake_case__ )
# get the vision config dict if we are loading from AlignConfig
if config_dict.get("""model_type""" ) == "align":
UpperCAmelCase = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls , """model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
f'''You are using a model of type {config_dict['model_type']} to instantiate a model of type '''
f'''{cls.model_type}. This is not supported for all configurations of models and can yield errors.''' )
return cls.from_dict(snake_case__ , **snake_case__ )
class UpperCamelCase_ ( a_ ):
_A : str = 'align'
_A : Optional[Any] = True
def __init__( self , snake_case__=None , snake_case__=None , snake_case__=6_40 , snake_case__=1.0 , snake_case__=0.02 , **snake_case__ , ) -> Tuple:
"""simple docstring"""
super().__init__(**snake_case__ )
if text_config is None:
UpperCAmelCase = {}
logger.info("""text_config is None. Initializing the AlignTextConfig with default values.""" )
if vision_config is None:
UpperCAmelCase = {}
logger.info("""vision_config is None. Initializing the AlignVisionConfig with default values.""" )
UpperCAmelCase = AlignTextConfig(**snake_case__ )
UpperCAmelCase = AlignVisionConfig(**snake_case__ )
UpperCAmelCase = projection_dim
UpperCAmelCase = temperature_init_value
UpperCAmelCase = initializer_range
@classmethod
def UpperCamelCase_ ( cls , snake_case__ , snake_case__ , **snake_case__ ) -> Optional[int]:
"""simple docstring"""
return cls(text_config=text_config.to_dict() , vision_config=vision_config.to_dict() , **snake_case__ )
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = copy.deepcopy(self.__dict__ )
UpperCAmelCase = self.text_config.to_dict()
UpperCAmelCase = self.vision_config.to_dict()
UpperCAmelCase = self.__class__.model_type
return output
| 378 |
"""simple docstring"""
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : List[Any] = logging.get_logger(__name__)
lowerCAmelCase_ : Optional[Any] = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json''',
'''facebook/encodec_48khz''': '''https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json''',
}
class UpperCamelCase_ ( a_ ):
_A : Dict = 'encodec'
def __init__( self , snake_case__=[1.5, 3.0, 6.0, 12.0, 24.0] , snake_case__=2_40_00 , snake_case__=1 , snake_case__=False , snake_case__=None , snake_case__=None , snake_case__=1_28 , snake_case__=32 , snake_case__=1 , snake_case__=[8, 5, 4, 2] , snake_case__="weight_norm" , snake_case__=7 , snake_case__=7 , snake_case__=3 , snake_case__=2 , snake_case__=True , snake_case__="reflect" , snake_case__=2 , snake_case__=2 , snake_case__=1.0 , snake_case__=10_24 , snake_case__=None , snake_case__=True , **snake_case__ , ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase = target_bandwidths
UpperCAmelCase = sampling_rate
UpperCAmelCase = audio_channels
UpperCAmelCase = normalize
UpperCAmelCase = chunk_length_s
UpperCAmelCase = overlap
UpperCAmelCase = hidden_size
UpperCAmelCase = num_filters
UpperCAmelCase = num_residual_layers
UpperCAmelCase = upsampling_ratios
UpperCAmelCase = norm_type
UpperCAmelCase = kernel_size
UpperCAmelCase = last_kernel_size
UpperCAmelCase = residual_kernel_size
UpperCAmelCase = dilation_growth_rate
UpperCAmelCase = use_causal_conv
UpperCAmelCase = pad_mode
UpperCAmelCase = compress
UpperCAmelCase = num_lstm_layers
UpperCAmelCase = trim_right_ratio
UpperCAmelCase = codebook_size
UpperCAmelCase = codebook_dim if codebook_dim is not None else hidden_size
UpperCAmelCase = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
f'''self.norm_type must be one of `"weight_norm"`, `"time_group_norm"`), got {self.norm_type}''' )
super().__init__(**snake_case__ )
@property
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def UpperCamelCase_ ( self ) -> Optional[int]:
"""simple docstring"""
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length ) )
@property
def UpperCamelCase_ ( self ) -> int:
"""simple docstring"""
UpperCAmelCase = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def UpperCamelCase_ ( self ) -> int:
"""simple docstring"""
return int(10_00 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 378 | 1 |
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
UpperCamelCase = False
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
pass
@slow
@require_torch_gpu
class lowerCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __a ( self :Optional[int] ):
UpperCamelCase__ :Optional[Any] = VersatileDiffusionImageVariationPipeline.from_pretrained("""shi-labs/versatile-diffusion""" )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
UpperCamelCase__ :str = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg""" )
UpperCamelCase__ :List[Any] = torch.manual_seed(0 )
UpperCamelCase__ :Any = pipe(
image=lowerCamelCase__ , generator=lowerCamelCase__ , guidance_scale=7.5 , num_inference_steps=50 , output_type="""numpy""" , ).images
UpperCamelCase__ :Optional[int] = image[0, 2_53:2_56, 2_53:2_56, -1]
assert image.shape == (1, 5_12, 5_12, 3)
UpperCamelCase__ :Any = np.array([0.0441, 0.0469, 0.0507, 0.0575, 0.0632, 0.0650, 0.0865, 0.0909, 0.0945] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2 | 45 |
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 A ( lowercase__ : dict ) -> tuple:
return (data["data"], data["target"])
def A ( lowercase__ : np.ndarray , lowercase__ : np.ndarray ) -> XGBClassifier:
UpperCamelCase__ :Tuple = XGBClassifier()
classifier.fit(lowercase__ , lowercase__ )
return classifier
def A ( ) -> None:
UpperCamelCase__ :str = load_iris()
UpperCamelCase__ , UpperCamelCase__ :int = data_handling(lowercase__ )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ :int = train_test_split(
lowercase__ , lowercase__ , test_size=0.25 )
UpperCamelCase__ :Optional[int] = iris["""target_names"""]
# Create an XGBoost Classifier from the training data
UpperCamelCase__ :Optional[Any] = xgboost(lowercase__ , lowercase__ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
lowercase__ , lowercase__ , lowercase__ , display_labels=lowercase__ , cmap="""Blues""" , normalize="""true""" , )
plt.title("""Normalized Confusion Matrix - IRIS Dataset""" )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main() | 45 | 1 |
_UpperCamelCase : Dict ='Input must be a string of 8 numbers plus letter'
_UpperCamelCase : Any ='TRWAGMYFPDXBNJZSQVHLCKE'
def a__ (__lowercase :str ) -> bool:
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
_A : Optional[Any] = f"""Expected string as input, found {type(UpperCamelCase__ ).__name__}"""
raise TypeError(UpperCamelCase__ )
_A : List[str] = spanish_id.replace('''-''' , '''''' ).upper()
if len(UpperCamelCase__ ) != 9:
raise ValueError(UpperCamelCase__ )
try:
_A : Union[str, Any] = int(spanish_id_clean[0:8] )
_A : List[Any] = spanish_id_clean[8]
except ValueError as ex:
raise ValueError(UpperCamelCase__ ) from ex
if letter.isdigit():
raise ValueError(UpperCamelCase__ )
return letter == LOOKUP_LETTERS[number % 23]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 720 |
import math
from typing import Any, Callable, List, Optional, Tuple, Union
import numpy as np
import torch
from ...models import TaFilmDecoder
from ...schedulers import DDPMScheduler
from ...utils import is_onnx_available, logging, randn_tensor
if is_onnx_available():
from ..onnx_utils import OnnxRuntimeModel
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
from .continous_encoder import SpectrogramContEncoder
from .notes_encoder import SpectrogramNotesEncoder
_UpperCamelCase : Union[str, Any] =logging.get_logger(__name__) # pylint: disable=invalid-name
_UpperCamelCase : List[Any] =256
class UpperCAmelCase__ ( __snake_case ):
__snake_case : Tuple = ["melgan"]
def __init__( self ,A__ ,A__ ,A__ ,A__ ,A__ ,):
super().__init__()
# From MELGAN
_A : Any = math.log(1E-5 ) # Matches MelGAN training.
_A : int = 4.0 # Largest value for most examples
_A : int = 128
self.register_modules(
notes_encoder=A__ ,continuous_encoder=A__ ,decoder=A__ ,scheduler=A__ ,melgan=A__ ,)
def A__ ( self ,A__ ,A__=(-1.0, 1.0) ,A__=False ):
_A , _A : int = output_range
if clip:
_A : int = torch.clip(A__ ,self.min_value ,self.max_value )
# Scale to [0, 1].
_A : Optional[Any] = (features - self.min_value) / (self.max_value - self.min_value)
# Scale to [min_out, max_out].
return zero_one * (max_out - min_out) + min_out
def A__ ( self ,A__ ,A__=(-1.0, 1.0) ,A__=False ):
_A , _A : Dict = input_range
_A : Tuple = torch.clip(A__ ,A__ ,A__ ) if clip else outputs
# Scale to [0, 1].
_A : Any = (outputs - min_out) / (max_out - min_out)
# Scale to [self.min_value, self.max_value].
return zero_one * (self.max_value - self.min_value) + self.min_value
def A__ ( self ,A__ ,A__ ,A__ ):
_A : Tuple = input_tokens > 0
_A , _A : str = self.notes_encoder(
encoder_input_tokens=A__ ,encoder_inputs_mask=A__ )
_A , _A : List[str] = self.continuous_encoder(
encoder_inputs=A__ ,encoder_inputs_mask=A__ )
return [(tokens_encoded, tokens_mask), (continuous_encoded, continuous_mask)]
def A__ ( self ,A__ ,A__ ,A__ ):
_A : str = noise_time
if not torch.is_tensor(A__ ):
_A : Any = torch.tensor([timesteps] ,dtype=torch.long ,device=input_tokens.device )
elif torch.is_tensor(A__ ) and len(timesteps.shape ) == 0:
_A : Union[str, Any] = timesteps[None].to(input_tokens.device )
# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
_A : int = timesteps * torch.ones(input_tokens.shape[0] ,dtype=timesteps.dtype ,device=timesteps.device )
_A : Dict = self.decoder(
encodings_and_masks=A__ ,decoder_input_tokens=A__ ,decoder_noise_time=A__ )
return logits
@torch.no_grad()
def __call__( self ,A__ ,A__ = None ,A__ = 100 ,A__ = True ,A__ = "numpy" ,A__ = None ,A__ = 1 ,):
if (callback_steps is None) or (
callback_steps is not None and (not isinstance(A__ ,A__ ) or callback_steps <= 0)
):
raise ValueError(
f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
f""" {type(A__ )}.""" )
_A : Any = np.zeros([1, TARGET_FEATURE_LENGTH, self.n_dims] ,dtype=np.floataa )
_A : Optional[int] = np.zeros([1, 0, self.n_dims] ,np.floataa )
_A : Dict = torch.ones((1, TARGET_FEATURE_LENGTH) ,dtype=A__ ,device=self.device )
for i, encoder_input_tokens in enumerate(A__ ):
if i == 0:
_A : str = torch.from_numpy(pred_mel[:1].copy() ).to(
device=self.device ,dtype=self.decoder.dtype )
# The first chunk has no previous context.
_A : Dict = torch.zeros((1, TARGET_FEATURE_LENGTH) ,dtype=A__ ,device=self.device )
else:
# The full song pipeline does not feed in a context feature, so the mask
# will be all 0s after the feature converter. Because we know we're
# feeding in a full context chunk from the previous prediction, set it
# to all 1s.
_A : Optional[int] = ones
_A : Tuple = self.scale_features(
A__ ,output_range=[-1.0, 1.0] ,clip=A__ )
_A : Tuple = self.encode(
input_tokens=torch.IntTensor([encoder_input_tokens] ).to(device=self.device ) ,continuous_inputs=A__ ,continuous_mask=A__ ,)
# Sample encoder_continuous_inputs shaped gaussian noise to begin loop
_A : Any = randn_tensor(
shape=encoder_continuous_inputs.shape ,generator=A__ ,device=self.device ,dtype=self.decoder.dtype ,)
# set step values
self.scheduler.set_timesteps(A__ )
# Denoising diffusion loop
for j, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
_A : Union[str, Any] = self.decode(
encodings_and_masks=A__ ,input_tokens=A__ ,noise_time=t / self.scheduler.config.num_train_timesteps ,)
# Compute previous output: x_t -> x_t-1
_A : List[str] = self.scheduler.step(A__ ,A__ ,A__ ,generator=A__ ).prev_sample
_A : Union[str, Any] = self.scale_to_features(A__ ,input_range=[-1.0, 1.0] )
_A : Optional[Any] = mel[:1]
_A : int = mel.cpu().float().numpy()
_A : Optional[Any] = np.concatenate([full_pred_mel, pred_mel[:1]] ,axis=1 )
# call the callback, if provided
if callback is not None and i % callback_steps == 0:
callback(A__ ,A__ )
logger.info('''Generated segment''' ,A__ )
if output_type == "numpy" and not is_onnx_available():
raise ValueError(
'''Cannot return output in \'np\' format if ONNX is not available. Make sure to have ONNX installed or set \'output_type\' to \'mel\'.''' )
elif output_type == "numpy" and self.melgan is None:
raise ValueError(
'''Cannot return output in \'np\' format if melgan component is not defined. Make sure to define `self.melgan` or set \'output_type\' to \'mel\'.''' )
if output_type == "numpy":
_A : Optional[Any] = self.melgan(input_features=full_pred_mel.astype(np.floataa ) )
else:
_A : Dict = full_pred_mel
if not return_dict:
return (output,)
return AudioPipelineOutput(audios=A__ )
| 332 | 0 |
'''simple docstring'''
import fire
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save
def UpperCAmelCase__ ( UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : List[Any]=10_24 , UpperCAmelCase_ : List[Any]=10_24 , UpperCAmelCase_ : List[str]=False , **UpperCAmelCase_ : str ) -> Dict:
__lowerCamelCase : str = AutoTokenizer.from_pretrained(UpperCAmelCase_ )
__lowerCamelCase : Union[str, Any] = SeqaSeqDataset(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , type_path='train' , **UpperCAmelCase_ )
__lowerCamelCase : List[str] = tok.pad_token_id
def get_lens(UpperCAmelCase_ : Optional[Any] ):
__lowerCamelCase : Union[str, Any] = tqdm(
DataLoader(UpperCAmelCase_ , batch_size=5_12 , num_workers=8 , shuffle=UpperCAmelCase_ , collate_fn=ds.collate_fn ) , desc=str(ds.len_file ) , )
__lowerCamelCase : Optional[Any] = []
for batch in dl:
__lowerCamelCase : int = batch['input_ids'].ne(UpperCAmelCase_ ).sum(1 ).tolist()
__lowerCamelCase : Any = batch['labels'].ne(UpperCAmelCase_ ).sum(1 ).tolist()
if consider_target:
for src, tgt in zip(UpperCAmelCase_ , UpperCAmelCase_ ):
max_lens.append(max(UpperCAmelCase_ , UpperCAmelCase_ ) )
else:
max_lens.extend(UpperCAmelCase_ )
return max_lens
__lowerCamelCase : int = get_lens(UpperCAmelCase_ )
__lowerCamelCase : Any = SeqaSeqDataset(UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , type_path='val' , **UpperCAmelCase_ )
__lowerCamelCase : Union[str, Any] = get_lens(UpperCAmelCase_ )
pickle_save(UpperCAmelCase_ , train_ds.len_file )
pickle_save(UpperCAmelCase_ , val_ds.len_file )
if __name__ == "__main__":
fire.Fire(save_len_file)
| 13 |
"""simple docstring"""
import cmath
import math
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> complex:
UpperCAmelCase__ : str = math.radians(lowerCAmelCase )
UpperCAmelCase__ : Optional[int] = math.radians(lowerCAmelCase )
# Convert voltage and current to rectangular form
UpperCAmelCase__ : Union[str, Any] = cmath.rect(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase__ : List[str] = cmath.rect(lowerCAmelCase , lowerCAmelCase )
# Calculate apparent power
return voltage_rect * current_rect
if __name__ == "__main__":
import doctest
doctest.testmod()
| 182 | 0 |
from __future__ import annotations
SCREAMING_SNAKE_CASE : List[str] = tuple[int, int, int]
SCREAMING_SNAKE_CASE : List[Any] = tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
SCREAMING_SNAKE_CASE : int = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
# -------------------------- default selection --------------------------
# rotors --------------------------
SCREAMING_SNAKE_CASE : int = """EGZWVONAHDCLFQMSIPJBYUKXTR"""
SCREAMING_SNAKE_CASE : int = """FOBHMDKEXQNRAULPGSJVTYICZW"""
SCREAMING_SNAKE_CASE : Optional[int] = """ZJXESIUQLHAVRMDOYGTNFWPBKC"""
# reflector --------------------------
SCREAMING_SNAKE_CASE : Dict = {
"""A""": """N""",
"""N""": """A""",
"""B""": """O""",
"""O""": """B""",
"""C""": """P""",
"""P""": """C""",
"""D""": """Q""",
"""Q""": """D""",
"""E""": """R""",
"""R""": """E""",
"""F""": """S""",
"""S""": """F""",
"""G""": """T""",
"""T""": """G""",
"""H""": """U""",
"""U""": """H""",
"""I""": """V""",
"""V""": """I""",
"""J""": """W""",
"""W""": """J""",
"""K""": """X""",
"""X""": """K""",
"""L""": """Y""",
"""Y""": """L""",
"""M""": """Z""",
"""Z""": """M""",
}
# -------------------------- extra rotors --------------------------
SCREAMING_SNAKE_CASE : Any = """RMDJXFUWGISLHVTCQNKYPBEZOA"""
SCREAMING_SNAKE_CASE : List[str] = """SGLCPQWZHKXAREONTFBVIYJUDM"""
SCREAMING_SNAKE_CASE : Any = """HVSICLTYKQUBXDWAJZOMFGPREN"""
SCREAMING_SNAKE_CASE : List[str] = """RZWQHFMVDBKICJLNTUXAGYPSOE"""
SCREAMING_SNAKE_CASE : List[str] = """LFKIJODBEGAMQPXVUHYSTCZRWN"""
SCREAMING_SNAKE_CASE : Dict = """KOAEGVDHXPQZMLFTYWJNBRCIUS"""
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> str:
# Checks if there are 3 unique rotors
if (unique_rotsel := len(set(_lowerCamelCase ) )) < 3:
_lowercase : Optional[Any] = F'''Please use 3 unique rotors (not {unique_rotsel})'''
raise Exception(_lowerCamelCase )
# Checks if rotor positions are valid
_lowercase , _lowercase , _lowercase : Optional[Any] = rotpos
if not 0 < rotorposa <= len(_lowerCamelCase ):
_lowercase : Dict = F'''First rotor position is not within range of 1..26 ({rotorposa}'''
raise ValueError(_lowerCamelCase )
if not 0 < rotorposa <= len(_lowerCamelCase ):
_lowercase : Optional[Any] = F'''Second rotor position is not within range of 1..26 ({rotorposa})'''
raise ValueError(_lowerCamelCase )
if not 0 < rotorposa <= len(_lowerCamelCase ):
_lowercase : Any = F'''Third rotor position is not within range of 1..26 ({rotorposa})'''
raise ValueError(_lowerCamelCase )
# Validates string and returns dict
_lowercase : Tuple = _plugboard(_lowerCamelCase )
return rotpos, rotsel, pbdict
def UpperCamelCase_( lowerCamelCase_ ) -> Optional[Any]:
# tests the input string if it
# a) is type string
# b) has even length (so pairs can be made)
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
_lowercase : List[str] = F'''Plugboard setting isn\'t type string ({type(_lowerCamelCase )})'''
raise TypeError(_lowerCamelCase )
elif len(_lowerCamelCase ) % 2 != 0:
_lowercase : Union[str, Any] = F'''Odd number of symbols ({len(_lowerCamelCase )})'''
raise Exception(_lowerCamelCase )
elif pbstring == "":
return {}
pbstring.replace(' ' , '' )
# Checks if all characters are unique
_lowercase : Tuple = set()
for i in pbstring:
if i not in abc:
_lowercase : Optional[Any] = F'''\'{i}\' not in list of symbols'''
raise Exception(_lowerCamelCase )
elif i in tmppbl:
_lowercase : str = F'''Duplicate symbol ({i})'''
raise Exception(_lowerCamelCase )
else:
tmppbl.add(_lowerCamelCase )
del tmppbl
# Created the dictionary
_lowercase : Optional[Any] = {}
for j in range(0 , len(_lowerCamelCase ) - 1 , 2 ):
_lowercase : Tuple = pbstring[j + 1]
_lowercase : Optional[int] = pbstring[j]
return pb
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = (rotora, rotora, rotora) , lowerCamelCase_ = "" , ) -> Optional[Any]:
_lowercase : Dict = text.upper()
_lowercase , _lowercase , _lowercase : Optional[int] = _validator(
_lowerCamelCase , _lowerCamelCase , plugb.upper() )
_lowercase , _lowercase , _lowercase : Dict = rotor_position
_lowercase , _lowercase , _lowercase : List[str] = rotor_selection
rotorposa -= 1
rotorposa -= 1
rotorposa -= 1
_lowercase : Any = []
# encryption/decryption process --------------------------
for symbol in text:
if symbol in abc:
# 1st plugboard --------------------------
if symbol in plugboard:
_lowercase : List[Any] = plugboard[symbol]
# rotor ra --------------------------
_lowercase : int = abc.index(_lowerCamelCase ) + rotorposa
_lowercase : Tuple = rotora[index % len(_lowerCamelCase )]
# rotor rb --------------------------
_lowercase : Optional[int] = abc.index(_lowerCamelCase ) + rotorposa
_lowercase : List[str] = rotora[index % len(_lowerCamelCase )]
# rotor rc --------------------------
_lowercase : int = abc.index(_lowerCamelCase ) + rotorposa
_lowercase : Optional[int] = rotora[index % len(_lowerCamelCase )]
# reflector --------------------------
# this is the reason you don't need another machine to decipher
_lowercase : List[Any] = reflector[symbol]
# 2nd rotors
_lowercase : Union[str, Any] = abc[rotora.index(_lowerCamelCase ) - rotorposa]
_lowercase : str = abc[rotora.index(_lowerCamelCase ) - rotorposa]
_lowercase : int = abc[rotora.index(_lowerCamelCase ) - rotorposa]
# 2nd plugboard
if symbol in plugboard:
_lowercase : str = plugboard[symbol]
# moves/resets rotor positions
rotorposa += 1
if rotorposa >= len(_lowerCamelCase ):
_lowercase : int = 0
rotorposa += 1
if rotorposa >= len(_lowerCamelCase ):
_lowercase : Any = 0
rotorposa += 1
if rotorposa >= len(_lowerCamelCase ):
_lowercase : List[str] = 0
# else:
# pass
# Error could be also raised
# raise ValueError(
# 'Invalid symbol('+repr(symbol)+')')
result.append(_lowerCamelCase )
return "".join(_lowerCamelCase )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Union[str, Any] = """This is my Python script that emulates the Enigma machine from WWII."""
SCREAMING_SNAKE_CASE : Optional[Any] = (1, 1, 1)
SCREAMING_SNAKE_CASE : Union[str, Any] = """pictures"""
SCREAMING_SNAKE_CASE : str = (rotora, rotora, rotora)
SCREAMING_SNAKE_CASE : Optional[int] = enigma(message, rotor_pos, rotor_sel, pb)
print("Encrypted message:", en)
print("Decrypted message:", enigma(en, rotor_pos, rotor_sel, pb))
| 700 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
)
from transformers.models.deit.modeling_deit import DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class _lowerCamelCase:
def __init__( self, lowerCamelCase, lowerCamelCase=13, lowerCamelCase=30, lowerCamelCase=2, lowerCamelCase=3, lowerCamelCase=True, lowerCamelCase=True, lowerCamelCase=32, lowerCamelCase=5, lowerCamelCase=4, lowerCamelCase=37, lowerCamelCase="gelu", lowerCamelCase=0.1, lowerCamelCase=0.1, lowerCamelCase=10, lowerCamelCase=0.0_2, lowerCamelCase=3, lowerCamelCase=None, lowerCamelCase=2, ) -> Optional[int]:
"""simple docstring"""
_lowercase : Any = parent
_lowercase : int = batch_size
_lowercase : int = image_size
_lowercase : str = patch_size
_lowercase : int = num_channels
_lowercase : Any = is_training
_lowercase : Union[str, Any] = use_labels
_lowercase : Dict = hidden_size
_lowercase : List[str] = num_hidden_layers
_lowercase : Optional[Any] = num_attention_heads
_lowercase : Optional[int] = intermediate_size
_lowercase : Tuple = hidden_act
_lowercase : str = hidden_dropout_prob
_lowercase : Optional[Any] = attention_probs_dropout_prob
_lowercase : Tuple = type_sequence_label_size
_lowercase : List[str] = initializer_range
_lowercase : Any = scope
_lowercase : Union[str, Any] = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
_lowercase : Union[str, Any] = (image_size // patch_size) ** 2
_lowercase : Any = num_patches + 2
def UpperCamelCase ( self) -> str:
"""simple docstring"""
_lowercase : Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])
_lowercase : str = None
if self.use_labels:
_lowercase : List[Any] = ids_tensor([self.batch_size], self.type_sequence_label_size)
_lowercase : str = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self) -> int:
"""simple docstring"""
return DeiTConfig(
image_size=self.image_size, patch_size=self.patch_size, num_channels=self.num_channels, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, is_decoder=lowerCamelCase, initializer_range=self.initializer_range, encoder_stride=self.encoder_stride, )
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Optional[int]:
"""simple docstring"""
_lowercase : Any = DeiTModel(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : Optional[int] = model(lowerCamelCase)
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> int:
"""simple docstring"""
_lowercase : Optional[Any] = DeiTForMaskedImageModeling(config=lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : List[Any] = model(lowerCamelCase)
self.parent.assertEqual(
result.reconstruction.shape, (self.batch_size, self.num_channels, self.image_size, self.image_size))
# test greyscale images
_lowercase : Any = 1
_lowercase : Optional[Any] = DeiTForMaskedImageModeling(lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : List[str] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
_lowercase : Optional[int] = model(lowerCamelCase)
self.parent.assertEqual(result.reconstruction.shape, (self.batch_size, 1, self.image_size, self.image_size))
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> Any:
"""simple docstring"""
_lowercase : str = self.type_sequence_label_size
_lowercase : Dict = DeiTForImageClassification(lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : List[Any] = model(lowerCamelCase, labels=lowerCamelCase)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
# test greyscale images
_lowercase : Optional[Any] = 1
_lowercase : Optional[Any] = DeiTForImageClassification(lowerCamelCase)
model.to(lowerCamelCase)
model.eval()
_lowercase : List[str] = floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
_lowercase : List[Any] = model(lowerCamelCase, labels=lowerCamelCase)
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase : Tuple = self.prepare_config_and_inputs()
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) : List[str] = config_and_inputs
_lowercase : List[str] = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class _lowerCamelCase( _a, _a, unittest.TestCase ):
lowercase_ : Optional[Any] = (
(
DeiTModel,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
)
if is_torch_available()
else ()
)
lowercase_ : Optional[Any] = (
{
"""feature-extraction""": DeiTModel,
"""image-classification""": (DeiTForImageClassification, DeiTForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
lowercase_ : Dict = False
lowercase_ : List[str] = False
lowercase_ : Union[str, Any] = False
def UpperCamelCase ( self) -> Optional[Any]:
"""simple docstring"""
_lowercase : int = DeiTModelTester(self)
_lowercase : Optional[Any] = ConfigTester(self, config_class=lowerCamelCase, has_text_modality=lowerCamelCase, hidden_size=37)
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds')
def UpperCamelCase ( self) -> str:
"""simple docstring"""
pass
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase , _lowercase : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : int = model_class(lowerCamelCase)
self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
_lowercase : Union[str, Any] = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase, nn.Linear))
def UpperCamelCase ( self) -> int:
"""simple docstring"""
_lowercase , _lowercase : Any = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase : Any = model_class(lowerCamelCase)
_lowercase : Optional[Any] = inspect.signature(model.forward)
# signature.parameters is an OrderedDict => so arg_names order is deterministic
_lowercase : Union[str, Any] = [*signature.parameters.keys()]
_lowercase : str = ['pixel_values']
self.assertListEqual(arg_names[:1], lowerCamelCase)
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase)
def UpperCamelCase ( self) -> str:
"""simple docstring"""
_lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowerCamelCase)
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
_lowercase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase, lowerCamelCase=False) -> Any:
"""simple docstring"""
_lowercase : Dict = super()._prepare_for_class(lowerCamelCase, lowerCamelCase, return_labels=lowerCamelCase)
if return_labels:
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
if not self.model_tester.is_training:
return
_lowercase , _lowercase : str = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : Tuple = True
for model_class in self.all_model_classes:
# DeiTForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(lowerCamelCase)
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
_lowercase : Optional[int] = model_class(lowerCamelCase)
model.to(lowerCamelCase)
model.train()
_lowercase : Optional[Any] = self._prepare_for_class(lowerCamelCase, lowerCamelCase, return_labels=lowerCamelCase)
_lowercase : List[str] = model(**lowerCamelCase).loss
loss.backward()
def UpperCamelCase ( self) -> Optional[Any]:
"""simple docstring"""
_lowercase , _lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
_lowercase : Dict = False
_lowercase : Optional[int] = True
for model_class in self.all_model_classes:
if model_class in get_values(lowerCamelCase) or not model_class.supports_gradient_checkpointing:
continue
# DeiTForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "DeiTForImageClassificationWithTeacher":
continue
_lowercase : str = model_class(lowerCamelCase)
model.gradient_checkpointing_enable()
model.to(lowerCamelCase)
model.train()
_lowercase : Union[str, Any] = self._prepare_for_class(lowerCamelCase, lowerCamelCase, return_labels=lowerCamelCase)
_lowercase : List[Any] = model(**lowerCamelCase).loss
loss.backward()
def UpperCamelCase ( self) -> Union[str, Any]:
"""simple docstring"""
_lowercase , _lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
_lowercase : int = [
{'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
{'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
{'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(lowerCamelCase),
*get_values(lowerCamelCase),
]
or model_class.__name__ == "DeiTForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=F'''Testing {model_class} with {problem_type["title"]}'''):
_lowercase : List[Any] = problem_type['title']
_lowercase : str = problem_type['num_labels']
_lowercase : Optional[int] = model_class(lowerCamelCase)
model.to(lowerCamelCase)
model.train()
_lowercase : Tuple = self._prepare_for_class(lowerCamelCase, lowerCamelCase, return_labels=lowerCamelCase)
if problem_type["num_labels"] > 1:
_lowercase : Dict = inputs['labels'].unsqueeze(1).repeat(1, problem_type['num_labels'])
_lowercase : Optional[int] = inputs['labels'].to(problem_type['dtype'])
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=lowerCamelCase) as warning_list:
_lowercase : Dict = model(**lowerCamelCase).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message):
raise ValueError(
F'''Something is going wrong in the regression problem: intercepted {w.message}''')
loss.backward()
@slow
def UpperCamelCase ( self) -> str:
"""simple docstring"""
for model_name in DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase : Tuple = DeiTModel.from_pretrained(lowerCamelCase)
self.assertIsNotNone(lowerCamelCase)
def UpperCamelCase_( ) -> List[str]:
_lowercase : Optional[Any] = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_torch
@require_vision
class _lowerCamelCase( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224')
if is_vision_available()
else None
)
@slow
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
_lowercase : Optional[int] = DeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224').to(
lowerCamelCase)
_lowercase : List[str] = self.default_image_processor
_lowercase : List[str] = prepare_img()
_lowercase : Tuple = image_processor(images=lowerCamelCase, return_tensors='pt').to(lowerCamelCase)
# forward pass
with torch.no_grad():
_lowercase : int = model(**lowerCamelCase)
# verify the logits
_lowercase : Any = torch.Size((1, 10_00))
self.assertEqual(outputs.logits.shape, lowerCamelCase)
_lowercase : Union[str, Any] = torch.tensor([-1.0_2_6_6, 0.1_9_1_2, -1.2_8_6_1]).to(lowerCamelCase)
self.assertTrue(torch.allclose(outputs.logits[0, :3], lowerCamelCase, atol=1E-4))
@slow
@require_accelerate
@require_torch_gpu
def UpperCamelCase ( self) -> List[Any]:
"""simple docstring"""
_lowercase : Tuple = DeiTModel.from_pretrained(
'facebook/deit-base-distilled-patch16-224', torch_dtype=torch.floataa, device_map='auto')
_lowercase : Union[str, Any] = self.default_image_processor
_lowercase : Union[str, Any] = prepare_img()
_lowercase : int = image_processor(images=lowerCamelCase, return_tensors='pt')
_lowercase : Union[str, Any] = inputs.pixel_values.to(lowerCamelCase)
# forward pass to make sure inference works in fp16
with torch.no_grad():
_lowercase : Optional[int] = model(lowerCamelCase)
| 354 | 0 |
"""simple docstring"""
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_batch_size
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
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 UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Tuple = tmp_path / """cache"""
UpperCamelCase : List[Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase : Dict = ParquetDatasetReader(SCREAMING_SNAKE_CASE , cache_dir=SCREAMING_SNAKE_CASE , keep_in_memory=SCREAMING_SNAKE_CASE ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@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 UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Any = tmp_path / """cache"""
UpperCamelCase : Optional[Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
UpperCamelCase : Union[str, Any] = features.copy() if features else default_expected_features
UpperCamelCase : Optional[int] = (
Features({feature: Value(SCREAMING_SNAKE_CASE ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase : Tuple = ParquetDatasetReader(SCREAMING_SNAKE_CASE , features=SCREAMING_SNAKE_CASE , cache_dir=SCREAMING_SNAKE_CASE ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : List[Any] = tmp_path / """cache"""
UpperCamelCase : Optional[int] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
UpperCamelCase : Union[str, Any] = ParquetDatasetReader(SCREAMING_SNAKE_CASE , cache_dir=SCREAMING_SNAKE_CASE , split=SCREAMING_SNAKE_CASE ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
assert dataset.split == split if split else "train"
@pytest.mark.parametrize("""path_type""" , [str, list] )
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
if issubclass(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : int = parquet_path
elif issubclass(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : List[Any] = [parquet_path]
UpperCamelCase : List[str] = tmp_path / """cache"""
UpperCamelCase : Dict = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
UpperCamelCase : List[str] = ParquetDatasetReader(SCREAMING_SNAKE_CASE , cache_dir=SCREAMING_SNAKE_CASE ).read()
_check_parquet_dataset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=("train",) ):
assert isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
for split in splits:
UpperCamelCase : Union[str, Any] = 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 UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Tuple = tmp_path / """cache"""
UpperCamelCase : List[Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
UpperCamelCase : str = ParquetDatasetReader(
{"""train""": parquet_path} , cache_dir=SCREAMING_SNAKE_CASE , keep_in_memory=SCREAMING_SNAKE_CASE ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@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 UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Tuple = tmp_path / """cache"""
UpperCamelCase : str = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
UpperCamelCase : Union[str, Any] = features.copy() if features else default_expected_features
UpperCamelCase : int = (
Features({feature: Value(SCREAMING_SNAKE_CASE ) for feature, dtype in features.items()} ) if features is not None else None
)
UpperCamelCase : Union[str, Any] = ParquetDatasetReader({"""train""": parquet_path} , features=SCREAMING_SNAKE_CASE , cache_dir=SCREAMING_SNAKE_CASE ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
@pytest.mark.parametrize("""split""" , [None, NamedSplit("""train""" ), """train""", """test"""] )
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
if split:
UpperCamelCase : Optional[int] = {split: parquet_path}
else:
UpperCamelCase : Any = """train"""
UpperCamelCase : str = {"""train""": parquet_path, """test""": parquet_path}
UpperCamelCase : Union[str, Any] = tmp_path / """cache"""
UpperCamelCase : Union[str, Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
UpperCamelCase : Optional[Any] = ParquetDatasetReader(SCREAMING_SNAKE_CASE , cache_dir=SCREAMING_SNAKE_CASE ).read()
_check_parquet_datasetdict(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , splits=list(path.keys() ) )
assert all(dataset[split].split == split for split in path.keys() )
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Any = ParquetDatasetWriter(SCREAMING_SNAKE_CASE , tmp_path / """foo.parquet""" )
assert writer.write() > 0
UpperCamelCase : str = pq.ParquetFile(tmp_path / """foo.parquet""" )
UpperCamelCase : Optional[Any] = pf.read()
assert dataset.data.table == output_table
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
UpperCamelCase : Any = str(shared_datadir / """test_image_rgb.jpg""" )
UpperCamelCase : Any = {"""image""": [image_path]}
UpperCamelCase : List[Any] = Features({"""image""": Image()} )
UpperCamelCase : Optional[Any] = Dataset.from_dict(SCREAMING_SNAKE_CASE , features=SCREAMING_SNAKE_CASE )
UpperCamelCase : Tuple = ParquetDatasetWriter(SCREAMING_SNAKE_CASE , tmp_path / """foo.parquet""" )
assert writer.write() > 0
UpperCamelCase : Any = Dataset.from_parquet(str(tmp_path / """foo.parquet""" ) )
assert dataset.features == reloaded_dataset.features
UpperCamelCase : Dict = ParquetDatasetReader(str(tmp_path / """foo.parquet""" ) , streaming=SCREAMING_SNAKE_CASE ).read()
assert dataset.features == reloaded_iterable_dataset.features
@pytest.mark.parametrize(
"""feature, expected""" , [
(Features({"""foo""": Value("""int32""" )} ), None),
(Features({"""image""": Image(), """foo""": Value("""int32""" )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_IMAGE_DATASETS),
(Features({"""nested""": Sequence(Audio() )} ), config.PARQUET_ROW_GROUP_SIZE_FOR_AUDIO_DATASETS),
] , )
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
assert get_writer_batch_size(SCREAMING_SNAKE_CASE ) == expected
| 102 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__magic_name__ : int = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ : List[Any] = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
__magic_name__ : Optional[int] = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
__magic_name__ : str = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
__magic_name__ : Optional[Any] = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
__magic_name__ : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 102 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
snake_case = logging.get_logger(__name__)
snake_case = """▁"""
snake_case = {"""vocab_file""": """sentencepiece.bpe.model"""}
snake_case = {
"""vocab_file""": {
"""facebook/mbart-large-en-ro""": (
"""https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
),
"""facebook/mbart-large-cc25""": (
"""https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
),
}
}
snake_case = {
"""facebook/mbart-large-en-ro""": 1_024,
"""facebook/mbart-large-cc25""": 1_024,
}
# fmt: off
snake_case = ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]
class SCREAMING_SNAKE_CASE ( __a ):
'''simple docstring'''
UpperCamelCase_ : str = VOCAB_FILES_NAMES
UpperCamelCase_ : Dict = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCamelCase_ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
UpperCamelCase_ : List[str] = ['''input_ids''', '''attention_mask''']
UpperCamelCase_ : Union[str, Any] = []
UpperCamelCase_ : str = []
def __init__( self : Optional[int] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : int="<s>" , UpperCAmelCase_ : str="</s>" , UpperCAmelCase_ : Tuple="</s>" , UpperCAmelCase_ : Union[str, Any]="<s>" , UpperCAmelCase_ : Union[str, Any]="<unk>" , UpperCAmelCase_ : Optional[Any]="<pad>" , UpperCAmelCase_ : List[Any]="<mask>" , UpperCAmelCase_ : Optional[int]=None , UpperCAmelCase_ : str=None , UpperCAmelCase_ : Optional[Any]=None , UpperCAmelCase_ : Optional[Dict[str, Any]] = None , UpperCAmelCase_ : Union[str, Any]=None , **UpperCAmelCase_ : Optional[int] , ):
SCREAMING_SNAKE_CASE : List[Any] = AddedToken(a_ , lstrip=a_ , rstrip=a_ ) if isinstance(a_ , a_ ) else mask_token
SCREAMING_SNAKE_CASE : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=a_ , eos_token=a_ , unk_token=a_ , sep_token=a_ , cls_token=a_ , pad_token=a_ , mask_token=a_ , tokenizer_file=a_ , src_lang=a_ , tgt_lang=a_ , additional_special_tokens=a_ , sp_model_kwargs=self.sp_model_kwargs , **a_ , )
SCREAMING_SNAKE_CASE : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(a_ ) )
