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 |
|---|---|---|---|---|
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase : List[str] = logging.get_logger(__name__)
__lowerCamelCase : str = {
"google/mobilenet_v2_1.4_224": "https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json",
"google/mobilenet_v2_1.0_224": "https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json",
"google/mobilenet_v2_0.75_160": "https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json",
"google/mobilenet_v2_0.35_96": "https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json",
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class __magic_name__ ( A__ ):
lowercase : Any ='''mobilenet_v2'''
def __init__( self : Optional[Any] , UpperCamelCase__ : str=3 , UpperCamelCase__ : Tuple=2_24 , UpperCamelCase__ : Optional[Any]=1.0 , UpperCamelCase__ : Tuple=8 , UpperCamelCase__ : Union[str, Any]=8 , UpperCamelCase__ : Dict=6 , UpperCamelCase__ : Any=32 , UpperCamelCase__ : str=True , UpperCamelCase__ : Tuple=True , UpperCamelCase__ : Optional[Any]="relu6" , UpperCamelCase__ : Optional[Any]=True , UpperCamelCase__ : Dict=0.8 , UpperCamelCase__ : List[str]=0.02 , UpperCamelCase__ : Any=0.0_01 , UpperCamelCase__ : Union[str, Any]=2_55 , **UpperCamelCase__ : Optional[int] , ) -> List[Any]:
'''simple docstring'''
super().__init__(**UpperCamelCase__ )
if depth_multiplier <= 0:
raise ValueError("depth_multiplier must be greater than zero." )
UpperCAmelCase = num_channels
UpperCAmelCase = image_size
UpperCAmelCase = depth_multiplier
UpperCAmelCase = depth_divisible_by
UpperCAmelCase = min_depth
UpperCAmelCase = expand_ratio
UpperCAmelCase = output_stride
UpperCAmelCase = first_layer_is_expansion
UpperCAmelCase = finegrained_output
UpperCAmelCase = hidden_act
UpperCAmelCase = tf_padding
UpperCAmelCase = classifier_dropout_prob
UpperCAmelCase = initializer_range
UpperCAmelCase = layer_norm_eps
UpperCAmelCase = semantic_loss_ignore_index
class __magic_name__ ( A__ ):
lowercase : Any =version.parse('''1.11''' )
@property
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict([("pixel_values", {0: "batch"})] )
@property
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([("logits", {0: "batch"})] )
else:
return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})] )
@property
def SCREAMING_SNAKE_CASE_ ( self : int ) -> float:
'''simple docstring'''
return 1e-4
| 323 |
'''simple docstring'''
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
lowercase ='\\n@inproceedings{wang2019glue,\n title={{GLUE}: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding},\n author={Wang, Alex and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R.},\n note={In the Proceedings of ICLR.},\n year={2019}\n}\n'
lowercase ='\\nGLUE, the General Language Understanding Evaluation benchmark\n(https://gluebenchmark.com/) is a collection of resources for training,\nevaluating, and analyzing natural language understanding systems.\n'
lowercase ='\nCompute GLUE evaluation metric associated to each GLUE dataset.\nArgs:\n predictions: list of predictions to score.\n Each translation should be tokenized into a list of tokens.\n references: list of lists of references for each translation.\n Each reference should be tokenized into a list of tokens.\nReturns: depending on the GLUE subset, one or several of:\n "accuracy": Accuracy\n "f1": F1 score\n "pearson": Pearson Correlation\n "spearmanr": Spearman Correlation\n "matthews_correlation": Matthew Correlation\nExamples:\n\n >>> glue_metric = datasets.load_metric(\'glue\', \'sst2\') # \'sst2\' or any of ["mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0}\n\n >>> glue_metric = datasets.load_metric(\'glue\', \'mrpc\') # \'mrpc\' or \'qqp\'\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'accuracy\': 1.0, \'f1\': 1.0}\n\n >>> glue_metric = datasets.load_metric(\'glue\', \'stsb\')\n >>> references = [0., 1., 2., 3., 4., 5.]\n >>> predictions = [0., 1., 2., 3., 4., 5.]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print({"pearson": round(results["pearson"], 2), "spearmanr": round(results["spearmanr"], 2)})\n {\'pearson\': 1.0, \'spearmanr\': 1.0}\n\n >>> glue_metric = datasets.load_metric(\'glue\', \'cola\')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'matthews_correlation\': 1.0}\n'
def lowerCamelCase__ ( __lowerCamelCase : Optional[Any] , __lowerCamelCase : str ):
'''simple docstring'''
return float((preds == labels).mean() )
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
_UpperCAmelCase : Union[str, Any] =simple_accuracy(__lowerCamelCase , __lowerCamelCase )
_UpperCAmelCase : List[Any] =float(fa_score(y_true=__lowerCamelCase , y_pred=__lowerCamelCase ) )
return {
"accuracy": acc,
"f1": fa,
}
def lowerCamelCase__ ( __lowerCamelCase : str , __lowerCamelCase : int ):
'''simple docstring'''
_UpperCAmelCase : Dict =float(pearsonr(__lowerCamelCase , __lowerCamelCase )[0] )
_UpperCAmelCase : int =float(spearmanr(__lowerCamelCase , __lowerCamelCase )[0] )
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class __magic_name__ ( datasets.Metric ):
def lowerCAmelCase ( self) -> str:
'''simple docstring'''
if self.config_name not in [
"sst2",
"mnli",
"mnli_mismatched",
"mnli_matched",
"cola",
"stsb",
"mrpc",
"qqp",
"qnli",
"rte",
"wnli",
"hans",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
'"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]')
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'),
'references': datasets.Value('int64' if self.config_name != 'stsb' else 'float32'),
}) , codebase_urls=[] , reference_urls=[] , format='numpy' , )
def lowerCAmelCase ( self , snake_case , snake_case) -> int:
'''simple docstring'''
if self.config_name == "cola":
return {"matthews_correlation": matthews_corrcoef(snake_case , snake_case)}
elif self.config_name == "stsb":
return pearson_and_spearman(snake_case , snake_case)
elif self.config_name in ["mrpc", "qqp"]:
return acc_and_fa(snake_case , snake_case)
elif self.config_name in ["sst2", "mnli", "mnli_mismatched", "mnli_matched", "qnli", "rte", "wnli", "hans"]:
return {"accuracy": simple_accuracy(snake_case , snake_case)}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["sst2", "mnli", "mnli_mismatched", "mnli_matched", '
'"cola", "stsb", "mrpc", "qqp", "qnli", "rte", "wnli", "hans"]')
| 446 | 0 |
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 _SCREAMING_SNAKE_CASE ( _A ):
__SCREAMING_SNAKE_CASE :int = 42
@flax_register_to_config
class _SCREAMING_SNAKE_CASE ( nn.Module ,_A ,_A ):
__SCREAMING_SNAKE_CASE :Union[str, Any] = 32
__SCREAMING_SNAKE_CASE :Dict = 4
__SCREAMING_SNAKE_CASE :List[str] = 4
__SCREAMING_SNAKE_CASE :List[Any] = (
"""CrossAttnDownBlock2D""",
"""CrossAttnDownBlock2D""",
"""CrossAttnDownBlock2D""",
"""DownBlock2D""",
)
__SCREAMING_SNAKE_CASE :Tuple = ("""UpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""", """CrossAttnUpBlock2D""")
__SCREAMING_SNAKE_CASE :Union[str, Any] = False
__SCREAMING_SNAKE_CASE :Tuple = (320, 640, 1280, 1280)
__SCREAMING_SNAKE_CASE :Tuple = 2
__SCREAMING_SNAKE_CASE :int = 8
__SCREAMING_SNAKE_CASE :Optional[int] = None
__SCREAMING_SNAKE_CASE :List[str] = 1280
__SCREAMING_SNAKE_CASE :Tuple = 0.0
__SCREAMING_SNAKE_CASE :Optional[Any] = False
__SCREAMING_SNAKE_CASE :Union[str, Any] = jnp.floataa
__SCREAMING_SNAKE_CASE :Optional[int] = True
__SCREAMING_SNAKE_CASE :List[Any] = 0
__SCREAMING_SNAKE_CASE :Optional[Any] = False
def snake_case__ ( self : List[str] , a__ : jax.random.KeyArray ):
# init input tensors
__magic_name__ = (1, self.in_channels, self.sample_size, self.sample_size)
__magic_name__ = jnp.zeros(__lowerCamelCase , dtype=jnp.floataa )
__magic_name__ = jnp.ones((1,) , dtype=jnp.intaa )
__magic_name__ = jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
__magic_name__ = jax.random.split(__lowerCamelCase )
__magic_name__ = {"params": params_rng, "dropout": dropout_rng}
return self.init(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase )["params"]
def snake_case__ ( self : int ):
__magic_name__ = self.block_out_channels
__magic_name__ = 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.
__magic_name__ = self.num_attention_heads or self.attention_head_dim
# input
__magic_name__ = nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
__magic_name__ = FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
__magic_name__ = FlaxTimestepEmbedding(__lowerCamelCase , dtype=self.dtype )
__magic_name__ = self.only_cross_attention
if isinstance(__lowerCamelCase , __lowerCamelCase ):
__magic_name__ = (only_cross_attention,) * len(self.down_block_types )
if isinstance(__lowerCamelCase , __lowerCamelCase ):
__magic_name__ = (num_attention_heads,) * len(self.down_block_types )
# down
__magic_name__ = []
__magic_name__ = block_out_channels[0]
for i, down_block_type in enumerate(self.down_block_types ):
__magic_name__ = output_channel
__magic_name__ = block_out_channels[i]
__magic_name__ = i == len(__lowerCamelCase ) - 1
if down_block_type == "CrossAttnDownBlock2D":
__magic_name__ = 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:
__magic_name__ = 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 )
__magic_name__ = down_blocks
# mid
__magic_name__ = 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
__magic_name__ = []
__magic_name__ = list(reversed(__lowerCamelCase ) )
__magic_name__ = list(reversed(__lowerCamelCase ) )
__magic_name__ = list(reversed(__lowerCamelCase ) )
__magic_name__ = reversed_block_out_channels[0]
for i, up_block_type in enumerate(self.up_block_types ):
__magic_name__ = output_channel
__magic_name__ = reversed_block_out_channels[i]
__magic_name__ = reversed_block_out_channels[min(i + 1 , len(__lowerCamelCase ) - 1 )]
__magic_name__ = i == len(__lowerCamelCase ) - 1
if up_block_type == "CrossAttnUpBlock2D":
__magic_name__ = 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:
__magic_name__ = 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 )
__magic_name__ = output_channel
__magic_name__ = up_blocks
# out
__magic_name__ = nn.GroupNorm(num_groups=32 , epsilon=1E-5 )
__magic_name__ = nn.Conv(
self.out_channels , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
def __call__( self : Dict , a__ : Dict , a__ : Tuple , a__ : Optional[Any] , a__ : int=None , a__ : Any=None , a__ : bool = True , a__ : bool = False , ):
# 1. time
if not isinstance(__lowerCamelCase , jnp.ndarray ):
__magic_name__ = jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(__lowerCamelCase , jnp.ndarray ) and len(timesteps.shape ) == 0:
__magic_name__ = timesteps.astype(dtype=jnp.floataa )
__magic_name__ = jnp.expand_dims(__lowerCamelCase , 0 )
__magic_name__ = self.time_proj(__lowerCamelCase )
__magic_name__ = self.time_embedding(__lowerCamelCase )
# 2. pre-process
__magic_name__ = jnp.transpose(__lowerCamelCase , (0, 2, 3, 1) )
__magic_name__ = self.conv_in(__lowerCamelCase )
# 3. down
__magic_name__ = (sample,)
for down_block in self.down_blocks:
if isinstance(__lowerCamelCase , __lowerCamelCase ):
__magic_name__ = down_block(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , deterministic=not train )
else:
__magic_name__ = down_block(__lowerCamelCase , __lowerCamelCase , deterministic=not train )
down_block_res_samples += res_samples
if down_block_additional_residuals is not None:
__magic_name__ = ()
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,)
__magic_name__ = new_down_block_res_samples
# 4. mid
__magic_name__ = 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:
__magic_name__ = down_block_res_samples[-(self.layers_per_block + 1) :]
__magic_name__ = down_block_res_samples[: -(self.layers_per_block + 1)]
if isinstance(__lowerCamelCase , __lowerCamelCase ):
__magic_name__ = up_block(
__lowerCamelCase , temb=__lowerCamelCase , encoder_hidden_states=__lowerCamelCase , res_hidden_states_tuple=__lowerCamelCase , deterministic=not train , )
else:
__magic_name__ = up_block(__lowerCamelCase , temb=__lowerCamelCase , res_hidden_states_tuple=__lowerCamelCase , deterministic=not train )
# 6. post-process
__magic_name__ = self.conv_norm_out(__lowerCamelCase )
__magic_name__ = nn.silu(__lowerCamelCase )
__magic_name__ = self.conv_out(__lowerCamelCase )
__magic_name__ = jnp.transpose(__lowerCamelCase , (0, 3, 1, 2) )
if not return_dict:
return (sample,)
return FlaxUNetaDConditionOutput(sample=__lowerCamelCase )
| 717 |
'''simple docstring'''
import json
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
from transformers import (
CONFIG_MAPPING,
IMAGE_PROCESSOR_MAPPING,
AutoConfig,
AutoImageProcessor,
CLIPConfig,
CLIPImageProcessor,
)
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER
sys.path.append(str(Path(__file__).parent.parent.parent.parent / "utils"))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_image_processing import CustomImageProcessor # noqa E402
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def snake_case__ ( self : Optional[int] ):
__magic_name__ = 0
def snake_case__ ( self : Any ):
__magic_name__ = AutoImageProcessor.from_pretrained('''openai/clip-vit-base-patch32''' )
self.assertIsInstance(a__ , a__ )
def snake_case__ ( self : List[Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
__magic_name__ = Path(a__ ) / '''preprocessor_config.json'''
__magic_name__ = Path(a__ ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(a__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(a__ , '''w''' ) )
__magic_name__ = AutoImageProcessor.from_pretrained(a__ )
self.assertIsInstance(a__ , a__ )
def snake_case__ ( self : int ):
# Ensure we can load the image processor from the feature extractor config
with tempfile.TemporaryDirectory() as tmpdirname:
__magic_name__ = Path(a__ ) / '''preprocessor_config.json'''
__magic_name__ = Path(a__ ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(a__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(a__ , '''w''' ) )
__magic_name__ = AutoImageProcessor.from_pretrained(a__ )
self.assertIsInstance(a__ , a__ )
def snake_case__ ( self : Dict ):
with tempfile.TemporaryDirectory() as tmpdirname:
__magic_name__ = CLIPConfig()
# Create a dummy config file with image_proceesor_type
__magic_name__ = Path(a__ ) / '''preprocessor_config.json'''
__magic_name__ = Path(a__ ) / '''config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(a__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(a__ , '''w''' ) )
# remove image_processor_type to make sure config.json alone is enough to load image processor locally
__magic_name__ = AutoImageProcessor.from_pretrained(a__ ).to_dict()
config_dict.pop('''image_processor_type''' )
__magic_name__ = CLIPImageProcessor(**a__ )
# save in new folder
model_config.save_pretrained(a__ )
config.save_pretrained(a__ )
__magic_name__ = AutoImageProcessor.from_pretrained(a__ )
# make sure private variable is not incorrectly saved
__magic_name__ = json.loads(config.to_json_string() )
self.assertTrue('''_processor_class''' not in dict_as_saved )
self.assertIsInstance(a__ , a__ )
def snake_case__ ( self : List[Any] ):
with tempfile.TemporaryDirectory() as tmpdirname:
__magic_name__ = Path(a__ ) / '''preprocessor_config.json'''
json.dump(
{'''image_processor_type''': '''CLIPImageProcessor''', '''processor_class''': '''CLIPProcessor'''} , open(a__ , '''w''' ) , )
__magic_name__ = AutoImageProcessor.from_pretrained(a__ )
self.assertIsInstance(a__ , a__ )
def snake_case__ ( self : Dict ):
with self.assertRaisesRegex(
a__ , '''clip-base is not a local folder and is not a valid model identifier''' ):
__magic_name__ = AutoImageProcessor.from_pretrained('''clip-base''' )
def snake_case__ ( self : int ):
with self.assertRaisesRegex(
a__ , r'''aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)''' ):
__magic_name__ = AutoImageProcessor.from_pretrained(a__ , revision='''aaaaaa''' )
def snake_case__ ( self : Optional[int] ):
with self.assertRaisesRegex(
a__ , '''hf-internal-testing/config-no-model does not appear to have a file named preprocessor_config.json.''' , ):
__magic_name__ = AutoImageProcessor.from_pretrained('''hf-internal-testing/config-no-model''' )
def snake_case__ ( self : Any ):
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(a__ ):
__magic_name__ = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
# If remote code is disabled, we can't load this config.
with self.assertRaises(a__ ):
__magic_name__ = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=a__ )
__magic_name__ = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=a__ )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
# Test image processor can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(a__ )
__magic_name__ = AutoImageProcessor.from_pretrained(a__ , trust_remote_code=a__ )
self.assertEqual(reloaded_image_processor.__class__.__name__ , '''NewImageProcessor''' )
def snake_case__ ( self : List[Any] ):
try:
AutoConfig.register('''custom''' , a__ )
AutoImageProcessor.register(a__ , a__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(a__ ):
AutoImageProcessor.register(a__ , a__ )
with tempfile.TemporaryDirectory() as tmpdirname:
__magic_name__ = Path(a__ ) / '''preprocessor_config.json'''
__magic_name__ = Path(a__ ) / '''config.json'''
json.dump(
{'''feature_extractor_type''': '''CLIPFeatureExtractor''', '''processor_class''': '''CLIPProcessor'''} , open(a__ , '''w''' ) , )
json.dump({'''model_type''': '''clip'''} , open(a__ , '''w''' ) )
__magic_name__ = CustomImageProcessor.from_pretrained(a__ )
# Now that the config is registered, it can be used as any other config with the auto-API
with tempfile.TemporaryDirectory() as tmp_dir:
image_processor.save_pretrained(a__ )
__magic_name__ = AutoImageProcessor.from_pretrained(a__ )
self.assertIsInstance(a__ , a__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
def snake_case__ ( self : str ):
class _SCREAMING_SNAKE_CASE ( __a ):
__SCREAMING_SNAKE_CASE :Tuple = True
try:
AutoConfig.register('''custom''' , a__ )
AutoImageProcessor.register(a__ , a__ )
# If remote code is not set, the default is to use local
__magic_name__ = AutoImageProcessor.from_pretrained('''hf-internal-testing/test_dynamic_image_processor''' )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote code is disabled, we load the local one.
__magic_name__ = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=a__ )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(image_processor.is_local )
# If remote is enabled, we load from the Hub
__magic_name__ = AutoImageProcessor.from_pretrained(
'''hf-internal-testing/test_dynamic_image_processor''' , trust_remote_code=a__ )
self.assertEqual(image_processor.__class__.__name__ , '''NewImageProcessor''' )
self.assertTrue(not hasattr(a__ , '''is_local''' ) )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in IMAGE_PROCESSOR_MAPPING._extra_content:
del IMAGE_PROCESSOR_MAPPING._extra_content[CustomConfig]
| 245 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_MODEL_FOR_PRETRAINING_MAPPING,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertModel,
)
@require_tf
class A_ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , unittest.TestCase ):
'''simple docstring'''
_lowerCAmelCase = (
(
TFMobileBertModel,
TFMobileBertForMaskedLM,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertForMultipleChoice,
)
if is_tf_available()
else ()
)
_lowerCAmelCase = (
{
'feature-extraction': TFMobileBertModel,
'fill-mask': TFMobileBertForMaskedLM,
'question-answering': TFMobileBertForQuestionAnswering,
'text-classification': TFMobileBertForSequenceClassification,
'token-classification': TFMobileBertForTokenClassification,
'zero-shot': TFMobileBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_lowerCAmelCase = False
_lowerCAmelCase = False
def a ( self , A_ , A_ , A_=False ):
_UpperCamelCase = super()._prepare_for_class(_a , _a , return_labels=_a )
if return_labels:
if model_class in get_values(_a ):
_UpperCamelCase = tf.zeros(self.model_tester.batch_size , dtype=tf.intaa )
return inputs_dict
class A_ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self , A_ , A_=13 , A_=7 , A_=True , A_=True , A_=True , A_=True , A_=99 , A_=32 , A_=32 , A_=2 , A_=4 , A_=37 , A_="gelu" , A_=0.1 , A_=0.1 , A_=5_12 , A_=16 , A_=2 , A_=0.02 , A_=3 , A_=4 , A_=None , ):
_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 = embedding_size
def a ( self ):
_UpperCamelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_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 = MobileBertConfig(
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 , embedding_size=self.embedding_size , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
_UpperCamelCase = TFMobileBertModel(config=_a )
_UpperCamelCase = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_UpperCamelCase = model(_a )
_UpperCamelCase = [input_ids, input_mask]
_UpperCamelCase = model(_a )
_UpperCamelCase = model(_a )
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 a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
_UpperCamelCase = TFMobileBertForMaskedLM(config=_a )
_UpperCamelCase = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_UpperCamelCase = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
_UpperCamelCase = TFMobileBertForNextSentencePrediction(config=_a )
_UpperCamelCase = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_UpperCamelCase = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, 2) )
def a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
_UpperCamelCase = TFMobileBertForPreTraining(config=_a )
_UpperCamelCase = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_UpperCamelCase = model(_a )
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 a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
_UpperCamelCase = self.num_labels
_UpperCamelCase = TFMobileBertForSequenceClassification(config=_a )
_UpperCamelCase = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_UpperCamelCase = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
_UpperCamelCase = self.num_choices
_UpperCamelCase = TFMobileBertForMultipleChoice(config=_a )
_UpperCamelCase = tf.tile(tf.expand_dims(_a , 1 ) , (1, self.num_choices, 1) )
_UpperCamelCase = tf.tile(tf.expand_dims(_a , 1 ) , (1, self.num_choices, 1) )
_UpperCamelCase = tf.tile(tf.expand_dims(_a , 1 ) , (1, self.num_choices, 1) )
_UpperCamelCase = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
"token_type_ids": multiple_choice_token_type_ids,
}
_UpperCamelCase = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
_UpperCamelCase = self.num_labels
_UpperCamelCase = TFMobileBertForTokenClassification(config=_a )
_UpperCamelCase = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_UpperCamelCase = model(_a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def a ( self , A_ , A_ , A_ , A_ , A_ , A_ , A_ ):
_UpperCamelCase = TFMobileBertForQuestionAnswering(config=_a )
_UpperCamelCase = {"input_ids": input_ids, "attention_mask": input_mask, "token_type_ids": token_type_ids}
_UpperCamelCase = model(_a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def a ( self ):
_UpperCamelCase = self.prepare_config_and_inputs()
(
(
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) , (
_UpperCamelCase
) ,
) = config_and_inputs
_UpperCamelCase = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
def a ( self ):
_UpperCamelCase = TFMobileBertModelTest.TFMobileBertModelTester(self )
_UpperCamelCase = ConfigTester(self , config_class=_a , hidden_size=37 )
def a ( self ):
self.config_tester.run_common_tests()
def a ( self ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_model(*_a )
def a ( self ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_masked_lm(*_a )
def a ( self ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_multiple_choice(*_a )
def a ( self ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_next_sequence_prediction(*_a )
def a ( self ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_pretraining(*_a )
def a ( self ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_question_answering(*_a )
def a ( self ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_sequence_classification(*_a )
def a ( self ):
_UpperCamelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_mobilebert_for_token_classification(*_a )
@slow
def a ( self ):
# for model_name in TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
for model_name in ["google/mobilebert-uncased"]:
_UpperCamelCase = TFMobileBertModel.from_pretrained(_a )
self.assertIsNotNone(_a )
@require_tf
class A_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def a ( self ):
_UpperCamelCase = TFMobileBertForPreTraining.from_pretrained("google/mobilebert-uncased" )
_UpperCamelCase = tf.constant([[0, 1, 2, 3, 4, 5]] )
_UpperCamelCase = model(_a )[0]
_UpperCamelCase = [1, 6, 3_05_22]
self.assertEqual(output.shape , _a )
_UpperCamelCase = tf.constant(
[
[
[-4.591_9547, -9.24_8295, -9.64_5256],
[-6.730_6175, -6.44_0284, -6.605_2837],
[-7.274_3506, -6.784_7915, -6.02_4673],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _a , atol=1e-4 )
| 138 |
"""simple docstring"""
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : str ) -> List[Any]:
# Initialise PyTorch model
__a = RemBertConfig.from_json_file(lowerCAmelCase__ )
print('''Building PyTorch model from configuration: {}'''.format(str(lowerCAmelCase__ ) ) )
__a = RemBertModel(lowerCAmelCase__ )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# Save pytorch-model
print('''Save PyTorch model to {}'''.format(lowerCAmelCase__ ) )
torch.save(model.state_dict() , lowerCAmelCase__ )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--rembert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained RemBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
lowercase_ = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 695 | 0 |
'''simple docstring'''
def snake_case_ (UpperCamelCase : int = 50 ):
'''simple docstring'''
_a = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] += ways_number[
row_length - tile_start - tile_length
]
return ways_number[length]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 715 |
'''simple docstring'''
from manim import *
class A ( _a ):
def __lowerCAmelCase ( self : Tuple ) -> Dict:
"""simple docstring"""
_a = Rectangle(height=0.5 , width=0.5 )
_a = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
_a = [mem.copy() for i in range(6 )]
_a = [mem.copy() for i in range(6 )]
_a = VGroup(*lowerCAmelCase_ ).arrange(lowerCAmelCase_ , buff=0 )
_a = VGroup(*lowerCAmelCase_ ).arrange(lowerCAmelCase_ , buff=0 )
_a = VGroup(lowerCAmelCase_ , lowerCAmelCase_ ).arrange(lowerCAmelCase_ , buff=0 )
_a = Text('''CPU''' , font_size=24 )
_a = Group(lowerCAmelCase_ , lowerCAmelCase_ ).arrange(lowerCAmelCase_ , buff=0.5 , aligned_edge=lowerCAmelCase_ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowerCAmelCase_ )
_a = [mem.copy() for i in range(4 )]
_a = VGroup(*lowerCAmelCase_ ).arrange(lowerCAmelCase_ , buff=0 )
_a = Text('''GPU''' , font_size=24 )
_a = Group(lowerCAmelCase_ , lowerCAmelCase_ ).arrange(lowerCAmelCase_ , buff=0.5 , aligned_edge=lowerCAmelCase_ )
gpu.move_to([-1, -1, 0] )
self.add(lowerCAmelCase_ )
_a = [mem.copy() for i in range(6 )]
_a = VGroup(*lowerCAmelCase_ ).arrange(lowerCAmelCase_ , buff=0 )
_a = Text('''Model''' , font_size=24 )
_a = Group(lowerCAmelCase_ , lowerCAmelCase_ ).arrange(lowerCAmelCase_ , buff=0.5 , aligned_edge=lowerCAmelCase_ )
model.move_to([3, -1.0, 0] )
self.add(lowerCAmelCase_ )
_a = []
for i, rect in enumerate(lowerCAmelCase_ ):
rect.set_stroke(lowerCAmelCase_ )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
_a = Rectangle(height=0.4_6 / 4 , width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(lowerCAmelCase_ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.0_2 , direction=lowerCAmelCase_ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=lowerCAmelCase_ , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=lowerCAmelCase_ , buff=0.0 )
self.add(lowerCAmelCase_ )
cpu_targs.append(lowerCAmelCase_ )
_a = [mem.copy() for i in range(6 )]
_a = VGroup(*lowerCAmelCase_ ).arrange(lowerCAmelCase_ , buff=0 )
_a = Text('''Loaded Checkpoint''' , font_size=24 )
_a = Group(lowerCAmelCase_ , lowerCAmelCase_ ).arrange(lowerCAmelCase_ , aligned_edge=lowerCAmelCase_ , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
_a = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_a = MarkupText(
F'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(lowerCAmelCase_ , lowerCAmelCase_ )
_a = MarkupText(
F'<span fgcolor=\'{BLUE}\'>●</span> Checkpoint' , font_size=18 , )
blue_text.next_to(lowerCAmelCase_ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
_a = MarkupText(
F'Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(lowerCAmelCase_ ) , Write(lowerCAmelCase_ ) )
self.play(Write(lowerCAmelCase_ , run_time=1 ) , Create(lowerCAmelCase_ , run_time=1 ) )
_a = []
_a = []
for i, rect in enumerate(lowerCAmelCase_ ):
_a = fill.copy().set_fill(lowerCAmelCase_ , opacity=0.7 )
target.move_to(lowerCAmelCase_ )
first_animations.append(GrowFromCenter(lowerCAmelCase_ , run_time=1 ) )
_a = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(lowerCAmelCase_ , run_time=1.5 ) )
self.play(*lowerCAmelCase_ )
self.play(*lowerCAmelCase_ )
self.wait()
| 377 | 0 |
'''simple docstring'''
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
UpperCamelCase_ = {
"""vocab_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json""",
},
"""merges_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""allenai/led-base-16384""": """https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json""",
},
}
UpperCamelCase_ = {
"""allenai/led-base-16384""": 16384,
}
class __SCREAMING_SNAKE_CASE ( lowercase__ ):
lowerCamelCase_ = VOCAB_FILES_NAMES
lowerCamelCase_ = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase_ = LEDTokenizer
lowerCamelCase_ = ['input_ids', 'attention_mask']
def __init__( self : Optional[int] , UpperCAmelCase__ : Tuple=None , UpperCAmelCase__ : Optional[Any]=None , UpperCAmelCase__ : List[str]=None , UpperCAmelCase__ : Dict="replace" , UpperCAmelCase__ : Any="<s>" , UpperCAmelCase__ : Any="</s>" , UpperCAmelCase__ : Union[str, Any]="</s>" , UpperCAmelCase__ : Dict="<s>" , UpperCAmelCase__ : List[str]="<unk>" , UpperCAmelCase__ : List[Any]="<pad>" , UpperCAmelCase__ : Tuple="<mask>" , UpperCAmelCase__ : int=False , UpperCAmelCase__ : int=True , **UpperCAmelCase__ : Optional[Any] , ):
'''simple docstring'''
super().__init__(
UpperCAmelCase__ , UpperCAmelCase__ , tokenizer_file=UpperCAmelCase__ , errors=UpperCAmelCase__ , bos_token=UpperCAmelCase__ , eos_token=UpperCAmelCase__ , sep_token=UpperCAmelCase__ , cls_token=UpperCAmelCase__ , unk_token=UpperCAmelCase__ , pad_token=UpperCAmelCase__ , mask_token=UpperCAmelCase__ , add_prefix_space=UpperCAmelCase__ , trim_offsets=UpperCAmelCase__ , **UpperCAmelCase__ , )
lowercase : Optional[int] =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('''add_prefix_space''' , UpperCAmelCase__ ) != add_prefix_space:
lowercase : Any =getattr(UpperCAmelCase__ , pre_tok_state.pop('''type''' ) )
lowercase : Optional[Any] =add_prefix_space
lowercase : Union[str, Any] =pre_tok_class(**UpperCAmelCase__ )
lowercase : str =add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
lowercase : Tuple ='''post_processor'''
lowercase : Union[str, Any] =getattr(self.backend_tokenizer , UpperCAmelCase__ , UpperCAmelCase__ )
if tokenizer_component_instance:
lowercase : List[str] =json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
lowercase : int =tuple(state['''sep'''] )
if "cls" in state:
lowercase : Tuple =tuple(state['''cls'''] )
lowercase : Union[str, Any] =False
if state.get('''add_prefix_space''' , UpperCAmelCase__ ) != add_prefix_space:
lowercase : List[Any] =add_prefix_space
lowercase : List[str] =True
if state.get('''trim_offsets''' , UpperCAmelCase__ ) != trim_offsets:
lowercase : Tuple =trim_offsets
lowercase : Optional[int] =True
if changes_to_apply:
lowercase : Any =getattr(UpperCAmelCase__ , state.pop('''type''' ) )
lowercase : str =component_class(**UpperCAmelCase__ )
setattr(self.backend_tokenizer , UpperCAmelCase__ , UpperCAmelCase__ )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def lowerCamelCase_ ( self : List[str] ):
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error('''Using mask_token, but it is not set yet.''' )
return None
return str(self._mask_token )
@mask_token.setter
def lowerCamelCase_ ( self : List[str] , UpperCAmelCase__ : int ):
'''simple docstring'''
lowercase : List[Any] =AddedToken(UpperCAmelCase__ , lstrip=UpperCAmelCase__ , rstrip=UpperCAmelCase__ ) if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) else value
lowercase : Tuple =value
def lowerCamelCase_ ( self : int , *UpperCAmelCase__ : List[str] , **UpperCAmelCase__ : List[str] ):
'''simple docstring'''
lowercase : Optional[Any] =kwargs.get('''is_split_into_words''' , UpperCAmelCase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
'''to use it with pretokenized inputs.''' )
return super()._batch_encode_plus(*UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCamelCase_ ( self : Optional[int] , *UpperCAmelCase__ : Any , **UpperCAmelCase__ : List[str] ):
'''simple docstring'''
lowercase : Tuple =kwargs.get('''is_split_into_words''' , UpperCAmelCase__ )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
'''to use it with pretokenized inputs.''' )
return super()._encode_plus(*UpperCAmelCase__ , **UpperCAmelCase__ )
def lowerCamelCase_ ( self : Tuple , UpperCAmelCase__ : str , UpperCAmelCase__ : Optional[str] = None ):
'''simple docstring'''
lowercase : List[Any] =self._tokenizer.model.save(UpperCAmelCase__ , name=UpperCAmelCase__ )
return tuple(UpperCAmelCase__ )
def lowerCamelCase_ ( self : int , UpperCAmelCase__ : List[Any] , UpperCAmelCase__ : Tuple=None ):
'''simple docstring'''
lowercase : str =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowerCamelCase_ ( self : List[Any] , UpperCAmelCase__ : List[int] , UpperCAmelCase__ : Optional[List[int]] = None ):
'''simple docstring'''
lowercase : List[str] =[self.sep_token_id]
lowercase : List[str] =[self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowerCamelCase_ ( self : Optional[Any] , UpperCAmelCase__ : Union[Dict[str, EncodedInput], BatchEncoding] , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , UpperCAmelCase__ : Optional[int] = None , UpperCAmelCase__ : Optional[bool] = None , ):
'''simple docstring'''
lowercase : Optional[int] =super()._pad(
encoded_inputs=UpperCAmelCase__ , max_length=UpperCAmelCase__ , padding_strategy=UpperCAmelCase__ , pad_to_multiple_of=UpperCAmelCase__ , return_attention_mask=UpperCAmelCase__ , )
# Load from model defaults
if return_attention_mask is None:
lowercase : int ='''attention_mask''' in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
lowercase : int =encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
lowercase : Optional[int] =len(encoded_inputs['''global_attention_mask'''] ) != len(UpperCAmelCase__ )
if needs_to_be_padded:
lowercase : List[Any] =len(UpperCAmelCase__ ) - len(encoded_inputs['''global_attention_mask'''] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
lowercase : Dict =(
encoded_inputs['''global_attention_mask'''] + [-1] * difference
)
elif self.padding_side == "left":
lowercase : Union[str, Any] =[-1] * difference + encoded_inputs[
'''global_attention_mask'''
]
else:
raise ValueError('''Invalid padding strategy:''' + str(self.padding_side ) )
return encoded_inputs
| 92 | '''simple docstring'''
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_detectrona,
require_pytesseract,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
from transformers.image_utils import load_image
else:
class __UpperCAmelCase :
@staticmethod
def UpperCAmelCase_ ( *_lowerCamelCase , **_lowerCamelCase ):
pass
def snake_case_ ( __snake_case : int) -> Union[str, Any]:
return None
# This is a pinned image from a specific revision of a document question answering space, hosted by HuggingFace,
# so we can expect it to be available.
A_ : Any =(
'''https://huggingface.co/spaces/impira/docquery/resolve/2f6c96314dc84dfda62d40de9da55f2f5165d403/invoice.png'''
)
@is_pipeline_test
@require_torch
@require_vision
class __UpperCAmelCase ( unittest.TestCase ):
__A : Union[str, Any] = MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING
@require_pytesseract
@require_vision
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
lowerCAmelCase_ = pipeline(
'''document-question-answering''' , model=_lowerCamelCase , tokenizer=_lowerCamelCase , image_processor=_lowerCamelCase )
lowerCAmelCase_ = INVOICE_URL
lowerCAmelCase_ = list(zip(*apply_tesseract(load_image(_lowerCamelCase ) , _lowerCamelCase , '''''' ) ) )
lowerCAmelCase_ = '''What is the placebo?'''
lowerCAmelCase_ = [
{
'''image''': load_image(_lowerCamelCase ),
'''question''': question,
},
{
'''image''': image,
'''question''': question,
},
{
'''image''': image,
'''question''': question,
'''word_boxes''': word_boxes,
},
]
return dqa_pipeline, examples
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ):
lowerCAmelCase_ = dqa_pipeline(_lowerCamelCase , top_k=2 )
self.assertEqual(
_lowerCamelCase , [
[
{'''score''': ANY(_lowerCamelCase ), '''answer''': ANY(_lowerCamelCase ), '''start''': ANY(_lowerCamelCase ), '''end''': ANY(_lowerCamelCase )},
{'''score''': ANY(_lowerCamelCase ), '''answer''': ANY(_lowerCamelCase ), '''start''': ANY(_lowerCamelCase ), '''end''': ANY(_lowerCamelCase )},
]
]
* 3 , )
@require_torch
@require_detectrona
@require_pytesseract
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = pipeline('''document-question-answering''' , model='''hf-internal-testing/tiny-random-layoutlmv2''' )
lowerCAmelCase_ = INVOICE_URL
lowerCAmelCase_ = '''How many cats are there?'''
lowerCAmelCase_ = [
{'''score''': 0.00_01, '''answer''': '''oy 2312/2019''', '''start''': 38, '''end''': 39},
{'''score''': 0.00_01, '''answer''': '''oy 2312/2019 DUE''', '''start''': 38, '''end''': 40},
]
lowerCAmelCase_ = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(_lowerCamelCase , decimals=4 ) , _lowerCamelCase )
lowerCAmelCase_ = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(nested_simplify(_lowerCamelCase , decimals=4 ) , _lowerCamelCase )
# This image does not detect ANY text in it, meaning layoutlmv2 should fail.
# Empty answer probably
lowerCAmelCase_ = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
lowerCAmelCase_ = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(_lowerCamelCase , [] )
# We can optionnally pass directly the words and bounding boxes
lowerCAmelCase_ = '''./tests/fixtures/tests_samples/COCO/000000039769.png'''
lowerCAmelCase_ = []
lowerCAmelCase_ = []
lowerCAmelCase_ = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , words=_lowerCamelCase , boxes=_lowerCamelCase , top_k=2 )
self.assertEqual(_lowerCamelCase , [] )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , )
lowerCAmelCase_ = INVOICE_URL
lowerCAmelCase_ = '''What is the invoice number?'''
lowerCAmelCase_ = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
lowerCAmelCase_ = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
lowerCAmelCase_ = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
[
{'''score''': 0.99_44, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.00_09, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
],
]
* 2 , )
@slow
@require_torch
@require_detectrona
@require_pytesseract
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = pipeline(
'''document-question-answering''' , model='''tiennvcs/layoutlmv2-base-uncased-finetuned-docvqa''' , revision='''9977165''' , max_seq_len=50 , )
lowerCAmelCase_ = INVOICE_URL
lowerCAmelCase_ = '''What is the invoice number?'''
lowerCAmelCase_ = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
lowerCAmelCase_ = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
lowerCAmelCase_ = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
[
{'''score''': 0.99_74, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
{'''score''': 0.99_48, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
@slow
@require_torch
@require_pytesseract
@require_vision
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=_lowerCamelCase )
lowerCAmelCase_ = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=_lowerCamelCase , revision='''3dc6de3''' , )
lowerCAmelCase_ = INVOICE_URL
lowerCAmelCase_ = '''What is the invoice number?'''
lowerCAmelCase_ = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
lowerCAmelCase_ = dqa_pipeline({'''image''': image, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
lowerCAmelCase_ = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
[
{'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
]
]
* 2 , )
lowerCAmelCase_ = list(zip(*apply_tesseract(load_image(_lowerCamelCase ) , _lowerCamelCase , '''''' ) ) )
# This model should also work if `image` is set to None
lowerCAmelCase_ = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'''score''': 0.42_51, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.08_19, '''answer''': '''1110212019''', '''start''': 23, '''end''': 23},
] , )
@slow
@require_torch
@require_pytesseract
@require_vision
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = AutoTokenizer.from_pretrained(
'''impira/layoutlm-document-qa''' , revision='''3dc6de3''' , add_prefix_space=_lowerCamelCase )
lowerCAmelCase_ = pipeline(
'''document-question-answering''' , model='''impira/layoutlm-document-qa''' , tokenizer=_lowerCamelCase , revision='''3dc6de3''' , max_seq_len=50 , )
lowerCAmelCase_ = INVOICE_URL
lowerCAmelCase_ = '''What is the invoice number?'''
lowerCAmelCase_ = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
lowerCAmelCase_ = dqa_pipeline(
[{'''image''': image, '''question''': question}, {'''image''': image, '''question''': question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
[
{'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
]
]
* 2 , )
lowerCAmelCase_ = list(zip(*apply_tesseract(load_image(_lowerCamelCase ) , _lowerCamelCase , '''''' ) ) )
# This model should also work if `image` is set to None
lowerCAmelCase_ = dqa_pipeline({'''image''': None, '''word_boxes''': word_boxes, '''question''': question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCamelCase , decimals=4 ) , [
{'''score''': 0.99_99, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
{'''score''': 0.99_98, '''answer''': '''us-001''', '''start''': 16, '''end''': 16},
] , )
@slow
@require_torch
def UpperCAmelCase_ ( self ):
lowerCAmelCase_ = pipeline(
'''document-question-answering''' , model='''naver-clova-ix/donut-base-finetuned-docvqa''' , tokenizer=AutoTokenizer.from_pretrained('''naver-clova-ix/donut-base-finetuned-docvqa''' ) , feature_extractor='''naver-clova-ix/donut-base-finetuned-docvqa''' , )
lowerCAmelCase_ = INVOICE_URL
lowerCAmelCase_ = '''What is the invoice number?'''
lowerCAmelCase_ = dqa_pipeline(image=_lowerCamelCase , question=_lowerCamelCase , top_k=2 )
self.assertEqual(nested_simplify(_lowerCamelCase , decimals=4 ) , [{'''answer''': '''us-001'''}] )
@require_tf
@unittest.skip('''Document question answering not implemented in TF''' )
def UpperCAmelCase_ ( self ):
pass
| 274 | 0 |
"""simple docstring"""
import os
import sys
UpperCamelCase_ : Tuple = os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
UpperCamelCase_ : Dict = [
'''torch''',
'''numpy''',
'''tokenizers''',
'''filelock''',
'''requests''',
'''tqdm''',
'''regex''',
'''sentencepiece''',
'''sacremoses''',
'''importlib_metadata''',
'''huggingface_hub''',
]
@add_start_docstrings(AutoConfig.__doc__ )
def A_ (*__a , **__a ):
'''simple docstring'''
return AutoConfig.from_pretrained(*__a , **__a )
@add_start_docstrings(AutoTokenizer.__doc__ )
def A_ (*__a , **__a ):
'''simple docstring'''
return AutoTokenizer.from_pretrained(*__a , **__a )
@add_start_docstrings(AutoModel.__doc__ )
def A_ (*__a , **__a ):
'''simple docstring'''
return AutoModel.from_pretrained(*__a , **__a )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def A_ (*__a , **__a ):
'''simple docstring'''
return AutoModelForCausalLM.from_pretrained(*__a , **__a )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def A_ (*__a , **__a ):
'''simple docstring'''
return AutoModelForMaskedLM.from_pretrained(*__a , **__a )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def A_ (*__a , **__a ):
'''simple docstring'''
return AutoModelForSequenceClassification.from_pretrained(*__a , **__a )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def A_ (*__a , **__a ):
'''simple docstring'''
return AutoModelForQuestionAnswering.from_pretrained(*__a , **__a )
| 717 |
"""simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCAmelCase ( _lowercase , unittest.TestCase ):
"""simple docstring"""
snake_case = TransfoXLTokenizer
snake_case = False
snake_case = False
def lowerCamelCase__ ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
super().setUp()
A_ = [
"<unk>",
"[CLS]",
"[SEP]",
"want",
"unwanted",
"wa",
"un",
"running",
",",
"low",
"l",
]
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] ) )
def lowerCamelCase__ ( self : str , **_snake_case : Any ) -> Optional[Any]:
"""simple docstring"""
A_ = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **_snake_case )
def lowerCamelCase__ ( self : int , _snake_case : Optional[Any] ) -> Any:
"""simple docstring"""
A_ = "<unk> UNwanted , running"
A_ = "<unk> unwanted, running"
return input_text, output_text
def lowerCamelCase__ ( self : Dict ) -> int:
"""simple docstring"""
A_ = TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=_snake_case )
A_ = tokenizer.tokenize("<unk> UNwanted , running" )
self.assertListEqual(_snake_case , ["<unk>", "unwanted", ",", "running"] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_snake_case ) , [0, 4, 8, 7] )
def lowerCamelCase__ ( self : List[str] ) -> int:
"""simple docstring"""
A_ = TransfoXLTokenizer(lower_case=_snake_case )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo ! how \n Are yoU ? " ) , ["hello", "!", "how", "are", "you", "?"] )
def lowerCamelCase__ ( self : Union[str, Any] ) -> str:
"""simple docstring"""
A_ = TransfoXLTokenizer(lower_case=_snake_case )
self.assertListEqual(
tokenizer.tokenize(" \tHeLLo ! how \n Are yoU ? " ) , ["HeLLo", "!", "how", "Are", "yoU", "?"] )
def lowerCamelCase__ ( self : Dict ) -> Tuple:
"""simple docstring"""
A_ = TransfoXLTokenizer(lower_case=_snake_case )
A_ = "Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"
A_ = [
"Hello",
"(",
"bracket",
")",
"and",
"side",
"@-@",
"scrolled",
"[",
"and",
"]",
"Henry",
"'s",
"$",
"5",
"@,@",
"000",
"with",
"3",
"@.@",
"34",
"m",
".",
"What",
"'s",
"up",
"!",
"?",
]
self.assertListEqual(tokenizer.tokenize(_snake_case ) , _snake_case )
self.assertEqual(tokenizer.convert_tokens_to_string(_snake_case ) , _snake_case )
def lowerCamelCase__ ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
A_ = self.get_tokenizer()
A_ = len(_snake_case )
tokenizer.add_tokens(["new1", "new2"] )
tokenizer.move_added_token("new1" , 1 )
# Check that moved token is not copied (duplicate)
self.assertEqual(len(_snake_case ) , original_len + 2 )
# Check that token is moved to specified id
self.assertEqual(tokenizer.encode("new1" ) , [1] )
self.assertEqual(tokenizer.decode([1] ) , "new1" )
| 482 | 0 |
def UpperCamelCase__( UpperCamelCase__ : int , UpperCamelCase__ : float , UpperCamelCase__ : float )->float:
return round(float(moles / volume ) * nfactor )
def UpperCamelCase__( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float )->float:
return round(float((moles * 0.0821 * temperature) / (volume) ) )
def UpperCamelCase__( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float )->float:
return round(float((moles * 0.0821 * temperature) / (pressure) ) )
def UpperCamelCase__( UpperCamelCase__ : float , UpperCamelCase__ : float , UpperCamelCase__ : float )->float:
return round(float((pressure * volume) / (0.0821 * moles) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 190 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
a__: str = {
'configuration_mega': ['MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MegaConfig', 'MegaOnnxConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__: Union[str, Any] = [
'MEGA_PRETRAINED_MODEL_ARCHIVE_LIST',
'MegaForCausalLM',
'MegaForMaskedLM',
'MegaForMultipleChoice',
'MegaForQuestionAnswering',
'MegaForSequenceClassification',
'MegaForTokenClassification',
'MegaModel',
'MegaPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mega import MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP, MegaConfig, MegaOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mega import (
MEGA_PRETRAINED_MODEL_ARCHIVE_LIST,
MegaForCausalLM,
MegaForMaskedLM,
MegaForMultipleChoice,
MegaForQuestionAnswering,
MegaForSequenceClassification,
MegaForTokenClassification,
MegaModel,
MegaPreTrainedModel,
)
else:
import sys
a__: Union[str, Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 190 | 1 |
"""simple docstring"""
import os
import jsonlines
import numpy as np
from tqdm import tqdm
SCREAMING_SNAKE_CASE = 2048
SCREAMING_SNAKE_CASE = 4096
SCREAMING_SNAKE_CASE = 42
SCREAMING_SNAKE_CASE = os.environ.pop('PROCESS_TRAIN', 'false')
SCREAMING_SNAKE_CASE = {'null': 0, 'short': 1, 'long': 2, 'yes': 3, 'no': 4}
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
def choose_first(__UpperCAmelCase ,__UpperCAmelCase=False ):
assert isinstance(__UpperCAmelCase ,__UpperCAmelCase )
if len(__UpperCAmelCase ) == 1:
_lowercase : int = answer[0]
return {k: [answer[k]] for k in answer} if is_long_answer else answer
for a in answer:
if is_long_answer:
_lowercase : str = {k: [a[k]] for k in a}
if len(a['start_token'] ) > 0:
break
return a
_lowercase : Optional[int] = {'id': example['id']}
_lowercase : str = example['annotations']
_lowercase : List[str] = annotation['yes_no_answer']
if 0 in yes_no_answer or 1 in yes_no_answer:
_lowercase : Dict = ['yes'] if 1 in yes_no_answer else ['no']
_lowercase : Tuple = []
_lowercase : int = []
_lowercase : Union[str, Any] = ['<cls>']
else:
_lowercase : Dict = ['short']
_lowercase : int = choose_first(annotation['short_answers'] )
if len(out['start_token'] ) == 0:
# answer will be long if short is not available
_lowercase : Union[str, Any] = ['long']
_lowercase : Union[str, Any] = choose_first(annotation['long_answer'] ,is_long_answer=__UpperCAmelCase )
_lowercase : Tuple = []
answer.update(__UpperCAmelCase )
# disregard some samples
if len(answer['start_token'] ) > 1 or answer["start_token"] == answer["end_token"]:
_lowercase : List[str] = True
else:
_lowercase : str = False
_lowercase : Dict = ['start_token', 'end_token', 'start_byte', 'end_byte', 'text']
if not all(isinstance(answer[k] ,__UpperCAmelCase ) for k in cols ):
raise ValueError('Issue in ID' ,example['id'] )
return answer
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase=False ):
"""simple docstring"""
_lowercase : List[str] = _get_single_answer(__UpperCAmelCase )
# bytes are of no use
del answer["start_byte"]
del answer["end_byte"]
# handle yes_no answers explicitly
if answer["category"][0] in ["yes", "no"]: # category is list with one element
_lowercase : int = example['document']['tokens']
_lowercase : str = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
return {
"context": " ".join(__UpperCAmelCase ),
"answer": {
"start_token": -100, # ignore index in cross-entropy
"end_token": -100, # ignore index in cross-entropy
"category": answer["category"],
"span": answer["category"], # extra
},
}
# later, help in removing all no answers
if answer["start_token"] == [-1]:
return {
"context": "None",
"answer": {
"start_token": -1,
"end_token": -1,
"category": "null",
"span": "None", # extra
},
}
# handling normal samples
_lowercase : str = ['start_token', 'end_token']
answer.update({k: answer[k][0] if len(answer[k] ) > 0 else answer[k] for k in cols} ) # e.g. [10] == 10
_lowercase : Union[str, Any] = example['document']['tokens']
_lowercase : Union[str, Any] = answer['start_token']
_lowercase : Dict = answer['end_token']
_lowercase : List[str] = []
for i in range(len(doc['token'] ) ):
if not doc["is_html"][i]:
context.append(doc['token'][i] )
else:
if answer["start_token"] > i:
start_token -= 1
if answer["end_token"] > i:
end_token -= 1
_lowercase : List[str] = ' '.join(context[start_token:end_token] )
# checking above code
if assertion:
_lowercase : int = doc['is_html'][answer['start_token'] : answer['end_token']]
_lowercase : int = doc['token'][answer['start_token'] : answer['end_token']]
_lowercase : Tuple = ' '.join([old[i] for i in range(len(__UpperCAmelCase ) ) if not is_html[i]] )
if new != old:
print('ID:' ,example['id'] )
print('New:' ,__UpperCAmelCase ,end='\n' )
print('Old:' ,__UpperCAmelCase ,end='\n\n' )
return {
"context": " ".join(__UpperCAmelCase ),
"answer": {
"start_token": start_token,
"end_token": end_token - 1, # this makes it inclusive
"category": answer["category"], # either long or short
"span": new, # extra
},
}
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase=2_048 ,__UpperCAmelCase=4_096 ,__UpperCAmelCase=True ):
"""simple docstring"""
_lowercase : str = get_context_and_ans(__UpperCAmelCase ,assertion=__UpperCAmelCase )
_lowercase : Union[str, Any] = out['answer']
# later, removing these samples
if answer["start_token"] == -1:
return {
"example_id": example["id"],
"input_ids": [[-1]],
"labels": {
"start_token": [-1],
"end_token": [-1],
"category": ["null"],
},
}
_lowercase : Union[str, Any] = tokenizer(example['question']['text'] ,out['context'] ).input_ids
_lowercase : Union[str, Any] = input_ids.index(tokenizer.sep_token_id ) + 1
# return yes/no
if answer["category"][0] in ["yes", "no"]: # category is list with one element
_lowercase : Optional[int] = []
_lowercase : List[str] = []
_lowercase : str = input_ids[:q_len]
_lowercase : Union[str, Any] = range(__UpperCAmelCase ,len(__UpperCAmelCase ) ,max_length - doc_stride )
for i in doc_start_indices:
_lowercase : Tuple = i + max_length - q_len
_lowercase : Union[str, Any] = input_ids[i:end_index]
inputs.append(q_indices + slice )
category.append(answer['category'][0] )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": [-100] * len(__UpperCAmelCase ),
"end_token": [-100] * len(__UpperCAmelCase ),
"category": category,
},
}
_lowercase : Optional[Any] = out['context'].split()
_lowercase : Dict = splitted_context[answer['end_token']]
_lowercase : Optional[Any] = len(
tokenizer(
' '.join(splitted_context[: answer['start_token']] ) ,add_special_tokens=__UpperCAmelCase ,).input_ids )
_lowercase : Any = len(
tokenizer(' '.join(splitted_context[: answer['end_token']] ) ,add_special_tokens=__UpperCAmelCase ).input_ids )
answer["start_token"] += q_len
answer["end_token"] += q_len
# fixing end token
_lowercase : Tuple = len(tokenizer(__UpperCAmelCase ,add_special_tokens=__UpperCAmelCase ).input_ids )
if num_sub_tokens > 1:
answer["end_token"] += num_sub_tokens - 1
_lowercase : Optional[Any] = input_ids[answer['start_token'] : answer['end_token'] + 1] # right & left are inclusive
_lowercase : Any = answer['start_token']
_lowercase : Optional[int] = answer['end_token']
if assertion:
_lowercase : Optional[int] = tokenizer.decode(__UpperCAmelCase )
if answer["span"] != new:
print('ISSUE IN TOKENIZATION' )
print('OLD:' ,answer['span'] )
print('NEW:' ,__UpperCAmelCase ,end='\n\n' )
if len(__UpperCAmelCase ) <= max_length:
return {
"example_id": example["id"],
"input_ids": [input_ids],
"labels": {
"start_token": [answer["start_token"]],
"end_token": [answer["end_token"]],
"category": answer["category"],
},
}
_lowercase : Optional[Any] = input_ids[:q_len]
_lowercase : int = range(__UpperCAmelCase ,len(__UpperCAmelCase ) ,max_length - doc_stride )
_lowercase : Tuple = []
_lowercase : Optional[Any] = []
_lowercase : str = []
_lowercase : List[str] = [] # null, yes, no, long, short
for i in doc_start_indices:
_lowercase : Any = i + max_length - q_len
_lowercase : List[str] = input_ids[i:end_index]
inputs.append(q_indices + slice )
assert len(inputs[-1] ) <= max_length, "Issue in truncating length"
if start_token >= i and end_token <= end_index - 1:
_lowercase : str = start_token - i + q_len
_lowercase : Tuple = end_token - i + q_len
answers_category.append(answer['category'][0] ) # ["short"] -> "short"
else:
_lowercase : Dict = -100
_lowercase : Union[str, Any] = -100
answers_category.append('null' )
_lowercase : str = inputs[-1][start_token : end_token + 1]
answers_start_token.append(__UpperCAmelCase )
answers_end_token.append(__UpperCAmelCase )
if assertion:
if new != old and new != [tokenizer.cls_token_id]:
print('ISSUE in strided for ID:' ,example['id'] )
print('New:' ,tokenizer.decode(__UpperCAmelCase ) )
print('Old:' ,tokenizer.decode(__UpperCAmelCase ) ,end='\n\n' )
if slice[-1] == tokenizer.sep_token_id:
break
return {
"example_id": example["id"],
"input_ids": inputs,
"labels": {
"start_token": answers_start_token,
"end_token": answers_end_token,
"category": answers_category,
},
}
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ,__UpperCAmelCase=2_048 ,__UpperCAmelCase=4_096 ,__UpperCAmelCase=False ):
"""simple docstring"""
_lowercase : Union[str, Any] = get_strided_contexts_and_ans(
__UpperCAmelCase ,__UpperCAmelCase ,doc_stride=__UpperCAmelCase ,max_length=__UpperCAmelCase ,assertion=__UpperCAmelCase ,)
return example
def __lowerCAmelCase( __UpperCAmelCase ,__UpperCAmelCase ):
"""simple docstring"""
with jsonlines.open(__UpperCAmelCase ,'a' ) as writer:
for example in tqdm(__UpperCAmelCase ,total=len(__UpperCAmelCase ) ,desc='Saving samples ... ' ):
_lowercase : List[Any] = example['labels']
for ids, start, end, cat in zip(
example['input_ids'] ,labels['start_token'] ,labels['end_token'] ,labels['category'] ,):
if start == -1 and end == -1:
continue # leave waste samples with no answer
if cat == "null" and np.random.rand() < 0.6:
continue # removing 50 % samples
writer.write(
{
'input_ids': ids,
'start_token': start,
'end_token': end,
'category': CATEGORY_MAPPING[cat],
} )
if __name__ == "__main__":
from datasets import load_dataset
from transformers import BigBirdTokenizer
SCREAMING_SNAKE_CASE = load_dataset('natural_questions')
SCREAMING_SNAKE_CASE = BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')
SCREAMING_SNAKE_CASE = data['train' if PROCESS_TRAIN == 'true' else 'validation']
SCREAMING_SNAKE_CASE = {
'tokenizer': tokenizer,
'doc_stride': DOC_STRIDE,
'max_length': MAX_LENGTH,
'assertion': False,
}
SCREAMING_SNAKE_CASE = data.map(prepare_inputs, fn_kwargs=fn_kwargs)
SCREAMING_SNAKE_CASE = data.remove_columns(['annotations', 'document', 'id', 'question'])
print(data)
np.random.seed(SEED)
SCREAMING_SNAKE_CASE = 'nq-training.jsonl' if PROCESS_TRAIN == 'true' else 'nq-validation.jsonl'
save_to_disk(data, file_name=cache_file_name)
| 283 | """simple docstring"""
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
SCREAMING_SNAKE_CASE = pytest.mark.integration
SCREAMING_SNAKE_CASE = {'comet'}
SCREAMING_SNAKE_CASE = importlib.util.find_spec('fairseq') is not None
SCREAMING_SNAKE_CASE = {'code_eval'}
SCREAMING_SNAKE_CASE = os.name == 'nt'
SCREAMING_SNAKE_CASE = {'bertscore', 'frugalscore', 'perplexity'}
SCREAMING_SNAKE_CASE = importlib.util.find_spec('transformers') is not None
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
@wraps(__UpperCAmelCase )
def wrapper(self ,__UpperCAmelCase ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest('"test requires Fairseq"' )
else:
test_case(self ,__UpperCAmelCase )
return wrapper
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
@wraps(__UpperCAmelCase )
def wrapper(self ,__UpperCAmelCase ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest('"test requires transformers"' )
else:
test_case(self ,__UpperCAmelCase )
return wrapper
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
@wraps(__UpperCAmelCase )
def wrapper(self ,__UpperCAmelCase ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest('"test not supported on Windows"' )
else:
test_case(self ,__UpperCAmelCase )
return wrapper
def __lowerCAmelCase( ):
"""simple docstring"""
_lowercase : int = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob('./metrics/*/' )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
@local
class _lowerCamelCase (parameterized.TestCase ):
_snake_case = {}
_snake_case = None
@pytest.mark.filterwarnings('ignore:metric_module_factory is deprecated:FutureWarning' )
@pytest.mark.filterwarnings('ignore:load_metric is deprecated:FutureWarning' )
def __UpperCAmelCase ( self : str , lowerCamelCase_ : List[str] ):
"""simple docstring"""
_lowercase : Optional[Any] = '[...]'
_lowercase : str = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('metrics' , lowerCamelCase_ ) ).module_path )
_lowercase : Dict = datasets.load.import_main_class(metric_module.__name__ , dataset=lowerCamelCase_ )
# check parameters
_lowercase : Optional[int] = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(lowerCamelCase_ , metric_module.__name__ ):
with self.use_local_metrics():
try:
_lowercase : Optional[Any] = doctest.testmod(lowerCamelCase_ , verbose=lowerCamelCase_ , raise_on_error=lowerCamelCase_ )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@slow
def __UpperCAmelCase ( self : Any , lowerCamelCase_ : Dict ):
"""simple docstring"""
_lowercase : Optional[Any] = '[...]'
_lowercase : Dict = importlib.import_module(
datasets.load.metric_module_factory(os.path.join('metrics' , lowerCamelCase_ ) ).module_path )
# run doctest
with self.use_local_metrics():
_lowercase : str = doctest.testmod(lowerCamelCase_ , verbose=lowerCamelCase_ , raise_on_error=lowerCamelCase_ )
self.assertEqual(results.failed , 0 )
self.assertGreater(results.attempted , 1 )
@contextmanager
def __UpperCAmelCase ( self : Tuple , lowerCamelCase_ : int , lowerCamelCase_ : str ):
"""simple docstring"""
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](lowerCamelCase_ ):
yield
else:
yield
@contextmanager
def __UpperCAmelCase ( self : Dict ):
"""simple docstring"""
def load_local_metric(lowerCamelCase_ : Optional[Any] , *lowerCamelCase_ : Any , **lowerCamelCase_ : Optional[int] ):
return load_metric(os.path.join('metrics' , lowerCamelCase_ ) , *lowerCamelCase_ , **lowerCamelCase_ )
with patch('datasets.load_metric' ) as mock_load_metric:
_lowercase : str = load_local_metric
yield
@classmethod
def __UpperCAmelCase ( cls : Tuple , lowerCamelCase_ : Tuple ):
"""simple docstring"""
def wrapper(lowerCamelCase_ : int ):
_lowercase : Any = contextmanager(lowerCamelCase_ )
_lowercase : Any = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher('bleurt' )
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string('sv' ,'' ,'' ) # handle pytest cli flags
class _lowerCamelCase (__lowerCamelCase ):
def __UpperCAmelCase ( self : Optional[Any] , lowerCamelCase_ : str ):
"""simple docstring"""
assert len(input_dict['input_ids'] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch('bleurt.score._create_predictor' ) as mock_create_predictor:
_lowercase : Dict = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher('bertscore' )
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
import torch
def bert_cos_score_idf(__UpperCAmelCase ,__UpperCAmelCase ,*__UpperCAmelCase ,**__UpperCAmelCase ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(__UpperCAmelCase ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch('bert_score.scorer.get_model' ), patch(
'bert_score.scorer.bert_cos_score_idf' ) as mock_bert_cos_score_idf:
_lowercase : Tuple = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher('comet' )
def __lowerCAmelCase( __UpperCAmelCase ):
"""simple docstring"""
def load_from_checkpoint(__UpperCAmelCase ):
class _lowerCamelCase :
def __UpperCAmelCase ( self : Tuple , lowerCamelCase_ : str , *lowerCamelCase_ : List[Any] , **lowerCamelCase_ : List[str] ):
"""simple docstring"""
assert len(lowerCamelCase_ ) == 2
_lowercase : Union[str, Any] = [0.19, 0.92]
return scores, sum(lowerCamelCase_ ) / len(lowerCamelCase_ )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch('comet.download_model' ) as mock_download_model:
_lowercase : Dict = None
with patch('comet.load_from_checkpoint' ) as mock_load_from_checkpoint:
_lowercase : str = load_from_checkpoint
yield
def __lowerCAmelCase( ):
"""simple docstring"""
_lowercase : Tuple = load_metric(os.path.join('metrics' ,'seqeval' ) )
_lowercase : int = 'ERROR'
_lowercase : Union[str, Any] = F'''Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'''
with pytest.raises(__UpperCAmelCase ,match=re.escape(__UpperCAmelCase ) ):
metric.compute(predictions=[] ,references=[] ,scheme=__UpperCAmelCase )
| 283 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import ShapEPipeline
else:
from .camera import create_pan_cameras
from .pipeline_shap_e import ShapEPipeline
from .pipeline_shap_e_img2img import ShapEImgaImgPipeline
from .renderer import (
BoundingBoxVolume,
ImportanceRaySampler,
MLPNeRFModelOutput,
MLPNeRSTFModel,
ShapEParamsProjModel,
ShapERenderer,
StratifiedRaySampler,
VoidNeRFModel,
)
| 68 |
'''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 __magic_name__ ( lowerCAmelCase ):
"""simple docstring"""
def __init__( self , *lowerCamelCase , lowerCamelCase=None , lowerCamelCase=None , **lowerCamelCase ):
'''simple docstring'''
super().__init__(*lowerCamelCase , **lowerCamelCase )
__A : Tuple = eval_examples
__A : List[str] = post_process_function
def lowerCAmelCase__ ( self , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase=None , lowerCamelCase = "eval" ):
'''simple docstring'''
__A : Any = self.eval_dataset if eval_dataset is None else eval_dataset
__A : Dict = self.get_eval_dataloader(lowerCamelCase )
__A : str = self.eval_examples if eval_examples is None else eval_examples
# Temporarily disable metric computation, we will do it in the loop here.
__A : Any = self.compute_metrics
__A : Optional[int] = None
__A : Tuple = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
__A : Dict = time.time()
try:
__A : List[str] = eval_loop(
lowerCamelCase , description="Evaluation" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowerCamelCase , metric_key_prefix=lowerCamelCase , )
finally:
__A : List[str] = compute_metrics
__A : List[Any] = 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
__A : List[Any] = self.post_process_function(lowerCamelCase , lowerCamelCase , output.predictions )
__A : List[str] = 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}_" ):
__A : Union[str, Any] = metrics.pop(lowerCamelCase )
metrics.update(output.metrics )
else:
__A : Any = 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() )
__A : str = self.callback_handler.on_evaluate(self.args , self.state , self.control , lowerCamelCase )
return metrics
def lowerCAmelCase__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase=None , lowerCamelCase = "test" ):
'''simple docstring'''
__A : Tuple = self.get_test_dataloader(lowerCamelCase )
# Temporarily disable metric computation, we will do it in the loop here.
__A : Union[str, Any] = self.compute_metrics
__A : Union[str, Any] = None
__A : Optional[Any] = self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
__A : Tuple = time.time()
try:
__A : List[str] = eval_loop(
lowerCamelCase , description="Prediction" , prediction_loss_only=True if compute_metrics is None else None , ignore_keys=lowerCamelCase , metric_key_prefix=lowerCamelCase , )
finally:
__A : int = compute_metrics
__A : List[Any] = 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
__A : Dict = self.post_process_function(lowerCamelCase , lowerCamelCase , output.predictions , "predict" )
__A : Tuple = 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}_" ):
__A : Any = metrics.pop(lowerCamelCase )
metrics.update(output.metrics )
return PredictionOutput(predictions=predictions.predictions , label_ids=predictions.label_ids , metrics=lowerCamelCase )
| 111 | 0 |
"""simple docstring"""
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowercase_ ( _UpperCAmelCase ):
"""simple docstring"""
A_ : List[Any] = {}
A_ : Dict = job['''started_at''']
A_ : Optional[Any] = job['''completed_at''']
A_ : Optional[Any] = date_parser.parse(_UpperCAmelCase )
A_ : str = date_parser.parse(_UpperCAmelCase )
A_ : Union[str, Any] = round((end_datetime - start_datetime).total_seconds() / 60.0 )
A_ : Optional[int] = start
A_ : Optional[int] = end
A_ : Any = duration_in_min
return job_info
def lowercase_ ( _UpperCAmelCase , _UpperCAmelCase=None ):
"""simple docstring"""
A_ : List[Any] = None
if token is not None:
A_ : Dict = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f"""Bearer {token}"""}
A_ : List[str] = f"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
A_ : Tuple = requests.get(_UpperCAmelCase , headers=_UpperCAmelCase ).json()
A_ : Optional[int] = {}
try:
job_time.update({job['''name''']: extract_time_from_single_job(_UpperCAmelCase ) for job in result['''jobs''']} )
A_ : int = math.ceil((result['''total_count'''] - 100) / 100 )
for i in range(_UpperCAmelCase ):
A_ : Any = requests.get(url + f"""&page={i + 2}""" , headers=_UpperCAmelCase ).json()
job_time.update({job['''name''']: extract_time_from_single_job(_UpperCAmelCase ) for job in result['''jobs''']} )
return job_time
except Exception:
print(f"""Unknown error, could not fetch links:\n{traceback.format_exc()}""" )
return {}
if __name__ == "__main__":
_lowerCamelCase : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
_lowerCamelCase : Union[str, Any] = parser.parse_args()
_lowerCamelCase : Union[str, Any] = get_job_time(args.workflow_run_id)
_lowerCamelCase : List[Any] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(f'{k}: {v["duration"]}')
| 361 |
"""simple docstring"""
import argparse
import torch
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_from_original_stable_diffusion_ckpt
if __name__ == "__main__":
_lowerCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
# !wget https://raw.githubusercontent.com/CompVis/stable-diffusion/main/configs/stable-diffusion/v1-inference.yaml
parser.add_argument(
'--original_config_file',
default=None,
type=str,
help='The YAML config file corresponding to the original architecture.',
)
parser.add_argument(
'--num_in_channels',
default=None,
type=int,
help='The number of input channels. If `None` number of input channels will be automatically inferred.',
)
parser.add_argument(
'--scheduler_type',
default='pndm',
type=str,
help='Type of scheduler to use. Should be one of [\'pndm\', \'lms\', \'ddim\', \'euler\', \'euler-ancestral\', \'dpm\']',
)
parser.add_argument(
'--pipeline_type',
default=None,
type=str,
help=(
'The pipeline type. One of \'FrozenOpenCLIPEmbedder\', \'FrozenCLIPEmbedder\', \'PaintByExample\''
'. If `None` pipeline will be automatically inferred.'
),
)
parser.add_argument(
'--image_size',
default=None,
type=int,
help=(
'The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'
' Base. Use 768 for Stable Diffusion v2.'
),
)
parser.add_argument(
'--prediction_type',
default=None,
type=str,
help=(
'The prediction type that the model was trained on. Use \'epsilon\' for Stable Diffusion v1.X and Stable'
' Diffusion v2 Base. Use \'v_prediction\' for Stable Diffusion v2.'
),
)
parser.add_argument(
'--extract_ema',
action='store_true',
help=(
'Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'
' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'
' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'
),
)
parser.add_argument(
'--upcast_attention',
action='store_true',
help=(
'Whether the attention computation should always be upcasted. This is necessary when running stable'
' diffusion 2.1.'
),
)
parser.add_argument(
'--from_safetensors',
action='store_true',
help='If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.',
)
parser.add_argument(
'--to_safetensors',
action='store_true',
help='Whether to store pipeline in safetensors format or not.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--device', type=str, help='Device to use (e.g. cpu, cuda:0, cuda:1, etc.)')
parser.add_argument(
'--stable_unclip',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP model. One of \'txt2img\' or \'img2img\'.',
)
parser.add_argument(
'--stable_unclip_prior',
type=str,
default=None,
required=False,
help='Set if this is a stable unCLIP txt2img model. Selects which prior to use. If `--stable_unclip` is set to `txt2img`, the karlo prior (https://huggingface.co/kakaobrain/karlo-v1-alpha/tree/main/prior) is selected by default.',
)
parser.add_argument(
'--clip_stats_path',
type=str,
help='Path to the clip stats file. Only required if the stable unclip model\'s config specifies `model.params.noise_aug_config.params.clip_stats_path`.',
required=False,
)
parser.add_argument(
'--controlnet', action='store_true', default=None, help='Set flag if this is a controlnet checkpoint.'
)
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--vae_path',
type=str,
default=None,
required=False,
help='Set to a path, hub id to an already converted vae to not convert it again.',
)
_lowerCamelCase : Optional[Any] = parser.parse_args()
_lowerCamelCase : Optional[Any] = download_from_original_stable_diffusion_ckpt(
checkpoint_path=args.checkpoint_path,
original_config_file=args.original_config_file,
image_size=args.image_size,
prediction_type=args.prediction_type,
model_type=args.pipeline_type,
extract_ema=args.extract_ema,
scheduler_type=args.scheduler_type,
num_in_channels=args.num_in_channels,
upcast_attention=args.upcast_attention,
from_safetensors=args.from_safetensors,
device=args.device,
stable_unclip=args.stable_unclip,
stable_unclip_prior=args.stable_unclip_prior,
clip_stats_path=args.clip_stats_path,
controlnet=args.controlnet,
vae_path=args.vae_path,
)
if args.half:
pipe.to(torch_dtype=torch.floataa)
if args.controlnet:
# only save the controlnet model
pipe.controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
else:
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 361 | 1 |
'''simple docstring'''
import inspect
import unittest
from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_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 transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
from transformers.models.mobilevitva.modeling_mobilevitva import (
MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
make_divisible,
)
if is_vision_available():
from PIL import Image
from transformers import MobileViTImageProcessor
class a__ ( a__ ):
'''simple docstring'''
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
lowerCAmelCase__ = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCamelCase_ , '''width_multiplier''' ) )
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ , lowerCamelCase_=13 , lowerCamelCase_=64 , lowerCamelCase_=2 , lowerCamelCase_=3 , lowerCamelCase_="swish" , lowerCamelCase_=3 , lowerCamelCase_=32 , lowerCamelCase_=0.1 , lowerCamelCase_=0.02 , lowerCamelCase_=True , lowerCamelCase_=True , lowerCamelCase_=10 , lowerCamelCase_=None , lowerCamelCase_=0.25 , lowerCamelCase_=0.0 , lowerCamelCase_=0.0 , ) -> Tuple:
lowerCAmelCase__ = parent
lowerCAmelCase__ = batch_size
lowerCAmelCase__ = image_size
lowerCAmelCase__ = patch_size
lowerCAmelCase__ = num_channels
lowerCAmelCase__ = make_divisible(5_12 * width_multiplier , divisor=8 )
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = conv_kernel_size
lowerCAmelCase__ = output_stride
lowerCAmelCase__ = classifier_dropout_prob
lowerCAmelCase__ = use_labels
lowerCAmelCase__ = is_training
lowerCAmelCase__ = num_labels
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = scope
lowerCAmelCase__ = width_multiplier
lowerCAmelCase__ = ffn_dropout
lowerCAmelCase__ = attn_dropout
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
lowerCAmelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCAmelCase__ = None
lowerCAmelCase__ = None
if self.use_labels:
lowerCAmelCase__ = ids_tensor([self.batch_size] , self.num_labels )
lowerCAmelCase__ = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowerCAmelCase__ = self.get_config()
return config, pixel_values, labels, pixel_labels
def __SCREAMING_SNAKE_CASE ( self ) -> List[str]:
return MobileViTVaConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_act=self.hidden_act , conv_kernel_size=self.conv_kernel_size , output_stride=self.output_stride , classifier_dropout_prob=self.classifier_dropout_prob , initializer_range=self.initializer_range , width_multiplier=self.width_multiplier , ffn_dropout=self.ffn_dropout_prob , attn_dropout=self.attn_dropout_prob , )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> Any:
lowerCAmelCase__ = MobileViTVaModel(config=lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCAmelCase__ = model(lowerCamelCase_ )
self.parent.assertEqual(
result.last_hidden_state.shape , (
self.batch_size,
self.last_hidden_size,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> int:
lowerCAmelCase__ = self.num_labels
lowerCAmelCase__ = MobileViTVaForImageClassification(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCAmelCase__ = model(lowerCamelCase_ , labels=lowerCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __SCREAMING_SNAKE_CASE ( self , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ) -> List[Any]:
lowerCAmelCase__ = self.num_labels
lowerCAmelCase__ = MobileViTVaForSemanticSegmentation(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
lowerCAmelCase__ = model(lowerCamelCase_ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
lowerCAmelCase__ = model(lowerCamelCase_ , labels=lowerCamelCase_ )
self.parent.assertEqual(
result.logits.shape , (
self.batch_size,
self.num_labels,
self.image_size // self.output_stride,
self.image_size // self.output_stride,
) , )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
lowerCAmelCase__ = self.prepare_config_and_inputs()
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ = config_and_inputs
lowerCAmelCase__ = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class a__ ( a__ , a__ , unittest.TestCase ):
'''simple docstring'''
lowercase__ : Tuple = (
(MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
if is_torch_available()
else ()
)
lowercase__ : int = (
{
"feature-extraction": MobileViTVaModel,
"image-classification": MobileViTVaForImageClassification,
"image-segmentation": MobileViTVaForSemanticSegmentation,
}
if is_torch_available()
else {}
)
lowercase__ : Dict = False
lowercase__ : Optional[int] = False
lowercase__ : int = False
lowercase__ : List[str] = False
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
lowerCAmelCase__ = MobileViTVaModelTester(self )
lowerCAmelCase__ = MobileViTVaConfigTester(self , config_class=lowerCamelCase_ , has_text_modality=lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> Any:
self.config_tester.run_common_tests()
@unittest.skip(reason='''MobileViTV2 does not use inputs_embeds''' )
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
pass
@unittest.skip(reason='''MobileViTV2 does not support input and output embeddings''' )
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
pass
@unittest.skip(reason='''MobileViTV2 does not output attentions''' )
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
pass
@require_torch_multi_gpu
@unittest.skip(reason='''Got `CUDA error: misaligned address` for tests after this one being run.''' )
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
pass
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
pass
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = model_class(lowerCamelCase_ )
lowerCAmelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase__ = [*signature.parameters.keys()]
lowerCAmelCase__ = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
def check_hidden_states_output(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ ):
lowerCAmelCase__ = model_class(lowerCamelCase_ )
model.to(lowerCamelCase_ )
model.eval()
with torch.no_grad():
lowerCAmelCase__ = model(**self._prepare_for_class(lowerCamelCase_ , lowerCamelCase_ ) )
lowerCAmelCase__ = outputs.hidden_states
lowerCAmelCase__ = 5
self.assertEqual(len(lowerCamelCase_ ) , lowerCamelCase_ )
# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
# with the width and height being successively divided by 2.
lowerCAmelCase__ = 2
for i in range(len(lowerCamelCase_ ) ):
self.assertListEqual(
list(hidden_states[i].shape[-2:] ) , [self.model_tester.image_size // divisor, self.model_tester.image_size // divisor] , )
divisor *= 2
self.assertEqual(self.model_tester.output_stride , divisor // 2 )
lowerCAmelCase__ , lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase__ = True
check_hidden_states_output(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase__ = True
check_hidden_states_output(lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase_ )
def __SCREAMING_SNAKE_CASE ( self ) -> int:
lowerCAmelCase__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowerCamelCase_ )
@slow
def __SCREAMING_SNAKE_CASE ( self ) -> Dict:
for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase__ = MobileViTVaModel.from_pretrained(lowerCamelCase_ )
self.assertIsNotNone(lowerCamelCase_ )
def _snake_case ( ) -> int:
lowerCAmelCase__ = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class a__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def __SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
return (
MobileViTImageProcessor.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''' )
if is_vision_available()
else None
)
@slow
def __SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
lowerCAmelCase__ = MobileViTVaForImageClassification.from_pretrained('''apple/mobilevitv2-1.0-imagenet1k-256''' ).to(
lowerCamelCase_ )
lowerCAmelCase__ = self.default_image_processor
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=lowerCamelCase_ , return_tensors='''pt''' ).to(lowerCamelCase_ )
# forward pass
with torch.no_grad():
lowerCAmelCase__ = model(**lowerCamelCase_ )
# verify the logits
lowerCAmelCase__ = torch.Size((1, 10_00) )
self.assertEqual(outputs.logits.shape , lowerCamelCase_ )
lowerCAmelCase__ = torch.tensor([-1.63_36e00, -7.32_04e-02, -5.18_83e-01] ).to(lowerCamelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase_ , atol=1e-4 ) )
@slow
def __SCREAMING_SNAKE_CASE ( self ) -> List[str]:
lowerCAmelCase__ = MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
lowerCAmelCase__ = model.to(lowerCamelCase_ )
lowerCAmelCase__ = MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=lowerCamelCase_ , return_tensors='''pt''' ).to(lowerCamelCase_ )
# forward pass
with torch.no_grad():
lowerCAmelCase__ = model(**lowerCamelCase_ )
lowerCAmelCase__ = outputs.logits
# verify the logits
lowerCAmelCase__ = torch.Size((1, 21, 32, 32) )
self.assertEqual(logits.shape , lowerCamelCase_ )
lowerCAmelCase__ = torch.tensor(
[
[[7.0_863, 7.1_525, 6.8_201], [6.6_931, 6.8_770, 6.8_933], [6.2_978, 7.0_366, 6.9_636]],
[[-3.7_134, -3.6_712, -3.6_675], [-3.5_825, -3.3_549, -3.4_777], [-3.3_435, -3.3_979, -3.2_857]],
[[-2.9_329, -2.8_003, -2.7_369], [-3.0_564, -2.4_780, -2.0_207], [-2.6_889, -1.9_298, -1.7_640]],
] , device=lowerCamelCase_ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , lowerCamelCase_ , atol=1e-4 ) )
@slow
def __SCREAMING_SNAKE_CASE ( self ) -> Tuple:
lowerCAmelCase__ = MobileViTVaForSemanticSegmentation.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
lowerCAmelCase__ = model.to(lowerCamelCase_ )
lowerCAmelCase__ = MobileViTImageProcessor.from_pretrained('''shehan97/mobilevitv2-1.0-voc-deeplabv3''' )
lowerCAmelCase__ = prepare_img()
lowerCAmelCase__ = image_processor(images=lowerCamelCase_ , return_tensors='''pt''' ).to(lowerCamelCase_ )
# forward pass
with torch.no_grad():
lowerCAmelCase__ = model(**lowerCamelCase_ )
lowerCAmelCase__ = outputs.logits.detach().cpu()
lowerCAmelCase__ = image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_ , target_sizes=[(50, 60)] )
lowerCAmelCase__ = torch.Size((50, 60) )
self.assertEqual(segmentation[0].shape , lowerCamelCase_ )
lowerCAmelCase__ = image_processor.post_process_semantic_segmentation(outputs=lowerCamelCase_ )
lowerCAmelCase__ = torch.Size((32, 32) )
self.assertEqual(segmentation[0].shape , lowerCamelCase_ ) | 90 |
'''simple docstring'''
import logging
import os
from typing import Dict, List, Optional, Union
import torch
import torch.nn as nn
from accelerate.utils.imports import (
is_abit_bnb_available,
is_abit_bnb_available,
is_bnb_available,
)
from ..big_modeling import dispatch_model, init_empty_weights
from .dataclasses import BnbQuantizationConfig
from .modeling import (
find_tied_parameters,
get_balanced_memory,
infer_auto_device_map,
load_checkpoint_in_model,
offload_weight,
set_module_tensor_to_device,
)
if is_bnb_available():
import bitsandbytes as bnb
from copy import deepcopy
__UpperCAmelCase = logging.getLogger(__name__)
def _snake_case ( A , A , A = None , A = None , A = None , A = None , A = None , A = False , ) -> Union[str, Any]:
lowerCAmelCase__ = bnb_quantization_config.load_in_abit
lowerCAmelCase__ = bnb_quantization_config.load_in_abit
if load_in_abit and not is_abit_bnb_available():
raise ImportError(
'''You have a version of `bitsandbytes` that is not compatible with 8bit quantization,'''
''' make sure you have the latest version of `bitsandbytes` installed.''' )
if load_in_abit and not is_abit_bnb_available():
raise ValueError(
'''You have a version of `bitsandbytes` that is not compatible with 4bit quantization,'''
'''make sure you have the latest version of `bitsandbytes` installed.''' )
lowerCAmelCase__ = []
# custom device map
if isinstance(A , A ) and len(device_map.keys() ) > 1:
lowerCAmelCase__ = [key for key, value in device_map.items() if value in ['''disk''', '''cpu''']]
# We keep some modules such as the lm_head in their original dtype for numerical stability reasons
if bnb_quantization_config.skip_modules is None:
lowerCAmelCase__ = get_keys_to_not_convert(A )
# add cpu modules to skip modules only for 4-bit modules
if load_in_abit:
bnb_quantization_config.skip_modules.extend(A )
lowerCAmelCase__ = bnb_quantization_config.skip_modules
# We add the modules we want to keep in full precision
if bnb_quantization_config.keep_in_fpaa_modules is None:
lowerCAmelCase__ = []
lowerCAmelCase__ = bnb_quantization_config.keep_in_fpaa_modules
modules_to_not_convert.extend(A )
# compatibility with peft
lowerCAmelCase__ = load_in_abit
lowerCAmelCase__ = load_in_abit
lowerCAmelCase__ = get_parameter_device(A )
if model_device.type != "meta":
# quantization of an already loaded model
logger.warning(
'''It is not recommended to quantize a loaded model. '''
'''The model should be instantiated under the `init_empty_weights` context manager.''' )
lowerCAmelCase__ = replace_with_bnb_layers(A , A , modules_to_not_convert=A )
# convert param to the right dtype
lowerCAmelCase__ = bnb_quantization_config.torch_dtype
for name, param in model.state_dict().items():
if any(module_to_keep_in_fpaa in name for module_to_keep_in_fpaa in keep_in_fpaa_modules ):
param.to(torch.floataa )
if param.dtype != torch.floataa:
lowerCAmelCase__ = name.replace('''.weight''' , '''''' ).replace('''.bias''' , '''''' )
lowerCAmelCase__ = getattr(A , A , A )
if param is not None:
param.to(torch.floataa )
elif torch.is_floating_point(A ):
param.to(A )
if model_device.type == "cuda":
# move everything to cpu in the first place because we can't do quantization if the weights are already on cuda
model.cuda(torch.cuda.current_device() )
torch.cuda.empty_cache()
elif torch.cuda.is_available():
model.to(torch.cuda.current_device() )
else:
raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
logger.info(
F"""The model device type is {model_device.type}. However, cuda is needed for quantization."""
'''We move the model to cuda.''' )
return model
elif weights_location is None:
raise RuntimeError(
F"""`weights_location` needs to be the folder path containing the weights of the model, but we found {weights_location} """ )
else:
with init_empty_weights():
lowerCAmelCase__ = replace_with_bnb_layers(
A , A , modules_to_not_convert=A )
lowerCAmelCase__ = get_quantized_model_device_map(
A , A , A , max_memory=A , no_split_module_classes=A , )
if offload_state_dict is None and device_map is not None and "disk" in device_map.values():
lowerCAmelCase__ = True
lowerCAmelCase__ = any(x in list(device_map.values() ) for x in ['''cpu''', '''disk'''] )
load_checkpoint_in_model(
A , A , A , dtype=bnb_quantization_config.torch_dtype , offload_folder=A , offload_state_dict=A , keep_in_fpaa_modules=bnb_quantization_config.keep_in_fpaa_modules , offload_abit_bnb=load_in_abit and offload , )
return dispatch_model(A , device_map=A , offload_dir=A )
def _snake_case ( A , A , A=None , A=None , A=None ) -> List[Any]:
if device_map is None:
if torch.cuda.is_available():
lowerCAmelCase__ = {'''''': torch.cuda.current_device()}
else:
raise RuntimeError('''No GPU found. A GPU is needed for quantization.''' )
logger.info('''The device_map was not initialized.''' '''Setting device_map to `{\'\':torch.cuda.current_device()}`.''' )
if isinstance(A , A ):
if device_map not in ["auto", "balanced", "balanced_low_0", "sequential"]:
raise ValueError(
'''If passing a string for `device_map`, please choose \'auto\', \'balanced\', \'balanced_low_0\' or '''
'''\'sequential\'.''' )
lowerCAmelCase__ = {}
special_dtypes.update(
{
name: bnb_quantization_config.torch_dtype
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.skip_modules )
} )
special_dtypes.update(
{
name: torch.floataa
for name, _ in model.named_parameters()
if any(m in name for m in bnb_quantization_config.keep_in_fpaa_modules )
} )
lowerCAmelCase__ = {}
lowerCAmelCase__ = special_dtypes
lowerCAmelCase__ = no_split_module_classes
lowerCAmelCase__ = bnb_quantization_config.target_dtype
# get max_memory for each device.
if device_map != "sequential":
lowerCAmelCase__ = get_balanced_memory(
A , low_zero=(device_map == '''balanced_low_0''') , max_memory=A , **A , )
lowerCAmelCase__ = max_memory
lowerCAmelCase__ = infer_auto_device_map(A , **A )
if isinstance(A , A ):
# check if don't have any quantized module on the cpu
lowerCAmelCase__ = bnb_quantization_config.skip_modules + bnb_quantization_config.keep_in_fpaa_modules
lowerCAmelCase__ = {
key: device_map[key] for key in device_map.keys() if key not in modules_not_to_convert
}
for device in ["cpu", "disk"]:
if device in device_map_without_some_modules.values():
if bnb_quantization_config.load_in_abit:
raise ValueError(
'''
Some modules are dispatched on the CPU or the disk. Make sure you have enough GPU RAM to fit
the quantized model. If you want to dispatch the model on the CPU or the disk while keeping
these modules in `torch_dtype`, you need to pass a custom `device_map` to
`load_and_quantize_model`. Check
https://huggingface.co/docs/accelerate/main/en/usage_guides/quantization#offload-modules-to-cpu-and-disk
for more details.
''' )
else:
logger.info(
'''Some modules are are offloaded to the CPU or the disk. Note that these modules will be converted to 8-bit''' )
del device_map_without_some_modules
return device_map
def _snake_case ( A , A , A=None , A=None ) -> Any:
if modules_to_not_convert is None:
lowerCAmelCase__ = []
lowerCAmelCase__ , lowerCAmelCase__ = _replace_with_bnb_layers(
A , A , A , A )
if not has_been_replaced:
logger.warning(
'''You are loading your model in 8bit or 4bit but no linear modules were found in your model.'''
''' this can happen for some architectures such as gpt2 that uses Conv1D instead of Linear layers.'''
''' Please double check your model architecture, or submit an issue on github if you think this is'''
''' a bug.''' )
return model
def _snake_case ( A , A , A=None , A=None , ) -> Optional[Any]:
lowerCAmelCase__ = False
for name, module in model.named_children():
if current_key_name is None:
lowerCAmelCase__ = []
current_key_name.append(A )
if isinstance(A , nn.Linear ) and name not in modules_to_not_convert:
# Check if the current key is not in the `modules_to_not_convert`
lowerCAmelCase__ = '''.'''.join(A )
lowerCAmelCase__ = True
for key in modules_to_not_convert:
if (
(key in current_key_name_str) and (key + "." in current_key_name_str)
) or key == current_key_name_str:
lowerCAmelCase__ = False
break
if proceed:
# Load bnb module with empty weight and replace ``nn.Linear` module
if bnb_quantization_config.load_in_abit:
lowerCAmelCase__ = bnb.nn.LinearabitLt(
module.in_features , module.out_features , module.bias is not None , has_fpaa_weights=A , threshold=bnb_quantization_config.llm_inta_threshold , )
elif bnb_quantization_config.load_in_abit:
lowerCAmelCase__ = bnb.nn.Linearabit(
module.in_features , module.out_features , module.bias is not None , bnb_quantization_config.bnb_abit_compute_dtype , compress_statistics=bnb_quantization_config.bnb_abit_use_double_quant , quant_type=bnb_quantization_config.bnb_abit_quant_type , )
else:
raise ValueError('''load_in_8bit and load_in_4bit can\'t be both False''' )
lowerCAmelCase__ = module.weight.data
if module.bias is not None:
lowerCAmelCase__ = module.bias.data
bnb_module.requires_grad_(A )
setattr(A , A , A )
lowerCAmelCase__ = True
if len(list(module.children() ) ) > 0:
lowerCAmelCase__ , lowerCAmelCase__ = _replace_with_bnb_layers(
A , A , A , A )
lowerCAmelCase__ = has_been_replaced | _has_been_replaced
# Remove the last key for recursion
current_key_name.pop(-1 )
return model, has_been_replaced
def _snake_case ( A ) -> Tuple:
# Create a copy of the model
with init_empty_weights():
lowerCAmelCase__ = deepcopy(A ) # this has 0 cost since it is done inside `init_empty_weights` context manager`
lowerCAmelCase__ = find_tied_parameters(A )
# For compatibility with Accelerate < 0.18
if isinstance(A , A ):
lowerCAmelCase__ = sum(list(tied_params.values() ) , [] ) + list(tied_params.keys() )
else:
lowerCAmelCase__ = sum(A , [] )
lowerCAmelCase__ = len(A ) > 0
# Check if it is a base model
lowerCAmelCase__ = False
if hasattr(A , '''base_model_prefix''' ):
lowerCAmelCase__ = not hasattr(A , model.base_model_prefix )
# Ignore this for base models (BertModel, GPT2Model, etc.)
if (not has_tied_params) and is_base_model:
return []
# otherwise they have an attached head
lowerCAmelCase__ = list(model.named_children() )
lowerCAmelCase__ = [list_modules[-1][0]]
# add last module together with tied weights
lowerCAmelCase__ = set(A ) - set(A )
lowerCAmelCase__ = list(set(A ) ) + list(A )
# remove ".weight" from the keys
lowerCAmelCase__ = ['''.weight''', '''.bias''']
lowerCAmelCase__ = []
for name in list_untouched:
for name_to_remove in names_to_remove:
if name_to_remove in name:
lowerCAmelCase__ = name.replace(A , '''''' )
filtered_module_names.append(A )
return filtered_module_names
def _snake_case ( A ) -> Optional[int]:
for m in model.modules():
if isinstance(A , bnb.nn.Linearabit ):
return True
return False
def _snake_case ( A ) -> Union[str, Any]:
return next(parameter.parameters() ).device
def _snake_case ( A , A , A , A , A , A , A ) -> Any:
# if it is not quantized, we quantize and offload the quantized weights and the SCB stats
if fpaa_statistics is None:
set_module_tensor_to_device(A , A , 0 , dtype=A , value=A )
lowerCAmelCase__ = param_name
lowerCAmelCase__ = model
if "." in tensor_name:
lowerCAmelCase__ = tensor_name.split('''.''' )
for split in splits[:-1]:
lowerCAmelCase__ = getattr(A , A )
if new_module is None:
raise ValueError(F"""{module} has no attribute {split}.""" )
lowerCAmelCase__ = new_module
lowerCAmelCase__ = splits[-1]
# offload weights
lowerCAmelCase__ = False
offload_weight(module._parameters[tensor_name] , A , A , index=A )
if hasattr(module._parameters[tensor_name] , '''SCB''' ):
offload_weight(
module._parameters[tensor_name].SCB , param_name.replace('''weight''' , '''SCB''' ) , A , index=A , )
else:
offload_weight(A , A , A , index=A )
offload_weight(A , param_name.replace('''weight''' , '''SCB''' ) , A , index=A )
set_module_tensor_to_device(A , A , '''meta''' , dtype=A , value=torch.empty(*param.size() ) ) | 90 | 1 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
lowerCamelCase__ : Union[str, Any] = logging.get_logger(__name__)
lowerCamelCase__ : Dict = {
"google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class lowercase__( _UpperCAmelCase ):
'''simple docstring'''
UpperCamelCase = """umt5"""
UpperCamelCase = ["""past_key_values"""]
def __init__( self :Any , lowerCamelCase_ :Union[str, Any]=25_01_12 , lowerCamelCase_ :Tuple=5_12 , lowerCamelCase_ :List[Any]=64 , lowerCamelCase_ :str=10_24 , lowerCamelCase_ :List[str]=8 , lowerCamelCase_ :Dict=None , lowerCamelCase_ :Optional[int]=6 , lowerCamelCase_ :int=32 , lowerCamelCase_ :Dict=1_28 , lowerCamelCase_ :List[Any]=0.1 , lowerCamelCase_ :List[Any]=1E-6 , lowerCamelCase_ :List[str]=1.0 , lowerCamelCase_ :Union[str, Any]="gated-gelu" , lowerCamelCase_ :Union[str, Any]=True , lowerCamelCase_ :Dict=True , lowerCamelCase_ :Tuple="T5Tokenizer" , lowerCamelCase_ :List[str]=True , lowerCamelCase_ :int=0 , lowerCamelCase_ :int=1 , lowerCamelCase_ :int=0 , **lowerCamelCase_ :Optional[Any] , ) -> List[str]:
'''simple docstring'''
super().__init__(
is_encoder_decoder=lowerCamelCase_ , tokenizer_class=lowerCamelCase_ , tie_word_embeddings=lowerCamelCase_ , pad_token_id=lowerCamelCase_ , eos_token_id=lowerCamelCase_ , decoder_start_token_id=lowerCamelCase_ , **lowerCamelCase_ , )
SCREAMING_SNAKE_CASE : List[str] = vocab_size
SCREAMING_SNAKE_CASE : List[Any] = d_model
SCREAMING_SNAKE_CASE : List[Any] = d_kv
SCREAMING_SNAKE_CASE : Optional[int] = d_ff
SCREAMING_SNAKE_CASE : List[str] = num_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
SCREAMING_SNAKE_CASE : Dict = num_heads
SCREAMING_SNAKE_CASE : int = relative_attention_num_buckets
SCREAMING_SNAKE_CASE : str = relative_attention_max_distance
SCREAMING_SNAKE_CASE : str = dropout_rate
SCREAMING_SNAKE_CASE : int = layer_norm_epsilon
SCREAMING_SNAKE_CASE : Optional[Any] = initializer_factor
SCREAMING_SNAKE_CASE : List[str] = feed_forward_proj
SCREAMING_SNAKE_CASE : List[str] = use_cache
SCREAMING_SNAKE_CASE : Any = self.feed_forward_proj.split('''-''' )
SCREAMING_SNAKE_CASE : Tuple = act_info[-1]
SCREAMING_SNAKE_CASE : Optional[Any] = act_info[0] == '''gated'''
if len(lowerCamelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCamelCase_ ) > 2:
raise ValueError(
f"`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer."
