code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
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import unittest
from transformers import 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
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
OpenAIGPTConfig,
OpenAIGPTDoubleHeadsModel,
OpenAIGPTForSequenceClassification,
OpenAIGPTLMHeadModel,
OpenAIGPTModel,
)
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : Optional[Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : Optional[int]=13 ,lowerCamelCase__ : Optional[Any]=7 ,lowerCamelCase__ : Tuple=True ,lowerCamelCase__ : Optional[int]=True ,lowerCamelCase__ : int=True ,lowerCamelCase__ : List[str]=99 ,lowerCamelCase__ : Union[str, Any]=32 ,lowerCamelCase__ : Union[str, Any]=5 ,lowerCamelCase__ : Optional[int]=4 ,lowerCamelCase__ : Any=37 ,lowerCamelCase__ : Union[str, Any]="gelu" ,lowerCamelCase__ : List[str]=0.1 ,lowerCamelCase__ : str=0.1 ,lowerCamelCase__ : List[Any]=512 ,lowerCamelCase__ : int=16 ,lowerCamelCase__ : Optional[Any]=2 ,lowerCamelCase__ : List[str]=0.02 ,lowerCamelCase__ : Any=3 ,lowerCamelCase__ : List[Any]=4 ,lowerCamelCase__ : Union[str, Any]=None ,) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_token_type_ids
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = type_sequence_label_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = num_choices
SCREAMING_SNAKE_CASE = scope
SCREAMING_SNAKE_CASE = self.vocab_size - 1
def SCREAMING_SNAKE_CASE__ ( self : int ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE = None
if self.use_token_type_ids:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] ,self.num_choices )
SCREAMING_SNAKE_CASE = OpenAIGPTConfig(
vocab_size=self.vocab_size ,n_embd=self.hidden_size ,n_layer=self.num_hidden_layers ,n_head=self.num_attention_heads ,n_positions=self.max_position_embeddings ,pad_token_id=self.pad_token_id ,)
SCREAMING_SNAKE_CASE = ids_tensor([self.num_hidden_layers, self.num_attention_heads] ,2 )
return (
config,
input_ids,
head_mask,
token_type_ids,
sequence_labels,
token_labels,
choice_labels,
)
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : int ,lowerCamelCase__ : int ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : List[Any] ,*lowerCamelCase__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = OpenAIGPTModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,token_type_ids=lowerCamelCase__ ,head_mask=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,token_type_ids=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : int ,lowerCamelCase__ : str ,*lowerCamelCase__ : Any ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = OpenAIGPTLMHeadModel(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,token_type_ids=lowerCamelCase__ ,labels=lowerCamelCase__ )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : Any ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : str ,*lowerCamelCase__ : str ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = OpenAIGPTDoubleHeadsModel(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,token_type_ids=lowerCamelCase__ ,labels=lowerCamelCase__ )
self.parent.assertEqual(result.loss.shape ,() )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : int ,*lowerCamelCase__ : Any ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = OpenAIGPTForSequenceClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,token_type_ids=lowerCamelCase__ ,labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
),
) = config_and_inputs
SCREAMING_SNAKE_CASE = {
"""input_ids""": input_ids,
"""token_type_ids""": token_type_ids,
"""head_mask""": head_mask,
}
return config, inputs_dict
@require_torch
class UpperCamelCase__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Union[str, Any] = (
(OpenAIGPTModel, OpenAIGPTLMHeadModel, OpenAIGPTDoubleHeadsModel, OpenAIGPTForSequenceClassification)
if is_torch_available()
else ()
)
__snake_case : Union[str, Any] = (
(OpenAIGPTLMHeadModel,) if is_torch_available() else ()
) # TODO (PVP): Add Double HeadsModel when generate() function is changed accordingly
__snake_case : Optional[int] = (
{
"feature-extraction": OpenAIGPTModel,
"text-classification": OpenAIGPTForSequenceClassification,
"text-generation": OpenAIGPTLMHeadModel,
"zero-shot": OpenAIGPTForSequenceClassification,
}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : Any ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Optional[int] ) -> Any:
'''simple docstring'''
if pipeline_test_casse_name == "ZeroShotClassificationPipelineTests":
# Get `tokenizer does not have a padding token` error for both fast/slow tokenizers.
# `OpenAIGPTConfig` was never used in pipeline tests, either because of a missing checkpoint or because a
# tiny config could not be created.
return True
return False
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : Dict ,lowerCamelCase__ : str ,lowerCamelCase__ : Union[str, Any]=False ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = super()._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ ,return_labels=lowerCamelCase__ )
if return_labels:
if model_class.__name__ == "OpenAIGPTDoubleHeadsModel":
SCREAMING_SNAKE_CASE = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices, self.model_tester.seq_length) ,dtype=torch.long ,device=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = inputs_dict["""labels"""]
SCREAMING_SNAKE_CASE = inputs_dict["""labels"""]
SCREAMING_SNAKE_CASE = torch.zeros(
(self.model_tester.batch_size, self.model_tester.num_choices) ,dtype=torch.long ,device=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = torch.zeros(
self.model_tester.batch_size ,dtype=torch.long ,device=lowerCamelCase__ )
return inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = OpenAIGPTModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self ,config_class=lowerCamelCase__ ,n_embd=37 )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_model(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_lm_head_model(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_double_lm_head_model(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_openai_gpt_for_sequence_classification(*lowerCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Dict:
'''simple docstring'''
for model_name in OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = OpenAIGPTModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = OpenAIGPTLMHeadModel.from_pretrained("""openai-gpt""" )
model.to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.tensor([[481, 4735, 544]] ,dtype=torch.long ,device=lowerCamelCase__ ) # the president is
SCREAMING_SNAKE_CASE = [
481,
4735,
544,
246,
963,
870,
762,
239,
244,
40477,
244,
249,
719,
881,
487,
544,
240,
244,
603,
481,
] # the president is a very good man. " \n " i\'m sure he is, " said the
SCREAMING_SNAKE_CASE = model.generate(lowerCamelCase__ ,do_sample=lowerCamelCase__ )
self.assertListEqual(output_ids[0].tolist() ,lowerCamelCase__ )
| 296 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE_ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
if "://" in dataset_path:
SCREAMING_SNAKE_CASE = dataset_path.split("""://""" )[1]
return dataset_path
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
if fs is not None and fs.protocol != "file":
return True
else:
return False
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = not is_remote_filesystem(_SCREAMING_SNAKE_CASE )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(_SCREAMING_SNAKE_CASE ) , fs._strip_protocol(_SCREAMING_SNAKE_CASE ) )
else:
fs.mv(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , recursive=_SCREAMING_SNAKE_CASE )
def __lowercase ( ) -> None:
'''simple docstring'''
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = threading.Lock()
| 296 | 1 |
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@require_torch
class UpperCamelCase__ ( unittest.TestCase , lowerCAmelCase_ ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_tool("""text-to-speech""" )
self.tool.setup()
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = self.tool("""hey""" )
SCREAMING_SNAKE_CASE = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] ,torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485] ) ,) )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Tuple:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = self.tool("""hey""" )
SCREAMING_SNAKE_CASE = result.to_raw()
self.assertTrue(
torch.allclose(
resulting_tensor[:3] ,torch.tensor([-0.0005966668832115829, -0.0003657640190795064, -0.00013439502799883485] ) ,) )
| 296 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,)
return model
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE = KarrasVeScheduler()
SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""numpy""" ,return_dict=lowerCamelCase__ )[0]
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """google/ncsnpp-celebahq-256"""
SCREAMING_SNAKE_CASE = UNetaDModel.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = KarrasVeScheduler()
SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=20 ,generator=lowerCamelCase__ ,output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 296 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}
class UpperCamelCase__ ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Tuple = "focalnet"
def __init__( self : Union[str, Any] ,lowerCamelCase__ : Union[str, Any]=224 ,lowerCamelCase__ : Optional[int]=4 ,lowerCamelCase__ : str=3 ,lowerCamelCase__ : Any=96 ,lowerCamelCase__ : Dict=False ,lowerCamelCase__ : Optional[int]=[192, 384, 768, 768] ,lowerCamelCase__ : Optional[Any]=[2, 2, 6, 2] ,lowerCamelCase__ : Optional[Any]=[2, 2, 2, 2] ,lowerCamelCase__ : Optional[Any]=[3, 3, 3, 3] ,lowerCamelCase__ : int="gelu" ,lowerCamelCase__ : List[Any]=4.0 ,lowerCamelCase__ : List[Any]=0.0 ,lowerCamelCase__ : int=0.1 ,lowerCamelCase__ : str=False ,lowerCamelCase__ : int=1e-4 ,lowerCamelCase__ : Dict=False ,lowerCamelCase__ : int=False ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : List[str]=0.02 ,lowerCamelCase__ : Any=1e-5 ,lowerCamelCase__ : List[Any]=32 ,lowerCamelCase__ : List[str]=None ,lowerCamelCase__ : Any=None ,**lowerCamelCase__ : Any ,) -> Any:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = embed_dim
SCREAMING_SNAKE_CASE = use_conv_embed
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = focal_levels
SCREAMING_SNAKE_CASE = focal_windows
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = mlp_ratio
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = use_layerscale
SCREAMING_SNAKE_CASE = layerscale_value
SCREAMING_SNAKE_CASE = use_post_layernorm
SCREAMING_SNAKE_CASE = use_post_layernorm_in_modulation
SCREAMING_SNAKE_CASE = normalize_modulator
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = encoder_stride
SCREAMING_SNAKE_CASE = ["""stem"""] + [F"""stage{idx}""" for idx in range(1 ,len(self.depths ) + 1 )]
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = get_aligned_output_features_output_indices(
out_features=lowerCamelCase__ ,out_indices=lowerCamelCase__ ,stage_names=self.stage_names )
| 296 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,lowerCamelCase__ : Callable ,lowerCamelCase__ : Optional[Features] = None ,lowerCamelCase__ : str = None ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : Optional[dict] = None ,lowerCamelCase__ : Optional[int] = None ,**lowerCamelCase__ : Optional[Any] ,) -> List[str]:
'''simple docstring'''
super().__init__(
features=lowerCamelCase__ ,cache_dir=lowerCamelCase__ ,keep_in_memory=lowerCamelCase__ ,streaming=lowerCamelCase__ ,num_proc=lowerCamelCase__ ,**lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = Generator(
cache_dir=lowerCamelCase__ ,features=lowerCamelCase__ ,generator=lowerCamelCase__ ,gen_kwargs=lowerCamelCase__ ,**lowerCamelCase__ ,)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
if self.streaming:
SCREAMING_SNAKE_CASE = self.builder.as_streaming_dataset(split="""train""" )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
self.builder.download_and_prepare(
download_config=lowerCamelCase__ ,download_mode=lowerCamelCase__ ,verification_mode=lowerCamelCase__ ,base_path=lowerCamelCase__ ,num_proc=self.num_proc ,)
SCREAMING_SNAKE_CASE = self.builder.as_dataset(
split="""train""" ,verification_mode=lowerCamelCase__ ,in_memory=self.keep_in_memory )
return dataset
| 296 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE_ = random.Random()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Any:
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE = global_rng
SCREAMING_SNAKE_CASE = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
@require_torch
@require_torchaudio
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[Any] ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Union[str, Any]=7 ,lowerCamelCase__ : Optional[int]=400 ,lowerCamelCase__ : Tuple=2000 ,lowerCamelCase__ : Any=10 ,lowerCamelCase__ : Tuple=160 ,lowerCamelCase__ : List[Any]=8 ,lowerCamelCase__ : Dict=0.0 ,lowerCamelCase__ : str=4000 ,lowerCamelCase__ : Dict=False ,lowerCamelCase__ : Tuple=True ,) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = min_seq_length
SCREAMING_SNAKE_CASE = max_seq_length
SCREAMING_SNAKE_CASE = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE = padding_value
SCREAMING_SNAKE_CASE = sampling_rate
SCREAMING_SNAKE_CASE = return_attention_mask
SCREAMING_SNAKE_CASE = do_normalize
SCREAMING_SNAKE_CASE = feature_size
SCREAMING_SNAKE_CASE = chunk_length
SCREAMING_SNAKE_CASE = hop_length
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : int=False ,lowerCamelCase__ : Optional[Any]=False ) -> str:
'''simple docstring'''
def _flatten(lowerCamelCase__ : List[Any] ):
return list(itertools.chain(*lowerCamelCase__ ) )
if equal_length:
SCREAMING_SNAKE_CASE = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Any = WhisperFeatureExtractor if is_speech_available() else None
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = WhisperFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = feat_extract_first.save_pretrained(lowerCamelCase__ )[0]
check_json_file_has_correct_format(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_first.mel_filters
SCREAMING_SNAKE_CASE = feat_extract_second.mel_filters
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_first.mel_filters
SCREAMING_SNAKE_CASE = feat_extract_second.mel_filters
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in range(800 ,1400 ,200 )]
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]
# Test feature size
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,padding="""max_length""" ,return_tensors="""np""" ).input_features
self.assertTrue(input_features.ndim == 3 )
self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames )
self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size )
# Test not batched input
SCREAMING_SNAKE_CASE = feature_extractor(speech_inputs[0] ,return_tensors="""np""" ).input_features
SCREAMING_SNAKE_CASE = feature_extractor(np_speech_inputs[0] ,return_tensors="""np""" ).input_features
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ,atol=1e-3 ) )
# Test batched
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""np""" ).input_features
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCamelCase__ ,lowerCamelCase__ ):
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ,atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE = np.asarray(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""np""" ).input_features
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCamelCase__ ,lowerCamelCase__ ):
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ,atol=1e-3 ) )
# Test truncation required
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in range(200 ,(feature_extractor.n_samples + 500) ,200 )]
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]
SCREAMING_SNAKE_CASE = [x[: feature_extractor.n_samples] for x in speech_inputs]
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs_truncated]
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""np""" ).input_features
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(lowerCamelCase__ ,lowerCamelCase__ ):
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ,atol=1e-3 ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
import torch
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE = np.random.rand(100 ,32 ).astype(np.floataa )
SCREAMING_SNAKE_CASE = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
SCREAMING_SNAKE_CASE = feature_extractor.pad([{"""input_features""": inputs}] ,return_tensors="""np""" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
SCREAMING_SNAKE_CASE = feature_extractor.pad([{"""input_features""": inputs}] ,return_tensors="""pt""" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : Any ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,"""clean""" ,split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE = ds.sort("""id""" ).select(range(lowerCamelCase__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = torch.tensor(
[
0.1193, -0.0946, -0.1098, -0.0196, 0.0225, -0.0690, -0.1736, 0.0951,
0.0971, -0.0817, -0.0702, 0.0162, 0.0260, 0.0017, -0.0192, -0.1678,
0.0709, -0.1867, -0.0655, -0.0274, -0.0234, -0.1884, -0.0516, -0.0554,
-0.0274, -0.1425, -0.1423, 0.0837, 0.0377, -0.0854
] )
# fmt: on
SCREAMING_SNAKE_CASE = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE = WhisperFeatureExtractor()
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""pt""" ).input_features
self.assertEqual(input_features.shape ,(1, 80, 3000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] ,lowerCamelCase__ ,atol=1e-4 ) )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
SCREAMING_SNAKE_CASE = self._load_datasamples(1 )[0]
SCREAMING_SNAKE_CASE = ((audio - audio.min()) / (audio.max() - audio.min())) * 65535 # Rescale to [0, 65535] to show issue
SCREAMING_SNAKE_CASE = feat_extract.zero_mean_unit_var_norm([audio] ,attention_mask=lowerCamelCase__ )[0]
self.assertTrue(np.all(np.mean(lowerCamelCase__ ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(lowerCamelCase__ ) - 1 ) < 1e-3 ) )
| 296 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[float] = field(
default=0.0 , metadata={"help": "The label smoothing epsilon to apply (if not zero)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Whether to SortishSamler or not."} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "whether to use adafactor"} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Encoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Decoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(default=lowerCAmelCase_ , metadata={"help": "Dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Attention dropout probability. Goes into model.config."} )
__snake_case : Optional[str] = field(
default="linear" , metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}"} , )
| 296 | 1 |
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__) # pylint: disable=invalid-name
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Dict ,lowerCamelCase__ : CLIPSegForImageSegmentation ,lowerCamelCase__ : CLIPSegProcessor ,lowerCamelCase__ : AutoencoderKL ,lowerCamelCase__ : CLIPTextModel ,lowerCamelCase__ : CLIPTokenizer ,lowerCamelCase__ : UNetaDConditionModel ,lowerCamelCase__ : Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] ,lowerCamelCase__ : StableDiffusionSafetyChecker ,lowerCamelCase__ : CLIPImageProcessor ,) -> Union[str, Any]:
'''simple docstring'''
super().__init__()
if hasattr(scheduler.config ,"""steps_offset""" ) and scheduler.config.steps_offset != 1:
SCREAMING_SNAKE_CASE = (
F"""The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"""
F""" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure """
"""to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
""" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
""" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
""" file"""
)
deprecate("""steps_offset!=1""" ,"""1.0.0""" ,lowerCamelCase__ ,standard_warn=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = dict(scheduler.config )
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = FrozenDict(lowerCamelCase__ )
if hasattr(scheduler.config ,"""skip_prk_steps""" ) and scheduler.config.skip_prk_steps is False:
SCREAMING_SNAKE_CASE = (
F"""The configuration file of this scheduler: {scheduler} has not set the configuration"""
""" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
""" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
""" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
""" Hub, it would be very nice if you could open a Pull request for the"""
""" `scheduler/scheduler_config.json` file"""
)
deprecate("""skip_prk_steps not set""" ,"""1.0.0""" ,lowerCamelCase__ ,standard_warn=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = dict(scheduler.config )
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = FrozenDict(lowerCamelCase__ )
if safety_checker is None:
logger.warning(
F"""You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"""
""" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
""" results in services or applications open to the public. Both the diffusers team and Hugging Face"""
""" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
""" it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
""" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .""" )
self.register_modules(
segmentation_model=lowerCamelCase__ ,segmentation_processor=lowerCamelCase__ ,vae=lowerCamelCase__ ,text_encoder=lowerCamelCase__ ,tokenizer=lowerCamelCase__ ,unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ ,safety_checker=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,)
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : Optional[Union[str, int]] = "auto" ) -> Union[str, Any]:
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
SCREAMING_SNAKE_CASE = self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Any:
'''simple docstring'''
self.enable_attention_slicing(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> List[Any]:
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
SCREAMING_SNAKE_CASE = torch.device("""cuda""" )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(lowerCamelCase__ ,lowerCamelCase__ )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
if self.device != torch.device("""meta""" ) or not hasattr(self.unet ,"""_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(lowerCamelCase__ ,"""_hf_hook""" )
and hasattr(module._hf_hook ,"""execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
def __call__( self : Tuple ,lowerCamelCase__ : Union[str, List[str]] ,lowerCamelCase__ : Union[torch.FloatTensor, PIL.Image.Image] ,lowerCamelCase__ : str ,lowerCamelCase__ : int = 512 ,lowerCamelCase__ : int = 512 ,lowerCamelCase__ : int = 50 ,lowerCamelCase__ : float = 7.5 ,lowerCamelCase__ : Optional[Union[str, List[str]]] = None ,lowerCamelCase__ : Optional[int] = 1 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : Optional[torch.Generator] = None ,lowerCamelCase__ : Optional[torch.FloatTensor] = None ,lowerCamelCase__ : Optional[str] = "pil" ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Optional[Callable[[int, int, torch.FloatTensor], None]] = None ,lowerCamelCase__ : int = 1 ,**lowerCamelCase__ : Optional[int] ,) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.segmentation_processor(
text=[text] ,images=[image] ,padding="""max_length""" ,return_tensors="""pt""" ).to(self.device )
SCREAMING_SNAKE_CASE = self.segmentation_model(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
SCREAMING_SNAKE_CASE = self.numpy_to_pil(lowerCamelCase__ )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
SCREAMING_SNAKE_CASE = StableDiffusionInpaintPipeline(
vae=self.vae ,text_encoder=self.text_encoder ,tokenizer=self.tokenizer ,unet=self.unet ,scheduler=self.scheduler ,safety_checker=self.safety_checker ,feature_extractor=self.feature_extractor ,)
return inpainting_pipeline(
prompt=lowerCamelCase__ ,image=lowerCamelCase__ ,mask_image=lowerCamelCase__ ,height=lowerCamelCase__ ,width=lowerCamelCase__ ,num_inference_steps=lowerCamelCase__ ,guidance_scale=lowerCamelCase__ ,negative_prompt=lowerCamelCase__ ,num_images_per_prompt=lowerCamelCase__ ,eta=lowerCamelCase__ ,generator=lowerCamelCase__ ,latents=lowerCamelCase__ ,output_type=lowerCamelCase__ ,return_dict=lowerCamelCase__ ,callback=lowerCamelCase__ ,callback_steps=lowerCamelCase__ ,)
| 296 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[str] = TextToVideoSDPipeline
__snake_case : int = TEXT_TO_IMAGE_PARAMS
__snake_case : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
__snake_case : Dict = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""") ,up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""") ,cross_attention_dim=32 ,attention_head_dim=4 ,)
SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.00085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=lowerCamelCase__ ,set_alpha_to_one=lowerCamelCase__ ,)
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = 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 )
SCREAMING_SNAKE_CASE = 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=1000 ,hidden_act="""gelu""" ,projection_dim=512 ,)
SCREAMING_SNAKE_CASE = CLIPTextModel(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : int=0 ) -> List[Any]:
'''simple docstring'''
if str(lowerCamelCase__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCamelCase__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe.to(lowerCamelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """np"""
SCREAMING_SNAKE_CASE = sd_pipe(**lowerCamelCase__ ).frames
SCREAMING_SNAKE_CASE = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
SCREAMING_SNAKE_CASE = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=1e-2 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=25 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=2 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 296 | 1 |
from collections import defaultdict
from math import gcd
def __lowercase ( _SCREAMING_SNAKE_CASE = 1_50_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = defaultdict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , _SCREAMING_SNAKE_CASE , 2 ):
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) > 1:
continue
SCREAMING_SNAKE_CASE = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(_SCREAMING_SNAKE_CASE , limit + 1 , _SCREAMING_SNAKE_CASE ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 296 |
def __lowercase ( _SCREAMING_SNAKE_CASE = 10 ) -> str:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or n < 0:
raise ValueError("""Invalid input""" )
SCREAMING_SNAKE_CASE = 10**n
SCREAMING_SNAKE_CASE = 2_84_33 * (pow(2 , 7_83_04_57 , _SCREAMING_SNAKE_CASE )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(1_0) = }''')
| 296 | 1 |
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.modeling_flax_pytorch_utils import (
convert_pytorch_state_dict_to_flax,
load_flax_weights_in_pytorch_model,
)
from transformers.models.gptj.modeling_flax_gptj import FlaxGPTJForCausalLM, FlaxGPTJModel
if is_torch_available():
import torch
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : Tuple ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Optional[int]=14 ,lowerCamelCase__ : str=7 ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : str=False ,lowerCamelCase__ : List[str]=True ,lowerCamelCase__ : Any=99 ,lowerCamelCase__ : Tuple=32 ,lowerCamelCase__ : Optional[Any]=4 ,lowerCamelCase__ : Tuple=4 ,lowerCamelCase__ : str=4 ,lowerCamelCase__ : List[Any]=37 ,lowerCamelCase__ : Optional[int]="gelu" ,lowerCamelCase__ : Optional[int]=0.1 ,lowerCamelCase__ : Optional[int]=0.1 ,lowerCamelCase__ : List[Any]=512 ,lowerCamelCase__ : List[str]=0.02 ,) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_input_mask
SCREAMING_SNAKE_CASE = use_token_type_ids
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = rotary_dim
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = vocab_size - 1
SCREAMING_SNAKE_CASE = vocab_size - 1
SCREAMING_SNAKE_CASE = vocab_size - 1
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE = GPTJConfig(
vocab_size=self.vocab_size ,n_embd=self.hidden_size ,n_layer=self.num_hidden_layers ,n_head=self.num_attention_heads ,n_positions=self.max_position_embeddings ,use_cache=lowerCamelCase__ ,bos_token_id=self.bos_token_id ,eos_token_id=self.eos_token_id ,pad_token_id=self.pad_token_id ,rotary_dim=self.rotary_dim ,)
return (config, input_ids, input_mask)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : Dict ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 20
SCREAMING_SNAKE_CASE = model_class_name(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model.init_cache(input_ids.shape[0] ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = jnp.ones((input_ids.shape[0], max_decoder_length) ,dtype="""i4""" )
SCREAMING_SNAKE_CASE = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] ,(input_ids.shape[0], input_ids.shape[-1] - 1) )
SCREAMING_SNAKE_CASE = model(
input_ids[:, :-1] ,attention_mask=lowerCamelCase__ ,past_key_values=lowerCamelCase__ ,position_ids=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,dtype="""i4""" )
SCREAMING_SNAKE_CASE = model(
input_ids[:, -1:] ,attention_mask=lowerCamelCase__ ,past_key_values=outputs_cache.past_key_values ,position_ids=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 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 SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Any ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Optional[int] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 20
SCREAMING_SNAKE_CASE = model_class_name(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = jnp.concatenate(
[attention_mask, jnp.zeros((attention_mask.shape[0], max_decoder_length - attention_mask.shape[1]) )] ,axis=-1 ,)
SCREAMING_SNAKE_CASE = model.init_cache(input_ids.shape[0] ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = jnp.broadcast_to(
jnp.arange(input_ids.shape[-1] - 1 )[None, :] ,(input_ids.shape[0], input_ids.shape[-1] - 1) )
SCREAMING_SNAKE_CASE = model(
input_ids[:, :-1] ,attention_mask=lowerCamelCase__ ,past_key_values=lowerCamelCase__ ,position_ids=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = jnp.array(input_ids.shape[0] * [[input_ids.shape[-1] - 1]] ,dtype="""i4""" )
SCREAMING_SNAKE_CASE = model(
input_ids[:, -1:] ,past_key_values=outputs_cache.past_key_values ,attention_mask=lowerCamelCase__ ,position_ids=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 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 UpperCamelCase__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[Any] = (FlaxGPTJModel, FlaxGPTJForCausalLM) if is_flax_available() else ()
__snake_case : List[str] = (FlaxGPTJForCausalLM,) if is_flax_available() else ()
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = FlaxGPTJModelTester(self )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Any:
'''simple docstring'''
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.check_use_cache_forward_with_attn_mask(
lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
@tooslow
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = GPTaTokenizer.from_pretrained("""gpt2""" ,pad_token="""<|endoftext|>""" ,padding_side="""left""" )
SCREAMING_SNAKE_CASE = tokenizer(["""Hello this is a long string""", """Hey"""] ,return_tensors="""np""" ,padding=lowerCamelCase__ ,truncation=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = FlaxGPTJForCausalLM.from_pretrained("""EleutherAI/gpt-j-6B""" )
SCREAMING_SNAKE_CASE = False
SCREAMING_SNAKE_CASE = model.config.eos_token_id
SCREAMING_SNAKE_CASE = jax.jit(model.generate )
SCREAMING_SNAKE_CASE = jit_generate(
inputs["""input_ids"""] ,attention_mask=inputs["""attention_mask"""] ,pad_token_id=tokenizer.pad_token_id ).sequences
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(lowerCamelCase__ ,skip_special_tokens=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = [
"""Hello this is a long string of text.\n\nI'm trying to get the text of the""",
"""Hey, I'm a little late to the party. I'm going to""",
]
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
SCREAMING_SNAKE_CASE = self._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
SCREAMING_SNAKE_CASE = model_class.__name__[4:] # Skip the "Flax" at the beginning
SCREAMING_SNAKE_CASE = getattr(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = pt_inputs["""input_ids"""].shape
SCREAMING_SNAKE_CASE = np.random.randint(0 ,seq_length - 1 ,size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = pt_model_class(lowerCamelCase__ ).eval()
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ ,dtype=jnp.floataa )
SCREAMING_SNAKE_CASE = convert_pytorch_state_dict_to_flax(pt_model.state_dict() ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = fx_state
with torch.no_grad():
SCREAMING_SNAKE_CASE = pt_model(**lowerCamelCase__ ).to_tuple()
SCREAMING_SNAKE_CASE = fx_model(**lowerCamelCase__ ).to_tuple()
self.assertEqual(len(lowerCamelCase__ ) ,len(lowerCamelCase__ ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(lowerCamelCase__ ,lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] ,pt_output[:, -1].numpy() ,4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
pt_model.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model_class.from_pretrained(lowerCamelCase__ ,from_pt=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = fx_model_loaded(**lowerCamelCase__ ).to_tuple()
self.assertEqual(
len(lowerCamelCase__ ) ,len(lowerCamelCase__ ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output_loaded, pt_output in zip(lowerCamelCase__ ,lowerCamelCase__ ):
self.assert_almost_equals(fx_output_loaded[:, -1] ,pt_output[:, -1].numpy() ,4e-2 )
@is_pt_flax_cross_test
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
# prepare inputs
SCREAMING_SNAKE_CASE = self._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {k: torch.tensor(v.tolist() ) for k, v in prepared_inputs_dict.items()}
# load corresponding PyTorch class
SCREAMING_SNAKE_CASE = model_class.__name__[4:] # Skip the "Flax" at the beginning
SCREAMING_SNAKE_CASE = getattr(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = pt_model_class(lowerCamelCase__ ).eval()
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ ,dtype=jnp.floataa )
SCREAMING_SNAKE_CASE = load_flax_weights_in_pytorch_model(lowerCamelCase__ ,fx_model.params )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = pt_inputs["""input_ids"""].shape
SCREAMING_SNAKE_CASE = np.random.randint(0 ,seq_length - 1 ,size=(batch_size,) )
for batch_idx, start_index in enumerate(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 1
# make sure weights are tied in PyTorch
pt_model.tie_weights()
with torch.no_grad():
SCREAMING_SNAKE_CASE = pt_model(**lowerCamelCase__ ).to_tuple()
SCREAMING_SNAKE_CASE = fx_model(**lowerCamelCase__ ).to_tuple()
self.assertEqual(len(lowerCamelCase__ ) ,len(lowerCamelCase__ ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(lowerCamelCase__ ,lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] ,pt_output[:, -1].numpy() ,4e-2 )
with tempfile.TemporaryDirectory() as tmpdirname:
fx_model.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = pt_model_class.from_pretrained(lowerCamelCase__ ,from_flax=lowerCamelCase__ )
with torch.no_grad():
SCREAMING_SNAKE_CASE = pt_model_loaded(**lowerCamelCase__ ).to_tuple()
self.assertEqual(
len(lowerCamelCase__ ) ,len(lowerCamelCase__ ) ,"""Output lengths differ between Flax and PyTorch""" )
for fx_output, pt_output in zip(lowerCamelCase__ ,lowerCamelCase__ ):
self.assert_almost_equals(fx_output[:, -1] ,pt_output[:, -1].numpy() ,4e-2 )
@tooslow
def SCREAMING_SNAKE_CASE__ ( self : str ) -> str:
'''simple docstring'''
for model_class_name in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class_name.from_pretrained("""EleutherAI/gpt-j-6B""" )
SCREAMING_SNAKE_CASE = model(np.ones((1, 1) ) )
self.assertIsNotNone(lowerCamelCase__ )
| 296 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""LlamaForCausalLM""",
"""LlamaModel""",
"""LlamaPreTrainedModel""",
"""LlamaForSequenceClassification""",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 296 | 1 |
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from .tokenization_codegen import CodeGenTokenizer
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}
SCREAMING_SNAKE_CASE_ = {
"""vocab_file""": {
"""Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/vocab.json""",
},
"""merges_file""": {
"""Salesforce/codegen-350M-mono""": """https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/merges.txt""",
},
"""tokenizer_file""": {
"""Salesforce/codegen-350M-mono""": (
"""https://huggingface.co/Salesforce/codegen-350M-mono/resolve/main/tokenizer.json"""
),
},
}
SCREAMING_SNAKE_CASE_ = {
"""Salesforce/codegen-350M-mono""": 2_0_4_8,
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[Any] = VOCAB_FILES_NAMES
__snake_case : Optional[int] = PRETRAINED_VOCAB_FILES_MAP
__snake_case : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case : Optional[int] = ["input_ids", "attention_mask"]
__snake_case : str = CodeGenTokenizer
def __init__( self : Optional[Any] ,lowerCamelCase__ : Optional[int]=None ,lowerCamelCase__ : Union[str, Any]=None ,lowerCamelCase__ : str=None ,lowerCamelCase__ : int="<|endoftext|>" ,lowerCamelCase__ : str="<|endoftext|>" ,lowerCamelCase__ : str="<|endoftext|>" ,lowerCamelCase__ : Tuple=False ,**lowerCamelCase__ : List[Any] ,) -> List[str]:
'''simple docstring'''
super().__init__(
lowerCamelCase__ ,lowerCamelCase__ ,tokenizer_file=lowerCamelCase__ ,unk_token=lowerCamelCase__ ,bos_token=lowerCamelCase__ ,eos_token=lowerCamelCase__ ,add_prefix_space=lowerCamelCase__ ,**lowerCamelCase__ ,)
if kwargs.pop("""add_bos_token""" ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = kwargs.pop("""name_or_path""" ,"""""" )
raise ValueError(
"""Currenty GPT2's fast tokenizer does NOT support adding a BOS token."""
"""Instead you should use GPT2's slow tokenizer class `CodeGenTokenizer` as follows: \n"""
F"""`CodeGenTokenizer.from_pretrained('{model_id}')`\nor\n"""
F"""`AutoTokenizer.from_pretrained('{model_id}', use_fast=False)`\n"""
"""This issue will be fixed soon, see: https://github.com/huggingface/tokenizers/pull/1005."""
""" so that the fast tokenizer works correctly.""" )
SCREAMING_SNAKE_CASE = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("""add_prefix_space""" ,lowerCamelCase__ ) != add_prefix_space:
SCREAMING_SNAKE_CASE = getattr(lowerCamelCase__ ,pre_tok_state.pop("""type""" ) )
SCREAMING_SNAKE_CASE = add_prefix_space
SCREAMING_SNAKE_CASE = pre_tok_class(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = add_prefix_space
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,*lowerCamelCase__ : Dict ,**lowerCamelCase__ : Optional[Any] ) -> BatchEncoding:
'''simple docstring'''
SCREAMING_SNAKE_CASE = kwargs.get("""is_split_into_words""" ,lowerCamelCase__ )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._batch_encode_plus(*lowerCamelCase__ ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ,*lowerCamelCase__ : int ,**lowerCamelCase__ : Dict ) -> BatchEncoding:
'''simple docstring'''
SCREAMING_SNAKE_CASE = kwargs.get("""is_split_into_words""" ,lowerCamelCase__ )
assert self.add_prefix_space or not is_split_into_words, (
F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True """
"to use it with pretokenized inputs."
)
return super()._encode_plus(*lowerCamelCase__ ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._tokenizer.model.save(lowerCamelCase__ ,name=lowerCamelCase__ )
return tuple(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Union[int, List[int], "np.ndarray", "torch.Tensor", "tf.Tensor"] ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : Optional[List[str]] = None ,**lowerCamelCase__ : List[Any] ,) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = super().decode(
token_ids=lowerCamelCase__ ,skip_special_tokens=lowerCamelCase__ ,clean_up_tokenization_spaces=lowerCamelCase__ ,**lowerCamelCase__ ,)
if truncate_before_pattern is not None and len(lowerCamelCase__ ) > 0:
SCREAMING_SNAKE_CASE = self.truncate(lowerCamelCase__ ,lowerCamelCase__ )
return decoded_text
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : List[str] ) -> str:
'''simple docstring'''
def find_re(lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Optional[Any] ):
SCREAMING_SNAKE_CASE = pattern.search(lowerCamelCase__ ,lowerCamelCase__ )
return m.start() if m else -1
SCREAMING_SNAKE_CASE = [re.compile(lowerCamelCase__ ,re.MULTILINE ) for pattern in truncate_before_pattern]
SCREAMING_SNAKE_CASE = list(re.finditer("""^print""" ,lowerCamelCase__ ,re.MULTILINE ) )
if len(lowerCamelCase__ ) > 1:
SCREAMING_SNAKE_CASE = completion[: prints[1].start()]
SCREAMING_SNAKE_CASE = list(re.finditer("""^def""" ,lowerCamelCase__ ,re.MULTILINE ) )
if len(lowerCamelCase__ ) > 1:
SCREAMING_SNAKE_CASE = completion[: defs[1].start()]
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = [
pos for pos in [find_re(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ) for terminal in terminals] if pos != -1
]
if len(lowerCamelCase__ ) > 0:
return completion[: min(lowerCamelCase__ )]
else:
return completion
| 296 |
from pathlib import Path
import fire
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
for path in src_dir.iterdir():
SCREAMING_SNAKE_CASE = [x.rstrip() for x in list(path.open().readlines() )][:n]
SCREAMING_SNAKE_CASE = dest_dir.joinpath(path.name )
print(_SCREAMING_SNAKE_CASE )
dest_path.open("""w""" ).write("""\n""".join(_SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
fire.Fire(minify)
| 296 | 1 |
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__) # pylint: disable=invalid-name
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
if not path:
return "pipe"
for ext in PipelineDataFormat.SUPPORTED_FORMATS:
if path.endswith(_SCREAMING_SNAKE_CASE ):
return ext
raise Exception(
F"""Unable to determine file format from file extension {path}. """
F"""Please provide the format through --format {PipelineDataFormat.SUPPORTED_FORMATS}""" )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = pipeline(
task=args.task , model=args.model if args.model else None , config=args.config , tokenizer=args.tokenizer , device=args.device , )
SCREAMING_SNAKE_CASE = try_infer_format_from_ext(args.input ) if args.format == """infer""" else args.format
SCREAMING_SNAKE_CASE = PipelineDataFormat.from_str(
format=_SCREAMING_SNAKE_CASE , output_path=args.output , input_path=args.input , column=args.column if args.column else nlp.default_input_names , overwrite=args.overwrite , )
return RunCommand(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Any ,lowerCamelCase__ : Pipeline ,lowerCamelCase__ : PipelineDataFormat ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = nlp
SCREAMING_SNAKE_CASE = reader
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : ArgumentParser ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parser.add_parser("""run""" ,help="""Run a pipeline through the CLI""" )
run_parser.add_argument("""--task""" ,choices=get_supported_tasks() ,help="""Task to run""" )
run_parser.add_argument("""--input""" ,type=lowerCamelCase__ ,help="""Path to the file to use for inference""" )
run_parser.add_argument("""--output""" ,type=lowerCamelCase__ ,help="""Path to the file that will be used post to write results.""" )
run_parser.add_argument("""--model""" ,type=lowerCamelCase__ ,help="""Name or path to the model to instantiate.""" )
run_parser.add_argument("""--config""" ,type=lowerCamelCase__ ,help="""Name or path to the model's config to instantiate.""" )
run_parser.add_argument(
"""--tokenizer""" ,type=lowerCamelCase__ ,help="""Name of the tokenizer to use. (default: same as the model name)""" )
run_parser.add_argument(
"""--column""" ,type=lowerCamelCase__ ,help="""Name of the column to use as input. (For multi columns input as QA use column1,columns2)""" ,)
run_parser.add_argument(
"""--format""" ,type=lowerCamelCase__ ,default="""infer""" ,choices=PipelineDataFormat.SUPPORTED_FORMATS ,help="""Input format to read from""" ,)
run_parser.add_argument(
"""--device""" ,type=lowerCamelCase__ ,default=-1 ,help="""Indicate the device to run onto, -1 indicates CPU, >= 0 indicates GPU (default: -1)""" ,)
run_parser.add_argument("""--overwrite""" ,action="""store_true""" ,help="""Allow overwriting the output file.""" )
run_parser.set_defaults(func=lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self._nlp, []
for entry in self._reader:
SCREAMING_SNAKE_CASE = nlp(**lowerCamelCase__ ) if self._reader.is_multi_columns else nlp(lowerCamelCase__ )
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
outputs.append(lowerCamelCase__ )
else:
outputs += output
# Saving data
if self._nlp.binary_output:
SCREAMING_SNAKE_CASE = self._reader.save_binary(lowerCamelCase__ )
logger.warning(F"""Current pipeline requires output to be in binary format, saving at {binary_path}""" )
else:
self._reader.save(lowerCamelCase__ )
| 296 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
SCREAMING_SNAKE_CASE_ = get_tests_dir("""fixtures/dummy-config.json""")
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
'''simple docstring'''
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""fake-roberta""" )
os.makedirs(lowerCamelCase__ ,exist_ok=lowerCamelCase__ )
with open(os.path.join(lowerCamelCase__ ,"""config.json""" ) ,"""w""" ) as f:
f.write(json.dumps({} ) )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertEqual(type(lowerCamelCase__ ) ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
try:
AutoConfig.register("""custom""" ,lowerCamelCase__ )
# Wrong model type will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""model""" ,lowerCamelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""bert""" ,lowerCamelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""bert-base is not a local folder and is not a valid model identifier""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,revision="""aaaaaa""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" ,):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(reloaded_config.__class__.__name__ ,"""NewModelConfig""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = "new-model"
try:
AutoConfig.register("""new-model""" ,lowerCamelCase__ )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 296 | 1 |
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # 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 six # noqa: F401 # Here to have a nice missing dependency error message early on
from rouge_score import rouge_scorer, scoring
import datasets
SCREAMING_SNAKE_CASE_ = """\
@inproceedings{lin-2004-rouge,
title = \"{ROUGE}: A Package for Automatic Evaluation of Summaries\",
author = \"Lin, Chin-Yew\",
booktitle = \"Text Summarization Branches Out\",
month = jul,
year = \"2004\",
address = \"Barcelona, Spain\",
publisher = \"Association for Computational Linguistics\",
url = \"https://www.aclweb.org/anthology/W04-1013\",
pages = \"74--81\",
}
"""
SCREAMING_SNAKE_CASE_ = """\
ROUGE, or Recall-Oriented Understudy for Gisting Evaluation, is a set of metrics and a software package used for
evaluating automatic summarization and machine translation software in natural language processing.
The metrics compare an automatically produced summary or translation against a reference or a set of references (human-produced) summary or translation.
Note that ROUGE is case insensitive, meaning that upper case letters are treated the same way as lower case letters.
This metrics is a wrapper around Google Research reimplementation of ROUGE:
https://github.com/google-research/google-research/tree/master/rouge
"""
SCREAMING_SNAKE_CASE_ = """
Calculates average rouge scores for a list of hypotheses and references
Args:
predictions: list of predictions to score. Each prediction
should be a string with tokens separated by spaces.
references: list of reference for each prediction. Each
reference should be a string with tokens separated by spaces.
rouge_types: A list of rouge types to calculate.
Valid names:
`\"rouge{n}\"` (e.g. `\"rouge1\"`, `\"rouge2\"`) where: {n} is the n-gram based scoring,
`\"rougeL\"`: Longest common subsequence based scoring.
`\"rougeLSum\"`: rougeLsum splits text using `\"\n\"`.