SCREAMING_SNAKE_CASE : List[Any] = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
SCREAMING_SNAKE_CASE : List[Any] = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
SCREAMING_SNAKE_CASE : List[Any] = 1
SCREAMING_SNAKE_CASE : List[str] = len(self.sp_model )
SCREAMING_SNAKE_CASE : Tuple = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a_ )
}
SCREAMING_SNAKE_CASE : Any = {v: k for k, v in self.lang_code_to_id.items()}
SCREAMING_SNAKE_CASE : Any = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
SCREAMING_SNAKE_CASE : int = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
SCREAMING_SNAKE_CASE : Optional[int] = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
SCREAMING_SNAKE_CASE : Union[str, Any] = src_lang if src_lang is not None else """en_XX"""
SCREAMING_SNAKE_CASE : Dict = self.lang_code_to_id[self._src_lang]
SCREAMING_SNAKE_CASE : Optional[Any] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self : List[str] ):
SCREAMING_SNAKE_CASE : Optional[Any] = self.__dict__.copy()
SCREAMING_SNAKE_CASE : str = None
SCREAMING_SNAKE_CASE : Any = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : List[Any] , UpperCAmelCase_ : Optional[int] ):
SCREAMING_SNAKE_CASE : int = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
SCREAMING_SNAKE_CASE : Union[str, Any] = {}
SCREAMING_SNAKE_CASE : Optional[Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def _A ( self : Any ):
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def _A ( self : List[Any] ):
return self._src_lang
@src_lang.setter
def _A ( self : List[Any] , UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE : Optional[int] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _A ( self : Any , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None , UpperCAmelCase_ : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=a_ , token_ids_a=a_ , already_has_special_tokens=a_ )
SCREAMING_SNAKE_CASE : str = [1] * len(self.prefix_tokens )
SCREAMING_SNAKE_CASE : List[str] = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(a_ )) + suffix_ones
return prefix_ones + ([0] * len(a_ )) + ([0] * len(a_ )) + suffix_ones
def _A ( self : Dict , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _A ( self : List[str] , UpperCAmelCase_ : List[int] , UpperCAmelCase_ : Optional[List[int]] = None ):
SCREAMING_SNAKE_CASE : str = [self.sep_token_id]
SCREAMING_SNAKE_CASE : 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 _A ( self : Optional[Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] , UpperCAmelCase_ : Optional[str] , **UpperCAmelCase_ : Any ):
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model" )
SCREAMING_SNAKE_CASE : List[Any] = src_lang
SCREAMING_SNAKE_CASE : Dict = self(a_ , add_special_tokens=a_ , return_tensors=a_ , **a_ )
SCREAMING_SNAKE_CASE : Dict = self.convert_tokens_to_ids(a_ )
SCREAMING_SNAKE_CASE : Optional[Any] = tgt_lang_id
return inputs
def _A ( self : Optional[int] ):
SCREAMING_SNAKE_CASE : Optional[Any] = {self.convert_ids_to_tokens(a_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _A ( self : List[Any] , UpperCAmelCase_ : str ):
return self.sp_model.encode(a_ , out_type=a_ )
def _A ( self : Optional[int] , UpperCAmelCase_ : List[Any] ):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE : Any = self.sp_model.PieceToId(a_ )
# Need to return unknown token if the SP model returned 0
return spm_id + self.fairseq_offset if spm_id else self.unk_token_id
def _A ( self : Optional[int] , UpperCAmelCase_ : List[Any] ):
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def _A ( self : List[str] , UpperCAmelCase_ : Union[str, Any] ):
SCREAMING_SNAKE_CASE : Optional[int] = """""".join(a_ ).replace(a_ , " " ).strip()
return out_string
def _A ( self : Dict , UpperCAmelCase_ : str , UpperCAmelCase_ : Optional[str] = None ):
if not os.path.isdir(a_ ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE : List[str] = os.path.join(
a_ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , a_ )
elif not os.path.isfile(self.vocab_file ):
with open(a_ , "wb" ) as fi:
SCREAMING_SNAKE_CASE : str = self.sp_model.serialized_model_proto()
fi.write(a_ )
return (out_vocab_file,)
def _A ( self : Optional[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : str = "en_XX" , UpperCAmelCase_ : Optional[List[str]] = None , UpperCAmelCase_ : str = "ro_RO" , **UpperCAmelCase_ : Dict , ):
SCREAMING_SNAKE_CASE : Optional[Any] = src_lang
SCREAMING_SNAKE_CASE : int = tgt_lang
return super().prepare_seqaseq_batch(a_ , a_ , **a_ )
def _A ( self : Optional[Any] ):
return self.set_src_lang_special_tokens(self.src_lang )
def _A ( self : Tuple ):
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def _A ( self : Optional[Any] , UpperCAmelCase_ : Any ):
SCREAMING_SNAKE_CASE : List[Any] = self.lang_code_to_id[src_lang]
SCREAMING_SNAKE_CASE : Dict = []
SCREAMING_SNAKE_CASE : List[str] = [self.eos_token_id, self.cur_lang_code]
def _A ( self : Tuple , UpperCAmelCase_ : str ):
SCREAMING_SNAKE_CASE : Tuple = self.lang_code_to_id[lang]
SCREAMING_SNAKE_CASE : str = []
SCREAMING_SNAKE_CASE : Union[str, Any] = [self.eos_token_id, self.cur_lang_code]
| 706 |
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
snake_case = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : str , UpperCAmelCase_ : Union[str, Any]=-1 ):
# in NER datasets, the last column is usually reserved for NER label
SCREAMING_SNAKE_CASE : int = label_idx
def _A ( self : Any , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Union[Split, str] ):
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Optional[Any] = mode.value
SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(UpperCAmelCase_ , f'''{mode}.txt''' )
SCREAMING_SNAKE_CASE : int = 1
SCREAMING_SNAKE_CASE : Any = []
with open(UpperCAmelCase_ , encoding="utf-8" ) as f:
SCREAMING_SNAKE_CASE : Optional[int] = []
SCREAMING_SNAKE_CASE : List[str] = []
for line in f:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
if words:
examples.append(InputExample(guid=f'''{mode}-{guid_index}''' , words=UpperCAmelCase_ , labels=UpperCAmelCase_ ) )
guid_index += 1
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : Optional[int] = []
else:
SCREAMING_SNAKE_CASE : str = line.split(" " )
words.append(splits[0] )
if len(UpperCAmelCase_ ) > 1:
labels.append(splits[self.label_idx].replace("\n" , "" ) )
else:
# Examples could have no label for mode = "test"
labels.append("O" )
if words:
examples.append(InputExample(guid=f'''{mode}-{guid_index}''' , words=UpperCAmelCase_ , labels=UpperCAmelCase_ ) )
return examples
def _A ( self : Tuple , UpperCAmelCase_ : TextIO , UpperCAmelCase_ : TextIO , UpperCAmelCase_ : List ):
SCREAMING_SNAKE_CASE : List[str] = 0
for line in test_input_reader:
if line.startswith("-DOCSTART-" ) or line == "" or line == "\n":
writer.write(UpperCAmelCase_ )
if not preds_list[example_id]:
example_id += 1
elif preds_list[example_id]:
SCREAMING_SNAKE_CASE : Optional[int] = line.split()[0] + " " + preds_list[example_id].pop(0 ) + "\n"
writer.write(UpperCAmelCase_ )
else:
logger.warning("Maximum sequence length exceeded: No prediction for '%s'." , line.split()[0] )
def _A ( self : Optional[Any] , UpperCAmelCase_ : str ):
if path:
with open(UpperCAmelCase_ , "r" ) as f:
SCREAMING_SNAKE_CASE : List[Any] = f.read().splitlines()
if "O" not in labels:
SCREAMING_SNAKE_CASE : Tuple = ["O"] + labels
return labels
else:
return ["O", "B-MISC", "I-MISC", "B-PER", "I-PER", "B-ORG", "I-ORG", "B-LOC", "I-LOC"]
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def __init__( self : Union[str, Any] ):
# in CONLL2003 dataset chunk column is second-to-last
super().__init__(label_idx=-2 )
def _A ( self : Optional[int] , UpperCAmelCase_ : str ):
if path:
with open(UpperCAmelCase_ , "r" ) as f:
SCREAMING_SNAKE_CASE : Dict = f.read().splitlines()
if "O" not in labels:
SCREAMING_SNAKE_CASE : str = ["O"] + labels
return labels
else:
return [
"O",
"B-ADVP",
"B-INTJ",
"B-LST",
"B-PRT",
"B-NP",
"B-SBAR",
"B-VP",
"B-ADJP",
"B-CONJP",
"B-PP",
"I-ADVP",
"I-INTJ",
"I-LST",
"I-PRT",
"I-NP",
"I-SBAR",
"I-VP",
"I-ADJP",
"I-CONJP",
"I-PP",
]
class SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
def _A ( self : Dict , UpperCAmelCase_ : Any , UpperCAmelCase_ : Union[Split, str] ):
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : str = mode.value
SCREAMING_SNAKE_CASE : Union[str, Any] = os.path.join(UpperCAmelCase_ , f'''{mode}.txt''' )
SCREAMING_SNAKE_CASE : Optional[Any] = 1
SCREAMING_SNAKE_CASE : str = []
with open(UpperCAmelCase_ , encoding="utf-8" ) as f:
for sentence in parse_incr(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : List[Any] = []
SCREAMING_SNAKE_CASE : List[str] = []
for token in sentence:
words.append(token["form"] )
labels.append(token["upos"] )
assert len(UpperCAmelCase_ ) == len(UpperCAmelCase_ )
if words:
examples.append(InputExample(guid=f'''{mode}-{guid_index}''' , words=UpperCAmelCase_ , labels=UpperCAmelCase_ ) )
guid_index += 1
return examples
def _A ( self : str , UpperCAmelCase_ : TextIO , UpperCAmelCase_ : TextIO , UpperCAmelCase_ : List ):
SCREAMING_SNAKE_CASE : Dict = 0
for sentence in parse_incr(UpperCAmelCase_ ):
SCREAMING_SNAKE_CASE : Union[str, Any] = preds_list[example_id]
SCREAMING_SNAKE_CASE : Any = ""
for token in sentence:
out += f'''{token['form']} ({token['upos']}|{s_p.pop(0 )}) '''
out += "\n"
writer.write(UpperCAmelCase_ )
example_id += 1
def _A ( self : Dict , UpperCAmelCase_ : str ):
if path:
with open(UpperCAmelCase_ , "r" ) as f:
return f.read().splitlines()
else:
return [
"ADJ",
"ADP",
"ADV",
"AUX",
"CCONJ",
"DET",
"INTJ",
"NOUN",
"NUM",
"PART",
"PRON",
"PROPN",
"PUNCT",
"SCONJ",
"SYM",
"VERB",
"X",
]
| 488 | 0 |
"""simple docstring"""
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class lowercase_ ( yaml.SafeLoader ):
'''simple docstring'''
def lowerCAmelCase_ ( self : List[str] , _UpperCAmelCase : List[Any] ):
_A = [self.constructed_objects[key_node] for key_node, _ in node.value]
_A = [tuple(_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else key for key in keys]
_A = Counter(_UpperCAmelCase )
_A = [key for key in counter if counter[key] > 1]
if duplicate_keys:
raise TypeError(F'''Got duplicate yaml keys: {duplicate_keys}''' )
def lowerCAmelCase_ ( self : Tuple , _UpperCAmelCase : int , _UpperCAmelCase : Union[str, Any]=False ):
_A = super().construct_mapping(_UpperCAmelCase , deep=_UpperCAmelCase )
self._check_no_duplicates_on_constructed_node(_UpperCAmelCase )
return mapping
def _snake_case ( _snake_case : str ) -> Tuple[Optional[str], str]:
'''simple docstring'''
_A = list(readme_content.splitlines() )
if full_content and full_content[0] == "---" and "---" in full_content[1:]:
_A = full_content[1:].index('---' ) + 1
_A = '\n'.join(full_content[1:sep_idx] )
return yamlblock, "\n".join(full_content[sep_idx + 1 :] )
return None, "\n".join(_snake_case )
class lowercase_ ( __lowerCAmelCase ):
'''simple docstring'''
# class attributes
UpperCAmelCase : Union[str, Any] = {'''train_eval_index'''} # train-eval-index in the YAML metadata
@classmethod
def lowerCAmelCase_ ( cls : int , _UpperCAmelCase : Path ):
with open(_UpperCAmelCase , encoding='utf-8' ) as readme_file:
_A , _A = _split_yaml_from_readme(readme_file.read() )
if yaml_string is not None:
return cls.from_yaml_string(_UpperCAmelCase )
else:
return cls()
def lowerCAmelCase_ ( self : str , _UpperCAmelCase : Path ):
if path.exists():
with open(_UpperCAmelCase , encoding='utf-8' ) as readme_file:
_A = readme_file.read()
else:
_A = None
_A = self._to_readme(_UpperCAmelCase )
with open(_UpperCAmelCase , 'w' , encoding='utf-8' ) as readme_file:
readme_file.write(_UpperCAmelCase )
def lowerCAmelCase_ ( self : int , _UpperCAmelCase : Optional[str] = None ):
if readme_content is not None:
_A , _A = _split_yaml_from_readme(_UpperCAmelCase )
_A = '---\n' + self.to_yaml_string() + '---\n' + content
else:
_A = '---\n' + self.to_yaml_string() + '---\n'
return full_content
@classmethod
def lowerCAmelCase_ ( cls : List[str] , _UpperCAmelCase : str ):
_A = yaml.load(_UpperCAmelCase , Loader=_NoDuplicateSafeLoader ) or {}
# Convert the YAML keys to DatasetMetadata fields
_A = {
(key.replace('-' , '_' ) if key.replace('-' , '_' ) in cls._FIELDS_WITH_DASHES else key): value
for key, value in metadata_dict.items()
}
return cls(**_UpperCAmelCase )
def lowerCAmelCase_ ( self : Dict ):
return yaml.safe_dump(
{
(key.replace('_' , '-' ) if key in self._FIELDS_WITH_DASHES else key): value
for key, value in self.items()
} , sort_keys=_UpperCAmelCase , allow_unicode=_UpperCAmelCase , encoding='utf-8' , ).decode('utf-8' )
a = {
'''image-classification''': [],
'''translation''': [],
'''image-segmentation''': [],
'''fill-mask''': [],
'''automatic-speech-recognition''': [],
'''token-classification''': [],
'''sentence-similarity''': [],
'''audio-classification''': [],
'''question-answering''': [],
'''summarization''': [],
'''zero-shot-classification''': [],
'''table-to-text''': [],
'''feature-extraction''': [],
'''other''': [],
'''multiple-choice''': [],
'''text-classification''': [],
'''text-to-image''': [],
'''text2text-generation''': [],
'''zero-shot-image-classification''': [],
'''tabular-classification''': [],
'''tabular-regression''': [],
'''image-to-image''': [],
'''tabular-to-text''': [],
'''unconditional-image-generation''': [],
'''text-retrieval''': [],
'''text-to-speech''': [],
'''object-detection''': [],
'''audio-to-audio''': [],
'''text-generation''': [],
'''conversational''': [],
'''table-question-answering''': [],
'''visual-question-answering''': [],
'''image-to-text''': [],
'''reinforcement-learning''': [],
'''voice-activity-detection''': [],
'''time-series-forecasting''': [],
'''document-question-answering''': [],
}
if __name__ == "__main__":
from argparse import ArgumentParser
a = ArgumentParser(usage='''Validate the yaml metadata block of a README.md file.''')
ap.add_argument('''readme_filepath''')
a = ap.parse_args()
a = Path(args.readme_filepath)
a = DatasetMetadata.from_readme(readme_filepath)
print(dataset_metadata)
dataset_metadata.to_readme(readme_filepath)
| 7 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLogitsProcessor,
FlaxForcedEOSTokenLogitsProcessor,
FlaxLogitsProcessorList,
FlaxMinLengthLogitsProcessor,
FlaxTemperatureLogitsWarper,
FlaxTopKLogitsWarper,
FlaxTopPLogitsWarper,
)
@require_flax
class UpperCamelCase__ (unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> Optional[Any]:
lowerCamelCase : List[Any] = jnp.ones((batch_size, length) ) / length
return scores
def _lowercase ( self ) -> Optional[int]:
lowerCamelCase : Optional[Any] = None
lowerCamelCase : Optional[Any] = 20
lowerCamelCase : List[Any] = self._get_uniform_logits(batch_size=2 , length=UpperCamelCase__ )
# tweak scores to not be uniform anymore
lowerCamelCase : Optional[int] = scores.at[1, 5].set((1 / length) + 0.1 ) # peak, 1st batch
lowerCamelCase : List[Any] = scores.at[1, 10].set((1 / length) - 0.4 ) # valley, 1st batch
# compute softmax
lowerCamelCase : List[Any] = jax.nn.softmax(UpperCamelCase__ , axis=-1 )
lowerCamelCase : str = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase : Tuple = FlaxTemperatureLogitsWarper(temperature=1.3 )
lowerCamelCase : List[Any] = jax.nn.softmax(temp_dist_warper_sharper(UpperCamelCase__ , scores.copy() , cur_len=UpperCamelCase__ ) , axis=-1 )
lowerCamelCase : Optional[int] = jax.nn.softmax(temp_dist_warper_smoother(UpperCamelCase__ , scores.copy() , cur_len=UpperCamelCase__ ) , axis=-1 )
# uniform distribution stays uniform
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_sharp[0, :] , atol=1e-3 ) )
self.assertTrue(jnp.allclose(probs[0, :] , warped_prob_smooth[0, :] , atol=1e-3 ) )
# sharp peaks get higher, valleys get lower
self.assertLess(probs[1, :].max() , warped_prob_sharp[1, :].max() )
self.assertGreater(probs[1, :].min() , warped_prob_sharp[1, :].min() )
# smooth peaks get lower, valleys get higher
self.assertGreater(probs[1, :].max() , warped_prob_smooth[1, :].max() )
self.assertLess(probs[1, :].min() , warped_prob_smooth[1, :].min() )
def _lowercase ( self ) -> List[str]:
lowerCamelCase : Dict = None
lowerCamelCase : List[str] = 10
lowerCamelCase : Optional[int] = 2
# create ramp distribution
lowerCamelCase : Dict = np.broadcast_to(np.arange(UpperCamelCase__ )[None, :] , (batch_size, vocab_size) ).copy()
lowerCamelCase : Tuple = ramp_logits[1:, : vocab_size // 2] + vocab_size
lowerCamelCase : List[Any] = FlaxTopKLogitsWarper(3 )
lowerCamelCase : str = top_k_warp(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
# check that correct tokens are filtered
self.assertListEqual(jnp.isinf(scores[0] ).tolist() , 7 * [True] + 3 * [False] )
self.assertListEqual(jnp.isinf(scores[1] ).tolist() , 2 * [True] + 3 * [False] + 5 * [True] )
# check special case
lowerCamelCase : Union[str, Any] = 5
lowerCamelCase : Tuple = FlaxTopKLogitsWarper(top_k=1 , filter_value=0.0 , min_tokens_to_keep=3 )
lowerCamelCase : Union[str, Any] = np.broadcast_to(np.arange(UpperCamelCase__ )[None, :] , (batch_size, length) ).copy()
lowerCamelCase : Union[str, Any] = top_k_warp_safety_check(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
# min_tokens overwrites k: 3 tokens are kept => 2 tokens are nullified
self.assertListEqual((scores == 0.0).sum(axis=-1 ).tolist() , [2, 2] )
def _lowercase ( self ) -> Optional[Any]:
lowerCamelCase : Dict = None
lowerCamelCase : Tuple = 10
lowerCamelCase : int = 2
# create distribution and take log (inverse to Softmax as taken in TopPLogitsWarper)
lowerCamelCase : int = np.log(np.array([[0.3, 0.1, 0.1, 0.5], [0.15, 0.3, 0.3, 0.25]] ) )
lowerCamelCase : Dict = FlaxTopPLogitsWarper(0.8 )
lowerCamelCase : List[str] = np.exp(top_p_warp(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ ) )
# dist should be filtered to keep min num values so that sum is >= top_p
# exp (-inf) => 0
lowerCamelCase : List[str] = np.array([[0.3, 0.0, 0.0, 0.5], [0.0, 0.3, 0.3, 0.25]] )
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 ) )
# check edge cases with negative and extreme logits
lowerCamelCase : List[str] = np.broadcast_to(np.arange(UpperCamelCase__ )[None, :] , (batch_size, vocab_size) ).copy() - (
vocab_size // 2
)
# make ramp_logits more extreme
lowerCamelCase : str = ramp_logits[1] * 100.0
# make sure at least 2 tokens are kept
lowerCamelCase : str = FlaxTopPLogitsWarper(0.9 , min_tokens_to_keep=2 , filter_value=0.0 )
lowerCamelCase : Optional[Any] = top_p_warp(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
# first batch should keep three tokens, second batch would keep only 1, but due to `min_tokens_to_keep=2` keeps 2.
self.assertListEqual((filtered_dist != 0.0).sum(axis=-1 ).tolist() , [3, 2] )
def _lowercase ( self ) -> Optional[Any]:
lowerCamelCase : int = 20
lowerCamelCase : Optional[Any] = 4
lowerCamelCase : List[str] = 0
lowerCamelCase : List[str] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase__ )
# check that min length is applied at length 5
lowerCamelCase : str = ids_tensor((batch_size, 20) , vocab_size=20 )
lowerCamelCase : Any = 5
lowerCamelCase : Optional[int] = self._get_uniform_logits(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Optional[Any] = min_dist_processor(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
self.assertListEqual(scores_before_min_length[:, eos_token_id].tolist() , 4 * [-float("inf" )] )
# check that min length is not applied anymore at length 15
lowerCamelCase : Union[str, Any] = self._get_uniform_logits(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : List[Any] = 15
lowerCamelCase : str = min_dist_processor(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
self.assertFalse(jnp.isinf(UpperCamelCase__ ).any() )
def _lowercase ( self ) -> List[str]:
lowerCamelCase : Any = 20
lowerCamelCase : List[str] = 4
lowerCamelCase : Optional[Any] = 0
lowerCamelCase : Union[str, Any] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__ )
# check that all scores are -inf except the bos_token_id score
lowerCamelCase : Any = ids_tensor((batch_size, 1) , vocab_size=20 )
lowerCamelCase : Any = 1
lowerCamelCase : Tuple = self._get_uniform_logits(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = logits_processor(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
self.assertTrue(jnp.isneginf(scores[:, bos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, bos_token_id].tolist() , 4 * [0] ) # score for bos_token_id shold be zero
# check that bos_token_id is not forced if current length is greater than 1
lowerCamelCase : str = 3
lowerCamelCase : Union[str, Any] = self._get_uniform_logits(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Optional[int] = logits_processor(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
self.assertFalse(jnp.isinf(UpperCamelCase__ ).any() )
def _lowercase ( self ) -> Tuple:
lowerCamelCase : Optional[int] = 20
lowerCamelCase : str = 4
lowerCamelCase : str = 0
lowerCamelCase : Any = 5
lowerCamelCase : Optional[int] = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__ , eos_token_id=UpperCamelCase__ )
# check that all scores are -inf except the eos_token_id when max_length is reached
lowerCamelCase : List[str] = ids_tensor((batch_size, 4) , vocab_size=20 )
lowerCamelCase : Any = 4
lowerCamelCase : Any = self._get_uniform_logits(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : List[str] = logits_processor(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
self.assertTrue(jnp.isneginf(scores[:, eos_token_id + 1 :] ).all() )
self.assertListEqual(scores[:, eos_token_id].tolist() , 4 * [0] ) # score for eos_token_id should be zero
# check that eos_token_id is not forced if max_length is not reached
lowerCamelCase : Optional[Any] = 3
lowerCamelCase : Optional[Any] = self._get_uniform_logits(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Optional[int] = logits_processor(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
self.assertFalse(jnp.isinf(UpperCamelCase__ ).any() )
def _lowercase ( self ) -> Optional[int]:
lowerCamelCase : str = 4
lowerCamelCase : List[str] = 10
lowerCamelCase : List[str] = 15
lowerCamelCase : Optional[int] = 2
lowerCamelCase : List[Any] = 1
lowerCamelCase : str = 15
# dummy input_ids and scores
lowerCamelCase : Dict = ids_tensor((batch_size, sequence_length) , UpperCamelCase__ )
lowerCamelCase : Tuple = input_ids.copy()
lowerCamelCase : List[Any] = self._get_uniform_logits(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Optional[Any] = scores.copy()
# instantiate all dist processors
lowerCamelCase : Optional[Any] = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase : str = FlaxTopKLogitsWarper(3 )
lowerCamelCase : Tuple = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase : List[str] = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase__ )
lowerCamelCase : str = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__ )
lowerCamelCase : List[str] = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__ , eos_token_id=UpperCamelCase__ )
lowerCamelCase : Optional[int] = 10
# no processor list
lowerCamelCase : Dict = temp_dist_warp(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
lowerCamelCase : Tuple = top_k_warp(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
lowerCamelCase : List[str] = top_p_warp(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = min_dist_proc(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
lowerCamelCase : Optional[int] = bos_dist_proc(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
lowerCamelCase : Optional[Any] = eos_dist_proc(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
# with processor list
lowerCamelCase : List[Any] = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase : Union[str, Any] = processor(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
# scores should be equal
self.assertTrue(jnp.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
def _lowercase ( self ) -> Any:
lowerCamelCase : List[Any] = 4
lowerCamelCase : Optional[int] = 10
lowerCamelCase : Dict = 15
lowerCamelCase : Optional[int] = 2
lowerCamelCase : Dict = 1
lowerCamelCase : Optional[Any] = 15
# dummy input_ids and scores
lowerCamelCase : List[Any] = ids_tensor((batch_size, sequence_length) , UpperCamelCase__ )
lowerCamelCase : Any = input_ids.copy()
lowerCamelCase : Dict = self._get_uniform_logits(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Dict = scores.copy()
# instantiate all dist processors
lowerCamelCase : int = FlaxTemperatureLogitsWarper(temperature=0.5 )
lowerCamelCase : List[Any] = FlaxTopKLogitsWarper(3 )
lowerCamelCase : List[str] = FlaxTopPLogitsWarper(0.8 )
# instantiate all logits processors
lowerCamelCase : int = FlaxMinLengthLogitsProcessor(min_length=10 , eos_token_id=UpperCamelCase__ )
lowerCamelCase : List[Any] = FlaxForcedBOSTokenLogitsProcessor(bos_token_id=UpperCamelCase__ )
lowerCamelCase : Optional[Any] = FlaxForcedEOSTokenLogitsProcessor(max_length=UpperCamelCase__ , eos_token_id=UpperCamelCase__ )
lowerCamelCase : Dict = 10
# no processor list
def run_no_processor_list(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase : Dict = temp_dist_warp(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
lowerCamelCase : Tuple = top_k_warp(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
lowerCamelCase : Optional[int] = top_p_warp(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
lowerCamelCase : int = min_dist_proc(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
lowerCamelCase : Optional[Any] = bos_dist_proc(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
lowerCamelCase : Dict = eos_dist_proc(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
return scores
# with processor list
def run_processor_list(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
lowerCamelCase : Optional[Any] = FlaxLogitsProcessorList(
[temp_dist_warp, top_k_warp, top_p_warp, min_dist_proc, bos_dist_proc, eos_dist_proc] )
lowerCamelCase : Tuple = processor(UpperCamelCase__ , UpperCamelCase__ , cur_len=UpperCamelCase__ )
return scores
lowerCamelCase : Dict = jax.jit(UpperCamelCase__ )
lowerCamelCase : Optional[int] = jax.jit(UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = jitted_run_no_processor_list(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Optional[Any] = jitted_run_processor_list(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# scores should be equal
self.assertTrue(jnp.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 ) )
# input_ids should never be changed
self.assertListEqual(input_ids.tolist() , input_ids_comp.tolist() )
| 311 | 0 |
"""simple docstring"""
def lowerCamelCase_ ( _lowerCamelCase ):
if not isinstance(_lowerCamelCase , _lowerCamelCase ):
raise TypeError('only integers accepted as input' )
else:
lowerCamelCase__ : List[Any] = str(abs(_lowerCamelCase ) )
lowerCamelCase__ : Any = [list(_lowerCamelCase ) for char in range(len(_lowerCamelCase ) )]
for index in range(len(_lowerCamelCase ) ):
num_transpositions[index].pop(_lowerCamelCase )
return max(
int(''.join(list(_lowerCamelCase ) ) ) for transposition in num_transpositions )
if __name__ == "__main__":
__import__("doctest").testmod()
| 721 |
"""simple docstring"""
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from tqdm import tqdm
import transformers
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, StoppingCriteria, StoppingCriteriaList
A_ : str = ["\nclass", "\ndef", "\n#", "\n@", "\nprint", "\nif"]
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_=None, lowerCamelCase_=1 ):
'''simple docstring'''
lowerCamelCase__ : Any = tokenizer
lowerCamelCase__ : Optional[Any] = dataset
lowerCamelCase__ : int = len(lowerCamelCase_ ) if n_tasks is None else n_tasks
lowerCamelCase__ : Any = n_copies
def __iter__(self ):
'''simple docstring'''
lowerCamelCase__ : Dict = []
for task in range(self.n_tasks ):
# without strip, the model generate commented codes ...
prompts.append(self.tokenizer.eos_token + self.dataset[task]['prompt'].strip() )
lowerCamelCase__ : Optional[int] = self.tokenizer(lowerCamelCase_, padding=lowerCamelCase_, return_tensors='pt' )
for task in range(self.n_tasks ):
for _ in range(self.n_copies ):
yield {
"ids": outputs.input_ids[task],
"task_id": task,
"input_len": outputs.attention_mask[task].sum(),
}
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__(self, lowerCamelCase_, lowerCamelCase_, lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = start_length
lowerCamelCase__ : List[str] = eof_strings
lowerCamelCase__ : List[str] = tokenizer
def __call__(self, lowerCamelCase_, lowerCamelCase_, **lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase__ : Any = self.tokenizer.batch_decode(input_ids[:, self.start_length :] )
lowerCamelCase__ : Optional[Any] = []
for decoded_generation in decoded_generations:
done.append(any(stop_string in decoded_generation for stop_string in self.eof_strings ) )
return all(lowerCamelCase_ )
def lowerCamelCase_ ( _lowerCamelCase ):
lowerCamelCase__ : Optional[Any] = re.split('(%s)' % '|'.join(_lowerCamelCase ) , _lowerCamelCase )
# last string should be ""
return "".join(string_list[:-2] )
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase=20 , **_lowerCamelCase ):
lowerCamelCase__ : List[str] = defaultdict(_lowerCamelCase ) # dict of list of generated tokens
for step, batch in tqdm(enumerate(_lowerCamelCase ) ):
with torch.no_grad():
lowerCamelCase__ : str = batch['ids'].shape[-1]
lowerCamelCase__ : int = accelerator.unwrap_model(_lowerCamelCase ).generate(
input_ids=batch['ids'][:, : batch['input_len']] , num_return_sequences=_lowerCamelCase , **_lowerCamelCase )
# each task is generated batch_size times
lowerCamelCase__ : Optional[Any] = batch['task_id'].repeat(_lowerCamelCase )
lowerCamelCase__ : List[Any] = accelerator.pad_across_processes(
_lowerCamelCase , dim=1 , pad_index=tokenizer.pad_token_id )
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = accelerator.gather((generated_tokens, generated_tasks) )
lowerCamelCase__ : List[Any] = generated_tokens.cpu().numpy()
lowerCamelCase__ : Union[str, Any] = generated_tasks.cpu().numpy()
for task, generated_tokens in zip(_lowerCamelCase , _lowerCamelCase ):
gen_token_dict[task].append(_lowerCamelCase )
lowerCamelCase__ : str = [[] for _ in range(_lowerCamelCase )]
for task, generated_tokens in gen_token_dict.items():
for s in generated_tokens:
lowerCamelCase__ : Optional[Any] = tokenizer.decode(_lowerCamelCase , skip_special_tokens=_lowerCamelCase , clean_up_tokenization_spaces=_lowerCamelCase )
code_gens[task].append(remove_last_block(_lowerCamelCase ) )
return code_gens
def lowerCamelCase_ ( ):
# Setup configuration
lowerCamelCase__ : int = HfArgumentParser(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = parser.parse_args()
transformers.logging.set_verbosity_error()
# enables code execution in code_eval metric
lowerCamelCase__ : List[str] = args.HF_ALLOW_CODE_EVAL
# make sure tokenizer plays nice with multiprocessing
lowerCamelCase__ : Tuple = 'false'
if args.num_workers is None:
lowerCamelCase__ : List[Any] = multiprocessing.cpu_count()
# Use dataset load to feed to accelerate
lowerCamelCase__ : List[Any] = Accelerator()
set_seed(args.seed , device_specific=_lowerCamelCase )
# Load model and tokenizer
lowerCamelCase__ : Any = AutoTokenizer.from_pretrained(args.model_ckpt )
lowerCamelCase__ : Optional[int] = tokenizer.eos_token
lowerCamelCase__ : Any = AutoModelForCausalLM.from_pretrained(args.model_ckpt )
# Generation settings
lowerCamelCase__ : Optional[Any] = {
'do_sample': args.do_sample,
'temperature': args.temperature,
'max_new_tokens': args.max_new_tokens,
'top_p': args.top_p,
'top_k': args.top_k,
'stopping_criteria': StoppingCriteriaList([EndOfFunctionCriteria(0 , _lowerCamelCase , _lowerCamelCase )] ),
}
# Load evaluation dataset and metric
lowerCamelCase__ : Any = load_dataset('openai_humaneval' )
lowerCamelCase__ : Optional[int] = load_metric('code_eval' )
lowerCamelCase__ : List[Any] = args.num_tasks if args.num_tasks is not None else len(human_eval['test'] )
lowerCamelCase__ : Optional[int] = args.n_samples // args.batch_size
lowerCamelCase__ : Tuple = TokenizedDataset(_lowerCamelCase , human_eval['test'] , n_copies=_lowerCamelCase , n_tasks=_lowerCamelCase )
# do not confuse args.batch_size, which is actually the num_return_sequences
lowerCamelCase__ : Union[str, Any] = DataLoader(_lowerCamelCase , batch_size=1 )
# Run a quick test to see if code evaluation is enabled
try:
lowerCamelCase__ : List[Any] = code_eval_metric.compute(references=[''] , predictions=[['']] )
except ValueError as exception:
print(
'Code evaluation not enabled. Read the warning below carefully and then use `--HF_ALLOW_CODE_EVAL="1"`'
' flag to enable code evaluation.' )
raise exception
lowerCamelCase__ , lowerCamelCase__ : str = accelerator.prepare(_lowerCamelCase , _lowerCamelCase )
lowerCamelCase__ : Any = complete_code(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , n_tasks=_lowerCamelCase , batch_size=args.batch_size , **_lowerCamelCase , )
if accelerator.is_main_process:
lowerCamelCase__ : List[str] = []
for task in tqdm(range(_lowerCamelCase ) ):
lowerCamelCase__ : int = human_eval['test'][task]['test']
lowerCamelCase__ : Union[str, Any] = f'''check({human_eval['test'][task]['entry_point']})'''
references.append('\n' + test_func + '\n' + entry_point )
# Evaluate completions with "code_eval" metric
lowerCamelCase__ , lowerCamelCase__ : Any = code_eval_metric.compute(
references=_lowerCamelCase , predictions=_lowerCamelCase , num_workers=args.num_workers )
print(f'''Results: {pass_at_k}''' )
# Save results to json file
with open(args.output_file , 'w' ) as fp:
json.dump(_lowerCamelCase , _lowerCamelCase )
# For some reason the folliwng seems to be necessary sometimes for code_eval to work nice with multiprocessing
# https://stackoverflow.com/questions/60804599/python-multiprocessing-keeps-spawning-the-whole-script
if __name__ == "__main__":
main()
| 696 | 0 |
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class A :
'''simple docstring'''
def __init__( self : Optional[int] , _UpperCamelCase : Dict , ):
_lowercase: Any = parent
_lowercase: Tuple = 13
_lowercase: int = 7
_lowercase: Any = True
_lowercase: List[str] = True
_lowercase: Optional[int] = True
_lowercase: List[Any] = 99
_lowercase: Any = 32
_lowercase: Optional[Any] = 2
_lowercase: List[Any] = 4
_lowercase: Dict = 37
_lowercase: List[Any] = "gelu"
_lowercase: Any = 0.1
_lowercase: Dict = 0.1
_lowercase: Optional[int] = 512
_lowercase: Any = 16
_lowercase: Tuple = 2
_lowercase: Optional[Any] = 0.0_2
_lowercase: Any = 3
_lowercase: Dict = 4
_lowercase: str = None
def UpperCAmelCase__ ( self : List[str]):
_lowercase: Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
_lowercase: Any = None
if self.use_input_mask:
_lowercase: List[Any] = random_attention_mask([self.batch_size, self.seq_length])
_lowercase: Union[str, Any] = None
_lowercase: Union[str, Any] = None
_lowercase: List[Any] = None
if self.use_labels:
_lowercase: int = ids_tensor([self.batch_size] , self.type_sequence_label_size)
_lowercase: str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels)
_lowercase: List[Any] = ids_tensor([self.batch_size] , self.num_choices)
_lowercase: Union[str, Any] = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def UpperCAmelCase__ ( self : List[Any]):
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
): Optional[Any] = self.prepare_config_and_inputs()
_lowercase: List[Any] = True
_lowercase: str = floats_tensor([self.batch_size, self.seq_length, self.hidden_size])
_lowercase: str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2)
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def UpperCAmelCase__ ( self : Union[str, Any] , _UpperCamelCase : List[str] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Any , _UpperCamelCase : Dict , _UpperCamelCase : Optional[int] , _UpperCamelCase : Union[str, Any]):
_lowercase: Tuple = TFEsmModel(config=_UpperCamelCase)
_lowercase: str = {"input_ids": input_ids, "attention_mask": input_mask}
_lowercase: Optional[int] = model(_UpperCamelCase)
_lowercase: Optional[int] = [input_ids, input_mask]
_lowercase: int = model(_UpperCamelCase)
_lowercase: Tuple = model(_UpperCamelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase__ ( self : Any , _UpperCamelCase : Optional[int] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : str , _UpperCamelCase : List[str] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : int , _UpperCamelCase : Tuple , _UpperCamelCase : Any , ):
_lowercase: Dict = True
_lowercase: Optional[Any] = TFEsmModel(config=_UpperCamelCase)
_lowercase: Any = {
"input_ids": input_ids,
"attention_mask": input_mask,
"encoder_hidden_states": encoder_hidden_states,
"encoder_attention_mask": encoder_attention_mask,
}
_lowercase: Optional[int] = model(_UpperCamelCase)
_lowercase: Optional[int] = [input_ids, input_mask]
_lowercase: List[Any] = model(_UpperCamelCase , encoder_hidden_states=_UpperCamelCase)
# Also check the case where encoder outputs are not passed
_lowercase: Dict = model(_UpperCamelCase , attention_mask=_UpperCamelCase)
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size))
def UpperCAmelCase__ ( self : List[str] , _UpperCamelCase : str , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Any , _UpperCamelCase : int , _UpperCamelCase : Tuple , _UpperCamelCase : int):
_lowercase: int = TFEsmForMaskedLM(config=_UpperCamelCase)
_lowercase: List[str] = model([input_ids, input_mask])
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size))
def UpperCAmelCase__ ( self : str , _UpperCamelCase : List[Any] , _UpperCamelCase : str , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Dict , _UpperCamelCase : Any , _UpperCamelCase : List[str]):
_lowercase: Union[str, Any] = self.num_labels
_lowercase: int = TFEsmForTokenClassification(config=_UpperCamelCase)
_lowercase: Tuple = {"input_ids": input_ids, "attention_mask": input_mask}
_lowercase: Union[str, Any] = model(_UpperCamelCase)
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels))
def UpperCAmelCase__ ( self : Tuple):
_lowercase: Tuple = self.prepare_config_and_inputs()
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
): List[Any] = config_and_inputs
_lowercase: str = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class A ( lowerCamelCase_ , lowerCamelCase_ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase : Optional[Any] = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
lowerCamelCase : Tuple = (
{
'feature-extraction': TFEsmModel,
'fill-mask': TFEsmForMaskedLM,
'text-classification': TFEsmForSequenceClassification,
'token-classification': TFEsmForTokenClassification,
'zero-shot': TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
lowerCamelCase : str = False
lowerCamelCase : Tuple = False
def UpperCAmelCase__ ( self : Tuple):
_lowercase: List[str] = TFEsmModelTester(self)
_lowercase: str = ConfigTester(self , config_class=_UpperCamelCase , hidden_size=37)
def UpperCAmelCase__ ( self : int):
self.config_tester.run_common_tests()
def UpperCAmelCase__ ( self : Tuple):
_lowercase: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCamelCase)
def UpperCAmelCase__ ( self : Any):
_lowercase: List[str] = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*_UpperCamelCase)
def UpperCAmelCase__ ( self : Union[str, Any]):
_lowercase: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase)
def UpperCAmelCase__ ( self : Tuple):
_lowercase: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase)
@slow
def UpperCAmelCase__ ( self : Optional[int]):
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase: Tuple = TFEsmModel.from_pretrained(_UpperCamelCase)
self.assertIsNotNone(_UpperCamelCase)
@unittest.skip("Protein models do not support embedding resizing.")
def UpperCAmelCase__ ( self : List[str]):
pass
@unittest.skip("Protein models do not support embedding resizing.")