'''Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. '''
'''\'gated-gelu\' or \'relu\'''' )
if feed_forward_proj == "gated-gelu":
SCREAMING_SNAKE_CASE : Any = '''gelu_new'''
@property
def __lowerCAmelCase ( self :Any ) -> Any:
'''simple docstring'''
return self.d_model
@property
def __lowerCAmelCase ( self :Optional[Any] ) -> Optional[int]:
'''simple docstring'''
return self.num_heads
@property
def __lowerCAmelCase ( self :Tuple ) -> List[str]:
'''simple docstring'''
return self.num_layers
class lowercase__( _UpperCAmelCase ):
'''simple docstring'''
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def __lowerCAmelCase ( self :int ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = {
'''input_ids''': {0: '''batch''', 1: '''encoder_sequence'''},
'''attention_mask''': {0: '''batch''', 1: '''encoder_sequence'''},
}
if self.use_past:
SCREAMING_SNAKE_CASE : int = '''past_encoder_sequence + sequence'''
SCREAMING_SNAKE_CASE : str = {0: '''batch'''}
SCREAMING_SNAKE_CASE : List[Any] = {0: '''batch''', 1: '''past_decoder_sequence + sequence'''}
else:
SCREAMING_SNAKE_CASE : Tuple = {0: '''batch''', 1: '''decoder_sequence'''}
SCREAMING_SNAKE_CASE : List[str] = {0: '''batch''', 1: '''decoder_sequence'''}
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase_ , direction='''inputs''' )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def __lowerCAmelCase ( self :Optional[Any] ) -> int:
'''simple docstring'''
return 13
@property
def __lowerCAmelCase ( self :List[str] ) -> float:
'''simple docstring'''
return 5E-4
| 18 |
"""simple docstring"""
from __future__ import annotations
from fractions import Fraction
def __A ( a_ : int , a_ : int )-> bool:
'''simple docstring'''
return (
num != den and num % 10 == den // 10 and (num // 10) / (den % 10) == num / den
)
def __A ( a_ : int )-> list[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Union[str, Any] = []
SCREAMING_SNAKE_CASE : List[str] = 11
SCREAMING_SNAKE_CASE : Union[str, Any] = int('''1''' + '''0''' * digit_len )
for num in range(a_ , a_ ):
while den <= 99:
if (num != den) and (num % 10 == den // 10) and (den % 10 != 0):
if is_digit_cancelling(a_ , a_ ):
solutions.append(F"{num}/{den}" )
den += 1
num += 1
SCREAMING_SNAKE_CASE : Optional[Any] = 10
return solutions
def __A ( a_ : int = 2 )-> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE : Optional[Any] = 1.0
for fraction in fraction_list(a_ ):
SCREAMING_SNAKE_CASE : List[str] = Fraction(a_ )
result *= frac.denominator / frac.numerator
return int(a_ )
if __name__ == "__main__":
print(solution())
| 18 | 1 |
def UpperCamelCase( __UpperCamelCase : str ):
assert column_title.isupper()
lowerCAmelCase_ : Optional[Any] = 0
lowerCAmelCase_ : List[Any] = len(_UpperCAmelCase ) - 1
lowerCAmelCase_ : Tuple = 0
while index >= 0:
lowerCAmelCase_ : Tuple = (ord(column_title[index] ) - 64) * pow(26 ,_UpperCAmelCase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 171 |
'''simple docstring'''
__lowerCAmelCase ={
"meter": "m",
"kilometer": "km",
"megametre": "Mm",
"gigametre": "Gm",
"terametre": "Tm",
"petametre": "Pm",
"exametre": "Em",
"zettametre": "Zm",
"yottametre": "Ym",
}
# Exponent of the factor(meter)
__lowerCAmelCase ={
"m": 0,
"km": 3,
"Mm": 6,
"Gm": 9,
"Tm": 12,
"Pm": 15,
"Em": 18,
"Zm": 21,
"Ym": 24,
}
def a ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ) -> float:
"""simple docstring"""
a_ = from_type.lower().strip('s' )
a_ = to_type.lower().strip('s' )
a_ = UNIT_SYMBOL.get(_UpperCAmelCase , _UpperCAmelCase )
a_ = UNIT_SYMBOL.get(_UpperCAmelCase , _UpperCAmelCase )
if from_sanitized not in METRIC_CONVERSION:
a_ = (
F'''Invalid \'from_type\' value: {from_type!r}.\n'''
F'''Conversion abbreviations are: {', '.join(_UpperCAmelCase )}'''
)
raise ValueError(_UpperCAmelCase )
if to_sanitized not in METRIC_CONVERSION:
a_ = (
F'''Invalid \'to_type\' value: {to_type!r}.\n'''
F'''Conversion abbreviations are: {', '.join(_UpperCAmelCase )}'''
)
raise ValueError(_UpperCAmelCase )
a_ = METRIC_CONVERSION[from_sanitized]
a_ = METRIC_CONVERSION[to_sanitized]
a_ = 1
if from_exponent > to_exponent:
a_ = from_exponent - to_exponent
else:
a_ = -(to_exponent - from_exponent)
return value * pow(1_0 , _UpperCAmelCase )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 697 | 0 |
from math import pi, sqrt, tan
def _a ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if side_length < 0:
raise ValueError('surface_area_cube() only accepts non-negative values' )
return 6 * side_length**2
def _a ( __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
if length < 0 or breadth < 0 or height < 0:
raise ValueError('surface_area_cuboid() only accepts non-negative values' )
return 2 * ((length * breadth) + (breadth * height) + (length * height))
def _a ( __SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
if radius < 0:
raise ValueError('surface_area_sphere() only accepts non-negative values' )
return 4 * pi * radius**2
def _a ( __SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
if radius < 0:
raise ValueError('surface_area_hemisphere() only accepts non-negative values' )
return 3 * pi * radius**2
def _a ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError('surface_area_cone() only accepts non-negative values' )
return pi * radius * (radius + (height**2 + radius**2) ** 0.5)
def _a ( __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
if radius_a < 0 or radius_a < 0 or height < 0:
raise ValueError(
'surface_area_conical_frustum() only accepts non-negative values' )
_lowerCAmelCase = (height**2 + (radius_a - radius_a) ** 2) ** 0.5
return pi * ((slant_height * (radius_a + radius_a)) + radius_a**2 + radius_a**2)
def _a ( __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
if radius < 0 or height < 0:
raise ValueError('surface_area_cylinder() only accepts non-negative values' )
return 2 * pi * radius * (height + radius)
def _a ( __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
if torus_radius < 0 or tube_radius < 0:
raise ValueError('surface_area_torus() only accepts non-negative values' )
if torus_radius < tube_radius:
raise ValueError(
'surface_area_torus() does not support spindle or self intersecting tori' )
return 4 * pow(__UpperCAmelCase , 2 ) * torus_radius * tube_radius
def _a ( __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
if length < 0 or width < 0:
raise ValueError('area_rectangle() only accepts non-negative values' )
return length * width
def _a ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if side_length < 0:
raise ValueError('area_square() only accepts non-negative values' )
return side_length**2
def _a ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError('area_triangle() only accepts non-negative values' )
return (base * height) / 2
def _a ( __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Union[str, Any] ):
"""simple docstring"""
if sidea < 0 or sidea < 0 or sidea < 0:
raise ValueError('area_triangle_three_sides() only accepts non-negative values' )
elif sidea + sidea < sidea or sidea + sidea < sidea or sidea + sidea < sidea:
raise ValueError('Given three sides do not form a triangle' )
_lowerCAmelCase = (sidea + sidea + sidea) / 2
_lowerCAmelCase = sqrt(
semi_perimeter
* (semi_perimeter - sidea)
* (semi_perimeter - sidea)
* (semi_perimeter - sidea) )
return area
def _a ( __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
if base < 0 or height < 0:
raise ValueError('area_parallelogram() only accepts non-negative values' )
return base * height
def _a ( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
if basea < 0 or basea < 0 or height < 0:
raise ValueError('area_trapezium() only accepts non-negative values' )
return 1 / 2 * (basea + basea) * height
def _a ( __SCREAMING_SNAKE_CASE : Dict ):
"""simple docstring"""
if radius < 0:
raise ValueError('area_circle() only accepts non-negative values' )
return pi * radius**2
def _a ( __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
if radius_x < 0 or radius_y < 0:
raise ValueError('area_ellipse() only accepts non-negative values' )
return pi * radius_x * radius_y
def _a ( __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : Optional[Any] ):
"""simple docstring"""
if diagonal_a < 0 or diagonal_a < 0:
raise ValueError('area_rhombus() only accepts non-negative values' )
return 1 / 2 * diagonal_a * diagonal_a
def _a ( __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : str ):
"""simple docstring"""
if not isinstance(__UpperCAmelCase , __UpperCAmelCase ) or sides < 3:
raise ValueError(
'area_reg_polygon() only accepts integers greater than or \\nequal to three as number of sides' )
elif length < 0:
raise ValueError(
'area_reg_polygon() only accepts non-negative values as \\nlength of a side' )
return (sides * length**2) / (4 * tan(pi / sides ))
return (sides * length**2) / (4 * tan(pi / sides ))
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True) # verbose so we can see methods missing tests
print('[DEMO] Areas of various geometric shapes: \n')
print(F"Rectangle: {area_rectangle(10, 20) = }")
print(F"Square: {area_square(10) = }")
print(F"Triangle: {area_triangle(10, 10) = }")
print(F"Triangle: {area_triangle_three_sides(5, 12, 13) = }")
print(F"Parallelogram: {area_parallelogram(10, 20) = }")
print(F"Rhombus: {area_rhombus(10, 20) = }")
print(F"Trapezium: {area_trapezium(10, 20, 30) = }")
print(F"Circle: {area_circle(20) = }")
print(F"Ellipse: {area_ellipse(10, 20) = }")
print('\nSurface Areas of various geometric shapes: \n')
print(F"Cube: {surface_area_cube(20) = }")
print(F"Cuboid: {surface_area_cuboid(10, 20, 30) = }")
print(F"Sphere: {surface_area_sphere(20) = }")
print(F"Hemisphere: {surface_area_hemisphere(20) = }")
print(F"Cone: {surface_area_cone(10, 20) = }")
print(F"Conical Frustum: {surface_area_conical_frustum(10, 20, 30) = }")
print(F"Cylinder: {surface_area_cylinder(10, 20) = }")
print(F"Torus: {surface_area_torus(20, 10) = }")
print(F"Equilateral Triangle: {area_reg_polygon(3, 10) = }")
print(F"Square: {area_reg_polygon(4, 10) = }")
print(F"Reqular Pentagon: {area_reg_polygon(5, 10) = }")
| 713 |
def _a ( __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : List[str] ):
"""simple docstring"""
_lowerCAmelCase = 0
_lowerCAmelCase = len(__SCREAMING_SNAKE_CASE ) - 1
while left <= right:
# avoid divided by 0 during interpolation
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
_lowerCAmelCase = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(__SCREAMING_SNAKE_CASE ):
return None
_lowerCAmelCase = sorted_collection[point]
if current_item == item:
return point
else:
if point < left:
_lowerCAmelCase = left
_lowerCAmelCase = point
elif point > right:
_lowerCAmelCase = right
_lowerCAmelCase = point
else:
if item < current_item:
_lowerCAmelCase = point - 1
else:
_lowerCAmelCase = point + 1
return None
def _a ( __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
if sorted_collection[left] == sorted_collection[right]:
if sorted_collection[left] == item:
return left
else:
return None
_lowerCAmelCase = left + ((item - sorted_collection[left]) * (right - left)) // (
sorted_collection[right] - sorted_collection[left]
)
# out of range check
if point < 0 or point >= len(__SCREAMING_SNAKE_CASE ):
return None
if sorted_collection[point] == item:
return point
elif point < left:
return interpolation_search_by_recursion(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
elif point > right:
return interpolation_search_by_recursion(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
else:
if sorted_collection[point] > item:
return interpolation_search_by_recursion(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , point - 1 )
else:
return interpolation_search_by_recursion(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , point + 1 , __SCREAMING_SNAKE_CASE )
def _a ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if collection != sorted(__SCREAMING_SNAKE_CASE ):
raise ValueError('Collection must be ascending sorted' )
return True
if __name__ == "__main__":
import sys
_UpperCamelCase: Tuple =0
if debug == 1:
_UpperCamelCase: int =[10, 30, 40, 45, 50, 66, 77, 93]
try:
__assert_sorted(collection)
except ValueError:
sys.exit('Sequence must be ascending sorted to apply interpolation search')
_UpperCamelCase: str =67
_UpperCamelCase: List[str] =interpolation_search(collection, target)
if result is not None:
print(F"{target} found at positions: {result}")
else:
print('Not found')
| 585 | 0 |
import random
from .binary_exp_mod import bin_exp_mod
def _A ( lowerCAmelCase_ : Optional[Any] , lowerCAmelCase_ : Optional[int]=1000 ):
"""simple docstring"""
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowerCAmelCase__ = n - 1
lowerCAmelCase__ = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
lowerCAmelCase__ = 0
while count < prec:
lowerCAmelCase__ = random.randint(2 , n - 1 )
lowerCAmelCase__ = bin_exp_mod(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
if b != 1:
lowerCAmelCase__ = True
for _ in range(lowerCAmelCase_ ):
if b == n - 1:
lowerCAmelCase__ = False
break
lowerCAmelCase__ = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
UpperCamelCase = abs(int(input('Enter bound : ').strip()))
print('Here\'s the list of primes:')
print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 61 |
import unittest
from transformers import MraConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_torch_available():
import torch
from transformers import (
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
MraModel,
)
from transformers.models.mra.modeling_mra import MRA_PRETRAINED_MODEL_ARCHIVE_LIST
class _a :
def __init__(self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=8, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=99, SCREAMING_SNAKE_CASE_=16, SCREAMING_SNAKE_CASE_=5, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=36, SCREAMING_SNAKE_CASE_="gelu", SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=512, SCREAMING_SNAKE_CASE_=16, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=0.0_2, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=4, SCREAMING_SNAKE_CASE_=None, ) -> str:
UpperCAmelCase_: int = parent
UpperCAmelCase_: Dict = batch_size
UpperCAmelCase_: Optional[int] = seq_length
UpperCAmelCase_: int = is_training
UpperCAmelCase_: List[Any] = use_input_mask
UpperCAmelCase_: int = use_token_type_ids
UpperCAmelCase_: Tuple = use_labels
UpperCAmelCase_: Tuple = vocab_size
UpperCAmelCase_: int = hidden_size
UpperCAmelCase_: List[str] = num_hidden_layers
UpperCAmelCase_: List[str] = num_attention_heads
UpperCAmelCase_: Any = intermediate_size
UpperCAmelCase_: str = hidden_act
UpperCAmelCase_: Optional[int] = hidden_dropout_prob
UpperCAmelCase_: Optional[Any] = attention_probs_dropout_prob
UpperCAmelCase_: List[str] = max_position_embeddings
UpperCAmelCase_: Optional[int] = type_vocab_size
UpperCAmelCase_: Tuple = type_sequence_label_size
UpperCAmelCase_: Tuple = initializer_range
UpperCAmelCase_: str = num_labels
UpperCAmelCase_: str = num_choices
UpperCAmelCase_: Optional[int] = scope
def __snake_case (self ) -> Optional[Any]:
UpperCAmelCase_: Tuple = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
UpperCAmelCase_: List[str] = None
if self.use_input_mask:
UpperCAmelCase_: Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase_: List[str] = None
if self.use_token_type_ids:
UpperCAmelCase_: str = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
UpperCAmelCase_: str = None
UpperCAmelCase_: List[Any] = None
UpperCAmelCase_: Tuple = None
if self.use_labels:
UpperCAmelCase_: Tuple = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase_: str = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
UpperCAmelCase_: Tuple = ids_tensor([self.batch_size], self.num_choices )
UpperCAmelCase_: List[Any] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __snake_case (self ) -> List[Any]:
return MraConfig(
vocab_size=self.vocab_size, hidden_size=self.hidden_size, num_hidden_layers=self.num_hidden_layers, num_attention_heads=self.num_attention_heads, intermediate_size=self.intermediate_size, hidden_act=self.hidden_act, hidden_dropout_prob=self.hidden_dropout_prob, attention_probs_dropout_prob=self.attention_probs_dropout_prob, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, is_decoder=SCREAMING_SNAKE_CASE_, initializer_range=self.initializer_range, )
def __snake_case (self ) -> Optional[int]:
UpperCAmelCase_: Optional[Any] = self.get_config()
UpperCAmelCase_: Dict = 300
return config
def __snake_case (self ) -> List[Any]:
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
): Any = self.prepare_config_and_inputs()
UpperCAmelCase_: List[str] = True
UpperCAmelCase_: Any = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
UpperCAmelCase_: List[str] = ids_tensor([self.batch_size, self.seq_length], vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Optional[Any]:
UpperCAmelCase_: Optional[int] = MraModel(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCAmelCase_: Optional[Any] = model(SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Tuple = model(SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, ) -> Union[str, Any]:
UpperCAmelCase_: Any = True
UpperCAmelCase_: Union[str, Any] = MraModel(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCAmelCase_: Optional[Any] = model(
SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_, encoder_hidden_states=SCREAMING_SNAKE_CASE_, encoder_attention_mask=SCREAMING_SNAKE_CASE_, )
UpperCAmelCase_: List[Any] = model(
SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_, encoder_hidden_states=SCREAMING_SNAKE_CASE_, )
UpperCAmelCase_: Tuple = model(SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[Any]:
UpperCAmelCase_: int = MraForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCAmelCase_: int = model(SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_, labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[str]:
UpperCAmelCase_: Any = MraForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCAmelCase_: str = model(
SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_, start_positions=SCREAMING_SNAKE_CASE_, end_positions=SCREAMING_SNAKE_CASE_, )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> Optional[int]:
UpperCAmelCase_: List[str] = self.num_labels
UpperCAmelCase_: Any = MraForSequenceClassification(SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCAmelCase_: List[str] = model(SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_, labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[Any]:
UpperCAmelCase_: Tuple = self.num_labels
UpperCAmelCase_: Any = MraForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCAmelCase_: List[str] = model(SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_, labels=SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ) -> List[Any]:
UpperCAmelCase_: Optional[Any] = self.num_choices
UpperCAmelCase_: int = MraForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
model.to(SCREAMING_SNAKE_CASE_ )
model.eval()
UpperCAmelCase_: Dict = input_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
UpperCAmelCase_: List[str] = token_type_ids.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
UpperCAmelCase_: List[Any] = input_mask.unsqueeze(1 ).expand(-1, self.num_choices, -1 ).contiguous()
UpperCAmelCase_: List[Any] = model(
SCREAMING_SNAKE_CASE_, attention_mask=SCREAMING_SNAKE_CASE_, token_type_ids=SCREAMING_SNAKE_CASE_, labels=SCREAMING_SNAKE_CASE_, )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) )
def __snake_case (self ) -> Union[str, Any]:
UpperCAmelCase_: int = self.prepare_config_and_inputs()
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
): Any = config_and_inputs
UpperCAmelCase_: Dict = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class _a ( _lowerCAmelCase , unittest.TestCase ):
A = (
(
MraModel,
MraForMaskedLM,
MraForMultipleChoice,
MraForQuestionAnswering,
MraForSequenceClassification,
MraForTokenClassification,
)
if is_torch_available()
else ()
)
A = False
A = False
A = False
A = False
A = ()
def __snake_case (self ) -> Tuple:
UpperCAmelCase_: Dict = MraModelTester(self )
UpperCAmelCase_: List[Any] = ConfigTester(self, config_class=SCREAMING_SNAKE_CASE_, hidden_size=37 )
def __snake_case (self ) -> Tuple:
self.config_tester.run_common_tests()
def __snake_case (self ) -> str:
UpperCAmelCase_: int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Optional[int]:
UpperCAmelCase_: List[str] = self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
UpperCAmelCase_: str = type
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Union[str, Any]:
UpperCAmelCase_: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> List[str]:
UpperCAmelCase_: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Dict:
UpperCAmelCase_: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Tuple:
UpperCAmelCase_: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Any:
UpperCAmelCase_: str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
@slow
def __snake_case (self ) -> Any:
for model_name in MRA_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase_: List[str] = MraModel.from_pretrained(SCREAMING_SNAKE_CASE_ )
self.assertIsNotNone(SCREAMING_SNAKE_CASE_ )
@unittest.skip(reason="""MRA does not output attentions""" )
def __snake_case (self ) -> List[str]:
return
@require_torch
class _a ( unittest.TestCase ):
@slow
def __snake_case (self ) -> Tuple:
UpperCAmelCase_: Union[str, Any] = MraModel.from_pretrained("""uw-madison/mra-base-512-4""" )
UpperCAmelCase_: Optional[Any] = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
UpperCAmelCase_: Optional[Any] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCAmelCase_: Optional[int] = torch.Size((1, 256, 768) )
self.assertEqual(output.shape, SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: int = torch.tensor(
[[[-0.0_1_4_0, 0.0_8_3_0, -0.0_3_8_1], [0.1_5_4_6, 0.1_4_0_2, 0.0_2_2_0], [0.1_1_6_2, 0.0_8_5_1, 0.0_1_6_5]]] )
self.assertTrue(torch.allclose(output[:, :3, :3], SCREAMING_SNAKE_CASE_, atol=1E-4 ) )
@slow
def __snake_case (self ) -> Any:
UpperCAmelCase_: List[str] = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-512-4""" )
UpperCAmelCase_: Dict = torch.arange(256 ).unsqueeze(0 )
with torch.no_grad():
UpperCAmelCase_: List[str] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCAmelCase_: Any = 50265
UpperCAmelCase_: Any = torch.Size((1, 256, vocab_size) )
self.assertEqual(output.shape, SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Dict = torch.tensor(
[[[9.2_5_9_5, -3.6_0_3_8, 1_1.8_8_1_9], [9.3_8_6_9, -3.2_6_9_3, 1_1.0_9_5_6], [1_1.8_5_2_4, -3.4_9_3_8, 1_3.1_2_1_0]]] )
self.assertTrue(torch.allclose(output[:, :3, :3], SCREAMING_SNAKE_CASE_, atol=1E-4 ) )
@slow
def __snake_case (self ) -> Optional[int]:
UpperCAmelCase_: Optional[int] = MraForMaskedLM.from_pretrained("""uw-madison/mra-base-4096-8-d3""" )
UpperCAmelCase_: List[Any] = torch.arange(4096 ).unsqueeze(0 )
with torch.no_grad():
UpperCAmelCase_: List[Any] = model(SCREAMING_SNAKE_CASE_ )[0]
UpperCAmelCase_: Dict = 50265
UpperCAmelCase_: int = torch.Size((1, 4096, vocab_size) )
self.assertEqual(output.shape, SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: List[Any] = torch.tensor(
[[[5.4_7_8_9, -2.3_5_6_4, 7.5_0_6_4], [7.9_0_6_7, -1.3_3_6_9, 9.9_6_6_8], [9.0_7_1_2, -1.8_1_0_6, 7.0_3_8_0]]] )
self.assertTrue(torch.allclose(output[:, :3, :3], SCREAMING_SNAKE_CASE_, atol=1E-4 ) )
| 556 | 0 |
'''simple docstring'''
import math
import sys
def _UpperCamelCase ( __A ) -> int:
'''simple docstring'''
if number != int(__A ):
raise ValueError("the value of input must be a natural number" )
if number < 0:
raise ValueError("the value of input must not be a negative number" )
if number == 0:
return 1
UpperCamelCase__ = [-1] * (number + 1)
UpperCamelCase__ = 0
for i in range(1 , number + 1 ):
UpperCamelCase__ = sys.maxsize
UpperCamelCase__ = int(math.sqrt(__A ) )
for j in range(1 , root + 1 ):
UpperCamelCase__ = 1 + answers[i - (j**2)]
UpperCamelCase__ = min(__A , __A )
UpperCamelCase__ = answer
return answers[number]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 711 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ ( a__ , unittest.TestCase ):
__UpperCAmelCase = BioGptTokenizer
__UpperCAmelCase = False
def __a ( self ):
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
UpperCamelCase__ = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"w</w>",
"r</w>",
"t</w>",
"lo",
"low",
"er</w>",
"low</w>",
"lowest</w>",
"newer</w>",
"wider</w>",
"<unk>",
]
UpperCamelCase__ = dict(zip(a , range(len(a ) ) ) )
UpperCamelCase__ = ["l o 123", "lo w 1456", "e r</w> 1789", ""]
UpperCamelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["vocab_file"] )
UpperCamelCase__ = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file , "w" ) as fp:
fp.write(json.dumps(a ) )
with open(self.merges_file , "w" ) as fp:
fp.write("\n".join(a ) )
def __a ( self , a ):
UpperCamelCase__ = "lower newer"
UpperCamelCase__ = "lower newer"
return input_text, output_text
def __a ( self ):
UpperCamelCase__ = BioGptTokenizer(self.vocab_file , self.merges_file )
UpperCamelCase__ = "lower"
UpperCamelCase__ = ["low", "er</w>"]
UpperCamelCase__ = tokenizer.tokenize(a )
self.assertListEqual(a , a )
UpperCamelCase__ = tokens + ["<unk>"]
UpperCamelCase__ = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(a ) , a )
@slow
def __a ( self ):
UpperCamelCase__ = BioGptTokenizer.from_pretrained("microsoft/biogpt" )
UpperCamelCase__ = tokenizer.encode("sequence builders" , add_special_tokens=a )
UpperCamelCase__ = tokenizer.encode("multi-sequence build" , add_special_tokens=a )
UpperCamelCase__ = tokenizer.build_inputs_with_special_tokens(a )
UpperCamelCase__ = tokenizer.build_inputs_with_special_tokens(a , a )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 223 | 0 |
from collections.abc import Generator
def _A () ->Generator[int, None, None]:
'''simple docstring'''
lowerCamelCase__ ,lowerCamelCase__ : Union[str, Any] = 0, 1
while True:
lowerCamelCase__ ,lowerCamelCase__ : Any = b, a + b
yield b
def _A (UpperCamelCase : int = 1000 ) ->int:
'''simple docstring'''
lowerCamelCase__ : Tuple = 1
lowerCamelCase__ : Dict = fibonacci_generator()
while len(str(next(UpperCamelCase ) ) ) < n:
answer += 1
return answer + 1
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 157 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_lowercase = logging.get_logger(__name__) # pylint: disable=invalid-name
_lowercase = '''
Examples:
```py
>>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline
>>> import torch
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained("kandinsky-community/kandinsky-2-2-prior")
>>> pipe_prior.to("cuda")
>>> prompt = "red cat, 4k photo"
>>> out = pipe_prior(prompt)
>>> image_emb = out.image_embeds
>>> zero_image_emb = out.negative_image_embeds
>>> pipe = KandinskyV22Pipeline.from_pretrained("kandinsky-community/kandinsky-2-2-decoder")
>>> pipe.to("cuda")
>>> image = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... height=768,
... width=768,
... num_inference_steps=50,
... ).images
>>> image[0].save("cat.png")
```
'''
def _A (UpperCamelCase : Any , UpperCamelCase : Tuple , UpperCamelCase : List[Any]=8 ) ->Optional[int]:
'''simple docstring'''
lowerCamelCase__ : Optional[int] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
lowerCamelCase__ : int = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class __A ( A_ ):
def __init__(self , __magic_name__ , __magic_name__ , __magic_name__ , ):
super().__init__()
self.register_modules(
unet=__magic_name__ , scheduler=__magic_name__ , movq=__magic_name__ , )
lowerCamelCase__ : Union[str, Any] = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def _snake_case (self , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ ):
if latents is None:
lowerCamelCase__ : Any = randn_tensor(__magic_name__ , generator=__magic_name__ , device=__magic_name__ , dtype=__magic_name__ )
else:
if latents.shape != shape:
raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}" )
lowerCamelCase__ : Optional[int] = latents.to(__magic_name__ )
lowerCamelCase__ : Dict = latents * scheduler.init_noise_sigma
return latents
def _snake_case (self , __magic_name__=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
lowerCamelCase__ : int = torch.device(f"cuda:{gpu_id}" )
lowerCamelCase__ : Any = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__magic_name__ , __magic_name__ )
def _snake_case (self , __magic_name__=0 ):
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
lowerCamelCase__ : Any = torch.device(f"cuda:{gpu_id}" )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=__magic_name__ )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
lowerCamelCase__ : str = None
for cpu_offloaded_model in [self.unet, self.movq]:
lowerCamelCase__ ,lowerCamelCase__ : Optional[Any] = cpu_offload_with_hook(__magic_name__ , __magic_name__ , prev_module_hook=__magic_name__ )
# We'll offload the last model manually.
lowerCamelCase__ : Tuple = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def _snake_case (self ):
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(__magic_name__ , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__magic_name__ )
def __call__(self , __magic_name__ , __magic_name__ , __magic_name__ = 512 , __magic_name__ = 512 , __magic_name__ = 100 , __magic_name__ = 4.0 , __magic_name__ = 1 , __magic_name__ = None , __magic_name__ = None , __magic_name__ = "pil" , __magic_name__ = True , ):
lowerCamelCase__ : Any = self._execution_device
lowerCamelCase__ : List[Any] = guidance_scale > 1.0
if isinstance(__magic_name__ , __magic_name__ ):
lowerCamelCase__ : List[Any] = torch.cat(__magic_name__ , dim=0 )
lowerCamelCase__ : str = image_embeds.shape[0] * num_images_per_prompt
if isinstance(__magic_name__ , __magic_name__ ):
lowerCamelCase__ : Tuple = torch.cat(__magic_name__ , dim=0 )
if do_classifier_free_guidance:
lowerCamelCase__ : Tuple = image_embeds.repeat_interleave(__magic_name__ , dim=0 )
lowerCamelCase__ : str = negative_image_embeds.repeat_interleave(__magic_name__ , dim=0 )
lowerCamelCase__ : Any = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__magic_name__ )
self.scheduler.set_timesteps(__magic_name__ , device=__magic_name__ )
lowerCamelCase__ : Optional[int] = self.scheduler.timesteps
lowerCamelCase__ : Tuple = self.unet.config.in_channels
lowerCamelCase__ ,lowerCamelCase__ : int = downscale_height_and_width(__magic_name__ , __magic_name__ , self.movq_scale_factor )
# create initial latent
lowerCamelCase__ : Union[str, Any] = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __magic_name__ , __magic_name__ , __magic_name__ , self.scheduler , )
for i, t in enumerate(self.progress_bar(__magic_name__ ) ):
# expand the latents if we are doing classifier free guidance
lowerCamelCase__ : List[str] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
lowerCamelCase__ : str = {"""image_embeds""": image_embeds}
lowerCamelCase__ : Any = self.unet(
sample=__magic_name__ , timestep=__magic_name__ , encoder_hidden_states=__magic_name__ , added_cond_kwargs=__magic_name__ , return_dict=__magic_name__ , )[0]
if do_classifier_free_guidance:
lowerCamelCase__ ,lowerCamelCase__ : List[str] = noise_pred.split(latents.shape[1] , dim=1 )
lowerCamelCase__ ,lowerCamelCase__ : Any = noise_pred.chunk(2 )
lowerCamelCase__ ,lowerCamelCase__ : str = variance_pred.chunk(2 )
lowerCamelCase__ : Dict = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
lowerCamelCase__ : List[str] = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
lowerCamelCase__ ,lowerCamelCase__ : Dict = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
lowerCamelCase__ : Tuple = self.scheduler.step(
__magic_name__ , __magic_name__ , __magic_name__ , generator=__magic_name__ , )[0]
# post-processing
lowerCamelCase__ : Optional[int] = self.movq.decode(__magic_name__ , force_not_quantize=__magic_name__ )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}" )
if output_type in ["np", "pil"]:
lowerCamelCase__ : Tuple = image * 0.5 + 0.5
lowerCamelCase__ : Tuple = image.clamp(0 , 1 )
lowerCamelCase__ : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
lowerCamelCase__ : Tuple = self.numpy_to_pil(__magic_name__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__magic_name__ )
| 157 | 1 |
import random
from typing import Any
def snake_case ( lowerCamelCase ):
'''simple docstring'''
for _ in range(len(__snake_case ) ):
__lowercase = random.randint(0 , len(__snake_case ) - 1 )
__lowercase = random.randint(0 , len(__snake_case ) - 1 )
__lowercase , __lowercase = data[b], data[a]
return data
if __name__ == "__main__":
__UpperCamelCase : Optional[int] = [0, 1, 2, 3, 4, 5, 6, 7]
__UpperCamelCase : int = ["""python""", """says""", """hello""", """!"""]
print("""Fisher-Yates Shuffle:""")
print("""List""", integers, strings)
print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))
| 707 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestCase ):
__snake_case :Union[str, Any] = 'hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'
def _a ( self : Any , _lowerCAmelCase : str=0 ) -> str:
"""simple docstring"""
__lowercase = np.random.RandomState(_lowerCAmelCase )
__lowercase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def _a ( self : int ) -> List[Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.65_072, 0.58_492, 0.48_219, 0.55_521, 0.53_180, 0.55_939, 0.50_697, 0.39_800, 0.46_455] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ) -> str:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.65_863, 0.59_425, 0.49_326, 0.56_313, 0.53_875, 0.56_627, 0.51_065, 0.39_777, 0.46_330] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Optional[int] ) -> Optional[int]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Tuple ) -> int:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_755, 0.60_786, 0.47_402, 0.49_488, 0.51_869, 0.49_819, 0.47_985, 0.38_957, 0.44_279] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Tuple ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_817, 0.60_812, 0.47_384, 0.49_530, 0.51_894, 0.49_814, 0.47_984, 0.38_958, 0.44_271] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
__lowercase = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = pipe(**_lowerCAmelCase ).images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 128, 128, 3)
__lowercase = np.array([0.53_895, 0.60_808, 0.47_933, 0.49_608, 0.51_886, 0.49_950, 0.48_053, 0.38_957, 0.44_200] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def _a ( self : List[str] ) -> List[str]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs["""prompt"""]]
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs.pop("""prompt""" )]
__lowercase = pipe.tokenizer(
_lowerCAmelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = text_inputs["""input_ids"""]
__lowercase = pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]
__lowercase = prompt_embeds
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
def _a ( self : int ) -> str:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * ["""this is a negative prompt"""]
__lowercase = negative_prompt
__lowercase = 3 * [inputs["""prompt"""]]
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
__lowercase = self.get_dummy_inputs()
__lowercase = 3 * [inputs.pop("""prompt""" )]
__lowercase = []
for p in [prompt, negative_prompt]:
__lowercase = pipe.tokenizer(
_lowerCAmelCase , padding="""max_length""" , max_length=pipe.tokenizer.model_max_length , truncation=_lowerCAmelCase , return_tensors="""np""" , )
__lowercase = text_inputs["""input_ids"""]
embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )
__lowercase , __lowercase = embeds
# forward
__lowercase = pipe(**_lowerCAmelCase )
__lowercase = output.images[0, -3:, -3:, -1]
assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1e-4
@nightly
@require_onnxruntime
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
@property
def _a ( self : Dict ) -> str:
"""simple docstring"""
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def _a ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase = ort.SessionOptions()
__lowercase = False
return options
def _a ( self : List[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""CompVis/stable-diffusion-v1-4""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """A painting of a squirrel eating a burger"""
np.random.seed(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=6.0 , num_inference_steps=10 , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.0_452, 0.0_390, 0.0_087, 0.0_350, 0.0_617, 0.0_364, 0.0_544, 0.0_523, 0.0_720] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Tuple ) -> Any:
"""simple docstring"""
__lowercase = DDIMScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """open neural network exchange"""
__lowercase = np.random.RandomState(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.2_867, 0.1_974, 0.1_481, 0.7_294, 0.7_251, 0.6_667, 0.4_194, 0.5_642, 0.6_486] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : Dict ) -> Dict:
"""simple docstring"""
__lowercase = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , subfolder="""scheduler""" , revision="""onnx""" )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , scheduler=_lowerCAmelCase , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
sd_pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """open neural network exchange"""
__lowercase = np.random.RandomState(0 )
__lowercase = sd_pipe([prompt] , guidance_scale=7.5 , num_inference_steps=10 , generator=_lowerCAmelCase , output_type="""np""" )
__lowercase = output.images
__lowercase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__lowercase = np.array([0.2_306, 0.1_959, 0.1_593, 0.6_549, 0.6_394, 0.5_408, 0.5_065, 0.6_010, 0.6_161] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _a ( self : str ) -> List[str]:
"""simple docstring"""
__lowercase = 0
def test_callback_fn(_lowerCAmelCase : int , _lowerCAmelCase : int , _lowerCAmelCase : np.ndarray ) -> None:
__lowercase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 0:
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array(
[-0.6_772, -0.3_835, -1.2_456, 0.1_905, -1.0_974, 0.6_967, -1.9_353, 0.0_178, 1.0_167] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
elif step == 5:
assert latents.shape == (1, 4, 64, 64)
__lowercase = latents[0, -3:, -3:, -1]
__lowercase = np.array(
[-0.3_351, 0.2_241, -0.1_837, -0.2_325, -0.6_577, 0.3_393, -0.0_241, 0.5_899, 1.3_875] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1e-3
__lowercase = False
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
__lowercase = """Andromeda galaxy in a bottle"""
__lowercase = np.random.RandomState(0 )
pipe(
prompt=_lowerCAmelCase , num_inference_steps=5 , guidance_scale=7.5 , generator=_lowerCAmelCase , callback=_lowerCAmelCase , callback_steps=1 , )
assert test_callback_fn.has_been_called
assert number_of_steps == 6
def _a ( self : Union[str, Any] ) -> Tuple:
"""simple docstring"""
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(
"""runwayml/stable-diffusion-v1-5""" , revision="""onnx""" , safety_checker=_lowerCAmelCase , feature_extractor=_lowerCAmelCase , provider=self.gpu_provider , sess_options=self.gpu_options , )
assert isinstance(_lowerCAmelCase , _lowerCAmelCase )
assert pipe.safety_checker is None
__lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
# check that there's no error when saving a pipeline with one of the models being None
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCAmelCase )
__lowercase = OnnxStableDiffusionPipeline.from_pretrained(_lowerCAmelCase )
# sanity check that the pipeline still works
assert pipe.safety_checker is None
__lowercase = pipe("""example prompt""" , num_inference_steps=2 ).images[0]
assert image is not None
| 53 | 0 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def snake_case__ ( UpperCAmelCase : Tuple , UpperCAmelCase : Optional[int] , UpperCAmelCase : List[str] , UpperCAmelCase : str ):
if isinstance(UpperCAmelCase , UpperCAmelCase ):
lowerCAmelCase__ :Optional[int] = np.full((len(UpperCAmelCase ), sequence_length, 2) , UpperCAmelCase )
else:
lowerCAmelCase__ :Union[str, Any] = np.full((len(UpperCAmelCase ), sequence_length) , UpperCAmelCase )
for i, tensor in enumerate(UpperCAmelCase ):
if padding_side == "right":
if isinstance(UpperCAmelCase , UpperCAmelCase ):
lowerCAmelCase__ :int = tensor[:sequence_length]
else:
lowerCAmelCase__ :int = tensor[:sequence_length]
else:
if isinstance(UpperCAmelCase , UpperCAmelCase ):
lowerCAmelCase__ :Union[str, Any] = tensor[:sequence_length]
else:
lowerCAmelCase__ :List[Any] = tensor[:sequence_length]
return out_tensor.tolist()
def snake_case__ ( UpperCAmelCase : Optional[Any] ):
lowerCAmelCase__ :str = ord(UpperCAmelCase )
if (cp >= 3_3 and cp <= 4_7) or (cp >= 5_8 and cp <= 6_4) or (cp >= 9_1 and cp <= 9_6) or (cp >= 1_2_3 and cp <= 1_2_6):
return True
lowerCAmelCase__ :str = unicodedata.category(UpperCAmelCase )
if cat.startswith("P" ):
return True
return False
@dataclass
class _UpperCAmelCase ( _A ):
"""simple docstring"""
A = 42
A = True
A = None
A = None
A = -1_00
A = "pt"
def snake_case_ ( self , _lowerCAmelCase ):
'''simple docstring'''
import torch
lowerCAmelCase__ :Optional[int] = "label" if "label" in features[0].keys() else "labels"
lowerCAmelCase__ :List[Any] = [feature[label_name] for feature in features] if label_name in features[0].keys() else None
lowerCAmelCase__ :Optional[Any] = self.tokenizer.pad(
_lowerCAmelCase , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="pt" if labels is None else None , )
if labels is None:
return batch
lowerCAmelCase__ :Any = torch.tensor(batch["entity_ids"] ).shape[1]
lowerCAmelCase__ :Dict = self.tokenizer.padding_side
if padding_side == "right":
lowerCAmelCase__ :int = [
list(_lowerCAmelCase ) + [self.label_pad_token_id] * (sequence_length - len(_lowerCAmelCase )) for label in labels
]
else:
lowerCAmelCase__ :str = [
[self.label_pad_token_id] * (sequence_length - len(_lowerCAmelCase )) + list(_lowerCAmelCase ) for label in labels
]
lowerCAmelCase__ :Tuple = [feature["ner_tags"] for feature in features]
lowerCAmelCase__ :str = padding_tensor(_lowerCAmelCase , -1 , _lowerCAmelCase , _lowerCAmelCase )
lowerCAmelCase__ :List[Any] = [feature["original_entity_spans"] for feature in features]
lowerCAmelCase__ :Optional[int] = padding_tensor(_lowerCAmelCase , (-1, -1) , _lowerCAmelCase , _lowerCAmelCase )
lowerCAmelCase__ :List[str] = {k: torch.tensor(_lowerCAmelCase , dtype=torch.intaa ) for k, v in batch.items()}
return batch
| 145 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
_a : int = False
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def snake_case_ ( self ):
'''simple docstring'''
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_ ( self ):
'''simple docstring'''
lowerCAmelCase__ :Any = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCAmelCase__ :Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
lowerCAmelCase__ :Dict = torch.manual_seed(0 )
lowerCAmelCase__ :List[str] = pipe.dual_guided(
prompt="first prompt" , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" , ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(_lowerCAmelCase )
lowerCAmelCase__ :Dict = VersatileDiffusionPipeline.from_pretrained(_lowerCAmelCase , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCAmelCase__ :List[Any] = generator.manual_seed(0 )
lowerCAmelCase__ :Any = pipe.dual_guided(
prompt="first prompt" , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" , ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def snake_case_ ( self ):
'''simple docstring'''
lowerCAmelCase__ :Tuple = VersatileDiffusionPipeline.from_pretrained("shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
lowerCAmelCase__ :Optional[Any] = "cyberpunk 2077"
lowerCAmelCase__ :List[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/versatile_diffusion/benz.jpg" )
lowerCAmelCase__ :Tuple = torch.manual_seed(0 )
lowerCAmelCase__ :Any = pipe.dual_guided(
prompt=_lowerCAmelCase , image=_lowerCAmelCase , text_to_image_strength=0.75 , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" , ).images
lowerCAmelCase__ :List[Any] = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase__ :str = np.array([0.1448, 0.1619, 0.1741, 0.1086, 0.1147, 0.1128, 0.1199, 0.1165, 0.1001] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
lowerCAmelCase__ :Tuple = "A painting of a squirrel eating a burger "
lowerCAmelCase__ :str = torch.manual_seed(0 )
lowerCAmelCase__ :Tuple = pipe.text_to_image(
prompt=_lowerCAmelCase , generator=_lowerCAmelCase , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" ).images
lowerCAmelCase__ :int = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase__ :Optional[Any] = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
lowerCAmelCase__ :Any = pipe.image_variation(_lowerCAmelCase , generator=_lowerCAmelCase , output_type="numpy" ).images
lowerCAmelCase__ :Optional[int] = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase__ :int = np.array([0.3076, 0.3123, 0.3284, 0.3782, 0.3770, 0.3894, 0.4297, 0.4331, 0.4456] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1
| 145 | 1 |
"""simple docstring"""
import copy
import tempfile
import unittest
from huggingface_hub import HfFolder, delete_repo
from parameterized import parameterized
from requests.exceptions import HTTPError
from transformers import AutoConfig, GenerationConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@parameterized.expand([(None,), ("""foo.json""",)] )
def UpperCamelCase__ ( self ,lowerCamelCase_ ) -> int:
A = GenerationConfig(
do_sample=lowerCamelCase_ ,temperature=0.7 ,length_penalty=1.0 ,bad_words_ids=[[1, 2, 3], [4, 5]] ,)
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase_ ,config_name=lowerCamelCase_ )
A = GenerationConfig.from_pretrained(lowerCamelCase_ ,config_name=lowerCamelCase_ )
# Checks parameters that were specified
self.assertEqual(loaded_config.do_sample ,lowerCamelCase_ )
self.assertEqual(loaded_config.temperature ,0.7 )
self.assertEqual(loaded_config.length_penalty ,1.0 )
self.assertEqual(loaded_config.bad_words_ids ,[[1, 2, 3], [4, 5]] )
# Checks parameters that were not specified (defaults)
self.assertEqual(loaded_config.top_k ,5_0 )
self.assertEqual(loaded_config.max_length ,2_0 )
self.assertEqual(loaded_config.max_time ,lowerCamelCase_ )
def UpperCamelCase__ ( self ) -> Optional[Any]:
A = AutoConfig.from_pretrained("""gpt2""" )
A = GenerationConfig.from_model_config(lowerCamelCase_ )
A = GenerationConfig()
# The generation config has loaded a few non-default parameters from the model config
self.assertNotEqual(lowerCamelCase_ ,lowerCamelCase_ )
# One of those parameters is eos_token_id -- check if it matches
self.assertNotEqual(generation_config_from_model.eos_token_id ,default_generation_config.eos_token_id )
self.assertEqual(generation_config_from_model.eos_token_id ,model_config.eos_token_id )
def UpperCamelCase__ ( self ) -> Dict:
A = GenerationConfig()
A = {
"""max_new_tokens""": 1_0_2_4,
"""foo""": """bar""",
}
A = copy.deepcopy(lowerCamelCase_ )
A = generation_config.update(**lowerCamelCase_ )
# update_kwargs was not modified (no side effects)
self.assertEqual(lowerCamelCase_ ,lowerCamelCase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(generation_config.max_new_tokens ,1_0_2_4 )
# `.update()` returns a dictionary of unused kwargs
self.assertEqual(lowerCamelCase_ ,{"""foo""": """bar"""} )
def UpperCamelCase__ ( self ) -> str:
A = GenerationConfig()
A = """bar"""
with tempfile.TemporaryDirectory("""test-generation-config""" ) as tmp_dir:
generation_config.save_pretrained(lowerCamelCase_ )
A = GenerationConfig.from_pretrained(lowerCamelCase_ )
# update_kwargs was used to update the config on valid attributes
self.assertEqual(new_config.foo ,"""bar""" )
A = GenerationConfig.from_model_config(lowerCamelCase_ )
assert not hasattr(lowerCamelCase_ ,"""foo""" ) # no new kwargs should be initialized if from config
def UpperCamelCase__ ( self ) -> Optional[int]:
A = GenerationConfig()
self.assertEqual(default_config.temperature ,1.0 )
self.assertEqual(default_config.do_sample ,lowerCamelCase_ )
self.assertEqual(default_config.num_beams ,1 )
A = GenerationConfig(
do_sample=lowerCamelCase_ ,temperature=0.7 ,length_penalty=1.0 ,bad_words_ids=[[1, 2, 3], [4, 5]] ,)
self.assertEqual(config.temperature ,0.7 )
self.assertEqual(config.do_sample ,lowerCamelCase_ )
self.assertEqual(config.num_beams ,1 )
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase_ )
A = GenerationConfig.from_pretrained(lowerCamelCase_ ,temperature=1.0 )
self.assertEqual(loaded_config.temperature ,1.0 )
self.assertEqual(loaded_config.do_sample ,lowerCamelCase_ )
self.assertEqual(loaded_config.num_beams ,1 ) # default value
@is_staging_test
class lowerCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def UpperCamelCase__ ( cls ) -> Dict:
A = TOKEN
HfFolder.save_token(lowerCamelCase_ )
@classmethod
def UpperCamelCase__ ( cls ) -> Tuple:
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 ) -> Dict:
A = GenerationConfig(
do_sample=lowerCamelCase_ ,temperature=0.7 ,length_penalty=1.0 ,)
config.push_to_hub("""test-generation-config""" ,use_auth_token=self._token )
A = GenerationConfig.from_pretrained(f'{USER}/test-generation-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_ ,getattr(lowerCamelCase_ ,lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token ,repo_id="""test-generation-config""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCamelCase_ ,repo_id="""test-generation-config""" ,push_to_hub=lowerCamelCase_ ,use_auth_token=self._token )
A = GenerationConfig.from_pretrained(f'{USER}/test-generation-config' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_ ,getattr(lowerCamelCase_ ,lowerCamelCase_ ) )
def UpperCamelCase__ ( self ) -> Tuple:
A = GenerationConfig(
do_sample=lowerCamelCase_ ,temperature=0.7 ,length_penalty=1.0 ,)
config.push_to_hub("""valid_org/test-generation-config-org""" ,use_auth_token=self._token )
A = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_ ,getattr(lowerCamelCase_ ,lowerCamelCase_ ) )
# Reset repo
delete_repo(token=self._token ,repo_id="""valid_org/test-generation-config-org""" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
lowerCamelCase_ ,repo_id="""valid_org/test-generation-config-org""" ,push_to_hub=lowerCamelCase_ ,use_auth_token=self._token )
A = GenerationConfig.from_pretrained("""valid_org/test-generation-config-org""" )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(lowerCamelCase_ ,getattr(lowerCamelCase_ ,lowerCamelCase_ ) )
| 255 |
"""simple docstring"""
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxSeqaSeqConfigWithPast
from ...utils import logging
UpperCAmelCase =logging.get_logger(__name__)
UpperCAmelCase ={
"google/umt5-small": "https://huggingface.co/google/umt5-small/resolve/main/config.json",
# See all umt5 models at https://huggingface.co/models?filter=umt5
}
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowerCamelCase = '''umt5'''
_lowerCamelCase = ['''past_key_values''']
def __init__( self ,lowerCamelCase_=2_5_0_1_1_2 ,lowerCamelCase_=5_1_2 ,lowerCamelCase_=6_4 ,lowerCamelCase_=1_0_2_4 ,lowerCamelCase_=8 ,lowerCamelCase_=None ,lowerCamelCase_=6 ,lowerCamelCase_=3_2 ,lowerCamelCase_=1_2_8 ,lowerCamelCase_=0.1 ,lowerCamelCase_=1E-6 ,lowerCamelCase_=1.0 ,lowerCamelCase_="gated-gelu" ,lowerCamelCase_=True ,lowerCamelCase_=True ,lowerCamelCase_="T5Tokenizer" ,lowerCamelCase_=True ,lowerCamelCase_=0 ,lowerCamelCase_=1 ,lowerCamelCase_=0 ,**lowerCamelCase_ ,) -> Dict:
super().__init__(
is_encoder_decoder=lowerCamelCase_ ,tokenizer_class=lowerCamelCase_ ,tie_word_embeddings=lowerCamelCase_ ,pad_token_id=lowerCamelCase_ ,eos_token_id=lowerCamelCase_ ,decoder_start_token_id=lowerCamelCase_ ,**lowerCamelCase_ ,)
A = vocab_size
A = d_model
A = d_kv
A = d_ff
A = num_layers
A = (
num_decoder_layers if num_decoder_layers is not None else self.num_layers
) # default = symmetry
A = num_heads
A = relative_attention_num_buckets
A = relative_attention_max_distance
A = dropout_rate
A = layer_norm_epsilon
A = initializer_factor
A = feed_forward_proj
A = use_cache
A = self.feed_forward_proj.split("""-""" )
A = act_info[-1]
A = act_info[0] == """gated"""
if len(lowerCamelCase_ ) > 1 and act_info[0] != "gated" or len(lowerCamelCase_ ) > 2:
raise ValueError(
f'`feed_forward_proj`: {feed_forward_proj} is not a valid activation function of the dense layer.'
"""Please make sure `feed_forward_proj` is of the format `gated-{ACT_FN}` or `{ACT_FN}`, e.g. """
"""'gated-gelu' or 'relu'""" )
if feed_forward_proj == "gated-gelu":
A = """gelu_new"""
@property
def UpperCamelCase__ ( self ) -> Dict:
return self.d_model
@property
def UpperCamelCase__ ( self ) -> Any:
return self.num_heads
@property
def UpperCamelCase__ ( self ) -> int:
return self.num_layers
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.inputs
def UpperCamelCase__ ( self ) -> Mapping[str, Mapping[int, str]]:
A = {
"""input_ids""": {0: """batch""", 1: """encoder_sequence"""},
"""attention_mask""": {0: """batch""", 1: """encoder_sequence"""},
}
if self.use_past:
A = """past_encoder_sequence + sequence"""
A = {0: """batch"""}
A = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
else:
A = {0: """batch""", 1: """decoder_sequence"""}
A = {0: """batch""", 1: """decoder_sequence"""}
if self.use_past:
self.fill_with_past_key_values_(lowerCamelCase_ ,direction="""inputs""" )
return common_inputs
@property
# Copied from transformers.models.t5.configuration_t5.T5OnnxConfig.default_onnx_opset
def UpperCamelCase__ ( self ) -> int:
return 1_3
@property
def UpperCamelCase__ ( self ) -> float:
return 5E-4
| 255 | 1 |
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('''0.12.2'''):
raise Exception('''requires fairseq >= 0.12.2''')
if version.parse(fairseq.__version__) > version.parse('''2'''):
raise Exception('''requires fairseq < v2''')
logging.set_verbosity_info()
__a : Any = logging.get_logger(__name__)
__a : Union[str, Any] = '''Hello, World!'''
__a : Dict = '''en_XX'''
def snake_case_ ( SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_ ) -> List[Any]:
lowercase__ : str = Path("data_bin" )
lowercase__ : str = FairseqXmodModel.from_pretrained(
model_name_or_path=str(Path(lowerCAmelCase__ ).parent ) ,checkpoint_file=Path(lowerCAmelCase__ ).name ,_name="xmod_base" ,arch="xmod_base" ,task="multilingual_masked_lm" ,data_name_or_path=str(lowerCAmelCase__ ) ,bpe="sentencepiece" ,sentencepiece_model=str(Path(lowerCAmelCase__ ).parent / "sentencepiece.bpe.model" ) ,src_dict=str(data_dir / "dict.txt" ) ,)
xmod.eval() # disable dropout
print(lowerCAmelCase__ )
lowercase__ : Optional[int] = xmod.model.encoder.sentence_encoder
lowercase__ : Any = XmodConfig(
vocab_size=xmod_sent_encoder.embed_tokens.num_embeddings ,hidden_size=xmod.cfg.model.encoder_embed_dim ,num_hidden_layers=xmod.cfg.model.encoder_layers ,num_attention_heads=xmod.cfg.model.encoder_attention_heads ,intermediate_size=xmod.cfg.model.encoder_ffn_embed_dim ,max_position_embeddings=5_14 ,type_vocab_size=1 ,layer_norm_eps=1E-5 ,pre_norm=xmod.cfg.model.encoder_normalize_before ,adapter_reduction_factor=getattr(xmod.cfg.model ,"bottleneck" ,2 ) ,adapter_layer_norm=xmod.cfg.model.adapter_layer_norm ,adapter_reuse_layer_norm=xmod.cfg.model.adapter_reuse_layer_norm ,ln_before_adapter=xmod.cfg.model.ln_before_adapter ,languages=xmod.cfg.model.languages ,)
if classification_head:
lowercase__ : Dict = xmod.model.classification_heads["mnli"].out_proj.weight.shape[0]
print("Our X-MOD config:" ,lowerCAmelCase__ )
lowercase__ : Union[str, Any] = XmodForSequenceClassification(lowerCAmelCase__ ) if classification_head else XmodForMaskedLM(lowerCAmelCase__ )
model.eval()
# Now let's copy all the weights.
# Embeddings
lowercase__ : Dict = xmod_sent_encoder.embed_tokens.weight
lowercase__ : int = xmod_sent_encoder.embed_positions.weight
lowercase__ : Optional[int] = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c xmod doesn't use them.
lowercase__ : Dict = xmod_sent_encoder.layernorm_embedding.weight
lowercase__ : Tuple = xmod_sent_encoder.layernorm_embedding.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
lowercase__ : Dict = model.roberta.encoder.layer[i]
lowercase__ : Union[str, Any] = xmod_sent_encoder.layers[i]
# self attention
lowercase__ : List[str] = layer.attention.self
if not (
xmod_layer.self_attn.k_proj.weight.data.shape
== xmod_layer.self_attn.q_proj.weight.data.shape
== xmod_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
):
raise AssertionError("Dimensions of self-attention weights do not match." )
lowercase__ : Optional[Any] = xmod_layer.self_attn.q_proj.weight
lowercase__ : int = xmod_layer.self_attn.q_proj.bias
lowercase__ : List[str] = xmod_layer.self_attn.k_proj.weight
lowercase__ : Any = xmod_layer.self_attn.k_proj.bias
lowercase__ : str = xmod_layer.self_attn.v_proj.weight
lowercase__ : Optional[int] = xmod_layer.self_attn.v_proj.bias
# self-attention output
lowercase__ : Optional[int] = layer.attention.output
if self_output.dense.weight.shape != xmod_layer.self_attn.out_proj.weight.shape:
raise AssertionError("Dimensions of self-attention output weights do not match." )
lowercase__ : Dict = xmod_layer.self_attn.out_proj.weight
lowercase__ : List[Any] = xmod_layer.self_attn.out_proj.bias
lowercase__ : Optional[int] = xmod_layer.self_attn_layer_norm.weight
lowercase__ : Optional[Any] = xmod_layer.self_attn_layer_norm.bias
# intermediate
lowercase__ : Union[str, Any] = layer.intermediate
if intermediate.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of intermediate weights do not match." )
lowercase__ : int = xmod_layer.fca.weight
lowercase__ : Tuple = xmod_layer.fca.bias
# output
lowercase__ : List[str] = layer.output
if bert_output.dense.weight.shape != xmod_layer.fca.weight.shape:
raise AssertionError("Dimensions of feed-forward weights do not match." )
lowercase__ : str = xmod_layer.fca.weight
lowercase__ : int = xmod_layer.fca.bias
lowercase__ : Optional[Any] = xmod_layer.final_layer_norm.weight
lowercase__ : List[Any] = xmod_layer.final_layer_norm.bias
if bert_output.adapter_layer_norm is not None:
lowercase__ : Tuple = xmod_layer.adapter_layer_norm.weight
lowercase__ : Tuple = xmod_layer.adapter_layer_norm.bias
if sorted(bert_output.adapter_modules.keys() ) != sorted(xmod_layer.adapter_modules.keys() ):
raise AssertionError("Lists of language adapters do not match." )
for lang_code, adapter in xmod_layer.adapter_modules.items():
lowercase__ : List[Any] = bert_output.adapter_modules[lang_code]
lowercase__ : Tuple = xmod_layer.adapter_modules[lang_code]
lowercase__ : Tuple = from_adapter.fca.weight
lowercase__ : str = from_adapter.fca.bias
lowercase__ : List[str] = from_adapter.fca.weight
lowercase__ : Union[str, Any] = from_adapter.fca.bias
# end of layer
if xmod_sent_encoder.layer_norm is not None:
lowercase__ : Any = xmod_sent_encoder.layer_norm.weight
lowercase__ : Any = xmod_sent_encoder.layer_norm.bias
if classification_head:
lowercase__ : Optional[int] = xmod.model.classification_heads["mnli"].dense.weight
lowercase__ : Any = xmod.model.classification_heads["mnli"].dense.bias
lowercase__ : Union[str, Any] = xmod.model.classification_heads["mnli"].out_proj.weight
lowercase__ : List[str] = xmod.model.classification_heads["mnli"].out_proj.bias
else:
# LM Head
lowercase__ : List[str] = xmod.model.encoder.lm_head.dense.weight
lowercase__ : List[Any] = xmod.model.encoder.lm_head.dense.bias
lowercase__ : Optional[Any] = xmod.model.encoder.lm_head.layer_norm.weight
lowercase__ : Any = xmod.model.encoder.lm_head.layer_norm.bias
lowercase__ : Tuple = xmod.model.encoder.lm_head.weight
lowercase__ : List[str] = xmod.model.encoder.lm_head.bias
# Let's check that we get the same results.
lowercase__ : List[str] = xmod.encode(lowerCAmelCase__ ).unsqueeze(0 ) # batch of size 1
model.roberta.set_default_language(lowerCAmelCase__ )
lowercase__ : str = model(lowerCAmelCase__ )[0]
if classification_head:
lowercase__ : str = xmod.model.classification_heads["mnli"](xmod.extract_features(lowerCAmelCase__ ) )
else:
lowercase__ : Any = xmod.model(lowerCAmelCase__ ,lang_id=[SAMPLE_LANGUAGE] )[0]
print(our_output.shape ,their_output.shape )
lowercase__ : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(F"""max_absolute_diff = {max_absolute_diff}""" ) # ~ 1e-7
lowercase__ : Tuple = torch.allclose(lowerCAmelCase__ ,lowerCAmelCase__ ,atol=1E-3 )
print("Do both models output the same tensors?" ,"🔥" if success else "💩" )
if not success:
raise Exception("Something went wRoNg" )
Path(lowerCAmelCase__ ).mkdir(parents=lowerCAmelCase__ ,exist_ok=lowerCAmelCase__ )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(lowerCAmelCase__ )
if __name__ == "__main__":
__a : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--xmod_checkpoint_path''', default=None, type=str, required=True, help='''Path the official PyTorch dump.'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
)
parser.add_argument(
'''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
)
__a : Union[str, Any] = parser.parse_args()
convert_xmod_checkpoint_to_pytorch(
args.xmod_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
) | 397 |
"""simple docstring"""
from __future__ import annotations
def lowercase__ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> tuple[int, int]:
'''simple docstring'''
if b == 0:
return (1, 0)
((a__) , (a__)) : List[Any] = extended_euclid(lowerCAmelCase__ , a % b )
a__ : str = a // b
return (y, x - k * y)
def lowercase__ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> int:
'''simple docstring'''
((a__) , (a__)) : Tuple = extended_euclid(lowerCAmelCase__ , lowerCAmelCase__ )
a__ : List[str] = na * na
a__ : Union[str, Any] = ra * x * na + ra * y * na
return (n % m + m) % m
def lowercase__ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> int:
'''simple docstring'''
((a__) , (a__)) : Optional[Any] = extended_euclid(lowerCAmelCase__ , lowerCAmelCase__ )
if b < 0:
a__ : Optional[int] = (b % n + n) % n
return b
def lowercase__ ( lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int , lowerCAmelCase__ : int ) -> int:
'''simple docstring'''
a__ , a__ : List[Any] = invert_modulo(lowerCAmelCase__ , lowerCAmelCase__ ), invert_modulo(lowerCAmelCase__ , lowerCAmelCase__ )
a__ : Dict = na * na
a__ : Any = ra * x * na + ra * y * na
return (n % m + m) % m
if __name__ == "__main__":
from doctest import testmod
testmod(name='''chinese_remainder_theorem''', verbose=True)
testmod(name='''chinese_remainder_theorem2''', verbose=True)
testmod(name='''invert_modulo''', verbose=True)
testmod(name='''extended_euclid''', verbose=True) | 642 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json""",
"""studio-ousia/luke-large""": """https://huggingface.co/studio-ousia/luke-large/resolve/main/config.json""",
}
class __snake_case ( SCREAMING_SNAKE_CASE):
'''simple docstring'''
UpperCamelCase__ : Tuple = """luke"""
def __init__( self , a_=50_267 , a_=500_000 , a_=768 , a_=256 , a_=12 , a_=12 , a_=3_072 , a_="gelu" , a_=0.1 , a_=0.1 , a_=512 , a_=2 , a_=0.02 , a_=1E-1_2 , a_=True , a_=None , a_=1 , a_=0 , a_=2 , **a_ , ):
super().__init__(pad_token_id=a_ , bos_token_id=a_ , eos_token_id=a_ , **a_ )
a__ = vocab_size
a__ = entity_vocab_size
a__ = hidden_size
a__ = entity_emb_size
a__ = num_hidden_layers
a__ = num_attention_heads
a__ = hidden_act
a__ = intermediate_size
a__ = hidden_dropout_prob
a__ = attention_probs_dropout_prob
a__ = max_position_embeddings
a__ = type_vocab_size
a__ = initializer_range
a__ = layer_norm_eps
a__ = use_entity_aware_attention
a__ = classifier_dropout
| 707 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytesseract,
require_tf,
require_timm,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __snake_case :
'''simple docstring'''
@staticmethod
def _a ( *a_ , **a_ ):
pass
@is_pipeline_test
@require_vision
@require_timm
@require_torch
class __snake_case ( unittest.TestCase):
'''simple docstring'''
UpperCamelCase__ : Dict = MODEL_FOR_OBJECT_DETECTION_MAPPING
def _a ( self , a_ , a_ , a_ ):
a__ = ObjectDetectionPipeline(model=a_ , image_processor=a_ )
return object_detector, ["./tests/fixtures/tests_samples/COCO/000000039769.png"]
def _a ( self , a_ , a_ ):
a__ = object_detector("""./tests/fixtures/tests_samples/COCO/000000039769.png""" , threshold=0.0 )
self.assertGreater(len(a_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
a_ , {
"""score""": ANY(a_ ),
"""label""": ANY(a_ ),
"""box""": {"""xmin""": ANY(a_ ), """ymin""": ANY(a_ ), """xmax""": ANY(a_ ), """ymax""": ANY(a_ )},
} , )
import datasets
a__ = datasets.load_dataset("""hf-internal-testing/fixtures_image_utils""" , """image""" , split="""test""" )
a__ = [
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"""],
]
a__ = object_detector(a_ , threshold=0.0 )
self.assertEqual(len(a_ ) , len(a_ ) )
for outputs in batch_outputs:
self.assertGreater(len(a_ ) , 0 )
for detected_object in outputs:
self.assertEqual(
a_ , {
"""score""": ANY(a_ ),
"""label""": ANY(a_ ),
"""box""": {"""xmin""": ANY(a_ ), """ymin""": ANY(a_ ), """xmax""": ANY(a_ ), """ymax""": ANY(a_ )},
} , )
@require_tf
@unittest.skip("""Object detection not implemented in TF""" )
def _a ( self ):
pass
@require_torch
def _a ( self ):
a__ = """hf-internal-testing/tiny-detr-mobilenetsv3"""
a__ = AutoModelForObjectDetection.from_pretrained(a_ )
a__ = AutoFeatureExtractor.from_pretrained(a_ )
a__ = ObjectDetectionPipeline(model=a_ , feature_extractor=a_ )
a__ = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" , threshold=0.0 )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
{"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
{"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
] , )
a__ = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] , threshold=0.0 , )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
[
{"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
{"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
],
[
{"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
{"""score""": 0.3_376, """label""": """LABEL_0""", """box""": {"""xmin""": 159, """ymin""": 120, """xmax""": 480, """ymax""": 359}},
],
] , )
@require_torch
@slow
def _a ( self ):
a__ = """facebook/detr-resnet-50"""
a__ = AutoModelForObjectDetection.from_pretrained(a_ )
a__ = AutoFeatureExtractor.from_pretrained(a_ )
a__ = ObjectDetectionPipeline(model=a_ , feature_extractor=a_ )
a__ = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
{"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
] , )
a__ = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
[
{"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
[
{"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
] , )
@require_torch
@slow
def _a ( self ):
a__ = """facebook/detr-resnet-50"""
a__ = pipeline("""object-detection""" , model=a_ )
a__ = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
{"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
] , )
a__ = object_detector(
[
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
"""http://images.cocodataset.org/val2017/000000039769.jpg""",
] )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
[
{"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
[
{"""score""": 0.9_982, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 70, """xmax""": 175, """ymax""": 117}},
{"""score""": 0.9_960, """label""": """remote""", """box""": {"""xmin""": 333, """ymin""": 72, """xmax""": 368, """ymax""": 187}},
{"""score""": 0.9_955, """label""": """couch""", """box""": {"""xmin""": 0, """ymin""": 1, """xmax""": 639, """ymax""": 473}},
{"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
],
] , )
@require_torch
@slow
def _a ( self ):
a__ = 0.9_985
a__ = """facebook/detr-resnet-50"""
a__ = pipeline("""object-detection""" , model=a_ )
a__ = object_detector("""http://images.cocodataset.org/val2017/000000039769.jpg""" , threshold=a_ )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
{"""score""": 0.9_988, """label""": """cat""", """box""": {"""xmin""": 13, """ymin""": 52, """xmax""": 314, """ymax""": 470}},
{"""score""": 0.9_987, """label""": """cat""", """box""": {"""xmin""": 345, """ymin""": 23, """xmax""": 640, """ymax""": 368}},
] , )
@require_torch
@require_pytesseract
@slow
def _a ( self ):
a__ = """Narsil/layoutlmv3-finetuned-funsd"""
a__ = 0.9_993
a__ = pipeline("""object-detection""" , model=a_ , threshold=a_ )
a__ = object_detector(
"""https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png""" )
self.assertEqual(
nested_simplify(a_ , decimals=4 ) , [
{"""score""": 0.9_993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
{"""score""": 0.9_993, """label""": """I-ANSWER""", """box""": {"""xmin""": 294, """ymin""": 254, """xmax""": 343, """ymax""": 264}},
] , )
| 351 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinvaConfig, SwinvaForImageClassification
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
_UpperCAmelCase = SwinvaConfig()
_UpperCAmelCase = swinva_name.split("_" )
_UpperCAmelCase = name_split[1]
if "to" in name_split[3]:
_UpperCAmelCase = int(name_split[3][-3:] )
else:
_UpperCAmelCase = int(name_split[3] )
if "to" in name_split[2]:
_UpperCAmelCase = int(name_split[2][-2:] )
else:
_UpperCAmelCase = int(name_split[2][6:] )
if model_size == "tiny":
_UpperCAmelCase = 96
_UpperCAmelCase = (2, 2, 6, 2)
_UpperCAmelCase = (3, 6, 12, 24)
elif model_size == "small":
_UpperCAmelCase = 96
_UpperCAmelCase = (2, 2, 18, 2)
_UpperCAmelCase = (3, 6, 12, 24)
elif model_size == "base":
_UpperCAmelCase = 128
_UpperCAmelCase = (2, 2, 18, 2)
_UpperCAmelCase = (4, 8, 16, 32)
else:
_UpperCAmelCase = 192
_UpperCAmelCase = (2, 2, 18, 2)
_UpperCAmelCase = (6, 12, 24, 48)
if "to" in swinva_name:
_UpperCAmelCase = (12, 12, 12, 6)
if ("22k" in swinva_name) and ("to" not in swinva_name):
_UpperCAmelCase = 2_1841
_UpperCAmelCase = "huggingface/label-files"
_UpperCAmelCase = "imagenet-22k-id2label.json"
_UpperCAmelCase = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
else:
_UpperCAmelCase = 1000
_UpperCAmelCase = "huggingface/label-files"
_UpperCAmelCase = "imagenet-1k-id2label.json"
_UpperCAmelCase = json.load(open(hf_hub_download(UpperCamelCase__ , UpperCamelCase__ , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase = {int(UpperCamelCase__ ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
_UpperCAmelCase = img_size
_UpperCAmelCase = num_classes
_UpperCAmelCase = embed_dim
_UpperCAmelCase = depths
_UpperCAmelCase = num_heads
_UpperCAmelCase = window_size
return config
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if "patch_embed.proj" in name:
_UpperCAmelCase = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "patch_embed.norm" in name:
_UpperCAmelCase = name.replace("patch_embed.norm" , "embeddings.norm" )
if "layers" in name:
_UpperCAmelCase = "encoder." + name
if "attn.proj" in name:
_UpperCAmelCase = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
_UpperCAmelCase = name.replace("attn" , "attention.self" )
if "norm1" in name:
_UpperCAmelCase = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
_UpperCAmelCase = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
_UpperCAmelCase = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
_UpperCAmelCase = name.replace("mlp.fc2" , "output.dense" )
if "q_bias" in name:
_UpperCAmelCase = name.replace("q_bias" , "query.bias" )
if "k_bias" in name:
_UpperCAmelCase = name.replace("k_bias" , "key.bias" )
if "v_bias" in name:
_UpperCAmelCase = name.replace("v_bias" , "value.bias" )
if "cpb_mlp" in name:
_UpperCAmelCase = name.replace("cpb_mlp" , "continuous_position_bias_mlp" )
if name == "norm.weight":
_UpperCAmelCase = "layernorm.weight"
if name == "norm.bias":
_UpperCAmelCase = "layernorm.bias"
if "head" in name:
_UpperCAmelCase = name.replace("head" , "classifier" )
else:
_UpperCAmelCase = "swinv2." + name
return name
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
for key in orig_state_dict.copy().keys():
_UpperCAmelCase = orig_state_dict.pop(UpperCamelCase__ )
if "mask" in key:
continue
elif "qkv" in key:
_UpperCAmelCase = key.split("." )
_UpperCAmelCase = int(key_split[1] )
_UpperCAmelCase = int(key_split[3] )
_UpperCAmelCase = model.swinva.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_UpperCAmelCase = val[:dim, :]
_UpperCAmelCase = val[dim : dim * 2, :]
_UpperCAmelCase = val[-dim:, :]
else:
_UpperCAmelCase = val[:dim]
_UpperCAmelCase = val[
dim : dim * 2
]
_UpperCAmelCase = val[-dim:]
else:
_UpperCAmelCase = val
return orig_state_dict
def __lowerCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
_UpperCAmelCase = timm.create_model(UpperCamelCase__ , pretrained=UpperCamelCase__ )
timm_model.eval()
_UpperCAmelCase = get_swinva_config(UpperCamelCase__ )
_UpperCAmelCase = SwinvaForImageClassification(UpperCamelCase__ )
model.eval()
_UpperCAmelCase = convert_state_dict(timm_model.state_dict() , UpperCamelCase__ )
model.load_state_dict(UpperCamelCase__ )
_UpperCAmelCase = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase = AutoImageProcessor.from_pretrained("microsoft/{}".format(swinva_name.replace("_" , "-" ) ) )
_UpperCAmelCase = Image.open(requests.get(UpperCamelCase__ , stream=UpperCamelCase__ ).raw )
_UpperCAmelCase = image_processor(images=UpperCamelCase__ , return_tensors="pt" )
_UpperCAmelCase = timm_model(inputs["pixel_values"] )
_UpperCAmelCase = model(**UpperCamelCase__ ).logits
assert torch.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1E-3 )
print(f"Saving model {swinva_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(UpperCamelCase__ )
print(f"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(UpperCamelCase__ )
model.push_to_hub(
repo_path_or_name=Path(UpperCamelCase__ , UpperCamelCase__ ) , organization="nandwalritik" , commit_message="Add model" , )
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--swinv2_name''',
default='''swinv2_tiny_patch4_window8_256''',
type=str,
help='''Name of the Swinv2 timm model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
__magic_name__ = parser.parse_args()
convert_swinva_checkpoint(args.swinva_name, args.pytorch_dump_folder_path)
| 657 |
"""simple docstring"""
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
return "".join([hex(UpperCamelCase__ )[2:].zfill(2 ).upper() for byte in list(UpperCamelCase__ )] )
def __lowerCamelCase ( UpperCamelCase__ ):
"""simple docstring"""
if (len(UpperCamelCase__ ) % 2) != 0:
raise ValueError(
"Base16 encoded data is invalid:\nData does not have an even number of hex digits." )
# Check the character set - the standard base16 alphabet
# is uppercase according to RFC3548 section 6
if not set(UpperCamelCase__ ) <= set("0123456789ABCDEF" ):
raise ValueError(
"Base16 encoded data is invalid:\nData is not uppercase hex or it contains invalid characters." )
# For every two hexadecimal digits (= a byte), turn it into an integer.
# Then, string the result together into bytes, and return it.
return bytes(int(data[i] + data[i + 1] , 16 ) for i in range(0 , len(UpperCamelCase__ ) , 2 ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 657 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
'configuration_mobilebert': [
'MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'MobileBertConfig',
'MobileBertOnnxConfig',
],
'tokenization_mobilebert': ['MobileBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ['MobileBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'MobileBertForMaskedLM',
'MobileBertForMultipleChoice',
'MobileBertForNextSentencePrediction',
'MobileBertForPreTraining',
'MobileBertForQuestionAnswering',
'MobileBertForSequenceClassification',
'MobileBertForTokenClassification',
'MobileBertLayer',
'MobileBertModel',
'MobileBertPreTrainedModel',
'load_tf_weights_in_mobilebert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
'TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFMobileBertForMaskedLM',
'TFMobileBertForMultipleChoice',
'TFMobileBertForNextSentencePrediction',
'TFMobileBertForPreTraining',
'TFMobileBertForQuestionAnswering',
'TFMobileBertForSequenceClassification',
'TFMobileBertForTokenClassification',
'TFMobileBertMainLayer',
'TFMobileBertModel',
'TFMobileBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_mobilebert import (
MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
MobileBertConfig,
MobileBertOnnxConfig,
)
from .tokenization_mobilebert import MobileBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_mobilebert_fast import MobileBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mobilebert import (
MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MobileBertForMaskedLM,
MobileBertForMultipleChoice,
MobileBertForNextSentencePrediction,
MobileBertForPreTraining,
MobileBertForQuestionAnswering,
MobileBertForSequenceClassification,
MobileBertForTokenClassification,
MobileBertLayer,
MobileBertModel,
MobileBertPreTrainedModel,
load_tf_weights_in_mobilebert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_mobilebert import (
TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFMobileBertForMaskedLM,
TFMobileBertForMultipleChoice,
TFMobileBertForNextSentencePrediction,
TFMobileBertForPreTraining,
TFMobileBertForQuestionAnswering,
TFMobileBertForSequenceClassification,
TFMobileBertForTokenClassification,
TFMobileBertMainLayer,
TFMobileBertModel,
TFMobileBertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 711 |
from __future__ import annotations
def _snake_case ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> List[Any]:
"""simple docstring"""
print(f'''Vertex\tShortest Distance from vertex {src}''' )
for i, d in enumerate(SCREAMING_SNAKE_CASE ):
print(f'''{i}\t\t{d}''' )
def _snake_case ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> Tuple:
"""simple docstring"""
for j in range(SCREAMING_SNAKE_CASE ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : Any = (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 _snake_case ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> list[float]:
"""simple docstring"""
_lowerCAmelCase : Tuple = [float("inf" )] * vertex_count
_lowerCAmelCase : int = 0.0
for _ in range(vertex_count - 1 ):
for j in range(SCREAMING_SNAKE_CASE ):
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase : int = (graph[j][k] for k in ["src", "dst", "weight"])
if distance[u] != float("inf" ) and distance[u] + w < distance[v]:
_lowerCAmelCase : List[str] = distance[u] + w
_lowerCAmelCase : int = check_negative_cycle(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
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)
| 503 | 0 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
TFTransfoXLForSequenceClassification,
TFTransfoXLLMHeadModel,
TFTransfoXLModel,
)
class SCREAMING_SNAKE_CASE_ :
def __init__( self , lowercase , ) -> Optional[Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[Any] = parent
__SCREAMING_SNAKE_CASE : int = 1_3
__SCREAMING_SNAKE_CASE : Tuple = 7
__SCREAMING_SNAKE_CASE : Dict = 3_0
__SCREAMING_SNAKE_CASE : Optional[Any] = self.seq_length + self.mem_len
__SCREAMING_SNAKE_CASE : Tuple = 1_5
__SCREAMING_SNAKE_CASE : int = True
__SCREAMING_SNAKE_CASE : Any = True
__SCREAMING_SNAKE_CASE : List[Any] = 9_9
__SCREAMING_SNAKE_CASE : List[Any] = [1_0, 5_0, 8_0]
__SCREAMING_SNAKE_CASE : List[str] = 3_2
__SCREAMING_SNAKE_CASE : Tuple = 3_2
__SCREAMING_SNAKE_CASE : Union[str, Any] = 4
__SCREAMING_SNAKE_CASE : Optional[Any] = 8
__SCREAMING_SNAKE_CASE : List[Any] = 1_2_8
__SCREAMING_SNAKE_CASE : Union[str, Any] = 2
__SCREAMING_SNAKE_CASE : Union[str, Any] = 2
__SCREAMING_SNAKE_CASE : Any = None
__SCREAMING_SNAKE_CASE : List[Any] = 1
__SCREAMING_SNAKE_CASE : Optional[int] = 0
__SCREAMING_SNAKE_CASE : Any = 3
__SCREAMING_SNAKE_CASE : str = self.vocab_size - 1
__SCREAMING_SNAKE_CASE : str = 0.0_1
def _snake_case ( self ) -> Any:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE : Any = None
if self.use_labels:
__SCREAMING_SNAKE_CASE : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__SCREAMING_SNAKE_CASE : Optional[Any] = TransfoXLConfig(
vocab_size=self.vocab_size , mem_len=self.mem_len , clamp_len=self.clamp_len , cutoffs=self.cutoffs , d_model=self.hidden_size , d_embed=self.d_embed , n_head=self.num_attention_heads , d_head=self.d_head , d_inner=self.d_inner , div_val=self.div_val , n_layer=self.num_hidden_layers , eos_token_id=self.eos_token_id , pad_token_id=self.vocab_size - 1 , init_range=self.init_range , num_labels=self.num_labels , )
return (config, input_ids_a, input_ids_a, lm_labels)
def _snake_case ( self ) -> List[str]:
'''simple docstring'''
random.seed(self.seed )
tf.random.set_seed(self.seed )
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = TFTransfoXLModel(lowercase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = model(lowercase ).to_tuple()
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''input_ids''': input_ids_a, '''mems''': mems_a}
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : List[Any] = model(lowercase ).to_tuple()
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertEqual(hidden_states_a.shape , (self.batch_size, self.seq_length, self.hidden_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase ) -> Tuple:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = TFTransfoXLLMHeadModel(lowercase )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowercase ).to_tuple()
__SCREAMING_SNAKE_CASE : List[Any] = {'''input_ids''': input_ids_a, '''labels''': lm_labels}
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[int] = model(lowercase ).to_tuple()
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Optional[Any] = model([input_ids_a, mems_a] ).to_tuple()
__SCREAMING_SNAKE_CASE : Optional[Any] = {'''input_ids''': input_ids_a, '''mems''': mems_a, '''labels''': lm_labels}
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowercase ).to_tuple()
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
self.parent.assertEqual(lm_logits_a.shape , (self.batch_size, self.seq_length, self.vocab_size) )
self.parent.assertListEqual(
[mem.shape for mem in mems_a] , [(self.mem_len, self.batch_size, self.hidden_size)] * self.num_hidden_layers , )
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase ) -> Any:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Dict = TFTransfoXLForSequenceClassification(lowercase )
__SCREAMING_SNAKE_CASE : Union[str, Any] = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def _snake_case ( self ) -> Optional[int]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Union[str, Any] = self.prepare_config_and_inputs()
((__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE) , (__SCREAMING_SNAKE_CASE)) : Tuple = config_and_inputs
__SCREAMING_SNAKE_CASE : Optional[int] = {'''input_ids''': input_ids_a}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE_ ( snake_case , snake_case , unittest.TestCase ):
__a : Tuple = (
(TFTransfoXLModel, TFTransfoXLLMHeadModel, TFTransfoXLForSequenceClassification) if is_tf_available() else ()
)
__a : Union[str, Any] = () if is_tf_available() else ()
__a : Optional[int] = (
{
'''feature-extraction''': TFTransfoXLModel,
'''text-classification''': TFTransfoXLForSequenceClassification,
'''text-generation''': TFTransfoXLLMHeadModel,
'''zero-shot''': TFTransfoXLForSequenceClassification,
}
if is_tf_available()
else {}
)
# TODO: add this test when TFTransfoXLLMHead has a linear output layer implemented
__a : str = False
__a : List[str] = False
__a : Tuple = False
__a : str = False
def _snake_case ( self , lowercase , lowercase , lowercase , lowercase , lowercase ) -> str:
'''simple docstring'''
if pipeline_test_casse_name == "TextGenerationPipelineTests":
# Get `ValueError: AttributeError: 'NoneType' object has no attribute 'new_ones'` or `AssertionError`.
# `TransfoXLConfig` was never used in pipeline tests: cannot create a simple
# tokenizer.
return True
return False
def _snake_case ( self ) -> List[Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : int = TFTransfoXLModelTester(self )
__SCREAMING_SNAKE_CASE : List[str] = ConfigTester(self , config_class=lowercase , d_embed=3_7 )
def _snake_case ( self ) -> Dict:
'''simple docstring'''
self.config_tester.run_common_tests()
def _snake_case ( self ) -> Any:
'''simple docstring'''
self.model_tester.set_seed()
__SCREAMING_SNAKE_CASE : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_model(*lowercase )
def _snake_case ( self ) -> str:
'''simple docstring'''
self.model_tester.set_seed()
__SCREAMING_SNAKE_CASE : Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_lm_head(*lowercase )
def _snake_case ( self ) -> Optional[Any]:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_transfo_xl_for_sequence_classification(*lowercase )
def _snake_case ( self ) -> int:
'''simple docstring'''
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE : int = self.model_tester.prepare_config_and_inputs_for_common()
__SCREAMING_SNAKE_CASE : str = [TFTransfoXLForSequenceClassification]
for model_class in self.all_model_classes:
__SCREAMING_SNAKE_CASE : int = model_class(lowercase )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class in list_other_models_with_output_ebd:
__SCREAMING_SNAKE_CASE : int = model.get_output_embeddings()
assert isinstance(lowercase , tf.keras.layers.Layer )
__SCREAMING_SNAKE_CASE : List[Any] = model.get_bias()
assert name is None
else:
__SCREAMING_SNAKE_CASE : Any = model.get_output_embeddings()
assert x is None
__SCREAMING_SNAKE_CASE : Any = model.get_bias()
assert name is None
def _snake_case ( self ) -> Optional[Any]:
'''simple docstring'''
pass
@slow
def _snake_case ( self ) -> Optional[Any]:
'''simple docstring'''
for model_name in TF_TRANSFO_XL_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__SCREAMING_SNAKE_CASE : List[Any] = TFTransfoXLModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
@unittest.skip(reason='''This model doesn\'t play well with fit() due to not returning a single loss.''' )
def _snake_case ( self ) -> Dict:
'''simple docstring'''
pass
@require_tf
class SCREAMING_SNAKE_CASE_ ( unittest.TestCase ):
@unittest.skip('''Skip test until #12651 is resolved.''' )
@slow
def _snake_case ( self ) -> str:
'''simple docstring'''
__SCREAMING_SNAKE_CASE : Any = TFTransfoXLLMHeadModel.from_pretrained('''transfo-xl-wt103''' )
# fmt: off
__SCREAMING_SNAKE_CASE : List[str] = tf.convert_to_tensor([[3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0]] , dtype=tf.intaa ) # noqa: E231
# fmt: on
# In 1991 , the remains of Russian Tsar Nicholas II and his family
# ( except for Alexei and Maria ) are discovered .
# The voice of Nicholas's young son , Tsarevich Alexei Nikolaevich , narrates the
# remainder of the story . 1883 Western Siberia ,
# a young Grigori Rasputin is asked by his father and a group of men to perform magic .
# Rasputin has a vision and denounces one of the men as a horse thief . Although his
# father initially slaps him for making such an accusation , Rasputin watches as the
# man is chased outside and beaten . Twenty years later , Rasputin sees a vision of
# the Virgin Mary , prompting him to become a priest . Rasputin quickly becomes famous ,
# with people , even a bishop , begging for his blessing . <eod> </s> <eos>
# fmt: off
__SCREAMING_SNAKE_CASE : Union[str, Any] = [3_3,1_2_9_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_2,1_7_0_6,1_7,2_0_0_9_8,5,3_2_1_5,2_1,3_7,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,6_2_2_4,8_3_1,1_6_0_0_2,2,8,6_0_3,7_8_9_6_7,2_9_5_4_6,2_3,8_0_3,2_0,2_5,4_1_6,5,8,2_3_2,4,2_7_7,6,1_8_5_5,4_6_0_1,3,2_9_5_4_6,5_4,8,3_6_0_9,5,5_7_2_1_1,4_9,4,1,2_7_7,1_8,8,1_7_5_5,1_5_6_9_1,3,3_4_1,2_5,4_1_6,6_9_3,4_2_5_7_3,7_1,1_7,4_0_1,9_4,3_1,1_7_9_1_9,2,2_9_5_4_6,7_8_7_3,1_8,1,4_3_5,2_3,1_1_0_1_1,7_5_5,5,5_1_6_7,3,7_9_8_3,9_8,8_4,2,2_9_5_4_6,3_2_6_7,8,3_6_0_9,4,1,4_8_6_5,1_0_7_5,2,6_0_8_7,7_1,6,3_4_6,8,5_8_5_4,3,2_9_5_4_6,8_2_4,1_4_0_0,1_8_6_8,2,1_9,1_6_0,2,3_1_1,8,5_4_9_6,2,2_0_9_2_0,1_7,2_5,1_5_0_9_7,3,2_4,2_4,0,3_3,1,1_8_5_7,2,1,1_0_0_9,4,1_1_0_9,1_1_7_3_9,4_7_6_2,3_5_8,5,2_5,2_4_5,2_8,1_1_1_0,3,1_3,1_0_4_1,4,2_4,6_0_3,4_9_0,2,7_1_4_7_7,2_0_0_9_8,1_0_4_4_4_7,2,2_0_9_6_1,1,2_6_0_4,4,1,3_2_9,3,0] # noqa: E231
# fmt: on
# In 1991, the remains of Russian Tsar Nicholas II and his family (
# except for Alexei and Maria ) are discovered. The voice of young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.
# 1883 Western Siberia, a young Grigori Rasputin is asked by his father
# and a group of men to perform magic. Rasputin has a vision and
# denounces one of the men as a horse thief. Although his father initially
# slaps him for making such an accusation, Rasputin watches as the man
# is chased outside and beaten. Twenty years later, Rasputin sees a vision
# of the Virgin Mary, prompting him to become a priest.
# Rasputin quickly becomes famous, with people, even a bishop, begging for
# his blessing. <unk> <unk> <eos> In the 1990s, the remains of Russian Tsar
# Nicholas II and his family were discovered. The voice of <unk> young son,
# Tsarevich Alexei Nikolaevich, narrates the remainder of the story.<eos>
__SCREAMING_SNAKE_CASE : List[str] = model.generate(lowercase , max_length=2_0_0 , do_sample=lowercase )
self.assertListEqual(output_ids[0].numpy().tolist() , lowercase )
| 158 |
'''simple docstring'''
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
_A = logging.getLogger(__name__)
@dataclass
class SCREAMING_SNAKE_CASE_ ( snake_case ):
__a : Optional[float] = field(
default=0.0 , metadata={'''help''': '''The label smoothing epsilon to apply (if not zero).'''} )
__a : bool = field(default=snake_case , metadata={'''help''': '''Whether to SortishSamler or not.'''} )
__a : bool = field(
default=snake_case , metadata={'''help''': '''Whether to use generate to calculate generative metrics (ROUGE, BLEU).'''} )
__a : bool = field(default=snake_case , metadata={'''help''': '''whether to use adafactor'''} )
__a : Optional[float] = field(
default=snake_case , metadata={'''help''': '''Encoder layer dropout probability. Goes into model.config.'''} )
__a : Optional[float] = field(
default=snake_case , metadata={'''help''': '''Decoder layer dropout probability. Goes into model.config.'''} )
__a : Optional[float] = field(default=snake_case , metadata={'''help''': '''Dropout probability. Goes into model.config.'''} )
__a : Optional[float] = field(
default=snake_case , metadata={'''help''': '''Attention dropout probability. Goes into model.config.'''} )
__a : Optional[str] = field(
default='''linear''' , metadata={'''help''': f'''Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}'''} , )
| 158 | 1 |
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class UpperCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self , UpperCamelCase_ , UpperCamelCase_ ):
super().__init__()
self.register_modules(unet=UpperCamelCase_ , scheduler=UpperCamelCase_ )
@torch.no_grad()
def __call__( self , UpperCamelCase_ = 1 , UpperCamelCase_ = None , UpperCamelCase_ = 50 , UpperCamelCase_ = "pil" , UpperCamelCase_ = True , **UpperCamelCase_ , ):
lowercase_ :Dict = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=UpperCamelCase_ , )
lowercase_ :List[Any] = image.to(self.device )
# set step values
self.scheduler.set_timesteps(UpperCamelCase_ )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
lowercase_ :List[str] = self.unet(UpperCamelCase_ , UpperCamelCase_ ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
lowercase_ :Dict = self.scheduler.step(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ).prev_sample
lowercase_ :Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1 )
lowercase_ :Tuple = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
lowercase_ :Tuple = self.numpy_to_pil(UpperCamelCase_ )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=UpperCamelCase_ ), "This is a local test"
| 441 |
from __future__ import annotations
def UpperCamelCase ( _a , _a = None , _a = None ) -> None:
'''simple docstring'''
if start is None:
lowercase_ :str = 0
if end is None:
lowercase_ :str = len(_a ) - 1
if start >= end:
return
lowercase_ :Dict = (start + end) // 2
slowsort(_a , _a , _a )
slowsort(_a , mid + 1 , _a )
if sequence[end] < sequence[mid]:
lowercase_ , lowercase_ :List[Any] = sequence[mid], sequence[end]
slowsort(_a , _a , end - 1 )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 441 | 1 |
'''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 json
import os
from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
DYNAMO_BACKENDS,
_ask_field,
_ask_options,
_convert_dynamo_backend,
_convert_mixed_precision,
_convert_sagemaker_distributed_mode,
_convert_yes_no_to_bool,
)
if is_botoa_available():
import botoa # noqa: F401
def __snake_case ( SCREAMING_SNAKE_CASE_ : List[Any] ) -> str:
"""simple docstring"""
UpperCAmelCase = botoa.client('''iam''' )
UpperCAmelCase = {
'''Version''': '''2012-10-17''',
'''Statement''': [
{'''Effect''': '''Allow''', '''Principal''': {'''Service''': '''sagemaker.amazonaws.com'''}, '''Action''': '''sts:AssumeRole'''}
],
}
try:
# create the role, associated with the chosen trust policy
iam_client.create_role(
RoleName=SCREAMING_SNAKE_CASE_ , AssumeRolePolicyDocument=json.dumps(SCREAMING_SNAKE_CASE_ , indent=2 ) )
UpperCAmelCase = {
'''Version''': '''2012-10-17''',
'''Statement''': [
{
'''Effect''': '''Allow''',
'''Action''': [
'''sagemaker:*''',
'''ecr:GetDownloadUrlForLayer''',
'''ecr:BatchGetImage''',
'''ecr:BatchCheckLayerAvailability''',
'''ecr:GetAuthorizationToken''',
'''cloudwatch:PutMetricData''',
'''cloudwatch:GetMetricData''',
'''cloudwatch:GetMetricStatistics''',
'''cloudwatch:ListMetrics''',
'''logs:CreateLogGroup''',
'''logs:CreateLogStream''',
'''logs:DescribeLogStreams''',
'''logs:PutLogEvents''',
'''logs:GetLogEvents''',
'''s3:CreateBucket''',
'''s3:ListBucket''',
'''s3:GetBucketLocation''',
'''s3:GetObject''',
'''s3:PutObject''',
],
'''Resource''': '''*''',
}
],
}
# attach policy to role
iam_client.put_role_policy(
RoleName=SCREAMING_SNAKE_CASE_ , PolicyName=f"{role_name}_policy_permission" , PolicyDocument=json.dumps(SCREAMING_SNAKE_CASE_ , indent=2 ) , )
except iam_client.exceptions.EntityAlreadyExistsException:
print(f"role {role_name} already exists. Using existing one" )
def __snake_case ( SCREAMING_SNAKE_CASE_ : str ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = botoa.client('''iam''' )
return iam_client.get_role(RoleName=SCREAMING_SNAKE_CASE_ )["Role"]["Arn"]
def __snake_case ( ) -> str:
"""simple docstring"""
UpperCAmelCase = _ask_options(
'''How do you want to authorize?''' , ['''AWS Profile''', '''Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) '''] , SCREAMING_SNAKE_CASE_ , )
UpperCAmelCase = None
if credentials_configuration == 0:
UpperCAmelCase = _ask_field('''Enter your AWS Profile name: [default] ''' , default='''default''' )
UpperCAmelCase = aws_profile
else:
print(
'''Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'''
'''`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`''' )
UpperCAmelCase = _ask_field('''AWS Access Key ID: ''' )
UpperCAmelCase = aws_access_key_id
UpperCAmelCase = _ask_field('''AWS Secret Access Key: ''' )
UpperCAmelCase = aws_secret_access_key
UpperCAmelCase = _ask_field('''Enter your AWS Region: [us-east-1]''' , default='''us-east-1''' )
UpperCAmelCase = aws_region
UpperCAmelCase = _ask_options(
'''Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?''' , ['''Provide IAM Role name''', '''Create new IAM role using credentials'''] , SCREAMING_SNAKE_CASE_ , )
if role_management == 0:
UpperCAmelCase = _ask_field('''Enter your IAM role name: ''' )
else:
UpperCAmelCase = '''accelerate_sagemaker_execution_role'''
print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials" )
_create_iam_role_for_sagemaker(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase = _ask_field(
'''Do you want to use custom Docker image? [yes/NO]: ''' , _convert_yes_no_to_bool , default=SCREAMING_SNAKE_CASE_ , error_message='''Please enter yes or no.''' , )
UpperCAmelCase = None
if is_custom_docker_image:
UpperCAmelCase = _ask_field('''Enter your Docker image: ''' , lambda SCREAMING_SNAKE_CASE_ : str(SCREAMING_SNAKE_CASE_ ).lower() )
UpperCAmelCase = _ask_field(
'''Do you want to provide SageMaker input channels with data locations? [yes/NO]: ''' , _convert_yes_no_to_bool , default=SCREAMING_SNAKE_CASE_ , error_message='''Please enter yes or no.''' , )
UpperCAmelCase = None
if is_sagemaker_inputs_enabled:
UpperCAmelCase = _ask_field(
'''Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): ''' , lambda SCREAMING_SNAKE_CASE_ : str(SCREAMING_SNAKE_CASE_ ).lower() , )
UpperCAmelCase = _ask_field(
'''Do you want to enable SageMaker metrics? [yes/NO]: ''' , _convert_yes_no_to_bool , default=SCREAMING_SNAKE_CASE_ , error_message='''Please enter yes or no.''' , )
UpperCAmelCase = None
if is_sagemaker_metrics_enabled:
UpperCAmelCase = _ask_field(
'''Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): ''' , lambda SCREAMING_SNAKE_CASE_ : str(SCREAMING_SNAKE_CASE_ ).lower() , )
UpperCAmelCase = _ask_options(
'''What is the distributed mode?''' , ['''No distributed training''', '''Data parallelism'''] , _convert_sagemaker_distributed_mode , )
UpperCAmelCase = {}
UpperCAmelCase = _ask_field(
'''Do you wish to optimize your script with torch dynamo?[yes/NO]:''' , _convert_yes_no_to_bool , default=SCREAMING_SNAKE_CASE_ , error_message='''Please enter yes or no.''' , )
if use_dynamo:
UpperCAmelCase = '''dynamo_'''
UpperCAmelCase = _ask_options(
'''Which dynamo backend would you like to use?''' , [x.lower() for x in DYNAMO_BACKENDS] , _convert_dynamo_backend , default=2 , )
UpperCAmelCase = _ask_field(
'''Do you want to customize the defaults sent to torch.compile? [yes/NO]: ''' , _convert_yes_no_to_bool , default=SCREAMING_SNAKE_CASE_ , error_message='''Please enter yes or no.''' , )
if use_custom_options:
UpperCAmelCase = _ask_options(
'''Which mode do you want to use?''' , SCREAMING_SNAKE_CASE_ , lambda SCREAMING_SNAKE_CASE_ : TORCH_DYNAMO_MODES[int(SCREAMING_SNAKE_CASE_ )] , default='''default''' , )
UpperCAmelCase = _ask_field(
'''Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: ''' , _convert_yes_no_to_bool , default=SCREAMING_SNAKE_CASE_ , error_message='''Please enter yes or no.''' , )
UpperCAmelCase = _ask_field(
'''Do you want to enable dynamic shape tracing? [yes/NO]: ''' , _convert_yes_no_to_bool , default=SCREAMING_SNAKE_CASE_ , error_message='''Please enter yes or no.''' , )
UpperCAmelCase = '''Which EC2 instance type you want to use for your training?'''
if distributed_type != SageMakerDistributedType.NO:
UpperCAmelCase = _ask_options(
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , lambda SCREAMING_SNAKE_CASE_ : SAGEMAKER_PARALLEL_EC2_INSTANCES[int(SCREAMING_SNAKE_CASE_ )] )
else:
eca_instance_query += "? [ml.p3.2xlarge]:"
UpperCAmelCase = _ask_field(SCREAMING_SNAKE_CASE_ , lambda SCREAMING_SNAKE_CASE_ : str(SCREAMING_SNAKE_CASE_ ).lower() , default='''ml.p3.2xlarge''' )
UpperCAmelCase = 1
if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
UpperCAmelCase = _ask_field(
'''How many machines do you want use? [1]: ''' , SCREAMING_SNAKE_CASE_ , default=1 , )
UpperCAmelCase = _ask_options(
'''Do you wish to use FP16 or BF16 (mixed precision)?''' , ['''no''', '''fp16''', '''bf16''', '''fp8'''] , _convert_mixed_precision , )
if use_dynamo and mixed_precision == "no":
print(
'''Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.''' )
return SageMakerConfig(
image_uri=SCREAMING_SNAKE_CASE_ , compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER , distributed_type=SCREAMING_SNAKE_CASE_ , use_cpu=SCREAMING_SNAKE_CASE_ , dynamo_config=SCREAMING_SNAKE_CASE_ , eca_instance_type=SCREAMING_SNAKE_CASE_ , profile=SCREAMING_SNAKE_CASE_ , region=SCREAMING_SNAKE_CASE_ , iam_role_name=SCREAMING_SNAKE_CASE_ , mixed_precision=SCREAMING_SNAKE_CASE_ , num_machines=SCREAMING_SNAKE_CASE_ , sagemaker_inputs_file=SCREAMING_SNAKE_CASE_ , sagemaker_metrics_file=SCREAMING_SNAKE_CASE_ , )
| 51 |
'''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
| 51 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__lowerCAmelCase = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be smaller than N_POPULATION.
__lowerCAmelCase = 5_0
# Probability that an element of a generation can mutate, changing one of its genes.
# This will guarantee that all genes will be used during evolution.
__lowerCAmelCase = 0.4
# Just a seed to improve randomness required by the algorithm.
random.seed(random.randint(0, 1_0_0_0))
def _lowercase ( a__ : Dict , a__ : Optional[Any] ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = len([g for position, g in enumerate(_lowercase ) if g == main_target[position]] )
return (item, float(_lowercase ))
def _lowercase ( a__ : Optional[int] , a__ : str ) -> Optional[Any]:
"""simple docstring"""
_UpperCamelCase = random.randint(0 , len(_lowercase ) - 1 )
_UpperCamelCase = parent_a[:random_slice] + parent_a[random_slice:]
_UpperCamelCase = parent_a[:random_slice] + parent_a[random_slice:]
return (child_a, child_a)
def _lowercase ( a__ : Optional[Any] , a__ : Any ) -> Dict:
"""simple docstring"""
_UpperCamelCase = list(_lowercase )
if random.uniform(0 , 1 ) < MUTATION_PROBABILITY:
_UpperCamelCase = random.choice(_lowercase )
return "".join(_lowercase )
def _lowercase ( a__ : Optional[Any] , a__ : Optional[int] , a__ : Tuple , ) -> int:
"""simple docstring"""
_UpperCamelCase = []
# Generate more children proportionally to the fitness score.
_UpperCamelCase = int(parent_a[1] * 1_00 ) + 1
_UpperCamelCase = 10 if child_n >= 10 else child_n
for _ in range(_lowercase ):
_UpperCamelCase = population_score[random.randint(0 , _lowercase )][0]
_UpperCamelCase = crossover(parent_a[0] , _lowercase )
# Append new string to the population list.
pop.append(mutate(_lowercase , _lowercase ) )
pop.append(mutate(_lowercase , _lowercase ) )
return pop
def _lowercase ( a__ : Union[str, Any] , a__ : List[Any] , a__ : List[Any] = True ) -> Any:
"""simple docstring"""
if N_POPULATION < N_SELECTED:
_UpperCamelCase = f'''{N_POPULATION} must be bigger than {N_SELECTED}'''
raise ValueError(_lowercase )
# Verify that the target contains no genes besides the ones inside genes variable.
_UpperCamelCase = sorted({c for c in target if c not in genes} )
if not_in_genes_list:
_UpperCamelCase = f'''{not_in_genes_list} is not in genes list, evolution cannot converge'''
raise ValueError(_lowercase )
# Generate random starting population.
_UpperCamelCase = []
for _ in range(_lowercase ):
population.append("".join([random.choice(_lowercase ) for i in range(len(_lowercase ) )] ) )
# Just some logs to know what the algorithms is doing.
_UpperCamelCase = 0, 0
# This loop will end when we find a perfect match for our target.
while True:
generation += 1
total_population += len(_lowercase )
# Random population created. Now it's time to evaluate.
# Adding a bit of concurrency can make everything faster,
#
# import concurrent.futures
# population_score: list[tuple[str, float]] = []
# with concurrent.futures.ThreadPoolExecutor(
# max_workers=NUM_WORKERS) as executor:
# futures = {executor.submit(evaluate, item) for item in population}
# concurrent.futures.wait(futures)
# population_score = [item.result() for item in futures]
#
# but with a simple algorithm like this, it will probably be slower.
# We just need to call evaluate for every item inside the population.
_UpperCamelCase = [evaluate(_lowercase , _lowercase ) for item in population]
# Check if there is a matching evolution.
_UpperCamelCase = sorted(_lowercase , key=lambda a__ : x[1] , reverse=_lowercase )
if population_score[0][0] == target:
return (generation, total_population, population_score[0][0])
# Print the best result every 10 generation.
# Just to know that the algorithm is working.
if debug and generation % 10 == 0:
print(
f'''\nGeneration: {generation}'''
f'''\nTotal Population:{total_population}'''
f'''\nBest score: {population_score[0][1]}'''
f'''\nBest string: {population_score[0][0]}''' )
# Flush the old population, keeping some of the best evolutions.
# Keeping this avoid regression of evolution.
_UpperCamelCase = population[: int(N_POPULATION / 3 )]
population.clear()
population.extend(_lowercase )
# Normalize population score to be between 0 and 1.
_UpperCamelCase = [
(item, score / len(_lowercase )) for item, score in population_score
]
# This is selection
for i in range(_lowercase ):
population.extend(select(population_score[int(_lowercase )] , _lowercase , _lowercase ) )
# Check if the population has already reached the maximum value and if so,
# break the cycle. If this check is disabled, the algorithm will take
# forever to compute large strings, but will also calculate small strings in
# a far fewer generations.
if len(_lowercase ) > N_POPULATION:
break
if __name__ == "__main__":
__lowerCAmelCase = (
'This is a genetic algorithm to evaluate, combine, evolve, and mutate a string!'