See details in https://github.com/huggingface/datasets/issues/617
use_stemmer: Bool indicating whether Porter stemmer should be used to strip word suffixes.
use_aggregator: Return aggregates if this is set to True
Returns:
rouge1: rouge_1 (precision, recall, f1),
rouge2: rouge_2 (precision, recall, f1),
rougeL: rouge_l (precision, recall, f1),
rougeLsum: rouge_lsum (precision, recall, f1)
Examples:
>>> rouge = datasets.load_metric('rouge')
>>> predictions = [\"hello there\", \"general kenobi\"]
>>> references = [\"hello there\", \"general kenobi\"]
>>> results = rouge.compute(predictions=predictions, references=references)
>>> print(list(results.keys()))
['rouge1', 'rouge2', 'rougeL', 'rougeLsum']
>>> print(results[\"rouge1\"])
AggregateScore(low=Score(precision=1.0, recall=1.0, fmeasure=1.0), mid=Score(precision=1.0, recall=1.0, fmeasure=1.0), high=Score(precision=1.0, recall=1.0, fmeasure=1.0))
>>> print(results[\"rouge1\"].mid.fmeasure)
1.0
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase__ ( datasets.Metric ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" ,id="""sequence""" ),
"""references""": datasets.Value("""string""" ,id="""sequence""" ),
} ) ,codebase_urls=["""https://github.com/google-research/google-research/tree/master/rouge"""] ,reference_urls=[
"""https://en.wikipedia.org/wiki/ROUGE_(metric)""",
"""https://github.com/google-research/google-research/tree/master/rouge""",
] ,)
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Tuple=None ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : Optional[Any]=False ) -> Dict:
'''simple docstring'''
if rouge_types is None:
SCREAMING_SNAKE_CASE = ["""rouge1""", """rouge2""", """rougeL""", """rougeLsum"""]
SCREAMING_SNAKE_CASE = rouge_scorer.RougeScorer(rouge_types=lowerCamelCase__ ,use_stemmer=lowerCamelCase__ )
if use_aggregator:
SCREAMING_SNAKE_CASE = scoring.BootstrapAggregator()
else:
SCREAMING_SNAKE_CASE = []
for ref, pred in zip(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = scorer.score(lowerCamelCase__ ,lowerCamelCase__ )
if use_aggregator:
aggregator.add_scores(lowerCamelCase__ )
else:
scores.append(lowerCamelCase__ )
if use_aggregator:
SCREAMING_SNAKE_CASE = aggregator.aggregate()
else:
SCREAMING_SNAKE_CASE = {}
for key in scores[0]:
SCREAMING_SNAKE_CASE = [score[key] for score in scores]
return result
| 296 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Dict = "git_vision_model"
def __init__( self : List[Any] ,lowerCamelCase__ : Dict=768 ,lowerCamelCase__ : Union[str, Any]=3072 ,lowerCamelCase__ : Optional[int]=12 ,lowerCamelCase__ : Tuple=12 ,lowerCamelCase__ : Tuple=3 ,lowerCamelCase__ : Optional[Any]=224 ,lowerCamelCase__ : Union[str, Any]=16 ,lowerCamelCase__ : List[Any]="quick_gelu" ,lowerCamelCase__ : Optional[Any]=1e-5 ,lowerCamelCase__ : str=0.0 ,lowerCamelCase__ : Optional[int]=0.02 ,**lowerCamelCase__ : Union[str, Any] ,) -> Optional[int]:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = hidden_act
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Tuple ,lowerCamelCase__ : Union[str, os.PathLike] ,**lowerCamelCase__ : int ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = cls.get_config_dict(lowerCamelCase__ ,**lowerCamelCase__ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("""model_type""" ) == "git":
SCREAMING_SNAKE_CASE = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowerCamelCase__ ,**lowerCamelCase__ )
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Dict = "git"
def __init__( self : Optional[int] ,lowerCamelCase__ : int=None ,lowerCamelCase__ : str=30522 ,lowerCamelCase__ : Tuple=768 ,lowerCamelCase__ : Union[str, Any]=6 ,lowerCamelCase__ : str=12 ,lowerCamelCase__ : List[str]=3072 ,lowerCamelCase__ : Dict="gelu" ,lowerCamelCase__ : Tuple=0.1 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : List[str]=1024 ,lowerCamelCase__ : List[str]=0.02 ,lowerCamelCase__ : str=1e-1_2 ,lowerCamelCase__ : Optional[int]=0 ,lowerCamelCase__ : Optional[int]="absolute" ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : str=False ,lowerCamelCase__ : int=101 ,lowerCamelCase__ : int=102 ,lowerCamelCase__ : Dict=None ,**lowerCamelCase__ : List[Any] ,) -> Optional[Any]:
'''simple docstring'''
super().__init__(bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,pad_token_id=lowerCamelCase__ ,**lowerCamelCase__ )
if vision_config is None:
SCREAMING_SNAKE_CASE = {}
logger.info("""vision_config is None. initializing the GitVisionConfig with default values.""" )
SCREAMING_SNAKE_CASE = GitVisionConfig(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = tie_word_embeddings
SCREAMING_SNAKE_CASE = num_image_with_embedding
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = eos_token_id
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
| 296 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
"""https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[int] = "roberta-prelayernorm"
def __init__( self : Optional[Any] ,lowerCamelCase__ : Dict=50265 ,lowerCamelCase__ : str=768 ,lowerCamelCase__ : Optional[Any]=12 ,lowerCamelCase__ : str=12 ,lowerCamelCase__ : int=3072 ,lowerCamelCase__ : str="gelu" ,lowerCamelCase__ : int=0.1 ,lowerCamelCase__ : List[str]=0.1 ,lowerCamelCase__ : Any=512 ,lowerCamelCase__ : int=2 ,lowerCamelCase__ : Dict=0.02 ,lowerCamelCase__ : Any=1e-1_2 ,lowerCamelCase__ : Optional[int]=1 ,lowerCamelCase__ : str=0 ,lowerCamelCase__ : str=2 ,lowerCamelCase__ : Any="absolute" ,lowerCamelCase__ : str=True ,lowerCamelCase__ : int=None ,**lowerCamelCase__ : Optional[Any] ,) -> str:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase__ ,bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = classifier_dropout
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 296 |
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
SCREAMING_SNAKE_CASE_ = random.Random()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE = global_rng
SCREAMING_SNAKE_CASE = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[int]=7 ,lowerCamelCase__ : Optional[Any]=400 ,lowerCamelCase__ : List[str]=2000 ,lowerCamelCase__ : List[str]=2048 ,lowerCamelCase__ : Any=128 ,lowerCamelCase__ : List[str]=1 ,lowerCamelCase__ : str=512 ,lowerCamelCase__ : Optional[Any]=30 ,lowerCamelCase__ : Tuple=44100 ,) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = min_seq_length
SCREAMING_SNAKE_CASE = max_seq_length
SCREAMING_SNAKE_CASE = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE = spectrogram_length
SCREAMING_SNAKE_CASE = feature_size
SCREAMING_SNAKE_CASE = num_audio_channels
SCREAMING_SNAKE_CASE = hop_length
SCREAMING_SNAKE_CASE = chunk_length
SCREAMING_SNAKE_CASE = sampling_rate
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[int]:
'''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 SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : Tuple=False ,lowerCamelCase__ : Union[str, Any]=False ) -> str:
'''simple docstring'''
def _flatten(lowerCamelCase__ : List[Any] ):
return list(itertools.chain(*lowerCamelCase__ ) )
if equal_length:
SCREAMING_SNAKE_CASE = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[Any] = TvltFeatureExtractor
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TvltFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase__ ,"""spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""sampling_rate""" ) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = feat_extract_first.save_pretrained(lowerCamelCase__ )[0]
check_json_file_has_correct_format(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in range(800 ,1400 ,200 )]
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,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
SCREAMING_SNAKE_CASE = feature_extractor(
lowerCamelCase__ ,return_tensors="""np""" ,sampling_rate=44100 ,mask_audio=lowerCamelCase__ ).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.
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE = np.asarray(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,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 SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,"""clean""" ,split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE = ds.sort("""id""" ).select(range(lowerCamelCase__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape ,(1, 1, 192, 128) )
SCREAMING_SNAKE_CASE = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,lowerCamelCase__ ,atol=1e-4 ) )
| 296 | 1 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
from .config import config_command_parser
from .config_args import default_config_file, load_config_from_file # noqa: F401
from .default import default_command_parser
from .update import update_command_parser
def __lowercase ( _SCREAMING_SNAKE_CASE=None ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = argparse.ArgumentParser(add_help=_SCREAMING_SNAKE_CASE , allow_abbrev=_SCREAMING_SNAKE_CASE )
# The main config parser
SCREAMING_SNAKE_CASE = config_command_parser(_SCREAMING_SNAKE_CASE )
# The subparser to add commands to
SCREAMING_SNAKE_CASE = config_parser.add_subparsers(title="""subcommands""" , dest="""subcommand""" )
# Then add other parsers with the parent parser
default_command_parser(_SCREAMING_SNAKE_CASE , parents=[parent_parser] )
update_command_parser(_SCREAMING_SNAKE_CASE , parents=[parent_parser] )
return config_parser
def __lowercase ( ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_config_parser()
SCREAMING_SNAKE_CASE = config_parser.parse_args()
if not hasattr(_SCREAMING_SNAKE_CASE , """func""" ):
config_parser.print_help()
exit(1 )
# Run
args.func(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
main()
| 296 |
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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
SCREAMING_SNAKE_CASE = [1_44, 1_92, 2_40]
SCREAMING_SNAKE_CASE = [16, 32, 64, 96, 1_28, 1_60, 6_40]
elif "mobilevit_xs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [96, 1_20, 1_44]
SCREAMING_SNAKE_CASE = [16, 32, 48, 64, 80, 96, 3_84]
elif "mobilevit_xxs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [64, 80, 96]
SCREAMING_SNAKE_CASE = [16, 16, 24, 48, 64, 80, 3_20]
SCREAMING_SNAKE_CASE = 0.05
SCREAMING_SNAKE_CASE = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = 5_12
SCREAMING_SNAKE_CASE = 16
SCREAMING_SNAKE_CASE = 21
SCREAMING_SNAKE_CASE = """pascal-voc-id2label.json"""
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
SCREAMING_SNAKE_CASE = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
SCREAMING_SNAKE_CASE = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.weight""" , """.layernorm.weight""" )
if F""".global_rep.{i}.bias""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.bias""" , """.layernorm.bias""" )
if ".global_rep." in name:
SCREAMING_SNAKE_CASE = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
SCREAMING_SNAKE_CASE = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
SCREAMING_SNAKE_CASE = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
SCREAMING_SNAKE_CASE = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
SCREAMING_SNAKE_CASE = """mobilevit.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Dict:
'''simple docstring'''
if base_model:
SCREAMING_SNAKE_CASE = """"""
else:
SCREAMING_SNAKE_CASE = """mobilevit."""
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if key[:8] == "encoder.":
SCREAMING_SNAKE_CASE = key[8:]
if "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[0][6:] ) - 1
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
SCREAMING_SNAKE_CASE = layer.transformer.layer[transformer_num].attention.attention.all_head_size
SCREAMING_SNAKE_CASE = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[:dim]
SCREAMING_SNAKE_CASE = val[dim : dim * 2]
SCREAMING_SNAKE_CASE = val[-dim:]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_mobilevit_config(_SCREAMING_SNAKE_CASE )
# load original state_dict
SCREAMING_SNAKE_CASE = torch.load(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = MobileViTForSemanticSegmentation(_SCREAMING_SNAKE_CASE ).eval()
else:
SCREAMING_SNAKE_CASE = MobileViTForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
SCREAMING_SNAKE_CASE = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileViTImageProcessor
SCREAMING_SNAKE_CASE = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
[[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
[[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
[[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
[[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
[[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
[[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
else:
assert logits.shape == (1, 10_00)
if mobilevit_name == "mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
elif mobilevit_name == "mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
elif mobilevit_name == "mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
SCREAMING_SNAKE_CASE = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
SCREAMING_SNAKE_CASE = model_mapping[mobilevit_name]
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
model.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--mobilevit_name""",
default="""mobilevit_s""",
type=str,
help=(
"""Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
""" 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
),
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 296 | 1 |
from __future__ import annotations
from math import gcd
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 2 , _SCREAMING_SNAKE_CASE = 1 , _SCREAMING_SNAKE_CASE = 3 , ) -> int | None:
'''simple docstring'''
if num < 2:
raise ValueError("""The input value cannot be less than 2""" )
# Because of the relationship between ``f(f(x))`` and ``f(x)``, this
# algorithm struggles to find factors that are divisible by two.
# As a workaround, we specifically check for two and even inputs.
# See: https://math.stackexchange.com/a/2856214/165820
if num > 2 and num % 2 == 0:
return 2
# Pollard's Rho algorithm requires a function that returns pseudorandom
# values between 0 <= X < ``num``. It doesn't need to be random in the
# sense that the output value is cryptographically secure or difficult
# to calculate, it only needs to be random in the sense that all output
# values should be equally likely to appear.
# For this reason, Pollard suggested using ``f(x) = (x**2 - 1) % num``
# However, the success of Pollard's algorithm isn't guaranteed and is
# determined in part by the initial seed and the chosen random function.
# To make retries easier, we will instead use ``f(x) = (x**2 + C) % num``
# where ``C`` is a value that we can modify between each attempt.
def rand_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
return (pow(_SCREAMING_SNAKE_CASE , 2 ) + step) % modulus
for _ in range(_SCREAMING_SNAKE_CASE ):
# These track the position within the cycle detection logic.
SCREAMING_SNAKE_CASE = seed
SCREAMING_SNAKE_CASE = seed
while True:
# At each iteration, the tortoise moves one step and the hare moves two.
SCREAMING_SNAKE_CASE = rand_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = rand_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = rand_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# At some point both the tortoise and the hare will enter a cycle whose
# length ``p`` is a divisor of ``num``. Once in that cycle, at some point
# the tortoise and hare will end up on the same value modulo ``p``.
# We can detect when this happens because the position difference between
# the tortoise and the hare will share a common divisor with ``num``.
SCREAMING_SNAKE_CASE = gcd(hare - tortoise , _SCREAMING_SNAKE_CASE )
if divisor == 1:
# No common divisor yet, just keep searching.
continue
else:
# We found a common divisor!
if divisor == num:
# Unfortunately, the divisor is ``num`` itself and is useless.
break
else:
# The divisor is a nontrivial factor of ``num``!
return divisor
# If we made it here, then this attempt failed.
# We need to pick a new starting seed for the tortoise and hare
# in addition to a new step value for the random function.
# To keep this example implementation deterministic, the
# new values will be generated based on currently available
# values instead of using something like ``random.randint``.
# We can use the hare's position as the new seed.
# This is actually what Richard Brent's the "optimized" variant does.
SCREAMING_SNAKE_CASE = hare
# The new step value for the random function can just be incremented.
# At first the results will be similar to what the old function would
# have produced, but the value will quickly diverge after a bit.
step += 1
# We haven't found a divisor within the requested number of attempts.
# We were unlucky or ``num`` itself is actually prime.
return None
if __name__ == "__main__":
import argparse
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument(
"""num""",
type=int,
help="""The value to find a divisor of""",
)
parser.add_argument(
"""--attempts""",
type=int,
default=3,
help="""The number of attempts before giving up""",
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
SCREAMING_SNAKE_CASE_ = pollard_rho(args.num, attempts=args.attempts)
if divisor is None:
print(F'''{args.num} is probably prime''')
else:
SCREAMING_SNAKE_CASE_ = args.num // divisor
print(F'''{args.num} = {divisor} * {quotient}''')
| 296 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[str] = FLAX_MODEL_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModel)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_PRETRAINING_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Any = FLAX_MODEL_FOR_MASKED_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : int = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[int] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[Any] = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Tuple = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[str] = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[Any] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 296 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
"""configuration_falcon""": ["""FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FalconConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""FALCON_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""FalconForCausalLM""",
"""FalconModel""",
"""FalconPreTrainedModel""",
"""FalconForSequenceClassification""",
"""FalconForTokenClassification""",
"""FalconForQuestionAnswering""",
]
if TYPE_CHECKING:
from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_falcon import (
FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
FalconForCausalLM,
FalconForQuestionAnswering,
FalconForSequenceClassification,
FalconForTokenClassification,
FalconModel,
FalconPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 296 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = "table-transformer"
__snake_case : Union[str, Any] = ["past_key_values"]
__snake_case : List[Any] = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self : Optional[int] ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : Optional[Any]=None ,lowerCamelCase__ : List[Any]=3 ,lowerCamelCase__ : Optional[int]=100 ,lowerCamelCase__ : List[Any]=6 ,lowerCamelCase__ : Dict=2048 ,lowerCamelCase__ : List[Any]=8 ,lowerCamelCase__ : Dict=6 ,lowerCamelCase__ : Dict=2048 ,lowerCamelCase__ : Any=8 ,lowerCamelCase__ : Optional[int]=0.0 ,lowerCamelCase__ : int=0.0 ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : Optional[int]="relu" ,lowerCamelCase__ : Tuple=256 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : Optional[Any]=0.0 ,lowerCamelCase__ : Tuple=0.0 ,lowerCamelCase__ : List[Any]=0.02 ,lowerCamelCase__ : int=1.0 ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : Optional[Any]="sine" ,lowerCamelCase__ : List[str]="resnet50" ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : int=1 ,lowerCamelCase__ : Dict=5 ,lowerCamelCase__ : Tuple=2 ,lowerCamelCase__ : Union[str, Any]=1 ,lowerCamelCase__ : str=1 ,lowerCamelCase__ : Any=5 ,lowerCamelCase__ : Tuple=2 ,lowerCamelCase__ : str=0.1 ,**lowerCamelCase__ : List[str] ,) -> Optional[int]:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
SCREAMING_SNAKE_CASE = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = backbone_config.get("""model_type""" )
SCREAMING_SNAKE_CASE = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE = config_class.from_dict(lowerCamelCase__ )
# set timm attributes to None
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = None, None, None
SCREAMING_SNAKE_CASE = use_timm_backbone
SCREAMING_SNAKE_CASE = backbone_config
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = num_queries
SCREAMING_SNAKE_CASE = d_model
SCREAMING_SNAKE_CASE = encoder_ffn_dim
SCREAMING_SNAKE_CASE = encoder_layers
SCREAMING_SNAKE_CASE = encoder_attention_heads
SCREAMING_SNAKE_CASE = decoder_ffn_dim
SCREAMING_SNAKE_CASE = decoder_layers
SCREAMING_SNAKE_CASE = decoder_attention_heads
SCREAMING_SNAKE_CASE = dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = activation_dropout
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = init_std
SCREAMING_SNAKE_CASE = init_xavier_std
SCREAMING_SNAKE_CASE = encoder_layerdrop
SCREAMING_SNAKE_CASE = decoder_layerdrop
SCREAMING_SNAKE_CASE = encoder_layers
SCREAMING_SNAKE_CASE = auxiliary_loss
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = backbone
SCREAMING_SNAKE_CASE = use_pretrained_backbone
SCREAMING_SNAKE_CASE = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE = class_cost
SCREAMING_SNAKE_CASE = bbox_cost
SCREAMING_SNAKE_CASE = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE = mask_loss_coefficient
SCREAMING_SNAKE_CASE = dice_loss_coefficient
SCREAMING_SNAKE_CASE = bbox_loss_coefficient
SCREAMING_SNAKE_CASE = giou_loss_coefficient
SCREAMING_SNAKE_CASE = eos_coefficient
super().__init__(is_encoder_decoder=lowerCamelCase__ ,**lowerCamelCase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return self.d_model
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = version.parse("1.11" )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> float:
'''simple docstring'''
return 1e-5
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> int:
'''simple docstring'''
return 12
| 296 | 1 |
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
AutoencoderKL,
DDIMScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_AUDIO_BATCH_PARAMS, TEXT_TO_AUDIO_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Tuple = AudioLDMPipeline
__snake_case : Tuple = TEXT_TO_AUDIO_PARAMS
__snake_case : Optional[int] = TEXT_TO_AUDIO_BATCH_PARAMS
__snake_case : Union[str, Any] = frozenset(
[
"num_inference_steps",
"num_waveforms_per_prompt",
"generator",
"latents",
"output_type",
"return_dict",
"callback",
"callback_steps",
] )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> int:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = 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, 64) ,class_embed_type="""simple_projection""" ,projection_class_embeddings_input_dim=32 ,class_embeddings_concat=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.00085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=lowerCamelCase__ ,set_alpha_to_one=lowerCamelCase__ ,)
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=1 ,out_channels=1 ,down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,latent_channels=4 ,)
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = ClapTextConfig(
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=1000 ,projection_dim=32 ,)
SCREAMING_SNAKE_CASE = ClapTextModelWithProjection(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = RobertaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-roberta""" ,model_max_length=77 )
SCREAMING_SNAKE_CASE = SpeechTaHifiGanConfig(
model_in_dim=8 ,sampling_rate=16000 ,upsample_initial_channel=16 ,upsample_rates=[2, 2] ,upsample_kernel_sizes=[4, 4] ,resblock_kernel_sizes=[3, 7] ,resblock_dilation_sizes=[[1, 3, 5], [1, 3, 5]] ,normalize_before=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = SpeechTaHifiGan(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
"""vocoder""": vocoder,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Optional[Any]=0 ) -> str:
'''simple docstring'''
if str(lowerCamelCase__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCamelCase__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A hammer hitting a wooden surface""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) == 256
SCREAMING_SNAKE_CASE = audio[:10]
SCREAMING_SNAKE_CASE = np.array(
[-0.0050, 0.0050, -0.0060, 0.0033, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0033] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 3 * [inputs["""prompt"""]]
# forward
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 3 * [inputs.pop("""prompt""" )]
SCREAMING_SNAKE_CASE = audioldm_pipe.tokenizer(
lowerCamelCase__ ,padding="""max_length""" ,max_length=audioldm_pipe.tokenizer.model_max_length ,truncation=lowerCamelCase__ ,return_tensors="""pt""" ,)
SCREAMING_SNAKE_CASE = text_inputs["""input_ids"""].to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.text_encoder(
lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = prompt_embeds.text_embeds
# additional L_2 normalization over each hidden-state
SCREAMING_SNAKE_CASE = F.normalize(lowerCamelCase__ ,dim=-1 )
SCREAMING_SNAKE_CASE = prompt_embeds
# forward
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 3 * ["""this is a negative prompt"""]
SCREAMING_SNAKE_CASE = negative_prompt
SCREAMING_SNAKE_CASE = 3 * [inputs["""prompt"""]]
# forward
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 3 * [inputs.pop("""prompt""" )]
SCREAMING_SNAKE_CASE = []
for p in [prompt, negative_prompt]:
SCREAMING_SNAKE_CASE = audioldm_pipe.tokenizer(
lowerCamelCase__ ,padding="""max_length""" ,max_length=audioldm_pipe.tokenizer.model_max_length ,truncation=lowerCamelCase__ ,return_tensors="""pt""" ,)
SCREAMING_SNAKE_CASE = text_inputs["""input_ids"""].to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.text_encoder(
lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = text_embeds.text_embeds
# additional L_2 normalization over each hidden-state
SCREAMING_SNAKE_CASE = F.normalize(lowerCamelCase__ ,dim=-1 )
embeds.append(lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = embeds
# forward
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert np.abs(audio_a - audio_a ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = PNDMScheduler(skip_prk_steps=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """egg cracking"""
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ ,negative_prompt=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) == 256
SCREAMING_SNAKE_CASE = audio[:10]
SCREAMING_SNAKE_CASE = np.array(
[-0.0051, 0.0050, -0.0060, 0.0034, -0.0026, 0.0033, -0.0027, 0.0033, -0.0028, 0.0032] )
assert np.abs(audio_slice - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = PNDMScheduler(skip_prk_steps=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """A hammer hitting a wooden surface"""
# test num_waveforms_per_prompt=1 (default)
SCREAMING_SNAKE_CASE = audioldm_pipe(lowerCamelCase__ ,num_inference_steps=2 ).audios
assert audios.shape == (1, 256)
# test num_waveforms_per_prompt=1 (default) for batch of prompts
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = audioldm_pipe([prompt] * batch_size ,num_inference_steps=2 ).audios
assert audios.shape == (batch_size, 256)
# test num_waveforms_per_prompt for single prompt
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = audioldm_pipe(lowerCamelCase__ ,num_inference_steps=2 ,num_waveforms_per_prompt=lowerCamelCase__ ).audios
assert audios.shape == (num_waveforms_per_prompt, 256)
# test num_waveforms_per_prompt for batch of prompts
SCREAMING_SNAKE_CASE = 2
SCREAMING_SNAKE_CASE = audioldm_pipe(
[prompt] * batch_size ,num_inference_steps=2 ,num_waveforms_per_prompt=lowerCamelCase__ ).audios
assert audios.shape == (batch_size * num_waveforms_per_prompt, 256)
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.vocoder.config.sampling_rate
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe(audio_length_in_s=0.016 ,**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) / vocoder_sampling_rate == 0.016
SCREAMING_SNAKE_CASE = audioldm_pipe(audio_length_in_s=0.032 ,**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output.audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) / vocoder_sampling_rate == 0.032
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = AudioLDMPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = ["""hey"""]
SCREAMING_SNAKE_CASE = audioldm_pipe(lowerCamelCase__ ,num_inference_steps=1 )
SCREAMING_SNAKE_CASE = output.audios.shape
assert audio_shape == (1, 256)
SCREAMING_SNAKE_CASE = audioldm_pipe.vocoder.config
config.model_in_dim *= 2
SCREAMING_SNAKE_CASE = SpeechTaHifiGan(lowerCamelCase__ ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe(lowerCamelCase__ ,num_inference_steps=1 )
SCREAMING_SNAKE_CASE = output.audios.shape
# waveform shape is unchanged, we just have 2x the number of mel channels in the spectrogram
assert audio_shape == (1, 256)
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
self._test_inference_batch_single_identical(test_mean_pixel_difference=lowerCamelCase__ )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__ )
@slow
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Any="cpu" ,lowerCamelCase__ : Optional[int]=torch.floataa ,lowerCamelCase__ : List[str]=0 ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = np.random.RandomState(lowerCamelCase__ ).standard_normal((1, 8, 128, 16) )
SCREAMING_SNAKE_CASE = torch.from_numpy(lowerCamelCase__ ).to(device=lowerCamelCase__ ,dtype=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A hammer hitting a wooden surface""",
"""latents""": latents,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 2.5,
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 25
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ ).audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) == 81920
SCREAMING_SNAKE_CASE = audio[77230:77240]
SCREAMING_SNAKE_CASE = np.array(
[-0.4884, -0.4607, 0.0023, 0.5007, 0.5896, 0.5151, 0.3813, -0.0208, -0.3687, -0.4315] )
SCREAMING_SNAKE_CASE = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AudioLDMPipeline.from_pretrained("""cvssp/audioldm""" )
SCREAMING_SNAKE_CASE = LMSDiscreteScheduler.from_config(audioldm_pipe.scheduler.config )
SCREAMING_SNAKE_CASE = audioldm_pipe.to(lowerCamelCase__ )
audioldm_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = audioldm_pipe(**lowerCamelCase__ ).audios[0]
assert audio.ndim == 1
assert len(lowerCamelCase__ ) == 81920
SCREAMING_SNAKE_CASE = audio[27780:27790]
SCREAMING_SNAKE_CASE = np.array([-0.2131, -0.0873, -0.0124, -0.0189, 0.0569, 0.1373, 0.1883, 0.2886, 0.3297, 0.2212] )
SCREAMING_SNAKE_CASE = np.abs(expected_slice - audio_slice ).max()
assert max_diff < 3e-2
| 296 |
from collections import defaultdict
from math import gcd
def __lowercase ( _SCREAMING_SNAKE_CASE = 1_50_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = defaultdict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , _SCREAMING_SNAKE_CASE , 2 ):
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) > 1:
continue
SCREAMING_SNAKE_CASE = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(_SCREAMING_SNAKE_CASE , limit + 1 , _SCREAMING_SNAKE_CASE ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 296 | 1 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = job["""started_at"""]
SCREAMING_SNAKE_CASE = job["""completed_at"""]
SCREAMING_SNAKE_CASE = date_parser.parse(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = date_parser.parse(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = round((end_datetime - start_datetime).total_seconds() / 60.0 )
SCREAMING_SNAKE_CASE = start
SCREAMING_SNAKE_CASE = end
SCREAMING_SNAKE_CASE = duration_in_min
return job_info
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = None
if token is not None:
SCREAMING_SNAKE_CASE = {"""Accept""": """application/vnd.github+json""", """Authorization""": F"""Bearer {token}"""}
SCREAMING_SNAKE_CASE = F"""https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100"""
SCREAMING_SNAKE_CASE = requests.get(_SCREAMING_SNAKE_CASE , headers=_SCREAMING_SNAKE_CASE ).json()
SCREAMING_SNAKE_CASE = {}
try:
job_time.update({job["""name"""]: extract_time_from_single_job(_SCREAMING_SNAKE_CASE ) for job in result["""jobs"""]} )
SCREAMING_SNAKE_CASE = math.ceil((result["""total_count"""] - 1_00) / 1_00 )
for i in range(_SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE = requests.get(url + F"""&page={i + 2}""" , headers=_SCREAMING_SNAKE_CASE ).json()
job_time.update({job["""name"""]: extract_time_from_single_job(_SCREAMING_SNAKE_CASE ) 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__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument("""--workflow_run_id""", type=str, required=True, help="""A GitHub Actions workflow run id.""")
SCREAMING_SNAKE_CASE_ = parser.parse_args()
SCREAMING_SNAKE_CASE_ = get_job_time(args.workflow_run_id)
SCREAMING_SNAKE_CASE_ = 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']}''')
| 296 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __lowercase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
SCREAMING_SNAKE_CASE = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
# Let's go
SCREAMING_SNAKE_CASE = parser.parse_args()
if not hasattr(_SCREAMING_SNAKE_CASE , """func""" ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE = args.func(_SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 296 | 1 |
import operator as op
SCREAMING_SNAKE_CASE_ = """scaler.pt"""
SCREAMING_SNAKE_CASE_ = """pytorch_model"""
SCREAMING_SNAKE_CASE_ = """random_states"""
SCREAMING_SNAKE_CASE_ = """optimizer"""
SCREAMING_SNAKE_CASE_ = """scheduler"""
SCREAMING_SNAKE_CASE_ = """pytorch_model.bin"""
SCREAMING_SNAKE_CASE_ = """pytorch_model.bin.index.json"""
SCREAMING_SNAKE_CASE_ = """model.safetensors"""
SCREAMING_SNAKE_CASE_ = """model.safetensors.index.json"""
SCREAMING_SNAKE_CASE_ = """1.10.2"""
SCREAMING_SNAKE_CASE_ = """py38"""
SCREAMING_SNAKE_CASE_ = """4.17.0"""
SCREAMING_SNAKE_CASE_ = ["""ml.p3.16xlarge""", """ml.p3dn.24xlarge""", """ml.p4dn.24xlarge"""]
SCREAMING_SNAKE_CASE_ = ["""FULL_SHARD""", """SHARD_GRAD_OP""", """NO_SHARD""", """HYBRID_SHARD""", """HYBRID_SHARD_ZERO2"""]
SCREAMING_SNAKE_CASE_ = ["""TRANSFORMER_BASED_WRAP""", """SIZE_BASED_WRAP""", """NO_WRAP"""]
SCREAMING_SNAKE_CASE_ = ["""BACKWARD_PRE""", """BACKWARD_POST""", """NO_PREFETCH"""]
SCREAMING_SNAKE_CASE_ = ["""FULL_STATE_DICT""", """LOCAL_STATE_DICT""", """SHARDED_STATE_DICT"""]
SCREAMING_SNAKE_CASE_ = """2.0.1"""
SCREAMING_SNAKE_CASE_ = ["""pdsh""", """standard""", """openmpi""", """mvapich"""]
SCREAMING_SNAKE_CASE_ = ["""default""", """reduce-overhead""", """max-autotune"""]
SCREAMING_SNAKE_CASE_ = {""">""": op.gt, """>=""": op.ge, """==""": op.eq, """!=""": op.ne, """<=""": op.le, """<""": op.lt}
# These are the args for `torch.distributed.launch` for pytorch < 1.9
SCREAMING_SNAKE_CASE_ = [
"""nnodes""",
"""nproc_per_node""",
"""rdzv_backend""",
"""rdzv_endpoint""",
"""rdzv_id""",
"""rdzv_conf""",
"""standalone""",
"""max_restarts""",
"""monitor_interval""",
"""start_method""",
"""role""",
"""module""",
"""m""",
"""no_python""",
"""run_path""",
"""log_dir""",
"""r""",
"""redirects""",
"""t""",
"""tee""",
"""node_rank""",
"""master_addr""",
"""master_port""",
]
SCREAMING_SNAKE_CASE_ = ["""DEEPSPEED""", """MULTI_GPU""", """FSDP""", """MEGATRON_LM"""]
SCREAMING_SNAKE_CASE_ = ["""DEEPSPEED""", """MULTI_XPU""", """FSDP"""]
| 296 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = SwinConfig()
SCREAMING_SNAKE_CASE = swin_name.split("""_""" )
SCREAMING_SNAKE_CASE = name_split[1]
SCREAMING_SNAKE_CASE = int(name_split[4] )
SCREAMING_SNAKE_CASE = int(name_split[3][-1] )
if model_size == "tiny":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 6, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "small":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "base":
SCREAMING_SNAKE_CASE = 1_28
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (4, 8, 16, 32)
else:
SCREAMING_SNAKE_CASE = 1_92
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (6, 12, 24, 48)
if "in22k" in swin_name:
SCREAMING_SNAKE_CASE = 2_18_41
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = img_size
SCREAMING_SNAKE_CASE = num_classes
SCREAMING_SNAKE_CASE = embed_dim
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = window_size
return config
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
SCREAMING_SNAKE_CASE = """encoder.""" + name
if "attn.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "norm.weight":
SCREAMING_SNAKE_CASE = """layernorm.weight"""
if name == "norm.bias":
SCREAMING_SNAKE_CASE = """layernorm.bias"""
if "head" in name:
SCREAMING_SNAKE_CASE = name.replace("""head""" , """classifier""" )
else:
SCREAMING_SNAKE_CASE = """swin.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[1] )
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[
:dim
]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
SCREAMING_SNAKE_CASE = get_swin_config(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = SwinForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
SCREAMING_SNAKE_CASE = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" , """-""" ) ) )
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
SCREAMING_SNAKE_CASE = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = timm_model(inputs["""pixel_values"""] )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}""" )
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__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swin_name""",
default="""swin_tiny_patch4_window7_224""",
type=str,
help="""Name of the Swin 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."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 296 | 1 |
import itertools
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, Optional, Union
import pandas as pd
import pyarrow as pa
import datasets
import datasets.config
from datasets.features.features import require_storage_cast
from datasets.table import table_cast
from datasets.utils.py_utils import Literal
SCREAMING_SNAKE_CASE_ = datasets.utils.logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = ["""names""", """prefix"""]
SCREAMING_SNAKE_CASE_ = ["""warn_bad_lines""", """error_bad_lines""", """mangle_dupe_cols"""]
SCREAMING_SNAKE_CASE_ = ["""encoding_errors""", """on_bad_lines"""]
SCREAMING_SNAKE_CASE_ = ["""date_format"""]
@dataclass
class UpperCamelCase__ ( datasets.BuilderConfig ):
'''simple docstring'''
__snake_case : str = ","
__snake_case : Optional[str] = None
__snake_case : Optional[Union[int, List[int], str]] = "infer"
__snake_case : Optional[List[str]] = None
__snake_case : Optional[List[str]] = None
__snake_case : Optional[Union[int, str, List[int], List[str]]] = None
__snake_case : Optional[Union[List[int], List[str]]] = None
__snake_case : Optional[str] = None
__snake_case : bool = True
__snake_case : Optional[Literal["c", "python", "pyarrow"]] = None
__snake_case : Dict[Union[int, str], Callable[[Any], Any]] = None
__snake_case : Optional[list] = None
__snake_case : Optional[list] = None
__snake_case : bool = False
__snake_case : Optional[Union[int, List[int]]] = None
__snake_case : Optional[int] = None
__snake_case : Optional[Union[str, List[str]]] = None
__snake_case : bool = True
__snake_case : bool = True
__snake_case : bool = False
__snake_case : bool = True
__snake_case : Optional[str] = None
__snake_case : str = "."
__snake_case : Optional[str] = None
__snake_case : str = '"'
__snake_case : int = 0
__snake_case : Optional[str] = None
__snake_case : Optional[str] = None
__snake_case : Optional[str] = None
__snake_case : Optional[str] = None
__snake_case : bool = True
__snake_case : bool = True
__snake_case : int = 0
__snake_case : bool = True
__snake_case : bool = False
__snake_case : Optional[str] = None
__snake_case : int = 10000
__snake_case : Optional[datasets.Features] = None
__snake_case : Optional[str] = "strict"
__snake_case : Literal["error", "warn", "skip"] = "error"
__snake_case : Optional[str] = None
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
if self.delimiter is not None:
SCREAMING_SNAKE_CASE = self.delimiter
if self.column_names is not None:
SCREAMING_SNAKE_CASE = self.column_names
@property
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {
"""sep""": self.sep,
"""header""": self.header,
"""names""": self.names,
"""index_col""": self.index_col,
"""usecols""": self.usecols,
"""prefix""": self.prefix,
"""mangle_dupe_cols""": self.mangle_dupe_cols,
"""engine""": self.engine,
"""converters""": self.converters,
"""true_values""": self.true_values,
"""false_values""": self.false_values,
"""skipinitialspace""": self.skipinitialspace,
"""skiprows""": self.skiprows,
"""nrows""": self.nrows,
"""na_values""": self.na_values,
"""keep_default_na""": self.keep_default_na,
"""na_filter""": self.na_filter,
"""verbose""": self.verbose,
"""skip_blank_lines""": self.skip_blank_lines,
"""thousands""": self.thousands,
"""decimal""": self.decimal,
"""lineterminator""": self.lineterminator,
"""quotechar""": self.quotechar,
"""quoting""": self.quoting,
"""escapechar""": self.escapechar,
"""comment""": self.comment,
"""encoding""": self.encoding,
"""dialect""": self.dialect,
"""error_bad_lines""": self.error_bad_lines,
"""warn_bad_lines""": self.warn_bad_lines,
"""skipfooter""": self.skipfooter,
"""doublequote""": self.doublequote,
"""memory_map""": self.memory_map,
"""float_precision""": self.float_precision,
"""chunksize""": self.chunksize,
"""encoding_errors""": self.encoding_errors,
"""on_bad_lines""": self.on_bad_lines,
"""date_format""": self.date_format,
}
# some kwargs must not be passed if they don't have a default value
# some others are deprecated and we can also not pass them if they are the default value
for pd_read_csv_parameter in _PANDAS_READ_CSV_NO_DEFAULT_PARAMETERS + _PANDAS_READ_CSV_DEPRECATED_PARAMETERS:
if pd_read_csv_kwargs[pd_read_csv_parameter] == getattr(CsvConfig() ,lowerCamelCase__ ):
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 2.0 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 2):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_2_0_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
# Remove 1.3 new arguments
if not (datasets.config.PANDAS_VERSION.major >= 1 and datasets.config.PANDAS_VERSION.minor >= 3):
for pd_read_csv_parameter in _PANDAS_READ_CSV_NEW_1_3_0_PARAMETERS:
del pd_read_csv_kwargs[pd_read_csv_parameter]
return pd_read_csv_kwargs
class UpperCamelCase__ ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
__snake_case : Union[str, Any] = CsvConfig
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
SCREAMING_SNAKE_CASE = dl_manager.download_and_extract(self.config.data_files )
if isinstance(lowerCamelCase__ ,(str, list, tuple) ):
SCREAMING_SNAKE_CASE = data_files
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = [files]
SCREAMING_SNAKE_CASE = [dl_manager.iter_files(lowerCamelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={"""files""": files} )]
SCREAMING_SNAKE_CASE = []
for split_name, files in data_files.items():
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = [files]
SCREAMING_SNAKE_CASE = [dl_manager.iter_files(lowerCamelCase__ ) for file in files]
splits.append(datasets.SplitGenerator(name=lowerCamelCase__ ,gen_kwargs={"""files""": files} ) )
return splits
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : pa.Table ) -> pa.Table:
'''simple docstring'''
if self.config.features is not None:
SCREAMING_SNAKE_CASE = self.config.features.arrow_schema
if all(not require_storage_cast(lowerCamelCase__ ) for feature in self.config.features.values() ):
# cheaper cast
SCREAMING_SNAKE_CASE = pa.Table.from_arrays([pa_table[field.name] for field in schema] ,schema=lowerCamelCase__ )
else:
# more expensive cast; allows str <-> int/float or str to Audio for example
SCREAMING_SNAKE_CASE = table_cast(lowerCamelCase__ ,lowerCamelCase__ )
return pa_table
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : List[Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.config.features.arrow_schema if self.config.features else None
# dtype allows reading an int column as str
SCREAMING_SNAKE_CASE = (
{
name: dtype.to_pandas_dtype() if not require_storage_cast(lowerCamelCase__ ) else object
for name, dtype, feature in zip(schema.names ,schema.types ,self.config.features.values() )
}
if schema is not None
else None
)
for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase__ ) ):
SCREAMING_SNAKE_CASE = pd.read_csv(lowerCamelCase__ ,iterator=lowerCamelCase__ ,dtype=lowerCamelCase__ ,**self.config.pd_read_csv_kwargs )
try:
for batch_idx, df in enumerate(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = pa.Table.from_pandas(lowerCamelCase__ )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield (file_idx, batch_idx), self._cast_table(lowerCamelCase__ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(lowerCamelCase__ )}: {e}""" )
raise
| 296 |
import os
from distutils.util import strtobool
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
for e in env_keys:
SCREAMING_SNAKE_CASE = int(os.environ.get(_SCREAMING_SNAKE_CASE , -1 ) )
if val >= 0:
return val
return default
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) )
return strtobool(_SCREAMING_SNAKE_CASE ) == 1 # As its name indicates `strtobool` actually returns an int...