def UpperCAmelCase__ ( self : Tuple):
pass
def UpperCAmelCase__ ( self : Optional[Any]):
_lowercase , _lowercase: Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowercase: List[str] = model_class(_UpperCamelCase)
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer)
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
_lowercase: List[str] = model.get_bias()
assert isinstance(_UpperCamelCase , _UpperCamelCase)
for k, v in name.items():
assert isinstance(_UpperCamelCase , tf.Variable)
else:
_lowercase: List[str] = model.get_output_embeddings()
assert x is None
_lowercase: int = model.get_bias()
assert name is None
@require_tf
class A ( unittest.TestCase ):
'''simple docstring'''
@slow
def UpperCAmelCase__ ( self : Any):
_lowercase: Any = TFEsmForMaskedLM.from_pretrained("facebook/esm2_t6_8M_UR50D")
_lowercase: List[str] = tf.constant([[0, 1, 2, 3, 4, 5]])
_lowercase: Optional[Any] = model(_UpperCamelCase)[0]
_lowercase: Union[str, Any] = [1, 6, 33]
self.assertEqual(list(output.numpy().shape) , _UpperCamelCase)
# compare the actual values for a slice.
_lowercase: Optional[int] = tf.constant(
[
[
[8.9_2_1_5_1_8, -10.589_814, -6.4_6_7_1_3_0_7],
[-6.3_9_6_7_1_5_6, -13.911_377, -1.1_2_1_1_9_1_5],
[-7.7_8_1_2_4_7, -13.951_557, -3.7_4_0_5_9_2],
]
])
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-2))
@slow
def UpperCAmelCase__ ( self : Optional[Any]):
_lowercase: List[str] = TFEsmModel.from_pretrained("facebook/esm2_t6_8M_UR50D")
_lowercase: Tuple = tf.constant([[0, 6, 4, 13, 5, 4, 16, 12, 11, 7, 2]])
_lowercase: Union[str, Any] = model(_UpperCamelCase)[0]
# compare the actual values for a slice.
_lowercase: Any = tf.constant(
[
[
[0.1_4_4_4_3_0_9_2, 0.5_4_1_2_5_3_2_7, 0.3_2_4_7_7_3_9],
[0.3_0_3_4_0_4_8_4, 0.0_0_5_2_6_6_7_6, 0.3_1_0_7_7_7_2_2],
[0.3_2_2_7_8_0_4_3, -0.2_4_9_8_7_0_9_6, 0.3_4_1_4_6_2_8],
]
])
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1e-4))
| 226 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class __lowerCamelCase ( unittest.TestCase ):
@parameterized.expand([(None,), ('''foo.json''',)] )
def UpperCAmelCase__ ( self , UpperCAmelCase ):
lowerCamelCase_ = GenerationConfig(
do_sample=UpperCAmelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCAmelCase , config_name=UpperCAmelCase )
lowerCamelCase_ = GenerationConfig.from_pretrained(UpperCAmelCase , config_name=UpperCAmelCase )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample , UpperCAmelCase )
self.assertEqual(loaded_config.temperature , 0.7 )
self.assertEqual(loaded_config.length_penalty , 1.0 )
self.assertEqual(loaded_config.bad_words_ids , [[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k , 50 )
self.assertEqual(loaded_config.max_length , 20 )
self.assertEqual(loaded_config.max_time , UpperCAmelCase )
def UpperCAmelCase__ ( self ):
lowerCamelCase_ = AutoConfig.from_pretrained('''gpt2''' )
lowerCamelCase_ = GenerationConfig.from_model_config(UpperCAmelCase )
lowerCamelCase_ = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(UpperCAmelCase , UpperCAmelCase )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id , default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id , model_config.eos_token_id )
def UpperCAmelCase__ ( self ):
lowerCamelCase_ = GenerationConfig()
lowerCamelCase_ = {
'''max_new_tokens''': 1024,
'''foo''': '''bar''',
}
lowerCamelCase_ = copy.deepcopy(UpperCAmelCase )
lowerCamelCase_ = generation_config.update(**UpperCAmelCase )
# update_kwargs was not modified (no side effects)
self.assertEqual(UpperCAmelCase , UpperCAmelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens , 1024 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(UpperCAmelCase , {'''foo''': '''bar'''} )
def UpperCAmelCase__ ( self ):
lowerCamelCase_ = GenerationConfig()
lowerCamelCase_ = '''bar'''
with tempfile.TemporaryDirectory('''test-generation-config''' ) as tmp_dir:
generation_config.save_pretrained(UpperCAmelCase )
lowerCamelCase_ = GenerationConfig.from_pretrained(UpperCAmelCase )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo , '''bar''' )
lowerCamelCase_ = GenerationConfig.from_model_config(UpperCAmelCase )
assert not hasattr(UpperCAmelCase , '''foo''' ) # no new kwargs should be initialized if from config
def UpperCAmelCase__ ( self ):
lowerCamelCase_ = GenerationConfig()
self.assertEqual(default_config.temperature , 1.0 )
self.assertEqual(default_config.do_sample , UpperCAmelCase )
self.assertEqual(default_config.num_beams , 1 )
lowerCamelCase_ = GenerationConfig(
do_sample=UpperCAmelCase , temperature=0.7 , length_penalty=1.0 , bad_words_ids=[[1, 2, 3], [4, 5]] , )
self.assertEqual(config.temperature , 0.7 )
self.assertEqual(config.do_sample , UpperCAmelCase )
self.assertEqual(config.num_beams , 1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCAmelCase )
lowerCamelCase_ = GenerationConfig.from_pretrained(UpperCAmelCase , temperature=1.0 )
self.assertEqual(loaded_config.temperature , 1.0 )
self.assertEqual(loaded_config.do_sample , UpperCAmelCase )
self.assertEqual(loaded_config.num_beams , 1 ) # default value
@is_staging_test
class __lowerCamelCase ( unittest.TestCase ):
@classmethod
def UpperCAmelCase__ ( cls ):
lowerCamelCase_ = TOKEN
HfFolder.save_token(UpperCAmelCase )
@classmethod
def UpperCAmelCase__ ( cls ):
try:
delete_repo(token=cls._token , repo_id='''test-generation-config''' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='''valid_org/test-generation-config-org''' )
except HTTPError:
pass
def UpperCAmelCase__ ( self ):
lowerCamelCase_ = GenerationConfig(
do_sample=UpperCAmelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''test-generation-config''' , use_auth_token=self._token )
lowerCamelCase_ = GenerationConfig.from_pretrained(f"{USER}/test-generation-config" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''test-generation-config''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
UpperCAmelCase , repo_id='''test-generation-config''' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
lowerCamelCase_ = GenerationConfig.from_pretrained(f"{USER}/test-generation-config" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase__ ( self ):
lowerCamelCase_ = GenerationConfig(
do_sample=UpperCAmelCase , temperature=0.7 , length_penalty=1.0 , )
config.push_to_hub('''valid_org/test-generation-config-org''' , use_auth_token=self._token )
lowerCamelCase_ = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='''valid_org/test-generation-config-org''' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
UpperCAmelCase , repo_id='''valid_org/test-generation-config-org''' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
lowerCamelCase_ = GenerationConfig.from_pretrained('''valid_org/test-generation-config-org''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
| 29 | 0 |
"""simple docstring"""
import math
import os
import unittest
from transformers import MegatronBertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_PRETRAINING_MAPPING,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
)
class __lowercase :
"""simple docstring"""
def __init__(self , lowercase__ , lowercase__=13 , lowercase__=7 , lowercase__=True , lowercase__=True , lowercase__=True , lowercase__=True , lowercase__=99 , lowercase__=64 , lowercase__=32 , lowercase__=5 , lowercase__=4 , lowercase__=37 , lowercase__="gelu" , lowercase__=0.1 , lowercase__=0.1 , lowercase__=5_12 , lowercase__=16 , lowercase__=2 , lowercase__=0.02 , lowercase__=3 , lowercase__=4 , lowercase__=None , ):
snake_case_ : Optional[Any] = parent
snake_case_ : Optional[Any] = batch_size
snake_case_ : str = seq_length
snake_case_ : Union[str, Any] = is_training
snake_case_ : Tuple = use_input_mask
snake_case_ : int = use_token_type_ids
snake_case_ : List[Any] = use_labels
snake_case_ : List[Any] = vocab_size
snake_case_ : Any = hidden_size
snake_case_ : Tuple = embedding_size
snake_case_ : List[Any] = num_hidden_layers
snake_case_ : Optional[Any] = num_attention_heads
snake_case_ : List[str] = intermediate_size
snake_case_ : List[str] = hidden_act
snake_case_ : Dict = hidden_dropout_prob
snake_case_ : List[Any] = attention_probs_dropout_prob
snake_case_ : Tuple = max_position_embeddings
snake_case_ : Optional[int] = type_vocab_size
snake_case_ : List[str] = type_sequence_label_size
snake_case_ : Optional[int] = initializer_range
snake_case_ : Any = num_labels
snake_case_ : Any = num_choices
snake_case_ : Optional[int] = scope
def __UpperCamelCase (self ):
snake_case_ : List[str] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ : Union[str, Any] = None
if self.use_input_mask:
snake_case_ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ : Dict = None
if self.use_token_type_ids:
snake_case_ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ : Dict = None
snake_case_ : Tuple = None
snake_case_ : Optional[Any] = None
if self.use_labels:
snake_case_ : str = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ : str = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ : int = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ : Optional[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase (self ):
return MegatronBertConfig(
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 , embedding_size=self.embedding_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=__snake_case , initializer_range=self.initializer_range , )
def __UpperCamelCase (self , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
snake_case_ : Dict = MegatronBertModel(config=__snake_case )
model.to(__snake_case )
model.eval()
snake_case_ : Tuple = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case )
snake_case_ : str = model(__snake_case , token_type_ids=__snake_case )
snake_case_ : Optional[int] = model(__snake_case )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.hidden_size) )
def __UpperCamelCase (self , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
snake_case_ : Dict = MegatronBertForMaskedLM(config=__snake_case )
model.to(__snake_case )
model.eval()
snake_case_ : List[Any] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase (self , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
snake_case_ : Any = MegatronBertForCausalLM(config=__snake_case )
model.to(__snake_case )
model.eval()
snake_case_ : str = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase (self , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
snake_case_ : Dict = MegatronBertForNextSentencePrediction(config=__snake_case )
model.to(__snake_case )
model.eval()
snake_case_ : int = model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def __UpperCamelCase (self , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
snake_case_ : Dict = MegatronBertForPreTraining(config=__snake_case )
model.to(__snake_case )
model.eval()
snake_case_ : Optional[Any] = model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , next_sentence_label=__snake_case , )
self.parent.assertEqual(result.prediction_logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertEqual(result.seq_relationship_logits.shape , (self.batch_size, 2) )
def __UpperCamelCase (self , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
snake_case_ : Dict = MegatronBertForQuestionAnswering(config=__snake_case )
model.to(__snake_case )
model.eval()
snake_case_ : Tuple = model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , start_positions=__snake_case , end_positions=__snake_case , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __UpperCamelCase (self , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
snake_case_ : int = self.num_labels
snake_case_ : List[str] = MegatronBertForSequenceClassification(__snake_case )
model.to(__snake_case )
model.eval()
snake_case_ : List[str] = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __UpperCamelCase (self , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
snake_case_ : Optional[Any] = self.num_labels
snake_case_ : int = MegatronBertForTokenClassification(config=__snake_case )
model.to(__snake_case )
model.eval()
snake_case_ : Any = model(__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase (self , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
snake_case_ : Optional[Any] = self.num_choices
snake_case_ : Tuple = MegatronBertForMultipleChoice(config=__snake_case )
model.to(__snake_case )
model.eval()
snake_case_ : List[Any] = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ : List[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ : List[str] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
snake_case_ : Dict = model(
__snake_case , attention_mask=__snake_case , token_type_ids=__snake_case , labels=__snake_case , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __UpperCamelCase (self ):
snake_case_ : List[str] = self.prepare_config_and_inputs()
(
snake_case_
) : Optional[Any] = config_and_inputs
snake_case_ : List[Any] = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class __lowercase ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase):
"""simple docstring"""
_A : Tuple = (
(
MegatronBertModel,
MegatronBertForMaskedLM,
MegatronBertForCausalLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
)
if is_torch_available()
else ()
)
_A : Tuple = (
{
"""feature-extraction""": MegatronBertModel,
"""fill-mask""": MegatronBertForMaskedLM,
"""question-answering""": MegatronBertForQuestionAnswering,
"""text-classification""": MegatronBertForSequenceClassification,
"""text-generation""": MegatronBertForCausalLM,
"""token-classification""": MegatronBertForTokenClassification,
"""zero-shot""": MegatronBertForSequenceClassification,
}
if is_torch_available()
else {}
)
_A : List[str] = True
# test_resize_embeddings = False
_A : Union[str, Any] = False
def __UpperCamelCase (self , lowercase__ , lowercase__ , lowercase__=False ):
snake_case_ : Union[str, Any] = super()._prepare_for_class(__snake_case , __snake_case , return_labels=__snake_case )
if return_labels:
if model_class in get_values(__snake_case ):
snake_case_ : int = torch.zeros(
(self.model_tester.batch_size, self.model_tester.seq_length) , dtype=torch.long , device=__snake_case )
snake_case_ : str = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=__snake_case )
return inputs_dict
def __UpperCamelCase (self ):
snake_case_ : List[Any] = MegatronBertModelTester(self )
snake_case_ : Union[str, Any] = ConfigTester(self , config_class=__snake_case , hidden_size=37 )
def __UpperCamelCase (self ):
self.config_tester.run_common_tests()
def __UpperCamelCase (self ):
snake_case_ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_model(*__snake_case )
def __UpperCamelCase (self ):
snake_case_ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_masked_lm(*__snake_case )
def __UpperCamelCase (self ):
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_multiple_choice(*__snake_case )
def __UpperCamelCase (self ):
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_next_sequence_prediction(*__snake_case )
def __UpperCamelCase (self ):
snake_case_ : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_pretraining(*__snake_case )
def __UpperCamelCase (self ):
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_question_answering(*__snake_case )
def __UpperCamelCase (self ):
snake_case_ : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_sequence_classification(*__snake_case )
def __UpperCamelCase (self ):
snake_case_ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_megatron_bert_for_token_classification(*__snake_case )
def SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ : Optional[Any] ):
return torch.tensor(
a_ , dtype=torch.long , device=a_ , )
a_ = 1E-4
@require_torch
@require_sentencepiece
@require_tokenizers
class __lowercase ( unittest.TestCase):
"""simple docstring"""
@slow
@unittest.skip("""Model is not available.""" )
def __UpperCamelCase (self ):
snake_case_ : Union[str, Any] = '''nvidia/megatron-bert-uncased-345m'''
if "MYDIR" in os.environ:
snake_case_ : Any = os.path.join(os.environ["""MYDIR"""] , __snake_case )
snake_case_ : Union[str, Any] = MegatronBertModel.from_pretrained(__snake_case )
model.to(__snake_case )
model.half()
snake_case_ : List[str] = _long_tensor([[1_01, 71_10, 10_05, 10_56, 20_23, 1_13_33, 1_74_13, 10_29, 1_02]] )
with torch.no_grad():
snake_case_ : Any = model(__snake_case )[0]
snake_case_ : Tuple = torch.Size((1, 9, 10_24) )
self.assertEqual(output.shape , __snake_case )
snake_case_ : str = [-0.6040, -0.2517, -0.1025, 0.3420, -0.6758, -0.0017, -0.1089, -0.1990, 0.5728]
for ii in range(3 ):
for jj in range(3 ):
snake_case_ : int = output[0, ii, jj]
snake_case_ : str = expected[3 * ii + jj]
snake_case_ : Union[str, Any] = '''ii={} jj={} a={} b={}'''.format(__snake_case , __snake_case , __snake_case , __snake_case )
self.assertTrue(math.isclose(__snake_case , __snake_case , rel_tol=__snake_case , abs_tol=__snake_case ) , msg=__snake_case )
| 704 |
"""simple docstring"""
from copy import deepcopy
class __lowercase :
"""simple docstring"""
def __init__(self , lowercase__ = None , lowercase__ = None ):
if arr is None and size is not None:
snake_case_ : str = size
snake_case_ : Optional[Any] = [0] * size
elif arr is not None:
self.init(lowercase__ )
else:
raise ValueError("""Either arr or size must be specified""" )
def __UpperCamelCase (self , lowercase__ ):
snake_case_ : Optional[Any] = len(lowercase__ )
snake_case_ : int = deepcopy(lowercase__ )
for i in range(1 , self.size ):
snake_case_ : Optional[Any] = self.next_(lowercase__ )
if j < self.size:
self.tree[j] += self.tree[i]
def __UpperCamelCase (self ):
snake_case_ : Dict = self.tree[:]
for i in range(self.size - 1 , 0 , -1 ):
snake_case_ : Optional[int] = self.next_(lowercase__ )
if j < self.size:
arr[j] -= arr[i]
return arr
@staticmethod
def __UpperCamelCase (lowercase__ ):
return index + (index & (-index))
@staticmethod
def __UpperCamelCase (lowercase__ ):
return index - (index & (-index))
def __UpperCamelCase (self , lowercase__ , lowercase__ ):
if index == 0:
self.tree[0] += value
return
while index < self.size:
self.tree[index] += value
snake_case_ : Tuple = self.next_(lowercase__ )
def __UpperCamelCase (self , lowercase__ , lowercase__ ):
self.add(lowercase__ , value - self.get(lowercase__ ) )
def __UpperCamelCase (self , lowercase__ ):
if right == 0:
return 0
snake_case_ : List[str] = self.tree[0]
right -= 1 # make right inclusive
while right > 0:
result += self.tree[right]
snake_case_ : Optional[int] = self.prev(lowercase__ )
return result
def __UpperCamelCase (self , lowercase__ , lowercase__ ):
return self.prefix(lowercase__ ) - self.prefix(lowercase__ )
def __UpperCamelCase (self , lowercase__ ):
return self.query(lowercase__ , index + 1 )
def __UpperCamelCase (self , lowercase__ ):
value -= self.tree[0]
if value < 0:
return -1
snake_case_ : Tuple = 1 # Largest power of 2 <= size
while j * 2 < self.size:
j *= 2
snake_case_ : Tuple = 0
while j > 0:
if i + j < self.size and self.tree[i + j] <= value:
value -= self.tree[i + j]
i += j
j //= 2
return i
if __name__ == "__main__":
import doctest
doctest.testmod()
| 48 | 0 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ArrayaD, ClassLabel, Features, Image, Value
from datasets.features.features import ArrayaDExtensionType, cast_to_python_objects
from datasets.keyhash import DuplicatedKeysError, InvalidKeyError
from .utils import require_pil
class lowerCAmelCase_ ( _a ):
def snake_case_ ( self ) -> int:
UpperCamelCase : str = pa.array(TypedSequence([1, 2, 3] ) )
self.assertEqual(arr.type, pa.intaa() )
def snake_case_ ( self ) -> str:
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Tuple = pa.array(TypedSequence([1, 2, 3] ), type=pa.intaa() )
def snake_case_ ( self ) -> int:
with self.assertRaises(SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : Any = pa.array(TypedSequence([1, 2, 3], try_type=Value('bool' ), type=Value('int64' ) ) )
def snake_case_ ( self ) -> Dict:
UpperCamelCase : Tuple = pa.array(TypedSequence([1, 2, 3], type=Value('int32' ) ) )
self.assertEqual(arr.type, pa.intaa() )
def snake_case_ ( self ) -> List[str]:
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
UpperCamelCase : Dict = pa.array(TypedSequence(['foo', 'bar'], type=Value('int64' ) ) )
def snake_case_ ( self ) -> Union[str, Any]:
UpperCamelCase : Optional[Any] = pa.array(TypedSequence([1, 2, 3], try_type=Value('int32' ) ) )
self.assertEqual(arr.type, pa.intaa() )
def snake_case_ ( self ) -> List[Any]:
UpperCamelCase : List[Any] = pa.array(TypedSequence(['foo', 'bar'], try_type=Value('int64' ) ) )
self.assertEqual(arr.type, pa.string() )
def snake_case_ ( self ) -> List[str]:
UpperCamelCase : Union[str, Any] = pa.array(TypedSequence([[[1, 2, 3]]], type=ArrayaD((1, 3), 'int64' ) ) )
self.assertEqual(arr.type, ArrayaDExtensionType((1, 3), 'int64' ) )
def snake_case_ ( self ) -> Union[str, Any]:
with self.assertRaises((TypeError, pa.lib.ArrowInvalid) ):
UpperCamelCase : List[Any] = pa.array(TypedSequence(['foo', 'bar'], type=ArrayaD((1, 3), 'int64' ) ) )
def snake_case_ ( self ) -> List[Any]:
UpperCamelCase : Optional[int] = pa.array(TypedSequence([[[1, 2, 3]]], try_type=ArrayaD((1, 3), 'int64' ) ) )
self.assertEqual(arr.type, ArrayaDExtensionType((1, 3), 'int64' ) )
def snake_case_ ( self ) -> Tuple:
UpperCamelCase : Tuple = pa.array(TypedSequence(['foo', 'bar'], try_type=ArrayaD((1, 3), 'int64' ) ) )
self.assertEqual(arr.type, pa.string() )
@require_pil
def snake_case_ ( self ) -> List[str]:
import PIL.Image
UpperCamelCase : Any = PIL.Image.fromarray(np.arange(10, dtype=np.uinta ).reshape(2, 5 ) )
with patch(
'datasets.arrow_writer.cast_to_python_objects', side_effect=SCREAMING_SNAKE_CASE_ ) as mock_cast_to_python_objects:
UpperCamelCase : str = pa.array(TypedSequence([{'path': None, 'bytes': B'image_bytes'}, pil_image], type=Image() ) )
UpperCamelCase , UpperCamelCase : Tuple = mock_cast_to_python_objects.call_args_list[-1]
self.assertIn('optimize_list_casting', SCREAMING_SNAKE_CASE_ )
self.assertFalse(kwargs['optimize_list_casting'] )
def UpperCamelCase ( snake_case__ : List[str] , snake_case__ : int ) -> Optional[Any]:
UpperCamelCase : str = pa.BufferReader(snake_case__ ) if isinstance(snake_case__ , pa.Buffer ) else pa.memory_map(snake_case__ )
UpperCamelCase : Union[str, Any] = pa.ipc.open_stream(snake_case__ )
UpperCamelCase : Dict = f.read_all()
assert len(pa_table.to_batches() ) == expected_num_chunks
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
del pa_table
@pytest.mark.parametrize('writer_batch_size' , [None, 1, 10] )
@pytest.mark.parametrize(
'fields' , [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def UpperCamelCase ( snake_case__ : int , snake_case__ : Tuple ) -> List[str]:
UpperCamelCase : Dict = pa.BufferOutputStream()
UpperCamelCase : Union[str, Any] = pa.schema(snake_case__ ) if fields else None
with ArrowWriter(stream=snake_case__ , schema=snake_case__ , writer_batch_size=snake_case__ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
UpperCamelCase , UpperCamelCase : Union[str, Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : Any = {'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(snake_case__ , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def UpperCamelCase ( ) -> Tuple:
UpperCamelCase : str = pa.BufferOutputStream()
UpperCamelCase : str = Features({'labels': ClassLabel(names=['neg', 'pos'] )} )
with ArrowWriter(stream=snake_case__ , features=snake_case__ ) as writer:
writer.write({'labels': 0} )
writer.write({'labels': 1} )
UpperCamelCase , UpperCamelCase : Optional[int] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == features.arrow_schema
assert writer._schema.metadata == features.arrow_schema.metadata
UpperCamelCase : List[str] = pa.BufferReader(output.getvalue() )
UpperCamelCase : Union[str, Any] = pa.ipc.open_stream(snake_case__ )
UpperCamelCase : Optional[int] = f.read_all()
UpperCamelCase : List[str] = pa_table.schema
assert pa_table.num_rows == 2
assert schema == features.arrow_schema
assert schema.metadata == features.arrow_schema.metadata
assert features == Features.from_arrow_schema(snake_case__ )
@pytest.mark.parametrize('writer_batch_size' , [None, 1, 10] )
def UpperCamelCase ( snake_case__ : Optional[Any] ) -> str:
UpperCamelCase : Union[str, Any] = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case__ , writer_batch_size=snake_case__ , hash_salt='split_name' , check_duplicates=snake_case__ , ) as writer:
with pytest.raises(snake_case__ ):
writer.write({'col_1': 'foo', 'col_2': 1} , key=[1, 2] )
UpperCamelCase , UpperCamelCase : Dict = writer.finalize()
@pytest.mark.parametrize('writer_batch_size' , [None, 2, 10] )
def UpperCamelCase ( snake_case__ : Optional[Any] ) -> Optional[int]:
UpperCamelCase : Optional[int] = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case__ , writer_batch_size=snake_case__ , hash_salt='split_name' , check_duplicates=snake_case__ , ) as writer:
with pytest.raises(snake_case__ ):
writer.write({'col_1': 'foo', 'col_2': 1} , key=10 )
writer.write({'col_1': 'bar', 'col_2': 2} , key=10 )
UpperCamelCase , UpperCamelCase : Optional[Any] = writer.finalize()
@pytest.mark.parametrize('writer_batch_size' , [None, 2, 10] )
def UpperCamelCase ( snake_case__ : str ) -> Optional[int]:
UpperCamelCase : Optional[Any] = pa.BufferOutputStream()
with ArrowWriter(
stream=snake_case__ , writer_batch_size=snake_case__ , hash_salt='split_name' , check_duplicates=snake_case__ , ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} , key=1 )
writer.write({'col_1': 'bar', 'col_2': 2} , key=2 )
UpperCamelCase , UpperCamelCase : List[str] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size' , [None, 1, 10] )
@pytest.mark.parametrize(
'fields' , [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def UpperCamelCase ( snake_case__ : Optional[int] , snake_case__ : Union[str, Any] ) -> List[Any]:
UpperCamelCase : Tuple = pa.BufferOutputStream()
UpperCamelCase : Dict = pa.schema(snake_case__ ) if fields else None
with ArrowWriter(stream=snake_case__ , schema=snake_case__ , writer_batch_size=snake_case__ ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
writer.write_batch({'col_1': [], 'col_2': []} )
UpperCamelCase , UpperCamelCase : Union[str, Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : List[Any] = {'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(snake_case__ , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size' , [None, 1, 10] )
@pytest.mark.parametrize(
'fields' , [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def UpperCamelCase ( snake_case__ : Optional[Any] , snake_case__ : Optional[int] ) -> List[str]:
UpperCamelCase : Union[str, Any] = pa.BufferOutputStream()
UpperCamelCase : List[Any] = pa.schema(snake_case__ ) if fields else None
with ArrowWriter(stream=snake_case__ , schema=snake_case__ , writer_batch_size=snake_case__ ) as writer:
writer.write_table(pa.Table.from_pydict({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} ) )
UpperCamelCase , UpperCamelCase : int = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : Optional[Any] = {'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(snake_case__ , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
@pytest.mark.parametrize('writer_batch_size' , [None, 1, 10] )
@pytest.mark.parametrize(
'fields' , [None, {'col_1': pa.string(), 'col_2': pa.intaa()}, {'col_1': pa.string(), 'col_2': pa.intaa()}] )
def UpperCamelCase ( snake_case__ : Any , snake_case__ : Dict ) -> Optional[int]:
UpperCamelCase : Any = pa.BufferOutputStream()
UpperCamelCase : Any = pa.schema(snake_case__ ) if fields else None
with ArrowWriter(stream=snake_case__ , schema=snake_case__ , writer_batch_size=snake_case__ ) as writer:
writer.write_row(pa.Table.from_pydict({'col_1': ['foo'], 'col_2': [1]} ) )
writer.write_row(pa.Table.from_pydict({'col_1': ['bar'], 'col_2': [2]} ) )
UpperCamelCase , UpperCamelCase : Optional[int] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
if not fields:
UpperCamelCase : Optional[Any] = {'col_1': pa.string(), 'col_2': pa.intaa()}
assert writer._schema == pa.schema(snake_case__ , metadata=writer._schema.metadata )
_check_output(output.getvalue() , expected_num_chunks=num_examples if writer_batch_size == 1 else 1 )
def UpperCamelCase ( ) -> List[Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
UpperCamelCase : str = {'col_1': pa.string(), 'col_2': pa.intaa()}
UpperCamelCase : int = os.path.join(snake_case__ , 'test.arrow' )
with ArrowWriter(path=snake_case__ , schema=pa.schema(snake_case__ ) ) as writer:
writer.write_batch({'col_1': ['foo', 'bar'], 'col_2': [1, 2]} )
UpperCamelCase , UpperCamelCase : Any = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert writer._schema == pa.schema(snake_case__ , metadata=writer._schema.metadata )
_check_output(snake_case__ , 1 )
def UpperCamelCase ( snake_case__ : str ) -> Dict:
if pa.types.is_list(snake_case__ ):
return get_base_dtype(arr_type.value_type )
else:
return arr_type
def UpperCamelCase ( snake_case__ : List[str] , snake_case__ : Optional[Any] ) -> Union[str, Any]:
if isinstance(lst[0] , snake_case__ ):
change_first_primitive_element_in_list(lst[0] , snake_case__ )
else:
UpperCamelCase : Dict = value
@pytest.mark.parametrize('optimized_int_type, expected_dtype' , [(None, pa.intaa()), (Value('int32' ), pa.intaa())] )
@pytest.mark.parametrize('sequence' , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def UpperCamelCase ( snake_case__ : int , snake_case__ : Tuple , snake_case__ : Any ) -> Tuple:
UpperCamelCase : Dict = pa.array(TypedSequence(snake_case__ , optimized_int_type=snake_case__ ) )
assert get_base_dtype(arr.type ) == expected_dtype
@pytest.mark.parametrize(
'col, expected_dtype' , [
('attention_mask', pa.inta()),
('special_tokens_mask', pa.inta()),
('token_type_ids', pa.inta()),
('input_ids', pa.intaa()),
('other', pa.intaa()),
] , )
@pytest.mark.parametrize('sequence' , [[1, 2, 3], [[1, 2, 3]], [[[1, 2, 3]]]] )
def UpperCamelCase ( snake_case__ : str , snake_case__ : Tuple , snake_case__ : Tuple ) -> Dict:
UpperCamelCase : Optional[Any] = pa.array(OptimizedTypedSequence(snake_case__ , col=snake_case__ ) )
assert get_base_dtype(arr.type ) == expected_dtype
# not in range
if col != "other":
# avoids errors due to in-place modifications
UpperCamelCase : Tuple = copy.deepcopy(snake_case__ )
UpperCamelCase : str = np.iinfo(expected_dtype.to_pandas_dtype() ).max + 1
change_first_primitive_element_in_list(snake_case__ , snake_case__ )
UpperCamelCase : List[str] = pa.array(OptimizedTypedSequence(snake_case__ , col=snake_case__ ) )
assert get_base_dtype(arr.type ) == pa.intaa()
@pytest.mark.parametrize('raise_exception' , [False, True] )
def UpperCamelCase ( snake_case__ : Dict , snake_case__ : int ) -> Any:
UpperCamelCase : List[Any] = str(tmp_path / 'dataset-train.arrow' )
try:
with ArrowWriter(path=snake_case__ ) as writer:
if raise_exception:
raise pa.lib.ArrowInvalid()
else:
writer.stream.close()
except pa.lib.ArrowInvalid:
pass
finally:
assert writer.stream.closed
def UpperCamelCase ( snake_case__ : int ) -> Optional[Any]:
UpperCamelCase : List[Any] = 'mock://dataset-train.arrow'
with ArrowWriter(path=snake_case__ , storage_options=mockfs.storage_options ) as writer:
assert isinstance(writer._fs , type(snake_case__ ) )
assert writer._fs.storage_options == mockfs.storage_options
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
UpperCamelCase , UpperCamelCase : Optional[Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
assert mockfs.exists(snake_case__ )
def UpperCamelCase ( ) -> Union[str, Any]:
UpperCamelCase : str = pa.BufferOutputStream()
with ParquetWriter(stream=snake_case__ ) as writer:
writer.write({'col_1': 'foo', 'col_2': 1} )
writer.write({'col_1': 'bar', 'col_2': 2} )
UpperCamelCase , UpperCamelCase : Union[str, Any] = writer.finalize()
assert num_examples == 2
assert num_bytes > 0
UpperCamelCase : int = pa.BufferReader(output.getvalue() )
UpperCamelCase : Optional[Any] = pq.read_table(snake_case__ )
assert pa_table.to_pydict() == {"col_1": ["foo", "bar"], "col_2": [1, 2]}
@require_pil
@pytest.mark.parametrize('embed_local_files' , [False, True] )
def UpperCamelCase ( snake_case__ : Tuple , snake_case__ : List[str] ) -> str:
import PIL.Image
UpperCamelCase : str = str(tmp_path / 'test_image_rgb.jpg' )
PIL.Image.fromarray(np.zeros((5, 5) , dtype=np.uinta ) ).save(snake_case__ , format='png' )
UpperCamelCase : Optional[int] = pa.BufferOutputStream()
with ParquetWriter(
stream=snake_case__ , features=Features({'image': Image()} ) , embed_local_files=snake_case__ ) as writer:
writer.write({'image': image_path} )
writer.finalize()
UpperCamelCase : Union[str, Any] = pa.BufferReader(output.getvalue() )
UpperCamelCase : str = pq.read_table(snake_case__ )
UpperCamelCase : List[str] = pa_table.to_pydict()
if embed_local_files:
assert isinstance(out['image'][0]['path'] , snake_case__ )
with open(snake_case__ , 'rb' ) as f:
assert out["image"][0]["bytes"] == f.read()
else:
assert out["image"][0]["path"] == image_path
assert out["image"][0]["bytes"] is None
def UpperCamelCase ( ) -> str:
UpperCamelCase : str = pa.schema([pa.field('col_1' , pa.string() , nullable=snake_case__ )] )
UpperCamelCase : Tuple = pa.BufferOutputStream()
with ArrowWriter(stream=snake_case__ ) as writer:
writer._build_writer(inferred_schema=snake_case__ )
assert writer._schema == pa.schema([pa.field('col_1' , pa.string() )] )
| 40 |
"""simple docstring"""
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( _a , unittest.TestCase ):
_a = BertJapaneseTokenizer
_a = False
_a = True
def __lowercase ( self : Any ):
super().setUp()
lowerCAmelCase = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""こんにちは""",
"""こん""",
"""にちは""",
"""ばんは""",
"""##こん""",
"""##にちは""",
"""##ばんは""",
"""世界""",
"""##世界""",
"""、""",
"""##、""",
"""。""",
"""##。""",
]
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def __lowercase ( self : int , lowerCAmelCase : List[Any] ):
lowerCAmelCase = """こんにちは、世界。 \nこんばんは、世界。"""
lowerCAmelCase = """こんにちは 、 世界 。 こんばんは 、 世界 。"""
return input_text, output_text
def __lowercase ( self : Optional[Any] , lowerCAmelCase : List[Any] ):
lowerCAmelCase , lowerCAmelCase = self.get_input_output_texts(lowerCAmelCase )
lowerCAmelCase = tokenizer.encode(lowerCAmelCase , add_special_tokens=lowerCAmelCase )
lowerCAmelCase = tokenizer.decode(lowerCAmelCase , clean_up_tokenization_spaces=lowerCAmelCase )
return text, ids
def __lowercase ( self : List[str] ):
pass # TODO add if relevant
def __lowercase ( self : Optional[Any] ):
pass # TODO add if relevant
def __lowercase ( self : Any ):
pass # TODO add if relevant
def __lowercase ( self : List[Any] ):
lowerCAmelCase = self.tokenizer_class(self.vocab_file )
lowerCAmelCase = tokenizer.tokenize("""こんにちは、世界。\nこんばんは、世界。""" )
self.assertListEqual(lowerCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def __lowercase ( self : int ):
lowerCAmelCase = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""mecab""" )
self.assertIsNotNone(lowerCAmelCase )
lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
lowerCAmelCase = tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowerCAmelCase , """wb""" ) as handle:
pickle.dump(lowerCAmelCase , lowerCAmelCase )
with open(lowerCAmelCase , """rb""" ) as handle:
lowerCAmelCase = pickle.load(lowerCAmelCase )
lowerCAmelCase = tokenizer_new.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
def __lowercase ( self : str ):
lowerCAmelCase = MecabTokenizer(mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップルストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def __lowercase ( self : int ):
try:
lowerCAmelCase = MecabTokenizer(mecab_dic="""unidic_lite""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def __lowercase ( self : Optional[int] ):
try:
lowerCAmelCase = MecabTokenizer(mecab_dic="""unidic""" )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def __lowercase ( self : Any ):
lowerCAmelCase = MecabTokenizer(do_lower_case=lowerCAmelCase , mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップルストア""", """で""", """iphone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
def __lowercase ( self : Optional[int] ):
try:
lowerCAmelCase = MecabTokenizer(
do_lower_case=lowerCAmelCase , normalize_text=lowerCAmelCase , mecab_option="""-d /usr/local/lib/mecab/dic/jumandic""" )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
def __lowercase ( self : int ):
lowerCAmelCase = MecabTokenizer(normalize_text=lowerCAmelCase , mecab_dic="""ipadic""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱｯﾌﾟﾙストア""", """で""", """iPhone""", """８""", """が""", """発売""", """さ""", """れ""", """た""", """ """, """。"""] , )
@require_sudachi
def __lowercase ( self : List[Any] ):
lowerCAmelCase = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""sudachi""" )
self.assertIsNotNone(lowerCAmelCase )
lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
lowerCAmelCase = tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowerCAmelCase , """wb""" ) as handle:
pickle.dump(lowerCAmelCase , lowerCAmelCase )
with open(lowerCAmelCase , """rb""" ) as handle:
lowerCAmelCase = pickle.load(lowerCAmelCase )
lowerCAmelCase = tokenizer_new.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
@require_sudachi
def __lowercase ( self : str ):
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iPhone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def __lowercase ( self : str ):
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""A""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国""", """人""", """参政""", """権"""] )
@require_sudachi
def __lowercase ( self : Dict ):
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""B""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国人""", """参政権"""] )
@require_sudachi
def __lowercase ( self : int ):
lowerCAmelCase = SudachiTokenizer(sudachi_dict_type="""core""" , sudachi_split_mode="""C""" )
self.assertListEqual(tokenizer.tokenize("""外国人参政権""" ) , ["""外国人参政権"""] )
@require_sudachi
def __lowercase ( self : str ):
lowerCAmelCase = SudachiTokenizer(do_lower_case=lowerCAmelCase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """アップル""", """ストア""", """で""", """iphone""", """8""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """ """, """。""", """ """, """ """] , )
@require_sudachi
def __lowercase ( self : Tuple ):
lowerCAmelCase = SudachiTokenizer(normalize_text=lowerCAmelCase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , [""" """, """\t""", """ｱｯﾌﾟﾙ""", """ストア""", """で""", """iPhone""", """８""", """ """, """が""", """ """, """ """, """\n """, """発売""", """さ""", """れ""", """た""", """\u3000""", """。""", """ """, """ """] , )
@require_sudachi
def __lowercase ( self : List[Any] ):
lowerCAmelCase = SudachiTokenizer(trim_whitespace=lowerCAmelCase , sudachi_dict_type="""core""" )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れ""", """た""", """。"""] , )
@require_jumanpp
def __lowercase ( self : List[Any] ):
lowerCAmelCase = self.tokenizer_class(self.vocab_file , word_tokenizer_type="""jumanpp""" )
self.assertIsNotNone(lowerCAmelCase )
lowerCAmelCase = """こんにちは、世界。\nこんばんは、世界。"""
lowerCAmelCase = tokenizer.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , ["""こんにちは""", """、""", """世界""", """。""", """こん""", """##ばんは""", """、""", """世界""", """。"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
lowerCAmelCase = os.path.join(self.tmpdirname , """tokenizer.bin""" )
with open(lowerCAmelCase , """wb""" ) as handle:
pickle.dump(lowerCAmelCase , lowerCAmelCase )
with open(lowerCAmelCase , """rb""" ) as handle:
lowerCAmelCase = pickle.load(lowerCAmelCase )
lowerCAmelCase = tokenizer_new.tokenize(lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
@require_jumanpp
def __lowercase ( self : Optional[Any] ):
lowerCAmelCase = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def __lowercase ( self : Optional[Any] ):
lowerCAmelCase = JumanppTokenizer(do_lower_case=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iphone""", """8""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def __lowercase ( self : int ):
lowerCAmelCase = JumanppTokenizer(normalize_text=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""ｱ""", """ｯ""", """ﾌ""", """ﾟ""", """ﾙ""", """ストア""", """で""", """iPhone""", """８""", """\u3000""", """が""", """\u3000""", """\u3000""", """\u3000""", """発売""", """さ""", """れた""", """\u3000""", """。"""] , )
@require_jumanpp
def __lowercase ( self : Any ):
lowerCAmelCase = JumanppTokenizer(trim_whitespace=lowerCAmelCase )
self.assertListEqual(
tokenizer.tokenize(""" \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 """ ) , ["""アップル""", """ストア""", """で""", """iPhone""", """8""", """が""", """発売""", """さ""", """れた""", """。"""] , )
@require_jumanpp
def __lowercase ( self : Tuple ):
lowerCAmelCase = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize("""ありがとうございますm(_ _)ｍ見つけるのが大変です。""" ) , ["""ありがとう""", """ございます""", """m(_ _)m""", """見つける""", """の""", """が""", """大変です""", """。"""] , )
def __lowercase ( self : str ):
lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こんにちは""", """こん""", """にちは""", """ばんは""", """##こん""", """##にちは""", """##ばんは"""]
lowerCAmelCase = {}
for i, token in enumerate(lowerCAmelCase ):
lowerCAmelCase = i
lowerCAmelCase = WordpieceTokenizer(vocab=lowerCAmelCase , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ) , ["""こんにちは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは""" ) , ["""こん""", """##ばんは"""] )
self.assertListEqual(tokenizer.tokenize("""こんばんは こんばんにちは こんにちは""" ) , ["""こん""", """##ばんは""", """[UNK]""", """こんにちは"""] )
def __lowercase ( self : Dict ):
lowerCAmelCase = BertJapaneseTokenizer.from_pretrained("""nlp-waseda/roberta-base-japanese-with-auto-jumanpp""" )
lowerCAmelCase = tokenizer.subword_tokenizer
lowerCAmelCase = subword_tokenizer.tokenize("""国境 の 長い トンネル を 抜ける と 雪国 であった 。""" )
self.assertListEqual(lowerCAmelCase , ["""▁国境""", """▁の""", """▁長い""", """▁トンネル""", """▁を""", """▁抜ける""", """▁と""", """▁雪""", """国""", """▁であった""", """▁。"""] )
lowerCAmelCase = subword_tokenizer.tokenize("""こんばんは こんばん にち は こんにちは""" )
self.assertListEqual(lowerCAmelCase , ["""▁こん""", """ばん""", """は""", """▁こん""", """ばん""", """▁に""", """ち""", """▁は""", """▁こんにちは"""] )
def __lowercase ( self : str ):
lowerCAmelCase = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese""" )
lowerCAmelCase = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowerCAmelCase )
lowerCAmelCase = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowerCAmelCase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase , lowerCAmelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( _a , unittest.TestCase ):
_a = BertJapaneseTokenizer
_a = False
def __lowercase ( self : Union[str, Any] ):
super().setUp()
lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def __lowercase ( self : Optional[int] , **lowerCAmelCase : Optional[Any] ):
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type="""character""" , **lowerCAmelCase )
def __lowercase ( self : List[str] , lowerCAmelCase : Union[str, Any] ):
lowerCAmelCase = """こんにちは、世界。 \nこんばんは、世界。"""
lowerCAmelCase = """こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。"""
return input_text, output_text
def __lowercase ( self : List[Any] ):
pass # TODO add if relevant
def __lowercase ( self : Optional[Any] ):
pass # TODO add if relevant
def __lowercase ( self : int ):
pass # TODO add if relevant
def __lowercase ( self : Union[str, Any] ):
lowerCAmelCase = self.tokenizer_class(self.vocab_file , subword_tokenizer_type="""character""" )
lowerCAmelCase = tokenizer.tokenize("""こんにちは、世界。 \nこんばんは、世界。""" )
self.assertListEqual(
lowerCAmelCase , ["""こ""", """ん""", """に""", """ち""", """は""", """、""", """世""", """界""", """。""", """こ""", """ん""", """ば""", """ん""", """は""", """、""", """世""", """界""", """。"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(lowerCAmelCase ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def __lowercase ( self : Any ):
lowerCAmelCase = ["""[UNK]""", """[CLS]""", """[SEP]""", """こ""", """ん""", """に""", """ち""", """は""", """ば""", """世""", """界""", """、""", """。"""]
lowerCAmelCase = {}
for i, token in enumerate(lowerCAmelCase ):
lowerCAmelCase = i
lowerCAmelCase = CharacterTokenizer(vocab=lowerCAmelCase , unk_token="""[UNK]""" )
self.assertListEqual(tokenizer.tokenize("""""" ) , [] )
self.assertListEqual(tokenizer.tokenize("""こんにちは""" ) , ["""こ""", """ん""", """に""", """ち""", """は"""] )
self.assertListEqual(tokenizer.tokenize("""こんにちほ""" ) , ["""こ""", """ん""", """に""", """ち""", """[UNK]"""] )
def __lowercase ( self : Tuple ):
lowerCAmelCase = self.tokenizer_class.from_pretrained("""cl-tohoku/bert-base-japanese-char""" )
lowerCAmelCase = tokenizer.encode("""ありがとう。""" , add_special_tokens=lowerCAmelCase )
lowerCAmelCase = tokenizer.encode("""どういたしまして。""" , add_special_tokens=lowerCAmelCase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(lowerCAmelCase , lowerCAmelCase )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowercase ( self : Optional[int] ):
lowerCAmelCase = """cl-tohoku/bert-base-japanese"""
lowerCAmelCase = AutoTokenizer.from_pretrained(lowerCAmelCase )
self.assertIsInstance(lowerCAmelCase , lowerCAmelCase )
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowercase ( self : List[str] ):
lowerCAmelCase = """cl-tohoku/bert-base-japanese"""
with self.assertLogs("""transformers""" , level="""WARNING""" ) as cm:
BertTokenizer.from_pretrained(lowerCAmelCase )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
lowerCAmelCase = """bert-base-cased"""
with self.assertLogs("""transformers""" , level="""WARNING""" ) as cm:
BertJapaneseTokenizer.from_pretrained(lowerCAmelCase )
self.assertTrue(
cm.records[0].message.startswith(
"""The tokenizer class you load from this checkpoint is not the same type as the class this function"""
""" is called from.""" ) )
| 169 | 0 |
'''simple docstring'''
def snake_case_ ( __snake_case : int = 1000) -> int:
lowerCAmelCase_ = -1
lowerCAmelCase_ = 0
for a in range(1 , n // 3):
# Solving the two equations a**2+b**2=c**2 and a+b+c=N eliminating c
lowerCAmelCase_ = (n * n - 2 * a * n) // (2 * n - 2 * a)
lowerCAmelCase_ = n - a - b
if c * c == (a * a + b * b):
lowerCAmelCase_ = a * b * c
if candidate >= product:
lowerCAmelCase_ = candidate
return product
if __name__ == "__main__":
print(f'''{solution() = }''')
| 606 | '''simple docstring'''
from itertools import zip_longest
import requests
from bsa import BeautifulSoup
from pandas import DataFrame
def snake_case_ ( __snake_case : str = "laptop") -> DataFrame:
lowerCAmelCase_ = F'''https://www.amazon.in/laptop/s?k={product}'''
lowerCAmelCase_ = {
'''User-Agent''': '''Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36
(KHTML, like Gecko)Chrome/44.0.2403.157 Safari/537.36''',
'''Accept-Language''': '''en-US, en;q=0.5''',
}
lowerCAmelCase_ = BeautifulSoup(requests.get(__snake_case , headers=__snake_case).text)
# Initialize a Pandas dataframe with the column titles
lowerCAmelCase_ = DataFrame(
columns=[
'''Product Title''',
'''Product Link''',
'''Current Price of the product''',
'''Product Rating''',
'''MRP of the product''',
'''Discount''',
])
# Loop through each entry and store them in the dataframe
for item, _ in zip_longest(
soup.find_all(
'''div''' , attrs={'''class''': '''s-result-item''', '''data-component-type''': '''s-search-result'''} , ) , soup.find_all('''div''' , attrs={'''class''': '''a-row a-size-base a-color-base'''}) , ):
try:
lowerCAmelCase_ = item.ha.text
lowerCAmelCase_ = '''https://www.amazon.in/''' + item.ha.a['''href''']
lowerCAmelCase_ = item.find('''span''' , attrs={'''class''': '''a-offscreen'''}).text
try:
lowerCAmelCase_ = item.find('''span''' , attrs={'''class''': '''a-icon-alt'''}).text
except AttributeError:
lowerCAmelCase_ = '''Not available'''
try:
lowerCAmelCase_ = (
'''₹'''
+ item.find(
'''span''' , attrs={'''class''': '''a-price a-text-price'''}).text.split('''₹''')[1]
)
except AttributeError:
lowerCAmelCase_ = ''''''
try:
lowerCAmelCase_ = float(
(
(
float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
- float(product_price.strip('''₹''').replace(''',''' , ''''''))
)
/ float(product_mrp.strip('''₹''').replace(''',''' , ''''''))
)
* 100)
except ValueError:
lowerCAmelCase_ = float('''nan''')
except AttributeError:
pass
lowerCAmelCase_ = [
product_title,
product_link,
product_price,
product_rating,
product_mrp,
discount,
]
lowerCAmelCase_ = ''' '''
lowerCAmelCase_ = ''' '''
data_frame.index += 1
return data_frame
if __name__ == "__main__":
A_ : Optional[int] ='''headphones'''
get_amazon_product_data(product).to_csv(f'''Amazon Product Data for {product}.csv''')
| 606 | 1 |
"""simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class _UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
__snake_case = CustomTokenizer
pass
| 434 |
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase__ ( __magic_name__ : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("""List is empty""" )
return sum(__magic_name__ ) / len(__magic_name__ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 38 | 0 |
UpperCamelCase : str = """0.21.0"""
from .accelerator import Accelerator
from .big_modeling import (
cpu_offload,
cpu_offload_with_hook,
disk_offload,
dispatch_model,
init_empty_weights,
init_on_device,
load_checkpoint_and_dispatch,
)
from .data_loader import skip_first_batches
from .launchers import debug_launcher, notebook_launcher
from .state import PartialState
from .utils import (
DeepSpeedPlugin,
DistributedDataParallelKwargs,
DistributedType,
FullyShardedDataParallelPlugin,
GradScalerKwargs,
InitProcessGroupKwargs,
find_executable_batch_size,
infer_auto_device_map,
is_rich_available,
load_checkpoint_in_model,
synchronize_rng_states,
)
if is_rich_available():
from .utils import rich
| 719 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassification,
TFTrainer,
TFTrainingArguments,
)
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
def UpperCamelCase_ ( __a , __a , __a , __a , __a , __a = None , ) -> Union[str, Any]:
a__ : Optional[Any] = {}
if train_file is not None:
a__ : str = [train_file]
if eval_file is not None:
a__ : Dict = [eval_file]
if test_file is not None:
a__ : Tuple = [test_file]
a__ : int = datasets.load_dataset("csv" , data_files=__a )
a__ : List[str] = list(ds[list(files.keys() )[0]].features.keys() )
a__ : Any = features_name.pop(__a )
a__ : Union[str, Any] = list(set(ds[list(files.keys() )[0]][label_name] ) )
a__ : Any = {label: i for i, label in enumerate(__a )}
a__ : Union[str, Any] = tokenizer.model_input_names
a__ : Optional[int] = {}
if len(__a ) == 1:
for k in files.keys():
a__ : Any = ds[k].map(
lambda __a : tokenizer.batch_encode_plus(
example[features_name[0]] , truncation=__a , max_length=__a , padding="max_length" ) , batched=__a , )
elif len(__a ) == 2:
for k in files.keys():
a__ : Any = ds[k].map(
lambda __a : tokenizer.batch_encode_plus(
(example[features_name[0]], example[features_name[1]]) , truncation=__a , max_length=__a , padding="max_length" , ) , batched=__a , )
def gen_train():
for ex in transformed_ds[datasets.Split.TRAIN]:
a__ : Union[str, Any] = {k: v for k, v in ex.items() if k in input_names}
a__ : Union[str, Any] = labelaid[ex[label_name]]
yield (d, label)
def gen_val():
for ex in transformed_ds[datasets.Split.VALIDATION]:
a__ : Tuple = {k: v for k, v in ex.items() if k in input_names}
a__ : Tuple = labelaid[ex[label_name]]
yield (d, label)
def gen_test():
for ex in transformed_ds[datasets.Split.TEST]:
a__ : List[Any] = {k: v for k, v in ex.items() if k in input_names}
a__ : List[Any] = labelaid[ex[label_name]]
yield (d, label)
a__ : Tuple = (
tf.data.Dataset.from_generator(
__a , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TRAIN in transformed_ds
else None
)
if train_ds is not None:
a__ : Any = train_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TRAIN] ) ) )
a__ : str = (
tf.data.Dataset.from_generator(
__a , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.VALIDATION in transformed_ds
else None
)
if val_ds is not None:
a__ : Dict = val_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.VALIDATION] ) ) )
a__ : Optional[int] = (
tf.data.Dataset.from_generator(
__a , ({k: tf.intaa for k in input_names}, tf.intaa) , ({k: tf.TensorShape([None] ) for k in input_names}, tf.TensorShape([] )) , )
if datasets.Split.TEST in transformed_ds
else None
)
if test_ds is not None:
a__ : Any = test_ds.apply(tf.data.experimental.assert_cardinality(len(ds[datasets.Split.TEST] ) ) )
return train_ds, val_ds, test_ds, labelaid
UpperCamelCase : Tuple = logging.getLogger(__name__)
@dataclass
class A__ :
"""simple docstring"""
_lowercase = field(metadata={'help': 'Which column contains the label'} )
_lowercase = field(default=A__ , metadata={'help': 'The path of the training file'} )
_lowercase = field(default=A__ , metadata={'help': 'The path of the development file'} )
_lowercase = field(default=A__ , metadata={'help': 'The path of the test file'} )
_lowercase = field(
default=1_2_8 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
_lowercase = field(
default=A__ , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
@dataclass
class A__ :
"""simple docstring"""
_lowercase = field(
metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
_lowercase = field(
default=A__ , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
_lowercase = field(
default=A__ , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
_lowercase = field(default=A__ , metadata={'help': 'Set this flag to use fast tokenization.'} )
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
_lowercase = field(
default=A__ , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
def UpperCamelCase_ ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
a__ : str = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments) )
a__, a__, a__ : Any = parser.parse_args_into_dataclasses()
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'''Output directory ({training_args.output_dir}) already exists and is not empty. Use'''
" --overwrite_output_dir to overcome." )
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s" , datefmt="%m/%d/%Y %H:%M:%S" , level=logging.INFO , )
logger.info(
f'''n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1 )}, '''
f'''16-bits training: {training_args.fpaa}''' )
logger.info(f'''Training/evaluation parameters {training_args}''' )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
a__ : Dict = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , )
a__, a__, a__, a__ : Dict = get_tfds(
train_file=data_args.train_file , eval_file=data_args.dev_file , test_file=data_args.test_file , tokenizer=__a , label_column_id=data_args.label_column_id , max_seq_length=data_args.max_seq_length , )
a__ : int = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=len(__a ) , labelaid=__a , idalabel={id: label for label, id in labelaid.items()} , finetuning_task="text-classification" , cache_dir=model_args.cache_dir , )
with training_args.strategy.scope():
a__ : Optional[int] = TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_pt=bool(".bin" in model_args.model_name_or_path ) , config=__a , cache_dir=model_args.cache_dir , )
def compute_metrics(__a ) -> Dict:
a__ : List[str] = np.argmax(p.predictions , axis=1 )
return {"acc": (preds == p.label_ids).mean()}
# Initialize our Trainer
a__ : Optional[int] = TFTrainer(
model=__a , args=__a , train_dataset=__a , eval_dataset=__a , compute_metrics=__a , )
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
a__ : Union[str, Any] = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
a__ : str = trainer.evaluate()
a__ : List[str] = os.path.join(training_args.output_dir , "eval_results.txt" )
with open(__a , "w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in result.items():
logger.info(f''' {key} = {value}''' )
writer.write(f'''{key} = {value}\n''' )
results.update(__a )
return results
if __name__ == "__main__":
main()
| 151 | 0 |
from collections import defaultdict
from math import ceil, sqrt
def a ( a = 100_0000 , a = 10 ) ->int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = defaultdict(a )
for outer_width in range(3 , (t_limit // 4) + 2 ):
if outer_width * outer_width > t_limit:
SCREAMING_SNAKE_CASE = max(
ceil(sqrt(outer_width * outer_width - t_limit ) ) , 1 )
else:
SCREAMING_SNAKE_CASE = 1
hole_width_lower_bound += (outer_width - hole_width_lower_bound) % 2
for hole_width in range(a , outer_width - 1 , 2 ):
count[outer_width * outer_width - hole_width * hole_width] += 1
return sum(1 for n in count.values() if 1 <= n <= 10 )
if __name__ == "__main__":
print(F'''{solution() = }''') | 201 |
# Function to print upper half of diamond (pyramid)
def a ( a ) ->Optional[Any]:
'''simple docstring'''
for i in range(0 , a ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(''' ''' , end='''''' )
for _ in range(0 , i + 1 ): # printing stars
print('''* ''' , end='''''' )
print()
def a ( a ) ->Union[str, Any]:
'''simple docstring'''
for i in range(a , 0 , -1 ):
for _ in range(a , 0 , -1 ): # printing stars
print('''* ''' , end='''''' )
print()
for _ in range(n - i + 1 , 0 , -1 ): # printing spaces
print(''' ''' , end='''''' )
def a ( a ) ->Optional[int]:
'''simple docstring'''
if n <= 0:
print(''' ... .... nothing printing :(''' )
return
floyd(a ) # upper half
reverse_floyd(a ) # lower half
if __name__ == "__main__":
print(R'| /\ | |- | |- |--| |\ /| |-')
print(R'|/ \| |- |_ |_ |__| | \/ | |_')
__lowerCAmelCase = 1
while K:
__lowerCAmelCase = int(input('enter the number and , and see the magic : '))
print()
pretty_print(user_number)
__lowerCAmelCase = int(input('press 0 to exit... and 1 to continue...'))
print('Good Bye...') | 201 | 1 |
'''simple docstring'''
from datetime import datetime
import requests
def lowerCamelCase ( UpperCAmelCase__ : str ) -> bytes:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :int = 'https://downloadgram.net/wp-json/wppress/video-downloader/video?url='
SCREAMING_SNAKE_CASE__ :Optional[int] = requests.get(base_url + url ).json()[0]['urls'][0]['src']
return requests.get(UpperCAmelCase__ ).content
if __name__ == "__main__":
UpperCamelCase_ = input('''Enter Video/IGTV url: ''').strip()
UpperCamelCase_ = f"{datetime.now():%Y-%m-%d_%H:%M:%S}.mp4"
with open(file_name, '''wb''') as fp:
fp.write(download_video(url))
print(f"Done. Video saved to disk as {file_name}.")
| 320 | '''simple docstring'''
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
UpperCamelCase_ = logging.getLogger(__name__)
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Optional[Any] ) -> List[Any]:
'''simple docstring'''
if os.path.exists(UpperCAmelCase__ ):
if os.path.exists(os.path.join(UpperCAmelCase__ , 'config.json' ) ) and os.path.isfile(
os.path.join(UpperCAmelCase__ , 'config.json' ) ):
os.remove(os.path.join(UpperCAmelCase__ , 'config.json' ) )
if os.path.exists(os.path.join(UpperCAmelCase__ , 'pytorch_model.bin' ) ) and os.path.isfile(
os.path.join(UpperCAmelCase__ , 'pytorch_model.bin' ) ):
os.remove(os.path.join(UpperCAmelCase__ , 'pytorch_model.bin' ) )
else:
os.makedirs(UpperCAmelCase__ )
model.save_pretrained(UpperCAmelCase__ )
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : List[Any]=False ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :int = 2
if unlogit:
SCREAMING_SNAKE_CASE__ :int = torch.pow(UpperCAmelCase__ , UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = p * torch.log(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :str = 0
return -plogp.sum(dim=-1 )
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] ) -> List[str]:
'''simple docstring'''
logger.info('lv, h >\t' + '\t'.join(F'''{x + 1}''' for x in range(len(UpperCAmelCase__ ) ) ) )
for row in range(len(UpperCAmelCase__ ) ):
if tensor.dtype != torch.long:
logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:.5f}''' for x in tensor[row].cpu().data ) )
else:
logger.info(F'''layer {row + 1}:\t''' + '\t'.join(F'''{x:d}''' for x in tensor[row].cpu().data ) )
def lowerCamelCase ( UpperCAmelCase__ : Dict , UpperCAmelCase__ : Tuple , UpperCAmelCase__ : Optional[Any] , UpperCAmelCase__ : int=True , UpperCAmelCase__ : Optional[int]=True , UpperCAmelCase__ : Optional[int]=None , UpperCAmelCase__ : str=False ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ :Tuple = model.config.num_hidden_layers, model.config.num_attention_heads
SCREAMING_SNAKE_CASE__ :List[str] = torch.zeros(UpperCAmelCase__ , UpperCAmelCase__ ).to(args.device )
SCREAMING_SNAKE_CASE__ :Optional[int] = torch.zeros(UpperCAmelCase__ , UpperCAmelCase__ ).to(args.device )
if head_mask is None:
SCREAMING_SNAKE_CASE__ :Any = torch.ones(UpperCAmelCase__ , UpperCAmelCase__ ).to(args.device )
head_mask.requires_grad_(requires_grad=UpperCAmelCase__ )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
SCREAMING_SNAKE_CASE__ :Optional[Any] = None
SCREAMING_SNAKE_CASE__ :Dict = 0.0
SCREAMING_SNAKE_CASE__ :Any = 0.0
for step, inputs in enumerate(tqdm(UpperCAmelCase__ , desc='Iteration' , disable=args.local_rank not in [-1, 0] ) ):
SCREAMING_SNAKE_CASE__ :Union[str, Any] = tuple(t.to(args.device ) for t in inputs )
((SCREAMING_SNAKE_CASE__) , ) :Dict = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
SCREAMING_SNAKE_CASE__ :Optional[Any] = model(UpperCAmelCase__ , labels=UpperCAmelCase__ , head_mask=UpperCAmelCase__ )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ :Union[str, Any] = (
outputs[0],
outputs[1],
outputs[-1],
) # Loss and logits are the first, attention the last
loss.backward() # Backpropagate to populate the gradients in the head mask
total_loss += loss.detach().cpu().numpy()
if compute_entropy:
for layer, attn in enumerate(UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE__ :Optional[int] = entropy(attn.detach() , UpperCAmelCase__ )
attn_entropy[layer] += masked_entropy.sum(-1 ).sum(0 ).sum(0 ).detach()
if compute_importance:
head_importance += head_mask.grad.abs().detach()
tot_tokens += torch.ones_like(UpperCAmelCase__ ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
SCREAMING_SNAKE_CASE__ :List[str] = 2
SCREAMING_SNAKE_CASE__ :Optional[int] = torch.pow(torch.pow(UpperCAmelCase__ , UpperCAmelCase__ ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-20
if not args.dont_normalize_global_importance:
SCREAMING_SNAKE_CASE__ :Dict = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info('Attention entropies' )
print_ad_tensor(UpperCAmelCase__ )
if compute_importance:
logger.info('Head importance scores' )
print_ad_tensor(UpperCAmelCase__ )
logger.info('Head ranked by importance scores' )
SCREAMING_SNAKE_CASE__ :str = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
SCREAMING_SNAKE_CASE__ :Any = torch.arange(
head_importance.numel() , device=args.device )
SCREAMING_SNAKE_CASE__ :Optional[Any] = head_ranks.view_as(UpperCAmelCase__ )
print_ad_tensor(UpperCAmelCase__ )
return attn_entropy, head_importance, total_loss
def lowerCamelCase ( UpperCAmelCase__ : int , UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : Optional[int] ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ :Optional[int] = compute_heads_importance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , compute_entropy=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :List[Any] = 1 / loss # instead of downsteam score use the LM loss
logger.info('Pruning: original score: %f, threshold: %f' , UpperCAmelCase__ , original_score * args.masking_threshold )
SCREAMING_SNAKE_CASE__ :Optional[int] = torch.ones_like(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
SCREAMING_SNAKE_CASE__ :str = original_score
while current_score >= original_score * args.masking_threshold:
SCREAMING_SNAKE_CASE__ :Any = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
SCREAMING_SNAKE_CASE__ :str = float('Inf' )
SCREAMING_SNAKE_CASE__ :str = head_importance.view(-1 ).sort()[1]
if len(UpperCAmelCase__ ) <= num_to_mask:
print('BREAK BY num_to_mask' )
break
# mask heads
SCREAMING_SNAKE_CASE__ :Optional[int] = current_heads_to_mask[:num_to_mask]
logger.info('Heads to mask: %s' , str(current_heads_to_mask.tolist() ) )
SCREAMING_SNAKE_CASE__ :List[Any] = new_head_mask.view(-1 )
SCREAMING_SNAKE_CASE__ :str = 0.0
SCREAMING_SNAKE_CASE__ :Any = new_head_mask.view_as(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :str = new_head_mask.clone().detach()
print_ad_tensor(UpperCAmelCase__ )
# Compute metric and head importance again
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ :int = compute_heads_importance(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , compute_entropy=UpperCAmelCase__ , head_mask=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Any = 1 / loss
logger.info(
'Masking: current score: %f, remaining heads %d (%.1f percents)' , UpperCAmelCase__ , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 1_0_0 , )
logger.info('Final head mask' )
print_ad_tensor(UpperCAmelCase__ )
np.save(os.path.join(args.output_dir , 'head_mask.npy' ) , head_mask.detach().cpu().numpy() )
return head_mask
def lowerCamelCase ( UpperCAmelCase__ : Optional[int] , UpperCAmelCase__ : str , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :Any = datetime.now()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ :str = compute_heads_importance(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , compute_entropy=UpperCAmelCase__ , compute_importance=UpperCAmelCase__ , head_mask=UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Any = 1 / loss
SCREAMING_SNAKE_CASE__ :List[Any] = datetime.now() - before_time
SCREAMING_SNAKE_CASE__ :Union[str, Any] = sum(p.numel() for p in model.parameters() )
SCREAMING_SNAKE_CASE__ :Dict = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(UpperCAmelCase__ ) )
}
for k, v in heads_to_prune.items():
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE__ :Any = [
v,
]
assert sum(len(UpperCAmelCase__ ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :List[Any] = sum(p.numel() for p in model.parameters() )
SCREAMING_SNAKE_CASE__ :Optional[int] = datetime.now()
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ :Optional[Any] = compute_heads_importance(
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , compute_entropy=UpperCAmelCase__ , compute_importance=UpperCAmelCase__ , head_mask=UpperCAmelCase__ , actually_pruned=UpperCAmelCase__ , )
SCREAMING_SNAKE_CASE__ :List[Any] = 1 / loss
SCREAMING_SNAKE_CASE__ :Optional[int] = datetime.now() - before_time
logger.info(
'Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)' , UpperCAmelCase__ , UpperCAmelCase__ , pruned_num_params / original_num_params * 1_0_0 , )
logger.info('Pruning: score with masking: %f score with pruning: %f' , UpperCAmelCase__ , UpperCAmelCase__ )
logger.info('Pruning: speed ratio (original timing / new timing): %f percents' , original_time / new_time * 1_0_0 )
save_model(UpperCAmelCase__ , args.output_dir )
def lowerCamelCase ( ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--data_dir' , default=UpperCAmelCase__ , type=UpperCAmelCase__ , required=UpperCAmelCase__ , help='The input data dir. Should contain the .tsv files (or other data files) for the task.' , )
parser.add_argument(
'--model_name_or_path' , default=UpperCAmelCase__ , type=UpperCAmelCase__ , required=UpperCAmelCase__ , help='Path to pretrained model or model identifier from huggingface.co/models' , )
parser.add_argument(
'--output_dir' , default=UpperCAmelCase__ , type=UpperCAmelCase__ , required=UpperCAmelCase__ , help='The output directory where the model predictions and checkpoints will be written.' , )
# Other parameters
parser.add_argument(
'--config_name' , default='' , type=UpperCAmelCase__ , help='Pretrained config name or path if not the same as model_name_or_path' , )
parser.add_argument(
'--tokenizer_name' , default='' , type=UpperCAmelCase__ , help='Pretrained tokenizer name or path if not the same as model_name_or_path' , )
parser.add_argument(
'--cache_dir' , default=UpperCAmelCase__ , type=UpperCAmelCase__ , help='Where do you want to store the pre-trained models downloaded from s3' , )
parser.add_argument(
'--data_subset' , type=UpperCAmelCase__ , default=-1 , help='If > 0: limit the data to a subset of data_subset instances.' )
parser.add_argument(
'--overwrite_output_dir' , action='store_true' , help='Whether to overwrite data in output directory' )
parser.add_argument(
'--overwrite_cache' , action='store_true' , help='Overwrite the cached training and evaluation sets' )
parser.add_argument(
'--dont_normalize_importance_by_layer' , action='store_true' , help='Don\'t normalize importance score by layers' )
parser.add_argument(
'--dont_normalize_global_importance' , action='store_true' , help='Don\'t normalize all importance scores between 0 and 1' , )
parser.add_argument(
'--try_masking' , action='store_true' , help='Whether to try to mask head until a threshold of accuracy.' )
parser.add_argument(
'--masking_threshold' , default=0.9 , type=UpperCAmelCase__ , help='masking threshold in term of metrics (stop masking when metric < threshold * original metric value).' , )
parser.add_argument(
'--masking_amount' , default=0.1 , type=UpperCAmelCase__ , help='Amount to heads to masking at each masking step.' )
parser.add_argument('--metric_name' , default='acc' , type=UpperCAmelCase__ , help='Metric to use for head masking.' )
parser.add_argument(
'--max_seq_length' , default=1_2_8 , type=UpperCAmelCase__ , help=(
'The maximum total input sequence length after WordPiece tokenization. \n'
'Sequences longer than this will be truncated, sequences shorter padded.'
) , )
parser.add_argument('--batch_size' , default=1 , type=UpperCAmelCase__ , help='Batch size.' )
parser.add_argument('--seed' , type=UpperCAmelCase__ , default=4_2 )
parser.add_argument('--local_rank' , type=UpperCAmelCase__ , default=-1 , help='local_rank for distributed training on gpus' )
parser.add_argument('--no_cuda' , action='store_true' , help='Whether not to use CUDA when available' )
parser.add_argument('--server_ip' , type=UpperCAmelCase__ , default='' , help='Can be used for distant debugging.' )
parser.add_argument('--server_port' , type=UpperCAmelCase__ , default='' , help='Can be used for distant debugging.' )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = parser.parse_args()
if args.server_ip and args.server_port:
# Distant debugging - see https://code.visualstudio.com/docs/python/debugging#_attach-to-a-local-script
import ptvsd
print('Waiting for debugger attach' )
ptvsd.enable_attach(address=(args.server_ip, args.server_port) , redirect_output=UpperCAmelCase__ )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
SCREAMING_SNAKE_CASE__ :Tuple = torch.device('cuda' if torch.cuda.is_available() and not args.no_cuda else 'cpu' )
SCREAMING_SNAKE_CASE__ :Tuple = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
SCREAMING_SNAKE_CASE__ :Optional[int] = torch.device('cuda' , args.local_rank )
SCREAMING_SNAKE_CASE__ :List[Any] = 1
torch.distributed.init_process_group(backend='nccl' ) # Initializes the distributed backend
# Setup logging
logging.basicConfig(level=logging.INFO if args.local_rank in [-1, 0] else logging.WARN )
logger.info('device: {} n_gpu: {}, distributed: {}'.format(args.device , args.n_gpu , bool(args.local_rank != -1 ) ) )
SCREAMING_SNAKE_CASE__ :Any = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
SCREAMING_SNAKE_CASE__ :Tuple = nn.parallel.DistributedDataParallel(
UpperCAmelCase__ , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=UpperCAmelCase__ )
elif args.n_gpu > 1:
SCREAMING_SNAKE_CASE__ :Optional[int] = nn.DataParallel(UpperCAmelCase__ )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=UpperCAmelCase__ )
torch.save(UpperCAmelCase__ , os.path.join(args.output_dir , 'run_args.bin' ) )
logger.info('Training/evaluation parameters %s' , UpperCAmelCase__ )
# Prepare dataset
SCREAMING_SNAKE_CASE__ :Optional[Any] = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
SCREAMING_SNAKE_CASE__ :Optional[Any] = (torch.from_numpy(UpperCAmelCase__ ),)
SCREAMING_SNAKE_CASE__ :List[str] = TensorDataset(*UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = RandomSampler(UpperCAmelCase__ )
SCREAMING_SNAKE_CASE__ :int = DataLoader(UpperCAmelCase__ , sampler=UpperCAmelCase__ , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
# Try head masking (set heads to zero until the score goes under a threshole)
# and head pruning (remove masked heads and see the effect on the network)
if args.try_masking and args.masking_threshold > 0.0 and args.masking_threshold < 1.0:
SCREAMING_SNAKE_CASE__ :Tuple = mask_heads(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
prune_heads(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
main()
| 320 | 1 |
'''simple docstring'''
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
lowerCAmelCase : Dict = _symbol_database.Default()
lowerCAmelCase : Union[str, Any] = _descriptor_pool.Default().AddSerializedFile(
B'\n\x19sentencepiece_model.proto\x12\rsentencepiece"\x80\x0c\n\x0bTrainerSpec\x12\r\n\x05input\x18\x01 \x03(\t\x12\x14\n\x0cinput_format\x18\x07 \x01(\t\x12\x14\n\x0cmodel_prefix\x18\x02 \x01(\t\x12\x41\n\nmodel_type\x18\x03 \x01(\x0e\x32$.sentencepiece.TrainerSpec.ModelType:\x07UNIGRAM\x12\x18\n\nvocab_size\x18\x04 \x01(\x05:\x04\x38\x30\x30\x30\x12\x17\n\x0f\x61\x63\x63\x65pt_language\x18\x05 \x03(\t\x12 \n\x15self_test_sample_size\x18\x06 \x01(\x05:\x01\x30\x12*\n\x1b\x65nable_differential_privacy\x18\x32 \x01(\x08:\x05\x66\x61lse\x12+\n differential_privacy_noise_level\x18\x33 \x01(\x02:\x01\x30\x12\x32\n\'differential_privacy_clipping_threshold\x18\x34 \x01(\x04:\x01\x30\x12"\n\x12\x63haracter_coverage\x18\n \x01(\x02:\x06\x30.9995\x12\x1e\n\x13input_sentence_size\x18\x0b \x01(\x04:\x01\x30\x12$\n\x16shuffle_input_sentence\x18\x13 \x01(\x08:\x04true\x12 \n\x14mining_sentence_size\x18\x0c \x01(\x05\x42\x02\x18\x01\x12"\n\x16training_sentence_size\x18\r \x01(\x05\x42\x02\x18\x01\x12(\n\x17seed_sentencepiece_size\x18\x0e \x01(\x05:\x07\x31\x30\x30\x30\x30\x30\x30\x12\x1e\n\x10shrinking_factor\x18\x0f \x01(\x02:\x04\x30.75\x12!\n\x13max_sentence_length\x18\x12 \x01(\x05:\x04\x34\x31\x39\x32\x12\x17\n\x0bnum_threads\x18\x10 \x01(\x05:\x02\x31\x36\x12\x1d\n\x12num_sub_iterations\x18\x11 \x01(\x05:\x01\x32\x12$\n\x18max_sentencepiece_length\x18\x14 \x01(\x05:\x02\x31\x36\x12%\n\x17split_by_unicode_script\x18\x15 \x01(\x08:\x04true\x12\x1d\n\x0fsplit_by_number\x18\x17 \x01(\x08:\x04true\x12!\n\x13split_by_whitespace\x18\x16 \x01(\x08:\x04true\x12)\n\x1atreat_whitespace_as_suffix\x18\x18 \x01(\x08:\x05\x66\x61lse\x12+\n\x1c\x61llow_whitespace_only_pieces\x18\x1a \x01(\x08:\x05\x66\x61lse\x12\x1b\n\x0csplit_digits\x18\x19 \x01(\x08:\x05\x66\x61lse\x12#\n\x19pretokenization_delimiter\x18\x35 \x01(\t:\x00\x12\x17\n\x0f\x63ontrol_symbols\x18\x1e \x03(\t\x12\x1c\n\x14user_defined_symbols\x18\x1f \x03(\t\x12\x16\n\x0erequired_chars\x18$ \x01(\t\x12\x1c\n\rbyte_fallback\x18# \x01(\x08:\x05\x66\x61lse\x12+\n\x1dvocabulary_output_piece_score\x18 \x01(\x08:\x04true\x12\x1e\n\x10hard_vocab_limit\x18! \x01(\x08:\x04true\x12\x1c\n\ruse_all_vocab\x18" \x01(\x08:\x05\x66\x61lse\x12\x11\n\x06unk_id\x18( \x01(\x05:\x01\x30\x12\x11\n\x06\x62os_id\x18) \x01(\x05:\x01\x31\x12\x11\n\x06\x65os_id\x18* \x01(\x05:\x01\x32\x12\x12\n\x06pad_id\x18+ \x01(\x05:\x02-1\x12\x18\n\tunk_piece\x18- \x01(\t:\x05<unk>\x12\x16\n\tbos_piece\x18. \x01(\t:\x03<s>\x12\x17\n\teos_piece\x18/ \x01(\t:\x04</s>\x12\x18\n\tpad_piece\x18\x30 \x01(\t:\x05<pad>\x12\x1a\n\x0bunk_surface\x18, \x01(\t:\x05 \xe2\x81\x87 \x12+\n\x1ctrain_extremely_large_corpus\x18\x31 \x01(\x08:\x05\x66\x61lse"5\n\tModelType\x12\x0b\n\x07UNIGRAM\x10\x01\x12\x07\n\x03\x42PE\x10\x02\x12\x08\n\x04WORD\x10\x03\x12\x08\n\x04\x43HAR\x10\x04*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xd1\x01\n\x0eNormalizerSpec\x12\x0c\n\x04name\x18\x01 \x01(\t\x12\x1c\n\x14precompiled_charsmap\x18\x02 \x01(\x0c\x12\x1e\n\x10\x61\x64\x64_dummy_prefix\x18\x03 \x01(\x08:\x04true\x12&\n\x18remove_extra_whitespaces\x18\x04 \x01(\x08:\x04true\x12 \n\x12\x65scape_whitespaces\x18\x05 \x01(\x08:\x04true\x12\x1e\n\x16normalization_rule_tsv\x18\x06 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"y\n\x0cSelfTestData\x12\x33\n\x07samples\x18\x01 \x03(\x0b\x32".sentencepiece.SelfTestData.Sample\x1a)\n\x06Sample\x12\r\n\x05input\x18\x01 \x01(\t\x12\x10\n\x08\x65xpected\x18\x02 \x01(\t*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02"\xfe\x03\n\nModelProto\x12\x37\n\x06pieces\x18\x01 \x03(\x0b\x32\'.sentencepiece.ModelProto.SentencePiece\x12\x30\n\x0ctrainer_spec\x18\x02 \x01(\x0b\x32\x1a.sentencepiece.TrainerSpec\x12\x36\n\x0fnormalizer_spec\x18\x03 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x12\x33\n\x0eself_test_data\x18\x04 \x01(\x0b\x32\x1b.sentencepiece.SelfTestData\x12\x38\n\x11\x64\x65normalizer_spec\x18\x05 \x01(\x0b\x32\x1d.sentencepiece.NormalizerSpec\x1a\xd2\x01\n\rSentencePiece\x12\r\n\x05piece\x18\x01 \x01(\t\x12\r\n\x05score\x18\x02 \x01(\x02\x12\x42\n\x04type\x18\x03 \x01(\x0e\x32,.sentencepiece.ModelProto.SentencePiece.Type:\x06NORMAL"T\n\x04Type\x12\n\n\x06NORMAL\x10\x01\x12\x0b\n\x07UNKNOWN\x10\x02\x12\x0b\n\x07\x43ONTROL\x10\x03\x12\x10\n\x0cUSER_DEFINED\x10\x04\x12\x08\n\x04\x42YTE\x10\x06\x12\n\n\x06UNUSED\x10\x05*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02*\t\x08\xc8\x01\x10\x80\x80\x80\x80\x02\x42\x02H\x03'
)
lowerCAmelCase : Optional[Any] = globals()
_builder.BuildMessageAndEnumDescriptors(DESCRIPTOR, _globals)
_builder.BuildTopDescriptorsAndMessages(DESCRIPTOR, 'sentencepiece_model_pb2', _globals)
if _descriptor._USE_C_DESCRIPTORS is False:
lowerCAmelCase : Optional[int] = None
lowerCAmelCase : str = B'H\003'
# (generated by protobuf compiler, but `_TRAINERSPEC` is not defined)
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["mining_sentence_size"]._serialized_options = b"\030\001"
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._options = None
# _TRAINERSPEC.fields_by_name["training_sentence_size"]._serialized_options = b"\030\001"
lowerCAmelCase : Optional[int] = 45
lowerCAmelCase : Tuple = 15_81
lowerCAmelCase : Tuple = 15_17
lowerCAmelCase : Tuple = 15_70
lowerCAmelCase : Union[str, Any] = 15_84
lowerCAmelCase : Optional[int] = 17_93
lowerCAmelCase : int = 17_95
lowerCAmelCase : Dict = 19_16
lowerCAmelCase : List[Any] = 18_64
lowerCAmelCase : Any = 19_05
lowerCAmelCase : Any = 19_19
lowerCAmelCase : str = 24_29
lowerCAmelCase : str = 22_08
lowerCAmelCase : Any = 24_18
lowerCAmelCase : Dict = 23_23
lowerCAmelCase : Optional[int] = 24_07
# @@protoc_insertion_point(module_scope)
| 3 |
'''simple docstring'''
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def A ( self : Optional[Any] ):
"""simple docstring"""
__snake_case = mock.Mock()
__snake_case = 500
__snake_case = {}
__snake_case = HTTPError
__snake_case = {}
# Download this model to make sure it's in the cache.
__snake_case = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" , return_value=a_ ) as mock_head:
__snake_case = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def A ( self : Optional[Any] ):
"""simple docstring"""