)
__lowerCAmelCase = list(
""" ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklm"""
"""nopqrstuvwxyz.,;!?+-*#@^\'èéòà€ù=)(&%$£/\\"""
)
__lowerCAmelCase = basic(target_str, genes_list)
print(
F'''\nGeneration: {generation}\nTotal Population: {population}\nTarget: {target}'''
)
| 712 |
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 lowerCamelCase_ ( unittest.TestCase ):
def __init__( self , lowerCamelCase_ ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = parent
def lowercase ( self ) -> Any:
"""simple docstring"""
return {}
def _lowercase ( ) -> List[Any]:
"""simple docstring"""
_UpperCamelCase = "<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR=\"FFFFFF\">\n <HR>\n <a href=\"http://google.com\">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style=\"color:#0000FF\">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>"
_UpperCamelCase = "\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n "
return [html_string_a, html_string_a]
@require_bsa
class lowerCamelCase_ ( lowercase , unittest.TestCase ):
__lowercase : List[Any] = MarkupLMFeatureExtractor if is_bsa_available() else None
def lowercase ( self ) -> List[str]:
"""simple docstring"""
_UpperCamelCase = MarkupLMFeatureExtractionTester(self )
@property
def lowercase ( self ) -> Optional[int]:
"""simple docstring"""
return self.feature_extract_tester.prepare_feat_extract_dict()
def lowercase ( self ) -> Tuple:
"""simple docstring"""
_UpperCamelCase = self.feature_extraction_class()
# Test not batched input
_UpperCamelCase = get_html_strings()[0]
_UpperCamelCase = feature_extractor(lowerCamelCase_ )
# fmt: off
_UpperCamelCase = [["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"]]
_UpperCamelCase = [["/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 , lowerCamelCase_ )
self.assertEqual(encoding.xpaths , lowerCamelCase_ )
# Test batched
_UpperCamelCase = get_html_strings()
_UpperCamelCase = feature_extractor(lowerCamelCase_ )
# fmt: off
_UpperCamelCase = expected_nodes + [["My First Heading", "My first paragraph."]]
_UpperCamelCase = expected_xpaths + [["/html/body/h1", "/html/body/p"]]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , lowerCamelCase_ )
self.assertEqual(encoding.xpaths , lowerCamelCase_ )
| 589 | 0 |
'''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 : Tuple = logging.get_logger(__name__)
__lowercase : Optional[int] = {
'microsoft/beit-base-patch16-224-pt22k': (
'https://huggingface.co/microsoft/beit-base-patch16-224-pt22k/resolve/main/config.json'
),
# See all BEiT models at https://huggingface.co/models?filter=beit
}
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "beit"
def __init__( self , __a=8192 , __a=768 , __a=12 , __a=12 , __a=3072 , __a="gelu" , __a=0.0 , __a=0.0 , __a=0.02 , __a=1E-1_2 , __a=224 , __a=16 , __a=3 , __a=False , __a=False , __a=False , __a=False , __a=0.1 , __a=0.1 , __a=True , __a=[3, 5, 7, 11] , __a=[1, 2, 3, 6] , __a=True , __a=0.4 , __a=256 , __a=1 , __a=False , __a=255 , **__a , ):
'''simple docstring'''
super().__init__(**__a )
__a : int = vocab_size
__a : str = hidden_size
__a : Tuple = num_hidden_layers
__a : Optional[int] = num_attention_heads
__a : int = intermediate_size
__a : Union[str, Any] = hidden_act
__a : List[Any] = hidden_dropout_prob
__a : List[str] = attention_probs_dropout_prob
__a : List[Any] = initializer_range
__a : Optional[int] = layer_norm_eps
__a : Optional[Any] = image_size
__a : Optional[Any] = patch_size
__a : str = num_channels
__a : Optional[Any] = use_mask_token
__a : Dict = use_absolute_position_embeddings
__a : Tuple = use_relative_position_bias
__a : Any = use_shared_relative_position_bias
__a : Tuple = layer_scale_init_value
__a : str = drop_path_rate
__a : List[Any] = use_mean_pooling
# decode head attributes (semantic segmentation)
__a : Optional[Any] = out_indices
__a : str = pool_scales
# auxiliary head attributes (semantic segmentation)
__a : Dict = use_auxiliary_head
__a : Optional[int] = auxiliary_loss_weight
__a : str = auxiliary_channels
__a : Optional[Any] = auxiliary_num_convs
__a : Optional[Any] = auxiliary_concat_input
__a : Any = semantic_loss_ignore_index
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = version.parse("1.11" )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return OrderedDict(
[
('pixel_values', {0: 'batch', 1: 'num_channels', 2: 'height', 3: 'width'}),
] )
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
return 1E-4
| 476 |
'''simple docstring'''
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 __UpperCamelCase ( lowerCAmelCase_ , unittest.TestCase ):
A_ = CpmAntTokenizer
A_ = False
def __UpperCAmelCase ( self ):
'''simple docstring'''
super().setUp()
__a : str = [
'<d>',
'</d>',
'<s>',
'</s>',
'</_>',
'<unk>',
'<pad>',
'</n>',
'我',
'是',
'C',
'P',
'M',
'A',
'n',
't',
]
__a : str = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
@tooslow
def __UpperCAmelCase ( self ):
'''simple docstring'''
__a : List[Any] = CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b' )
__a : int = '今天天气真好！'
__a : int = ['今天', '天气', '真', '好', '！']
__a : Optional[Any] = tokenizer.tokenize(__a )
self.assertListEqual(__a , __a )
__a : Dict = '今天天气真好！'
__a : Union[str, Any] = [tokenizer.bos_token] + tokens
__a : int = [6, 9802, 1_4962, 2082, 831, 244]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__a ) , __a )
__a : Any = tokenizer.decode(__a )
self.assertEqual(__a , __a )
| 476 | 1 |
'''simple docstring'''
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_squeezebert import SqueezeBertTokenizer
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_SCREAMING_SNAKE_CASE = {
'''vocab_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/vocab.txt'''
),
'''squeezebert/squeezebert-mnli''': '''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/vocab.txt''',
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/vocab.txt'''
),
},
'''tokenizer_file''': {
'''squeezebert/squeezebert-uncased''': (
'''https://huggingface.co/squeezebert/squeezebert-uncased/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli/resolve/main/tokenizer.json'''
),
'''squeezebert/squeezebert-mnli-headless''': (
'''https://huggingface.co/squeezebert/squeezebert-mnli-headless/resolve/main/tokenizer.json'''
),
},
}
_SCREAMING_SNAKE_CASE = {
'''squeezebert/squeezebert-uncased''': 5_12,
'''squeezebert/squeezebert-mnli''': 5_12,
'''squeezebert/squeezebert-mnli-headless''': 5_12,
}
_SCREAMING_SNAKE_CASE = {
'''squeezebert/squeezebert-uncased''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli''': {'''do_lower_case''': True},
'''squeezebert/squeezebert-mnli-headless''': {'''do_lower_case''': True},
}
class lowerCAmelCase_ ( UpperCamelCase__ ):
__lowerCamelCase : Dict = VOCAB_FILES_NAMES
__lowerCamelCase : Any = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Dict = PRETRAINED_INIT_CONFIGURATION
__lowerCamelCase : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : Optional[Any] = SqueezeBertTokenizer
def __init__( self , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=True , _lowerCAmelCase="[UNK]" , _lowerCAmelCase="[SEP]" , _lowerCAmelCase="[PAD]" , _lowerCAmelCase="[CLS]" , _lowerCAmelCase="[MASK]" , _lowerCAmelCase=True , _lowerCAmelCase=None , **_lowerCAmelCase , ) -> List[Any]:
super().__init__(
_lowerCAmelCase , tokenizer_file=_lowerCAmelCase , do_lower_case=_lowerCAmelCase , unk_token=_lowerCAmelCase , sep_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , cls_token=_lowerCAmelCase , mask_token=_lowerCAmelCase , tokenize_chinese_chars=_lowerCAmelCase , strip_accents=_lowerCAmelCase , **_lowerCAmelCase , )
_lowerCAmelCase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , _lowerCAmelCase ) != do_lower_case
or normalizer_state.get("strip_accents" , _lowerCAmelCase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , _lowerCAmelCase ) != tokenize_chinese_chars
):
_lowerCAmelCase = getattr(_lowerCAmelCase , normalizer_state.pop("type" ) )
_lowerCAmelCase = do_lower_case
_lowerCAmelCase = strip_accents
_lowerCAmelCase = tokenize_chinese_chars
_lowerCAmelCase = normalizer_class(**_lowerCAmelCase )
_lowerCAmelCase = do_lower_case
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase=None ) -> str:
_lowerCAmelCase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> List[int]:
_lowerCAmelCase = [self.sep_token_id]
_lowerCAmelCase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> Tuple[str]:
_lowerCAmelCase = self._tokenizer.model.save(_lowerCAmelCase , name=_lowerCAmelCase )
return tuple(_lowerCAmelCase )
| 717 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE = "▁"
_SCREAMING_SNAKE_CASE = {"vocab_file": "sentencepiece.bpe.model"}
_SCREAMING_SNAKE_CASE = {
"vocab_file": {
"facebook/xglm-564M": "https://huggingface.co/facebook/xglm-564M/resolve/main/sentencepiece.bpe.model",
}
}
_SCREAMING_SNAKE_CASE = {
"facebook/xglm-564M": 20_48,
}
class lowerCAmelCase_ ( __magic_name__ ):
__lowerCamelCase : int = VOCAB_FILES_NAMES
__lowerCamelCase : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
__lowerCamelCase : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__lowerCamelCase : str = ["input_ids", "attention_mask"]
def __init__( self , _lowerCAmelCase , _lowerCAmelCase="<s>" , _lowerCAmelCase="</s>" , _lowerCAmelCase="</s>" , _lowerCAmelCase="<s>" , _lowerCAmelCase="<unk>" , _lowerCAmelCase="<pad>" , _lowerCAmelCase = None , **_lowerCAmelCase , ) -> None:
_lowerCAmelCase = {} if sp_model_kwargs is None else sp_model_kwargs
# Compatibility with the original tokenizer
_lowerCAmelCase = 7
_lowerCAmelCase = [f'''<madeupword{i}>''' for i in range(self.num_madeup_words )]
_lowerCAmelCase = kwargs.get("additional_special_tokens" , [] )
kwargs["additional_special_tokens"] += [
word for word in madeup_words if word not in kwargs["additional_special_tokens"]
]
super().__init__(
bos_token=_lowerCAmelCase , eos_token=_lowerCAmelCase , unk_token=_lowerCAmelCase , sep_token=_lowerCAmelCase , cls_token=_lowerCAmelCase , pad_token=_lowerCAmelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCAmelCase , )
_lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_lowerCAmelCase ) )
_lowerCAmelCase = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
_lowerCAmelCase = 1
# Mimic fairseq token-to-id alignment for the first 4 token
_lowerCAmelCase = {"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}
_lowerCAmelCase = len(self.sp_model )
_lowerCAmelCase = {f'''<madeupword{i}>''': sp_size + i + self.fairseq_offset for i in range(self.num_madeup_words )}
self.fairseq_tokens_to_ids.update(_lowerCAmelCase )
_lowerCAmelCase = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> List[str]:
_lowerCAmelCase = self.__dict__.copy()
_lowerCAmelCase = None
_lowerCAmelCase = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , _lowerCAmelCase ) -> Optional[int]:
_lowerCAmelCase = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
_lowerCAmelCase = {}
_lowerCAmelCase = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> List[int]:
if token_ids_a is None:
return [self.sep_token_id] + token_ids_a
_lowerCAmelCase = [self.sep_token_id]
return sep + token_ids_a + sep + sep + token_ids_a
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None , _lowerCAmelCase = 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 ))
return [1] + ([0] * len(_lowerCAmelCase )) + [1, 1] + ([0] * len(_lowerCAmelCase ))
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> List[int]:
_lowerCAmelCase = [self.sep_token_id]
if token_ids_a is None:
return len(sep + token_ids_a ) * [0]
return len(sep + token_ids_a + sep + sep + token_ids_a ) * [0]
@property
def _snake_case ( self ) -> str:
return len(self.sp_model ) + self.fairseq_offset + self.num_madeup_words
def _snake_case ( self ) -> Any:
_lowerCAmelCase = {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 , _lowerCAmelCase ) -> List[str]:
return self.sp_model.encode(_lowerCAmelCase , out_type=_lowerCAmelCase )
def _snake_case ( self , _lowerCAmelCase ) -> List[str]:
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_lowerCAmelCase = self.sp_model.PieceToId(_lowerCAmelCase )
# 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 _snake_case ( self , _lowerCAmelCase ) -> 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 _snake_case ( self , _lowerCAmelCase ) -> Union[str, Any]:
_lowerCAmelCase = "".join(_lowerCAmelCase ).replace(_lowerCAmelCase , " " ).strip()
return out_string
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase = None ) -> Tuple[str]:
if not os.path.isdir(_lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
_lowerCAmelCase = os.path.join(
_lowerCAmelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCAmelCase ) 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:
_lowerCAmelCase = self.sp_model.serialized_model_proto()
fi.write(_lowerCAmelCase )
return (out_vocab_file,)
| 489 | 0 |
"""simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
from decord import VideoReader
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING
a : str = logging.get_logger(__name__)
@add_end_docstrings(SCREAMING_SNAKE_CASE__ )
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self , *snake_case__ , **snake_case__ ):
'''simple docstring'''
super().__init__(*snake_case__ , **snake_case__ )
requires_backends(self , "decord" )
self.check_model_type(snake_case__ )
def UpperCAmelCase_ ( self , snake_case__=None , snake_case__=None , snake_case__=None ):
'''simple docstring'''
lowercase__ : Optional[Any]= {}
if frame_sampling_rate is not None:
lowercase__ : Optional[int]= frame_sampling_rate
if num_frames is not None:
lowercase__ : Union[str, Any]= num_frames
lowercase__ : Optional[Any]= {}
if top_k is not None:
lowercase__ : Tuple= top_k
return preprocess_params, {}, postprocess_params
def __call__( self , snake_case__ , **snake_case__ ):
'''simple docstring'''
return super().__call__(snake_case__ , **snake_case__ )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__=None , snake_case__=1 ):
'''simple docstring'''
if num_frames is None:
lowercase__ : Any= self.model.config.num_frames
if video.startswith("http://" ) or video.startswith("https://" ):
lowercase__ : Dict= BytesIO(requests.get(snake_case__ ).content )
lowercase__ : List[str]= VideoReader(snake_case__ )
videoreader.seek(0 )
lowercase__ : Optional[int]= 0
lowercase__ : Optional[int]= num_frames * frame_sampling_rate - 1
lowercase__ : str= np.linspace(snake_case__ , snake_case__ , num=snake_case__ , dtype=np.intaa )
lowercase__ : Tuple= videoreader.get_batch(snake_case__ ).asnumpy()
lowercase__ : Optional[Any]= list(snake_case__ )
lowercase__ : Dict= self.image_processor(snake_case__ , return_tensors=self.framework )
return model_inputs
def UpperCAmelCase_ ( self , snake_case__ ):
'''simple docstring'''
lowercase__ : Union[str, Any]= self.model(**snake_case__ )
return model_outputs
def UpperCAmelCase_ ( self , snake_case__ , snake_case__=5 ):
'''simple docstring'''
if top_k > self.model.config.num_labels:
lowercase__ : List[str]= self.model.config.num_labels
if self.framework == "pt":
lowercase__ : Tuple= model_outputs.logits.softmax(-1 )[0]
lowercase__, lowercase__ : Tuple= probs.topk(snake_case__ )
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
lowercase__ : int= scores.tolist()
lowercase__ : List[Any]= ids.tolist()
return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(snake_case__ , snake_case__ )]
| 218 |
"""simple docstring"""
from __future__ import annotations
class __UpperCAmelCase:
"""simple docstring"""
def __init__( self , snake_case__ ):
'''simple docstring'''
lowercase__ : str= data
lowercase__ : Node | None= None
lowercase__ : Node | None= None
def lowercase__(A ) ->None: # In Order traversal of the tree
"""simple docstring"""
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowercase__(A ) ->int:
"""simple docstring"""
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowercase__(A ) ->bool:
"""simple docstring"""
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowercase__() ->None: # Main function for testing.
"""simple docstring"""
lowercase__ : int= Node(1 )
lowercase__ : Union[str, Any]= Node(2 )
lowercase__ : Optional[int]= Node(3 )
lowercase__ : Optional[Any]= Node(4 )
lowercase__ : Optional[Any]= Node(5 )
lowercase__ : Tuple= Node(6 )
lowercase__ : Any= Node(7 )
lowercase__ : Tuple= Node(8 )
lowercase__ : List[Any]= Node(9 )
print(is_full_binary_tree(A ) )
print(depth_of_tree(A ) )
print("Tree is: " )
display(A )
if __name__ == "__main__":
main()
| 218 | 1 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available
if is_torch_available() and is_datasets_available() and is_faiss_available():
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
def _a ( self ) -> List[Any]:
__UpperCamelCase =tempfile.mkdtemp()
__UpperCamelCase =8
# DPR tok
__UpperCamelCase =[
'[UNK]',
'[CLS]',
'[SEP]',
'[PAD]',
'[MASK]',
'want',
'##want',
'##ed',
'wa',
'un',
'runn',
'##ing',
',',
'low',
'lowest',
]
__UpperCamelCase =os.path.join(self.tmpdirname , 'dpr_tokenizer' )
os.makedirs(A_ , exist_ok=A_ )
__UpperCamelCase =os.path.join(A_ , DPR_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] ) )
# BART tok
__UpperCamelCase =[
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
__UpperCamelCase =dict(zip(A_ , range(len(A_ ) ) ) )
__UpperCamelCase =['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
__UpperCamelCase ={'unk_token': '<unk>'}
__UpperCamelCase =os.path.join(self.tmpdirname , 'bart_tokenizer' )
os.makedirs(A_ , exist_ok=A_ )
__UpperCamelCase =os.path.join(A_ , BART_VOCAB_FILES_NAMES['vocab_file'] )
__UpperCamelCase =os.path.join(A_ , BART_VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(A_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(A_ ) )
def _a ( self ) -> DPRQuestionEncoderTokenizer:
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'dpr_tokenizer' ) )
def _a ( self ) -> BartTokenizer:
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname , 'bart_tokenizer' ) )
def _a ( self ) -> Any:
shutil.rmtree(self.tmpdirname )
@require_tokenizers
def _a ( self ) -> Optional[Any]:
__UpperCamelCase =os.path.join(self.tmpdirname , 'rag_tokenizer' )
__UpperCamelCase =RagConfig(question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() )
__UpperCamelCase =RagTokenizer(question_encoder=self.get_dpr_tokenizer() , generator=self.get_bart_tokenizer() )
rag_config.save_pretrained(A_ )
rag_tokenizer.save_pretrained(A_ )
__UpperCamelCase =RagTokenizer.from_pretrained(A_ , config=A_ )
self.assertIsInstance(new_rag_tokenizer.question_encoder , A_ )
self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab() , rag_tokenizer.question_encoder.get_vocab() )
self.assertIsInstance(new_rag_tokenizer.generator , A_ )
self.assertEqual(new_rag_tokenizer.generator.get_vocab() , rag_tokenizer.generator.get_vocab() )
@slow
def _a ( self ) -> Optional[Any]:
__UpperCamelCase =RagTokenizer.from_pretrained('facebook/rag-token-nq' )
__UpperCamelCase =[
'who got the first nobel prize in physics',
'when is the next deadpool movie being released',
'which mode is used for short wave broadcast service',
'who is the owner of reading football club',
'when is the next scandal episode coming out',
'when is the last time the philadelphia won the superbowl',
'what is the most current adobe flash player version',
'how many episodes are there in dragon ball z',
'what is the first step in the evolution of the eye',
'where is gall bladder situated in human body',
'what is the main mineral in lithium batteries',
'who is the president of usa right now',
'where do the greasers live in the outsiders',
'panda is a national animal of which country',
'what is the name of manchester united stadium',
]
__UpperCamelCase =tokenizer(A_ )
self.assertIsNotNone(A_ )
@slow
def _a ( self ) -> Any:
__UpperCamelCase =RagTokenizer.from_pretrained('facebook/rag-sequence-nq' )
__UpperCamelCase =[
'who got the first nobel prize in physics',
'when is the next deadpool movie being released',
'which mode is used for short wave broadcast service',
'who is the owner of reading football club',
'when is the next scandal episode coming out',
'when is the last time the philadelphia won the superbowl',
'what is the most current adobe flash player version',
'how many episodes are there in dragon ball z',
'what is the first step in the evolution of the eye',
'where is gall bladder situated in human body',
'what is the main mineral in lithium batteries',
'who is the president of usa right now',
'where do the greasers live in the outsiders',
'panda is a national animal of which country',
'what is the name of manchester united stadium',
]
__UpperCamelCase =tokenizer(A_ )
self.assertIsNotNone(A_ )
| 682 |
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
_A = logging.getLogger(__name__)
class UpperCAmelCase__ :
"""simple docstring"""
def __init__( self ) -> int:
__UpperCamelCase =False
def _a ( self , A_ , A_ , A_ , A_ ) -> List[Any]:
if not self.initialized:
__UpperCamelCase =RagRetriever(
A_ , question_encoder_tokenizer=A_ , generator_tokenizer=A_ , index=A_ , init_retrieval=A_ , )
__UpperCamelCase =True
def _a ( self ) -> Optional[Any]:
self.retriever.index.init_index()
def _a ( self , A_ , A_ ) -> Dict:
__UpperCamelCase , __UpperCamelCase =self.retriever._main_retrieve(A_ , A_ )
return doc_ids, retrieved_doc_embeds
class UpperCAmelCase__ ( A_ ):
"""simple docstring"""
def __init__( self , A_ , A_ , A_ , A_ , A_=None ) -> Dict:
if index is not None and index.is_initialized() and len(A_ ) > 0:
raise ValueError(
'When using Ray for distributed fine-tuning, '
'you\'ll need to provide the paths instead, '
'as the dataset and the index are loaded '
'separately. More info in examples/rag/use_own_knowledge_dataset.py ' )
super().__init__(
A_ , question_encoder_tokenizer=A_ , generator_tokenizer=A_ , index=A_ , init_retrieval=A_ , )
__UpperCamelCase =retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(A_ , A_ , A_ , A_ )
for worker in self.retrieval_workers
] )
def _a ( self ) -> Union[str, Any]:
logger.info('initializing retrieval' )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def _a ( self , A_ , A_ ) -> Optional[int]:
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
__UpperCamelCase =self.retrieval_workers[random.randint(0 , len(self.retrieval_workers ) - 1 )]
__UpperCamelCase , __UpperCamelCase =ray.get(random_worker.retrieve.remote(A_ , A_ ) )
else:
__UpperCamelCase , __UpperCamelCase =self._main_retrieve(A_ , A_ )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(A_ )
@classmethod
def _a ( cls , A_ , A_=None , **A_ ) -> List[str]:
return super(A_ , cls ).get_tokenizers(A_ , A_ , **A_ )
@classmethod
def _a ( cls , A_ , A_ , A_=None , **A_ ) -> str:
__UpperCamelCase =kwargs.pop('config' , A_ ) or RagConfig.from_pretrained(A_ , **A_ )
__UpperCamelCase =RagTokenizer.from_pretrained(A_ , config=A_ )
__UpperCamelCase =rag_tokenizer.question_encoder
__UpperCamelCase =rag_tokenizer.generator
if indexed_dataset is not None:
__UpperCamelCase ='custom'
__UpperCamelCase =CustomHFIndex(config.retrieval_vector_size , A_ )
else:
__UpperCamelCase =cls._build_index(A_ )
return cls(
A_ , question_encoder_tokenizer=A_ , generator_tokenizer=A_ , retrieval_workers=A_ , index=A_ , )
| 682 | 1 |
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def a__ ( snake_case__ : Tuple , snake_case__ : Dict=1 ):
if n_shave_prefix_segments >= 0:
return ".".join(path.split(""".""" )[n_shave_prefix_segments:] )
else:
return ".".join(path.split(""".""" )[:n_shave_prefix_segments] )
def a__ ( snake_case__ : str , snake_case__ : Tuple=0 ):
_UpperCAmelCase : Dict = []
for old_item in old_list:
_UpperCAmelCase : Optional[Any] = old_item.replace("""in_layers.0""" , """norm1""" )
_UpperCAmelCase : Tuple = new_item.replace("""in_layers.2""" , """conv1""" )
_UpperCAmelCase : Dict = new_item.replace("""out_layers.0""" , """norm2""" )
_UpperCAmelCase : Tuple = new_item.replace("""out_layers.3""" , """conv2""" )
_UpperCAmelCase : List[str] = new_item.replace("""emb_layers.1""" , """time_emb_proj""" )
_UpperCAmelCase : List[str] = new_item.replace("""skip_connection""" , """conv_shortcut""" )
_UpperCAmelCase : int = shave_segments(_UpperCamelCase , n_shave_prefix_segments=_UpperCamelCase )
mapping.append({"""old""": old_item, """new""": new_item} )
return mapping
def a__ ( snake_case__ : List[Any] , snake_case__ : List[Any]=0 ):
_UpperCAmelCase : Any = []
for old_item in old_list:
_UpperCAmelCase : Optional[int] = old_item
_UpperCAmelCase : str = new_item.replace("""norm.weight""" , """group_norm.weight""" )
_UpperCAmelCase : List[Any] = new_item.replace("""norm.bias""" , """group_norm.bias""" )
_UpperCAmelCase : str = new_item.replace("""proj_out.weight""" , """proj_attn.weight""" )
_UpperCAmelCase : Optional[int] = new_item.replace("""proj_out.bias""" , """proj_attn.bias""" )
_UpperCAmelCase : int = shave_segments(_UpperCamelCase , n_shave_prefix_segments=_UpperCamelCase )
mapping.append({"""old""": old_item, """new""": new_item} )
return mapping
def a__ ( snake_case__ : Dict , snake_case__ : int , snake_case__ : Optional[int] , snake_case__ : Dict=None , snake_case__ : int=None , snake_case__ : List[Any]=None ):
assert isinstance(_UpperCamelCase , _UpperCamelCase ), "Paths should be a list of dicts containing 'old' and 'new' keys."
# Splits the attention layers into three variables.
if attention_paths_to_split is not None:
for path, path_map in attention_paths_to_split.items():
_UpperCAmelCase : str = old_checkpoint[path]
_UpperCAmelCase : Optional[Any] = old_tensor.shape[0] // 3
_UpperCAmelCase : Dict = (-1, channels) if len(old_tensor.shape ) == 3 else (-1)
_UpperCAmelCase : Optional[Any] = old_tensor.shape[0] // config["""num_head_channels"""] // 3
_UpperCAmelCase : str = old_tensor.reshape((num_heads, 3 * channels // num_heads) + old_tensor.shape[1:] )
_UpperCAmelCase,_UpperCAmelCase,_UpperCAmelCase : List[str] = old_tensor.split(channels // num_heads , dim=1 )
_UpperCAmelCase : Optional[Any] = query.reshape(_UpperCamelCase )
_UpperCAmelCase : Any = key.reshape(_UpperCamelCase )
_UpperCAmelCase : Optional[Any] = value.reshape(_UpperCamelCase )
for path in paths:
_UpperCAmelCase : List[Any] = path["""new"""]
# These have already been assigned
if attention_paths_to_split is not None and new_path in attention_paths_to_split:
continue
# Global renaming happens here
_UpperCAmelCase : int = new_path.replace("""middle_block.0""" , """mid_block.resnets.0""" )
_UpperCAmelCase : str = new_path.replace("""middle_block.1""" , """mid_block.attentions.0""" )
_UpperCAmelCase : Union[str, Any] = new_path.replace("""middle_block.2""" , """mid_block.resnets.1""" )
if additional_replacements is not None:
for replacement in additional_replacements:
_UpperCAmelCase : Any = new_path.replace(replacement["""old"""] , replacement["""new"""] )
# proj_attn.weight has to be converted from conv 1D to linear
if "proj_attn.weight" in new_path:
_UpperCAmelCase : Tuple = old_checkpoint[path["""old"""]][:, :, 0]
else:
_UpperCAmelCase : List[Any] = old_checkpoint[path["""old"""]]
def a__ ( snake_case__ : List[str] , snake_case__ : List[str] ):
_UpperCAmelCase : Any = {}
_UpperCAmelCase : Optional[int] = checkpoint["""time_embed.0.weight"""]
_UpperCAmelCase : Union[str, Any] = checkpoint["""time_embed.0.bias"""]
_UpperCAmelCase : Tuple = checkpoint["""time_embed.2.weight"""]
_UpperCAmelCase : Optional[Any] = checkpoint["""time_embed.2.bias"""]
_UpperCAmelCase : int = checkpoint["""input_blocks.0.0.weight"""]
_UpperCAmelCase : List[Any] = checkpoint["""input_blocks.0.0.bias"""]
_UpperCAmelCase : Optional[int] = checkpoint["""out.0.weight"""]
_UpperCAmelCase : Tuple = checkpoint["""out.0.bias"""]
_UpperCAmelCase : Any = checkpoint["""out.2.weight"""]
_UpperCAmelCase : Any = checkpoint["""out.2.bias"""]
# Retrieves the keys for the input blocks only
_UpperCAmelCase : List[str] = len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """input_blocks""" in layer} )
_UpperCAmelCase : Optional[int] = {
layer_id: [key for key in checkpoint if f'''input_blocks.{layer_id}''' in key]
for layer_id in range(_UpperCamelCase )
}
# Retrieves the keys for the middle blocks only
_UpperCAmelCase : Dict = len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """middle_block""" in layer} )
_UpperCAmelCase : List[Any] = {
layer_id: [key for key in checkpoint if f'''middle_block.{layer_id}''' in key]
for layer_id in range(_UpperCamelCase )
}
# Retrieves the keys for the output blocks only
_UpperCAmelCase : List[Any] = len({""".""".join(layer.split(""".""" )[:2] ) for layer in checkpoint if """output_blocks""" in layer} )
_UpperCAmelCase : Dict = {
layer_id: [key for key in checkpoint if f'''output_blocks.{layer_id}''' in key]
for layer_id in range(_UpperCamelCase )
}
for i in range(1 , _UpperCamelCase ):
_UpperCAmelCase : str = (i - 1) // (config["""num_res_blocks"""] + 1)
_UpperCAmelCase : str = (i - 1) % (config["""num_res_blocks"""] + 1)
_UpperCAmelCase : Any = [key for key in input_blocks[i] if f'''input_blocks.{i}.0''' in key]
_UpperCAmelCase : Dict = [key for key in input_blocks[i] if f'''input_blocks.{i}.1''' in key]
if f'''input_blocks.{i}.0.op.weight''' in checkpoint:
_UpperCAmelCase : int = checkpoint[
f'''input_blocks.{i}.0.op.weight'''
]
_UpperCAmelCase : Dict = checkpoint[
f'''input_blocks.{i}.0.op.bias'''
]
continue
_UpperCAmelCase : int = renew_resnet_paths(_UpperCamelCase )
_UpperCAmelCase : Optional[Any] = {"""old""": f'''input_blocks.{i}.0''', """new""": f'''down_blocks.{block_id}.resnets.{layer_in_block_id}'''}
_UpperCAmelCase : str = {"""old""": """resnets.2.op""", """new""": """downsamplers.0.op"""}
assign_to_checkpoint(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , additional_replacements=[meta_path, resnet_op] , config=_UpperCamelCase )
if len(_UpperCamelCase ):
_UpperCAmelCase : Tuple = renew_attention_paths(_UpperCamelCase )
_UpperCAmelCase : Tuple = {
"""old""": f'''input_blocks.{i}.1''',
"""new""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
_UpperCAmelCase : List[str] = {
f'''input_blocks.{i}.1.qkv.bias''': {
"""key""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
"""query""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
"""value""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
f'''input_blocks.{i}.1.qkv.weight''': {
"""key""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
"""query""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
"""value""": f'''down_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , additional_replacements=[meta_path] , attention_paths_to_split=_UpperCamelCase , config=_UpperCamelCase , )
_UpperCAmelCase : Optional[Any] = middle_blocks[0]
_UpperCAmelCase : Dict = middle_blocks[1]
_UpperCAmelCase : List[str] = middle_blocks[2]
_UpperCAmelCase : int = renew_resnet_paths(_UpperCamelCase )
assign_to_checkpoint(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , config=_UpperCamelCase )
_UpperCAmelCase : str = renew_resnet_paths(_UpperCamelCase )
assign_to_checkpoint(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , config=_UpperCamelCase )
_UpperCAmelCase : List[Any] = renew_attention_paths(_UpperCamelCase )
_UpperCAmelCase : List[Any] = {
"""middle_block.1.qkv.bias""": {
"""key""": """mid_block.attentions.0.key.bias""",
"""query""": """mid_block.attentions.0.query.bias""",
"""value""": """mid_block.attentions.0.value.bias""",
},
"""middle_block.1.qkv.weight""": {
"""key""": """mid_block.attentions.0.key.weight""",
"""query""": """mid_block.attentions.0.query.weight""",
"""value""": """mid_block.attentions.0.value.weight""",
},
}
assign_to_checkpoint(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , attention_paths_to_split=_UpperCamelCase , config=_UpperCamelCase )
for i in range(_UpperCamelCase ):
_UpperCAmelCase : Dict = i // (config["""num_res_blocks"""] + 1)
_UpperCAmelCase : Any = i % (config["""num_res_blocks"""] + 1)
_UpperCAmelCase : Tuple = [shave_segments(_UpperCamelCase , 2 ) for name in output_blocks[i]]
_UpperCAmelCase : List[Any] = {}
for layer in output_block_layers:
_UpperCAmelCase,_UpperCAmelCase : Dict = layer.split(""".""" )[0], shave_segments(_UpperCamelCase , 1 )
if layer_id in output_block_list:
output_block_list[layer_id].append(_UpperCamelCase )
else:
_UpperCAmelCase : Any = [layer_name]
if len(_UpperCamelCase ) > 1:
_UpperCAmelCase : List[str] = [key for key in output_blocks[i] if f'''output_blocks.{i}.0''' in key]
_UpperCAmelCase : List[Any] = [key for key in output_blocks[i] if f'''output_blocks.{i}.1''' in key]
_UpperCAmelCase : int = renew_resnet_paths(_UpperCamelCase )
_UpperCAmelCase : str = renew_resnet_paths(_UpperCamelCase )
_UpperCAmelCase : List[str] = {"""old""": f'''output_blocks.{i}.0''', """new""": f'''up_blocks.{block_id}.resnets.{layer_in_block_id}'''}
assign_to_checkpoint(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , additional_replacements=[meta_path] , config=_UpperCamelCase )
if ["conv.weight", "conv.bias"] in output_block_list.values():
_UpperCAmelCase : Dict = list(output_block_list.values() ).index(["""conv.weight""", """conv.bias"""] )
_UpperCAmelCase : Dict = checkpoint[
f'''output_blocks.{i}.{index}.conv.weight'''
]
_UpperCAmelCase : Any = checkpoint[
f'''output_blocks.{i}.{index}.conv.bias'''
]
# Clear attentions as they have been attributed above.
if len(_UpperCamelCase ) == 2:
_UpperCAmelCase : str = []
if len(_UpperCamelCase ):
_UpperCAmelCase : List[Any] = renew_attention_paths(_UpperCamelCase )
_UpperCAmelCase : int = {
"""old""": f'''output_blocks.{i}.1''',
"""new""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}''',
}
_UpperCAmelCase : Any = {
f'''output_blocks.{i}.1.qkv.bias''': {
"""key""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.bias''',
"""query""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.bias''',
"""value""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.bias''',
},
f'''output_blocks.{i}.1.qkv.weight''': {
"""key""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.key.weight''',
"""query""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.query.weight''',
"""value""": f'''up_blocks.{block_id}.attentions.{layer_in_block_id}.value.weight''',
},
}
assign_to_checkpoint(
_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , additional_replacements=[meta_path] , attention_paths_to_split=to_split if any("""qkv""" in key for key in attentions ) else None , config=_UpperCamelCase , )
else:
_UpperCAmelCase : List[str] = renew_resnet_paths(_UpperCamelCase , n_shave_prefix_segments=1 )
for path in resnet_0_paths:
_UpperCAmelCase : Optional[int] = """.""".join(["""output_blocks""", str(_UpperCamelCase ), path["""old"""]] )
_UpperCAmelCase : Optional[Any] = """.""".join(["""up_blocks""", str(_UpperCamelCase ), """resnets""", str(_UpperCamelCase ), path["""new"""]] )
_UpperCAmelCase : Union[str, Any] = checkpoint[old_path]
return new_checkpoint
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : List[str] = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path', default=None, type=str, required=True, help='Path to the checkpoint to convert.'
)
parser.add_argument(
'--config_file',
default=None,
type=str,
required=True,
help='The config json file corresponding to the architecture.',
)
parser.add_argument('--dump_path', default=None, type=str, required=True, help='Path to the output model.')
SCREAMING_SNAKE_CASE__ : Dict = parser.parse_args()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.load(args.checkpoint_path)
with open(args.config_file) as f:
SCREAMING_SNAKE_CASE__ : Any = json.loads(f.read())
SCREAMING_SNAKE_CASE__ : Union[str, Any] = convert_ldm_checkpoint(checkpoint, config)
if "ldm" in config:
del config["ldm"]
SCREAMING_SNAKE_CASE__ : Optional[int] = UNetaDModel(**config)
model.load_state_dict(converted_checkpoint)
try:
SCREAMING_SNAKE_CASE__ : List[str] = DDPMScheduler.from_config('/'.join(args.checkpoint_path.split('/')[:-1]))
SCREAMING_SNAKE_CASE__ : Optional[int] = VQModel.from_pretrained('/'.join(args.checkpoint_path.split('/')[:-1]))
SCREAMING_SNAKE_CASE__ : int = LDMPipeline(unet=model, scheduler=scheduler, vae=vqvae)
pipe.save_pretrained(args.dump_path)
except: # noqa: E722
model.save_pretrained(args.dump_path)
| 643 |
from __future__ import annotations
import math
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> list:
"""simple docstring"""
if len(_UpperCamelCase) != 2 or len(a[0]) != 2 or len(_UpperCamelCase) != 2 or len(b[0]) != 2:
raise Exception('Matrices are not 2x2')
UpperCamelCase = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> Union[str, Any]:
"""simple docstring"""
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row]))]
for row in range(len(_UpperCamelCase))
]
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> Optional[int]:
"""simple docstring"""
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row]))]
for row in range(len(_UpperCamelCase))
]
def lowercase__ ( _UpperCamelCase) -> tuple[list, list, list, list]:
"""simple docstring"""
if len(_UpperCamelCase) % 2 != 0 or len(a[0]) % 2 != 0:
raise Exception('Odd matrices are not supported!')
UpperCamelCase = len(_UpperCamelCase)
UpperCamelCase = matrix_length // 2
UpperCamelCase = [[a[i][j] for j in range(_UpperCamelCase , _UpperCamelCase)] for i in range(_UpperCamelCase)]
UpperCamelCase = [
[a[i][j] for j in range(_UpperCamelCase , _UpperCamelCase)] for i in range(_UpperCamelCase , _UpperCamelCase)
]
UpperCamelCase = [[a[i][j] for j in range(_UpperCamelCase)] for i in range(_UpperCamelCase)]
UpperCamelCase = [[a[i][j] for j in range(_UpperCamelCase)] for i in range(_UpperCamelCase , _UpperCamelCase)]
return top_left, top_right, bot_left, bot_right
def lowercase__ ( _UpperCamelCase) -> tuple[int, int]:
"""simple docstring"""
return len(_UpperCamelCase), len(matrix[0])
def lowercase__ ( _UpperCamelCase) -> None:
"""simple docstring"""
print('\n'.join(str(_UpperCamelCase) for line in matrix))
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> list:
"""simple docstring"""
if matrix_dimensions(_UpperCamelCase) == (2, 2):
return default_matrix_multiplication(_UpperCamelCase , _UpperCamelCase)
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = split_matrix(_UpperCamelCase)
UpperCamelCase , UpperCamelCase , UpperCamelCase , UpperCamelCase = split_matrix(_UpperCamelCase)
UpperCamelCase = actual_strassen(_UpperCamelCase , matrix_subtraction(_UpperCamelCase , _UpperCamelCase))
UpperCamelCase = actual_strassen(matrix_addition(_UpperCamelCase , _UpperCamelCase) , _UpperCamelCase)
UpperCamelCase = actual_strassen(matrix_addition(_UpperCamelCase , _UpperCamelCase) , _UpperCamelCase)
UpperCamelCase = actual_strassen(_UpperCamelCase , matrix_subtraction(_UpperCamelCase , _UpperCamelCase))
UpperCamelCase = actual_strassen(matrix_addition(_UpperCamelCase , _UpperCamelCase) , matrix_addition(_UpperCamelCase , _UpperCamelCase))
UpperCamelCase = actual_strassen(matrix_subtraction(_UpperCamelCase , _UpperCamelCase) , matrix_addition(_UpperCamelCase , _UpperCamelCase))
UpperCamelCase = actual_strassen(matrix_subtraction(_UpperCamelCase , _UpperCamelCase) , matrix_addition(_UpperCamelCase , _UpperCamelCase))
UpperCamelCase = matrix_addition(matrix_subtraction(matrix_addition(_UpperCamelCase , _UpperCamelCase) , _UpperCamelCase) , _UpperCamelCase)
UpperCamelCase = matrix_addition(_UpperCamelCase , _UpperCamelCase)
UpperCamelCase = matrix_addition(_UpperCamelCase , _UpperCamelCase)
UpperCamelCase = matrix_subtraction(matrix_subtraction(matrix_addition(_UpperCamelCase , _UpperCamelCase) , _UpperCamelCase) , _UpperCamelCase)
# construct the new matrix from our 4 quadrants
UpperCamelCase = []
for i in range(len(_UpperCamelCase)):
new_matrix.append(top_left[i] + top_right[i])
for i in range(len(_UpperCamelCase)):
new_matrix.append(bot_left[i] + bot_right[i])
return new_matrix
def lowercase__ ( _UpperCamelCase , _UpperCamelCase) -> list:
"""simple docstring"""
if matrix_dimensions(_UpperCamelCase)[1] != matrix_dimensions(_UpperCamelCase)[0]:
UpperCamelCase = (
'Unable to multiply these matrices, please check the dimensions.\n'
F'Matrix A: {matrixa}\n'
F'Matrix B: {matrixa}'
)
raise Exception(_UpperCamelCase)
UpperCamelCase = matrix_dimensions(_UpperCamelCase)
UpperCamelCase = matrix_dimensions(_UpperCamelCase)
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
UpperCamelCase = max(*_UpperCamelCase , *_UpperCamelCase)
UpperCamelCase = int(math.pow(2 , math.ceil(math.loga(_UpperCamelCase))))
UpperCamelCase = matrixa
UpperCamelCase = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , _UpperCamelCase):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , _UpperCamelCase):
new_matrixa[i].append(0)
else:
new_matrixa.append([0] * maxim)
if i < dimensiona[0]:
for _ in range(dimensiona[1] , _UpperCamelCase):
new_matrixa[i].append(0)
else:
new_matrixa.append([0] * maxim)
UpperCamelCase = actual_strassen(_UpperCamelCase , _UpperCamelCase)
# Removing the additional zeros
for i in range(0 , _UpperCamelCase):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , _UpperCamelCase):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
__magic_name__ : Optional[Any] = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
__magic_name__ : List[str] = [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
print(strassen(matrixa, matrixa))
| 280 | 0 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple
import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = None
_lowerCamelCase = None
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __init__( self ,lowerCamelCase_=1 ,lowerCamelCase_=0 ,lowerCamelCase_=2 ,lowerCamelCase_=5_1_2 ,lowerCamelCase_="cls" ,lowerCamelCase_=False ,lowerCamelCase_=True ,**lowerCamelCase_ ,) -> Optional[int]:
super().__init__(pad_token_id=lowerCamelCase_ ,bos_token_id=lowerCamelCase_ ,eos_token_id=lowerCamelCase_ ,**lowerCamelCase_ )
A = project_dim
A = pooler_fn
A = learn_encoder
A = use_attention_mask
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_lowerCamelCase = [R'''pooler''', R'''logit_scale''']
_lowerCamelCase = [R'''position_ids''', R'''predictions.decoder.bias''']
_lowerCamelCase = '''roberta'''
_lowerCamelCase = RobertaSeriesConfig
def __init__( self ,lowerCamelCase_ ) -> Optional[Any]:
super().__init__(lowerCamelCase_ )
A = XLMRobertaModel(lowerCamelCase_ )
A = nn.Linear(config.hidden_size ,config.project_dim )
A = getattr(lowerCamelCase_ ,"""has_pre_transformation""" ,lowerCamelCase_ )
if self.has_pre_transformation:
A = nn.Linear(config.hidden_size ,config.project_dim )
A = nn.LayerNorm(config.hidden_size ,eps=config.layer_norm_eps )
self.post_init()
def UpperCamelCase__ ( self ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,lowerCamelCase_ = None ,) -> Optional[Any]:
A = return_dict if return_dict is not None else self.config.use_return_dict
A = self.base_model(
input_ids=lowerCamelCase_ ,attention_mask=lowerCamelCase_ ,token_type_ids=lowerCamelCase_ ,position_ids=lowerCamelCase_ ,head_mask=lowerCamelCase_ ,inputs_embeds=lowerCamelCase_ ,encoder_hidden_states=lowerCamelCase_ ,encoder_attention_mask=lowerCamelCase_ ,output_attentions=lowerCamelCase_ ,output_hidden_states=True if self.has_pre_transformation else output_hidden_states ,return_dict=lowerCamelCase_ ,)
if self.has_pre_transformation:
A = outputs["""hidden_states"""][-2]
A = self.pre_LN(lowerCamelCase_ )
A = self.transformation_pre(lowerCamelCase_ )
return TransformationModelOutput(
projection_state=lowerCamelCase_ ,last_hidden_state=outputs.last_hidden_state ,hidden_states=outputs.hidden_states ,attentions=outputs.attentions ,)
else:
A = self.transformation(outputs.last_hidden_state )
return TransformationModelOutput(
projection_state=lowerCamelCase_ ,last_hidden_state=outputs.last_hidden_state ,hidden_states=outputs.hidden_states ,attentions=outputs.attentions ,)
| 255 |
"""simple docstring"""
UpperCAmelCase =256
# Modulus to hash a string
UpperCAmelCase =1_000_003
def _A ( _a : str , _a : str ):
"""simple docstring"""
A = len(_a )
A = len(_a )
if p_len > t_len:
return False
A = 0
A = 0
A = 1
# Calculating the hash of pattern and substring of text
for i in range(_a ):
A = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
A = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
A = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
A = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def _A ( ):
"""simple docstring"""
A = """abc1abc12"""
A = """alskfjaldsabc1abc1abc12k23adsfabcabc"""
A = """alskfjaldsk23adsfabcabc"""
assert rabin_karp(_a , _a ) and not rabin_karp(_a , _a )
# Test 2)
A = """ABABX"""
A = """ABABZABABYABABX"""
assert rabin_karp(_a , _a )
# Test 3)
A = """AAAB"""
A = """ABAAAAAB"""
assert rabin_karp(_a , _a )
# Test 4)
A = """abcdabcy"""
A = """abcxabcdabxabcdabcdabcy"""
assert rabin_karp(_a , _a )
# Test 5)
A = """Lü"""
A = """Lüsai"""
assert rabin_karp(_a , _a )
A = """Lue"""
assert not rabin_karp(_a , _a )
print("""Success.""" )
if __name__ == "__main__":
test_rabin_karp()
| 255 | 1 |
"""simple docstring"""
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import DeiTConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
)
from transformers.models.deit.modeling_tf_deit import TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DeiTImageProcessor
class a :
def __init__( self : int , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Union[str, Any]=13 , __SCREAMING_SNAKE_CASE : List[Any]=30 , __SCREAMING_SNAKE_CASE : Dict=2 , __SCREAMING_SNAKE_CASE : str=3 , __SCREAMING_SNAKE_CASE : Any=True , __SCREAMING_SNAKE_CASE : str=True , __SCREAMING_SNAKE_CASE : Any=32 , __SCREAMING_SNAKE_CASE : Any=2 , __SCREAMING_SNAKE_CASE : Optional[int]=4 , __SCREAMING_SNAKE_CASE : List[str]=37 , __SCREAMING_SNAKE_CASE : Optional[int]="gelu" , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.1 , __SCREAMING_SNAKE_CASE : Union[str, Any]=0.1 , __SCREAMING_SNAKE_CASE : Any=10 , __SCREAMING_SNAKE_CASE : int=0.02 , __SCREAMING_SNAKE_CASE : Union[str, Any]=3 , __SCREAMING_SNAKE_CASE : str=None , __SCREAMING_SNAKE_CASE : Optional[int]=2 , ) -> int:
lowerCamelCase_ = parent
lowerCamelCase_ = batch_size
lowerCamelCase_ = image_size
lowerCamelCase_ = patch_size
lowerCamelCase_ = num_channels
lowerCamelCase_ = is_training
lowerCamelCase_ = use_labels
lowerCamelCase_ = hidden_size
lowerCamelCase_ = num_hidden_layers
lowerCamelCase_ = num_attention_heads
lowerCamelCase_ = intermediate_size
lowerCamelCase_ = hidden_act
lowerCamelCase_ = hidden_dropout_prob
lowerCamelCase_ = attention_probs_dropout_prob
lowerCamelCase_ = type_sequence_label_size
lowerCamelCase_ = initializer_range
lowerCamelCase_ = scope
lowerCamelCase_ = encoder_stride
# in DeiT, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distilation tokens)
lowerCamelCase_ = (image_size // patch_size) ** 2
lowerCamelCase_ = num_patches + 2
def UpperCamelCase ( self : Any ) -> Any:
lowerCamelCase_ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowerCamelCase_ = None
if self.use_labels:
lowerCamelCase_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowerCamelCase_ = self.get_config()
return config, pixel_values, labels
def UpperCamelCase ( self : List[str] ) -> 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=__SCREAMING_SNAKE_CASE , initializer_range=self.initializer_range , encoder_stride=self.encoder_stride , )
def UpperCamelCase ( self : str , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : List[Any] ) -> Optional[Any]:
lowerCamelCase_ = TFDeiTModel(config=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCamelCase ( self : int , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : str ) -> List[Any]:
lowerCamelCase_ = TFDeiTForMaskedImageModeling(config=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(
result.reconstruction.shape , (self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
lowerCamelCase_ = 1
lowerCamelCase_ = TFDeiTForMaskedImageModeling(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase_ = model(__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.reconstruction.shape , (self.batch_size, 1, self.image_size, self.image_size) )
def UpperCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : str ) -> Any:
lowerCamelCase_ = self.type_sequence_label_size
lowerCamelCase_ = TFDeiTForImageClassification(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowerCamelCase_ = 1
lowerCamelCase_ = TFDeiTForImageClassification(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowerCamelCase_ = model(__SCREAMING_SNAKE_CASE , labels=__SCREAMING_SNAKE_CASE )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCamelCase ( self : List[Any] ) -> str:
lowerCamelCase_ = self.prepare_config_and_inputs()
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = config_and_inputs
lowerCamelCase_ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_tf
class a ( __snake_case , __snake_case , unittest.TestCase ):
SCREAMING_SNAKE_CASE : Any = (
(
TFDeiTModel,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE : List[str] = (
{
"""feature-extraction""": TFDeiTModel,
"""image-classification""": (TFDeiTForImageClassification, TFDeiTForImageClassificationWithTeacher),
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : Optional[int] = False
SCREAMING_SNAKE_CASE : Tuple = False
def UpperCamelCase ( self : str ) -> Dict:
lowerCamelCase_ = TFDeiTModelTester(self )
lowerCamelCase_ = ConfigTester(self , config_class=__SCREAMING_SNAKE_CASE , has_text_modality=__SCREAMING_SNAKE_CASE , hidden_size=37 )
def UpperCamelCase ( self : int ) -> List[str]:
self.config_tester.run_common_tests()
@unittest.skip(reason='DeiT does not use inputs_embeds' )
def UpperCamelCase ( self : str ) -> List[Any]:
pass
def UpperCamelCase ( self : List[str] ) -> str:
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(__SCREAMING_SNAKE_CASE )
self.assertIsInstance(model.get_input_embeddings() , (tf.keras.layers.Layer) )
lowerCamelCase_ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(__SCREAMING_SNAKE_CASE , tf.keras.layers.Dense ) )
def UpperCamelCase ( self : List[Any] ) -> Tuple:
lowerCamelCase_ , lowerCamelCase_ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCamelCase_ = model_class(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCamelCase_ = [*signature.parameters.keys()]
lowerCamelCase_ = ['pixel_values']
self.assertListEqual(arg_names[:1] , __SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : int ) -> List[Any]:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : str ) -> Optional[int]:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*__SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : List[Any] ) -> Dict:
lowerCamelCase_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*__SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any]=False ) -> Union[str, Any]:
lowerCamelCase_ = super()._prepare_for_class(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , return_labels=__SCREAMING_SNAKE_CASE )
if return_labels:
if "labels" in inputs_dict and "labels" not in inspect.signature(model_class.call ).parameters:
del inputs_dict["labels"]
return inputs_dict
@slow
def UpperCamelCase ( self : Any ) -> List[str]:
for model_name in TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCamelCase_ = TFDeiTModel.from_pretrained(__SCREAMING_SNAKE_CASE )
self.assertIsNotNone(__SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( ) -> Union[str, Any]:
lowerCamelCase_ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_tf
@require_vision
class a ( unittest.TestCase ):
@cached_property
def UpperCamelCase ( self : List[str] ) -> List[str]:
return (
DeiTImageProcessor.from_pretrained('facebook/deit-base-distilled-patch16-224' )
if is_vision_available()
else None
)
@slow
def UpperCamelCase ( self : Optional[Any] ) -> Optional[int]:
lowerCamelCase_ = TFDeiTForImageClassificationWithTeacher.from_pretrained('facebook/deit-base-distilled-patch16-224' )
lowerCamelCase_ = self.default_image_processor
lowerCamelCase_ = prepare_img()
lowerCamelCase_ = image_processor(images=__SCREAMING_SNAKE_CASE , return_tensors='tf' )
# forward pass
lowerCamelCase_ = model(**__SCREAMING_SNAKE_CASE )
# verify the logits
lowerCamelCase_ = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape , __SCREAMING_SNAKE_CASE )
lowerCamelCase_ = tf.constant([-1.0_266, 0.1_912, -1.2_861] )
self.assertTrue(np.allclose(outputs.logits[0, :3] , __SCREAMING_SNAKE_CASE , atol=1e-4 ) )
| 549 |
"""simple docstring"""
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
_SCREAMING_SNAKE_CASE : str = logging.getLogger(__name__)
class a ( __snake_case ):
SCREAMING_SNAKE_CASE : Optional[Any] = """summarization"""
SCREAMING_SNAKE_CASE : Any = ["""loss"""]
SCREAMING_SNAKE_CASE : Optional[int] = ROUGE_KEYS
SCREAMING_SNAKE_CASE : Optional[int] = """rouge2"""
def __init__( self : Optional[int] , __SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : List[str] ) -> Union[str, Any]:
if hparams.sortish_sampler and hparams.gpus > 1:
lowerCamelCase_ = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError('Dynamic Batch size does not work for multi-gpu training' )
if hparams.sortish_sampler:
raise ValueError('--sortish_sampler and --max_tokens_per_batch may not be used simultaneously' )
super().__init__(__SCREAMING_SNAKE_CASE , num_labels=__SCREAMING_SNAKE_CASE , mode=self.mode , **__SCREAMING_SNAKE_CASE )
use_task_specific_params(self.model , 'summarization' )
save_git_info(self.hparams.output_dir )
lowerCamelCase_ = Path(self.output_dir ) / 'metrics.json'
lowerCamelCase_ = Path(self.output_dir ) / 'hparams.pkl'
pickle_save(self.hparams , self.hparams_save_path )
lowerCamelCase_ = 0
lowerCamelCase_ = defaultdict(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = self.config.model_type
lowerCamelCase_ = self.config.tgt_vocab_size if self.model_type == 'fsmt' else self.config.vocab_size
lowerCamelCase_ = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
lowerCamelCase_ = {
'train': self.hparams.n_train,
'val': self.hparams.n_val,
'test': self.hparams.n_test,
}
lowerCamelCase_ = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
lowerCamelCase_ = {
'train': self.hparams.max_target_length,
'val': self.hparams.val_max_target_length,
'test': self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], F'''target_lens: {self.target_lens}'''
assert self.target_lens["train"] <= self.target_lens["test"], F'''target_lens: {self.target_lens}'''
if self.hparams.freeze_embeds:
freeze_embeds(self.model )
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder() )
assert_all_frozen(self.model.get_encoder() )
lowerCamelCase_ = get_git_info()['repo_sha']
lowerCamelCase_ = hparams.num_workers
lowerCamelCase_ = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer , __SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
lowerCamelCase_ = self.decoder_start_token_id
lowerCamelCase_ = (
SeqaSeqDataset if hasattr(self.tokenizer , 'prepare_seq2seq_batch' ) else LegacySeqaSeqDataset
)
lowerCamelCase_ = False
lowerCamelCase_ = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
lowerCamelCase_ = self.hparams.eval_max_gen_length
else:
lowerCamelCase_ = self.model.config.max_length
lowerCamelCase_ = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : Dict[str, torch.Tensor] ) -> Dict[str, List[str]]:
lowerCamelCase_ = {
k: self.tokenizer.batch_decode(v.tolist() ) if 'mask' not in k else v.shape for k, v in batch.items()
}
save_json(__SCREAMING_SNAKE_CASE , Path(self.output_dir ) / 'text_batch.json' )
save_json({k: v.tolist() for k, v in batch.items()} , Path(self.output_dir ) / 'tok_batch.json' )
lowerCamelCase_ = True
return readable_batch
def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : int , **__SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
return self.model(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : List[int] ) -> int:
lowerCamelCase_ = self.tokenizer.batch_decode(
__SCREAMING_SNAKE_CASE , skip_special_tokens=__SCREAMING_SNAKE_CASE , clean_up_tokenization_spaces=__SCREAMING_SNAKE_CASE )
return lmap(str.strip , __SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : dict ) -> Tuple:
lowerCamelCase_ = self.tokenizer.pad_token_id
lowerCamelCase_ , lowerCamelCase_ = batch['input_ids'], batch['attention_mask']
lowerCamelCase_ = batch['labels']
if isinstance(self.model , __SCREAMING_SNAKE_CASE ):
lowerCamelCase_ = self.model._shift_right(__SCREAMING_SNAKE_CASE )
else:
lowerCamelCase_ = shift_tokens_right(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
lowerCamelCase_ = decoder_input_ids
self.save_readable_batch(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = self(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE , decoder_input_ids=__SCREAMING_SNAKE_CASE , use_cache=__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = outputs['logits']
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
lowerCamelCase_ = nn.CrossEntropyLoss(ignore_index=__SCREAMING_SNAKE_CASE )
assert lm_logits.shape[-1] == self.vocab_size
lowerCamelCase_ = ce_loss_fct(lm_logits.view(-1 , lm_logits.shape[-1] ) , tgt_ids.view(-1 ) )
else:
lowerCamelCase_ = nn.functional.log_softmax(__SCREAMING_SNAKE_CASE , dim=-1 )
lowerCamelCase_ , lowerCamelCase_ = label_smoothed_nll_loss(
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , self.hparams.label_smoothing , ignore_index=__SCREAMING_SNAKE_CASE )
return (loss,)
@property
def UpperCamelCase ( self : Optional[Any] ) -> int:
return self.tokenizer.pad_token_id
def UpperCamelCase ( self : Dict , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Any ) -> Dict:
lowerCamelCase_ = self._step(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = dict(zip(self.loss_names , __SCREAMING_SNAKE_CASE ) )
# tokens per batch
lowerCamelCase_ = batch['input_ids'].ne(self.pad ).sum() + batch['labels'].ne(self.pad ).sum()
lowerCamelCase_ = batch['input_ids'].shape[0]
lowerCamelCase_ = batch['input_ids'].eq(self.pad ).sum()
lowerCamelCase_ = batch['input_ids'].eq(self.pad ).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def UpperCamelCase ( self : Optional[Any] , __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : int ) -> Dict:
return self._generative_step(__SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : str , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any]="val" ) -> Dict:
self.step_count += 1
lowerCamelCase_ = {k: torch.stack([x[k] for x in outputs] ).mean() for k in self.loss_names}
lowerCamelCase_ = losses['loss']
lowerCamelCase_ = {
k: np.array([x[k] for x in outputs] ).mean() for k in self.metric_names + ['gen_time', 'gen_len']
}
lowerCamelCase_ = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
lowerCamelCase_ = torch.tensor(__SCREAMING_SNAKE_CASE ).type_as(__SCREAMING_SNAKE_CASE )
generative_metrics.update({k: v.item() for k, v in losses.items()} )
losses.update(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = {F'''{prefix}_avg_{k}''': x for k, x in losses.items()}
lowerCamelCase_ = self.step_count
self.metrics[prefix].append(__SCREAMING_SNAKE_CASE ) # callback writes this to self.metrics_save_path
lowerCamelCase_ = flatten_list([x['preds'] for x in outputs] )
return {
"log": all_metrics,
"preds": preds,
F'''{prefix}_loss''': loss,
F'''{prefix}_{self.val_metric}''': metric_tensor,
}
def UpperCamelCase ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> Dict:
return calculate_rouge(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : List[str] , __SCREAMING_SNAKE_CASE : dict ) -> dict:
lowerCamelCase_ = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
lowerCamelCase_ = self.model.generate(
batch['input_ids'] , attention_mask=batch['attention_mask'] , use_cache=__SCREAMING_SNAKE_CASE , decoder_start_token_id=self.decoder_start_token_id , num_beams=self.eval_beams , max_length=self.eval_max_length , )
lowerCamelCase_ = (time.time() - ta) / batch['input_ids'].shape[0]
lowerCamelCase_ = self.ids_to_clean_text(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = self.ids_to_clean_text(batch['labels'] )
lowerCamelCase_ = self._step(__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = dict(zip(self.loss_names , __SCREAMING_SNAKE_CASE ) )
lowerCamelCase_ = self.calc_generative_metrics(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowerCamelCase_ = np.mean(lmap(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
base_metrics.update(gen_time=__SCREAMING_SNAKE_CASE , gen_len=__SCREAMING_SNAKE_CASE , preds=__SCREAMING_SNAKE_CASE , target=__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
return base_metrics
def UpperCamelCase ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Tuple , __SCREAMING_SNAKE_CASE : int ) -> Any:
return self._generative_step(__SCREAMING_SNAKE_CASE )
def UpperCamelCase ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Tuple:
return self.validation_epoch_end(__SCREAMING_SNAKE_CASE , prefix='test' )
def UpperCamelCase ( self : Any , __SCREAMING_SNAKE_CASE : Optional[int] ) -> SeqaSeqDataset:
lowerCamelCase_ = self.n_obs[type_path]
lowerCamelCase_ = self.target_lens[type_path]
lowerCamelCase_ = self.dataset_class(
self.tokenizer , type_path=__SCREAMING_SNAKE_CASE , n_obs=__SCREAMING_SNAKE_CASE , max_target_length=__SCREAMING_SNAKE_CASE , **self.dataset_kwargs , )
return dataset
def UpperCamelCase ( self : Tuple , __SCREAMING_SNAKE_CASE : str , __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : bool = False ) -> DataLoader:
lowerCamelCase_ = self.get_dataset(__SCREAMING_SNAKE_CASE )
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
lowerCamelCase_ = dataset.make_sortish_sampler(__SCREAMING_SNAKE_CASE , distributed=self.hparams.gpus > 1 )
return DataLoader(
__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE , collate_fn=dataset.collate_fn , shuffle=__SCREAMING_SNAKE_CASE , num_workers=self.num_workers , sampler=__SCREAMING_SNAKE_CASE , )
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
lowerCamelCase_ = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch , distributed=self.hparams.gpus > 1 )
return DataLoader(
__SCREAMING_SNAKE_CASE , batch_sampler=__SCREAMING_SNAKE_CASE , collate_fn=dataset.collate_fn , num_workers=self.num_workers , )
else:
return DataLoader(
__SCREAMING_SNAKE_CASE , batch_size=__SCREAMING_SNAKE_CASE , collate_fn=dataset.collate_fn , shuffle=__SCREAMING_SNAKE_CASE , num_workers=self.num_workers , sampler=__SCREAMING_SNAKE_CASE , )
def UpperCamelCase ( self : Dict ) -> DataLoader:
lowerCamelCase_ = self.get_dataloader('train' , batch_size=self.hparams.train_batch_size , shuffle=__SCREAMING_SNAKE_CASE )
return dataloader
def UpperCamelCase ( self : int ) -> DataLoader:
return self.get_dataloader('val' , batch_size=self.hparams.eval_batch_size )
def UpperCamelCase ( self : int ) -> DataLoader:
return self.get_dataloader('test' , batch_size=self.hparams.eval_batch_size )
@staticmethod
def UpperCamelCase ( __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : Optional[int] ) -> Dict:
BaseTransformer.add_model_specific_args(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
add_generic_args(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
parser.add_argument(
'--max_source_length' , default=1024 , type=__SCREAMING_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(
'--max_target_length' , default=56 , type=__SCREAMING_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(
'--val_max_target_length' , default=142 , type=__SCREAMING_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(
'--test_max_target_length' , default=142 , type=__SCREAMING_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('--freeze_encoder' , action='store_true' )
parser.add_argument('--freeze_embeds' , action='store_true' )
parser.add_argument('--sortish_sampler' , action='store_true' , default=__SCREAMING_SNAKE_CASE )
parser.add_argument('--overwrite_output_dir' , action='store_true' , default=__SCREAMING_SNAKE_CASE )
parser.add_argument('--max_tokens_per_batch' , type=__SCREAMING_SNAKE_CASE , default=__SCREAMING_SNAKE_CASE )
parser.add_argument('--logger_name' , type=__SCREAMING_SNAKE_CASE , choices=['default', 'wandb', 'wandb_shared'] , default='default' )
parser.add_argument('--n_train' , type=__SCREAMING_SNAKE_CASE , default=-1 , required=__SCREAMING_SNAKE_CASE , help='# examples. -1 means use all.' )
parser.add_argument('--n_val' , type=__SCREAMING_SNAKE_CASE , default=500 , required=__SCREAMING_SNAKE_CASE , help='# examples. -1 means use all.' )
parser.add_argument('--n_test' , type=__SCREAMING_SNAKE_CASE , default=-1 , required=__SCREAMING_SNAKE_CASE , help='# examples. -1 means use all.' )
parser.add_argument(
'--task' , type=__SCREAMING_SNAKE_CASE , default='summarization' , required=__SCREAMING_SNAKE_CASE , help='# examples. -1 means use all.' )
parser.add_argument('--label_smoothing' , type=__SCREAMING_SNAKE_CASE , default=0.0 , required=__SCREAMING_SNAKE_CASE )
parser.add_argument('--src_lang' , type=__SCREAMING_SNAKE_CASE , default='' , required=__SCREAMING_SNAKE_CASE )
parser.add_argument('--tgt_lang' , type=__SCREAMING_SNAKE_CASE , default='' , required=__SCREAMING_SNAKE_CASE )
parser.add_argument('--eval_beams' , type=__SCREAMING_SNAKE_CASE , default=__SCREAMING_SNAKE_CASE , required=__SCREAMING_SNAKE_CASE )
parser.add_argument(
'--val_metric' , type=__SCREAMING_SNAKE_CASE , default=__SCREAMING_SNAKE_CASE , required=__SCREAMING_SNAKE_CASE , choices=['bleu', 'rouge2', 'loss', None] )
parser.add_argument('--eval_max_gen_length' , type=__SCREAMING_SNAKE_CASE , default=__SCREAMING_SNAKE_CASE , help='never generate more than n tokens' )
parser.add_argument('--save_top_k' , type=__SCREAMING_SNAKE_CASE , default=1 , required=__SCREAMING_SNAKE_CASE , help='How many checkpoints to save' )
parser.add_argument(
'--early_stopping_patience' , type=__SCREAMING_SNAKE_CASE , default=-1 , required=__SCREAMING_SNAKE_CASE , help=(
'-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'
' val_check_interval will effect it.'
) , )
return parser
class a ( __snake_case ):
SCREAMING_SNAKE_CASE : Union[str, Any] = """translation"""
SCREAMING_SNAKE_CASE : List[str] = ["""loss"""]
SCREAMING_SNAKE_CASE : str = ["""bleu"""]
SCREAMING_SNAKE_CASE : Optional[int] = """bleu"""
def __init__( self : str , __SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Tuple ) -> Optional[Any]:
super().__init__(__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
lowerCamelCase_ = hparams.src_lang
lowerCamelCase_ = hparams.tgt_lang
def UpperCamelCase ( self : Optional[int] , __SCREAMING_SNAKE_CASE : List[Any] , __SCREAMING_SNAKE_CASE : List[str] ) -> dict:
return calculate_bleu(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def lowerCamelCase__ ( _lowerCamelCase : str , _lowerCamelCase : Tuple=None ) -> SummarizationModule:
Path(args.output_dir ).mkdir(exist_ok=_lowerCamelCase )
check_output_dir(_lowerCamelCase , expected_items=3 )
if model is None:
if "summarization" in args.task:
lowerCamelCase_ = SummarizationModule(_lowerCamelCase )
else:
lowerCamelCase_ = TranslationModule(_lowerCamelCase )
lowerCamelCase_ = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith('/tmp' )
or str(args.output_dir ).startswith('/var' )
):
lowerCamelCase_ = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
lowerCamelCase_ = os.environ.get('WANDB_PROJECT' , _lowerCamelCase )
lowerCamelCase_ = WandbLogger(name=model.output_dir.name , project=_lowerCamelCase )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
lowerCamelCase_ = WandbLogger(name=model.output_dir.name , project=F'''hf_{dataset}''' )
if args.early_stopping_patience >= 0:
lowerCamelCase_ = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
lowerCamelCase_ = False
lowerCamelCase_ = args.val_metric == 'loss'
lowerCamelCase_ = generic_train(
_lowerCamelCase , _lowerCamelCase , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , _lowerCamelCase ) , early_stopping_callback=_lowerCamelCase , logger=_lowerCamelCase , )
pickle_save(model.hparams , model.output_dir / 'hparams.pkl' )
if not args.do_predict:
return model
lowerCamelCase_ = ''
lowerCamelCase_ = sorted(glob.glob(os.path.join(args.output_dir , '*.ckpt' ) , recursive=_lowerCamelCase ) )
if checkpoints:
lowerCamelCase_ = checkpoints[-1]
lowerCamelCase_ = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
_SCREAMING_SNAKE_CASE : List[str] = argparse.ArgumentParser()
_SCREAMING_SNAKE_CASE : Any = pl.Trainer.add_argparse_args(parser)
_SCREAMING_SNAKE_CASE : Any = SummarizationModule.add_model_specific_args(parser, os.getcwd())
_SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args()
main(args)
| 549 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
class _a ( UpperCAmelCase__ , unittest.TestCase ):
"""simple docstring"""
A_ = CanineTokenizer
A_ = False
def _UpperCAmelCase ( self ) -> Any:
super().setUp()
UpperCamelCase_ = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def _UpperCAmelCase ( self ) -> List[str]:
return CanineTokenizer.from_pretrained('google/canine-s' )
def _UpperCAmelCase ( self , **_UpperCAmelCase ) -> CanineTokenizer:
UpperCamelCase_ = self.tokenizer_class.from_pretrained(self.tmpdirname , **_UpperCAmelCase )
UpperCamelCase_ = 1024
return tokenizer
@require_torch
def _UpperCAmelCase ( self ) -> Optional[Any]:
UpperCamelCase_ = self.canine_tokenizer
UpperCamelCase_ = ['Life is like a box of chocolates.', 'You never know what you\'re gonna get.']
# fmt: off
UpperCamelCase_ = [57344, 76, 105, 102, 101, 32, 105, 115, 32, 108, 105, 107, 101, 32, 97, 32, 98, 111, 120, 32, 111, 102, 32, 99, 104, 111, 99, 111, 108, 97, 116, 101, 115, 46, 57345, 0, 0, 0, 0]
# fmt: on
UpperCamelCase_ = tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors='pt' )
self.assertIsInstance(_UpperCAmelCase , _UpperCAmelCase )
UpperCamelCase_ = list(batch.input_ids.numpy()[0] )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
self.assertEqual((2, 39) , batch.input_ids.shape )
self.assertEqual((2, 39) , batch.attention_mask.shape )
@require_torch
def _UpperCAmelCase ( self ) -> List[str]:
UpperCamelCase_ = self.canine_tokenizer
UpperCamelCase_ = ['Once there was a man.', 'He wrote a test in HuggingFace Tranformers.']
UpperCamelCase_ = tokenizer(_UpperCAmelCase , padding=_UpperCAmelCase , return_tensors='pt' )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn('input_ids' , _UpperCAmelCase )
self.assertIn('attention_mask' , _UpperCAmelCase )
self.assertIn('token_type_ids' , _UpperCAmelCase )
@require_torch
def _UpperCAmelCase ( self ) -> List[str]:
UpperCamelCase_ = self.canine_tokenizer
UpperCamelCase_ = [
'What\'s the weater?',
'It\'s about 25 degrees.',
]
UpperCamelCase_ = tokenizer(
text_target=_UpperCAmelCase , max_length=32 , padding='max_length' , truncation=_UpperCAmelCase , return_tensors='pt' )
self.assertEqual(32 , targets['input_ids'].shape[1] )
def _UpperCAmelCase ( self ) -> Dict:
# safety check on max_len default value so we are sure the test works
UpperCamelCase_ = 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_ = 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_ = tempfile.mkdtemp()
UpperCamelCase_ = ' He is very happy, UNwant\u00E9d,running'
UpperCamelCase_ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
tokenizer.save_pretrained(_UpperCAmelCase )
UpperCamelCase_ = tokenizer.__class__.from_pretrained(_UpperCAmelCase )
UpperCamelCase_ = after_tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
shutil.rmtree(_UpperCAmelCase )
UpperCamelCase_ = 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_ = tempfile.mkdtemp()
UpperCamelCase_ = ' He is very happy, UNwant\u00E9d,running'
UpperCamelCase_ = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
UpperCamelCase_ = chr(0xE007 )
additional_special_tokens.append(_UpperCAmelCase )
tokenizer.add_special_tokens({'additional_special_tokens': additional_special_tokens} )
UpperCamelCase_ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
tokenizer.save_pretrained(_UpperCAmelCase )
UpperCamelCase_ = tokenizer.__class__.from_pretrained(_UpperCAmelCase )
UpperCamelCase_ = after_tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertListEqual(_UpperCAmelCase , _UpperCAmelCase )
self.assertIn(_UpperCAmelCase , after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length , 42 )
UpperCamelCase_ = tokenizer.__class__.from_pretrained(_UpperCAmelCase , model_max_length=43 )
self.assertEqual(tokenizer.model_max_length , 43 )
shutil.rmtree(_UpperCAmelCase )
def _UpperCAmelCase ( self ) -> int:
UpperCamelCase_ = self.get_tokenizers(do_lower_case=_UpperCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
UpperCamelCase_ , UpperCamelCase_ = self.get_clean_sequence(_UpperCAmelCase )
# a special token for Canine can be defined as follows:
UpperCamelCase_ = 0xE005
UpperCamelCase_ = chr(_UpperCAmelCase )
tokenizer.add_special_tokens({'cls_token': special_token} )
UpperCamelCase_ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertEqual(len(_UpperCAmelCase ) , 1 )
UpperCamelCase_ = tokenizer.decode(ids + encoded_special_token , clean_up_tokenization_spaces=_UpperCAmelCase )
UpperCamelCase_ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
UpperCamelCase_ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
UpperCamelCase_ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
self.assertEqual(_UpperCAmelCase , input_encoded + special_token_id )
UpperCamelCase_ = tokenizer.decode(_UpperCAmelCase , skip_special_tokens=_UpperCAmelCase )
self.assertTrue(special_token not in decoded )
def _UpperCAmelCase ( self ) -> Dict:
UpperCamelCase_ = self.get_tokenizers(do_lower_case=_UpperCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
UpperCamelCase_ = chr(0xE005 )
UpperCamelCase_ = chr(0xE006 )
# `add_tokens` method stores special tokens only in `tokenizer.unique_no_split_tokens`. (in tokenization_utils.py)
tokenizer.add_tokens([SPECIAL_TOKEN_1] , special_tokens=_UpperCAmelCase )
# `add_special_tokens` method stores special tokens in `tokenizer.additional_special_tokens`,
# which also occur in `tokenizer.all_special_tokens`. (in tokenization_utils_base.py)
tokenizer.add_special_tokens({'additional_special_tokens': [SPECIAL_TOKEN_2]} )
UpperCamelCase_ = tokenizer.tokenize(_UpperCAmelCase )
UpperCamelCase_ = tokenizer.tokenize(_UpperCAmelCase )
self.assertEqual(len(_UpperCAmelCase ) , 1 )
self.assertEqual(len(_UpperCAmelCase ) , 1 )
self.assertEqual(token_a[0] , _UpperCAmelCase )
self.assertEqual(token_a[0] , _UpperCAmelCase )
@require_tokenizers
def _UpperCAmelCase ( self ) -> List[str]:
UpperCamelCase_ = self.get_tokenizers(do_lower_case=_UpperCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
# a special token for Canine can be defined as follows:
UpperCamelCase_ = 0xE006
UpperCamelCase_ = chr(_UpperCAmelCase )
UpperCamelCase_ = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase )
tokenizer.add_special_tokens({'additional_special_tokens': [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(_UpperCAmelCase )
tokenizer.from_pretrained(_UpperCAmelCase )
def _UpperCAmelCase ( self ) -> List[Any]:
UpperCamelCase_ = []
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_ = json.load(_UpperCAmelCase )
with open(os.path.join(_UpperCAmelCase , 'tokenizer_config.json' ) , encoding='utf-8' ) as json_file:
UpperCamelCase_ = json.load(_UpperCAmelCase )
# a special token for Canine can be defined as follows:
UpperCamelCase_ = 0xE006
UpperCamelCase_ = chr(_UpperCAmelCase )
UpperCamelCase_ = [new_token_a]
UpperCamelCase_ = [new_token_a]
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_ = tokenizer_class.from_pretrained(_UpperCAmelCase , extra_ids=0 )
self.assertIn(_UpperCAmelCase , tokenizer_without_change_in_init.additional_special_tokens )
# self.assertIn("an_additional_special_token",tokenizer_without_change_in_init.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer_without_change_in_init.convert_ids_to_tokens(
tokenizer_without_change_in_init.convert_tokens_to_ids([new_token_a] ) ) , )
UpperCamelCase_ = 0xE007
UpperCamelCase_ = chr(_UpperCAmelCase )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
UpperCamelCase_ = [AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase )]
UpperCamelCase_ = tokenizer_class.from_pretrained(
_UpperCAmelCase , additional_special_tokens=_UpperCAmelCase , extra_ids=0 )
self.assertIn(_UpperCAmelCase , tokenizer.additional_special_tokens )
# self.assertIn(new_token_2,tokenizer.get_vocab()) # ByT5Tokenization no vocab
self.assertEqual(
[new_token_a] , tokenizer.convert_ids_to_tokens(tokenizer.convert_tokens_to_ids([new_token_a] ) ) )
@require_tokenizers
def _UpperCAmelCase ( self ) -> Dict:
UpperCamelCase_ = self.get_tokenizers(do_lower_case=_UpperCAmelCase )
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
UpperCamelCase_ = 'hello world'
if self.space_between_special_tokens:
UpperCamelCase_ = '[CLS] hello world [SEP]'
else:
UpperCamelCase_ = input
UpperCamelCase_ = tokenizer.encode(_UpperCAmelCase , add_special_tokens=_UpperCAmelCase )
UpperCamelCase_ = tokenizer.decode(_UpperCAmelCase , spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(_UpperCAmelCase , [output, output.lower()] )
def _UpperCAmelCase ( self ) -> Union[str, Any]:
UpperCamelCase_ = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(f"""{tokenizer.__class__.__name__}""" ):
UpperCamelCase_ = [
'bos_token',
'eos_token',
'unk_token',
'sep_token',
'pad_token',
'cls_token',
'mask_token',
]
UpperCamelCase_ = 'a'
UpperCamelCase_ = ord(_UpperCAmelCase )
for attr in attributes_list:
setattr(_UpperCAmelCase , attr + '_id' , _UpperCAmelCase )
self.assertEqual(getattr(_UpperCAmelCase , _UpperCAmelCase ) , _UpperCAmelCase )
self.assertEqual(getattr(_UpperCAmelCase , attr + '_id' ) , _UpperCAmelCase )
setattr(_UpperCAmelCase , attr + '_id' , _UpperCAmelCase )
self.assertEqual(getattr(_UpperCAmelCase , _UpperCAmelCase ) , _UpperCAmelCase )
self.assertEqual(getattr(_UpperCAmelCase , attr + '_id' ) , _UpperCAmelCase )
setattr(_UpperCAmelCase , 'additional_special_tokens_ids' , [] )
self.assertListEqual(getattr(_UpperCAmelCase , 'additional_special_tokens' ) , [] )
self.assertListEqual(getattr(_UpperCAmelCase , 'additional_special_tokens_ids' ) , [] )
UpperCamelCase_ = 0xE006
UpperCamelCase_ = chr(_UpperCAmelCase )
setattr(_UpperCAmelCase , 'additional_special_tokens_ids' , [additional_special_token_id] )
self.assertListEqual(getattr(_UpperCAmelCase , 'additional_special_tokens' ) , [additional_special_token] )
self.assertListEqual(getattr(_UpperCAmelCase , 'additional_special_tokens_ids' ) , [additional_special_token_id] )
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> Any:
pass
def _UpperCAmelCase ( self ) -> List[str]:
pass
def _UpperCAmelCase ( self ) -> List[Any]:
pass
def _UpperCAmelCase ( self ) -> Tuple:
pass
def _UpperCAmelCase ( self ) -> Any:
pass
def _UpperCAmelCase ( self ) -> Dict:
pass
def _UpperCAmelCase ( self ) -> Optional[Any]:
pass
| 618 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
snake_case__ : List[str] = logging.get_logger(__name__)
class _a ( UpperCAmelCase__ ):
"""simple docstring"""
def __init__( self , *_UpperCAmelCase , **_UpperCAmelCase ) -> None:
warnings.warn(
'The class VideoMAEFeatureExtractor is deprecated and will be removed in version 5 of Transformers.'
' Please use VideoMAEImageProcessor instead.' , _UpperCAmelCase , )
super().__init__(*_UpperCAmelCase , **_UpperCAmelCase )
| 618 | 1 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case : Optional[int] = logging.get_logger(__name__)
_snake_case : List[Any] = {
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
_snake_case : str = {
'b0': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 224,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 240,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 1408,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 260,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 1536,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 300,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 1792,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 380,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 2048,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 456,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 2304,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 528,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 2560,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 600,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def snake_case_ (UpperCamelCase : List[Any] ):
'''simple docstring'''
_a = EfficientNetConfig()
_a = CONFIG_MAP[model_name]['''hidden_dim''']
_a = CONFIG_MAP[model_name]['''width_coef''']
_a = CONFIG_MAP[model_name]['''depth_coef''']
_a = CONFIG_MAP[model_name]['''image_size''']
_a = CONFIG_MAP[model_name]['''dropout_rate''']
_a = CONFIG_MAP[model_name]['''dw_padding''']
_a = '''huggingface/label-files'''
_a = '''imagenet-1k-id2label.json'''
_a = 1000
_a = json.load(open(hf_hub_download(lowercase_ , lowercase_ , repo_type='''dataset''' ) , '''r''' ) )
_a = {int(lowercase_ ): v for k, v in idalabel.items()}
_a = idalabel
_a = {v: k for k, v in idalabel.items()}
return config
def snake_case_ ():
'''simple docstring'''
_a = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_a = Image.open(requests.get(lowercase_ , stream=lowercase_ ).raw )
return im
def snake_case_ (UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
_a = CONFIG_MAP[model_name]['''image_size''']
_a = EfficientNetImageProcessor(
size={'''height''': size, '''width''': size} , image_mean=[0.485, 0.456, 0.406] , image_std=[0.47853944, 0.4732864, 0.47434163] , do_center_crop=lowercase_ , )
return preprocessor
def snake_case_ (UpperCamelCase : List[str] ):
'''simple docstring'''
_a = [v.split('''_''' )[0].split('''block''' )[1] for v in original_param_names if v.startswith('''block''' )]
_a = sorted(set(lowercase_ ) )
_a = len(lowercase_ )
_a = {b: str(lowercase_ ) for b, i in zip(lowercase_ , range(lowercase_ ) )}
_a = []
rename_keys.append(('''stem_conv/kernel:0''', '''embeddings.convolution.weight''') )
rename_keys.append(('''stem_bn/gamma:0''', '''embeddings.batchnorm.weight''') )
rename_keys.append(('''stem_bn/beta:0''', '''embeddings.batchnorm.bias''') )
rename_keys.append(('''stem_bn/moving_mean:0''', '''embeddings.batchnorm.running_mean''') )
rename_keys.append(('''stem_bn/moving_variance:0''', '''embeddings.batchnorm.running_var''') )
for b in block_names:
_a = block_name_mapping[b]
rename_keys.append((f'block{b}_expand_conv/kernel:0', f'encoder.blocks.{hf_b}.expansion.expand_conv.weight') )
rename_keys.append((f'block{b}_expand_bn/gamma:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.weight') )
rename_keys.append((f'block{b}_expand_bn/beta:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.bias') )
rename_keys.append(
(f'block{b}_expand_bn/moving_mean:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean') )
rename_keys.append(
(f'block{b}_expand_bn/moving_variance:0', f'encoder.blocks.{hf_b}.expansion.expand_bn.running_var') )
rename_keys.append(
(f'block{b}_dwconv/depthwise_kernel:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight') )
rename_keys.append((f'block{b}_bn/gamma:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight') )
rename_keys.append((f'block{b}_bn/beta:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias') )
rename_keys.append(
(f'block{b}_bn/moving_mean:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean') )
rename_keys.append(
(f'block{b}_bn/moving_variance:0', f'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var') )
rename_keys.append((f'block{b}_se_reduce/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight') )
rename_keys.append((f'block{b}_se_reduce/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias') )
rename_keys.append((f'block{b}_se_expand/kernel:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.weight') )
rename_keys.append((f'block{b}_se_expand/bias:0', f'encoder.blocks.{hf_b}.squeeze_excite.expand.bias') )
rename_keys.append(
(f'block{b}_project_conv/kernel:0', f'encoder.blocks.{hf_b}.projection.project_conv.weight') )
rename_keys.append((f'block{b}_project_bn/gamma:0', f'encoder.blocks.{hf_b}.projection.project_bn.weight') )
rename_keys.append((f'block{b}_project_bn/beta:0', f'encoder.blocks.{hf_b}.projection.project_bn.bias') )
rename_keys.append(
(f'block{b}_project_bn/moving_mean:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_mean') )
rename_keys.append(
(f'block{b}_project_bn/moving_variance:0', f'encoder.blocks.{hf_b}.projection.project_bn.running_var') )
rename_keys.append(('''top_conv/kernel:0''', '''encoder.top_conv.weight''') )
rename_keys.append(('''top_bn/gamma:0''', '''encoder.top_bn.weight''') )
rename_keys.append(('''top_bn/beta:0''', '''encoder.top_bn.bias''') )
rename_keys.append(('''top_bn/moving_mean:0''', '''encoder.top_bn.running_mean''') )
rename_keys.append(('''top_bn/moving_variance:0''', '''encoder.top_bn.running_var''') )
_a = {}
for item in rename_keys:
if item[0] in original_param_names:
_a = '''efficientnet.''' + item[1]
_a = '''classifier.weight'''
_a = '''classifier.bias'''
return key_mapping
def snake_case_ (UpperCamelCase : int , UpperCamelCase : Union[str, Any] , UpperCamelCase : Union[str, Any] ):
'''simple docstring'''
for key, value in tf_params.items():
if "normalization" in key:
continue
_a = key_mapping[key]
if "_conv" in key and "kernel" in key:
_a = torch.from_numpy(lowercase_ ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
_a = torch.from_numpy(lowercase_ ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
_a = torch.from_numpy(np.transpose(lowercase_ ) )
else:
_a = torch.from_numpy(lowercase_ )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(lowercase_ )
@torch.no_grad()
def snake_case_ (UpperCamelCase : Tuple , UpperCamelCase : Union[str, Any] , UpperCamelCase : Any , UpperCamelCase : Optional[Any] ):
'''simple docstring'''
_a = model_classes[model_name](
include_top=lowercase_ , weights='''imagenet''' , input_tensor=lowercase_ , input_shape=lowercase_ , pooling=lowercase_ , classes=1000 , classifier_activation='''softmax''' , )
_a = original_model.trainable_variables
_a = original_model.non_trainable_variables
_a = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
_a = param.numpy()
_a = list(tf_params.keys() )
# Load HuggingFace model
_a = get_efficientnet_config(lowercase_ )
_a = EfficientNetForImageClassification(lowercase_ ).eval()
_a = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print('''Converting parameters...''' )
_a = rename_keys(lowercase_ )
replace_params(lowercase_ , lowercase_ , lowercase_ )
# Initialize preprocessor and preprocess input image
_a = convert_image_processor(lowercase_ )
_a = preprocessor(images=prepare_img() , return_tensors='''pt''' )
# HF model inference
hf_model.eval()
with torch.no_grad():
_a = hf_model(**lowercase_ )
_a = outputs.logits.detach().numpy()
# Original model inference
_a = False
_a = CONFIG_MAP[model_name]['''image_size''']
_a = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
_a = image.img_to_array(lowercase_ )
_a = np.expand_dims(lowercase_ , axis=0 )
_a = original_model.predict(lowercase_ )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(lowercase_ , lowercase_ , atol=1e-3 ), "The predicted logits are not the same."
print('''Model outputs match!''' )
if save_model:
# Create folder to save model
if not os.path.isdir(lowercase_ ):
os.mkdir(lowercase_ )
# Save converted model and image processor
hf_model.save_pretrained(lowercase_ )
preprocessor.save_pretrained(lowercase_ )
if push_to_hub:
# Push model and image processor to hub
print(f'Pushing converted {model_name} to the hub...' )
_a = f'efficientnet-{model_name}'
preprocessor.push_to_hub(lowercase_ )
hf_model.push_to_hub(lowercase_ )
if __name__ == "__main__":
_snake_case : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
_snake_case : Tuple = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)
| 22 |
import math
import flax.linen as nn
import jax.numpy as jnp
def A_ ( lowercase_ , lowercase_ , lowercase_ = 1 , lowercase_ = 1 , lowercase_ = 1.0E4 , lowercase_ = False , lowercase_ = 1.0 , ) -> jnp.ndarray:
assert timesteps.ndim == 1, "Timesteps should be a 1d-array"
assert embedding_dim % 2 == 0, f'''Embedding dimension {embedding_dim} should be even'''
_snake_case : Union[str, Any] = float(embedding_dim // 2 )
_snake_case : Optional[int] = math.log(max_timescale / min_timescale ) / (num_timescales - freq_shift)
_snake_case : Union[str, Any] = min_timescale * jnp.exp(jnp.arange(lowercase_ , dtype=jnp.floataa ) * -log_timescale_increment )
_snake_case : Any = jnp.expand_dims(lowercase_ , 1 ) * jnp.expand_dims(lowercase_ , 0 )
# scale embeddings
_snake_case : Any = scale * emb
if flip_sin_to_cos:
_snake_case : str = jnp.concatenate([jnp.cos(lowercase_ ), jnp.sin(lowercase_ )] , axis=1 )
else:
_snake_case : Optional[int] = jnp.concatenate([jnp.sin(lowercase_ ), jnp.cos(lowercase_ )] , axis=1 )
_snake_case : Optional[Any] = jnp.reshape(lowercase_ , [jnp.shape(lowercase_ )[0], embedding_dim] )
return signal
class A (nn.Module ):
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = jnp.floataa
@nn.compact
def __call__( self , lowercase_ ) -> Optional[Any]:
'''simple docstring'''
_snake_case : List[str] = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_1''' )(lowercase_ )
_snake_case : List[Any] = nn.silu(lowercase_ )
_snake_case : List[str] = nn.Dense(self.time_embed_dim , dtype=self.dtype , name='''linear_2''' )(lowercase_ )
return temb
class A (nn.Module ):
_SCREAMING_SNAKE_CASE = 32
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = 1
@nn.compact
def __call__( self , lowercase_ ) -> Any:
'''simple docstring'''
return get_sinusoidal_embeddings(
lowercase_ , embedding_dim=self.dim , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.freq_shift )
| 326 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class __lowerCamelCase :
def __init__( self , snake_case_ , snake_case_=None , snake_case_=None , snake_case_=None , snake_case_="resnet50" , snake_case_=3 , snake_case_=32 , snake_case_=3 , snake_case_=True , snake_case_=True , ) -> Any:
UpperCamelCase__ = parent
UpperCamelCase__ = out_indices if out_indices is not None else [4]
UpperCamelCase__ = stage_names
UpperCamelCase__ = out_features
UpperCamelCase__ = backbone
UpperCamelCase__ = batch_size
UpperCamelCase__ = image_size
UpperCamelCase__ = num_channels
UpperCamelCase__ = use_pretrained_backbone
UpperCamelCase__ = is_training
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
UpperCamelCase__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCamelCase__ = self.get_config()
return config, pixel_values
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def SCREAMING_SNAKE_CASE__ ( self , snake_case_ , snake_case_ ) -> Optional[Any]:
UpperCamelCase__ = TimmBackbone(config=snake_case_ )
model.to(snake_case_ )
model.eval()
with torch.no_grad():
UpperCamelCase__ = model(snake_case_ )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 14, 14) , )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
UpperCamelCase__ = self.prepare_config_and_inputs()
UpperCamelCase__ , UpperCamelCase__ = config_and_inputs
UpperCamelCase__ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class __lowerCamelCase ( _a , _a , _a , unittest.TestCase ):
a : Optional[int] =(TimmBackbone,) if is_torch_available() else ()
a : Any ={"""feature-extraction""": TimmBackbone} if is_torch_available() else {}
a : Optional[Any] =False
a : Any =False
a : Any =False
a : str =False
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
UpperCamelCase__ = TimmBackboneModelTester(self )
UpperCamelCase__ = ConfigTester(self , config_class=snake_case_ , has_text_modality=snake_case_ )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
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 SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
UpperCamelCase__ = 'resnet18'
UpperCamelCase__ = 'microsoft/resnet-18'
UpperCamelCase__ = AutoBackbone.from_pretrained(snake_case_ , use_timm_backbone=snake_case_ )
UpperCamelCase__ = AutoBackbone.from_pretrained(snake_case_ )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
UpperCamelCase__ = AutoBackbone.from_pretrained(snake_case_ , use_timm_backbone=snake_case_ , out_indices=[1, 2, 3] )
UpperCamelCase__ = AutoBackbone.from_pretrained(snake_case_ , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip('TimmBackbone doesn\'t support feed forward chunking' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
pass
@unittest.skip('TimmBackbone doesn\'t have num_hidden_layers attribute' )
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
pass
@unittest.skip('TimmBackbone initialization is managed on the timm side' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[Any]:
pass
@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Any:
pass
@unittest.skip('TimmBackbone models doesn\'t have inputs_embeds' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
pass
@unittest.skip('TimmBackbone model cannot be created without specifying a backbone checkpoint' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[Any]:
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone' )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[Any]:
pass
@unittest.skip('model weights aren\'t tied in TimmBackbone.' )
def SCREAMING_SNAKE_CASE__ ( self ) -> List[str]:
pass
@unittest.skip('model weights aren\'t tied in TimmBackbone.' )
def SCREAMING_SNAKE_CASE__ ( self ) -> int:
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
pass
@unittest.skip('Only checkpoints on timm can be loaded into TimmBackbone' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Dict:
pass
@unittest.skip('TimmBackbone doesn\'t have hidden size info in its configuration.' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
pass
@unittest.skip('TimmBackbone doesn\'t support output_attentions.' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
pass
@unittest.skip('Safetensors is not supported by timm.' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def SCREAMING_SNAKE_CASE__ ( self ) -> Union[str, Any]:
pass
def SCREAMING_SNAKE_CASE__ ( self ) -> str:
UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ = model_class(snake_case_ )
UpperCamelCase__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCamelCase__ = [*signature.parameters.keys()]
UpperCamelCase__ = ['pixel_values']
self.assertListEqual(arg_names[:1] , snake_case_ )
def SCREAMING_SNAKE_CASE__ ( self ) -> Optional[int]:
UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
UpperCamelCase__ = True
UpperCamelCase__ = self.has_attentions
# no need to test all models as different heads yield the same functionality
UpperCamelCase__ = self.all_model_classes[0]
UpperCamelCase__ = model_class(snake_case_ )
model.to(snake_case_ )
UpperCamelCase__ = self._prepare_for_class(snake_case_ , snake_case_ )
UpperCamelCase__ = model(**snake_case_ )
UpperCamelCase__ = outputs[0][-1]
# Encoder-/Decoder-only models
UpperCamelCase__ = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
UpperCamelCase__ = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=snake_case_ )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def SCREAMING_SNAKE_CASE__ ( self ) -> Tuple:
UpperCamelCase__ , UpperCamelCase__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCamelCase__ = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
UpperCamelCase__ = model(**snake_case_ )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
UpperCamelCase__ = copy.deepcopy(snake_case_ )
UpperCamelCase__ = None
UpperCamelCase__ = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
UpperCamelCase__ = model(**snake_case_ )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
UpperCamelCase__ = copy.deepcopy(snake_case_ )
UpperCamelCase__ = False
UpperCamelCase__ = model_class(snake_case_ )
model.to(snake_case_ )
model.eval()
UpperCamelCase__ = model(**snake_case_ )
| 703 |
"""simple docstring"""
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("""9.1.0"""):
A__ : str= {
"""linear""": PIL.Image.Resampling.BILINEAR,
"""bilinear""": PIL.Image.Resampling.BILINEAR,
"""bicubic""": PIL.Image.Resampling.BICUBIC,
"""lanczos""": PIL.Image.Resampling.LANCZOS,
"""nearest""": PIL.Image.Resampling.NEAREST,
}
else:
A__ : str= {
"""linear""": PIL.Image.LINEAR,
"""bilinear""": PIL.Image.BILINEAR,
"""bicubic""": PIL.Image.BICUBIC,
"""lanczos""": PIL.Image.LANCZOS,
"""nearest""": PIL.Image.NEAREST,
}
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> int:
"""simple docstring"""
UpperCamelCase__ = (images / 2 + 0.5).clamp(0 , 1 )
UpperCamelCase__ = images.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
UpperCamelCase__ = numpy_to_pil(SCREAMING_SNAKE_CASE )
return images
def lowerCAmelCase_( SCREAMING_SNAKE_CASE ) -> Optional[int]:
"""simple docstring"""
if images.ndim == 3:
UpperCamelCase__ = images[None, ...]