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="no" ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) )
return value
| 296 | 1 |
import math
SCREAMING_SNAKE_CASE_ = 1_0
SCREAMING_SNAKE_CASE_ = 7
SCREAMING_SNAKE_CASE_ = BALLS_PER_COLOUR * NUM_COLOURS
def __lowercase ( _SCREAMING_SNAKE_CASE = 20 ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = math.comb(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = math.comb(NUM_BALLS - BALLS_PER_COLOUR , _SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = NUM_COLOURS * (1 - missing_colour / total)
return F"""{result:.9f}"""
if __name__ == "__main__":
print(solution(2_0))
| 296 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __lowercase ( _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> List[str]:
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
__snake_case : List[str] = list_field(
default=[] , metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
} , )
__snake_case : List[int] = list_field(
default=[8] , metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
__snake_case : List[int] = list_field(
default=[8, 32, 128, 512] , metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Use FP16 to accelerate inference."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Benchmark training of model"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Verbose memory tracing"} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
} , )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Trace memory line by line"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Save result to a CSV file"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Save all print statements in a log file"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Whether to print environment information"} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
} , )
__snake_case : str = field(
default=F"inference_time_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving time results to csv."} , )
__snake_case : str = field(
default=F"inference_memory_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving memory results to csv."} , )
__snake_case : str = field(
default=F"train_time_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving time results to csv for training."} , )
__snake_case : str = field(
default=F"train_memory_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving memory results to csv for training."} , )
__snake_case : str = field(
default=F"env_info_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving environment information."} , )
__snake_case : str = field(
default=F"log_{round(time() )}.csv" , metadata={"help": "Log filename used if print statements are saved in log."} , )
__snake_case : int = field(default=3 , metadata={"help": "Times an experiment will be run."} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
} , )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
""" are deprecated in general and it is advised to use external Benchmarking libraries """
""" to benchmark Transformer models.""" ,lowerCamelCase__ ,)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return json.dumps(dataclasses.asdict(self ) ,indent=2 )
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
if len(self.models ) <= 0:
raise ValueError(
"""Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
""" bert-base-cased` or `args.models = ['bert-base-cased'].""" )
return self.models
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> int:
'''simple docstring'''
if not self.multi_process:
return False
elif self.is_tpu:
logger.info("""Multiprocessing is currently not possible on TPU.""" )
return False
else:
return True
| 296 | 1 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Tuple ,*lowerCamelCase__ : int ,**lowerCamelCase__ : Optional[int] ) -> None:
'''simple docstring'''
warnings.warn(
"""The class LayoutLMv2FeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use LayoutLMv2ImageProcessor instead.""" ,lowerCamelCase__ ,)
super().__init__(*lowerCamelCase__ ,**lowerCamelCase__ )
| 296 |
import math
import unittest
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Tuple:
'''simple docstring'''
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) ,"""Zero doesn't have any positive factors, primes must have exactly two.""" ,)
self.assertFalse(
is_prime(1 ) ,"""One only has 1 positive factor, primes must have exactly two.""" ,)
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 296 | 1 |
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 UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Optional[Any] = BioGptTokenizer
__snake_case : List[str] = False
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[str]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE = [
"""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>""",
]
SCREAMING_SNAKE_CASE = dict(zip(lowerCamelCase__ ,range(len(lowerCamelCase__ ) ) ) )
SCREAMING_SNAKE_CASE = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) )
with open(self.merges_file ,"""w""" ) as fp:
fp.write("""\n""".join(lowerCamelCase__ ) )
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Tuple ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """lower newer"""
SCREAMING_SNAKE_CASE = """lower newer"""
return input_text, output_text
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = BioGptTokenizer(self.vocab_file ,self.merges_file )
SCREAMING_SNAKE_CASE = """lower"""
SCREAMING_SNAKE_CASE = ["""low""", """er</w>"""]
SCREAMING_SNAKE_CASE = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokens + ["""<unk>"""]
SCREAMING_SNAKE_CASE = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) ,lowerCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
SCREAMING_SNAKE_CASE = tokenizer.encode("""sequence builders""" ,add_special_tokens=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer.encode("""multi-sequence build""" ,add_special_tokens=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer.build_inputs_with_special_tokens(lowerCamelCase__ ,lowerCamelCase__ )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 296 |
import random
class UpperCamelCase__ :
'''simple docstring'''
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : str ) -> tuple[list[int], list[int]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [ord(lowerCamelCase__ ) for i in text]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i in plain:
SCREAMING_SNAKE_CASE = random.randint(1 ,300 )
SCREAMING_SNAKE_CASE = (i + k) * k
cipher.append(lowerCamelCase__ )
key.append(lowerCamelCase__ )
return cipher, key
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : list[int] ,lowerCamelCase__ : list[int] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
for i in range(len(lowerCamelCase__ ) ):
SCREAMING_SNAKE_CASE = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowerCamelCase__ ) )
return "".join(lowerCamelCase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 296 | 1 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.utils.logging import set_verbosity_info
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
return {key.lstrip("""-""" ): value for key, value in zip(unknown_args[::2] , unknown_args[1::2] )}
def __lowercase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ArgumentParser(
"""HuggingFace Datasets CLI tool""" , usage="""datasets-cli <command> [<args>]""" , allow_abbrev=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = parser.add_subparsers(help="""datasets-cli command helpers""" )
set_verbosity_info()
# Register commands
ConvertCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
TestCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
RunBeamCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
DummyDataCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
# Parse args
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = parser.parse_known_args()
if not hasattr(_SCREAMING_SNAKE_CASE , """func""" ):
parser.print_help()
exit(1 )
SCREAMING_SNAKE_CASE = parse_unknown_args(_SCREAMING_SNAKE_CASE )
# Run
SCREAMING_SNAKE_CASE = args.func(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 296 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = "efficientformer"
def __init__( self : Optional[int] ,lowerCamelCase__ : List[int] = [3, 2, 6, 4] ,lowerCamelCase__ : List[int] = [48, 96, 224, 448] ,lowerCamelCase__ : List[bool] = [True, True, True, True] ,lowerCamelCase__ : int = 448 ,lowerCamelCase__ : int = 32 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : int = 7 ,lowerCamelCase__ : int = 5 ,lowerCamelCase__ : int = 8 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 16 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 2 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : float = 1e-5 ,lowerCamelCase__ : str = "gelu" ,lowerCamelCase__ : float = 0.02 ,lowerCamelCase__ : float = 1e-1_2 ,lowerCamelCase__ : int = 224 ,lowerCamelCase__ : float = 1e-0_5 ,**lowerCamelCase__ : str ,) -> None:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = mlp_expansion_ratio
SCREAMING_SNAKE_CASE = downsamples
SCREAMING_SNAKE_CASE = dim
SCREAMING_SNAKE_CASE = key_dim
SCREAMING_SNAKE_CASE = attention_ratio
SCREAMING_SNAKE_CASE = resolution
SCREAMING_SNAKE_CASE = pool_size
SCREAMING_SNAKE_CASE = downsample_patch_size
SCREAMING_SNAKE_CASE = downsample_stride
SCREAMING_SNAKE_CASE = downsample_pad
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = num_metaad_blocks
SCREAMING_SNAKE_CASE = distillation
SCREAMING_SNAKE_CASE = use_layer_scale
SCREAMING_SNAKE_CASE = layer_scale_init_value
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = batch_norm_eps
| 296 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""EleutherAI/gpt-neox-20b""": """https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json""",
# See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[int] = "gpt_neox"
def __init__( self : Any ,lowerCamelCase__ : Optional[int]=50432 ,lowerCamelCase__ : List[Any]=6144 ,lowerCamelCase__ : Dict=44 ,lowerCamelCase__ : Dict=64 ,lowerCamelCase__ : Tuple=24576 ,lowerCamelCase__ : List[str]="gelu" ,lowerCamelCase__ : int=0.25 ,lowerCamelCase__ : Any=10000 ,lowerCamelCase__ : Dict=0.0 ,lowerCamelCase__ : Any=0.0 ,lowerCamelCase__ : str=0.1 ,lowerCamelCase__ : int=2048 ,lowerCamelCase__ : Union[str, Any]=0.02 ,lowerCamelCase__ : List[str]=1e-5 ,lowerCamelCase__ : Any=True ,lowerCamelCase__ : List[str]=0 ,lowerCamelCase__ : List[Any]=2 ,lowerCamelCase__ : str=False ,lowerCamelCase__ : Tuple=True ,lowerCamelCase__ : Optional[Any]=None ,**lowerCamelCase__ : List[Any] ,) -> str:
'''simple docstring'''
super().__init__(bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = rotary_pct
SCREAMING_SNAKE_CASE = rotary_emb_base
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = hidden_dropout
SCREAMING_SNAKE_CASE = classifier_dropout
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = tie_word_embeddings
SCREAMING_SNAKE_CASE = use_parallel_residual
SCREAMING_SNAKE_CASE = rope_scaling
self._rope_scaling_validation()
if self.hidden_size % self.num_attention_heads != 0:
raise ValueError(
"""The hidden size is not divisble by the number of attention heads! Make sure to update them!""" )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling ,lowerCamelCase__ ) or len(self.rope_scaling ) != 2:
raise ValueError(
"""`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, """
F"""got {self.rope_scaling}""" )
SCREAMING_SNAKE_CASE = self.rope_scaling.get("""type""" ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.rope_scaling.get("""factor""" ,lowerCamelCase__ )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
F"""`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}""" )
if rope_scaling_factor is None or not isinstance(lowerCamelCase__ ,lowerCamelCase__ ) or rope_scaling_factor <= 1.0:
raise ValueError(F"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )
| 296 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE_ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
if "://" in dataset_path:
SCREAMING_SNAKE_CASE = dataset_path.split("""://""" )[1]
return dataset_path
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
if fs is not None and fs.protocol != "file":
return True
else:
return False
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = not is_remote_filesystem(_SCREAMING_SNAKE_CASE )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(_SCREAMING_SNAKE_CASE ) , fs._strip_protocol(_SCREAMING_SNAKE_CASE ) )
else:
fs.mv(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , recursive=_SCREAMING_SNAKE_CASE )
def __lowercase ( ) -> None:
'''simple docstring'''
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = threading.Lock()
| 296 | 1 |
import warnings
from ..trainer import Trainer
from ..utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : str ,lowerCamelCase__ : Dict=None ,**lowerCamelCase__ : str ) -> Union[str, Any]:
'''simple docstring'''
warnings.warn(
"""`SageMakerTrainer` is deprecated and will be removed in v5 of Transformers. You can use `Trainer` """
"""instead.""" ,lowerCamelCase__ ,)
super().__init__(args=lowerCamelCase__ ,**lowerCamelCase__ )
| 296 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,)
return model
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE = KarrasVeScheduler()
SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""numpy""" ,return_dict=lowerCamelCase__ )[0]
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """google/ncsnpp-celebahq-256"""
SCREAMING_SNAKE_CASE = UNetaDModel.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = KarrasVeScheduler()
SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=20 ,generator=lowerCamelCase__ ,output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 296 | 1 |
def __lowercase ( _SCREAMING_SNAKE_CASE = 1_00_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = limit + 1
SCREAMING_SNAKE_CASE = [0] * limit
for first_term in range(1 , _SCREAMING_SNAKE_CASE ):
for n in range(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE = first_term + n / first_term
if common_difference % 4: # d must be divisble by 4
continue
else:
common_difference /= 4
if (
first_term > common_difference
and first_term < 4 * common_difference
): # since x,y,z are positive integers
frequency[n] += 1 # so z>0 and a>d ,also 4d<a
SCREAMING_SNAKE_CASE = sum(1 for x in frequency[1:limit] if x == 10 )
return count
if __name__ == "__main__":
print(F'''{solution() = }''')
| 296 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,lowerCamelCase__ : Callable ,lowerCamelCase__ : Optional[Features] = None ,lowerCamelCase__ : str = None ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : Optional[dict] = None ,lowerCamelCase__ : Optional[int] = None ,**lowerCamelCase__ : Optional[Any] ,) -> List[str]:
'''simple docstring'''
super().__init__(
features=lowerCamelCase__ ,cache_dir=lowerCamelCase__ ,keep_in_memory=lowerCamelCase__ ,streaming=lowerCamelCase__ ,num_proc=lowerCamelCase__ ,**lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = Generator(
cache_dir=lowerCamelCase__ ,features=lowerCamelCase__ ,generator=lowerCamelCase__ ,gen_kwargs=lowerCamelCase__ ,**lowerCamelCase__ ,)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
if self.streaming:
SCREAMING_SNAKE_CASE = self.builder.as_streaming_dataset(split="""train""" )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
self.builder.download_and_prepare(
download_config=lowerCamelCase__ ,download_mode=lowerCamelCase__ ,verification_mode=lowerCamelCase__ ,base_path=lowerCamelCase__ ,num_proc=self.num_proc ,)
SCREAMING_SNAKE_CASE = self.builder.as_dataset(
split="""train""" ,verification_mode=lowerCamelCase__ ,in_memory=self.keep_in_memory )
return dataset
| 296 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[str] = FLAX_MODEL_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModel)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_PRETRAINING_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Any = FLAX_MODEL_FOR_MASKED_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : int = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[int] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[Any] = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Tuple = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[str] = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[Any] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 296 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[float] = field(
default=0.0 , metadata={"help": "The label smoothing epsilon to apply (if not zero)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Whether to SortishSamler or not."} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "whether to use adafactor"} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Encoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Decoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(default=lowerCAmelCase_ , metadata={"help": "Dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Attention dropout probability. Goes into model.config."} )
__snake_case : Optional[str] = field(
default="linear" , metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}"} , )
| 296 | 1 |
SCREAMING_SNAKE_CASE_ = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.git
"""
SCREAMING_SNAKE_CASE_ = [{"""type""": """code""", """content""": INSTALL_CONTENT}]
SCREAMING_SNAKE_CASE_ = {
"""{processor_class}""": """FakeProcessorClass""",
"""{model_class}""": """FakeModelClass""",
"""{object_class}""": """FakeObjectClass""",
}
| 296 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[str] = TextToVideoSDPipeline
__snake_case : int = TEXT_TO_IMAGE_PARAMS
__snake_case : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
__snake_case : Dict = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""") ,up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""") ,cross_attention_dim=32 ,attention_head_dim=4 ,)
SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.00085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=lowerCamelCase__ ,set_alpha_to_one=lowerCamelCase__ ,)
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = 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 )
SCREAMING_SNAKE_CASE = 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=1000 ,hidden_act="""gelu""" ,projection_dim=512 ,)
SCREAMING_SNAKE_CASE = CLIPTextModel(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : int=0 ) -> List[Any]:
'''simple docstring'''
if str(lowerCamelCase__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCamelCase__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe.to(lowerCamelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """np"""
SCREAMING_SNAKE_CASE = sd_pipe(**lowerCamelCase__ ).frames
SCREAMING_SNAKE_CASE = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
SCREAMING_SNAKE_CASE = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=1e-2 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=25 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=2 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 296 | 1 |
import os
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(grid[0] )
SCREAMING_SNAKE_CASE = len(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
# Check vertically, horizontally, diagonally at the same time (only works
# for nxn grid)
for i in range(_SCREAMING_SNAKE_CASE ):
for j in range(n_rows - 3 ):
SCREAMING_SNAKE_CASE = grid[j][i] * grid[j + 1][i] * grid[j + 2][i] * grid[j + 3][i]
SCREAMING_SNAKE_CASE = grid[i][j] * grid[i][j + 1] * grid[i][j + 2] * grid[i][j + 3]
# Left-to-right diagonal (\) product
if i < n_columns - 3:
SCREAMING_SNAKE_CASE = (
grid[i][j]
* grid[i + 1][j + 1]
* grid[i + 2][j + 2]
* grid[i + 3][j + 3]
)
# Right-to-left diagonal(/) product
if i > 2:
SCREAMING_SNAKE_CASE = (
grid[i][j]
* grid[i - 1][j + 1]
* grid[i - 2][j + 2]
* grid[i - 3][j + 3]
)
SCREAMING_SNAKE_CASE = max(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if max_product > largest:
SCREAMING_SNAKE_CASE = max_product
return largest
def __lowercase ( ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
with open(os.path.dirname(_SCREAMING_SNAKE_CASE ) + """/grid.txt""" ) as file:
for line in file:
grid.append(line.strip("""\n""" ).split(""" """ ) )
SCREAMING_SNAKE_CASE = [[int(_SCREAMING_SNAKE_CASE ) for i in grid[j]] for j in range(len(_SCREAMING_SNAKE_CASE ) )]
return largest_product(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(solution())
| 296 |
def __lowercase ( _SCREAMING_SNAKE_CASE = 10 ) -> str:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or n < 0:
raise ValueError("""Invalid input""" )
SCREAMING_SNAKE_CASE = 10**n
SCREAMING_SNAKE_CASE = 2_84_33 * (pow(2 , 7_83_04_57 , _SCREAMING_SNAKE_CASE )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(1_0) = }''')
| 296 | 1 |
# limitations under the License.
# NOTE: This file is deprecated and will be removed in a future version.
# It only exists so that temporarely `from diffusers.pipelines import DiffusionPipeline` works
from .pipelines import DiffusionPipeline, ImagePipelineOutput # noqa: F401
from .utils import deprecate
deprecate(
"""pipelines_utils""",
"""0.22.0""",
"""Importing `DiffusionPipeline` or `ImagePipelineOutput` from diffusers.pipeline_utils is deprecated. Please import from diffusers.pipelines.pipeline_utils instead.""",
standard_warn=False,
stacklevel=3,
)
| 296 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""LlamaForCausalLM""",
"""LlamaModel""",
"""LlamaPreTrainedModel""",
"""LlamaForSequenceClassification""",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 296 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils import ModelMixin
from .unet_ad_blocks import get_down_block, get_mid_block, get_out_block, get_up_block
@dataclass
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : torch.FloatTensor
class UpperCamelCase__ ( lowerCAmelCase_ , lowerCAmelCase_ ):
'''simple docstring'''
@register_to_config
def __init__( self : Tuple ,lowerCamelCase__ : int = 65536 ,lowerCamelCase__ : Optional[int] = None ,lowerCamelCase__ : int = 2 ,lowerCamelCase__ : int = 2 ,lowerCamelCase__ : int = 0 ,lowerCamelCase__ : str = "fourier" ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : Tuple[str] = ("DownBlock1DNoSkip", "DownBlock1D", "AttnDownBlock1D") ,lowerCamelCase__ : Tuple[str] = ("AttnUpBlock1D", "UpBlock1D", "UpBlock1DNoSkip") ,lowerCamelCase__ : Tuple[str] = "UNetMidBlock1D" ,lowerCamelCase__ : str = None ,lowerCamelCase__ : Tuple[int] = (32, 32, 64) ,lowerCamelCase__ : str = None ,lowerCamelCase__ : int = 8 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : bool = False ,) -> List[Any]:
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE = sample_size
# time
if time_embedding_type == "fourier":
SCREAMING_SNAKE_CASE = GaussianFourierProjection(
embedding_size=8 ,set_W_to_weight=lowerCamelCase__ ,log=lowerCamelCase__ ,flip_sin_to_cos=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 2 * block_out_channels[0]
elif time_embedding_type == "positional":
SCREAMING_SNAKE_CASE = Timesteps(
block_out_channels[0] ,flip_sin_to_cos=lowerCamelCase__ ,downscale_freq_shift=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = block_out_channels[0]
if use_timestep_embedding:
SCREAMING_SNAKE_CASE = block_out_channels[0] * 4
SCREAMING_SNAKE_CASE = TimestepEmbedding(
in_channels=lowerCamelCase__ ,time_embed_dim=lowerCamelCase__ ,act_fn=lowerCamelCase__ ,out_dim=block_out_channels[0] ,)
SCREAMING_SNAKE_CASE = nn.ModuleList([] )
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = nn.ModuleList([] )
SCREAMING_SNAKE_CASE = None
# down
SCREAMING_SNAKE_CASE = in_channels
for i, down_block_type in enumerate(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = output_channel
SCREAMING_SNAKE_CASE = block_out_channels[i]
if i == 0:
input_channel += extra_in_channels
SCREAMING_SNAKE_CASE = i == len(lowerCamelCase__ ) - 1
SCREAMING_SNAKE_CASE = get_down_block(
lowerCamelCase__ ,num_layers=lowerCamelCase__ ,in_channels=lowerCamelCase__ ,out_channels=lowerCamelCase__ ,temb_channels=block_out_channels[0] ,add_downsample=not is_final_block or downsample_each_block ,)
self.down_blocks.append(lowerCamelCase__ )
# mid
SCREAMING_SNAKE_CASE = get_mid_block(
lowerCamelCase__ ,in_channels=block_out_channels[-1] ,mid_channels=block_out_channels[-1] ,out_channels=block_out_channels[-1] ,embed_dim=block_out_channels[0] ,num_layers=lowerCamelCase__ ,add_downsample=lowerCamelCase__ ,)
# up
SCREAMING_SNAKE_CASE = list(reversed(lowerCamelCase__ ) )
SCREAMING_SNAKE_CASE = reversed_block_out_channels[0]
if out_block_type is None:
SCREAMING_SNAKE_CASE = out_channels
else:
SCREAMING_SNAKE_CASE = block_out_channels[0]
for i, up_block_type in enumerate(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = output_channel
SCREAMING_SNAKE_CASE = (
reversed_block_out_channels[i + 1] if i < len(lowerCamelCase__ ) - 1 else final_upsample_channels
)
SCREAMING_SNAKE_CASE = i == len(lowerCamelCase__ ) - 1
SCREAMING_SNAKE_CASE = get_up_block(
lowerCamelCase__ ,num_layers=lowerCamelCase__ ,in_channels=lowerCamelCase__ ,out_channels=lowerCamelCase__ ,temb_channels=block_out_channels[0] ,add_upsample=not is_final_block ,)
self.up_blocks.append(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output_channel
# out
SCREAMING_SNAKE_CASE = norm_num_groups if norm_num_groups is not None else min(block_out_channels[0] // 4 ,32 )
SCREAMING_SNAKE_CASE = get_out_block(
out_block_type=lowerCamelCase__ ,num_groups_out=lowerCamelCase__ ,embed_dim=block_out_channels[0] ,out_channels=lowerCamelCase__ ,act_fn=lowerCamelCase__ ,fc_dim=block_out_channels[-1] // 4 ,)
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : torch.FloatTensor ,lowerCamelCase__ : Union[torch.Tensor, float, int] ,lowerCamelCase__ : bool = True ,) -> Union[UNetaDOutput, Tuple]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = timestep
if not torch.is_tensor(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = torch.tensor([timesteps] ,dtype=torch.long ,device=sample.device )
elif torch.is_tensor(lowerCamelCase__ ) and len(timesteps.shape ) == 0:
SCREAMING_SNAKE_CASE = timesteps[None].to(sample.device )
SCREAMING_SNAKE_CASE = self.time_proj(lowerCamelCase__ )
if self.config.use_timestep_embedding:
SCREAMING_SNAKE_CASE = self.time_mlp(lowerCamelCase__ )
else:
SCREAMING_SNAKE_CASE = timestep_embed[..., None]
SCREAMING_SNAKE_CASE = timestep_embed.repeat([1, 1, sample.shape[2]] ).to(sample.dtype )
SCREAMING_SNAKE_CASE = timestep_embed.broadcast_to((sample.shape[:1] + timestep_embed.shape[1:]) )
# 2. down
SCREAMING_SNAKE_CASE = ()
for downsample_block in self.down_blocks:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = downsample_block(hidden_states=lowerCamelCase__ ,temb=lowerCamelCase__ )
down_block_res_samples += res_samples
# 3. mid
if self.mid_block:
SCREAMING_SNAKE_CASE = self.mid_block(lowerCamelCase__ ,lowerCamelCase__ )
# 4. up
for i, upsample_block in enumerate(self.up_blocks ):
SCREAMING_SNAKE_CASE = down_block_res_samples[-1:]
SCREAMING_SNAKE_CASE = down_block_res_samples[:-1]
SCREAMING_SNAKE_CASE = upsample_block(lowerCamelCase__ ,res_hidden_states_tuple=lowerCamelCase__ ,temb=lowerCamelCase__ )
# 5. post-process
if self.out_block:
SCREAMING_SNAKE_CASE = self.out_block(lowerCamelCase__ ,lowerCamelCase__ )
if not return_dict:
return (sample,)
return UNetaDOutput(sample=lowerCamelCase__ )
| 296 |
from pathlib import Path
import fire
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
for path in src_dir.iterdir():
SCREAMING_SNAKE_CASE = [x.rstrip() for x in list(path.open().readlines() )][:n]
SCREAMING_SNAKE_CASE = dest_dir.joinpath(path.name )
print(_SCREAMING_SNAKE_CASE )
dest_path.open("""w""" ).write("""\n""".join(_SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
fire.Fire(minify)
| 296 | 1 |
import os
SCREAMING_SNAKE_CASE_ = {"""I""": 1, """V""": 5, """X""": 1_0, """L""": 5_0, """C""": 1_0_0, """D""": 5_0_0, """M""": 1_0_0_0}
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 0
while index < len(_SCREAMING_SNAKE_CASE ) - 1:
SCREAMING_SNAKE_CASE = SYMBOLS[numerals[index]]
SCREAMING_SNAKE_CASE = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """"""
SCREAMING_SNAKE_CASE = num // 10_00
numerals += m_count * "M"
num %= 10_00
SCREAMING_SNAKE_CASE = num // 1_00
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 1_00
SCREAMING_SNAKE_CASE = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def __lowercase ( _SCREAMING_SNAKE_CASE = "/p089_roman.txt" ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
with open(os.path.dirname(_SCREAMING_SNAKE_CASE ) + roman_numerals_filename ) as filea:
SCREAMING_SNAKE_CASE = filea.readlines()
for line in lines:
SCREAMING_SNAKE_CASE = line.strip()
SCREAMING_SNAKE_CASE = parse_roman_numerals(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = generate_roman_numerals(_SCREAMING_SNAKE_CASE )
savings += len(_SCREAMING_SNAKE_CASE ) - len(_SCREAMING_SNAKE_CASE )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 296 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
SCREAMING_SNAKE_CASE_ = get_tests_dir("""fixtures/dummy-config.json""")
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
'''simple docstring'''
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""fake-roberta""" )
os.makedirs(lowerCamelCase__ ,exist_ok=lowerCamelCase__ )
with open(os.path.join(lowerCamelCase__ ,"""config.json""" ) ,"""w""" ) as f:
f.write(json.dumps({} ) )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertEqual(type(lowerCamelCase__ ) ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
try:
AutoConfig.register("""custom""" ,lowerCamelCase__ )
# Wrong model type will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""model""" ,lowerCamelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""bert""" ,lowerCamelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""bert-base is not a local folder and is not a valid model identifier""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,revision="""aaaaaa""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" ,):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(reloaded_config.__class__.__name__ ,"""NewModelConfig""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = "new-model"
try:
AutoConfig.register("""new-model""" ,lowerCamelCase__ )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 296 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
"""configuration_resnet""": ["""RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ResNetConfig""", """ResNetOnnxConfig"""]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ResNetForImageClassification""",
"""ResNetModel""",
"""ResNetPreTrainedModel""",
"""ResNetBackbone""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFResNetForImageClassification""",
"""TFResNetModel""",
"""TFResNetPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""FlaxResNetForImageClassification""",
"""FlaxResNetModel""",
"""FlaxResNetPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_resnet import RESNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ResNetConfig, ResNetOnnxConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_resnet import (
RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
ResNetBackbone,
ResNetForImageClassification,
ResNetModel,
ResNetPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_resnet import (
TF_RESNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFResNetForImageClassification,
TFResNetModel,
TFResNetPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_resnet import FlaxResNetForImageClassification, FlaxResNetModel, FlaxResNetPreTrainedModel
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 296 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Dict = "git_vision_model"
def __init__( self : List[Any] ,lowerCamelCase__ : Dict=768 ,lowerCamelCase__ : Union[str, Any]=3072 ,lowerCamelCase__ : Optional[int]=12 ,lowerCamelCase__ : Tuple=12 ,lowerCamelCase__ : Tuple=3 ,lowerCamelCase__ : Optional[Any]=224 ,lowerCamelCase__ : Union[str, Any]=16 ,lowerCamelCase__ : List[Any]="quick_gelu" ,lowerCamelCase__ : Optional[Any]=1e-5 ,lowerCamelCase__ : str=0.0 ,lowerCamelCase__ : Optional[int]=0.02 ,**lowerCamelCase__ : Union[str, Any] ,) -> Optional[int]:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = hidden_act
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Tuple ,lowerCamelCase__ : Union[str, os.PathLike] ,**lowerCamelCase__ : int ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = cls.get_config_dict(lowerCamelCase__ ,**lowerCamelCase__ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("""model_type""" ) == "git":
SCREAMING_SNAKE_CASE = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowerCamelCase__ ,**lowerCamelCase__ )
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Dict = "git"
def __init__( self : Optional[int] ,lowerCamelCase__ : int=None ,lowerCamelCase__ : str=30522 ,lowerCamelCase__ : Tuple=768 ,lowerCamelCase__ : Union[str, Any]=6 ,lowerCamelCase__ : str=12 ,lowerCamelCase__ : List[str]=3072 ,lowerCamelCase__ : Dict="gelu" ,lowerCamelCase__ : Tuple=0.1 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : List[str]=1024 ,lowerCamelCase__ : List[str]=0.02 ,lowerCamelCase__ : str=1e-1_2 ,lowerCamelCase__ : Optional[int]=0 ,lowerCamelCase__ : Optional[int]="absolute" ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : str=False ,lowerCamelCase__ : int=101 ,lowerCamelCase__ : int=102 ,lowerCamelCase__ : Dict=None ,**lowerCamelCase__ : List[Any] ,) -> Optional[Any]:
'''simple docstring'''
super().__init__(bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,pad_token_id=lowerCamelCase__ ,**lowerCamelCase__ )
if vision_config is None:
SCREAMING_SNAKE_CASE = {}
logger.info("""vision_config is None. initializing the GitVisionConfig with default values.""" )
SCREAMING_SNAKE_CASE = GitVisionConfig(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = tie_word_embeddings
SCREAMING_SNAKE_CASE = num_image_with_embedding
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = eos_token_id
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
| 296 | 1 |
from math import ceil, sqrt
def __lowercase ( _SCREAMING_SNAKE_CASE = 1_00_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
SCREAMING_SNAKE_CASE = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
SCREAMING_SNAKE_CASE = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F'''{solution() = }''')
| 296 |
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
SCREAMING_SNAKE_CASE_ = random.Random()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE = global_rng
SCREAMING_SNAKE_CASE = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[int]=7 ,lowerCamelCase__ : Optional[Any]=400 ,lowerCamelCase__ : List[str]=2000 ,lowerCamelCase__ : List[str]=2048 ,lowerCamelCase__ : Any=128 ,lowerCamelCase__ : List[str]=1 ,lowerCamelCase__ : str=512 ,lowerCamelCase__ : Optional[Any]=30 ,lowerCamelCase__ : Tuple=44100 ,) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = min_seq_length
SCREAMING_SNAKE_CASE = max_seq_length
SCREAMING_SNAKE_CASE = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE = spectrogram_length
SCREAMING_SNAKE_CASE = feature_size
SCREAMING_SNAKE_CASE = num_audio_channels
SCREAMING_SNAKE_CASE = hop_length
SCREAMING_SNAKE_CASE = chunk_length
SCREAMING_SNAKE_CASE = sampling_rate
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[int]:
'''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 SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : Tuple=False ,lowerCamelCase__ : Union[str, Any]=False ) -> str:
'''simple docstring'''
def _flatten(lowerCamelCase__ : List[Any] ):
return list(itertools.chain(*lowerCamelCase__ ) )
if equal_length:
SCREAMING_SNAKE_CASE = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[Any] = TvltFeatureExtractor
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TvltFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase__ ,"""spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""sampling_rate""" ) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = feat_extract_first.save_pretrained(lowerCamelCase__ )[0]
check_json_file_has_correct_format(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in range(800 ,1400 ,200 )]
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,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
SCREAMING_SNAKE_CASE = feature_extractor(
lowerCamelCase__ ,return_tensors="""np""" ,sampling_rate=44100 ,mask_audio=lowerCamelCase__ ).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.
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE = np.asarray(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,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 SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,"""clean""" ,split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE = ds.sort("""id""" ).select(range(lowerCamelCase__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape ,(1, 1, 192, 128) )
SCREAMING_SNAKE_CASE = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,lowerCamelCase__ ,atol=1e-4 ) )
| 296 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""LlamaForCausalLM""",
"""LlamaModel""",
"""LlamaPreTrainedModel""",
"""LlamaForSequenceClassification""",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 296 |
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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
SCREAMING_SNAKE_CASE = [1_44, 1_92, 2_40]
SCREAMING_SNAKE_CASE = [16, 32, 64, 96, 1_28, 1_60, 6_40]
elif "mobilevit_xs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [96, 1_20, 1_44]
SCREAMING_SNAKE_CASE = [16, 32, 48, 64, 80, 96, 3_84]
elif "mobilevit_xxs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [64, 80, 96]
SCREAMING_SNAKE_CASE = [16, 16, 24, 48, 64, 80, 3_20]
SCREAMING_SNAKE_CASE = 0.05
SCREAMING_SNAKE_CASE = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = 5_12
SCREAMING_SNAKE_CASE = 16
SCREAMING_SNAKE_CASE = 21
SCREAMING_SNAKE_CASE = """pascal-voc-id2label.json"""
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
SCREAMING_SNAKE_CASE = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
SCREAMING_SNAKE_CASE = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.weight""" , """.layernorm.weight""" )
if F""".global_rep.{i}.bias""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.bias""" , """.layernorm.bias""" )
if ".global_rep." in name:
SCREAMING_SNAKE_CASE = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
SCREAMING_SNAKE_CASE = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
SCREAMING_SNAKE_CASE = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
SCREAMING_SNAKE_CASE = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
SCREAMING_SNAKE_CASE = """mobilevit.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Dict:
'''simple docstring'''
if base_model:
SCREAMING_SNAKE_CASE = """"""
else:
SCREAMING_SNAKE_CASE = """mobilevit."""
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if key[:8] == "encoder.":
SCREAMING_SNAKE_CASE = key[8:]
if "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[0][6:] ) - 1
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
SCREAMING_SNAKE_CASE = layer.transformer.layer[transformer_num].attention.attention.all_head_size
SCREAMING_SNAKE_CASE = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[:dim]
SCREAMING_SNAKE_CASE = val[dim : dim * 2]
SCREAMING_SNAKE_CASE = val[-dim:]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_mobilevit_config(_SCREAMING_SNAKE_CASE )
# load original state_dict
SCREAMING_SNAKE_CASE = torch.load(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = MobileViTForSemanticSegmentation(_SCREAMING_SNAKE_CASE ).eval()
else:
SCREAMING_SNAKE_CASE = MobileViTForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
SCREAMING_SNAKE_CASE = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileViTImageProcessor
SCREAMING_SNAKE_CASE = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
[[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
[[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
[[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
[[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
[[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
[[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
else:
assert logits.shape == (1, 10_00)
if mobilevit_name == "mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
elif mobilevit_name == "mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
elif mobilevit_name == "mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
SCREAMING_SNAKE_CASE = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
SCREAMING_SNAKE_CASE = model_mapping[mobilevit_name]
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
model.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--mobilevit_name""",
default="""mobilevit_s""",
type=str,
help=(
"""Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
""" 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
),
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 296 | 1 |
def __lowercase ( ) -> str:
'''simple docstring'''
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 2
while i * i <= n:
SCREAMING_SNAKE_CASE = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def __lowercase ( ) -> Any:
'''simple docstring'''
return next(i for i in triangle_number_generator() if count_divisors(_SCREAMING_SNAKE_CASE ) > 5_00 )
if __name__ == "__main__":
print(solution())
| 296 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[str] = FLAX_MODEL_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModel)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_PRETRAINING_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Any = FLAX_MODEL_FOR_MASKED_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : int = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[int] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[Any] = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Tuple = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[str] = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[Any] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 296 | 1 |
import argparse
from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging
logging.set_verbosity_info()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TaConfig.from_json_file(_SCREAMING_SNAKE_CASE )
print(F"""Building PyTorch model from configuration: {config}""" )
SCREAMING_SNAKE_CASE = TaForConditionalGeneration(_SCREAMING_SNAKE_CASE )
# Load weights from tf checkpoint
load_tf_weights_in_ta(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Save pytorch-model
print(F"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = 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(
"""--config_file""",
default=None,
type=str,
required=True,
help=(
"""The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture."""
),
)
parser.add_argument(
"""--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)
| 296 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = "table-transformer"
__snake_case : Union[str, Any] = ["past_key_values"]
__snake_case : List[Any] = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self : Optional[int] ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : Optional[Any]=None ,lowerCamelCase__ : List[Any]=3 ,lowerCamelCase__ : Optional[int]=100 ,lowerCamelCase__ : List[Any]=6 ,lowerCamelCase__ : Dict=2048 ,lowerCamelCase__ : List[Any]=8 ,lowerCamelCase__ : Dict=6 ,lowerCamelCase__ : Dict=2048 ,lowerCamelCase__ : Any=8 ,lowerCamelCase__ : Optional[int]=0.0 ,lowerCamelCase__ : int=0.0 ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : Optional[int]="relu" ,lowerCamelCase__ : Tuple=256 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : Optional[Any]=0.0 ,lowerCamelCase__ : Tuple=0.0 ,lowerCamelCase__ : List[Any]=0.02 ,lowerCamelCase__ : int=1.0 ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : Optional[Any]="sine" ,lowerCamelCase__ : List[str]="resnet50" ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : int=1 ,lowerCamelCase__ : Dict=5 ,lowerCamelCase__ : Tuple=2 ,lowerCamelCase__ : Union[str, Any]=1 ,lowerCamelCase__ : str=1 ,lowerCamelCase__ : Any=5 ,lowerCamelCase__ : Tuple=2 ,lowerCamelCase__ : str=0.1 ,**lowerCamelCase__ : List[str] ,) -> Optional[int]:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
SCREAMING_SNAKE_CASE = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = backbone_config.get("""model_type""" )
SCREAMING_SNAKE_CASE = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE = config_class.from_dict(lowerCamelCase__ )
# set timm attributes to None
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = None, None, None
SCREAMING_SNAKE_CASE = use_timm_backbone
SCREAMING_SNAKE_CASE = backbone_config
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = num_queries
SCREAMING_SNAKE_CASE = d_model
SCREAMING_SNAKE_CASE = encoder_ffn_dim
SCREAMING_SNAKE_CASE = encoder_layers
SCREAMING_SNAKE_CASE = encoder_attention_heads
SCREAMING_SNAKE_CASE = decoder_ffn_dim
SCREAMING_SNAKE_CASE = decoder_layers
SCREAMING_SNAKE_CASE = decoder_attention_heads
SCREAMING_SNAKE_CASE = dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = activation_dropout
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = init_std
SCREAMING_SNAKE_CASE = init_xavier_std
SCREAMING_SNAKE_CASE = encoder_layerdrop
SCREAMING_SNAKE_CASE = decoder_layerdrop
SCREAMING_SNAKE_CASE = encoder_layers
SCREAMING_SNAKE_CASE = auxiliary_loss
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = backbone
SCREAMING_SNAKE_CASE = use_pretrained_backbone
SCREAMING_SNAKE_CASE = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE = class_cost
SCREAMING_SNAKE_CASE = bbox_cost
SCREAMING_SNAKE_CASE = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE = mask_loss_coefficient
SCREAMING_SNAKE_CASE = dice_loss_coefficient
SCREAMING_SNAKE_CASE = bbox_loss_coefficient
SCREAMING_SNAKE_CASE = giou_loss_coefficient
SCREAMING_SNAKE_CASE = eos_coefficient
super().__init__(is_encoder_decoder=lowerCamelCase__ ,**lowerCamelCase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return self.d_model
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = version.parse("1.11" )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> float:
'''simple docstring'''
return 1e-5
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> int:
'''simple docstring'''
return 12
| 296 | 1 |
def __lowercase ( ) -> int:
'''simple docstring'''
return [
a * b * (10_00 - a - b)
for a in range(1 , 9_99 )
for b in range(_SCREAMING_SNAKE_CASE , 9_99 )
if (a * a + b * b == (10_00 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'''{solution() = }''')
| 296 |
from collections import defaultdict
from math import gcd
def __lowercase ( _SCREAMING_SNAKE_CASE = 1_50_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = defaultdict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , _SCREAMING_SNAKE_CASE , 2 ):
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) > 1:
continue
SCREAMING_SNAKE_CASE = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(_SCREAMING_SNAKE_CASE , limit + 1 , _SCREAMING_SNAKE_CASE ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 296 | 1 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
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.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch
if is_faiss_available():
import faiss
@require_faiss
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = 8
# DPR tok
SCREAMING_SNAKE_CASE = [
"""[UNK]""",
"""[CLS]""",
"""[SEP]""",
"""[PAD]""",
"""[MASK]""",
"""want""",
"""##want""",
"""##ed""",
"""wa""",
"""un""",
"""runn""",
"""##ing""",
""",""",
"""low""",
"""lowest""",
]
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,"""dpr_tokenizer""" )
os.makedirs(lowerCamelCase__ ,exist_ok=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,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
SCREAMING_SNAKE_CASE = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""\u0120""",
"""\u0120l""",
"""\u0120n""",
"""\u0120lo""",
"""\u0120low""",
"""er""",
"""\u0120lowest""",
"""\u0120newer""",
"""\u0120wider""",
"""<unk>""",
]
SCREAMING_SNAKE_CASE = dict(zip(lowerCamelCase__ ,range(len(lowerCamelCase__ ) ) ) )
SCREAMING_SNAKE_CASE = ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
SCREAMING_SNAKE_CASE = {"""unk_token""": """<unk>"""}
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,"""bart_tokenizer""" )
os.makedirs(lowerCamelCase__ ,exist_ok=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,BART_VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,BART_VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) + """\n""" )
with open(self.merges_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write("""\n""".join(lowerCamelCase__ ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> DPRQuestionEncoderTokenizer:
'''simple docstring'''
return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"""dpr_tokenizer""" ) )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> DPRContextEncoderTokenizer:
'''simple docstring'''
return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"""dpr_tokenizer""" ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> BartTokenizer:
'''simple docstring'''
return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname ,"""bart_tokenizer""" ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Dataset.from_dict(
{
"""id""": ["""0""", """1"""],
"""text""": ["""foo""", """bar"""],
"""title""": ["""Foo""", """Bar"""],
"""embeddings""": [np.ones(self.retrieval_vector_size ), 2 * np.ones(self.retrieval_vector_size )],
} )
dataset.add_faiss_index("""embeddings""" ,string_factory="""Flat""" ,metric_type=faiss.METRIC_INNER_PRODUCT )
return dataset
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_dataset()
SCREAMING_SNAKE_CASE = RagConfig(
retrieval_vector_size=self.retrieval_vector_size ,question_encoder=DPRConfig().to_dict() ,generator=BartConfig().to_dict() ,)
with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
SCREAMING_SNAKE_CASE = dataset
SCREAMING_SNAKE_CASE = RagRetriever(
lowerCamelCase__ ,question_encoder_tokenizer=self.get_dpr_tokenizer() ,generator_tokenizer=self.get_bart_tokenizer() ,)
return retriever
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,lowerCamelCase__ : bool ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_dataset()
SCREAMING_SNAKE_CASE = RagConfig(
retrieval_vector_size=self.retrieval_vector_size ,question_encoder=DPRConfig().to_dict() ,generator=BartConfig().to_dict() ,index_name="""custom""" ,)
if from_disk:
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,"""dataset""" )
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,"""index.faiss""" )
dataset.get_index("""embeddings""" ).save(os.path.join(self.tmpdirname ,"""index.faiss""" ) )
dataset.drop_index("""embeddings""" )
dataset.save_to_disk(os.path.join(self.tmpdirname ,"""dataset""" ) )
del dataset
SCREAMING_SNAKE_CASE = RagRetriever(
lowerCamelCase__ ,question_encoder_tokenizer=self.get_dpr_tokenizer() ,generator_tokenizer=self.get_bart_tokenizer() ,)
else:
SCREAMING_SNAKE_CASE = RagRetriever(
lowerCamelCase__ ,question_encoder_tokenizer=self.get_dpr_tokenizer() ,generator_tokenizer=self.get_bart_tokenizer() ,index=CustomHFIndex(config.retrieval_vector_size ,lowerCamelCase__ ) ,)
return retriever
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Dataset.from_dict(
{
"""id""": ["""0""", """1"""],
"""text""": ["""foo""", """bar"""],
"""title""": ["""Foo""", """Bar"""],
"""embeddings""": [np.ones(self.retrieval_vector_size + 1 ), 2 * np.ones(self.retrieval_vector_size + 1 )],
} )
dataset.add_faiss_index("""embeddings""" ,string_factory="""Flat""" ,metric_type=faiss.METRIC_INNER_PRODUCT )
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,"""hf_bert_base.hnswSQ8_correct_phi_128.c_index""" )
dataset.save_faiss_index("""embeddings""" ,index_file_name + """.index.dpr""" )
pickle.dump(dataset["""id"""] ,open(index_file_name + """.index_meta.dpr""" ,"""wb""" ) )
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,"""psgs_w100.tsv.pkl""" )
SCREAMING_SNAKE_CASE = {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
pickle.dump(lowerCamelCase__ ,open(lowerCamelCase__ ,"""wb""" ) )
SCREAMING_SNAKE_CASE = RagConfig(
retrieval_vector_size=self.retrieval_vector_size ,question_encoder=DPRConfig().to_dict() ,generator=BartConfig().to_dict() ,index_name="""legacy""" ,index_path=self.tmpdirname ,)
SCREAMING_SNAKE_CASE = RagRetriever(
lowerCamelCase__ ,question_encoder_tokenizer=self.get_dpr_tokenizer() ,generator_tokenizer=self.get_bart_tokenizer() )
return retriever
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = self.get_dummy_canonical_hf_index_retriever()
SCREAMING_SNAKE_CASE = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = retriever.retrieve(lowerCamelCase__ ,n_docs=lowerCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCamelCase__ ) ,2 )
self.assertEqual(sorted(doc_dicts[0] ) ,["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) ,lowerCamelCase__ )
self.assertEqual(doc_dicts[0]["""id"""][0] ,"""1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] ,"""0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() ,[[1], [0]] )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_canonical_hf_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
with patch("""transformers.models.rag.retrieval_rag.load_dataset""" ) as mock_load_dataset:
SCREAMING_SNAKE_CASE = self.get_dummy_dataset()
retriever.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = RagRetriever.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
SCREAMING_SNAKE_CASE = retriever.retrieve(lowerCamelCase__ ,n_docs=1 )
self.assertTrue(out is not None )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = retriever.retrieve(lowerCamelCase__ ,n_docs=lowerCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCamelCase__ ) ,2 )
self.assertEqual(sorted(doc_dicts[0] ) ,["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) ,lowerCamelCase__ )
self.assertEqual(doc_dicts[0]["""id"""][0] ,"""1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] ,"""0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() ,[[1], [0]] )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = RagRetriever.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
SCREAMING_SNAKE_CASE = retriever.retrieve(lowerCamelCase__ ,n_docs=1 )
self.assertTrue(out is not None )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = retriever.retrieve(lowerCamelCase__ ,n_docs=lowerCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCamelCase__ ) ,2 )
self.assertEqual(sorted(doc_dicts[0] ) ,["""embeddings""", """id""", """text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""id"""] ) ,lowerCamelCase__ )
self.assertEqual(doc_dicts[0]["""id"""][0] ,"""1""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""id"""][0] ,"""0""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() ,[[1], [0]] )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase__ )
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = RagRetriever.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
SCREAMING_SNAKE_CASE = retriever.retrieve(lowerCamelCase__ ,n_docs=1 )
self.assertTrue(out is not None )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = self.get_dummy_legacy_index_retriever()
SCREAMING_SNAKE_CASE = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = retriever.retrieve(lowerCamelCase__ ,n_docs=lowerCamelCase__ )
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertEqual(len(lowerCamelCase__ ) ,2 )
self.assertEqual(sorted(doc_dicts[0] ) ,["""text""", """title"""] )
self.assertEqual(len(doc_dicts[0]["""text"""] ) ,lowerCamelCase__ )
self.assertEqual(doc_dicts[0]["""text"""][0] ,"""bar""" ) # max inner product is reached with second doc
self.assertEqual(doc_dicts[1]["""text"""][0] ,"""foo""" ) # max inner product is reached with first doc
self.assertListEqual(doc_ids.tolist() ,[[1], [0]] )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dummy_legacy_index_retriever()
with tempfile.TemporaryDirectory() as tmp_dirname:
retriever.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = RagRetriever.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
SCREAMING_SNAKE_CASE = retriever.retrieve(lowerCamelCase__ ,n_docs=1 )
self.assertTrue(out is not None )
@require_torch
@require_tokenizers
@require_sentencepiece
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
import torch
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = self.get_dummy_canonical_hf_index_retriever()
SCREAMING_SNAKE_CASE = [[5, 7], [10, 11]]
SCREAMING_SNAKE_CASE = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
SCREAMING_SNAKE_CASE = retriever(lowerCamelCase__ ,lowerCamelCase__ ,prefix=retriever.config.generator.prefix ,n_docs=lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = (
out["""context_input_ids"""],
out["""context_attention_mask"""],
out["""retrieved_doc_embeds"""],
)
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,np.ndarray )
SCREAMING_SNAKE_CASE = retriever(
lowerCamelCase__ ,lowerCamelCase__ ,prefix=retriever.config.generator.prefix ,n_docs=lowerCamelCase__ ,return_tensors="""pt""" ,)
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = ( # noqa: F841
out["""context_input_ids"""],
out["""context_attention_mask"""],
out["""retrieved_doc_embeds"""],
out["""doc_ids"""],
)
self.assertEqual(retrieved_doc_embeds.shape ,(2, n_docs, self.retrieval_vector_size) )
self.assertIsInstance(lowerCamelCase__ ,torch.Tensor )
self.assertIsInstance(lowerCamelCase__ ,torch.Tensor )
self.assertIsInstance(lowerCamelCase__ ,torch.Tensor )
@require_torch
@require_tokenizers
@require_sentencepiece
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_dpr_ctx_encoder_tokenizer()
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = self.get_dummy_custom_hf_index_retriever(from_disk=lowerCamelCase__ )
retriever.set_ctx_encoder_tokenizer(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = [[5, 7], [10, 11]]
SCREAMING_SNAKE_CASE = np.array(
[np.ones(self.retrieval_vector_size ), -np.ones(self.retrieval_vector_size )] ,dtype=np.floataa )
SCREAMING_SNAKE_CASE = retriever(lowerCamelCase__ ,lowerCamelCase__ ,prefix=retriever.config.generator.prefix ,n_docs=lowerCamelCase__ )
self.assertEqual(
len(lowerCamelCase__ ) ,6 ) # check whether the retriever output consist of 6 attributes including tokenized docs
self.assertEqual(
all(k in out for k in ("""tokenized_doc_ids""", """tokenized_doc_attention_mask""") ) ,lowerCamelCase__ ) # check for doc token related keys in dictionary.