__snake_case = mock.Mock()
__snake_case = 500
__snake_case = {}
__snake_case = HTTPError
__snake_case = {}
# Download this model to make sure it's in the cache.
__snake_case = GPTaTokenizerFast.from_pretrained("gpt2" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" , return_value=a_ ) as mock_head:
__snake_case = GPTaTokenizerFast.from_pretrained("gpt2" )
# This check we did call the fake head request
mock_head.assert_called()
def A ( self : Optional[Any] ):
"""simple docstring"""
try:
__snake_case = tempfile.mktemp()
with open(a_ , "wb" ) as f:
http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" , a_ )
__snake_case = AlbertTokenizer.from_pretrained(a_ )
finally:
os.remove(a_ )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile("tokenizer.json" ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open("tokenizer.json" , "wb" ) as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json" , a_ )
__snake_case = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size , 1_000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("tokenizer.json" )
def A ( self : str ):
"""simple docstring"""
__snake_case = AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" )
@is_staging_test
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
__SCREAMING_SNAKE_CASE = ["""[UNK]""", """[CLS]""", """[SEP]""", """[PAD]""", """[MASK]""", """bla""", """blou"""]
@classmethod
def A ( cls : List[Any] ):
"""simple docstring"""
__snake_case = TOKEN
HfFolder.save_token(a_ )
@classmethod
def A ( cls : List[Any] ):
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="test-tokenizer" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-tokenizer-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-tokenizer" )
except HTTPError:
pass
def A ( self : int ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case = os.path.join(a_ , "vocab.txt" )
with open(a_ , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
__snake_case = BertTokenizer(a_ )
tokenizer.push_to_hub("test-tokenizer" , use_auth_token=self._token )
__snake_case = BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer''' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id="test-tokenizer" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(a_ , repo_id="test-tokenizer" , push_to_hub=a_ , use_auth_token=self._token )
__snake_case = BertTokenizer.from_pretrained(f'''{USER}/test-tokenizer''' )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
def A ( self : int ):
"""simple docstring"""
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case = os.path.join(a_ , "vocab.txt" )
with open(a_ , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
__snake_case = BertTokenizer(a_ )
tokenizer.push_to_hub("valid_org/test-tokenizer-org" , use_auth_token=self._token )
__snake_case = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-tokenizer-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
a_ , repo_id="valid_org/test-tokenizer-org" , push_to_hub=a_ , use_auth_token=self._token )
__snake_case = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab , tokenizer.vocab )
@require_tokenizers
def A ( self : List[str] ):
"""simple docstring"""
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case = os.path.join(a_ , "vocab.txt" )
with open(a_ , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
__snake_case = CustomTokenizer(a_ )
# No fast custom tokenizer
tokenizer.push_to_hub("test-dynamic-tokenizer" , use_auth_token=self._token )
__snake_case = AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer''' , trust_remote_code=a_ )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , "CustomTokenizer" )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
__snake_case = os.path.join(a_ , "vocab.txt" )
with open(a_ , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
__snake_case = BertTokenizerFast.from_pretrained(a_ )
bert_tokenizer.save_pretrained(a_ )
__snake_case = CustomTokenizerFast.from_pretrained(a_ )
tokenizer.push_to_hub("test-dynamic-tokenizer" , use_auth_token=self._token )
__snake_case = AutoTokenizer.from_pretrained(f'''{USER}/test-dynamic-tokenizer''' , trust_remote_code=a_ )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , "CustomTokenizerFast" )
__snake_case = AutoTokenizer.from_pretrained(
f'''{USER}/test-dynamic-tokenizer''' , use_fast=a_ , trust_remote_code=a_ )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ , "CustomTokenizer" )
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def A ( self : Optional[int] ):
"""simple docstring"""
__snake_case = Trie()
trie.add("Hello 友達" )
self.assertEqual(trie.data , {"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}} )
trie.add("Hello" )
trie.data
self.assertEqual(trie.data , {"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}} )
def A ( self : str ):
"""simple docstring"""
__snake_case = Trie()
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) , ["[CLS] This is a extra_id_100"] )
trie.add("[CLS]" )
trie.add("extra_id_1" )
trie.add("extra_id_100" )
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) , ["[CLS]", " This is a ", "extra_id_100"] )
def A ( self : Optional[Any] ):
"""simple docstring"""
__snake_case = Trie()
trie.add("A" )
self.assertEqual(trie.split("ABC" ) , ["A", "BC"] )
self.assertEqual(trie.split("BCA" ) , ["BC", "A"] )
def A ( self : List[Any] ):
"""simple docstring"""
__snake_case = Trie()
trie.add("TOKEN]" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) , ["This is something ", "[SPECIAL_TOKEN]"] )
def A ( self : str ):
"""simple docstring"""
__snake_case = Trie()
trie.add("A" )
trie.add("P" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) , ["This is something ", "[SPECIAL_TOKEN]"] )
def A ( self : Optional[int] ):
"""simple docstring"""
__snake_case = Trie()
trie.add("AB" )
trie.add("B" )
trie.add("C" )
self.assertEqual(trie.split("ABC" ) , ["AB", "C"] )
def A ( self : Tuple ):
"""simple docstring"""
__snake_case = Trie()
trie.add("ABC" )
trie.add("B" )
trie.add("CD" )
self.assertEqual(trie.split("ABCD" ) , ["ABC", "D"] )
def A ( self : Any ):
"""simple docstring"""
__snake_case = Trie()
__snake_case = trie.cut_text("ABC" , [0, 0, 2, 1, 2, 3] )
self.assertEqual(a_ , ["AB", "C"] )
| 69 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.utils import is_torch_available
from .test_pipelines_common import ANY
if is_torch_available():
import torch
@is_pipeline_test
class lowerCAmelCase_ ( unittest.TestCase):
lowerCamelCase_ = MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
lowerCamelCase_ = TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
def _snake_case ( self : Dict , __A : List[Any] , __A : Optional[int] , __A : int ) ->Dict:
"""simple docstring"""
a__ :Tuple = TextaTextGenerationPipeline(model=__A , tokenizer=__A )
return generator, ["Something to write", "Something else"]
def _snake_case ( self : Optional[int] , __A : Optional[Any] , __A : List[Any] ) ->Dict:
"""simple docstring"""
a__ :Optional[int] = generator("Something there" )
self.assertEqual(__A , [{"generated_text": ANY(__A )}] )
# These are encoder decoder, they don't just append to incoming string
self.assertFalse(outputs[0]["generated_text"].startswith("Something there" ) )
a__ :Dict = generator(["This is great !", "Something else"] , num_return_sequences=2 , do_sample=__A )
self.assertEqual(
__A , [
[{"generated_text": ANY(__A )}, {"generated_text": ANY(__A )}],
[{"generated_text": ANY(__A )}, {"generated_text": ANY(__A )}],
] , )
a__ :int = generator(
["This is great !", "Something else"] , num_return_sequences=2 , batch_size=2 , do_sample=__A )
self.assertEqual(
__A , [
[{"generated_text": ANY(__A )}, {"generated_text": ANY(__A )}],
[{"generated_text": ANY(__A )}, {"generated_text": ANY(__A )}],
] , )
with self.assertRaises(__A ):
generator(4 )
@require_torch
def _snake_case ( self : List[Any] ) ->Dict:
"""simple docstring"""
a__ :int = pipeline("text2text-generation" , model="patrickvonplaten/t5-tiny-random" , framework="pt" )
# do_sample=False necessary for reproducibility
a__ :Dict = generator("Something there" , do_sample=__A )
self.assertEqual(__A , [{"generated_text": ""}] )
a__ :int = 3
a__ :str = generator(
"Something there" , num_return_sequences=__A , num_beams=__A , )
a__ :int = [
{"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide Beide"},
{"generated_text": "Beide Beide Beide Beide Beide Beide Beide Beide"},
{"generated_text": ""},
]
self.assertEqual(__A , __A )
a__ :str = generator("This is a test" , do_sample=__A , num_return_sequences=2 , return_tensors=__A )
self.assertEqual(
__A , [
{"generated_token_ids": ANY(torch.Tensor )},
{"generated_token_ids": ANY(torch.Tensor )},
] , )
a__ :Dict = generator.model.config.eos_token_id
a__ :List[Any] = "<pad>"
a__ :Optional[int] = generator(
["This is a test", "This is a second test"] , do_sample=__A , num_return_sequences=2 , batch_size=2 , return_tensors=__A , )
self.assertEqual(
__A , [
[
{"generated_token_ids": ANY(torch.Tensor )},
{"generated_token_ids": ANY(torch.Tensor )},
],
[
{"generated_token_ids": ANY(torch.Tensor )},
{"generated_token_ids": ANY(torch.Tensor )},
],
] , )
@require_tf
def _snake_case ( self : List[Any] ) ->Any:
"""simple docstring"""
a__ :Optional[Any] = pipeline("text2text-generation" , model="patrickvonplaten/t5-tiny-random" , framework="tf" )
# do_sample=False necessary for reproducibility
a__ :Any = generator("Something there" , do_sample=__A )
self.assertEqual(__A , [{"generated_text": ""}] )
| 702 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case__ = logging.get_logger(__name__)
snake_case__ = {
'''asapp/sew-tiny-100k''': '''https://huggingface.co/asapp/sew-tiny-100k/resolve/main/config.json''',
# See all SEW models at https://huggingface.co/models?filter=sew
}
class lowerCAmelCase_ ( _a):
lowerCamelCase_ = 'sew'
def __init__( self : Any , __A : str=32 , __A : Dict=768 , __A : int=12 , __A : Dict=12 , __A : Dict=3072 , __A : int=2 , __A : Union[str, Any]="gelu" , __A : Union[str, Any]=0.1 , __A : Optional[Any]=0.1 , __A : Union[str, Any]=0.1 , __A : str=0.0 , __A : Union[str, Any]=0.1 , __A : Optional[Any]=0.1 , __A : Tuple=0.02 , __A : Any=1E-5 , __A : Optional[Any]="group" , __A : str="gelu" , __A : Dict=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , __A : Tuple=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , __A : int=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , __A : List[str]=False , __A : Tuple=128 , __A : Tuple=16 , __A : Optional[int]=True , __A : Union[str, Any]=0.05 , __A : List[str]=10 , __A : Optional[int]=2 , __A : List[Any]=0.0 , __A : Optional[Any]=10 , __A : Tuple=0 , __A : Tuple="mean" , __A : Any=False , __A : str=False , __A : Dict=256 , __A : Union[str, Any]=0 , __A : Optional[int]=1 , __A : Optional[Any]=2 , **__A : Tuple , ) ->Tuple:
"""simple docstring"""
super().__init__(**__A , pad_token_id=__A , bos_token_id=__A , eos_token_id=__A )
a__ :List[Any] = hidden_size
a__ :List[Any] = feat_extract_norm
a__ :List[str] = feat_extract_activation
a__ :Any = list(__A )
a__ :Dict = list(__A )
a__ :Optional[int] = list(__A )
a__ :Any = conv_bias
a__ :List[str] = num_conv_pos_embeddings
a__ :str = num_conv_pos_embedding_groups
a__ :Optional[int] = len(self.conv_dim )
a__ :List[Any] = num_hidden_layers
a__ :str = intermediate_size
a__ :Dict = squeeze_factor
a__ :List[Any] = hidden_act
a__ :Optional[Any] = num_attention_heads
a__ :Tuple = hidden_dropout
a__ :Tuple = attention_dropout
a__ :List[Any] = activation_dropout
a__ :str = feat_proj_dropout
a__ :Any = final_dropout
a__ :Dict = layerdrop
a__ :List[str] = layer_norm_eps
a__ :Tuple = initializer_range
a__ :Dict = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"Configuration for convolutional layers is incorrect."
"It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,"
F'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
F'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
a__ :int = apply_spec_augment
a__ :Optional[Any] = mask_time_prob
a__ :List[Any] = mask_time_length
a__ :Any = mask_time_min_masks
a__ :Any = mask_feature_prob
a__ :Dict = mask_feature_length
a__ :Union[str, Any] = mask_feature_min_masks
# ctc loss
a__ :Dict = ctc_loss_reduction
a__ :Any = ctc_zero_infinity
# sequence classification
a__ :Optional[int] = use_weighted_layer_sum
a__ :Tuple = classifier_proj_size
@property
def _snake_case ( self : Dict ) ->Union[str, Any]:
"""simple docstring"""
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 373 | 0 |
import pytest
from datasets.splits import SplitDict, SplitInfo
from datasets.utils.py_utils import asdict
@pytest.mark.parametrize(
'''split_dict''' , [
SplitDict(),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=13_37 , num_examples=42 , dataset_name='''my_dataset''' )} ),
SplitDict({'''train''': SplitInfo(name='''train''' , num_bytes=13_37 , num_examples=42 )} ),
SplitDict({'''train''': SplitInfo()} ),
] , )
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = split_dict._to_yaml_list()
assert len(SCREAMING_SNAKE_CASE ) == len(SCREAMING_SNAKE_CASE )
lowercase__ = SplitDict._from_yaml_list(SCREAMING_SNAKE_CASE )
for split_name, split_info in split_dict.items():
# dataset_name field is deprecated, and is therefore not part of the YAML dump
lowercase__ = None
# the split name of split_dict takes over the name of the split info object
lowercase__ = split_name
assert split_dict == reloaded
@pytest.mark.parametrize(
'''split_info''' , [SplitInfo(), SplitInfo(dataset_name=SCREAMING_SNAKE_CASE ), SplitInfo(dataset_name='''my_dataset''' )] )
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
lowercase__ = asdict(SplitDict({'''train''': split_info} ) )
assert "dataset_name" in split_dict_asdict["train"]
assert split_dict_asdict["train"]["dataset_name"] == split_info.dataset_name
| 43 |
"""simple docstring"""
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
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 transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def __snake_case ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : List[Any]="shi-labs/oneformer_demo" ) -> Tuple:
'''simple docstring'''
with open(hf_hub_download(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , repo_type="dataset" ) , "r" ) as f:
_UpperCAmelCase : Optional[int] = json.load(SCREAMING_SNAKE_CASE__ )
_UpperCAmelCase : int = {}
_UpperCAmelCase : Union[str, Any] = []
_UpperCAmelCase : List[Any] = []
for key, info in class_info.items():
_UpperCAmelCase : List[str] = info["name"]
class_names.append(info["name"] )
if info["isthing"]:
thing_ids.append(int(SCREAMING_SNAKE_CASE__ ) )
_UpperCAmelCase : Tuple = thing_ids
_UpperCAmelCase : str = class_names
return metadata
class UpperCAmelCase_ ( unittest.TestCase ):
def __init__( self : Optional[Any] , A : Tuple , A : str=7 , A : Union[str, Any]=3 , A : Union[str, Any]=3_0 , A : Dict=4_0_0 , A : List[str]=None , A : str=True , A : Union[str, Any]=True , A : Optional[Any]=[0.5, 0.5, 0.5] , A : str=[0.5, 0.5, 0.5] , A : Optional[Any]=1_0 , A : Optional[int]=False , A : int=2_5_5 , A : List[Any]="shi-labs/oneformer_demo" , A : int="ade20k_panoptic.json" , A : str=1_0 , ):
_UpperCAmelCase : int = parent
_UpperCAmelCase : Any = batch_size
_UpperCAmelCase : str = num_channels
_UpperCAmelCase : str = min_resolution
_UpperCAmelCase : List[str] = max_resolution
_UpperCAmelCase : List[Any] = do_resize
_UpperCAmelCase : List[Any] = {"shortest_edge": 3_2, "longest_edge": 1_3_3_3} if size is None else size
_UpperCAmelCase : Optional[Any] = do_normalize
_UpperCAmelCase : Optional[int] = image_mean
_UpperCAmelCase : Dict = image_std
_UpperCAmelCase : Any = class_info_file
_UpperCAmelCase : Optional[int] = prepare_metadata(A , A )
_UpperCAmelCase : Any = num_text
_UpperCAmelCase : Dict = repo_path
# for the post_process_functions
_UpperCAmelCase : str = 2
_UpperCAmelCase : Any = 1_0
_UpperCAmelCase : Optional[int] = 1_0
_UpperCAmelCase : Tuple = 3
_UpperCAmelCase : List[str] = 4
_UpperCAmelCase : int = num_labels
_UpperCAmelCase : Optional[Any] = do_reduce_labels
_UpperCAmelCase : Any = ignore_index
def snake_case_ ( self : Optional[int] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def snake_case_ ( self : str , A : int , A : Optional[Any]=False ):
if not batched:
_UpperCAmelCase : List[str] = image_inputs[0]
if isinstance(A , Image.Image ):
_UpperCAmelCase , _UpperCAmelCase : Union[str, Any] = image.size
else:
_UpperCAmelCase , _UpperCAmelCase : Any = image.shape[1], image.shape[2]
if w < h:
_UpperCAmelCase : Optional[int] = int(self.size["shortest_edge"] * h / w )
_UpperCAmelCase : Tuple = self.size["shortest_edge"]
elif w > h:
_UpperCAmelCase : Dict = self.size["shortest_edge"]
_UpperCAmelCase : Dict = int(self.size["shortest_edge"] * w / h )
else:
_UpperCAmelCase : Optional[int] = self.size["shortest_edge"]
_UpperCAmelCase : Optional[int] = self.size["shortest_edge"]
else:
_UpperCAmelCase : List[str] = []
for image in image_inputs:
_UpperCAmelCase , _UpperCAmelCase : int = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_UpperCAmelCase : int = max(A , key=lambda A : item[0] )[0]
_UpperCAmelCase : Dict = max(A , key=lambda A : item[1] )[1]
return expected_height, expected_width
def snake_case_ ( self : List[str] ):
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) , masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) , )
@require_torch
@require_vision
class UpperCAmelCase_ ( _UpperCamelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE : str = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
__SCREAMING_SNAKE_CASE : Tuple = image_processing_class
def snake_case_ ( self : Tuple ):
_UpperCAmelCase : Optional[Any] = OneFormerImageProcessorTester(self )
@property
def snake_case_ ( self : Optional[Any] ):
return self.image_processing_tester.prepare_image_processor_dict()
def snake_case_ ( self : Union[str, Any] ):
_UpperCAmelCase : str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(A , "image_mean" ) )
self.assertTrue(hasattr(A , "image_std" ) )
self.assertTrue(hasattr(A , "do_normalize" ) )
self.assertTrue(hasattr(A , "do_resize" ) )
self.assertTrue(hasattr(A , "size" ) )
self.assertTrue(hasattr(A , "ignore_index" ) )
self.assertTrue(hasattr(A , "class_info_file" ) )
self.assertTrue(hasattr(A , "num_text" ) )
self.assertTrue(hasattr(A , "repo_path" ) )
self.assertTrue(hasattr(A , "metadata" ) )
self.assertTrue(hasattr(A , "do_reduce_labels" ) )
def snake_case_ ( self : List[Any] ):
pass
def snake_case_ ( self : Union[str, Any] ):
# Initialize image_processor
_UpperCAmelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_UpperCAmelCase : Optional[int] = prepare_image_inputs(self.image_processing_tester , equal_resolution=A )
for image in image_inputs:
self.assertIsInstance(A , Image.Image )
# Test not batched input
_UpperCAmelCase : Optional[Any] = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt" ).pixel_values
_UpperCAmelCase , _UpperCAmelCase : Optional[Any] = self.image_processing_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCAmelCase , _UpperCAmelCase : Dict = self.image_processing_tester.get_expected_values(A , batched=A )
_UpperCAmelCase : Optional[Any] = image_processor(
A , ["semantic"] * len(A ) , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case_ ( self : str ):
# Initialize image_processor
_UpperCAmelCase : Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_UpperCAmelCase : Any = prepare_image_inputs(self.image_processing_tester , equal_resolution=A , numpify=A )
for image in image_inputs:
self.assertIsInstance(A , np.ndarray )
# Test not batched input
_UpperCAmelCase : int = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt" ).pixel_values
_UpperCAmelCase , _UpperCAmelCase : List[Any] = self.image_processing_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCAmelCase , _UpperCAmelCase : int = self.image_processing_tester.get_expected_values(A , batched=A )
_UpperCAmelCase : List[str] = image_processor(
A , ["semantic"] * len(A ) , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case_ ( self : Optional[int] ):
# Initialize image_processor
_UpperCAmelCase : Dict = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_UpperCAmelCase : str = prepare_image_inputs(self.image_processing_tester , equal_resolution=A , torchify=A )
for image in image_inputs:
self.assertIsInstance(A , torch.Tensor )
# Test not batched input
_UpperCAmelCase : Tuple = image_processor(image_inputs[0] , ["semantic"] , return_tensors="pt" ).pixel_values
_UpperCAmelCase , _UpperCAmelCase : Any = self.image_processing_tester.get_expected_values(A )
self.assertEqual(
encoded_images.shape , (1, self.image_processing_tester.num_channels, expected_height, expected_width) , )
# Test batched
_UpperCAmelCase , _UpperCAmelCase : List[str] = self.image_processing_tester.get_expected_values(A , batched=A )
_UpperCAmelCase : Optional[int] = image_processor(
A , ["semantic"] * len(A ) , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) , )
def snake_case_ ( self : Optional[int] , A : Tuple=False , A : Optional[Any]=False , A : int="np" ):
_UpperCAmelCase : List[Any] = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
_UpperCAmelCase : List[str] = self.image_processing_tester.num_labels
_UpperCAmelCase : Any = None
_UpperCAmelCase : List[Any] = None
_UpperCAmelCase : Dict = prepare_image_inputs(self.image_processing_tester , equal_resolution=A )
if with_segmentation_maps:
_UpperCAmelCase : Union[str, Any] = num_labels
if is_instance_map:
_UpperCAmelCase : Optional[int] = list(range(A ) ) * 2
_UpperCAmelCase : Union[str, Any] = dict(enumerate(A ) )
_UpperCAmelCase : Tuple = [
np.random.randint(0 , high * 2 , (img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
_UpperCAmelCase : Optional[int] = [Image.fromarray(A ) for annotation in annotations]
_UpperCAmelCase : int = image_processor(
A , ["semantic"] * len(A ) , A , return_tensors="pt" , instance_id_to_semantic_id=A , pad_and_return_pixel_mask=A , )
return inputs
def snake_case_ ( self : Any ):
pass
def snake_case_ ( self : Dict ):
def common(A : List[Any]=False , A : List[str]=None ):
_UpperCAmelCase : str = self.comm_get_image_processor_inputs(
with_segmentation_maps=A , is_instance_map=A , segmentation_type=A )
_UpperCAmelCase : Optional[int] = inputs["mask_labels"]
_UpperCAmelCase : str = inputs["class_labels"]
_UpperCAmelCase : List[str] = inputs["pixel_values"]
_UpperCAmelCase : Any = inputs["text_inputs"]
# check the batch_size
for mask_label, class_label, text_input in zip(A , A , A ):
self.assertEqual(mask_label.shape[0] , class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] , pixel_values.shape[2:] )
self.assertEqual(len(A ) , self.image_processing_tester.num_text )
common()
common(is_instance_map=A )
common(is_instance_map=A , segmentation_type="pil" )
common(is_instance_map=A , segmentation_type="pil" )
def snake_case_ ( self : int ):
_UpperCAmelCase : Optional[Any] = np.zeros((2_0, 5_0) )
_UpperCAmelCase : Dict = 1
_UpperCAmelCase : Optional[int] = 1
_UpperCAmelCase : Optional[int] = 1
_UpperCAmelCase : List[str] = binary_mask_to_rle(A )
self.assertEqual(len(A ) , 4 )
self.assertEqual(rle[0] , 2_1 )
self.assertEqual(rle[1] , 4_5 )
def snake_case_ ( self : Optional[Any] ):
_UpperCAmelCase : Optional[Any] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=7_7 , task_seq_length=7_7 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
_UpperCAmelCase : int = self.image_processing_tester.get_fake_oneformer_outputs()
_UpperCAmelCase : List[str] = fature_extractor.post_process_semantic_segmentation(A )
self.assertEqual(len(A ) , self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape , (
self.image_processing_tester.height,
self.image_processing_tester.width,
) , )
_UpperCAmelCase : Tuple = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
_UpperCAmelCase : Optional[Any] = fature_extractor.post_process_semantic_segmentation(A , target_sizes=A )
self.assertEqual(segmentation[0].shape , target_sizes[0] )
def snake_case_ ( self : Dict ):
_UpperCAmelCase : Tuple = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=7_7 , task_seq_length=7_7 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
_UpperCAmelCase : Union[str, Any] = self.image_processing_tester.get_fake_oneformer_outputs()
_UpperCAmelCase : Tuple = image_processor.post_process_instance_segmentation(A , threshold=0 )
self.assertTrue(len(A ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("segmentation" in el )
self.assertTrue("segments_info" in el )
self.assertEqual(type(el["segments_info"] ) , A )
self.assertEqual(
el["segmentation"].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
def snake_case_ ( self : Any ):
_UpperCAmelCase : Optional[int] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes , max_seq_length=7_7 , task_seq_length=7_7 , class_info_file="ade20k_panoptic.json" , num_text=self.image_processing_tester.num_text , repo_path="shi-labs/oneformer_demo" , )
_UpperCAmelCase : Optional[Any] = self.image_processing_tester.get_fake_oneformer_outputs()
_UpperCAmelCase : Tuple = image_processor.post_process_panoptic_segmentation(A , threshold=0 )
self.assertTrue(len(A ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("segmentation" in el )
self.assertTrue("segments_info" in el )
self.assertEqual(type(el["segments_info"] ) , A )
self.assertEqual(
el["segmentation"].shape , (self.image_processing_tester.height, self.image_processing_tester.width) )
| 289 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCamelCase : int = 100):
A_ : Any = n * (n + 1) * (2 * n + 1) / 6
A_ : str = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares)
if __name__ == "__main__":
print(f"""{solution() = }""")
| 27 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
__magic_name__ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class __lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
a_ = ["""pixel_values"""]
def __init__( self : Optional[Any] ,_a : bool = True ,_a : Dict[str, int] = None ,_a : PILImageResampling = PILImageResampling.BICUBIC ,_a : bool = True ,_a : Dict[str, int] = None ,_a : bool = True ,_a : Union[int, float] = 1 / 255 ,_a : bool = True ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : bool = True ,**_a : Dict ,):
'''simple docstring'''
super().__init__(**_a )
A_ : Tuple = size if size is not None else {"""shortest_edge""": 224}
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a )
A_ : Tuple = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a ,param_name="""crop_size""" )
A_ : Any = do_resize
A_ : List[str] = size
A_ : Union[str, Any] = resample
A_ : Dict = do_center_crop
A_ : List[str] = crop_size
A_ : Any = do_rescale
A_ : Union[str, Any] = rescale_factor
A_ : Any = do_normalize
A_ : List[str] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
A_ : List[Any] = image_std if image_std is not None else OPENAI_CLIP_STD
A_ : Tuple = do_convert_rgb
def _a ( self : Optional[int] ,_a : np.ndarray ,_a : Dict[str, int] ,_a : PILImageResampling = PILImageResampling.BICUBIC ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Optional[Any] ,):
'''simple docstring'''
A_ : Optional[Any] = get_size_dict(_a ,default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}' )
A_ : Tuple = get_resize_output_image_size(_a ,size=size["""shortest_edge"""] ,default_to_square=_a )
return resize(_a ,size=_a ,resample=_a ,data_format=_a ,**_a )
def _a ( self : List[Any] ,_a : np.ndarray ,_a : Dict[str, int] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Optional[int] ,):
'''simple docstring'''
A_ : Optional[int] = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}' )
return center_crop(_a ,size=(size["""height"""], size["""width"""]) ,data_format=_a ,**_a )
def _a ( self : Any ,_a : np.ndarray ,_a : Union[int, float] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : Any ,):
'''simple docstring'''
return rescale(_a ,scale=_a ,data_format=_a ,**_a )
def _a ( self : Any ,_a : np.ndarray ,_a : Union[float, List[float]] ,_a : Union[float, List[float]] ,_a : Optional[Union[str, ChannelDimension]] = None ,**_a : List[str] ,):
'''simple docstring'''
return normalize(_a ,mean=_a ,std=_a ,data_format=_a ,**_a )
def _a ( self : Optional[Any] ,_a : ImageInput ,_a : bool = None ,_a : Dict[str, int] = None ,_a : PILImageResampling = None ,_a : bool = None ,_a : int = None ,_a : bool = None ,_a : float = None ,_a : bool = None ,_a : Optional[Union[float, List[float]]] = None ,_a : Optional[Union[float, List[float]]] = None ,_a : bool = None ,_a : Optional[Union[str, TensorType]] = None ,_a : Optional[ChannelDimension] = ChannelDimension.FIRST ,**_a : int ,):
'''simple docstring'''
A_ : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
A_ : Tuple = size if size is not None else self.size
A_ : Optional[int] = get_size_dict(_a ,param_name="""size""" ,default_to_square=_a )
A_ : List[str] = resample if resample is not None else self.resample
A_ : int = do_center_crop if do_center_crop is not None else self.do_center_crop
A_ : Any = crop_size if crop_size is not None else self.crop_size
A_ : int = get_size_dict(_a ,param_name="""crop_size""" ,default_to_square=_a )
A_ : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
A_ : int = rescale_factor if rescale_factor is not None else self.rescale_factor
A_ : Any = do_normalize if do_normalize is not None else self.do_normalize
A_ : int = image_mean if image_mean is not None else self.image_mean
A_ : int = image_std if image_std is not None else self.image_std
A_ : List[str] = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
A_ : int = make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None:
raise ValueError("""Size must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
A_ : Optional[int] = [convert_to_rgb(_a ) for image in images]
# All transformations expect numpy arrays.
A_ : Dict = [to_numpy_array(_a ) for image in images]
if do_resize:
A_ : int = [self.resize(image=_a ,size=_a ,resample=_a ) for image in images]
if do_center_crop:
A_ : Tuple = [self.center_crop(image=_a ,size=_a ) for image in images]
if do_rescale:
A_ : List[str] = [self.rescale(image=_a ,scale=_a ) for image in images]
if do_normalize:
A_ : Any = [self.normalize(image=_a ,mean=_a ,std=_a ) for image in images]
A_ : List[str] = [to_channel_dimension_format(_a ,_a ) for image in images]
A_ : List[str] = {"""pixel_values""": images}
return BatchFeature(data=_a ,tensor_type=_a )
| 27 | 1 |
"""simple docstring"""
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class A( tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None , SCREAMING_SNAKE_CASE__ = None ) -> Optional[Any]:
"""simple docstring"""
super().__init__()
_UpperCamelCase :Optional[int] = pad_token_id
_UpperCamelCase :Optional[Any] = max_length
_UpperCamelCase :Any = vocab
_UpperCamelCase :Union[str, Any] = merges
_UpperCamelCase :int = BytePairTokenizer(__UpperCamelCase , __UpperCamelCase , sequence_length=__UpperCamelCase )
@classmethod
def _UpperCamelCase( cls , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> Any:
"""simple docstring"""
_UpperCamelCase :Dict = [''' '''.join(__UpperCamelCase ) for m in tokenizer.bpe_ranks.keys()]
_UpperCamelCase :Union[str, Any] = tokenizer.get_vocab()
return cls(__UpperCamelCase , __UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
@classmethod
def _UpperCamelCase( cls , SCREAMING_SNAKE_CASE__ , *SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ ) -> str:
"""simple docstring"""
_UpperCamelCase :str = GPTaTokenizer.from_pretrained(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
return cls.from_tokenizer(__UpperCamelCase , *__UpperCamelCase , **__UpperCamelCase )
@classmethod
def _UpperCamelCase( cls , SCREAMING_SNAKE_CASE__ ) -> Any:
"""simple docstring"""
return cls(**__UpperCamelCase )
def _UpperCamelCase( self ) -> List[str]:
"""simple docstring"""
return {
"vocab": self.vocab,
"merges": self.merges,
"max_length": self.max_length,
"pad_token_id": self.pad_token_id,
}
def _UpperCamelCase( self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = None ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase :Tuple = self.tf_tokenizer(__UpperCamelCase )
_UpperCamelCase :Optional[int] = tf.ones_like(__UpperCamelCase )
if self.pad_token_id is not None:
# pad the tokens up to max length
_UpperCamelCase :List[Any] = max_length if max_length is not None else self.max_length
if max_length is not None:
_UpperCamelCase :Tuple = pad_model_inputs(
__UpperCamelCase , max_seq_length=__UpperCamelCase , pad_value=self.pad_token_id )
return {"attention_mask": attention_mask, "input_ids": input_ids}
| 355 |
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> bool:
A__ : List[Any] =len(snake_case_ ) + 1
A__ : List[Any] =len(snake_case_ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with prefix string of length j of
# given pattern.
# "dp" stands for dynamic programming.
A__ : Dict =[[0 for i in range(snake_case_ )] for j in range(snake_case_ )]
# since string of zero length match pattern of zero length
A__ : Dict =1
# since pattern of zero length will never match with string of non-zero length
for i in range(1, snake_case_ ):
A__ : Optional[Any] =0
# since string of zero length will match with pattern where there
# is at least one * alternatively
for j in range(1, snake_case_ ):
A__ : str =dp[0][j - 2] if pattern[j - 1] == '''*''' else 0
# now using bottom-up approach to find for all remaining lengths
for i in range(1, snake_case_ ):
for j in range(1, snake_case_ ):
if input_string[i - 1] == pattern[j - 1] or pattern[j - 1] == ".":
A__ : str =dp[i - 1][j - 1]
elif pattern[j - 1] == "*":
if dp[i][j - 2] == 1:
A__ : str =1
elif pattern[j - 2] in (input_string[i - 1], "."):
A__ : Union[str, Any] =dp[i - 1][j]
else:
A__ : Optional[int] =0
else:
A__ : str =0
return bool(dp[-1][-1] )
if __name__ == "__main__":
import doctest
doctest.testmod()
# inputing the strings
# input_string = input("input a string :")
# pattern = input("input a pattern :")
__lowerCamelCase : int = "aab"
__lowerCamelCase : Dict = "c*a*b"
# using function to check whether given string matches the given pattern
if match_pattern(input_string, pattern):
print(F"{input_string} matches the given pattern {pattern}")
else:
print(F"{input_string} does not match with the given pattern {pattern}")
| 416 | 0 |
from __future__ import annotations
__UpperCamelCase : List[Any] = 1.6021E-19 # units = C
def __UpperCAmelCase ( _snake_case : float, _snake_case : float, _snake_case : float, ):
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif conductivity < 0:
raise ValueError("Conductivity cannot be negative" )
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative" )
elif mobility < 0:
raise ValueError("mobility cannot be negative" )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod() | 703 | """simple docstring"""
import math
from collections.abc import Callable
def __UpperCAmelCase ( _snake_case : Callable[[float], float], _snake_case : float, _snake_case : float ):
_lowercase = xa
_lowercase = xa
while True:
if x_n == x_na or function(_snake_case ) == function(_snake_case ):
raise ZeroDivisionError("float division by zero, could not find root" )
_lowercase = x_na - (
function(_snake_case ) / ((function(_snake_case ) - function(_snake_case )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 1_0**-5:
return x_na
_lowercase = x_na
_lowercase = x_na
def __UpperCAmelCase ( _snake_case : float ):
return math.pow(_snake_case, 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5)) | 227 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a : str = logging.get_logger(__name__)
_a : int = {
"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 _lowercase ( __lowercase ):
_SCREAMING_SNAKE_CASE : List[str] = "vivit"
def __init__( self : Union[str, Any] , SCREAMING_SNAKE_CASE_ : Optional[Any]=224 , SCREAMING_SNAKE_CASE_ : List[str]=32 , SCREAMING_SNAKE_CASE_ : List[Any]=[2, 16, 16] , SCREAMING_SNAKE_CASE_ : Optional[int]=3 , SCREAMING_SNAKE_CASE_ : List[Any]=768 , SCREAMING_SNAKE_CASE_ : Any=12 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=12 , SCREAMING_SNAKE_CASE_ : Optional[Any]=3072 , SCREAMING_SNAKE_CASE_ : List[str]="gelu_fast" , SCREAMING_SNAKE_CASE_ : Any=0.0 , SCREAMING_SNAKE_CASE_ : int=0.0 , SCREAMING_SNAKE_CASE_ : int=0.0_2 , SCREAMING_SNAKE_CASE_ : List[Any]=1e-06 , SCREAMING_SNAKE_CASE_ : int=True , **SCREAMING_SNAKE_CASE_ : Optional[int] , ) -> Optional[int]:
__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 = initializer_range
__snake_case = layer_norm_eps
__snake_case = image_size
__snake_case = num_frames
__snake_case = tubelet_size
__snake_case = num_channels
__snake_case = qkv_bias
super().__init__(**SCREAMING_SNAKE_CASE_ )
| 56 |
'''simple docstring'''
# 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.utils import ComputeEnvironment
from .cluster import get_cluster_input
from .config_args import cache_dir, default_config_file, default_yaml_config_file, load_config_from_file # noqa: F401
from .config_utils import _ask_field, _ask_options, _convert_compute_environment # noqa: F401
from .sagemaker import get_sagemaker_input
_a : str = "Launches a series of prompts to create and save a `default_config.yaml` configuration file for your training system. Should always be ran first on your machine"
def _a () -> Dict:
"""simple docstring"""
__snake_case = _ask_options(
'In which compute environment are you running?' , ['This machine', 'AWS (Amazon SageMaker)'] , _convert_compute_environment , )
if compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
__snake_case = get_sagemaker_input()
else:
__snake_case = get_cluster_input()
return config
def _a (lowercase__ : Union[str, Any]=None ) -> int:
"""simple docstring"""
if subparsers is not None:
__snake_case = subparsers.add_parser('config' , description=lowercase__ )
else:
__snake_case = argparse.ArgumentParser('Accelerate config command' , description=lowercase__ )
parser.add_argument(
'--config_file' , default=lowercase__ , 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=lowercase__ )
return parser
def _a (lowercase__ : List[str] ) -> Union[str, Any]:
"""simple docstring"""
__snake_case = get_user_input()
if args.config_file is not None:
__snake_case = args.config_file
else:
if not os.path.isdir(lowercase__ ):
os.makedirs(lowercase__ )
__snake_case = default_yaml_config_file
if config_file.endswith('.json' ):
config.to_json_file(lowercase__ )
else:
config.to_yaml_file(lowercase__ )
print(f'accelerate configuration saved at {config_file}' )
def _a () -> int:
"""simple docstring"""
__snake_case = config_command_parser()
__snake_case = parser.parse_args()
config_command(lowercase__ )
if __name__ == "__main__":
main()
| 56 | 1 |
"""simple docstring"""
from __future__ import annotations
from math import pi
def lowercase ( a__ : float , a__ : float , a__ : float ) -> dict[str, float]:
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError('''One and only one argument must be 0''' )
if inductance < 0:
raise ValueError('''Inductance cannot be negative''' )
if frequency < 0:
raise ValueError('''Frequency cannot be negative''' )
if reactance < 0:
raise ValueError('''Inductive reactance cannot be negative''' )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError('''Exactly one argument must be 0''' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 342 | """simple docstring"""
from datetime import datetime as dt
import os
from github import Github
UpperCAmelCase = [
"""good first issue""",
"""good second issue""",
"""good difficult issue""",
"""feature request""",
"""new model""",
"""wip""",
]
def lowercase ( ) -> int:
_UpperCamelCase = Github(os.environ['''GITHUB_TOKEN'''] )
_UpperCamelCase = g.get_repo('''huggingface/transformers''' )
_UpperCamelCase = repo.get_issues(state='''open''' )
for issue in open_issues:
_UpperCamelCase = sorted([comment for comment in issue.get_comments()] , key=lambda a__ : i.created_at , reverse=a__ )
_UpperCamelCase = comments[0] if len(a__ ) > 0 else None
if (
last_comment is not None
and last_comment.user.login == "github-actions[bot]"
and (dt.utcnow() - issue.updated_at).days > 7
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would close issue {issue.number} since it has been 7 days of inactivity since bot mention.")
issue.edit(state='''closed''' )
elif (
(dt.utcnow() - issue.updated_at).days > 23
and (dt.utcnow() - issue.created_at).days >= 30
and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels() )
):
# print(f"Would add stale comment to {issue.number}")
issue.create_comment(
'''This issue has been automatically marked as stale because it has not had '''
'''recent activity. If you think this still needs to be addressed '''
'''please comment on this thread.\n\nPlease note that issues that do not follow the '''
'''[contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) '''
'''are likely to be ignored.''' )
if __name__ == "__main__":
main()
| 342 | 1 |
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
SCREAMING_SNAKE_CASE : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[Any] = {"vocab_file": "vocab.txt", "emoji_file": "emoji.json"}
SCREAMING_SNAKE_CASE : List[str] = {
"vocab_file": {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt",
},
"emoji_file": {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json",
},
}
SCREAMING_SNAKE_CASE : Any = {
"abeja/gpt-neox-japanese-2.7b": 2048,
}
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> Dict:
with open(lowerCamelCase_ , 'r' , encoding='utf-8' ) as f:
_lowercase : int = json.loads(f.read() )
_lowercase : Optional[Any] = collections.OrderedDict()
_lowercase : Any = collections.OrderedDict()
_lowercase : Optional[Any] = collections.OrderedDict()
with open(lowerCamelCase_ , 'r' , encoding='utf-8' ) as f:
_lowercase : List[str] = f.readlines()
_lowercase : List[str] = [[t.rstrip('\n' )] if (t == ',' or ',' not in t) else t.rstrip('\n' ).split(',' ) for t in token]
for idx, b in enumerate(lowerCamelCase_ ):
_lowercase : Optional[Any] = b
_lowercase : Dict = idx
for wd in b:
_lowercase : Optional[int] = idx
return vocab, raw_vocab, ids_to_tokens, emoji
class _lowerCamelCase( _a ):
lowercase_ : str = VOCAB_FILES_NAMES
lowercase_ : List[str] = PRETRAINED_VOCAB_FILES_MAP
lowercase_ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase_ : Union[str, Any] = ["""input_ids""", """attention_mask"""]
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase="<|endoftext|>", lowerCamelCase="<|endoftext|>", lowerCamelCase="<|startoftext|>", lowerCamelCase="<|endoftext|>", lowerCamelCase=False, **lowerCamelCase, ) -> Optional[int]:
"""simple docstring"""
super().__init__(
unk_token=lowerCamelCase, pad_token=lowerCamelCase, bos_token=lowerCamelCase, eos_token=lowerCamelCase, do_clean_text=lowerCamelCase, **lowerCamelCase, )
if not os.path.isfile(lowerCamelCase):
raise ValueError(
F'''Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'''
' model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`')
if not os.path.isfile(lowerCamelCase):
raise ValueError(
F'''Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'''
' pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`')
_lowercase : Tuple = do_clean_text
_lowercase , _lowercase , _lowercase , _lowercase : Dict = load_vocab_and_emoji(lowerCamelCase, lowerCamelCase)
_lowercase : str = SubWordJapaneseTokenizer(
vocab=self.vocab, ids_to_tokens=self.ids_to_tokens, emoji=self.emoji)
@property
def UpperCamelCase ( self) -> Optional[int]:
"""simple docstring"""
return len(self.raw_vocab)
def UpperCamelCase ( self) -> List[str]:
"""simple docstring"""
return dict(self.raw_vocab, **self.added_tokens_encoder)
def UpperCamelCase ( self, lowerCamelCase) -> Optional[Any]:
"""simple docstring"""
return self.subword_tokenizer.tokenize(lowerCamelCase, clean=self.do_clean_text)
def UpperCamelCase ( self, lowerCamelCase) -> Tuple:
"""simple docstring"""
return self.vocab.get(lowerCamelCase, self.vocab.get(self.unk_token))
def UpperCamelCase ( self, lowerCamelCase) -> Tuple:
"""simple docstring"""
return self.subword_tokenizer.convert_id_to_token(lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase) -> Optional[int]:
"""simple docstring"""
_lowercase : str = ''.join(lowerCamelCase).strip()
return out_string
def UpperCamelCase ( self, lowerCamelCase) -> List[int]:
"""simple docstring"""
_lowercase : str = []
for is_user, text in conversation.iter_texts():
input_ids.extend(self.encode(lowerCamelCase, add_special_tokens=lowerCamelCase) + [self.eos_token_id])
if len(lowerCamelCase) > self.model_max_length:
_lowercase : List[str] = input_ids[-self.model_max_length :]
return input_ids
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase = None) -> Tuple[str]:
"""simple docstring"""
_lowercase : Tuple = 0
if os.path.isdir(lowerCamelCase):
_lowercase : int = os.path.join(
lowerCamelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'])
_lowercase : Union[str, Any] = os.path.join(
lowerCamelCase, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['emoji_file'])
else:
_lowercase : Dict = (
(filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['vocab_file']
)
_lowercase : str = (
(filename_prefix + '-' if filename_prefix else '') + save_directory + VOCAB_FILES_NAMES['emoji_file']
)
with open(lowerCamelCase, 'w', encoding='utf-8') as writer:
for token_index, token in self.ids_to_tokens.items():
if index != token_index:
logger.warning(
F'''Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'''
' Please check that the vocabulary is not corrupted!')