UpperCamelCase__ = (images * 2_55).round().astype('uint8' )
if images.shape[-1] == 1:
# special case for grayscale (single channel) images
UpperCamelCase__ = [Image.fromarray(image.squeeze() , mode='L' ) for image in images]
else:
UpperCamelCase__ = [Image.fromarray(SCREAMING_SNAKE_CASE ) for image in images]
return pil_images
| 20 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def _SCREAMING_SNAKE_CASE ( ):
_A , _A = 9, 1_4 # noqa: F841
_A = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8],
[7, 8, 7],
[7, 6, 1],
[2, 8, 2],
[8, 6, 6],
[2, 3, 7],
[2, 5, 4],
[6, 5, 2],
[3, 5, 1_4],
[3, 4, 9],
[5, 4, 1_0],
[1, 7, 1_1],
]
_A = defaultdict(__snake_case )
for nodea, nodea, cost in edges:
adjancency[nodea].append([nodea, cost] )
adjancency[nodea].append([nodea, cost] )
_A = mst(__snake_case )
_A = [
[7, 6, 1],
[2, 8, 2],
[6, 5, 2],
[0, 1, 4],
[2, 5, 4],
[2, 3, 7],
[0, 7, 8],
[3, 4, 9],
]
for answer in expected:
_A = tuple(answer[:2] )
_A = tuple(edge[::-1] )
assert edge in result or reverse in result
| 107 |
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a_ : List[str] = logging.get_logger(__name__)
a_ : Any = {
'microsoft/unispeech-large-1500h-cv': (
'https://huggingface.co/microsoft/unispeech-large-1500h-cv/resolve/main/config.json'
),
# See all UniSpeech models at https://huggingface.co/models?filter=unispeech
}
class _snake_case ( A__ ):
_lowercase : Optional[int] = '''unispeech'''
def __init__( self , a=32 , a=768 , a=12 , a=12 , a=3072 , a="gelu" , a=0.1 , a=0.1 , a=0.1 , a=0.0 , a=0.0 , a=0.1 , a=0.1 , a=0.02 , a=1E-5 , a="group" , a="gelu" , a=(512, 512, 512, 512, 512, 512, 512) , a=(5, 2, 2, 2, 2, 2, 2) , a=(10, 3, 3, 3, 3, 2, 2) , a=False , a=128 , a=16 , a=False , a=True , a=0.05 , a=10 , a=2 , a=0.0 , a=10 , a=0 , a=320 , a=2 , a=0.1 , a=100 , a=256 , a=256 , a=0.1 , a="mean" , a=False , a=False , a=256 , a=80 , a=0 , a=1 , a=2 , a=0.5 , **a , ) -> Optional[int]:
super().__init__(**a , pad_token_id=a , bos_token_id=a , eos_token_id=a)
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = feat_extract_norm
SCREAMING_SNAKE_CASE = feat_extract_activation
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = list(a)
SCREAMING_SNAKE_CASE = conv_bias
SCREAMING_SNAKE_CASE = num_conv_pos_embeddings
SCREAMING_SNAKE_CASE = num_conv_pos_embedding_groups
SCREAMING_SNAKE_CASE = len(self.conv_dim)
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = activation_dropout
SCREAMING_SNAKE_CASE = feat_proj_dropout
SCREAMING_SNAKE_CASE = final_dropout
SCREAMING_SNAKE_CASE = layerdrop
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = num_ctc_classes
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = do_stable_layer_norm
SCREAMING_SNAKE_CASE = use_weighted_layer_sum
SCREAMING_SNAKE_CASE = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='
' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='
f''' {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel)}`.''')
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
SCREAMING_SNAKE_CASE = apply_spec_augment
SCREAMING_SNAKE_CASE = mask_time_prob
SCREAMING_SNAKE_CASE = mask_time_length
SCREAMING_SNAKE_CASE = mask_time_min_masks
SCREAMING_SNAKE_CASE = mask_feature_prob
SCREAMING_SNAKE_CASE = mask_feature_length
SCREAMING_SNAKE_CASE = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
SCREAMING_SNAKE_CASE = num_codevectors_per_group
SCREAMING_SNAKE_CASE = num_codevector_groups
SCREAMING_SNAKE_CASE = contrastive_logits_temperature
SCREAMING_SNAKE_CASE = feat_quantizer_dropout
SCREAMING_SNAKE_CASE = num_negatives
SCREAMING_SNAKE_CASE = codevector_dim
SCREAMING_SNAKE_CASE = proj_codevector_dim
SCREAMING_SNAKE_CASE = diversity_loss_weight
# ctc loss
SCREAMING_SNAKE_CASE = ctc_loss_reduction
SCREAMING_SNAKE_CASE = ctc_zero_infinity
# pretraining loss
SCREAMING_SNAKE_CASE = replace_prob
@property
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
return functools.reduce(operator.mul , self.conv_stride , 1)
| 73 | 0 |
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize("""dataset_size""" , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize("""input_in_memory_max_size""" , ["""default""", 0, 100 * 2**20, 900 * 2**20] )
def __UpperCamelCase ( _A : int , _A : Dict , _A : Tuple ) ->Optional[int]:
"""simple docstring"""
if input_in_memory_max_size != "default":
monkeypatch.setattr(datasets.config , """IN_MEMORY_MAX_SIZE""" , _A )
lowerCamelCase_ =datasets.config.IN_MEMORY_MAX_SIZE
if input_in_memory_max_size == "default":
assert in_memory_max_size == 0
else:
assert in_memory_max_size == input_in_memory_max_size
if dataset_size and in_memory_max_size:
lowerCamelCase_ =dataset_size < in_memory_max_size
else:
lowerCamelCase_ =False
lowerCamelCase_ =is_small_dataset(_A )
assert result == expected
| 75 |
from collections import deque
from math import floor
from random import random
from time import time
class _SCREAMING_SNAKE_CASE :
def __init__( self )-> List[str]:
lowerCamelCase_ ={}
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1 )-> List[Any]:
if self.graph.get(_SCREAMING_SNAKE_CASE ):
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
lowerCamelCase_ =[[w, v]]
if not self.graph.get(_SCREAMING_SNAKE_CASE ):
lowerCamelCase_ =[]
def _snake_case ( self )-> str:
return list(self.graph )
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )-> Dict:
if self.graph.get(_SCREAMING_SNAKE_CASE ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE=-2 , _SCREAMING_SNAKE_CASE=-1 )-> Optional[Any]:
if s == d:
return []
lowerCamelCase_ =[]
lowerCamelCase_ =[]
if s == -2:
lowerCamelCase_ =list(self.graph )[0]
stack.append(_SCREAMING_SNAKE_CASE )
visited.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase_ =s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(_SCREAMING_SNAKE_CASE )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase_ =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(_SCREAMING_SNAKE_CASE ) != 0:
lowerCamelCase_ =stack[len(_SCREAMING_SNAKE_CASE ) - 1]
else:
lowerCamelCase_ =ss
# check if se have reached the starting point
if len(_SCREAMING_SNAKE_CASE ) == 0:
return visited
def _snake_case ( self , _SCREAMING_SNAKE_CASE=-1 )-> Optional[int]:
if c == -1:
lowerCamelCase_ =floor(random() * 1_0000 ) + 10
for i in range(_SCREAMING_SNAKE_CASE ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
lowerCamelCase_ =floor(random() * c ) + 1
if n != i:
self.add_pair(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 1 )
def _snake_case ( self , _SCREAMING_SNAKE_CASE=-2 )-> Any:
lowerCamelCase_ =deque()
lowerCamelCase_ =[]
if s == -2:
lowerCamelCase_ =list(self.graph )[0]
d.append(_SCREAMING_SNAKE_CASE )
visited.append(_SCREAMING_SNAKE_CASE )
while d:
lowerCamelCase_ =d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> List[Any]:
lowerCamelCase_ =0
for x in self.graph:
for y in self.graph[x]:
if y[1] == u:
count += 1
return count
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> List[str]:
return len(self.graph[u] )
def _snake_case ( self , _SCREAMING_SNAKE_CASE=-2 )-> Union[str, Any]:
lowerCamelCase_ =[]
lowerCamelCase_ =[]
if s == -2:
lowerCamelCase_ =list(self.graph )[0]
stack.append(_SCREAMING_SNAKE_CASE )
visited.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =s
lowerCamelCase_ =[]
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase_ =s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase_ =node[1]
break
# check if all the children are visited
if s == ss:
sorted_nodes.append(stack.pop() )
if len(_SCREAMING_SNAKE_CASE ) != 0:
lowerCamelCase_ =stack[len(_SCREAMING_SNAKE_CASE ) - 1]
else:
lowerCamelCase_ =ss
# check if se have reached the starting point
if len(_SCREAMING_SNAKE_CASE ) == 0:
return sorted_nodes
def _snake_case ( self )-> str:
lowerCamelCase_ =[]
lowerCamelCase_ =[]
lowerCamelCase_ =list(self.graph )[0]
stack.append(_SCREAMING_SNAKE_CASE )
visited.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =-2
lowerCamelCase_ =[]
lowerCamelCase_ =s
lowerCamelCase_ =False
lowerCamelCase_ =set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase_ =s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCamelCase_ =len(_SCREAMING_SNAKE_CASE ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase_ =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCamelCase_ =True
if len(_SCREAMING_SNAKE_CASE ) != 0:
lowerCamelCase_ =stack[len(_SCREAMING_SNAKE_CASE ) - 1]
else:
lowerCamelCase_ =False
indirect_parents.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =s
lowerCamelCase_ =ss
# check if se have reached the starting point
if len(_SCREAMING_SNAKE_CASE ) == 0:
return list(_SCREAMING_SNAKE_CASE )
def _snake_case ( self )-> Tuple:
lowerCamelCase_ =[]
lowerCamelCase_ =[]
lowerCamelCase_ =list(self.graph )[0]
stack.append(_SCREAMING_SNAKE_CASE )
visited.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =-2
lowerCamelCase_ =[]
lowerCamelCase_ =s
lowerCamelCase_ =False
lowerCamelCase_ =set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase_ =s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCamelCase_ =len(_SCREAMING_SNAKE_CASE ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase_ =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCamelCase_ =True
if len(_SCREAMING_SNAKE_CASE ) != 0:
lowerCamelCase_ =stack[len(_SCREAMING_SNAKE_CASE ) - 1]
else:
lowerCamelCase_ =False
indirect_parents.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =s
lowerCamelCase_ =ss
# check if se have reached the starting point
if len(_SCREAMING_SNAKE_CASE ) == 0:
return False
def _snake_case ( self , _SCREAMING_SNAKE_CASE=-2 , _SCREAMING_SNAKE_CASE=-1 )-> List[str]:
lowerCamelCase_ =time()
self.dfs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCamelCase_ =time()
return end - begin
def _snake_case ( self , _SCREAMING_SNAKE_CASE=-2 )-> List[str]:
lowerCamelCase_ =time()
self.bfs(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =time()
return end - begin
class _SCREAMING_SNAKE_CASE :
def __init__( self )-> Optional[Any]:
lowerCamelCase_ ={}
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1 )-> List[str]:
# check if the u exists
if self.graph.get(_SCREAMING_SNAKE_CASE ):
# if there already is a edge
if self.graph[u].count([w, v] ) == 0:
self.graph[u].append([w, v] )
else:
# if u does not exist
lowerCamelCase_ =[[w, v]]
# add the other way
if self.graph.get(_SCREAMING_SNAKE_CASE ):
# if there already is a edge
if self.graph[v].count([w, u] ) == 0:
self.graph[v].append([w, u] )
else:
# if u does not exist
lowerCamelCase_ =[[w, u]]
def _snake_case ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )-> Tuple:
if self.graph.get(_SCREAMING_SNAKE_CASE ):
for _ in self.graph[u]:
if _[1] == v:
self.graph[u].remove(_SCREAMING_SNAKE_CASE )
# the other way round
if self.graph.get(_SCREAMING_SNAKE_CASE ):
for _ in self.graph[v]:
if _[1] == u:
self.graph[v].remove(_SCREAMING_SNAKE_CASE )
def _snake_case ( self , _SCREAMING_SNAKE_CASE=-2 , _SCREAMING_SNAKE_CASE=-1 )-> int:
if s == d:
return []
lowerCamelCase_ =[]
lowerCamelCase_ =[]
if s == -2:
lowerCamelCase_ =list(self.graph )[0]
stack.append(_SCREAMING_SNAKE_CASE )
visited.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =s
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase_ =s
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
if node[1] == d:
visited.append(_SCREAMING_SNAKE_CASE )
return visited
else:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase_ =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
if len(_SCREAMING_SNAKE_CASE ) != 0:
lowerCamelCase_ =stack[len(_SCREAMING_SNAKE_CASE ) - 1]
else:
lowerCamelCase_ =ss
# check if se have reached the starting point
if len(_SCREAMING_SNAKE_CASE ) == 0:
return visited
def _snake_case ( self , _SCREAMING_SNAKE_CASE=-1 )-> Optional[int]:
if c == -1:
lowerCamelCase_ =floor(random() * 1_0000 ) + 10
for i in range(_SCREAMING_SNAKE_CASE ):
# every vertex has max 100 edges
for _ in range(floor(random() * 102 ) + 1 ):
lowerCamelCase_ =floor(random() * c ) + 1
if n != i:
self.add_pair(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 1 )
def _snake_case ( self , _SCREAMING_SNAKE_CASE=-2 )-> List[str]:
lowerCamelCase_ =deque()
lowerCamelCase_ =[]
if s == -2:
lowerCamelCase_ =list(self.graph )[0]
d.append(_SCREAMING_SNAKE_CASE )
visited.append(_SCREAMING_SNAKE_CASE )
while d:
lowerCamelCase_ =d.popleft()
if len(self.graph[s] ) != 0:
for node in self.graph[s]:
if visited.count(node[1] ) < 1:
d.append(node[1] )
visited.append(node[1] )
return visited
def _snake_case ( self , _SCREAMING_SNAKE_CASE )-> Union[str, Any]:
return len(self.graph[u] )
def _snake_case ( self )-> Any:
lowerCamelCase_ =[]
lowerCamelCase_ =[]
lowerCamelCase_ =list(self.graph )[0]
stack.append(_SCREAMING_SNAKE_CASE )
visited.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =-2
lowerCamelCase_ =[]
lowerCamelCase_ =s
lowerCamelCase_ =False
lowerCamelCase_ =set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase_ =s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCamelCase_ =len(_SCREAMING_SNAKE_CASE ) - 1
while len_stack >= 0:
if stack[len_stack] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
anticipating_nodes.add(stack[len_stack] )
len_stack -= 1
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase_ =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCamelCase_ =True
if len(_SCREAMING_SNAKE_CASE ) != 0:
lowerCamelCase_ =stack[len(_SCREAMING_SNAKE_CASE ) - 1]
else:
lowerCamelCase_ =False
indirect_parents.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =s
lowerCamelCase_ =ss
# check if se have reached the starting point
if len(_SCREAMING_SNAKE_CASE ) == 0:
return list(_SCREAMING_SNAKE_CASE )
def _snake_case ( self )-> Any:
lowerCamelCase_ =[]
lowerCamelCase_ =[]
lowerCamelCase_ =list(self.graph )[0]
stack.append(_SCREAMING_SNAKE_CASE )
visited.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =-2
lowerCamelCase_ =[]
lowerCamelCase_ =s
lowerCamelCase_ =False
lowerCamelCase_ =set()
while True:
# check if there is any non isolated nodes
if len(self.graph[s] ) != 0:
lowerCamelCase_ =s
for node in self.graph[s]:
if (
visited.count(node[1] ) > 0
and node[1] != parent
and indirect_parents.count(node[1] ) > 0
and not on_the_way_back
):
lowerCamelCase_ =len(_SCREAMING_SNAKE_CASE ) - 1
while len_stack_minus_one >= 0:
if stack[len_stack_minus_one] == node[1]:
anticipating_nodes.add(node[1] )
break
else:
return True
if visited.count(node[1] ) < 1:
stack.append(node[1] )
visited.append(node[1] )
lowerCamelCase_ =node[1]
break
# check if all the children are visited
if s == ss:
stack.pop()
lowerCamelCase_ =True
if len(_SCREAMING_SNAKE_CASE ) != 0:
lowerCamelCase_ =stack[len(_SCREAMING_SNAKE_CASE ) - 1]
else:
lowerCamelCase_ =False
indirect_parents.append(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =s
lowerCamelCase_ =ss
# check if se have reached the starting point
if len(_SCREAMING_SNAKE_CASE ) == 0:
return False
def _snake_case ( self )-> Optional[Any]:
return list(self.graph )
def _snake_case ( self , _SCREAMING_SNAKE_CASE=-2 , _SCREAMING_SNAKE_CASE=-1 )-> str:
lowerCamelCase_ =time()
self.dfs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
lowerCamelCase_ =time()
return end - begin
def _snake_case ( self , _SCREAMING_SNAKE_CASE=-2 )-> Dict:
lowerCamelCase_ =time()
self.bfs(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ =time()
return end - begin
| 75 | 1 |
"""simple docstring"""
from __future__ import annotations
__lowerCAmelCase : List[Any] = 10
def __lowerCAmelCase ( __UpperCamelCase : list[int] ):
'''simple docstring'''
snake_case_ : Optional[Any] = 1
snake_case_ : Any = max(__UpperCamelCase )
while placement <= max_digit:
# declare and initialize empty buckets
snake_case_ : list[list] = [[] for _ in range(__UpperCamelCase )]
# split list_of_ints between the buckets
for i in list_of_ints:
snake_case_ : str = int((i / placement) % RADIX )
buckets[tmp].append(__UpperCamelCase )
# put each buckets' contents into list_of_ints
snake_case_ : Optional[int] = 0
for b in range(__UpperCamelCase ):
for i in buckets[b]:
snake_case_ : str = i
a += 1
# move to next
placement *= RADIX
return list_of_ints
if __name__ == "__main__":
import doctest
doctest.testmod()
| 58 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE_ ( __A : int = 10**12 ) -> int:
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 0
_SCREAMING_SNAKE_CASE = 1
_SCREAMING_SNAKE_CASE = 1
while numerator <= 2 * min_total - 1:
prev_numerator += 2 * numerator
numerator += 2 * prev_numerator
prev_denominator += 2 * denominator
denominator += 2 * prev_denominator
return (denominator + 1) // 2
if __name__ == "__main__":
print(f'''{solution() = }''')
| 418 | 0 |
def lowerCamelCase ( UpperCAmelCase__ : Tuple , UpperCAmelCase__ : str , UpperCAmelCase__ : List[Any]=False ) -> List[Any]:
'''simple docstring'''
if isinstance(UpperCAmelCase__ , UpperCAmelCase__ ) and isinstance(UpperCAmelCase__ , UpperCAmelCase__ ):
SCREAMING_SNAKE_CASE__ :str = len(set_a.intersection(UpperCAmelCase__ ) )
if alternative_union:
SCREAMING_SNAKE_CASE__ :List[str] = len(UpperCAmelCase__ ) + len(UpperCAmelCase__ )
else:
SCREAMING_SNAKE_CASE__ :str = len(set_a.union(UpperCAmelCase__ ) )
return intersection / union
if isinstance(UpperCAmelCase__ , (list, tuple) ) and isinstance(UpperCAmelCase__ , (list, tuple) ):
SCREAMING_SNAKE_CASE__ :List[str] = [element for element in set_a if element in set_b]
if alternative_union:
SCREAMING_SNAKE_CASE__ :Optional[Any] = len(UpperCAmelCase__ ) + len(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) / union
else:
SCREAMING_SNAKE_CASE__ :Union[str, Any] = set_a + [element for element in set_b if element not in set_a]
return len(UpperCAmelCase__ ) / len(UpperCAmelCase__ )
return len(UpperCAmelCase__ ) / len(UpperCAmelCase__ )
return None
if __name__ == "__main__":
UpperCamelCase_ = {'''a''', '''b''', '''c''', '''d''', '''e'''}
UpperCamelCase_ = {'''c''', '''d''', '''e''', '''f''', '''h''', '''i'''}
print(jaccard_similarity(set_a, set_b))
| 700 | '''simple docstring'''
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class _SCREAMING_SNAKE_CASE( _SCREAMING_SNAKE_CASE , unittest.TestCase ):
A_ : int = LongformerTokenizer
A_ : int = True
A_ : Optional[Any] = LongformerTokenizerFast
A_ : Tuple = True
def __lowerCamelCase ( self : int ) -> Tuple:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE__ :Union[str, Any] = [
'l',
'o',
'w',
'e',
'r',
's',
't',
'i',
'd',
'n',
'\u0120',
'\u0120l',
'\u0120n',
'\u0120lo',
'\u0120low',
'er',
'\u0120lowest',
'\u0120newer',
'\u0120wider',
'<unk>',
]
SCREAMING_SNAKE_CASE__ :Optional[int] = dict(zip(UpperCamelCase_ , range(len(UpperCamelCase_ ) ) ) )
SCREAMING_SNAKE_CASE__ :Dict = ['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
SCREAMING_SNAKE_CASE__ :Any = {'unk_token': '<unk>'}
SCREAMING_SNAKE_CASE__ :Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['merges_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as fp:
fp.write(json.dumps(UpperCamelCase_ ) + '\n' )
with open(self.merges_file , 'w' , encoding='utf-8' ) as fp:
fp.write('\n'.join(UpperCamelCase_ ) )
def __lowerCamelCase ( self : Tuple , **UpperCamelCase_ : Union[str, Any] ) -> List[str]:
kwargs.update(self.special_tokens_map )
return self.tokenizer_class.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def __lowerCamelCase ( self : Union[str, Any] , **UpperCamelCase_ : Union[str, Any] ) -> Any:
kwargs.update(self.special_tokens_map )
return self.rust_tokenizer_class.from_pretrained(self.tmpdirname , **UpperCamelCase_ )
def __lowerCamelCase ( self : List[str] , UpperCamelCase_ : Tuple ) -> List[Any]:
SCREAMING_SNAKE_CASE__ :Tuple = 'lower newer'
SCREAMING_SNAKE_CASE__ :Tuple = 'lower newer'
return input_text, output_text
def __lowerCamelCase ( self : Optional[Any] ) -> List[Any]:
SCREAMING_SNAKE_CASE__ :str = self.tokenizer_class(self.vocab_file , self.merges_file , **self.special_tokens_map )
SCREAMING_SNAKE_CASE__ :Any = 'lower newer'
SCREAMING_SNAKE_CASE__ :Optional[Any] = ['l', 'o', 'w', 'er', '\u0120', 'n', 'e', 'w', 'er']
SCREAMING_SNAKE_CASE__ :Optional[Any] = tokenizer.tokenize(UpperCamelCase_ ) # , add_prefix_space=True)
self.assertListEqual(UpperCamelCase_ , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :List[Any] = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE__ :List[Any] = [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase_ ) , UpperCamelCase_ )
def __lowerCamelCase ( self : Optional[Any] ) -> Tuple:
SCREAMING_SNAKE_CASE__ :Optional[int] = self.get_tokenizer()
self.assertListEqual(tokenizer.encode('Hello world!' , add_special_tokens=UpperCamelCase_ ) , [0, 3_14_14, 2_32, 3_28, 2] )
self.assertListEqual(
tokenizer.encode('Hello world! cécé herlolip 418' , add_special_tokens=UpperCamelCase_ ) , [0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2] , )
@slow
def __lowerCamelCase ( self : List[str] ) -> Optional[int]:
SCREAMING_SNAKE_CASE__ :int = self.tokenizer_class.from_pretrained('allenai/longformer-base-4096' )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = tokenizer.encode('sequence builders' , add_special_tokens=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Dict = tokenizer.encode('multi-sequence build' , add_special_tokens=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Optional[int] = tokenizer.encode(
'sequence builders' , add_special_tokens=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :str = tokenizer.encode(
'sequence builders' , 'multi-sequence build' , add_special_tokens=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Dict = tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :List[Any] = tokenizer.build_inputs_with_special_tokens(UpperCamelCase_ , UpperCamelCase_ )
assert encoded_sentence == encoded_text_from_decode
assert encoded_pair == encoded_pair_from_decode
def __lowerCamelCase ( self : Dict ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ :List[Any] = self.get_tokenizer()
SCREAMING_SNAKE_CASE__ :Any = 'Encode this sequence.'
SCREAMING_SNAKE_CASE__ :int = tokenizer.byte_encoder[' '.encode('utf-8' )[0]]
# Testing encoder arguments
SCREAMING_SNAKE_CASE__ :str = tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :str = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertNotEqual(UpperCamelCase_ , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Tuple = tokenizer.convert_ids_to_tokens(encoded[0] )[0]
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
tokenizer.add_special_tokens({'bos_token': '<s>'} )
SCREAMING_SNAKE_CASE__ :List[Any] = tokenizer.encode(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = tokenizer.convert_ids_to_tokens(encoded[1] )[0]
self.assertNotEqual(UpperCamelCase_ , UpperCamelCase_ )
# Testing spaces after special tokens
SCREAMING_SNAKE_CASE__ :Optional[int] = '<mask>'
tokenizer.add_special_tokens(
{'mask_token': AddedToken(UpperCamelCase_ , lstrip=UpperCamelCase_ , rstrip=UpperCamelCase_ )} ) # mask token has a left space
SCREAMING_SNAKE_CASE__ :Any = tokenizer.convert_tokens_to_ids(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :List[Any] = 'Encode <mask> sequence'
SCREAMING_SNAKE_CASE__ :Optional[Any] = 'Encode <mask>sequence'
SCREAMING_SNAKE_CASE__ :Tuple = tokenizer.encode(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = encoded.index(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Dict = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertEqual(UpperCamelCase_ , UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = tokenizer.encode(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Any = encoded.index(UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Optional[int] = tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1] )[0]
self.assertNotEqual(UpperCamelCase_ , UpperCamelCase_ )
def __lowerCamelCase ( self : Dict ) -> List[str]:
pass
def __lowerCamelCase ( self : List[Any] ) -> int:
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ :Union[str, Any] = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = self.tokenizer_class.from_pretrained(UpperCamelCase_ , **UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :str = 'A, <mask> AllenNLP sentence.'
SCREAMING_SNAKE_CASE__ :str = tokenizer_r.encode_plus(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , return_token_type_ids=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :List[Any] = tokenizer_p.encode_plus(UpperCamelCase_ , add_special_tokens=UpperCamelCase_ , return_token_type_ids=UpperCamelCase_ )
# token_type_ids should put 0 everywhere
self.assertEqual(sum(tokens_r['token_type_ids'] ) , sum(tokens_p['token_type_ids'] ) )
# attention_mask should put 1 everywhere, so sum over length should be 1
self.assertEqual(
sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] ) , sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] ) , )
SCREAMING_SNAKE_CASE__ :int = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
SCREAMING_SNAKE_CASE__ :str = tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )
# Rust correctly handles the space before the mask while python doesnt
self.assertSequenceEqual(tokens_p['input_ids'] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(tokens_r['input_ids'] , [0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2] )
self.assertSequenceEqual(
UpperCamelCase_ , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
self.assertSequenceEqual(
UpperCamelCase_ , ['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
def __lowerCamelCase ( self : Dict ) -> List[str]:
for trim_offsets, add_prefix_space in itertools.product([True, False] , repeat=2 ):
SCREAMING_SNAKE_CASE__ :int = self.rust_tokenizer_class.from_pretrained(
self.tmpdirname , use_fast=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Tuple = json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__() )
SCREAMING_SNAKE_CASE__ :str = json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__() )
self.assertEqual(pre_tokenizer_state['add_prefix_space'] , UpperCamelCase_ )
self.assertEqual(post_processor_state['add_prefix_space'] , UpperCamelCase_ )
self.assertEqual(post_processor_state['trim_offsets'] , UpperCamelCase_ )
def __lowerCamelCase ( self : Dict ) -> List[Any]:
# Test which aims to verify that the offsets are well adapted to the argument `add_prefix_space` and
# `trim_offsets`
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
SCREAMING_SNAKE_CASE__ :Tuple = 'hello' # `hello` is a token in the vocabulary of `pretrained_name`
SCREAMING_SNAKE_CASE__ :Any = f'''{text_of_1_token} {text_of_1_token}'''
SCREAMING_SNAKE_CASE__ :Optional[Any] = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase_ , use_fast=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = tokenizer_r(UpperCamelCase_ , return_offsets_mapping=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(UpperCamelCase_ ) + 1, len(UpperCamelCase_ ) + 1 + len(UpperCamelCase_ )) , )
SCREAMING_SNAKE_CASE__ :Tuple = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase_ , use_fast=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Union[str, Any] = tokenizer_r(UpperCamelCase_ , return_offsets_mapping=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(UpperCamelCase_ ) + 1, len(UpperCamelCase_ ) + 1 + len(UpperCamelCase_ )) , )
SCREAMING_SNAKE_CASE__ :str = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase_ , use_fast=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Tuple = tokenizer_r(UpperCamelCase_ , return_offsets_mapping=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(UpperCamelCase_ ), len(UpperCamelCase_ ) + 1 + len(UpperCamelCase_ )) , )
SCREAMING_SNAKE_CASE__ :int = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase_ , use_fast=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :str = tokenizer_r(UpperCamelCase_ , return_offsets_mapping=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, len(UpperCamelCase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (len(UpperCamelCase_ ), len(UpperCamelCase_ ) + 1 + len(UpperCamelCase_ )) , )
SCREAMING_SNAKE_CASE__ :int = f''' {text}'''
# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
# pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
# )
# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
# self.assertEqual(
# encoding.offset_mapping[1],
# (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
# )
SCREAMING_SNAKE_CASE__ :int = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase_ , use_fast=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Optional[Any] = tokenizer_r(UpperCamelCase_ , return_offsets_mapping=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertEqual(encoding.offset_mapping[0] , (1, 1 + len(UpperCamelCase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(UpperCamelCase_ ) + 1, 1 + len(UpperCamelCase_ ) + 1 + len(UpperCamelCase_ )) , )
SCREAMING_SNAKE_CASE__ :Any = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase_ , use_fast=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :Dict = tokenizer_r(UpperCamelCase_ , return_offsets_mapping=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(UpperCamelCase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(UpperCamelCase_ ), 1 + len(UpperCamelCase_ ) + 1 + len(UpperCamelCase_ )) , )
SCREAMING_SNAKE_CASE__ :Tuple = self.rust_tokenizer_class.from_pretrained(
UpperCamelCase_ , use_fast=UpperCamelCase_ , add_prefix_space=UpperCamelCase_ , trim_offsets=UpperCamelCase_ )
SCREAMING_SNAKE_CASE__ :List[str] = tokenizer_r(UpperCamelCase_ , return_offsets_mapping=UpperCamelCase_ , add_special_tokens=UpperCamelCase_ )
self.assertEqual(encoding.offset_mapping[0] , (0, 1 + len(UpperCamelCase_ )) )
self.assertEqual(
encoding.offset_mapping[1] , (1 + len(UpperCamelCase_ ), 1 + len(UpperCamelCase_ ) + 1 + len(UpperCamelCase_ )) , )
| 320 | 0 |
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def _A ( _lowercase ) -> Tuple:
"""simple docstring"""
monkeypatch.setattr('datasets.utils.deprecation_utils._emitted_deprecation_warnings' , set() )
@pytest.fixture
def _A ( _lowercase ) -> int:
"""simple docstring"""
class __lowerCamelCase :
def __init__( self: Optional[Any],A_: Tuple ):
'''simple docstring'''
__UpperCamelCase = metric_id
class __lowerCamelCase :
_lowercase = [MetricMock(_a ) for metric_id in ["""accuracy""", """mse""", """precision""", """codeparrot/apps_metric"""]]
def snake_case_ ( self: Any ):
'''simple docstring'''
return self._metrics
monkeypatch.setattr('datasets.inspect.huggingface_hub' , HfhMock() )
@pytest.mark.parametrize(
'func, args' , [(load_metric, ('metrics/mse',)), (list_metrics, ()), (inspect_metric, ('metrics/mse', 'tmp_path'))] )
def _A ( _lowercase , _lowercase , _lowercase , _lowercase , _lowercase ) -> str:
"""simple docstring"""
if "tmp_path" in args:
__UpperCamelCase = tuple(arg if arg != 'tmp_path' else tmp_path for arg in args )
with pytest.warns(_lowercase , match='https://huggingface.co/docs/evaluate' ):
func(*_lowercase )
| 1 |
import requests
lowercase_ = """YOUR API KEY"""
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = giphy_api_key ) -> list:
lowercase__ = '+'.join(query.split() )
lowercase__ = F"""https://api.giphy.com/v1/gifs/search?q={formatted_query}&api_key={api_key}"""
lowercase__ = requests.get(_SCREAMING_SNAKE_CASE ).json()['data']
return [gif["url"] for gif in gifs]
if __name__ == "__main__":
print("""\n""".join(get_gifs("""space ship""")))
| 235 | 0 |
from __future__ import annotations
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : str = str(SCREAMING_SNAKE_CASE__ )
return len(SCREAMING_SNAKE_CASE__ ) == 9 and set(SCREAMING_SNAKE_CASE__ ) == set('123456789' )
def UpperCamelCase__ ( ):
for base_num in range(9_999 , 4_999 , -1 ):
__lowerCamelCase : Tuple = 100_002 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE__ ):
return candidate
for base_num in range(333 , 99 , -1 ):
__lowerCamelCase : int = 1_002_003 * base_num
if is_9_pandigital(SCREAMING_SNAKE_CASE__ ):
return candidate
return None
if __name__ == "__main__":
print(F"""{solution() = }""")
| 708 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class A_ :
'''simple docstring'''
__snake_case = 42 # [batch_size x 3]
__snake_case = 42 # [batch_size x 3]
__snake_case = 42 # [batch_size x 3]
__snake_case = 42 # [batch_size x 3]
__snake_case = 42
__snake_case = 42
__snake_case = 42
__snake_case = 42
__snake_case = 42
def _snake_case ( self: str ):
assert self.x.shape[0] == self.y.shape[0] == self.z.shape[0] == self.origin.shape[0]
assert self.x.shape[1] == self.y.shape[1] == self.z.shape[1] == self.origin.shape[1] == 3
assert len(self.x.shape ) == len(self.y.shape ) == len(self.z.shape ) == len(self.origin.shape ) == 2
def _snake_case ( self: Dict ):
return torch.from_numpy(np.array([self.width, self.height] , dtype=np.floataa ) )
def _snake_case ( self: List[str] ):
return torch.from_numpy(np.array([self.x_fov, self.y_fov] , dtype=np.floataa ) )
def _snake_case ( self: Dict ):
__lowerCamelCase : Any = torch.arange(self.height * self.width )
__lowerCamelCase : List[str] = torch.stack(
[
pixel_indices % self.width,
torch.div(a , self.width , rounding_mode='trunc' ),
] , axis=1 , )
return coords
@property
def _snake_case ( self: Optional[int] ):
__lowerCamelCase , *__lowerCamelCase : int = self.shape
__lowerCamelCase : Optional[Any] = int(np.prod(a ) )
__lowerCamelCase : Dict = self.get_image_coords()
__lowerCamelCase : Optional[Any] = torch.broadcast_to(coords.unsqueeze(0 ) , [batch_size * inner_batch_size, *coords.shape] )
__lowerCamelCase : Tuple = self.get_camera_rays(a )
__lowerCamelCase : Union[str, Any] = rays.view(a , inner_batch_size * self.height * self.width , 2 , 3 )
return rays
def _snake_case ( self: Optional[Any] , a: torch.Tensor ):
__lowerCamelCase , *__lowerCamelCase , __lowerCamelCase : Union[str, Any] = coords.shape
assert n_coords == 2
assert batch_size == self.origin.shape[0]
__lowerCamelCase : Union[str, Any] = coords.view(a , -1 , 2 )
__lowerCamelCase : Dict = self.resolution()
__lowerCamelCase : List[Any] = self.fov()
__lowerCamelCase : str = (flat.float() / (res - 1)) * 2 - 1
__lowerCamelCase : Union[str, Any] = fracs * torch.tan(fov / 2 )
__lowerCamelCase : Dict = fracs.view(a , -1 , 2 )
__lowerCamelCase : Dict = (
self.z.view(a , 1 , 3 )
+ self.x.view(a , 1 , 3 ) * fracs[:, :, :1]
+ self.y.view(a , 1 , 3 ) * fracs[:, :, 1:]
)
__lowerCamelCase : int = directions / directions.norm(dim=-1 , keepdim=a )
__lowerCamelCase : Any = torch.stack(
[
torch.broadcast_to(self.origin.view(a , 1 , 3 ) , [batch_size, directions.shape[1], 3] ),
directions,
] , dim=2 , )
return rays.view(a , *a , 2 , 3 )
def _snake_case ( self: int , a: int , a: int ):
assert width * self.height == height * self.width, "The aspect ratio should not change."
return DifferentiableProjectiveCamera(
origin=self.origin , x=self.x , y=self.y , z=self.z , width=a , height=a , x_fov=self.x_fov , y_fov=self.y_fov , )
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
__lowerCamelCase : Dict = []
__lowerCamelCase : Optional[int] = []
__lowerCamelCase : str = []
__lowerCamelCase : Optional[int] = []
for theta in np.linspace(0 , 2 * np.pi , num=20 ):
__lowerCamelCase : Tuple = np.array([np.sin(SCREAMING_SNAKE_CASE__ ), np.cos(SCREAMING_SNAKE_CASE__ ), -0.5] )
z /= np.sqrt(np.sum(z**2 ) )
__lowerCamelCase : Optional[Any] = -z * 4
__lowerCamelCase : Any = np.array([np.cos(SCREAMING_SNAKE_CASE__ ), -np.sin(SCREAMING_SNAKE_CASE__ ), 0.0] )
__lowerCamelCase : Optional[int] = np.cross(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
origins.append(SCREAMING_SNAKE_CASE__ )
xs.append(SCREAMING_SNAKE_CASE__ )
ys.append(SCREAMING_SNAKE_CASE__ )
zs.append(SCREAMING_SNAKE_CASE__ )
return DifferentiableProjectiveCamera(
origin=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE__ , axis=0 ) ).float() , x=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE__ , axis=0 ) ).float() , y=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE__ , axis=0 ) ).float() , z=torch.from_numpy(np.stack(SCREAMING_SNAKE_CASE__ , axis=0 ) ).float() , width=SCREAMING_SNAKE_CASE__ , height=SCREAMING_SNAKE_CASE__ , x_fov=0.7 , y_fov=0.7 , shape=(1, len(SCREAMING_SNAKE_CASE__ )) , )
| 230 | 0 |
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class snake_case :
def __lowercase( self : List[Any] )-> List[Any]:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
] , mid_block_type='UNetMidBlock2DSimpleCrossAttn' , up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='text' , addition_embed_type_num_heads=2 , cross_attention_norm='group_norm' , resnet_time_scale_shift='scale_shift' , act_fn='gelu' , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : int = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0001 , beta_end=0.02 , thresholding=a_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='epsilon' , variance_type='learned_range' , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowercase( self : Tuple )-> int:
"""simple docstring"""
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[Any] = TaEncoderModel.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict = AutoTokenizer.from_pretrained('hf-internal-testing/tiny-random-t5' )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
'ResnetDownsampleBlock2D',
'SimpleCrossAttnDownBlock2D',
] , mid_block_type='UNetMidBlock2DSimpleCrossAttn' , up_block_types=['SimpleCrossAttnUpBlock2D', 'ResnetUpsampleBlock2D'] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type='text' , addition_embed_type_num_heads=2 , cross_attention_norm='group_norm' , resnet_time_scale_shift='scale_shift' , act_fn='gelu' , class_embed_type='timestep' , mid_block_scale_factor=1.414 , time_embedding_act_fn='gelu' , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : str = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0001 , beta_end=0.02 , thresholding=a_ , dynamic_thresholding_ratio=0.95 , sample_max_value=1.0 , prediction_type='epsilon' , variance_type='learned_range' , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule='squaredcos_cap_v2' , beta_start=0.0001 , beta_end=0.02 , )
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Tuple = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowercase( self : Optional[int] )-> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Dict = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : Tuple = inputs['prompt']
SCREAMING_SNAKE_CASE__ : int = inputs['generator']
SCREAMING_SNAKE_CASE__ : Optional[int] = inputs['num_inference_steps']
SCREAMING_SNAKE_CASE__ : Dict = inputs['output_type']
if "image" in inputs:
SCREAMING_SNAKE_CASE__ : List[Any] = inputs['image']
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if "mask_image" in inputs:
SCREAMING_SNAKE_CASE__ : Optional[Any] = inputs['mask_image']
else:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
if "original_image" in inputs:
SCREAMING_SNAKE_CASE__ : Tuple = inputs['original_image']
else:
SCREAMING_SNAKE_CASE__ : Any = None
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Any = pipe.encode_prompt(a_ )
# inputs with prompt converted to embeddings
SCREAMING_SNAKE_CASE__ : Any = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
SCREAMING_SNAKE_CASE__ : Dict = image
if mask_image is not None:
SCREAMING_SNAKE_CASE__ : Union[str, Any] = mask_image
if original_image is not None:
SCREAMING_SNAKE_CASE__ : Dict = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(a_ , a_ , a_ )
SCREAMING_SNAKE_CASE__ : Optional[int] = pipe(**a_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.pipeline_class.from_pretrained(a_ )
pipe_loaded.to(a_ )
pipe_loaded.set_progress_bar_config(disable=a_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(a_ , a_ ) is None , F'''`{optional_component}` did not stay set to None after loading.''' , )
SCREAMING_SNAKE_CASE__ : int = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : List[Any] = inputs['generator']
SCREAMING_SNAKE_CASE__ : str = inputs['num_inference_steps']
SCREAMING_SNAKE_CASE__ : List[Any] = inputs['output_type']
# inputs with prompt converted to embeddings
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
'prompt_embeds': prompt_embeds,
'negative_prompt_embeds': negative_prompt_embeds,
'generator': generator,
'num_inference_steps': num_inference_steps,
'output_type': output_type,
}
if image is not None:
SCREAMING_SNAKE_CASE__ : List[str] = image
if mask_image is not None:
SCREAMING_SNAKE_CASE__ : Tuple = mask_image
if original_image is not None:
SCREAMING_SNAKE_CASE__ : Optional[Any] = original_image
SCREAMING_SNAKE_CASE__ : List[str] = pipe_loaded(**a_ )[0]
SCREAMING_SNAKE_CASE__ : Any = np.abs(to_np(a_ ) - to_np(a_ ) ).max()
self.assertLess(a_ , 1e-4 )
def __lowercase( self : Optional[Any] )-> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_dummy_components()
SCREAMING_SNAKE_CASE__ : Any = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
SCREAMING_SNAKE_CASE__ : List[str] = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = pipe(**a_ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.pipeline_class.from_pretrained(a_ )
pipe_loaded.to(a_ )
pipe_loaded.set_progress_bar_config(disable=a_ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
SCREAMING_SNAKE_CASE__ : int = self.get_dummy_inputs(a_ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe_loaded(**a_ )[0]
SCREAMING_SNAKE_CASE__ : Dict = np.abs(to_np(a_ ) - to_np(a_ ) ).max()
self.assertLess(a_ , 1e-4 )
| 85 | from pathlib import Path
import numpy as np
from PIL import Image
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : List[Any] = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]
return 0.2989 * r + 0.5870 * g + 0.1140 * b
def _a ( lowercase__ : np.ndarray ):
'''simple docstring'''
return (gray > 1_27) & (gray <= 2_55)
def _a ( lowercase__ : np.ndarray , lowercase__ : np.ndarray ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : List[Any] = np.zeros_like(lowercase__ )
SCREAMING_SNAKE_CASE__ : str = np.zeros(
(image.shape[0] + kernel.shape[0] - 1, image.shape[1] + kernel.shape[1] - 1) )
# Copy image to padded image
SCREAMING_SNAKE_CASE__ : Optional[Any] = image
# Iterate over image & apply kernel
for x in range(image.shape[1] ):
for y in range(image.shape[0] ):
SCREAMING_SNAKE_CASE__ : List[Any] = (
kernel * image_padded[y : y + kernel.shape[0], x : x + kernel.shape[1]]
).sum()
SCREAMING_SNAKE_CASE__ : List[str] = int(summation > 0 )
return output
if __name__ == "__main__":
# read original image
SCREAMING_SNAKE_CASE__ : int = Path(__file__).resolve().parent / "image_data" / "lena.jpg"
SCREAMING_SNAKE_CASE__ : int = np.array(Image.open(lena_path))
# kernel to be applied
SCREAMING_SNAKE_CASE__ : str = np.array([[0, 1, 0], [1, 1, 1], [0, 1, 0]])
SCREAMING_SNAKE_CASE__ : Optional[int] = dilation(gray_to_binary(rgb_to_gray(lena)), structuring_element)
# Save the output image
SCREAMING_SNAKE_CASE__ : Optional[int] = Image.fromarray(output).convert("RGB")
pil_img.save("result_dilation.png")
| 85 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
snake_case__ = {
'configuration_clap': [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapAudioConfig',
'ClapConfig',
'ClapTextConfig',
],
'processing_clap': ['ClapProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
snake_case__ = [
'CLAP_PRETRAINED_MODEL_ARCHIVE_LIST',
'ClapModel',
'ClapPreTrainedModel',
'ClapTextModel',
'ClapTextModelWithProjection',
'ClapAudioModel',
'ClapAudioModelWithProjection',
]
snake_case__ = ['ClapFeatureExtractor']
if TYPE_CHECKING:
from .configuration_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioConfig,
ClapConfig,
ClapTextConfig,
)
from .processing_clap import ClapProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_clap import ClapFeatureExtractor
from .modeling_clap import (
CLAP_PRETRAINED_MODEL_ARCHIVE_LIST,
ClapAudioModel,
ClapAudioModelWithProjection,
ClapModel,
ClapPreTrainedModel,
ClapTextModel,
ClapTextModelWithProjection,
)
else:
import sys
snake_case__ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 638 | import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
snake_case__ = logging.get_logger(__name__)
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase=False ) -> List[Any]:
'''simple docstring'''
_lowerCamelCase = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'blocks.{i}.norm1.weight', F'vit.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'blocks.{i}.norm1.bias', F'vit.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append((F'blocks.{i}.attn.proj.weight', F'vit.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append((F'blocks.{i}.attn.proj.bias', F'vit.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((F'blocks.{i}.norm2.weight', F'vit.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'blocks.{i}.norm2.bias', F'vit.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc1.weight', F'vit.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc1.bias', F'vit.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((F'blocks.{i}.mlp.fc2.weight', F'vit.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'blocks.{i}.mlp.fc2.bias', F'vit.encoder.layer.{i}.output.dense.bias') )
# projection layer + position embeddings
rename_keys.extend(
[
('''cls_token''', '''vit.embeddings.cls_token'''),
('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
('''pos_embed''', '''vit.embeddings.position_embeddings'''),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
_lowerCamelCase = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
return rename_keys
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=False ) -> str:
'''simple docstring'''
for i in range(config.num_hidden_layers ):
if base_model:
_lowerCamelCase = ''''''
else:
_lowerCamelCase = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
_lowerCamelCase = state_dict.pop(F'blocks.{i}.attn.qkv.weight' )
_lowerCamelCase = state_dict.pop(F'blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
_lowerCamelCase = in_proj_weight[
: config.hidden_size, :
]
_lowerCamelCase = in_proj_bias[: config.hidden_size]
_lowerCamelCase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
_lowerCamelCase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
_lowerCamelCase = in_proj_weight[
-config.hidden_size :, :
]
_lowerCamelCase = in_proj_bias[-config.hidden_size :]
def __magic_name__( __UpperCAmelCase ) -> Dict:
'''simple docstring'''
_lowerCamelCase = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(__UpperCAmelCase , __UpperCAmelCase )
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[Any]:
'''simple docstring'''
_lowerCamelCase = dct.pop(__UpperCAmelCase )
_lowerCamelCase = val
def __magic_name__( ) -> List[str]:
'''simple docstring'''
_lowerCamelCase = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
_lowerCamelCase = Image.open(requests.get(__UpperCAmelCase , stream=__UpperCAmelCase ).raw )
return im
@torch.no_grad()
def __magic_name__( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase=True ) -> str:
'''simple docstring'''
_lowerCamelCase = ViTConfig()
# patch_size
if model_name[-1] == "8":
_lowerCamelCase = 8
# set labels if required
if not base_model:
_lowerCamelCase = 1000
_lowerCamelCase = '''huggingface/label-files'''
_lowerCamelCase = '''imagenet-1k-id2label.json'''
_lowerCamelCase = json.load(open(hf_hub_download(__UpperCAmelCase , __UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
_lowerCamelCase = {int(__UpperCAmelCase ): v for k, v in idalabel.items()}
_lowerCamelCase = idalabel
_lowerCamelCase = {v: k for k, v in idalabel.items()}
# size of the architecture
if model_name in ["dino_vits8", "dino_vits16"]:
_lowerCamelCase = 384
_lowerCamelCase = 1536
_lowerCamelCase = 12
_lowerCamelCase = 6
# load original model from torch hub
_lowerCamelCase = torch.hub.load('''facebookresearch/dino:main''' , __UpperCAmelCase )
original_model.eval()
# load state_dict of original model, remove and rename some keys
_lowerCamelCase = original_model.state_dict()
if base_model:
remove_classification_head_(__UpperCAmelCase )
_lowerCamelCase = create_rename_keys(__UpperCAmelCase , base_model=__UpperCAmelCase )
for src, dest in rename_keys:
rename_key(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
read_in_q_k_v(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
# load HuggingFace model
if base_model:
_lowerCamelCase = ViTModel(__UpperCAmelCase , add_pooling_layer=__UpperCAmelCase ).eval()
else:
_lowerCamelCase = ViTForImageClassification(__UpperCAmelCase ).eval()
model.load_state_dict(__UpperCAmelCase )
# Check outputs on an image, prepared by ViTImageProcessor
_lowerCamelCase = ViTImageProcessor()
_lowerCamelCase = image_processor(images=prepare_img() , return_tensors='''pt''' )
_lowerCamelCase = encoding['''pixel_values''']
_lowerCamelCase = model(__UpperCAmelCase )
if base_model:
_lowerCamelCase = original_model(__UpperCAmelCase )
assert torch.allclose(__UpperCAmelCase , outputs.last_hidden_state[:, 0, :] , atol=1E-1 )
else:
_lowerCamelCase = original_model(__UpperCAmelCase )
assert logits.shape == outputs.logits.shape
assert torch.allclose(__UpperCAmelCase , outputs.logits , atol=1E-3 )
Path(__UpperCAmelCase ).mkdir(exist_ok=__UpperCAmelCase )
print(F'Saving model {model_name} to {pytorch_dump_folder_path}' )
model.save_pretrained(__UpperCAmelCase )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(__UpperCAmelCase )
if __name__ == "__main__":
snake_case__ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='dino_vitb16',
type=str,
help='Name of the model trained with DINO you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
parser.add_argument(
'--base_model',
action='store_true',
help='Whether to only convert the base model (no projection head weights).',
)
parser.set_defaults(base_model=True)
snake_case__ = parser.parse_args()
convert_vit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.base_model) | 638 | 1 |
'''simple docstring'''
from __future__ import annotations
from typing import Generic, TypeVar
__lowerCamelCase = TypeVar('''T''')
class UpperCAmelCase ( Generic[T] ):
def __init__( self : Dict , __lowerCamelCase : T ):
UpperCAmelCase__ :Any = data
UpperCAmelCase__ :Dict = self
UpperCAmelCase__ :Optional[Any] = 0
class UpperCAmelCase ( Generic[T] ):
def __init__( self : Optional[int] ):
# map from node name to the node object
UpperCAmelCase__ :dict[T, DisjointSetTreeNode[T]] = {}
def __SCREAMING_SNAKE_CASE ( self : List[str] , __lowerCamelCase : T ):
# create a new set with x as its member
UpperCAmelCase__ :Any = DisjointSetTreeNode(__lowerCamelCase )
def __SCREAMING_SNAKE_CASE ( self : Optional[int] , __lowerCamelCase : T ):
# find the set x belongs to (with path-compression)
UpperCAmelCase__ :Optional[int] = self.map[data]
if elem_ref != elem_ref.parent:
UpperCAmelCase__ :Optional[int] = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def __SCREAMING_SNAKE_CASE ( self : int , __lowerCamelCase : DisjointSetTreeNode[T] , __lowerCamelCase : DisjointSetTreeNode[T] ):
# helper function for union operation
if nodea.rank > nodea.rank:
UpperCAmelCase__ :Any = nodea
else:
UpperCAmelCase__ :Optional[int] = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def __SCREAMING_SNAKE_CASE ( self : List[str] , __lowerCamelCase : T , __lowerCamelCase : T ):
# merge 2 disjoint sets
self.link(self.find_set(__lowerCamelCase ) , self.find_set(__lowerCamelCase ) )
class UpperCAmelCase ( Generic[T] ):
def __init__( self : Union[str, Any] ):
# connections: map from the node to the neighbouring nodes (with weights)
UpperCAmelCase__ :dict[T, dict[T, int]] = {}
def __SCREAMING_SNAKE_CASE ( self : Any , __lowerCamelCase : T ):
# add a node ONLY if its not present in the graph
if node not in self.connections:
UpperCAmelCase__ :Dict = {}
def __SCREAMING_SNAKE_CASE ( self : Union[str, Any] , __lowerCamelCase : T , __lowerCamelCase : T , __lowerCamelCase : int ):
# add an edge with the given weight
self.add_node(__lowerCamelCase )
self.add_node(__lowerCamelCase )
UpperCAmelCase__ :Union[str, Any] = weight
UpperCAmelCase__ :Dict = weight
def __SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
UpperCAmelCase__ :Tuple = []
UpperCAmelCase__ :List[Any] = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda __lowerCamelCase : x[2] )
# creating the disjoint set
UpperCAmelCase__ :List[Any] = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(__lowerCamelCase )
# MST generation
UpperCAmelCase__ :str = 0
UpperCAmelCase__ :Union[str, Any] = 0
UpperCAmelCase__ :Optional[Any] = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ :Optional[int] = edges[index]
index += 1
UpperCAmelCase__ :int = disjoint_set.find_set(__lowerCamelCase )
UpperCAmelCase__ :Tuple = disjoint_set.find_set(__lowerCamelCase )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase )
disjoint_set.union(__lowerCamelCase , __lowerCamelCase )
return graph
| 467 |
'''simple docstring'''
def a__ ( UpperCamelCase_ : int | float | str ):
try:
UpperCAmelCase__ :Union[str, Any] = float(UpperCamelCase_ )
except ValueError:
raise ValueError('''Please enter a valid number''' )
UpperCAmelCase__ :List[str] = decimal - int(UpperCamelCase_ )
if fractional_part == 0:
return int(UpperCamelCase_ ), 1
else:
UpperCAmelCase__ :List[Any] = len(str(UpperCamelCase_ ).split('''.''' )[1] )
UpperCAmelCase__ :Tuple = int(decimal * (10**number_of_frac_digits) )
UpperCAmelCase__ :int = 10**number_of_frac_digits
UpperCAmelCase__ , UpperCAmelCase__ :List[str] = denominator, numerator
while True:
UpperCAmelCase__ :Optional[Any] = dividend % divisor
if remainder == 0:
break
UpperCAmelCase__ , UpperCAmelCase__ :List[str] = divisor, remainder
UpperCAmelCase__ , UpperCAmelCase__ :Tuple = numerator / divisor, denominator / divisor
return int(UpperCamelCase_ ), int(UpperCamelCase_ )
if __name__ == "__main__":
print(F'''{decimal_to_fraction(2) = }''')
print(F'''{decimal_to_fraction(89.0) = }''')
print(F'''{decimal_to_fraction("67") = }''')
print(F'''{decimal_to_fraction("45.0") = }''')
print(F'''{decimal_to_fraction(1.5) = }''')
print(F'''{decimal_to_fraction("6.25") = }''')
print(F'''{decimal_to_fraction("78td") = }''')
| 467 | 1 |
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Image
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
A = field(default='image-classification' , metadata={'include_in_asdict_even_if_is_default': True} )
A = Features({'image': Image()} )
A = Features({'labels': ClassLabel} )
A = "image"
A = "labels"
def __a ( self ,__SCREAMING_SNAKE_CASE ):
if self.label_column not in features:
raise ValueError(f"""Column {self.label_column} is not present in features.""" )
if not isinstance(features[self.label_column] ,__SCREAMING_SNAKE_CASE ):
raise ValueError(f"""Column {self.label_column} is not a ClassLabel.""" )
SCREAMING_SNAKE_CASE : Union[str, Any] = copy.deepcopy(self )
SCREAMING_SNAKE_CASE : str = self.label_schema.copy()
SCREAMING_SNAKE_CASE : Any = features[self.label_column]
SCREAMING_SNAKE_CASE : List[str] = label_schema
return task_template
@property
def __a ( self ):
return {
self.image_column: "image",
self.label_column: "labels",
}
| 220 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')
require_version('datasets>=1.8.0', 'To fix: pip install -r examples/pytorch/text-classification/requirements.txt')
__UpperCAmelCase = logging.getLogger(__name__)
@dataclass
class _a :
"""simple docstring"""
A = field(
default=1_28 , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
)
} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Overwrite the cached preprocessed datasets or not.'} )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of training examples to this '
'value if set.'
)
} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
'value if set.'
)
} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={
'help': (
'For debugging purposes or quicker training, truncate the number of prediction examples to this '
'value if set.'
)
} , )
@dataclass
class _a :
"""simple docstring"""
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'} )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Evaluation language. Also train language if `train_language` is set to None.'} )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Train language if it is different from the evaluation language.'} )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
A = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
A = field(
default=SCREAMING_SNAKE_CASE , metadata={'help': 'Will enable to load a pretrained model whose head dimensions are different.'} , )
def SCREAMING_SNAKE_CASE_ ( ) -> Union[str, Any]:
# 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.
SCREAMING_SNAKE_CASE : List[Any] = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_xnli' , snake_case_ )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
SCREAMING_SNAKE_CASE : Optional[int] = training_args.get_process_log_level()
logger.setLevel(snake_case_ )
datasets.utils.logging.set_verbosity(snake_case_ )
transformers.utils.logging.set_verbosity(snake_case_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
SCREAMING_SNAKE_CASE : int = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
SCREAMING_SNAKE_CASE : Tuple = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
SCREAMING_SNAKE_CASE : Tuple = load_dataset(
'xnli' , model_args.language , split='train' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
SCREAMING_SNAKE_CASE : Optional[Any] = load_dataset(
'xnli' , model_args.train_language , split='train' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE : List[str] = train_dataset.features['label'].names
if training_args.do_eval:
SCREAMING_SNAKE_CASE : List[str] = load_dataset(
'xnli' , model_args.language , split='validation' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE : str = eval_dataset.features['label'].names
if training_args.do_predict:
SCREAMING_SNAKE_CASE : List[Any] = load_dataset(
'xnli' , model_args.language , split='test' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE : List[Any] = predict_dataset.features['label'].names
# Labels
SCREAMING_SNAKE_CASE : List[str] = len(snake_case_ )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
SCREAMING_SNAKE_CASE : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=snake_case_ , idalabel={str(snake_case_ ): label for i, label in enumerate(snake_case_ )} , labelaid={label: i for i, label in enumerate(snake_case_ )} , finetuning_task='xnli' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE : Dict = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
SCREAMING_SNAKE_CASE : Dict = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=snake_case_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE : Dict = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
SCREAMING_SNAKE_CASE : Tuple = False
def preprocess_function(snake_case_ : Optional[Any] ):
# Tokenize the texts
return tokenizer(
examples['premise'] , examples['hypothesis'] , padding=snake_case_ , max_length=data_args.max_seq_length , truncation=snake_case_ , )
if training_args.do_train:
if data_args.max_train_samples is not None:
SCREAMING_SNAKE_CASE : List[Any] = min(len(snake_case_ ) , data_args.max_train_samples )
SCREAMING_SNAKE_CASE : List[Any] = train_dataset.select(range(snake_case_ ) )
with training_args.main_process_first(desc='train dataset map pre-processing' ):
SCREAMING_SNAKE_CASE : int = train_dataset.map(
snake_case_ , batched=snake_case_ , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on train dataset' , )
# Log a few random samples from the training set:
for index in random.sample(range(len(snake_case_ ) ) , 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
SCREAMING_SNAKE_CASE : str = min(len(snake_case_ ) , data_args.max_eval_samples )
SCREAMING_SNAKE_CASE : List[str] = eval_dataset.select(range(snake_case_ ) )
with training_args.main_process_first(desc='validation dataset map pre-processing' ):
SCREAMING_SNAKE_CASE : Optional[Any] = eval_dataset.map(
snake_case_ , batched=snake_case_ , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on validation dataset' , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
SCREAMING_SNAKE_CASE : Tuple = min(len(snake_case_ ) , data_args.max_predict_samples )
SCREAMING_SNAKE_CASE : Optional[Any] = predict_dataset.select(range(snake_case_ ) )
with training_args.main_process_first(desc='prediction dataset map pre-processing' ):
SCREAMING_SNAKE_CASE : Any = predict_dataset.map(
snake_case_ , batched=snake_case_ , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on prediction dataset' , )
# Get the metric function
SCREAMING_SNAKE_CASE : List[str] = evaluate.load('xnli' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(snake_case_ : EvalPrediction ):
SCREAMING_SNAKE_CASE : Optional[int] = p.predictions[0] if isinstance(p.predictions , snake_case_ ) else p.predictions
SCREAMING_SNAKE_CASE : Optional[Any] = np.argmax(snake_case_ , axis=1 )
return metric.compute(predictions=snake_case_ , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
SCREAMING_SNAKE_CASE : List[str] = default_data_collator
elif training_args.fpaa:
SCREAMING_SNAKE_CASE : int = DataCollatorWithPadding(snake_case_ , pad_to_multiple_of=8 )
else:
SCREAMING_SNAKE_CASE : str = None
# Initialize our Trainer
SCREAMING_SNAKE_CASE : Optional[int] = Trainer(
model=snake_case_ , args=snake_case_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=snake_case_ , tokenizer=snake_case_ , data_collator=snake_case_ , )
# Training
if training_args.do_train:
SCREAMING_SNAKE_CASE : Optional[int] = None
if training_args.resume_from_checkpoint is not None:
SCREAMING_SNAKE_CASE : Dict = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
SCREAMING_SNAKE_CASE : Optional[int] = last_checkpoint
SCREAMING_SNAKE_CASE : Union[str, Any] = trainer.train(resume_from_checkpoint=snake_case_ )
SCREAMING_SNAKE_CASE : Any = train_result.metrics
SCREAMING_SNAKE_CASE : int = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(snake_case_ )
)
SCREAMING_SNAKE_CASE : Tuple = min(snake_case_ , len(snake_case_ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , snake_case_ )
trainer.save_metrics('train' , snake_case_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
SCREAMING_SNAKE_CASE : int = trainer.evaluate(eval_dataset=snake_case_ )
SCREAMING_SNAKE_CASE : Optional[Any] = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(snake_case_ )
SCREAMING_SNAKE_CASE : Union[str, Any] = min(snake_case_ , len(snake_case_ ) )
trainer.log_metrics('eval' , snake_case_ )
trainer.save_metrics('eval' , snake_case_ )
# Prediction
if training_args.do_predict:
logger.info('*** Predict ***' )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = trainer.predict(snake_case_ , metric_key_prefix='predict' )
SCREAMING_SNAKE_CASE : Optional[int] = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(snake_case_ )
)
SCREAMING_SNAKE_CASE : Optional[Any] = min(snake_case_ , len(snake_case_ ) )
trainer.log_metrics('predict' , snake_case_ )
trainer.save_metrics('predict' , snake_case_ )
SCREAMING_SNAKE_CASE : int = np.argmax(snake_case_ , axis=1 )
SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(training_args.output_dir , 'predictions.txt' )
if trainer.is_world_process_zero():
with open(snake_case_ , 'w' ) as writer:
writer.write('index\tprediction\n' )
for index, item in enumerate(snake_case_ ):
SCREAMING_SNAKE_CASE : str = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
if __name__ == "__main__":
main()
| 220 | 1 |
from __future__ import annotations
from typing import Generic, TypeVar
lowercase_ = TypeVar("""T""")
class SCREAMING_SNAKE_CASE (Generic[T] ):
def __init__( self : Optional[int] , a : Any )-> List[str]:
"""simple docstring"""
lowercase__ = data
lowercase__ = self
lowercase__ = 0
class SCREAMING_SNAKE_CASE (Generic[T] ):
def __init__( self : str )-> Tuple:
"""simple docstring"""
lowercase__ = {}
def SCREAMING_SNAKE_CASE_ ( self : List[str] , a : Optional[int] )-> Optional[Any]:
"""simple docstring"""
lowercase__ = DisjointSetTreeNode(a )
def SCREAMING_SNAKE_CASE_ ( self : Tuple , a : Dict )-> Optional[Any]:
"""simple docstring"""
lowercase__ = self.map[data]
if elem_ref != elem_ref.parent:
lowercase__ = self.find_set(elem_ref.parent.data )
return elem_ref.parent
def SCREAMING_SNAKE_CASE_ ( self : Any , a : List[str] , a : Any )-> List[Any]:
"""simple docstring"""
if nodea.rank > nodea.rank:
lowercase__ = nodea
else:
lowercase__ = nodea
if nodea.rank == nodea.rank:
nodea.rank += 1
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , a : str , a : Optional[int] )-> Union[str, Any]:
"""simple docstring"""
self.link(self.find_set(a ) , self.find_set(a ) )
class SCREAMING_SNAKE_CASE (Generic[T] ):
def __init__( self : Union[str, Any] )-> Tuple:
"""simple docstring"""
lowercase__ = {}
def SCREAMING_SNAKE_CASE_ ( self : Optional[int] , a : Optional[int] )-> Any:
"""simple docstring"""
if node not in self.connections:
lowercase__ = {}
def SCREAMING_SNAKE_CASE_ ( self : List[str] , a : str , a : str , a : Tuple )-> Union[str, Any]:
"""simple docstring"""
self.add_node(a )
self.add_node(a )
lowercase__ = weight
lowercase__ = weight
def SCREAMING_SNAKE_CASE_ ( self : List[Any] )-> List[Any]:
"""simple docstring"""
lowercase__ = []
lowercase__ = set()
for start in self.connections:
for end in self.connections[start]:
if (start, end) not in seen:
seen.add((end, start) )
edges.append((start, end, self.connections[start][end]) )
edges.sort(key=lambda a : x[2] )
# creating the disjoint set
lowercase__ = DisjointSetTree[T]()
for node in self.connections:
disjoint_set.make_set(a )
# MST generation
lowercase__ = 0
lowercase__ = 0
lowercase__ = GraphUndirectedWeighted[T]()
while num_edges < len(self.connections ) - 1:
lowercase__ , lowercase__ , lowercase__ = edges[index]
index += 1
lowercase__ = disjoint_set.find_set(a )
lowercase__ = disjoint_set.find_set(a )
if parent_u != parent_v:
num_edges += 1
graph.add_edge(a , a , a )
disjoint_set.union(a , a )
return graph
| 235 | """simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor
if is_flax_available():
import os
# The slow tests are often failing with OOM error on GPU
# This makes JAX allocate exactly what is needed on demand, and deallocate memory that is no longer needed
# but will be slower as stated here https://jax.readthedocs.io/en/latest/gpu_memory_allocation.html
SCREAMING_SNAKE_CASE__:List[Any] = """platform"""
import jax
import jax.numpy as jnp
from transformers.models.blenderbot_small.modeling_flax_blenderbot_small import (
FlaxBlenderbotSmallForConditionalGeneration,
FlaxBlenderbotSmallModel,
shift_tokens_right,
)
def _lowerCamelCase( a , a , a=None , a=None , a=None , a=None , a=None , a=None , ):
if attention_mask is None:
__a = np.where(input_ids != config.pad_token_id , 1 , 0 )
if decoder_attention_mask is None:
__a = np.where(decoder_input_ids != config.pad_token_id , 1 , 0 )
if head_mask is None:
__a = np.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
__a = np.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
__a = np.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
}
class snake_case__ :
def __init__( self , lowerCamelCase , lowerCamelCase=13 , lowerCamelCase=7 , lowerCamelCase=True , lowerCamelCase=False , lowerCamelCase=99 , lowerCamelCase=16 , lowerCamelCase=2 , lowerCamelCase=4 , lowerCamelCase=4 , lowerCamelCase="gelu" , lowerCamelCase=0.1 , lowerCamelCase=0.1 , lowerCamelCase=32 , lowerCamelCase=2 , lowerCamelCase=1 , lowerCamelCase=0 , lowerCamelCase=0.02 , ):
__a = parent
__a = batch_size
__a = seq_length
__a = is_training
__a = use_labels
__a = vocab_size
__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 = max_position_embeddings
__a = eos_token_id
__a = pad_token_id
__a = bos_token_id
__a = initializer_range
def a__ ( self ):
__a = np.clip(ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size ) , 3 , self.vocab_size )
__a = np.concatenate((input_ids, 2 * np.ones((self.batch_size, 1) , dtype=np.intaa )) , -1 )
__a = shift_tokens_right(lowerCamelCase , 1 , 2 )
__a = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , initializer_range=self.initializer_range , use_cache=lowerCamelCase , )
__a = prepare_blenderbot_inputs_dict(lowerCamelCase , lowerCamelCase , lowerCamelCase )
return config, inputs_dict
def a__ ( self ):
__a , __a = self.prepare_config_and_inputs()
return config, inputs_dict
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__a = 20
__a = model_class_name(lowerCamelCase )
__a = model.encode(inputs_dict["input_ids"] )
__a , __a = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
__a = model.init_cache(decoder_input_ids.shape[0] , lowerCamelCase , lowerCamelCase )
__a = jnp.ones((decoder_input_ids.shape[0], max_decoder_length) , dtype="i4" )
__a = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__a = model.decode(
decoder_input_ids[:, :-1] , lowerCamelCase , decoder_attention_mask=lowerCamelCase , past_key_values=lowerCamelCase , decoder_position_ids=lowerCamelCase , )
__a = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
__a = model.decode(
decoder_input_ids[:, -1:] , lowerCamelCase , decoder_attention_mask=lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_position_ids=lowerCamelCase , )
__a = model.decode(lowerCamelCase , lowerCamelCase )
__a = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"Max diff is {diff}" )
def a__ ( self , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__a = 20
__a = model_class_name(lowerCamelCase )
__a = model.encode(inputs_dict["input_ids"] )
__a , __a = (
inputs_dict["decoder_input_ids"],
inputs_dict["decoder_attention_mask"],
)
__a = jnp.concatenate(
[
decoder_attention_mask,
jnp.zeros((decoder_attention_mask.shape[0], max_decoder_length - decoder_attention_mask.shape[1]) ),
] , axis=-1 , )
__a = model.init_cache(decoder_input_ids.shape[0] , lowerCamelCase , lowerCamelCase )
__a = jnp.broadcast_to(
jnp.arange(decoder_input_ids.shape[-1] - 1 )[None, :] , (decoder_input_ids.shape[0], decoder_input_ids.shape[-1] - 1) , )
__a = model.decode(
decoder_input_ids[:, :-1] , lowerCamelCase , decoder_attention_mask=lowerCamelCase , past_key_values=lowerCamelCase , decoder_position_ids=lowerCamelCase , )
__a = jnp.array(decoder_input_ids.shape[0] * [[decoder_input_ids.shape[-1] - 1]] , dtype="i4" )
__a = model.decode(
decoder_input_ids[:, -1:] , lowerCamelCase , past_key_values=outputs_cache.past_key_values , decoder_attention_mask=lowerCamelCase , decoder_position_ids=lowerCamelCase , )
__a = model.decode(lowerCamelCase , lowerCamelCase , decoder_attention_mask=lowerCamelCase )
__a = np.max(np.abs((outputs_cache_next[0][:, -1, :5] - outputs[0][:, -1, :5]) ) )
self.parent.assertTrue(diff < 1E-3 , msg=F"Max diff is {diff}" )
@require_flax
class snake_case__ ( unittest.TestCase ):
_snake_case : Dict = 99
def a__ ( self ):
__a = np.array(
[
[71, 82, 18, 33, 46, 91, 2],
[68, 34, 26, 58, 30, 82, 2],
[5, 97, 17, 39, 94, 40, 2],
[76, 83, 94, 25, 70, 78, 2],
[87, 59, 41, 35, 48, 66, 2],
[55, 13, 16, 58, 5, 2, 1], # note padding
[64, 27, 31, 51, 12, 75, 2],
[52, 64, 86, 17, 83, 39, 2],
[48, 61, 9, 24, 71, 82, 2],
[26, 1, 60, 48, 22, 13, 2],
[21, 5, 62, 28, 14, 76, 2],
[45, 98, 37, 86, 59, 48, 2],
[70, 70, 50, 9, 28, 0, 2],
] , dtype=np.intaa , )
__a = input_ids.shape[0]
__a = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=24 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=32 , decoder_ffn_dim=32 , max_position_embeddings=48 , eos_token_id=2 , pad_token_id=1 , bos_token_id=0 , )
return config, input_ids, batch_size
def a__ ( self ):
__a , __a , __a = self._get_config_and_data()
__a = FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase )
__a = lm_model(input_ids=lowerCamelCase )
__a = (batch_size, input_ids.shape[1], config.vocab_size)
self.assertEqual(outputs["logits"].shape , lowerCamelCase )
def a__ ( self ):
__a = BlenderbotSmallConfig(
vocab_size=self.vocab_size , d_model=14 , encoder_layers=2 , decoder_layers=2 , encoder_attention_heads=2 , decoder_attention_heads=2 , encoder_ffn_dim=8 , decoder_ffn_dim=8 , max_position_embeddings=48 , )
__a = FlaxBlenderbotSmallForConditionalGeneration(lowerCamelCase )
__a = np.array([[71, 82, 18, 33, 46, 91, 2], [68, 34, 26, 58, 30, 2, 1]] , dtype=np.intaa )
__a = np.array([[82, 71, 82, 18, 2], [58, 68, 2, 1, 1]] , dtype=np.intaa )
__a = lm_model(input_ids=lowerCamelCase , decoder_input_ids=lowerCamelCase )
__a = (*summary.shape, config.vocab_size)
self.assertEqual(outputs["logits"].shape , lowerCamelCase )
def a__ ( self ):
__a = np.array([[71, 82, 18, 33, 2, 1, 1], [68, 34, 26, 58, 30, 82, 2]] , dtype=np.intaa )
__a = shift_tokens_right(lowerCamelCase , 1 , 2 )
__a = np.equal(lowerCamelCase , 1 ).astype(np.floataa ).sum()
__a = np.equal(lowerCamelCase , 1 ).astype(np.floataa ).sum()
self.assertEqual(shifted.shape , input_ids.shape )
self.assertEqual(lowerCamelCase , n_pad_before - 1 )
self.assertTrue(np.equal(shifted[:, 0] , 2 ).all() )
@require_flax
class snake_case__ ( snake_case_, unittest.TestCase, snake_case_ ):
_snake_case : Any = True
_snake_case : int = (
(
FlaxBlenderbotSmallModel,
FlaxBlenderbotSmallForConditionalGeneration,
)
if is_flax_available()
else ()
)
_snake_case : str = (FlaxBlenderbotSmallForConditionalGeneration,) if is_flax_available() else ()
def a__ ( self ):
__a = FlaxBlenderbotSmallModelTester(self )
def a__ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def a__ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
self.model_tester.check_use_cache_forward_with_attn_mask(lowerCamelCase , lowerCamelCase , lowerCamelCase )
def a__ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__a = self._prepare_for_class(lowerCamelCase , lowerCamelCase )
__a = model_class(lowerCamelCase )
@jax.jit
def encode_jitted(lowerCamelCase , lowerCamelCase=None , **lowerCamelCase ):
return model.encode(input_ids=lowerCamelCase , attention_mask=lowerCamelCase )
with self.subTest("JIT Enabled" ):
__a = encode_jitted(**lowerCamelCase ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
__a = encode_jitted(**lowerCamelCase ).to_tuple()
self.assertEqual(len(lowerCamelCase ) , len(lowerCamelCase ) )
for jitted_output, output in zip(lowerCamelCase , lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
def a__ ( self ):
__a , __a = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
__a = model_class(lowerCamelCase )
__a = model.encode(inputs_dict["input_ids"] , inputs_dict["attention_mask"] )
__a = {
"decoder_input_ids": inputs_dict["decoder_input_ids"],
"decoder_attention_mask": inputs_dict["decoder_attention_mask"],
"encoder_outputs": encoder_outputs,
}
@jax.jit
def decode_jitted(lowerCamelCase , lowerCamelCase , lowerCamelCase ):
return model.decode(
decoder_input_ids=lowerCamelCase , decoder_attention_mask=lowerCamelCase , encoder_outputs=lowerCamelCase , )
with self.subTest("JIT Enabled" ):
__a = decode_jitted(**lowerCamelCase ).to_tuple()
with self.subTest("JIT Disabled" ):
with jax.disable_jit():
__a = decode_jitted(**lowerCamelCase ).to_tuple()
self.assertEqual(len(lowerCamelCase ) , len(lowerCamelCase ) )
for jitted_output, output in zip(lowerCamelCase , lowerCamelCase ):
self.assertEqual(jitted_output.shape , output.shape )
@slow
def a__ ( self ):
for model_class_name in self.all_model_classes:
__a = model_class_name.from_pretrained("facebook/blenderbot_small-90M" )
# FlaxBlenderbotForSequenceClassification expects eos token in input_ids
__a = np.ones((1, 1) ) * model.config.eos_token_id
__a = model(lowerCamelCase )
self.assertIsNotNone(lowerCamelCase )
| 528 | 0 |
"""simple docstring"""
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from accelerate.utils import write_basic_config
# DataLoaders built from `test_samples/MRPC` for quick testing
# Should mock `{script_name}.get_dataloaders` via:
# @mock.patch("{script_name}.get_dataloaders", mocked_dataloaders)
A: Any = [
"cross_validation.py",
"gradient_accumulation.py",
"local_sgd.py",
"multi_process_metrics.py",
"memory.py",
"automatic_gradient_accumulation.py",
"fsdp_with_peak_mem_tracking.py",
"deepspeed_with_config_support.py",
"megatron_lm_gpt_pretraining.py",
]
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = None ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str = None
UpperCAmelCase : Optional[Any] = os.path.abspath(os.path.join("""examples""" , """by_feature""" ) )
UpperCAmelCase : str = os.path.abspath("""examples""" )
for item in os.listdir(_SCREAMING_SNAKE_CASE ):
if item not in EXCLUDE_EXAMPLES:
UpperCAmelCase : int = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if os.path.isfile(_SCREAMING_SNAKE_CASE ) and ".py" in item_path:
with self.subTest(
tested_script=_SCREAMING_SNAKE_CASE , feature_script=_SCREAMING_SNAKE_CASE , tested_section="""main()""" if parser_only else """training_function()""" , ):
UpperCAmelCase : Optional[int] = compare_against_test(
os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCAmelCase : List[str] = """\n""".join(_SCREAMING_SNAKE_CASE )
if special_strings is not None:
for string in special_strings:
UpperCAmelCase : List[Any] = diff.replace(_SCREAMING_SNAKE_CASE , """""" )
self.assertEqual(_SCREAMING_SNAKE_CASE , """""" )
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
self.one_complete_example("""complete_nlp_example.py""" , _SCREAMING_SNAKE_CASE )
self.one_complete_example("""complete_nlp_example.py""" , _SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Tuple = os.path.abspath(os.path.join("""examples""" , """cv_example.py""" ) )
UpperCAmelCase : Tuple = [
""" """ * 16 + """{\n\n""",
""" """ * 20 + """\"accuracy\": eval_metric[\"accuracy\"],\n\n""",
""" """ * 20 + """\"f1\": eval_metric[\"f1\"],\n\n""",
""" """ * 20 + """\"train_loss\": total_loss.item() / len(train_dataloader),\n\n""",
""" """ * 20 + """\"epoch\": epoch,\n\n""",
""" """ * 16 + """},\n\n""",
""" """ * 16 + """step=epoch,\n""",
""" """ * 12,
""" """ * 8 + """for step, batch in enumerate(active_dataloader):\n""",
]
self.one_complete_example("""complete_cv_example.py""" , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
self.one_complete_example("""complete_cv_example.py""" , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
@mock.patch.dict(os.environ , {'TESTING_MOCKED_DATALOADERS': '1'} )
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : Optional[int] = False
@classmethod
def SCREAMING_SNAKE_CASE ( cls ) -> Any:
'''simple docstring'''
super().setUpClass()
UpperCAmelCase : List[str] = tempfile.mkdtemp()
UpperCAmelCase : Union[str, Any] = os.path.join(cls._tmpdir , """default_config.yml""" )
write_basic_config(save_location=cls.configPath )
UpperCAmelCase : Union[str, Any] = ["""accelerate""", """launch""", """--config_file""", cls.configPath]
@classmethod
def SCREAMING_SNAKE_CASE ( cls ) -> str:
'''simple docstring'''
super().tearDownClass()
shutil.rmtree(cls._tmpdir )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Tuple = F"\n examples/by_feature/checkpointing.py\n --checkpointing_steps epoch\n --output_dir {self.tmpdir}\n ".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , """epoch_0""" ) ) )
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = F"\n examples/by_feature/checkpointing.py\n --checkpointing_steps 1\n --output_dir {self.tmpdir}\n ".split()
UpperCAmelCase : Tuple = run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(self.tmpdir , """step_2""" ) ) )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : List[Any] = F"\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , 'epoch_0' )}\n ".split()
UpperCAmelCase : str = run_command(self._launch_args + testargs , return_stdout=_SCREAMING_SNAKE_CASE )
self.assertNotIn("""epoch 0:""" , _SCREAMING_SNAKE_CASE )
self.assertIn("""epoch 1:""" , _SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
'''simple docstring'''
UpperCAmelCase : Any = F"\n examples/by_feature/checkpointing.py\n --resume_from_checkpoint {os.path.join(self.tmpdir , 'step_2' )}\n ".split()
UpperCAmelCase : Any = run_command(self._launch_args + testargs , return_stdout=_SCREAMING_SNAKE_CASE )
if torch.cuda.is_available():
UpperCAmelCase : Any = torch.cuda.device_count()
else:
UpperCAmelCase : Union[str, Any] = 1
if num_processes > 1:
self.assertNotIn("""epoch 0:""" , _SCREAMING_SNAKE_CASE )
self.assertIn("""epoch 1:""" , _SCREAMING_SNAKE_CASE )
else:
self.assertIn("""epoch 0:""" , _SCREAMING_SNAKE_CASE )
self.assertIn("""epoch 1:""" , _SCREAMING_SNAKE_CASE )
@slow
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Dict = """
examples/by_feature/cross_validation.py
--num_folds 2
""".split()
with mock.patch.dict(os.environ , {"""TESTING_MOCKED_DATALOADERS""": """0"""} ):
UpperCAmelCase : Dict = run_command(self._launch_args + testargs , return_stdout=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Tuple = re.findall("""({.+})""" , _SCREAMING_SNAKE_CASE )
UpperCAmelCase : Tuple = [r for r in results if """accuracy""" in r][-1]
UpperCAmelCase : str = ast.literal_eval(_SCREAMING_SNAKE_CASE )
self.assertGreaterEqual(results["""accuracy"""] , 0.75 )
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : int = ["""examples/by_feature/multi_process_metrics.py"""]
run_command(self._launch_args + testargs )
@require_trackers
@mock.patch.dict(os.environ , {"""WANDB_MODE""": """offline"""} )
def SCREAMING_SNAKE_CASE ( self ) -> str:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmpdir:
UpperCAmelCase : List[Any] = F"\n examples/by_feature/tracking.py\n --with_tracking\n --project_dir {tmpdir}\n ".split()
run_command(self._launch_args + testargs )
self.assertTrue(os.path.exists(os.path.join(_SCREAMING_SNAKE_CASE , """tracking""" ) ) )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : str = ["""examples/by_feature/gradient_accumulation.py"""]
run_command(self._launch_args + testargs )
def SCREAMING_SNAKE_CASE ( self ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : List[Any] = ["""examples/by_feature/local_sgd.py"""]
run_command(self._launch_args + testargs )
| 359 |
"""simple docstring"""
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ , unittest.TestCase ):
__lowerCAmelCase : List[Any] = TransfoXLTokenizer
__lowerCAmelCase : Optional[int] = False
__lowerCAmelCase : Optional[Any] = False
def SCREAMING_SNAKE_CASE ( self ) -> List[str]:
'''simple docstring'''
super().setUp()
UpperCAmelCase : Optional[Any] = [
"""<unk>""",
"""[CLS]""",
"""[SEP]""",
"""want""",
"""unwanted""",
"""wa""",
"""un""",
"""running""",
""",""",
"""low""",
"""l""",
]
UpperCAmelCase : int = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as vocab_writer:
vocab_writer.write("""""".join([x + """\n""" for x in vocab_tokens] ) )
def SCREAMING_SNAKE_CASE ( self , **_SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] = True
return TransfoXLTokenizer.from_pretrained(self.tmpdirname , **_SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase : int = """<unk> UNwanted , running"""
UpperCAmelCase : Dict = """<unk> unwanted, running"""
return input_text, output_text
def SCREAMING_SNAKE_CASE ( self ) -> Any:
'''simple docstring'''
UpperCAmelCase : List[str] = TransfoXLTokenizer(vocab_file=self.vocab_file , lower_case=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Dict = tokenizer.tokenize("""<unk> UNwanted , running""" )
self.assertListEqual(_SCREAMING_SNAKE_CASE , ["""<unk>""", """unwanted""", """,""", """running"""] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(_SCREAMING_SNAKE_CASE ) , [0, 4, 8, 7] )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
'''simple docstring'''
UpperCAmelCase : Tuple = TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo ! how \n Are yoU ? """ ) , ["""hello""", """!""", """how""", """are""", """you""", """?"""] )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase : Optional[Any] = TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE )
self.assertListEqual(
tokenizer.tokenize(""" \tHeLLo ! how \n Are yoU ? """ ) , ["""HeLLo""", """!""", """how""", """Are""", """yoU""", """?"""] )
def SCREAMING_SNAKE_CASE ( self ) -> int:
'''simple docstring'''
UpperCAmelCase : List[Any] = TransfoXLTokenizer(lower_case=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Dict = """Hello (bracket) and side-scrolled [and] Henry's $5,000 with 3.34 m. What's up!?"""
UpperCAmelCase : Optional[Any] = [
"""Hello""",
"""(""",
"""bracket""",
""")""",
"""and""",
"""side""",
"""@-@""",
"""scrolled""",
"""[""",
"""and""",
"""]""",
"""Henry""",
"""'s""",
"""$""",
"""5""",
"""@,@""",
"""000""",
"""with""",
"""3""",
"""@.@""",
"""34""",
"""m""",
""".""",
"""What""",
"""'s""",
"""up""",
"""!""",
"""?""",
]
self.assertListEqual(tokenizer.tokenize(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
self.assertEqual(tokenizer.convert_tokens_to_string(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
'''simple docstring'''
UpperCAmelCase : List[str] = self.get_tokenizer()
UpperCAmelCase : Tuple = len(_SCREAMING_SNAKE_CASE )
tokenizer.add_tokens(["""new1""", """new2"""] )
tokenizer.move_added_token("""new1""" , 1 )
# Check that moved token is not copied (duplicate)
self.assertEqual(len(_SCREAMING_SNAKE_CASE ) , original_len + 2 )
# Check that token is moved to specified id
self.assertEqual(tokenizer.encode("""new1""" ) , [1] )
self.assertEqual(tokenizer.decode([1] ) , """new1""" )
| 359 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
logging.set_verbosity_info()
_A : Dict = logging.get_logger(__name__)
def __magic_name__ ( __snake_case : Any ) -> str:
lowercase : List[Any] = ASTConfig()
if "10-10" in model_name:
pass
elif "speech-commands" in model_name:
lowercase : List[str] = 128
elif "12-12" in model_name:
lowercase : Optional[int] = 12
lowercase : Optional[int] = 12
elif "14-14" in model_name:
lowercase : Union[str, Any] = 14
lowercase : Union[str, Any] = 14
elif "16-16" in model_name:
lowercase : Union[str, Any] = 16
lowercase : List[str] = 16
else:
raise ValueError("Model not supported" )
lowercase : List[str] = "huggingface/label-files"
if "speech-commands" in model_name:
lowercase : Optional[int] = 35
lowercase : int = "speech-commands-v2-id2label.json"
else:
lowercase : Any = 527
lowercase : Optional[int] = "audioset-id2label.json"
lowercase : Tuple = json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type="dataset" ) , "r" ) )
lowercase : Union[str, Any] = {int(__snake_case ): v for k, v in idalabel.items()}
lowercase : Optional[int] = idalabel
lowercase : Optional[Any] = {v: k for k, v in idalabel.items()}
return config
def __magic_name__ ( __snake_case : str ) -> Any:
if "module.v" in name:
lowercase : List[Any] = name.replace("module.v" , "audio_spectrogram_transformer" )
if "cls_token" in name:
lowercase : Any = name.replace("cls_token" , "embeddings.cls_token" )
if "dist_token" in name:
lowercase : Optional[Any] = name.replace("dist_token" , "embeddings.distillation_token" )
if "pos_embed" in name:
lowercase : Tuple = name.replace("pos_embed" , "embeddings.position_embeddings" )
if "patch_embed.proj" in name:
lowercase : List[str] = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
# transformer blocks
if "blocks" in name:
lowercase : Dict = name.replace("blocks" , "encoder.layer" )
if "attn.proj" in name:
lowercase : str = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
lowercase : List[Any] = name.replace("attn" , "attention.self" )
if "norm1" in name:
lowercase : Union[str, Any] = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
lowercase : int = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
lowercase : str = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
lowercase : List[str] = name.replace("mlp.fc2" , "output.dense" )
# final layernorm
if "audio_spectrogram_transformer.norm" in name:
lowercase : Optional[int] = name.replace("audio_spectrogram_transformer.norm" , "audio_spectrogram_transformer.layernorm" )
# classifier head
if "module.mlp_head.0" in name:
lowercase : Optional[int] = name.replace("module.mlp_head.0" , "classifier.layernorm" )
if "module.mlp_head.1" in name:
lowercase : Any = name.replace("module.mlp_head.1" , "classifier.dense" )
return name
def __magic_name__ ( __snake_case : str , __snake_case : Union[str, Any] ) -> Union[str, Any]:
for key in orig_state_dict.copy().keys():
lowercase : Any = orig_state_dict.pop(__snake_case )
if "qkv" in key:
lowercase : str = key.split("." )
lowercase : Union[str, Any] = int(key_split[3] )
lowercase : str = config.hidden_size
if "weight" in key:
lowercase : Optional[Any] = val[:dim, :]
lowercase : Optional[Any] = val[dim : dim * 2, :]
lowercase : List[str] = val[-dim:, :]
else:
lowercase : Tuple = val[:dim]
lowercase : str = val[dim : dim * 2]
lowercase : List[Any] = val[-dim:]
else:
lowercase : str = val
return orig_state_dict
def __magic_name__ ( __snake_case : Optional[Any] ) -> Optional[int]:
lowercase : int = [
"module.v.head.weight",
"module.v.head.bias",
"module.v.head_dist.weight",
"module.v.head_dist.bias",
]
for k in ignore_keys:
state_dict.pop(__snake_case , __snake_case )
@torch.no_grad()
def __magic_name__ ( __snake_case : List[Any] , __snake_case : Optional[int] , __snake_case : Optional[Any]=False ) -> Any:
lowercase : Optional[Any] = get_audio_spectrogram_transformer_config(__snake_case )
lowercase : List[str] = {
"ast-finetuned-audioset-10-10-0.4593": (
"https://www.dropbox.com/s/ca0b1v2nlxzyeb4/audioset_10_10_0.4593.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.450": (
"https://www.dropbox.com/s/1tv0hovue1bxupk/audioset_10_10_0.4495.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.448": (
"https://www.dropbox.com/s/6u5sikl4b9wo4u5/audioset_10_10_0.4483.pth?dl=1"
),
"ast-finetuned-audioset-10-10-0.448-v2": (
"https://www.dropbox.com/s/kt6i0v9fvfm1mbq/audioset_10_10_0.4475.pth?dl=1"
),
"ast-finetuned-audioset-12-12-0.447": (
"https://www.dropbox.com/s/snfhx3tizr4nuc8/audioset_12_12_0.4467.pth?dl=1"
),
"ast-finetuned-audioset-14-14-0.443": (
"https://www.dropbox.com/s/z18s6pemtnxm4k7/audioset_14_14_0.4431.pth?dl=1"
),
"ast-finetuned-audioset-16-16-0.442": (
"https://www.dropbox.com/s/mdsa4t1xmcimia6/audioset_16_16_0.4422.pth?dl=1"
),
"ast-finetuned-speech-commands-v2": (
"https://www.dropbox.com/s/q0tbqpwv44pquwy/speechcommands_10_10_0.9812.pth?dl=1"
),
}
# load original state_dict
lowercase : Dict = model_name_to_url[model_name]
lowercase : List[Any] = torch.hub.load_state_dict_from_url(__snake_case , map_location="cpu" )
# remove some keys
remove_keys(__snake_case )
# rename some keys
lowercase : List[str] = convert_state_dict(__snake_case , __snake_case )
# load 🤗 model
lowercase : int = ASTForAudioClassification(__snake_case )
model.eval()
model.load_state_dict(__snake_case )
# verify outputs on dummy input
# source: https://github.com/YuanGongND/ast/blob/79e873b8a54d0a3b330dd522584ff2b9926cd581/src/run.py#L62
lowercase : List[Any] = -4.2_67_73_93 if "speech-commands" not in model_name else -6.84_59_78
lowercase : List[Any] = 4.5_68_99_74 if "speech-commands" not in model_name else 5.5_65_45_26
lowercase : Tuple = 1024 if "speech-commands" not in model_name else 128
lowercase : Optional[int] = ASTFeatureExtractor(mean=__snake_case , std=__snake_case , max_length=__snake_case )
if "speech-commands" in model_name:
lowercase : Any = load_dataset("speech_commands" , "v0.02" , split="validation" )
lowercase : List[str] = dataset[0]["audio"]["array"]
else:
lowercase : Union[str, Any] = hf_hub_download(
repo_id="nielsr/audio-spectogram-transformer-checkpoint" , filename="sample_audio.flac" , repo_type="dataset" , )
lowercase , lowercase : str = torchaudio.load(__snake_case )
lowercase : str = waveform.squeeze().numpy()
lowercase : List[str] = feature_extractor(__snake_case , sampling_rate=1_6000 , return_tensors="pt" )
# forward pass
lowercase : Union[str, Any] = model(**__snake_case )
lowercase : Optional[int] = outputs.logits
if model_name == "ast-finetuned-audioset-10-10-0.4593":
lowercase : Optional[Any] = torch.tensor([-0.87_60, -7.00_42, -8.66_02] )
elif model_name == "ast-finetuned-audioset-10-10-0.450":
lowercase : Any = torch.tensor([-1.19_86, -7.09_03, -8.27_18] )
elif model_name == "ast-finetuned-audioset-10-10-0.448":
lowercase : List[str] = torch.tensor([-2.61_28, -8.00_80, -9.43_44] )
elif model_name == "ast-finetuned-audioset-10-10-0.448-v2":
lowercase : Any = torch.tensor([-1.50_80, -7.45_34, -8.89_17] )
elif model_name == "ast-finetuned-audioset-12-12-0.447":
lowercase : List[str] = torch.tensor([-0.50_50, -6.58_33, -8.08_43] )
elif model_name == "ast-finetuned-audioset-14-14-0.443":
lowercase : Dict = torch.tensor([-0.38_26, -7.03_36, -8.24_13] )
elif model_name == "ast-finetuned-audioset-16-16-0.442":
lowercase : str = torch.tensor([-1.21_13, -6.91_01, -8.34_70] )
elif model_name == "ast-finetuned-speech-commands-v2":
lowercase : Any = torch.tensor([6.15_89, -8.05_66, -8.79_84] )
else:
raise ValueError("Unknown model name" )
if not torch.allclose(logits[0, :3] , __snake_case , atol=1E-4 ):
raise ValueError("Logits don't match" )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
Path(__snake_case ).mkdir(exist_ok=__snake_case )
print(f"""Saving model {model_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(__snake_case )
print(f"""Saving feature extractor to {pytorch_dump_folder_path}""" )
feature_extractor.save_pretrained(__snake_case )
if push_to_hub:
print("Pushing model and feature extractor to the hub..." )
model.push_to_hub(f"""MIT/{model_name}""" )
feature_extractor.push_to_hub(f"""MIT/{model_name}""" )
if __name__ == "__main__":
_A : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""ast-finetuned-audioset-10-10-0.4593""",
type=str,
help="""Name of the Audio Spectrogram Transformer model you'd like to convert.""",
)
parser.add_argument(
"""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
_A : str = parser.parse_args()
convert_audio_spectrogram_transformer_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 361 |
"""simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A : Optional[int] = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A : Any = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
_A : Tuple = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 361 | 1 |
"""simple docstring"""
import argparse
import os
import re
_UpperCamelCase : Dict = 'src/diffusers'
# Pattern that looks at the indentation in a line.
_UpperCamelCase : Tuple = re.compile(R'^(\s*)\S')
# Pattern that matches `"key":" and puts `key` in group 0.
_UpperCamelCase : int = re.compile(R'^\s*"([^"]+)":')
# Pattern that matches `_import_structure["key"]` and puts `key` in group 0.
_UpperCamelCase : str = re.compile(R'^\s*_import_structure\["([^"]+)"\]')
# Pattern that matches `"key",` and puts `key` in group 0.
_UpperCamelCase : int = re.compile(R'^\s*"([^"]+)",\s*$')
# Pattern that matches any `[stuff]` and puts `stuff` in group 0.
_UpperCamelCase : List[str] = re.compile(R'\[([^\]]+)\]')
def _SCREAMING_SNAKE_CASE ( __snake_case : Any ):
'''simple docstring'''
lowercase = _re_indent.search(__snake_case )
return "" if search is None else search.groups()[0]
def _SCREAMING_SNAKE_CASE ( __snake_case : List[Any] , __snake_case : str="" , __snake_case : Optional[Any]=None , __snake_case : Union[str, Any]=None ):
'''simple docstring'''
lowercase = 0
lowercase = code.split('\n' )
if start_prompt is not None:
while not lines[index].startswith(__snake_case ):
index += 1
lowercase = ['\n'.join(lines[:index] )]
else:
lowercase = []
# We split into blocks until we get to the `end_prompt` (or the end of the block).
lowercase = [lines[index]]
index += 1
while index < len(__snake_case ) and (end_prompt is None or not lines[index].startswith(__snake_case )):
if len(lines[index] ) > 0 and get_indent(lines[index] ) == indent_level:
if len(__snake_case ) > 0 and get_indent(current_block[-1] ).startswith(indent_level + ' ' ):
current_block.append(lines[index] )
blocks.append('\n'.join(__snake_case ) )
if index < len(__snake_case ) - 1:
lowercase = [lines[index + 1]]
index += 1
else:
lowercase = []
else:
blocks.append('\n'.join(__snake_case ) )
lowercase = [lines[index]]
else:
current_block.append(lines[index] )
index += 1
# Adds current block if it's nonempty.
if len(__snake_case ) > 0:
blocks.append('\n'.join(__snake_case ) )
# Add final block after end_prompt if provided.
if end_prompt is not None and index < len(__snake_case ):
blocks.append('\n'.join(lines[index:] ) )
return blocks
def _SCREAMING_SNAKE_CASE ( __snake_case : List[str] ):
'''simple docstring'''
def _inner(__snake_case : Union[str, Any] ):
return key(__snake_case ).lower().replace('_' , '' )
return _inner
def _SCREAMING_SNAKE_CASE ( __snake_case : List[Any] , __snake_case : Optional[Any]=None ):
'''simple docstring'''
def noop(__snake_case : List[Any] ):
return x
if key is None:
lowercase = noop
# Constants are all uppercase, they go first.
lowercase = [obj for obj in objects if key(__snake_case ).isupper()]
# Classes are not all uppercase but start with a capital, they go second.
lowercase = [obj for obj in objects if key(__snake_case )[0].isupper() and not key(__snake_case ).isupper()]
# Functions begin with a lowercase, they go last.
lowercase = [obj for obj in objects if not key(__snake_case )[0].isupper()]
lowercase = ignore_underscore(__snake_case )
return sorted(__snake_case , key=__snake_case ) + sorted(__snake_case , key=__snake_case ) + sorted(__snake_case , key=__snake_case )
def _SCREAMING_SNAKE_CASE ( __snake_case : str ):
'''simple docstring'''
def _replace(__snake_case : Optional[Any] ):
lowercase = match.groups()[0]
if "," not in imports:
return f'[{imports}]'
lowercase = [part.strip().replace('"' , '' ) for part in imports.split(',' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowercase = keys[:-1]
return "[" + ", ".join([f'"{k}"' for k in sort_objects(__snake_case )] ) + "]"
lowercase = import_statement.split('\n' )
if len(__snake_case ) > 3:
# Here we have to sort internal imports that are on several lines (one per name):
# key: [
# "object1",
# "object2",
# ...
# ]
# We may have to ignore one or two lines on each side.
lowercase = 2 if lines[1].strip() == '[' else 1
lowercase = [(i, _re_strip_line.search(__snake_case ).groups()[0]) for i, line in enumerate(lines[idx:-idx] )]
lowercase = sort_objects(__snake_case , key=lambda __snake_case : x[1] )
lowercase = [lines[x[0] + idx] for x in sorted_indices]
return "\n".join(lines[:idx] + sorted_lines + lines[-idx:] )
elif len(__snake_case ) == 3:
# Here we have to sort internal imports that are on one separate line:
# key: [
# "object1", "object2", ...
# ]
if _re_bracket_content.search(lines[1] ) is not None:
lowercase = _re_bracket_content.sub(_replace , lines[1] )
else:
lowercase = [part.strip().replace('"' , '' ) for part in lines[1].split(',' )]
# We will have a final empty element if the line finished with a comma.
if len(keys[-1] ) == 0:
lowercase = keys[:-1]
lowercase = get_indent(lines[1] ) + ', '.join([f'"{k}"' for k in sort_objects(__snake_case )] )
return "\n".join(__snake_case )
else:
# Finally we have to deal with imports fitting on one line
lowercase = _re_bracket_content.sub(_replace , __snake_case )
return import_statement
def _SCREAMING_SNAKE_CASE ( __snake_case : int , __snake_case : Union[str, Any]=True ):
'''simple docstring'''
with open(__snake_case , 'r' ) as f:
lowercase = f.read()
if "_import_structure" not in code:
return
# Blocks of indent level 0
lowercase = split_code_in_indented_blocks(
__snake_case , start_prompt='_import_structure = {' , end_prompt='if TYPE_CHECKING:' )
# We ignore block 0 (everything until start_prompt) and the last block (everything after end_prompt).
for block_idx in range(1 , len(__snake_case ) - 1 ):
# Check if the block contains some `_import_structure`s thingy to sort.
lowercase = main_blocks[block_idx]
lowercase = block.split('\n' )
# Get to the start of the imports.
lowercase = 0
while line_idx < len(__snake_case ) and "_import_structure" not in block_lines[line_idx]:
# Skip dummy import blocks
if "import dummy" in block_lines[line_idx]:
lowercase = len(__snake_case )
else:
line_idx += 1
if line_idx >= len(__snake_case ):
continue
# Ignore beginning and last line: they don't contain anything.
lowercase = '\n'.join(block_lines[line_idx:-1] )
lowercase = get_indent(block_lines[1] )
# Slit the internal block into blocks of indent level 1.
lowercase = split_code_in_indented_blocks(__snake_case , indent_level=__snake_case )
# We have two categories of import key: list or _import_structure[key].append/extend
lowercase = _re_direct_key if '_import_structure' in block_lines[0] else _re_indirect_key
# Grab the keys, but there is a trap: some lines are empty or just comments.
lowercase = [(pattern.search(__snake_case ).groups()[0] if pattern.search(__snake_case ) is not None else None) for b in internal_blocks]
# We only sort the lines with a key.
lowercase = [(i, key) for i, key in enumerate(__snake_case ) if key is not None]
lowercase = [x[0] for x in sorted(__snake_case , key=lambda __snake_case : x[1] )]
# We reorder the blocks by leaving empty lines/comments as they were and reorder the rest.
lowercase = 0
lowercase = []
for i in range(len(__snake_case ) ):
if keys[i] is None:
reordered_blocks.append(internal_blocks[i] )
else:
lowercase = sort_objects_in_import(internal_blocks[sorted_indices[count]] )
reordered_blocks.append(__snake_case )
count += 1
# And we put our main block back together with its first and last line.
lowercase = '\n'.join(block_lines[:line_idx] + reordered_blocks + [block_lines[-1]] )
if code != "\n".join(__snake_case ):
if check_only:
return True
else:
print(f'Overwriting {file}.' )
with open(__snake_case , 'w' ) as f:
f.write('\n'.join(__snake_case ) )
def _SCREAMING_SNAKE_CASE ( __snake_case : str=True ):
'''simple docstring'''
lowercase = []
for root, _, files in os.walk(__snake_case ):
if "__init__.py" in files:
lowercase = sort_imports(os.path.join(__snake_case , '__init__.py' ) , check_only=__snake_case )
if result:
lowercase = [os.path.join(__snake_case , '__init__.py' )]
if len(__snake_case ) > 0:
raise ValueError(f'Would overwrite {len(__snake_case )} files, run `make style`.' )
if __name__ == "__main__":
_UpperCamelCase : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--check_only', action='store_true', help='Whether to only check or fix style.')