| 296 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __lowercase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
SCREAMING_SNAKE_CASE = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
# Let's go
SCREAMING_SNAKE_CASE = parser.parse_args()
if not hasattr(_SCREAMING_SNAKE_CASE , """func""" ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE = args.func(_SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 296 | 1 |
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {"""vocab_file""": """spiece.model"""}
SCREAMING_SNAKE_CASE_ = {
"""vocab_file""": {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model""",
"""xlnet-large-cased""": """https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model""",
}
}
SCREAMING_SNAKE_CASE_ = {
"""xlnet-base-cased""": None,
"""xlnet-large-cased""": None,
}
# Segments (not really needed)
SCREAMING_SNAKE_CASE_ = 0
SCREAMING_SNAKE_CASE_ = 1
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = 3
SCREAMING_SNAKE_CASE_ = 4
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Any = VOCAB_FILES_NAMES
__snake_case : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
__snake_case : str = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case : int = "left"
def __init__( self : Optional[int] ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : str=False ,lowerCamelCase__ : Any=True ,lowerCamelCase__ : Any=False ,lowerCamelCase__ : Union[str, Any]="<s>" ,lowerCamelCase__ : int="</s>" ,lowerCamelCase__ : Any="<unk>" ,lowerCamelCase__ : str="<sep>" ,lowerCamelCase__ : Tuple="<pad>" ,lowerCamelCase__ : Optional[int]="<cls>" ,lowerCamelCase__ : Any="<mask>" ,lowerCamelCase__ : Union[str, Any]=["<eop>", "<eod>"] ,lowerCamelCase__ : Optional[Dict[str, Any]] = None ,**lowerCamelCase__ : Union[str, Any] ,) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else mask_token
SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowerCamelCase__ ,remove_space=lowerCamelCase__ ,keep_accents=lowerCamelCase__ ,bos_token=lowerCamelCase__ ,eos_token=lowerCamelCase__ ,unk_token=lowerCamelCase__ ,sep_token=lowerCamelCase__ ,pad_token=lowerCamelCase__ ,cls_token=lowerCamelCase__ ,mask_token=lowerCamelCase__ ,additional_special_tokens=lowerCamelCase__ ,sp_model_kwargs=self.sp_model_kwargs ,**lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = 3
SCREAMING_SNAKE_CASE = do_lower_case
SCREAMING_SNAKE_CASE = remove_space
SCREAMING_SNAKE_CASE = keep_accents
SCREAMING_SNAKE_CASE = vocab_file
SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCamelCase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[str]:
'''simple docstring'''
return len(self.sp_model )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(lowerCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : Dict ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.__dict__.copy()
SCREAMING_SNAKE_CASE = None
return state
def __setstate__( self : List[str] ,lowerCamelCase__ : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self ,"""sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,lowerCamelCase__ : int ) -> Any:
'''simple docstring'''
if self.remove_space:
SCREAMING_SNAKE_CASE = """ """.join(inputs.strip().split() )
else:
SCREAMING_SNAKE_CASE = inputs
SCREAMING_SNAKE_CASE = outputs.replace("""``""" ,"""\"""" ).replace("""''""" ,"""\"""" )
if not self.keep_accents:
SCREAMING_SNAKE_CASE = unicodedata.normalize("""NFKD""" ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """""".join([c for c in outputs if not unicodedata.combining(lowerCamelCase__ )] )
if self.do_lower_case:
SCREAMING_SNAKE_CASE = outputs.lower()
return outputs
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : str ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.preprocess_text(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.sp_model.encode(lowerCamelCase__ ,out_type=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = []
for piece in pieces:
if len(lowerCamelCase__ ) > 1 and piece[-1] == str(""",""" ) and piece[-2].isdigit():
SCREAMING_SNAKE_CASE = self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCamelCase__ ,"""""" ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
SCREAMING_SNAKE_CASE = cur_pieces[1:]
else:
SCREAMING_SNAKE_CASE = cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(lowerCamelCase__ )
else:
new_pieces.append(lowerCamelCase__ )
return new_pieces
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : Any ) -> Union[str, Any]:
'''simple docstring'''
return self.sp_model.PieceToId(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
return self.sp_model.IdToPiece(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : str ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """""".join(lowerCamelCase__ ).replace(lowerCamelCase__ ,""" """ ).strip()
return out_string
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : bool = True ,**lowerCamelCase__ : List[Any] ,) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = kwargs.pop("""use_source_tokenizer""" ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.convert_ids_to_tokens(lowerCamelCase__ ,skip_special_tokens=lowerCamelCase__ )
# To avoid mixing byte-level and unicode for byte-level BPT
# we need to build string separately for added tokens and byte-level tokens
# cf. https://github.com/huggingface/transformers/issues/1133
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for token in filtered_tokens:
if skip_special_tokens and token in self.all_special_ids:
continue
if token in self.added_tokens_encoder:
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowerCamelCase__ ) )
SCREAMING_SNAKE_CASE = []
sub_texts.append(lowerCamelCase__ )
else:
current_sub_text.append(lowerCamelCase__ )
if current_sub_text:
sub_texts.append(self.convert_tokens_to_string(lowerCamelCase__ ) )
# Mimic the behavior of the Rust tokenizer:
# By default, there are no spaces between special tokens
SCREAMING_SNAKE_CASE = """""".join(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = (
clean_up_tokenization_spaces
if clean_up_tokenization_spaces is not None
else self.clean_up_tokenization_spaces
)
if clean_up_tokenization_spaces:
SCREAMING_SNAKE_CASE = self.clean_up_tokenization(lowerCamelCase__ )
return clean_text
else:
return text
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ,lowerCamelCase__ : bool = False ) -> List[int]:
'''simple docstring'''
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 not None:
return ([0] * len(lowerCamelCase__ )) + [1] + ([0] * len(lowerCamelCase__ )) + [1, 1]
return ([0] * len(lowerCamelCase__ )) + [1, 1]
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
SCREAMING_SNAKE_CASE = 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:
SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase__ )
return (out_vocab_file,)
| 296 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = SwinConfig()
SCREAMING_SNAKE_CASE = swin_name.split("""_""" )
SCREAMING_SNAKE_CASE = name_split[1]
SCREAMING_SNAKE_CASE = int(name_split[4] )
SCREAMING_SNAKE_CASE = int(name_split[3][-1] )
if model_size == "tiny":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 6, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "small":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "base":
SCREAMING_SNAKE_CASE = 1_28
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (4, 8, 16, 32)
else:
SCREAMING_SNAKE_CASE = 1_92
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (6, 12, 24, 48)
if "in22k" in swin_name:
SCREAMING_SNAKE_CASE = 2_18_41
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = img_size
SCREAMING_SNAKE_CASE = num_classes
SCREAMING_SNAKE_CASE = embed_dim
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = window_size
return config
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
SCREAMING_SNAKE_CASE = """encoder.""" + name
if "attn.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "norm.weight":
SCREAMING_SNAKE_CASE = """layernorm.weight"""
if name == "norm.bias":
SCREAMING_SNAKE_CASE = """layernorm.bias"""
if "head" in name:
SCREAMING_SNAKE_CASE = name.replace("""head""" , """classifier""" )
else:
SCREAMING_SNAKE_CASE = """swin.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[1] )
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[
:dim
]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
SCREAMING_SNAKE_CASE = get_swin_config(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = SwinForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
SCREAMING_SNAKE_CASE = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" , """-""" ) ) )
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
SCREAMING_SNAKE_CASE = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = timm_model(inputs["""pixel_values"""] )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}""" )
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__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swin_name""",
default="""swin_tiny_patch4_window7_224""",
type=str,
help="""Name of the Swin 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."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 296 | 1 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
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 TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(lowerCamelCase__ ,"""embed_dim""" ) )
self.parent.assertTrue(hasattr(lowerCamelCase__ ,"""num_heads""" ) )
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : Any ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Tuple=13 ,lowerCamelCase__ : Dict=64 ,lowerCamelCase__ : Union[str, Any]=3 ,lowerCamelCase__ : str=[16, 48, 96] ,lowerCamelCase__ : int=[1, 3, 6] ,lowerCamelCase__ : int=[1, 2, 10] ,lowerCamelCase__ : Tuple=[7, 3, 3] ,lowerCamelCase__ : Dict=[4, 2, 2] ,lowerCamelCase__ : str=[2, 1, 1] ,lowerCamelCase__ : Any=[2, 2, 2] ,lowerCamelCase__ : Optional[Any]=[False, False, True] ,lowerCamelCase__ : int=[0.0, 0.0, 0.0] ,lowerCamelCase__ : int=0.02 ,lowerCamelCase__ : Union[str, Any]=1e-1_2 ,lowerCamelCase__ : int=True ,lowerCamelCase__ : Union[str, Any]=True ,lowerCamelCase__ : Any=2 ,) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = patch_sizes
SCREAMING_SNAKE_CASE = patch_stride
SCREAMING_SNAKE_CASE = patch_padding
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = embed_dim
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = stride_kv
SCREAMING_SNAKE_CASE = depth
SCREAMING_SNAKE_CASE = cls_token
SCREAMING_SNAKE_CASE = attention_drop_rate
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = None
if self.use_labels:
# create a random int32 tensor of given shape
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] ,self.num_labels )
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values, labels
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
return CvtConfig(
image_size=self.image_size ,num_labels=self.num_labels ,num_channels=self.num_channels ,embed_dim=self.embed_dim ,num_heads=self.num_heads ,patch_sizes=self.patch_sizes ,patch_padding=self.patch_padding ,patch_stride=self.patch_stride ,stride_kv=self.stride_kv ,depth=self.depth ,cls_token=self.cls_token ,attention_drop_rate=self.attention_drop_rate ,initializer_range=self.initializer_range ,)
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Dict ,lowerCamelCase__ : int ,lowerCamelCase__ : List[str] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TFCvtModel(config=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,training=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = (self.image_size, self.image_size)
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
SCREAMING_SNAKE_CASE = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
SCREAMING_SNAKE_CASE = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.embed_dim[-1], height, width) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : List[str] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = TFCvtForImageClassification(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,labels=lowerCamelCase__ ,training=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_tf
class UpperCamelCase__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[str] = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
__snake_case : Optional[int] = (
{"feature-extraction": TFCvtModel, "image-classification": TFCvtForImageClassification}
if is_tf_available()
else {}
)
__snake_case : List[Any] = False
__snake_case : int = False
__snake_case : Optional[int] = False
__snake_case : int = False
__snake_case : List[Any] = False
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TFCvtModelTester(self )
SCREAMING_SNAKE_CASE = TFCvtConfigTester(self ,config_class=lowerCamelCase__ ,has_text_modality=lowerCamelCase__ ,hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.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()
@unittest.skip(reason="""Cvt does not output attentions""" )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""Cvt does not use inputs_embeds""" )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[str]:
'''simple docstring'''
pass
@unittest.skip(reason="""Cvt does not support input and output embeddings""" )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[int]:
'''simple docstring'''
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 ,reason="""TF does not support backprop for grouped convolutions on CPU.""" ,)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices("""GPU""" ) ) == 0 ,reason="""TF does not support backprop for grouped convolutions on CPU.""" ,)
@slow
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> List[str]:
'''simple docstring'''
super().test_keras_fit()
@unittest.skip(reason="""Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = tf.keras.mixed_precision.Policy("""mixed_float16""" )
tf.keras.mixed_precision.set_global_policy(lowerCamelCase__ )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy("""float32""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> int:
'''simple docstring'''
def check_hidden_states_output(lowerCamelCase__ : str ,lowerCamelCase__ : str ,lowerCamelCase__ : List[str] ):
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ ) )
SCREAMING_SNAKE_CASE = outputs.hidden_states
SCREAMING_SNAKE_CASE = len(self.model_tester.depth )
self.assertEqual(len(lowerCamelCase__ ) ,lowerCamelCase__ )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) ,[
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] ,)
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[int]:
'''simple docstring'''
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = TFCvtModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def __lowercase ( ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_tf
@require_vision
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=lowerCamelCase__ ,return_tensors="""tf""" )
# forward pass
SCREAMING_SNAKE_CASE = model(**lowerCamelCase__ )
# verify the logits
SCREAMING_SNAKE_CASE = tf.TensorShape((1, 1000) )
self.assertEqual(outputs.logits.shape ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tf.constant([0.9285, 0.9015, -0.3150] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() ,lowerCamelCase__ ,atol=1e-4 ) )
| 296 |
import os
from distutils.util import strtobool
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
for e in env_keys:
SCREAMING_SNAKE_CASE = int(os.environ.get(_SCREAMING_SNAKE_CASE , -1 ) )
if val >= 0:
return val
return default
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) )
return strtobool(_SCREAMING_SNAKE_CASE ) == 1 # As its name indicates `strtobool` actually returns an int...
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="no" ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) )
return value
| 296 | 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 UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Tuple = CanineTokenizer
__snake_case : Dict = False
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
super().setUp()
SCREAMING_SNAKE_CASE = CanineTokenizer()
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
return CanineTokenizer.from_pretrained("""google/canine-s""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,**lowerCamelCase__ : List[Any] ) -> CanineTokenizer:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.tokenizer_class.from_pretrained(self.tmpdirname ,**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 1024
return tokenizer
@require_torch
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.canine_tokenizer
SCREAMING_SNAKE_CASE = ["""Life is like a box of chocolates.""", """You never know what you're gonna get."""]
# fmt: off
SCREAMING_SNAKE_CASE = [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
SCREAMING_SNAKE_CASE = tokenizer(lowerCamelCase__ ,padding=lowerCamelCase__ ,return_tensors="""pt""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = list(batch.input_ids.numpy()[0] )
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
self.assertEqual((2, 39) ,batch.input_ids.shape )
self.assertEqual((2, 39) ,batch.attention_mask.shape )
@require_torch
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.canine_tokenizer
SCREAMING_SNAKE_CASE = ["""Once there was a man.""", """He wrote a test in HuggingFace Tranformers."""]
SCREAMING_SNAKE_CASE = tokenizer(lowerCamelCase__ ,padding=lowerCamelCase__ ,return_tensors="""pt""" )
# check if input_ids, attention_mask and token_type_ids are returned
self.assertIn("""input_ids""" ,lowerCamelCase__ )
self.assertIn("""attention_mask""" ,lowerCamelCase__ )
self.assertIn("""token_type_ids""" ,lowerCamelCase__ )
@require_torch
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.canine_tokenizer
SCREAMING_SNAKE_CASE = [
"""What's the weater?""",
"""It's about 25 degrees.""",
]
SCREAMING_SNAKE_CASE = tokenizer(
text_target=lowerCamelCase__ ,max_length=32 ,padding="""max_length""" ,truncation=lowerCamelCase__ ,return_tensors="""pt""" )
self.assertEqual(32 ,targets["""input_ids"""].shape[1] )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = """ He is very happy, UNwant\u00E9d,running"""
SCREAMING_SNAKE_CASE = tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
tokenizer.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer.__class__.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = after_tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
shutil.rmtree(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = """ He is very happy, UNwant\u00E9d,running"""
SCREAMING_SNAKE_CASE = tokenizer.additional_special_tokens
# We can add a new special token for Canine as follows:
SCREAMING_SNAKE_CASE = chr(0Xe_007 )
additional_special_tokens.append(lowerCamelCase__ )
tokenizer.add_special_tokens({"""additional_special_tokens""": additional_special_tokens} )
SCREAMING_SNAKE_CASE = tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
tokenizer.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer.__class__.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = after_tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
self.assertIn(lowerCamelCase__ ,after_tokenizer.additional_special_tokens )
self.assertEqual(after_tokenizer.model_max_length ,42 )
SCREAMING_SNAKE_CASE = tokenizer.__class__.from_pretrained(lowerCamelCase__ ,model_max_length=43 )
self.assertEqual(tokenizer.model_max_length ,43 )
shutil.rmtree(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_tokenizers(do_lower_case=lowerCamelCase__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.get_clean_sequence(lowerCamelCase__ )
# a special token for Canine can be defined as follows:
SCREAMING_SNAKE_CASE = 0Xe_005
SCREAMING_SNAKE_CASE = chr(lowerCamelCase__ )
tokenizer.add_special_tokens({"""cls_token""": special_token} )
SCREAMING_SNAKE_CASE = tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
self.assertEqual(len(lowerCamelCase__ ) ,1 )
SCREAMING_SNAKE_CASE = tokenizer.decode(ids + encoded_special_token ,clean_up_tokenization_spaces=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
self.assertEqual(lowerCamelCase__ ,input_encoded + special_token_id )
SCREAMING_SNAKE_CASE = tokenizer.decode(lowerCamelCase__ ,skip_special_tokens=lowerCamelCase__ )
self.assertTrue(special_token not in decoded )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_tokenizers(do_lower_case=lowerCamelCase__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
SCREAMING_SNAKE_CASE = chr(0Xe_005 )
SCREAMING_SNAKE_CASE = chr(0Xe_006 )
# `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=lowerCamelCase__ )
# `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]} )
SCREAMING_SNAKE_CASE = tokenizer.tokenize(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer.tokenize(lowerCamelCase__ )
self.assertEqual(len(lowerCamelCase__ ) ,1 )
self.assertEqual(len(lowerCamelCase__ ) ,1 )
self.assertEqual(token_a[0] ,lowerCamelCase__ )
self.assertEqual(token_a[0] ,lowerCamelCase__ )
@require_tokenizers
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_tokenizers(do_lower_case=lowerCamelCase__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
# a special token for Canine can be defined as follows:
SCREAMING_SNAKE_CASE = 0Xe_006
SCREAMING_SNAKE_CASE = chr(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ )
tokenizer.add_special_tokens({"""additional_special_tokens""": [new_token]} )
with tempfile.TemporaryDirectory() as tmp_dir_name:
tokenizer.save_pretrained(lowerCamelCase__ )
tokenizer.from_pretrained(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
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(lowerCamelCase__ )
with open(os.path.join(lowerCamelCase__ ,"""special_tokens_map.json""" ) ,encoding="""utf-8""" ) as json_file:
SCREAMING_SNAKE_CASE = json.load(lowerCamelCase__ )
with open(os.path.join(lowerCamelCase__ ,"""tokenizer_config.json""" ) ,encoding="""utf-8""" ) as json_file:
SCREAMING_SNAKE_CASE = json.load(lowerCamelCase__ )
# a special token for Canine can be defined as follows:
SCREAMING_SNAKE_CASE = 0Xe_006
SCREAMING_SNAKE_CASE = chr(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = [new_token_a]
SCREAMING_SNAKE_CASE = [new_token_a]
with open(os.path.join(lowerCamelCase__ ,"""special_tokens_map.json""" ) ,"""w""" ,encoding="""utf-8""" ) as outfile:
json.dump(lowerCamelCase__ ,lowerCamelCase__ )
with open(os.path.join(lowerCamelCase__ ,"""tokenizer_config.json""" ) ,"""w""" ,encoding="""utf-8""" ) as outfile:
json.dump(lowerCamelCase__ ,lowerCamelCase__ )
# 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
SCREAMING_SNAKE_CASE = tokenizer_class.from_pretrained(lowerCamelCase__ ,extra_ids=0 )
self.assertIn(lowerCamelCase__ ,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] ) ) ,)
SCREAMING_SNAKE_CASE = 0Xe_007
SCREAMING_SNAKE_CASE = chr(lowerCamelCase__ )
# Now we test that we can change the value of additional_special_tokens in the from_pretrained
SCREAMING_SNAKE_CASE = [AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ )]
SCREAMING_SNAKE_CASE = tokenizer_class.from_pretrained(
lowerCamelCase__ ,additional_special_tokens=lowerCamelCase__ ,extra_ids=0 )
self.assertIn(lowerCamelCase__ ,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 SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_tokenizers(do_lower_case=lowerCamelCase__ )
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
SCREAMING_SNAKE_CASE = """hello world"""
if self.space_between_special_tokens:
SCREAMING_SNAKE_CASE = """[CLS] hello world [SEP]"""
else:
SCREAMING_SNAKE_CASE = input
SCREAMING_SNAKE_CASE = tokenizer.encode(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer.decode(lowerCamelCase__ ,spaces_between_special_tokens=self.space_between_special_tokens )
self.assertIn(lowerCamelCase__ ,[output, output.lower()] )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F"""{tokenizer.__class__.__name__}""" ):
SCREAMING_SNAKE_CASE = [
"""bos_token""",
"""eos_token""",
"""unk_token""",
"""sep_token""",
"""pad_token""",
"""cls_token""",
"""mask_token""",
]
SCREAMING_SNAKE_CASE = """a"""
SCREAMING_SNAKE_CASE = ord(lowerCamelCase__ )
for attr in attributes_list:
setattr(lowerCamelCase__ ,attr + """_id""" ,lowerCamelCase__ )
self.assertEqual(getattr(lowerCamelCase__ ,lowerCamelCase__ ) ,lowerCamelCase__ )
self.assertEqual(getattr(lowerCamelCase__ ,attr + """_id""" ) ,lowerCamelCase__ )
setattr(lowerCamelCase__ ,attr + """_id""" ,lowerCamelCase__ )
self.assertEqual(getattr(lowerCamelCase__ ,lowerCamelCase__ ) ,lowerCamelCase__ )
self.assertEqual(getattr(lowerCamelCase__ ,attr + """_id""" ) ,lowerCamelCase__ )
setattr(lowerCamelCase__ ,"""additional_special_tokens_ids""" ,[] )
self.assertListEqual(getattr(lowerCamelCase__ ,"""additional_special_tokens""" ) ,[] )
self.assertListEqual(getattr(lowerCamelCase__ ,"""additional_special_tokens_ids""" ) ,[] )
SCREAMING_SNAKE_CASE = 0Xe_006
SCREAMING_SNAKE_CASE = chr(lowerCamelCase__ )
setattr(lowerCamelCase__ ,"""additional_special_tokens_ids""" ,[additional_special_token_id] )
self.assertListEqual(getattr(lowerCamelCase__ ,"""additional_special_tokens""" ) ,[additional_special_token] )
self.assertListEqual(getattr(lowerCamelCase__ ,"""additional_special_tokens_ids""" ) ,[additional_special_token_id] )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Any:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> List[str]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Any:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[str]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
pass
| 296 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __lowercase ( _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> List[str]:
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
__snake_case : List[str] = list_field(
default=[] , metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
} , )
__snake_case : List[int] = list_field(
default=[8] , metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
__snake_case : List[int] = list_field(
default=[8, 32, 128, 512] , metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Use FP16 to accelerate inference."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Benchmark training of model"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Verbose memory tracing"} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
} , )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Trace memory line by line"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Save result to a CSV file"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Save all print statements in a log file"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Whether to print environment information"} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
} , )
__snake_case : str = field(
default=F"inference_time_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving time results to csv."} , )
__snake_case : str = field(
default=F"inference_memory_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving memory results to csv."} , )
__snake_case : str = field(
default=F"train_time_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving time results to csv for training."} , )
__snake_case : str = field(
default=F"train_memory_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving memory results to csv for training."} , )
__snake_case : str = field(
default=F"env_info_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving environment information."} , )
__snake_case : str = field(
default=F"log_{round(time() )}.csv" , metadata={"help": "Log filename used if print statements are saved in log."} , )
__snake_case : int = field(default=3 , metadata={"help": "Times an experiment will be run."} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
} , )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
""" are deprecated in general and it is advised to use external Benchmarking libraries """
""" to benchmark Transformer models.""" ,lowerCamelCase__ ,)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return json.dumps(dataclasses.asdict(self ) ,indent=2 )
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
if len(self.models ) <= 0:
raise ValueError(
"""Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
""" bert-base-cased` or `args.models = ['bert-base-cased'].""" )
return self.models
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> int:
'''simple docstring'''
if not self.multi_process:
return False
elif self.is_tpu:
logger.info("""Multiprocessing is currently not possible on TPU.""" )
return False
else:
return True
| 296 | 1 |
import inspect
import unittest
from huggingface_hub import hf_hub_download
from transformers import ASTConfig
from transformers.testing_utils import require_torch, require_torchaudio, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_torchaudio_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import ASTForAudioClassification, ASTModel
from transformers.models.audio_spectrogram_transformer.modeling_audio_spectrogram_transformer import (
AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
)
if is_torchaudio_available():
import torchaudio
from transformers import ASTFeatureExtractor
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : int ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Tuple=13 ,lowerCamelCase__ : Optional[int]=2 ,lowerCamelCase__ : Any=24 ,lowerCamelCase__ : int=16 ,lowerCamelCase__ : Optional[int]=True ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : Union[str, Any]=32 ,lowerCamelCase__ : List[Any]=5 ,lowerCamelCase__ : Any=4 ,lowerCamelCase__ : List[Any]=37 ,lowerCamelCase__ : Optional[int]="gelu" ,lowerCamelCase__ : List[Any]=0.1 ,lowerCamelCase__ : Optional[Any]=0.1 ,lowerCamelCase__ : str=10 ,lowerCamelCase__ : int=0.02 ,lowerCamelCase__ : Tuple=None ,lowerCamelCase__ : str=2 ,lowerCamelCase__ : int=2 ,) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = max_length
SCREAMING_SNAKE_CASE = num_mel_bins
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = type_sequence_label_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = scope
SCREAMING_SNAKE_CASE = frequency_stride
SCREAMING_SNAKE_CASE = time_stride
# in AST, the seq length equals the number of patches + 2 (we add 2 for the [CLS] and distillation tokens)
SCREAMING_SNAKE_CASE = (self.num_mel_bins - self.patch_size) // self.frequency_stride + 1
SCREAMING_SNAKE_CASE = (self.max_length - self.patch_size) // self.time_stride + 1
SCREAMING_SNAKE_CASE = frequency_out_dimension * time_out_dimension
SCREAMING_SNAKE_CASE = num_patches + 2
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.max_length, self.num_mel_bins] )
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE = self.get_config()
return config, input_values, labels
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
'''simple docstring'''
return ASTConfig(
patch_size=self.patch_size ,max_length=self.max_length ,num_mel_bins=self.num_mel_bins ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,is_decoder=lowerCamelCase__ ,initializer_range=self.initializer_range ,frequency_stride=self.frequency_stride ,time_stride=self.time_stride ,)
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : int ,lowerCamelCase__ : Any ,lowerCamelCase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ASTModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
(
(
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
), (
SCREAMING_SNAKE_CASE
),
) = config_and_inputs
SCREAMING_SNAKE_CASE = {"""input_values""": input_values}
return config, inputs_dict
@require_torch
class UpperCamelCase__ ( lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Optional[Any] = (
(
ASTModel,
ASTForAudioClassification,
)
if is_torch_available()
else ()
)
__snake_case : Optional[Any] = (
{"audio-classification": ASTForAudioClassification, "feature-extraction": ASTModel}
if is_torch_available()
else {}
)
__snake_case : Tuple = False
__snake_case : List[str] = False
__snake_case : Optional[Any] = False
__snake_case : Optional[Any] = False
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Any ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Any ) -> Tuple:
'''simple docstring'''
if pipeline_test_casse_name == "AudioClassificationPipelineTests":
return True
return False
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ASTModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self ,config_class=lowerCamelCase__ ,has_text_modality=lowerCamelCase__ ,hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Tuple:
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="""AST does not use inputs_embeds""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
SCREAMING_SNAKE_CASE = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowerCamelCase__ ,nn.Linear ) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["""input_values"""]
self.assertListEqual(arg_names[:1] ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Tuple:
'''simple docstring'''
for model_name in AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
SCREAMING_SNAKE_CASE = ASTModel.from_pretrained(lowerCamelCase__ )
self.assertIsNotNone(lowerCamelCase__ )
def __lowercase ( ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = hf_hub_download(
repo_id="""nielsr/audio-spectogram-transformer-checkpoint""" , filename="""sample_audio.flac""" , repo_type="""dataset""" )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = torchaudio.load(_SCREAMING_SNAKE_CASE )
return audio, sampling_rate
@require_torch
@require_torchaudio
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> str:
'''simple docstring'''
return (
ASTFeatureExtractor.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" )
if is_torchaudio_available()
else None
)
@slow
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.default_feature_extractor
SCREAMING_SNAKE_CASE = ASTForAudioClassification.from_pretrained("""MIT/ast-finetuned-audioset-10-10-0.4593""" ).to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.default_feature_extractor
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = prepare_audio()
SCREAMING_SNAKE_CASE = audio.squeeze().numpy()
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,sampling_rate=lowerCamelCase__ ,return_tensors="""pt""" ).to(lowerCamelCase__ )
# forward pass
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(**lowerCamelCase__ )
# verify the logits
SCREAMING_SNAKE_CASE = torch.Size((1, 527) )
self.assertEqual(outputs.logits.shape ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.tensor([-0.8760, -7.0042, -8.6602] ).to(lowerCamelCase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,lowerCamelCase__ ,atol=1e-4 ) )
| 296 |
import math
import unittest
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Tuple:
'''simple docstring'''
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) ,"""Zero doesn't have any positive factors, primes must have exactly two.""" ,)
self.assertFalse(
is_prime(1 ) ,"""One only has 1 positive factor, primes must have exactly two.""" ,)
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 296 | 1 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = "efficientformer"
def __init__( self : Optional[int] ,lowerCamelCase__ : List[int] = [3, 2, 6, 4] ,lowerCamelCase__ : List[int] = [48, 96, 224, 448] ,lowerCamelCase__ : List[bool] = [True, True, True, True] ,lowerCamelCase__ : int = 448 ,lowerCamelCase__ : int = 32 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : int = 7 ,lowerCamelCase__ : int = 5 ,lowerCamelCase__ : int = 8 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 16 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 2 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : float = 1e-5 ,lowerCamelCase__ : str = "gelu" ,lowerCamelCase__ : float = 0.02 ,lowerCamelCase__ : float = 1e-1_2 ,lowerCamelCase__ : int = 224 ,lowerCamelCase__ : float = 1e-0_5 ,**lowerCamelCase__ : str ,) -> None:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = mlp_expansion_ratio
SCREAMING_SNAKE_CASE = downsamples
SCREAMING_SNAKE_CASE = dim
SCREAMING_SNAKE_CASE = key_dim
SCREAMING_SNAKE_CASE = attention_ratio
SCREAMING_SNAKE_CASE = resolution
SCREAMING_SNAKE_CASE = pool_size
SCREAMING_SNAKE_CASE = downsample_patch_size
SCREAMING_SNAKE_CASE = downsample_stride
SCREAMING_SNAKE_CASE = downsample_pad
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = num_metaad_blocks
SCREAMING_SNAKE_CASE = distillation
SCREAMING_SNAKE_CASE = use_layer_scale
SCREAMING_SNAKE_CASE = layer_scale_init_value
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = batch_norm_eps
| 296 |
import random
class UpperCamelCase__ :
'''simple docstring'''
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : str ) -> tuple[list[int], list[int]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [ord(lowerCamelCase__ ) for i in text]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i in plain:
SCREAMING_SNAKE_CASE = random.randint(1 ,300 )
SCREAMING_SNAKE_CASE = (i + k) * k
cipher.append(lowerCamelCase__ )
key.append(lowerCamelCase__ )
return cipher, key
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : list[int] ,lowerCamelCase__ : list[int] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
for i in range(len(lowerCamelCase__ ) ):
SCREAMING_SNAKE_CASE = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowerCamelCase__ ) )
return "".join(lowerCamelCase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 296 | 1 |
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.models.regnet.modeling_flax_regnet import FlaxRegNetForImageClassification, FlaxRegNetModel
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Any ,lowerCamelCase__ : str ,lowerCamelCase__ : Union[str, Any]=3 ,lowerCamelCase__ : str=32 ,lowerCamelCase__ : Optional[Any]=3 ,lowerCamelCase__ : Dict=10 ,lowerCamelCase__ : Optional[Any]=[10, 20, 30, 40] ,lowerCamelCase__ : Dict=[1, 1, 2, 1] ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : List[str]="relu" ,lowerCamelCase__ : str=3 ,lowerCamelCase__ : Union[str, Any]=None ,) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = embeddings_size
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = scope
SCREAMING_SNAKE_CASE = len(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
SCREAMING_SNAKE_CASE = self.get_config()
return config, pixel_values
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
return RegNetConfig(
num_channels=self.num_channels ,embeddings_size=self.embeddings_size ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,hidden_act=self.hidden_act ,num_labels=self.num_labels ,image_size=self.image_size ,)
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Optional[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = FlaxRegNetModel(config=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )
# Output shape (b, c, h, w)
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32) ,)
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Union[str, Any] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = FlaxRegNetForImageClassification(config=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_flax
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Optional[Any] = (FlaxRegNetModel, FlaxRegNetForImageClassification) if is_flax_available() else ()
__snake_case : int = False
__snake_case : Optional[Any] = False
__snake_case : List[str] = False
def SCREAMING_SNAKE_CASE__ ( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = FlaxRegNetModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self ,config_class=lowerCamelCase__ ,has_text_modality=lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
return
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowerCamelCase__ )
@unittest.skip(reason="""RegNet does not use inputs_embeds""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> str:
'''simple docstring'''
pass
@unittest.skip(reason="""RegNet does not support input and output embeddings""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> int:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = inspect.signature(model.__call__ )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
SCREAMING_SNAKE_CASE = [*signature.parameters.keys()]
SCREAMING_SNAKE_CASE = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
def check_hidden_states_output(lowerCamelCase__ : Any ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Dict ):
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(**self._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ ) )
SCREAMING_SNAKE_CASE = outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states
SCREAMING_SNAKE_CASE = self.model_tester.num_stages
self.assertEqual(len(lowerCamelCase__ ) ,expected_num_stages + 1 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
SCREAMING_SNAKE_CASE = True
check_hidden_states_output(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
with self.subTest(model_class.__name__ ):
SCREAMING_SNAKE_CASE = self._prepare_for_class(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model_class(lowerCamelCase__ )
@jax.jit
def model_jitted(lowerCamelCase__ : Tuple ,**lowerCamelCase__ : Dict ):
return model(pixel_values=lowerCamelCase__ ,**lowerCamelCase__ )
with self.subTest("""JIT Enabled""" ):
SCREAMING_SNAKE_CASE = model_jitted(**lowerCamelCase__ ).to_tuple()
with self.subTest("""JIT Disabled""" ):
with jax.disable_jit():
SCREAMING_SNAKE_CASE = model_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 __lowercase ( ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_flax
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
return AutoImageProcessor.from_pretrained("""facebook/regnet-y-040""" ) if is_vision_available() else None
@slow
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = FlaxRegNetForImageClassification.from_pretrained("""facebook/regnet-y-040""" )
SCREAMING_SNAKE_CASE = self.default_image_processor
SCREAMING_SNAKE_CASE = prepare_img()
SCREAMING_SNAKE_CASE = image_processor(images=lowerCamelCase__ ,return_tensors="""np""" )
SCREAMING_SNAKE_CASE = model(**lowerCamelCase__ )
# verify the logits
SCREAMING_SNAKE_CASE = (1, 1000)
self.assertEqual(outputs.logits.shape ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = jnp.array([-0.4180, -1.5051, -3.4836] )
self.assertTrue(jnp.allclose(outputs.logits[0, :3] ,lowerCamelCase__ ,atol=1e-4 ) )
| 296 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = "efficientformer"
def __init__( self : Optional[int] ,lowerCamelCase__ : List[int] = [3, 2, 6, 4] ,lowerCamelCase__ : List[int] = [48, 96, 224, 448] ,lowerCamelCase__ : List[bool] = [True, True, True, True] ,lowerCamelCase__ : int = 448 ,lowerCamelCase__ : int = 32 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : int = 7 ,lowerCamelCase__ : int = 5 ,lowerCamelCase__ : int = 8 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 16 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 2 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : float = 1e-5 ,lowerCamelCase__ : str = "gelu" ,lowerCamelCase__ : float = 0.02 ,lowerCamelCase__ : float = 1e-1_2 ,lowerCamelCase__ : int = 224 ,lowerCamelCase__ : float = 1e-0_5 ,**lowerCamelCase__ : str ,) -> None:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = mlp_expansion_ratio
SCREAMING_SNAKE_CASE = downsamples
SCREAMING_SNAKE_CASE = dim
SCREAMING_SNAKE_CASE = key_dim
SCREAMING_SNAKE_CASE = attention_ratio
SCREAMING_SNAKE_CASE = resolution
SCREAMING_SNAKE_CASE = pool_size
SCREAMING_SNAKE_CASE = downsample_patch_size
SCREAMING_SNAKE_CASE = downsample_stride
SCREAMING_SNAKE_CASE = downsample_pad
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = num_metaad_blocks
SCREAMING_SNAKE_CASE = distillation
SCREAMING_SNAKE_CASE = use_layer_scale
SCREAMING_SNAKE_CASE = layer_scale_init_value
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = batch_norm_eps
| 296 | 1 |
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[int] = "vision-encoder-decoder"
__snake_case : Union[str, Any] = True
def __init__( self : List[Any] ,**lowerCamelCase__ : Dict ) -> Any:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
F"""A configuraton of type {self.model_type} cannot be instantiated because """
F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}""" )
SCREAMING_SNAKE_CASE = kwargs.pop("""encoder""" )
SCREAMING_SNAKE_CASE = encoder_config.pop("""model_type""" )
SCREAMING_SNAKE_CASE = kwargs.pop("""decoder""" )
SCREAMING_SNAKE_CASE = decoder_config.pop("""model_type""" )
SCREAMING_SNAKE_CASE = AutoConfig.for_model(lowerCamelCase__ ,**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.for_model(lowerCamelCase__ ,**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = True
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Optional[Any] ,lowerCamelCase__ : PretrainedConfig ,lowerCamelCase__ : PretrainedConfig ,**lowerCamelCase__ : Tuple ) -> PretrainedConfig:
'''simple docstring'''
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
return cls(encoder=encoder_config.to_dict() ,decoder=decoder_config.to_dict() ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE = self.encoder.to_dict()
SCREAMING_SNAKE_CASE = self.decoder.to_dict()
SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Dict = version.parse("1.11" )
@property
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> float:
'''simple docstring'''
return 1e-4
@property
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = OrderedDict()
SCREAMING_SNAKE_CASE = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
SCREAMING_SNAKE_CASE = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
SCREAMING_SNAKE_CASE = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : "PreTrainedTokenizerBase" ,lowerCamelCase__ : int = -1 ,lowerCamelCase__ : int = -1 ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : Optional["TensorType"] = None ,) -> Mapping[str, Any]:
'''simple docstring'''
import torch
SCREAMING_SNAKE_CASE = OrderedDict()
SCREAMING_SNAKE_CASE = super().generate_dummy_inputs(
lowerCamelCase__ ,batch_size=lowerCamelCase__ ,seq_length=lowerCamelCase__ ,is_pair=lowerCamelCase__ ,framework=lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = dummy_input["""input_ids"""].shape
SCREAMING_SNAKE_CASE = (batch, encoder_sequence, self._config.encoder_hidden_size)
SCREAMING_SNAKE_CASE = dummy_input.pop("""input_ids""" )
SCREAMING_SNAKE_CASE = dummy_input.pop("""attention_mask""" )
SCREAMING_SNAKE_CASE = torch.zeros(lowerCamelCase__ )
return common_inputs
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> None:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : PretrainedConfig ) -> OnnxConfig:
'''simple docstring'''
return VisionEncoderDecoderEncoderOnnxConfig(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : PretrainedConfig ,lowerCamelCase__ : PretrainedConfig ,lowerCamelCase__ : str = "default" ) -> OnnxConfig:
'''simple docstring'''
SCREAMING_SNAKE_CASE = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(lowerCamelCase__ ,lowerCamelCase__ )
| 296 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE_ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
if "://" in dataset_path:
SCREAMING_SNAKE_CASE = dataset_path.split("""://""" )[1]
return dataset_path
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
if fs is not None and fs.protocol != "file":
return True
else:
return False
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = not is_remote_filesystem(_SCREAMING_SNAKE_CASE )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(_SCREAMING_SNAKE_CASE ) , fs._strip_protocol(_SCREAMING_SNAKE_CASE ) )
else:
fs.mv(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , recursive=_SCREAMING_SNAKE_CASE )
def __lowercase ( ) -> None:
'''simple docstring'''
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = threading.Lock()
| 296 | 1 |
from PIL import Image
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Image:
'''simple docstring'''
def brightness(_SCREAMING_SNAKE_CASE ) -> float:
return 1_28 + level + (c - 1_28)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
# Load image
with Image.open("""image_data/lena.jpg""") as img:
# Change brightness to 100
SCREAMING_SNAKE_CASE_ = change_brightness(img, 1_0_0)
brigt_img.save("""image_data/lena_brightness.png""", format="""png""")
| 296 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,)
return model
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE = KarrasVeScheduler()
SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""numpy""" ,return_dict=lowerCamelCase__ )[0]
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """google/ncsnpp-celebahq-256"""
SCREAMING_SNAKE_CASE = UNetaDModel.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = KarrasVeScheduler()
SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=20 ,generator=lowerCamelCase__ ,output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 296 | 1 |
def __lowercase ( _SCREAMING_SNAKE_CASE = 10**12 ) -> int:
'''simple docstring'''
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() = }''')
| 296 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,lowerCamelCase__ : Callable ,lowerCamelCase__ : Optional[Features] = None ,lowerCamelCase__ : str = None ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : Optional[dict] = None ,lowerCamelCase__ : Optional[int] = None ,**lowerCamelCase__ : Optional[Any] ,) -> List[str]:
'''simple docstring'''
super().__init__(
features=lowerCamelCase__ ,cache_dir=lowerCamelCase__ ,keep_in_memory=lowerCamelCase__ ,streaming=lowerCamelCase__ ,num_proc=lowerCamelCase__ ,**lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = Generator(
cache_dir=lowerCamelCase__ ,features=lowerCamelCase__ ,generator=lowerCamelCase__ ,gen_kwargs=lowerCamelCase__ ,**lowerCamelCase__ ,)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
if self.streaming:
SCREAMING_SNAKE_CASE = self.builder.as_streaming_dataset(split="""train""" )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
self.builder.download_and_prepare(
download_config=lowerCamelCase__ ,download_mode=lowerCamelCase__ ,verification_mode=lowerCamelCase__ ,base_path=lowerCamelCase__ ,num_proc=self.num_proc ,)
SCREAMING_SNAKE_CASE = self.builder.as_dataset(
split="""train""" ,verification_mode=lowerCamelCase__ ,in_memory=self.keep_in_memory )
return dataset
| 296 | 1 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[float] = field(
default=0.0 , metadata={"help": "The label smoothing epsilon to apply (if not zero)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Whether to SortishSamler or not."} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "whether to use adafactor"} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Encoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Decoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(default=lowerCAmelCase_ , metadata={"help": "Dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Attention dropout probability. Goes into model.config."} )
__snake_case : Optional[str] = field(
default="linear" , metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}"} , )
| 296 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[float] = field(
default=0.0 , metadata={"help": "The label smoothing epsilon to apply (if not zero)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Whether to SortishSamler or not."} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "whether to use adafactor"} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Encoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Decoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(default=lowerCAmelCase_ , metadata={"help": "Dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Attention dropout probability. Goes into model.config."} )
__snake_case : Optional[str] = field(
default="linear" , metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}"} , )
| 296 | 1 |
from collections.abc import Callable
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : List[str] ,lowerCamelCase__ : Callable | None = None ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