_lowercase : Union[str, Any] = token_index
writer.write(','.join(lowerCamelCase) + '\n')
index += 1
with open(lowerCamelCase, 'w', encoding='utf-8') as writer:
json.dump(self.emoji, lowerCamelCase)
return vocab_file, emoji_file
class _lowerCamelCase( _a ):
def __init__( self, lowerCamelCase, lowerCamelCase, lowerCamelCase) -> List[Any]:
"""simple docstring"""
_lowercase : List[Any] = vocab # same as swe
_lowercase : int = ids_to_tokens # same as bpe
_lowercase : Tuple = emoji
_lowercase : Tuple = np.max([len(lowerCamelCase) for w in self.vocab.keys()])
_lowercase : Dict = re.compile(R'(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)')
_lowercase : Tuple = re.compile(R'[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*')
_lowercase : List[Any] = re.compile(R'[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}')
_lowercase : Union[str, Any] = re.compile(
R'([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*')
_lowercase : List[Any] = re.compile(
R'(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*')
_lowercase : Optional[Any] = re.compile(
R'((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*')
_lowercase : List[str] = '─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿'
_lowercase : int = '▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟'
_lowercase : str = str.maketrans({k: '<BLOCK>' for k in keisen + blocks})
def __len__( self) -> List[str]:
"""simple docstring"""
return len(self.ids_to_tokens)
def UpperCamelCase ( self, lowerCamelCase) -> Tuple:
"""simple docstring"""
_lowercase : Union[str, Any] = self.content_repattera.sub('<URL>', lowerCamelCase)
_lowercase : Dict = self.content_repattera.sub('<EMAIL>', lowerCamelCase)
_lowercase : List[str] = self.content_repattera.sub('<TEL>', lowerCamelCase)
_lowercase : Optional[int] = self.content_repattera.sub('<DATE>', lowerCamelCase)
_lowercase : Tuple = self.content_repattera.sub('<DATE>', lowerCamelCase)
_lowercase : str = self.content_repattera.sub('<PRICE>', lowerCamelCase)
_lowercase : int = content.translate(self.content_transa)
while "<BLOCK><BLOCK>" in content:
_lowercase : Optional[Any] = content.replace('<BLOCK><BLOCK>', '<BLOCK>')
return content
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase=False) -> List[Any]:
"""simple docstring"""
_lowercase : int = text.replace(' ', '<SP>')
_lowercase : Optional[Any] = text.replace(' ', '<SP>')
_lowercase : Tuple = text.replace('\r\n', '<BR>')
_lowercase : Tuple = text.replace('\n', '<BR>')
_lowercase : Dict = text.replace('\r', '<BR>')
_lowercase : str = text.replace('\t', '<TAB>')
_lowercase : Optional[Any] = text.replace('—', 'ー')
_lowercase : Union[str, Any] = text.replace('−', 'ー')
for k, v in self.emoji["emoji"].items():
if k in text:
_lowercase : Optional[int] = text.replace(lowerCamelCase, lowerCamelCase)
if clean:
_lowercase : Tuple = self.clean_text(lowerCamelCase)
def check_simbol(lowerCamelCase):
_lowercase : Optional[int] = x.encode()
if len(lowerCamelCase) == 1 and len(lowerCamelCase) == 2:
_lowercase : List[Any] = (int(e[0]) << 8) + int(e[1])
if (
(c >= 0xC2A1 and c <= 0xC2BF)
or (c >= 0xC780 and c <= 0xC783)
or (c >= 0xCAB9 and c <= 0xCBBF)
or (c >= 0xCC80 and c <= 0xCDA2)
):
return True
return False
def checkuae(lowerCamelCase):
_lowercase : int = x.encode()
if len(lowerCamelCase) == 1 and len(lowerCamelCase) == 3:
_lowercase : Optional[Any] = (int(e[0]) << 16) + (int(e[1]) << 8) + int(e[2])
if c >= 0xE28080 and c <= 0xE2B07F:
return True
return False
_lowercase : Dict = 0
_lowercase : Optional[int] = []
while pos < len(lowerCamelCase):
_lowercase : Optional[Any] = min(len(lowerCamelCase), pos + self.maxlen + 1) if text[pos] == '<' else pos + 3
_lowercase : Optional[int] = [] # (token_id, token, pos)
for e in range(lowerCamelCase, lowerCamelCase, -1):
_lowercase : Optional[Any] = text[pos:e]
if wd in self.vocab:
if wd[0] == "<" and len(lowerCamelCase) > 2:
_lowercase : List[Any] = [(self.vocab[wd], wd, e)]
break
else:
candidates.append((self.vocab[wd], wd, e))
if len(lowerCamelCase) > 0:
# the smallest token_id is adopted
_lowercase , _lowercase , _lowercase : List[str] = sorted(lowerCamelCase, key=lambda lowerCamelCase: x[0])[0]
result.append(lowerCamelCase)
_lowercase : List[str] = e
else:
_lowercase : Optional[int] = pos + 1
_lowercase : int = text[pos:end]
if check_simbol(lowerCamelCase):
result.append('<KIGOU>')
elif checkuae(lowerCamelCase):
result.append('<U2000U2BFF>')
else:
for i in wd.encode('utf-8'):
result.append('<|byte%d|>' % i)
_lowercase : Optional[Any] = end
return result
def UpperCamelCase ( self, lowerCamelCase, lowerCamelCase="\n") -> List[str]:
"""simple docstring"""
_lowercase : List[Any] = []
_lowercase : str = []
_lowercase : str = self.ids_to_tokens[index][0]
if word[:6] == "<|byte" and word[-2:] == "|>":
byte_tokens.append(int(word[6:-2]))
else:
if len(lowerCamelCase) > 0:
words.append(bytearray(lowerCamelCase).decode('utf-8', errors='replace'))
_lowercase : Optional[Any] = []
if word[:7] == "<|emoji" and word[-2:] == "|>":
words.append(self.emoji['emoji_inv'][word])
elif word == "<SP>":
words.append(' ')
elif word == "<BR>":
words.append(lowerCamelCase)
elif word == "<TAB>":
words.append('\t')
elif word == "<BLOCK>":
words.append('▀')
elif word == "<KIGOU>":
words.append('ǀ')
elif word == "<U2000U2BFF>":
words.append('‖')
else:
words.append(lowerCamelCase)
if len(lowerCamelCase) > 0:
words.append(bytearray(lowerCamelCase).decode('utf-8', errors='replace'))
_lowercase : Union[str, Any] = ''.join(lowerCamelCase)
return text
| 89 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {"vocab_file": "spiece.model"}
_snake_case = {
"vocab_file": {
"albert-base-v1": "https://huggingface.co/albert-base-v1/resolve/main/spiece.model",
"albert-large-v1": "https://huggingface.co/albert-large-v1/resolve/main/spiece.model",
"albert-xlarge-v1": "https://huggingface.co/albert-xlarge-v1/resolve/main/spiece.model",
"albert-xxlarge-v1": "https://huggingface.co/albert-xxlarge-v1/resolve/main/spiece.model",
"albert-base-v2": "https://huggingface.co/albert-base-v2/resolve/main/spiece.model",
"albert-large-v2": "https://huggingface.co/albert-large-v2/resolve/main/spiece.model",
"albert-xlarge-v2": "https://huggingface.co/albert-xlarge-v2/resolve/main/spiece.model",
"albert-xxlarge-v2": "https://huggingface.co/albert-xxlarge-v2/resolve/main/spiece.model",
}
}
_snake_case = {
"albert-base-v1": 512,
"albert-large-v1": 512,
"albert-xlarge-v1": 512,
"albert-xxlarge-v1": 512,
"albert-base-v2": 512,
"albert-large-v2": 512,
"albert-xlarge-v2": 512,
"albert-xxlarge-v2": 512,
}
_snake_case = "▁"
class lowercase ( UpperCamelCase__ ):
_a = VOCAB_FILES_NAMES
_a = PRETRAINED_VOCAB_FILES_MAP
_a = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self , _a , _a=True , _a=True , _a=False , _a="[CLS]" , _a="[SEP]" , _a="<unk>" , _a="[SEP]" , _a="<pad>" , _a="[CLS]" , _a="[MASK]" , _a = None , **_a , ) -> None:
# Mask token behave like a normal word, i.e. include the space before it and
# is included in the raw text, there should be a match in a non-normalized sentence.
_A : Dict = (
AddedToken(_a , lstrip=_a , rstrip=_a , normalized=_a )
if isinstance(_a , _a )
else mask_token
)
_A : Tuple = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=_a , remove_space=_a , keep_accents=_a , bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , pad_token=_a , cls_token=_a , mask_token=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
_A : Optional[int] = do_lower_case
_A : List[Any] = remove_space
_A : Dict = keep_accents
_A : int = vocab_file
_A : List[str] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_a )
@property
def a__ ( self ) -> List[str]:
return len(self.sp_model )
def a__ ( self ) -> Optional[Any]:
_A : Dict = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Any:
_A : List[str] = self.__dict__.copy()
_A : Dict = None
return state
def __setstate__( self , _a ) -> Any:
_A : Optional[Any] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
_A : int = {}
_A : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def a__ ( self , _a ) -> Union[str, Any]:
if self.remove_space:
_A : List[str] = """ """.join(inputs.strip().split() )
else:
_A : Any = inputs
_A : List[str] = outputs.replace("""``""" , """\"""" ).replace("""''""" , """\"""" )
if not self.keep_accents:
_A : Optional[int] = unicodedata.normalize("""NFKD""" , _a )
_A : Tuple = """""".join([c for c in outputs if not unicodedata.combining(_a )] )
if self.do_lower_case:
_A : Optional[Any] = outputs.lower()
return outputs
def a__ ( self , _a ) -> List[str]:
_A : Any = self.preprocess_text(_a )
_A : int = self.sp_model.encode(_a , out_type=_a )
_A : int = []
for piece in pieces:
if len(_a ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
_A : Optional[int] = self.sp_model.EncodeAsPieces(piece[:-1].replace(_a , """""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
_A : List[Any] = cur_pieces[1:]
else:
_A : Optional[Any] = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(_a )
else:
new_pieces.append(_a )
return new_pieces
def a__ ( self , _a ) -> Any:
return self.sp_model.PieceToId(_a )
def a__ ( self , _a ) -> int:
return self.sp_model.IdToPiece(_a )
def a__ ( self , _a ) -> str:
_A : Optional[Any] = []
_A : Union[str, Any] = """"""
_A : Any = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(_a ) + token
_A : Union[str, Any] = True
_A : Optional[Any] = []
else:
current_sub_tokens.append(_a )
_A : Optional[int] = False
out_string += self.sp_model.decode(_a )
return out_string.strip()
def a__ ( self , _a , _a = None ) -> List[int]:
_A : Dict = [self.sep_token_id]
_A : Optional[Any] = [self.cls_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def a__ ( self , _a , _a = None , _a = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is not None:
return [1] + ([0] * len(_a )) + [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1]
def a__ ( self , _a , _a = None ) -> List[int]:
_A : str = [self.sep_token_id]
_A : List[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 a__ ( self , _a , _a = None ) -> Tuple[str]:
if not os.path.isdir(_a ):
logger.error(F'''Vocabulary path ({save_directory}) should be a directory''' )
return
_A : Any = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , """wb""" ) as fi:
_A : str = self.sp_model.serialized_model_proto()
fi.write(_a )
return (out_vocab_file,)
| 307 | 0 |
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class UpperCAmelCase__ ( unittest.TestCase ):
def __init__( self ,A__ ):
_A : str = parent
def A__ ( self ):
return {}
def a__ () -> int:
_A : Union[str, Any] = """<HTML>
<HEAD>
<TITLE>sample document</TITLE>
</HEAD>
<BODY BGCOLOR=\"FFFFFF\">
<HR>
<a href=\"http://google.com\">Goog</a>
<H1>This is one header</H1>
<H2>This is a another Header</H2>
<P>Travel from
<P>
<B>SFO to JFK</B>
<BR>
<B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>
<HR>
<div style=\"color:#0000FF\">
<h3>Traveler <b> name </b> is
<p> John Doe </p>
</div>"""
_A : Optional[Any] = """
<!DOCTYPE html>
<html>
<body>
<h1>My First Heading</h1>
<p>My first paragraph.</p>
</body>
</html>
"""
return [html_string_a, html_string_a]
@require_bsa
class UpperCAmelCase__ ( _UpperCAmelCase , unittest.TestCase ):
__snake_case : str = MarkupLMFeatureExtractor if is_bsa_available() else None
def A__ ( self ):
_A : List[Any] = MarkupLMFeatureExtractionTester(self )
@property
def A__ ( self ):
return self.feature_extract_tester.prepare_feat_extract_dict()
def A__ ( self ):
# Initialize feature_extractor
_A : List[Any] = self.feature_extraction_class()
# Test not batched input
_A : Union[str, Any] = get_html_strings()[0]
_A : Tuple = feature_extractor(lowercase_ )
# fmt: off
_A : List[Any] = [["""sample document""", """Goog""", """This is one header""", """This is a another Header""", """Travel from""", """SFO to JFK""", """on May 2, 2015 at 2:00 pm. For details go to confirm.com""", """Traveler""", """name""", """is""", """John Doe"""]]
_A : Union[str, Any] = [["""/html/head/title""", """/html/body/a""", """/html/body/h1""", """/html/body/h2""", """/html/body/p""", """/html/body/p/p/b[1]""", """/html/body/p/p/b[2]/i""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/b""", """/html/body/p/p/div/h3""", """/html/body/p/p/div/h3/p"""]]
# fmt: on
self.assertEqual(encoding.nodes ,lowercase_ )
self.assertEqual(encoding.xpaths ,lowercase_ )
# Test batched
_A : Tuple = get_html_strings()
_A : Optional[Any] = feature_extractor(lowercase_ )
# fmt: off
_A : List[str] = expected_nodes + [["""My First Heading""", """My first paragraph."""]]
_A : int = expected_xpaths + [["""/html/body/h1""", """/html/body/p"""]]
self.assertEqual(len(encoding.nodes ) ,2 )
self.assertEqual(len(encoding.xpaths ) ,2 )
self.assertEqual(encoding.nodes ,lowercase_ )
self.assertEqual(encoding.xpaths ,lowercase_ )
| 708 |
from pathlib import Path
import numpy as np
from PIL import Image
def a__ (__lowercase :np.ndarray ) -> np.ndarray:
_A , _A , _A : Optional[int] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def a__ (__lowercase :np.ndarray ) -> np.ndarray:
return (gray > 127) & (gray <= 255)
def a__ (__lowercase :np.ndarray , __lowercase :np.ndarray ) -> np.ndarray:
_A : Optional[int] = np.zeros_like(__lowercase )
_A : str = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
_A : Optional[int] = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
_A : List[Any] = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
_A : List[Any] = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
_UpperCamelCase : Optional[Any] =Path(__file__).resolve().parent / 'image_data' / 'lena.jpg'
_UpperCamelCase : List[Any] =np.array(Image.open(lena_path))
# kernel to be applied
_UpperCamelCase : Dict =np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
_UpperCamelCase : Optional[Any] =dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
_UpperCamelCase : Any =Image.fromarray(output).convert('RGB')
pil_img.save('result_dilation.png')
| 332 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__A : Any = {
'''configuration_pix2struct''': [
'''PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''Pix2StructConfig''',
'''Pix2StructTextConfig''',
'''Pix2StructVisionConfig''',
],
'''processing_pix2struct''': ['''Pix2StructProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Optional[int] = ['''Pix2StructImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A : Union[str, Any] = [
'''PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''Pix2StructPreTrainedModel''',
'''Pix2StructForConditionalGeneration''',
'''Pix2StructVisionModel''',
'''Pix2StructTextModel''',
]
if TYPE_CHECKING:
from .configuration_pixastruct import (
PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP,
PixaStructConfig,
PixaStructTextConfig,
PixaStructVisionConfig,
)
from .processing_pixastruct import PixaStructProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_pixastruct import PixaStructImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_pixastruct import (
PIX2STRUCT_PRETRAINED_MODEL_ARCHIVE_LIST,
PixaStructForConditionalGeneration,
PixaStructPreTrainedModel,
PixaStructTextModel,
PixaStructVisionModel,
)
else:
import sys
__A : int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 343 |
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ) -> Optional[Any]:
'''simple docstring'''
def wrapper(*_UpperCAmelCase, **_UpperCAmelCase ):
lowerCAmelCase : str = timeit.default_timer()
lowerCAmelCase : str = func(*_UpperCAmelCase, **_UpperCAmelCase )
lowerCAmelCase : Optional[int] = timeit.default_timer() - starttime
return delta
lowerCAmelCase : Union[str, Any] = func.__name__
return wrapper
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase=100, _UpperCAmelCase=None ) -> Any:
'''simple docstring'''
lowerCAmelCase : Dict = []
lowerCAmelCase : Optional[int] = seq_shapes or {}
for i in range(_UpperCAmelCase ):
lowerCAmelCase : Any = {}
for col_id, (k, v) in enumerate(features.items() ):
if isinstance(_UpperCAmelCase, _ArrayXD ):
lowerCAmelCase : Dict = np.random.rand(*v.shape ).astype(v.dtype )
elif isinstance(_UpperCAmelCase, datasets.Value ):
if v.dtype == "string":
lowerCAmelCase : Any = 'The small grey turtle was surprisingly fast when challenged.'
else:
lowerCAmelCase : Optional[Any] = np.random.randint(10, size=1 ).astype(v.dtype ).item()
elif isinstance(_UpperCAmelCase, datasets.Sequence ):
while isinstance(_UpperCAmelCase, datasets.Sequence ):
lowerCAmelCase : int = v.feature
lowerCAmelCase : Optional[int] = seq_shapes[k]
lowerCAmelCase : str = np.random.rand(*_UpperCAmelCase ).astype(v.dtype )
lowerCAmelCase : Any = data
dummy_data.append((i, example) )
return dummy_data
def SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase, _UpperCAmelCase, _UpperCAmelCase=100, _UpperCAmelCase=None ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase : Any = generate_examples(_UpperCAmelCase, num_examples=_UpperCAmelCase, seq_shapes=_UpperCAmelCase )
with ArrowWriter(features=_UpperCAmelCase, path=_UpperCAmelCase ) as writer:
for key, record in dummy_data:
lowerCAmelCase : Any = features.encode_example(_UpperCAmelCase )
writer.write(_UpperCAmelCase )
lowerCAmelCase , lowerCAmelCase : Optional[int] = writer.finalize()
if not num_final_examples == num_examples:
raise ValueError(
f"Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}." )
lowerCAmelCase : int = datasets.Dataset.from_file(filename=_UpperCAmelCase, info=datasets.DatasetInfo(features=_UpperCAmelCase ) )
return dataset
| 343 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase : Union[str, Any] = logging.get_logger(__name__)
lowercase : Tuple = {
'sail/poolformer_s12': 'https://huggingface.co/sail/poolformer_s12/resolve/main/config.json',
# See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class A ( __snake_case ):
__magic_name__ = '''poolformer'''
def __init__( self , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=16 , SCREAMING_SNAKE_CASE=3 , SCREAMING_SNAKE_CASE=4.0 , SCREAMING_SNAKE_CASE=[2, 2, 6, 2] , SCREAMING_SNAKE_CASE=[64, 128, 320, 512] , SCREAMING_SNAKE_CASE=[7, 3, 3, 3] , SCREAMING_SNAKE_CASE=[4, 2, 2, 2] , SCREAMING_SNAKE_CASE=[2, 1, 1, 1] , SCREAMING_SNAKE_CASE=4 , SCREAMING_SNAKE_CASE=0.0 , SCREAMING_SNAKE_CASE="gelu" , SCREAMING_SNAKE_CASE=True , SCREAMING_SNAKE_CASE=1e-5 , SCREAMING_SNAKE_CASE=0.02 , **SCREAMING_SNAKE_CASE , ) -> Dict:
"""simple docstring"""
A : str = num_channels
A : Any = patch_size
A : Union[str, Any] = stride
A : List[Any] = padding
A : Any = pool_size
A : Tuple = hidden_sizes
A : Any = mlp_ratio
A : List[str] = depths
A : Optional[Any] = patch_sizes
A : Union[str, Any] = strides
A : Optional[Any] = num_encoder_blocks
A : Optional[Any] = drop_path_rate
A : List[Any] = hidden_act
A : Any = use_layer_scale
A : List[str] = layer_scale_init_value
A : List[str] = initializer_range
super().__init__(**SCREAMING_SNAKE_CASE )
class A ( __snake_case ):
__magic_name__ = version.parse('''1.11''' )
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
"""simple docstring"""
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def __lowerCAmelCase ( self ) -> float:
"""simple docstring"""
return 2e-3
| 705 |
'''simple docstring'''
import math_equivalence # From: git+https://github.com/hendrycks/math.git
import datasets
lowercase : Tuple = '\\n@article{hendrycksmath2021,\n title={Measuring Mathematical Problem Solving With the MATH Dataset},\n author={Dan Hendrycks\n and Collin Burns\n and Saurav Kadavath\n and Akul Arora\n and Steven Basart\n and Eric Tang\n and Dawn Song\n and Jacob Steinhardt},\n journal={arXiv preprint arXiv:2103.03874},\n year={2021}\n}\n'
lowercase : List[str] = '\\nThis metric is used to assess performance on the Mathematics Aptitude Test of Heuristics (MATH) dataset.\nIt first canonicalizes the inputs (e.g., converting "1/2" to "\\frac{1}{2}") and then computes accuracy.\n'
lowercase : Any = R'\nCalculates accuracy after canonicalizing inputs.\n\nArgs:\n predictions: list of predictions to score. Each prediction\n is a string that contains natural language and LaTex.\n references: list of reference for each prediction. Each\n reference is a string that contains natural language\n and LaTex.\nReturns:\n accuracy: accuracy after canonicalizing inputs\n (e.g., converting "1/2" to "\\frac{1}{2}")\n\nExamples:\n >>> metric = datasets.load_metric("competition_math")\n >>> results = metric.compute(references=["\\frac{1}{2}"], predictions=["1/2"])\n >>> print(results)\n {\'accuracy\': 1.0}\n'
@datasets.utils.file_utils.add_end_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
def __lowerCAmelCase ( self ) -> Tuple:
"""simple docstring"""
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': datasets.Value('''string''' ),
'''references''': datasets.Value('''string''' ),
} ) , homepage='''https://github.com/hendrycks/math''' , codebase_urls=['''https://github.com/hendrycks/math'''] , )
def __lowerCAmelCase ( self , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
A : int = 0.0
for i, j in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
n_correct += 1.0 if math_equivalence.is_equiv(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) else 0.0
A : Tuple = n_correct / len(SCREAMING_SNAKE_CASE )
return {
"accuracy": accuracy,
}
| 343 | 0 |
"""simple docstring"""
SCREAMING_SNAKE_CASE__:int = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
SCREAMING_SNAKE_CASE__:Tuple = [
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
SCREAMING_SNAKE_CASE__:Dict = [
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
SCREAMING_SNAKE_CASE__:List[Any] = [
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
SCREAMING_SNAKE_CASE__:Any = [
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
SCREAMING_SNAKE_CASE__:int = [
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
SCREAMING_SNAKE_CASE__:Optional[int] = [
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
SCREAMING_SNAKE_CASE__:Dict = [
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 528 | """simple docstring"""
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
SCREAMING_SNAKE_CASE__:Dict = logging.get_logger(__name__)
class snake_case__ ( snake_case_ ):
def __init__( self , *lowerCamelCase , **lowerCamelCase ):
warnings.warn(
"The class DonutFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use DonutImageProcessor instead." , lowerCamelCase , )
super().__init__(*lowerCamelCase , **lowerCamelCase )
| 528 | 1 |
from math import isqrt
def _SCREAMING_SNAKE_CASE ( snake_case ) -> bool:
return all(number % divisor != 0 for divisor in range(2 , isqrt(snake_case ) + 1 ) )
def _SCREAMING_SNAKE_CASE ( snake_case = 1_0**6 ) -> int:
_UpperCAmelCase = 0
_UpperCAmelCase = 1
_UpperCAmelCase = 7
while prime_candidate < max_prime:
primes_count += is_prime(snake_case )
cube_index += 1
prime_candidate += 6 * cube_index
return primes_count
if __name__ == "__main__":
print(F'{solution() = }') | 175 |
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case ) -> float:
_UpperCAmelCase = sorted(numsa + numsa )
_UpperCAmelCase , _UpperCAmelCase = 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()
a = [float(x) for x in input("Enter the elements of first array: ").split()]
a = [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)}') | 175 | 1 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCAmelCase_ ( unittest.TestCase ):
@property
def __UpperCAmelCase ( self ):
torch.manual_seed(0 )
UpperCAmelCase__ : List[Any] = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") , up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") , )
return model
def __UpperCAmelCase ( self ):
UpperCAmelCase__ : int = self.dummy_uncond_unet
UpperCAmelCase__ : Union[str, Any] = ScoreSdeVeScheduler()
UpperCAmelCase__ : Union[str, Any] = ScoreSdeVePipeline(unet=_lowerCAmelCase , scheduler=_lowerCAmelCase )
sde_ve.to(_lowerCAmelCase )
sde_ve.set_progress_bar_config(disable=_lowerCAmelCase )
UpperCAmelCase__ : Any = torch.manual_seed(0 )
UpperCAmelCase__ : Union[str, Any] = sde_ve(num_inference_steps=2 , output_type="""numpy""" , generator=_lowerCAmelCase ).images
UpperCAmelCase__ : Tuple = torch.manual_seed(0 )
UpperCAmelCase__ : Any = sde_ve(num_inference_steps=2 , output_type="""numpy""" , generator=_lowerCAmelCase , return_dict=_lowerCAmelCase )[
0
]
UpperCAmelCase__ : Any = image[0, -3:, -3:, -1]
UpperCAmelCase__ : List[str] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
UpperCAmelCase__ : Optional[Any] = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class UpperCAmelCase_ ( unittest.TestCase ):
def __UpperCAmelCase ( self ):
UpperCAmelCase__ : Optional[int] = """google/ncsnpp-church-256"""
UpperCAmelCase__ : str = UNetaDModel.from_pretrained(_lowerCAmelCase )
UpperCAmelCase__ : int = ScoreSdeVeScheduler.from_pretrained(_lowerCAmelCase )
UpperCAmelCase__ : Tuple = ScoreSdeVePipeline(unet=_lowerCAmelCase , scheduler=_lowerCAmelCase )
sde_ve.to(_lowerCAmelCase )
sde_ve.set_progress_bar_config(disable=_lowerCAmelCase )
UpperCAmelCase__ : List[str] = torch.manual_seed(0 )
UpperCAmelCase__ : List[str] = sde_ve(num_inference_steps=10 , output_type="""numpy""" , generator=_lowerCAmelCase ).images
UpperCAmelCase__ : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
UpperCAmelCase__ : List[Any] = np.array([0.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 79 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
UpperCAmelCase : Dict = '\\n@inproceedings{snover-etal-2006-study,\n title = "A Study of Translation Edit Rate with Targeted Human Annotation",\n author = "Snover, Matthew and\n Dorr, Bonnie and\n Schwartz, Rich and\n Micciulla, Linnea and\n Makhoul, John",\n booktitle = "Proceedings of the 7th Conference of the Association for Machine Translation in the Americas: Technical Papers",\n month = aug # " 8-12",\n year = "2006",\n address = "Cambridge, Massachusetts, USA",\n publisher = "Association for Machine Translation in the Americas",\n url = "https://aclanthology.org/2006.amta-papers.25",\n pages = "223--231",\n}\n@inproceedings{post-2018-call,\n title = "A Call for Clarity in Reporting {BLEU} Scores",\n author = "Post, Matt",\n booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n month = oct,\n year = "2018",\n address = "Belgium, Brussels",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W18-6319",\n pages = "186--191",\n}\n'
UpperCAmelCase : List[Any] = '\\nTER (Translation Edit Rate, also called Translation Error Rate) is a metric to quantify the edit operations that a\nhypothesis requires to match a reference translation. We use the implementation that is already present in sacrebleu\n(https://github.com/mjpost/sacreBLEU#ter), which in turn is inspired by the TERCOM implementation, which can be found\nhere: https://github.com/jhclark/tercom.\n\nThe implementation here is slightly different from sacrebleu in terms of the required input format. The length of\nthe references and hypotheses lists need to be the same, so you may need to transpose your references compared to\nsacrebleu\'s required input format. See https://github.com/huggingface/datasets/issues/3154#issuecomment-950746534\n\nSee the README.md file at https://github.com/mjpost/sacreBLEU#ter for more information.\n'
UpperCAmelCase : int = '\nProduces TER scores alongside the number of edits and reference length.\n\nArgs:\n predictions (list of str): The system stream (a sequence of segments).\n references (list of list of str): A list of one or more reference streams (each a sequence of segments).\n normalized (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n ignore_punct (boolean): If `True`, applies basic tokenization and normalization to sentences. Defaults to `False`.\n support_zh_ja_chars (boolean): If `True`, tokenization/normalization supports processing of Chinese characters,\n as well as Japanese Kanji, Hiragana, Katakana, and Phonetic Extensions of Katakana.\n Only applies if `normalized = True`. Defaults to `False`.\n case_sensitive (boolean): If `False`, makes all predictions and references lowercase to ignore differences in case. Defaults to `False`.\n\nReturns:\n \'score\' (float): TER score (num_edits / sum_ref_lengths * 100)\n \'num_edits\' (int): The cumulative number of edits\n \'ref_length\' (float): The cumulative average reference length\n\nExamples:\n Example 1:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 150.0, \'num_edits\': 15, \'ref_length\': 10.0}\n\n Example 2:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 62.5, \'num_edits\': 5, \'ref_length\': 8.0}\n\n Example 3:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... normalized=True,\n ... case_sensitive=True)\n >>> print(results)\n {\'score\': 57.14285714285714, \'num_edits\': 6, \'ref_length\': 10.5}\n\n Example 4:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 0.0, \'num_edits\': 0, \'ref_length\': 8.0}\n\n Example 5:\n >>> predictions = ["does this sentence match??",\n ... "what about this sentence?",\n ... "What did the TER metric user say to the developer?"]\n >>> references = [["does this sentence match", "does this sentence match!?!"],\n ... ["wHaT aBoUt ThIs SeNtEnCe?", "wHaT aBoUt ThIs SeNtEnCe?"],\n ... ["Your jokes are...", "...TERrible"]]\n >>> ter = datasets.load_metric("ter")\n >>> results = ter.compute(predictions=predictions,\n ... references=references,\n ... ignore_punct=True,\n ... case_sensitive=False)\n >>> print(results)\n {\'score\': 100.0, \'num_edits\': 10, \'ref_length\': 10.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def lowerCamelCase__ ( self : str ):
'''simple docstring'''
if version.parse(scb.__version__ ) < version.parse("""1.4.12""" ):
raise ImportWarning(
"""To use `sacrebleu`, the module `sacrebleu>=1.4.12` is required, and the current version of `sacrebleu` doesn't match this condition.\n"""
"""You can install it with `pip install \"sacrebleu>=1.4.12\"`.""" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="""http://www.cs.umd.edu/~snover/tercom/""" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence""" ),
"""references""": datasets.Sequence(datasets.Value("""string""" , id="""sequence""" ) , id="""references""" ),
} ) , codebase_urls=["""https://github.com/mjpost/sacreBLEU#ter"""] , reference_urls=[
"""https://github.com/jhclark/tercom""",
] , )
def lowerCamelCase__ ( self : List[Any] , UpperCamelCase : int , UpperCamelCase : Dict , UpperCamelCase : bool = False , UpperCamelCase : bool = False , UpperCamelCase : bool = False , UpperCamelCase : bool = False , ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = len(references[0] )
if any(len(UpperCamelCase ) != references_per_prediction for refs in references ):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
__UpperCAmelCase : Any = [[refs[i] for refs in references] for i in range(UpperCamelCase )]
__UpperCAmelCase : List[str] = TER(
normalized=UpperCamelCase , no_punct=UpperCamelCase , asian_support=UpperCamelCase , case_sensitive=UpperCamelCase , )
__UpperCAmelCase : Tuple = sb_ter.corpus_score(UpperCamelCase , UpperCamelCase )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length}
| 139 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class UpperCAmelCase ( unittest.TestCase ):
def __lowerCAmelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def __lowerCAmelCase ( self ):
_lowerCAmelCase = 1
_lowerCAmelCase = 3
_lowerCAmelCase = (32, 32)
_lowerCAmelCase = floats_tensor((batch_size, num_channels) + sizes , rng=random.Random(0 ) ).to(_lowerCAmelCase )
return image
@property
def __lowerCAmelCase ( self ):
torch.manual_seed(0 )
_lowerCAmelCase = UNetaDConditionModel(
block_out_channels=(32, 32, 64) , layers_per_block=2 , sample_size=32 , in_channels=7 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , attention_head_dim=8 , use_linear_projection=_lowerCAmelCase , only_cross_attention=(True, True, False) , num_class_embeds=100 , )
return model
@property
def __lowerCAmelCase ( self ):
torch.manual_seed(0 )
_lowerCAmelCase = AutoencoderKL(
block_out_channels=[32, 32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
return model
@property
def __lowerCAmelCase ( self ):
torch.manual_seed(0 )
_lowerCAmelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , hidden_act='''gelu''' , projection_dim=512 , )
return CLIPTextModel(_lowerCAmelCase )
def __lowerCAmelCase ( self ):
_lowerCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.dummy_cond_unet_upscale
_lowerCAmelCase = DDPMScheduler()
_lowerCAmelCase = DDIMScheduler(prediction_type='''v_prediction''' )
_lowerCAmelCase = self.dummy_vae
_lowerCAmelCase = self.dummy_text_encoder
_lowerCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
_lowerCAmelCase = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCAmelCase = Image.fromarray(np.uinta(_lowerCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
# make sure here that pndm scheduler skips prk
_lowerCAmelCase = StableDiffusionUpscalePipeline(
unet=_lowerCAmelCase , low_res_scheduler=_lowerCAmelCase , scheduler=_lowerCAmelCase , vae=_lowerCAmelCase , text_encoder=_lowerCAmelCase , tokenizer=_lowerCAmelCase , max_noise_level=350 , )
_lowerCAmelCase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
_lowerCAmelCase = '''A painting of a squirrel eating a burger'''
_lowerCAmelCase = torch.Generator(device=_lowerCAmelCase ).manual_seed(0 )
_lowerCAmelCase = sd_pipe(
[prompt] , image=_lowerCAmelCase , generator=_lowerCAmelCase , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type='''np''' , )
_lowerCAmelCase = output.images
_lowerCAmelCase = torch.Generator(device=_lowerCAmelCase ).manual_seed(0 )
_lowerCAmelCase = sd_pipe(
[prompt] , image=_lowerCAmelCase , generator=_lowerCAmelCase , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type='''np''' , return_dict=_lowerCAmelCase , )[0]
_lowerCAmelCase = image[0, -3:, -3:, -1]
_lowerCAmelCase = image_from_tuple[0, -3:, -3:, -1]
_lowerCAmelCase = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
_lowerCAmelCase = np.array([0.3_113, 0.3_910, 0.4_272, 0.4_859, 0.5_061, 0.4_652, 0.5_362, 0.5_715, 0.5_661] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ):
_lowerCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_lowerCAmelCase = self.dummy_cond_unet_upscale
_lowerCAmelCase = DDPMScheduler()
_lowerCAmelCase = DDIMScheduler(prediction_type='''v_prediction''' )
_lowerCAmelCase = self.dummy_vae
_lowerCAmelCase = self.dummy_text_encoder
_lowerCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
_lowerCAmelCase = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCAmelCase = Image.fromarray(np.uinta(_lowerCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
# make sure here that pndm scheduler skips prk
_lowerCAmelCase = StableDiffusionUpscalePipeline(
unet=_lowerCAmelCase , low_res_scheduler=_lowerCAmelCase , scheduler=_lowerCAmelCase , vae=_lowerCAmelCase , text_encoder=_lowerCAmelCase , tokenizer=_lowerCAmelCase , max_noise_level=350 , )
_lowerCAmelCase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
_lowerCAmelCase = '''A painting of a squirrel eating a burger'''
_lowerCAmelCase = sd_pipe(
2 * [prompt] , image=2 * [low_res_image] , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type='''np''' , )
_lowerCAmelCase = output.images
assert image.shape[0] == 2
_lowerCAmelCase = torch.Generator(device=_lowerCAmelCase ).manual_seed(0 )
_lowerCAmelCase = sd_pipe(
[prompt] , image=_lowerCAmelCase , generator=_lowerCAmelCase , num_images_per_prompt=2 , guidance_scale=6.0 , noise_level=20 , num_inference_steps=2 , output_type='''np''' , )
_lowerCAmelCase = output.images
assert image.shape[0] == 2
@unittest.skipIf(torch_device != '''cuda''' , '''This test requires a GPU''' )
def __lowerCAmelCase ( self ):
_lowerCAmelCase = self.dummy_cond_unet_upscale
_lowerCAmelCase = DDPMScheduler()
_lowerCAmelCase = DDIMScheduler(prediction_type='''v_prediction''' )
_lowerCAmelCase = self.dummy_vae
_lowerCAmelCase = self.dummy_text_encoder
_lowerCAmelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
_lowerCAmelCase = self.dummy_image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCAmelCase = Image.fromarray(np.uinta(_lowerCAmelCase ) ).convert('''RGB''' ).resize((64, 64) )
# put models in fp16, except vae as it overflows in fp16
_lowerCAmelCase = unet.half()
_lowerCAmelCase = text_encoder.half()
# make sure here that pndm scheduler skips prk
_lowerCAmelCase = StableDiffusionUpscalePipeline(
unet=_lowerCAmelCase , low_res_scheduler=_lowerCAmelCase , scheduler=_lowerCAmelCase , vae=_lowerCAmelCase , text_encoder=_lowerCAmelCase , tokenizer=_lowerCAmelCase , max_noise_level=350 , )
_lowerCAmelCase = sd_pipe.to(_lowerCAmelCase )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
_lowerCAmelCase = '''A painting of a squirrel eating a burger'''
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = sd_pipe(
[prompt] , image=_lowerCAmelCase , generator=_lowerCAmelCase , num_inference_steps=2 , output_type='''np''' , ).images
_lowerCAmelCase = low_res_image.size[0] * 4
assert image.shape == (1, expected_height_width, expected_height_width, 3)
@slow
@require_torch_gpu
class UpperCAmelCase ( unittest.TestCase ):
def __lowerCAmelCase ( self ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __lowerCAmelCase ( self ):
_lowerCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-upscale/low_res_cat.png''' )
_lowerCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
'''/upsampled_cat.npy''' )
_lowerCAmelCase = '''stabilityai/stable-diffusion-x4-upscaler'''
_lowerCAmelCase = StableDiffusionUpscalePipeline.from_pretrained(_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
_lowerCAmelCase = '''a cat sitting on a park bench'''
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , generator=_lowerCAmelCase , output_type='''np''' , )
_lowerCAmelCase = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 1E-3
def __lowerCAmelCase ( self ):
_lowerCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-upscale/low_res_cat.png''' )
_lowerCAmelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd2-upscale'''
'''/upsampled_cat_fp16.npy''' )
_lowerCAmelCase = '''stabilityai/stable-diffusion-x4-upscaler'''
_lowerCAmelCase = StableDiffusionUpscalePipeline.from_pretrained(
_lowerCAmelCase , torch_dtype=torch.floataa , )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing()
_lowerCAmelCase = '''a cat sitting on a park bench'''
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , generator=_lowerCAmelCase , output_type='''np''' , )
_lowerCAmelCase = output.images[0]
assert image.shape == (512, 512, 3)
assert np.abs(expected_image - image ).max() < 5E-1
def __lowerCAmelCase ( self ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_lowerCAmelCase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/sd2-upscale/low_res_cat.png''' )
_lowerCAmelCase = '''stabilityai/stable-diffusion-x4-upscaler'''
_lowerCAmelCase = StableDiffusionUpscalePipeline.from_pretrained(
_lowerCAmelCase , torch_dtype=torch.floataa , )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
_lowerCAmelCase = '''a cat sitting on a park bench'''
_lowerCAmelCase = torch.manual_seed(0 )
_lowerCAmelCase = pipe(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , generator=_lowerCAmelCase , num_inference_steps=5 , output_type='''np''' , )
_lowerCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 2.9 GB is allocated
assert mem_bytes < 2.9 * 10**9 | 664 |
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm
UpperCAmelCase_ = re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
UpperCAmelCase_ = 1_0
UpperCAmelCase_ = 2_5_6
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : List[str] )->Optional[MinHash]:
if len(_SCREAMING_SNAKE_CASE ) < MIN_NUM_TOKENS:
return None
_lowerCAmelCase = MinHash(num_perm=_SCREAMING_SNAKE_CASE )
for token in set(_SCREAMING_SNAKE_CASE ):
min_hash.update(token.encode() )
return min_hash
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : str )->Set[str]:
return {t for t in NON_ALPHA.split(_SCREAMING_SNAKE_CASE ) if len(t.strip() ) > 0}
class UpperCAmelCase :
def __init__( self , *,
_lowerCAmelCase = 0.85 , ):
_lowerCAmelCase = duplication_jaccard_threshold
_lowerCAmelCase = NUM_PERM
_lowerCAmelCase = MinHashLSH(threshold=self._duplication_jaccard_threshold , num_perm=self._num_perm )
_lowerCAmelCase = defaultdict(_lowerCAmelCase )
def __lowerCAmelCase ( self , _lowerCAmelCase , _lowerCAmelCase ):
_lowerCAmelCase = self._index.query(_lowerCAmelCase )
if code_key in self._index.keys:
print(F'''Duplicate key {code_key}''' )
return
self._index.insert(_lowerCAmelCase , _lowerCAmelCase )
if len(_lowerCAmelCase ) > 0:
for base_duplicate in close_duplicates:
if base_duplicate in self._duplicate_clusters:
self._duplicate_clusters[base_duplicate].add(_lowerCAmelCase )
break
else:
self._duplicate_clusters[close_duplicates[0]].add(_lowerCAmelCase )
def __lowerCAmelCase ( self ):
_lowerCAmelCase = []
for base, duplicates in self._duplicate_clusters.items():
_lowerCAmelCase = [base] + list(_lowerCAmelCase )
# reformat the cluster to be a list of dict
_lowerCAmelCase = [{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
duplicate_clusters.append(_lowerCAmelCase )
return duplicate_clusters
def __lowerCAmelCase ( self , _lowerCAmelCase ):
_lowerCAmelCase = self.get_duplicate_clusters()
with open(_lowerCAmelCase , '''w''' ) as f:
json.dump(_lowerCAmelCase , _lowerCAmelCase )
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : str )->Optional[Any]:
_lowerCAmelCase , _lowerCAmelCase = element
_lowerCAmelCase = get_min_hash([t for t in NON_ALPHA.split(data['''content'''] ) if len(t.strip() ) > 0] )
if min_hash is not None:
return (index, data["repo_name"], data["path"]), min_hash
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : Type[Dataset] )->Any:
with mp.Pool() as pool:
for data in pool.imap_unordered(
_compute_min_hash , ThreadedIterator(_SCREAMING_SNAKE_CASE , max_queue_size=1_0_0_0_0 ) , chunksize=1_0_0 , ):
if data is not None:
yield data
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : Type[Dataset] , _SCREAMING_SNAKE_CASE : float )->str:
_lowerCAmelCase = DuplicationIndex(duplication_jaccard_threshold=_SCREAMING_SNAKE_CASE )
for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(_SCREAMING_SNAKE_CASE ) ) , max_queue_size=1_0_0 ) ):
di.add(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Returns a List[Cluster] where Cluster is List[str] with the filenames.