_UpperCamelCase : Tuple = parser.parse_args()
sort_imports_in_all_inits(check_only=args.check_only)
| 134 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SwiftFormerConfig,
SwiftFormerForImageClassification,
ViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCamelCase : Tuple = logging.get_logger(__name__)
_UpperCamelCase : Dict = torch.device('cpu')
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowercase = 'http://images.cocodataset.org/val2017/000000039769.jpg'
lowercase = Image.open(requests.get(__snake_case , stream=__snake_case ).raw )
return im
def _SCREAMING_SNAKE_CASE ( __snake_case : List[str] ):
'''simple docstring'''
if swiftformer_name == "swiftformer_xs":
return torch.tensor([-2.1_703e00, 2.1_107e00, -2.0_811e00, 8.8_685e-01, 2.4_360e-01] )
elif swiftformer_name == "swiftformer_s":
return torch.tensor([3.9_636e-01, 2.3_478e-01, -1.6_963e00, -1.7_381e00, -8.6_337e-01] )
elif swiftformer_name == "swiftformer_l1":
return torch.tensor([-4.2_768e-01, -4.7_429e-01, -1.0_897e00, -1.0_248e00, 3.5_523e-02] )
elif swiftformer_name == "swiftformer_l3":
return torch.tensor([-2.5_330e-01, 2.4_211e-01, -6.0_185e-01, -8.2_789e-01, -6.0_446e-02] )
def _SCREAMING_SNAKE_CASE ( __snake_case : Optional[int] , __snake_case : str , __snake_case : Any ):
'''simple docstring'''
lowercase = dct.pop(__snake_case )
lowercase = val
def _SCREAMING_SNAKE_CASE ( __snake_case : Any ):
'''simple docstring'''
lowercase = []
for k in state_dict.keys():
lowercase = k
if ".pwconv" in k:
lowercase = k_new.replace('.pwconv' , '.point_wise_conv' )
if ".dwconv" in k:
lowercase = k_new.replace('.dwconv' , '.depth_wise_conv' )
if ".Proj." in k:
lowercase = k_new.replace('.Proj.' , '.proj.' )
if "patch_embed" in k_new:
lowercase = k_new.replace('patch_embed' , 'swiftformer.patch_embed.patch_embedding' )
if "network" in k_new:
lowercase = k_new.split('.' )
if ls[2].isdigit():
lowercase = 'swiftformer.encoder.network.' + ls[1] + '.blocks.' + ls[2] + '.' + '.'.join(ls[3:] )
else:
lowercase = k_new.replace('network' , 'swiftformer.encoder.network' )
rename_keys.append((k, k_new) )
return rename_keys
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( __snake_case : Union[str, Any] , __snake_case : Tuple , __snake_case : List[str] ):
'''simple docstring'''
lowercase = SwiftFormerConfig()
# dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
lowercase = 10_00
lowercase = 'huggingface/label-files'
lowercase = 'imagenet-1k-id2label.json'
lowercase = json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type='dataset' ) , 'r' ) )
lowercase = {int(__snake_case ): v for k, v in idalabel.items()}
lowercase = idalabel
lowercase = {v: k for k, v in idalabel.items()}
# size of the architecture
if swiftformer_name == "swiftformer_xs":
lowercase = [3, 3, 6, 4]
lowercase = [48, 56, 1_12, 2_20]
elif swiftformer_name == "swiftformer_s":
lowercase = [3, 3, 9, 6]
lowercase = [48, 64, 1_68, 2_24]
elif swiftformer_name == "swiftformer_l1":
lowercase = [4, 3, 10, 5]
lowercase = [48, 96, 1_92, 3_84]
elif swiftformer_name == "swiftformer_l3":
lowercase = [4, 4, 12, 6]
lowercase = [64, 1_28, 3_20, 5_12]
# load state_dict of original model, remove and rename some keys
if original_ckpt:
if original_ckpt.startswith('https' ):
lowercase = torch.hub.load_state_dict_from_url(__snake_case , map_location='cpu' , check_hash=__snake_case )
else:
lowercase = torch.load(__snake_case , map_location='cpu' )
lowercase = checkpoint
lowercase = create_rename_keys(__snake_case )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(__snake_case , __snake_case , __snake_case )
# load HuggingFace model
lowercase = SwiftFormerForImageClassification(__snake_case ).eval()
hf_model.load_state_dict(__snake_case )
# prepare test inputs
lowercase = prepare_img()
lowercase = ViTImageProcessor.from_pretrained('preprocessor_config' )
lowercase = processor(images=__snake_case , return_tensors='pt' )
# compare outputs from both models
lowercase = get_expected_output(__snake_case )
lowercase = hf_model(inputs['pixel_values'] ).logits
assert hf_logits.shape == torch.Size([1, 10_00] )
assert torch.allclose(hf_logits[0, 0:5] , __snake_case , atol=1e-3 )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
print(f'Saving model {swiftformer_name} to {pytorch_dump_folder_path}' )
hf_model.save_pretrained(__snake_case )
if __name__ == "__main__":
_UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--swiftformer_name',
default='swiftformer_xs',
choices=['swiftformer_xs', 'swiftformer_s', 'swiftformer_l1', 'swiftformer_l3'],
type=str,
help='Name of the SwiftFormer model you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='./converted_outputs/',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--original_ckpt', default=None, type=str, help='Path to the original model checkpoint.')
_UpperCamelCase : Union[str, Any] = parser.parse_args()
convert_swiftformer_checkpoint(args.swiftformer_name, args.pytorch_dump_folder_path, args.original_ckpt)
| 134 | 1 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
import sklearn # noqa: F401 # Here to have a nice missing dependency error message early on
import tqdm # noqa: F401 # Here to have a nice missing dependency error message early on
from mauve import compute_mauve # From: mauve-text
import datasets
_SCREAMING_SNAKE_CASE = "\\n@inproceedings{pillutla-etal:mauve:neurips2021,\n title={MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers},\n author={Pillutla, Krishna and Swayamdipta, Swabha and Zellers, Rowan and Thickstun, John and Welleck, Sean and Choi, Yejin and Harchaoui, Zaid},\n booktitle = {NeurIPS},\n year = {2021}\n}\n\n"
_SCREAMING_SNAKE_CASE = "\\nMAUVE is a library built on PyTorch and HuggingFace Transformers to measure the gap between neural text and human text with the eponymous MAUVE measure.\n\nMAUVE summarizes both Type I and Type II errors measured softly using Kullback–Leibler (KL) divergences.\n\nFor details, see the MAUVE paper: https://arxiv.org/abs/2102.01454 (Neurips, 2021).\n\nThis metrics is a wrapper around the official implementation of MAUVE:\nhttps://github.com/krishnap25/mauve\n"
_SCREAMING_SNAKE_CASE = "\nCalculates MAUVE scores between two lists of generated text and reference text.\nArgs:\n predictions: list of generated text to score. Each predictions\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\nOptional Args:\n num_buckets: the size of the histogram to quantize P and Q. Options: 'auto' (default) or an integer\n pca_max_data: the number data points to use for PCA dimensionality reduction prior to clustering. If -1, use all the data. Default -1\n kmeans_explained_var: amount of variance of the data to keep in dimensionality reduction by PCA. Default 0.9\n kmeans_num_redo: number of times to redo k-means clustering (the best objective is kept). Default 5\n kmeans_max_iter: maximum number of k-means iterations. Default 500\n featurize_model_name: name of the model from which features are obtained. Default 'gpt2-large' Use one of ['gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'].\n device_id: Device for featurization. Supply a GPU id (e.g. 0 or 3) to use GPU. If no GPU with this id is found, use CPU\n max_text_length: maximum number of tokens to consider. Default 1024\n divergence_curve_discretization_size: Number of points to consider on the divergence curve. Default 25\n mauve_scaling_factor: \"c\" from the paper. Default 5.\n verbose: If True (default), print running time updates\n seed: random seed to initialize k-means cluster assignments.\nReturns:\n mauve: MAUVE score, a number between 0 and 1. Larger values indicate that P and Q are closer,\n frontier_integral: Frontier Integral, a number between 0 and 1. Smaller values indicate that P and Q are closer,\n divergence_curve: a numpy.ndarray of shape (m, 2); plot it with matplotlib to view the divergence curve,\n p_hist: a discrete distribution, which is a quantized version of the text distribution p_text,\n q_hist: same as above, but with q_text.\nExamples:\n\n >>> # faiss segfaults in doctest for some reason, so the .compute call is not tested with doctest\n >>> import datasets\n >>> mauve = datasets.load_metric('mauve')\n >>> predictions = [\"hello there\", \"general kenobi\"]\n >>> references = [\"hello there\", \"general kenobi\"]\n >>> out = mauve.compute(predictions=predictions, references=references) # doctest: +SKIP\n >>> print(out.mauve) # doctest: +SKIP\n 1.0\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION ,_KWARGS_DESCRIPTION )
class lowerCAmelCase_ ( datasets.Metric ):
def _snake_case ( self ) -> Union[str, Any]:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage="https://github.com/krishnap25/mauve" , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence" ),
"references": datasets.Value("string" , id="sequence" ),
} ) , codebase_urls=["https://github.com/krishnap25/mauve"] , reference_urls=[
"https://arxiv.org/abs/2102.01454",
"https://github.com/krishnap25/mauve",
] , )
def _snake_case ( self , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase="auto" , _lowerCAmelCase=-1 , _lowerCAmelCase=0.9 , _lowerCAmelCase=5 , _lowerCAmelCase=500 , _lowerCAmelCase="gpt2-large" , _lowerCAmelCase=-1 , _lowerCAmelCase=1024 , _lowerCAmelCase=25 , _lowerCAmelCase=5 , _lowerCAmelCase=True , _lowerCAmelCase=25 , ) -> Optional[int]:
_lowerCAmelCase = compute_mauve(
p_text=_lowerCAmelCase , q_text=_lowerCAmelCase , p_features=_lowerCAmelCase , q_features=_lowerCAmelCase , p_tokens=_lowerCAmelCase , q_tokens=_lowerCAmelCase , num_buckets=_lowerCAmelCase , pca_max_data=_lowerCAmelCase , kmeans_explained_var=_lowerCAmelCase , kmeans_num_redo=_lowerCAmelCase , kmeans_max_iter=_lowerCAmelCase , featurize_model_name=_lowerCAmelCase , device_id=_lowerCAmelCase , max_text_length=_lowerCAmelCase , divergence_curve_discretization_size=_lowerCAmelCase , mauve_scaling_factor=_lowerCAmelCase , verbose=_lowerCAmelCase , seed=_lowerCAmelCase , )
return out
| 18 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():
import torch
from transformers import AutoModelForCausalLM
@require_torch
class lowerCAmelCase_ ( unittest.TestCase ):
def _snake_case ( self ) -> Union[str, Any]:
_lowerCAmelCase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_lowerCAmelCase = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(_lowerCAmelCase )
_lowerCAmelCase = -1
_lowerCAmelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCAmelCase )
_lowerCAmelCase = model.generate(_lowerCAmelCase , max_new_tokens=10 , do_sample=_lowerCAmelCase )
_lowerCAmelCase = tokenizer.decode(greedy_ids[0] )
with CaptureStdout() as cs:
_lowerCAmelCase = TextStreamer(_lowerCAmelCase )
model.generate(_lowerCAmelCase , max_new_tokens=10 , do_sample=_lowerCAmelCase , streamer=_lowerCAmelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_lowerCAmelCase = cs.out[:-1]
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase )
def _snake_case ( self ) -> Union[str, Any]:
_lowerCAmelCase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_lowerCAmelCase = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(_lowerCAmelCase )
_lowerCAmelCase = -1
_lowerCAmelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCAmelCase )
_lowerCAmelCase = model.generate(_lowerCAmelCase , max_new_tokens=10 , do_sample=_lowerCAmelCase )
_lowerCAmelCase = tokenizer.decode(greedy_ids[0] )
_lowerCAmelCase = TextIteratorStreamer(_lowerCAmelCase )
_lowerCAmelCase = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
_lowerCAmelCase = Thread(target=model.generate , kwargs=_lowerCAmelCase )
thread.start()
_lowerCAmelCase = ""
for new_text in streamer:
streamer_text += new_text
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase )
def _snake_case ( self ) -> List[str]:
_lowerCAmelCase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_lowerCAmelCase = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(_lowerCAmelCase )
_lowerCAmelCase = -1
_lowerCAmelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCAmelCase )
_lowerCAmelCase = model.generate(_lowerCAmelCase , max_new_tokens=10 , do_sample=_lowerCAmelCase )
_lowerCAmelCase = greedy_ids[:, input_ids.shape[1] :]
_lowerCAmelCase = tokenizer.decode(new_greedy_ids[0] )
with CaptureStdout() as cs:
_lowerCAmelCase = TextStreamer(_lowerCAmelCase , skip_prompt=_lowerCAmelCase )
model.generate(_lowerCAmelCase , max_new_tokens=10 , do_sample=_lowerCAmelCase , streamer=_lowerCAmelCase )
# The greedy text should be printed to stdout, except for the final "\n" in the streamer
_lowerCAmelCase = cs.out[:-1]
self.assertEqual(_lowerCAmelCase , _lowerCAmelCase )
def _snake_case ( self ) -> Dict:
# Tests that we can pass `decode_kwargs` to the streamer to control how the tokens are decoded. Must be tested
# with actual models -- the dummy models' tokenizers are not aligned with their models, and
# `skip_special_tokens=True` has no effect on them
_lowerCAmelCase = AutoTokenizer.from_pretrained("distilgpt2" )
_lowerCAmelCase = AutoModelForCausalLM.from_pretrained("distilgpt2" ).to(_lowerCAmelCase )
_lowerCAmelCase = -1
_lowerCAmelCase = torch.ones((1, 5) , device=_lowerCAmelCase ).long() * model.config.bos_token_id
with CaptureStdout() as cs:
_lowerCAmelCase = TextStreamer(_lowerCAmelCase , skip_special_tokens=_lowerCAmelCase )
model.generate(_lowerCAmelCase , max_new_tokens=1 , do_sample=_lowerCAmelCase , streamer=_lowerCAmelCase )
# The prompt contains a special token, so the streamer should not print it. As such, the output text, when
# re-tokenized, must only contain one token
_lowerCAmelCase = cs.out[:-1] # Remove the final "\n"
_lowerCAmelCase = tokenizer(_lowerCAmelCase , return_tensors="pt" )
self.assertEqual(streamer_text_tokenized.input_ids.shape , (1, 1) )
def _snake_case ( self ) -> Union[str, Any]:
_lowerCAmelCase = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
_lowerCAmelCase = AutoModelForCausalLM.from_pretrained("hf-internal-testing/tiny-random-gpt2" ).to(_lowerCAmelCase )
_lowerCAmelCase = -1
_lowerCAmelCase = ids_tensor((1, 5) , vocab_size=model.config.vocab_size ).to(_lowerCAmelCase )
_lowerCAmelCase = TextIteratorStreamer(_lowerCAmelCase , timeout=0.001 )
_lowerCAmelCase = {"input_ids": input_ids, "max_new_tokens": 10, "do_sample": False, "streamer": streamer}
_lowerCAmelCase = Thread(target=model.generate , kwargs=_lowerCAmelCase )
thread.start()
# The streamer will timeout after 0.001 seconds, so an exception will be raised
with self.assertRaises(_lowerCAmelCase ):
_lowerCAmelCase = ""
for new_text in streamer:
streamer_text += new_text
| 18 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a_ : Union[str, Any] = {
'configuration_convbert': ['CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ConvBertConfig', 'ConvBertOnnxConfig'],
'tokenization_convbert': ['ConvBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : Tuple = ['ConvBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : List[Any] = [
'CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ConvBertForMaskedLM',
'ConvBertForMultipleChoice',
'ConvBertForQuestionAnswering',
'ConvBertForSequenceClassification',
'ConvBertForTokenClassification',
'ConvBertLayer',
'ConvBertModel',
'ConvBertPreTrainedModel',
'load_tf_weights_in_convbert',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ : str = [
'TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFConvBertForMaskedLM',
'TFConvBertForMultipleChoice',
'TFConvBertForQuestionAnswering',
'TFConvBertForSequenceClassification',
'TFConvBertForTokenClassification',
'TFConvBertLayer',
'TFConvBertModel',
'TFConvBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_convbert import CONVBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, ConvBertConfig, ConvBertOnnxConfig
from .tokenization_convbert import ConvBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_convbert_fast import ConvBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_convbert import (
CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
ConvBertForMaskedLM,
ConvBertForMultipleChoice,
ConvBertForQuestionAnswering,
ConvBertForSequenceClassification,
ConvBertForTokenClassification,
ConvBertLayer,
ConvBertModel,
ConvBertPreTrainedModel,
load_tf_weights_in_convbert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_convbert import (
TF_CONVBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertLayer,
TFConvBertModel,
TFConvBertPreTrainedModel,
)
else:
import sys
a_ : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__) | 678 |
def _SCREAMING_SNAKE_CASE ( snake_case_ : list[list[int]] , snake_case_ : int , snake_case_ : int , snake_case_ : set ):
__magic_name__ , __magic_name__ = len(snake_case_ ), len(grid[0] )
if (
min(snake_case_ , snake_case_ ) < 0
or row == row_length
or col == col_length
or (row, col) in visit
or grid[row][col] == 1
):
return 0
if row == row_length - 1 and col == col_length - 1:
return 1
visit.add((row, col) )
__magic_name__ = 0
count += depth_first_search(snake_case_ , row + 1 , snake_case_ , snake_case_ )
count += depth_first_search(snake_case_ , row - 1 , snake_case_ , snake_case_ )
count += depth_first_search(snake_case_ , snake_case_ , col + 1 , snake_case_ )
count += depth_first_search(snake_case_ , snake_case_ , col - 1 , snake_case_ )
visit.remove((row, col) )
return count
if __name__ == "__main__":
import doctest
doctest.testmod() | 678 | 1 |
'''simple docstring'''
def _lowercase ( UpperCamelCase__ : list ):
__A : Optional[Any] = len(UpperCamelCase__ )
for _ in range(UpperCamelCase__ ):
for i in range(_ % 2, arr_size - 1, 2 ):
if arr[i + 1] < arr[i]:
__A ,__A : Tuple = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
UpperCAmelCase_ : Union[str, Any] = list(range(1_0, 0, -1))
print(f'''Original: {arr}. Sorted: {odd_even_transposition(arr)}''')
| 365 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase_ : Dict = {
'configuration_distilbert': [
'DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP',
'DistilBertConfig',
'DistilBertOnnxConfig',
],
'tokenization_distilbert': ['DistilBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[Any] = ['DistilBertTokenizerFast']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : int = [
'DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'DistilBertForMaskedLM',
'DistilBertForMultipleChoice',
'DistilBertForQuestionAnswering',
'DistilBertForSequenceClassification',
'DistilBertForTokenClassification',
'DistilBertModel',
'DistilBertPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : Optional[int] = [
'TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'TFDistilBertForMaskedLM',
'TFDistilBertForMultipleChoice',
'TFDistilBertForQuestionAnswering',
'TFDistilBertForSequenceClassification',
'TFDistilBertForTokenClassification',
'TFDistilBertMainLayer',
'TFDistilBertModel',
'TFDistilBertPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCAmelCase_ : List[str] = [
'FlaxDistilBertForMaskedLM',
'FlaxDistilBertForMultipleChoice',
'FlaxDistilBertForQuestionAnswering',
'FlaxDistilBertForSequenceClassification',
'FlaxDistilBertForTokenClassification',
'FlaxDistilBertModel',
'FlaxDistilBertPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
UpperCAmelCase_ : Optional[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 365 | 1 |
def __UpperCAmelCase ( lowerCamelCase_ : list ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : Tuple = len(lowerCAmelCase__ )
for _ in range(lowerCAmelCase__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
SCREAMING_SNAKE_CASE_ : int = arr[i + 1], arr[i]
return arr
if __name__ == "__main__":
UpperCamelCase__ : Union[str, Any] = list(range(10, 0, -1))
print(F"""Original: {arr}. Sorted: {odd_even_transposition(arr)}""")
| 709 |
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : Tuple = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : List[str] = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : List[str] = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : Union[str, Any] = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : str = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : Optional[int] = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : Any = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : str = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : Union[str, Any] = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : List[Any] = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : Dict = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : Optional[int] = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
class lowerCAmelCase_ ( metaclass=lowerCamelCase_ ):
__a : str = ["flax"]
def __init__( self ,*snake_case__ ,**snake_case__ ):
requires_backends(self ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
@classmethod
def snake_case ( cls ,*snake_case__ ,**snake_case__ ):
requires_backends(cls ,['flax'] )
| 685 | 0 |
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class _UpperCamelCase( unittest.TestCase ):
@slow
def __lowerCAmelCase ( self : int ):
'''simple docstring'''
__a : Any = FlaxMTaForConditionalGeneration.from_pretrained('google/mt5-small' )
__a : int = AutoTokenizer.from_pretrained('google/mt5-small' )
__a : Any = tokenizer('Hello there' , return_tensors='np' ).input_ids
__a : Any = tokenizer('Hi I am' , return_tensors='np' ).input_ids
__a : List[str] = shift_tokens_right(SCREAMING_SNAKE_CASE__ , model.config.pad_token_id , model.config.decoder_start_token_id )
__a : Tuple = model(SCREAMING_SNAKE_CASE__ , decoder_input_ids=SCREAMING_SNAKE_CASE__ ).logits
__a : Union[str, Any] = optax.softmax_cross_entropy(SCREAMING_SNAKE_CASE__ , onehot(SCREAMING_SNAKE_CASE__ , logits.shape[-1] ) ).mean()
__a : Tuple = -(labels.shape[-1] * loss.item())
__a : Optional[Any] = -84.9_127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE ) < 1e-4 )
| 47 | from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
default_hp_space_ray,
default_hp_space_sigopt,
default_hp_space_wandb,
)
from .utils import logging
_UpperCAmelCase = logging.get_logger(__name__)
class UpperCAmelCase :
'''simple docstring'''
lowerCamelCase_ = 42
lowerCamelCase_ = None
@staticmethod
def lowerCAmelCase_ ( ):
"""simple docstring"""
raise NotImplementedError
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , **lowercase ):
"""simple docstring"""
raise NotImplementedError
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
raise NotImplementedError
def lowerCAmelCase_ ( self ):
"""simple docstring"""
if not self.is_available():
raise RuntimeError(
F'''You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}.''' )
@classmethod
def lowerCAmelCase_ ( cls ):
"""simple docstring"""
return F'''`pip install {cls.pip_package or cls.name}`'''
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''optuna'''
@staticmethod
def lowerCAmelCase_ ( ):
"""simple docstring"""
return is_optuna_available()
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , **lowercase ):
"""simple docstring"""
return run_hp_search_optuna(lowercase , lowercase , lowercase , **lowercase )
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
return default_hp_space_optuna(lowercase )
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''ray'''
lowerCamelCase_ = '''\'ray[tune]\''''
@staticmethod
def lowerCAmelCase_ ( ):
"""simple docstring"""
return is_ray_available()
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , **lowercase ):
"""simple docstring"""
return run_hp_search_ray(lowercase , lowercase , lowercase , **lowercase )
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
return default_hp_space_ray(lowercase )
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''sigopt'''
@staticmethod
def lowerCAmelCase_ ( ):
"""simple docstring"""
return is_sigopt_available()
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , **lowercase ):
"""simple docstring"""
return run_hp_search_sigopt(lowercase , lowercase , lowercase , **lowercase )
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
return default_hp_space_sigopt(lowercase )
class UpperCAmelCase ( __A ):
'''simple docstring'''
lowerCamelCase_ = '''wandb'''
@staticmethod
def lowerCAmelCase_ ( ):
"""simple docstring"""
return is_wandb_available()
def lowerCAmelCase_ ( self , lowercase , lowercase , lowercase , **lowercase ):
"""simple docstring"""
return run_hp_search_wandb(lowercase , lowercase , lowercase , **lowercase )
def lowerCAmelCase_ ( self , lowercase ):
"""simple docstring"""
return default_hp_space_wandb(lowercase )
_UpperCAmelCase = {
HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend]
}
def UpperCamelCase ( ):
'''simple docstring'''
A_ : List[str] = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()]
if len(__lowercase ) > 0:
A_ : List[str] = available_backends[0].name
if len(__lowercase ) > 1:
logger.info(
f'''{len(__lowercase )} hyperparameter search backends available. Using {name} as the default.''' )
return name
raise RuntimeError(
'No hyperparameter search backend available.\n'
+ '\n'.join(
f''' - To install {backend.name} run {backend.pip_install()}'''
for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() ) )
| 558 | 0 |
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__) # pylint: disable=invalid-name
_SCREAMING_SNAKE_CASE = """
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)[\"depth\"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline(\"depth-estimation\")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-prior\", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to(\"cuda\")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... \"kandinsky-community/kandinsky-2-2-controlnet-depth\", torch_dtype=torch.float16
... )
>>> pipe = pipe.to(\"cuda\")
>>> img = load_image(
... \"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main\"
... \"/kandinsky/cat.png\"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to(\"cuda\")
>>> prompt = \"A robot, 4k photo\"
>>> negative_prior_prompt = \"lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature\"
>>> generator = torch.Generator(device=\"cuda\").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save(\"robot_cat.png\")
```
"""
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a=8 ):
snake_case_ : Optional[int] = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
snake_case_ : str = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class SCREAMING_SNAKE_CASE_ ( snake_case_ ):
def __init__( self : Tuple , _A : UNetaDConditionModel , _A : DDPMScheduler , _A : VQModel , ) -> List[str]:
"""simple docstring"""
super().__init__()
self.register_modules(
unet=_A , scheduler=_A , movq=_A , )
snake_case_ : Tuple = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def UpperCAmelCase_ ( self : Any , _A : Dict , _A : Optional[Any] , _A : Union[str, Any] , _A : Union[str, Any] , _A : Dict , _A : List[Any] ) -> List[str]:
"""simple docstring"""
if latents is None:
snake_case_ : Optional[int] = randn_tensor(_A , generator=_A , device=_A , dtype=_A )
else:
if latents.shape != shape:
raise ValueError(F"""Unexpected latents shape, got {latents.shape}, expected {shape}""" )
snake_case_ : List[str] = latents.to(_A )
snake_case_ : Union[str, Any] = latents * scheduler.init_noise_sigma
return latents
def UpperCAmelCase_ ( self : List[str] , _A : List[str]=0 ) -> Optional[int]:
"""simple docstring"""
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError('Please install accelerate via `pip install accelerate`' )
snake_case_ : Optional[int] = torch.device(F"""cuda:{gpu_id}""" )
snake_case_ : Any = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_A , _A )
def UpperCAmelCase_ ( self : int , _A : List[str]=0 ) -> int:
"""simple docstring"""
if is_accelerate_available() and is_accelerate_version('>=' , '0.17.0.dev0' ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.' )
snake_case_ : Union[str, Any] = torch.device(F"""cuda:{gpu_id}""" )
if self.device.type != "cpu":
self.to('cpu' , silence_dtype_warnings=_A )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
snake_case_ : str = None
for cpu_offloaded_model in [self.unet, self.movq]:
snake_case_ ,snake_case_ : Optional[int] = cpu_offload_with_hook(_A , _A , prev_module_hook=_A )
# We'll offload the last model manually.
snake_case_ : Tuple = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def UpperCAmelCase_ ( self : Optional[Any] ) -> List[str]:
"""simple docstring"""
if not hasattr(self.unet , '_hf_hook' ):
return self.device
for module in self.unet.modules():
if (
hasattr(_A , '_hf_hook' )
and hasattr(module._hf_hook , 'execution_device' )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(_A )
def __call__( self : str , _A : Union[torch.FloatTensor, List[torch.FloatTensor]] , _A : Union[torch.FloatTensor, List[torch.FloatTensor]] , _A : torch.FloatTensor , _A : int = 512 , _A : int = 512 , _A : int = 100 , _A : float = 4.0 , _A : int = 1 , _A : Optional[Union[torch.Generator, List[torch.Generator]]] = None , _A : Optional[torch.FloatTensor] = None , _A : Optional[str] = "pil" , _A : bool = True , ) -> str:
"""simple docstring"""
snake_case_ : Any = self._execution_device
snake_case_ : Tuple = guidance_scale > 1.0
if isinstance(_A , _A ):
snake_case_ : int = torch.cat(_A , dim=0 )
if isinstance(_A , _A ):
snake_case_ : Any = torch.cat(_A , dim=0 )
if isinstance(_A , _A ):
snake_case_ : int = torch.cat(_A , dim=0 )
snake_case_ : Dict = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
snake_case_ : int = image_embeds.repeat_interleave(_A , dim=0 )
snake_case_ : Optional[int] = negative_image_embeds.repeat_interleave(_A , dim=0 )
snake_case_ : int = hint.repeat_interleave(_A , dim=0 )
snake_case_ : Optional[Any] = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=_A )
snake_case_ : List[str] = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=_A )
self.scheduler.set_timesteps(_A , device=_A )
snake_case_ : Tuple = self.scheduler.timesteps
snake_case_ : Union[str, Any] = self.movq.config.latent_channels
snake_case_ ,snake_case_ : Union[str, Any] = downscale_height_and_width(_A , _A , self.movq_scale_factor )
# create initial latent
snake_case_ : Union[str, Any] = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , _A , _A , _A , self.scheduler , )
for i, t in enumerate(self.progress_bar(_A ) ):
# expand the latents if we are doing classifier free guidance
snake_case_ : List[Any] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
snake_case_ : Optional[Any] = {'image_embeds': image_embeds, 'hint': hint}
snake_case_ : int = self.unet(
sample=_A , timestep=_A , encoder_hidden_states=_A , added_cond_kwargs=_A , return_dict=_A , )[0]
if do_classifier_free_guidance:
snake_case_ ,snake_case_ : Optional[int] = noise_pred.split(latents.shape[1] , dim=1 )
snake_case_ ,snake_case_ : Dict = noise_pred.chunk(2 )
snake_case_ ,snake_case_ : Tuple = variance_pred.chunk(2 )
snake_case_ : Tuple = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
snake_case_ : Optional[Any] = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , 'variance_type' )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
snake_case_ ,snake_case_ : Any = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
snake_case_ : Any = self.scheduler.step(
_A , _A , _A , generator=_A , )[0]
# post-processing
snake_case_ : List[Any] = self.movq.decode(_A , force_not_quantize=_A )['sample']
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F"""Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}""" )
if output_type in ["np", "pil"]:
snake_case_ : int = image * 0.5 + 0.5
snake_case_ : Any = image.clamp(0 , 1 )
snake_case_ : Optional[Any] = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
snake_case_ : List[Any] = self.numpy_to_pil(_A )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_A )
| 534 |
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
_SCREAMING_SNAKE_CASE = (7_20, 12_80) # Height, Width
_SCREAMING_SNAKE_CASE = (0.4, 0.6) # if height or width lower than this scale, drop it.
_SCREAMING_SNAKE_CASE = 1 / 1_00
_SCREAMING_SNAKE_CASE = """"""
_SCREAMING_SNAKE_CASE = """"""
_SCREAMING_SNAKE_CASE = """"""
_SCREAMING_SNAKE_CASE = 2_50
def SCREAMING_SNAKE_CASE__ ( ):
snake_case_ ,snake_case_ : Any = get_dataset(__a , __a )
for index in range(__a ):
snake_case_ : Dict = random.sample(range(len(__a ) ) , 4 )
snake_case_ ,snake_case_ ,snake_case_ : Optional[int] = update_image_and_anno(
__a , __a , __a , __a , __a , filter_scale=__a , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case_ : List[str] = random_chars(32 )
snake_case_ : Dict = path.split(os.sep )[-1].rsplit('.' , 1 )[0]
snake_case_ : Any = f"""{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"""
cva.imwrite(f"""{file_root}.jpg""" , __a , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(f"""Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}""" )
snake_case_ : Dict = []
for anno in new_annos:
snake_case_ : int = anno[3] - anno[1]
snake_case_ : Dict = anno[4] - anno[2]
snake_case_ : int = anno[1] + width / 2
snake_case_ : str = anno[2] + height / 2
snake_case_ : Union[str, Any] = f"""{anno[0]} {x_center} {y_center} {width} {height}"""
annos_list.append(__a )
with open(f"""{file_root}.txt""" , 'w' ) as outfile:
outfile.write('\n'.join(line for line in annos_list ) )
def SCREAMING_SNAKE_CASE__ ( __a , __a ):
snake_case_ : List[Any] = []
snake_case_ : Optional[int] = []
for label_file in glob.glob(os.path.join(__a , '*.txt' ) ):
snake_case_ : List[str] = label_file.split(os.sep )[-1].rsplit('.' , 1 )[0]
with open(__a ) as in_file:
snake_case_ : Dict = in_file.readlines()
snake_case_ : int = os.path.join(__a , f"""{label_name}.jpg""" )
snake_case_ : int = []
for obj_list in obj_lists:
snake_case_ : str = obj_list.rstrip('\n' ).split(' ' )
snake_case_ : Tuple = float(obj[1] ) - float(obj[3] ) / 2
snake_case_ : str = float(obj[2] ) - float(obj[4] ) / 2
snake_case_ : Optional[Any] = float(obj[1] ) + float(obj[3] ) / 2
snake_case_ : List[Any] = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(__a )
labels.append(__a )
return img_paths, labels
def SCREAMING_SNAKE_CASE__ ( __a , __a , __a , __a , __a , __a = 0.0 , ):
snake_case_ : str = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
snake_case_ : Tuple = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ : int = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
snake_case_ : Optional[Any] = int(scale_x * output_size[1] )
snake_case_ : int = int(scale_y * output_size[0] )
snake_case_ : str = []
snake_case_ : Dict = []
for i, index in enumerate(__a ):
snake_case_ : Any = all_img_list[index]
path_list.append(__a )
snake_case_ : List[str] = all_annos[index]
snake_case_ : Any = cva.imread(__a )
if i == 0: # top-left
snake_case_ : Optional[Any] = cva.resize(__a , (divid_point_x, divid_point_y) )
snake_case_ : Dict = img
for bbox in img_annos:
snake_case_ : Dict = bbox[1] * scale_x
snake_case_ : Tuple = bbox[2] * scale_y
snake_case_ : Optional[Any] = bbox[3] * scale_x
snake_case_ : str = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
snake_case_ : Tuple = cva.resize(__a , (output_size[1] - divid_point_x, divid_point_y) )
snake_case_ : Optional[Any] = img
for bbox in img_annos:
snake_case_ : int = scale_x + bbox[1] * (1 - scale_x)
snake_case_ : Dict = bbox[2] * scale_y
snake_case_ : Union[str, Any] = scale_x + bbox[3] * (1 - scale_x)
snake_case_ : Union[str, Any] = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
snake_case_ : Optional[Any] = cva.resize(__a , (divid_point_x, output_size[0] - divid_point_y) )
snake_case_ : int = img
for bbox in img_annos:
snake_case_ : int = bbox[1] * scale_x
snake_case_ : List[str] = scale_y + bbox[2] * (1 - scale_y)
snake_case_ : List[str] = bbox[3] * scale_x
snake_case_ : Union[str, Any] = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
snake_case_ : str = cva.resize(
__a , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
snake_case_ : Dict = img
for bbox in img_annos:
snake_case_ : Dict = scale_x + bbox[1] * (1 - scale_x)
snake_case_ : Tuple = scale_y + bbox[2] * (1 - scale_y)
snake_case_ : Union[str, Any] = scale_x + bbox[3] * (1 - scale_x)
snake_case_ : List[Any] = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
snake_case_ : Dict = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def SCREAMING_SNAKE_CASE__ ( __a ):
assert number_char > 1, "The number of character should greater than 1"
snake_case_ : Tuple = ascii_lowercase + digits
return "".join(random.choice(__a ) for _ in range(__a ) )
if __name__ == "__main__":
main()
print("""DONE ✅""")
| 534 | 1 |
'''simple docstring'''
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
UpperCamelCase_ = "true"
def lowercase__( __UpperCamelCase: Dict ,__UpperCamelCase: Any=82 ,__UpperCamelCase: Union[str, Any]=16 ):
"""simple docstring"""
set_seed(42 )
SCREAMING_SNAKE_CASE : List[Any] = RegressionModel()
SCREAMING_SNAKE_CASE : List[str] = deepcopy(__UpperCamelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = RegressionDataset(length=__UpperCamelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = DataLoader(__UpperCamelCase ,batch_size=__UpperCamelCase )
model.to(accelerator.device )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = accelerator.prepare(__UpperCamelCase ,__UpperCamelCase )
return model, ddp_model, dataloader
def lowercase__( __UpperCamelCase: Accelerator ,__UpperCamelCase: Tuple=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = AutoTokenizer.from_pretrained('hf-internal-testing/mrpc-bert-base-cased' )
SCREAMING_SNAKE_CASE : Union[str, Any] = load_dataset('glue' ,'mrpc' ,split='validation' )
def tokenize_function(__UpperCamelCase: str ):
SCREAMING_SNAKE_CASE : Optional[int] = tokenizer(examples['sentence1'] ,examples['sentence2'] ,truncation=__UpperCamelCase ,max_length=__UpperCamelCase )
return outputs
with accelerator.main_process_first():
SCREAMING_SNAKE_CASE : Union[str, Any] = dataset.map(
__UpperCamelCase ,batched=__UpperCamelCase ,remove_columns=['idx', 'sentence1', 'sentence2'] ,)
SCREAMING_SNAKE_CASE : Any = tokenized_datasets.rename_column('label' ,'labels' )
def collate_fn(__UpperCamelCase: List[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 lowercase__( __UpperCamelCase: Optional[Any] ,__UpperCamelCase: Union[str, Any] ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = Accelerator(dispatch_batches=__UpperCamelCase ,split_batches=__UpperCamelCase )
SCREAMING_SNAKE_CASE : Union[str, Any] = get_dataloader(__UpperCamelCase ,not dispatch_batches )
SCREAMING_SNAKE_CASE : Optional[Any] = AutoModelForSequenceClassification.from_pretrained(
'hf-internal-testing/mrpc-bert-base-cased' ,return_dict=__UpperCamelCase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = accelerator.prepare(__UpperCamelCase ,__UpperCamelCase )
return {"ddp": [ddp_model, ddp_dataloader, "cuda:0"], "no": [model, dataloader, accelerator.device]}, accelerator
def lowercase__( __UpperCamelCase: Tuple ,__UpperCamelCase: Optional[Any] ,__UpperCamelCase: int ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = []
for batch in dataloader:
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = batch.values()
with torch.no_grad():
SCREAMING_SNAKE_CASE : int = model(__UpperCamelCase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = accelerator.gather_for_metrics((logit, target) )
logits_and_targets.append((logit, target) )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[int] = [], []
for logit, targ in logits_and_targets:
logits.append(__UpperCamelCase )
targs.append(__UpperCamelCase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Any = torch.cat(__UpperCamelCase ), torch.cat(__UpperCamelCase )
return logits, targs
def lowercase__( __UpperCamelCase: Accelerator ,__UpperCamelCase: int=82 ,__UpperCamelCase: List[Any]=False ,__UpperCamelCase: Optional[Any]=False ,__UpperCamelCase: str=16 ):
"""simple docstring"""
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Optional[Any] = get_basic_setup(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : Dict = generate_predictions(__UpperCamelCase ,__UpperCamelCase ,__UpperCamelCase )
assert (
len(__UpperCamelCase ) == num_samples
), f"Unexpected number of inputs:\n Expected: {num_samples}\n Actual: {len(__UpperCamelCase )}"
def lowercase__( __UpperCamelCase: bool = False ,__UpperCamelCase: bool = False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = evaluate.load('glue' ,'mrpc' )
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[Any] = get_mrpc_setup(__UpperCamelCase ,__UpperCamelCase )
# First do baseline
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : List[str] = setup['no']
model.to(__UpperCamelCase )
model.eval()
for batch in dataloader:
batch.to(__UpperCamelCase )
with torch.inference_mode():
SCREAMING_SNAKE_CASE : Optional[Any] = model(**__UpperCamelCase )
SCREAMING_SNAKE_CASE : Optional[Any] = outputs.logits.argmax(dim=-1 )
metric.add_batch(predictions=__UpperCamelCase ,references=batch['labels'] )
SCREAMING_SNAKE_CASE : List[Any] = metric.compute()
# Then do distributed
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : str = setup['ddp']
model.eval()
for batch in dataloader:
with torch.inference_mode():
SCREAMING_SNAKE_CASE : List[str] = model(**__UpperCamelCase )
SCREAMING_SNAKE_CASE : str = outputs.logits.argmax(dim=-1 )
SCREAMING_SNAKE_CASE : Tuple = batch['labels']
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE : int = accelerator.gather_for_metrics((preds, references) )
metric.add_batch(predictions=__UpperCamelCase ,references=__UpperCamelCase )
SCREAMING_SNAKE_CASE : Optional[Any] = 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 lowercase__( ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = 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]:
SCREAMING_SNAKE_CASE : Dict = 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**' )
SCREAMING_SNAKE_CASE : Union[str, Any] = Accelerator()
test_torch_metrics(__UpperCamelCase ,5_12 )
accelerator.state._reset_state()
def lowercase__( __UpperCamelCase: Union[str, Any] ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 28 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ConvNextConfig, UperNetConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import UperNetForSemanticSegmentation
from transformers.models.upernet.modeling_upernet import UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _UpperCAmelCase :
"""simple docstring"""
def __init__( self : List[str] , lowerCAmelCase_ : Optional[int] , lowerCAmelCase_ : Dict=1_3 , lowerCAmelCase_ : str=3_2 , lowerCAmelCase_ : Optional[Any]=3 , lowerCAmelCase_ : Any=4 , lowerCAmelCase_ : str=[1_0, 2_0, 3_0, 4_0] , lowerCAmelCase_ : Tuple=[2, 2, 3, 2] , lowerCAmelCase_ : str=True , lowerCAmelCase_ : str=True , lowerCAmelCase_ : Optional[int]=3_7 , lowerCAmelCase_ : Dict="gelu" , lowerCAmelCase_ : List[Any]=1_0 , lowerCAmelCase_ : str=0.02 , lowerCAmelCase_ : Dict=["stage2", "stage3", "stage4"] , lowerCAmelCase_ : Dict=3 , lowerCAmelCase_ : List[Any]=None , ) -> int:
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = image_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = num_stages
__lowerCAmelCase = hidden_sizes
__lowerCAmelCase = depths
__lowerCAmelCase = is_training
__lowerCAmelCase = use_labels
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = out_features
__lowerCAmelCase = num_labels
__lowerCAmelCase = scope
__lowerCAmelCase = num_stages
def lowercase ( self : Dict ) -> List[str]:
__lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__lowerCAmelCase = None
if self.use_labels:
__lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def lowercase ( self : List[str] ) -> Union[str, Any]:
return ConvNextConfig(
num_channels=self.num_channels , num_stages=self.num_stages , hidden_sizes=self.hidden_sizes , depths=self.depths , is_training=self.is_training , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , out_features=self.out_features , )
def lowercase ( self : Dict ) -> List[str]:
return UperNetConfig(
backbone_config=self.get_backbone_config() , hidden_size=5_1_2 , pool_scales=[1, 2, 3, 6] , use_auxiliary_head=lowerCAmelCase_ , auxiliary_loss_weight=0.4 , auxiliary_in_channels=4_0 , auxiliary_channels=2_5_6 , auxiliary_num_convs=1 , auxiliary_concat_input=lowerCAmelCase_ , loss_ignore_index=2_5_5 , num_labels=self.num_labels , )
def lowercase ( self : List[Any] , lowerCAmelCase_ : Tuple , lowerCAmelCase_ : Union[str, Any] , lowerCAmelCase_ : int ) -> Optional[Any]:
__lowerCAmelCase = UperNetForSemanticSegmentation(config=lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
__lowerCAmelCase = model(lowerCAmelCase_ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def lowercase ( self : Union[str, Any] ) -> Union[str, Any]:
__lowerCAmelCase = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = config_and_inputs
__lowerCAmelCase = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class _UpperCAmelCase ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
"""simple docstring"""
a_ = (UperNetForSemanticSegmentation,) if is_torch_available() else ()
a_ = {"""image-segmentation""": UperNetForSemanticSegmentation} if is_torch_available() else {}
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
a_ = False
def lowercase ( self : Optional[int] ) -> Dict:
__lowerCAmelCase = UperNetModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=lowerCAmelCase_ , has_text_modality=lowerCAmelCase_ , hidden_size=3_7 )
def lowercase ( self : List[str] ) -> int:
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 lowercase ( self : Tuple ) -> Union[str, Any]:
return
def lowercase ( self : Optional[int] ) -> Optional[Any]:
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(lowerCAmelCase_ )
__lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__lowerCAmelCase = [*signature.parameters.keys()]
__lowerCAmelCase = ['pixel_values']
self.assertListEqual(arg_names[:1] , lowerCAmelCase_ )
def lowercase ( self : List[Any] ) -> Union[str, Any]:
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*lowerCAmelCase_ )
@unittest.skip(reason='UperNet does not use inputs_embeds' )
def lowercase ( self : Optional[int] ) -> Dict:
pass
@unittest.skip(reason='UperNet does not support input and output embeddings' )
def lowercase ( self : Optional[Any] ) -> Dict:
pass
@unittest.skip(reason='UperNet does not have a base model' )
def lowercase ( self : Optional[int] ) -> List[Any]:
pass
@unittest.skip(reason='UperNet does not have a base model' )
def lowercase ( self : str ) -> Dict:
pass
@require_torch_multi_gpu
@unittest.skip(reason='UperNet has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def lowercase ( self : Optional[Any] ) -> Optional[int]:
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def lowercase ( self : Tuple ) -> List[Any]:
pass
def lowercase ( self : Union[str, Any] ) -> Tuple:
def check_hidden_states_output(lowerCAmelCase_ : List[str] , lowerCAmelCase_ : int , lowerCAmelCase_ : Optional[Any] ):
__lowerCAmelCase = model_class(lowerCAmelCase_ )
model.to(lowerCAmelCase_ )
model.eval()
with torch.no_grad():
__lowerCAmelCase = model(**self._prepare_for_class(lowerCAmelCase_ , lowerCAmelCase_ ) )
__lowerCAmelCase = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
__lowerCAmelCase = self.model_tester.num_stages
self.assertEqual(len(lowerCAmelCase_ ) , expected_num_stages + 1 )
# ConvNext's feature maps are of shape (batch_size, num_channels, height, width)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [self.model_tester.image_size // 4, self.model_tester.image_size // 4] , )
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__lowerCAmelCase = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__lowerCAmelCase = True
check_hidden_states_output(lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ )
def lowercase ( self : Any ) -> Tuple:
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = _config_zero_init(lowerCAmelCase_ )
__lowerCAmelCase = _config_zero_init(configs_no_init.backbone_config )
for model_class in self.all_model_classes:
__lowerCAmelCase = model_class(config=lowerCAmelCase_ )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" , )
@unittest.skip(reason='UperNet does not have tied weights' )
def lowercase ( self : Any ) -> int:
pass
@slow
def lowercase ( self : Optional[int] ) -> Optional[int]:
for model_name in UPERNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__lowerCAmelCase = UperNetForSemanticSegmentation.from_pretrained(lowerCAmelCase_ )
self.assertIsNotNone(lowerCAmelCase_ )
def a_ ( ):
__lowerCAmelCase = hf_hub_download(
repo_id='hf-internal-testing/fixtures_ade20k', repo_type='dataset', filename='ADE_val_00000001.jpg' )
__lowerCAmelCase = Image.open(lowerCAmelCase_ ).convert('RGB' )
return image
@require_torch
@require_vision
@slow
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self : Dict ) -> Union[str, Any]:
__lowerCAmelCase = AutoImageProcessor.from_pretrained('openmmlab/upernet-swin-tiny' )
__lowerCAmelCase = UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-swin-tiny' ).to(lowerCAmelCase_ )
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = processor(images=lowerCAmelCase_ , return_tensors='pt' ).to(lowerCAmelCase_ )
with torch.no_grad():
__lowerCAmelCase = model(**lowerCAmelCase_ )
__lowerCAmelCase = torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
__lowerCAmelCase = torch.tensor(
[[-7.59_58, -7.59_58, -7.43_02], [-7.59_58, -7.59_58, -7.43_02], [-7.47_97, -7.47_97, -7.30_68]] ).to(lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , lowerCAmelCase_ , atol=1e-4 ) )
def lowercase ( self : List[Any] ) -> List[str]:
__lowerCAmelCase = AutoImageProcessor.from_pretrained('openmmlab/upernet-convnext-tiny' )
__lowerCAmelCase = UperNetForSemanticSegmentation.from_pretrained('openmmlab/upernet-convnext-tiny' ).to(lowerCAmelCase_ )
__lowerCAmelCase = prepare_img()
__lowerCAmelCase = processor(images=lowerCAmelCase_ , return_tensors='pt' ).to(lowerCAmelCase_ )
with torch.no_grad():
__lowerCAmelCase = model(**lowerCAmelCase_ )
__lowerCAmelCase = torch.Size((1, model.config.num_labels, 5_1_2, 5_1_2) )
self.assertEqual(outputs.logits.shape , lowerCAmelCase_ )
__lowerCAmelCase = torch.tensor(
[[-8.81_10, -8.81_10, -8.65_21], [-8.81_10, -8.81_10, -8.65_21], [-8.77_46, -8.77_46, -8.61_30]] ).to(lowerCAmelCase_ )
self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] , lowerCAmelCase_ , atol=1e-4 ) )
| 53 | 0 |
"""simple docstring"""
from __future__ import annotations
from numpy import array, cos, cross, floataa, radians, sin
from numpy.typing import NDArray
def snake_case__ ( _snake_case : float , _snake_case : float , _snake_case : bool = False ):
"""simple docstring"""
if radian_mode:
return [magnitude * cos(_snake_case ), magnitude * sin(_snake_case )]
return [magnitude * cos(radians(_snake_case ) ), magnitude * sin(radians(_snake_case ) )]
def snake_case__ ( _snake_case : NDArray[floataa] , _snake_case : NDArray[floataa] , _snake_case : float = 10**-1 ):
"""simple docstring"""
UpperCamelCase__ = cross(_snake_case , _snake_case )
UpperCamelCase__ = sum(_snake_case )
return abs(_snake_case ) < eps
if __name__ == "__main__":
# Test to check if it works
A : List[str] = array(
[
polar_force(718.4, 180 - 30),
polar_force(879.54, 45),
polar_force(100, -90),
]
)
A : NDArray[floataa] = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem 1 in image_data/2D_problems.jpg
A : Tuple = array(
[
polar_force(30 * 9.81, 15),
polar_force(215, 180 - 45),
polar_force(264, 90 - 30),
]
)
A : int = array([[0, 0], [0, 0], [0, 0]])
assert in_static_equilibrium(forces, location)
# Problem in image_data/2D_problems_1.jpg
A : int = array([[0, -2_000], [0, -1_200], [0, 15_600], [0, -12_400]])
A : Optional[int] = array([[0, 0], [6, 0], [10, 0], [12, 0]])
assert in_static_equilibrium(forces, location)
import doctest
doctest.testmod() | 304 | """simple docstring"""
import math
import random
from typing import Any
from .hill_climbing import SearchProblem
def snake_case__ ( _snake_case : List[str] , _snake_case : bool = True , _snake_case : float = math.inf , _snake_case : float = -math.inf , _snake_case : float = math.inf , _snake_case : float = -math.inf , _snake_case : bool = False , _snake_case : float = 1_00 , _snake_case : float = 0.01 , _snake_case : float = 1 , ):
"""simple docstring"""
UpperCamelCase__ = False
UpperCamelCase__ = search_prob
UpperCamelCase__ = start_temperate
UpperCamelCase__ = []
UpperCamelCase__ = 0
UpperCamelCase__ = None
while not search_end:
UpperCamelCase__ = current_state.score()
if best_state is None or current_score > best_state.score():
UpperCamelCase__ = current_state
scores.append(_snake_case )
iterations += 1
UpperCamelCase__ = None
UpperCamelCase__ = current_state.get_neighbors()
while (
next_state is None and neighbors
): # till we do not find a neighbor that we can move to
UpperCamelCase__ = random.randint(0 , len(_snake_case ) - 1 ) # picking a random neighbor
UpperCamelCase__ = neighbors.pop(_snake_case )
UpperCamelCase__ = picked_neighbor.score() - current_score
if (
picked_neighbor.x > max_x
or picked_neighbor.x < min_x
or picked_neighbor.y > max_y
or picked_neighbor.y < min_y
):
continue # neighbor outside our bounds
if not find_max:
UpperCamelCase__ = change * -1 # in case we are finding minimum
if change > 0: # improves the solution
UpperCamelCase__ = picked_neighbor
else:
UpperCamelCase__ = (math.e) ** (
change / current_temp
) # probability generation function
if random.random() < probability: # random number within probability
UpperCamelCase__ = picked_neighbor
UpperCamelCase__ = current_temp - (current_temp * rate_of_decrease)
if current_temp < threshold_temp or next_state is None:
# temperature below threshold, or could not find a suitable neighbor
UpperCamelCase__ = True
else:
UpperCamelCase__ = next_state
if visualization:
from matplotlib import pyplot as plt
plt.plot(range(_snake_case ) , _snake_case )
plt.xlabel("Iterations" )
plt.ylabel("Function values" )
plt.show()
return best_state
if __name__ == "__main__":
def snake_case__ ( _snake_case : List[Any] , _snake_case : int ):
"""simple docstring"""
return (x**2) + (y**2)
# starting the problem with initial coordinates (12, 47)
A : Tuple = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
A : Optional[Any] = simulated_annealing(
prob, find_max=False, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
'The minimum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
F"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
# starting the problem with initial coordinates (12, 47)
A : List[str] = SearchProblem(x=12, y=47, step_size=1, function_to_optimize=test_fa)
A : int = simulated_annealing(
prob, find_max=True, max_x=100, min_x=5, max_y=50, min_y=-5, visualization=True
)
print(
'The maximum score for f(x, y) = x^2 + y^2 with the domain 100 > x > 5 '
F"and 50 > y > - 5 found via hill climbing: {local_min.score()}"
)
def snake_case__ ( _snake_case : Optional[Any] , _snake_case : Optional[Any] ):
"""simple docstring"""
return (3 * x**2) - (6 * y)
A : Any = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
A : Any = simulated_annealing(prob, find_max=False, visualization=True)
print(
'The minimum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
F"{local_min.score()}"
)
A : Optional[int] = SearchProblem(x=3, y=4, step_size=1, function_to_optimize=test_fa)
A : Any = simulated_annealing(prob, find_max=True, visualization=True)
print(
'The maximum score for f(x, y) = 3*x^2 - 6*y found via hill climbing: '
F"{local_min.score()}"
) | 304 | 1 |
"""simple docstring"""
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_DEFAULT_MEAN,
IMAGENET_DEFAULT_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, logging
lowercase__ :Any = logging.get_logger(__name__)
class snake_case ( __UpperCAmelCase ):
'''simple docstring'''
_A : str = ['pixel_values']
def __init__( self : Optional[int] , __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 / 255 , __lowercase : bool = True , __lowercase : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN , __lowercase : Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD , **__lowercase : str , ):
'''simple docstring'''
super().__init__(**__lowercase )
__UpperCAmelCase : Tuple = size if size is not None else {'''shortest_edge''': 224}
__UpperCAmelCase : Optional[Any] = get_size_dict(__lowercase , default_to_square=__lowercase )
__UpperCAmelCase : Any = crop_size if crop_size is not None else {'''height''': 224, '''width''': 224}
__UpperCAmelCase : str = get_size_dict(__lowercase , param_name='''crop_size''' )
__UpperCAmelCase : int = do_resize
__UpperCAmelCase : Optional[int] = size
__UpperCAmelCase : Dict = resample
__UpperCAmelCase : Optional[Any] = do_center_crop
__UpperCAmelCase : Tuple = crop_size
__UpperCAmelCase : Dict = do_rescale
__UpperCAmelCase : int = rescale_factor
__UpperCAmelCase : Optional[Any] = do_normalize
__UpperCAmelCase : List[str] = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
__UpperCAmelCase : Optional[Any] = image_std if image_std is not None else IMAGENET_DEFAULT_STD
def A_ ( self : Optional[int] , __lowercase : np.ndarray , __lowercase : Dict[str, int] , __lowercase : PILImageResampling = PILImageResampling.BICUBIC , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : Optional[Any] , ):
'''simple docstring'''
__UpperCAmelCase : List[Any] = get_size_dict(__lowercase , default_to_square=__lowercase )
# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
if "shortest_edge" in size:
__UpperCAmelCase : List[Any] = int((256 / 224) * size['''shortest_edge'''] )
__UpperCAmelCase : int = get_resize_output_image_size(__lowercase , size=__lowercase , default_to_square=__lowercase )
__UpperCAmelCase : Optional[int] = {'''height''': output_size[0], '''width''': output_size[1]}
if "height" not in size_dict or "width" not in size_dict:
raise ValueError(
f'''Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}''' )
return resize(
__lowercase , size=(size_dict['''height'''], size_dict['''width''']) , resample=__lowercase , data_format=__lowercase , **__lowercase )
def A_ ( self : Any , __lowercase : np.ndarray , __lowercase : Dict[str, int] , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : str , ):
'''simple docstring'''
__UpperCAmelCase : Tuple = get_size_dict(__lowercase )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size dict must have keys \'height\' and \'width\'. Got {size.keys()}''' )
return center_crop(__lowercase , size=(size['''height'''], size['''width''']) , data_format=__lowercase , **__lowercase )
def A_ ( self : Tuple , __lowercase : np.ndarray , __lowercase : Union[int, float] , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : Dict , ):
'''simple docstring'''
return rescale(__lowercase , scale=__lowercase , data_format=__lowercase , **__lowercase )
def A_ ( self : List[Any] , __lowercase : np.ndarray , __lowercase : Union[float, List[float]] , __lowercase : Union[float, List[float]] , __lowercase : Optional[Union[str, ChannelDimension]] = None , **__lowercase : Optional[Any] , ):
'''simple docstring'''
return normalize(__lowercase , mean=__lowercase , std=__lowercase , data_format=__lowercase , **__lowercase )
def A_ ( self : str , __lowercase : ImageInput , __lowercase : Optional[bool] = None , __lowercase : Optional[Dict[str, int]] = None , __lowercase : PILImageResampling = None , __lowercase : Optional[bool] = None , __lowercase : Optional[Dict[str, int]] = None , __lowercase : Optional[bool] = None , __lowercase : Optional[float] = None , __lowercase : Optional[bool] = None , __lowercase : Optional[Union[float, Iterable[float]]] = None , __lowercase : Optional[Union[float, Iterable[float]]] = None , __lowercase : Optional[TensorType] = None , __lowercase : ChannelDimension = ChannelDimension.FIRST , **__lowercase : int , ):
'''simple docstring'''
__UpperCAmelCase : Union[str, Any] = do_resize if do_resize is not None else self.do_resize
__UpperCAmelCase : Dict = resample if resample is not None else self.resample
__UpperCAmelCase : List[str] = do_center_crop if do_center_crop is not None else self.do_center_crop
__UpperCAmelCase : Union[str, Any] = do_rescale if do_rescale is not None else self.do_rescale
__UpperCAmelCase : List[Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
__UpperCAmelCase : str = do_normalize if do_normalize is not None else self.do_normalize
__UpperCAmelCase : Optional[Any] = image_mean if image_mean is not None else self.image_mean
__UpperCAmelCase : str = image_std if image_std is not None else self.image_std
__UpperCAmelCase : Union[str, Any] = size if size is not None else self.size
__UpperCAmelCase : str = get_size_dict(__lowercase , default_to_square=__lowercase )
__UpperCAmelCase : int = crop_size if crop_size is not None else self.crop_size
__UpperCAmelCase : Union[str, Any] = get_size_dict(__lowercase , param_name='''crop_size''' )
__UpperCAmelCase : 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.''' )
# All transformations expect numpy arrays.
__UpperCAmelCase : List[str] = [to_numpy_array(__lowercase ) for image in images]
if do_resize:
__UpperCAmelCase : str = [self.resize(__lowercase , __lowercase , __lowercase ) for image in images]
if do_center_crop:
__UpperCAmelCase : Any = [self.center_crop(__lowercase , __lowercase ) for image in images]
if do_rescale:
__UpperCAmelCase : Dict = [self.rescale(__lowercase , __lowercase ) for image in images]
if do_normalize:
__UpperCAmelCase : int = [self.normalize(__lowercase , __lowercase , __lowercase ) for image in images]
__UpperCAmelCase : Any = [to_channel_dimension_format(__lowercase , __lowercase ) for image in images]
__UpperCAmelCase : Any = {'''pixel_values''': images}
return BatchFeature(data=__lowercase , tensor_type=__lowercase ) | 522 |
"""simple docstring"""
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
lowercase__ :Union[str, Any] = HfApi()
lowercase__ :Optional[Any] = {}
# fmt: off
lowercase__ :Optional[int] = torch.tensor([
-0.7_515, -1.6_883, 0.2_420, 0.0_300, 0.6_347, 1.3_433, -1.1_743, -3.7_467,
1.2_342, -2.2_485, 0.4_636, 0.8_076, -0.7_991, 0.3_969, 0.8_498, 0.9_189,
-1.8_887, -3.3_522, 0.7_639, 0.2_040, 0.6_271, -2.7_148, -1.6_316, 3.0_839,
0.3_186, 0.2_721, -0.9_759, -1.2_461, 2.6_257, 1.3_557
])
lowercase__ :Optional[Any] = torch.tensor([
-2.3_639, -2.5_344, 0.0_054, -0.6_674, 1.5_990, 1.0_158, 0.3_124, -2.1_436,
1.8_795, -2.5_429, -0.1_566, -0.3_973, 1.2_490, 2.6_447, 1.2_283, -0.5_208,
-2.8_154, -3.5_119, 2.3_838, 1.2_033, 1.7_201, -2.1_256, -1.4_576, 2.7_948,
2.4_204, -0.9_752, -1.2_546, 0.8_027, 3.2_758, 3.1_365
])
lowercase__ :Optional[Any] = torch.tensor([
-0.6_531, -0.6_891, -0.3_172, -0.5_375, -0.9_140, -0.5_367, -0.1_175, -0.7_869,
-0.3_808, -0.4_513, -0.2_098, -0.0_083, 0.3_183, 0.5_140, 0.2_247, -0.1_304,
-0.1_302, -0.2_802, -0.2_084, -0.2_025, -0.4_967, -0.4_873, -0.0_861, 0.6_925,
0.0_250, 0.1_290, -0.1_543, 0.6_316, 1.0_460, 1.4_943
])
lowercase__ :List[Any] = torch.tensor([
0.0_911, 0.1_107, 0.0_182, 0.0_435, -0.0_805, -0.0_608, 0.0_381, 0.2_172,
-0.0_280, 0.1_327, -0.0_299, -0.0_255, -0.0_050, -0.1_170, -0.1_046, 0.0_309,
0.1_367, 0.1_728, -0.0_533, -0.0_748, -0.0_534, 0.1_624, 0.0_384, -0.1_805,
-0.0_707, 0.0_642, 0.0_220, -0.0_134, -0.1_333, -0.1_505
])
lowercase__ :List[Any] = torch.tensor([
0.1_321, 0.1_337, 0.0_440, 0.0_622, -0.0_591, -0.0_370, 0.0_503, 0.2_133,
-0.0_177, 0.1_415, -0.0_116, -0.0_112, 0.0_044, -0.0_980, -0.0_789, 0.0_395,
0.1_502, 0.1_785, -0.0_488, -0.0_514, -0.0_404, 0.1_539, 0.0_454, -0.1_559,
-0.0_665, 0.0_659, 0.0_383, -0.0_005, -0.1_266, -0.1_386
])
lowercase__ :Optional[int] = torch.tensor([
0.1_154, 0.1_218, 0.0_307, 0.0_526, -0.0_711, -0.0_541, 0.0_366, 0.2_078,
-0.0_267, 0.1_317, -0.0_226, -0.0_193, -0.0_014, -0.1_055, -0.0_902, 0.0_330,
0.1_391, 0.1_709, -0.0_562, -0.0_693, -0.0_560, 0.1_482, 0.0_381, -0.1_683,
-0.0_681, 0.0_661, 0.0_331, -0.0_046, -0.1_268, -0.1_431
])
lowercase__ :Optional[Any] = torch.tensor([
0.1_192, 0.1_240, 0.0_414, 0.0_606, -0.0_557, -0.0_412, 0.0_430, 0.2_042,
-0.0_200, 0.1_385, -0.0_115, -0.0_132, 0.0_017, -0.0_965, -0.0_802, 0.0_398,
0.1_433, 0.1_747, -0.0_458, -0.0_533, -0.0_407, 0.1_545, 0.0_419, -0.1_574,
-0.0_645, 0.0_626, 0.0_341, -0.0_010, -0.1_199, -0.1_390
])
lowercase__ :List[str] = torch.tensor([
0.1_075, 0.1_074, 0.0_205, 0.0_431, -0.0_774, -0.0_607, 0.0_298, 0.2_042,
-0.0_320, 0.1_267, -0.0_281, -0.0_250, -0.0_064, -0.1_091, -0.0_946, 0.0_290,
0.1_328, 0.1_650, -0.0_580, -0.0_738, -0.0_586, 0.1_440, 0.0_337, -0.1_746,
-0.0_712, 0.0_605, 0.0_250, -0.0_099, -0.1_316, -0.1_473
])
lowercase__ :str = torch.tensor([
-1.4_572, -2.0_481, -0.0_414, -0.6_005, 1.4_136, 0.5_848, 0.4_028, -2.7_330,
1.2_212, -2.1_228, 0.2_155, 0.4_039, 0.7_662, 2.0_535, 0.7_477, -0.3_243,
-2.1_758, -2.7_648, 1.6_947, 0.7_026, 1.2_338, -1.6_078, -0.8_682, 2.2_810,
1.8_574, -0.5_718, -0.5_586, -0.0_186, 2.3_415, 2.1_251])
lowercase__ :Union[str, Any] = torch.tensor([
-1.3_690, -1.9_720, -0.4_090, -0.6_966, 1.4_660, 0.9_938, -0.1_385, -2.7_324,
0.7_736, -1.8_917, 0.2_923, 0.4_293, 0.1_693, 1.4_112, 1.1_887, -0.3_181,
-2.2_160, -2.6_381, 1.3_170, 0.8_163, 0.9_240, -1.6_544, -0.6_099, 2.5_259,
1.6_430, -0.9_090, -0.9_392, -0.0_126, 2.4_268, 2.3_266
])
lowercase__ :List[Any] = torch.tensor([
-1.3_525, -1.9_628, -0.3_956, -0.6_860, 1.4_664, 1.0_014, -0.1_259, -2.7_212,
0.7_772, -1.8_811, 0.2_996, 0.4_388, 0.1_704, 1.4_029, 1.1_701, -0.3_027,
-2.2_053, -2.6_287, 1.3_350, 0.8_131, 0.9_274, -1.6_292, -0.6_098, 2.5_131,
1.6_505, -0.8_958, -0.9_298, -0.0_151, 2.4_257, 2.3_355
])
lowercase__ :Optional[Any] = torch.tensor([
-2.0_585, -2.7_897, -0.2_850, -0.8_940, 1.9_052, 0.5_702, 0.6_345, -3.8_959,
1.5_932, -3.2_319, 0.1_974, 0.0_287, 1.7_566, 2.6_543, 0.8_387, -0.5_351,
-3.2_736, -4.3_375, 2.9_029, 1.6_390, 1.4_640, -2.1_701, -1.9_013, 2.9_341,
3.4_981, -0.6_255, -1.1_644, -0.1_591, 3.7_097, 3.2_066
])
lowercase__ :Optional[int] = torch.tensor([
-2.3_139, -2.5_594, -0.0_197, -0.6_785, 1.7_001, 1.1_606, 0.3_075, -2.1_740,
1.8_071, -2.5_630, -0.0_926, -0.3_811, 1.2_116, 2.6_246, 1.2_731, -0.5_398,
-2.8_153, -3.6_140, 2.3_893, 1.3_262, 1.6_258, -2.1_856, -1.3_267, 2.8_395,
2.3_779, -1.0_623, -1.2_468, 0.8_959, 3.3_367, 3.2_243
])
lowercase__ :int = torch.tensor([
-2.0_628, -2.7_667, -0.2_089, -0.8_263, 2.0_539, 0.5_992, 0.6_495, -3.8_336,
1.6_025, -3.2_817, 0.1_721, -0.0_633, 1.7_516, 2.7_039, 0.8_100, -0.5_908,
-3.2_113, -4.4_343, 2.9_257, 1.3_632, 1.5_562, -2.1_489, -1.9_894, 3.0_560,
3.3_396, -0.7_328, -1.0_417, 0.0_383, 3.7_093, 3.2_343
])
lowercase__ :List[str] = torch.tensor([
-1.4_574, -2.0_569, -0.0_473, -0.6_117, 1.4_018, 0.5_769, 0.4_129, -2.7_344,
1.2_241, -2.1_397, 0.2_000, 0.3_937, 0.7_616, 2.0_453, 0.7_324, -0.3_391,
-2.1_746, -2.7_744, 1.6_963, 0.6_921, 1.2_187, -1.6_172, -0.8_877, 2.2_439,
1.8_471, -0.5_839, -0.5_605, -0.0_464, 2.3_250, 2.1_219
])
# fmt: on
lowercase__ :Union[str, Any] = api.list_models(filter='diffusers')
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
lowercase__ :List[Any] = '/home/patrick/google_checkpoints/' + mod.modelId.split('/')[-1]
print(f"""Started running {mod.modelId}!!!""")
if mod.modelId.startswith('CompVis'):
lowercase__ :str = UNetaDModel.from_pretrained(local_checkpoint, subfolder='unet')
else:
lowercase__ :List[str] = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
lowercase__ :Optional[int] = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
lowercase__ :List[Any] = torch.tensor([1_0] * noise.shape[0])
with torch.no_grad():
lowercase__ :int = model(noise, time_step).sample
assert torch.allclose(
logits[0, 0, 0, :3_0], results['_'.join('_'.join(mod.modelId.split('/')).split('-'))], atol=1E-3
)
print(f"""{mod.modelId} has passed successfully!!!""") | 522 | 1 |
'''simple docstring'''
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
__A : Optional[int] = [
# (stable-diffusion, HF Diffusers)
("time_embed.0.weight", "time_embedding.linear_1.weight"),
("time_embed.0.bias", "time_embedding.linear_1.bias"),
("time_embed.2.weight", "time_embedding.linear_2.weight"),
("time_embed.2.bias", "time_embedding.linear_2.bias"),
("input_blocks.0.0.weight", "conv_in.weight"),
("input_blocks.0.0.bias", "conv_in.bias"),
("out.0.weight", "conv_norm_out.weight"),
("out.0.bias", "conv_norm_out.bias"),
("out.2.weight", "conv_out.weight"),
("out.2.bias", "conv_out.bias"),
]
__A : Dict = [
# (stable-diffusion, HF Diffusers)
("in_layers.0", "norm1"),
("in_layers.2", "conv1"),
("out_layers.0", "norm2"),
("out_layers.3", "conv2"),
("emb_layers.1", "time_emb_proj"),
("skip_connection", "conv_shortcut"),
]
__A : Tuple = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
__A : Tuple = F"""down_blocks.{i}.resnets.{j}."""
__A : List[Any] = F"""input_blocks.{3*i + j + 1}.0."""
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
__A : Any = F"""down_blocks.{i}.attentions.{j}."""
__A : Union[str, Any] = F"""input_blocks.{3*i + j + 1}.1."""
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
__A : Optional[int] = F"""up_blocks.{i}.resnets.{j}."""
__A : List[Any] = F"""output_blocks.{3*i + j}.0."""
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
__A : Tuple = F"""up_blocks.{i}.attentions.{j}."""
__A : int = F"""output_blocks.{3*i + j}.1."""
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
__A : str = F"""down_blocks.{i}.downsamplers.0.conv."""
__A : List[Any] = F"""input_blocks.{3*(i+1)}.0.op."""
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
__A : Optional[Any] = F"""up_blocks.{i}.upsamplers.0."""
__A : List[Any] = F"""output_blocks.{3*i + 2}.{1 if i == 0 else 2}."""
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
__A : List[Any] = "mid_block.attentions.0."
__A : Any = "middle_block.1."
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
__A : Tuple = F"""mid_block.resnets.{j}."""
__A : int = F"""middle_block.{2*j}."""
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def lowerCAmelCase_ ( a : Any ):
a__ = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
a__ = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
a__ = v.replace(_lowerCamelCase , _lowerCamelCase )
a__ = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
a__ = v.replace(_lowerCamelCase , _lowerCamelCase )
a__ = v
a__ = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
__A : str = [
# (stable-diffusion, HF Diffusers)
("nin_shortcut", "conv_shortcut"),
("norm_out", "conv_norm_out"),
("mid.attn_1.", "mid_block.attentions.0."),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
__A : Union[str, Any] = F"""encoder.down_blocks.{i}.resnets.{j}."""
__A : Optional[Any] = F"""encoder.down.{i}.block.{j}."""
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
__A : List[Any] = F"""down_blocks.{i}.downsamplers.0."""
__A : Tuple = F"""down.{i}.downsample."""
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
__A : Any = F"""up_blocks.{i}.upsamplers.0."""
__A : Any = F"""up.{3-i}.upsample."""
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
__A : Dict = F"""decoder.up_blocks.{i}.resnets.{j}."""
__A : Union[str, Any] = F"""decoder.up.{3-i}.block.{j}."""
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
__A : Optional[Any] = F"""mid_block.resnets.{i}."""
__A : Tuple = F"""mid.block_{i+1}."""
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
__A : int = [
# (stable-diffusion, HF Diffusers)
("norm.", "group_norm."),
("q.", "query."),
("k.", "key."),
("v.", "value."),
("proj_out.", "proj_attn."),
]
def lowerCAmelCase_ ( a : Optional[int] ):
return w.reshape(*w.shape , 1 , 1 )
def lowerCAmelCase_ ( a : List[Any] ):
a__ = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
a__ = v.replace(_lowerCamelCase , _lowerCamelCase )
a__ = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
a__ = v.replace(_lowerCamelCase , _lowerCamelCase )
a__ = v
a__ = {v: vae_state_dict[k] for k, v in mapping.items()}
a__ = ["q", "k", "v", "proj_out"]
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if f'''mid.attn_1.{weight_name}.weight''' in k:
print(f'''Reshaping {k} for SD format''' )
a__ = reshape_weight_for_sd(_lowerCamelCase )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
__A : List[Any] = [
# (stable-diffusion, HF Diffusers)
("resblocks.", "text_model.encoder.layers."),
("ln_1", "layer_norm1"),
("ln_2", "layer_norm2"),
(".c_fc.", ".fc1."),
(".c_proj.", ".fc2."),
(".attn", ".self_attn"),
("ln_final.", "transformer.text_model.final_layer_norm."),
("token_embedding.weight", "transformer.text_model.embeddings.token_embedding.weight"),
("positional_embedding", "transformer.text_model.embeddings.position_embedding.weight"),
]
__A : Dict = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
__A : Tuple = re.compile('|'.join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
__A : Any = {"q": 0, "k": 1, "v": 2}
def lowerCAmelCase_ ( a : List[str] ):
a__ = {}
a__ = {}
a__ = {}
for k, v in text_enc_dict.items():
if (
k.endswith('.self_attn.q_proj.weight' )
or k.endswith('.self_attn.k_proj.weight' )
or k.endswith('.self_attn.v_proj.weight' )
):
a__ = k[: -len('.q_proj.weight' )]
a__ = k[-len('q_proj.weight' )]
if k_pre not in capture_qkv_weight:
a__ = [None, None, None]
a__ = v
continue
if (
k.endswith('.self_attn.q_proj.bias' )
or k.endswith('.self_attn.k_proj.bias' )
or k.endswith('.self_attn.v_proj.bias' )
):
a__ = k[: -len('.q_proj.bias' )]
a__ = k[-len('q_proj.bias' )]
if k_pre not in capture_qkv_bias:
a__ = [None, None, None]
a__ = v
continue
a__ = textenc_pattern.sub(lambda a : protected[re.escape(m.group(0 ) )] , _lowerCamelCase )
a__ = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing' )
a__ = textenc_pattern.sub(lambda a : protected[re.escape(m.group(0 ) )] , _lowerCamelCase )
a__ = torch.cat(_lowerCamelCase )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception('CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing' )
a__ = textenc_pattern.sub(lambda a : protected[re.escape(m.group(0 ) )] , _lowerCamelCase )
a__ = torch.cat(_lowerCamelCase )
return new_state_dict
def lowerCAmelCase_ ( a : Any ):
return text_enc_dict
if __name__ == "__main__":
__A : List[Any] = argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
)
__A : str = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
__A : List[Any] = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
__A : int = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
__A : Dict = osp.join(args.model_path, 'text_encoder', 'model.safetensors')
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
__A : List[str] = load_file(unet_path, device='cpu')
else:
__A : int = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
__A : Tuple = torch.load(unet_path, map_location='cpu')
if osp.exists(vae_path):
__A : Union[str, Any] = load_file(vae_path, device='cpu')
else:
__A : Union[str, Any] = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
__A : Union[str, Any] = torch.load(vae_path, map_location='cpu')
if osp.exists(text_enc_path):
__A : Union[str, Any] = load_file(text_enc_path, device='cpu')
else:
__A : Dict = osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
__A : Tuple = torch.load(text_enc_path, map_location='cpu')
# Convert the UNet model
__A : int = convert_unet_state_dict(unet_state_dict)
__A : List[str] = {"model.diffusion_model." + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
__A : List[str] = convert_vae_state_dict(vae_state_dict)
__A : List[str] = {"first_stage_model." + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
__A : List[str] = "text_model.encoder.layers.22.layer_norm2.bias" in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
__A : int = {"transformer." + k: v for k, v in text_enc_dict.items()}
__A : Optional[Any] = convert_text_enc_state_dict_vaa(text_enc_dict)
__A : List[Any] = {"cond_stage_model.model." + k: v for k, v in text_enc_dict.items()}
else:
__A : Dict = convert_text_enc_state_dict(text_enc_dict)
__A : Tuple = {"cond_stage_model.transformer." + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
__A : Optional[int] = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
__A : Tuple = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
__A : Dict = {"state_dict": state_dict}
torch.save(state_dict, args.checkpoint_path)
| 704 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
__A : Optional[int] = logging.get_logger(__name__)
def lowerCAmelCase_ ( a : List[Any] ):
if isinstance(a , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(a , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(a ):
return [[videos]]
raise ValueError(f'''Could not make batched video from {videos}''' )
class _UpperCamelCase ( _A ):
'''simple docstring'''
SCREAMING_SNAKE_CASE:List[str] = ['pixel_values']
def __init__( self , _a = True , _a = None , _a = PILImageResampling.BILINEAR , _a = True , _a = None , _a = True , _a = 1 / 255 , _a = True , _a = True , _a = None , _a = None , **_a , ):
"""simple docstring"""
super().__init__(**_a )
a__ = size if size is not None else {'shortest_edge': 256}
a__ = get_size_dict(_a , default_to_square=_a )
a__ = crop_size if crop_size is not None else {'height': 224, 'width': 224}
a__ = get_size_dict(_a , param_name='crop_size' )
a__ = do_resize
a__ = size
a__ = do_center_crop
a__ = crop_size
a__ = resample
a__ = do_rescale
a__ = rescale_factor
a__ = offset
a__ = do_normalize
a__ = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
a__ = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase__ ( self , _a , _a , _a = PILImageResampling.BILINEAR , _a = None , **_a , ):
"""simple docstring"""
a__ = get_size_dict(_a , default_to_square=_a )
if "shortest_edge" in size:
a__ = get_resize_output_image_size(_a , size['shortest_edge'] , default_to_square=_a )
elif "height" in size and "width" in size:
a__ = (size['height'], size['width'])
else:
raise ValueError(F'''Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}''' )
return resize(_a , size=_a , resample=_a , data_format=_a , **_a )
def lowercase__ ( self , _a , _a , _a = None , **_a , ):
"""simple docstring"""
a__ = get_size_dict(_a )
if "height" not in size or "width" not in size:
raise ValueError(F'''Size must have \'height\' and \'width\' as keys. Got {size.keys()}''' )
return center_crop(_a , size=(size['height'], size['width']) , data_format=_a , **_a )
def lowercase__ ( self , _a , _a , _a = True , _a = None , **_a , ):
"""simple docstring"""
a__ = image.astype(np.floataa )
if offset:
a__ = image - (scale / 2)
return rescale(_a , scale=_a , data_format=_a , **_a )
def lowercase__ ( self , _a , _a , _a , _a = None , **_a , ):
"""simple docstring"""
return normalize(_a , mean=_a , std=_a , data_format=_a , **_a )
def lowercase__ ( self , _a , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = ChannelDimension.FIRST , ):
"""simple docstring"""
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_center_crop and crop_size is None:
raise ValueError('Crop size must be specified if do_center_crop is True.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
if offset and not do_rescale:
raise ValueError('For offset, do_rescale must also be set to True.' )