# Stores indexes of each item for supporting updates and deletion.
SCREAMING_SNAKE_CASE = {}
# Stores current size of heap.
SCREAMING_SNAKE_CASE = 0
# Stores function used to evaluate the score of an item on which basis ordering
# will be done.
SCREAMING_SNAKE_CASE = key or (lambda lowerCamelCase__ : x)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : int ) -> int | None:
'''simple docstring'''
return int((i - 1) / 2 ) if i > 0 else None
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : int ) -> int | None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = int(2 * i + 1 )
return left if 0 < left < self.size else None
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : int ) -> int | None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = int(2 * i + 2 )
return right if 0 < right < self.size else None
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : int ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = (
self.pos_map[self.arr[j][0]],
self.pos_map[self.arr[i][0]],
)
# Then swap the items in the list.
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.arr[j], self.arr[i]
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : int ,lowerCamelCase__ : int ) -> bool:
'''simple docstring'''
return self.arr[i][1] < self.arr[j][1]
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,lowerCamelCase__ : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._left(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self._right(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = i
if left is not None and not self._cmp(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = left
if right is not None and not self._cmp(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = right
return valid_parent
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : int ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._parent(lowerCamelCase__ )
while parent is not None and not self._cmp(lowerCamelCase__ ,lowerCamelCase__ ):
self._swap(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = parent, self._parent(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : int ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._get_valid_parent(lowerCamelCase__ )
while valid_parent != index:
self._swap(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = valid_parent, self._get_valid_parent(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : int ,lowerCamelCase__ : int ) -> None:
'''simple docstring'''
if item not in self.pos_map:
return
SCREAMING_SNAKE_CASE = self.pos_map[item]
SCREAMING_SNAKE_CASE = [item, self.key(lowerCamelCase__ )]
# Make sure heap is right in both up and down direction.
# Ideally only one of them will make any change.
self._heapify_up(lowerCamelCase__ )
self._heapify_down(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : int ) -> None:
'''simple docstring'''
if item not in self.pos_map:
return
SCREAMING_SNAKE_CASE = self.pos_map[item]
del self.pos_map[item]
SCREAMING_SNAKE_CASE = self.arr[self.size - 1]
SCREAMING_SNAKE_CASE = index
self.size -= 1
# Make sure heap is right in both up and down direction. Ideally only one
# of them will make any change- so no performance loss in calling both.
if self.size > index:
self._heapify_up(lowerCamelCase__ )
self._heapify_down(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : int ,lowerCamelCase__ : int ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(self.arr )
if arr_len == self.size:
self.arr.append([item, self.key(lowerCamelCase__ )] )
else:
SCREAMING_SNAKE_CASE = [item, self.key(lowerCamelCase__ )]
SCREAMING_SNAKE_CASE = self.size
self.size += 1
self._heapify_up(self.size - 1 )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> tuple | None:
'''simple docstring'''
return self.arr[0] if self.size else None
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> tuple | None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_top()
if top_item_tuple:
self.delete_item(top_item_tuple[0] )
return top_item_tuple
def __lowercase ( ) -> None:
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 296 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[str] = TextToVideoSDPipeline
__snake_case : int = TEXT_TO_IMAGE_PARAMS
__snake_case : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
__snake_case : Dict = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""") ,up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""") ,cross_attention_dim=32 ,attention_head_dim=4 ,)
SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.00085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=lowerCamelCase__ ,set_alpha_to_one=lowerCamelCase__ ,)
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = 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 )
SCREAMING_SNAKE_CASE = 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=1000 ,hidden_act="""gelu""" ,projection_dim=512 ,)
SCREAMING_SNAKE_CASE = CLIPTextModel(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : int=0 ) -> List[Any]:
'''simple docstring'''
if str(lowerCamelCase__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCamelCase__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe.to(lowerCamelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """np"""
SCREAMING_SNAKE_CASE = sd_pipe(**lowerCamelCase__ ).frames
SCREAMING_SNAKE_CASE = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
SCREAMING_SNAKE_CASE = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=1e-2 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=25 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=2 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 296 | 1 |
import math
from collections.abc import Callable
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
SCREAMING_SNAKE_CASE = xa
SCREAMING_SNAKE_CASE = xa
while True:
if x_n == x_na or function(_SCREAMING_SNAKE_CASE ) == function(_SCREAMING_SNAKE_CASE ):
raise ZeroDivisionError("""float division by zero, could not find root""" )
SCREAMING_SNAKE_CASE = x_na - (
function(_SCREAMING_SNAKE_CASE ) / ((function(_SCREAMING_SNAKE_CASE ) - function(_SCREAMING_SNAKE_CASE )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
SCREAMING_SNAKE_CASE = x_na
SCREAMING_SNAKE_CASE = x_na
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
return math.pow(_SCREAMING_SNAKE_CASE , 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5))
| 296 |
def __lowercase ( _SCREAMING_SNAKE_CASE = 10 ) -> str:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or n < 0:
raise ValueError("""Invalid input""" )
SCREAMING_SNAKE_CASE = 10**n
SCREAMING_SNAKE_CASE = 2_84_33 * (pow(2 , 7_83_04_57 , _SCREAMING_SNAKE_CASE )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(1_0) = }''')
| 296 | 1 |
# Author: OMKAR PATHAK, Nwachukwu Chidiebere
# Use a Python dictionary to construct the graph.
from __future__ import annotations
from pprint import pformat
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE_ = TypeVar("""T""")
class UpperCamelCase__ ( Generic[T] ):
'''simple docstring'''
def __init__( self : Optional[Any] ,lowerCamelCase__ : bool = True ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {} # dictionary of lists
SCREAMING_SNAKE_CASE = directed
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : T ,lowerCamelCase__ : T ) -> GraphAdjacencyList[T]:
'''simple docstring'''
if not self.directed: # For undirected graphs
# if both source vertex and destination vertex are both present in the
# adjacency list, add destination vertex to source vertex list of adjacent
# vertices and add source vertex to destination vertex list of adjacent
# vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCamelCase__ )
self.adj_list[destination_vertex].append(lowerCamelCase__ )
# if only source vertex is present in adjacency list, add destination vertex
# to source vertex list of adjacent vertices, then create a new vertex with
# destination vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = [source_vertex]
# if only destination vertex is present in adjacency list, add source vertex
# to destination vertex list of adjacent vertices, then create a new vertex
# with source vertex as key and assign a list containing the source vertex
# as it's first adjacent vertex.
elif destination_vertex in self.adj_list:
self.adj_list[destination_vertex].append(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and assign a list
# containing the destination vertex as it's first adjacent vertex also
# create a new vertex with destination vertex as key and assign a list
# containing the source vertex as it's first adjacent vertex.
else:
SCREAMING_SNAKE_CASE = [destination_vertex]
SCREAMING_SNAKE_CASE = [source_vertex]
else: # For directed graphs
# if both source vertex and destination vertex are present in adjacency
# list, add destination vertex to source vertex list of adjacent vertices.
if source_vertex in self.adj_list and destination_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCamelCase__ )
# if only source vertex is present in adjacency list, add destination
# vertex to source vertex list of adjacent vertices and create a new vertex
# with destination vertex as key, which has no adjacent vertex
elif source_vertex in self.adj_list:
self.adj_list[source_vertex].append(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = []
# if only destination vertex is present in adjacency list, create a new
# vertex with source vertex as key and assign a list containing destination
# vertex as first adjacent vertex
elif destination_vertex in self.adj_list:
SCREAMING_SNAKE_CASE = [destination_vertex]
# if both source vertex and destination vertex are not present in adjacency
# list, create a new vertex with source vertex as key and a list containing
# destination vertex as it's first adjacent vertex. Then create a new vertex
# with destination vertex as key, which has no adjacent vertex
else:
SCREAMING_SNAKE_CASE = [destination_vertex]
SCREAMING_SNAKE_CASE = []
return self
def __repr__( self : Tuple ) -> str:
'''simple docstring'''
return pformat(self.adj_list )
| 296 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""LlamaForCausalLM""",
"""LlamaModel""",
"""LlamaPreTrainedModel""",
"""LlamaForSequenceClassification""",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 296 | 1 |
from math import factorial, radians
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 18 , _SCREAMING_SNAKE_CASE = 10 ) -> float:
'''simple docstring'''
SCREAMING_SNAKE_CASE = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0)
# Converting from degrees to radians
SCREAMING_SNAKE_CASE = radians(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = angle_in_radians
SCREAMING_SNAKE_CASE = 3
SCREAMING_SNAKE_CASE = -1
for _ in range(_SCREAMING_SNAKE_CASE ):
result += (b * (angle_in_radians**a)) / factorial(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__import__("""doctest""").testmod()
| 296 |
from pathlib import Path
import fire
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
for path in src_dir.iterdir():
SCREAMING_SNAKE_CASE = [x.rstrip() for x in list(path.open().readlines() )][:n]
SCREAMING_SNAKE_CASE = dest_dir.joinpath(path.name )
print(_SCREAMING_SNAKE_CASE )
dest_path.open("""w""" ).write("""\n""".join(_SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
fire.Fire(minify)
| 296 | 1 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.models.wavaveca import WavaVecaCTCTokenizer, WavaVecaFeatureExtractor
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.testing_utils import require_pyctcdecode, require_torch, require_torchaudio, slow
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_pyctcdecode_available, is_torch_available
from ..wavaveca.test_feature_extraction_wavaveca import floats_list
if is_pyctcdecode_available():
from huggingface_hub import snapshot_download
from pyctcdecode import BeamSearchDecoderCTC
from transformers.models.wavaveca_with_lm import WavaVecaProcessorWithLM
from transformers.models.wavaveca_with_lm.processing_wavaveca_with_lm import WavaVecaDecoderWithLMOutput
if is_torch_available():
from transformers import WavaVecaForCTC
@require_pyctcdecode
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """| <pad> <unk> <s> </s> a b c d e f g h i j k""".split()
SCREAMING_SNAKE_CASE = dict(zip(lowerCamelCase__ ,range(len(lowerCamelCase__ ) ) ) )
SCREAMING_SNAKE_CASE = {
"""unk_token""": """<unk>""",
"""bos_token""": """<s>""",
"""eos_token""": """</s>""",
}
SCREAMING_SNAKE_CASE = {
"""feature_size""": 1,
"""padding_value""": 0.0,
"""sampling_rate""": 16000,
"""return_attention_mask""": False,
"""do_normalize""": True,
}
SCREAMING_SNAKE_CASE = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,lowerCamelCase__ )
with open(self.vocab_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) + """\n""" )
with open(self.feature_extraction_file ,"""w""" ,encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) + """\n""" )
# load decoder from hub
SCREAMING_SNAKE_CASE = """hf-internal-testing/ngram-beam-search-decoder"""
def SCREAMING_SNAKE_CASE__ ( self : int ,**lowerCamelCase__ : Tuple ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.add_kwargs_tokens_map.copy()
kwargs.update(lowerCamelCase__ )
return WavaVecaCTCTokenizer.from_pretrained(self.tmpdirname ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ,**lowerCamelCase__ : int ) -> Dict:
'''simple docstring'''
return WavaVecaFeatureExtractor.from_pretrained(self.tmpdirname ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,**lowerCamelCase__ : List[Any] ) -> Any:
'''simple docstring'''
return BeamSearchDecoderCTC.load_from_hf_hub(self.decoder_name ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
shutil.rmtree(self.tmpdirname )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = self.get_decoder()
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,decoder=lowerCamelCase__ )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM.from_pretrained(self.tmpdirname )
# tokenizer
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer.get_vocab() )
self.assertIsInstance(processor.tokenizer ,lowerCamelCase__ )
# feature extractor
self.assertEqual(processor.feature_extractor.to_json_string() ,feature_extractor.to_json_string() )
self.assertIsInstance(processor.feature_extractor ,lowerCamelCase__ )
# decoder
self.assertEqual(processor.decoder._alphabet.labels ,decoder._alphabet.labels )
self.assertEqual(
processor.decoder.model_container[decoder._model_key]._unigram_set ,decoder.model_container[decoder._model_key]._unigram_set ,)
self.assertIsInstance(processor.decoder ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM(
tokenizer=self.get_tokenizer() ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
processor.save_pretrained(self.tmpdirname )
# make sure that error is thrown when decoder alphabet doesn't match
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM.from_pretrained(
self.tmpdirname ,alpha=5.0 ,beta=3.0 ,score_boundary=-7.0 ,unk_score_offset=3 )
# decoder
self.assertEqual(processor.language_model.alpha ,5.0 )
self.assertEqual(processor.language_model.beta ,3.0 )
self.assertEqual(processor.language_model.score_boundary ,-7.0 )
self.assertEqual(processor.language_model.unk_score_offset ,3 )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_tokenizer()
# add token to trigger raise
tokenizer.add_tokens(["""xx"""] )
with self.assertRaisesRegex(lowerCamelCase__ ,"""include""" ):
WavaVecaProcessorWithLM(
tokenizer=lowerCamelCase__ ,feature_extractor=self.get_feature_extractor() ,decoder=self.get_decoder() )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_decoder()
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,decoder=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = floats_list((3, 1000) )
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""np""" )
SCREAMING_SNAKE_CASE = processor(lowerCamelCase__ ,return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() ,input_processor[key].sum() ,delta=1e-2 )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_decoder()
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,decoder=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """This is a test string"""
SCREAMING_SNAKE_CASE = processor(text=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer(lowerCamelCase__ )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : Tuple=(2, 10, 16) ,lowerCamelCase__ : List[Any]=77 ) -> Union[str, Any]:
'''simple docstring'''
np.random.seed(lowerCamelCase__ )
return np.random.rand(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_decoder()
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,decoder=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self._get_dummy_logits(shape=(10, 16) ,seed=13 )
SCREAMING_SNAKE_CASE = processor.decode(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = decoder.decode_beams(lowerCamelCase__ )[0]
self.assertEqual(decoded_decoder[0] ,decoded_processor.text )
self.assertEqual("""</s> <s> </s>""" ,decoded_processor.text )
self.assertEqual(decoded_decoder[-2] ,decoded_processor.logit_score )
self.assertEqual(decoded_decoder[-1] ,decoded_processor.lm_score )
@parameterized.expand([[None], ["""fork"""], ["""spawn"""]] )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : Tuple ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_decoder()
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,decoder=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self._get_dummy_logits()
# note: pool should be instantiated *after* Wav2Vec2ProcessorWithLM.
# otherwise, the LM won't be available to the pool's sub-processes.
# manual logic used to allow parameterized test for both pool=None and pool=Pool(...)
if pool_context is None:
SCREAMING_SNAKE_CASE = processor.batch_decode(lowerCamelCase__ )
else:
with get_context(lowerCamelCase__ ).Pool() as pool:
SCREAMING_SNAKE_CASE = processor.batch_decode(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = list(lowerCamelCase__ )
with get_context("""fork""" ).Pool() as p:
SCREAMING_SNAKE_CASE = decoder.decode_beams_batch(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = [], [], []
for beams in decoded_beams:
texts_decoder.append(beams[0][0] )
logit_scores_decoder.append(beams[0][-2] )
lm_scores_decoder.append(beams[0][-1] )
self.assertListEqual(lowerCamelCase__ ,decoded_processor.text )
self.assertListEqual(["""<s> <s> </s>""", """<s> <s> <s>"""] ,decoded_processor.text )
self.assertListEqual(lowerCamelCase__ ,decoded_processor.logit_score )
self.assertListEqual(lowerCamelCase__ ,decoded_processor.lm_score )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_decoder()
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,decoder=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self._get_dummy_logits()
SCREAMING_SNAKE_CASE = 15
SCREAMING_SNAKE_CASE = -20.0
SCREAMING_SNAKE_CASE = -4.0
SCREAMING_SNAKE_CASE = processor.batch_decode(
lowerCamelCase__ ,beam_width=lowerCamelCase__ ,beam_prune_logp=lowerCamelCase__ ,token_min_logp=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = decoded_processor_out.text
SCREAMING_SNAKE_CASE = list(lowerCamelCase__ )
with get_context("""fork""" ).Pool() as pool:
SCREAMING_SNAKE_CASE = decoder.decode_beams_batch(
lowerCamelCase__ ,lowerCamelCase__ ,beam_width=lowerCamelCase__ ,beam_prune_logp=lowerCamelCase__ ,token_min_logp=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = [d[0][0] for d in decoded_decoder_out]
SCREAMING_SNAKE_CASE = [d[0][2] for d in decoded_decoder_out]
SCREAMING_SNAKE_CASE = [d[0][3] for d in decoded_decoder_out]
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
self.assertListEqual(["""</s> <s> <s>""", """<s> <s> <s>"""] ,lowerCamelCase__ )
self.assertTrue(np.array_equal(lowerCamelCase__ ,decoded_processor_out.logit_score ) )
self.assertTrue(np.allclose([-20.054, -18.447] ,lowerCamelCase__ ,atol=1e-3 ) )
self.assertTrue(np.array_equal(lowerCamelCase__ ,decoded_processor_out.lm_score ) )
self.assertTrue(np.allclose([-15.554, -13.9474] ,lowerCamelCase__ ,atol=1e-3 ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_decoder()
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,decoder=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self._get_dummy_logits()
SCREAMING_SNAKE_CASE = 2.0
SCREAMING_SNAKE_CASE = 5.0
SCREAMING_SNAKE_CASE = -20.0
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = processor.batch_decode(
lowerCamelCase__ ,alpha=lowerCamelCase__ ,beta=lowerCamelCase__ ,unk_score_offset=lowerCamelCase__ ,lm_score_boundary=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = decoded_processor_out.text
SCREAMING_SNAKE_CASE = list(lowerCamelCase__ )
decoder.reset_params(
alpha=lowerCamelCase__ ,beta=lowerCamelCase__ ,unk_score_offset=lowerCamelCase__ ,lm_score_boundary=lowerCamelCase__ ,)
with get_context("""fork""" ).Pool() as pool:
SCREAMING_SNAKE_CASE = decoder.decode_beams_batch(
lowerCamelCase__ ,lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = [d[0][0] for d in decoded_decoder_out]
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
self.assertListEqual(["""<s> </s> <s> </s> </s>""", """</s> </s> <s> </s> </s>"""] ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = processor.decoder.model_container[processor.decoder._model_key]
self.assertEqual(lm_model.alpha ,2.0 )
self.assertEqual(lm_model.beta ,5.0 )
self.assertEqual(lm_model.unk_score_offset ,-20.0 )
self.assertEqual(lm_model.score_boundary ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
SCREAMING_SNAKE_CASE = processor.decoder.model_container[processor.decoder._model_key]
SCREAMING_SNAKE_CASE = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
SCREAMING_SNAKE_CASE = os.listdir(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = ["""alphabet.json""", """language_model"""]
downloaded_decoder_files.sort()
expected_decoder_files.sort()
# test that only decoder relevant files from
# https://huggingface.co/hf-internal-testing/processor_with_lm/tree/main
# are downloaded and none of the rest (e.g. README.md, ...)
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = snapshot_download("""hf-internal-testing/processor_with_lm""" )
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = processor.decoder.model_container[processor.decoder._model_key]
SCREAMING_SNAKE_CASE = Path(language_model._kenlm_model.path.decode("""utf-8""" ) ).parent.parent.absolute()
SCREAMING_SNAKE_CASE = os.listdir(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = os.listdir(lowerCamelCase__ )
local_decoder_files.sort()
expected_decoder_files.sort()
# test that both decoder form hub and local files in cache are the same
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained("""hf-internal-testing/processor_with_lm""" )
SCREAMING_SNAKE_CASE = floats_list((3, 1000) )
SCREAMING_SNAKE_CASE = processor_wavaveca(lowerCamelCase__ ,return_tensors="""np""" )
SCREAMING_SNAKE_CASE = processor_auto(lowerCamelCase__ ,return_tensors="""np""" )
for key in input_wavaveca.keys():
self.assertAlmostEqual(input_wavaveca[key].sum() ,input_auto[key].sum() ,delta=1e-2 )
SCREAMING_SNAKE_CASE = self._get_dummy_logits()
SCREAMING_SNAKE_CASE = processor_wavaveca.batch_decode(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = processor_auto.batch_decode(lowerCamelCase__ )
self.assertListEqual(decoded_wavaveca.text ,decoded_auto.text )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_feature_extractor()
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE = self.get_decoder()
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM(tokenizer=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,decoder=lowerCamelCase__ )
self.assertListEqual(
processor.model_input_names ,feature_extractor.model_input_names ,msg="""`processor` and `feature_extractor` model input names do not match""" ,)
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : Tuple ,lowerCamelCase__ : Any ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [d[key] for d in offsets]
return retrieved_list
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
SCREAMING_SNAKE_CASE = self._get_dummy_logits()[0]
SCREAMING_SNAKE_CASE = processor.decode(lowerCamelCase__ ,output_word_offsets=lowerCamelCase__ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(""" """.join(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ) ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""] ,"""end_offset""" ) ,[1, 3, 5] )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = WavaVecaProcessorWithLM.from_pretrained("""hf-internal-testing/processor_with_lm""" )
SCREAMING_SNAKE_CASE = self._get_dummy_logits()
SCREAMING_SNAKE_CASE = processor.batch_decode(lowerCamelCase__ ,output_word_offsets=lowerCamelCase__ )
# check Wav2Vec2CTCTokenizerOutput keys for word
self.assertEqual(len(outputs.keys() ) ,4 )
self.assertTrue("""text""" in outputs )
self.assertTrue("""word_offsets""" in outputs )
self.assertTrue(isinstance(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertListEqual(
[""" """.join(self.get_from_offsets(lowerCamelCase__ ,"""word""" ) ) for o in outputs["""word_offsets"""]] ,outputs.text )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""word""" ) ,["""<s>""", """<s>""", """</s>"""] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""start_offset""" ) ,[0, 2, 4] )
self.assertListEqual(self.get_from_offsets(outputs["""word_offsets"""][0] ,"""end_offset""" ) ,[1, 3, 5] )
@slow
@require_torch
@require_torchaudio
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
import torch
SCREAMING_SNAKE_CASE = load_dataset("""common_voice""" ,"""en""" ,split="""train""" ,streaming=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = ds.cast_column("""audio""" ,datasets.Audio(sampling_rate=16000 ) )
SCREAMING_SNAKE_CASE = iter(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = next(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
SCREAMING_SNAKE_CASE = WavaVecaForCTC.from_pretrained("""patrickvonplaten/wav2vec2-base-100h-with-lm""" )
# compare to filename `common_voice_en_100038.mp3` of dataset viewer on https://huggingface.co/datasets/common_voice/viewer/en/train
SCREAMING_SNAKE_CASE = processor(sample["""audio"""]["""array"""] ,return_tensors="""pt""" ).input_values
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ).logits.cpu().numpy()
SCREAMING_SNAKE_CASE = processor.decode(logits[0] ,output_word_offsets=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model.config.inputs_to_logits_ratio / processor.feature_extractor.sampling_rate
SCREAMING_SNAKE_CASE = [
{
"""start_time""": d["""start_offset"""] * time_offset,
"""end_time""": d["""end_offset"""] * time_offset,
"""word""": d["""word"""],
}
for d in output["""word_offsets"""]
]
SCREAMING_SNAKE_CASE = """WHY DOES MILISANDRA LOOK LIKE SHE WANTS TO CONSUME JOHN SNOW ON THE RIVER AT THE WALL"""
# output words
self.assertEqual(""" """.join(self.get_from_offsets(lowerCamelCase__ ,"""word""" ) ) ,lowerCamelCase__ )
self.assertEqual(""" """.join(self.get_from_offsets(lowerCamelCase__ ,"""word""" ) ) ,output.text )
# output times
SCREAMING_SNAKE_CASE = torch.tensor(self.get_from_offsets(lowerCamelCase__ ,"""start_time""" ) )
SCREAMING_SNAKE_CASE = torch.tensor(self.get_from_offsets(lowerCamelCase__ ,"""end_time""" ) )
# fmt: off
SCREAMING_SNAKE_CASE = torch.tensor([1.4199, 1.6599, 2.2599, 3.0, 3.24, 3.5999, 3.7999, 4.0999, 4.26, 4.94, 5.28, 5.6599, 5.78, 5.94, 6.32, 6.5399, 6.6599] )
SCREAMING_SNAKE_CASE = torch.tensor([1.5399, 1.8999, 2.9, 3.16, 3.5399, 3.72, 4.0199, 4.1799, 4.76, 5.1599, 5.5599, 5.6999, 5.86, 6.1999, 6.38, 6.6199, 6.94] )
# fmt: on
self.assertTrue(torch.allclose(lowerCamelCase__ ,lowerCamelCase__ ,atol=0.01 ) )
self.assertTrue(torch.allclose(lowerCamelCase__ ,lowerCamelCase__ ,atol=0.01 ) )
| 296 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
SCREAMING_SNAKE_CASE_ = get_tests_dir("""fixtures/dummy-config.json""")
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
'''simple docstring'''
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""fake-roberta""" )
os.makedirs(lowerCamelCase__ ,exist_ok=lowerCamelCase__ )
with open(os.path.join(lowerCamelCase__ ,"""config.json""" ) ,"""w""" ) as f:
f.write(json.dumps({} ) )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertEqual(type(lowerCamelCase__ ) ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
try:
AutoConfig.register("""custom""" ,lowerCamelCase__ )
# Wrong model type will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""model""" ,lowerCamelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""bert""" ,lowerCamelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""bert-base is not a local folder and is not a valid model identifier""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,revision="""aaaaaa""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" ,):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(reloaded_config.__class__.__name__ ,"""NewModelConfig""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = "new-model"
try:
AutoConfig.register("""new-model""" ,lowerCamelCase__ )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 296 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = """▁"""
SCREAMING_SNAKE_CASE_ = {"""vocab_file""": """sentencepiece.bpe.model"""}
SCREAMING_SNAKE_CASE_ = {
"""vocab_file""": {
"""facebook/mbart-large-en-ro""": (
"""https://huggingface.co/facebook/mbart-large-en-ro/resolve/main/sentencepiece.bpe.model"""
),
"""facebook/mbart-large-cc25""": (
"""https://huggingface.co/facebook/mbart-large-cc25/resolve/main/sentencepiece.bpe.model"""
),
}
}
SCREAMING_SNAKE_CASE_ = {
"""facebook/mbart-large-en-ro""": 1_0_2_4,
"""facebook/mbart-large-cc25""": 1_0_2_4,
}
# fmt: off
SCREAMING_SNAKE_CASE_ = ["""ar_AR""", """cs_CZ""", """de_DE""", """en_XX""", """es_XX""", """et_EE""", """fi_FI""", """fr_XX""", """gu_IN""", """hi_IN""", """it_IT""", """ja_XX""", """kk_KZ""", """ko_KR""", """lt_LT""", """lv_LV""", """my_MM""", """ne_NP""", """nl_XX""", """ro_RO""", """ru_RU""", """si_LK""", """tr_TR""", """vi_VN""", """zh_CN"""]
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : List[Any] = VOCAB_FILES_NAMES
__snake_case : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
__snake_case : Any = PRETRAINED_VOCAB_FILES_MAP
__snake_case : Optional[Any] = ["input_ids", "attention_mask"]
__snake_case : List[int] = []
__snake_case : List[int] = []
def __init__( self : Any ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Any="<s>" ,lowerCamelCase__ : Optional[int]="</s>" ,lowerCamelCase__ : Optional[Any]="</s>" ,lowerCamelCase__ : Optional[Any]="<s>" ,lowerCamelCase__ : Dict="<unk>" ,lowerCamelCase__ : List[str]="<pad>" ,lowerCamelCase__ : Any="<mask>" ,lowerCamelCase__ : List[Any]=None ,lowerCamelCase__ : int=None ,lowerCamelCase__ : Tuple=None ,lowerCamelCase__ : Optional[Dict[str, Any]] = None ,lowerCamelCase__ : str=None ,**lowerCamelCase__ : str ,) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AddedToken(lowerCamelCase__ ,lstrip=lowerCamelCase__ ,rstrip=lowerCamelCase__ ) if isinstance(lowerCamelCase__ ,lowerCamelCase__ ) else mask_token
SCREAMING_SNAKE_CASE = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=lowerCamelCase__ ,eos_token=lowerCamelCase__ ,unk_token=lowerCamelCase__ ,sep_token=lowerCamelCase__ ,cls_token=lowerCamelCase__ ,pad_token=lowerCamelCase__ ,mask_token=lowerCamelCase__ ,tokenizer_file=lowerCamelCase__ ,src_lang=lowerCamelCase__ ,tgt_lang=lowerCamelCase__ ,additional_special_tokens=lowerCamelCase__ ,sp_model_kwargs=self.sp_model_kwargs ,**lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(lowerCamelCase__ ) )
SCREAMING_SNAKE_CASE = vocab_file
# Original fairseq vocab and spm vocab must be "aligned":
# Vocab | 0 | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9
# -------- | ------- | ------- | ------ | ------- | --- | --- | --- | ----- | ----- | ----
# fairseq | '<s>' | '<pad>' | '</s>' | '<unk>' | ',' | '.' | '▁' | 's' | '▁de' | '-'
# spm | '<unk>' | '<s>' | '</s>' | ',' | '.' | '▁' | 's' | '▁de' | '-' | '▁a'
# Mimic fairseq token-to-id alignment for the first 4 token
SCREAMING_SNAKE_CASE = {"""<s>""": 0, """<pad>""": 1, """</s>""": 2, """<unk>""": 3}
# The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = len(self.sp_model )
SCREAMING_SNAKE_CASE = {
code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(lowerCamelCase__ )
}
SCREAMING_SNAKE_CASE = {v: k for k, v in self.lang_code_to_id.items()}
SCREAMING_SNAKE_CASE = len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset
self.fairseq_tokens_to_ids.update(self.lang_code_to_id )
SCREAMING_SNAKE_CASE = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
SCREAMING_SNAKE_CASE = list(self.lang_code_to_id.keys() )
if additional_special_tokens is not None:
# Only add those special tokens if they are not already there.
self._additional_special_tokens.extend(
[t for t in additional_special_tokens if t not in self._additional_special_tokens] )
SCREAMING_SNAKE_CASE = src_lang if src_lang is not None else """en_XX"""
SCREAMING_SNAKE_CASE = self.lang_code_to_id[self._src_lang]
SCREAMING_SNAKE_CASE = tgt_lang
self.set_src_lang_special_tokens(self._src_lang )
def __getstate__( self : List[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.__dict__.copy()
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
return state
def __setstate__( self : Union[str, Any] ,lowerCamelCase__ : Any ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = d
# for backward compatibility
if not hasattr(self ,"""sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE = {}
SCREAMING_SNAKE_CASE = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
@property
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Union[str, Any]:
'''simple docstring'''
return len(self.sp_model ) + len(self.lang_code_to_id ) + self.fairseq_offset + 1 # Plus 1 for the mask token
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
return self._src_lang
@src_lang.setter
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,lowerCamelCase__ : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ,lowerCamelCase__ : bool = False ) -> List[int]:
'''simple docstring'''
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCamelCase__ ,token_ids_a=lowerCamelCase__ ,already_has_special_tokens=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = [1] * len(self.prefix_tokens )
SCREAMING_SNAKE_CASE = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(lowerCamelCase__ )) + suffix_ones
return prefix_ones + ([0] * len(lowerCamelCase__ )) + ([0] * len(lowerCamelCase__ )) + suffix_ones
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
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 SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : List[int] ,lowerCamelCase__ : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [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 SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[str] ,lowerCamelCase__ : Optional[str] ,**lowerCamelCase__ : Union[str, Any] ) -> List[str]:
'''simple docstring'''
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE = src_lang
SCREAMING_SNAKE_CASE = self(lowerCamelCase__ ,add_special_tokens=lowerCamelCase__ ,return_tensors=lowerCamelCase__ ,**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.convert_tokens_to_ids(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tgt_lang_id
return inputs
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {self.convert_ids_to_tokens(lowerCamelCase__ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : str ) -> List[str]:
'''simple docstring'''
return self.sp_model.encode(lowerCamelCase__ ,out_type=lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : Tuple ) -> Tuple:
'''simple docstring'''
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
SCREAMING_SNAKE_CASE = 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 SCREAMING_SNAKE_CASE__ ( self : List[Any] ,lowerCamelCase__ : Optional[Any] ) -> str:
'''simple docstring'''
if index in self.fairseq_ids_to_tokens:
return self.fairseq_ids_to_tokens[index]
return self.sp_model.IdToPiece(index - self.fairseq_offset )
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Dict ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """""".join(lowerCamelCase__ ).replace(lowerCamelCase__ ,""" """ ).strip()
return out_string
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
if not os.path.isdir(lowerCamelCase__ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
SCREAMING_SNAKE_CASE = 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:
SCREAMING_SNAKE_CASE = self.sp_model.serialized_model_proto()
fi.write(lowerCamelCase__ )
return (out_vocab_file,)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : str = "en_XX" ,lowerCamelCase__ : Optional[List[str]] = None ,lowerCamelCase__ : str = "ro_RO" ,**lowerCamelCase__ : List[str] ,) -> BatchEncoding:
'''simple docstring'''
SCREAMING_SNAKE_CASE = src_lang
SCREAMING_SNAKE_CASE = tgt_lang
return super().prepare_seqaseq_batch(lowerCamelCase__ ,lowerCamelCase__ ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
return self.set_src_lang_special_tokens(self.src_lang )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return self.set_tgt_lang_special_tokens(self.tgt_lang )
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Union[str, Any] ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.lang_code_to_id[src_lang]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = [self.eos_token_id, self.cur_lang_code]
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.lang_code_to_id[lang]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = [self.eos_token_id, self.cur_lang_code]
| 296 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Dict = "git_vision_model"
def __init__( self : List[Any] ,lowerCamelCase__ : Dict=768 ,lowerCamelCase__ : Union[str, Any]=3072 ,lowerCamelCase__ : Optional[int]=12 ,lowerCamelCase__ : Tuple=12 ,lowerCamelCase__ : Tuple=3 ,lowerCamelCase__ : Optional[Any]=224 ,lowerCamelCase__ : Union[str, Any]=16 ,lowerCamelCase__ : List[Any]="quick_gelu" ,lowerCamelCase__ : Optional[Any]=1e-5 ,lowerCamelCase__ : str=0.0 ,lowerCamelCase__ : Optional[int]=0.02 ,**lowerCamelCase__ : Union[str, Any] ,) -> Optional[int]:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = hidden_act
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Tuple ,lowerCamelCase__ : Union[str, os.PathLike] ,**lowerCamelCase__ : int ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = cls.get_config_dict(lowerCamelCase__ ,**lowerCamelCase__ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("""model_type""" ) == "git":
SCREAMING_SNAKE_CASE = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowerCamelCase__ ,**lowerCamelCase__ )
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Dict = "git"
def __init__( self : Optional[int] ,lowerCamelCase__ : int=None ,lowerCamelCase__ : str=30522 ,lowerCamelCase__ : Tuple=768 ,lowerCamelCase__ : Union[str, Any]=6 ,lowerCamelCase__ : str=12 ,lowerCamelCase__ : List[str]=3072 ,lowerCamelCase__ : Dict="gelu" ,lowerCamelCase__ : Tuple=0.1 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : List[str]=1024 ,lowerCamelCase__ : List[str]=0.02 ,lowerCamelCase__ : str=1e-1_2 ,lowerCamelCase__ : Optional[int]=0 ,lowerCamelCase__ : Optional[int]="absolute" ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : str=False ,lowerCamelCase__ : int=101 ,lowerCamelCase__ : int=102 ,lowerCamelCase__ : Dict=None ,**lowerCamelCase__ : List[Any] ,) -> Optional[Any]:
'''simple docstring'''
super().__init__(bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,pad_token_id=lowerCamelCase__ ,**lowerCamelCase__ )
if vision_config is None:
SCREAMING_SNAKE_CASE = {}
logger.info("""vision_config is None. initializing the GitVisionConfig with default values.""" )
SCREAMING_SNAKE_CASE = GitVisionConfig(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = tie_word_embeddings
SCREAMING_SNAKE_CASE = num_image_with_embedding
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = eos_token_id
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
| 296 | 1 |
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_checkpoint
SCREAMING_SNAKE_CASE_ = {
"""169M""": 1_2,
"""430M""": 2_4,
"""1B5""": 2_4,
"""3B""": 3_2,
"""7B""": 3_2,
"""14B""": 4_0,
}
SCREAMING_SNAKE_CASE_ = {
"""169M""": 7_6_8,
"""430M""": 1_0_2_4,
"""1B5""": 2_0_4_8,
"""3B""": 2_5_6_0,
"""7B""": 4_0_9_6,
"""14B""": 5_1_2_0,
}
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = list(state_dict.keys() )
for name in state_dict_keys:
SCREAMING_SNAKE_CASE = state_dict.pop(_SCREAMING_SNAKE_CASE )
# emb -> embedding
if name.startswith("""emb.""" ):
SCREAMING_SNAKE_CASE = name.replace("""emb.""" , """embeddings.""" )
# ln_0 -> pre_ln (only present at block 0)
if name.startswith("""blocks.0.ln0""" ):
SCREAMING_SNAKE_CASE = name.replace("""blocks.0.ln0""" , """blocks.0.pre_ln""" )
# att -> attention
SCREAMING_SNAKE_CASE = re.sub(r"""blocks\.(\d+)\.att""" , r"""blocks.\1.attention""" , _SCREAMING_SNAKE_CASE )
# ffn -> feed_forward
SCREAMING_SNAKE_CASE = re.sub(r"""blocks\.(\d+)\.ffn""" , r"""blocks.\1.feed_forward""" , _SCREAMING_SNAKE_CASE )
# time_mix_k -> time_mix_key and reshape
if name.endswith(""".time_mix_k""" ):
SCREAMING_SNAKE_CASE = name.replace(""".time_mix_k""" , """.time_mix_key""" )
# time_mix_v -> time_mix_value and reshape
if name.endswith(""".time_mix_v""" ):
SCREAMING_SNAKE_CASE = name.replace(""".time_mix_v""" , """.time_mix_value""" )
# time_mix_r -> time_mix_key and reshape
if name.endswith(""".time_mix_r""" ):
SCREAMING_SNAKE_CASE = name.replace(""".time_mix_r""" , """.time_mix_receptance""" )
if name != "head.weight":
SCREAMING_SNAKE_CASE = """rwkv.""" + name
SCREAMING_SNAKE_CASE = weight
return state_dict
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=None ) -> Optional[Any]:
'''simple docstring'''
if tokenizer_file is None:
print("""No `--tokenizer_file` provided, we will use the default tokenizer.""" )
SCREAMING_SNAKE_CASE = 5_02_77
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""EleutherAI/gpt-neox-20b""" )
else:
SCREAMING_SNAKE_CASE = PreTrainedTokenizerFast(tokenizer_file=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = len(_SCREAMING_SNAKE_CASE )
tokenizer.save_pretrained(_SCREAMING_SNAKE_CASE )
# 2. Build the config
SCREAMING_SNAKE_CASE = list(NUM_HIDDEN_LAYERS_MAPPING.keys() )
if size is None:
# Try to infer size from the checkpoint name
for candidate in possible_sizes:
if candidate in checkpoint_file:
SCREAMING_SNAKE_CASE = candidate
break
if size is None:
raise ValueError("""Could not infer the size, please provide it with the `--size` argument.""" )
if size not in possible_sizes:
raise ValueError(F"""`size` should be one of {possible_sizes}, got {size}.""" )
SCREAMING_SNAKE_CASE = RwkvConfig(
vocab_size=_SCREAMING_SNAKE_CASE , num_hidden_layers=NUM_HIDDEN_LAYERS_MAPPING[size] , hidden_size=HIDEN_SIZE_MAPPING[size] , )
config.save_pretrained(_SCREAMING_SNAKE_CASE )
# 3. Download model file then convert state_dict
SCREAMING_SNAKE_CASE = hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = torch.load(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
SCREAMING_SNAKE_CASE = convert_state_dict(_SCREAMING_SNAKE_CASE )
# 4. Split in shards and save
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = shard_checkpoint(_SCREAMING_SNAKE_CASE )
for shard_file, shard in shards.items():
torch.save(_SCREAMING_SNAKE_CASE , os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
if index is not None:
SCREAMING_SNAKE_CASE = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# Save the index as well
with open(_SCREAMING_SNAKE_CASE , """w""" , encoding="""utf-8""" ) as f:
SCREAMING_SNAKE_CASE = json.dumps(_SCREAMING_SNAKE_CASE , indent=2 , sort_keys=_SCREAMING_SNAKE_CASE ) + """\n"""
f.write(_SCREAMING_SNAKE_CASE )
# 5. Clean up shards (for some reason the file PyTorch saves take the same space as the whole state_dict
print(
"""Cleaning up shards. This may error with an OOM error, it this is the case don't worry you still have converted the model.""" )
SCREAMING_SNAKE_CASE = list(shards.keys() )
del state_dict
del shards
gc.collect()
for shard_file in shard_files:
SCREAMING_SNAKE_CASE = torch.load(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
torch.save({k: v.cpu().clone() for k, v in state_dict.items()} , os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) )
del state_dict
gc.collect()
if push_to_hub:
if model_name is None:
raise ValueError("""Please provide a `model_name` to push the model to the Hub.""" )
SCREAMING_SNAKE_CASE = AutoModelForCausalLM.from_pretrained(_SCREAMING_SNAKE_CASE )
model.push_to_hub(_SCREAMING_SNAKE_CASE , max_shard_size="""2GB""" )
tokenizer.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--repo_id""", default=None, type=str, required=True, help="""Repo ID from which to pull the checkpoint."""