return di.get_duplicate_clusters()
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : str , _SCREAMING_SNAKE_CASE : str )->float:
_lowerCAmelCase = get_tokens(_SCREAMING_SNAKE_CASE )
_lowerCAmelCase = get_tokens(_SCREAMING_SNAKE_CASE )
return len(tokensa & tokensa ) / len(tokensa | tokensa )
UpperCAmelCase_ = None
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : Optional[Any] , _SCREAMING_SNAKE_CASE : Any )->List[Any]:
_lowerCAmelCase = []
for elementa in cluster:
_lowerCAmelCase = _shared_dataset[elementa['''base_index''']]['''content''']
for elementa in extremes:
_lowerCAmelCase = _shared_dataset[elementa['''base_index''']]['''content''']
if jaccard_similarity(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) >= jaccard_threshold:
elementa["copies"] += 1
break
else:
_lowerCAmelCase = 1
extremes.append(_SCREAMING_SNAKE_CASE )
return extremes
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : str )->Tuple:
global _shared_dataset
_lowerCAmelCase = dataset
_lowerCAmelCase = []
_lowerCAmelCase = partial(_find_cluster_extremes_shared , jaccard_threshold=_SCREAMING_SNAKE_CASE )
with mp.Pool() as pool:
for extremes in tqdm(
pool.imap_unordered(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) , total=len(_SCREAMING_SNAKE_CASE ) , ):
extremes_list.append(_SCREAMING_SNAKE_CASE )
return extremes_list
def UpperCAmelCase__ ( _SCREAMING_SNAKE_CASE : Type[Dataset] , _SCREAMING_SNAKE_CASE : float = 0.85 )->Tuple[Type[Dataset], List[List[Dict]]]:
_lowerCAmelCase = make_duplicate_clusters(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_lowerCAmelCase = {x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
_lowerCAmelCase = {}
_lowerCAmelCase = find_extremes(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for extremes in extremes_clusters:
for element in extremes:
_lowerCAmelCase = element
_lowerCAmelCase = duplicate_indices - set(extreme_dict.keys() )
_lowerCAmelCase = dataset.filter(lambda _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : idx not in remove_indices , with_indices=_SCREAMING_SNAKE_CASE )
# update duplicate_clusters
for cluster in duplicate_clusters:
for element in cluster:
_lowerCAmelCase = element['''base_index'''] in extreme_dict
if element["is_extreme"]:
_lowerCAmelCase = extreme_dict[element['''base_index''']]['''copies''']
print(f'''Original dataset size: {len(_SCREAMING_SNAKE_CASE )}''' )
print(f'''Number of duplicate clusters: {len(_SCREAMING_SNAKE_CASE )}''' )
print(f'''Files in duplicate cluster: {len(_SCREAMING_SNAKE_CASE )}''' )
print(f'''Unique files in duplicate cluster: {len(_SCREAMING_SNAKE_CASE )}''' )
print(f'''Filtered dataset size: {len(_SCREAMING_SNAKE_CASE )}''' )
return ds_filter, duplicate_clusters | 664 | 1 |
"""simple docstring"""
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 snake_case__ ( _snake_case : List[Any]=32 , _snake_case : Tuple=10 , _snake_case : str=1_00 , _snake_case : Optional[int]=10_26 , _snake_case : Any=True , _snake_case : str="data/tokenized_stories_train_wikitext103.jbl" , _snake_case : Any="igf_context_pairs.jbl" , ):
"""simple docstring"""
set_seed(3 )
# generate train_data and objective_set
UpperCamelCase__ , UpperCamelCase__ = generate_datasets(
_snake_case , _snake_case , number=_snake_case , min_len=10_26 , trim=_snake_case )
# 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?
UpperCamelCase__ = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
# load pretrained model
UpperCamelCase__ = load_gpta("gpt2" ).to(_snake_case )
print("computing perplexity on objective set" )
UpperCamelCase__ = compute_perplexity(_snake_case , _snake_case , _snake_case ).item()
print("perplexity on objective set:" , _snake_case )
# collect igf pairs and save to file demo.jbl
collect_objective_set(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def snake_case__ ( _snake_case : Any , _snake_case : str=15 , _snake_case : str=1_28 , _snake_case : int=1_00 , _snake_case : Tuple="igf_model.pt" , ):
"""simple docstring"""
set_seed(42 )
# Load pre-trained model
UpperCamelCase__ = GPTaLMHeadModel.from_pretrained("gpt2" )
# Initialize secondary learner to use embedding weights of model
UpperCamelCase__ = SecondaryLearner(_snake_case )
# Train secondary learner
UpperCamelCase__ = train_secondary_learner(
_snake_case , _snake_case , max_epochs=_snake_case , batch_size=_snake_case , eval_freq=1_00 , igf_model_path=_snake_case , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def snake_case__ ( _snake_case : List[Any] , _snake_case : Optional[int] , _snake_case : Optional[int] , _snake_case : List[str]=32 , _snake_case : Tuple=10_00 , _snake_case : List[Any]=16 , _snake_case : str=1.0 , _snake_case : List[str]=recopy_gpta , _snake_case : Optional[int]=None , _snake_case : Optional[int]=10 , _snake_case : Optional[int]="gpt2_finetuned.pt" , ):
"""simple docstring"""
UpperCamelCase__ = torch.device("cuda:0" if torch.cuda.is_available() else "cpu" )
UpperCamelCase__ = RandomSampler(_snake_case )
UpperCamelCase__ = DataLoader(_snake_case , sampler=_snake_case )
UpperCamelCase__ = max_steps // (len(_snake_case )) + 1
UpperCamelCase__ = 0
UpperCamelCase__ = torch.zeros((1, context_len) , dtype=torch.long , device=_snake_case )
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ = recopy_model(_snake_case , _snake_case , _snake_case )
model.train()
if secondary_learner is not None:
secondary_learner.to(_snake_case )
secondary_learner.eval()
UpperCamelCase__ = []
UpperCamelCase__ = 0
UpperCamelCase__ = []
UpperCamelCase__ = []
# Compute the performance of the transformer model at the beginning
UpperCamelCase__ = compute_perplexity(_snake_case , _snake_case , _snake_case )
test_perps.append(_snake_case )
print("Test perplexity, step" , _snake_case , ":" , _snake_case )
for epoch in range(int(_snake_case ) ):
for step, example in enumerate(_snake_case ):
torch.cuda.empty_cache()
UpperCamelCase__ = random.randint(0 , example.size(2 ) - context_len - 1 )
UpperCamelCase__ = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
UpperCamelCase__ = model(_snake_case , labels=_snake_case )
UpperCamelCase__ = True
if secondary_learner is not None:
UpperCamelCase__ = secondary_learner.forward(
torch.tensor(_snake_case , dtype=torch.long , device=_snake_case ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(_snake_case ) )
# 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:
UpperCamelCase__ = -1
if predicted_q < threshold:
UpperCamelCase__ = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
UpperCamelCase__ = 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()
UpperCamelCase__ = 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:
UpperCamelCase__ = compute_perplexity(_snake_case , _snake_case , _snake_case )
test_perps.append(_snake_case )
print("Test perplexity, step" , _snake_case , ":" , _snake_case )
# 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() , _snake_case )
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 snake_case__ ( ):
"""simple docstring"""
UpperCamelCase__ = argparse.ArgumentParser(description="Fine-tune a transformer model with IGF on a language modeling task" )
# Required parameters
parser.add_argument(
"--data_dir" , default=_snake_case , type=_snake_case , required=_snake_case , help="The input data dir. Should contain data files for WikiText." , )
parser.add_argument(
"--model_name_or_path" , default=_snake_case , type=_snake_case , required=_snake_case , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--data_file" , type=_snake_case , default=_snake_case , 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=_snake_case , default=_snake_case , help="A jbl file containing the context and information gain pairs to train secondary learner." , )
parser.add_argument(
"--output_dir" , default=_snake_case , type=_snake_case , required=_snake_case , help="The output directory where the final fine-tuned model is stored." , )
parser.add_argument(
"--tokenizer_name" , default=_snake_case , type=_snake_case , help="Pretrained tokenizer name or path if not the same as model_name" , )
parser.add_argument("--seed" , type=_snake_case , default=_snake_case , help="A seed for reproducible training." )
parser.add_argument(
"--context_len" , default=32 , type=_snake_case , 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=1_00 , type=_snake_case , help="number of articles that are long enough to be used as our objective set" , )
parser.add_argument(
"--eval_freq" , default=1_00 , type=_snake_case , help="secondary model evaluation is triggered at eval_freq" )
parser.add_argument("--max_steps" , default=10_00 , type=_snake_case , help="To calculate training epochs" )
parser.add_argument(
"--secondary_learner_batch_size" , default=1_28 , type=_snake_case , help="batch size of training data for secondary learner" , )
parser.add_argument(
"--batch_size" , default=16 , type=_snake_case , help="batch size of training data of language model(gpt2) " )
parser.add_argument(
"--eval_interval" , default=10 , type=_snake_case , 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=1_00 , type=_snake_case , help="The number of examples split to be used as objective_set/test_data" )
parser.add_argument(
"--min_len" , default=10_26 , type=_snake_case , help="The minimum length of the article to be used as objective set" )
parser.add_argument(
"--secondary_learner_max_epochs" , default=15 , type=_snake_case , help="number of epochs to train secondary learner" )
parser.add_argument("--trim" , default=_snake_case , type=_snake_case , help="truncate the example if it exceeds context length" )
parser.add_argument(
"--threshold" , default=1.0 , type=_snake_case , 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=_snake_case , help="finetuned_model_name" )
parser.add_argument(
"--recopy_model" , default=_snake_case , type=_snake_case , 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=1_00 , min_len=10_26 , trim=_snake_case , data_file="data/tokenized_stories_train_wikitext103.jbl" , igf_data_file="igf_context_pairs.jbl" , )
# Load train data for secondary learner
UpperCamelCase__ = joblib.load("data/IGF_values.jbl" )
# Train secondary learner
UpperCamelCase__ = training_secondary_learner(
_snake_case , secondary_learner_max_epochs=15 , secondary_learner_batch_size=1_28 , eval_freq=1_00 , igf_model_path="igf_model.pt" , )
# load pretrained gpt2 model
UpperCamelCase__ = GPTaLMHeadModel.from_pretrained("gpt2" )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
UpperCamelCase__ , UpperCamelCase__ = generate_datasets(
context_len=32 , file="data/tokenized_stories_train_wikitext103.jbl" , number=1_00 , min_len=10_26 , trim=_snake_case )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
_snake_case , _snake_case , _snake_case , context_len=32 , max_steps=10_00 , batch_size=16 , threshold=1.0 , recopy_model=_snake_case , secondary_learner=_snake_case , eval_interval=10 , finetuned_model_name="gpt2_finetuned.pt" , )
if __name__ == "__main__":
main() | 516 | """simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def snake_case__ ( _snake_case : float ):
"""simple docstring"""
if num <= 0:
raise ValueError("math domain error" )
return quad(_snake_case , 0 , _snake_case , args=(_snake_case) )[0]
def snake_case__ ( _snake_case : float , _snake_case : float ):
"""simple docstring"""
return math.pow(_snake_case , z - 1 ) * math.exp(-x )
if __name__ == "__main__":
from doctest import testmod
testmod() | 516 | 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 a ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
A__ : torch.FloatTensor
A__ : Optional[torch.FloatTensor] = None
def A__ ( A__ , A__=0.999 , A__="cosine" , ) -> str:
'''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 a ( _SCREAMING_SNAKE_CASE, _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
A__ : Tuple = 1
@register_to_config
def __init__( self , snake_case_ = 1000 , snake_case_ = 0.00_01 , snake_case_ = 0.02 , snake_case_ = "linear" , snake_case_ = None , snake_case_ = True , snake_case_ = True , snake_case_ = 0 , snake_case_ = "epsilon" , snake_case_ = 1.0 , **snake_case_ , ) -> Union[str, Any]:
if kwargs.get("set_alpha_to_one" , snake_case_ ) 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" , snake_case_ , standard_warn=snake_case_ )
_UpperCAmelCase = kwargs["set_alpha_to_one"]
if trained_betas is not None:
_UpperCAmelCase = torch.tensor(snake_case_ , dtype=torch.floataa )
elif beta_schedule == "linear":
_UpperCAmelCase = torch.linspace(snake_case_ , snake_case_ , snake_case_ , 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 , snake_case_ , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
_UpperCAmelCase = betas_for_alpha_bar(snake_case_ )
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 , snake_case_ ).copy().astype(np.intaa ) )
def __A ( self , snake_case_ , snake_case_ = None ) -> torch.FloatTensor:
return sample
def __A ( self , snake_case_ , snake_case_ = None ) -> List[str]:
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 , snake_case_ ) * step_ratio).round().copy().astype(np.intaa )
_UpperCAmelCase = torch.from_numpy(snake_case_ ).to(snake_case_ )
self.timesteps += self.config.steps_offset
def __A ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ = 0.0 , snake_case_ = False , snake_case_ = None , snake_case_ = 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=snake_case_ , pred_original_sample=snake_case_ )
def __len__( self ) -> Optional[Any]:
return self.config.num_train_timesteps
| 579 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''edbeeching/decision-transformer-gym-hopper-medium''': (
'''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class a ( _SCREAMING_SNAKE_CASE ):
"""simple docstring"""
A__ : Optional[int] = "decision_transformer"
A__ : List[Any] = ["past_key_values"]
A__ : Optional[int] = {
"max_position_embeddings": "n_positions",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self , snake_case_=17 , snake_case_=4 , snake_case_=128 , snake_case_=4096 , snake_case_=True , snake_case_=1 , snake_case_=1024 , snake_case_=3 , snake_case_=1 , snake_case_=None , snake_case_="relu" , snake_case_=0.1 , snake_case_=0.1 , snake_case_=0.1 , snake_case_=1e-5 , snake_case_=0.02 , snake_case_=True , snake_case_=True , snake_case_=50256 , snake_case_=50256 , snake_case_=False , snake_case_=False , **snake_case_ , ) -> Union[str, Any]:
_UpperCAmelCase = state_dim
_UpperCAmelCase = act_dim
_UpperCAmelCase = hidden_size
_UpperCAmelCase = max_ep_len
_UpperCAmelCase = action_tanh
_UpperCAmelCase = vocab_size
_UpperCAmelCase = n_positions
_UpperCAmelCase = n_layer
_UpperCAmelCase = n_head
_UpperCAmelCase = n_inner
_UpperCAmelCase = activation_function
_UpperCAmelCase = resid_pdrop
_UpperCAmelCase = embd_pdrop
_UpperCAmelCase = attn_pdrop
_UpperCAmelCase = layer_norm_epsilon
_UpperCAmelCase = initializer_range
_UpperCAmelCase = scale_attn_weights
_UpperCAmelCase = use_cache
_UpperCAmelCase = scale_attn_by_inverse_layer_idx
_UpperCAmelCase = reorder_and_upcast_attn
_UpperCAmelCase = bos_token_id
_UpperCAmelCase = eos_token_id
super().__init__(bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ )
| 579 | 1 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
if is_vision_available():
from PIL import Image
else:
class snake_case__ :
@staticmethod
def __lowerCAmelCase ( *lowercase : Any , **lowercase : str ):
'''simple docstring'''
pass
def lowercase_ ( _lowercase : Image ):
'''simple docstring'''
UpperCAmelCase : str = hashlib.mda(image.tobytes() )
return m.hexdigest()[:10]
def lowercase_ ( _lowercase : Image ):
'''simple docstring'''
UpperCAmelCase : int = np.array(_lowercase )
UpperCAmelCase : Union[str, Any] = npimg.shape
return {"hash": hashimage(_lowercase ), "shape": shape}
@is_pipeline_test
@require_vision
@require_torch
class snake_case__ ( unittest.TestCase ):
SCREAMING_SNAKE_CASE__ = dict(
(list(MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if MODEL_FOR_MASK_GENERATION_MAPPING else []) )
SCREAMING_SNAKE_CASE__ = dict(
(list(TF_MODEL_FOR_MASK_GENERATION_MAPPING.items() ) if TF_MODEL_FOR_MASK_GENERATION_MAPPING else []) )
def __lowerCAmelCase ( self : List[Any] , lowercase : List[str] , lowercase : Dict , lowercase : str ):
'''simple docstring'''
UpperCAmelCase : Optional[Any] = MaskGenerationPipeline(model=lowercase , image_processor=lowercase )
return image_segmenter, [
"./tests/fixtures/tests_samples/COCO/000000039769.png",
"./tests/fixtures/tests_samples/COCO/000000039769.png",
]
def __lowerCAmelCase ( self : Optional[Any] , lowercase : Dict , lowercase : Dict ):
'''simple docstring'''
pass
@require_tf
@unittest.skip("Image segmentation not implemented in TF" )
def __lowerCAmelCase ( self : Dict ):
'''simple docstring'''
pass
@slow
@require_torch
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase : Any = pipeline("mask-generation" , model="facebook/sam-vit-huge" )
UpperCAmelCase : str = image_segmenter("http://images.cocodataset.org/val2017/000000039769.jpg" , points_per_batch=2_56 )
# Shortening by hashing
UpperCAmelCase : Optional[Any] = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(lowercase ), "scores": outputs["scores"][i]}]
# fmt: off
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (4_80, 6_40)}, "scores": 1.0_4_4_4},
{"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.0_2_1},
{"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.0_1_6_7},
{"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.0_1_3_2},
{"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.0_0_5_3},
{"mask": {"hash": "e2d0b7a0b7", "shape": (4_80, 6_40)}, "scores": 0.9_9_6_7},
{"mask": {"hash": "453c7844bd", "shape": (4_80, 6_40)}, "scores": 0.9_9_3},
{"mask": {"hash": "3d44f2926d", "shape": (4_80, 6_40)}, "scores": 0.9_9_0_9},
{"mask": {"hash": "64033ddc3f", "shape": (4_80, 6_40)}, "scores": 0.9_8_7_9},
{"mask": {"hash": "801064ff79", "shape": (4_80, 6_40)}, "scores": 0.9_8_3_4},
{"mask": {"hash": "6172f276ef", "shape": (4_80, 6_40)}, "scores": 0.9_7_1_6},
{"mask": {"hash": "b49e60e084", "shape": (4_80, 6_40)}, "scores": 0.9_6_1_2},
{"mask": {"hash": "a811e775fd", "shape": (4_80, 6_40)}, "scores": 0.9_5_9_9},
{"mask": {"hash": "a6a8ebcf4b", "shape": (4_80, 6_40)}, "scores": 0.9_5_5_2},
{"mask": {"hash": "9d8257e080", "shape": (4_80, 6_40)}, "scores": 0.9_5_3_2},
{"mask": {"hash": "32de6454a8", "shape": (4_80, 6_40)}, "scores": 0.9_5_1_6},
{"mask": {"hash": "af3d4af2c8", "shape": (4_80, 6_40)}, "scores": 0.9_4_9_9},
{"mask": {"hash": "3c6db475fb", "shape": (4_80, 6_40)}, "scores": 0.9_4_8_3},
{"mask": {"hash": "c290813fb9", "shape": (4_80, 6_40)}, "scores": 0.9_4_6_4},
{"mask": {"hash": "b6f0b8f606", "shape": (4_80, 6_40)}, "scores": 0.9_4_3},
{"mask": {"hash": "92ce16bfdf", "shape": (4_80, 6_40)}, "scores": 0.9_4_3},
{"mask": {"hash": "c749b25868", "shape": (4_80, 6_40)}, "scores": 0.9_4_0_8},
{"mask": {"hash": "efb6cab859", "shape": (4_80, 6_40)}, "scores": 0.9_3_3_5},
{"mask": {"hash": "1ff2eafb30", "shape": (4_80, 6_40)}, "scores": 0.9_3_2_6},
{"mask": {"hash": "788b798e24", "shape": (4_80, 6_40)}, "scores": 0.9_2_6_2},
{"mask": {"hash": "abea804f0e", "shape": (4_80, 6_40)}, "scores": 0.8_9_9_9},
{"mask": {"hash": "7b9e8ddb73", "shape": (4_80, 6_40)}, "scores": 0.8_9_8_6},
{"mask": {"hash": "cd24047c8a", "shape": (4_80, 6_40)}, "scores": 0.8_9_8_4},
{"mask": {"hash": "6943e6bcbd", "shape": (4_80, 6_40)}, "scores": 0.8_8_7_3},
{"mask": {"hash": "b5f47c9191", "shape": (4_80, 6_40)}, "scores": 0.8_8_7_1}
] , )
# fmt: on
@require_torch
@slow
def __lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
UpperCAmelCase : List[Any] = "facebook/sam-vit-huge"
UpperCAmelCase : Optional[int] = pipeline("mask-generation" , model=lowercase )
UpperCAmelCase : Dict = image_segmenter(
"http://images.cocodataset.org/val2017/000000039769.jpg" , pred_iou_thresh=1 , points_per_batch=2_56 )
# Shortening by hashing
UpperCAmelCase : str = []
for i, o in enumerate(outputs["masks"] ):
new_outupt += [{"mask": mask_to_test_readable(lowercase ), "scores": outputs["scores"][i]}]
self.assertEqual(
nested_simplify(lowercase , decimals=4 ) , [
{"mask": {"hash": "115ad19f5f", "shape": (4_80, 6_40)}, "scores": 1.0_4_4_4},
{"mask": {"hash": "6affa964c6", "shape": (4_80, 6_40)}, "scores": 1.0_2_1_0},
{"mask": {"hash": "dfe28a0388", "shape": (4_80, 6_40)}, "scores": 1.0_1_6_7},
{"mask": {"hash": "c0a5f4a318", "shape": (4_80, 6_40)}, "scores": 1.0_1_3_2},
{"mask": {"hash": "fe8065c197", "shape": (4_80, 6_40)}, "scores": 1.0_0_5_3},
] , )
| 595 |
"""simple docstring"""
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
"""The `inpainting.py` script is outdated. Please use directly `from diffusers import"""
""" StableDiffusionInpaintPipeline` instead."""
)
| 595 | 1 |
from __future__ import annotations
def _snake_case ( lowerCAmelCase : list[float] , lowerCAmelCase : list[float] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Any = sorted(numsa + numsa )
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = divmod(len(lowerCAmelCase ) , 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[Any] = [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)}''')
| 316 | from typing import Any
import numpy as np
def _snake_case ( lowerCAmelCase : np.ndarray ):
"""simple docstring"""
return np.array_equal(lowerCAmelCase , matrix.conjugate().T )
def _snake_case ( lowerCAmelCase : np.ndarray , lowerCAmelCase : np.ndarray ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = v.conjugate().T
SCREAMING_SNAKE_CASE_ : int = v_star.dot(lowerCAmelCase )
assert isinstance(lowerCAmelCase , np.ndarray )
return (v_star_dot.dot(lowerCAmelCase )) / (v_star.dot(lowerCAmelCase ))
def _snake_case ( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = np.array([[2, 2 + 1J, 4], [2 - 1J, 3, 1J], [4, -1J, 1]] )
SCREAMING_SNAKE_CASE_ : Dict = np.array([[1], [2], [3]] )
assert is_hermitian(lowerCAmelCase ), f'{a} is not hermitian.'
print(rayleigh_quotient(lowerCAmelCase , lowerCAmelCase ) )
SCREAMING_SNAKE_CASE_ : int = np.array([[1, 2, 4], [2, 3, -1], [4, -1, 1]] )
assert is_hermitian(lowerCAmelCase ), f'{a} is not hermitian.'
assert rayleigh_quotient(lowerCAmelCase , lowerCAmelCase ) == float(3 )
if __name__ == "__main__":
import doctest
doctest.testmod()
tests()
| 316 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_onnx_available,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
@dataclass
class __lowerCAmelCase ( lowercase_ ):
lowerCAmelCase__ = 4_2
lowerCAmelCase__ = 4_2
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_cycle_diffusion import CycleDiffusionPipeline
from .pipeline_stable_diffusion import StableDiffusionPipeline
from .pipeline_stable_diffusion_attend_and_excite import StableDiffusionAttendAndExcitePipeline
from .pipeline_stable_diffusion_imgaimg import StableDiffusionImgaImgPipeline
from .pipeline_stable_diffusion_inpaint import StableDiffusionInpaintPipeline
from .pipeline_stable_diffusion_inpaint_legacy import StableDiffusionInpaintPipelineLegacy
from .pipeline_stable_diffusion_instruct_pixapix import StableDiffusionInstructPixaPixPipeline
from .pipeline_stable_diffusion_latent_upscale import StableDiffusionLatentUpscalePipeline
from .pipeline_stable_diffusion_ldmad import StableDiffusionLDMaDPipeline
from .pipeline_stable_diffusion_model_editing import StableDiffusionModelEditingPipeline
from .pipeline_stable_diffusion_panorama import StableDiffusionPanoramaPipeline
from .pipeline_stable_diffusion_paradigms import StableDiffusionParadigmsPipeline
from .pipeline_stable_diffusion_sag import StableDiffusionSAGPipeline
from .pipeline_stable_diffusion_upscale import StableDiffusionUpscalePipeline
from .pipeline_stable_unclip import StableUnCLIPPipeline
from .pipeline_stable_unclip_imgaimg import StableUnCLIPImgaImgPipeline
from .safety_checker import StableDiffusionSafetyChecker
from .stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.25.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import StableDiffusionImageVariationPipeline
else:
from .pipeline_stable_diffusion_image_variation import StableDiffusionImageVariationPipeline
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(""">=""", """4.26.0""")):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import (
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionPixaPixZeroPipeline,
)
else:
from .pipeline_stable_diffusion_depthaimg import StableDiffusionDepthaImgPipeline
from .pipeline_stable_diffusion_diffedit import StableDiffusionDiffEditPipeline
from .pipeline_stable_diffusion_pixapix_zero import StableDiffusionPixaPixZeroPipeline
try:
if not (
is_torch_available()
and is_transformers_available()
and is_k_diffusion_available()
and is_k_diffusion_version(""">=""", """0.0.12""")
):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipeline_stable_diffusion_k_diffusion import StableDiffusionKDiffusionPipeline
try:
if not (is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_onnx_objects import * # noqa F403
else:
from .pipeline_onnx_stable_diffusion import OnnxStableDiffusionPipeline, StableDiffusionOnnxPipeline
from .pipeline_onnx_stable_diffusion_imgaimg import OnnxStableDiffusionImgaImgPipeline
from .pipeline_onnx_stable_diffusion_inpaint import OnnxStableDiffusionInpaintPipeline
from .pipeline_onnx_stable_diffusion_inpaint_legacy import OnnxStableDiffusionInpaintPipelineLegacy
from .pipeline_onnx_stable_diffusion_upscale import OnnxStableDiffusionUpscalePipeline
if is_transformers_available() and is_flax_available():
import flax
@flax.struct.dataclass
class __lowerCAmelCase ( lowercase_ ):
lowerCAmelCase__ = 4_2
lowerCAmelCase__ = 4_2
from ...schedulers.scheduling_pndm_flax import PNDMSchedulerState
from .pipeline_flax_stable_diffusion import FlaxStableDiffusionPipeline
from .pipeline_flax_stable_diffusion_imgaimg import FlaxStableDiffusionImgaImgPipeline
from .pipeline_flax_stable_diffusion_inpaint import FlaxStableDiffusionInpaintPipeline
from .safety_checker_flax import FlaxStableDiffusionSafetyChecker
| 175 |
'''simple docstring'''
__snake_case : Optional[Any] = 8.314462 # Unit - J mol-1 K-1
def __lowerCamelCase ( __snake_case : float, __snake_case : float, __snake_case : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / volume
def __lowerCamelCase ( __snake_case : float, __snake_case : float, __snake_case : float ) -> float:
"""simple docstring"""
if moles < 0 or kelvin < 0 or pressure < 0:
raise ValueError("""Invalid inputs. Enter positive value.""" )
return moles * kelvin * UNIVERSAL_GAS_CONSTANT / pressure
if __name__ == "__main__":
from doctest import testmod
testmod()
| 215 | 0 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class snake_case ( __a ):
'''simple docstring'''
@staticmethod
@abstractmethod
def _SCREAMING_SNAKE_CASE ( _lowerCamelCase : ArgumentParser ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ):
'''simple docstring'''
raise NotImplementedError()
| 706 |
"""simple docstring"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version('>=', '4.25.0')):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import UnCLIPImageVariationPipeline, UnCLIPPipeline
else:
from .pipeline_unclip import UnCLIPPipeline
from .pipeline_unclip_image_variation import UnCLIPImageVariationPipeline
from .text_proj import UnCLIPTextProjModel
| 215 | 0 |
import string
# frequency taken from https://en.wikipedia.org/wiki/Letter_frequency
lowerCAmelCase__ : int ={
'''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,
}
lowerCAmelCase__ : Dict ='''ETAOINSHRDLCUMWFGYPBVKJXQZ'''
lowerCAmelCase__ : Any ='''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def __lowercase ( a__ ) -> dict[str, int]:
__SCREAMING_SNAKE_CASE = {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 __lowercase ( a__ ) -> str:
return x[0]
def __lowercase ( a__ ) -> str:
__SCREAMING_SNAKE_CASE = get_letter_count(a__ )
__SCREAMING_SNAKE_CASE = {
freq: [] for letter, freq in letter_to_freq.items()
}
for letter in LETTERS:
freq_to_letter[letter_to_freq[letter]].append(a__ )
__SCREAMING_SNAKE_CASE = {}
for freq in freq_to_letter:
freq_to_letter[freq].sort(key=ETAOIN.find , reverse=a__ )
__SCREAMING_SNAKE_CASE = ''.join(freq_to_letter[freq] )
__SCREAMING_SNAKE_CASE = list(freq_to_letter_str.items() )
freq_pairs.sort(key=a__ , reverse=a__ )
__SCREAMING_SNAKE_CASE = [freq_pair[1] for freq_pair in freq_pairs]
return "".join(a__ )
def __lowercase ( a__ ) -> int:
__SCREAMING_SNAKE_CASE = get_frequency_order(a__ )
__SCREAMING_SNAKE_CASE = 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()
| 148 |
import argparse
from collections import defaultdict
import yaml
lowerCAmelCase__ : Optional[int] ='''docs/source/en/_toctree.yml'''
def __lowercase ( a__ ) -> List[Any]:
__SCREAMING_SNAKE_CASE = defaultdict(a__ )
__SCREAMING_SNAKE_CASE = []
__SCREAMING_SNAKE_CASE = []
for doc in doc_list:
if "local" in doc:
counts[doc["local"]] += 1
if doc["title"].lower() == "overview":
overview_doc.append({'local': doc['local'], 'title': doc['title']} )
else:
new_doc_list.append(a__ )
__SCREAMING_SNAKE_CASE = new_doc_list
__SCREAMING_SNAKE_CASE = [key for key, value in counts.items() if value > 1]
__SCREAMING_SNAKE_CASE = []
for duplicate_key in duplicates:
__SCREAMING_SNAKE_CASE = list({doc['title'] for doc in doc_list if doc['local'] == duplicate_key} )
if len(a__ ) > 1:
raise ValueError(
f"""{duplicate_key} is present several times in the documentation table of content at """
'`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '
'others.' )
# Only add this once
new_doc.append({'local': duplicate_key, 'title': titles[0]} )
# Add none duplicate-keys
new_doc.extend([doc for doc in doc_list if 'local' not in counts or counts[doc['local']] == 1] )
__SCREAMING_SNAKE_CASE = sorted(a__ , key=lambda a__ : s["title"].lower() )
# "overview" gets special treatment and is always first
if len(a__ ) > 1:
raise ValueError('{doc_list} has two \'overview\' docs which is not allowed.' )
overview_doc.extend(a__ )
# Sort
return overview_doc
def __lowercase ( a__=False ) -> List[Any]:
with open(a__ , encoding='utf-8' ) as f:
__SCREAMING_SNAKE_CASE = yaml.safe_load(f.read() )
# Get to the API doc
__SCREAMING_SNAKE_CASE = 0
while content[api_idx]["title"] != "API":
api_idx += 1
__SCREAMING_SNAKE_CASE = content[api_idx]['sections']
# Then to the model doc
__SCREAMING_SNAKE_CASE = 0
while api_doc[scheduler_idx]["title"] != "Schedulers":
scheduler_idx += 1
__SCREAMING_SNAKE_CASE = api_doc[scheduler_idx]['sections']
__SCREAMING_SNAKE_CASE = clean_doc_toc(a__ )
__SCREAMING_SNAKE_CASE = False
if new_scheduler_doc != scheduler_doc:
__SCREAMING_SNAKE_CASE = True
if overwrite:
__SCREAMING_SNAKE_CASE = new_scheduler_doc
if diff:
if overwrite:
__SCREAMING_SNAKE_CASE = api_doc
with open(a__ , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(a__ , allow_unicode=a__ ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
def __lowercase ( a__=False ) -> Union[str, Any]:
with open(a__ , encoding='utf-8' ) as f:
__SCREAMING_SNAKE_CASE = yaml.safe_load(f.read() )
# Get to the API doc
__SCREAMING_SNAKE_CASE = 0
while content[api_idx]["title"] != "API":
api_idx += 1
__SCREAMING_SNAKE_CASE = content[api_idx]['sections']
# Then to the model doc
__SCREAMING_SNAKE_CASE = 0
while api_doc[pipeline_idx]["title"] != "Pipelines":
pipeline_idx += 1
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = api_doc[pipeline_idx]['sections']
__SCREAMING_SNAKE_CASE = []
# sort sub pipeline docs
for pipeline_doc in pipeline_docs:
if "section" in pipeline_doc:
__SCREAMING_SNAKE_CASE = pipeline_doc['section']
__SCREAMING_SNAKE_CASE = clean_doc_toc(a__ )
if overwrite:
__SCREAMING_SNAKE_CASE = new_sub_pipeline_doc
new_pipeline_docs.append(a__ )
# sort overall pipeline doc
__SCREAMING_SNAKE_CASE = clean_doc_toc(a__ )
if new_pipeline_docs != pipeline_docs:
__SCREAMING_SNAKE_CASE = True
if overwrite:
__SCREAMING_SNAKE_CASE = new_pipeline_docs
if diff:
if overwrite:
__SCREAMING_SNAKE_CASE = api_doc
with open(a__ , 'w' , encoding='utf-8' ) as f:
f.write(yaml.dump(a__ , allow_unicode=a__ ) )
else:
raise ValueError(
'The model doc part of the table of content is not properly sorted, run `make style` to fix this.' )
if __name__ == "__main__":
lowerCAmelCase__ : str =argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
lowerCAmelCase__ : Optional[int] =parser.parse_args()
check_scheduler_doc(args.fix_and_overwrite)
check_pipeline_doc(args.fix_and_overwrite)
| 148 | 1 |
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
lowercase_: Union[str, Any] = logging.get_logger(__name__)
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
if isinstance(UpperCAmelCase_ , np.ndarray):
return list(tensor.shape)
snake_case__ : List[Any] = tf.shape(UpperCAmelCase_)
if tensor.shape == tf.TensorShape(UpperCAmelCase_):
return dynamic
snake_case__ : Tuple = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(UpperCAmelCase_)]
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ = None , UpperCAmelCase_ = None):
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1e-9 , axis=UpperCAmelCase_ , name=UpperCAmelCase_)
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=1e-5 , UpperCAmelCase_=-1):
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(UpperCAmelCase_ , UpperCAmelCase_):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""")
# Get mean and variance on the axis to be normalized
snake_case__ , snake_case__ : Any = tf.nn.moments(UpperCAmelCase_ , axes=[axis] , keepdims=UpperCAmelCase_)
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
snake_case__ : Optional[Any] = [1] * inputs.shape.rank
snake_case__ : Optional[int] = shape_list(UpperCAmelCase_)[axis]
snake_case__ : Tuple = tf.reshape(UpperCAmelCase_ , UpperCAmelCase_)
snake_case__ : Tuple = tf.reshape(UpperCAmelCase_ , UpperCAmelCase_)
# Compute layer normalization using the batch_normalization
# function.
snake_case__ : List[str] = tf.nn.batch_normalization(
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , offset=UpperCAmelCase_ , scale=UpperCAmelCase_ , variance_epsilon=UpperCAmelCase_ , )
return outputs
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_=0 , UpperCAmelCase_=-1):
"""simple docstring"""
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
snake_case__ : Optional[int] = tf.shape(UpperCAmelCase_)
snake_case__ : List[Any] = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1])
snake_case__ : Optional[Any] = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0)
return tf.reshape(UpperCAmelCase_ , UpperCAmelCase_)
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
if not isinstance(UpperCAmelCase_ , tf.Tensor):
snake_case__ : int = tf.convert_to_tensor(UpperCAmelCase_) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
snake_case__ : Dict = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
snake_case__ : List[Any] = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
snake_case__ : Dict = (
tf.cast(1 , encoder_attention_mask.dtype) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = "input_ids"):
"""simple docstring"""
tf.debugging.assert_less(
UpperCAmelCase_ , tf.cast(UpperCAmelCase_ , dtype=tensor.dtype) , message=(
F'The maximum value of {tensor_name} ({tf.math.reduce_max(UpperCAmelCase_)}) must be smaller than the embedding '
F'layer\'s input dimension ({embed_dim}). The likely cause is some problem at tokenization time.'
) , )
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
snake_case__ : List[str] = 64_512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
snake_case__ : Tuple = [x for x in data if len(UpperCAmelCase_) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
F'they are larger than {HDF5_OBJECT_HEADER_LIMIT} '
F'bytes: {bad_attributes}')
snake_case__ : Optional[int] = np.asarray(UpperCAmelCase_)
snake_case__ : Tuple = 1
snake_case__ : Optional[Any] = np.array_split(UpperCAmelCase_ , UpperCAmelCase_)
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data):
num_chunks += 1
snake_case__ : Tuple = np.array_split(UpperCAmelCase_ , UpperCAmelCase_)
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(UpperCAmelCase_):
snake_case__ : List[str] = chunk_data
else:
snake_case__ : Union[str, Any] = data
def _lowercase ( UpperCAmelCase_ , UpperCAmelCase_):
"""simple docstring"""
if name in group.attrs:
snake_case__ : int = [n.decode("""utf8""") if hasattr(UpperCAmelCase_ , """decode""") else n for n in group.attrs[name]]
else:
snake_case__ : Tuple = []
snake_case__ : Optional[Any] = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""") if hasattr(UpperCAmelCase_ , """decode""") else n for n in group.attrs["""%s%d""" % (name, chunk_id)]])
chunk_id += 1
return data
def _lowercase ( UpperCAmelCase_):
"""simple docstring"""
def _expand_single_ad_tensor(UpperCAmelCase_):
if isinstance(UpperCAmelCase_ , tf.Tensor) and t.shape.rank == 1:
return tf.expand_dims(UpperCAmelCase_ , axis=-1)
return t
return tf.nest.map_structure(_expand_single_ad_tensor , UpperCAmelCase_)
| 127 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_: str = logging.get_logger(__name__)
lowercase_: List[str] = {
'sayakpaul/vit-msn-base': 'https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json',
# See all ViT MSN models at https://huggingface.co/models?filter=vit_msn
}
class lowercase__ (__snake_case ):
"""simple docstring"""
__UpperCamelCase : Any = 'vit_msn'
def __init__( self : Optional[int] , __a : List[str]=7_6_8 , __a : Union[str, Any]=1_2 , __a : Dict=1_2 , __a : Union[str, Any]=3_0_7_2 , __a : int="gelu" , __a : int=0.0 , __a : Dict=0.0 , __a : Optional[int]=0.02 , __a : Any=1e-06 , __a : Any=2_2_4 , __a : Tuple=1_6 , __a : List[Any]=3 , __a : Tuple=True , **__a : Optional[Any] , ):
super().__init__(**__a )
snake_case__ : str = hidden_size
snake_case__ : Union[str, Any] = num_hidden_layers
snake_case__ : Tuple = num_attention_heads
snake_case__ : Optional[int] = intermediate_size
snake_case__ : int = hidden_act
snake_case__ : List[str] = hidden_dropout_prob
snake_case__ : List[Any] = attention_probs_dropout_prob
snake_case__ : Tuple = initializer_range
snake_case__ : Dict = layer_norm_eps
snake_case__ : Union[str, Any] = image_size
snake_case__ : Optional[int] = patch_size
snake_case__ : Optional[int] = num_channels
snake_case__ : Dict = qkv_bias
| 127 | 1 |
'''simple docstring'''
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase = HfArgumentParser(UpperCamelCase__ )
UpperCAmelCase = parser.parse_args_into_dataclasses()[0]
UpperCAmelCase = TensorFlowBenchmark(args=UpperCamelCase__ )
try:
UpperCAmelCase = parser.parse_args_into_dataclasses()[0]
except ValueError as e:
UpperCAmelCase = '''Arg --no_{0} is no longer used, please use --no-{0} instead.'''