# All transformations expect numpy arrays.
a__ = to_numpy_array(_a )
if do_resize:
a__ = self.resize(image=_a , size=_a , resample=_a )
if do_center_crop:
a__ = self.center_crop(_a , size=_a )
if do_rescale:
a__ = self.rescale(image=_a , scale=_a , offset=_a )
if do_normalize:
a__ = self.normalize(image=_a , mean=_a , std=_a )
a__ = to_channel_dimension_format(_a , _a )
return image
def lowercase__ ( self , _a , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = None , _a = ChannelDimension.FIRST , **_a , ):
"""simple docstring"""
a__ = do_resize if do_resize is not None else self.do_resize
a__ = resample if resample is not None else self.resample
a__ = do_center_crop if do_center_crop is not None else self.do_center_crop
a__ = do_rescale if do_rescale is not None else self.do_rescale
a__ = rescale_factor if rescale_factor is not None else self.rescale_factor
a__ = offset if offset is not None else self.offset
a__ = do_normalize if do_normalize is not None else self.do_normalize
a__ = image_mean if image_mean is not None else self.image_mean
a__ = image_std if image_std is not None else self.image_std
a__ = size if size is not None else self.size
a__ = get_size_dict(_a , default_to_square=_a )
a__ = crop_size if crop_size is not None else self.crop_size
a__ = get_size_dict(_a , param_name='crop_size' )
if not valid_images(_a ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
a__ = make_batched(_a )
a__ = [
[
self._preprocess_image(
image=_a , do_resize=_a , size=_a , resample=_a , do_center_crop=_a , crop_size=_a , do_rescale=_a , rescale_factor=_a , offset=_a , do_normalize=_a , image_mean=_a , image_std=_a , data_format=_a , )
for img in video
]
for video in videos
]
a__ = {'pixel_values': videos}
return BatchFeature(data=_a , tensor_type=_a )
| 126 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokenization_utils_base import PreTrainedTokenizerBase
_a : Optional[Any] = logging.get_logger(__name__)
_a : int = {
'allenai/longformer-base-4096': 'https://huggingface.co/allenai/longformer-base-4096/resolve/main/config.json',
'allenai/longformer-large-4096': 'https://huggingface.co/allenai/longformer-large-4096/resolve/main/config.json',
'allenai/longformer-large-4096-finetuned-triviaqa': (
'https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/config.json'
),
'allenai/longformer-base-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/config.json'
),
'allenai/longformer-large-4096-extra.pos.embd.only': (
'https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/config.json'
),
}
class a_ ( __a ):
A__ : int = 'longformer'
def __init__( self : Optional[int] , UpperCAmelCase__ : Union[List[int], int] = 512 , UpperCAmelCase__ : int = 2 , UpperCAmelCase__ : int = 1 , UpperCAmelCase__ : int = 0 , UpperCAmelCase__ : int = 2 , UpperCAmelCase__ : int = 30_522 , UpperCAmelCase__ : int = 768 , UpperCAmelCase__ : int = 12 , UpperCAmelCase__ : int = 12 , UpperCAmelCase__ : int = 3_072 , UpperCAmelCase__ : str = "gelu" , UpperCAmelCase__ : float = 0.1 , UpperCAmelCase__ : float = 0.1 , UpperCAmelCase__ : int = 512 , UpperCAmelCase__ : int = 2 , UpperCAmelCase__ : float = 0.02 , UpperCAmelCase__ : float = 1e-1_2 , UpperCAmelCase__ : bool = False , **UpperCAmelCase__ : Optional[Any] , ):
"""simple docstring"""
super().__init__(pad_token_id=__lowerCAmelCase , **__lowerCAmelCase )
snake_case : int = attention_window
snake_case : int = sep_token_id
snake_case : Union[str, Any] = bos_token_id
snake_case : Union[str, Any] = eos_token_id
snake_case : int = vocab_size
snake_case : int = hidden_size
snake_case : Dict = num_hidden_layers
snake_case : Dict = num_attention_heads
snake_case : Optional[int] = hidden_act
snake_case : Optional[int] = intermediate_size
snake_case : int = hidden_dropout_prob
snake_case : Tuple = attention_probs_dropout_prob
snake_case : Dict = max_position_embeddings
snake_case : Optional[Any] = type_vocab_size
snake_case : Union[str, Any] = initializer_range
snake_case : Tuple = layer_norm_eps
snake_case : Optional[Any] = onnx_export
class a_ ( __a ):
def __init__( self : Optional[int] , UpperCAmelCase__ : "PretrainedConfig" , UpperCAmelCase__ : str = "default" , UpperCAmelCase__ : "List[PatchingSpec]" = None ):
"""simple docstring"""
super().__init__(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase )
snake_case : List[str] = True
@property
def lowerCAmelCase( self : int ):
"""simple docstring"""
if self.task == "multiple-choice":
snake_case : Any = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
snake_case : List[Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''global_attention_mask''', dynamic_axis),
] )
@property
def lowerCAmelCase( self : List[str] ):
"""simple docstring"""
snake_case : int = super().outputs
if self.task == "default":
snake_case : Dict = {0: '''batch'''}
return outputs
@property
def lowerCAmelCase( self : List[Any] ):
"""simple docstring"""
return 1e-4
@property
def lowerCAmelCase( self : Union[str, Any] ):
"""simple docstring"""
return max(super().default_onnx_opset , 14 )
def lowerCAmelCase( self : str , UpperCAmelCase__ : "PreTrainedTokenizerBase" , UpperCAmelCase__ : int = -1 , UpperCAmelCase__ : int = -1 , UpperCAmelCase__ : bool = False , UpperCAmelCase__ : Optional[TensorType] = None , ):
"""simple docstring"""
snake_case : int = super().generate_dummy_inputs(
preprocessor=__lowerCAmelCase , batch_size=__lowerCAmelCase , seq_length=__lowerCAmelCase , is_pair=__lowerCAmelCase , framework=__lowerCAmelCase )
import torch
# for some reason, replacing this code by inputs["global_attention_mask"] = torch.randint(2, inputs["input_ids"].shape, dtype=torch.int64)
# makes the export fail randomly
snake_case : Dict = torch.zeros_like(inputs['''input_ids'''] )
# make every second token global
snake_case : Tuple = 1
return inputs
| 598 | '''simple docstring'''
def A_ ( _lowerCamelCase : List[Any] ):
_lowerCAmelCase = len(_lowerCamelCase )
_lowerCAmelCase = sum(_lowerCamelCase )
_lowerCAmelCase = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
_lowerCAmelCase = True
for i in range(1 , s + 1 ):
_lowerCAmelCase = False
for i in range(1 , n + 1 ):
for j in range(1 , s + 1 ):
_lowerCAmelCase = dp[i][j - 1]
if arr[i - 1] <= j:
_lowerCAmelCase = dp[i][j] or dp[i - 1][j - arr[i - 1]]
for j in range(int(s / 2 ) , -1 , -1 ):
if dp[n][j] is True:
_lowerCAmelCase = s - 2 * j
break
return diff
| 309 | 0 |
'''simple docstring'''
import warnings
from ..trainer import Trainer
from ..utils import logging
A_ = logging.get_logger(__name__)
class _snake_case ( _a ):
def __init__( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : Optional[Any]=None ,**SCREAMING_SNAKE_CASE__ : Tuple ):
warnings.warn(
"`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` "
"instead." ,SCREAMING_SNAKE_CASE__ ,)
super().__init__(args=SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
| 465 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
A_ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class _snake_case ( _a ):
_A : int = ['''pixel_values''']
def __init__( self : Optional[Any] ,SCREAMING_SNAKE_CASE__ : bool = True ,SCREAMING_SNAKE_CASE__ : Dict[str, int] = None ,SCREAMING_SNAKE_CASE__ : PILImageResampling = PILImageResampling.BICUBIC ,SCREAMING_SNAKE_CASE__ : bool = True ,SCREAMING_SNAKE_CASE__ : Dict[str, int] = None ,SCREAMING_SNAKE_CASE__ : bool = True ,SCREAMING_SNAKE_CASE__ : Union[int, float] = 1 / 255 ,SCREAMING_SNAKE_CASE__ : bool = True ,SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None ,SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None ,SCREAMING_SNAKE_CASE__ : bool = True ,**SCREAMING_SNAKE_CASE__ : Union[str, Any] ,):
super().__init__(**SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Any = size if size is not None else {"shortest_edge": 224}
SCREAMING_SNAKE_CASE:int = get_size_dict(SCREAMING_SNAKE_CASE__ ,default_to_square=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:str = crop_size if crop_size is not None else {"height": 224, "width": 224}
SCREAMING_SNAKE_CASE:str = get_size_dict(SCREAMING_SNAKE_CASE__ ,default_to_square=SCREAMING_SNAKE_CASE__ ,param_name="crop_size" )
SCREAMING_SNAKE_CASE:List[Any] = do_resize
SCREAMING_SNAKE_CASE:Optional[int] = size
SCREAMING_SNAKE_CASE:List[Any] = resample
SCREAMING_SNAKE_CASE:Any = do_center_crop
SCREAMING_SNAKE_CASE:List[Any] = crop_size
SCREAMING_SNAKE_CASE:Tuple = do_rescale
SCREAMING_SNAKE_CASE:Optional[Any] = rescale_factor
SCREAMING_SNAKE_CASE:Dict = do_normalize
SCREAMING_SNAKE_CASE:int = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
SCREAMING_SNAKE_CASE:Union[str, Any] = image_std if image_std is not None else OPENAI_CLIP_STD
SCREAMING_SNAKE_CASE:Optional[int] = do_convert_rgb
def __UpperCamelCase ( self : Optional[int] ,SCREAMING_SNAKE_CASE__ : np.ndarray ,SCREAMING_SNAKE_CASE__ : Dict[str, int] ,SCREAMING_SNAKE_CASE__ : PILImageResampling = PILImageResampling.BICUBIC ,SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None ,**SCREAMING_SNAKE_CASE__ : Any ,):
SCREAMING_SNAKE_CASE:List[Any] = get_size_dict(SCREAMING_SNAKE_CASE__ ,default_to_square=SCREAMING_SNAKE_CASE__ )
if "shortest_edge" not in size:
raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}''' )
SCREAMING_SNAKE_CASE:Dict = get_resize_output_image_size(SCREAMING_SNAKE_CASE__ ,size=size["shortest_edge"] ,default_to_square=SCREAMING_SNAKE_CASE__ )
return resize(SCREAMING_SNAKE_CASE__ ,size=SCREAMING_SNAKE_CASE__ ,resample=SCREAMING_SNAKE_CASE__ ,data_format=SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : Union[str, Any] ,SCREAMING_SNAKE_CASE__ : np.ndarray ,SCREAMING_SNAKE_CASE__ : Dict[str, int] ,SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None ,**SCREAMING_SNAKE_CASE__ : Optional[int] ,):
SCREAMING_SNAKE_CASE:List[Any] = get_size_dict(SCREAMING_SNAKE_CASE__ )
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(SCREAMING_SNAKE_CASE__ ,size=(size["height"], size["width"]) ,data_format=SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : Dict ,SCREAMING_SNAKE_CASE__ : np.ndarray ,SCREAMING_SNAKE_CASE__ : Union[int, float] ,SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None ,**SCREAMING_SNAKE_CASE__ : List[Any] ,):
return rescale(SCREAMING_SNAKE_CASE__ ,scale=SCREAMING_SNAKE_CASE__ ,data_format=SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : Any ,SCREAMING_SNAKE_CASE__ : np.ndarray ,SCREAMING_SNAKE_CASE__ : Union[float, List[float]] ,SCREAMING_SNAKE_CASE__ : Union[float, List[float]] ,SCREAMING_SNAKE_CASE__ : Optional[Union[str, ChannelDimension]] = None ,**SCREAMING_SNAKE_CASE__ : Tuple ,):
return normalize(SCREAMING_SNAKE_CASE__ ,mean=SCREAMING_SNAKE_CASE__ ,std=SCREAMING_SNAKE_CASE__ ,data_format=SCREAMING_SNAKE_CASE__ ,**SCREAMING_SNAKE_CASE__ )
def __UpperCamelCase ( self : int ,SCREAMING_SNAKE_CASE__ : ImageInput ,SCREAMING_SNAKE_CASE__ : bool = None ,SCREAMING_SNAKE_CASE__ : Dict[str, int] = None ,SCREAMING_SNAKE_CASE__ : PILImageResampling = None ,SCREAMING_SNAKE_CASE__ : bool = None ,SCREAMING_SNAKE_CASE__ : int = None ,SCREAMING_SNAKE_CASE__ : bool = None ,SCREAMING_SNAKE_CASE__ : float = None ,SCREAMING_SNAKE_CASE__ : bool = None ,SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None ,SCREAMING_SNAKE_CASE__ : Optional[Union[float, List[float]]] = None ,SCREAMING_SNAKE_CASE__ : bool = None ,SCREAMING_SNAKE_CASE__ : Optional[Union[str, TensorType]] = None ,SCREAMING_SNAKE_CASE__ : Optional[ChannelDimension] = ChannelDimension.FIRST ,**SCREAMING_SNAKE_CASE__ : List[str] ,):
SCREAMING_SNAKE_CASE:Any = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE:Optional[int] = size if size is not None else self.size
SCREAMING_SNAKE_CASE:Dict = get_size_dict(SCREAMING_SNAKE_CASE__ ,param_name="size" ,default_to_square=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:Optional[int] = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE:Dict = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE:Optional[int] = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE:str = get_size_dict(SCREAMING_SNAKE_CASE__ ,param_name="crop_size" ,default_to_square=SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE:int = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE:Tuple = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE:List[Any] = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE:List[str] = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE:Any = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE:str = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
SCREAMING_SNAKE_CASE:Any = make_list_of_images(SCREAMING_SNAKE_CASE__ )
if not valid_images(SCREAMING_SNAKE_CASE__ ):
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:
SCREAMING_SNAKE_CASE:List[Any] = [convert_to_rgb(SCREAMING_SNAKE_CASE__ ) for image in images]
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE:Optional[int] = [to_numpy_array(SCREAMING_SNAKE_CASE__ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE:Union[str, Any] = [self.resize(image=SCREAMING_SNAKE_CASE__ ,size=SCREAMING_SNAKE_CASE__ ,resample=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE:Union[str, Any] = [self.center_crop(image=SCREAMING_SNAKE_CASE__ ,size=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE:Any = [self.rescale(image=SCREAMING_SNAKE_CASE__ ,scale=SCREAMING_SNAKE_CASE__ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE:List[str] = [self.normalize(image=SCREAMING_SNAKE_CASE__ ,mean=SCREAMING_SNAKE_CASE__ ,std=SCREAMING_SNAKE_CASE__ ) for image in images]
SCREAMING_SNAKE_CASE:List[Any] = [to_channel_dimension_format(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__ ) for image in images]
SCREAMING_SNAKE_CASE:Optional[int] = {"pixel_values": images}
return BatchFeature(data=SCREAMING_SNAKE_CASE__ ,tensor_type=SCREAMING_SNAKE_CASE__ )
| 465 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
lowercase_ = {"configuration_speech_encoder_decoder": ["SpeechEncoderDecoderConfig"]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["SpeechEncoderDecoderModel"]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase_ = ["FlaxSpeechEncoderDecoderModel"]
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
lowercase_ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 11 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_lowerCAmelCase = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""RobertaPreLayerNormConfig""",
"""RobertaPreLayerNormOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
"""ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaPreLayerNormForCausalLM""",
"""RobertaPreLayerNormForMaskedLM""",
"""RobertaPreLayerNormForMultipleChoice""",
"""RobertaPreLayerNormForQuestionAnswering""",
"""RobertaPreLayerNormForSequenceClassification""",
"""RobertaPreLayerNormForTokenClassification""",
"""RobertaPreLayerNormModel""",
"""RobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
"""TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaPreLayerNormForCausalLM""",
"""TFRobertaPreLayerNormForMaskedLM""",
"""TFRobertaPreLayerNormForMultipleChoice""",
"""TFRobertaPreLayerNormForQuestionAnswering""",
"""TFRobertaPreLayerNormForSequenceClassification""",
"""TFRobertaPreLayerNormForTokenClassification""",
"""TFRobertaPreLayerNormMainLayer""",
"""TFRobertaPreLayerNormModel""",
"""TFRobertaPreLayerNormPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
"""FlaxRobertaPreLayerNormForCausalLM""",
"""FlaxRobertaPreLayerNormForMaskedLM""",
"""FlaxRobertaPreLayerNormForMultipleChoice""",
"""FlaxRobertaPreLayerNormForQuestionAnswering""",
"""FlaxRobertaPreLayerNormForSequenceClassification""",
"""FlaxRobertaPreLayerNormForTokenClassification""",
"""FlaxRobertaPreLayerNormModel""",
"""FlaxRobertaPreLayerNormPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
RobertaPreLayerNormConfig,
RobertaPreLayerNormOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta_prelayernorm import (
ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaPreLayerNormForCausalLM,
RobertaPreLayerNormForMaskedLM,
RobertaPreLayerNormForMultipleChoice,
RobertaPreLayerNormForQuestionAnswering,
RobertaPreLayerNormForSequenceClassification,
RobertaPreLayerNormForTokenClassification,
RobertaPreLayerNormModel,
RobertaPreLayerNormPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta_prelayernorm import (
TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaPreLayerNormForCausalLM,
TFRobertaPreLayerNormForMaskedLM,
TFRobertaPreLayerNormForMultipleChoice,
TFRobertaPreLayerNormForQuestionAnswering,
TFRobertaPreLayerNormForSequenceClassification,
TFRobertaPreLayerNormForTokenClassification,
TFRobertaPreLayerNormMainLayer,
TFRobertaPreLayerNormModel,
TFRobertaPreLayerNormPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta_prelayernorm import (
FlaxRobertaPreLayerNormForCausalLM,
FlaxRobertaPreLayerNormForMaskedLM,
FlaxRobertaPreLayerNormForMultipleChoice,
FlaxRobertaPreLayerNormForQuestionAnswering,
FlaxRobertaPreLayerNormForSequenceClassification,
FlaxRobertaPreLayerNormForTokenClassification,
FlaxRobertaPreLayerNormModel,
FlaxRobertaPreLayerNormPreTrainedModel,
)
else:
import sys
_lowerCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 569 | 0 |
"""simple docstring"""
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"
from pathlib import Path
import json
import tempfile
from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES
A__ : Optional[int] = """tiny-wmt19-en-ru"""
# Build
# borrowed from a test
A__ : Any = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
A__ : List[str] = dict(zip(vocab, range(len(vocab))))
A__ : str = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
with tempfile.TemporaryDirectory() as tmpdirname:
A__ : str = Path(tmpdirname)
A__ : List[str] = build_dir / VOCAB_FILES_NAMES["""src_vocab_file"""]
A__ : Any = build_dir / VOCAB_FILES_NAMES["""tgt_vocab_file"""]
A__ : Optional[int] = build_dir / VOCAB_FILES_NAMES["""merges_file"""]
with open(src_vocab_file, """w""") as fp:
fp.write(json.dumps(vocab_tokens))
with open(tgt_vocab_file, """w""") as fp:
fp.write(json.dumps(vocab_tokens))
with open(merges_file, """w""") as fp:
fp.write("""\n""".join(merges))
A__ : Optional[Any] = FSMTTokenizer(
langs=["""en""", """ru"""],
src_vocab_size=len(vocab),
tgt_vocab_size=len(vocab),
src_vocab_file=src_vocab_file,
tgt_vocab_file=tgt_vocab_file,
merges_file=merges_file,
)
A__ : Tuple = FSMTConfig(
langs=["""ru""", """en"""],
src_vocab_size=1_000,
tgt_vocab_size=1_000,
d_model=4,
encoder_layers=1,
decoder_layers=1,
encoder_ffn_dim=4,
decoder_ffn_dim=4,
encoder_attention_heads=1,
decoder_attention_heads=1,
)
A__ : Tuple = FSMTForConditionalGeneration(config)
print(f"""num of params {tiny_model.num_parameters()}""")
# Test
A__ : Dict = tokenizer(["""Making tiny model"""], return_tensors="""pt""")
A__ : str = tiny_model(**batch)
print("""test output:""", len(outputs.logits[0]))
# Save
tiny_model.half() # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)
print(f"""Generated {mname_tiny}""")
# Upload
# transformers-cli upload tiny-wmt19-en-ru
| 660 |
"""simple docstring"""
from math import isqrt
def a__ ( lowerCAmelCase : int ):
'''simple docstring'''
UpperCAmelCase__ : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , lowerCAmelCase , lowerCAmelCase ):
UpperCAmelCase__ : List[Any] = False
return [i for i in range(2 , lowerCAmelCase ) if is_prime[i]]
def a__ ( lowerCAmelCase : int = 10**8 ):
'''simple docstring'''
UpperCAmelCase__ : Dict = calculate_prime_numbers(max_number // 2 )
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Optional[int] = 0
UpperCAmelCase__ : Tuple = len(lowerCAmelCase ) - 1
while left <= right:
while prime_numbers[left] * prime_numbers[right] >= max_number:
right -= 1
semiprimes_count += right - left + 1
left += 1
return semiprimes_count
if __name__ == "__main__":
print(f"""{solution() = }""")
| 660 | 1 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
_UpperCamelCase = (
'This metric will be removed from the library soon, metrics should be handled with the 🤗 Evaluate '
'library. You can have a look at this example script for pointers: '
'https://github.com/huggingface/transformers/blob/main/examples/pytorch/text-classification/run_glue.py'
)
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> str:
warnings.warn(UpperCamelCase__ ,UpperCamelCase__ )
requires_backends(UpperCamelCase__ ,'sklearn' )
return (preds == labels).mean()
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> Optional[Any]:
warnings.warn(UpperCamelCase__ ,UpperCamelCase__ )
requires_backends(UpperCamelCase__ ,'sklearn' )
__lowerCamelCase : str = simple_accuracy(UpperCamelCase__ ,UpperCamelCase__ )
__lowerCamelCase : List[Any] = fa_score(y_true=UpperCamelCase__ ,y_pred=UpperCamelCase__ )
return {
"acc": acc,
"f1": fa,
"acc_and_f1": (acc + fa) / 2,
}
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> Dict:
warnings.warn(UpperCamelCase__ ,UpperCamelCase__ )
requires_backends(UpperCamelCase__ ,'sklearn' )
__lowerCamelCase : str = pearsonr(UpperCamelCase__ ,UpperCamelCase__ )[0]
__lowerCamelCase : List[Any] = spearmanr(UpperCamelCase__ ,UpperCamelCase__ )[0]
return {
"pearson": pearson_corr,
"spearmanr": spearman_corr,
"corr": (pearson_corr + spearman_corr) / 2,
}
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ) -> Dict:
warnings.warn(UpperCamelCase__ ,UpperCamelCase__ )
requires_backends(UpperCamelCase__ ,'sklearn' )
assert len(UpperCamelCase__ ) == len(UpperCamelCase__ ), F'Predictions and labels have mismatched lengths {len(UpperCamelCase__ )} and {len(UpperCamelCase__ )}'
if task_name == "cola":
return {"mcc": matthews_corrcoef(UpperCamelCase__ ,UpperCamelCase__ )}
elif task_name == "sst-2":
return {"acc": simple_accuracy(UpperCamelCase__ ,UpperCamelCase__ )}
elif task_name == "mrpc":
return acc_and_fa(UpperCamelCase__ ,UpperCamelCase__ )
elif task_name == "sts-b":
return pearson_and_spearman(UpperCamelCase__ ,UpperCamelCase__ )
elif task_name == "qqp":
return acc_and_fa(UpperCamelCase__ ,UpperCamelCase__ )
elif task_name == "mnli":
return {"mnli/acc": simple_accuracy(UpperCamelCase__ ,UpperCamelCase__ )}
elif task_name == "mnli-mm":
return {"mnli-mm/acc": simple_accuracy(UpperCamelCase__ ,UpperCamelCase__ )}
elif task_name == "qnli":
return {"acc": simple_accuracy(UpperCamelCase__ ,UpperCamelCase__ )}
elif task_name == "rte":
return {"acc": simple_accuracy(UpperCamelCase__ ,UpperCamelCase__ )}
elif task_name == "wnli":
return {"acc": simple_accuracy(UpperCamelCase__ ,UpperCamelCase__ )}
elif task_name == "hans":
return {"acc": simple_accuracy(UpperCamelCase__ ,UpperCamelCase__ )}
else:
raise KeyError(UpperCamelCase__ )
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ) -> Any:
warnings.warn(UpperCamelCase__ ,UpperCamelCase__ )
requires_backends(UpperCamelCase__ ,'sklearn' )
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
raise ValueError(F'Predictions and labels have mismatched lengths {len(UpperCamelCase__ )} and {len(UpperCamelCase__ )}' )
if task_name == "xnli":
return {"acc": simple_accuracy(UpperCamelCase__ ,UpperCamelCase__ )}
else:
raise KeyError(UpperCamelCase__ )
| 459 |
"""simple docstring"""
from PIL import Image
def lowerCAmelCase__ ( UpperCamelCase__ , UpperCamelCase__ ):
'''simple docstring'''
def brightness(UpperCamelCase__ ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(UpperCamelCase__ )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
_snake_case = change_brightness(img, 100)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 389 | 0 |
"""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
__snake_case = logging.getLogger(__name__)
def __lowerCAmelCase ( lowercase : Any , lowercase : Tuple ) -> List[str]:
"""simple docstring"""
if os.path.exists(lowercase ):
if os.path.exists(os.path.join(lowercase , "config.json" ) ) and os.path.isfile(
os.path.join(lowercase , "config.json" ) ):
os.remove(os.path.join(lowercase , "config.json" ) )
if os.path.exists(os.path.join(lowercase , "pytorch_model.bin" ) ) and os.path.isfile(
os.path.join(lowercase , "pytorch_model.bin" ) ):
os.remove(os.path.join(lowercase , "pytorch_model.bin" ) )
else:
os.makedirs(lowercase )
model.save_pretrained(lowercase )
def __lowerCAmelCase ( lowercase : str , lowercase : Any=False ) -> Dict:
"""simple docstring"""
snake_case : Union[str, Any] = 2
if unlogit:
snake_case : Dict = torch.pow(lowercase , lowercase )
snake_case : List[Any] = p * torch.log(lowercase )
snake_case : Optional[Any] = 0
return -plogp.sum(dim=-1 )
def __lowerCAmelCase ( lowercase : List[str] ) -> str:
"""simple docstring"""
logger.info("lv, h >\t" + "\t".join(F'{x + 1}' for x in range(len(lowercase ) ) ) )
for row in range(len(lowercase ) ):
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 ( lowercase : List[Any] , lowercase : List[Any] , lowercase : List[str] , lowercase : List[str]=True , lowercase : List[Any]=True , lowercase : Union[str, Any]=None , lowercase : Optional[int]=False ) -> List[str]:
"""simple docstring"""
snake_case : Optional[Any] = model.config.num_hidden_layers, model.config.num_attention_heads
snake_case : int = torch.zeros(lowercase , lowercase ).to(args.device )
snake_case : List[str] = torch.zeros(lowercase , lowercase ).to(args.device )
if head_mask is None:
snake_case : str = torch.ones(lowercase , lowercase ).to(args.device )
head_mask.requires_grad_(requires_grad=lowercase )
# If actually pruned attention multi-head, set head mask to None to avoid shape mismatch
if actually_pruned:
snake_case : Optional[int] = None
snake_case : Optional[int] = 0.0
snake_case : Any = 0.0
for step, inputs in enumerate(tqdm(lowercase , desc="Iteration" , disable=args.local_rank not in [-1, 0] ) ):
snake_case : List[str] = tuple(t.to(args.device ) for t in inputs )
(snake_case) : List[Any] = inputs
# Do a forward pass (not with torch.no_grad() since we need gradients for importance score - see below)
snake_case : Tuple = model(lowercase , labels=lowercase , head_mask=lowercase )
# (loss), lm_logits, presents, (all hidden_states), (attentions)
snake_case : int = (
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(lowercase ):
snake_case : str = entropy(attn.detach() , lowercase )
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(lowercase ).float().detach().sum().data
# Normalize
attn_entropy /= tot_tokens
head_importance /= tot_tokens
# Layerwise importance normalization
if not args.dont_normalize_importance_by_layer:
snake_case : Optional[int] = 2
snake_case : Dict = torch.pow(torch.pow(lowercase , lowercase ).sum(-1 ) , 1 / exponent )
head_importance /= norm_by_layer.unsqueeze(-1 ) + 1e-2_0
if not args.dont_normalize_global_importance:
snake_case : List[Any] = (head_importance - head_importance.min()) / (head_importance.max() - head_importance.min())
# Print matrices
if compute_entropy:
logger.info("Attention entropies" )
print_ad_tensor(lowercase )
if compute_importance:
logger.info("Head importance scores" )
print_ad_tensor(lowercase )
logger.info("Head ranked by importance scores" )
snake_case : str = torch.zeros(head_importance.numel() , dtype=torch.long , device=args.device )
snake_case : Dict = torch.arange(
head_importance.numel() , device=args.device )
snake_case : List[Any] = head_ranks.view_as(lowercase )
print_ad_tensor(lowercase )
return attn_entropy, head_importance, total_loss
def __lowerCAmelCase ( lowercase : List[str] , lowercase : str , lowercase : str ) -> List[Any]:
"""simple docstring"""
snake_case : List[str] = compute_heads_importance(lowercase , lowercase , lowercase , compute_entropy=lowercase )
snake_case : Tuple = 1 / loss # instead of downsteam score use the LM loss
logger.info("Pruning: original score: %f, threshold: %f" , lowercase , original_score * args.masking_threshold )
snake_case : Optional[int] = torch.ones_like(lowercase )
snake_case : List[Any] = max(1 , int(new_head_mask.numel() * args.masking_amount ) )
snake_case : int = original_score
while current_score >= original_score * args.masking_threshold:
snake_case : str = new_head_mask.clone().detach() # save current head mask
# heads from least important to most - keep only not-masked heads
snake_case : str = float("Inf" )
snake_case : Optional[Any] = head_importance.view(-1 ).sort()[1]
if len(lowercase ) <= num_to_mask:
print("BREAK BY num_to_mask" )
break
# mask heads
snake_case : Optional[Any] = current_heads_to_mask[:num_to_mask]
logger.info("Heads to mask: %s" , str(current_heads_to_mask.tolist() ) )
snake_case : str = new_head_mask.view(-1 )
snake_case : List[str] = 0.0
snake_case : Any = new_head_mask.view_as(lowercase )
snake_case : List[str] = new_head_mask.clone().detach()
print_ad_tensor(lowercase )
# Compute metric and head importance again
snake_case : int = compute_heads_importance(
lowercase , lowercase , lowercase , compute_entropy=lowercase , head_mask=lowercase )
snake_case : Tuple = 1 / loss
logger.info(
"Masking: current score: %f, remaining heads %d (%.1f percents)" , lowercase , new_head_mask.sum() , new_head_mask.sum() / new_head_mask.numel() * 100 , )
logger.info("Final head mask" )
print_ad_tensor(lowercase )
np.save(os.path.join(args.output_dir , "head_mask.npy" ) , head_mask.detach().cpu().numpy() )
return head_mask
def __lowerCAmelCase ( lowercase : Optional[Any] , lowercase : List[Any] , lowercase : Optional[int] , lowercase : Any ) -> List[str]:
"""simple docstring"""
snake_case : str = datetime.now()
snake_case : Optional[int] = compute_heads_importance(
lowercase , lowercase , lowercase , compute_entropy=lowercase , compute_importance=lowercase , head_mask=lowercase )
snake_case : List[Any] = 1 / loss
snake_case : int = datetime.now() - before_time
snake_case : Dict = sum(p.numel() for p in model.parameters() )
snake_case : Any = {
layer: (1 - head_mask[layer].long()).nonzero().squeeze().tolist() for layer in range(len(lowercase ) )
}
for k, v in heads_to_prune.items():
if isinstance(lowercase , lowercase ):
snake_case : int = [
v,
]
assert sum(len(lowercase ) for h in heads_to_prune.values() ) == (1 - head_mask.long()).sum().item()
model.prune_heads(lowercase )
snake_case : str = sum(p.numel() for p in model.parameters() )
snake_case : List[str] = datetime.now()
snake_case : List[Any] = compute_heads_importance(
lowercase , lowercase , lowercase , compute_entropy=lowercase , compute_importance=lowercase , head_mask=lowercase , actually_pruned=lowercase , )
snake_case : Union[str, Any] = 1 / loss
snake_case : str = datetime.now() - before_time
logger.info(
"Pruning: original num of params: %.2e, after pruning %.2e (%.1f percents)" , lowercase , lowercase , pruned_num_params / original_num_params * 100 , )
logger.info("Pruning: score with masking: %f score with pruning: %f" , lowercase , lowercase )
logger.info("Pruning: speed ratio (original timing / new timing): %f percents" , original_time / new_time * 100 )
save_model(lowercase , args.output_dir )
def __lowerCAmelCase ( ) -> List[Any]:
"""simple docstring"""
snake_case : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--data_dir" , default=lowercase , type=lowercase , required=lowercase , 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=lowercase , type=lowercase , required=lowercase , help="Path to pretrained model or model identifier from huggingface.co/models" , )
parser.add_argument(
"--output_dir" , default=lowercase , type=lowercase , required=lowercase , help="The output directory where the model predictions and checkpoints will be written." , )
# Other parameters
parser.add_argument(
"--config_name" , default="" , type=lowercase , help="Pretrained config name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--tokenizer_name" , default="" , type=lowercase , help="Pretrained tokenizer name or path if not the same as model_name_or_path" , )
parser.add_argument(
"--cache_dir" , default=lowercase , type=lowercase , help="Where do you want to store the pre-trained models downloaded from s3" , )
parser.add_argument(
"--data_subset" , type=lowercase , 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=lowercase , help="masking threshold in term of metrics (stop masking when metric < threshold * original metric value)." , )
parser.add_argument(
"--masking_amount" , default=0.1 , type=lowercase , help="Amount to heads to masking at each masking step." )
parser.add_argument("--metric_name" , default="acc" , type=lowercase , help="Metric to use for head masking." )
parser.add_argument(
"--max_seq_length" , default=128 , type=lowercase , 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=lowercase , help="Batch size." )
parser.add_argument("--seed" , type=lowercase , default=42 )
parser.add_argument("--local_rank" , type=lowercase , 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=lowercase , default="" , help="Can be used for distant debugging." )
parser.add_argument("--server_port" , type=lowercase , default="" , help="Can be used for distant debugging." )
snake_case : str = 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=lowercase )
ptvsd.wait_for_attach()
# Setup devices and distributed training
if args.local_rank == -1 or args.no_cuda:
snake_case : List[str] = torch.device("cuda" if torch.cuda.is_available() and not args.no_cuda else "cpu" )
snake_case : int = 0 if args.no_cuda else torch.cuda.device_count()
else:
torch.cuda.set_device(args.local_rank )
snake_case : Union[str, Any] = torch.device("cuda" , args.local_rank )
snake_case : List[str] = 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 ) ) )
snake_case : Optional[Any] = GPTaLMHeadModel.from_pretrained(args.model_name_or_path )
# Distributed and parallel training
model.to(args.device )
if args.local_rank != -1:
snake_case : str = nn.parallel.DistributedDataParallel(
lowercase , device_ids=[args.local_rank] , output_device=args.local_rank , find_unused_parameters=lowercase )
elif args.n_gpu > 1:
snake_case : int = nn.DataParallel(lowercase )
# Print/save training arguments
os.makedirs(args.output_dir , exist_ok=lowercase )
torch.save(lowercase , os.path.join(args.output_dir , "run_args.bin" ) )
logger.info("Training/evaluation parameters %s" , lowercase )
# Prepare dataset
snake_case : List[str] = np.concatenate(
[
np.loadtxt(args.data_dir , dtype=np.intaa ),
] )
snake_case : Optional[int] = (torch.from_numpy(lowercase ),)
snake_case : Union[str, Any] = TensorDataset(*lowercase )
snake_case : Optional[int] = RandomSampler(lowercase )
snake_case : Optional[int] = DataLoader(lowercase , sampler=lowercase , batch_size=args.batch_size )
# Compute head entropy and importance score
compute_heads_importance(lowercase , lowercase , lowercase )
# 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:
snake_case : Any = mask_heads(lowercase , lowercase , lowercase )
prune_heads(lowercase , lowercase , lowercase , lowercase )
if __name__ == "__main__":
main()
| 720 |
"""simple docstring"""
# 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 = subprocess.check_output("""git merge-base main HEAD""".split()).decode("""utf-8""")
__snake_case = (
subprocess.check_output(F'''git diff --diff-filter=d --name-only {fork_point_sha}'''.split()).decode("""utf-8""").split()
)
__snake_case = """|""".join(sys.argv[1:])
__snake_case = re.compile(RF'''^({joined_dirs}).*?\.py$''')
__snake_case = [x for x in modified_files if regex.match(x)]
print(""" """.join(relevant_modified_files), end="""""")
| 117 | 0 |
"""simple docstring"""
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
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 torch import nn
from transformers import (
MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
TimesformerForVideoClassification,
TimesformerModel,
)
from transformers.models.timesformer.modeling_timesformer import TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from transformers import VideoMAEImageProcessor
class _UpperCamelCase :
def __init__(self , lowerCamelCase__ , lowerCamelCase__=1_3 , lowerCamelCase__=1_0 , lowerCamelCase__=3 , lowerCamelCase__=2 , lowerCamelCase__=2 , lowerCamelCase__=True , lowerCamelCase__=True , lowerCamelCase__=3_2 , lowerCamelCase__=5 , lowerCamelCase__=4 , lowerCamelCase__=3_7 , lowerCamelCase__="gelu" , lowerCamelCase__=0.1 , lowerCamelCase__=0.1 , lowerCamelCase__=1_0 , lowerCamelCase__=0.0_2 , lowerCamelCase__="divided_space_time" , lowerCamelCase__=None , ):
"""simple docstring"""
A__ = parent
A__ = batch_size
A__ = image_size
A__ = num_channels
A__ = patch_size
A__ = num_frames
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__ = attention_type
A__ = initializer_range
A__ = scope
A__ = num_labels
# in TimeSformer, the number of spatial tokens equals num_frames * num_patches per frame + 1 CLS token
A__ = (image_size // patch_size) ** 2
A__ = (num_frames) * self.num_patches_per_frame + 1
def A (self ):
"""simple docstring"""
A__ = floats_tensor(
[self.batch_size, self.num_frames, 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 A (self ):
"""simple docstring"""
A__ = TimesformerConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , num_frames=self.num_frames , 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 , initializer_range=self.initializer_range , attention_type=self.attention_type , )
A__ = self.num_labels
return config
def A (self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
A__ = TimesformerModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
A__ = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A (self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""simple docstring"""
A__ = TimesformerForVideoClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
A__ = model(lowerCamelCase__ )
# verify the logits shape
A__ = torch.Size((self.batch_size, self.num_labels) )
self.parent.assertEqual(result.logits.shape , lowerCamelCase__ )
def A (self ):
"""simple docstring"""
A__ = self.prepare_config_and_inputs()
A__ ,A__ ,A__ = config_and_inputs
A__ = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( __snake_case , __snake_case , unittest.TestCase):
__lowerCamelCase = (TimesformerModel, TimesformerForVideoClassification) if is_torch_available() else ()
__lowerCamelCase = (
{"feature-extraction": TimesformerModel, "video-classification": TimesformerForVideoClassification}
if is_torch_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def A (self ):
"""simple docstring"""
A__ = TimesformerModelTester(self )
A__ = ConfigTester(
self , config_class=lowerCamelCase__ , has_text_modality=lowerCamelCase__ , hidden_size=3_7 )
def A (self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__=False ):
"""simple docstring"""
A__ = copy.deepcopy(lowerCamelCase__ )
if return_labels:
if model_class in get_values(lowerCamelCase__ ):
A__ = torch.zeros(
self.model_tester.batch_size , dtype=torch.long , device=lowerCamelCase__ )
return inputs_dict
def A (self ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="""TimeSformer does not use inputs_embeds""" )
def A (self ):
"""simple docstring"""
pass
def A (self ):
"""simple docstring"""
A__ ,A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ , nn.Linear ) )
def A (self ):
"""simple docstring"""
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 A (self ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def A (self ):
"""simple docstring"""
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_video_classification(*lowerCamelCase__ )
@slow
def A (self ):
"""simple docstring"""
for model_name in TIMESFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = TimesformerModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def A (self ):
"""simple docstring"""
if not self.has_attentions:
pass
else:
A__ ,A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = True
for model_class in self.all_model_classes:
A__ = self.model_tester.seq_length
A__ = self.model_tester.num_frames
A__ = True
A__ = False
A__ = True
A__ = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
A__ = outputs.attentions
self.assertEqual(len(lowerCamelCase__ ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
A__ = True
A__ = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
A__ = outputs.attentions
self.assertEqual(len(lowerCamelCase__ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
A__ = len(lowerCamelCase__ )
# Check attention is always last and order is fine
A__ = True
A__ = True
A__ = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
self.assertEqual(out_len + 1 , len(lowerCamelCase__ ) )
A__ = outputs.attentions
self.assertEqual(len(lowerCamelCase__ ) , self.model_tester.num_hidden_layers )
# attentions has shape (batch_size x num_frames) x num_heads x (num_patches per frame + 1) x (num_patches per frame + 1)
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len // num_frames + 1, seq_len // num_frames + 1] , )
def A (self ):
"""simple docstring"""
def check_hidden_states_output(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
A__ = model_class(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowerCamelCase__ , lowerCamelCase__ ) )
A__ = outputs.hidden_states
A__ = self.model_tester.num_hidden_layers + 1
self.assertEqual(len(lowerCamelCase__ ) , lowerCamelCase__ )
A__ = self.model_tester.seq_length
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
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 _SCREAMING_SNAKE_CASE ( ):
A__ = hf_hub_download(
repo_id="""hf-internal-testing/spaghetti-video""" , filename="""eating_spaghetti.npy""" , repo_type="""dataset""" )
A__ = np.load(UpperCamelCase )
return list(UpperCamelCase )
@require_torch
@require_vision
class _UpperCamelCase ( unittest.TestCase):
@cached_property
def A (self ):
"""simple docstring"""
# logits were tested with a different mean and std, so we use the same here
return (
VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] , image_std=[0.5, 0.5, 0.5] )
if is_vision_available()
else None
)
@slow
def A (self ):
"""simple docstring"""
A__ = TimesformerForVideoClassification.from_pretrained("""facebook/timesformer-base-finetuned-k400""" ).to(
lowerCamelCase__ )
A__ = self.default_image_processor
A__ = prepare_video()
A__ = image_processor(video[:8] , return_tensors="""pt""" ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
A__ = model(**lowerCamelCase__ )
# verify the logits
A__ = torch.Size((1, 4_0_0) )
self.assertEqual(outputs.logits.shape , lowerCamelCase__ )
A__ = torch.tensor([-0.3_0_1_6, -0.7_7_1_3, -0.4_2_0_5] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowerCamelCase__ , atol=1E-4 ) )
| 574 |
"""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 _SCREAMING_SNAKE_CASE ( UpperCamelCase : str , UpperCamelCase : str , UpperCamelCase : str ):
def get_masked_lm_array(UpperCamelCase : str ):
A__ = F"""masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
A__ = tf.train.load_variable(UpperCamelCase , UpperCamelCase )
if "kernel" in name:
A__ = array.transpose()
return torch.from_numpy(UpperCamelCase )
def get_encoder_array(UpperCamelCase : str ):
A__ = F"""encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
A__ = tf.train.load_variable(UpperCamelCase , UpperCamelCase )
if "kernel" in name:
A__ = array.transpose()
return torch.from_numpy(UpperCamelCase )
def get_encoder_layer_array(UpperCamelCase : int , UpperCamelCase : str ):
A__ = F"""encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
A__ = tf.train.load_variable(UpperCamelCase , UpperCamelCase )
if "kernel" in name:
A__ = array.transpose()
return torch.from_numpy(UpperCamelCase )
def get_encoder_attention_layer_array(UpperCamelCase : int , UpperCamelCase : str , UpperCamelCase : int ):
A__ = F"""encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE"""
A__ = tf.train.load_variable(UpperCamelCase , UpperCamelCase )
A__ = array.reshape(UpperCamelCase )
if "kernel" in name:
A__ = array.transpose()
return torch.from_numpy(UpperCamelCase )
print(F"""Loading model based on config from {config_path}...""" )
A__ = BertConfig.from_json_file(UpperCamelCase )
A__ = BertForMaskedLM(UpperCamelCase )
# Layers
for layer_index in range(0 , config.num_hidden_layers ):
A__ = model.bert.encoder.layer[layer_index]
# Self-attention
A__ = layer.attention.self
A__ = get_encoder_attention_layer_array(
UpperCamelCase , """_query_dense/kernel""" , self_attn.query.weight.data.shape )
A__ = get_encoder_attention_layer_array(
UpperCamelCase , """_query_dense/bias""" , self_attn.query.bias.data.shape )
A__ = get_encoder_attention_layer_array(
UpperCamelCase , """_key_dense/kernel""" , self_attn.key.weight.data.shape )
A__ = get_encoder_attention_layer_array(
UpperCamelCase , """_key_dense/bias""" , self_attn.key.bias.data.shape )
A__ = get_encoder_attention_layer_array(
UpperCamelCase , """_value_dense/kernel""" , self_attn.value.weight.data.shape )
A__ = get_encoder_attention_layer_array(
UpperCamelCase , """_value_dense/bias""" , self_attn.value.bias.data.shape )
# Self-attention Output
A__ = layer.attention.output
A__ = get_encoder_attention_layer_array(
UpperCamelCase , """_output_dense/kernel""" , self_output.dense.weight.data.shape )
A__ = get_encoder_attention_layer_array(
UpperCamelCase , """_output_dense/bias""" , self_output.dense.bias.data.shape )
A__ = get_encoder_layer_array(UpperCamelCase , """_attention_layer_norm/gamma""" )
A__ = get_encoder_layer_array(UpperCamelCase , """_attention_layer_norm/beta""" )
# Intermediate
A__ = layer.intermediate
A__ = get_encoder_layer_array(UpperCamelCase , """_intermediate_dense/kernel""" )
A__ = get_encoder_layer_array(UpperCamelCase , """_intermediate_dense/bias""" )
# Output
A__ = layer.output
A__ = get_encoder_layer_array(UpperCamelCase , """_output_dense/kernel""" )
A__ = get_encoder_layer_array(UpperCamelCase , """_output_dense/bias""" )
A__ = get_encoder_layer_array(UpperCamelCase , """_output_layer_norm/gamma""" )
A__ = get_encoder_layer_array(UpperCamelCase , """_output_layer_norm/beta""" )
# Embeddings
A__ = get_encoder_array("""_position_embedding_layer/embeddings""" )
A__ = get_encoder_array("""_type_embedding_layer/embeddings""" )
A__ = get_encoder_array("""_embedding_norm_layer/gamma""" )
A__ = get_encoder_array("""_embedding_norm_layer/beta""" )
# LM Head
A__ = model.cls.predictions.transform
A__ = get_masked_lm_array("""dense/kernel""" )
A__ = get_masked_lm_array("""dense/bias""" )
A__ = get_masked_lm_array("""layer_norm/gamma""" )
A__ = get_masked_lm_array("""layer_norm/beta""" )
A__ = get_masked_lm_array("""embedding_table""" )
# Pooling
A__ = BertPooler(config=UpperCamelCase )
A__ = get_encoder_array("""_pooler_layer/kernel""" )
A__ = get_encoder_array("""_pooler_layer/bias""" )
# Export final model
model.save_pretrained(UpperCamelCase )
# Integration test - should load without any errors ;)
A__ = BertForMaskedLM.from_pretrained(UpperCamelCase )
print(new_model.eval() )
print("""Model conversion was done sucessfully!""" )
if __name__ == "__main__":
lowerCamelCase__ = 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.",
)
lowerCamelCase__ = parser.parse_args()
convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 574 | 1 |
"""simple docstring"""
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
UpperCAmelCase__ = get_tests_dir("""fixtures/test_sentencepiece.model""")
@require_sentencepiece
class a ( lowerCAmelCase_ , unittest.TestCase ):
_snake_case : Dict = XLMProphetNetTokenizer
_snake_case : Tuple = False
_snake_case : Tuple = True
def lowerCAmelCase_ ( self : List[Any] ):
super().setUp()
# We have a SentencePiece fixture for testing
_UpperCAmelCase = XLMProphetNetTokenizer(__lowerCAmelCase , keep_accents=__lowerCAmelCase )
tokenizer.save_pretrained(self.tmpdirname )
def lowerCAmelCase_ ( self : str ):
_UpperCAmelCase = """[PAD]"""
_UpperCAmelCase = 0
self.assertEqual(self.get_tokenizer()._convert_token_to_id(__lowerCAmelCase ) , __lowerCAmelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(__lowerCAmelCase ) , __lowerCAmelCase )
def lowerCAmelCase_ ( self : Union[str, Any] ):
_UpperCAmelCase = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """[PAD]""" )
self.assertEqual(vocab_keys[1] , """[CLS]""" )
self.assertEqual(vocab_keys[-1] , """j""" )
self.assertEqual(len(__lowerCAmelCase ) , 1012 )
def lowerCAmelCase_ ( self : str ):
self.assertEqual(self.get_tokenizer().vocab_size , 1012 )
def lowerCAmelCase_ ( self : List[Any] ):
_UpperCAmelCase = XLMProphetNetTokenizer(__lowerCAmelCase , keep_accents=__lowerCAmelCase )
_UpperCAmelCase = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(__lowerCAmelCase , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__lowerCAmelCase ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
_UpperCAmelCase = tokenizer.tokenize("""I was born in 92000, and this is falsé.""" )
self.assertListEqual(
__lowerCAmelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""9""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""é""",
""".""",
] , )
_UpperCAmelCase = tokenizer.convert_tokens_to_ids(__lowerCAmelCase )
self.assertListEqual(
__lowerCAmelCase , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, -9, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, -9, 4]
] , )
_UpperCAmelCase = tokenizer.convert_ids_to_tokens(__lowerCAmelCase )
self.assertListEqual(
__lowerCAmelCase , [
SPIECE_UNDERLINE + """I""",
SPIECE_UNDERLINE + """was""",
SPIECE_UNDERLINE + """b""",
"""or""",
"""n""",
SPIECE_UNDERLINE + """in""",
SPIECE_UNDERLINE + """""",
"""[UNK]""",
"""2""",
"""0""",
"""0""",
"""0""",
""",""",
SPIECE_UNDERLINE + """and""",
SPIECE_UNDERLINE + """this""",
SPIECE_UNDERLINE + """is""",
SPIECE_UNDERLINE + """f""",
"""al""",
"""s""",
"""[UNK]""",
""".""",
] , )
@cached_property
def lowerCAmelCase_ ( self : Tuple ):
return XLMProphetNetTokenizer.from_pretrained("""microsoft/xprophetnet-large-wiki100-cased""" )
@slow
def lowerCAmelCase_ ( self : Optional[Any] ):
_UpperCAmelCase = """Hello World!"""
_UpperCAmelCase = [3_5389, 6672, 49, 2]
self.assertListEqual(__lowerCAmelCase , self.big_tokenizer.encode(__lowerCAmelCase ) )
@slow
def lowerCAmelCase_ ( self : Tuple ):
# fmt: off
_UpperCAmelCase = {"""input_ids""": [[1_1073, 8_2783, 18, 26, 8_2783, 549, 5_1540, 248, 1_7209, 1301, 217, 20, 21_5186, 1325, 147, 1_7209, 1301, 217, 20, 5_6370, 53, 12_2020, 20, 1_6477, 27, 8_7355, 4548, 20, 4728, 7_8392, 17, 15_9969, 18, 26, 2_4491, 629, 15, 538, 2_2704, 5439, 15, 2788, 2_4491, 9885, 15, 4_3534, 605, 15, 814, 1_8403, 3_3200, 29, 15, 4_3534, 2_4458, 1_2410, 111, 2_4966, 8_3669, 9637, 14_4068, 26, 850, 2_2346, 27, 147, 2_4966, 8_3669, 8_3490, 26, 3_9113, 735, 27, 689, 656, 2800, 1339, 4600, 53, 12_2020, 11_5785, 34, 816, 1339, 4_6887, 18, 147, 5_3905, 1951, 4_2238, 4_1170, 1_7732, 834, 436, 15, 2_7523, 9_8733, 217, 147, 5542, 4981, 930, 1_7347, 16, 2], [2_0091, 629, 94, 8_2786, 58, 490, 20, 1528, 84, 5_3905, 344, 8_0592, 11_0128, 1_8822, 5267, 1306, 62, 15_2537, 308, 7997, 401, 12_4427, 549, 3_5442, 225, 109, 1_5055, 2_5748, 147, 7119, 4_3712, 34, 767, 13_5366, 18, 16, 2, 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], [592, 6_3784, 11_9466, 17, 14_7808, 8_8214, 18, 656, 81, 32, 3296, 1_0280, 16, 2, 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]], """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, 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, 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=__lowerCAmelCase , model_name="""microsoft/xprophetnet-large-wiki100-cased""" , revision="""1acad1643ddd54a44df6a1b797ada8373685d90e""" , )
| 275 | """simple docstring"""
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class a ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
_snake_case : Optional[int] = StableUnCLIPPipeline
_snake_case : List[str] = TEXT_TO_IMAGE_PARAMS
_snake_case : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS
_snake_case : List[Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
_snake_case : str = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
_snake_case : str = False
def lowerCAmelCase_ ( self : Dict ):
_UpperCAmelCase = 32
_UpperCAmelCase = embedder_hidden_size
# prior components
torch.manual_seed(0 )
_UpperCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
torch.manual_seed(0 )
_UpperCAmelCase = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=__lowerCAmelCase , projection_dim=__lowerCAmelCase , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCAmelCase = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=__lowerCAmelCase , num_layers=1 , )
torch.manual_seed(0 )
_UpperCAmelCase = DDPMScheduler(
variance_type="""fixed_small_log""" , prediction_type="""sample""" , num_train_timesteps=1000 , clip_sample=__lowerCAmelCase , clip_sample_range=5.0 , beta_schedule="""squaredcos_cap_v2""" , )
# regular denoising components
torch.manual_seed(0 )
_UpperCAmelCase = StableUnCLIPImageNormalizer(embedding_dim=__lowerCAmelCase )
_UpperCAmelCase = DDPMScheduler(beta_schedule="""squaredcos_cap_v2""" )
torch.manual_seed(0 )
_UpperCAmelCase = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
torch.manual_seed(0 )
_UpperCAmelCase = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=__lowerCAmelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
_UpperCAmelCase = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="""projection""" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=__lowerCAmelCase , layers_per_block=1 , upcast_attention=__lowerCAmelCase , use_linear_projection=__lowerCAmelCase , )
torch.manual_seed(0 )
_UpperCAmelCase = DDIMScheduler(
beta_schedule="""scaled_linear""" , beta_start=0.00_085 , beta_end=0.012 , prediction_type="""v_prediction""" , set_alpha_to_one=__lowerCAmelCase , steps_offset=1 , )
torch.manual_seed(0 )
_UpperCAmelCase = AutoencoderKL()
_UpperCAmelCase = {
# prior components
"""prior_tokenizer""": prior_tokenizer,
"""prior_text_encoder""": prior_text_encoder,
"""prior""": prior,
"""prior_scheduler""": prior_scheduler,
# image noising components
"""image_normalizer""": image_normalizer,
"""image_noising_scheduler""": image_noising_scheduler,
# regular denoising components
"""tokenizer""": tokenizer,
"""text_encoder""": text_encoder,
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
}
return components
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : Any , __lowerCAmelCase : int=0 ):
if str(__lowerCAmelCase ).startswith("""mps""" ):
_UpperCAmelCase = torch.manual_seed(__lowerCAmelCase )
else:
_UpperCAmelCase = torch.Generator(device=__lowerCAmelCase ).manual_seed(__lowerCAmelCase )
_UpperCAmelCase = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""prior_num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def lowerCAmelCase_ ( self : List[str] ):
_UpperCAmelCase = torch_device == """cpu"""
self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase )
def lowerCAmelCase_ ( self : Any ):
_UpperCAmelCase = torch_device in ["""cpu""", """mps"""]
self._test_inference_batch_single_identical(test_max_difference=__lowerCAmelCase )
@slow
@require_torch_gpu
class a ( unittest.TestCase ):
def lowerCAmelCase_ ( self : List[str] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ ( self : List[str] ):
_UpperCAmelCase = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy""" )
_UpperCAmelCase = StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" , torch_dtype=torch.floataa )
pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCAmelCase = torch.Generator(device="""cpu""" ).manual_seed(0 )
_UpperCAmelCase = pipe("""anime turle""" , generator=__lowerCAmelCase , output_type="""np""" )
_UpperCAmelCase = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__lowerCAmelCase , __lowerCAmelCase )
def lowerCAmelCase_ ( self : List[str] ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_UpperCAmelCase = StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" , torch_dtype=torch.floataa )
_UpperCAmelCase = pipe.to(__lowerCAmelCase )
pipe.set_progress_bar_config(disable=__lowerCAmelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
_UpperCAmelCase = pipe(
"""anime turtle""" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="""np""" , )
_UpperCAmelCase = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 275 | 1 |
"""simple docstring"""
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
__snake_case = pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def _lowerCamelCase ( lowerCamelCase__ : Dict , lowerCamelCase__ : int ):
inspect_dataset(lowerCamelCase__ , lowerCamelCase__ )
lowercase__ : Any = path + """.py"""
assert script_name in os.listdir(lowerCamelCase__ )
assert "__pycache__" not in os.listdir(lowerCamelCase__ )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def _lowerCamelCase ( lowerCamelCase__ : str , lowerCamelCase__ : Union[str, Any] ):
inspect_metric(lowerCamelCase__ , lowerCamelCase__ )
lowercase__ : Optional[int] = path + """.py"""
assert script_name in os.listdir(lowerCamelCase__ )
assert "__pycache__" not in os.listdir(lowerCamelCase__ )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def _lowerCamelCase ( lowerCamelCase__ : Dict , lowerCamelCase__ : Any , lowerCamelCase__ : Any ):
lowercase__ : List[str] = get_dataset_config_info(lowerCamelCase__ , config_name=lowerCamelCase__ )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def _lowerCamelCase ( lowerCamelCase__ : int , lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : Union[str, Any] ):
with pytest.raises(lowerCamelCase__ ):
get_dataset_config_info(lowerCamelCase__ , config_name=lowerCamelCase__ )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def _lowerCamelCase ( lowerCamelCase__ : Union[str, Any] , lowerCamelCase__ : List[Any] ):
lowercase__ : List[str] = get_dataset_config_names(lowerCamelCase__ )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def _lowerCamelCase ( lowerCamelCase__ : int , lowerCamelCase__ : Optional[int] , lowerCamelCase__ : Any ):
lowercase__ : Any = get_dataset_infos(lowerCamelCase__ )
assert list(infos.keys() ) == expected_configs
lowercase__ : List[Any] = expected_configs[0]
assert expected_config in infos
lowercase__ : int = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def _lowerCamelCase ( lowerCamelCase__ : Optional[Any] , lowerCamelCase__ : List[str] , lowerCamelCase__ : int ):
lowercase__ : str = get_dataset_infos(lowerCamelCase__ )
assert expected_config in infos
lowercase__ : str = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def _lowerCamelCase ( lowerCamelCase__ : Any , lowerCamelCase__ : List[str] , lowerCamelCase__ : Union[str, Any] ):
with pytest.raises(lowerCamelCase__ ):
get_dataset_split_names(lowerCamelCase__ , config_name=lowerCamelCase__ ) | 200 |
"""simple docstring"""
import argparse
import pathlib
import fairseq
import torch
from fairseq.models.roberta import RobertaModel as FairseqRobertaModel
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version
from transformers import XLMRobertaConfig, XLMRobertaXLForMaskedLM, XLMRobertaXLForSequenceClassification
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertSelfAttention,
BertSelfOutput,
)
from transformers.models.roberta.modeling_roberta import RobertaAttention
from transformers.utils import logging
if version.parse(fairseq.__version__) < version.parse('1.0.0a'):
raise Exception('requires fairseq >= 1.0.0a')
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
__snake_case = 'Hello world! cécé herlolip'
def _lowerCamelCase ( lowerCamelCase__ : str , lowerCamelCase__ : str , lowerCamelCase__ : bool ):
lowercase__ : int = FairseqRobertaModel.from_pretrained(lowerCamelCase__ )
roberta.eval() # disable dropout
lowercase__ : Tuple = roberta.model.encoder.sentence_encoder
lowercase__ : Tuple = XLMRobertaConfig(
vocab_size=roberta_sent_encoder.embed_tokens.num_embeddings , hidden_size=roberta.cfg.model.encoder_embed_dim , num_hidden_layers=roberta.cfg.model.encoder_layers , num_attention_heads=roberta.cfg.model.encoder_attention_heads , intermediate_size=roberta.cfg.model.encoder_ffn_embed_dim , max_position_embeddings=5_14 , type_vocab_size=1 , layer_norm_eps=1E-5 , )
if classification_head:
lowercase__ : Any = roberta.model.classification_heads["""mnli"""].out_proj.weight.shape[0]
print("""Our RoBERTa config:""" , lowerCamelCase__ )
lowercase__ : List[Any] = XLMRobertaXLForSequenceClassification(lowerCamelCase__ ) if classification_head else XLMRobertaXLForMaskedLM(lowerCamelCase__ )
model.eval()
# Now let's copy all the weights.
# Embeddings
lowercase__ : int = roberta_sent_encoder.embed_tokens.weight
lowercase__ : Union[str, Any] = roberta_sent_encoder.embed_positions.weight
lowercase__ : int = torch.zeros_like(
model.roberta.embeddings.token_type_embeddings.weight ) # just zero them out b/c RoBERTa doesn't use them.
lowercase__ : int = roberta_sent_encoder.layer_norm.weight
lowercase__ : List[Any] = roberta_sent_encoder.layer_norm.bias
for i in range(config.num_hidden_layers ):
# Encoder: start of layer
lowercase__ : BertLayer = model.roberta.encoder.layer[i]
lowercase__ : TransformerSentenceEncoderLayer = roberta_sent_encoder.layers[i]
lowercase__ : RobertaAttention = layer.attention
lowercase__ : str = roberta_layer.self_attn_layer_norm.weight
lowercase__ : Union[str, Any] = roberta_layer.self_attn_layer_norm.bias
# self attention
lowercase__ : BertSelfAttention = layer.attention.self
assert (
roberta_layer.self_attn.k_proj.weight.data.shape
== roberta_layer.self_attn.q_proj.weight.data.shape
== roberta_layer.self_attn.v_proj.weight.data.shape
== torch.Size((config.hidden_size, config.hidden_size) )
)
lowercase__ : Optional[Any] = roberta_layer.self_attn.q_proj.weight
lowercase__ : str = roberta_layer.self_attn.q_proj.bias
lowercase__ : Optional[int] = roberta_layer.self_attn.k_proj.weight
lowercase__ : Optional[int] = roberta_layer.self_attn.k_proj.bias
lowercase__ : int = roberta_layer.self_attn.v_proj.weight
lowercase__ : Union[str, Any] = roberta_layer.self_attn.v_proj.bias
# self-attention output
lowercase__ : BertSelfOutput = layer.attention.output
assert self_output.dense.weight.shape == roberta_layer.self_attn.out_proj.weight.shape
lowercase__ : Any = roberta_layer.self_attn.out_proj.weight
lowercase__ : Optional[int] = roberta_layer.self_attn.out_proj.bias
# this one is final layer norm
lowercase__ : Optional[Any] = roberta_layer.final_layer_norm.weight
lowercase__ : Any = roberta_layer.final_layer_norm.bias
# intermediate
lowercase__ : BertIntermediate = layer.intermediate
assert intermediate.dense.weight.shape == roberta_layer.fca.weight.shape
lowercase__ : Dict = roberta_layer.fca.weight
lowercase__ : Any = roberta_layer.fca.bias
# output
lowercase__ : BertOutput = layer.output
assert bert_output.dense.weight.shape == roberta_layer.fca.weight.shape
lowercase__ : Union[str, Any] = roberta_layer.fca.weight
lowercase__ : Optional[Any] = roberta_layer.fca.bias
# end of layer
if classification_head:
lowercase__ : Optional[Any] = roberta.model.classification_heads["""mnli"""].dense.weight
lowercase__ : str = roberta.model.classification_heads["""mnli"""].dense.bias
lowercase__ : str = roberta.model.classification_heads["""mnli"""].out_proj.weight
lowercase__ : List[str] = roberta.model.classification_heads["""mnli"""].out_proj.bias
else:
# LM Head
lowercase__ : Tuple = roberta.model.encoder.lm_head.dense.weight
lowercase__ : int = roberta.model.encoder.lm_head.dense.bias
lowercase__ : Any = roberta.model.encoder.lm_head.layer_norm.weight
lowercase__ : Union[str, Any] = roberta.model.encoder.lm_head.layer_norm.bias
lowercase__ : Dict = roberta.model.encoder.lm_head.weight
lowercase__ : List[Any] = roberta.model.encoder.lm_head.bias
# Let's check that we get the same results.
lowercase__ : torch.Tensor = roberta.encode(lowerCamelCase__ ).unsqueeze(0 ) # batch of size 1
lowercase__ : Any = model(lowerCamelCase__ )[0]
if classification_head:
lowercase__ : Optional[Any] = roberta.model.classification_heads["""mnli"""](roberta.extract_features(lowerCamelCase__ ) )
else:
lowercase__ : Tuple = roberta.model(lowerCamelCase__ )[0]
print(our_output.shape , their_output.shape )
lowercase__ : Tuple = torch.max(torch.abs(our_output - their_output ) ).item()
print(f'''max_absolute_diff = {max_absolute_diff}''' ) # ~ 1e-7
lowercase__ : int = torch.allclose(lowerCamelCase__ , lowerCamelCase__ , atol=1E-3 )
print("""Do both models output the same tensors?""" , """🔥""" if success else """💩""" )
if not success:
raise Exception("""Something went wRoNg""" )
pathlib.Path(lowerCamelCase__ ).mkdir(parents=lowerCamelCase__ , exist_ok=lowerCamelCase__ )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowerCamelCase__ )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--roberta_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--classification_head', action='store_true', help='Whether to convert a final classification head.'
)
__snake_case = parser.parse_args()
convert_xlm_roberta_xl_checkpoint_to_pytorch(
args.roberta_checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
) | 200 | 1 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __lowercase (UpperCamelCase__ ):
"""simple docstring"""
_snake_case = """"""
_snake_case = """hf-legacy""" # "hf://"" is reserved for hffs
def __init__( self , A = None , A = None , **A , ) -> List[str]:
super().__init__(self , **A )
snake_case : Dict = repo_info
snake_case : Union[str, Any] = token
snake_case : int = None
def UpperCAmelCase ( self ) -> Union[str, Any]:
if self.dir_cache is None:
snake_case : Dict = {}
for hf_file in self.repo_info.siblings:
# TODO(QL): add sizes
snake_case : Any = {
"""name""": hf_file.rfilename,
"""size""": None,
"""type""": """file""",
}
self.dir_cache.update(
{
str(A ): {"""name""": str(A ), """size""": None, """type""": """directory"""}
for d in list(PurePosixPath(hf_file.rfilename ).parents )[:-1]
} )
def UpperCAmelCase ( self , A , A = "rb" , **A , ) -> Dict:
if not isinstance(self.repo_info , A ):
raise NotImplementedError(f"""Open is only implemented for dataset repositories, but got {self.repo_info}""" )
snake_case : int = hf_hub_url(self.repo_info.id , A , revision=self.repo_info.sha )
return fsspec.open(
A , mode=A , headers=get_authentication_headers_for_url(A , use_auth_token=self.token ) , client_kwargs={"""trust_env""": True} , ).open()
def UpperCAmelCase ( self , A , **A ) -> Any:
self._get_dirs()
snake_case : int = self._strip_protocol(A )
if path in self.dir_cache:
return self.dir_cache[path]
else:
raise FileNotFoundError(A )
def UpperCAmelCase ( self , A , A=False , **A ) -> int:
self._get_dirs()
snake_case : Optional[int] = PurePosixPath(path.strip("""/""" ) )
snake_case : Optional[int] = {}
for p, f in self.dir_cache.items():
snake_case : Dict = PurePosixPath(p.strip("""/""" ) )
snake_case : Union[str, Any] = p.parent
if root == path:
snake_case : List[str] = f
snake_case : Optional[int] = list(paths.values() )
if detail:
return out
else:
return sorted(f["""name"""] for f in out )
| 684 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F401
from .text import text
def SCREAMING_SNAKE_CASE__ ( lowercase ) -> str:
snake_case : int = []
for line in lines:
snake_case : Dict = re.sub(R"""#.*""" ,"""""" ,lowercase ) # remove comments
if line:
filtered_lines.append(lowercase )
snake_case : Optional[int] = """\n""".join(lowercase )
# Make a hash from all this code
snake_case : List[str] = full_str.encode("""utf-8""" )
return shaaaa(lowercase ).hexdigest()
# get importable module names and hash for caching
lowerCamelCase : Any = {
'csv': (csv.__name__, _hash_python_lines(inspect.getsource(csv).splitlines())),
'json': (json.__name__, _hash_python_lines(inspect.getsource(json).splitlines())),
'pandas': (pandas.__name__, _hash_python_lines(inspect.getsource(pandas).splitlines())),
'parquet': (parquet.__name__, _hash_python_lines(inspect.getsource(parquet).splitlines())),
'arrow': (arrow.__name__, _hash_python_lines(inspect.getsource(arrow).splitlines())),
'text': (text.__name__, _hash_python_lines(inspect.getsource(text).splitlines())),
'imagefolder': (imagefolder.__name__, _hash_python_lines(inspect.getsource(imagefolder).splitlines())),
'audiofolder': (audiofolder.__name__, _hash_python_lines(inspect.getsource(audiofolder).splitlines())),
}
# Used to infer the module to use based on the data files extensions
lowerCamelCase : Optional[int] = {
'.csv': ('csv', {}),
'.tsv': ('csv', {'sep': '\t'}),
'.json': ('json', {}),
'.jsonl': ('json', {}),
'.parquet': ('parquet', {}),
'.arrow': ('arrow', {}),
'.txt': ('text', {}),
}
_EXTENSION_TO_MODULE.update({ext: ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('imagefolder', {}) for ext in imagefolder.ImageFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext: ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
_EXTENSION_TO_MODULE.update({ext.upper(): ('audiofolder', {}) for ext in audiofolder.AudioFolder.EXTENSIONS})
lowerCamelCase : Tuple = {'imagefolder', 'audiofolder'}
# Used to filter data files based on extensions given a module name
lowerCamelCase : Dict[str, List[str]] = {}
for _ext, (_module, _) in _EXTENSION_TO_MODULE.items():
_MODULE_TO_EXTENSIONS.setdefault(_module, []).append(_ext)
_MODULE_TO_EXTENSIONS["imagefolder"].append('.zip')
_MODULE_TO_EXTENSIONS["audiofolder"].append('.zip')
| 684 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
class lowercase__ ( unittest.TestCase , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def lowercase__ ( self : List[Any] ) -> str:
'''simple docstring'''
UpperCAmelCase_ = load_tool("text-classification" )
self.tool.setup()
UpperCAmelCase_ = load_tool("text-classification" , remote=_UpperCAmelCase )
def lowercase__ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
UpperCAmelCase_ = self.tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(_UpperCAmelCase , "positive" )
def lowercase__ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
UpperCAmelCase_ = self.remote_tool("That's quite cool" , ["positive", "negative"] )
self.assertEqual(_UpperCAmelCase , "positive" )
def lowercase__ ( self : str ) -> Any:
'''simple docstring'''
UpperCAmelCase_ = self.tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(_UpperCAmelCase , "positive" )
def lowercase__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase_ = self.remote_tool(text="That's quite cool" , labels=["positive", "negative"] )
self.assertEqual(_UpperCAmelCase , "positive" )
| 82 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'''alibaba-damo/mgp-str-base''': '''https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json''',
}
class lowerCAmelCase_ ( A__ ):
'''simple docstring'''
_snake_case = '''mgp-str'''
def __init__( self , snake_case_=[32, 128] , snake_case_=4 , snake_case_=3 , snake_case_=27 , snake_case_=38 , snake_case_=50_257 , snake_case_=30_522 , snake_case_=768 , snake_case_=12 , snake_case_=12 , snake_case_=4.0 , snake_case_=True , snake_case_=False , snake_case_=1e-5 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.0 , snake_case_=False , snake_case_=0.02 , **snake_case_ , ) -> List[Any]:
super().__init__(**snake_case_ )
__lowerCAmelCase = image_size
__lowerCAmelCase = patch_size
__lowerCAmelCase = num_channels
__lowerCAmelCase = max_token_length
__lowerCAmelCase = num_character_labels
__lowerCAmelCase = num_bpe_labels
__lowerCAmelCase = num_wordpiece_labels
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = mlp_ratio
__lowerCAmelCase = distilled
__lowerCAmelCase = layer_norm_eps
__lowerCAmelCase = drop_rate
__lowerCAmelCase = qkv_bias
__lowerCAmelCase = attn_drop_rate
__lowerCAmelCase = drop_path_rate
__lowerCAmelCase = output_aa_attentions
__lowerCAmelCase = initializer_range
| 465 | 0 |
'''simple docstring'''
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTTokenizer,
get_linear_schedule_with_warmup,
)
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)
lowerCAmelCase: Tuple = logging.getLogger(__name__)
def lowerCamelCase__ ( _A , _A ):
a : Optional[Any] = np.argmax(_A , axis=1 )
return np.sum(outputs == labels )
def lowerCamelCase__ ( _A ):
with open(_A , encoding='utf_8' ) as f:
a : Union[str, Any] = csv.reader(_A )
a : Any = []
next(_A ) # skip the first line
for line in tqdm(_A ):
output.append((' '.join(line[1:5] ), line[5], line[6], int(line[-1] ) - 1) )
return output
def lowerCamelCase__ ( _A , _A , _A , _A , _A , _A ):
a : str = []
for dataset in encoded_datasets:
a : Optional[int] = len(_A )
a : Optional[int] = np.zeros((n_batch, 2, input_len) , dtype=np.intaa )
a : str = np.zeros((n_batch, 2) , dtype=np.intaa )
a : Optional[Any] = np.full((n_batch, 2, input_len) , fill_value=-100 , dtype=np.intaa )
a : Dict = np.zeros((n_batch,) , dtype=np.intaa )
for (
i,
(story, conta, conta, mc_label),
) in enumerate(_A ):
a : int = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
a : Tuple = [start_token] + story[:cap_length] + [delimiter_token] + conta[:cap_length] + [clf_token]
a : int = with_conta
a : Dict = with_conta
a : int = len(_A ) - 1
a : Dict = len(_A ) - 1
a : Optional[int] = with_conta
a : Dict = with_conta
a : int = mc_label
a : List[Any] = (input_ids, mc_token_ids, lm_labels, mc_labels)
tensor_datasets.append(tuple(torch.tensor(_A ) for t in all_inputs ) )
return tensor_datasets
def lowerCamelCase__ ( ):
a : Dict = argparse.ArgumentParser()
parser.add_argument('--model_name' , type=_A , default='openai-gpt' , help='pretrained model name' )
parser.add_argument('--do_train' , action='store_true' , help='Whether to run training.' )
parser.add_argument('--do_eval' , action='store_true' , help='Whether to run eval on the dev set.' )
parser.add_argument(
'--output_dir' , default=_A , type=_A , required=_A , help='The output directory where the model predictions and checkpoints will be written.' , )
parser.add_argument('--train_dataset' , type=_A , default='' )
parser.add_argument('--eval_dataset' , type=_A , default='' )
parser.add_argument('--seed' , type=_A , default=42 )
parser.add_argument('--num_train_epochs' , type=_A , default=3 )
parser.add_argument('--train_batch_size' , type=_A , default=8 )
parser.add_argument('--eval_batch_size' , type=_A , default=16 )
parser.add_argument('--adam_epsilon' , default=1E-8 , type=_A , help='Epsilon for Adam optimizer.' )
parser.add_argument('--max_grad_norm' , type=_A , default=1 )
parser.add_argument(
'--max_steps' , default=-1 , type=_A , help=(
'If > 0: set total number of training steps to perform. Override num_train_epochs.'
) , )
parser.add_argument(
'--gradient_accumulation_steps' , type=_A , default=1 , help='Number of updates steps to accumulate before performing a backward/update pass.' , )
parser.add_argument('--learning_rate' , type=_A , default=6.25E-5 )
parser.add_argument('--warmup_steps' , default=0 , type=_A , help='Linear warmup over warmup_steps.' )
parser.add_argument('--lr_schedule' , type=_A , default='warmup_linear' )
parser.add_argument('--weight_decay' , type=_A , default=0.01 )
parser.add_argument('--lm_coef' , type=_A , default=0.9 )
parser.add_argument('--n_valid' , type=_A , default=374 )
parser.add_argument('--server_ip' , type=_A , default='' , help='Can be used for distant debugging.' )
parser.add_argument('--server_port' , type=_A , default='' , help='Can be used for distant debugging.' )
a : Tuple = parser.parse_args()
print(_A )
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=_A )
ptvsd.wait_for_attach()
random.seed(args.seed )
np.random.seed(args.seed )
torch.manual_seed(args.seed )
torch.cuda.manual_seed_all(args.seed )
a : List[str] = torch.device('cuda' if torch.cuda.is_available() else 'cpu' )
a : Any = torch.cuda.device_count()
logger.info('device: {}, n_gpu {}'.format(_A , _A ) )
if not args.do_train and not args.do_eval:
raise ValueError('At least one of `do_train` or `do_eval` must be True.' )
if not os.path.exists(args.output_dir ):
os.makedirs(args.output_dir )
# Load tokenizer and model
# This loading functions also add new tokens and embeddings called `special tokens`
# These new embeddings will be fine-tuned on the RocStories dataset
a : Union[str, Any] = ['_start_', '_delimiter_', '_classify_']
a : Union[str, Any] = OpenAIGPTTokenizer.from_pretrained(args.model_name )
tokenizer.add_tokens(_A )
a : Tuple = tokenizer.convert_tokens_to_ids(_A )
a : str = OpenAIGPTDoubleHeadsModel.from_pretrained(args.model_name )
model.resize_token_embeddings(len(_A ) )
model.to(_A )
# Load and encode the datasets
def tokenize_and_encode(_A ):
if isinstance(_A , _A ):
return tokenizer.convert_tokens_to_ids(tokenizer.tokenize(_A ) )
elif isinstance(_A , _A ):
return obj
return [tokenize_and_encode(_A ) for o in obj]
logger.info('Encoding dataset...' )
a : Dict = load_rocstories_dataset(args.train_dataset )
a : Any = load_rocstories_dataset(args.eval_dataset )
a : Dict = (train_dataset, eval_dataset)
a : str = tokenize_and_encode(_A )
# Compute the max input length for the Transformer
a : List[Any] = model.config.n_positions // 2 - 2
a : Dict = max(
len(story[:max_length] ) + max(len(conta[:max_length] ) , len(conta[:max_length] ) ) + 3
for dataset in encoded_datasets
for story, conta, conta, _ in dataset )
a : List[Any] = min(_A , model.config.n_positions ) # Max size of input for the pre-trained model
# Prepare inputs tensors and dataloaders
a : Tuple = pre_process_datasets(_A , _A , _A , *_A )
a , a : int = tensor_datasets[0], tensor_datasets[1]
a : Any = TensorDataset(*_A )
a : Union[str, Any] = RandomSampler(_A )
a : int = DataLoader(_A , sampler=_A , batch_size=args.train_batch_size )
a : Optional[Any] = TensorDataset(*_A )
a : Union[str, Any] = SequentialSampler(_A )
a : Dict = DataLoader(_A , sampler=_A , batch_size=args.eval_batch_size )
# Prepare optimizer
if args.do_train:
if args.max_steps > 0:
a : str = args.max_steps
a : List[Any] = args.max_steps // (len(_A ) // args.gradient_accumulation_steps) + 1
else:
a : Optional[Any] = len(_A ) // args.gradient_accumulation_steps * args.num_train_epochs
a : List[Any] = list(model.named_parameters() )
a : Optional[Any] = ['bias', 'LayerNorm.bias', 'LayerNorm.weight']
a : List[Any] = [
{
'params': [p for n, p in param_optimizer if not any(nd in n for nd in no_decay )],
'weight_decay': args.weight_decay,
},
{'params': [p for n, p in param_optimizer if any(nd in n for nd in no_decay )], 'weight_decay': 0.0},
]
a : Any = AdamW(_A , lr=args.learning_rate , eps=args.adam_epsilon )
a : List[str] = get_linear_schedule_with_warmup(
_A , num_warmup_steps=args.warmup_steps , num_training_steps=_A )
if args.do_train:
a , a , a : Any = 0, 0, None
model.train()
for _ in trange(int(args.num_train_epochs ) , desc='Epoch' ):
a : int = 0
a : str = 0
a : List[Any] = tqdm(_A , desc='Training' )
for step, batch in enumerate(_A ):
a : Dict = tuple(t.to(_A ) for t in batch )
a , a , a , a : Tuple = batch
a : Dict = model(_A , mc_token_ids=_A , lm_labels=_A , mc_labels=_A )
a : Tuple = args.lm_coef * losses[0] + losses[1]
loss.backward()
optimizer.step()
scheduler.step()
optimizer.zero_grad()
tr_loss += loss.item()
a : List[str] = (
loss.item() if exp_average_loss is None else 0.7 * exp_average_loss + 0.3 * loss.item()
)
nb_tr_steps += 1
a : Union[str, Any] = 'Training loss: {:.2e} lr: {:.2e}'.format(_A , scheduler.get_lr()[0] )
# Save a trained model
if args.do_train:
# Save a trained model, configuration and tokenizer
a : Tuple = model.module if hasattr(_A , 'module' ) else model # Only save the model itself
# If we save using the predefined names, we can load using `from_pretrained`
a : Any = os.path.join(args.output_dir , _A )
a : int = os.path.join(args.output_dir , _A )
torch.save(model_to_save.state_dict() , _A )
model_to_save.config.to_json_file(_A )
tokenizer.save_vocabulary(args.output_dir )
# Load a trained model and vocabulary that you have fine-tuned
a : List[Any] = OpenAIGPTDoubleHeadsModel.from_pretrained(args.output_dir )
a : Optional[int] = OpenAIGPTTokenizer.from_pretrained(args.output_dir )
model.to(_A )
if args.do_eval:
model.eval()
a , a : Any = 0, 0
a , a : Tuple = 0, 0
for batch in tqdm(_A , desc='Evaluating' ):
a : List[Any] = tuple(t.to(_A ) for t in batch )
a , a , a , a : str = batch
with torch.no_grad():
a , a , a , a : Optional[Any] = model(
_A , mc_token_ids=_A , lm_labels=_A , mc_labels=_A )
a : Dict = mc_logits.detach().cpu().numpy()
a : Union[str, Any] = mc_labels.to('cpu' ).numpy()
a : List[str] = accuracy(_A , _A )
eval_loss += mc_loss.mean().item()
eval_accuracy += tmp_eval_accuracy
nb_eval_examples += input_ids.size(0 )
nb_eval_steps += 1
a : Union[str, Any] = eval_loss / nb_eval_steps
a : int = eval_accuracy / nb_eval_examples
a : Tuple = tr_loss / nb_tr_steps if args.do_train else None
a : Union[str, Any] = {'eval_loss': eval_loss, 'eval_accuracy': eval_accuracy, 'train_loss': train_loss}
a : Tuple = os.path.join(args.output_dir , 'eval_results.txt' )
with open(_A , 'w' ) as writer:
logger.info('***** Eval results *****' )
for key in sorted(result.keys() ):
logger.info(' %s = %s' , _A , str(result[key] ) )
writer.write('%s = %s\n' % (key, str(result[key] )) )
if __name__ == "__main__":
main() | 195 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
lowerCAmelCase: Any = False
@skip_mps
class a__( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , unittest.TestCase ):
lowercase__ = StableDiffusionAttendAndExcitePipeline
lowercase__ = False
lowercase__ = TEXT_TO_IMAGE_PARAMS
lowercase__ = TEXT_TO_IMAGE_BATCH_PARAMS.union({"""token_indices"""} )
lowercase__ = TEXT_TO_IMAGE_IMAGE_PARAMS
lowercase__ = TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def lowercase_ ( cls : Union[str, Any] ):
super().setUpClass()
torch.use_deterministic_algorithms(__snake_case )
@classmethod
def lowercase_ ( cls : Optional[Any] ):
super().tearDownClass()
torch.use_deterministic_algorithms(__snake_case )
def lowercase_ ( self : List[Any] ):
torch.manual_seed(0 )
a : List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=1 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__snake_case , )
a : Optional[int] = DDIMScheduler(
beta_start=0.00085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=__snake_case , set_alpha_to_one=__snake_case , )
torch.manual_seed(0 )
a : str = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=1_28 , )
torch.manual_seed(0 )
a : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , hidden_act='gelu' , projection_dim=5_12 , )
a : Any = CLIPTextModel(__snake_case )
a : Tuple = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
a : List[str] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def lowercase_ ( self : str , __snake_case : Tuple , __snake_case : Optional[int]=0 ):
if str(__snake_case ).startswith('mps' ):
a : Any = torch.manual_seed(__snake_case )
else:
a : Any = torch.Generator(device=__snake_case ).manual_seed(__snake_case )
a : int = {
'prompt': 'a cat and a frog',
'token_indices': [2, 5],
'generator': generator,
'num_inference_steps': 1,
'guidance_scale': 6.0,
'output_type': 'numpy',
'max_iter_to_alter': 2,
'thresholds': {0: 0.7},
}
return inputs
def lowercase_ ( self : List[Any] ):
a : Union[str, Any] = 'cpu'
a : Any = self.get_dummy_components()
a : List[str] = self.pipeline_class(**__snake_case )
pipe.to(__snake_case )
pipe.set_progress_bar_config(disable=__snake_case )
a : Any = self.get_dummy_inputs(__snake_case )
a : int = pipe(**__snake_case ).images
a : int = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 64, 64, 3) )
a : str = np.array(
[0.63905364, 0.62897307, 0.48599017, 0.5133624, 0.5550048, 0.45769516, 0.50326973, 0.5023139, 0.45384496] )
a : int = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__snake_case , 1e-3 )
def lowercase_ ( self : Dict ):
super().test_cpu_offload_forward_pass(expected_max_diff=5e-4 )
def lowercase_ ( self : Tuple ):
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowercase_ ( self : Union[str, Any] ):
self._test_inference_batch_single_identical(batch_size=2 , expected_max_diff=7e-4 )
def lowercase_ ( self : Tuple ):
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def lowercase_ ( self : str ):
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4 )
def lowercase_ ( self : Any ):
super().test_save_load_local(expected_max_difference=5e-4 )
def lowercase_ ( self : List[Any] ):
super().test_save_load_optional_components(expected_max_difference=4e-4 )
@require_torch_gpu
@slow
class a__( unittest.TestCase ):
@classmethod
def lowercase_ ( cls : Union[str, Any] ):
super().setUpClass()
torch.use_deterministic_algorithms(__snake_case )
@classmethod
def lowercase_ ( cls : Union[str, Any] ):
super().tearDownClass()
torch.use_deterministic_algorithms(__snake_case )
def lowercase_ ( self : Union[str, Any] ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowercase_ ( self : Optional[Any] ):
a : List[Any] = torch.manual_seed(51 )
a : Dict = StableDiffusionAttendAndExcitePipeline.from_pretrained(
'CompVis/stable-diffusion-v1-4' , safety_checker=__snake_case , torch_dtype=torch.floataa )
pipe.to('cuda' )
a : Optional[Any] = 'a painting of an elephant with glasses'
a : Any = [5, 7]
a : Tuple = pipe(
prompt=__snake_case , token_indices=__snake_case , guidance_scale=7.5 , generator=__snake_case , num_inference_steps=5 , max_iter_to_alter=5 , output_type='numpy' , ).images[0]
a : str = load_numpy(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy' )
assert np.abs((expected_image - image).max() ) < 5e-1 | 195 | 1 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def a__ ( snake_case__ : str ):
if "cls_token" in name:
_UpperCAmelCase : Dict = name.replace("""cls_token""" , """vit.embeddings.cls_token""" )
if "mask_token" in name:
_UpperCAmelCase : Any = name.replace("""mask_token""" , """decoder.mask_token""" )
if "decoder_pos_embed" in name:
_UpperCAmelCase : Optional[int] = name.replace("""decoder_pos_embed""" , """decoder.decoder_pos_embed""" )
if "pos_embed" in name and "decoder" not in name:
_UpperCAmelCase : List[str] = name.replace("""pos_embed""" , """vit.embeddings.position_embeddings""" )
if "patch_embed.proj" in name:
_UpperCAmelCase : Optional[int] = name.replace("""patch_embed.proj""" , """vit.embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
_UpperCAmelCase : Union[str, Any] = name.replace("""patch_embed.norm""" , """vit.embeddings.norm""" )
if "decoder_blocks" in name:
_UpperCAmelCase : List[str] = name.replace("""decoder_blocks""" , """decoder.decoder_layers""" )
if "blocks" in name:
_UpperCAmelCase : Optional[int] = name.replace("""blocks""" , """vit.encoder.layer""" )
if "attn.proj" in name:
_UpperCAmelCase : Any = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
_UpperCAmelCase : Dict = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
_UpperCAmelCase : Any = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
_UpperCAmelCase : Tuple = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
_UpperCAmelCase : Dict = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
_UpperCAmelCase : int = name.replace("""mlp.fc2""" , """output.dense""" )
if "decoder_embed" in name:
_UpperCAmelCase : str = name.replace("""decoder_embed""" , """decoder.decoder_embed""" )
if "decoder_norm" in name:
_UpperCAmelCase : List[str] = name.replace("""decoder_norm""" , """decoder.decoder_norm""" )
if "decoder_pred" in name:
_UpperCAmelCase : Dict = name.replace("""decoder_pred""" , """decoder.decoder_pred""" )
if "norm.weight" in name and "decoder" not in name:
_UpperCAmelCase : Tuple = name.replace("""norm.weight""" , """vit.layernorm.weight""" )
if "norm.bias" in name and "decoder" not in name:
_UpperCAmelCase : List[str] = name.replace("""norm.bias""" , """vit.layernorm.bias""" )
return name
def a__ ( snake_case__ : Optional[int] , snake_case__ : str ):
for key in orig_state_dict.copy().keys():
_UpperCAmelCase : Any = orig_state_dict.pop(snake_case__ )
if "qkv" in key:
_UpperCAmelCase : str = key.split(""".""" )
_UpperCAmelCase : str = int(key_split[1] )
if "decoder_blocks" in key:
_UpperCAmelCase : Any = config.decoder_hidden_size
_UpperCAmelCase : Optional[Any] = """decoder.decoder_layers."""
if "weight" in key:
_UpperCAmelCase : Tuple = val[:dim, :]
_UpperCAmelCase : str = val[dim : dim * 2, :]
_UpperCAmelCase : Tuple = val[-dim:, :]
elif "bias" in key:
_UpperCAmelCase : List[Any] = val[:dim]
_UpperCAmelCase : str = val[dim : dim * 2]
_UpperCAmelCase : Optional[int] = val[-dim:]
else:
_UpperCAmelCase : Optional[int] = config.hidden_size
_UpperCAmelCase : Optional[int] = """vit.encoder.layer."""
if "weight" in key:
_UpperCAmelCase : Tuple = val[:dim, :]
_UpperCAmelCase : List[str] = val[dim : dim * 2, :]
_UpperCAmelCase : Any = val[-dim:, :]
elif "bias" in key:
_UpperCAmelCase : List[Any] = val[:dim]
_UpperCAmelCase : List[Any] = val[dim : dim * 2]
_UpperCAmelCase : Any = val[-dim:]
else:
_UpperCAmelCase : str = val
return orig_state_dict
def a__ ( snake_case__ : Union[str, Any] , snake_case__ : int ):
_UpperCAmelCase : Any = ViTMAEConfig()
if "large" in checkpoint_url:
_UpperCAmelCase : Optional[int] = 1024
_UpperCAmelCase : str = 4096
_UpperCAmelCase : Any = 24
_UpperCAmelCase : Dict = 16
elif "huge" in checkpoint_url:
_UpperCAmelCase : Optional[int] = 14
_UpperCAmelCase : Any = 1280
_UpperCAmelCase : Optional[Any] = 5120
_UpperCAmelCase : Optional[int] = 32
_UpperCAmelCase : str = 16
_UpperCAmelCase : Optional[Any] = ViTMAEForPreTraining(snake_case__ )
_UpperCAmelCase : str = torch.hub.load_state_dict_from_url(snake_case__ , map_location="""cpu""" )["""model"""]
_UpperCAmelCase : List[str] = ViTMAEImageProcessor(size=config.image_size )
_UpperCAmelCase : List[Any] = convert_state_dict(snake_case__ , snake_case__ )
model.load_state_dict(snake_case__ )
model.eval()
_UpperCAmelCase : Tuple = """https://user-images.githubusercontent.com/11435359/147738734-196fd92f-9260-48d5-ba7e-bf103d29364d.jpg"""
_UpperCAmelCase : List[Any] = Image.open(requests.get(snake_case__ , stream=snake_case__ ).raw )
_UpperCAmelCase : List[Any] = ViTMAEImageProcessor(size=config.image_size )
_UpperCAmelCase : List[Any] = image_processor(images=snake_case__ , return_tensors="""pt""" )
# forward pass
torch.manual_seed(2 )
_UpperCAmelCase : Optional[Any] = model(**snake_case__ )
_UpperCAmelCase : Tuple = outputs.logits
if "large" in checkpoint_url:
_UpperCAmelCase : List[str] = torch.tensor(
[[-0.7309, -0.7128, -1.0169], [-1.0161, -0.9058, -1.1878], [-1.0478, -0.9411, -1.1911]] )
elif "huge" in checkpoint_url:
_UpperCAmelCase : Optional[Any] = torch.tensor(
[[-1.1599, -0.9199, -1.2221], [-1.1952, -0.9269, -1.2307], [-1.2143, -0.9337, -1.2262]] )
else:
_UpperCAmelCase : Dict = torch.tensor(
[[-0.9192, -0.8481, -1.1259], [-1.1349, -1.0034, -1.2599], [-1.1757, -1.0429, -1.2726]] )
# verify logits
assert torch.allclose(logits[0, :3, :3] , snake_case__ , atol=1e-4 )
print(f'''Saving model to {pytorch_dump_folder_path}''' )
model.save_pretrained(snake_case__ )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(snake_case__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ : Optional[int] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://dl.fbaipublicfiles.com/mae/visualize/mae_visualize_vit_base.pth',
type=str,
help='URL of the checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
SCREAMING_SNAKE_CASE__ : Union[str, Any] = parser.parse_args()
convert_vit_mae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 643 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[int] = '▁'
SCREAMING_SNAKE_CASE__ : Optional[Any] = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
class _SCREAMING_SNAKE_CASE ( A , unittest.TestCase ):
__SCREAMING_SNAKE_CASE = BertGenerationTokenizer
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = True
def __snake_case( self ):
super().setUp()
_UpperCAmelCase : Optional[int] = BertGenerationTokenizer(A_ , keep_accents=A_ )
tokenizer.save_pretrained(self.tmpdirname )
def __snake_case( self ):
_UpperCAmelCase : Optional[int] = """<s>"""
_UpperCAmelCase : Any = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(A_ ) , A_ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(A_ ) , A_ )
def __snake_case( self ):
_UpperCAmelCase : Optional[int] = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , """<unk>""" )
self.assertEqual(vocab_keys[1] , """<s>""" )
self.assertEqual(vocab_keys[-1] , """<pad>""" )
self.assertEqual(len(A_ ) , 10_02 )
def __snake_case( self ):
self.assertEqual(self.get_tokenizer().vocab_size , 10_00 )
def __snake_case( self ):
_UpperCAmelCase : Union[str, Any] = BertGenerationTokenizer(A_ , keep_accents=A_ )
_UpperCAmelCase : List[Any] = tokenizer.tokenize("""This is a test""" )
self.assertListEqual(A_ , ["""▁This""", """▁is""", """▁a""", """▁t""", """est"""] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(A_ ) , [2_85, 46, 10, 1_70, 3_82] , )
_UpperCAmelCase : int = 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""",
""".""",
] , )
_UpperCAmelCase : List[str] = tokenizer.convert_tokens_to_ids(A_ )
self.assertListEqual(
A_ , [8, 21, 84, 55, 24, 19, 7, 0, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 0, 4] , )
_UpperCAmelCase : Tuple = 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>""",
""".""",
] , )
@cached_property
def __snake_case( self ):
return BertGenerationTokenizer.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
@slow
def __snake_case( self ):
_UpperCAmelCase : Optional[Any] = """Hello World!"""
_UpperCAmelCase : str = [1_85_36, 22_60, 1_01]
self.assertListEqual(A_ , self.big_tokenizer.encode(A_ ) )
@slow
def __snake_case( self ):
_UpperCAmelCase : List[str] = (
"""This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"""
""" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"""
)
_UpperCAmelCase : List[Any] = [
8_71,
4_19,
3_58,
9_46,
9_91,
25_21,
4_52,
3_58,
13_57,
3_87,
77_51,
35_36,
1_12,
9_85,
4_56,
1_26,
8_65,
9_38,
54_00,
57_34,
4_58,
13_68,
4_67,
7_86,
24_62,
52_46,
11_59,
6_33,
8_65,
45_19,
4_57,
5_82,
8_52,
25_57,
4_27,
9_16,
5_08,
4_05,
3_43_24,
4_97,
3_91,
4_08,
1_13_42,
12_44,
3_85,
1_00,
9_38,
9_85,
4_56,
5_74,
3_62,
1_25_97,
32_00,
31_29,
11_72,
]
self.assertListEqual(A_ , self.big_tokenizer.encode(A_ ) )
@require_torch
@slow
def __snake_case( self ):
import torch
from transformers import BertGenerationConfig, BertGenerationEncoder
# Build sequence
_UpperCAmelCase : Optional[Any] = list(self.big_tokenizer.get_vocab().keys() )[:10]
_UpperCAmelCase : Tuple = """ """.join(A_ )
_UpperCAmelCase : List[Any] = self.big_tokenizer.encode_plus(A_ , return_tensors="""pt""" , return_token_type_ids=A_ )
_UpperCAmelCase : Any = self.big_tokenizer.batch_encode_plus(
[sequence + """ """ + sequence] , return_tensors="""pt""" , return_token_type_ids=A_ )
_UpperCAmelCase : int = BertGenerationConfig()
_UpperCAmelCase : Dict = BertGenerationEncoder(A_ )
assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size
with torch.no_grad():
model(**A_ )
model(**A_ )
@slow
def __snake_case( self ):
# fmt: off
_UpperCAmelCase : Optional[int] = {"""input_ids""": [[3_92_86, 4_58, 3_63_35, 20_01, 4_56, 1_30_73, 1_32_66, 4_55, 1_13, 77_46, 17_41, 1_11_57, 3_91, 1_30_73, 1_32_66, 4_55, 1_13, 39_67, 3_54_12, 1_13, 49_36, 1_09, 38_70, 23_77, 1_13, 3_00_84, 4_57_20, 4_58, 1_34, 1_74_96, 1_12, 5_03, 1_16_72, 1_13, 1_18, 1_12, 56_65, 1_33_47, 3_86_87, 1_12, 14_96, 3_13_89, 1_12, 32_68, 4_72_64, 1_34, 9_62, 1_12, 1_63_77, 80_35, 2_31_30, 4_30, 1_21_69, 1_55_18, 2_85_92, 4_58, 1_46, 4_16_97, 1_09, 3_91, 1_21_69, 1_55_18, 1_66_89, 4_58, 1_46, 4_13_58, 1_09, 4_52, 7_26, 40_34, 1_11, 7_63, 3_54_12, 50_82, 3_88, 19_03, 1_11, 90_51, 3_91, 28_70, 4_89_18, 19_00, 11_23, 5_50, 9_98, 1_12, 95_86, 1_59_85, 4_55, 3_91, 4_10, 2_29_55, 3_76_36, 1_14], [4_48, 1_74_96, 4_19, 36_63, 3_85, 7_63, 1_13, 2_75_33, 28_70, 32_83, 1_30_43, 16_39, 2_47_13, 5_23, 6_56, 2_40_13, 1_85_50, 25_21, 5_17, 2_70_14, 2_12_44, 4_20, 12_12, 14_65, 3_91, 9_27, 48_33, 3_88, 5_78, 1_17_86, 1_14, 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], [4_84, 21_69, 76_87, 2_19_32, 1_81_46, 7_26, 3_63, 1_70_32, 33_91, 1_14, 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]], """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, 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, 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="""google/bert_for_seq_generation_L-24_bbc_encoder""" , revision="""c817d1fd1be2ffa69431227a1fe320544943d4db""" , )
| 643 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_mvp import MvpTokenizer
__magic_name__ = logging.get_logger(__name__)
__magic_name__ = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
# See all MVP models at https://huggingface.co/models?filter=mvp
__magic_name__ = {
'''vocab_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json''',
},
'''added_tokens.json''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json''',
},
'''merges_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''RUCAIBox/mvp''': '''https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json''',
},
}
__magic_name__ = {
'''RUCAIBox/mvp''': 1_024,
}
class __SCREAMING_SNAKE_CASE ( UpperCamelCase):
"""simple docstring"""
__UpperCAmelCase = VOCAB_FILES_NAMES
__UpperCAmelCase = PRETRAINED_VOCAB_FILES_MAP
__UpperCAmelCase = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__UpperCAmelCase = ["input_ids", "attention_mask"]
__UpperCAmelCase = MvpTokenizer
def __init__( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase="replace" , _UpperCAmelCase="<s>" , _UpperCAmelCase="</s>" , _UpperCAmelCase="</s>" , _UpperCAmelCase="<s>" , _UpperCAmelCase="<unk>" , _UpperCAmelCase="<pad>" , _UpperCAmelCase="<mask>" , _UpperCAmelCase=False , _UpperCAmelCase=True , **_UpperCAmelCase , ):
super().__init__(
_UpperCAmelCase , _UpperCAmelCase , tokenizer_file=_UpperCAmelCase , errors=_UpperCAmelCase , bos_token=_UpperCAmelCase , eos_token=_UpperCAmelCase , sep_token=_UpperCAmelCase , cls_token=_UpperCAmelCase , unk_token=_UpperCAmelCase , pad_token=_UpperCAmelCase , mask_token=_UpperCAmelCase , add_prefix_space=_UpperCAmelCase , trim_offsets=_UpperCAmelCase , **_UpperCAmelCase , )
__snake_case : Optional[Any] = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get('add_prefix_space' , _UpperCAmelCase ) != add_prefix_space:
__snake_case : Optional[int] = getattr(_UpperCAmelCase , pre_tok_state.pop('type' ) )
__snake_case : List[str] = add_prefix_space
__snake_case : Optional[Any] = pre_tok_class(**_UpperCAmelCase )
__snake_case : int = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
__snake_case : Optional[int] = 'post_processor'
__snake_case : str = getattr(self.backend_tokenizer , _UpperCAmelCase , _UpperCAmelCase )
if tokenizer_component_instance:
__snake_case : Optional[Any] = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
__snake_case : Optional[int] = tuple(state['sep'] )
if "cls" in state:
__snake_case : Optional[int] = tuple(state['cls'] )
__snake_case : int = False
if state.get('add_prefix_space' , _UpperCAmelCase ) != add_prefix_space:
__snake_case : List[Any] = add_prefix_space
__snake_case : List[str] = True
if state.get('trim_offsets' , _UpperCAmelCase ) != trim_offsets:
__snake_case : Dict = trim_offsets
__snake_case : Tuple = True
if changes_to_apply:
__snake_case : Union[str, Any] = getattr(_UpperCAmelCase , state.pop('type' ) )
__snake_case : str = component_class(**_UpperCAmelCase )
setattr(self.backend_tokenizer , _UpperCAmelCase , _UpperCAmelCase )
@property
def lowercase_ ( self ):
if self._mask_token is None:
if self.verbose:
logger.error('Using mask_token, but it is not set yet.' )
return None
return str(self._mask_token )
@mask_token.setter
def lowercase_ ( self , _UpperCAmelCase ):
__snake_case : str = AddedToken(_UpperCAmelCase , lstrip=_UpperCAmelCase , rstrip=_UpperCAmelCase ) if isinstance(_UpperCAmelCase , _UpperCAmelCase ) else value
__snake_case : Optional[int] = value
def lowercase_ ( self , *_UpperCAmelCase , **_UpperCAmelCase ):
__snake_case : str = kwargs.get('is_split_into_words' , _UpperCAmelCase )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'to use it with pretokenized inputs.' )
return super()._batch_encode_plus(*_UpperCAmelCase , **_UpperCAmelCase )
def lowercase_ ( self , *_UpperCAmelCase , **_UpperCAmelCase ):
__snake_case : Union[str, Any] = kwargs.get('is_split_into_words' , _UpperCAmelCase )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
'to use it with pretokenized inputs.' )
return super()._encode_plus(*_UpperCAmelCase , **_UpperCAmelCase )
def lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
__snake_case : Union[str, Any] = self._tokenizer.model.save(_UpperCAmelCase , name=_UpperCAmelCase )
return tuple(_UpperCAmelCase )
def lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase=None ):
__snake_case : List[Any] = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase = None ):
__snake_case : Optional[Any] = [self.sep_token_id]
__snake_case : List[str] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
| 679 | import logging
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
BERT_INPUTS_DOCSTRING,
BERT_START_DOCSTRING,
BertEncoder,
BertModel,
BertPreTrainedModel,
)
__magic_name__ = logging.getLogger(__name__)
class __SCREAMING_SNAKE_CASE ( UpperCamelCase):
"""simple docstring"""
def lowercase_ ( self , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase=None , _UpperCAmelCase=None ):
__snake_case : List[Any] = self.layer[current_layer](_UpperCAmelCase , _UpperCAmelCase , head_mask[current_layer] )
__snake_case : Optional[Any] = layer_outputs[0]
return hidden_states
@add_start_docstrings(
"The bare Bert Model transformer with PABEE outputting raw hidden-states without any specific head on top." , UpperCamelCase , )
class __SCREAMING_SNAKE_CASE ( UpperCamelCase):
"""simple docstring"""
def __init__( self , _UpperCAmelCase ):
super().__init__(_UpperCAmelCase )
__snake_case : List[Any] = BertEncoderWithPabee(_UpperCAmelCase )
self.init_weights()
__snake_case : str = 0
__snake_case : List[str] = 0
__snake_case : int = 0
__snake_case : Tuple = 0
def lowercase_ ( self , _UpperCAmelCase ):
__snake_case : Dict = threshold
def lowercase_ ( self , _UpperCAmelCase ):
__snake_case : List[Any] = patience
def lowercase_ ( self ):
__snake_case : Dict = 0
__snake_case : Dict = 0
def lowercase_ ( self ):
__snake_case : Union[str, Any] = self.inference_layers_num / self.inference_instances_num
__snake_case : int = (
F"""*** Patience = {self.patience} Avg. Inference Layers = {avg_inf_layers:.2f} Speed Up ="""
F""" {1 - avg_inf_layers / self.config.num_hidden_layers:.2f} ***"""
)
print(_UpperCAmelCase )
@add_start_docstrings_to_model_forward(_UpperCAmelCase )
def lowercase_ ( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=False , ):
if input_ids is not None and inputs_embeds is not None:
raise ValueError('You cannot specify both input_ids and inputs_embeds at the same time' )
elif input_ids is not None:
__snake_case : Union[str, Any] = input_ids.size()
elif inputs_embeds is not None:
__snake_case : int = inputs_embeds.size()[:-1]
else:
raise ValueError('You have to specify either input_ids or inputs_embeds' )
__snake_case : Optional[Any] = input_ids.device if input_ids is not None else inputs_embeds.device
if attention_mask is None:
__snake_case : List[str] = torch.ones(_UpperCAmelCase , device=_UpperCAmelCase )
if token_type_ids is None:
__snake_case : int = torch.zeros(_UpperCAmelCase , dtype=torch.long , device=_UpperCAmelCase )
# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
# ourselves in which case we just need to make it broadcastable to all heads.
__snake_case : torch.Tensor = self.get_extended_attention_mask(_UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase )
# If a 2D ou 3D attention mask is provided for the cross-attention
# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
if self.config.is_decoder and encoder_hidden_states is not None:
__snake_case , __snake_case , __snake_case : Optional[int] = encoder_hidden_states.size()
__snake_case : List[Any] = (encoder_batch_size, encoder_sequence_length)
if encoder_attention_mask is None:
__snake_case : Tuple = torch.ones(_UpperCAmelCase , device=_UpperCAmelCase )
__snake_case : Optional[int] = self.invert_attention_mask(_UpperCAmelCase )
else:
__snake_case : str = None
# Prepare head mask if needed
# 1.0 in head_mask indicate we keep the head
# attention_probs has shape bsz x n_heads x N x N
# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
__snake_case : int = self.get_head_mask(_UpperCAmelCase , self.config.num_hidden_layers )
__snake_case : Any = self.embeddings(
input_ids=_UpperCAmelCase , position_ids=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , inputs_embeds=_UpperCAmelCase )
__snake_case : List[str] = embedding_output
if self.training:
__snake_case : Dict = []
for i in range(self.config.num_hidden_layers ):
__snake_case : str = self.encoder.adaptive_forward(
_UpperCAmelCase , current_layer=_UpperCAmelCase , attention_mask=_UpperCAmelCase , head_mask=_UpperCAmelCase )
__snake_case : Optional[Any] = self.pooler(_UpperCAmelCase )
__snake_case : Any = output_layers[i](output_dropout(_UpperCAmelCase ) )
res.append(_UpperCAmelCase )
elif self.patience == 0: # Use all layers for inference
__snake_case : Dict = self.encoder(
_UpperCAmelCase , attention_mask=_UpperCAmelCase , head_mask=_UpperCAmelCase , encoder_hidden_states=_UpperCAmelCase , encoder_attention_mask=_UpperCAmelCase , )
__snake_case : str = self.pooler(encoder_outputs[0] )
__snake_case : Tuple = [output_layers[self.config.num_hidden_layers - 1](_UpperCAmelCase )]
else:
__snake_case : List[str] = 0
__snake_case : str = None
__snake_case : Tuple = 0
for i in range(self.config.num_hidden_layers ):
calculated_layer_num += 1
__snake_case : List[Any] = self.encoder.adaptive_forward(
_UpperCAmelCase , current_layer=_UpperCAmelCase , attention_mask=_UpperCAmelCase , head_mask=_UpperCAmelCase )
__snake_case : Any = self.pooler(_UpperCAmelCase )
__snake_case : int = output_layers[i](_UpperCAmelCase )
if regression:
__snake_case : Optional[int] = logits.detach()
if patient_result is not None:
__snake_case : Dict = patient_result.detach()
if (patient_result is not None) and torch.abs(patient_result - labels ) < self.regression_threshold:
patient_counter += 1
else:
__snake_case : Any = 0
else:
__snake_case : str = logits.detach().argmax(dim=1 )
if patient_result is not None:
__snake_case : List[str] = patient_result.detach().argmax(dim=1 )
if (patient_result is not None) and torch.all(labels.eq(_UpperCAmelCase ) ):
patient_counter += 1
else:
__snake_case : Dict = 0
__snake_case : str = logits
if patient_counter == self.patience:
break
__snake_case : str = [patient_result]
self.inference_layers_num += calculated_layer_num
self.inference_instances_num += 1
return res
@add_start_docstrings(
"Bert Model transformer with PABEE and a sequence classification/regression head on top (a linear layer on top of\n the pooled output) e.g. for GLUE tasks. " , UpperCamelCase , )
class __SCREAMING_SNAKE_CASE ( UpperCamelCase):
"""simple docstring"""
def __init__( self , _UpperCAmelCase ):
super().__init__(_UpperCAmelCase )
__snake_case : List[str] = config.num_labels
__snake_case : Dict = BertModelWithPabee(_UpperCAmelCase )
__snake_case : int = nn.Dropout(config.hidden_dropout_prob )
__snake_case : Optional[int] = nn.ModuleList(
[nn.Linear(config.hidden_size , self.config.num_labels ) for _ in range(config.num_hidden_layers )] )
self.init_weights()
@add_start_docstrings_to_model_forward(_UpperCAmelCase )
def lowercase_ ( self , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , _UpperCAmelCase=None , ):
__snake_case : List[str] = self.bert(
input_ids=_UpperCAmelCase , attention_mask=_UpperCAmelCase , token_type_ids=_UpperCAmelCase , position_ids=_UpperCAmelCase , head_mask=_UpperCAmelCase , inputs_embeds=_UpperCAmelCase , output_dropout=self.dropout , output_layers=self.classifiers , regression=self.num_labels == 1 , )
__snake_case : int = (logits[-1],)
if labels is not None:
__snake_case : List[Any] = None
__snake_case : Optional[int] = 0
for ix, logits_item in enumerate(_UpperCAmelCase ):
if self.num_labels == 1:
# We are doing regression
__snake_case : List[str] = MSELoss()
__snake_case : List[str] = loss_fct(logits_item.view(-1 ) , labels.view(-1 ) )
else:
__snake_case : List[str] = CrossEntropyLoss()
__snake_case : Optional[int] = loss_fct(logits_item.view(-1 , self.num_labels ) , labels.view(-1 ) )
if total_loss is None:
__snake_case : List[Any] = loss
else:
total_loss += loss * (ix + 1)
total_weights += ix + 1
__snake_case : int = (total_loss / total_weights,) + outputs
return outputs
| 679 | 1 |
'''simple docstring'''
from string import ascii_uppercase
UpperCAmelCase : Dict = {str(ord(c) - 5_5): c for c in ascii_uppercase}
def a__ ( a__ , a__ ):
"""simple docstring"""
if isinstance(a__ , a__ ):
raise TypeError("""int() can't convert non-string with explicit base""" )
if num < 0:
raise ValueError("""parameter must be positive int""" )
if isinstance(a__ , a__ ):
raise TypeError("""'str' object cannot be interpreted as an integer""" )
if isinstance(a__ , a__ ):
raise TypeError("""'float' object cannot be interpreted as an integer""" )
if base in (0, 1):
raise ValueError("""base must be >= 2""" )
if base > 36:
raise ValueError("""base must be <= 36""" )
__SCREAMING_SNAKE_CASE = """"""
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = 0
while div != 1:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = divmod(a__ , a__ )
if base >= 11 and 9 < mod < 36:
__SCREAMING_SNAKE_CASE = ALPHABET_VALUES[str(a__ )]
else:
__SCREAMING_SNAKE_CASE = str(a__ )
new_value += actual_value
__SCREAMING_SNAKE_CASE = num // base
__SCREAMING_SNAKE_CASE = div
if div == 0:
return str(new_value[::-1] )
elif div == 1:
new_value += str(a__ )
return str(new_value[::-1] )
return new_value[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
for base in range(2, 3_7):
for num in range(1_0_0_0):
assert int(decimal_to_any(num, base), base) == num, (
num,
base,
decimal_to_any(num, base),
int(decimal_to_any(num, base), base),
)
| 627 |
'''simple docstring'''
UpperCAmelCase : Tuple = range(2, 2_0 + 1)
UpperCAmelCase : int = [1_0**k for k in range(ks[-1] + 1)]
UpperCAmelCase : dict[int, dict[int, list[list[int]]]] = {}
def a__ ( a__ , a__ , a__ , a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = sum(a_i[j] for j in range(a__ , len(a__ ) ) )
__SCREAMING_SNAKE_CASE = sum(a_i[j] * base[j] for j in range(min(len(a__ ) , a__ ) ) )
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 0, 0
__SCREAMING_SNAKE_CASE = n - i
__SCREAMING_SNAKE_CASE = memo.get(a__ )
if sub_memo is not None:
__SCREAMING_SNAKE_CASE = sub_memo.get(a__ )
if jumps is not None and len(a__ ) > 0:
# find and make the largest jump without going over
__SCREAMING_SNAKE_CASE = -1
for _k in range(len(a__ ) - 1 , -1 , -1 ):
if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
__SCREAMING_SNAKE_CASE = _k
break
if max_jump >= 0:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = jumps[max_jump]
# since the difference between jumps is cached, add c
__SCREAMING_SNAKE_CASE = diff + c
for j in range(min(a__ , len(a__ ) ) ):
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = divmod(a__ , 10 )
if new_c > 0:
add(a__ , a__ , a__ )
else:
__SCREAMING_SNAKE_CASE = []
else:
__SCREAMING_SNAKE_CASE = {c: []}
__SCREAMING_SNAKE_CASE = sub_memo
if dn >= max_dn or c + diff >= base[k]:
return diff, dn
if k > ks[0]:
while True:
# keep doing smaller jumps
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = next_term(a__ , k - 1 , i + dn , a__ )
diff += _diff
dn += terms_jumped
if dn >= max_dn or c + diff >= base[k]:
break
else:
# would be too small a jump, just compute sequential terms instead
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = compute(a__ , a__ , i + dn , a__ )
diff += _diff
dn += terms_jumped
__SCREAMING_SNAKE_CASE = sub_memo[c]
# keep jumps sorted by # of terms skipped
__SCREAMING_SNAKE_CASE = 0
while j < len(a__ ):
if jumps[j][1] > dn:
break
j += 1
# cache the jump for this value digitsum(b) and c
sub_memo[c].insert(a__ , (diff, dn, k) )
return (diff, dn)
def a__ ( a__ , a__ , a__ , a__ ):
"""simple docstring"""
if i >= n:
return 0, i
if k > len(a__ ):
a_i.extend([0 for _ in range(k - len(a__ ) )] )
# note: a_i -> b * 10^k + c
# ds_b -> digitsum(b)
# ds_c -> digitsum(c)
__SCREAMING_SNAKE_CASE = i
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 0, 0, 0
for j in range(len(a__ ) ):
if j >= k:
ds_b += a_i[j]
else:
ds_c += a_i[j]
while i < n:
i += 1
__SCREAMING_SNAKE_CASE = ds_c + ds_b
diff += addend
__SCREAMING_SNAKE_CASE = 0
for j in range(a__ ):
__SCREAMING_SNAKE_CASE = a_i[j] + addend
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = divmod(a__ , 10 )
ds_c += a_i[j]
if addend > 0:
break
if addend > 0:
add(a__ , a__ , a__ )
return diff, i - start_i
def a__ ( a__ , a__ , a__ ):
"""simple docstring"""
for j in range(a__ , len(a__ ) ):
__SCREAMING_SNAKE_CASE = digits[j] + addend
if s >= 10:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = divmod(a__ , 10 )
__SCREAMING_SNAKE_CASE = addend // 10 + quotient
else:
__SCREAMING_SNAKE_CASE = s
__SCREAMING_SNAKE_CASE = addend // 10
if addend == 0:
break
while addend > 0:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = divmod(a__ , 10 )
digits.append(a__ )
def a__ ( a__ = 10**15 ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = [1]
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = 0
while True:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = next_term(a__ , 20 , i + dn , a__ )
dn += terms_jumped
if dn == n - i:
break
__SCREAMING_SNAKE_CASE = 0
for j in range(len(a__ ) ):
a_n += digits[j] * 10**j
return a_n
if __name__ == "__main__":
print(f"""{solution() = }""")
| 627 | 1 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Optional[int] = logging.get_logger(__name__)
class UpperCAmelCase ( _lowercase ):
UpperCAmelCase : int = '''encoder-decoder'''
UpperCAmelCase : Union[str, Any] = True
def __init__(self : Optional[int] , **A__ : List[str] ) -> Any:
super().__init__(**A__ )
assert (
"encoder" in kwargs and "decoder" in kwargs
), "Config has to be initialized with encoder and decoder config"
lowercase = kwargs.pop("encoder" )
lowercase = encoder_config.pop("model_type" )
lowercase = kwargs.pop("decoder" )
lowercase = decoder_config.pop("model_type" )
from ..auto.configuration_auto import AutoConfig
lowercase = AutoConfig.for_model(A__ , **A__ )
lowercase = AutoConfig.for_model(A__ , **A__ )
lowercase = True
@classmethod
def UpperCAmelCase__ (cls : Optional[int] , A__ : PretrainedConfig , A__ : PretrainedConfig , **A__ : Optional[Any] ) -> PretrainedConfig:
logger.info("Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config" )
lowercase = True
lowercase = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **A__ )
def UpperCAmelCase__ (self : int ) -> Any:
lowercase = copy.deepcopy(self.__dict__ )
lowercase = self.encoder.to_dict()
lowercase = self.decoder.to_dict()
lowercase = self.__class__.model_type
return output
| 459 |
'''simple docstring'''
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
__lowerCamelCase : str = "\\n@article{wang2019superglue,\n title={SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems},\n author={Wang, Alex and Pruksachatkun, Yada and Nangia, Nikita and Singh, Amanpreet and Michael, Julian and Hill, Felix and Levy, Omer and Bowman, Samuel R},\n journal={arXiv preprint arXiv:1905.00537},\n year={2019}\n}\n"
__lowerCamelCase : Optional[int] = "\\nSuperGLUE (https://super.gluebenchmark.com/) is a new benchmark styled after\nGLUE with a new set of more difficult language understanding tasks, improved\nresources, and a new public leaderboard.\n"
__lowerCamelCase : int = "\nCompute SuperGLUE evaluation metric associated to each SuperGLUE dataset.\nArgs:\n predictions: list of predictions to score. Depending on the SuperGlUE subset:\n - for 'record': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'prediction_text': the predicted answer text\n - for 'multirc': list of question-answer dictionaries with the following keys:\n - 'idx': index of the question-answer pair as specified by the dataset\n - 'prediction': the predicted answer label\n - otherwise: list of predicted labels\n references: list of reference labels. Depending on the SuperGLUE subset:\n - for 'record': list of question-answers dictionaries with the following keys:\n - 'idx': index of the question as specified by the dataset\n - 'answers': list of possible answers\n - otherwise: list of reference labels\nReturns: depending on the SuperGLUE subset:\n - for 'record':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1': F1 score\n - for 'multirc':\n - 'exact_match': Exact match between answer and gold answer\n - 'f1_m': Per-question macro-F1 score\n - 'f1_a': Average F1 score over all answers\n - for 'axb':\n 'matthews_correlation': Matthew Correlation\n - for 'cb':\n - 'accuracy': Accuracy\n - 'f1': F1 score\n - for all others:\n - 'accuracy': Accuracy\nExamples:\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'copa') # any of [\"copa\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"boolq\", \"axg\"]\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'cb')\n >>> predictions = [0, 1]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'accuracy': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'record')\n >>> predictions = [{'idx': {'passage': 0, 'query': 0}, 'prediction_text': 'answer'}]\n >>> references = [{'idx': {'passage': 0, 'query': 0}, 'answers': ['answer', 'another_answer']}]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'multirc')\n >>> predictions = [{'idx': {'answer': 0, 'paragraph': 0, 'question': 0}, 'prediction': 0}, {'idx': {'answer': 1, 'paragraph': 2, 'question': 3}, 'prediction': 1}]\n >>> references = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'exact_match': 1.0, 'f1_m': 1.0, 'f1_a': 1.0}\n\n >>> super_glue_metric = datasets.load_metric('super_glue', 'axb')\n >>> references = [0, 1]\n >>> predictions = [0, 1]\n >>> results = super_glue_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {'matthews_correlation': 1.0}\n"
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
return float((preds == labels).mean() )
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_="binary" ):
"""simple docstring"""
lowercase = simple_accuracy(lowerCAmelCase_ , lowerCAmelCase_ )
lowercase = float(fa_score(y_true=lowerCAmelCase_ , y_pred=lowerCAmelCase_ , average=lowerCAmelCase_ ) )
return {
"accuracy": acc,
"f1": fa,
}
def UpperCAmelCase_ ( lowerCAmelCase_ , lowerCAmelCase_ ):
"""simple docstring"""
lowercase = {}
for id_pred, label in zip(lowerCAmelCase_ , lowerCAmelCase_ ):
lowercase = f'{id_pred["idx"]["paragraph"]}-{id_pred["idx"]["question"]}'
lowercase = id_pred["prediction"]
if question_id in question_map:
question_map[question_id].append((pred, label) )
else:
lowercase = [(pred, label)]
lowercase , lowercase = [], []
for question, preds_labels in question_map.items():
lowercase , lowercase = zip(*lowerCAmelCase_ )
lowercase = fa_score(y_true=lowerCAmelCase_ , y_pred=lowerCAmelCase_ , average="macro" )
fas.append(lowerCAmelCase_ )
lowercase = int(sum(pred == label for pred, label in preds_labels ) == len(lowerCAmelCase_ ) )
ems.append(lowerCAmelCase_ )
lowercase = float(sum(lowerCAmelCase_ ) / len(lowerCAmelCase_ ) )
lowercase = sum(lowerCAmelCase_ ) / len(lowerCAmelCase_ )
lowercase = float(fa_score(y_true=lowerCAmelCase_ , y_pred=[id_pred["prediction"] for id_pred in ids_preds] ) )
return {"exact_match": em, "f1_m": fa_m, "f1_a": fa_a}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCAmelCase ( datasets.Metric ):
def UpperCAmelCase__ (self : Any ) -> str:
if self.config_name not in [
"boolq",
"cb",
"copa",
"multirc",
"record",
"rte",
"wic",
"wsc",
"wsc.fixed",
"axb",
"axg",
]:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(self._get_feature_types() ) , codebase_urls=[] , reference_urls=[] , format="numpy" if not self.config_name == "record" and not self.config_name == "multirc" else None , )
def UpperCAmelCase__ (self : int ) -> Optional[int]:
if self.config_name == "record":
return {
"predictions": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"prediction_text": datasets.Value("string" ),
},
"references": {
"idx": {
"passage": datasets.Value("int64" ),
"query": datasets.Value("int64" ),
},
"answers": datasets.Sequence(datasets.Value("string" ) ),
},
}
elif self.config_name == "multirc":
return {
"predictions": {
"idx": {
"answer": datasets.Value("int64" ),
"paragraph": datasets.Value("int64" ),
"question": datasets.Value("int64" ),
},
"prediction": datasets.Value("int64" ),
},
"references": datasets.Value("int64" ),
}
else:
return {
"predictions": datasets.Value("int64" ),
"references": datasets.Value("int64" ),
}
def UpperCAmelCase__ (self : Any , A__ : int , A__ : List[str] ) -> Dict:
if self.config_name == "axb":
return {"matthews_correlation": matthews_corrcoef(A__ , A__ )}
elif self.config_name == "cb":
return acc_and_fa(A__ , A__ , fa_avg="macro" )
elif self.config_name == "record":
lowercase = [
{
"qas": [
{"id": ref["idx"]["query"], "answers": [{"text": ans} for ans in ref["answers"]]}
for ref in references
]
}
]
lowercase = {pred["idx"]["query"]: pred["prediction_text"] for pred in predictions}
return evaluate_record(A__ , A__ )[0]
elif self.config_name == "multirc":
return evaluate_multirc(A__ , A__ )
elif self.config_name in ["copa", "rte", "wic", "wsc", "wsc.fixed", "boolq", "axg"]:
return {"accuracy": simple_accuracy(A__ , A__ )}
else:
raise KeyError(
"You should supply a configuration name selected in "
"[\"boolq\", \"cb\", \"copa\", \"multirc\", \"record\", \"rte\", \"wic\", \"wsc\", \"wsc.fixed\", \"axb\", \"axg\",]" )
| 459 | 1 |
'''simple docstring'''
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
if is_torch_available():
import torch
from transformers import TimmBackbone, TimmBackboneConfig
from ...test_pipeline_mixin import PipelineTesterMixin
class __UpperCamelCase :
def __init__( self , _lowerCAmelCase , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase=None , _lowerCAmelCase="resnet50" , _lowerCAmelCase=3 , _lowerCAmelCase=32 , _lowerCAmelCase=3 , _lowerCAmelCase=True , _lowerCAmelCase=True , ) -> Union[str, Any]:
'''simple docstring'''
lowercase = parent
lowercase = out_indices if out_indices is not None else [4]
lowercase = stage_names
lowercase = out_features
lowercase = backbone
lowercase = batch_size
lowercase = image_size
lowercase = num_channels
lowercase = use_pretrained_backbone
lowercase = is_training
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase = self.get_config()
return config, pixel_values
def _a ( self ) -> Tuple:
'''simple docstring'''
return TimmBackboneConfig(
image_size=self.image_size , num_channels=self.num_channels , out_features=self.out_features , out_indices=self.out_indices , stage_names=self.stage_names , use_pretrained_backbone=self.use_pretrained_backbone , backbone=self.backbone , )
def _a ( self , _lowerCAmelCase , _lowerCAmelCase ) -> str:
'''simple docstring'''
lowercase = TimmBackbone(config=lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
lowercase = model(lowercase_ )
self.parent.assertEqual(
result.feature_map[-1].shape , (self.batch_size, model.channels[-1], 14, 14) , )
def _a ( self ) -> str:
'''simple docstring'''
lowercase = self.prepare_config_and_inputs()
lowercase , lowercase = config_and_inputs
lowercase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
@require_timm
class __UpperCamelCase (lowercase__ , lowercase__ , lowercase__ , unittest.TestCase ):
__A = (TimmBackbone,) if is_torch_available() else ()
__A = {'''feature-extraction''': TimmBackbone} if is_torch_available() else {}
__A = False
__A = False
__A = False
__A = False
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
lowercase = TimmBackboneModelTester(self )
lowercase = ConfigTester(self , config_class=lowercase_ , has_text_modality=lowercase_ )
def _a ( self ) -> Dict:
'''simple docstring'''
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def _a ( self ) -> str:
'''simple docstring'''
lowercase = """resnet18"""
lowercase = """microsoft/resnet-18"""
lowercase = AutoBackbone.from_pretrained(lowercase_ , use_timm_backbone=lowercase_ )
lowercase = AutoBackbone.from_pretrained(lowercase_ )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(len(timm_model.stage_names ) , len(transformers_model.stage_names ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
# Out indices are set to the last layer by default. For timm models, we don't know
# the number of layers in advance, so we set it to (-1,), whereas for transformers
# models, we set it to [len(stage_names) - 1] (kept for backward compatibility).
self.assertEqual(timm_model.out_indices , (-1,) )
self.assertEqual(transformers_model.out_indices , [len(timm_model.stage_names ) - 1] )
lowercase = AutoBackbone.from_pretrained(lowercase_ , use_timm_backbone=lowercase_ , out_indices=[1, 2, 3] )
lowercase = AutoBackbone.from_pretrained(lowercase_ , out_indices=[1, 2, 3] )
self.assertEqual(timm_model.out_indices , transformers_model.out_indices )
self.assertEqual(len(timm_model.out_features ) , len(transformers_model.out_features ) )
self.assertEqual(timm_model.channels , transformers_model.channels )
@unittest.skip("""TimmBackbone doesn\'t support feed forward chunking""" )
def _a ( self ) -> int:
'''simple docstring'''
pass
@unittest.skip("""TimmBackbone doesn\'t have num_hidden_layers attribute""" )
def _a ( self ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip("""TimmBackbone initialization is managed on the timm side""" )
def _a ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip("""TimmBackbone models doesn\'t have inputs_embeds""" )
def _a ( self ) -> Any:
'''simple docstring'''
pass
@unittest.skip("""TimmBackbone models doesn\'t have inputs_embeds""" )
def _a ( self ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip("""TimmBackbone model cannot be created without specifying a backbone checkpoint""" )
def _a ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip("""model weights aren\'t tied in TimmBackbone.""" )
def _a ( self ) -> Dict:
'''simple docstring'''
pass
@unittest.skip("""model weights aren\'t tied in TimmBackbone.""" )
def _a ( self ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
def _a ( self ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip("""Only checkpoints on timm can be loaded into TimmBackbone""" )
def _a ( self ) -> Union[str, Any]:
'''simple docstring'''
pass
@unittest.skip("""TimmBackbone doesn\'t have hidden size info in its configuration.""" )
def _a ( self ) -> int:
'''simple docstring'''
pass
@unittest.skip("""TimmBackbone doesn\'t support output_attentions.""" )
def _a ( self ) -> str:
'''simple docstring'''
pass
@unittest.skip("""Safetensors is not supported by timm.""" )
def _a ( self ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skip("""Will be fixed soon by reducing the size of the model used for common tests.""" )
def _a ( self ) -> Optional[Any]:
'''simple docstring'''
pass
def _a ( self ) -> Any:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(lowercase_ )
lowercase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase = [*signature.parameters.keys()]
lowercase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowercase_ )
def _a ( self ) -> Any:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
lowercase = True
lowercase = self.has_attentions
# no need to test all models as different heads yield the same functionality
lowercase = self.all_model_classes[0]
lowercase = model_class(lowercase_ )
model.to(lowercase_ )
lowercase = self._prepare_for_class(lowercase_ , lowercase_ )
lowercase = model(**lowercase_ )
lowercase = outputs[0][-1]
# Encoder-/Decoder-only models
lowercase = outputs.hidden_states[0]
hidden_states.retain_grad()
if self.has_attentions:
lowercase = outputs.attentions[0]
attentions.retain_grad()
output.flatten()[0].backward(retain_graph=lowercase_ )
self.assertIsNotNone(hidden_states.grad )
if self.has_attentions:
self.assertIsNotNone(attentions.grad )
def _a ( self ) -> List[str]:
'''simple docstring'''
lowercase , lowercase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase = model(**lowercase_ )
self.assertEqual(len(result.feature_maps ) , len(config.out_indices ) )
self.assertEqual(len(model.channels ) , len(config.out_indices ) )
# Check output of last stage is taken if out_features=None, out_indices=None
lowercase = copy.deepcopy(lowercase_ )
lowercase = None
lowercase = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase = model(**lowercase_ )
self.assertEqual(len(result.feature_maps ) , 1 )
self.assertEqual(len(model.channels ) , 1 )
# Check backbone can be initialized with fresh weights
lowercase = copy.deepcopy(lowercase_ )
lowercase = False
lowercase = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
lowercase = model(**lowercase_ )
| 588 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_torch_tensor, logging
if is_torch_available():
import torch
_lowerCAmelCase: List[Any] = logging.get_logger(__name__)
class lowercase_ (lowercase__ ):
snake_case =['pixel_values']
def __init__( self , lowercase_ = True , lowercase_ = None , lowercase_ = PILImageResampling.BILINEAR , lowercase_ = True , lowercase_ = None , lowercase_ = True , lowercase_ = 1 / 255 , lowercase_ = True , lowercase_ = None , lowercase_ = None , **lowercase_ , ) -> None:
super().__init__(**lowercase_)
a__ =size if size is not None else {'shortest_edge': 256}
a__ =get_size_dict(lowercase_ , default_to_square=lowercase_)
a__ =crop_size if crop_size is not None else {'height': 224, 'width': 224}
a__ =get_size_dict(lowercase_ , param_name='crop_size')
a__ =do_resize
a__ =size
a__ =resample
a__ =do_center_crop
a__ =crop_size
a__ =do_rescale
a__ =rescale_factor
a__ =do_normalize
a__ =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
a__ =image_std if image_std is not None else IMAGENET_STANDARD_STD
def __UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_ = PILImageResampling.BICUBIC , lowercase_ = None , **lowercase_ , ) -> np.ndarray:
a__ =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()}""")
a__ =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 __UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_ = None , **lowercase_ , ) -> np.ndarray:
a__ =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` and `width`. Got {size.keys()}""")
return center_crop(lowercase_ , size=(size['height'], size['width']) , data_format=lowercase_ , **lowercase_)
def __UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_ = None , **lowercase_) -> np.ndarray:
return rescale(lowercase_ , scale=lowercase_ , data_format=lowercase_ , **lowercase_)
def __UpperCamelCase ( self , lowercase_ , lowercase_ , lowercase_ , lowercase_ = None , **lowercase_ , ) -> np.ndarray:
return normalize(lowercase_ , mean=lowercase_ , std=lowercase_ , data_format=lowercase_ , **lowercase_)
def __UpperCamelCase ( self , lowercase_ , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = None , lowercase_ = ChannelDimension.FIRST , **lowercase_ , ) -> Tuple:
a__ =do_resize if do_resize is not None else self.do_resize
a__ =size if size is not None else self.size
a__ =get_size_dict(lowercase_ , default_to_square=lowercase_)
a__ =resample if resample is not None else self.resample
a__ =do_center_crop if do_center_crop is not None else self.do_center_crop
a__ =crop_size if crop_size is not None else self.crop_size
a__ =get_size_dict(lowercase_ , param_name='crop_size')
a__ =do_rescale if do_rescale is not None else self.do_rescale
a__ =rescale_factor if rescale_factor is not None else self.rescale_factor
a__ =do_normalize if do_normalize is not None else self.do_normalize
a__ =image_mean if image_mean is not None else self.image_mean
a__ =image_std if image_std is not None else self.image_std
a__ =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.')
# All transformations expect numpy arrays.
a__ =[to_numpy_array(lowercase_) for image in images]
if do_resize:
a__ =[self.resize(image=lowercase_ , size=lowercase_ , resample=lowercase_) for image in images]
if do_center_crop:
a__ =[self.center_crop(image=lowercase_ , size=lowercase_) for image in images]
if do_rescale:
a__ =[self.rescale(image=lowercase_ , scale=lowercase_) for image in images]
if do_normalize:
a__ =[self.normalize(image=lowercase_ , mean=lowercase_ , std=lowercase_) for image in images]
a__ =[to_channel_dimension_format(lowercase_ , lowercase_) for image in images]
a__ ={'pixel_values': images}
return BatchFeature(data=lowercase_ , tensor_type=lowercase_)
def __UpperCamelCase ( self , lowercase_ , lowercase_ = None) -> str:
a__ =outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(lowercase_) != len(lowercase_):
raise ValueError(
'Make sure that you pass in as many target sizes as the batch dimension of the logits')
if is_torch_tensor(lowercase_):
a__ =target_sizes.numpy()
a__ =[]
for idx in range(len(lowercase_)):
a__ =torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0) , size=target_sizes[idx] , mode='bilinear' , align_corners=lowercase_)
a__ =resized_logits[0].argmax(dim=0)
semantic_segmentation.append(lowercase_)
else:
a__ =logits.argmax(dim=1)
a__ =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0])]
return semantic_segmentation
| 20 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class _lowerCAmelCase ( __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : List[str] = ['image_processor', 'tokenizer']
__SCREAMING_SNAKE_CASE : List[str] = 'BlipImageProcessor'
__SCREAMING_SNAKE_CASE : Any = ('BertTokenizer', 'BertTokenizerFast')
def __init__(self , lowercase , lowercase ):
A_ : Any = False
super().__init__(lowercase , lowercase )
A_ : Optional[Any] = self.image_processor
def __call__(self , lowercase = None , lowercase = None , lowercase = True , lowercase = False , lowercase = None , lowercase = None , lowercase = 0 , lowercase = None , lowercase = None , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = False , lowercase = True , lowercase = None , **lowercase , ):
if images is None and text is None:
raise ValueError("""You have to specify either images or text.""" )
# Get only text
if images is None:
A_ : Optional[int] = self.tokenizer
A_ : Optional[int] = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
return text_encoding
# add pixel_values
A_ : List[str] = self.image_processor(lowercase , return_tensors=lowercase )
if text is not None:
A_ : Optional[Any] = self.tokenizer(
text=lowercase , add_special_tokens=lowercase , padding=lowercase , truncation=lowercase , max_length=lowercase , stride=lowercase , pad_to_multiple_of=lowercase , return_attention_mask=lowercase , return_overflowing_tokens=lowercase , return_special_tokens_mask=lowercase , return_offsets_mapping=lowercase , return_token_type_ids=lowercase , return_length=lowercase , verbose=lowercase , return_tensors=lowercase , **lowercase , )
else:
A_ : str = None
if text_encoding is not None:
encoding_image_processor.update(lowercase )
return encoding_image_processor
def _a (self , *lowercase , **lowercase ):
return self.tokenizer.batch_decode(*lowercase , **lowercase )
def _a (self , *lowercase , **lowercase ):
return self.tokenizer.decode(*lowercase , **lowercase )
@property
def _a (self ):
A_ : Any = self.tokenizer.model_input_names
A_ : List[str] = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) ) | 718 |
'''simple docstring'''
import pytest
lowerCamelCase :Optional[Any] = '''__dummy_dataset1__'''
lowerCamelCase :List[Any] = '''
import json
import os
import datasets
REPO_URL = "https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/"
URLS = {"train": REPO_URL + "wikiann-bn-train.jsonl", "validation": REPO_URL + "wikiann-bn-validation.jsonl"}
class __DummyDataset1__(datasets.GeneratorBasedBuilder):
def _info(self):
features = datasets.Features(
{
"tokens": datasets.Sequence(datasets.Value("string")),
"ner_tags": datasets.Sequence(
datasets.features.ClassLabel(
names=[
"O",
"B-PER",
"I-PER",
"B-ORG",
"I-ORG",
"B-LOC",
"I-LOC",
]
)
),
"langs": datasets.Sequence(datasets.Value("string")),
"spans": datasets.Sequence(datasets.Value("string")),
}
)
return datasets.DatasetInfo(features=features)
def _split_generators(self, dl_manager):
dl_path = dl_manager.download(URLS)
return [
datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={"filepath": dl_path["train"]}),
datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={"filepath": dl_path["validation"]}),
]
def _generate_examples(self, filepath):
with open(filepath, "r", encoding="utf-8") as f:
for i, line in enumerate(f):
yield i, json.loads(line)
'''
@pytest.fixture
def a ( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def a ( ):
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def a ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
A_ : List[str] = dataset_loading_script_name
A_ : int = tmp_path / """datasets""" / script_name
script_dir.mkdir(parents=lowerCamelCase__ )
A_ : Tuple = script_dir / f'{script_name}.py'
with open(lowerCamelCase__ , """w""" ) as f:
f.write(lowerCamelCase__ )
return str(lowerCamelCase__ ) | 686 | 0 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> Optional[int]:
__lowerCamelCase : Optional[int] = AlbertConfig.from_json_file(_UpperCamelCase )
print(F"Building PyTorch model from configuration: {config}" )
__lowerCamelCase : List[Any] = AlbertForPreTraining(_UpperCamelCase )
# Load weights from tf checkpoint
load_tf_weights_in_albert(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
# Save pytorch-model
print(F"Save PyTorch model to {pytorch_dump_path}" )
torch.save(model.state_dict() , _UpperCamelCase )
if __name__ == "__main__":
a =argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--tf_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
)
parser.add_argument(
"""--albert_config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained ALBERT model. \n"""
"""This specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
a =parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.albert_config_file, args.pytorch_dump_path)
| 652 | '''simple docstring'''
import argparse
import math
import os
from copy import deepcopy
import torch
from audio_diffusion.models import DiffusionAttnUnetaD
from diffusion import sampling
from torch import nn
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
lowercase__ : int = {
"gwf-440k": {
"url": "https://model-server.zqevans2.workers.dev/gwf-440k.ckpt",
"sample_rate": 4_8000,
"sample_size": 6_5536,
},
"jmann-small-190k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-small-190k.ckpt",
"sample_rate": 4_8000,
"sample_size": 6_5536,
},
"jmann-large-580k": {
"url": "https://model-server.zqevans2.workers.dev/jmann-large-580k.ckpt",
"sample_rate": 4_8000,
"sample_size": 13_1072,
},
"maestro-uncond-150k": {
"url": "https://model-server.zqevans2.workers.dev/maestro-uncond-150k.ckpt",
"sample_rate": 1_6000,
"sample_size": 6_5536,
},
"unlocked-uncond-250k": {
"url": "https://model-server.zqevans2.workers.dev/unlocked-uncond-250k.ckpt",
"sample_rate": 1_6000,
"sample_size": 6_5536,
},
"honk-140k": {
"url": "https://model-server.zqevans2.workers.dev/honk-140k.ckpt",
"sample_rate": 1_6000,
"sample_size": 6_5536,
},
}
def __lowerCamelCase ( _UpperCamelCase : List[Any] , _UpperCamelCase : Optional[Any] ):
'''simple docstring'''
return torch.atana(_UpperCamelCase , _UpperCamelCase ) / math.pi * 2
def __lowerCamelCase ( _UpperCamelCase : List[str] ):
'''simple docstring'''
UpperCAmelCase_ = torch.sin(t * math.pi / 2 ) ** 2
UpperCAmelCase_ = (1 - sigma**2) ** 0.5
return alpha_sigma_to_t(_UpperCamelCase , _UpperCamelCase )
class lowerCamelCase ( lowerCamelCase ):
'''simple docstring'''
pass
class lowerCamelCase ( nn.Module ):
'''simple docstring'''
def __init__( self : Union[str, Any] , UpperCAmelCase__ : Tuple ) ->Optional[Any]:
super().__init__()
UpperCAmelCase_ = DiffusionAttnUnetaD(UpperCAmelCase__ , n_attn_layers=4 )
UpperCAmelCase_ = deepcopy(self.diffusion )
UpperCAmelCase_ = torch.quasirandom.SobolEngine(1 , scramble=UpperCAmelCase__ )
def __lowerCamelCase ( _UpperCamelCase : int ):
'''simple docstring'''
UpperCAmelCase_ = MODELS_MAP[model_name]['''url''']
os.system(F"""wget {url} ./""" )
return F"""./{model_name}.ckpt"""
lowercase__ : str = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
}
lowercase__ : Any = {
"8": "resnets.0",
"9": "attentions.0",
"10": "resnets.1",
"11": "attentions.1",
"12": "resnets.2",
"13": "attentions.2",
}
lowercase__ : Optional[Any] = {
"1": "resnets.0",
"2": "attentions.0",
"3": "resnets.1",
"4": "attentions.1",
"5": "resnets.2",
"6": "attentions.2",
"8": "resnets.3",
"9": "attentions.3",
"10": "resnets.4",
"11": "attentions.4",
"12": "resnets.5",
"13": "attentions.5",
}
lowercase__ : Optional[Any] = {
"0": "resnets.0",
"1": "resnets.1",
"2": "resnets.2",
"4": "resnets.0",
"5": "resnets.1",
"6": "resnets.2",
}
lowercase__ : str = {
"skip": "conv_skip",
"main.0": "conv_1",
"main.1": "group_norm_1",
"main.3": "conv_2",
"main.4": "group_norm_2",
}
lowercase__ : Optional[int] = {
"norm": "group_norm",
"qkv_proj": ["query", "key", "value"],
"out_proj": ["proj_attn"],
}
def __lowerCamelCase ( _UpperCamelCase : List[Any] ):
'''simple docstring'''
if name.startswith('''skip''' ):
return name.replace('''skip''' , RES_CONV_MAP['''skip'''] )
# name has to be of format main.{digit}
if not name.startswith('''main.''' ):
raise ValueError(F"""ResConvBlock error with {name}""" )
return name.replace(name[:6] , RES_CONV_MAP[name[:6]] )
def __lowerCamelCase ( _UpperCamelCase : Any ):
'''simple docstring'''
for key, value in ATTN_MAP.items():
if name.startswith(_UpperCamelCase ) and not isinstance(_UpperCamelCase , _UpperCamelCase ):
return name.replace(_UpperCamelCase , _UpperCamelCase )
elif name.startswith(_UpperCamelCase ):
return [name.replace(_UpperCamelCase , _UpperCamelCase ) for v in value]
raise ValueError(F"""Attn error with {name}""" )
def __lowerCamelCase ( _UpperCamelCase : Union[str, Any] , _UpperCamelCase : Any=13 ):
'''simple docstring'''
UpperCAmelCase_ = input_string
if string.split('''.''' )[0] == "timestep_embed":
return string.replace('''timestep_embed''' , '''time_proj''' )
UpperCAmelCase_ = 0
if string.startswith('''net.3.''' ):
depth += 1
UpperCAmelCase_ = string[6:]
elif string.startswith('''net.''' ):
UpperCAmelCase_ = string[4:]
while string.startswith('''main.7.''' ):
depth += 1
UpperCAmelCase_ = string[7:]
if string.startswith('''main.''' ):
UpperCAmelCase_ = string[5:]
# mid block
if string[:2].isdigit():
UpperCAmelCase_ = string[:2]
UpperCAmelCase_ = string[2:]
else:
UpperCAmelCase_ = string[0]
UpperCAmelCase_ = string[1:]
if depth == max_depth:
UpperCAmelCase_ = MID_NUM_TO_LAYER[layer_num]
UpperCAmelCase_ = '''mid_block'''
elif depth > 0 and int(_UpperCamelCase ) < 7:
UpperCAmelCase_ = DOWN_NUM_TO_LAYER[layer_num]
UpperCAmelCase_ = F"""down_blocks.{depth}"""
elif depth > 0 and int(_UpperCamelCase ) > 7:
UpperCAmelCase_ = UP_NUM_TO_LAYER[layer_num]
UpperCAmelCase_ = F"""up_blocks.{max_depth - depth - 1}"""
elif depth == 0:
UpperCAmelCase_ = DEPTH_0_TO_LAYER[layer_num]
UpperCAmelCase_ = F"""up_blocks.{max_depth - 1}""" if int(_UpperCamelCase ) > 3 else '''down_blocks.0'''
if not string_left.startswith('''.''' ):
raise ValueError(F"""Naming error with {input_string} and string_left: {string_left}.""" )
UpperCAmelCase_ = string_left[1:]
if "resnets" in new_layer:
UpperCAmelCase_ = convert_resconv_naming(_UpperCamelCase )
elif "attentions" in new_layer:
UpperCAmelCase_ = convert_attn_naming(_UpperCamelCase )
UpperCAmelCase_ = new_string_left
if not isinstance(_UpperCamelCase , _UpperCamelCase ):
UpperCAmelCase_ = prefix + '''.''' + new_layer + '''.''' + string_left
else:
UpperCAmelCase_ = [prefix + '''.''' + new_layer + '''.''' + s for s in string_left]
return new_string
def __lowerCamelCase ( _UpperCamelCase : int ):
'''simple docstring'''
UpperCAmelCase_ = {}
for k, v in state_dict.items():
if k.endswith('''kernel''' ):
# up- and downsample layers, don't have trainable weights
continue
UpperCAmelCase_ = rename(_UpperCamelCase )
# check if we need to transform from Conv => Linear for attention
if isinstance(_UpperCamelCase , _UpperCamelCase ):
UpperCAmelCase_ = transform_conv_attns(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase )
else:
UpperCAmelCase_ = v
return new_state_dict
def __lowerCamelCase ( _UpperCamelCase : Optional[int] , _UpperCamelCase : Tuple , _UpperCamelCase : List[Any] ):
'''simple docstring'''
if len(_UpperCamelCase ) == 1:
if len(v.shape ) == 3:
# weight
UpperCAmelCase_ = v[:, :, 0]
else:
# bias
UpperCAmelCase_ = v
else:
# qkv matrices
UpperCAmelCase_ = v.shape[0]
UpperCAmelCase_ = trippled_shape // 3
for i in range(3 ):
if len(v.shape ) == 3:
UpperCAmelCase_ = v[i * single_shape : (i + 1) * single_shape, :, 0]
else:
UpperCAmelCase_ = v[i * single_shape : (i + 1) * single_shape]
return new_state_dict
def __lowerCamelCase ( _UpperCamelCase : List[Any] ):
'''simple docstring'''
UpperCAmelCase_ = torch.device('''cuda''' if torch.cuda.is_available() else '''cpu''' )
UpperCAmelCase_ = args.model_path.split('''/''' )[-1].split('''.''' )[0]
if not os.path.isfile(args.model_path ):
assert (
model_name == args.model_path
), F"""Make sure to provide one of the official model names {MODELS_MAP.keys()}"""
UpperCAmelCase_ = download(_UpperCamelCase )
UpperCAmelCase_ = MODELS_MAP[model_name]['''sample_rate''']
UpperCAmelCase_ = MODELS_MAP[model_name]['''sample_size''']
UpperCAmelCase_ = Object()
UpperCAmelCase_ = sample_size
UpperCAmelCase_ = sample_rate
UpperCAmelCase_ = 0
UpperCAmelCase_ = UNetaDModel(sample_size=_UpperCamelCase , sample_rate=_UpperCamelCase )
UpperCAmelCase_ = diffusers_model.state_dict()
UpperCAmelCase_ = DiffusionUncond(_UpperCamelCase )
orig_model.load_state_dict(torch.load(args.model_path , map_location=_UpperCamelCase )['''state_dict'''] )
UpperCAmelCase_ = orig_model.diffusion_ema.eval()
UpperCAmelCase_ = orig_model.state_dict()
UpperCAmelCase_ = rename_orig_weights(_UpperCamelCase )
UpperCAmelCase_ = set(renamed_state_dict.keys() ) - set(diffusers_state_dict.keys() )
UpperCAmelCase_ = set(diffusers_state_dict.keys() ) - set(renamed_state_dict.keys() )
assert len(_UpperCamelCase ) == 0, F"""Problem with {renamed_minus_diffusers}"""
assert all(k.endswith('''kernel''' ) for k in list(_UpperCamelCase ) ), F"""Problem with {diffusers_minus_renamed}"""
for key, value in renamed_state_dict.items():
assert (
diffusers_state_dict[key].squeeze().shape == value.squeeze().shape
), F"""Shape for {key} doesn't match. Diffusers: {diffusers_state_dict[key].shape} vs. {value.shape}"""
if key == "time_proj.weight":
UpperCAmelCase_ = value.squeeze()
UpperCAmelCase_ = value
diffusers_model.load_state_dict(_UpperCamelCase )
UpperCAmelCase_ = 100
UpperCAmelCase_ = 33
UpperCAmelCase_ = IPNDMScheduler(num_train_timesteps=_UpperCamelCase )
UpperCAmelCase_ = torch.manual_seed(_UpperCamelCase )
UpperCAmelCase_ = torch.randn([1, 2, config.sample_size] , generator=_UpperCamelCase ).to(_UpperCamelCase )
UpperCAmelCase_ = torch.linspace(1 , 0 , steps + 1 , device=_UpperCamelCase )[:-1]
UpperCAmelCase_ = get_crash_schedule(_UpperCamelCase )
UpperCAmelCase_ = DanceDiffusionPipeline(unet=_UpperCamelCase , scheduler=_UpperCamelCase )
UpperCAmelCase_ = torch.manual_seed(33 )
UpperCAmelCase_ = pipe(num_inference_steps=_UpperCamelCase , generator=_UpperCamelCase ).audios
UpperCAmelCase_ = sampling.iplms_sample(_UpperCamelCase , _UpperCamelCase , _UpperCamelCase , {} )
UpperCAmelCase_ = generated.clamp(-1 , 1 )
UpperCAmelCase_ = (generated - audio).abs().sum()
UpperCAmelCase_ = (generated - audio).abs().max()
if args.save:
pipe.save_pretrained(args.checkpoint_path )
print('''Diff sum''' , _UpperCamelCase )
print('''Diff max''' , _UpperCamelCase )
assert diff_max < 1E-3, F"""Diff max: {diff_max} is too much :-/"""
print(F"""Conversion for {model_name} successful!""" )
if __name__ == "__main__":
lowercase__ : Union[str, Any] = argparse.ArgumentParser()
parser.add_argument("--model_path", default=None, type=str, required=True, help="Path to the model to convert.")
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=None, type=str, required=True, help="Path to the output model.")
lowercase__ : Any = parser.parse_args()
main(args)
| 390 | 0 |
import collections
import os
import re
from pathlib import Path
__a = 'src/transformers'
# Matches is_xxx_available()
__a = re.compile(R'is\_([a-z_]*)_available()')
# Catches a one-line _import_struct = {xxx}
__a = re.compile(R'^_import_structure\s+=\s+\{([^\}]+)\}')
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
__a = re.compile(R'\s+"\S*":\s+\[([^\]]*)\]')
# Catches a line if not is_foo_available
__a = re.compile(R'^\s*if\s+not\s+is\_[a-z_]*\_available\(\)')
# Catches a line _import_struct["bla"].append("foo")
__a = re.compile(R'^\s*_import_structure\["\S*"\]\.append\("(\S*)"\)')
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
__a = re.compile(R'^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]')
# Catches a line with an object between quotes and a comma: "MyModel",
__a = re.compile(R'^\s+"([^"]+)",')
# Catches a line with objects between brackets only: ["foo", "bar"],
__a = re.compile(R'^\s+\[([^\]]+)\]')
# Catches a line with from foo import bar, bla, boo
__a = re.compile(R'\s+from\s+\S*\s+import\s+([^\(\s].*)\n')
# Catches a line with try:
__a = re.compile(R'^\s*try:')
# Catches a line with else:
__a = re.compile(R'^\s*else:')
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
if _re_test_backend.search(_lowercase ) is None:
return None
UpperCAmelCase_ : int = [b[0] for b in _re_backend.findall(_lowercase )]
backends.sort()
return "_and_".join(_lowercase )
def lowerCamelCase__ ( _lowercase ):
'''simple docstring'''
with open(_lowercase , '''r''' , encoding='''utf-8''' , newline='''\n''' ) as f:
UpperCAmelCase_ : List[Any] = f.readlines()
UpperCAmelCase_ : Any = 0
while line_index < len(_lowercase ) and not lines[line_index].startswith('''_import_structure = {''' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(_lowercase ):
return None
# First grab the objects without a specific backend in _import_structure
UpperCAmelCase_ : Any = []
while not lines[line_index].startswith('''if TYPE_CHECKING''' ) and find_backend(lines[line_index] ) is None:
UpperCAmelCase_ : Tuple = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(_lowercase ):
UpperCAmelCase_ : int = _re_one_line_import_struct.search(_lowercase ).groups()[0]
UpperCAmelCase_ : Any = re.findall(r'''\[([^\]]+)\]''' , _lowercase )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(''', ''' )] )
line_index += 1
continue
UpperCAmelCase_ : Tuple = _re_import_struct_key_value.search(_lowercase )
if single_line_import_search is not None:
UpperCAmelCase_ : int = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(''', ''' ) if len(_lowercase ) > 0]
objects.extend(_lowercase )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
line_index += 1
UpperCAmelCase_ : List[str] = {'''none''': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('''if TYPE_CHECKING''' ):
# If the line is an if not is_backend_available, we grab all objects associated.
UpperCAmelCase_ : str = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCAmelCase_ : Any = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCAmelCase_ : Tuple = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 4 ):
UpperCAmelCase_ : str = lines[line_index]
if _re_import_struct_add_one.search(_lowercase ) is not None:
objects.append(_re_import_struct_add_one.search(_lowercase ).groups()[0] )
elif _re_import_struct_add_many.search(_lowercase ) is not None:
UpperCAmelCase_ : str = _re_import_struct_add_many.search(_lowercase ).groups()[0].split(''', ''' )
UpperCAmelCase_ : List[Any] = [obj[1:-1] for obj in imports if len(_lowercase ) > 0]
objects.extend(_lowercase )
elif _re_between_brackets.search(_lowercase ) is not None:
UpperCAmelCase_ : List[Any] = _re_between_brackets.search(_lowercase ).groups()[0].split(''', ''' )
UpperCAmelCase_ : int = [obj[1:-1] for obj in imports if len(_lowercase ) > 0]
objects.extend(_lowercase )
elif _re_quote_object.search(_lowercase ) is not None:
objects.append(_re_quote_object.search(_lowercase ).groups()[0] )
elif line.startswith(''' ''' * 8 + '''"''' ):
objects.append(line[9:-3] )
elif line.startswith(''' ''' * 12 + '''"''' ):
objects.append(line[13:-3] )
line_index += 1
UpperCAmelCase_ : Optional[Any] = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
UpperCAmelCase_ : List[Any] = []
while (
line_index < len(_lowercase )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('''else''' )
):
UpperCAmelCase_ : str = lines[line_index]
UpperCAmelCase_ : List[str] = _re_import.search(_lowercase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 8 ):
objects.append(line[8:-2] )
line_index += 1
UpperCAmelCase_ : Dict = {'''none''': objects}
# Let's continue with backend-specific objects
while line_index < len(_lowercase ):
# If the line is an if is_backend_available, we grab all objects associated.
UpperCAmelCase_ : Tuple = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
UpperCAmelCase_ : Tuple = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
UpperCAmelCase_ : str = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(''' ''' * 8 ):
UpperCAmelCase_ : Optional[int] = lines[line_index]
UpperCAmelCase_ : Optional[int] = _re_import.search(_lowercase )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(''', ''' ) )
elif line.startswith(''' ''' * 12 ):
objects.append(line[12:-2] )
line_index += 1
UpperCAmelCase_ : Dict = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def lowerCamelCase__ ( _lowercase , _lowercase ):
'''simple docstring'''
def find_duplicates(_lowercase ):
return [k for k, v in collections.Counter(_lowercase ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
UpperCAmelCase_ : Dict = []
for key in import_dict_objects.keys():
UpperCAmelCase_ : Tuple = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
UpperCAmelCase_ : List[Any] = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
UpperCAmelCase_ : Tuple = '''base imports''' if key == '''none''' else f'''{key} backend'''
errors.append(f'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = []
for root, _, files in os.walk(_lowercase ):
if "__init__.py" in files:
UpperCAmelCase_ : Optional[Any] = os.path.join(_lowercase , '''__init__.py''' )
UpperCAmelCase_ : Optional[int] = parse_init(_lowercase )
if objects is not None:
UpperCAmelCase_ : Any = analyze_results(*_lowercase )
if len(_lowercase ) > 0:
UpperCAmelCase_ : List[str] = f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('''\n'''.join(_lowercase ) )
if len(_lowercase ) > 0:
raise ValueError('''\n\n'''.join(_lowercase ) )
def lowerCamelCase__ ( ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = []
for path, directories, files in os.walk(_lowercase ):
for folder in directories:
# Ignore private modules
if folder.startswith('''_''' ):
directories.remove(_lowercase )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(_lowercase ) / folder).glob('''*.py''' ) ) ) == 0:
continue
UpperCAmelCase_ : Dict = str((Path(_lowercase ) / folder).relative_to(_lowercase ) )
UpperCAmelCase_ : Union[str, Any] = short_path.replace(os.path.sep , '''.''' )
submodules.append(_lowercase )
for fname in files:
if fname == "__init__.py":
continue
UpperCAmelCase_ : Optional[Any] = str((Path(_lowercase ) / fname).relative_to(_lowercase ) )
UpperCAmelCase_ : List[str] = short_path.replace('''.py''' , '''''' ).replace(os.path.sep , '''.''' )
if len(submodule.split('''.''' ) ) == 1:
submodules.append(_lowercase )
return submodules
__a = [
'convert_pytorch_checkpoint_to_tf2',
'modeling_flax_pytorch_utils',
'models.esm.openfold_utils',
]
def lowerCamelCase__ ( ):
'''simple docstring'''
from transformers.utils import direct_transformers_import
UpperCAmelCase_ : Optional[int] = direct_transformers_import(_lowercase )
UpperCAmelCase_ : Optional[Any] = set(transformers._import_structure.keys() )
# This contains all the base keys of the _import_structure object defined in the init, but if the user is missing
# some optional dependencies, they may not have all of them. Thus we read the init to read all additions and
# (potentiall re-) add them.
with open(os.path.join(_lowercase , '''__init__.py''' ) , '''r''' ) as f:
UpperCAmelCase_ : int = f.read()
import_structure_keys.update(set(re.findall(r'''import_structure\[\"([^\"]*)\"\]''' , _lowercase ) ) )
UpperCAmelCase_ : Tuple = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in import_structure_keys
]
if len(_lowercase ) > 0:
UpperCAmelCase_ : List[str] = '''\n'''.join(f'''- {module}''' for module in module_not_registered )
raise ValueError(
'''The following submodules are not properly registed in the main init of Transformers:\n'''
f'''{list_of_modules}\n'''
'''Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.''' )
if __name__ == "__main__":
check_all_inits()
check_submodules() | 705 |
import sacrebleu as scb
from packaging import version
from sacrebleu import TER
import datasets
__a = '\\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'
__a = '\\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'
__a = '\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 __a( datasets.Metric ):
"""simple docstring"""
def a__ ( self ) -> int:
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 a__ ( self ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,_SCREAMING_SNAKE_CASE = False ,) -> int:
UpperCAmelCase_ : Optional[Any] = len(references[0] )
if any(len(_SCREAMING_SNAKE_CASE ) != references_per_prediction for refs in references ):
raise ValueError('''Sacrebleu requires the same number of references for each prediction''' )
UpperCAmelCase_ : Dict = [[refs[i] for refs in references] for i in range(_SCREAMING_SNAKE_CASE )]
UpperCAmelCase_ : Union[str, Any] = TER(
normalized=_SCREAMING_SNAKE_CASE ,no_punct=_SCREAMING_SNAKE_CASE ,asian_support=_SCREAMING_SNAKE_CASE ,case_sensitive=_SCREAMING_SNAKE_CASE ,)
UpperCAmelCase_ : Tuple = sb_ter.corpus_score(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE )
return {"score": output.score, "num_edits": output.num_edits, "ref_length": output.ref_length} | 300 | 0 |
'''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
| 195 |
'''simple docstring'''
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
return base * power(UpperCAmelCase_ , (exponent - 1) ) if exponent else 1
if __name__ == "__main__":
print('Raise base to the power of exponent using recursion...')
snake_case_ : int = int(input('Enter the base: ').strip())
snake_case_ : Optional[int] = int(input('Enter the exponent: ').strip())
snake_case_ : Optional[int] = power(base, abs(exponent))
if exponent < 0: # power() does not properly deal w/ negative exponents
snake_case_ : List[Any] = 1 / result
print(F"""{base} to the power of {exponent} is {result}""")
| 195 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class UpperCamelCase :
_SCREAMING_SNAKE_CASE : List[Any] = PegasusConfig
_SCREAMING_SNAKE_CASE : List[Any] = {}
_SCREAMING_SNAKE_CASE : int = """gelu"""
def __init__( self :int , __magic_name__ :int , __magic_name__ :List[Any]=13 , __magic_name__ :Union[str, Any]=7 , __magic_name__ :Dict=True , __magic_name__ :List[Any]=False , __magic_name__ :List[str]=99 , __magic_name__ :int=32 , __magic_name__ :Dict=2 , __magic_name__ :Any=4 , __magic_name__ :List[str]=37 , __magic_name__ :Tuple=0.1 , __magic_name__ :Optional[Any]=0.1 , __magic_name__ :Any=40 , __magic_name__ :Optional[int]=2 , __magic_name__ :int=1 , __magic_name__ :Any=0 , ) ->Optional[Any]:
lowercase : Optional[Any] = parent
lowercase : Optional[Any] = batch_size
lowercase : Optional[Any] = seq_length
lowercase : List[Any] = is_training
lowercase : Optional[Any] = use_labels
lowercase : Dict = vocab_size
lowercase : Optional[Any] = hidden_size
lowercase : str = num_hidden_layers
lowercase : Union[str, Any] = num_attention_heads
lowercase : List[Any] = intermediate_size
lowercase : Union[str, Any] = hidden_dropout_prob
lowercase : Optional[Any] = attention_probs_dropout_prob
lowercase : List[Any] = max_position_embeddings
lowercase : Any = eos_token_id
lowercase : List[str] = pad_token_id
lowercase : Dict = bos_token_id
def __snake_case ( self :Dict ) ->Union[str, Any]:
lowercase : int = ids_tensor([self.batch_size, self.seq_length - 1] , self.vocab_size )
lowercase : Dict = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) , 1 )
lowercase : Tuple = tf.concat([input_ids, eos_tensor] , axis=1 )
lowercase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
lowercase : Tuple = self.config_cls(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , eos_token_ids=[2] , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , decoder_start_token_id=self.pad_token_id , **self.config_updates , )
lowercase : List[str] = prepare_pegasus_inputs_dict(__magic_name__ , __magic_name__ , __magic_name__ )
return config, inputs_dict
def __snake_case ( self :List[Any] , __magic_name__ :List[Any] , __magic_name__ :List[Any] ) ->Optional[int]:
lowercase : Any = TFPegasusModel(config=__magic_name__ ).get_decoder()
lowercase : Tuple = inputs_dict["""input_ids"""]
lowercase : List[str] = input_ids[:1, :]
lowercase : Optional[Any] = inputs_dict["""attention_mask"""][:1, :]
lowercase : Union[str, Any] = inputs_dict["""head_mask"""]
lowercase : Dict = 1
# first forward pass
lowercase : Optional[Any] = model(__magic_name__ , attention_mask=__magic_name__ , head_mask=__magic_name__ , use_cache=__magic_name__ )
lowercase , lowercase : Optional[int] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
lowercase : Union[str, Any] = ids_tensor((self.batch_size, 3) , config.vocab_size )
lowercase : Union[str, Any] = tf.cast(ids_tensor((self.batch_size, 3) , 2 ) , tf.inta )
# append to next input_ids and
lowercase : int = tf.concat([input_ids, next_tokens] , axis=-1 )
lowercase : Union[str, Any] = tf.concat([attention_mask, next_attn_mask] , axis=-1 )
lowercase : Any = model(__magic_name__ , attention_mask=__magic_name__ )[0]
lowercase : Dict = model(__magic_name__ , attention_mask=__magic_name__ , past_key_values=__magic_name__ )[0]
self.parent.assertEqual(next_tokens.shape[1] , output_from_past.shape[1] )
# select random slice
lowercase : Union[str, Any] = int(ids_tensor((1,) , output_from_past.shape[-1] ) )
lowercase : List[Any] = output_from_no_past[:, -3:, random_slice_idx]
lowercase : int = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(__magic_name__ , __magic_name__ , rtol=1E-3 )
def UpperCamelCase ( _A , _A , _A , _A=None , _A=None , _A=None , _A=None , _A=None , ) -> Union[str, Any]:
if attention_mask is None:
lowercase : Dict = tf.cast(tf.math.not_equal(_A , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
lowercase : List[str] = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
lowercase : Optional[int] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
lowercase : Union[str, Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
lowercase : Optional[Any] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class UpperCamelCase (__snake_case , __snake_case , unittest.TestCase ):
_SCREAMING_SNAKE_CASE : str = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE : Any = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
_SCREAMING_SNAKE_CASE : List[str] = (
{
"""conversational""": TFPegasusForConditionalGeneration,
"""feature-extraction""": TFPegasusModel,
"""summarization""": TFPegasusForConditionalGeneration,
"""text2text-generation""": TFPegasusForConditionalGeneration,
"""translation""": TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
_SCREAMING_SNAKE_CASE : List[str] = True
_SCREAMING_SNAKE_CASE : Tuple = False
_SCREAMING_SNAKE_CASE : Tuple = False
def __snake_case ( self :List[str] ) ->Any:
lowercase : Dict = TFPegasusModelTester(self )
lowercase : int = ConfigTester(self , config_class=__magic_name__ )
def __snake_case ( self :List[Any] ) ->List[Any]:
self.config_tester.run_common_tests()
def __snake_case ( self :Dict ) ->str:
lowercase : str = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*__magic_name__ )
@require_sentencepiece
@require_tokenizers
@require_tf
class UpperCamelCase (unittest.TestCase ):
_SCREAMING_SNAKE_CASE : List[str] = [
""" PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.""",
""" The London trio are up for best UK act and best album, as well as getting two nominations in the best song category.\"We got told like this morning 'Oh I think you're nominated'\", said Dappy.\"And I was like 'Oh yeah, which one?' And now we've got nominated for four awards. I mean, wow!\"Bandmate Fazer added: \"We thought it's best of us to come down and mingle with everyone and say hello to the cameras. And now we find we've got four nominations.\"The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn't be too disappointed if they didn't win this time around.\"At the end of the day we're grateful to be where we are in our careers.\"If it don't happen then it don't happen - live to fight another day and keep on making albums and hits for the fans.\"Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers' All These Things That I've Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year's Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border.\"We just done Edinburgh the other day,\" said Dappy.\"We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!\" """,
]
_SCREAMING_SNAKE_CASE : int = [
"""California's largest electricity provider has cut power to hundreds of thousands of customers in an effort to"""
""" reduce the risk of wildfires.""",
"""N-Dubz have revealed they\'re \"grateful\" to have been nominated for four Mobo Awards.""",
] # differs slightly from pytorch, likely due to numerical differences in linear layers
_SCREAMING_SNAKE_CASE : List[str] = """google/pegasus-xsum"""
@cached_property
def __snake_case ( self :Dict ) ->Optional[Any]:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def __snake_case ( self :List[Any] ) ->Union[str, Any]:
lowercase : Dict = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def __snake_case ( self :Any , **__magic_name__ :List[Any] ) ->Any:
lowercase : Union[str, Any] = self.translate_src_text(**__magic_name__ )
assert self.expected_text == generated_words
def __snake_case ( self :int , **__magic_name__ :int ) ->Dict:
lowercase : int = self.tokenizer(self.src_text , **__magic_name__ , padding=__magic_name__ , return_tensors="""tf""" )
lowercase : Optional[Any] = self.model.generate(
model_inputs.input_ids , attention_mask=model_inputs.attention_mask , num_beams=2 , use_cache=__magic_name__ , )
lowercase : List[str] = self.tokenizer.batch_decode(generated_ids.numpy() , skip_special_tokens=__magic_name__ )
return generated_words
@slow
def __snake_case ( self :Dict ) ->str:
self._assert_generated_batch_equal_expected()
| 348 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import logging
if is_vision_available():
import PIL
_lowerCAmelCase = logging.get_logger(__name__)
def UpperCamelCase ( _A ) -> List[List[ImageInput]]:
if isinstance(_A , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(_A , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(_A ):
return [[videos]]
raise ValueError(F"""Could not make batched video from {videos}""" )
class UpperCamelCase (__snake_case ):
_SCREAMING_SNAKE_CASE : Union[str, Any] = ["""pixel_values"""]
def __init__( self :Union[str, Any] , __magic_name__ :bool = True , __magic_name__ :Dict[str, int] = None , __magic_name__ :PILImageResampling = PILImageResampling.BILINEAR , __magic_name__ :bool = True , __magic_name__ :Dict[str, int] = None , __magic_name__ :bool = True , __magic_name__ :Union[int, float] = 1 / 255 , __magic_name__ :bool = True , __magic_name__ :bool = True , __magic_name__ :Optional[Union[float, List[float]]] = None , __magic_name__ :Optional[Union[float, List[float]]] = None , **__magic_name__ :List[str] , ) ->None:
super().__init__(**__magic_name__ )
lowercase : str = size if size is not None else {"""shortest_edge""": 256}
lowercase : Union[str, Any] = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
lowercase : str = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
lowercase : Union[str, Any] = get_size_dict(__magic_name__ , param_name="""crop_size""" )
lowercase : Union[str, Any] = do_resize
lowercase : Any = size
lowercase : int = do_center_crop
lowercase : Any = crop_size
lowercase : Tuple = resample
lowercase : str = do_rescale
lowercase : Tuple = rescale_factor
lowercase : Optional[Any] = offset
lowercase : Any = do_normalize
lowercase : List[Any] = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
lowercase : Optional[Any] = image_std if image_std is not None else IMAGENET_STANDARD_STD
def __snake_case ( self :Optional[int] , __magic_name__ :np.ndarray , __magic_name__ :Dict[str, int] , __magic_name__ :PILImageResampling = PILImageResampling.BILINEAR , __magic_name__ :Optional[Union[str, ChannelDimension]] = None , **__magic_name__ :Any , ) ->np.ndarray:
lowercase : Any = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
if "shortest_edge" in size:
lowercase : Union[str, Any] = get_resize_output_image_size(__magic_name__ , size["""shortest_edge"""] , default_to_square=__magic_name__ )
elif "height" in size and "width" in size:
lowercase : List[str] = (size["""height"""], size["""width"""])
else:
raise ValueError(f"""Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}""" )
return resize(__magic_name__ , size=__magic_name__ , resample=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __snake_case ( self :int , __magic_name__ :np.ndarray , __magic_name__ :Dict[str, int] , __magic_name__ :Optional[Union[str, ChannelDimension]] = None , **__magic_name__ :Dict , ) ->np.ndarray:
lowercase : Any = get_size_dict(__magic_name__ )
if "height" not in size or "width" not in size:
raise ValueError(f"""Size must have 'height' and 'width' as keys. Got {size.keys()}""" )
return center_crop(__magic_name__ , size=(size["""height"""], size["""width"""]) , data_format=__magic_name__ , **__magic_name__ )
def __snake_case ( self :Dict , __magic_name__ :np.ndarray , __magic_name__ :Union[int, float] , __magic_name__ :bool = True , __magic_name__ :Optional[Union[str, ChannelDimension]] = None , **__magic_name__ :Union[str, Any] , ) ->Union[str, Any]:
lowercase : Dict = image.astype(np.floataa )
if offset:
lowercase : List[str] = image - (scale / 2)
return rescale(__magic_name__ , scale=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __snake_case ( self :Dict , __magic_name__ :np.ndarray , __magic_name__ :Union[float, List[float]] , __magic_name__ :Union[float, List[float]] , __magic_name__ :Optional[Union[str, ChannelDimension]] = None , **__magic_name__ :Union[str, Any] , ) ->np.ndarray:
return normalize(__magic_name__ , mean=__magic_name__ , std=__magic_name__ , data_format=__magic_name__ , **__magic_name__ )
def __snake_case ( self :Tuple , __magic_name__ :ImageInput , __magic_name__ :bool = None , __magic_name__ :Dict[str, int] = None , __magic_name__ :PILImageResampling = None , __magic_name__ :bool = None , __magic_name__ :Dict[str, int] = None , __magic_name__ :bool = None , __magic_name__ :float = None , __magic_name__ :bool = None , __magic_name__ :bool = None , __magic_name__ :Optional[Union[float, List[float]]] = None , __magic_name__ :Optional[Union[float, List[float]]] = None , __magic_name__ :Optional[ChannelDimension] = ChannelDimension.FIRST , ) ->np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_center_crop and crop_size is None:
raise ValueError("""Crop size must be specified if do_center_crop is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
if offset and not do_rescale:
raise ValueError("""For offset, do_rescale must also be set to True.""" )
# All transformations expect numpy arrays.
lowercase : Union[str, Any] = to_numpy_array(__magic_name__ )
if do_resize:
lowercase : int = self.resize(image=__magic_name__ , size=__magic_name__ , resample=__magic_name__ )
if do_center_crop:
lowercase : Union[str, Any] = self.center_crop(__magic_name__ , size=__magic_name__ )
if do_rescale:
lowercase : Dict = self.rescale(image=__magic_name__ , scale=__magic_name__ , offset=__magic_name__ )
if do_normalize:
lowercase : Tuple = self.normalize(image=__magic_name__ , mean=__magic_name__ , std=__magic_name__ )
lowercase : List[Any] = to_channel_dimension_format(__magic_name__ , __magic_name__ )
return image
def __snake_case ( self :Optional[int] , __magic_name__ :ImageInput , __magic_name__ :bool = None , __magic_name__ :Dict[str, int] = None , __magic_name__ :PILImageResampling = None , __magic_name__ :bool = None , __magic_name__ :Dict[str, int] = None , __magic_name__ :bool = None , __magic_name__ :float = None , __magic_name__ :bool = None , __magic_name__ :bool = None , __magic_name__ :Optional[Union[float, List[float]]] = None , __magic_name__ :Optional[Union[float, List[float]]] = None , __magic_name__ :Optional[Union[str, TensorType]] = None , __magic_name__ :ChannelDimension = ChannelDimension.FIRST , **__magic_name__ :Any , ) ->PIL.Image.Image:
lowercase : List[str] = do_resize if do_resize is not None else self.do_resize
lowercase : Optional[int] = resample if resample is not None else self.resample
lowercase : int = do_center_crop if do_center_crop is not None else self.do_center_crop
lowercase : List[Any] = do_rescale if do_rescale is not None else self.do_rescale
lowercase : int = rescale_factor if rescale_factor is not None else self.rescale_factor
lowercase : Optional[Any] = offset if offset is not None else self.offset
lowercase : List[str] = do_normalize if do_normalize is not None else self.do_normalize
lowercase : Any = image_mean if image_mean is not None else self.image_mean
lowercase : Optional[int] = image_std if image_std is not None else self.image_std
lowercase : List[Any] = size if size is not None else self.size
lowercase : str = get_size_dict(__magic_name__ , default_to_square=__magic_name__ )
lowercase : str = crop_size if crop_size is not None else self.crop_size
lowercase : List[Any] = get_size_dict(__magic_name__ , param_name="""crop_size""" )
if not valid_images(__magic_name__ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
lowercase : int = make_batched(__magic_name__ )
lowercase : Dict = [
[
self._preprocess_image(
image=__magic_name__ , do_resize=__magic_name__ , size=__magic_name__ , resample=__magic_name__ , do_center_crop=__magic_name__ , crop_size=__magic_name__ , do_rescale=__magic_name__ , rescale_factor=__magic_name__ , offset=__magic_name__ , do_normalize=__magic_name__ , image_mean=__magic_name__ , image_std=__magic_name__ , data_format=__magic_name__ , )
for img in video
]
for video in videos
]
lowercase : List[str] = {"""pixel_values""": videos}
return BatchFeature(data=__magic_name__ , tensor_type=__magic_name__ )
| 348 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_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_torch_available():
import torch
if is_datasets_available():
from datasets import load_dataset
UpperCAmelCase_ = random.Random()
def lowerCAmelCase_ ( __UpperCAmelCase: List[str] , __UpperCAmelCase: Dict=1.0 , __UpperCAmelCase: str=None , __UpperCAmelCase: Tuple=None ) -> int:
if rng is None:
UpperCamelCase__ : List[Any] = global_rng
UpperCamelCase__ : Optional[Any] = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class lowercase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self, __magic_name__, __magic_name__=7, __magic_name__=400, __magic_name__=2000, __magic_name__=2048, __magic_name__=128, __magic_name__=1, __magic_name__=512, __magic_name__=30, __magic_name__=44100, ) -> str:
"""simple docstring"""
UpperCamelCase__ : Union[str, Any] = parent
UpperCamelCase__ : str = batch_size
UpperCamelCase__ : Dict = min_seq_length
UpperCamelCase__ : Dict = max_seq_length
UpperCamelCase__ : str = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
UpperCamelCase__ : Optional[int] = spectrogram_length
UpperCamelCase__ : Union[str, Any] = feature_size
UpperCamelCase__ : int = num_audio_channels
UpperCamelCase__ : List[Any] = hop_length
UpperCamelCase__ : Union[str, Any] = chunk_length
UpperCamelCase__ : Any = sampling_rate
def UpperCamelCase__ ( self ) -> List[Any]:
"""simple docstring"""
return {
"spectrogram_length": self.spectrogram_length,
"feature_size": self.feature_size,
"num_audio_channels": self.num_audio_channels,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"sampling_rate": self.sampling_rate,
}
def UpperCamelCase__ ( self, __magic_name__=False, __magic_name__=False ) -> Tuple:
"""simple docstring"""
def _flatten(__magic_name__ ):
return list(itertools.chain(*__magic_name__ ) )
if equal_length:
UpperCamelCase__ : Optional[int] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
UpperCamelCase__ : Any = [
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__ : Tuple = [np.asarray(__magic_name__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class lowercase__ ( __lowerCamelCase , unittest.TestCase ):
'''simple docstring'''
a : str = TvltFeatureExtractor
def UpperCamelCase__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : str = TvltFeatureExtractionTester(self )
def UpperCamelCase__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase__ : Any = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(__magic_name__, '''spectrogram_length''' ) )
self.assertTrue(hasattr(__magic_name__, '''feature_size''' ) )
self.assertTrue(hasattr(__magic_name__, '''num_audio_channels''' ) )
self.assertTrue(hasattr(__magic_name__, '''hop_length''' ) )
self.assertTrue(hasattr(__magic_name__, '''chunk_length''' ) )
self.assertTrue(hasattr(__magic_name__, '''sampling_rate''' ) )
def UpperCamelCase__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase__ : str = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase__ : str = feat_extract_first.save_pretrained(__magic_name__ )[0]
check_json_file_has_correct_format(__magic_name__ )
UpperCamelCase__ : Union[str, Any] = self.feature_extraction_class.from_pretrained(__magic_name__ )
UpperCamelCase__ : List[str] = feat_extract_first.to_dict()
UpperCamelCase__ : Union[str, Any] = feat_extract_second.to_dict()
UpperCamelCase__ : str = dict_first.pop('''mel_filters''' )
UpperCamelCase__ : List[Any] = dict_second.pop('''mel_filters''' )
self.assertTrue(np.allclose(__magic_name__, __magic_name__ ) )
self.assertEqual(__magic_name__, __magic_name__ )
def UpperCamelCase__ ( self ) -> int:
"""simple docstring"""
UpperCamelCase__ : Optional[int] = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCamelCase__ : Tuple = os.path.join(__magic_name__, '''feat_extract.json''' )
feat_extract_first.to_json_file(__magic_name__ )
UpperCamelCase__ : List[Any] = self.feature_extraction_class.from_json_file(__magic_name__ )
UpperCamelCase__ : List[Any] = feat_extract_first.to_dict()
UpperCamelCase__ : Tuple = feat_extract_second.to_dict()
UpperCamelCase__ : Any = dict_first.pop('''mel_filters''' )
UpperCamelCase__ : Union[str, Any] = dict_second.pop('''mel_filters''' )
self.assertTrue(np.allclose(__magic_name__, __magic_name__ ) )
self.assertEqual(__magic_name__, __magic_name__ )
def UpperCamelCase__ ( self ) -> List[Any]:
"""simple docstring"""
# Initialize feature_extractor
UpperCamelCase__ : Any = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
UpperCamelCase__ : int = [floats_list((1, x) )[0] for x in range(800, 1400, 200 )]
UpperCamelCase__ : List[str] = [np.asarray(__magic_name__ ) for speech_input in speech_inputs]
# Test not batched input
UpperCamelCase__ : int = feature_extractor(np_speech_inputs[0], return_tensors='''np''', sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test batched
UpperCamelCase__ : Union[str, Any] = feature_extractor(__magic_name__, return_tensors='''np''', sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test audio masking
UpperCamelCase__ : Optional[int] = feature_extractor(
__magic_name__, return_tensors='''np''', sampling_rate=44100, mask_audio=__magic_name__ ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
# Test 2-D numpy arrays are batched.
UpperCamelCase__ : Optional[int] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
UpperCamelCase__ : Tuple = np.asarray(__magic_name__ )
UpperCamelCase__ : int = feature_extractor(__magic_name__, return_tensors='''np''', sampling_rate=44100 ).audio_values
self.assertTrue(encoded_audios.ndim == 4 )
self.assertTrue(encoded_audios.shape[-1] == feature_extractor.feature_size )
self.assertTrue(encoded_audios.shape[-2] <= feature_extractor.spectrogram_length )
self.assertTrue(encoded_audios.shape[-3] == feature_extractor.num_channels )
def UpperCamelCase__ ( self, __magic_name__ ) -> str:
"""simple docstring"""
UpperCamelCase__ : Optional[int] = load_dataset('''hf-internal-testing/librispeech_asr_dummy''', '''clean''', split='''validation''' )
# automatic decoding with librispeech
UpperCamelCase__ : Optional[int] = ds.sort('''id''' ).select(range(__magic_name__ ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def UpperCamelCase__ ( self ) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ : str = self._load_datasamples(1 )
UpperCamelCase__ : Any = TvltFeatureExtractor()
UpperCamelCase__ : Tuple = feature_extractor(__magic_name__, return_tensors='''pt''' ).audio_values
self.assertEquals(audio_values.shape, (1, 1, 192, 128) )
UpperCamelCase__ : Any = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2], __magic_name__, atol=1E-4 ) )
| 253 |
def lowerCAmelCase_ ( __UpperCAmelCase: Union[str, Any] , __UpperCAmelCase: List[str] ) -> Optional[int]:
UpperCamelCase__ : Union[str, Any] = [1]
for i in range(2 , __UpperCAmelCase ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
UpperCamelCase__ : List[str] = []
UpperCamelCase__ : Any = list(range(__UpperCAmelCase ) )
# Find permutation
while factorials:
UpperCamelCase__ : Tuple = factorials.pop()
UpperCamelCase__ ,UpperCamelCase__ : Any = divmod(__UpperCAmelCase , __UpperCAmelCase )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 253 | 1 |
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__UpperCamelCase : int = logging.get_logger(__name__)
__UpperCamelCase : Optional[int] = {"vocab_file": "vocab.txt"}
__UpperCamelCase : int = {
"vocab_file": {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt",
}
}
__UpperCamelCase : Any = {
"YituTech/conv-bert-base": 5_1_2,
"YituTech/conv-bert-medium-small": 5_1_2,
"YituTech/conv-bert-small": 5_1_2,
}
__UpperCamelCase : Any = {
"YituTech/conv-bert-base": {"do_lower_case": True},
"YituTech/conv-bert-medium-small": {"do_lower_case": True},
"YituTech/conv-bert-small": {"do_lower_case": True},
}
class UpperCAmelCase_ ( lowercase__ ):
snake_case_ = VOCAB_FILES_NAMES
snake_case_ = PRETRAINED_VOCAB_FILES_MAP
snake_case_ = PRETRAINED_INIT_CONFIGURATION
snake_case_ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case_ = ConvBertTokenizer
def __init__( self : Tuple , _lowercase : List[Any]=None , _lowercase : List[Any]=None , _lowercase : Optional[int]=True , _lowercase : int="[UNK]" , _lowercase : List[Any]="[SEP]" , _lowercase : Any="[PAD]" , _lowercase : str="[CLS]" , _lowercase : Union[str, Any]="[MASK]" , _lowercase : Optional[Any]=True , _lowercase : Tuple=None , **_lowercase : Dict , ) -> Union[str, Any]:
super().__init__(
_lowercase , tokenizer_file=_lowercase , do_lower_case=_lowercase , unk_token=_lowercase , sep_token=_lowercase , pad_token=_lowercase , cls_token=_lowercase , mask_token=_lowercase , tokenize_chinese_chars=_lowercase , strip_accents=_lowercase , **_lowercase , )
_lowercase = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get("lowercase" , _lowercase ) != do_lower_case
or normalizer_state.get("strip_accents" , _lowercase ) != strip_accents
or normalizer_state.get("handle_chinese_chars" , _lowercase ) != tokenize_chinese_chars
):
_lowercase = getattr(_lowercase , normalizer_state.pop("type" ) )
_lowercase = do_lower_case
_lowercase = strip_accents
_lowercase = tokenize_chinese_chars
_lowercase = normalizer_class(**_lowercase )
_lowercase = do_lower_case
def _lowerCamelCase ( self : List[Any] , _lowercase : Tuple , _lowercase : Tuple=None ) -> Any:
_lowercase = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def _lowerCamelCase ( self : List[str] , _lowercase : List[int] , _lowercase : Optional[List[int]] = None ) -> List[int]:
_lowercase = [self.sep_token_id]
_lowercase = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _lowerCamelCase ( self : str , _lowercase : str , _lowercase : Optional[str] = None ) -> Tuple[str]:
_lowercase = self._tokenizer.model.save(_lowercase , name=_lowercase )
return tuple(_lowercase ) | 227 | """simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__UpperCamelCase : Union[str, Any] = 1_6
__UpperCamelCase : Optional[int] = 3_2
def __UpperCAmelCase ( _snake_case : Accelerator, _snake_case : int = 1_6, _snake_case : str = "bert-base-cased" ):
_lowercase = AutoTokenizer.from_pretrained(_snake_case )
_lowercase = load_dataset("glue", "mrpc" )
def tokenize_function(_snake_case : List[str] ):
# max_length=None => use the model max length (it's actually the default)
_lowercase = tokenizer(examples["sentence1"], examples["sentence2"], truncation=_snake_case, max_length=_snake_case )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
_lowercase = datasets.map(
_snake_case, batched=_snake_case, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=_snake_case )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowercase = tokenized_datasets.rename_column("label", "labels" )
def collate_fn(_snake_case : Tuple ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_snake_case, padding="max_length", max_length=1_2_8, return_tensors="pt" )
return tokenizer.pad(_snake_case, padding="longest", return_tensors="pt" )
# Instantiate dataloaders.
_lowercase = DataLoader(
tokenized_datasets["train"], shuffle=_snake_case, collate_fn=_snake_case, batch_size=_snake_case )
_lowercase = DataLoader(
tokenized_datasets["validation"], shuffle=_snake_case, collate_fn=_snake_case, batch_size=_snake_case )
return train_dataloader, eval_dataloader
def __UpperCAmelCase ( _snake_case : Union[str, Any], _snake_case : Tuple ):
# Initialize accelerator
_lowercase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowercase = config["lr"]
_lowercase = int(config["num_epochs"] )
_lowercase = int(config["seed"] )
_lowercase = int(config["batch_size"] )
_lowercase = args.model_name_or_path
set_seed(_snake_case )
_lowercase , _lowercase = get_dataloaders(_snake_case, _snake_case, _snake_case )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowercase = AutoModelForSequenceClassification.from_pretrained(_snake_case, return_dict=_snake_case )
# Instantiate optimizer
_lowercase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_lowercase = optimizer_cls(params=model.parameters(), lr=_snake_case )
if accelerator.state.deepspeed_plugin is not None:
_lowercase = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
_lowercase = 1
_lowercase = (len(_snake_case ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_lowercase = get_linear_schedule_with_warmup(
optimizer=_snake_case, num_warmup_steps=0, num_training_steps=_snake_case, )
else:
_lowercase = DummyScheduler(_snake_case, total_num_steps=_snake_case, warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase = accelerator.prepare(
_snake_case, _snake_case, _snake_case, _snake_case, _snake_case )
# We need to keep track of how many total steps we have iterated over
_lowercase = 0
# We also need to keep track of the stating epoch so files are named properly
_lowercase = 0
# Now we train the model
_lowercase = evaluate.load("glue", "mrpc" )
_lowercase = 0
_lowercase = {}
for epoch in range(_snake_case, _snake_case ):
model.train()
for step, batch in enumerate(_snake_case ):
_lowercase = model(**_snake_case )
_lowercase = outputs.loss
_lowercase = loss / gradient_accumulation_steps
accelerator.backward(_snake_case )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
_lowercase = 0
for step, batch in enumerate(_snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_lowercase = model(**_snake_case )
_lowercase = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_lowercase , _lowercase = accelerator.gather(
(predictions, batch["labels"]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(_snake_case ) - 1:
_lowercase = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_lowercase = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=_snake_case, references=_snake_case, )
_lowercase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""", _snake_case )
_lowercase = eval_metric["accuracy"]
if best_performance < eval_metric["accuracy"]:
_lowercase = eval_metric["accuracy"]
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir, "all_results.json" ), "w" ) as f:
json.dump(_snake_case, _snake_case )
def __UpperCAmelCase ( ):
_lowercase = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
parser.add_argument(
"--model_name_or_path", type=_snake_case, default="bert-base-cased", help="Path to pretrained model or model identifier from huggingface.co/models.", required=_snake_case, )
parser.add_argument(
"--output_dir", type=_snake_case, default=".", help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.", )
parser.add_argument(
"--performance_lower_bound", type=_snake_case, default=_snake_case, help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.", )
parser.add_argument(
"--num_epochs", type=_snake_case, default=3, help="Number of train epochs.", )
_lowercase = parser.parse_args()
_lowercase = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
training_function(_snake_case, _snake_case )
if __name__ == "__main__":
main() | 227 | 1 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ):
'''simple docstring'''
__UpperCAmelCase : Dict =[r"""h\.\d+\.attn\.bias""", r"""h\.\d+\.attn\.masked_bias"""]
@register_to_config
def __init__( self , __a , __a , __a = None , __a = 5_02_57 , __a = 10_24 , __a = 7_68 , __a = 12 , __a = 12 , __a = None , __a = "gelu_new" , __a = 0.1 , __a = 0.1 , __a = 0.1 , __a = 1e-5 , __a = 0.0_2 , __a = True , __a = True , __a = False , __a = False , ):
super().__init__()
__lowerCAmelCase = prefix_length
if prefix_inner_dim != n_embd and prefix_hidden_dim is None:
raise ValueError(
f"`prefix_hidden_dim` cannot be `None` when `prefix_inner_dim`: {prefix_hidden_dim} and"
f" `n_embd`: {n_embd} are not equal." )
__lowerCAmelCase = prefix_inner_dim
__lowerCAmelCase = prefix_hidden_dim
__lowerCAmelCase = (
nn.Linear(self.prefix_inner_dim , self.prefix_hidden_dim )
if self.prefix_hidden_dim is not None
else nn.Identity()
)
__lowerCAmelCase = (
nn.Linear(self.prefix_hidden_dim , __a ) if self.prefix_hidden_dim is not None else nn.Identity()
)
__lowerCAmelCase = GPTaConfig(
vocab_size=__a , n_positions=__a , n_embd=__a , n_layer=__a , n_head=__a , n_inner=__a , activation_function=__a , resid_pdrop=__a , embd_pdrop=__a , attn_pdrop=__a , layer_norm_epsilon=__a , initializer_range=__a , scale_attn_weights=__a , use_cache=__a , scale_attn_by_inverse_layer_idx=__a , reorder_and_upcast_attn=__a , )
__lowerCAmelCase = GPTaLMHeadModel(__a )
def snake_case ( self , __a , __a , __a = None , __a = None , ):
__lowerCAmelCase = self.transformer.transformer.wte(__a )
__lowerCAmelCase = self.encode_prefix(__a )
__lowerCAmelCase = self.decode_prefix(__a )
__lowerCAmelCase = torch.cat((prefix_embeds, embedding_text) , dim=1 )
if labels is not None:
__lowerCAmelCase = self.get_dummy_token(input_ids.shape[0] , input_ids.device )
__lowerCAmelCase = torch.cat((dummy_token, input_ids) , dim=1 )
__lowerCAmelCase = self.transformer(inputs_embeds=__a , labels=__a , attention_mask=__a )
if self.prefix_hidden_dim is not None:
return out, hidden
else:
return out
def snake_case ( self , __a , __a ):
return torch.zeros(__a , self.prefix_length , dtype=torch.intaa , device=__a )
def snake_case ( self , __a ):
return self.encode_prefix(__a )
@torch.no_grad()
def snake_case ( self , __a , __a , __a ):
__lowerCAmelCase = torch.split(__a , 1 , dim=0 )
__lowerCAmelCase = []
__lowerCAmelCase = []
for feature in features:
__lowerCAmelCase = self.decode_prefix(feature.to(__a ) ) # back to the clip feature
# Only support beam search for now
__lowerCAmelCase , __lowerCAmelCase = self.generate_beam(
input_embeds=__a , device=__a , eos_token_id=__a )
generated_tokens.append(output_tokens[0] )
generated_seq_lengths.append(seq_lengths[0] )
__lowerCAmelCase = torch.stack(__a )
__lowerCAmelCase = torch.stack(__a )
return generated_tokens, generated_seq_lengths
@torch.no_grad()
def snake_case ( self , __a=None , __a=None , __a=None , __a = 5 , __a = 67 , __a = 1.0 , __a = None , ):
__lowerCAmelCase = eos_token_id
__lowerCAmelCase = None
__lowerCAmelCase = None
__lowerCAmelCase = torch.ones(__a , device=__a , dtype=torch.int )
__lowerCAmelCase = torch.zeros(__a , device=__a , dtype=torch.bool )
if input_embeds is not None:
__lowerCAmelCase = input_embeds
else:
__lowerCAmelCase = self.transformer.transformer.wte(__a )
for i in range(__a ):
__lowerCAmelCase = self.transformer(inputs_embeds=__a )
__lowerCAmelCase = outputs.logits
__lowerCAmelCase = logits[:, -1, :] / (temperature if temperature > 0 else 1.0)
__lowerCAmelCase = logits.softmax(-1 ).log()
if scores is None:
__lowerCAmelCase , __lowerCAmelCase = logits.topk(__a , -1 )
__lowerCAmelCase = generated.expand(__a , *generated.shape[1:] )
__lowerCAmelCase , __lowerCAmelCase = next_tokens.permute(1 , 0 ), scores.squeeze(0 )
if tokens is None:
__lowerCAmelCase = next_tokens
else:
__lowerCAmelCase = tokens.expand(__a , *tokens.shape[1:] )
__lowerCAmelCase = torch.cat((tokens, next_tokens) , dim=1 )
else:
__lowerCAmelCase = -float(np.inf )
__lowerCAmelCase = 0
__lowerCAmelCase = scores[:, None] + logits
seq_lengths[~is_stopped] += 1
__lowerCAmelCase = scores_sum / seq_lengths[:, None]
__lowerCAmelCase , __lowerCAmelCase = scores_sum_average.view(-1 ).topk(__a , -1 )
__lowerCAmelCase = next_tokens // scores_sum.shape[1]
__lowerCAmelCase = seq_lengths[next_tokens_source]
__lowerCAmelCase = next_tokens % scores_sum.shape[1]
__lowerCAmelCase = next_tokens.unsqueeze(1 )
__lowerCAmelCase = tokens[next_tokens_source]
__lowerCAmelCase = torch.cat((tokens, next_tokens) , dim=1 )
__lowerCAmelCase = generated[next_tokens_source]
__lowerCAmelCase = scores_sum_average * seq_lengths
__lowerCAmelCase = is_stopped[next_tokens_source]
__lowerCAmelCase = self.transformer.transformer.wte(next_tokens.squeeze() ).view(generated.shape[0] , 1 , -1 )
__lowerCAmelCase = torch.cat((generated, next_token_embed) , dim=1 )
__lowerCAmelCase = is_stopped + next_tokens.eq(__a ).squeeze()
if is_stopped.all():
break
__lowerCAmelCase = scores / seq_lengths
__lowerCAmelCase = scores.argsort(descending=__a )
# tokens tensors are already padded to max_seq_length
__lowerCAmelCase = [tokens[i] for i in order]
__lowerCAmelCase = torch.stack(__a , dim=0 )
__lowerCAmelCase = torch.tensor([seq_lengths[i] for i in order] , dtype=seq_lengths.dtype )
return output_texts, seq_lengths
| 636 |
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, FalconConfig, 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, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
)
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , __a , __a=3 , __a=7 , __a=True , __a=True , __a=False , __a=True , __a=99 , __a=32 , __a=5 , __a=4 , __a=37 , __a="gelu" , __a=0.1 , __a=0.1 , __a=5_12 , __a=16 , __a=2 , __a=0.0_2 , __a=3 , __a=4 , __a=None , ):
__lowerCAmelCase = parent
__lowerCAmelCase = batch_size
__lowerCAmelCase = seq_length
__lowerCAmelCase = is_training
__lowerCAmelCase = use_input_mask
__lowerCAmelCase = use_token_type_ids
__lowerCAmelCase = use_labels
__lowerCAmelCase = vocab_size
__lowerCAmelCase = hidden_size
__lowerCAmelCase = num_hidden_layers
__lowerCAmelCase = num_attention_heads
__lowerCAmelCase = intermediate_size
__lowerCAmelCase = hidden_act
__lowerCAmelCase = hidden_dropout_prob
__lowerCAmelCase = attention_probs_dropout_prob
__lowerCAmelCase = max_position_embeddings
__lowerCAmelCase = type_vocab_size
__lowerCAmelCase = type_sequence_label_size
__lowerCAmelCase = initializer_range
__lowerCAmelCase = num_labels
__lowerCAmelCase = num_choices
__lowerCAmelCase = scope
def snake_case ( self ):
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowerCAmelCase = None
if self.use_input_mask:
__lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
__lowerCAmelCase = None
__lowerCAmelCase = None
__lowerCAmelCase = None
__lowerCAmelCase = None
if self.use_labels:
__lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
__lowerCAmelCase = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case ( self ):
return FalconConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=__a , initializer_range=self.initializer_range , pad_token_id=1 , new_decoder_architecture=__a , )
def snake_case ( self , __a , __a , __a , __a , __a , __a , __a ):
__lowerCAmelCase = FalconModel(config=__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(__a , attention_mask=__a )
__lowerCAmelCase = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , __a , __a , __a , __a , __a , __a , __a , __a , __a , ):
__lowerCAmelCase = True
__lowerCAmelCase = FalconModel(__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(
__a , attention_mask=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , )
__lowerCAmelCase = model(
__a , attention_mask=__a , encoder_hidden_states=__a , )
__lowerCAmelCase = model(__a , attention_mask=__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case ( self , __a , __a , __a , __a , __a , __a , __a , __a , __a , ):
__lowerCAmelCase = FalconForCausalLM(config=__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(__a , attention_mask=__a , labels=__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case ( self , __a , __a , __a , __a , __a , __a , __a , __a , __a , ):
__lowerCAmelCase = True
__lowerCAmelCase = True
__lowerCAmelCase = FalconForCausalLM(config=__a )
model.to(__a )
model.eval()
# first forward pass
__lowerCAmelCase = model(
__a , attention_mask=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , use_cache=__a , )
__lowerCAmelCase = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
__lowerCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size )
__lowerCAmelCase = ids_tensor((self.batch_size, 3) , vocab_size=2 )
# append to next input_ids and
__lowerCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1 )
__lowerCAmelCase = torch.cat([input_mask, next_mask] , dim=-1 )
__lowerCAmelCase = model(
__a , attention_mask=__a , encoder_hidden_states=__a , encoder_attention_mask=__a , output_hidden_states=__a , )["hidden_states"][0]
__lowerCAmelCase = 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
__lowerCAmelCase = ids_tensor((1,) , output_from_past.shape[-1] ).item()
__lowerCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
__lowerCAmelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__a , __a , atol=1e-3 ) )
def snake_case ( self ):
__lowerCAmelCase = self.prepare_config_and_inputs()
(
(
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) , (
__lowerCAmelCase
) ,
) = config_and_inputs
__lowerCAmelCase = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class _UpperCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ,lowerCAmelCase__ ,unittest.TestCase ):
'''simple docstring'''
__UpperCAmelCase : int =(
(
FalconModel,
FalconForCausalLM,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconForQuestionAnswering,
)
if is_torch_available()
else ()
)
__UpperCAmelCase : Union[str, Any] =(FalconForCausalLM,) if is_torch_available() else ()
__UpperCAmelCase : List[Any] =(
{
"""feature-extraction""": FalconModel,
"""text-classification""": FalconForSequenceClassification,
"""text-generation""": FalconForCausalLM,
"""question-answering""": FalconForQuestionAnswering,
"""token-classification""": FalconForTokenClassification,
"""zero-shot""": FalconForSequenceClassification,
}
if is_torch_available()
else {}
)
__UpperCAmelCase : Any =False
__UpperCAmelCase : Tuple =False
def snake_case ( self ):
__lowerCAmelCase = FalconModelTester(self )
__lowerCAmelCase = ConfigTester(self , config_class=__a , hidden_size=37 )
def snake_case ( self ):
self.config_tester.run_common_tests()
def snake_case ( self ):
__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def snake_case ( self ):
__lowerCAmelCase , *__lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for alibi in [True, False]:
__lowerCAmelCase = alibi
self.model_tester.create_and_check_model(__a , *__a )
def snake_case ( self ):
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = 3
__lowerCAmelCase = input_dict["input_ids"]
__lowerCAmelCase = input_ids.ne(1 ).to(__a )
__lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__lowerCAmelCase = FalconForSequenceClassification(__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(__a , attention_mask=__a , labels=__a )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def snake_case ( self ):
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = 3
__lowerCAmelCase = "single_label_classification"
__lowerCAmelCase = input_dict["input_ids"]
__lowerCAmelCase = input_ids.ne(1 ).to(__a )
__lowerCAmelCase = ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
__lowerCAmelCase = FalconForSequenceClassification(__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(__a , attention_mask=__a , labels=__a )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def snake_case ( self ):
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = input_dict["input_ids"]
__lowerCAmelCase = FalconForCausalLM(__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(__a , use_cache=__a )
__lowerCAmelCase = input_ids.shape[0]
__lowerCAmelCase = model._convert_to_rw_cache(result.past_key_values )
__lowerCAmelCase = model._convert_cache_to_standard_format(__a , __a )
for layer in range(len(__a ) ):
for tensor_idx in range(2 ):
self.assertTrue(rw_cache[layer][tensor_idx].ndim == 3 )
self.assertTrue(result.past_key_values[layer][tensor_idx].ndim == 4 )
self.assertTrue(
torch.all(result.past_key_values[layer][tensor_idx] == standard_cache[layer][tensor_idx] ) )
def snake_case ( self ):
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__lowerCAmelCase = 3
__lowerCAmelCase = "multi_label_classification"
__lowerCAmelCase = input_dict["input_ids"]
__lowerCAmelCase = input_ids.ne(1 ).to(__a )
__lowerCAmelCase = ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
__lowerCAmelCase = FalconForSequenceClassification(__a )
model.to(__a )
model.eval()
__lowerCAmelCase = model(__a , attention_mask=__a , labels=__a )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def snake_case ( self ):
# Falcon can have different numbers of KV-heads than the number of query heads, so we need
# to override this test to use the right head counts.
for model_class in self.all_generative_model_classes:
__lowerCAmelCase , __lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
# If it doesn't support cache, pass the test
if not hasattr(__a , "use_cache" ):
return
__lowerCAmelCase = model_class(__a ).to(__a )
if "use_cache" not in inputs:
__lowerCAmelCase = True
__lowerCAmelCase = model(**__a )
# If "past_key_values" is not returned, pass the test (e.g. RWKV uses a different cache name and format)
if "past_key_values" not in outputs:
return
__lowerCAmelCase = (
getattr(__a , "decoder_layers" , __a )
or getattr(__a , "num_decoder_layers" , __a )
or config.num_hidden_layers
)
__lowerCAmelCase = getattr(__a , "num_kv_heads" , config.num_attention_heads )
__lowerCAmelCase = getattr(__a , "d_model" , config.hidden_size )
__lowerCAmelCase = embed_dim // num_attention_heads
__lowerCAmelCase = outputs["past_key_values"]
self.assertEqual(len(__a ) , __a )
__lowerCAmelCase , __lowerCAmelCase = inputs["input_ids"].shape
for i in range(__a ):
if config.new_decoder_architecture:
__lowerCAmelCase = config.num_attention_heads
elif config.multi_query:
__lowerCAmelCase = 1
self.assertEqual(len(past_kv[0] ) , 2 ) # K V for the decoder = 2
self.assertEqual(
past_kv[i][0].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
self.assertEqual(
past_kv[i][1].shape , (batch_size, num_attention_heads, seq_length, per_head_embed_dim) )
@require_torch
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case ( self ):
__lowerCAmelCase = AutoTokenizer.from_pretrained("Rocketknight1/falcon-rw-1b" )
__lowerCAmelCase = FalconForCausalLM.from_pretrained("Rocketknight1/falcon-rw-1b" )
model.eval()
model.to(__a )
__lowerCAmelCase = tokenizer("My favorite food is" , return_tensors="pt" ).to(__a )
__lowerCAmelCase = (
"My favorite food is pizza. I love it so much that I have a pizza party every year for my birthday."