)
parser.add_argument(
"""--checkpoint_file""", default=None, type=str, required=True, help="""Name of the checkpoint file in the repo."""
)
parser.add_argument(
"""--output_dir""", default=None, type=str, required=True, help="""Where to save the converted model."""
)
parser.add_argument(
"""--tokenizer_file""",
default=None,
type=str,
help="""Path to the tokenizer file to use (if not provided, only the model is converted).""",
)
parser.add_argument(
"""--size""",
default=None,
type=str,
help="""Size of the model. Will be inferred from the `checkpoint_file` if not passed.""",
)
parser.add_argument(
"""--push_to_hub""",
action="""store_true""",
help="""Push to the Hub the converted model.""",
)
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help="""Name of the pushed model on the Hub, including the username / organization.""",
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_rmkv_checkpoint_to_hf_format(
args.repo_id,
args.checkpoint_file,
args.output_dir,
size=args.size,
tokenizer_file=args.tokenizer_file,
push_to_hub=args.push_to_hub,
model_name=args.model_name,
)
| 296 |
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
SCREAMING_SNAKE_CASE_ = random.Random()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE = global_rng
SCREAMING_SNAKE_CASE = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[int]=7 ,lowerCamelCase__ : Optional[Any]=400 ,lowerCamelCase__ : List[str]=2000 ,lowerCamelCase__ : List[str]=2048 ,lowerCamelCase__ : Any=128 ,lowerCamelCase__ : List[str]=1 ,lowerCamelCase__ : str=512 ,lowerCamelCase__ : Optional[Any]=30 ,lowerCamelCase__ : Tuple=44100 ,) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = min_seq_length
SCREAMING_SNAKE_CASE = max_seq_length
SCREAMING_SNAKE_CASE = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE = spectrogram_length
SCREAMING_SNAKE_CASE = feature_size
SCREAMING_SNAKE_CASE = num_audio_channels
SCREAMING_SNAKE_CASE = hop_length
SCREAMING_SNAKE_CASE = chunk_length
SCREAMING_SNAKE_CASE = sampling_rate
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[int]:
'''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 SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : Tuple=False ,lowerCamelCase__ : Union[str, Any]=False ) -> str:
'''simple docstring'''
def _flatten(lowerCamelCase__ : List[Any] ):
return list(itertools.chain(*lowerCamelCase__ ) )
if equal_length:
SCREAMING_SNAKE_CASE = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[Any] = TvltFeatureExtractor
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TvltFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase__ ,"""spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""sampling_rate""" ) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = feat_extract_first.save_pretrained(lowerCamelCase__ )[0]
check_json_file_has_correct_format(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in range(800 ,1400 ,200 )]
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,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
SCREAMING_SNAKE_CASE = feature_extractor(
lowerCamelCase__ ,return_tensors="""np""" ,sampling_rate=44100 ,mask_audio=lowerCamelCase__ ).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.
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE = np.asarray(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,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 SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,"""clean""" ,split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE = ds.sort("""id""" ).select(range(lowerCamelCase__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape ,(1, 1, 192, 128) )
SCREAMING_SNAKE_CASE = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,lowerCamelCase__ ,atol=1e-4 ) )
| 296 | 1 |
import cva
import numpy as np
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : List[str] ,lowerCamelCase__ : float ,lowerCamelCase__ : int ) -> List[Any]:
'''simple docstring'''
if k in (0.04, 0.06):
SCREAMING_SNAKE_CASE = k
SCREAMING_SNAKE_CASE = window_size
else:
raise ValueError("""invalid k value""" )
def __str__( self : List[Any] ) -> str:
'''simple docstring'''
return str(self.k )
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : str ) -> tuple[cva.Mat, list[list[int]]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = cva.imread(lowerCamelCase__ ,0 )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = img.shape
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = img.copy()
SCREAMING_SNAKE_CASE = cva.cvtColor(lowerCamelCase__ ,cva.COLOR_GRAY2RGB )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = np.gradient(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = dx**2
SCREAMING_SNAKE_CASE = dy**2
SCREAMING_SNAKE_CASE = dx * dy
SCREAMING_SNAKE_CASE = 0.04
SCREAMING_SNAKE_CASE = self.window_size // 2
for y in range(lowerCamelCase__ ,h - offset ):
for x in range(lowerCamelCase__ ,w - offset ):
SCREAMING_SNAKE_CASE = ixx[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE = iyy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE = ixy[
y - offset : y + offset + 1, x - offset : x + offset + 1
].sum()
SCREAMING_SNAKE_CASE = (wxx * wyy) - (wxy**2)
SCREAMING_SNAKE_CASE = wxx + wyy
SCREAMING_SNAKE_CASE = det - k * (trace**2)
# Can change the value
if r > 0.5:
corner_list.append([x, y, r] )
color_img.itemset((y, x, 0) ,0 )
color_img.itemset((y, x, 1) ,0 )
color_img.itemset((y, x, 2) ,255 )
return color_img, corner_list
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = HarrisCorner(0.04, 3)
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = edge_detect.detect("""path_to_image""")
cva.imwrite("""detect.png""", color_img)
| 296 |
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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
SCREAMING_SNAKE_CASE = [1_44, 1_92, 2_40]
SCREAMING_SNAKE_CASE = [16, 32, 64, 96, 1_28, 1_60, 6_40]
elif "mobilevit_xs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [96, 1_20, 1_44]
SCREAMING_SNAKE_CASE = [16, 32, 48, 64, 80, 96, 3_84]
elif "mobilevit_xxs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [64, 80, 96]
SCREAMING_SNAKE_CASE = [16, 16, 24, 48, 64, 80, 3_20]
SCREAMING_SNAKE_CASE = 0.05
SCREAMING_SNAKE_CASE = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = 5_12
SCREAMING_SNAKE_CASE = 16
SCREAMING_SNAKE_CASE = 21
SCREAMING_SNAKE_CASE = """pascal-voc-id2label.json"""
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
SCREAMING_SNAKE_CASE = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
SCREAMING_SNAKE_CASE = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.weight""" , """.layernorm.weight""" )
if F""".global_rep.{i}.bias""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.bias""" , """.layernorm.bias""" )
if ".global_rep." in name:
SCREAMING_SNAKE_CASE = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
SCREAMING_SNAKE_CASE = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
SCREAMING_SNAKE_CASE = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
SCREAMING_SNAKE_CASE = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
SCREAMING_SNAKE_CASE = """mobilevit.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Dict:
'''simple docstring'''
if base_model:
SCREAMING_SNAKE_CASE = """"""
else:
SCREAMING_SNAKE_CASE = """mobilevit."""
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if key[:8] == "encoder.":
SCREAMING_SNAKE_CASE = key[8:]
if "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[0][6:] ) - 1
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
SCREAMING_SNAKE_CASE = layer.transformer.layer[transformer_num].attention.attention.all_head_size
SCREAMING_SNAKE_CASE = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[:dim]
SCREAMING_SNAKE_CASE = val[dim : dim * 2]
SCREAMING_SNAKE_CASE = val[-dim:]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_mobilevit_config(_SCREAMING_SNAKE_CASE )
# load original state_dict
SCREAMING_SNAKE_CASE = torch.load(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = MobileViTForSemanticSegmentation(_SCREAMING_SNAKE_CASE ).eval()
else:
SCREAMING_SNAKE_CASE = MobileViTForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
SCREAMING_SNAKE_CASE = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileViTImageProcessor
SCREAMING_SNAKE_CASE = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
[[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
[[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
[[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
[[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
[[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
[[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
else:
assert logits.shape == (1, 10_00)
if mobilevit_name == "mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
elif mobilevit_name == "mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
elif mobilevit_name == "mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
SCREAMING_SNAKE_CASE = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
SCREAMING_SNAKE_CASE = model_mapping[mobilevit_name]
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
model.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--mobilevit_name""",
default="""mobilevit_s""",
type=str,
help=(
"""Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
""" 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
),
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 296 | 1 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = SwinConfig()
SCREAMING_SNAKE_CASE = swin_name.split("""_""" )
SCREAMING_SNAKE_CASE = name_split[1]
SCREAMING_SNAKE_CASE = int(name_split[4] )
SCREAMING_SNAKE_CASE = int(name_split[3][-1] )
if model_size == "tiny":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 6, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "small":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "base":
SCREAMING_SNAKE_CASE = 1_28
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (4, 8, 16, 32)
else:
SCREAMING_SNAKE_CASE = 1_92
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (6, 12, 24, 48)
if "in22k" in swin_name:
SCREAMING_SNAKE_CASE = 2_18_41
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = img_size
SCREAMING_SNAKE_CASE = num_classes
SCREAMING_SNAKE_CASE = embed_dim
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = window_size
return config
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
SCREAMING_SNAKE_CASE = """encoder.""" + name
if "attn.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "norm.weight":
SCREAMING_SNAKE_CASE = """layernorm.weight"""
if name == "norm.bias":
SCREAMING_SNAKE_CASE = """layernorm.bias"""
if "head" in name:
SCREAMING_SNAKE_CASE = name.replace("""head""" , """classifier""" )
else:
SCREAMING_SNAKE_CASE = """swin.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[1] )
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[
:dim
]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
SCREAMING_SNAKE_CASE = get_swin_config(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = SwinForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
SCREAMING_SNAKE_CASE = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" , """-""" ) ) )
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
SCREAMING_SNAKE_CASE = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = timm_model(inputs["""pixel_values"""] )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}""" )
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__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swin_name""",
default="""swin_tiny_patch4_window7_224""",
type=str,
help="""Name of the Swin 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."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 296 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[str] = FLAX_MODEL_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModel)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_PRETRAINING_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Any = FLAX_MODEL_FOR_MASKED_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : int = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[int] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[Any] = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Tuple = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[str] = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[Any] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 296 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""unc-nlp/lxmert-base-uncased""": """https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json""",
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[Any] = "lxmert"
__snake_case : str = {}
def __init__( self : Tuple ,lowerCamelCase__ : int=30522 ,lowerCamelCase__ : List[Any]=768 ,lowerCamelCase__ : int=12 ,lowerCamelCase__ : int=9500 ,lowerCamelCase__ : str=1600 ,lowerCamelCase__ : Union[str, Any]=400 ,lowerCamelCase__ : Optional[Any]=3072 ,lowerCamelCase__ : Optional[int]="gelu" ,lowerCamelCase__ : str=0.1 ,lowerCamelCase__ : Dict=0.1 ,lowerCamelCase__ : Dict=512 ,lowerCamelCase__ : Union[str, Any]=2 ,lowerCamelCase__ : List[Any]=0.02 ,lowerCamelCase__ : Optional[Any]=1e-1_2 ,lowerCamelCase__ : int=9 ,lowerCamelCase__ : List[Any]=5 ,lowerCamelCase__ : Any=5 ,lowerCamelCase__ : Any=2048 ,lowerCamelCase__ : Dict=4 ,lowerCamelCase__ : Optional[int]=6.67 ,lowerCamelCase__ : List[str]=True ,lowerCamelCase__ : int=True ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : Union[str, Any]=True ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : Any=True ,lowerCamelCase__ : Optional[Any]=True ,**lowerCamelCase__ : List[str] ,) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = num_qa_labels
SCREAMING_SNAKE_CASE = num_object_labels
SCREAMING_SNAKE_CASE = num_attr_labels
SCREAMING_SNAKE_CASE = l_layers
SCREAMING_SNAKE_CASE = x_layers
SCREAMING_SNAKE_CASE = r_layers
SCREAMING_SNAKE_CASE = visual_feat_dim
SCREAMING_SNAKE_CASE = visual_pos_dim
SCREAMING_SNAKE_CASE = visual_loss_normalizer
SCREAMING_SNAKE_CASE = task_matched
SCREAMING_SNAKE_CASE = task_mask_lm
SCREAMING_SNAKE_CASE = task_obj_predict
SCREAMING_SNAKE_CASE = task_qa
SCREAMING_SNAKE_CASE = visual_obj_loss
SCREAMING_SNAKE_CASE = visual_attr_loss
SCREAMING_SNAKE_CASE = visual_feat_loss
SCREAMING_SNAKE_CASE = {"""vision""": r_layers, """cross_encoder""": x_layers, """language""": l_layers}
super().__init__(**lowerCamelCase__ )
| 296 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = "table-transformer"
__snake_case : Union[str, Any] = ["past_key_values"]
__snake_case : List[Any] = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self : Optional[int] ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : Optional[Any]=None ,lowerCamelCase__ : List[Any]=3 ,lowerCamelCase__ : Optional[int]=100 ,lowerCamelCase__ : List[Any]=6 ,lowerCamelCase__ : Dict=2048 ,lowerCamelCase__ : List[Any]=8 ,lowerCamelCase__ : Dict=6 ,lowerCamelCase__ : Dict=2048 ,lowerCamelCase__ : Any=8 ,lowerCamelCase__ : Optional[int]=0.0 ,lowerCamelCase__ : int=0.0 ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : Optional[int]="relu" ,lowerCamelCase__ : Tuple=256 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : Optional[Any]=0.0 ,lowerCamelCase__ : Tuple=0.0 ,lowerCamelCase__ : List[Any]=0.02 ,lowerCamelCase__ : int=1.0 ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : Optional[Any]="sine" ,lowerCamelCase__ : List[str]="resnet50" ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : int=1 ,lowerCamelCase__ : Dict=5 ,lowerCamelCase__ : Tuple=2 ,lowerCamelCase__ : Union[str, Any]=1 ,lowerCamelCase__ : str=1 ,lowerCamelCase__ : Any=5 ,lowerCamelCase__ : Tuple=2 ,lowerCamelCase__ : str=0.1 ,**lowerCamelCase__ : List[str] ,) -> Optional[int]:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
SCREAMING_SNAKE_CASE = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = backbone_config.get("""model_type""" )
SCREAMING_SNAKE_CASE = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE = config_class.from_dict(lowerCamelCase__ )
# set timm attributes to None
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = None, None, None
SCREAMING_SNAKE_CASE = use_timm_backbone
SCREAMING_SNAKE_CASE = backbone_config
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = num_queries
SCREAMING_SNAKE_CASE = d_model
SCREAMING_SNAKE_CASE = encoder_ffn_dim
SCREAMING_SNAKE_CASE = encoder_layers
SCREAMING_SNAKE_CASE = encoder_attention_heads
SCREAMING_SNAKE_CASE = decoder_ffn_dim
SCREAMING_SNAKE_CASE = decoder_layers
SCREAMING_SNAKE_CASE = decoder_attention_heads
SCREAMING_SNAKE_CASE = dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = activation_dropout
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = init_std
SCREAMING_SNAKE_CASE = init_xavier_std
SCREAMING_SNAKE_CASE = encoder_layerdrop
SCREAMING_SNAKE_CASE = decoder_layerdrop
SCREAMING_SNAKE_CASE = encoder_layers
SCREAMING_SNAKE_CASE = auxiliary_loss
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = backbone
SCREAMING_SNAKE_CASE = use_pretrained_backbone
SCREAMING_SNAKE_CASE = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE = class_cost
SCREAMING_SNAKE_CASE = bbox_cost
SCREAMING_SNAKE_CASE = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE = mask_loss_coefficient
SCREAMING_SNAKE_CASE = dice_loss_coefficient
SCREAMING_SNAKE_CASE = bbox_loss_coefficient
SCREAMING_SNAKE_CASE = giou_loss_coefficient
SCREAMING_SNAKE_CASE = eos_coefficient
super().__init__(is_encoder_decoder=lowerCamelCase__ ,**lowerCamelCase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return self.d_model
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = version.parse("1.11" )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> float:
'''simple docstring'''
return 1e-5
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> int:
'''simple docstring'''
return 12
| 296 | 1 |
import unittest
import numpy as np
from diffusers import LMSDiscreteScheduler, OnnxStableDiffusionInpaintPipeline
from diffusers.utils.testing_utils import (
is_onnx_available,
load_image,
nightly,
require_onnxruntime,
require_torch_gpu,
)
from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin
if is_onnx_available():
import onnxruntime as ort
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
pass
@nightly
@require_onnxruntime
@require_torch_gpu
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ort.SessionOptions()
SCREAMING_SNAKE_CASE = False
return options
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
SCREAMING_SNAKE_CASE = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
SCREAMING_SNAKE_CASE = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" ,revision="""onnx""" ,safety_checker=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """A red cat sitting on a park bench"""
SCREAMING_SNAKE_CASE = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE = pipe(
prompt=lowerCamelCase__ ,image=lowerCamelCase__ ,mask_image=lowerCamelCase__ ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=lowerCamelCase__ ,output_type="""np""" ,)
SCREAMING_SNAKE_CASE = output.images
SCREAMING_SNAKE_CASE = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE = np.array([0.2514, 0.3007, 0.3517, 0.1790, 0.2382, 0.3167, 0.1944, 0.2273, 0.2464] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo.png""" )
SCREAMING_SNAKE_CASE = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/in_paint/overture-creations-5sI6fQgYIuo_mask.png""" )
SCREAMING_SNAKE_CASE = LMSDiscreteScheduler.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" ,subfolder="""scheduler""" ,revision="""onnx""" )
SCREAMING_SNAKE_CASE = OnnxStableDiffusionInpaintPipeline.from_pretrained(
"""runwayml/stable-diffusion-inpainting""" ,revision="""onnx""" ,scheduler=lowerCamelCase__ ,safety_checker=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """A red cat sitting on a park bench"""
SCREAMING_SNAKE_CASE = np.random.RandomState(0 )
SCREAMING_SNAKE_CASE = pipe(
prompt=lowerCamelCase__ ,image=lowerCamelCase__ ,mask_image=lowerCamelCase__ ,guidance_scale=7.5 ,num_inference_steps=20 ,generator=lowerCamelCase__ ,output_type="""np""" ,)
SCREAMING_SNAKE_CASE = output.images
SCREAMING_SNAKE_CASE = images[0, 255:258, 255:258, -1]
assert images.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE = np.array([0.0086, 0.0077, 0.0083, 0.0093, 0.0107, 0.0139, 0.0094, 0.0097, 0.0125] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 296 |
from collections import defaultdict
from math import gcd
def __lowercase ( _SCREAMING_SNAKE_CASE = 1_50_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = defaultdict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , _SCREAMING_SNAKE_CASE , 2 ):
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) > 1:
continue
SCREAMING_SNAKE_CASE = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(_SCREAMING_SNAKE_CASE , limit + 1 , _SCREAMING_SNAKE_CASE ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 296 | 1 |
from collections.abc import Iterator, MutableMapping
from dataclasses import dataclass
from typing import Generic, TypeVar
SCREAMING_SNAKE_CASE_ = TypeVar("""KEY""")
SCREAMING_SNAKE_CASE_ = TypeVar("""VAL""")
@dataclass(frozen=lowerCAmelCase_ , slots=lowerCAmelCase_ )
class UpperCamelCase__ ( Generic[KEY, VAL] ):
'''simple docstring'''
__snake_case : KEY
__snake_case : VAL
class UpperCamelCase__ ( _Item ):
'''simple docstring'''
def __init__( self : List[str] ) -> None:
'''simple docstring'''
super().__init__(lowerCamelCase__ ,lowerCamelCase__ )
def __bool__( self : List[Any] ) -> bool:
'''simple docstring'''
return False
SCREAMING_SNAKE_CASE_ = _DeletedItem()
class UpperCamelCase__ ( MutableMapping[KEY, VAL] ):
'''simple docstring'''
def __init__( self : Dict ,lowerCamelCase__ : int = 8 ,lowerCamelCase__ : float = 0.75 ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = initial_block_size
SCREAMING_SNAKE_CASE = [None] * initial_block_size
assert 0.0 < capacity_factor < 1.0
SCREAMING_SNAKE_CASE = capacity_factor
SCREAMING_SNAKE_CASE = 0
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : KEY ) -> int:
'''simple docstring'''
return hash(lowerCamelCase__ ) % len(self._buckets )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : int ) -> int:
'''simple docstring'''
return (ind + 1) % len(self._buckets )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : int ,lowerCamelCase__ : KEY ,lowerCamelCase__ : VAL ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._buckets[ind]
if not stored:
SCREAMING_SNAKE_CASE = _Item(lowerCamelCase__ ,lowerCamelCase__ )
self._len += 1
return True
elif stored.key == key:
SCREAMING_SNAKE_CASE = _Item(lowerCamelCase__ ,lowerCamelCase__ )
return True
else:
return False
def SCREAMING_SNAKE_CASE__ ( self : str ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE = len(self._buckets ) * self._capacity_factor
return len(self ) >= int(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> bool:
'''simple docstring'''
if len(self._buckets ) <= self._initial_block_size:
return False
SCREAMING_SNAKE_CASE = len(self._buckets ) * self._capacity_factor / 2
return len(self ) < limit
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : int ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._buckets
SCREAMING_SNAKE_CASE = [None] * new_size
SCREAMING_SNAKE_CASE = 0
for item in old_buckets:
if item:
self._add_item(item.key ,item.val )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) * 2 )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> None:
'''simple docstring'''
self._resize(len(self._buckets ) // 2 )
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : KEY ) -> Iterator[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._get_bucket_index(lowerCamelCase__ )
for _ in range(len(self._buckets ) ):
yield ind
SCREAMING_SNAKE_CASE = self._get_next_ind(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : 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[Any] ,lowerCamelCase__ : KEY ,lowerCamelCase__ : VAL ) -> None:
'''simple docstring'''
if self._is_full():
self._size_up()
self._add_item(lowerCamelCase__ ,lowerCamelCase__ )
def __delitem__( self : Any ,lowerCamelCase__ : KEY ) -> None:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = self._buckets[ind]
if item is None:
raise KeyError(lowerCamelCase__ )
if item is _deleted:
continue
if item.key == key:
SCREAMING_SNAKE_CASE = _deleted
self._len -= 1
break
if self._is_sparse():
self._size_down()
def __getitem__( self : Dict ,lowerCamelCase__ : KEY ) -> VAL:
'''simple docstring'''
for ind in self._iterate_buckets(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = 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 : Union[str, Any] ) -> int:
'''simple docstring'''
return self._len
def __iter__( self : Tuple ) -> Iterator[KEY]:
'''simple docstring'''
yield from (item.key for item in self._buckets if item)
def __repr__( self : List[str] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """ ,""".join(
F"""{item.key}: {item.val}""" for item in self._buckets if item )
return F"""HashMap({val_string})"""
| 296 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __lowercase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
SCREAMING_SNAKE_CASE = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
# Let's go
SCREAMING_SNAKE_CASE = parser.parse_args()
if not hasattr(_SCREAMING_SNAKE_CASE , """func""" ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE = args.func(_SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 296 | 1 |
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> list[list]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = current_set.copy()
for row_index, row in enumerate(_SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE = row[0]
for column_index, column in enumerate(_SCREAMING_SNAKE_CASE ):
if magnitude == 0:
SCREAMING_SNAKE_CASE = column
continue
SCREAMING_SNAKE_CASE = column / magnitude
# Subtract to cancel term
SCREAMING_SNAKE_CASE = current_set[0]
SCREAMING_SNAKE_CASE = [first_row]
SCREAMING_SNAKE_CASE = current_set[1::]
for row in current_set:
SCREAMING_SNAKE_CASE = []
# If first term is 0, it is already in form we want, so we preserve it
if row[0] == 0:
final_set.append(_SCREAMING_SNAKE_CASE )
continue
for column_index in range(len(_SCREAMING_SNAKE_CASE ) ):
temp_row.append(first_row[column_index] - row[column_index] )
final_set.append(_SCREAMING_SNAKE_CASE )
# Create next recursion iteration set
if len(final_set[0] ) != 3:
SCREAMING_SNAKE_CASE = final_set[0]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for row in final_set[1::]:
current_first_column.append(row[0] )
next_iteration.append(row[1::] )
SCREAMING_SNAKE_CASE = simplify(_SCREAMING_SNAKE_CASE )
for i in range(len(_SCREAMING_SNAKE_CASE ) ):
resultant[i].insert(0 , current_first_column[i] )
resultant.insert(0 , _SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = resultant
return final_set
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> list:
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
SCREAMING_SNAKE_CASE = len(_SCREAMING_SNAKE_CASE ) + 1
if any(len(_SCREAMING_SNAKE_CASE ) != _length for item in equations ):
raise IndexError("""solve_simultaneous() requires n lists of length n+1""" )
for row in equations:
if any(not isinstance(_SCREAMING_SNAKE_CASE , (int, float) ) for column in row ):
raise ValueError("""solve_simultaneous() requires lists of integers""" )
if len(_SCREAMING_SNAKE_CASE ) == 1:
return [equations[0][-1] / equations[0][0]]
SCREAMING_SNAKE_CASE = equations.copy()
if any(0 in row for row in data_set ):
SCREAMING_SNAKE_CASE = data_set.copy()
SCREAMING_SNAKE_CASE = []
for row_index, row in enumerate(_SCREAMING_SNAKE_CASE ):
if 0 not in row:
SCREAMING_SNAKE_CASE = data_set.pop(_SCREAMING_SNAKE_CASE )
break
if not full_row:
raise ValueError("""solve_simultaneous() requires at least 1 full equation""" )
data_set.insert(0 , _SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = data_set.copy()
SCREAMING_SNAKE_CASE = simplify(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = simplified[::-1]
SCREAMING_SNAKE_CASE = []
for row in simplified:
SCREAMING_SNAKE_CASE = row[-1]
if not solutions:
if row[-2] == 0:
solutions.append(0 )
continue
solutions.append(current_solution / row[-2] )
continue
SCREAMING_SNAKE_CASE = row.copy()[: len(_SCREAMING_SNAKE_CASE ) - 1 :]
while temp_row[0] == 0:
temp_row.pop(0 )
if len(_SCREAMING_SNAKE_CASE ) == 0:
solutions.append(0 )
continue
SCREAMING_SNAKE_CASE = temp_row[1::]
SCREAMING_SNAKE_CASE = temp_row[::-1]
for column_index, column in enumerate(_SCREAMING_SNAKE_CASE ):
current_solution -= column * solutions[column_index]
solutions.append(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = []
for item in solutions:
final.append(float(round(_SCREAMING_SNAKE_CASE , 5 ) ) )
return final[::-1]
if __name__ == "__main__":
import doctest
doctest.testmod()
SCREAMING_SNAKE_CASE_ = [
[2, 1, 1, 1, 1, 4],
[1, 2, 1, 1, 1, 5],
[1, 1, 2, 1, 1, 6],
[1, 1, 1, 2, 1, 7],
[1, 1, 1, 1, 2, 8],
]
print(solve_simultaneous(eq))
print(solve_simultaneous([[4, 2]]))
| 296 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = SwinConfig()
SCREAMING_SNAKE_CASE = swin_name.split("""_""" )
SCREAMING_SNAKE_CASE = name_split[1]
SCREAMING_SNAKE_CASE = int(name_split[4] )
SCREAMING_SNAKE_CASE = int(name_split[3][-1] )
if model_size == "tiny":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 6, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "small":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "base":
SCREAMING_SNAKE_CASE = 1_28
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (4, 8, 16, 32)
else:
SCREAMING_SNAKE_CASE = 1_92
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (6, 12, 24, 48)
if "in22k" in swin_name:
SCREAMING_SNAKE_CASE = 2_18_41
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = img_size
SCREAMING_SNAKE_CASE = num_classes
SCREAMING_SNAKE_CASE = embed_dim
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = window_size
return config
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
SCREAMING_SNAKE_CASE = """encoder.""" + name
if "attn.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "norm.weight":
SCREAMING_SNAKE_CASE = """layernorm.weight"""
if name == "norm.bias":
SCREAMING_SNAKE_CASE = """layernorm.bias"""
if "head" in name:
SCREAMING_SNAKE_CASE = name.replace("""head""" , """classifier""" )
else:
SCREAMING_SNAKE_CASE = """swin.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[1] )
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[
:dim
]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
SCREAMING_SNAKE_CASE = get_swin_config(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = SwinForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
SCREAMING_SNAKE_CASE = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" , """-""" ) ) )
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
SCREAMING_SNAKE_CASE = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = timm_model(inputs["""pixel_values"""] )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}""" )
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__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swin_name""",
default="""swin_tiny_patch4_window7_224""",
type=str,
help="""Name of the Swin 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."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 296 | 1 |
from __future__ import annotations
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str: # noqa: E741
'''simple docstring'''
while r - l > 1:
SCREAMING_SNAKE_CASE = (l + r) // 2
if v[m] >= key:
SCREAMING_SNAKE_CASE = m
else:
SCREAMING_SNAKE_CASE = m # noqa: E741
return r
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
if len(_SCREAMING_SNAKE_CASE ) == 0:
return 0
SCREAMING_SNAKE_CASE = [0] * len(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = v[0]
for i in range(1 , len(_SCREAMING_SNAKE_CASE ) ):
if v[i] < tail[0]:
SCREAMING_SNAKE_CASE = v[i]
elif v[i] > tail[length - 1]:
SCREAMING_SNAKE_CASE = v[i]
length += 1
else:
SCREAMING_SNAKE_CASE = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 296 |
import os
from distutils.util import strtobool
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
for e in env_keys:
SCREAMING_SNAKE_CASE = int(os.environ.get(_SCREAMING_SNAKE_CASE , -1 ) )
if val >= 0:
return val
return default
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) )
return strtobool(_SCREAMING_SNAKE_CASE ) == 1 # As its name indicates `strtobool` actually returns an int...
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="no" ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) )
return value
| 296 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"""configuration_bigbird_pegasus""": [
"""BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""BigBirdPegasusConfig""",
"""BigBirdPegasusOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""BigBirdPegasusForCausalLM""",
"""BigBirdPegasusForConditionalGeneration""",
"""BigBirdPegasusForQuestionAnswering""",
"""BigBirdPegasusForSequenceClassification""",
"""BigBirdPegasusModel""",
"""BigBirdPegasusPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP,
BigBirdPegasusConfig,
BigBirdPegasusOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bigbird_pegasus import (
BIGBIRD_PEGASUS_PRETRAINED_MODEL_ARCHIVE_LIST,
BigBirdPegasusForCausalLM,
BigBirdPegasusForConditionalGeneration,
BigBirdPegasusForQuestionAnswering,
BigBirdPegasusForSequenceClassification,
BigBirdPegasusModel,
BigBirdPegasusPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 296 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __lowercase ( _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> List[str]:
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
__snake_case : List[str] = list_field(
default=[] , metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
} , )
__snake_case : List[int] = list_field(
default=[8] , metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
__snake_case : List[int] = list_field(
default=[8, 32, 128, 512] , metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Use FP16 to accelerate inference."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Benchmark training of model"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Verbose memory tracing"} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
} , )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Trace memory line by line"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Save result to a CSV file"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Save all print statements in a log file"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Whether to print environment information"} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
} , )
__snake_case : str = field(
default=F"inference_time_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving time results to csv."} , )
__snake_case : str = field(
default=F"inference_memory_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving memory results to csv."} , )
__snake_case : str = field(
default=F"train_time_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving time results to csv for training."} , )
__snake_case : str = field(
default=F"train_memory_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving memory results to csv for training."} , )
__snake_case : str = field(
default=F"env_info_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving environment information."} , )
__snake_case : str = field(
default=F"log_{round(time() )}.csv" , metadata={"help": "Log filename used if print statements are saved in log."} , )
__snake_case : int = field(default=3 , metadata={"help": "Times an experiment will be run."} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
} , )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
""" are deprecated in general and it is advised to use external Benchmarking libraries """
""" to benchmark Transformer models.""" ,lowerCamelCase__ ,)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return json.dumps(dataclasses.asdict(self ) ,indent=2 )
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
if len(self.models ) <= 0:
raise ValueError(
"""Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
""" bert-base-cased` or `args.models = ['bert-base-cased'].""" )
return self.models
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> int:
'''simple docstring'''
if not self.multi_process:
return False
elif self.is_tpu:
logger.info("""Multiprocessing is currently not possible on TPU.""" )
return False
else:
return True
| 296 | 1 |
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Optional[int] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Any ,lowerCamelCase__ : Any=1024 ,lowerCamelCase__ : Optional[Any]=1024 ,lowerCamelCase__ : int=3.6 ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = tokenizer
SCREAMING_SNAKE_CASE = tokenizer.bos_token_id
SCREAMING_SNAKE_CASE = dataset
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = seq_length * chars_per_token * num_of_sequences
def __iter__( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = iter(self.dataset )
SCREAMING_SNAKE_CASE = True
while more_examples:
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = [], 0
while True:
if buffer_len >= self.input_characters:
break
try:
buffer.append(next(lowerCamelCase__ )["""content"""] )
buffer_len += len(buffer[-1] )
except StopIteration:
SCREAMING_SNAKE_CASE = False
break
SCREAMING_SNAKE_CASE = tokenizer(lowerCamelCase__ ,truncation=lowerCamelCase__ )["""input_ids"""]
SCREAMING_SNAKE_CASE = []
for tokenized_input in tokenized_inputs:
all_token_ids.extend(tokenized_input + [self.concat_token_id] )
for i in range(0 ,len(lowerCamelCase__ ) ,self.seq_length ):
SCREAMING_SNAKE_CASE = all_token_ids[i : i + self.seq_length]
if len(lowerCamelCase__ ) == self.seq_length:
yield torch.tensor(lowerCamelCase__ )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {"""streaming""": True}
SCREAMING_SNAKE_CASE = load_dataset(args.dataset_name , split="""train""" , **_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = ConstantLengthDataset(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , seq_length=args.seq_length )
SCREAMING_SNAKE_CASE = DataLoader(_SCREAMING_SNAKE_CASE , batch_size=args.batch_size )
return eval_dataloader
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
model.eval()
SCREAMING_SNAKE_CASE = []
for step, batch in enumerate(_SCREAMING_SNAKE_CASE ):
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = outputs.loss.repeat(args.batch_size )
losses.append(accelerator.gather(_SCREAMING_SNAKE_CASE ) )
if args.max_eval_steps > 0 and step >= args.max_eval_steps:
break
SCREAMING_SNAKE_CASE = torch.mean(torch.cat(_SCREAMING_SNAKE_CASE ) )
try:
SCREAMING_SNAKE_CASE = torch.exp(_SCREAMING_SNAKE_CASE )
except OverflowError:
SCREAMING_SNAKE_CASE = float("""inf""" )
return loss.item(), perplexity.item()
# Setup Accelerator
SCREAMING_SNAKE_CASE_ = Accelerator()
# Parse configuration
SCREAMING_SNAKE_CASE_ = HfArgumentParser(EvaluationArguments)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
set_seed(args.seed)
# Logging
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
# Load model and tokenizer
SCREAMING_SNAKE_CASE_ = AutoModelForCausalLM.from_pretrained(args.model_ckpt)
SCREAMING_SNAKE_CASE_ = AutoTokenizer.from_pretrained(args.model_ckpt)
# Load dataset and dataloader
SCREAMING_SNAKE_CASE_ = create_dataloader(args)