UpperCAmelCase = ''' '''.join(str(UpperCamelCase__ ).split(''' ''' )[:-1] )
UpperCAmelCase = ''''''
UpperCAmelCase = eval(str(UpperCamelCase__ ).split(''' ''' )[-1] )
UpperCAmelCase = []
for arg in depreciated_args:
# arg[2:] removes '--'
if arg[2:] in TensorFlowBenchmark.deprecated_args:
# arg[5:] removes '--no_'
full_error_msg += arg_error_msg.format(arg[5:] )
else:
wrong_args.append(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
UpperCAmelCase = full_error_msg + begin_error_msg + str(UpperCamelCase__ )
raise ValueError(UpperCamelCase__ )
benchmark.run()
if __name__ == "__main__":
main()
| 210 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
__magic_name__ = {'''configuration_gpt_neox''': ['''GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GPTNeoXConfig''']}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ['''GPTNeoXTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
'''GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GPTNeoXForCausalLM''',
'''GPTNeoXForQuestionAnswering''',
'''GPTNeoXForSequenceClassification''',
'''GPTNeoXForTokenClassification''',
'''GPTNeoXLayer''',
'''GPTNeoXModel''',
'''GPTNeoXPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_gpt_neox import GPT_NEOX_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoXConfig
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_gpt_neox_fast import GPTNeoXTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_gpt_neox import (
GPT_NEOX_PRETRAINED_MODEL_ARCHIVE_LIST,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXLayer,
GPTNeoXModel,
GPTNeoXPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 657 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
A__ : int = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple = ["""DeiTFeatureExtractor"""]
A__ : Union[str, Any] = ["""DeiTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Optional[int] = [
"""DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DeiTForImageClassification""",
"""DeiTForImageClassificationWithTeacher""",
"""DeiTForMaskedImageModeling""",
"""DeiTModel""",
"""DeiTPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Union[str, Any] = [
"""TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDeiTForImageClassification""",
"""TFDeiTForImageClassificationWithTeacher""",
"""TFDeiTForMaskedImageModeling""",
"""TFDeiTModel""",
"""TFDeiTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
A__ : Optional[int] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 660 |
"""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
from ..auto import CONFIG_MAPPING
A__ : Union[str, Any] = logging.get_logger(__name__)
A__ : Optional[int] = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = 'table-transformer'
_A = ['past_key_values']
_A = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , __UpperCamelCase=True , __UpperCamelCase=None , __UpperCamelCase=3 , __UpperCamelCase=1_00 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=6 , __UpperCamelCase=20_48 , __UpperCamelCase=8 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=True , __UpperCamelCase="relu" , __UpperCamelCase=2_56 , __UpperCamelCase=0.1 , __UpperCamelCase=0.0 , __UpperCamelCase=0.0 , __UpperCamelCase=0.02 , __UpperCamelCase=1.0 , __UpperCamelCase=False , __UpperCamelCase="sine" , __UpperCamelCase="resnet50" , __UpperCamelCase=True , __UpperCamelCase=False , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=1 , __UpperCamelCase=1 , __UpperCamelCase=5 , __UpperCamelCase=2 , __UpperCamelCase=0.1 , **__UpperCamelCase , )-> List[Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError("You can't specify both `backbone_config` and `use_timm_backbone`." )
if not use_timm_backbone:
if backbone_config is None:
logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone." )
UpperCAmelCase__ : Any = CONFIG_MAPPING["resnet"](out_features=["stage4"] )
elif isinstance(__UpperCamelCase , __UpperCamelCase ):
UpperCAmelCase__ : int = backbone_config.get("model_type" )
UpperCAmelCase__ : Optional[Any] = CONFIG_MAPPING[backbone_model_type]
UpperCAmelCase__ : int = config_class.from_dict(__UpperCamelCase )
# set timm attributes to None
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : List[str] = None, None, None
UpperCAmelCase__ : Optional[int] = use_timm_backbone
UpperCAmelCase__ : Dict = backbone_config
UpperCAmelCase__ : List[Any] = num_channels
UpperCAmelCase__ : Any = num_queries
UpperCAmelCase__ : int = d_model
UpperCAmelCase__ : Optional[int] = encoder_ffn_dim
UpperCAmelCase__ : str = encoder_layers
UpperCAmelCase__ : Dict = encoder_attention_heads
UpperCAmelCase__ : Optional[Any] = decoder_ffn_dim
UpperCAmelCase__ : Tuple = decoder_layers
UpperCAmelCase__ : Optional[Any] = decoder_attention_heads
UpperCAmelCase__ : List[str] = dropout
UpperCAmelCase__ : Tuple = attention_dropout
UpperCAmelCase__ : List[Any] = activation_dropout
UpperCAmelCase__ : Dict = activation_function
UpperCAmelCase__ : Optional[Any] = init_std
UpperCAmelCase__ : List[str] = init_xavier_std
UpperCAmelCase__ : int = encoder_layerdrop
UpperCAmelCase__ : Tuple = decoder_layerdrop
UpperCAmelCase__ : int = encoder_layers
UpperCAmelCase__ : Dict = auxiliary_loss
UpperCAmelCase__ : Union[str, Any] = position_embedding_type
UpperCAmelCase__ : List[str] = backbone
UpperCAmelCase__ : List[Any] = use_pretrained_backbone
UpperCAmelCase__ : List[str] = dilation
# Hungarian matcher
UpperCAmelCase__ : Dict = class_cost
UpperCAmelCase__ : Any = bbox_cost
UpperCAmelCase__ : Tuple = giou_cost
# Loss coefficients
UpperCAmelCase__ : Any = mask_loss_coefficient
UpperCAmelCase__ : Dict = dice_loss_coefficient
UpperCAmelCase__ : Any = bbox_loss_coefficient
UpperCAmelCase__ : Tuple = giou_loss_coefficient
UpperCAmelCase__ : List[Any] = eos_coefficient
super().__init__(is_encoder_decoder=__UpperCamelCase , **__UpperCamelCase )
@property
def lowerCAmelCase__ ( self )-> int:
return self.encoder_attention_heads
@property
def lowerCAmelCase__ ( self )-> int:
return self.d_model
class _lowercase ( lowerCAmelCase_ ):
'''simple docstring'''
_A = version.parse('1.11' )
@property
def lowerCAmelCase__ ( self )-> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
("pixel_mask", {0: "batch"}),
] )
@property
def lowerCAmelCase__ ( self )-> float:
return 1E-5
@property
def lowerCAmelCase__ ( self )-> int:
return 12
| 660 | 1 |
import math
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from .attention_processor import Attention
from .embeddings import get_timestep_embedding
from .modeling_utils import ModelMixin
class UpperCamelCase__ (SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
'''simple docstring'''
@register_to_config
def __init__( self , UpperCamelCase__ = 128 , UpperCamelCase__ = 256 , UpperCamelCase__ = 2000.0 , UpperCamelCase__ = 768 , UpperCamelCase__ = 12 , UpperCamelCase__ = 12 , UpperCamelCase__ = 64 , UpperCamelCase__ = 2048 , UpperCamelCase__ = 0.1 , ) -> List[Any]:
super().__init__()
lowerCamelCase : int = nn.Sequential(
nn.Linear(UpperCamelCase__ , d_model * 4 , bias=UpperCamelCase__ ) , nn.SiLU() , nn.Linear(d_model * 4 , d_model * 4 , bias=UpperCamelCase__ ) , nn.SiLU() , )
lowerCamelCase : Any = nn.Embedding(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : str = False
lowerCamelCase : Any = nn.Linear(UpperCamelCase__ , UpperCamelCase__ , bias=UpperCamelCase__ )
lowerCamelCase : Any = nn.Dropout(p=UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = nn.ModuleList()
for lyr_num in range(UpperCamelCase__ ):
# FiLM conditional T5 decoder
lowerCamelCase : int = DecoderLayer(d_model=UpperCamelCase__ , d_kv=UpperCamelCase__ , num_heads=UpperCamelCase__ , d_ff=UpperCamelCase__ , dropout_rate=UpperCamelCase__ )
self.decoders.append(UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = TaLayerNorm(UpperCamelCase__ )
lowerCamelCase : Dict = nn.Dropout(p=UpperCamelCase__ )
lowerCamelCase : int = nn.Linear(UpperCamelCase__ , UpperCamelCase__ , bias=UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> int:
lowerCamelCase : str = torch.mul(query_input.unsqueeze(-1 ) , key_input.unsqueeze(-2 ) )
return mask.unsqueeze(-3 )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
lowerCamelCase : List[Any] = decoder_input_tokens.shape
assert decoder_noise_time.shape == (batch,)
# decoder_noise_time is in [0, 1), so rescale to expected timing range.
lowerCamelCase : Any = get_timestep_embedding(
decoder_noise_time * self.config.max_decoder_noise_time , embedding_dim=self.config.d_model , max_period=self.config.max_decoder_noise_time , ).to(dtype=self.dtype )
lowerCamelCase : str = self.conditioning_emb(UpperCamelCase__ ).unsqueeze(1 )
assert conditioning_emb.shape == (batch, 1, self.config.d_model * 4)
lowerCamelCase : Optional[Any] = decoder_input_tokens.shape[1]
# If we want to use relative positions for audio context, we can just offset
# this sequence by the length of encodings_and_masks.
lowerCamelCase : List[Any] = torch.broadcast_to(
torch.arange(UpperCamelCase__ , device=decoder_input_tokens.device ) , (batch, seq_length) , )
lowerCamelCase : int = self.position_encoding(UpperCamelCase__ )
lowerCamelCase : str = self.continuous_inputs_projection(UpperCamelCase__ )
inputs += position_encodings
lowerCamelCase : List[Any] = self.dropout(UpperCamelCase__ )
# decoder: No padding present.
lowerCamelCase : Union[str, Any] = torch.ones(
decoder_input_tokens.shape[:2] , device=decoder_input_tokens.device , dtype=inputs.dtype )
# Translate encoding masks to encoder-decoder masks.
lowerCamelCase : Dict = [(x, self.encoder_decoder_mask(UpperCamelCase__ , UpperCamelCase__ )) for x, y in encodings_and_masks]
# cross attend style: concat encodings
lowerCamelCase : Optional[int] = torch.cat([x[0] for x in encodings_and_encdec_masks] , dim=1 )
lowerCamelCase : Union[str, Any] = torch.cat([x[1] for x in encodings_and_encdec_masks] , dim=-1 )
for lyr in self.decoders:
lowerCamelCase : int = lyr(
UpperCamelCase__ , conditioning_emb=UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , encoder_attention_mask=UpperCamelCase__ , )[0]
lowerCamelCase : Dict = self.decoder_norm(UpperCamelCase__ )
lowerCamelCase : Optional[Any] = self.post_dropout(UpperCamelCase__ )
lowerCamelCase : Dict = self.spec_out(UpperCamelCase__ )
return spec_out
class UpperCamelCase__ (nn.Module ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__=1e-6 ) -> Optional[int]:
super().__init__()
lowerCamelCase : str = nn.ModuleList()
# cond self attention: layer 0
self.layer.append(
TaLayerSelfAttentionCond(d_model=UpperCamelCase__ , d_kv=UpperCamelCase__ , num_heads=UpperCamelCase__ , dropout_rate=UpperCamelCase__ ) )
# cross attention: layer 1
self.layer.append(
TaLayerCrossAttention(
d_model=UpperCamelCase__ , d_kv=UpperCamelCase__ , num_heads=UpperCamelCase__ , dropout_rate=UpperCamelCase__ , layer_norm_epsilon=UpperCamelCase__ , ) )
# Film Cond MLP + dropout: last layer
self.layer.append(
TaLayerFFCond(d_model=UpperCamelCase__ , d_ff=UpperCamelCase__ , dropout_rate=UpperCamelCase__ , layer_norm_epsilon=UpperCamelCase__ ) )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , UpperCamelCase__=None , ) -> List[str]:
lowerCamelCase : Optional[int] = self.layer[0](
UpperCamelCase__ , conditioning_emb=UpperCamelCase__ , attention_mask=UpperCamelCase__ , )
if encoder_hidden_states is not None:
lowerCamelCase : Dict = torch.where(encoder_attention_mask > 0 , 0 , -1e10 ).to(
encoder_hidden_states.dtype )
lowerCamelCase : Any = self.layer[1](
UpperCamelCase__ , key_value_states=UpperCamelCase__ , attention_mask=UpperCamelCase__ , )
# Apply Film Conditional Feed Forward layer
lowerCamelCase : Dict = self.layer[-1](UpperCamelCase__ , UpperCamelCase__ )
return (hidden_states,)
class UpperCamelCase__ (nn.Module ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Any:
super().__init__()
lowerCamelCase : Union[str, Any] = TaLayerNorm(UpperCamelCase__ )
lowerCamelCase : Dict = TaFiLMLayer(in_features=d_model * 4 , out_features=UpperCamelCase__ )
lowerCamelCase : Optional[int] = Attention(query_dim=UpperCamelCase__ , heads=UpperCamelCase__ , dim_head=UpperCamelCase__ , out_bias=UpperCamelCase__ , scale_qk=UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = nn.Dropout(UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , ) -> Dict:
lowerCamelCase : Tuple = self.layer_norm(UpperCamelCase__ )
if conditioning_emb is not None:
lowerCamelCase : Dict = self.FiLMLayer(UpperCamelCase__ , UpperCamelCase__ )
# Self-attention block
lowerCamelCase : str = self.attention(UpperCamelCase__ )
lowerCamelCase : Dict = hidden_states + self.dropout(UpperCamelCase__ )
return hidden_states
class UpperCamelCase__ (nn.Module ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> List[Any]:
super().__init__()
lowerCamelCase : int = Attention(query_dim=UpperCamelCase__ , heads=UpperCamelCase__ , dim_head=UpperCamelCase__ , out_bias=UpperCamelCase__ , scale_qk=UpperCamelCase__ )
lowerCamelCase : Dict = TaLayerNorm(UpperCamelCase__ , eps=UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = nn.Dropout(UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__=None , UpperCamelCase__=None , ) -> Optional[int]:
lowerCamelCase : str = self.layer_norm(UpperCamelCase__ )
lowerCamelCase : Dict = self.attention(
UpperCamelCase__ , encoder_hidden_states=UpperCamelCase__ , attention_mask=attention_mask.squeeze(1 ) , )
lowerCamelCase : List[str] = hidden_states + self.dropout(UpperCamelCase__ )
return layer_output
class UpperCamelCase__ (nn.Module ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
super().__init__()
lowerCamelCase : Dict = TaDenseGatedActDense(d_model=UpperCamelCase__ , d_ff=UpperCamelCase__ , dropout_rate=UpperCamelCase__ )
lowerCamelCase : List[str] = TaFiLMLayer(in_features=d_model * 4 , out_features=UpperCamelCase__ )
lowerCamelCase : str = TaLayerNorm(UpperCamelCase__ , eps=UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = nn.Dropout(UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__=None ) -> int:
lowerCamelCase : Tuple = self.layer_norm(UpperCamelCase__ )
if conditioning_emb is not None:
lowerCamelCase : Union[str, Any] = self.film(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Any = self.DenseReluDense(UpperCamelCase__ )
lowerCamelCase : List[Any] = hidden_states + self.dropout(UpperCamelCase__ )
return hidden_states
class UpperCamelCase__ (nn.Module ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> Union[str, Any]:
super().__init__()
lowerCamelCase : List[Any] = nn.Linear(UpperCamelCase__ , UpperCamelCase__ , bias=UpperCamelCase__ )
lowerCamelCase : int = nn.Linear(UpperCamelCase__ , UpperCamelCase__ , bias=UpperCamelCase__ )
lowerCamelCase : Optional[int] = nn.Linear(UpperCamelCase__ , UpperCamelCase__ , bias=UpperCamelCase__ )
lowerCamelCase : List[str] = nn.Dropout(UpperCamelCase__ )
lowerCamelCase : List[str] = NewGELUActivation()
def _lowercase ( self , UpperCamelCase__ ) -> int:
lowerCamelCase : Tuple = self.act(self.wi_a(UpperCamelCase__ ) )
lowerCamelCase : Any = self.wi_a(UpperCamelCase__ )
lowerCamelCase : Any = hidden_gelu * hidden_linear
lowerCamelCase : Union[str, Any] = self.dropout(UpperCamelCase__ )
lowerCamelCase : int = self.wo(UpperCamelCase__ )
return hidden_states
class UpperCamelCase__ (nn.Module ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__=1e-6 ) -> List[str]:
super().__init__()
lowerCamelCase : Optional[Any] = nn.Parameter(torch.ones(UpperCamelCase__ ) )
lowerCamelCase : Optional[int] = eps
def _lowercase ( self , UpperCamelCase__ ) -> List[str]:
lowerCamelCase : List[str] = hidden_states.to(torch.floataa ).pow(2 ).mean(-1 , keepdim=UpperCamelCase__ )
lowerCamelCase : List[Any] = hidden_states * torch.rsqrt(variance + self.variance_epsilon )
# convert into half-precision if necessary
if self.weight.dtype in [torch.floataa, torch.bfloataa]:
lowerCamelCase : List[Any] = hidden_states.to(self.weight.dtype )
return self.weight * hidden_states
class UpperCamelCase__ (nn.Module ):
'''simple docstring'''
def _lowercase ( self , UpperCamelCase__ ) -> int:
return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi ) * (input + 0.044715 * torch.pow(UpperCamelCase__ , 3.0 )) ))
class UpperCamelCase__ (nn.Module ):
'''simple docstring'''
def __init__( self , UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
super().__init__()
lowerCamelCase : int = nn.Linear(UpperCamelCase__ , out_features * 2 , bias=UpperCamelCase__ )
def _lowercase ( self , UpperCamelCase__ , UpperCamelCase__ ) -> str:
lowerCamelCase : Tuple = self.scale_bias(UpperCamelCase__ )
lowerCamelCase : Optional[int] = torch.chunk(UpperCamelCase__ , 2 , -1 )
lowerCamelCase : Optional[int] = x * (1 + scale) + shift
return x
| 311 |
import unittest
import numpy as np
from transformers import DistilBertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.distilbert.modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
)
class lowercase ( unittest.TestCase):
'''simple docstring'''
def __init__( self : int , snake_case : Optional[Any] , snake_case : Optional[Any]=13 , snake_case : str=7 , snake_case : Optional[Any]=True , snake_case : int=True , snake_case : str=True , snake_case : List[str]=True , snake_case : Optional[Any]=99 , snake_case : Optional[int]=32 , snake_case : Optional[int]=5 , snake_case : Optional[Any]=4 , snake_case : Optional[Any]=37 , snake_case : str="gelu" , snake_case : List[str]=0.1 , snake_case : List[str]=0.1 , snake_case : int=512 , snake_case : Union[str, Any]=16 , snake_case : Optional[Any]=2 , snake_case : int=0.02 , snake_case : str=4 , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = parent
SCREAMING_SNAKE_CASE : str = batch_size
SCREAMING_SNAKE_CASE : Union[str, Any] = seq_length
SCREAMING_SNAKE_CASE : int = is_training
SCREAMING_SNAKE_CASE : List[str] = use_attention_mask
SCREAMING_SNAKE_CASE : Optional[Any] = use_token_type_ids
SCREAMING_SNAKE_CASE : Optional[int] = use_labels
SCREAMING_SNAKE_CASE : Optional[int] = vocab_size
SCREAMING_SNAKE_CASE : Optional[int] = hidden_size
SCREAMING_SNAKE_CASE : Any = num_hidden_layers
SCREAMING_SNAKE_CASE : Tuple = num_attention_heads
SCREAMING_SNAKE_CASE : Any = intermediate_size
SCREAMING_SNAKE_CASE : Tuple = hidden_act
SCREAMING_SNAKE_CASE : Optional[int] = hidden_dropout_prob
SCREAMING_SNAKE_CASE : str = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE : Tuple = max_position_embeddings
SCREAMING_SNAKE_CASE : Any = type_vocab_size
SCREAMING_SNAKE_CASE : List[Any] = type_sequence_label_size
SCREAMING_SNAKE_CASE : Tuple = initializer_range
SCREAMING_SNAKE_CASE : List[str] = num_choices
def lowerCamelCase_ ( self : List[Any] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
SCREAMING_SNAKE_CASE : Optional[int] = None
if self.use_attention_mask:
SCREAMING_SNAKE_CASE : Any = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE : Tuple = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , tie_weights_=snake_case , )
return config, input_ids, attention_mask
def lowerCamelCase_ ( self : int ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : Tuple = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = config_and_inputs
SCREAMING_SNAKE_CASE : int = {'input_ids': input_ids, 'attention_mask': attention_mask}
return config, inputs_dict
@require_flax
class lowercase ( SCREAMING_SNAKE_CASE_ , unittest.TestCase):
'''simple docstring'''
UpperCAmelCase : int = (
(
FlaxDistilBertModel,
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase_ ( self : Optional[int] ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : str = FlaxDistilBertModelTester(self )
@slow
def lowerCamelCase_ ( self : Any ):
'''simple docstring'''
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE : Dict = model_class_name.from_pretrained('distilbert-base-uncased' )
SCREAMING_SNAKE_CASE : Dict = model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case )
@require_flax
class lowercase ( unittest.TestCase):
'''simple docstring'''
@slow
def lowerCamelCase_ ( self : Tuple ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : List[Any] = FlaxDistilBertModel.from_pretrained('distilbert-base-uncased' )
SCREAMING_SNAKE_CASE : List[str] = np.array([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]] )
SCREAMING_SNAKE_CASE : Dict = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
SCREAMING_SNAKE_CASE : str = model(snake_case , attention_mask=snake_case )[0]
SCREAMING_SNAKE_CASE : int = (1, 11, 768)
self.assertEqual(output.shape , snake_case )
SCREAMING_SNAKE_CASE : Optional[int] = np.array([[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] , snake_case , atol=1E-4 ) ) | 352 | 0 |
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class __lowerCAmelCase ( unittest.TestCase ):
def lowerCamelCase ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
self.assertEqual(len(__UpperCAmelCase ) , len(__UpperCAmelCase ) )
for a, b in zip(__UpperCAmelCase , __UpperCAmelCase ):
self.assertAlmostEqual(__UpperCAmelCase , __UpperCAmelCase , delta=__UpperCAmelCase )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(__UpperCAmelCase ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1E-2 )
def lowerCamelCase ( self ):
'''simple docstring'''
__lowerCamelCase = None
ops.enable_eager_execution_internal()
__lowerCamelCase = tf.config.list_physical_devices('''CPU''' )
if len(__UpperCAmelCase ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
__lowerCamelCase = tf.config.list_logical_devices(device_type='''CPU''' )
__lowerCamelCase = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
__lowerCamelCase = GradientAccumulator()
__lowerCamelCase = tf.Variable([4.0, 3.0] )
__lowerCamelCase ,__lowerCamelCase = create_optimizer(5E-5 , 10 , 5 )
__lowerCamelCase = tf.Variable([0.0, 0.0] , trainable=__UpperCAmelCase )
def accumulate_on_replica(__UpperCAmelCase ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(__UpperCAmelCase , __UpperCAmelCase ):
with strategy.scope():
__lowerCamelCase = strategy.experimental_local_results(__UpperCAmelCase )
local_variables[0].assign(__UpperCAmelCase )
local_variables[1].assign(__UpperCAmelCase )
strategy.run(__UpperCAmelCase , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(__UpperCAmelCase )
def _check_local_values(__UpperCAmelCase , __UpperCAmelCase ):
__lowerCamelCase = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , __UpperCAmelCase , tol=1E-2 )
self.assertListAlmostEqual(values[1].value() , __UpperCAmelCase , tol=1E-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1E-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 622 |
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Accelerator
from accelerate.test_utils import RegressionDataset, RegressionModel
from accelerate.utils import is_tpu_available, set_seed
a_ = """true"""
def a__ ( _UpperCamelCase : Union[str, Any] ,_UpperCamelCase : List[str]=82 ,_UpperCamelCase : Optional[Any]=16 ):
set_seed(42 )
__lowerCamelCase = RegressionModel()
__lowerCamelCase = deepcopy(_UpperCamelCase )
__lowerCamelCase = RegressionDataset(length=_UpperCamelCase )
__lowerCamelCase = DataLoader(_UpperCamelCase ,batch_size=_UpperCamelCase )
model.to(accelerator.device )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return model, ddp_model, dataloader
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : str=False ):
__lowerCamelCase = AutoTokenizer.from_pretrained('''hf-internal-testing/mrpc-bert-base-cased''' )
__lowerCamelCase = load_dataset('''glue''' ,'''mrpc''' ,split='''validation''' )
def tokenize_function(_UpperCamelCase : int ):
__lowerCamelCase = tokenizer(examples['''sentence1'''] ,examples['''sentence2'''] ,truncation=_UpperCamelCase ,max_length=_UpperCamelCase )
return outputs
with accelerator.main_process_first():
__lowerCamelCase = dataset.map(
_UpperCamelCase ,batched=_UpperCamelCase ,remove_columns=['''idx''', '''sentence1''', '''sentence2'''] ,)
__lowerCamelCase = tokenized_datasets.rename_column('''label''' ,'''labels''' )
def collate_fn(_UpperCamelCase : Any ):
if use_longest:
return tokenizer.pad(_UpperCamelCase ,padding='''longest''' ,return_tensors='''pt''' )
return tokenizer.pad(_UpperCamelCase ,padding='''max_length''' ,max_length=1_28 ,return_tensors='''pt''' )
return DataLoader(_UpperCamelCase ,shuffle=_UpperCamelCase ,collate_fn=_UpperCamelCase ,batch_size=16 )
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : List[str] ):
__lowerCamelCase = Accelerator(dispatch_batches=_UpperCamelCase ,split_batches=_UpperCamelCase )
__lowerCamelCase = get_dataloader(_UpperCamelCase ,not dispatch_batches )
__lowerCamelCase = AutoModelForSequenceClassification.from_pretrained(
'''hf-internal-testing/mrpc-bert-base-cased''' ,return_dict=_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.prepare(_UpperCamelCase ,_UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def a__ ( _UpperCamelCase : Dict ,_UpperCamelCase : Optional[Any] ,_UpperCamelCase : Union[str, Any] ):
__lowerCamelCase = []
for batch in dataloader:
__lowerCamelCase ,__lowerCamelCase = batch.values()
with torch.no_grad():
__lowerCamelCase = model(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
__lowerCamelCase ,__lowerCamelCase = [], []
for logit, targ in logits_and_targets:
logits.append(_UpperCamelCase )
targs.append(_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = torch.cat(_UpperCamelCase ), torch.cat(_UpperCamelCase )
return logits, targs
def a__ ( _UpperCamelCase : Accelerator ,_UpperCamelCase : List[Any]=82 ,_UpperCamelCase : str=False ,_UpperCamelCase : List[str]=False ,_UpperCamelCase : Optional[int]=16 ):
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = get_basic_setup(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
__lowerCamelCase ,__lowerCamelCase = generate_predictions(_UpperCamelCase ,_UpperCamelCase ,_UpperCamelCase )
assert (
len(_UpperCamelCase ) == num_samples
), F"""Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(_UpperCamelCase )}"""
def a__ ( _UpperCamelCase : bool = False ,_UpperCamelCase : bool = False ):
__lowerCamelCase = evaluate.load('''glue''' ,'''mrpc''' )
__lowerCamelCase ,__lowerCamelCase = get_mrpc_setup(_UpperCamelCase ,_UpperCamelCase )
# First do baseline
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''no''']
model.to(_UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(_UpperCamelCase )
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=_UpperCamelCase ,references=batch['''labels'''] )
__lowerCamelCase = metric.compute()
# Then do distributed
__lowerCamelCase ,__lowerCamelCase ,__lowerCamelCase = setup['''ddp''']
model.eval()
for batch in dataloader:
with torch.inference_mode():
__lowerCamelCase = model(**_UpperCamelCase )
__lowerCamelCase = outputs.logits.argmax(dim=-1 )
__lowerCamelCase = batch['''labels''']
__lowerCamelCase ,__lowerCamelCase = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=_UpperCamelCase ,references=_UpperCamelCase )
__lowerCamelCase = metric.compute()
for key in "accuracy f1".split():
assert math.isclose(
baseline[key] ,distributed[key] ), F"""Baseline and Distributed are not the same for key {key}:\n\tBaseline: {baseline[key]}\n\tDistributed: {distributed[key]}\n"""
def a__ ( ):
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
datasets.utils.logging.set_verbosity_warning()
transformers.utils.logging.set_verbosity_warning()
else:
datasets.utils.logging.set_verbosity_error()
transformers.utils.logging.set_verbosity_error()
# These are a bit slower so they should only be ran on the GPU or TPU
if torch.cuda.is_available() or is_tpu_available():
if accelerator.is_local_main_process:
print('''**Testing gather_for_metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`""" )
test_mrpc(_UpperCamelCase ,_UpperCamelCase )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test torch metrics**''' )
for split_batches in [True, False]:
for dispatch_batches in [True, False]:
__lowerCamelCase = Accelerator(split_batches=_UpperCamelCase ,dispatch_batches=_UpperCamelCase )
if accelerator.is_local_main_process:
print(F"""With: `split_batches={split_batches}`, `dispatch_batches={dispatch_batches}`, length=99""" )
test_torch_metrics(_UpperCamelCase ,99 )
accelerator.state._reset_state()
if accelerator.is_local_main_process:
print('''**Test last batch is not dropped when perfectly divisible**''' )
__lowerCamelCase = Accelerator()
test_torch_metrics(_UpperCamelCase ,5_12 )
accelerator.state._reset_state()
def a__ ( _UpperCamelCase : Optional[int] ):
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
| 622 | 1 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from torch import nn
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import KandinskyVaaPriorPipeline, PriorTransformer, UnCLIPScheduler
from diffusers.utils import torch_device
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class lowercase_ ( _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase = KandinskyVaaPriorPipeline
__lowerCAmelCase = ["prompt"]
__lowerCAmelCase = ["prompt", "negative_prompt"]
__lowerCAmelCase = [
"num_images_per_prompt",
"generator",
"num_inference_steps",
"latents",
"negative_prompt",
"guidance_scale",
"output_type",
"return_dict",
]
__lowerCAmelCase = False
@property
def __UpperCAmelCase ( self : Dict ) -> Tuple:
return 32
@property
def __UpperCAmelCase ( self : Optional[int] ) -> Any:
return 32
@property
def __UpperCAmelCase ( self : Optional[Any] ) -> str:
return self.time_input_dim
@property
def __UpperCAmelCase ( self : Optional[Any] ) -> Optional[Any]:
return self.time_input_dim * 4
@property
def __UpperCAmelCase ( self : str ) -> Union[str, Any]:
return 1_00
@property
def __UpperCAmelCase ( self : List[Any] ) -> Optional[int]:
_A = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
return tokenizer
@property
def __UpperCAmelCase ( self : Dict ) -> Optional[Any]:
torch.manual_seed(0 )
_A = CLIPTextConfig(
bos_token_id=0, eos_token_id=2, hidden_size=self.text_embedder_hidden_size, projection_dim=self.text_embedder_hidden_size, intermediate_size=37, layer_norm_eps=1e-05, num_attention_heads=4, num_hidden_layers=5, pad_token_id=1, vocab_size=10_00, )
return CLIPTextModelWithProjection(UpperCamelCase__ )
@property
def __UpperCAmelCase ( self : Optional[Any] ) -> Any:
torch.manual_seed(0 )
_A = {
'num_attention_heads': 2,
'attention_head_dim': 12,
'embedding_dim': self.text_embedder_hidden_size,
'num_layers': 1,
}
_A = PriorTransformer(**UpperCamelCase__ )
# clip_std and clip_mean is initialized to be 0 so PriorTransformer.post_process_latents will always return 0 - set clip_std to be 1 so it won't return 0
_A = nn.Parameter(torch.ones(model.clip_std.shape ) )
return model
@property
def __UpperCAmelCase ( self : Dict ) -> Tuple:
torch.manual_seed(0 )
_A = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size, image_size=2_24, projection_dim=self.text_embedder_hidden_size, intermediate_size=37, num_attention_heads=4, num_channels=3, num_hidden_layers=5, patch_size=14, )
_A = CLIPVisionModelWithProjection(UpperCamelCase__ )
return model
@property
def __UpperCAmelCase ( self : Dict ) -> List[Any]:
_A = CLIPImageProcessor(
crop_size=2_24, do_center_crop=UpperCamelCase__, do_normalize=UpperCamelCase__, do_resize=UpperCamelCase__, image_mean=[0.48_145_466, 0.4_578_275, 0.40_821_073], image_std=[0.26_862_954, 0.26_130_258, 0.27_577_711], resample=3, size=2_24, )
return image_processor
def __UpperCAmelCase ( self : List[Any] ) -> Optional[int]:
_A = self.dummy_prior
_A = self.dummy_image_encoder
_A = self.dummy_text_encoder
_A = self.dummy_tokenizer
_A = self.dummy_image_processor
_A = UnCLIPScheduler(
variance_type='fixed_small_log', prediction_type='sample', num_train_timesteps=10_00, clip_sample=UpperCamelCase__, clip_sample_range=10.0, )
_A = {
'prior': prior,
'image_encoder': image_encoder,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'scheduler': scheduler,
'image_processor': image_processor,
}
return components
def __UpperCAmelCase ( self : List[Any], UpperCamelCase__ : List[Any], UpperCamelCase__ : Tuple=0 ) -> Optional[Any]:
if str(UpperCamelCase__ ).startswith('mps' ):
_A = torch.manual_seed(UpperCamelCase__ )
else:
_A = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
_A = {
'prompt': 'horse',
'generator': generator,
'guidance_scale': 4.0,
'num_inference_steps': 2,
'output_type': 'np',
}
return inputs
def __UpperCAmelCase ( self : Dict ) -> str:
_A = 'cpu'
_A = self.get_dummy_components()
_A = self.pipeline_class(**UpperCamelCase__ )
_A = pipe.to(UpperCamelCase__ )
pipe.set_progress_bar_config(disable=UpperCamelCase__ )
_A = pipe(**self.get_dummy_inputs(UpperCamelCase__ ) )
_A = output.image_embeds
_A = pipe(
**self.get_dummy_inputs(UpperCamelCase__ ), return_dict=UpperCamelCase__, )[0]
_A = image[0, -10:]
_A = image_from_tuple[0, -10:]
assert image.shape == (1, 32)
_A = np.array(
[-0.0_532, 1.7_120, 0.3_656, -1.0_852, -0.8_946, -1.1_756, 0.4_348, 0.2_482, 0.5_146, -0.1_156] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@skip_mps
def __UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
_A = torch_device == 'cpu'
_A = True
_A = False
self._test_inference_batch_single_identical(
test_max_difference=UpperCamelCase__, relax_max_difference=UpperCamelCase__, test_mean_pixel_difference=UpperCamelCase__, )
@skip_mps
def __UpperCAmelCase ( self : Optional[int] ) -> Union[str, Any]:
_A = torch_device == 'cpu'
_A = False
self._test_attention_slicing_forward_pass(
test_max_difference=UpperCamelCase__, test_mean_pixel_difference=UpperCamelCase__, )
| 107 |
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
HubertConfig,
HubertForCTC,
HubertModel,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
_UpperCamelCase = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""w2v_encoder.proj""": """lm_head""",
"""mask_emb""": """masked_spec_embed""",
}
def _a ( _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
for attribute in key.split(""".""" ):
UpperCAmelCase = getattr(_snake_case , _snake_case )
if weight_type is not None:
UpperCAmelCase = getattr(_snake_case , _snake_case ).shape
else:
UpperCAmelCase = hf_pointer.shape
assert hf_shape == value.shape, (
F'''Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be'''
F''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
UpperCAmelCase = value
elif weight_type == "weight_g":
UpperCAmelCase = value
elif weight_type == "weight_v":
UpperCAmelCase = value
elif weight_type == "bias":
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 ( _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = []
UpperCAmelCase = fairseq_model.state_dict()
UpperCAmelCase = hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor
for name, value in fairseq_dict.items():
UpperCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
_snake_case , _snake_case , _snake_case , _snake_case , hf_model.config.feat_extract_norm == """group""" , )
UpperCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
UpperCAmelCase = """hubert.""" + mapped_key if (is_finetuned and mapped_key != """lm_head""") else mapped_key
if key in name or (key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0] and not is_finetuned):
UpperCAmelCase = True
if "*" in mapped_key:
UpperCAmelCase = name.split(_snake_case )[0].split(""".""" )[-2]
UpperCAmelCase = mapped_key.replace("""*""" , _snake_case )
if "weight_g" in name:
UpperCAmelCase = """weight_g"""
elif "weight_v" in name:
UpperCAmelCase = """weight_v"""
elif "weight" in name:
UpperCAmelCase = """weight"""
elif "bias" in name:
UpperCAmelCase = """bias"""
else:
UpperCAmelCase = None
set_recursively(_snake_case , _snake_case , _snake_case , _snake_case , _snake_case )
continue
if not is_used:
unused_weights.append(_snake_case )
logger.warning(F'''Unused weights: {unused_weights}''' )
def _a ( _snake_case , _snake_case , _snake_case , _snake_case , _snake_case ):
"""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:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
UpperCAmelCase = value
logger.info(F'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
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:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
UpperCAmelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'''{full_name} has size {value.shape}, but'''
F''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
UpperCAmelCase = value
logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(_snake_case )
@torch.no_grad()
def _a ( _snake_case , _snake_case , _snake_case=None , _snake_case=None , _snake_case=True ):
"""simple docstring"""
if config_path is not None:
UpperCAmelCase = HubertConfig.from_pretrained(_snake_case )
else:
UpperCAmelCase = HubertConfig()
if is_finetuned:
if dict_path:
UpperCAmelCase = Dictionary.load(_snake_case )
# 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(_snake_case , """vocab.json""" )
if not os.path.isdir(_snake_case ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(_snake_case ) )
return
os.makedirs(_snake_case , exist_ok=_snake_case )
with open(_snake_case , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(target_dict.indices , _snake_case )
UpperCAmelCase = WavaVecaCTCTokenizer(
_snake_case , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=_snake_case , )
UpperCAmelCase = True if config.feat_extract_norm == """layer""" else False
UpperCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_6000 , padding_value=0 , do_normalize=_snake_case , return_attention_mask=_snake_case , )
UpperCAmelCase = WavaVecaProcessor(feature_extractor=_snake_case , tokenizer=_snake_case )
processor.save_pretrained(_snake_case )
UpperCAmelCase = HubertForCTC(_snake_case )
else:
UpperCAmelCase = HubertModel(_snake_case )
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 , UpperCAmelCase , UpperCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] )
UpperCAmelCase = model[0].eval()
recursively_load_weights(_snake_case , _snake_case , _snake_case )
hf_wavavec.save_pretrained(_snake_case )
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 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 = parser.parse_args()
convert_hubert_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 341 | 0 |
'''simple docstring'''
def lowercase_ ( lowercase__ , lowercase__ ) ->str:
_snake_case: list[list[str]] = [[] for _ in range(lowercase__ )]
_snake_case: Dict = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1 or len(lowercase__ ) <= key:
return input_string
for position, character in enumerate(lowercase__ ):
_snake_case: int = position % (lowest * 2) # puts it in bounds
_snake_case: Union[str, Any] = min(lowercase__ , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(lowercase__ )
_snake_case: int = [''.join(lowercase__ ) for row in temp_grid]
_snake_case: List[Any] = ''.join(lowercase__ )
return output_string
def lowercase_ ( lowercase__ , lowercase__ ) ->str:
_snake_case: Union[str, Any] = []
_snake_case: Tuple = key - 1
if key <= 0:
raise ValueError('Height of grid can\'t be 0 or negative' )
if key == 1:
return input_string
_snake_case: list[list[str]] = [[] for _ in range(lowercase__ )] # generates template
for position in range(len(lowercase__ ) ):
_snake_case: str = position % (lowest * 2) # puts it in bounds
_snake_case: Optional[Any] = min(lowercase__ , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append('*' )
_snake_case: Any = 0
for row in temp_grid: # fills in the characters
_snake_case: Dict = input_string[counter : counter + len(lowercase__ )]
grid.append(list(lowercase__ ) )
counter += len(lowercase__ )
_snake_case: Optional[Any] = '' # reads as zigzag
for position in range(len(lowercase__ ) ):
_snake_case: Any = position % (lowest * 2) # puts it in bounds
_snake_case: str = min(lowercase__ , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def lowercase_ ( lowercase__ ) ->dict[int, str]:
_snake_case: Dict = {}
for key_guess in range(1 , len(lowercase__ ) ): # tries every key
_snake_case: Optional[Any] = decrypt(lowercase__ , lowercase__ )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 721 |
'''simple docstring'''
from sklearn.metrics import matthews_corrcoef
import datasets
A : Dict = '\nCompute the Matthews correlation coefficient (MCC)\n\nThe Matthews correlation coefficient is used in machine learning as a\nmeasure of the quality of binary and multiclass classifications. It takes\ninto account true and false positives and negatives and is generally\nregarded as a balanced measure which can be used even if the classes are of\nvery different sizes. The MCC is in essence a correlation coefficient value\nbetween -1 and +1. A coefficient of +1 represents a perfect prediction, 0\nan average random prediction and -1 an inverse prediction. The statistic\nis also known as the phi coefficient. [source: Wikipedia]\n'
A : int = '\nArgs:\n predictions (list of int): Predicted labels, as returned by a model.\n references (list of int): Ground truth labels.\n sample_weight (list of int, float, or bool): Sample weights. Defaults to `None`.\nReturns:\n matthews_correlation (dict containing float): Matthews correlation.\nExamples:\n Example 1, a basic example with only predictions and references as inputs:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.54\n\n Example 2, the same example as above, but also including sample weights:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 3, 1, 1, 1, 2])\n >>> print(round(results[\'matthews_correlation\'], 2))\n 0.1\n\n Example 3, the same example as above, but with sample weights that cause a negative correlation:\n >>> matthews_metric = datasets.load_metric("matthews_correlation")\n >>> results = matthews_metric.compute(references=[1, 3, 2, 0, 3, 2],\n ... predictions=[1, 2, 2, 0, 3, 3],\n ... sample_weight=[0.5, 1, 0, 0, 0, 1])\n >>> print(round(results[\'matthews_correlation\'], 2))\n -0.25\n'
A : Dict = '\\n@article{scikit-learn,\n title={Scikit-learn: Machine Learning in {P}ython},\n author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.\n and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.\n and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and\n Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},\n journal={Journal of Machine Learning Research},\n volume={12},\n pages={2825--2830},\n year={2011}\n}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowerCamelCase ( datasets.Metric ):
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('int32' ),
'references': datasets.Value('int32' ),
} ) , reference_urls=[
'https://scikit-learn.org/stable/modules/generated/sklearn.metrics.matthews_corrcoef.html'
] , )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , __snake_case : int , __snake_case : List[Any] , __snake_case : Union[str, Any]=None ):
'''simple docstring'''
return {
"matthews_correlation": float(matthews_corrcoef(__snake_case , __snake_case , sample_weight=__snake_case ) ),
}
| 273 | 0 |
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