)
__lowerCAmelCase = model.generate(**__a , do_sample=__a , max_new_tokens=19 )
__lowerCAmelCase = tokenizer.batch_decode(__a )[0]
self.assertEqual(__a , __a )
@slow
def snake_case ( self ):
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
for repo in ["Rocketknight1/tiny-random-falcon-7b", "Rocketknight1/tiny-random-falcon-40b"]:
__lowerCAmelCase = AutoTokenizer.from_pretrained(__a )
__lowerCAmelCase = FalconForCausalLM.from_pretrained(__a )
model.eval()
model.to(__a )
__lowerCAmelCase = tokenizer("My favorite food is" , return_tensors="pt" ).to(__a )
# We just test that these run without errors - the models are randomly initialized
# and so the actual text outputs will be garbage
model.generate(**__a , do_sample=__a , max_new_tokens=4 )
model.generate(**__a , do_sample=__a , max_new_tokens=4 )
model.generate(**__a , num_beams=2 , max_new_tokens=4 )
@slow
def snake_case ( self ):
# The big models are way too big for the CI, so we use tiny random models that resemble their
# architectures but with much smaller and fewer layers
with torch.no_grad():
for repo in [
"Rocketknight1/falcon-rw-1b",
"Rocketknight1/tiny-random-falcon-7b",
"Rocketknight1/tiny-random-falcon-40b",
]:
__lowerCAmelCase = AutoTokenizer.from_pretrained(__a )
__lowerCAmelCase = FalconForCausalLM.from_pretrained(__a )
model.eval()
model.to(device=__a )
__lowerCAmelCase = tokenizer("My favorite food is" , return_tensors="pt" ).to(__a )
# Test results are the same with and without cache
__lowerCAmelCase = model.generate(**__a , do_sample=__a , max_new_tokens=20 , use_cache=__a )
__lowerCAmelCase = model.generate(**__a , do_sample=__a , max_new_tokens=20 , use_cache=__a )
self.assertTrue((outputs_cache - outputs_no_cache).sum().item() == 0 )
| 636 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, XLMRobertaTokenizer
from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCAmelCase__ ( __lowerCamelCase, __lowerCamelCase, __lowerCamelCase, unittest.TestCase ):
"""simple docstring"""
__UpperCAmelCase : str = AltDiffusionPipeline
__UpperCAmelCase : Optional[int] = TEXT_TO_IMAGE_PARAMS
__UpperCAmelCase : List[str] = TEXT_TO_IMAGE_BATCH_PARAMS
__UpperCAmelCase : Union[str, Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
__UpperCAmelCase : Union[str, Any] = TEXT_TO_IMAGE_IMAGE_PARAMS
def _UpperCamelCase ( self ):
torch.manual_seed(0 )
lowerCamelCase_ : int = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=32 , )
lowerCamelCase_ : Tuple = DDIMScheduler(
beta_start=0.0_00_85 , beta_end=0.0_12 , beta_schedule="scaled_linear" , clip_sample=_SCREAMING_SNAKE_CASE , set_alpha_to_one=_SCREAMING_SNAKE_CASE , )
torch.manual_seed(0 )
lowerCamelCase_ : List[str] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
# TODO: address the non-deterministic text encoder (fails for save-load tests)
# torch.manual_seed(0)
# text_encoder_config = RobertaSeriesConfig(
# hidden_size=32,
# project_dim=32,
# intermediate_size=37,
# layer_norm_eps=1e-05,
# num_attention_heads=4,
# num_hidden_layers=5,
# vocab_size=5002,
# )
# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)
torch.manual_seed(0 )
lowerCamelCase_ : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=5002 , )
lowerCamelCase_ : Optional[Any] = CLIPTextModel(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : Dict = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
lowerCamelCase_ : Union[str, Any] = 77
lowerCamelCase_ : str = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def _UpperCamelCase ( self , a_ , a_=0 ):
if str(_SCREAMING_SNAKE_CASE ).startswith("mps" ):
lowerCamelCase_ : Any = torch.manual_seed(_SCREAMING_SNAKE_CASE )
else:
lowerCamelCase_ : Dict = torch.Generator(device=_SCREAMING_SNAKE_CASE ).manual_seed(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : str = {
"prompt": "A painting of a squirrel eating a burger",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 6.0,
"output_type": "numpy",
}
return inputs
def _UpperCamelCase ( self ):
super().test_attention_slicing_forward_pass(expected_max_diff=3E-3 )
def _UpperCamelCase ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
def _UpperCamelCase ( self ):
lowerCamelCase_ : Tuple = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ : Optional[int] = self.get_dummy_components()
torch.manual_seed(0 )
lowerCamelCase_ : Tuple = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
lowerCamelCase_ : Tuple = RobertaSeriesModelWithTransformation(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : int = text_encoder
lowerCamelCase_ : List[Any] = AltDiffusionPipeline(**_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : Optional[Any] = alt_pipe.to(_SCREAMING_SNAKE_CASE )
alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : List[Any] = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : Optional[int] = "A photo of an astronaut"
lowerCamelCase_ : str = alt_pipe(**_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : Any = output.images
lowerCamelCase_ : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase_ : List[str] = np.array(
[0.5_74_81_62, 0.60_44_71_45, 0.48_82_12_17, 0.50_10_06_36, 0.5_43_11_85, 0.45_76_36_83, 0.49_65_76_96, 0.48_13_27_33, 0.47_57_30_93] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _UpperCamelCase ( self ):
lowerCamelCase_ : Optional[Any] = "cpu" # ensure determinism for the device-dependent torch.Generator
lowerCamelCase_ : str = self.get_dummy_components()
lowerCamelCase_ : Optional[Any] = PNDMScheduler(skip_prk_steps=_SCREAMING_SNAKE_CASE )
torch.manual_seed(0 )
lowerCamelCase_ : List[Any] = RobertaSeriesConfig(
hidden_size=32 , project_dim=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , vocab_size=5002 , )
# TODO: remove after fixing the non-deterministic text encoder
lowerCamelCase_ : Any = RobertaSeriesModelWithTransformation(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : Any = text_encoder
lowerCamelCase_ : List[Any] = AltDiffusionPipeline(**_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : Union[str, Any] = alt_pipe.to(_SCREAMING_SNAKE_CASE )
alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : List[str] = self.get_dummy_inputs(_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : List[str] = alt_pipe(**_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : List[str] = output.images
lowerCamelCase_ : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase_ : Optional[Any] = np.array(
[0.51_60_50_93, 0.5_70_72_41, 0.47_36_55_07, 0.50_57_88_86, 0.5_63_38_77, 0.4_64_25_03, 0.5_18_20_81, 0.48_76_34_84, 0.49_08_42_37] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class lowerCAmelCase__ ( unittest.TestCase ):
"""simple docstring"""
def _UpperCamelCase ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _UpperCamelCase ( self ):
lowerCamelCase_ : List[Any] = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion" , safety_checker=_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : Optional[Any] = alt_pipe.to(_SCREAMING_SNAKE_CASE )
alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : Optional[int] = "A painting of a squirrel eating a burger"
lowerCamelCase_ : Dict = torch.manual_seed(0 )
lowerCamelCase_ : Any = alt_pipe([prompt] , generator=_SCREAMING_SNAKE_CASE , guidance_scale=6.0 , num_inference_steps=20 , output_type="np" )
lowerCamelCase_ : Union[str, Any] = output.images
lowerCamelCase_ : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ : Any = np.array([0.10_10, 0.08_00, 0.07_94, 0.08_85, 0.08_43, 0.07_62, 0.07_69, 0.07_29, 0.05_86] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def _UpperCamelCase ( self ):
lowerCamelCase_ : Tuple = DDIMScheduler.from_pretrained("BAAI/AltDiffusion" , subfolder="scheduler" )
lowerCamelCase_ : Optional[int] = AltDiffusionPipeline.from_pretrained("BAAI/AltDiffusion" , scheduler=_SCREAMING_SNAKE_CASE , safety_checker=_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : Any = alt_pipe.to(_SCREAMING_SNAKE_CASE )
alt_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE )
lowerCamelCase_ : int = "A painting of a squirrel eating a burger"
lowerCamelCase_ : Dict = torch.manual_seed(0 )
lowerCamelCase_ : Optional[Any] = alt_pipe([prompt] , generator=_SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="numpy" )
lowerCamelCase_ : Union[str, Any] = output.images
lowerCamelCase_ : Union[str, Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCamelCase_ : int = np.array([0.40_19, 0.40_52, 0.38_10, 0.41_19, 0.39_16, 0.39_82, 0.46_51, 0.41_95, 0.53_23] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 713 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class lowerCAmelCase__ ( __lowerCamelCase ):
"""simple docstring"""
__UpperCAmelCase : Dict = '''EncodecFeatureExtractor'''
__UpperCAmelCase : Any = ('''T5Tokenizer''', '''T5TokenizerFast''')
def __init__( self , a_ , a_ ):
super().__init__(a_ , a_ )
lowerCamelCase_ : Optional[Any] = self.feature_extractor
lowerCamelCase_ : Optional[int] = False
def _UpperCamelCase ( self , a_=None , a_=None , a_=True ):
return self.tokenizer.get_decoder_prompt_ids(task=a_ , language=a_ , no_timestamps=a_ )
def __call__( self , *a_ , **a_ ):
# For backward compatibility
if self._in_target_context_manager:
return self.current_processor(*a_ , **a_ )
lowerCamelCase_ : str = kwargs.pop("audio" , a_ )
lowerCamelCase_ : List[str] = kwargs.pop("sampling_rate" , a_ )
lowerCamelCase_ : Optional[Any] = kwargs.pop("text" , a_ )
if len(a_ ) > 0:
lowerCamelCase_ : int = args[0]
lowerCamelCase_ : str = args[1:]
if audio is None and text is None:
raise ValueError("You need to specify either an `audio` or `text` input to process." )
if text is not None:
lowerCamelCase_ : Dict = self.tokenizer(a_ , **a_ )
if audio is not None:
lowerCamelCase_ : Optional[Any] = self.feature_extractor(a_ , *a_ , sampling_rate=a_ , **a_ )
if audio is None:
return inputs
elif text is None:
return audio_inputs
else:
lowerCamelCase_ : Dict = audio_inputs["input_values"]
if "padding_mask" in audio_inputs:
lowerCamelCase_ : int = audio_inputs["padding_mask"]
return inputs
def _UpperCamelCase ( self , *a_ , **a_ ):
lowerCamelCase_ : Dict = kwargs.pop("audio" , a_ )
lowerCamelCase_ : Optional[Any] = kwargs.pop("padding_mask" , a_ )
if len(a_ ) > 0:
lowerCamelCase_ : Optional[int] = args[0]
lowerCamelCase_ : Optional[Any] = args[1:]
if audio_values is not None:
return self._decode_audio(a_ , padding_mask=a_ )
else:
return self.tokenizer.batch_decode(*a_ , **a_ )
def _UpperCamelCase ( self , *a_ , **a_ ):
return self.tokenizer.decode(*a_ , **a_ )
def _UpperCamelCase ( self , a_ , a_ = None ):
lowerCamelCase_ : Any = to_numpy(a_ )
lowerCamelCase_ ,lowerCamelCase_ ,lowerCamelCase_ : List[str] = audio_values.shape
if padding_mask is None:
return list(a_ )
lowerCamelCase_ : Tuple = to_numpy(a_ )
# match the sequence length of the padding mask to the generated audio arrays by padding with the **non-padding**
# token (so that the generated audio values are **not** treated as padded tokens)
lowerCamelCase_ : List[str] = seq_len - padding_mask.shape[-1]
lowerCamelCase_ : int = 1 - self.feature_extractor.padding_value
lowerCamelCase_ : List[Any] = np.pad(a_ , ((0, 0), (0, difference)) , "constant" , constant_values=a_ )
lowerCamelCase_ : str = audio_values.tolist()
for i in range(a_ ):
lowerCamelCase_ : Dict = np.asarray(audio_values[i] )[
padding_mask[i][None, :] != self.feature_extractor.padding_value
]
lowerCamelCase_ : Dict = sliced_audio.reshape(a_ , -1 )
return audio_values
| 73 | 0 |
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
_SCREAMING_SNAKE_CASE : List[Any] = version.parse(importlib_metadata.version('nltk'))
if NLTK_VERSION >= version.Version('3.6.4'):
from nltk import word_tokenize
_SCREAMING_SNAKE_CASE : List[Any] = '\\n@inproceedings{banarjee2005,\n title = {{METEOR}: An Automatic Metric for {MT} Evaluation with Improved Correlation with Human Judgments},\n author = {Banerjee, Satanjeev and Lavie, Alon},\n booktitle = {Proceedings of the {ACL} Workshop on Intrinsic and Extrinsic Evaluation Measures for Machine Translation and/or Summarization},\n month = jun,\n year = {2005},\n address = {Ann Arbor, Michigan},\n publisher = {Association for Computational Linguistics},\n url = {https://www.aclweb.org/anthology/W05-0909},\n pages = {65--72},\n}\n'
_SCREAMING_SNAKE_CASE : List[Any] = '\\nMETEOR, an automatic metric for machine translation evaluation\nthat is based on a generalized concept of unigram matching between the\nmachine-produced translation and human-produced reference translations.\nUnigrams can be matched based on their surface forms, stemmed forms,\nand meanings; furthermore, METEOR can be easily extended to include more\nadvanced matching strategies. Once all generalized unigram matches\nbetween the two strings have been found, METEOR computes a score for\nthis matching using a combination of unigram-precision, unigram-recall, and\na measure of fragmentation that is designed to directly capture how\nwell-ordered the matched words in the machine translation are in relation\nto the reference.\n\nMETEOR gets an R correlation value of 0.347 with human evaluation on the Arabic\ndata and 0.331 on the Chinese data. This is shown to be an improvement on\nusing simply unigram-precision, unigram-recall and their harmonic F1\ncombination.\n'
_SCREAMING_SNAKE_CASE : List[Any] = '\nComputes METEOR score of translated segments against one or more references.\nArgs:\n predictions: list of predictions to score. Each prediction\n should be a string with tokens separated by spaces.\n references: list of reference for each prediction. Each\n reference should be a string with tokens separated by spaces.\n alpha: Parameter for controlling relative weights of precision and recall. default: 0.9\n beta: Parameter for controlling shape of penalty as a function of fragmentation. default: 3\n gamma: Relative weight assigned to fragmentation penalty. default: 0.5\nReturns:\n \'meteor\': meteor score.\nExamples:\n\n >>> meteor = datasets.load_metric(\'meteor\')\n >>> predictions = ["It is a guide to action which ensures that the military always obeys the commands of the party"]\n >>> references = ["It is a guide to action that ensures that the military will forever heed Party commands"]\n >>> results = meteor.compute(predictions=predictions, references=references)\n >>> print(round(results["meteor"], 4))\n 0.6944\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase__ ( self : Optional[int]):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"predictions": datasets.Value("string" , id="sequence"),
"references": datasets.Value("string" , id="sequence"),
}) , codebase_urls=["https://github.com/nltk/nltk/blob/develop/nltk/translate/meteor_score.py"] , reference_urls=[
"https://www.nltk.org/api/nltk.translate.html#module-nltk.translate.meteor_score",
"https://en.wikipedia.org/wiki/METEOR",
] , )
def UpperCAmelCase__ ( self : Union[str, Any] , _UpperCamelCase : Union[str, Any]):
import nltk
nltk.download("wordnet")
if NLTK_VERSION >= version.Version("3.6.5"):
nltk.download("punkt")
if NLTK_VERSION >= version.Version("3.6.6"):
nltk.download("omw-1.4")
def UpperCAmelCase__ ( self : int , _UpperCamelCase : Any , _UpperCamelCase : Dict , _UpperCamelCase : int=0.9 , _UpperCamelCase : List[str]=3 , _UpperCamelCase : Dict=0.5):
if NLTK_VERSION >= version.Version("3.6.5"):
_lowercase: List[str] = [
meteor_score.single_meteor_score(
word_tokenize(_UpperCamelCase) , word_tokenize(_UpperCamelCase) , alpha=_UpperCamelCase , beta=_UpperCamelCase , gamma=_UpperCamelCase)
for ref, pred in zip(_UpperCamelCase , _UpperCamelCase)
]
else:
_lowercase: Optional[int] = [
meteor_score.single_meteor_score(_UpperCamelCase , _UpperCamelCase , alpha=_UpperCamelCase , beta=_UpperCamelCase , gamma=_UpperCamelCase)
for ref, pred in zip(_UpperCamelCase , _UpperCamelCase)
]
return {"meteor": np.mean(_UpperCamelCase)}
| 226 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : str = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : str = {
'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
'kssteven/ibert-roberta-large-mnli': (
'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
),
}
class A ( lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase : Tuple = """ibert"""
def __init__( self : str , _UpperCamelCase : Optional[Any]=30_522 , _UpperCamelCase : List[Any]=768 , _UpperCamelCase : str=12 , _UpperCamelCase : Optional[Any]=12 , _UpperCamelCase : Tuple=3_072 , _UpperCamelCase : Dict="gelu" , _UpperCamelCase : List[Any]=0.1 , _UpperCamelCase : Dict=0.1 , _UpperCamelCase : str=512 , _UpperCamelCase : Union[str, Any]=2 , _UpperCamelCase : Tuple=0.0_2 , _UpperCamelCase : List[Any]=1e-12 , _UpperCamelCase : Any=1 , _UpperCamelCase : Optional[int]=0 , _UpperCamelCase : int=2 , _UpperCamelCase : Any="absolute" , _UpperCamelCase : Union[str, Any]=False , _UpperCamelCase : Optional[int]="none" , **_UpperCamelCase : Dict , ):
super().__init__(pad_token_id=_UpperCamelCase , bos_token_id=_UpperCamelCase , eos_token_id=_UpperCamelCase , **_UpperCamelCase)
_lowercase: Dict = vocab_size
_lowercase: int = hidden_size
_lowercase: Union[str, Any] = num_hidden_layers
_lowercase: Optional[Any] = num_attention_heads
_lowercase: Tuple = hidden_act
_lowercase: str = intermediate_size
_lowercase: List[str] = hidden_dropout_prob
_lowercase: Tuple = attention_probs_dropout_prob
_lowercase: Optional[Any] = max_position_embeddings
_lowercase: Tuple = type_vocab_size
_lowercase: List[str] = initializer_range
_lowercase: Optional[int] = layer_norm_eps
_lowercase: Optional[int] = position_embedding_type
_lowercase: Any = quant_mode
_lowercase: Dict = force_dequant
class A ( lowerCamelCase_ ):
'''simple docstring'''
@property
def UpperCAmelCase__ ( self : List[str]):
if self.task == "multiple-choice":
_lowercase: List[Any] = {0: "batch", 1: "choice", 2: "sequence"}
else:
_lowercase: Optional[int] = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
])
| 226 | 1 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
lowerCamelCase : Dict =logging.get_logger(__name__)
def _lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : Tuple , _SCREAMING_SNAKE_CASE : Union[str, Any] , _SCREAMING_SNAKE_CASE : List[Any] ) -> Any:
'''simple docstring'''
__A : Any = original_name.split('.' )[0]
__A : str = key.split('.' )
__A : int = int(key_list[key_list.index(_SCREAMING_SNAKE_CASE ) - 2] )
__A : int = int(key_list[key_list.index(_SCREAMING_SNAKE_CASE ) - 1] )
__A : Any = orig_block_num - offset
__A : str = key.replace(F'{orig_block_num}.{layer_num}.{original_name}' , F'block.{new_block_num}.{layer_num}.{new_name}' )
return key
def _lowercase ( _SCREAMING_SNAKE_CASE : int ) -> Tuple:
'''simple docstring'''
__A : List[str] = OrderedDict()
__A , __A : Tuple = 0, 0
for key, value in state_dict.items():
if key.startswith('network' ):
__A : Union[str, Any] = key.replace('network' , 'poolformer.encoder' )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith('bias' ) and "patch_embed" not in key:
patch_emb_offset += 1
__A : Dict = key[: key.find('proj' )]
__A : Union[str, Any] = key.replace(_SCREAMING_SNAKE_CASE , F'patch_embeddings.{total_embed_found}.' )
__A : Any = key.replace('proj' , 'projection' )
if key.endswith('bias' ):
total_embed_found += 1
if "patch_embeddings" in key:
__A : Any = 'poolformer.encoder.' + key
if "mlp.fc1" in key:
__A : Optional[Any] = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'mlp.fc1' , 'output.conv1' )
if "mlp.fc2" in key:
__A : List[Any] = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'mlp.fc2' , 'output.conv2' )
if "norm1" in key:
__A : List[str] = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'norm1' , 'before_norm' )
if "norm2" in key:
__A : str = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'norm2' , 'after_norm' )
if "layer_scale_1" in key:
__A : Dict = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'layer_scale_1' , 'layer_scale_1' )
if "layer_scale_2" in key:
__A : Optional[Any] = replace_key_with_offset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , 'layer_scale_2' , 'layer_scale_2' )
if "head" in key:
__A : Union[str, Any] = key.replace('head' , 'classifier' )
__A : List[Any] = value
return new_state_dict
def _lowercase ( ) -> Dict:
'''simple docstring'''
__A : List[Any] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
__A : List[Any] = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return image
@torch.no_grad()
def _lowercase ( _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : List[str] , _SCREAMING_SNAKE_CASE : int ) -> Union[str, Any]:
'''simple docstring'''
__A : Dict = PoolFormerConfig()
# set attributes based on model_name
__A : Optional[Any] = 'huggingface/label-files'
__A : List[str] = model_name[-3:]
__A : Dict = 1000
__A : List[Any] = 'imagenet-1k-id2label.json'
__A : Any = (1, 1000)
# set config attributes
__A : Optional[int] = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type='dataset' ) , 'r' ) )
__A : List[str] = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__A : Union[str, Any] = idalabel
__A : Optional[Any] = {v: k for k, v in idalabel.items()}
if size == "s12":
__A : Optional[int] = [2, 2, 6, 2]
__A : Union[str, Any] = [64, 128, 320, 512]
__A : Any = 4.0
__A : str = 0.9
elif size == "s24":
__A : Optional[Any] = [4, 4, 12, 4]
__A : Union[str, Any] = [64, 128, 320, 512]
__A : str = 4.0
__A : int = 0.9
elif size == "s36":
__A : Tuple = [6, 6, 18, 6]
__A : Optional[Any] = [64, 128, 320, 512]
__A : Any = 4.0
__A : int = 1E-6
__A : List[str] = 0.9
elif size == "m36":
__A : Tuple = [6, 6, 18, 6]
__A : List[str] = [96, 192, 384, 768]
__A : Tuple = 4.0
__A : int = 1E-6
__A : Any = 0.95
elif size == "m48":
__A : Union[str, Any] = [8, 8, 24, 8]
__A : Dict = [96, 192, 384, 768]
__A : Any = 4.0
__A : List[str] = 1E-6
__A : str = 0.95
else:
raise ValueError(F'Size {size} not supported' )
# load image processor
__A : Optional[Any] = PoolFormerImageProcessor(crop_pct=_SCREAMING_SNAKE_CASE )
# Prepare image
__A : Optional[int] = prepare_img()
__A : int = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors='pt' ).pixel_values
logger.info(F'Converting model {model_name}...' )
# load original state dict
__A : Any = torch.load(_SCREAMING_SNAKE_CASE , map_location=torch.device('cpu' ) )
# rename keys
__A : str = rename_keys(_SCREAMING_SNAKE_CASE )
# create HuggingFace model and load state dict
__A : List[Any] = PoolFormerForImageClassification(_SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
model.eval()
# Define image processor
__A : Dict = PoolFormerImageProcessor(crop_pct=_SCREAMING_SNAKE_CASE )
__A : Any = image_processor(images=prepare_img() , return_tensors='pt' ).pixel_values
# forward pass
__A : Optional[int] = model(_SCREAMING_SNAKE_CASE )
__A : Optional[int] = outputs.logits
# define expected logit slices for different models
if size == "s12":
__A : Union[str, Any] = torch.tensor([-0.30_45, -0.67_58, -0.48_69] )
elif size == "s24":
__A : str = torch.tensor([0.44_02, -0.13_74, -0.80_45] )
elif size == "s36":
__A : str = torch.tensor([-0.60_80, -0.51_33, -0.58_98] )
elif size == "m36":
__A : Optional[Any] = torch.tensor([0.39_52, 0.22_63, -1.26_68] )
elif size == "m48":
__A : Union[str, Any] = torch.tensor([0.11_67, -0.06_56, -0.34_23] )
else:
raise ValueError(F'Size {size} not supported' )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-2 )
# finally, save model and image processor
logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
lowerCamelCase : List[Any] =argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''poolformer_s12''',
type=str,
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
lowerCamelCase : Optional[Any] =parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 237 | """simple docstring"""
lowerCamelCase : int =[0, 2, 4, 6, 8]
lowerCamelCase : List[str] =[1, 3, 5, 7, 9]
def _lowercase ( _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : list[int] , _SCREAMING_SNAKE_CASE : int ) -> int:
'''simple docstring'''
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return 0
for i in range(length // 2 - 1 , -1 , -1 ):
remainder += digits[i] + digits[length - i - 1]
if remainder % 2 == 0:
return 0
remainder //= 10
return 1
if remaining_length == 1:
if remainder % 2 == 0:
return 0
__A : Union[str, Any] = 0
for digit in range(10 ):
__A : Dict = digit
result += reversible_numbers(
0 , (remainder + 2 * digit) // 10 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return result
__A : Union[str, Any] = 0
for digita in range(10 ):
__A : Tuple = digita
if (remainder + digita) % 2 == 0:
__A : Union[str, Any] = ODD_DIGITS
else:
__A : Optional[int] = EVEN_DIGITS
for digita in other_parity_digits:
__A : Union[str, Any] = digita
result += reversible_numbers(
remaining_length - 2 , (remainder + digita + digita) // 10 , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , )
return result
def _lowercase ( _SCREAMING_SNAKE_CASE : int = 9 ) -> int:
'''simple docstring'''
__A : Tuple = 0
for length in range(1 , max_power + 1 ):
result += reversible_numbers(_SCREAMING_SNAKE_CASE , 0 , [0] * length , _SCREAMING_SNAKE_CASE )
return result
if __name__ == "__main__":
print(F'{solution() = }')
| 237 | 1 |
from __future__ import annotations
import time
_UpperCAmelCase = list[tuple[int, int]]
_UpperCAmelCase = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0],
[1, 0, 1, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 0, 0],
]
_UpperCAmelCase = [[-1, 0], [0, -1], [1, 0], [0, 1]] # up, left, down, right
class snake_case_ :
def __init__( self : Optional[Any] , _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : int , _snake_case : Node | None )->Tuple:
'''simple docstring'''
__lowerCAmelCase : Tuple = pos_x
__lowerCAmelCase : int = pos_y
__lowerCAmelCase : Optional[int] = (pos_y, pos_x)
__lowerCAmelCase : List[str] = goal_x
__lowerCAmelCase : Union[str, Any] = goal_y
__lowerCAmelCase : int = parent
class snake_case_ :
def __init__( self : str , _snake_case : tuple[int, int] , _snake_case : tuple[int, int] )->List[Any]:
'''simple docstring'''
__lowerCAmelCase : List[Any] = Node(start[1] , start[0] , goal[1] , goal[0] , _snake_case )
__lowerCAmelCase : List[Any] = Node(goal[1] , goal[0] , goal[1] , goal[0] , _snake_case )
__lowerCAmelCase : Tuple = [self.start]
__lowerCAmelCase : Union[str, Any] = False
def UpperCAmelCase__ ( self : Optional[int] )->Path | None:
'''simple docstring'''
while self.node_queue:
__lowerCAmelCase : int = self.node_queue.pop(0 )
if current_node.pos == self.target.pos:
__lowerCAmelCase : Any = True
return self.retrace_path(_snake_case )
__lowerCAmelCase : int = self.get_successors(_snake_case )
for node in successors:
self.node_queue.append(_snake_case )
if not self.reached:
return [self.start.pos]
return None
def UpperCAmelCase__ ( self : List[Any] , _snake_case : Node )->list[Node]:
'''simple docstring'''
__lowerCAmelCase : Union[str, Any] = []
for action in delta:
__lowerCAmelCase : Union[str, Any] = parent.pos_x + action[1]
__lowerCAmelCase : List[Any] = parent.pos_y + action[0]
if not (0 <= pos_x <= len(grid[0] ) - 1 and 0 <= pos_y <= len(_snake_case ) - 1):
continue
if grid[pos_y][pos_x] != 0:
continue
successors.append(
Node(_snake_case , _snake_case , self.target.pos_y , self.target.pos_x , _snake_case ) )
return successors
def UpperCAmelCase__ ( self : Tuple , _snake_case : Node | None )->Path:
'''simple docstring'''
__lowerCAmelCase : str = node
__lowerCAmelCase : Optional[Any] = []
while current_node is not None:
path.append((current_node.pos_y, current_node.pos_x) )
__lowerCAmelCase : str = current_node.parent
path.reverse()
return path
class snake_case_ :
def __init__( self : Optional[Any] , _snake_case : Tuple , _snake_case : str )->Tuple:
'''simple docstring'''
__lowerCAmelCase : Dict = BreadthFirstSearch(_snake_case , _snake_case )
__lowerCAmelCase : int = BreadthFirstSearch(_snake_case , _snake_case )
__lowerCAmelCase : Optional[int] = False
def UpperCAmelCase__ ( self : List[Any] )->Path | None:
'''simple docstring'''
while self.fwd_bfs.node_queue or self.bwd_bfs.node_queue:
__lowerCAmelCase : Union[str, Any] = self.fwd_bfs.node_queue.pop(0 )
__lowerCAmelCase : Optional[int] = self.bwd_bfs.node_queue.pop(0 )
if current_bwd_node.pos == current_fwd_node.pos:
__lowerCAmelCase : Optional[Any] = True
return self.retrace_bidirectional_path(
_snake_case , _snake_case )
__lowerCAmelCase : List[str] = current_bwd_node
__lowerCAmelCase : str = current_fwd_node
__lowerCAmelCase : Tuple = {
self.fwd_bfs: self.fwd_bfs.get_successors(_snake_case ),
self.bwd_bfs: self.bwd_bfs.get_successors(_snake_case ),
}
for bfs in [self.fwd_bfs, self.bwd_bfs]:
for node in successors[bfs]:
bfs.node_queue.append(_snake_case )
if not self.reached:
return [self.fwd_bfs.start.pos]
return None
def UpperCAmelCase__ ( self : Dict , _snake_case : Node , _snake_case : Node )->Path:
'''simple docstring'''
__lowerCAmelCase : Any = self.fwd_bfs.retrace_path(_snake_case )
__lowerCAmelCase : Tuple = self.bwd_bfs.retrace_path(_snake_case )
bwd_path.pop()
bwd_path.reverse()
__lowerCAmelCase : Any = fwd_path + bwd_path
return path
if __name__ == "__main__":
# all coordinates are given in format [y,x]
import doctest
doctest.testmod()
_UpperCAmelCase = (0, 0)
_UpperCAmelCase = (len(grid) - 1, len(grid[0]) - 1)
for elem in grid:
print(elem)
_UpperCAmelCase = time.time()
_UpperCAmelCase = BreadthFirstSearch(init, goal)
_UpperCAmelCase = bfs.search()
_UpperCAmelCase = time.time() - start_bfs_time
print('Unidirectional BFS computation time : ', bfs_time)
_UpperCAmelCase = time.time()
_UpperCAmelCase = BidirectionalBreadthFirstSearch(init, goal)
_UpperCAmelCase = bd_bfs.search()
_UpperCAmelCase = time.time() - start_bd_bfs_time
print('Bidirectional BFS computation time : ', bd_bfs_time) | 504 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
EfficientNetConfig,
EfficientNetForImageClassification,
EfficientNetImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCAmelCase = logging.get_logger(__name__)
_UpperCAmelCase = {
'b0': efficientnet.EfficientNetBa,
'b1': efficientnet.EfficientNetBa,
'b2': efficientnet.EfficientNetBa,
'b3': efficientnet.EfficientNetBa,
'b4': efficientnet.EfficientNetBa,
'b5': efficientnet.EfficientNetBa,
'b6': efficientnet.EfficientNetBa,
'b7': efficientnet.EfficientNetBa,
}
_UpperCAmelCase = {
'b0': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.0,
'image_size': 224,
'dropout_rate': 0.2,
'dw_padding': [],
},
'b1': {
'hidden_dim': 1280,
'width_coef': 1.0,
'depth_coef': 1.1,
'image_size': 240,
'dropout_rate': 0.2,
'dw_padding': [16],
},
'b2': {
'hidden_dim': 1408,
'width_coef': 1.1,
'depth_coef': 1.2,
'image_size': 260,
'dropout_rate': 0.3,
'dw_padding': [5, 8, 16],
},
'b3': {
'hidden_dim': 1536,
'width_coef': 1.2,
'depth_coef': 1.4,
'image_size': 300,
'dropout_rate': 0.3,
'dw_padding': [5, 18],
},
'b4': {
'hidden_dim': 1792,
'width_coef': 1.4,
'depth_coef': 1.8,
'image_size': 380,
'dropout_rate': 0.4,
'dw_padding': [6],
},
'b5': {
'hidden_dim': 2048,
'width_coef': 1.6,
'depth_coef': 2.2,
'image_size': 456,
'dropout_rate': 0.4,
'dw_padding': [13, 27],
},
'b6': {
'hidden_dim': 2304,
'width_coef': 1.8,
'depth_coef': 2.6,
'image_size': 528,
'dropout_rate': 0.5,
'dw_padding': [31],
},
'b7': {
'hidden_dim': 2560,
'width_coef': 2.0,
'depth_coef': 3.1,
'image_size': 600,
'dropout_rate': 0.5,
'dw_padding': [18],
},
}
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :List[Any] ) -> Any:
__lowerCAmelCase : List[Any] = EfficientNetConfig()
__lowerCAmelCase : Tuple = CONFIG_MAP[model_name]["""hidden_dim"""]
__lowerCAmelCase : Dict = CONFIG_MAP[model_name]["""width_coef"""]
__lowerCAmelCase : Dict = CONFIG_MAP[model_name]["""depth_coef"""]
__lowerCAmelCase : str = CONFIG_MAP[model_name]["""image_size"""]
__lowerCAmelCase : Any = CONFIG_MAP[model_name]["""dropout_rate"""]
__lowerCAmelCase : Union[str, Any] = CONFIG_MAP[model_name]["""dw_padding"""]
__lowerCAmelCase : str = """huggingface/label-files"""
__lowerCAmelCase : Dict = """imagenet-1k-id2label.json"""
__lowerCAmelCase : str = 1_000
__lowerCAmelCase : Optional[Any] = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
__lowerCAmelCase : Optional[int] = {int(SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
__lowerCAmelCase : Dict = idalabel
__lowerCAmelCase : Dict = {v: k for k, v in idalabel.items()}
return config
def _SCREAMING_SNAKE_CASE ( ) -> Union[str, Any]:
__lowerCAmelCase : List[Any] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
__lowerCAmelCase : Any = Image.open(requests.get(SCREAMING_SNAKE_CASE , stream=SCREAMING_SNAKE_CASE ).raw )
return im
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Union[str, Any] ) -> List[str]:
__lowerCAmelCase : int = CONFIG_MAP[model_name]["""image_size"""]
__lowerCAmelCase : int = EfficientNetImageProcessor(
size={"""height""": size, """width""": size} , image_mean=[0.4_85, 0.4_56, 0.4_06] , image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63] , do_center_crop=SCREAMING_SNAKE_CASE , )
return preprocessor
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :int ) -> Any:
__lowerCAmelCase : str = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
__lowerCAmelCase : int = sorted(set(SCREAMING_SNAKE_CASE ) )
__lowerCAmelCase : Optional[int] = len(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Any = {b: str(SCREAMING_SNAKE_CASE ) for b, i in zip(SCREAMING_SNAKE_CASE , range(SCREAMING_SNAKE_CASE ) )}
__lowerCAmelCase : Union[str, Any] = []
rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )
for b in block_names:
__lowerCAmelCase : List[Any] = block_name_mapping[b]
rename_keys.append((F'''block{b}_expand_conv/kernel:0''', F'''encoder.blocks.{hf_b}.expansion.expand_conv.weight''') )
rename_keys.append((F'''block{b}_expand_bn/gamma:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.weight''') )
rename_keys.append((F'''block{b}_expand_bn/beta:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.bias''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_expand_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.expansion.expand_bn.running_var''') )
rename_keys.append(
(F'''block{b}_dwconv/depthwise_kernel:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight''') )
rename_keys.append((F'''block{b}_bn/gamma:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight''') )
rename_keys.append((F'''block{b}_bn/beta:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias''') )
rename_keys.append(
(F'''block{b}_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean''') )
rename_keys.append(
(F'''block{b}_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var''') )
rename_keys.append((F'''block{b}_se_reduce/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.weight''') )
rename_keys.append((F'''block{b}_se_reduce/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.reduce.bias''') )
rename_keys.append((F'''block{b}_se_expand/kernel:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.weight''') )
rename_keys.append((F'''block{b}_se_expand/bias:0''', F'''encoder.blocks.{hf_b}.squeeze_excite.expand.bias''') )
rename_keys.append(
(F'''block{b}_project_conv/kernel:0''', F'''encoder.blocks.{hf_b}.projection.project_conv.weight''') )
rename_keys.append((F'''block{b}_project_bn/gamma:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.weight''') )
rename_keys.append((F'''block{b}_project_bn/beta:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.bias''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_mean:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_mean''') )
rename_keys.append(
(F'''block{b}_project_bn/moving_variance:0''', F'''encoder.blocks.{hf_b}.projection.project_bn.running_var''') )
rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )
__lowerCAmelCase : str = {}
for item in rename_keys:
if item[0] in original_param_names:
__lowerCAmelCase : Tuple = """efficientnet.""" + item[1]
__lowerCAmelCase : Union[str, Any] = """classifier.weight"""
__lowerCAmelCase : Optional[Any] = """classifier.bias"""
return key_mapping
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :List[str] , SCREAMING_SNAKE_CASE :List[Any] , SCREAMING_SNAKE_CASE :Tuple ) -> List[Any]:
for key, value in tf_params.items():
if "normalization" in key:
continue
__lowerCAmelCase : Any = key_mapping[key]
if "_conv" in key and "kernel" in key:
__lowerCAmelCase : List[str] = torch.from_numpy(SCREAMING_SNAKE_CASE ).permute(3 , 2 , 0 , 1 )
elif "depthwise_kernel" in key:
__lowerCAmelCase : Tuple = torch.from_numpy(SCREAMING_SNAKE_CASE ).permute(2 , 3 , 0 , 1 )
elif "kernel" in key:
__lowerCAmelCase : Dict = torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE ) )
else:
__lowerCAmelCase : Optional[int] = torch.from_numpy(SCREAMING_SNAKE_CASE )
# Replace HF parameters with original TF model parameters
assert hf_params[hf_key].shape == new_hf_value.shape
hf_params[hf_key].copy_(SCREAMING_SNAKE_CASE )
@torch.no_grad()
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :Any , SCREAMING_SNAKE_CASE :List[Any] , SCREAMING_SNAKE_CASE :Dict , SCREAMING_SNAKE_CASE :Any ) -> List[str]:
__lowerCAmelCase : List[str] = model_classes[model_name](
include_top=SCREAMING_SNAKE_CASE , weights="""imagenet""" , input_tensor=SCREAMING_SNAKE_CASE , input_shape=SCREAMING_SNAKE_CASE , pooling=SCREAMING_SNAKE_CASE , classes=1_000 , classifier_activation="""softmax""" , )
__lowerCAmelCase : int = original_model.trainable_variables
__lowerCAmelCase : Tuple = original_model.non_trainable_variables
__lowerCAmelCase : Optional[int] = {param.name: param.numpy() for param in tf_params}
for param in tf_non_train_params:
__lowerCAmelCase : int = param.numpy()
__lowerCAmelCase : int = list(tf_params.keys() )
# Load HuggingFace model
__lowerCAmelCase : int = get_efficientnet_config(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = EfficientNetForImageClassification(SCREAMING_SNAKE_CASE ).eval()
__lowerCAmelCase : Union[str, Any] = hf_model.state_dict()
# Create src-to-dst parameter name mapping dictionary
print("""Converting parameters...""" )
__lowerCAmelCase : Any = rename_keys(SCREAMING_SNAKE_CASE )
replace_params(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Initialize preprocessor and preprocess input image
__lowerCAmelCase : Dict = convert_image_processor(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Dict = preprocessor(images=prepare_img() , return_tensors="""pt""" )
# HF model inference
hf_model.eval()
with torch.no_grad():
__lowerCAmelCase : Dict = hf_model(**SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Union[str, Any] = outputs.logits.detach().numpy()
# Original model inference
__lowerCAmelCase : List[str] = False
__lowerCAmelCase : int = CONFIG_MAP[model_name]["""image_size"""]
__lowerCAmelCase : Dict = prepare_img().resize((image_size, image_size) , resample=PIL.Image.NEAREST )
__lowerCAmelCase : Optional[int] = image.img_to_array(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : List[str] = np.expand_dims(SCREAMING_SNAKE_CASE , axis=0 )
__lowerCAmelCase : Any = original_model.predict(SCREAMING_SNAKE_CASE )
# Check whether original and HF model outputs match -> np.allclose
assert np.allclose(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , atol=1e-3 ), "The predicted logits are not the same."
print("""Model outputs match!""" )
if save_model:
# Create folder to save model
if not os.path.isdir(SCREAMING_SNAKE_CASE ):
os.mkdir(SCREAMING_SNAKE_CASE )
# Save converted model and image processor
hf_model.save_pretrained(SCREAMING_SNAKE_CASE )
preprocessor.save_pretrained(SCREAMING_SNAKE_CASE )
if push_to_hub:
# Push model and image processor to hub
print(F'''Pushing converted {model_name} to the hub...''' )
__lowerCAmelCase : Tuple = F'''efficientnet-{model_name}'''
preprocessor.push_to_hub(SCREAMING_SNAKE_CASE )
hf_model.push_to_hub(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
_UpperCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--model_name',
default='b0',
type=str,
help='Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default='hf_model',
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument('--save_model', action='store_true', help='Save model to local')
parser.add_argument('--push_to_hub', action='store_true', help='Push model and image processor to the hub')
_UpperCAmelCase = parser.parse_args()
convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub) | 504 | 1 |
"""simple docstring"""
from __future__ import annotations
def A ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = len(snake_case__ )
# If row is equal to the size of the board it means there are a queen in each row in
# the current board (possible_board)
if row == n:
# We convert the variable possible_board that looks like this: [1, 3, 0, 2] to
# this: ['. Q . . ', '. . . Q ', 'Q . . . ', '. . Q . ']
boards.append([""". """ * i + """Q """ + """. """ * (n - 1 - i) for i in possible_board] )
return
# We iterate each column in the row to find all possible results in each row
for col in range(snake_case__ ):
# We apply that we learned previously. First we check that in the current board
# (possible_board) there are not other same value because if there is it means
# that there are a collision in vertical. Then we apply the two formulas we
# learned before:
#
# 45º: y - x = b or 45: row - col = b
# 135º: y + x = b or row + col = b.
#
# And we verify if the results of this two formulas not exist in their variables
# respectively. (diagonal_right_collisions, diagonal_left_collisions)
#
# If any or these are True it means there is a collision so we continue to the
# next value in the for loop.
if (
col in possible_board
or row - col in diagonal_right_collisions
or row + col in diagonal_left_collisions
):
continue
# If it is False we call dfs function again and we update the inputs
depth_first_search(
[*possible_board, col] , [*diagonal_right_collisions, row - col] , [*diagonal_left_collisions, row + col] , snake_case__ , snake_case__ , )
def A ( snake_case__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = []
depth_first_search([] , [] , [] , snake_case__ , snake_case__ )
# Print all the boards
for board in boards:
for column in board:
print(snake_case__ )
print("""""" )
print(len(snake_case__ ) , """solutions were found.""" )
if __name__ == "__main__":
import doctest
doctest.testmod()
n_queens_solution(4)
| 616 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : Union[str, Any] = logging.get_logger(__name__)
A_ : int = {
"caidas/swin2sr-classicalsr-x2-64": (
"https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json"
),
}
class lowerCamelCase (A__ ):
lowerCamelCase__ : List[str] = 'swin2sr'
lowerCamelCase__ : Optional[Any] = {
'hidden_size': 'embed_dim',
'num_attention_heads': 'num_heads',
'num_hidden_layers': 'num_layers',
}
def __init__( self : Tuple , __UpperCAmelCase : Optional[int]=6_4 , __UpperCAmelCase : Tuple=1 , __UpperCAmelCase : Optional[Any]=3 , __UpperCAmelCase : List[str]=1_8_0 , __UpperCAmelCase : List[str]=[6, 6, 6, 6, 6, 6] , __UpperCAmelCase : Optional[Any]=[6, 6, 6, 6, 6, 6] , __UpperCAmelCase : Tuple=8 , __UpperCAmelCase : Union[str, Any]=2.0 , __UpperCAmelCase : int=True , __UpperCAmelCase : Optional[Any]=0.0 , __UpperCAmelCase : str=0.0 , __UpperCAmelCase : Dict=0.1 , __UpperCAmelCase : Dict="gelu" , __UpperCAmelCase : Tuple=False , __UpperCAmelCase : List[str]=0.02 , __UpperCAmelCase : List[Any]=1e-5 , __UpperCAmelCase : str=2 , __UpperCAmelCase : Optional[int]=1.0 , __UpperCAmelCase : Union[str, Any]="1conv" , __UpperCAmelCase : List[Any]="pixelshuffle" , **__UpperCAmelCase : Any , ) -> Optional[int]:
super().__init__(**__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = image_size
SCREAMING_SNAKE_CASE__ = patch_size
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = embed_dim
SCREAMING_SNAKE_CASE__ = depths
SCREAMING_SNAKE_CASE__ = len(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = num_heads
SCREAMING_SNAKE_CASE__ = window_size
SCREAMING_SNAKE_CASE__ = mlp_ratio
SCREAMING_SNAKE_CASE__ = qkv_bias
SCREAMING_SNAKE_CASE__ = hidden_dropout_prob
SCREAMING_SNAKE_CASE__ = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE__ = drop_path_rate
SCREAMING_SNAKE_CASE__ = hidden_act
SCREAMING_SNAKE_CASE__ = use_absolute_embeddings
SCREAMING_SNAKE_CASE__ = layer_norm_eps
SCREAMING_SNAKE_CASE__ = initializer_range
SCREAMING_SNAKE_CASE__ = upscale
SCREAMING_SNAKE_CASE__ = img_range
SCREAMING_SNAKE_CASE__ = resi_connection
SCREAMING_SNAKE_CASE__ = upsampler
| 616 | 1 |
__a :List[str] = 'Input must be a string of 8 numbers plus letter'
__a :Tuple = 'TRWAGMYFPDXBNJZSQVHLCKE'
def __snake_case ( __UpperCamelCase : str ):
"""simple docstring"""
if not isinstance(__UpperCamelCase ,__UpperCamelCase ):
A_ = f'''Expected string as input, found {type(__UpperCamelCase ).__name__}'''
raise TypeError(__UpperCamelCase )
A_ = spanish_id.replace("-" ,"" ).upper()
if len(__UpperCamelCase ) != 9:
raise ValueError(__UpperCamelCase )
try:
A_ = int(spanish_id_clean[0:8] )
A_ = 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() | 86 |
"""simple docstring"""
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('''4.31.0''')
require_version('''datasets>=1.8.0''', '''To fix: pip install -r examples/pytorch/text-classification/requirements.txt''')
_SCREAMING_SNAKE_CASE : str = logging.getLogger(__name__)
@dataclass
class a :
SCREAMING_SNAKE_CASE : Optional[int] = field(
default=1_2_8 , metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
} , )
SCREAMING_SNAKE_CASE : bool = field(
default=__snake_case , metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
SCREAMING_SNAKE_CASE : bool = field(
default=__snake_case , metadata={
"""help""": (
"""Whether to pad all samples to `max_seq_length`. """
"""If False, will pad the samples dynamically when batching to the maximum length in the batch."""
)
} , )
SCREAMING_SNAKE_CASE : Optional[int] = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
} , )
SCREAMING_SNAKE_CASE : Optional[int] = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
} , )
SCREAMING_SNAKE_CASE : Optional[int] = field(
default=__snake_case , metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
} , )
@dataclass
class a :
SCREAMING_SNAKE_CASE : str = field(
default=__snake_case , metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
SCREAMING_SNAKE_CASE : str = field(
default=__snake_case , metadata={"""help""": """Evaluation language. Also train language if `train_language` is set to None."""} )
SCREAMING_SNAKE_CASE : Optional[str] = field(
default=__snake_case , metadata={"""help""": """Train language if it is different from the evaluation language."""} )
SCREAMING_SNAKE_CASE : Optional[str] = field(
default=__snake_case , metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
SCREAMING_SNAKE_CASE : Optional[str] = field(
default=__snake_case , metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
SCREAMING_SNAKE_CASE : Optional[str] = field(
default=__snake_case , metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""} , )
SCREAMING_SNAKE_CASE : Optional[bool] = field(
default=__snake_case , metadata={"""help""": """arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()"""} , )
SCREAMING_SNAKE_CASE : bool = field(
default=__snake_case , metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""} , )
SCREAMING_SNAKE_CASE : str = field(
default="""main""" , metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""} , )
SCREAMING_SNAKE_CASE : bool = field(
default=__snake_case , metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
} , )
SCREAMING_SNAKE_CASE : bool = field(
default=__snake_case , metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""} , )
def lowerCamelCase__ ( ) -> Any:
# 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.
lowerCamelCase_ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('run_xnli' , _lowerCamelCase )
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
lowerCamelCase_ = training_args.get_process_log_level()
logger.setLevel(_lowerCamelCase )
datasets.utils.logging.set_verbosity(_lowerCamelCase )
transformers.utils.logging.set_verbosity(_lowerCamelCase )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
lowerCamelCase_ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
lowerCamelCase_ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
'Use --overwrite_output_dir to overcome.' )
elif last_checkpoint is not None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
lowerCamelCase_ = load_dataset(
'xnli' , model_args.language , split='train' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
lowerCamelCase_ = load_dataset(
'xnli' , model_args.train_language , split='train' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase_ = train_dataset.features['label'].names
if training_args.do_eval:
lowerCamelCase_ = load_dataset(
'xnli' , model_args.language , split='validation' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase_ = eval_dataset.features['label'].names
if training_args.do_predict:
lowerCamelCase_ = load_dataset(
'xnli' , model_args.language , split='test' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase_ = predict_dataset.features['label'].names
# Labels
lowerCamelCase_ = len(_lowerCamelCase )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
lowerCamelCase_ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=_lowerCamelCase , idalabel={str(_lowerCamelCase ): label for i, label in enumerate(_lowerCamelCase )} , labelaid={label: i for i, label in enumerate(_lowerCamelCase )} , finetuning_task='xnli' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase_ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
lowerCamelCase_ = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=_lowerCamelCase , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
lowerCamelCase_ = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
lowerCamelCase_ = False
def preprocess_function(_lowerCamelCase : Any ):
# Tokenize the texts
return tokenizer(
examples['premise'] , examples['hypothesis'] , padding=_lowerCamelCase , max_length=data_args.max_seq_length , truncation=_lowerCamelCase , )
if training_args.do_train:
if data_args.max_train_samples is not None:
lowerCamelCase_ = min(len(_lowerCamelCase ) , data_args.max_train_samples )
lowerCamelCase_ = train_dataset.select(range(_lowerCamelCase ) )
with training_args.main_process_first(desc='train dataset map pre-processing' ):
lowerCamelCase_ = train_dataset.map(
_lowerCamelCase , batched=_lowerCamelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on train dataset' , )
# Log a few random samples from the training set:
for index in random.sample(range(len(_lowerCamelCase ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
lowerCamelCase_ = min(len(_lowerCamelCase ) , data_args.max_eval_samples )
lowerCamelCase_ = eval_dataset.select(range(_lowerCamelCase ) )
with training_args.main_process_first(desc='validation dataset map pre-processing' ):
lowerCamelCase_ = eval_dataset.map(
_lowerCamelCase , batched=_lowerCamelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on validation dataset' , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
lowerCamelCase_ = min(len(_lowerCamelCase ) , data_args.max_predict_samples )
lowerCamelCase_ = predict_dataset.select(range(_lowerCamelCase ) )
with training_args.main_process_first(desc='prediction dataset map pre-processing' ):
lowerCamelCase_ = predict_dataset.map(
_lowerCamelCase , batched=_lowerCamelCase , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on prediction dataset' , )
# Get the metric function
lowerCamelCase_ = evaluate.load('xnli' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(_lowerCamelCase : EvalPrediction ):
lowerCamelCase_ = p.predictions[0] if isinstance(p.predictions , _lowerCamelCase ) else p.predictions
lowerCamelCase_ = np.argmax(_lowerCamelCase , axis=1 )
return metric.compute(predictions=_lowerCamelCase , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
lowerCamelCase_ = default_data_collator
elif training_args.fpaa:
lowerCamelCase_ = DataCollatorWithPadding(_lowerCamelCase , pad_to_multiple_of=8 )
else:
lowerCamelCase_ = None
# Initialize our Trainer
lowerCamelCase_ = Trainer(
model=_lowerCamelCase , args=_lowerCamelCase , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=_lowerCamelCase , tokenizer=_lowerCamelCase , data_collator=_lowerCamelCase , )
# Training
if training_args.do_train:
lowerCamelCase_ = None
if training_args.resume_from_checkpoint is not None:
lowerCamelCase_ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
lowerCamelCase_ = last_checkpoint
lowerCamelCase_ = trainer.train(resume_from_checkpoint=_lowerCamelCase )
lowerCamelCase_ = train_result.metrics
lowerCamelCase_ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(_lowerCamelCase )
)
lowerCamelCase_ = min(_lowerCamelCase , len(_lowerCamelCase ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , _lowerCamelCase )
trainer.save_metrics('train' , _lowerCamelCase )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
lowerCamelCase_ = trainer.evaluate(eval_dataset=_lowerCamelCase )
lowerCamelCase_ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(_lowerCamelCase )
lowerCamelCase_ = min(_lowerCamelCase , len(_lowerCamelCase ) )
trainer.log_metrics('eval' , _lowerCamelCase )
trainer.save_metrics('eval' , _lowerCamelCase )
# Prediction
if training_args.do_predict:
logger.info('*** Predict ***' )
lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ = trainer.predict(_lowerCamelCase , metric_key_prefix='predict' )
lowerCamelCase_ = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(_lowerCamelCase )
)
lowerCamelCase_ = min(_lowerCamelCase , len(_lowerCamelCase ) )
trainer.log_metrics('predict' , _lowerCamelCase )
trainer.save_metrics('predict' , _lowerCamelCase )
lowerCamelCase_ = np.argmax(_lowerCamelCase , axis=1 )
lowerCamelCase_ = os.path.join(training_args.output_dir , 'predictions.txt' )
if trainer.is_world_process_zero():
with open(_lowerCamelCase , 'w' ) as writer:
writer.write('index\tprediction\n' )
for index, item in enumerate(_lowerCamelCase ):
lowerCamelCase_ = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
if __name__ == "__main__":
main()
| 549 | 0 |
import argparse
from collections import defaultdict
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )-> Tuple:
"""simple docstring"""
lowercase = f'{file}_{class_name}_{test_name}'
done_test[_id] += 1
with open(UpperCAmelCase, '''r''' ) as f:
lowercase = f.readlines()
lowercase = f'class {class_name}('
lowercase = f'{4 * " "}def {test_name}('
lowercase = f'{8 * " "}{correct_line.split()[0]}'
lowercase = f'{16 * " "}{correct_line.split()[0]}'
lowercase = False
lowercase = False
lowercase = False
lowercase = False
lowercase = 0
lowercase = 0
lowercase = []
for line in lines:
if line.startswith(UpperCAmelCase ):
lowercase = True
elif in_class and line.startswith(UpperCAmelCase ):
lowercase = True
elif in_class and in_func and (line.startswith(UpperCAmelCase ) or line.startswith(UpperCAmelCase )):
lowercase = len(line.split(correct_line.split()[0] )[0] )
count += 1
if count == done_test[_id]:
lowercase = True
if in_class and in_func and in_line:
if ")" not in line:
continue
else:
lowercase = True
if in_class and in_func and in_line and insert_line:
new_lines.append(f'{spaces * " "}{correct_line}' )
lowercase = lowercase = lowercase = lowercase = False
else:
new_lines.append(UpperCAmelCase )
with open(UpperCAmelCase, '''w''' ) as f:
for line in new_lines:
f.write(UpperCAmelCase )
def __UpperCAmelCase ( UpperCAmelCase, UpperCAmelCase=None )-> str:
"""simple docstring"""
if fail is not None:
with open(UpperCAmelCase, '''r''' ) as f:
lowercase = {l.strip() for l in f.readlines()}
else:
lowercase = None
with open(UpperCAmelCase, '''r''' ) as f:
lowercase = f.readlines()
lowercase = defaultdict(UpperCAmelCase )
for line in correct_lines:
lowercase ,lowercase ,lowercase ,lowercase = line.split(''';''' )
if test_failures is None or "::".join([file, class_name, test_name] ) in test_failures:
overwrite_file(UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase, UpperCAmelCase )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument("--correct_filename", help="filename of tests with expected result")
parser.add_argument("--fail_filename", help="filename of test failures", type=str, default=None)
A_ = parser.parse_args()
main(args.correct_filename, args.fail_filename)
| 479 | from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
A_ = TypeVar("KEY")
A_ = TypeVar("VAL")
@dataclass(frozen=_A , slots=_A )
class __lowercase ( Generic[KEY, VAL] ):
lowercase = 42
lowercase = 42
class __lowercase ( _Item ):
def __init__( self : Optional[int] ) -> None:
'''simple docstring'''
super().__init__(__lowerCamelCase , __lowerCamelCase )
def __bool__( self : str ) -> bool:
'''simple docstring'''
return False
A_ = _DeletedItem()
class __lowercase ( MutableMapping[KEY, VAL] ):
def __init__( self : Any , __lowerCamelCase : int = 8 , __lowerCamelCase : float = 0.75 ) -> None:
'''simple docstring'''
lowercase = initial_block_size
lowercase = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
lowercase = capacity_factor
lowercase = 0
def __a ( self : Optional[Any] , __lowerCamelCase : KEY ) -> int:
'''simple docstring'''
return hash(__lowerCamelCase ) % len(self._buckets )
def __a ( self : List[Any] , __lowerCamelCase : int ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def __a ( self : Optional[Any] , __lowerCamelCase : int , __lowerCamelCase : KEY , __lowerCamelCase : VAL ) -> bool:
'''simple docstring'''
lowercase = self._buckets[ind]
if not stored:
lowercase = _Item(__lowerCamelCase , __lowerCamelCase )
self._len += 1
return True
elif stored.key == key:
lowercase = _Item(__lowerCamelCase , __lowerCamelCase )
return True
else:
return False
def __a ( self : Optional[Any] ) -> bool:
'''simple docstring'''
lowercase = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(__lowerCamelCase )
def __a ( self : Tuple ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
lowercase = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def __a ( self : Optional[Any] , __lowerCamelCase : int ) -> None:
'''simple docstring'''
lowercase = self._buckets
lowercase = [None] * new_size
lowercase = 0
for item in old_buckets:
if item:
self._add_item(item.key , item.val )
def __a ( self : List[str] ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def __a ( self : Any ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def __a ( self : Optional[int] , __lowerCamelCase : KEY ) -> Iterator[int]:
'''simple docstring'''
lowercase = self._get_bucket_index(__lowerCamelCase )
for _ in range(len(self._buckets ) ):
yield ind
lowercase = self._get_next_ind(__lowerCamelCase )
def __a ( self : Optional[int] , __lowerCamelCase : KEY , __lowerCamelCase : VAL ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(__lowerCamelCase ):
if self._try_set(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
break
def __setitem__( self : Optional[int] , __lowerCamelCase : KEY , __lowerCamelCase : VAL ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(__lowerCamelCase , __lowerCamelCase )
def __delitem__( self : List[str] , __lowerCamelCase : KEY ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(__lowerCamelCase ):
lowercase = self._buckets[ind]
if item is None:
raise KeyError(__lowerCamelCase )
if item is _deleted:
continue
if item.key == key:
lowercase = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self : Any , __lowerCamelCase : KEY ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(__lowerCamelCase ):
lowercase = self._buckets[ind]
if item is None:
break
if item is _deleted:
continue
if item.key == key:
return item.val
raise KeyError(__lowerCamelCase )
def __len__( self : str ) -> int:
'''simple docstring'''
return self._len
def __iter__( self : Union[str, Any] ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self : Any ) -> str:
'''simple docstring'''
lowercase = ''' ,'''.join(
f'{item.key}: {item.val}' for item in self._buckets if item )
return f'HashMap({val_string})'
| 479 | 1 |
import unittest
from transformers import (
MODEL_FOR_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_CAUSAL_LM_MAPPING,
TextGenerationPipeline,
logging,
pipeline,
)
from transformers.testing_utils import (
CaptureLogger,
is_pipeline_test,
require_accelerate,
require_tf,
require_torch,
require_torch_gpu,
require_torch_or_tf,
)
from .test_pipelines_common import ANY
@is_pipeline_test
@require_torch_or_tf
class lowerCamelCase__ ( unittest.TestCase):
"""simple docstring"""
a__ : Dict = MODEL_FOR_CAUSAL_LM_MAPPING
a__ : List[str] = TF_MODEL_FOR_CAUSAL_LM_MAPPING
@require_torch
def snake_case_ ( self : Any ) -> List[str]:
_A = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''pt''' )
# Using `do_sample=False` to force deterministic output
_A = text_generator('''This is a test''' , do_sample=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
] , )
_A = text_generator(['''This is a test''', '''This is a second test'''] )
self.assertEqual(
__lowerCAmelCase , [
[
{
'''generated_text''': (
'''This is a test ☃ ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy oscope.'''
''' oscope. FiliFili@@'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test ☃ segmental segmental segmental 议议eski eski flutter flutter Lacy'''
''' oscope. oscope. FiliFili@@'''
)
}
],
] , )
_A = text_generator('''This is a test''' , do_sample=__lowerCAmelCase , num_return_sequences=2 , return_tensors=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
{'''generated_token_ids''': ANY(__lowerCAmelCase )},
{'''generated_token_ids''': ANY(__lowerCAmelCase )},
] , )
_A = text_generator.model.config.eos_token_id
_A = '<pad>'
_A = text_generator(
['''This is a test''', '''This is a second test'''] , do_sample=__lowerCAmelCase , num_return_sequences=2 , batch_size=2 , return_tensors=__lowerCAmelCase , )
self.assertEqual(
__lowerCAmelCase , [
[
{'''generated_token_ids''': ANY(__lowerCAmelCase )},
{'''generated_token_ids''': ANY(__lowerCAmelCase )},
],
[
{'''generated_token_ids''': ANY(__lowerCAmelCase )},
{'''generated_token_ids''': ANY(__lowerCAmelCase )},
],
] , )
@require_tf
def snake_case_ ( self : Optional[Any] ) -> Optional[int]:
_A = pipeline(task='''text-generation''' , model='''sshleifer/tiny-ctrl''' , framework='''tf''' )
# Using `do_sample=False` to force deterministic output
_A = text_generator('''This is a test''' , do_sample=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
] , )
_A = text_generator(['''This is a test''', '''This is a second test'''] , do_sample=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
[
{
'''generated_text''': (
'''This is a test FeyFeyFey(Croatis.), s.), Cannes Cannes Cannes 閲閲Cannes Cannes Cannes 攵'''
''' please,'''
)
}
],
[
{
'''generated_text''': (
'''This is a second test Chieftain Chieftain prefecture prefecture prefecture Cannes Cannes'''
''' Cannes 閲閲Cannes Cannes Cannes 攵 please,'''
)
}
],
] , )
def snake_case_ ( self : Optional[Any] , __lowerCAmelCase : Tuple , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : int ) -> Tuple:
_A = TextGenerationPipeline(model=__lowerCAmelCase , tokenizer=__lowerCAmelCase )
return text_generator, ["This is a test", "Another test"]
def snake_case_ ( self : Any ) -> Union[str, Any]:
_A = 'Hello I believe in'
_A = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
_A = text_generator(__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [{'''generated_text''': '''Hello I believe in fe fe fe fe fe fe fe fe fe fe fe fe'''}] , )
_A = text_generator(__lowerCAmelCase , stop_sequence=''' fe''' )
self.assertEqual(__lowerCAmelCase , [{'''generated_text''': '''Hello I believe in fe'''}] )
def snake_case_ ( self : Any , __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : List[str] ) -> Tuple:
_A = text_generator.model
_A = text_generator.tokenizer
_A = text_generator('''This is a test''' )
self.assertEqual(__lowerCAmelCase , [{'''generated_text''': ANY(__lowerCAmelCase )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_A = text_generator('''This is a test''' , return_full_text=__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , [{'''generated_text''': ANY(__lowerCAmelCase )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_A = pipeline(task='''text-generation''' , model=__lowerCAmelCase , tokenizer=__lowerCAmelCase , return_full_text=__lowerCAmelCase )
_A = text_generator('''This is a test''' )
self.assertEqual(__lowerCAmelCase , [{'''generated_text''': ANY(__lowerCAmelCase )}] )
self.assertNotIn('''This is a test''' , outputs[0]['''generated_text'''] )
_A = text_generator('''This is a test''' , return_full_text=__lowerCAmelCase )
self.assertEqual(__lowerCAmelCase , [{'''generated_text''': ANY(__lowerCAmelCase )}] )
self.assertTrue(outputs[0]['''generated_text'''].startswith('''This is a test''' ) )
_A = text_generator(['''This is great !''', '''Something else'''] , num_return_sequences=2 , do_sample=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
[{'''generated_text''': ANY(__lowerCAmelCase )}, {'''generated_text''': ANY(__lowerCAmelCase )}],
[{'''generated_text''': ANY(__lowerCAmelCase )}, {'''generated_text''': ANY(__lowerCAmelCase )}],
] , )
if text_generator.tokenizer.pad_token is not None:
_A = text_generator(
['''This is great !''', '''Something else'''] , num_return_sequences=2 , batch_size=2 , do_sample=__lowerCAmelCase )
self.assertEqual(
__lowerCAmelCase , [
[{'''generated_text''': ANY(__lowerCAmelCase )}, {'''generated_text''': ANY(__lowerCAmelCase )}],
[{'''generated_text''': ANY(__lowerCAmelCase )}, {'''generated_text''': ANY(__lowerCAmelCase )}],
] , )
with self.assertRaises(__lowerCAmelCase ):
_A = text_generator('''test''' , return_full_text=__lowerCAmelCase , return_text=__lowerCAmelCase )
with self.assertRaises(__lowerCAmelCase ):
_A = text_generator('''test''' , return_full_text=__lowerCAmelCase , return_tensors=__lowerCAmelCase )
with self.assertRaises(__lowerCAmelCase ):
_A = text_generator('''test''' , return_text=__lowerCAmelCase , return_tensors=__lowerCAmelCase )
# Empty prompt is slighly special
# it requires BOS token to exist.
# Special case for Pegasus which will always append EOS so will
# work even without BOS.
if (
text_generator.tokenizer.bos_token_id is not None
or "Pegasus" in tokenizer.__class__.__name__
or "Git" in model.__class__.__name__
):
_A = text_generator('''''' )
self.assertEqual(__lowerCAmelCase , [{'''generated_text''': ANY(__lowerCAmelCase )}] )
else:
with self.assertRaises((ValueError, AssertionError) ):
_A = text_generator('''''' )
if text_generator.framework == "tf":
# TF generation does not support max_new_tokens, and it's impossible
# to control long generation with only max_length without
# fancy calculation, dismissing tests for now.
return
# We don't care about infinite range models.
# They already work.
# Skip this test for XGLM, since it uses sinusoidal positional embeddings which are resized on-the-fly.
_A = ['RwkvForCausalLM', 'XGLMForCausalLM', 'GPTNeoXForCausalLM']
if (
tokenizer.model_max_length < 1_00_00
and text_generator.model.__class__.__name__ not in EXTRA_MODELS_CAN_HANDLE_LONG_INPUTS
):
# Handling of large generations
with self.assertRaises((RuntimeError, IndexError, ValueError, AssertionError) ):
text_generator('''This is a test''' * 5_00 , max_new_tokens=20 )
_A = text_generator('''This is a test''' * 5_00 , handle_long_generation='''hole''' , max_new_tokens=20 )
# Hole strategy cannot work
with self.assertRaises(__lowerCAmelCase ):
text_generator(
'''This is a test''' * 5_00 , handle_long_generation='''hole''' , max_new_tokens=tokenizer.model_max_length + 10 , )
@require_torch
@require_accelerate
@require_torch_gpu
def snake_case_ ( self : Dict ) -> Optional[Any]:
import torch
# Classic `model_kwargs`
_A = pipeline(
model='''hf-internal-testing/tiny-random-bloom''' , model_kwargs={'''device_map''': '''auto''', '''torch_dtype''': torch.bfloataa} , )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_A = pipe('''This is a test''' )
self.assertEqual(
__lowerCAmelCase , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# Upgraded those two to real pipeline arguments (they just get sent for the model as they're unlikely to mean anything else.)
_A = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.bfloataa )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.bfloataa )
_A = pipe('''This is a test''' )
self.assertEqual(
__lowerCAmelCase , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
# torch_dtype will be automatically set to float32 if not provided - check: https://github.com/huggingface/transformers/pull/20602
_A = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' )
self.assertEqual(pipe.model.device , torch.device(0 ) )
self.assertEqual(pipe.model.lm_head.weight.dtype , torch.floataa )
_A = pipe('''This is a test''' )
self.assertEqual(
__lowerCAmelCase , [
{
'''generated_text''': (
'''This is a test test test test test test test test test test test test test test test test'''
''' test'''
)
}
] , )
@require_torch
@require_torch_gpu
def snake_case_ ( self : Any ) -> Tuple:
import torch
_A = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device=0 , torch_dtype=torch.floataa )
pipe('''This is a test''' )
@require_torch
@require_accelerate
@require_torch_gpu
def snake_case_ ( self : Any ) -> str:
import torch
_A = pipeline(model='''hf-internal-testing/tiny-random-bloom''' , device_map='''auto''' , torch_dtype=torch.floataa )
pipe('''This is a test''' , do_sample=__lowerCAmelCase , top_p=0.5 )
def snake_case_ ( self : int ) -> List[Any]:
_A = 'Hello world'
_A = pipeline('''text-generation''' , model='''hf-internal-testing/tiny-random-gpt2''' )
if text_generator.model.framework == "tf":
_A = logging.get_logger('''transformers.generation.tf_utils''' )
else:
_A = logging.get_logger('''transformers.generation.utils''' )
_A = 'Both `max_new_tokens`' # The beggining of the message to be checked in this test
# Both are set by the user -> log warning
with CaptureLogger(__lowerCAmelCase ) as cl:
_A = text_generator(__lowerCAmelCase , max_length=10 , max_new_tokens=1 )
self.assertIn(__lowerCAmelCase , cl.out )
# The user only sets one -> no warning
with CaptureLogger(__lowerCAmelCase ) as cl:
_A = text_generator(__lowerCAmelCase , max_new_tokens=1 )
self.assertNotIn(__lowerCAmelCase , cl.out )
with CaptureLogger(__lowerCAmelCase ) as cl:
_A = text_generator(__lowerCAmelCase , max_length=10 )
self.assertNotIn(__lowerCAmelCase , cl.out )
| 2 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# 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.
# this script dumps information about the environment
import os
import sys
import transformers
SCREAMING_SNAKE_CASE_ = "3"
print("Python version:", sys.version)
print("transformers version:", transformers.__version__)
try:
import torch
print("Torch version:", torch.__version__)
print("Cuda available:", torch.cuda.is_available())
print("Cuda version:", torch.version.cuda)
print("CuDNN version:", torch.backends.cudnn.version())
print("Number of GPUs available:", torch.cuda.device_count())
print("NCCL version:", torch.cuda.nccl.version())
except ImportError:
print("Torch version:", None)
try:
import deepspeed
print("DeepSpeed version:", deepspeed.__version__)
except ImportError:
print("DeepSpeed version:", None)
try:
import tensorflow as tf
print("TensorFlow version:", tf.__version__)
print("TF GPUs available:", bool(tf.config.list_physical_devices("GPU")))
print("Number of TF GPUs available:", len(tf.config.list_physical_devices("GPU")))
except ImportError:
print("TensorFlow version:", None)
| 517 | 0 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
A__ = iter(lowerCAmelCase__ )
while True:
A__ = tuple(itertools.islice(lowerCAmelCase__ ,lowerCAmelCase__ ) )
if not chunk:
return
yield chunk
def __lowerCamelCase ( lowerCAmelCase__ ):
A__ = ''.join([c.upper() for c in dirty if c in string.ascii_letters] )
A__ = ''
if len(lowerCAmelCase__ ) < 2:
return dirty
for i in range(len(lowerCAmelCase__ ) - 1 ):
clean += dirty[i]
if dirty[i] == dirty[i + 1]:
clean += "X"
clean += dirty[-1]
if len(lowerCAmelCase__ ) & 1:
clean += "X"
return clean
def __lowerCamelCase ( lowerCAmelCase__ ):
# I and J are used interchangeably to allow
# us to use a 5x5 table (25 letters)
A__ = 'ABCDEFGHIKLMNOPQRSTUVWXYZ'
# we're using a list instead of a '2d' array because it makes the math
# for setting up the table and doing the actual encoding/decoding simpler
A__ = []
# copy key chars into the table if they are in `alphabet` ignoring duplicates
for char in key.upper():
if char not in table and char in alphabet:
table.append(lowerCAmelCase__ )
# fill the rest of the table in with the remaining alphabet chars
for char in alphabet:
if char not in table:
table.append(lowerCAmelCase__ )
return table
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
A__ = generate_table(lowerCAmelCase__ )
A__ = prepare_input(lowerCAmelCase__ )
A__ = ''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(lowerCAmelCase__ ,2 ):
A__ , A__ = divmod(table.index(lowerCAmelCase__ ) ,5 )
A__ , A__ = divmod(table.index(lowerCAmelCase__ ) ,5 )
if rowa == rowa:
ciphertext += table[rowa * 5 + (cola + 1) % 5]
ciphertext += table[rowa * 5 + (cola + 1) % 5]
elif cola == cola:
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
ciphertext += table[((rowa + 1) % 5) * 5 + cola]
else: # rectangle
ciphertext += table[rowa * 5 + cola]
ciphertext += table[rowa * 5 + cola]
return ciphertext
def __lowerCamelCase ( lowerCAmelCase__ ,lowerCAmelCase__ ):
A__ = generate_table(lowerCAmelCase__ )
A__ = ''
# https://en.wikipedia.org/wiki/Playfair_cipher#Description
for chara, chara in chunker(lowerCAmelCase__ ,2 ):
A__ , A__ = divmod(table.index(lowerCAmelCase__ ) ,5 )
A__ , A__ = divmod(table.index(lowerCAmelCase__ ) ,5 )
if rowa == rowa:
plaintext += table[rowa * 5 + (cola - 1) % 5]
plaintext += table[rowa * 5 + (cola - 1) % 5]
elif cola == cola:
plaintext += table[((rowa - 1) % 5) * 5 + cola]
plaintext += table[((rowa - 1) % 5) * 5 + cola]
else: # rectangle
plaintext += table[rowa * 5 + cola]
plaintext += table[rowa * 5 + cola]
return plaintext
| 704 |
"""simple docstring"""
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMInverseScheduler,
DDIMScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
StableDiffusionDiffEditPipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device
from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class snake_case_ ( _lowerCamelCase , _lowerCamelCase , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_: Union[str, Any] = StableDiffusionDiffEditPipeline
SCREAMING_SNAKE_CASE_: Union[str, Any] = TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""height""", """width""", """image"""} | {"""image_latents"""}
SCREAMING_SNAKE_CASE_: int = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"""image"""} | {"""image_latents"""}
SCREAMING_SNAKE_CASE_: str = frozenset(
[] ) # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
SCREAMING_SNAKE_CASE_: int = frozenset([] )
def _UpperCAmelCase ( self ):
"""simple docstring"""
torch.manual_seed(0 )
A__ = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') , up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') , cross_attention_dim=32 , attention_head_dim=(2, 4) , use_linear_projection=__a , )
A__ = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=__a , set_alpha_to_one=__a , )
A__ = DDIMInverseScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=__a , set_alpha_to_zero=__a , )
torch.manual_seed(0 )
A__ = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=128 , )
torch.manual_seed(0 )
A__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , hidden_act='gelu' , projection_dim=512 , )
A__ = CLIPTextModel(__a )
A__ = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
A__ = {
'unet': unet,
'scheduler': scheduler,
'inverse_scheduler': inverse_scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
'safety_checker': None,
'feature_extractor': None,
}
return components
def _UpperCAmelCase ( self , __a , __a=0 ):
"""simple docstring"""
A__ = floats_tensor((1, 16, 16) , rng=random.Random(__a ) ).to(__a )
A__ = floats_tensor((1, 2, 4, 16, 16) , rng=random.Random(__a ) ).to(__a )
if str(__a ).startswith('mps' ):
A__ = torch.manual_seed(__a )
else:
A__ = torch.Generator(device=__a ).manual_seed(__a )
A__ = {
'prompt': 'a dog and a newt',
'mask_image': mask,
'image_latents': latents,
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def _UpperCAmelCase ( self , __a , __a=0 ):
"""simple docstring"""
A__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(__a ) ).to(__a )
A__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
A__ = Image.fromarray(np.uinta(__a ) ).convert('RGB' )
if str(__a ).startswith('mps' ):
A__ = torch.manual_seed(__a )
else:
A__ = torch.Generator(device=__a ).manual_seed(__a )
A__ = {
'image': image,
'source_prompt': 'a cat and a frog',
'target_prompt': 'a dog and a newt',
'generator': generator,
'num_inference_steps': 2,
'num_maps_per_mask': 2,
'mask_encode_strength': 1.0,
'guidance_scale': 6.0,
'output_type': 'numpy',
}
return inputs
def _UpperCAmelCase ( self , __a , __a=0 ):
"""simple docstring"""
A__ = floats_tensor((1, 3, 32, 32) , rng=random.Random(__a ) ).to(__a )
A__ = image.cpu().permute(0 , 2 , 3 , 1 )[0]
A__ = Image.fromarray(np.uinta(__a ) ).convert('RGB' )
if str(__a ).startswith('mps' ):
A__ = torch.manual_seed(__a )
else:
A__ = torch.Generator(device=__a ).manual_seed(__a )
A__ = {
'image': image,
'prompt': 'a cat and a frog',
'generator': generator,
'num_inference_steps': 2,
'inpaint_strength': 1.0,
'guidance_scale': 6.0,
'decode_latents': True,
'output_type': 'numpy',
}
return inputs
def _UpperCAmelCase ( self ):
"""simple docstring"""
if not hasattr(self.pipeline_class , '_optional_components' ):
return
A__ = self.get_dummy_components()
A__ = self.pipeline_class(**__a )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
# set all optional components to None and update pipeline config accordingly
for optional_component in pipe._optional_components:
setattr(__a , __a , __a )
pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components} )
A__ = self.get_dummy_inputs(__a )
A__ = pipe(**__a )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__a )
A__ = self.pipeline_class.from_pretrained(__a )
pipe_loaded.to(__a )
pipe_loaded.set_progress_bar_config(disable=__a )
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__a , __a ) is None , f'''`{optional_component}` did not stay set to None after loading.''' , )
A__ = self.get_dummy_inputs(__a )
A__ = pipe_loaded(**__a )[0]
A__ = np.abs(output - output_loaded ).max()
self.assertLess(__a , 1E-4 )
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ = 'cpu'
A__ = self.get_dummy_components()
A__ = self.pipeline_class(**__a )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
A__ = self.get_dummy_mask_inputs(__a )
A__ = pipe.generate_mask(**__a )
A__ = mask[0, -3:, -3:]
self.assertEqual(mask.shape , (1, 16, 16) )
A__ = np.array([0] * 9 )
A__ = np.abs(mask_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__a , 1E-3 )
self.assertEqual(mask[0, -3, -4] , 0 )
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ = 'cpu'
A__ = self.get_dummy_components()
A__ = self.pipeline_class(**__a )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
A__ = self.get_dummy_inversion_inputs(__a )
A__ = pipe.invert(**__a ).images
A__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
A__ = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] , )
A__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__a , 1E-3 )
def _UpperCAmelCase ( self ):
"""simple docstring"""
super().test_inference_batch_single_identical(expected_max_diff=5E-3 )
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ = 'cpu'
A__ = self.get_dummy_components()
A__ = {'beta_start': 0.0_0085, 'beta_end': 0.012, 'beta_schedule': 'scaled_linear'}
A__ = DPMSolverMultistepScheduler(**__a )
A__ = DPMSolverMultistepInverseScheduler(**__a )
A__ = self.pipeline_class(**__a )
pipe.to(__a )
pipe.set_progress_bar_config(disable=__a )
A__ = self.get_dummy_inversion_inputs(__a )
A__ = pipe.invert(**__a ).images
A__ = image[0, -1, -3:, -3:]
self.assertEqual(image.shape , (2, 32, 32, 3) )
A__ = np.array(
[0.5150, 0.5134, 0.5043, 0.5376, 0.4694, 0.5_1050, 0.5015, 0.4407, 0.4799] , )
A__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(__a , 1E-3 )
@require_torch_gpu
@slow
class snake_case_ ( unittest.TestCase ):
"""simple docstring"""
def _UpperCAmelCase ( self ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@classmethod
def _UpperCAmelCase ( cls ):
"""simple docstring"""
A__ = load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png' )
A__ = raw_image.convert('RGB' ).resize((768, 768) )
A__ = raw_image
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ = torch.manual_seed(0 )
A__ = StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1' , safety_checker=__a , torch_dtype=torch.floataa )
A__ = DDIMScheduler.from_config(pipe.scheduler.config )
A__ = DDIMInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__a )
A__ = 'a bowl of fruit'
A__ = 'a bowl of pears'
A__ = pipe.generate_mask(
image=self.raw_image , source_prompt=__a , target_prompt=__a , generator=__a , )
A__ = pipe.invert(
prompt=__a , image=self.raw_image , inpaint_strength=0.7 , generator=__a ).latents
A__ = pipe(
prompt=__a , mask_image=__a , image_latents=__a , generator=__a , negative_prompt=__a , inpaint_strength=0.7 , output_type='numpy' , ).images[0]
A__ = (
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
def _UpperCAmelCase ( self ):
"""simple docstring"""
A__ = torch.manual_seed(0 )
A__ = StableDiffusionDiffEditPipeline.from_pretrained(
'stabilityai/stable-diffusion-2-1' , safety_checker=__a , torch_dtype=torch.floataa )
A__ = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
A__ = DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=__a )
A__ = 'a bowl of fruit'
A__ = 'a bowl of pears'
A__ = pipe.generate_mask(
image=self.raw_image , source_prompt=__a , target_prompt=__a , generator=__a , )
A__ = pipe.invert(
prompt=__a , image=self.raw_image , inpaint_strength=0.7 , generator=__a , num_inference_steps=25 , ).latents
A__ = pipe(
prompt=__a , mask_image=__a , image_latents=__a , generator=__a , negative_prompt=__a , inpaint_strength=0.7 , num_inference_steps=25 , output_type='numpy' , ).images[0]
A__ = (
np.array(
load_image(
'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
'/diffedit/pears.png' ).resize((768, 768) ) )
/ 255
)
assert np.abs((expected_image - image).max() ) < 5E-1
| 554 | 0 |
'''simple docstring'''
from __future__ import annotations
class __lowerCamelCase :
"""simple docstring"""
def __init__( self : Dict , SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : str):
_A , _A : int = text, pattern
_A , _A : Union[str, Any] = len(SCREAMING_SNAKE_CASE), len(SCREAMING_SNAKE_CASE)
def A ( self : Any , SCREAMING_SNAKE_CASE : str):
for i in range(self.patLen - 1 , -1 , -1):
if char == self.pattern[i]:
return i
return -1
def A ( self : Any , SCREAMING_SNAKE_CASE : int):
for i in range(self.patLen - 1 , -1 , -1):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def A ( self : str):
# searches pattern in text and returns index positions
_A : int = []
for i in range(self.textLen - self.patLen + 1):
_A : List[Any] = self.mismatch_in_text(SCREAMING_SNAKE_CASE)
if mismatch_index == -1:
positions.append(SCREAMING_SNAKE_CASE)
else:
_A : Optional[Any] = self.match_in_pattern(self.text[mismatch_index])
_A : Optional[int] = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
A : List[str] = '''ABAABA'''
A : Optional[int] = '''AB'''
A : Tuple = BoyerMooreSearch(text, pattern)
A : Any = bms.bad_character_heuristic()
if len(positions) == 0:
print('''No match found''')
else:
print('''Pattern found in following positions: ''')
print(positions)
| 128 |
'''simple docstring'''
from __future__ import annotations
from statistics import mean
def lowerCAmelCase__ ( lowerCamelCase : list[int] ,lowerCamelCase : list[int] ,lowerCamelCase : int ):
_A : Optional[Any] = [0] * no_of_processes
_A : List[Any] = [0] * no_of_processes
# Initialize remaining_time to waiting_time.
for i in range(lowerCamelCase ):
_A : int = burst_time[i]
_A : list[int] = []
_A : Tuple = 0
_A : Dict = 0
# When processes are not completed,
# A process whose arrival time has passed \
# and has remaining execution time is put into the ready_process.
# The shortest process in the ready_process, target_process is executed.
while completed != no_of_processes:
_A : Optional[int] = []
_A : Optional[int] = -1
for i in range(lowerCamelCase ):
if (arrival_time[i] <= total_time) and (remaining_time[i] > 0):
ready_process.append(lowerCamelCase )
if len(lowerCamelCase ) > 0:
_A : List[str] = ready_process[0]
for i in ready_process:
if remaining_time[i] < remaining_time[target_process]:
_A : Tuple = i
total_time += burst_time[target_process]
completed += 1
_A : str = 0
_A : Optional[Any] = (
total_time - arrival_time[target_process] - burst_time[target_process]
)
else:
total_time += 1
return waiting_time
def lowerCAmelCase__ ( lowerCamelCase : list[int] ,lowerCamelCase : int ,lowerCamelCase : list[int] ):
_A : List[str] = [0] * no_of_processes
for i in range(lowerCamelCase ):
_A : Optional[int] = burst_time[i] + waiting_time[i]
return turn_around_time
if __name__ == "__main__":
print('''[TEST CASE 01]''')
A : int = 4
A : Any = [2, 5, 3, 7]
A : str = [0, 0, 0, 0]
A : str = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
A : Dict = calculate_turnaroundtime(
burst_time, no_of_processes, waiting_time
)
# Printing the Result
print('''PID\tBurst Time\tArrival Time\tWaiting Time\tTurnaround Time''')
for i, process_id in enumerate(list(range(1, 5))):
print(
f"""{process_id}\t{burst_time[i]}\t\t\t{arrival_time[i]}\t\t\t\t"""
f"""{waiting_time[i]}\t\t\t\t{turn_around_time[i]}"""
)
print(f"""\nAverage waiting time = {mean(waiting_time):.5f}""")
print(f"""Average turnaround time = {mean(turn_around_time):.5f}""")
| 128 | 1 |
from __future__ import annotations
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
if nth_term == "":
return [""]
lowercase_ = int(UpperCAmelCase__ )
lowercase_ = int(UpperCAmelCase__ )
lowercase_ = []
for temp in range(int(UpperCAmelCase__ ) ):
series.append(F'''1 / {pow(temp + 1 , int(UpperCAmelCase__ ) )}''' if series else """1""" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
a = int(input('Enter the last number (nth term) of the P-Series'))
a = int(input('Enter the power for P-Series'))
print('Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p')
print(p_series(nth_term, power))
| 708 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_utils import (
ChannelDimension,
ImageInput,
get_image_size,
infer_channel_dimension_format,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_torch_available, is_vision_available, logging
from ...utils.import_utils import requires_backends
if is_vision_available():
import textwrap
from PIL import Image, ImageDraw, ImageFont
if is_torch_available():
import torch
from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
a = False
a = logging.get_logger(__name__)
a = 'ybelkada/fonts'
def UpperCAmelCase_ ( ):
if is_torch_available() and not is_torch_greater_or_equal_than_1_11:
raise ImportError(
F'''You are using torch=={torch.__version__}, but torch>=1.11.0 is required to use '''
"""Pix2StructImageProcessor. Please upgrade torch.""" )
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ ):
requires_backends(UpperCAmelCase__ , ["""torch"""] )
_check_torch_version()
lowercase_ = image_tensor.unsqueeze(0 )
lowercase_ = torch.nn.functional.unfold(UpperCAmelCase__ , (patch_height, patch_width) , stride=(patch_height, patch_width) )
lowercase_ = patches.reshape(image_tensor.size(0 ) , image_tensor.size(1 ) , UpperCAmelCase__ , UpperCAmelCase__ , -1 )
lowercase_ = patches.permute(0 , 4 , 2 , 3 , 1 ).reshape(
image_tensor.size(2 ) // patch_height , image_tensor.size(3 ) // patch_width , image_tensor.size(1 ) * patch_height * patch_width , )
return patches.unsqueeze(0 )
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ = 3_6 , UpperCAmelCase__ = "black" , UpperCAmelCase__ = "white" , UpperCAmelCase__ = 5 , UpperCAmelCase__ = 5 , UpperCAmelCase__ = 5 , UpperCAmelCase__ = 5 , UpperCAmelCase__ = None , UpperCAmelCase__ = None , ):
requires_backends(UpperCAmelCase__ , """vision""" )
# Add new lines so that each line is no more than 80 characters.
lowercase_ = textwrap.TextWrapper(width=8_0 )
lowercase_ = wrapper.wrap(text=UpperCAmelCase__ )
lowercase_ = """\n""".join(UpperCAmelCase__ )
if font_bytes is not None and font_path is None:
lowercase_ = io.BytesIO(UpperCAmelCase__ )
elif font_path is not None:
lowercase_ = font_path
else:
lowercase_ = hf_hub_download(UpperCAmelCase__ , """Arial.TTF""" )
lowercase_ = ImageFont.truetype(UpperCAmelCase__ , encoding="""UTF-8""" , size=UpperCAmelCase__ )
# Use a temporary canvas to determine the width and height in pixels when
# rendering the text.
lowercase_ = ImageDraw.Draw(Image.new("""RGB""" , (1, 1) , UpperCAmelCase__ ) )
lowercase_ , lowercase_ , lowercase_ , lowercase_ = temp_draw.textbbox((0, 0) , UpperCAmelCase__ , UpperCAmelCase__ )
# Create the actual image with a bit of padding around the text.
lowercase_ = text_width + left_padding + right_padding
lowercase_ = text_height + top_padding + bottom_padding
lowercase_ = Image.new("""RGB""" , (image_width, image_height) , UpperCAmelCase__ )
lowercase_ = ImageDraw.Draw(UpperCAmelCase__ )
draw.text(xy=(left_padding, top_padding) , text=UpperCAmelCase__ , fill=UpperCAmelCase__ , font=UpperCAmelCase__ )
return image
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ , **UpperCAmelCase__ ):
requires_backends(UpperCAmelCase__ , """vision""" )
# Convert to PIL image if necessary
lowercase_ = to_pil_image(UpperCAmelCase__ )
lowercase_ = render_text(UpperCAmelCase__ , **UpperCAmelCase__ )
lowercase_ = max(header_image.width , image.width )
lowercase_ = int(image.height * (new_width / image.width) )
lowercase_ = int(header_image.height * (new_width / header_image.width) )
lowercase_ = Image.new("""RGB""" , (new_width, new_height + new_header_height) , """white""" )
new_image.paste(header_image.resize((new_width, new_header_height) ) , (0, 0) )
new_image.paste(image.resize((new_width, new_height) ) , (0, new_header_height) )
# Convert back to the original framework if necessary
lowercase_ = to_numpy_array(UpperCAmelCase__ )
if infer_channel_dimension_format(UpperCAmelCase__ ) == ChannelDimension.LAST:
lowercase_ = to_channel_dimension_format(UpperCAmelCase__ , ChannelDimension.LAST )
return new_image
class UpperCamelCase__ ( __magic_name__ ):
__SCREAMING_SNAKE_CASE : Tuple = ['flattened_patches']
def __init__( self : str , UpperCamelCase__ : bool = True , UpperCamelCase__ : bool = True , UpperCamelCase__ : Dict[str, int] = None , UpperCamelCase__ : int = 2_048 , UpperCamelCase__ : bool = False , **UpperCamelCase__ : Optional[int] , ):
'''simple docstring'''
super().__init__(**UpperCamelCase__ )
lowercase_ = patch_size if patch_size is not None else {"""height""": 16, """width""": 16}
lowercase_ = do_normalize
lowercase_ = do_convert_rgb
lowercase_ = max_patches
lowercase_ = is_vqa
def UpperCAmelCase__ ( self : Optional[Any] , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : int , UpperCamelCase__ : dict , **UpperCamelCase__ : Optional[int] ):
'''simple docstring'''
requires_backends(self.extract_flattened_patches , """torch""" )
_check_torch_version()
# convert to torch
lowercase_ = to_channel_dimension_format(UpperCamelCase__ , ChannelDimension.FIRST )
lowercase_ = torch.from_numpy(UpperCamelCase__ )
lowercase_ , lowercase_ = patch_size["""height"""], patch_size["""width"""]
lowercase_ , lowercase_ = get_image_size(UpperCamelCase__ )
# maximize scale s.t.
lowercase_ = math.sqrt(max_patches * (patch_height / image_height) * (patch_width / image_width) )
lowercase_ = max(min(math.floor(scale * image_height / patch_height ) , UpperCamelCase__ ) , 1 )
lowercase_ = max(min(math.floor(scale * image_width / patch_width ) , UpperCamelCase__ ) , 1 )
lowercase_ = max(num_feasible_rows * patch_height , 1 )
lowercase_ = max(num_feasible_cols * patch_width , 1 )
lowercase_ = torch.nn.functional.interpolate(
image.unsqueeze(0 ) , size=(resized_height, resized_width) , mode="""bilinear""" , align_corners=UpperCamelCase__ , antialias=UpperCamelCase__ , ).squeeze(0 )
# [1, rows, columns, patch_height * patch_width * image_channels]
lowercase_ = torch_extract_patches(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
lowercase_ = patches.shape
lowercase_ = patches_shape[1]
lowercase_ = patches_shape[2]
lowercase_ = patches_shape[3]
# [rows * columns, patch_height * patch_width * image_channels]
lowercase_ = patches.reshape([rows * columns, depth] )
# [rows * columns, 1]
lowercase_ = torch.arange(UpperCamelCase__ ).reshape([rows, 1] ).repeat(1 , UpperCamelCase__ ).reshape([rows * columns, 1] )
lowercase_ = torch.arange(UpperCamelCase__ ).reshape([1, columns] ).repeat(UpperCamelCase__ , 1 ).reshape([rows * columns, 1] )
# Offset by 1 so the ids do not contain zeros, which represent padding.
row_ids += 1
col_ids += 1
# Prepare additional patch features.
# [rows * columns, 1]
lowercase_ = row_ids.to(torch.floataa )
lowercase_ = col_ids.to(torch.floataa )
# [rows * columns, 2 + patch_height * patch_width * image_channels]
lowercase_ = torch.cat([row_ids, col_ids, patches] , -1 )
# [max_patches, 2 + patch_height * patch_width * image_channels]
lowercase_ = torch.nn.functional.pad(UpperCamelCase__ , [0, 0, 0, max_patches - (rows * columns)] ).float()
lowercase_ = to_numpy_array(UpperCamelCase__ )
return result
def UpperCAmelCase__ ( self : List[Any] , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase__ : Dict ):
'''simple docstring'''
if image.dtype == np.uinta:
lowercase_ = image.astype(np.floataa )
# take mean across the whole `image`
lowercase_ = np.mean(UpperCamelCase__ )
lowercase_ = np.std(UpperCamelCase__ )
lowercase_ = max(UpperCamelCase__ , 1.0 / math.sqrt(np.prod(image.shape ) ) )
return normalize(UpperCamelCase__ , mean=UpperCamelCase__ , std=UpperCamelCase__ , **UpperCamelCase__ )
def UpperCAmelCase__ ( self : str , UpperCamelCase__ : ImageInput , UpperCamelCase__ : Optional[str] = None , UpperCamelCase__ : bool = None , UpperCamelCase__ : Optional[bool] = None , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : Optional[Dict[str, int]] = None , UpperCamelCase__ : Optional[Union[str, TensorType]] = None , UpperCamelCase__ : ChannelDimension = ChannelDimension.FIRST , **UpperCamelCase__ : Union[str, Any] , ):
'''simple docstring'''
lowercase_ = do_normalize if do_normalize is not None else self.do_normalize
lowercase_ = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
lowercase_ = patch_size if patch_size is not None else self.patch_size
lowercase_ = max_patches if max_patches is not None else self.max_patches
lowercase_ = self.is_vqa
if kwargs.get("""data_format""" , UpperCamelCase__ ) is not None:
raise ValueError("""data_format is not an accepted input as the outputs are """ )
lowercase_ = make_list_of_images(UpperCamelCase__ )
if not valid_images(UpperCamelCase__ ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
lowercase_ = [convert_to_rgb(UpperCamelCase__ ) for image in images]
# All transformations expect numpy arrays.
lowercase_ = [to_numpy_array(UpperCamelCase__ ) for image in images]
if is_vqa:
if header_text is None:
raise ValueError("""A header text must be provided for VQA models.""" )
lowercase_ = kwargs.pop("""font_bytes""" , UpperCamelCase__ )
lowercase_ = kwargs.pop("""font_path""" , UpperCamelCase__ )
if isinstance(UpperCamelCase__ , UpperCamelCase__ ):
lowercase_ = [header_text] * len(UpperCamelCase__ )
lowercase_ = [
render_header(UpperCamelCase__ , header_text[i] , font_bytes=UpperCamelCase__ , font_path=UpperCamelCase__ )
for i, image in enumerate(UpperCamelCase__ )
]
if do_normalize:
lowercase_ = [self.normalize(image=UpperCamelCase__ ) for image in images]
# convert to torch tensor and permute
lowercase_ = [
self.extract_flattened_patches(image=UpperCamelCase__ , max_patches=UpperCamelCase__ , patch_size=UpperCamelCase__ )
for image in images
]
# create attention mask in numpy
lowercase_ = [(image.sum(axis=-1 ) != 0).astype(np.floataa ) for image in images]
lowercase_ = BatchFeature(
data={"""flattened_patches""": images, """attention_mask""": attention_masks} , tensor_type=UpperCamelCase__ )
return encoded_outputs
| 650 | 0 |
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
_SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
_SCREAMING_SNAKE_CASE : List[Any] = '▁'
_SCREAMING_SNAKE_CASE : Any = {'vocab_file': 'sentencepiece.bpe.model'}
_SCREAMING_SNAKE_CASE : Any = {
'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'
),
}
}
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
'facebook/mbart-large-en-ro': 1_024,
'facebook/mbart-large-cc25': 1_024,
}
# fmt: off
_SCREAMING_SNAKE_CASE : Dict = ['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 A ( lowerCamelCase_ ):
'''simple docstring'''
lowerCamelCase : Tuple = VOCAB_FILES_NAMES
lowerCamelCase : Any = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowerCamelCase : List[Any] = PRETRAINED_VOCAB_FILES_MAP
lowerCamelCase : Dict = ["""input_ids""", """attention_mask"""]
lowerCamelCase : List[int] = []
lowerCamelCase : List[int] = []
def __init__( self : Dict , _UpperCamelCase : str , _UpperCamelCase : Optional[Any]="<s>" , _UpperCamelCase : Dict="</s>" , _UpperCamelCase : Optional[Any]="</s>" , _UpperCamelCase : Union[str, Any]="<s>" , _UpperCamelCase : str="<unk>" , _UpperCamelCase : List[Any]="<pad>" , _UpperCamelCase : Dict="<mask>" , _UpperCamelCase : Optional[int]=None , _UpperCamelCase : Union[str, Any]=None , _UpperCamelCase : Optional[int]=None , _UpperCamelCase : Optional[Dict[str, Any]] = None , _UpperCamelCase : int=None , **_UpperCamelCase : List[str] , ):
# Mask token behave like a normal word, i.e. include the space before it
_lowercase: Dict = AddedToken(_UpperCamelCase , lstrip=_UpperCamelCase , rstrip=_UpperCamelCase) if isinstance(_UpperCamelCase , _UpperCamelCase) else mask_token
_lowercase: List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_UpperCamelCase , eos_token=_UpperCamelCase , unk_token=_UpperCamelCase , sep_token=_UpperCamelCase , cls_token=_UpperCamelCase , pad_token=_UpperCamelCase , mask_token=_UpperCamelCase , tokenizer_file=_UpperCamelCase , src_lang=_UpperCamelCase , tgt_lang=_UpperCamelCase , additional_special_tokens=_UpperCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_UpperCamelCase , )
_lowercase: Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.Load(str(_UpperCamelCase))
_lowercase: Tuple = 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
_lowercase: List[str] = {"<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
_lowercase: Any = 1
_lowercase: Dict = len(self.sp_model)
_lowercase: str = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(_UpperCamelCase)
}
_lowercase: int = {v: k for k, v in self.lang_code_to_id.items()}
_lowercase: 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)
_lowercase: List[str] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
_lowercase: Optional[Any] = 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])
_lowercase: str = src_lang if src_lang is not None else "en_XX"
_lowercase: int = self.lang_code_to_id[self._src_lang]
_lowercase: Optional[int] = tgt_lang
self.set_src_lang_special_tokens(self._src_lang)
def __getstate__( self : Optional[Any]):
_lowercase: Dict = self.__dict__.copy()
_lowercase: List[str] = None
_lowercase: Dict = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Optional[Any] , _UpperCamelCase : Union[str, Any]):
_lowercase: Optional[int] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs"):
_lowercase: Optional[int] = {}
_lowercase: Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs)
self.sp_model.LoadFromSerializedProto(self.sp_model_proto)
@property
def UpperCAmelCase__ ( self : Optional[Any]):
return len(self.sp_model) + len(self.lang_code_to_id) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def UpperCAmelCase__ ( self : Union[str, Any]):
return self._src_lang
@src_lang.setter
def UpperCAmelCase__ ( self : Optional[Any] , _UpperCamelCase : str):
_lowercase: Union[str, Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang)
def UpperCAmelCase__ ( self : str , _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=_UpperCamelCase , token_ids_a=_UpperCamelCase , already_has_special_tokens=_UpperCamelCase)
_lowercase: List[Any] = [1] * len(self.prefix_tokens)
_lowercase: List[Any] = [1] * len(self.suffix_tokens)
if token_ids_a is None:
return prefix_ones + ([0] * len(_UpperCamelCase)) + suffix_ones
return prefix_ones + ([0] * len(_UpperCamelCase)) + ([0] * len(_UpperCamelCase)) + suffix_ones
def UpperCAmelCase__ ( self : Optional[Any] , _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 UpperCAmelCase__ ( self : List[str] , _UpperCamelCase : List[int] , _UpperCamelCase : Optional[List[int]] = None):
_lowercase: Tuple = [self.sep_token_id]
_lowercase: str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]
def UpperCAmelCase__ ( self : str , _UpperCamelCase : Tuple , _UpperCamelCase : str , _UpperCamelCase : Optional[str] , _UpperCamelCase : Optional[str] , **_UpperCamelCase : Dict):
if src_lang is None or tgt_lang is None:
raise ValueError("Translation requires a `src_lang` and a `tgt_lang` for this model")
_lowercase: Dict = src_lang
_lowercase: Tuple = self(_UpperCamelCase , add_special_tokens=_UpperCamelCase , return_tensors=_UpperCamelCase , **_UpperCamelCase)
_lowercase: Dict = self.convert_tokens_to_ids(_UpperCamelCase)
_lowercase: int = tgt_lang_id
return inputs
def UpperCAmelCase__ ( self : Optional[int]):
_lowercase: Optional[int] = {self.convert_ids_to_tokens(_UpperCamelCase): i for i in range(self.vocab_size)}
vocab.update(self.added_tokens_encoder)
return vocab
def UpperCAmelCase__ ( self : Optional[int] , _UpperCamelCase : str):
return self.sp_model.encode(_UpperCamelCase , out_type=_UpperCamelCase)
def UpperCAmelCase__ ( self : Union[str, Any] , _UpperCamelCase : Dict):
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
_lowercase: Union[str, Any] = self.sp_model.PieceToId(_UpperCamelCase)
# 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 UpperCAmelCase__ ( self : Union[str, Any] , _UpperCamelCase : Optional[int]):
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 UpperCAmelCase__ ( self : str , _UpperCamelCase : Any):
_lowercase: Tuple = "".join(_UpperCamelCase).replace(_UpperCamelCase , " ").strip()
return out_string
def UpperCAmelCase__ ( self : Optional[int] , _UpperCamelCase : str , _UpperCamelCase : Optional[str] = None):
if not os.path.isdir(_UpperCamelCase):
logger.error(f"Vocabulary path ({save_directory}) should be a directory")
return
_lowercase: Any = 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) and os.path.isfile(self.vocab_file):
copyfile(self.vocab_file , _UpperCamelCase)
elif not os.path.isfile(self.vocab_file):
with open(_UpperCamelCase , "wb") as fi:
_lowercase: int = self.sp_model.serialized_model_proto()
fi.write(_UpperCamelCase)
return (out_vocab_file,)
def UpperCAmelCase__ ( self : str , _UpperCamelCase : List[str] , _UpperCamelCase : str = "en_XX" , _UpperCamelCase : Optional[List[str]] = None , _UpperCamelCase : str = "ro_RO" , **_UpperCamelCase : Dict , ):
_lowercase: Any = src_lang
_lowercase: Optional[Any] = tgt_lang
return super().prepare_seqaseq_batch(_UpperCamelCase , _UpperCamelCase , **_UpperCamelCase)
def UpperCAmelCase__ ( self : str):
return self.set_src_lang_special_tokens(self.src_lang)
def UpperCAmelCase__ ( self : Tuple):
return self.set_tgt_lang_special_tokens(self.tgt_lang)
def UpperCAmelCase__ ( self : str , _UpperCamelCase : Dict):
_lowercase: Any = self.lang_code_to_id[src_lang]
_lowercase: Dict = []
_lowercase: Tuple = [self.eos_token_id, self.cur_lang_code]
def UpperCAmelCase__ ( self : int , _UpperCamelCase : str):
_lowercase: Tuple = self.lang_code_to_id[lang]
_lowercase: str = []
_lowercase: Tuple = [self.eos_token_id, self.cur_lang_code]
| 226 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : Union[str, Any] = {
'configuration_vision_text_dual_encoder': ['VisionTextDualEncoderConfig'],
'processing_vision_text_dual_encoder': ['VisionTextDualEncoderProcessor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = ['VisionTextDualEncoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : Tuple = ['FlaxVisionTextDualEncoderModel']
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_SCREAMING_SNAKE_CASE : List[Any] = ['TFVisionTextDualEncoderModel']
if TYPE_CHECKING:
from .configuration_vision_text_dual_encoder import VisionTextDualEncoderConfig
from .processing_vision_text_dual_encoder import VisionTextDualEncoderProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vision_text_dual_encoder import VisionTextDualEncoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vision_text_dual_encoder import FlaxVisionTextDualEncoderModel
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vision_text_dual_encoder import TFVisionTextDualEncoderModel
else:
import sys
_SCREAMING_SNAKE_CASE : List[Any] = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 226 | 1 |
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( snake_case):
if not head:
return True
# split the list to two parts
__snake_case , __snake_case = head.next, head
while fast and fast.next:
__snake_case = fast.next.next
__snake_case = slow.next
__snake_case = slow.next
__snake_case = None # Don't forget here! But forget still works!
# reverse the second part
__snake_case = None
while second:
__snake_case = second.next
__snake_case = node
__snake_case = second
__snake_case = nxt
# compare two parts
# second part has the same or one less node
while node:
if node.val != head.val:
return False
__snake_case = node.next
__snake_case = head.next
return True
def SCREAMING_SNAKE_CASE ( snake_case):
if not head or not head.next:
return True
# 1. Get the midpoint (slow)
__snake_case = __snake_case = __snake_case = head
while fast and fast.next:
__snake_case , __snake_case = fast.next.next, slow.next
# 2. Push the second half into the stack
__snake_case = [slow.val]
while slow.next:
__snake_case = slow.next
stack.append(slow.val)
# 3. Comparison
while stack:
if stack.pop() != cur.val:
return False
__snake_case = cur.next
return True
def SCREAMING_SNAKE_CASE ( snake_case):
if not head or not head.next:
return True
__snake_case = {}
__snake_case = 0
while head:
if head.val in d:
d[head.val].append(snake_case)
else:
__snake_case = [pos]
__snake_case = head.next
pos += 1
__snake_case = pos - 1
__snake_case = 0
for v in d.values():
if len(snake_case) % 2 != 0:
middle += 1
else:
__snake_case = 0
for i in range(0, len(snake_case)):
if v[i] + v[len(snake_case) - 1 - step] != checksum:
return False
step += 1
if middle > 1:
return False
return True | 93 | """simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, 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.roformer.modeling_flax_roformer import (
FlaxRoFormerForMaskedLM,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerModel,
)
class _A ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Optional[Any] , A_ : Dict , A_ : List[Any]=13 , A_ : Dict=7 , A_ : Optional[int]=True , A_ : Optional[int]=True , A_ : List[Any]=True , A_ : Union[str, Any]=True , A_ : str=99 , A_ : Union[str, Any]=32 , A_ : Optional[int]=5 , A_ : Union[str, Any]=4 , A_ : Dict=37 , A_ : Any="gelu" , A_ : Tuple=0.1 , A_ : str=0.1 , A_ : List[str]=512 , A_ : List[str]=16 , A_ : Optional[int]=2 , A_ : Optional[Any]=0.02 , A_ : str=4 , ) -> Any:
__snake_case = parent
__snake_case = batch_size
__snake_case = seq_length
__snake_case = is_training
__snake_case = use_attention_mask
__snake_case = use_token_type_ids
__snake_case = use_labels
__snake_case = vocab_size
__snake_case = hidden_size
__snake_case = num_hidden_layers
__snake_case = num_attention_heads
__snake_case = intermediate_size
__snake_case = hidden_act
__snake_case = hidden_dropout_prob
__snake_case = attention_probs_dropout_prob
__snake_case = max_position_embeddings
__snake_case = type_vocab_size
__snake_case = type_sequence_label_size
__snake_case = initializer_range
__snake_case = num_choices
def lowercase ( self : List[Any] ) -> Any:
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__snake_case = None
if self.use_attention_mask:
__snake_case = random_attention_mask([self.batch_size, self.seq_length] )
__snake_case = None
if self.use_token_type_ids:
__snake_case = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__snake_case = RoFormerConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=A_ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowercase ( self : Dict ) -> Union[str, Any]:
__snake_case = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case , __snake_case = config_and_inputs
__snake_case = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
return config, inputs_dict
@require_flax
class _A ( _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase_ : List[str] = True
UpperCamelCase_ : List[str] = (
(
FlaxRoFormerModel,
FlaxRoFormerForMaskedLM,
FlaxRoFormerForSequenceClassification,
FlaxRoFormerForTokenClassification,
FlaxRoFormerForMultipleChoice,
FlaxRoFormerForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowercase ( self : str ) -> List[str]:
__snake_case = FlaxRoFormerModelTester(self )
@slow
def lowercase ( self : Optional[Any] ) -> List[Any]:
for model_class_name in self.all_model_classes:
__snake_case = model_class_name.from_pretrained('''junnyu/roformer_chinese_small''' , from_pt=A_ )
__snake_case = model(np.ones((1, 1) ) )
self.assertIsNotNone(A_ )
@require_flax
class _A ( unittest.TestCase ):
"""simple docstring"""
@slow
def lowercase ( self : List[str] ) -> List[str]:
__snake_case = FlaxRoFormerForMaskedLM.from_pretrained('''junnyu/roformer_chinese_base''' )
__snake_case = jnp.array([[0, 1, 2, 3, 4, 5]] )
__snake_case = model(A_ )[0]
__snake_case = 50_000
__snake_case = (1, 6, vocab_size)
self.assertEqual(output.shape , A_ )
__snake_case = jnp.array(
[[[-0.12_05, -1.02_65, 0.29_22], [-1.51_34, 0.19_74, 0.15_19], [-5.01_35, -3.90_03, -0.84_04]]] )
self.assertTrue(jnp.allclose(output[:, :3, :3] , A_ , atol=1E-4 ) ) | 93 | 1 |
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