# Prepare everything with our `accelerator`.
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = accelerator.prepare(model, eval_dataloader)
# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = evaluate(args)
logger.info(F'''loss/eval: {eval_loss}, perplexity: {perplexity}''')
| 296 |
import math
import unittest
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Tuple:
'''simple docstring'''
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) ,"""Zero doesn't have any positive factors, primes must have exactly two.""" ,)
self.assertFalse(
is_prime(1 ) ,"""One only has 1 positive factor, primes must have exactly two.""" ,)
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 296 | 1 |
from torch import nn
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(F"""Unsupported activation function: {act_fn}""" )
| 296 |
import random
class UpperCamelCase__ :
'''simple docstring'''
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : str ) -> tuple[list[int], list[int]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [ord(lowerCamelCase__ ) for i in text]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i in plain:
SCREAMING_SNAKE_CASE = random.randint(1 ,300 )
SCREAMING_SNAKE_CASE = (i + k) * k
cipher.append(lowerCamelCase__ )
key.append(lowerCamelCase__ )
return cipher, key
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : list[int] ,lowerCamelCase__ : list[int] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
for i in range(len(lowerCamelCase__ ) ):
SCREAMING_SNAKE_CASE = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowerCamelCase__ ) )
return "".join(lowerCamelCase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 296 | 1 |
# Lint as: python3
import itertools
import os
import re
SCREAMING_SNAKE_CASE_ = re.compile(r"""([A-Z]+)([A-Z][a-z])""")
SCREAMING_SNAKE_CASE_ = re.compile(r"""([a-z\d])([A-Z])""")
SCREAMING_SNAKE_CASE_ = re.compile(r"""(?<!_)_(?!_)""")
SCREAMING_SNAKE_CASE_ = re.compile(r"""(_{2,})""")
SCREAMING_SNAKE_CASE_ = r"""^\w+(\.\w+)*$"""
SCREAMING_SNAKE_CASE_ = r"""<>:/\|?*"""
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = _uppercase_uppercase_re.sub(r"""\1_\2""" , _SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = _lowercase_uppercase_re.sub(r"""\1_\2""" , _SCREAMING_SNAKE_CASE )
return name.lower()
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = _single_underscore_re.split(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = [_multiple_underscores_re.split(_SCREAMING_SNAKE_CASE ) for n in name]
return "".join(n.capitalize() for n in itertools.chain.from_iterable(_SCREAMING_SNAKE_CASE ) if n != """""" )
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
raise ValueError(F"""Should be a dataset name, not a path: {name}""" )
return camelcase_to_snakecase(_SCREAMING_SNAKE_CASE )
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
if os.path.basename(_SCREAMING_SNAKE_CASE ) != name:
raise ValueError(F"""Should be a dataset name, not a path: {name}""" )
if not re.match(_split_re , _SCREAMING_SNAKE_CASE ):
raise ValueError(F"""Split name should match '{_split_re}'' but got '{split}'.""" )
return F"""{filename_prefix_for_name(_SCREAMING_SNAKE_CASE )}-{split}"""
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = filename_prefix_for_split(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if filetype_suffix:
prefix += F""".{filetype_suffix}"""
SCREAMING_SNAKE_CASE = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return F"""{filepath}*"""
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = filename_prefix_for_split(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if shard_lengths:
SCREAMING_SNAKE_CASE = len(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = [F"""{prefix}-{shard_id:05d}-of-{num_shards:05d}""" for shard_id in range(_SCREAMING_SNAKE_CASE )]
if filetype_suffix:
SCREAMING_SNAKE_CASE = [filename + F""".{filetype_suffix}""" for filename in filenames]
return filenames
else:
SCREAMING_SNAKE_CASE = prefix
if filetype_suffix:
filename += F""".{filetype_suffix}"""
return [filename]
| 296 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = "efficientformer"
def __init__( self : Optional[int] ,lowerCamelCase__ : List[int] = [3, 2, 6, 4] ,lowerCamelCase__ : List[int] = [48, 96, 224, 448] ,lowerCamelCase__ : List[bool] = [True, True, True, True] ,lowerCamelCase__ : int = 448 ,lowerCamelCase__ : int = 32 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : int = 7 ,lowerCamelCase__ : int = 5 ,lowerCamelCase__ : int = 8 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 16 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 2 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : float = 1e-5 ,lowerCamelCase__ : str = "gelu" ,lowerCamelCase__ : float = 0.02 ,lowerCamelCase__ : float = 1e-1_2 ,lowerCamelCase__ : int = 224 ,lowerCamelCase__ : float = 1e-0_5 ,**lowerCamelCase__ : str ,) -> None:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = mlp_expansion_ratio
SCREAMING_SNAKE_CASE = downsamples
SCREAMING_SNAKE_CASE = dim
SCREAMING_SNAKE_CASE = key_dim
SCREAMING_SNAKE_CASE = attention_ratio
SCREAMING_SNAKE_CASE = resolution
SCREAMING_SNAKE_CASE = pool_size
SCREAMING_SNAKE_CASE = downsample_patch_size
SCREAMING_SNAKE_CASE = downsample_stride
SCREAMING_SNAKE_CASE = downsample_pad
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = num_metaad_blocks
SCREAMING_SNAKE_CASE = distillation
SCREAMING_SNAKE_CASE = use_layer_scale
SCREAMING_SNAKE_CASE = layer_scale_init_value
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = batch_norm_eps
| 296 | 1 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
SCREAMING_SNAKE_CASE_ = HfApi()
SCREAMING_SNAKE_CASE_ = {}
# fmt: off
SCREAMING_SNAKE_CASE_ = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076, -0.7991, 0.3969, 0.8498, 0.9189,
-1.8887, -3.3522, 0.7639, 0.2040, 0.6271, -2.7148, -1.6316, 3.0839,
0.3186, 0.2721, -0.9759, -1.2461, 2.6257, 1.3557
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
-2.3639, -2.5344, 0.0054, -0.6674, 1.5990, 1.0158, 0.3124, -2.1436,
1.8795, -2.5429, -0.1566, -0.3973, 1.2490, 2.6447, 1.2283, -0.5208,
-2.8154, -3.5119, 2.3838, 1.2033, 1.7201, -2.1256, -1.4576, 2.7948,
2.4204, -0.9752, -1.2546, 0.8027, 3.2758, 3.1365
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
-0.6531, -0.6891, -0.3172, -0.5375, -0.9140, -0.5367, -0.1175, -0.7869,
-0.3808, -0.4513, -0.2098, -0.0083, 0.3183, 0.5140, 0.2247, -0.1304,
-0.1302, -0.2802, -0.2084, -0.2025, -0.4967, -0.4873, -0.0861, 0.6925,
0.0250, 0.1290, -0.1543, 0.6316, 1.0460, 1.4943
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
0.0911, 0.1107, 0.0182, 0.0435, -0.0805, -0.0608, 0.0381, 0.2172,
-0.0280, 0.1327, -0.0299, -0.0255, -0.0050, -0.1170, -0.1046, 0.0309,
0.1367, 0.1728, -0.0533, -0.0748, -0.0534, 0.1624, 0.0384, -0.1805,
-0.0707, 0.0642, 0.0220, -0.0134, -0.1333, -0.1505
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
0.1321, 0.1337, 0.0440, 0.0622, -0.0591, -0.0370, 0.0503, 0.2133,
-0.0177, 0.1415, -0.0116, -0.0112, 0.0044, -0.0980, -0.0789, 0.0395,
0.1502, 0.1785, -0.0488, -0.0514, -0.0404, 0.1539, 0.0454, -0.1559,
-0.0665, 0.0659, 0.0383, -0.0005, -0.1266, -0.1386
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
0.1154, 0.1218, 0.0307, 0.0526, -0.0711, -0.0541, 0.0366, 0.2078,
-0.0267, 0.1317, -0.0226, -0.0193, -0.0014, -0.1055, -0.0902, 0.0330,
0.1391, 0.1709, -0.0562, -0.0693, -0.0560, 0.1482, 0.0381, -0.1683,
-0.0681, 0.0661, 0.0331, -0.0046, -0.1268, -0.1431
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
0.1192, 0.1240, 0.0414, 0.0606, -0.0557, -0.0412, 0.0430, 0.2042,
-0.0200, 0.1385, -0.0115, -0.0132, 0.0017, -0.0965, -0.0802, 0.0398,
0.1433, 0.1747, -0.0458, -0.0533, -0.0407, 0.1545, 0.0419, -0.1574,
-0.0645, 0.0626, 0.0341, -0.0010, -0.1199, -0.1390
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
0.1075, 0.1074, 0.0205, 0.0431, -0.0774, -0.0607, 0.0298, 0.2042,
-0.0320, 0.1267, -0.0281, -0.0250, -0.0064, -0.1091, -0.0946, 0.0290,
0.1328, 0.1650, -0.0580, -0.0738, -0.0586, 0.1440, 0.0337, -0.1746,
-0.0712, 0.0605, 0.0250, -0.0099, -0.1316, -0.1473
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
-1.4572, -2.0481, -0.0414, -0.6005, 1.4136, 0.5848, 0.4028, -2.7330,
1.2212, -2.1228, 0.2155, 0.4039, 0.7662, 2.0535, 0.7477, -0.3243,
-2.1758, -2.7648, 1.6947, 0.7026, 1.2338, -1.6078, -0.8682, 2.2810,
1.8574, -0.5718, -0.5586, -0.0186, 2.3415, 2.1251])
SCREAMING_SNAKE_CASE_ = torch.tensor([
-1.3690, -1.9720, -0.4090, -0.6966, 1.4660, 0.9938, -0.1385, -2.7324,
0.7736, -1.8917, 0.2923, 0.4293, 0.1693, 1.4112, 1.1887, -0.3181,
-2.2160, -2.6381, 1.3170, 0.8163, 0.9240, -1.6544, -0.6099, 2.5259,
1.6430, -0.9090, -0.9392, -0.0126, 2.4268, 2.3266
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
-1.3525, -1.9628, -0.3956, -0.6860, 1.4664, 1.0014, -0.1259, -2.7212,
0.7772, -1.8811, 0.2996, 0.4388, 0.1704, 1.4029, 1.1701, -0.3027,
-2.2053, -2.6287, 1.3350, 0.8131, 0.9274, -1.6292, -0.6098, 2.5131,
1.6505, -0.8958, -0.9298, -0.0151, 2.4257, 2.3355
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
-2.0585, -2.7897, -0.2850, -0.8940, 1.9052, 0.5702, 0.6345, -3.8959,
1.5932, -3.2319, 0.1974, 0.0287, 1.7566, 2.6543, 0.8387, -0.5351,
-3.2736, -4.3375, 2.9029, 1.6390, 1.4640, -2.1701, -1.9013, 2.9341,
3.4981, -0.6255, -1.1644, -0.1591, 3.7097, 3.2066
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
-2.3139, -2.5594, -0.0197, -0.6785, 1.7001, 1.1606, 0.3075, -2.1740,
1.8071, -2.5630, -0.0926, -0.3811, 1.2116, 2.6246, 1.2731, -0.5398,
-2.8153, -3.6140, 2.3893, 1.3262, 1.6258, -2.1856, -1.3267, 2.8395,
2.3779, -1.0623, -1.2468, 0.8959, 3.3367, 3.2243
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
-2.0628, -2.7667, -0.2089, -0.8263, 2.0539, 0.5992, 0.6495, -3.8336,
1.6025, -3.2817, 0.1721, -0.0633, 1.7516, 2.7039, 0.8100, -0.5908,
-3.2113, -4.4343, 2.9257, 1.3632, 1.5562, -2.1489, -1.9894, 3.0560,
3.3396, -0.7328, -1.0417, 0.0383, 3.7093, 3.2343
])
SCREAMING_SNAKE_CASE_ = torch.tensor([
-1.4574, -2.0569, -0.0473, -0.6117, 1.4018, 0.5769, 0.4129, -2.7344,
1.2241, -2.1397, 0.2000, 0.3937, 0.7616, 2.0453, 0.7324, -0.3391,
-2.1746, -2.7744, 1.6963, 0.6921, 1.2187, -1.6172, -0.8877, 2.2439,
1.8471, -0.5839, -0.5605, -0.0464, 2.3250, 2.1219
])
# fmt: on
SCREAMING_SNAKE_CASE_ = api.list_models(filter="""diffusers""")
for mod in models:
if "google" in mod.author or mod.modelId == "CompVis/ldm-celebahq-256":
SCREAMING_SNAKE_CASE_ = """/home/patrick/google_checkpoints/""" + mod.modelId.split("""/""")[-1]
print(F'''Started running {mod.modelId}!!!''')
if mod.modelId.startswith("""CompVis"""):
SCREAMING_SNAKE_CASE_ = UNetaDModel.from_pretrained(local_checkpoint, subfolder="""unet""")
else:
SCREAMING_SNAKE_CASE_ = UNetaDModel.from_pretrained(local_checkpoint)
torch.manual_seed(0)
random.seed(0)
SCREAMING_SNAKE_CASE_ = torch.randn(1, model.config.in_channels, model.config.sample_size, model.config.sample_size)
SCREAMING_SNAKE_CASE_ = torch.tensor([1_0] * noise.shape[0])
with torch.no_grad():
SCREAMING_SNAKE_CASE_ = 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!!!''')
| 296 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE_ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
if "://" in dataset_path:
SCREAMING_SNAKE_CASE = dataset_path.split("""://""" )[1]
return dataset_path
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
if fs is not None and fs.protocol != "file":
return True
else:
return False
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = not is_remote_filesystem(_SCREAMING_SNAKE_CASE )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(_SCREAMING_SNAKE_CASE ) , fs._strip_protocol(_SCREAMING_SNAKE_CASE ) )
else:
fs.mv(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , recursive=_SCREAMING_SNAKE_CASE )
def __lowercase ( ) -> None:
'''simple docstring'''
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = threading.Lock()
| 296 | 1 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __lowercase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
SCREAMING_SNAKE_CASE = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
# Let's go
SCREAMING_SNAKE_CASE = parser.parse_args()
if not hasattr(_SCREAMING_SNAKE_CASE , """func""" ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE = args.func(_SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 296 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,)
return model
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE = KarrasVeScheduler()
SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""numpy""" ,return_dict=lowerCamelCase__ )[0]
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """google/ncsnpp-celebahq-256"""
SCREAMING_SNAKE_CASE = UNetaDModel.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = KarrasVeScheduler()
SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=20 ,generator=lowerCamelCase__ ,output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 296 | 1 |
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
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
if is_vision_available():
import PIL
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Any = ["pixel_values"]
def __init__( self : Union[str, Any] ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Dict[str, int] = None ,lowerCamelCase__ : PILImageResampling = PILImageResampling.BICUBIC ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Dict[str, int] = None ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Union[int, float] = 1 / 255 ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Optional[Union[float, List[float]]] = None ,lowerCamelCase__ : Optional[Union[float, List[float]]] = None ,lowerCamelCase__ : bool = True ,**lowerCamelCase__ : int ,) -> None:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = size if size is not None else {"""shortest_edge""": 224}
SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase__ ,default_to_square=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else {"""height""": 224, """width""": 224}
SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase__ ,default_to_square=lowerCamelCase__ ,param_name="""crop_size""" )
SCREAMING_SNAKE_CASE = do_resize
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = resample
SCREAMING_SNAKE_CASE = do_center_crop
SCREAMING_SNAKE_CASE = crop_size
SCREAMING_SNAKE_CASE = do_rescale
SCREAMING_SNAKE_CASE = rescale_factor
SCREAMING_SNAKE_CASE = do_normalize
SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
SCREAMING_SNAKE_CASE = image_std if image_std is not None else OPENAI_CLIP_STD
SCREAMING_SNAKE_CASE = do_convert_rgb
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : np.ndarray ,lowerCamelCase__ : Dict[str, int] ,lowerCamelCase__ : PILImageResampling = PILImageResampling.BICUBIC ,lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__ : Tuple ,) -> np.ndarray:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase__ ,default_to_square=lowerCamelCase__ )
if "shortest_edge" not in size:
raise ValueError(F"""The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}""" )
SCREAMING_SNAKE_CASE = get_resize_output_image_size(lowerCamelCase__ ,size=size["""shortest_edge"""] ,default_to_square=lowerCamelCase__ )
return resize(lowerCamelCase__ ,size=lowerCamelCase__ ,resample=lowerCamelCase__ ,data_format=lowerCamelCase__ ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : np.ndarray ,lowerCamelCase__ : Dict[str, int] ,lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__ : Dict ,) -> np.ndarray:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase__ )
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(lowerCamelCase__ ,size=(size["""height"""], size["""width"""]) ,data_format=lowerCamelCase__ ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : np.ndarray ,lowerCamelCase__ : Union[int, float] ,lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__ : Any ,) -> Optional[Any]:
'''simple docstring'''
return rescale(lowerCamelCase__ ,scale=lowerCamelCase__ ,data_format=lowerCamelCase__ ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : np.ndarray ,lowerCamelCase__ : Union[float, List[float]] ,lowerCamelCase__ : Union[float, List[float]] ,lowerCamelCase__ : Optional[Union[str, ChannelDimension]] = None ,**lowerCamelCase__ : Tuple ,) -> np.ndarray:
'''simple docstring'''
return normalize(lowerCamelCase__ ,mean=lowerCamelCase__ ,std=lowerCamelCase__ ,data_format=lowerCamelCase__ ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : ImageInput ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : Dict[str, int] = None ,lowerCamelCase__ : PILImageResampling = None ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : int = None ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : float = None ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : Optional[Union[float, List[float]]] = None ,lowerCamelCase__ : Optional[Union[float, List[float]]] = None ,lowerCamelCase__ : bool = None ,lowerCamelCase__ : Optional[Union[str, TensorType]] = None ,lowerCamelCase__ : Optional[ChannelDimension] = ChannelDimension.FIRST ,**lowerCamelCase__ : List[str] ,) -> PIL.Image.Image:
'''simple docstring'''
SCREAMING_SNAKE_CASE = do_resize if do_resize is not None else self.do_resize
SCREAMING_SNAKE_CASE = size if size is not None else self.size
SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase__ ,param_name="""size""" ,default_to_square=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = resample if resample is not None else self.resample
SCREAMING_SNAKE_CASE = do_center_crop if do_center_crop is not None else self.do_center_crop
SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else self.crop_size
SCREAMING_SNAKE_CASE = get_size_dict(lowerCamelCase__ ,param_name="""crop_size""" ,default_to_square=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = do_rescale if do_rescale is not None else self.do_rescale
SCREAMING_SNAKE_CASE = rescale_factor if rescale_factor is not None else self.rescale_factor
SCREAMING_SNAKE_CASE = do_normalize if do_normalize is not None else self.do_normalize
SCREAMING_SNAKE_CASE = image_mean if image_mean is not None else self.image_mean
SCREAMING_SNAKE_CASE = image_std if image_std is not None else self.image_std
SCREAMING_SNAKE_CASE = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
SCREAMING_SNAKE_CASE = make_list_of_images(lowerCamelCase__ )
if not valid_images(lowerCamelCase__ ):
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 = [convert_to_rgb(lowerCamelCase__ ) for image in images]
# All transformations expect numpy arrays.
SCREAMING_SNAKE_CASE = [to_numpy_array(lowerCamelCase__ ) for image in images]
if do_resize:
SCREAMING_SNAKE_CASE = [self.resize(image=lowerCamelCase__ ,size=lowerCamelCase__ ,resample=lowerCamelCase__ ) for image in images]
if do_center_crop:
SCREAMING_SNAKE_CASE = [self.center_crop(image=lowerCamelCase__ ,size=lowerCamelCase__ ) for image in images]
if do_rescale:
SCREAMING_SNAKE_CASE = [self.rescale(image=lowerCamelCase__ ,scale=lowerCamelCase__ ) for image in images]
if do_normalize:
SCREAMING_SNAKE_CASE = [self.normalize(image=lowerCamelCase__ ,mean=lowerCamelCase__ ,std=lowerCamelCase__ ) for image in images]
SCREAMING_SNAKE_CASE = [to_channel_dimension_format(lowerCamelCase__ ,lowerCamelCase__ ) for image in images]
SCREAMING_SNAKE_CASE = {"""pixel_values""": images}
return BatchFeature(data=lowerCamelCase__ ,tensor_type=lowerCamelCase__ )
| 296 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,lowerCamelCase__ : Callable ,lowerCamelCase__ : Optional[Features] = None ,lowerCamelCase__ : str = None ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : Optional[dict] = None ,lowerCamelCase__ : Optional[int] = None ,**lowerCamelCase__ : Optional[Any] ,) -> List[str]:
'''simple docstring'''
super().__init__(
features=lowerCamelCase__ ,cache_dir=lowerCamelCase__ ,keep_in_memory=lowerCamelCase__ ,streaming=lowerCamelCase__ ,num_proc=lowerCamelCase__ ,**lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = Generator(
cache_dir=lowerCamelCase__ ,features=lowerCamelCase__ ,generator=lowerCamelCase__ ,gen_kwargs=lowerCamelCase__ ,**lowerCamelCase__ ,)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
if self.streaming:
SCREAMING_SNAKE_CASE = self.builder.as_streaming_dataset(split="""train""" )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
self.builder.download_and_prepare(
download_config=lowerCamelCase__ ,download_mode=lowerCamelCase__ ,verification_mode=lowerCamelCase__ ,base_path=lowerCamelCase__ ,num_proc=self.num_proc ,)
SCREAMING_SNAKE_CASE = self.builder.as_dataset(
split="""train""" ,verification_mode=lowerCamelCase__ ,in_memory=self.keep_in_memory )
return dataset
| 296 | 1 |
from ..utils import DummyObject, requires_backends
class UpperCamelCase__ ( metaclass=lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : List[str] = ["note_seq"]
def __init__( self : Dict ,*lowerCamelCase__ : Tuple ,**lowerCamelCase__ : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
requires_backends(self ,["""note_seq"""] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Union[str, Any] ,*lowerCamelCase__ : List[Any] ,**lowerCamelCase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,["""note_seq"""] )
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : int ,*lowerCamelCase__ : str ,**lowerCamelCase__ : str ) -> Optional[Any]:
'''simple docstring'''
requires_backends(cls ,["""note_seq"""] )
| 296 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[float] = field(
default=0.0 , metadata={"help": "The label smoothing epsilon to apply (if not zero)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Whether to SortishSamler or not."} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "whether to use adafactor"} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Encoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Decoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(default=lowerCAmelCase_ , metadata={"help": "Dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Attention dropout probability. Goes into model.config."} )
__snake_case : Optional[str] = field(
default="linear" , metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}"} , )
| 296 | 1 |
from __future__ import annotations
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_failure_array(_SCREAMING_SNAKE_CASE )
# 2) Step through text searching for pattern
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = 0, 0 # index into text, pattern
while i < len(_SCREAMING_SNAKE_CASE ):
if pattern[j] == text[i]:
if j == (len(_SCREAMING_SNAKE_CASE ) - 1):
return True
j += 1
# if this is a prefix in our pattern
# just go back far enough to continue
elif j > 0:
SCREAMING_SNAKE_CASE = failure[j - 1]
continue
i += 1
return False
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> list[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [0]
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = 1
while j < len(_SCREAMING_SNAKE_CASE ):
if pattern[i] == pattern[j]:
i += 1
elif i > 0:
SCREAMING_SNAKE_CASE = failure[i - 1]
continue
j += 1
failure.append(_SCREAMING_SNAKE_CASE )
return failure
if __name__ == "__main__":
# Test 1)
SCREAMING_SNAKE_CASE_ = """abc1abc12"""
SCREAMING_SNAKE_CASE_ = """alskfjaldsabc1abc1abc12k23adsfabcabc"""
SCREAMING_SNAKE_CASE_ = """alskfjaldsk23adsfabcabc"""
assert kmp(pattern, texta) and not kmp(pattern, texta)
# Test 2)
SCREAMING_SNAKE_CASE_ = """ABABX"""
SCREAMING_SNAKE_CASE_ = """ABABZABABYABABX"""
assert kmp(pattern, text)
# Test 3)
SCREAMING_SNAKE_CASE_ = """AAAB"""
SCREAMING_SNAKE_CASE_ = """ABAAAAAB"""
assert kmp(pattern, text)
# Test 4)
SCREAMING_SNAKE_CASE_ = """abcdabcy"""
SCREAMING_SNAKE_CASE_ = """abcxabcdabxabcdabcdabcy"""
assert kmp(pattern, text)
# Test 5)
SCREAMING_SNAKE_CASE_ = """aabaabaaa"""
assert get_failure_array(pattern) == [0, 1, 0, 1, 2, 3, 4, 5, 2]
| 296 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[str] = TextToVideoSDPipeline
__snake_case : int = TEXT_TO_IMAGE_PARAMS
__snake_case : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
__snake_case : Dict = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""") ,up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""") ,cross_attention_dim=32 ,attention_head_dim=4 ,)
SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.00085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=lowerCamelCase__ ,set_alpha_to_one=lowerCamelCase__ ,)
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = 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 )
SCREAMING_SNAKE_CASE = 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=1000 ,hidden_act="""gelu""" ,projection_dim=512 ,)
SCREAMING_SNAKE_CASE = CLIPTextModel(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : int=0 ) -> List[Any]:
'''simple docstring'''
if str(lowerCamelCase__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCamelCase__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe.to(lowerCamelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """np"""
SCREAMING_SNAKE_CASE = sd_pipe(**lowerCamelCase__ ).frames
SCREAMING_SNAKE_CASE = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
SCREAMING_SNAKE_CASE = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=1e-2 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=25 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=2 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 296 | 1 |
SCREAMING_SNAKE_CASE_ = 6_5_5_2_1
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = 0
for plain_chr in plain_text:
SCREAMING_SNAKE_CASE = (a + ord(_SCREAMING_SNAKE_CASE )) % MOD_ADLER
SCREAMING_SNAKE_CASE = (b + a) % MOD_ADLER
return (b << 16) | a
| 296 |
def __lowercase ( _SCREAMING_SNAKE_CASE = 10 ) -> str:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or n < 0:
raise ValueError("""Invalid input""" )
SCREAMING_SNAKE_CASE = 10**n
SCREAMING_SNAKE_CASE = 2_84_33 * (pow(2 , 7_83_04_57 , _SCREAMING_SNAKE_CASE )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(1_0) = }''')
| 296 | 1 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""xlm-roberta-base""": """https://huggingface.co/xlm-roberta-base/resolve/main/config.json""",
"""xlm-roberta-large""": """https://huggingface.co/xlm-roberta-large/resolve/main/config.json""",
"""xlm-roberta-large-finetuned-conll02-dutch""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json"""
),
"""xlm-roberta-large-finetuned-conll02-spanish""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json"""
),
"""xlm-roberta-large-finetuned-conll03-english""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json"""
),
"""xlm-roberta-large-finetuned-conll03-german""": (
"""https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[int] = "xlm-roberta"
def __init__( self : Any ,lowerCamelCase__ : Optional[int]=30522 ,lowerCamelCase__ : Optional[Any]=768 ,lowerCamelCase__ : Optional[int]=12 ,lowerCamelCase__ : Any=12 ,lowerCamelCase__ : Optional[Any]=3072 ,lowerCamelCase__ : Optional[Any]="gelu" ,lowerCamelCase__ : List[str]=0.1 ,lowerCamelCase__ : List[str]=0.1 ,lowerCamelCase__ : List[str]=512 ,lowerCamelCase__ : Any=2 ,lowerCamelCase__ : str=0.02 ,lowerCamelCase__ : List[Any]=1e-1_2 ,lowerCamelCase__ : int=1 ,lowerCamelCase__ : str=0 ,lowerCamelCase__ : List[str]=2 ,lowerCamelCase__ : int="absolute" ,lowerCamelCase__ : Optional[int]=True ,lowerCamelCase__ : List[str]=None ,**lowerCamelCase__ : Union[str, Any] ,) -> List[Any]:
'''simple docstring'''
super().__init__(pad_token_id=lowerCamelCase__ ,bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = classifier_dropout
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
SCREAMING_SNAKE_CASE = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
SCREAMING_SNAKE_CASE = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 296 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""LlamaForCausalLM""",
"""LlamaModel""",
"""LlamaPreTrainedModel""",
"""LlamaForSequenceClassification""",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 296 | 1 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 296 |
from pathlib import Path
import fire
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
for path in src_dir.iterdir():
SCREAMING_SNAKE_CASE = [x.rstrip() for x in list(path.open().readlines() )][:n]
SCREAMING_SNAKE_CASE = dest_dir.joinpath(path.name )
print(_SCREAMING_SNAKE_CASE )
dest_path.open("""w""" ).write("""\n""".join(_SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
fire.Fire(minify)
| 296 | 1 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : str = OpenAIGPTTokenizer
__snake_case : List[str] = OpenAIGPTTokenizerFast
__snake_case : Tuple = True
__snake_case : int = False
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> List[str]:
'''simple docstring'''
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
SCREAMING_SNAKE_CASE = [
"""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>""",
]
SCREAMING_SNAKE_CASE = dict(zip(lowerCamelCase__ ,range(len(lowerCamelCase__ ) ) ) )
SCREAMING_SNAKE_CASE = ["""#version: 0.2""", """l o""", """lo w""", """e r</w>""", """"""]
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file ,"""w""" ) as fp:
fp.write(json.dumps(lowerCamelCase__ ) )
with open(self.merges_file ,"""w""" ) as fp:
fp.write("""\n""".join(lowerCamelCase__ ) )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : Dict ) -> Optional[int]:
'''simple docstring'''
return "lower newer", "lower newer"
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = OpenAIGPTTokenizer(self.vocab_file ,self.merges_file )
SCREAMING_SNAKE_CASE = """lower"""
SCREAMING_SNAKE_CASE = ["""low""", """er</w>"""]
SCREAMING_SNAKE_CASE = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokens + ["""<unk>"""]
SCREAMING_SNAKE_CASE = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : List[Any]=15 ) -> Tuple:
'''simple docstring'''
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})""" ):
SCREAMING_SNAKE_CASE = self.rust_tokenizer_class.from_pretrained(lowerCamelCase__ ,**lowerCamelCase__ )
# Simple input
SCREAMING_SNAKE_CASE = """This is a simple input"""
SCREAMING_SNAKE_CASE = ["""This is a simple input 1""", """This is a simple input 2"""]
SCREAMING_SNAKE_CASE = ("""This is a simple input""", """This is a pair""")
SCREAMING_SNAKE_CASE = [
("""This is a simple input 1""", """This is a simple input 2"""),
("""This is a simple pair 1""", """This is a simple pair 2"""),
]
# Simple input tests
self.assertRaises(lowerCamelCase__ ,tokenizer_r.encode ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" )
# Simple input
self.assertRaises(lowerCamelCase__ ,tokenizer_r.encode_plus ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" )
# Simple input
self.assertRaises(
lowerCamelCase__ ,tokenizer_r.batch_encode_plus ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" ,)
# Pair input
self.assertRaises(lowerCamelCase__ ,tokenizer_r.encode ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" )
# Pair input
self.assertRaises(lowerCamelCase__ ,tokenizer_r.encode_plus ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" )
# Pair input
self.assertRaises(
lowerCamelCase__ ,tokenizer_r.batch_encode_plus ,lowerCamelCase__ ,max_length=lowerCamelCase__ ,padding="""max_length""" ,)
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[str]:
'''simple docstring'''
pass
@require_ftfy
@require_spacy
@require_tokenizers
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
pass
| 296 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
SCREAMING_SNAKE_CASE_ = get_tests_dir("""fixtures/dummy-config.json""")
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
'''simple docstring'''
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""fake-roberta""" )
os.makedirs(lowerCamelCase__ ,exist_ok=lowerCamelCase__ )
with open(os.path.join(lowerCamelCase__ ,"""config.json""" ) ,"""w""" ) as f:
f.write(json.dumps({} ) )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertEqual(type(lowerCamelCase__ ) ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
try:
AutoConfig.register("""custom""" ,lowerCamelCase__ )
# Wrong model type will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""model""" ,lowerCamelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""bert""" ,lowerCamelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""bert-base is not a local folder and is not a valid model identifier""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,revision="""aaaaaa""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" ,):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(reloaded_config.__class__.__name__ ,"""NewModelConfig""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = "new-model"
try:
AutoConfig.register("""new-model""" ,lowerCamelCase__ )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 296 | 1 |
import os
import sys
SCREAMING_SNAKE_CASE_ = 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,
)
SCREAMING_SNAKE_CASE_ = [
"""torch""",
"""numpy""",
"""tokenizers""",
"""filelock""",
"""requests""",
"""tqdm""",
"""regex""",
"""sentencepiece""",
"""sacremoses""",
"""importlib_metadata""",
"""huggingface_hub""",
]
@add_start_docstrings(AutoConfig.__doc__ )
def __lowercase ( *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
return AutoConfig.from_pretrained(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoTokenizer.__doc__ )
def __lowercase ( *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
return AutoTokenizer.from_pretrained(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoModel.__doc__ )
def __lowercase ( *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
return AutoModel.from_pretrained(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def __lowercase ( *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[Any]:
'''simple docstring'''
return AutoModelForCausalLM.from_pretrained(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def __lowercase ( *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
return AutoModelForMaskedLM.from_pretrained(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def __lowercase ( *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
return AutoModelForSequenceClassification.from_pretrained(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def __lowercase ( *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
return AutoModelForQuestionAnswering.from_pretrained(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
| 296 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Dict = "git_vision_model"
def __init__( self : List[Any] ,lowerCamelCase__ : Dict=768 ,lowerCamelCase__ : Union[str, Any]=3072 ,lowerCamelCase__ : Optional[int]=12 ,lowerCamelCase__ : Tuple=12 ,lowerCamelCase__ : Tuple=3 ,lowerCamelCase__ : Optional[Any]=224 ,lowerCamelCase__ : Union[str, Any]=16 ,lowerCamelCase__ : List[Any]="quick_gelu" ,lowerCamelCase__ : Optional[Any]=1e-5 ,lowerCamelCase__ : str=0.0 ,lowerCamelCase__ : Optional[int]=0.02 ,**lowerCamelCase__ : Union[str, Any] ,) -> Optional[int]:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = hidden_act
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Tuple ,lowerCamelCase__ : Union[str, os.PathLike] ,**lowerCamelCase__ : int ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = cls.get_config_dict(lowerCamelCase__ ,**lowerCamelCase__ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("""model_type""" ) == "git":
SCREAMING_SNAKE_CASE = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowerCamelCase__ ,**lowerCamelCase__ )
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Dict = "git"
def __init__( self : Optional[int] ,lowerCamelCase__ : int=None ,lowerCamelCase__ : str=30522 ,lowerCamelCase__ : Tuple=768 ,lowerCamelCase__ : Union[str, Any]=6 ,lowerCamelCase__ : str=12 ,lowerCamelCase__ : List[str]=3072 ,lowerCamelCase__ : Dict="gelu" ,lowerCamelCase__ : Tuple=0.1 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : List[str]=1024 ,lowerCamelCase__ : List[str]=0.02 ,lowerCamelCase__ : str=1e-1_2 ,lowerCamelCase__ : Optional[int]=0 ,lowerCamelCase__ : Optional[int]="absolute" ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : str=False ,lowerCamelCase__ : int=101 ,lowerCamelCase__ : int=102 ,lowerCamelCase__ : Dict=None ,**lowerCamelCase__ : List[Any] ,) -> Optional[Any]:
'''simple docstring'''
super().__init__(bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,pad_token_id=lowerCamelCase__ ,**lowerCamelCase__ )
if vision_config is None:
SCREAMING_SNAKE_CASE = {}
logger.info("""vision_config is None. initializing the GitVisionConfig with default values.""" )
SCREAMING_SNAKE_CASE = GitVisionConfig(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = tie_word_embeddings
SCREAMING_SNAKE_CASE = num_image_with_embedding
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = eos_token_id
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
| 296 | 1 |
import pytest
import requests
from datasets.utils.file_utils import http_head
from .utils import OfflineSimulationMode, RequestWouldHangIndefinitelyError, offline
@pytest.mark.integration
def __lowercase ( ) -> Optional[Any]:
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_TIMES_OUT ):
with pytest.raises(_SCREAMING_SNAKE_CASE ):
requests.request("""GET""" , """https://huggingface.co""" )
with pytest.raises(requests.exceptions.ConnectTimeout ):
requests.request("""GET""" , """https://huggingface.co""" , timeout=1.0 )
@pytest.mark.integration
def __lowercase ( ) -> str:
'''simple docstring'''
with offline(OfflineSimulationMode.CONNECTION_FAILS ):
with pytest.raises(requests.exceptions.ConnectionError ):
requests.request("""GET""" , """https://huggingface.co""" )
def __lowercase ( ) -> Tuple:
'''simple docstring'''
with offline(OfflineSimulationMode.HF_DATASETS_OFFLINE_SET_TO_1 ):
with pytest.raises(_SCREAMING_SNAKE_CASE ):
http_head("""https://huggingface.co""" )
| 296 |
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
SCREAMING_SNAKE_CASE_ = random.Random()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE = global_rng
SCREAMING_SNAKE_CASE = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[int]=7 ,lowerCamelCase__ : Optional[Any]=400 ,lowerCamelCase__ : List[str]=2000 ,lowerCamelCase__ : List[str]=2048 ,lowerCamelCase__ : Any=128 ,lowerCamelCase__ : List[str]=1 ,lowerCamelCase__ : str=512 ,lowerCamelCase__ : Optional[Any]=30 ,lowerCamelCase__ : Tuple=44100 ,) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = min_seq_length
SCREAMING_SNAKE_CASE = max_seq_length
SCREAMING_SNAKE_CASE = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE = spectrogram_length
SCREAMING_SNAKE_CASE = feature_size
SCREAMING_SNAKE_CASE = num_audio_channels
SCREAMING_SNAKE_CASE = hop_length
SCREAMING_SNAKE_CASE = chunk_length
SCREAMING_SNAKE_CASE = sampling_rate
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[int]:
'''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 SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : Tuple=False ,lowerCamelCase__ : Union[str, Any]=False ) -> str:
'''simple docstring'''
def _flatten(lowerCamelCase__ : List[Any] ):
return list(itertools.chain(*lowerCamelCase__ ) )
if equal_length:
SCREAMING_SNAKE_CASE = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[Any] = TvltFeatureExtractor
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TvltFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase__ ,"""spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""sampling_rate""" ) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = feat_extract_first.save_pretrained(lowerCamelCase__ )[0]
check_json_file_has_correct_format(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in range(800 ,1400 ,200 )]
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,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
SCREAMING_SNAKE_CASE = feature_extractor(
lowerCamelCase__ ,return_tensors="""np""" ,sampling_rate=44100 ,mask_audio=lowerCamelCase__ ).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.
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE = np.asarray(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,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 SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,"""clean""" ,split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE = ds.sort("""id""" ).select(range(lowerCamelCase__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape ,(1, 1, 192, 128) )
SCREAMING_SNAKE_CASE = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,lowerCamelCase__ ,atol=1e-4 ) )
| 296 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
# See all Cvt models at https://huggingface.co/models?filter=cvt
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : List[str] = "cvt"
def __init__( self : int ,lowerCamelCase__ : Dict=3 ,lowerCamelCase__ : int=[7, 3, 3] ,lowerCamelCase__ : Tuple=[4, 2, 2] ,lowerCamelCase__ : Union[str, Any]=[2, 1, 1] ,lowerCamelCase__ : Dict=[64, 192, 384] ,lowerCamelCase__ : str=[1, 3, 6] ,lowerCamelCase__ : Optional[int]=[1, 2, 10] ,lowerCamelCase__ : Union[str, Any]=[4.0, 4.0, 4.0] ,lowerCamelCase__ : str=[0.0, 0.0, 0.0] ,lowerCamelCase__ : List[str]=[0.0, 0.0, 0.0] ,lowerCamelCase__ : Dict=[0.0, 0.0, 0.1] ,lowerCamelCase__ : str=[True, True, True] ,lowerCamelCase__ : str=[False, False, True] ,lowerCamelCase__ : Tuple=["dw_bn", "dw_bn", "dw_bn"] ,lowerCamelCase__ : Optional[Any]=[3, 3, 3] ,lowerCamelCase__ : Union[str, Any]=[1, 1, 1] ,lowerCamelCase__ : List[str]=[2, 2, 2] ,lowerCamelCase__ : List[Any]=[1, 1, 1] ,lowerCamelCase__ : List[str]=[1, 1, 1] ,lowerCamelCase__ : Dict=0.02 ,lowerCamelCase__ : int=1e-1_2 ,**lowerCamelCase__ : Optional[Any] ,) -> List[str]:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = patch_sizes
SCREAMING_SNAKE_CASE = patch_stride
SCREAMING_SNAKE_CASE = patch_padding
SCREAMING_SNAKE_CASE = embed_dim
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = depth
SCREAMING_SNAKE_CASE = mlp_ratio
SCREAMING_SNAKE_CASE = attention_drop_rate
SCREAMING_SNAKE_CASE = drop_rate
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = qkv_bias
SCREAMING_SNAKE_CASE = cls_token
SCREAMING_SNAKE_CASE = qkv_projection_method
SCREAMING_SNAKE_CASE = kernel_qkv
SCREAMING_SNAKE_CASE = padding_kv
SCREAMING_SNAKE_CASE = stride_kv
SCREAMING_SNAKE_CASE = padding_q
SCREAMING_SNAKE_CASE = stride_q
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
| 296 |
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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
SCREAMING_SNAKE_CASE = [1_44, 1_92, 2_40]
SCREAMING_SNAKE_CASE = [16, 32, 64, 96, 1_28, 1_60, 6_40]
elif "mobilevit_xs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [96, 1_20, 1_44]
SCREAMING_SNAKE_CASE = [16, 32, 48, 64, 80, 96, 3_84]
elif "mobilevit_xxs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [64, 80, 96]
SCREAMING_SNAKE_CASE = [16, 16, 24, 48, 64, 80, 3_20]
SCREAMING_SNAKE_CASE = 0.05
SCREAMING_SNAKE_CASE = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = 5_12
SCREAMING_SNAKE_CASE = 16
SCREAMING_SNAKE_CASE = 21
SCREAMING_SNAKE_CASE = """pascal-voc-id2label.json"""
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
SCREAMING_SNAKE_CASE = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
SCREAMING_SNAKE_CASE = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.weight""" , """.layernorm.weight""" )
if F""".global_rep.{i}.bias""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.bias""" , """.layernorm.bias""" )
if ".global_rep." in name:
SCREAMING_SNAKE_CASE = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
SCREAMING_SNAKE_CASE = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
SCREAMING_SNAKE_CASE = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
SCREAMING_SNAKE_CASE = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
SCREAMING_SNAKE_CASE = """mobilevit.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Dict:
'''simple docstring'''
if base_model:
SCREAMING_SNAKE_CASE = """"""
else:
SCREAMING_SNAKE_CASE = """mobilevit."""
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if key[:8] == "encoder.":
SCREAMING_SNAKE_CASE = key[8:]
if "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[0][6:] ) - 1
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
SCREAMING_SNAKE_CASE = layer.transformer.layer[transformer_num].attention.attention.all_head_size
SCREAMING_SNAKE_CASE = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[:dim]
SCREAMING_SNAKE_CASE = val[dim : dim * 2]
SCREAMING_SNAKE_CASE = val[-dim:]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_mobilevit_config(_SCREAMING_SNAKE_CASE )
# load original state_dict
SCREAMING_SNAKE_CASE = torch.load(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = MobileViTForSemanticSegmentation(_SCREAMING_SNAKE_CASE ).eval()
else:
SCREAMING_SNAKE_CASE = MobileViTForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
SCREAMING_SNAKE_CASE = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileViTImageProcessor
SCREAMING_SNAKE_CASE = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
[[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
[[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
[[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
[[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
[[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
[[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
else:
assert logits.shape == (1, 10_00)
if mobilevit_name == "mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
elif mobilevit_name == "mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
elif mobilevit_name == "mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
SCREAMING_SNAKE_CASE = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
SCREAMING_SNAKE_CASE = model_mapping[mobilevit_name]
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
model.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--mobilevit_name""",
default="""mobilevit_s""",
type=str,
help=(
"""Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
""" 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
),
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 296 | 1 |
import unittest
from parameterized import parameterized
from transformers import AutoTokenizer, GPTNeoXConfig, is_torch_available, set_seed
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 (
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
GPTNeoXModel,
)
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : int ,lowerCamelCase__ : int ,lowerCamelCase__ : Optional[int]=13 ,lowerCamelCase__ : Any=7 ,lowerCamelCase__ : List[str]=True ,lowerCamelCase__ : Optional[int]=True ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : str=99 ,lowerCamelCase__ : Dict=64 ,lowerCamelCase__ : Any=5 ,lowerCamelCase__ : Any=4 ,lowerCamelCase__ : List[Any]=37 ,lowerCamelCase__ : str="gelu" ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : Optional[int]=0.1 ,lowerCamelCase__ : List[str]=512 ,lowerCamelCase__ : Union[str, Any]=16 ,lowerCamelCase__ : List[str]=2 ,lowerCamelCase__ : Optional[int]=0.02 ,lowerCamelCase__ : Any=3 ,lowerCamelCase__ : Dict=4 ,lowerCamelCase__ : List[str]=None ,) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_input_mask
SCREAMING_SNAKE_CASE = use_token_type_ids
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = type_vocab_size
SCREAMING_SNAKE_CASE = type_sequence_label_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = num_labels
SCREAMING_SNAKE_CASE = num_choices
SCREAMING_SNAKE_CASE = scope
SCREAMING_SNAKE_CASE = vocab_size - 1
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
SCREAMING_SNAKE_CASE = None
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,self.num_labels )
SCREAMING_SNAKE_CASE = self.get_config()
return config, input_ids, input_mask, token_labels
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
return GPTNeoXConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,type_vocab_size=self.type_vocab_size ,is_decoder=lowerCamelCase__ ,initializer_range=self.initializer_range ,pad_token_id=self.pad_token_id ,)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE = True
return config, input_ids, input_mask, token_labels
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = GPTNeoXModel(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Any ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : List[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = GPTNeoXModel(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : int ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : List[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = GPTNeoXForCausalLM(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : str ,lowerCamelCase__ : List[str] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = GPTNeoXForQuestionAnswering(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ )
self.parent.assertEqual(result.start_logits.shape ,(self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape ,(self.batch_size, self.seq_length) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Dict ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = GPTNeoXForSequenceClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : Any ,lowerCamelCase__ : Any ,lowerCamelCase__ : Dict ,lowerCamelCase__ : List[str] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.num_labels
SCREAMING_SNAKE_CASE = GPTNeoXForTokenClassification(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.num_labels) )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : str ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Tuple ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = GPTNeoXForCausalLM(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
# first forward pass
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,use_cache=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE = ids_tensor((self.batch_size, 3) ,config.vocab_size )
SCREAMING_SNAKE_CASE = ids_tensor((self.batch_size, 3) ,vocab_size=2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE = torch.cat([input_ids, next_tokens] ,dim=-1 )
SCREAMING_SNAKE_CASE = torch.cat([input_mask, next_mask] ,dim=-1 )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,output_hidden_states=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = output_from_no_past["""hidden_states"""][0]
SCREAMING_SNAKE_CASE = model(
lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,past_key_values=lowerCamelCase__ ,output_hidden_states=lowerCamelCase__ ,)["""hidden_states"""][0]
# select random slice
SCREAMING_SNAKE_CASE = ids_tensor((1,) ,output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE = 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(lowerCamelCase__ ,lowerCamelCase__ ,atol=1e-3 ) )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = config_and_inputs
SCREAMING_SNAKE_CASE = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase__ ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Tuple = (
(
GPTNeoXModel,
GPTNeoXForCausalLM,
GPTNeoXForQuestionAnswering,
GPTNeoXForSequenceClassification,
GPTNeoXForTokenClassification,
)
if is_torch_available()
else ()
)
__snake_case : Optional[int] = (GPTNeoXForCausalLM,) if is_torch_available() else ()
__snake_case : Tuple = (
{
"feature-extraction": GPTNeoXModel,
"question-answering": GPTNeoXForQuestionAnswering,
"text-classification": GPTNeoXForSequenceClassification,
"text-generation": GPTNeoXForCausalLM,
"token-classification": GPTNeoXForTokenClassification,
"zero-shot": GPTNeoXForSequenceClassification,
}
if is_torch_available()
else {}
)
__snake_case : List[str] = False
__snake_case : Optional[int] = False
__snake_case : Dict = False
__snake_case : Union[str, Any] = False
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = GPTNeoXModelTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self ,config_class=lowerCamelCase__ ,hidden_size=64 ,num_attention_heads=8 )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> str:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_decoder()
SCREAMING_SNAKE_CASE = None
self.model_tester.create_and_check_model_as_decoder(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*lowerCamelCase__ )
@unittest.skip(reason="""Feed forward chunking is not implemented""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
pass
@parameterized.expand([("""linear""",), ("""dynamic""",)] )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : int ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_common()
SCREAMING_SNAKE_CASE = ids_tensor([1, 10] ,config.vocab_size )
SCREAMING_SNAKE_CASE = ids_tensor([1, int(config.max_position_embeddings * 1.5 )] ,config.vocab_size )
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
SCREAMING_SNAKE_CASE = GPTNeoXModel(lowerCamelCase__ )
original_model.to(lowerCamelCase__ )
original_model.eval()
SCREAMING_SNAKE_CASE = original_model(lowerCamelCase__ ).last_hidden_state
SCREAMING_SNAKE_CASE = original_model(lowerCamelCase__ ).last_hidden_state
set_seed(42 ) # Fixed seed at init time so the two models get the same random weights
SCREAMING_SNAKE_CASE = {"""type""": scaling_type, """factor""": 10.0}
SCREAMING_SNAKE_CASE = GPTNeoXModel(lowerCamelCase__ )
scaled_model.to(lowerCamelCase__ )
scaled_model.eval()
SCREAMING_SNAKE_CASE = scaled_model(lowerCamelCase__ ).last_hidden_state
SCREAMING_SNAKE_CASE = scaled_model(lowerCamelCase__ ).last_hidden_state
# Dynamic scaling does not change the RoPE embeddings until it receives an input longer than the original
# maximum sequence length, so the outputs for the short input should match.
if scaling_type == "dynamic":
self.assertTrue(torch.allclose(lowerCamelCase__ ,lowerCamelCase__ ,atol=1e-5 ) )
else:
self.assertFalse(torch.allclose(lowerCamelCase__ ,lowerCamelCase__ ,atol=1e-5 ) )
# The output should be different for long inputs
self.assertFalse(torch.allclose(lowerCamelCase__ ,lowerCamelCase__ ,atol=1e-5 ) )
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("""EleutherAI/pythia-410m-deduped""" )
for checkpointing in [True, False]:
SCREAMING_SNAKE_CASE = GPTNeoXForCausalLM.from_pretrained("""EleutherAI/pythia-410m-deduped""" )
if checkpointing:
model.gradient_checkpointing_enable()
else:
model.gradient_checkpointing_disable()
model.to(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer("""My favorite food is""" ,return_tensors="""pt""" ).to(lowerCamelCase__ )
# The hub repo. is updated on 2023-04-04, resulting in poor outputs.
# See: https://github.com/huggingface/transformers/pull/24193
SCREAMING_SNAKE_CASE = """My favorite food is a good old-fashioned, old-fashioned, old-fashioned.\n\nI'm not sure"""
SCREAMING_SNAKE_CASE = model.generate(**lowerCamelCase__ ,do_sample=lowerCamelCase__ ,max_new_tokens=20 )
SCREAMING_SNAKE_CASE = tokenizer.batch_decode(lowerCamelCase__ )[0]
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
| 296 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Base model mapping
("""albert""", """FlaxAlbertModel"""),
("""bart""", """FlaxBartModel"""),
("""beit""", """FlaxBeitModel"""),
("""bert""", """FlaxBertModel"""),
("""big_bird""", """FlaxBigBirdModel"""),
("""blenderbot""", """FlaxBlenderbotModel"""),
("""blenderbot-small""", """FlaxBlenderbotSmallModel"""),
("""clip""", """FlaxCLIPModel"""),
("""distilbert""", """FlaxDistilBertModel"""),
("""electra""", """FlaxElectraModel"""),
("""gpt-sw3""", """FlaxGPT2Model"""),
("""gpt2""", """FlaxGPT2Model"""),
("""gpt_neo""", """FlaxGPTNeoModel"""),
("""gptj""", """FlaxGPTJModel"""),
("""longt5""", """FlaxLongT5Model"""),
("""marian""", """FlaxMarianModel"""),
("""mbart""", """FlaxMBartModel"""),
("""mt5""", """FlaxMT5Model"""),
("""opt""", """FlaxOPTModel"""),
("""pegasus""", """FlaxPegasusModel"""),
("""regnet""", """FlaxRegNetModel"""),
("""resnet""", """FlaxResNetModel"""),
("""roberta""", """FlaxRobertaModel"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormModel"""),
("""roformer""", """FlaxRoFormerModel"""),
("""t5""", """FlaxT5Model"""),
("""vision-text-dual-encoder""", """FlaxVisionTextDualEncoderModel"""),
("""vit""", """FlaxViTModel"""),
("""wav2vec2""", """FlaxWav2Vec2Model"""),
("""whisper""", """FlaxWhisperModel"""),
("""xglm""", """FlaxXGLMModel"""),
("""xlm-roberta""", """FlaxXLMRobertaModel"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for pre-training mapping
("""albert""", """FlaxAlbertForPreTraining"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForPreTraining"""),
("""big_bird""", """FlaxBigBirdForPreTraining"""),
("""electra""", """FlaxElectraForPreTraining"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
("""wav2vec2""", """FlaxWav2Vec2ForPreTraining"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Masked LM mapping
("""albert""", """FlaxAlbertForMaskedLM"""),
("""bart""", """FlaxBartForConditionalGeneration"""),
("""bert""", """FlaxBertForMaskedLM"""),
("""big_bird""", """FlaxBigBirdForMaskedLM"""),
("""distilbert""", """FlaxDistilBertForMaskedLM"""),
("""electra""", """FlaxElectraForMaskedLM"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""roberta""", """FlaxRobertaForMaskedLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMaskedLM"""),
("""roformer""", """FlaxRoFormerForMaskedLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForMaskedLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Seq2Seq Causal LM mapping
("""bart""", """FlaxBartForConditionalGeneration"""),
("""blenderbot""", """FlaxBlenderbotForConditionalGeneration"""),
("""blenderbot-small""", """FlaxBlenderbotSmallForConditionalGeneration"""),
("""encoder-decoder""", """FlaxEncoderDecoderModel"""),
("""longt5""", """FlaxLongT5ForConditionalGeneration"""),
("""marian""", """FlaxMarianMTModel"""),
("""mbart""", """FlaxMBartForConditionalGeneration"""),
("""mt5""", """FlaxMT5ForConditionalGeneration"""),
("""pegasus""", """FlaxPegasusForConditionalGeneration"""),
("""t5""", """FlaxT5ForConditionalGeneration"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Image-classsification
("""beit""", """FlaxBeitForImageClassification"""),
("""regnet""", """FlaxRegNetForImageClassification"""),
("""resnet""", """FlaxResNetForImageClassification"""),
("""vit""", """FlaxViTForImageClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""vision-encoder-decoder""", """FlaxVisionEncoderDecoderModel"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Causal LM mapping
("""bart""", """FlaxBartForCausalLM"""),
("""bert""", """FlaxBertForCausalLM"""),
("""big_bird""", """FlaxBigBirdForCausalLM"""),
("""electra""", """FlaxElectraForCausalLM"""),
("""gpt-sw3""", """FlaxGPT2LMHeadModel"""),
("""gpt2""", """FlaxGPT2LMHeadModel"""),
("""gpt_neo""", """FlaxGPTNeoForCausalLM"""),
("""gptj""", """FlaxGPTJForCausalLM"""),
("""opt""", """FlaxOPTForCausalLM"""),
("""roberta""", """FlaxRobertaForCausalLM"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForCausalLM"""),
("""xglm""", """FlaxXGLMForCausalLM"""),
("""xlm-roberta""", """FlaxXLMRobertaForCausalLM"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Sequence Classification mapping
("""albert""", """FlaxAlbertForSequenceClassification"""),
("""bart""", """FlaxBartForSequenceClassification"""),
("""bert""", """FlaxBertForSequenceClassification"""),
("""big_bird""", """FlaxBigBirdForSequenceClassification"""),
("""distilbert""", """FlaxDistilBertForSequenceClassification"""),
("""electra""", """FlaxElectraForSequenceClassification"""),
("""mbart""", """FlaxMBartForSequenceClassification"""),
("""roberta""", """FlaxRobertaForSequenceClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForSequenceClassification"""),
("""roformer""", """FlaxRoFormerForSequenceClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForSequenceClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Question Answering mapping
("""albert""", """FlaxAlbertForQuestionAnswering"""),
("""bart""", """FlaxBartForQuestionAnswering"""),
("""bert""", """FlaxBertForQuestionAnswering"""),
("""big_bird""", """FlaxBigBirdForQuestionAnswering"""),
("""distilbert""", """FlaxDistilBertForQuestionAnswering"""),
("""electra""", """FlaxElectraForQuestionAnswering"""),
("""mbart""", """FlaxMBartForQuestionAnswering"""),
("""roberta""", """FlaxRobertaForQuestionAnswering"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForQuestionAnswering"""),
("""roformer""", """FlaxRoFormerForQuestionAnswering"""),
("""xlm-roberta""", """FlaxXLMRobertaForQuestionAnswering"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Token Classification mapping
("""albert""", """FlaxAlbertForTokenClassification"""),
("""bert""", """FlaxBertForTokenClassification"""),
("""big_bird""", """FlaxBigBirdForTokenClassification"""),
("""distilbert""", """FlaxDistilBertForTokenClassification"""),
("""electra""", """FlaxElectraForTokenClassification"""),
("""roberta""", """FlaxRobertaForTokenClassification"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForTokenClassification"""),
("""roformer""", """FlaxRoFormerForTokenClassification"""),
("""xlm-roberta""", """FlaxXLMRobertaForTokenClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
# Model for Multiple Choice mapping
("""albert""", """FlaxAlbertForMultipleChoice"""),
("""bert""", """FlaxBertForMultipleChoice"""),
("""big_bird""", """FlaxBigBirdForMultipleChoice"""),
("""distilbert""", """FlaxDistilBertForMultipleChoice"""),
("""electra""", """FlaxElectraForMultipleChoice"""),
("""roberta""", """FlaxRobertaForMultipleChoice"""),
("""roberta-prelayernorm""", """FlaxRobertaPreLayerNormForMultipleChoice"""),
("""roformer""", """FlaxRoFormerForMultipleChoice"""),
("""xlm-roberta""", """FlaxXLMRobertaForMultipleChoice"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""bert""", """FlaxBertForNextSentencePrediction"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""speech-encoder-decoder""", """FlaxSpeechEncoderDecoderModel"""),
("""whisper""", """FlaxWhisperForConditionalGeneration"""),
]
)
SCREAMING_SNAKE_CASE_ = OrderedDict(
[
("""whisper""", """FlaxWhisperForAudioClassification"""),
]
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_PRETRAINING_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MASKED_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_CAUSAL_LM_MAPPING_NAMES)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES
)
SCREAMING_SNAKE_CASE_ = _LazyAutoMapping(
CONFIG_MAPPING_NAMES, FLAX_MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[str] = FLAX_MODEL_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModel)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_PRETRAINING_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForPreTraining, head_doc="""pretraining""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[Any] = FLAX_MODEL_FOR_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForCausalLM, head_doc="""causal language modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Any = FLAX_MODEL_FOR_MASKED_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForMaskedLM, head_doc="""masked language modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : int = FLAX_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSeqaSeqLM, head_doc="""sequence-to-sequence language modeling""", checkpoint_for_example="""t5-base"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[int] = FLAX_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSequenceClassification, head_doc="""sequence classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[Any] = FLAX_MODEL_FOR_QUESTION_ANSWERING_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForQuestionAnswering, head_doc="""question answering""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Tuple = FLAX_MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForTokenClassification, head_doc="""token classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Union[str, Any] = FLAX_MODEL_FOR_MULTIPLE_CHOICE_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForMultipleChoice, head_doc="""multiple choice""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : List[str] = FLAX_MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForNextSentencePrediction, head_doc="""next sentence prediction"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForImageClassification, head_doc="""image classification"""
)
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Dict = FLAX_MODEL_FOR_VISION_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(FlaxAutoModelForVisionaSeq, head_doc="""vision-to-text modeling""")
class UpperCamelCase__ ( _BaseAutoModelClass ):
'''simple docstring'''
__snake_case : Optional[Any] = FLAX_MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING
SCREAMING_SNAKE_CASE_ = auto_class_update(
FlaxAutoModelForSpeechSeqaSeq, head_doc="""sequence-to-sequence speech-to-text modeling"""
)
| 296 | 1 |
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
SCREAMING_SNAKE_CASE_ = {1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Tuple ,lowerCamelCase__ : List[Any] ) -> Optional[int]:
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE = torchvision.models.resnetaaa(pretrained=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = list(model.children() )[:-2]
SCREAMING_SNAKE_CASE = nn.Sequential(*lowerCamelCase__ )
SCREAMING_SNAKE_CASE = nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds] )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.pool(self.model(lowerCamelCase__ ) )
SCREAMING_SNAKE_CASE = torch.flatten(lowerCamelCase__ ,start_dim=2 )
SCREAMING_SNAKE_CASE = out.transpose(1 ,2 ).contiguous()
return out # BxNx2048
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : int ,lowerCamelCase__ : Any ,lowerCamelCase__ : int ,lowerCamelCase__ : Dict ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : str ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [json.loads(lowerCamelCase__ ) for l in open(lowerCamelCase__ )]
SCREAMING_SNAKE_CASE = os.path.dirname(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = tokenizer
SCREAMING_SNAKE_CASE = labels
SCREAMING_SNAKE_CASE = len(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = max_seq_length
SCREAMING_SNAKE_CASE = transforms
def __len__( self : Any ) -> Optional[int]:
'''simple docstring'''
return len(self.data )
def __getitem__( self : List[Any] ,lowerCamelCase__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,add_special_tokens=lowerCamelCase__ ) )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = sentence[0], sentence[1:-1], sentence[-1]
SCREAMING_SNAKE_CASE = sentence[: self.max_seq_length]
SCREAMING_SNAKE_CASE = torch.zeros(self.n_classes )
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = Image.open(os.path.join(self.data_dir ,self.data[index]["""img"""] ) ).convert("""RGB""" )
SCREAMING_SNAKE_CASE = self.transforms(lowerCamelCase__ )
return {
"image_start_token": start_token,
"image_end_token": end_token,
"sentence": sentence,
"image": image,
"label": label,
}
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Counter()
for row in self.data:
label_freqs.update(row["""label"""] )
return label_freqs
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [len(row["""sentence"""] ) for row in batch]
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = len(_SCREAMING_SNAKE_CASE ), max(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = torch.zeros(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , dtype=torch.long )
SCREAMING_SNAKE_CASE = torch.zeros(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , dtype=torch.long )
for i_batch, (input_row, length) in enumerate(zip(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ):
SCREAMING_SNAKE_CASE = input_row["""sentence"""]
SCREAMING_SNAKE_CASE = 1
SCREAMING_SNAKE_CASE = torch.stack([row["""image"""] for row in batch] )
SCREAMING_SNAKE_CASE = torch.stack([row["""label"""] for row in batch] )
SCREAMING_SNAKE_CASE = torch.stack([row["""image_start_token"""] for row in batch] )
SCREAMING_SNAKE_CASE = torch.stack([row["""image_end_token"""] for row in batch] )
return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def __lowercase ( ) -> List[Any]:
'''simple docstring'''
return [
"Crime",
"Drama",
"Thriller",
"Action",
"Comedy",
"Romance",
"Documentary",
"Short",
"Mystery",
"History",
"Family",
"Adventure",
"Fantasy",
"Sci-Fi",
"Western",
"Horror",
"Sport",
"War",
"Music",
"Musical",
"Animation",
"Biography",
"Film-Noir",
]
def __lowercase ( ) -> List[str]:
'''simple docstring'''
return transforms.Compose(
[
transforms.Resize(2_56 ),
transforms.CenterCrop(2_24 ),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.46_777_044, 0.44_531_429, 0.40_661_017] , std=[0.12_221_994, 0.12_145_835, 0.14_380_469] , ),
] )
| 296 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = "table-transformer"
__snake_case : Union[str, Any] = ["past_key_values"]
__snake_case : List[Any] = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self : Optional[int] ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : Optional[Any]=None ,lowerCamelCase__ : List[Any]=3 ,lowerCamelCase__ : Optional[int]=100 ,lowerCamelCase__ : List[Any]=6 ,lowerCamelCase__ : Dict=2048 ,lowerCamelCase__ : List[Any]=8 ,lowerCamelCase__ : Dict=6 ,lowerCamelCase__ : Dict=2048 ,lowerCamelCase__ : Any=8 ,lowerCamelCase__ : Optional[int]=0.0 ,lowerCamelCase__ : int=0.0 ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : Optional[int]="relu" ,lowerCamelCase__ : Tuple=256 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : Optional[Any]=0.0 ,lowerCamelCase__ : Tuple=0.0 ,lowerCamelCase__ : List[Any]=0.02 ,lowerCamelCase__ : int=1.0 ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : Optional[Any]="sine" ,lowerCamelCase__ : List[str]="resnet50" ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : int=1 ,lowerCamelCase__ : Dict=5 ,lowerCamelCase__ : Tuple=2 ,lowerCamelCase__ : Union[str, Any]=1 ,lowerCamelCase__ : str=1 ,lowerCamelCase__ : Any=5 ,lowerCamelCase__ : Tuple=2 ,lowerCamelCase__ : str=0.1 ,**lowerCamelCase__ : List[str] ,) -> Optional[int]:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
SCREAMING_SNAKE_CASE = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = backbone_config.get("""model_type""" )
SCREAMING_SNAKE_CASE = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE = config_class.from_dict(lowerCamelCase__ )
# set timm attributes to None
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = None, None, None
SCREAMING_SNAKE_CASE = use_timm_backbone
SCREAMING_SNAKE_CASE = backbone_config
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = num_queries
SCREAMING_SNAKE_CASE = d_model
SCREAMING_SNAKE_CASE = encoder_ffn_dim
SCREAMING_SNAKE_CASE = encoder_layers
SCREAMING_SNAKE_CASE = encoder_attention_heads
SCREAMING_SNAKE_CASE = decoder_ffn_dim
SCREAMING_SNAKE_CASE = decoder_layers
SCREAMING_SNAKE_CASE = decoder_attention_heads
SCREAMING_SNAKE_CASE = dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = activation_dropout
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = init_std
SCREAMING_SNAKE_CASE = init_xavier_std
SCREAMING_SNAKE_CASE = encoder_layerdrop
SCREAMING_SNAKE_CASE = decoder_layerdrop
SCREAMING_SNAKE_CASE = encoder_layers
SCREAMING_SNAKE_CASE = auxiliary_loss
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = backbone
SCREAMING_SNAKE_CASE = use_pretrained_backbone
SCREAMING_SNAKE_CASE = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE = class_cost
SCREAMING_SNAKE_CASE = bbox_cost
SCREAMING_SNAKE_CASE = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE = mask_loss_coefficient
SCREAMING_SNAKE_CASE = dice_loss_coefficient
SCREAMING_SNAKE_CASE = bbox_loss_coefficient
SCREAMING_SNAKE_CASE = giou_loss_coefficient
SCREAMING_SNAKE_CASE = eos_coefficient
super().__init__(is_encoder_decoder=lowerCamelCase__ ,**lowerCamelCase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return self.d_model
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = version.parse("1.11" )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> float:
'''simple docstring'''
return 1e-5
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> int:
'''simple docstring'''
return 12
| 296 | 1 |
import unittest
from transformers import RoFormerTokenizer, RoFormerTokenizerFast
from transformers.testing_utils import require_rjieba, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_rjieba
@require_tokenizers
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : int = RoFormerTokenizer
__snake_case : Optional[Any] = RoFormerTokenizerFast
__snake_case : Any = True
__snake_case : int = True
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Tuple:
'''simple docstring'''
super().setUp()
def SCREAMING_SNAKE_CASE__ ( self : str ,**lowerCamelCase__ : Dict ) -> Tuple:
'''simple docstring'''
return self.tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ,**lowerCamelCase__ : Dict ) -> List[str]:
'''simple docstring'''
return self.rust_tokenizer_class.from_pretrained("""junnyu/roformer_chinese_base""" ,**lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """永和服装饰品有限公司,今天天气非常好"""
SCREAMING_SNAKE_CASE = """永和 服装 饰品 有限公司 , 今 天 天 气 非常 好"""
return input_text, output_text
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_tokenizer()
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.get_chinese_input_output_texts()
SCREAMING_SNAKE_CASE = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ ,output_text.split() )
SCREAMING_SNAKE_CASE = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.get_rust_tokenizer()
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = self.get_chinese_input_output_texts()
SCREAMING_SNAKE_CASE = tokenizer.tokenize(lowerCamelCase__ )
self.assertListEqual(lowerCamelCase__ ,output_text.split() )
SCREAMING_SNAKE_CASE = tokens + [tokenizer.unk_token]
SCREAMING_SNAKE_CASE = [22943, 21332, 34431, 45904, 117, 306, 1231, 1231, 2653, 33994, 1266, 100]
self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__ ) ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> int:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[int]:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
pass
| 296 |
from collections import defaultdict
from math import gcd
def __lowercase ( _SCREAMING_SNAKE_CASE = 1_50_00_00 ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = defaultdict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = 2
while 2 * euclid_m * (euclid_m + 1) <= limit:
for euclid_n in range((euclid_m % 2) + 1 , _SCREAMING_SNAKE_CASE , 2 ):
if gcd(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) > 1:
continue
SCREAMING_SNAKE_CASE = 2 * euclid_m * (euclid_m + euclid_n)
for perimeter in range(_SCREAMING_SNAKE_CASE , limit + 1 , _SCREAMING_SNAKE_CASE ):
frequencies[perimeter] += 1
euclid_m += 1
return sum(1 for frequency in frequencies.values() if frequency == 1 )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 296 | 1 |
import unittest
from transformers import DonutProcessor
SCREAMING_SNAKE_CASE_ = """naver-clova-ix/donut-base"""
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = DonutProcessor.from_pretrained(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {
"""name""": """John Doe""",
"""age""": """99""",
"""city""": """Atlanta""",
"""state""": """GA""",
"""zip""": """30301""",
"""phone""": """123-4567""",
"""nicknames""": [{"""nickname""": """Johnny"""}, {"""nickname""": """JD"""}],
}
SCREAMING_SNAKE_CASE = (
"""<s_name>John Doe</s_name><s_age>99</s_age><s_city>Atlanta</s_city>"""
"""<s_state>GA</s_state><s_zip>30301</s_zip><s_phone>123-4567</s_phone>"""
"""<s_nicknames><s_nickname>Johnny</s_nickname>"""
"""<sep/><s_nickname>JD</s_nickname></s_nicknames>"""
)
SCREAMING_SNAKE_CASE = self.processor.tokenajson(lowerCamelCase__ )
self.assertDictEqual(lowerCamelCase__ ,lowerCamelCase__ )
| 296 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def __lowercase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ArgumentParser("""Diffusers CLI tool""" , usage="""diffusers-cli <command> [<args>]""" )
SCREAMING_SNAKE_CASE = parser.add_subparsers(help="""diffusers-cli command helpers""" )
# Register commands
EnvironmentCommand.register_subcommand(_SCREAMING_SNAKE_CASE )
# Let's go
SCREAMING_SNAKE_CASE = parser.parse_args()
if not hasattr(_SCREAMING_SNAKE_CASE , """func""" ):
parser.print_help()
exit(1 )
# Run
SCREAMING_SNAKE_CASE = args.func(_SCREAMING_SNAKE_CASE )
service.run()
if __name__ == "__main__":
main()
| 296 | 1 |
import itertools
from dataclasses import dataclass
from typing import List, Optional
import pyarrow as pa
import pyarrow.parquet as pq
import datasets
from datasets.table import table_cast
SCREAMING_SNAKE_CASE_ = datasets.utils.logging.get_logger(__name__)
@dataclass
class UpperCamelCase__ ( datasets.BuilderConfig ):
'''simple docstring'''
__snake_case : int = 10000
__snake_case : Optional[List[str]] = None
__snake_case : Optional[datasets.Features] = None
class UpperCamelCase__ ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
__snake_case : List[Any] = ParquetConfig
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Tuple:
'''simple docstring'''
return datasets.DatasetInfo(features=self.config.features )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : Any ) -> int:
'''simple docstring'''
if not self.config.data_files:
raise ValueError(F"""At least one data file must be specified, but got data_files={self.config.data_files}""" )
SCREAMING_SNAKE_CASE = dl_manager.download_and_extract(self.config.data_files )
if isinstance(lowerCamelCase__ ,(str, list, tuple) ):
SCREAMING_SNAKE_CASE = data_files
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
SCREAMING_SNAKE_CASE = [dl_manager.iter_files(lowerCamelCase__ ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={"""files""": files} )]
SCREAMING_SNAKE_CASE = []
for split_name, files in data_files.items():
if isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
SCREAMING_SNAKE_CASE = [dl_manager.iter_files(lowerCamelCase__ ) for file in files]
# Infer features is they are stoed in the arrow schema
if self.info.features is None:
for file in itertools.chain.from_iterable(lowerCamelCase__ ):
with open(lowerCamelCase__ ,"""rb""" ) as f:
SCREAMING_SNAKE_CASE = datasets.Features.from_arrow_schema(pq.read_schema(lowerCamelCase__ ) )
break
splits.append(datasets.SplitGenerator(name=lowerCamelCase__ ,gen_kwargs={"""files""": files} ) )
return splits
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : pa.Table ) -> pa.Table:
'''simple docstring'''
if self.info.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
SCREAMING_SNAKE_CASE = table_cast(lowerCamelCase__ ,self.info.features.arrow_schema )
return pa_table
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : List[str] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.info.features.arrow_schema if self.info.features is not None else None
if self.info.features is not None and self.config.columns is not None:
if sorted(field.name for field in schema ) != sorted(self.config.columns ):
raise ValueError(
F"""Tried to load parquet data with columns '{self.config.columns}' with mismatching features '{self.info.features}'""" )
for file_idx, file in enumerate(itertools.chain.from_iterable(lowerCamelCase__ ) ):
with open(lowerCamelCase__ ,"""rb""" ) as f:
SCREAMING_SNAKE_CASE = pq.ParquetFile(lowerCamelCase__ )
try:
for batch_idx, record_batch in enumerate(
parquet_file.iter_batches(batch_size=self.config.batch_size ,columns=self.config.columns ) ):
SCREAMING_SNAKE_CASE = pa.Table.from_batches([record_batch] )
# Uncomment for debugging (will print the Arrow table size and elements)
# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
yield F"""{file_idx}_{batch_idx}""", self._cast_table(lowerCamelCase__ )
except ValueError as e:
logger.error(F"""Failed to read file '{file}' with error {type(lowerCamelCase__ )}: {e}""" )
raise
| 296 |
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = SwinConfig()
SCREAMING_SNAKE_CASE = swin_name.split("""_""" )
SCREAMING_SNAKE_CASE = name_split[1]
SCREAMING_SNAKE_CASE = int(name_split[4] )
SCREAMING_SNAKE_CASE = int(name_split[3][-1] )
if model_size == "tiny":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 6, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "small":
SCREAMING_SNAKE_CASE = 96
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (3, 6, 12, 24)
elif model_size == "base":
SCREAMING_SNAKE_CASE = 1_28
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (4, 8, 16, 32)
else:
SCREAMING_SNAKE_CASE = 1_92
SCREAMING_SNAKE_CASE = (2, 2, 18, 2)
SCREAMING_SNAKE_CASE = (6, 12, 24, 48)
if "in22k" in swin_name:
SCREAMING_SNAKE_CASE = 2_18_41
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = img_size
SCREAMING_SNAKE_CASE = num_classes
SCREAMING_SNAKE_CASE = embed_dim
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = num_heads
SCREAMING_SNAKE_CASE = window_size
return config
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
if "patch_embed.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.proj""" , """embeddings.patch_embeddings.projection""" )
if "patch_embed.norm" in name:
SCREAMING_SNAKE_CASE = name.replace("""patch_embed.norm""" , """embeddings.norm""" )
if "layers" in name:
SCREAMING_SNAKE_CASE = """encoder.""" + name
if "attn.proj" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in name:
SCREAMING_SNAKE_CASE = name.replace("""attn""" , """attention.self""" )
if "norm1" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
SCREAMING_SNAKE_CASE = name.replace("""norm2""" , """layernorm_after""" )
if "mlp.fc1" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
SCREAMING_SNAKE_CASE = name.replace("""mlp.fc2""" , """output.dense""" )
if name == "norm.weight":
SCREAMING_SNAKE_CASE = """layernorm.weight"""
if name == "norm.bias":
SCREAMING_SNAKE_CASE = """layernorm.bias"""
if "head" in name:
SCREAMING_SNAKE_CASE = name.replace("""head""" , """classifier""" )
else:
SCREAMING_SNAKE_CASE = """swin.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if "mask" in key:
continue
elif "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[1] )
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2, :
]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[
:dim
]
SCREAMING_SNAKE_CASE = val[
dim : dim * 2
]
SCREAMING_SNAKE_CASE = val[
-dim:
]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = timm.create_model(_SCREAMING_SNAKE_CASE , pretrained=_SCREAMING_SNAKE_CASE )
timm_model.eval()
SCREAMING_SNAKE_CASE = get_swin_config(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = SwinForImageClassification(_SCREAMING_SNAKE_CASE )
model.eval()
SCREAMING_SNAKE_CASE = convert_state_dict(timm_model.state_dict() , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = AutoImageProcessor.from_pretrained("""microsoft/{}""".format(swin_name.replace("""_""" , """-""" ) ) )
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
SCREAMING_SNAKE_CASE = image_processor(images=_SCREAMING_SNAKE_CASE , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = timm_model(inputs["""pixel_values"""] )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE ).logits
assert torch.allclose(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , atol=1E-3 )
print(F"""Saving model {swin_name} to {pytorch_dump_folder_path}""" )
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__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--swin_name""",
default="""swin_tiny_patch4_window7_224""",
type=str,
help="""Name of the Swin 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."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 296 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
SCREAMING_SNAKE_CASE_ = {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig""", """ViTOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""ViTFeatureExtractor"""]
SCREAMING_SNAKE_CASE_ = ["""ViTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""VIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""ViTForImageClassification""",
"""ViTForMaskedImageModeling""",
"""ViTModel""",
"""ViTPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""TFViTForImageClassification""",
"""TFViTModel""",
"""TFViTPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""FlaxViTForImageClassification""",
"""FlaxViTModel""",
"""FlaxViTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 296 |
import os
from distutils.util import strtobool
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple:
'''simple docstring'''
for e in env_keys:
SCREAMING_SNAKE_CASE = int(os.environ.get(_SCREAMING_SNAKE_CASE , -1 ) )
if val >= 0:
return val
return default
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) )
return strtobool(_SCREAMING_SNAKE_CASE ) == 1 # As its name indicates `strtobool` actually returns an int...
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="no" ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = os.environ.get(_SCREAMING_SNAKE_CASE , str(_SCREAMING_SNAKE_CASE ) )
return value
| 296 | 1 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, RandomSampler
from transformers import GPTaLMHeadModel
def __lowercase ( _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=10 , _SCREAMING_SNAKE_CASE=1_00 , _SCREAMING_SNAKE_CASE=10_26 , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE="data/tokenized_stories_train_wikitext103.jbl" , _SCREAMING_SNAKE_CASE="igf_context_pairs.jbl" , ) -> int:
'''simple docstring'''
set_seed(3 )
# generate train_data and objective_set
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = generate_datasets(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , number=_SCREAMING_SNAKE_CASE , min_len=10_26 , trim=_SCREAMING_SNAKE_CASE )
# keeps model same across runs
set_seed(4 )
# model, lm_optimizer, lm_scheduler = recopy_gpt2(model, device, max_steps) # store original model weights
# can we train on GPU?
SCREAMING_SNAKE_CASE = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# load pretrained model
SCREAMING_SNAKE_CASE = load_gpta("""gpt2""" ).to(_SCREAMING_SNAKE_CASE )
print("""computing perplexity on objective set""" )
SCREAMING_SNAKE_CASE = compute_perplexity(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).item()
print("""perplexity on objective set:""" , _SCREAMING_SNAKE_CASE )
# collect igf pairs and save to file demo.jbl
collect_objective_set(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# clean up, delete model and data we don't need anymore
del model, train_data, objective_set
torch.cuda.empty_cache()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=15 , _SCREAMING_SNAKE_CASE=1_28 , _SCREAMING_SNAKE_CASE=1_00 , _SCREAMING_SNAKE_CASE="igf_model.pt" , ) -> Dict:
'''simple docstring'''
set_seed(42 )
# Load pre-trained model
SCREAMING_SNAKE_CASE = GPTaLMHeadModel.from_pretrained("""gpt2""" )
# Initialize secondary learner to use embedding weights of model
SCREAMING_SNAKE_CASE = SecondaryLearner(_SCREAMING_SNAKE_CASE )
# Train secondary learner
SCREAMING_SNAKE_CASE = train_secondary_learner(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , max_epochs=_SCREAMING_SNAKE_CASE , batch_size=_SCREAMING_SNAKE_CASE , eval_freq=1_00 , igf_model_path=_SCREAMING_SNAKE_CASE , )
del model, secondary_learner_train_data
torch.cuda.empty_cache()
return secondary_learner
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE=10_00 , _SCREAMING_SNAKE_CASE=16 , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=recopy_gpta , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=10 , _SCREAMING_SNAKE_CASE="gpt2_finetuned.pt" , ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
SCREAMING_SNAKE_CASE = RandomSampler(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = DataLoader(_SCREAMING_SNAKE_CASE , sampler=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = max_steps // (len(_SCREAMING_SNAKE_CASE )) + 1
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = torch.zeros((1, context_len) , dtype=torch.long , device=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = recopy_model(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.train()
if secondary_learner is not None:
secondary_learner.to(_SCREAMING_SNAKE_CASE )
secondary_learner.eval()
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
# Compute the performance of the transformer model at the beginning
SCREAMING_SNAKE_CASE = compute_perplexity(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
test_perps.append(_SCREAMING_SNAKE_CASE )
print("""Test perplexity, step""" , _SCREAMING_SNAKE_CASE , """:""" , _SCREAMING_SNAKE_CASE )
for epoch in range(int(_SCREAMING_SNAKE_CASE ) ):
for step, example in enumerate(_SCREAMING_SNAKE_CASE ):
torch.cuda.empty_cache()
SCREAMING_SNAKE_CASE = random.randint(0 , example.size(2 ) - context_len - 1 )
SCREAMING_SNAKE_CASE = example[0, 0, start : start + context_len]
lm_optimizer.zero_grad()
SCREAMING_SNAKE_CASE = model(_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = True
if secondary_learner is not None:
SCREAMING_SNAKE_CASE = secondary_learner.forward(
torch.tensor(_SCREAMING_SNAKE_CASE , dtype=torch.long , device=_SCREAMING_SNAKE_CASE ).unsqueeze(0 ) )[0].item()
observed_qs.append(float(_SCREAMING_SNAKE_CASE ) )
# Here we implement the simple non-constant threshold for the predicted IG(X) value
# We will decay the selectivity of our secondary learner filter from
# 1 standard deviation above average to 1 below average after 10 batches.
if global_step == 10:
SCREAMING_SNAKE_CASE = -1
if predicted_q < threshold:
SCREAMING_SNAKE_CASE = False
# If we passed the filter, add the context to the batch!
if do_backprop:
contexts.append(np.array(context.cpu() ) )
SCREAMING_SNAKE_CASE = outputs[0]
lm_loss.backward()
examples += 1
del outputs
# Once the batch is filled with enough contexts, backprop on the batch.
if examples == batch_size:
torch.cuda.empty_cache()
SCREAMING_SNAKE_CASE = 0
# Do LM backprop
torch.nn.utils.clip_grad_norm_(model.parameters() , 3.0 )
lm_optimizer.step()
lm_scheduler.step() # Update learning rate schedule
global_step += 1
# Compute the performance of the transformer model at this batch
if global_step % eval_interval == 0:
SCREAMING_SNAKE_CASE = compute_perplexity(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
test_perps.append(_SCREAMING_SNAKE_CASE )
print("""Test perplexity, step""" , _SCREAMING_SNAKE_CASE , """:""" , _SCREAMING_SNAKE_CASE )
# Break out of the loop after 60 batches
if max_steps > 0 and global_step > 60:
break
if max_steps > 0 and global_step > 60:
break
# save finetuned transformer model
torch.save(model.state_dict() , _SCREAMING_SNAKE_CASE )
torch.cuda.empty_cache()
# Do some cleaning up so we can reinitialize for the next run of this function
del lm_optimizer
del lm_scheduler
return model
def __lowercase ( ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = argparse.ArgumentParser(description="""Fine-tune a transformer model with IGF on a language modeling task""" )
# Required parameters
parser.add_argument(
"""--data_dir""" , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE , help="""The input data dir. Should contain data files for WikiText.""" , )
parser.add_argument(
"""--model_name_or_path""" , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE , help="""Path to pretrained model or model identifier from huggingface.co/models""" , )
parser.add_argument(
"""--data_file""" , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , help=(
"""A jbl file containing tokenized data which can be split as objective dataset, """
"""train_dataset and test_dataset."""
) , )
parser.add_argument(
"""--igf_data_file""" , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , help="""A jbl file containing the context and information gain pairs to train secondary learner.""" , )
parser.add_argument(
"""--output_dir""" , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , required=_SCREAMING_SNAKE_CASE , help="""The output directory where the final fine-tuned model is stored.""" , )
parser.add_argument(
"""--tokenizer_name""" , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , help="""Pretrained tokenizer name or path if not the same as model_name""" , )
parser.add_argument("""--seed""" , type=_SCREAMING_SNAKE_CASE , default=_SCREAMING_SNAKE_CASE , help="""A seed for reproducible training.""" )
parser.add_argument(
"""--context_len""" , default=32 , 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(
"""--size_objective_set""" , default=1_00 , type=_SCREAMING_SNAKE_CASE , help="""number of articles that are long enough to be used as our objective set""" , )
parser.add_argument(
"""--eval_freq""" , default=1_00 , type=_SCREAMING_SNAKE_CASE , help="""secondary model evaluation is triggered at eval_freq""" )
parser.add_argument("""--max_steps""" , default=10_00 , type=_SCREAMING_SNAKE_CASE , help="""To calculate training epochs""" )
parser.add_argument(
"""--secondary_learner_batch_size""" , default=1_28 , type=_SCREAMING_SNAKE_CASE , help="""batch size of training data for secondary learner""" , )
parser.add_argument(
"""--batch_size""" , default=16 , type=_SCREAMING_SNAKE_CASE , help="""batch size of training data of language model(gpt2) """ )
parser.add_argument(
"""--eval_interval""" , default=10 , type=_SCREAMING_SNAKE_CASE , help=(
"""decay the selectivity of our secondary learner filter from"""
"""1 standard deviation above average to 1 below average after 10 batches"""
) , )
parser.add_argument(
"""--number""" , default=1_00 , type=_SCREAMING_SNAKE_CASE , help="""The number of examples split to be used as objective_set/test_data""" )
parser.add_argument(
"""--min_len""" , default=10_26 , type=_SCREAMING_SNAKE_CASE , help="""The minimum length of the article to be used as objective set""" )
parser.add_argument(
"""--secondary_learner_max_epochs""" , default=15 , type=_SCREAMING_SNAKE_CASE , help="""number of epochs to train secondary learner""" )
parser.add_argument("""--trim""" , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , help="""truncate the example if it exceeds context length""" )
parser.add_argument(
"""--threshold""" , default=1.0 , type=_SCREAMING_SNAKE_CASE , help=(
"""The threshold value used by secondary learner to filter the train_data and allow only"""
""" informative data as input to the model"""
) , )
parser.add_argument("""--finetuned_model_name""" , default="""gpt2_finetuned.pt""" , type=_SCREAMING_SNAKE_CASE , help="""finetuned_model_name""" )
parser.add_argument(
"""--recopy_model""" , default=_SCREAMING_SNAKE_CASE , type=_SCREAMING_SNAKE_CASE , help="""Reset the model to the original pretrained GPT-2 weights after each iteration""" , )
# function calls
# Collecting *n* pairs of context and information gain(X, IG(X)) for training the secondary learner
generate_n_pairs(
context_len=32 , max_steps=10 , size_objective_set=1_00 , min_len=10_26 , trim=_SCREAMING_SNAKE_CASE , data_file="""data/tokenized_stories_train_wikitext103.jbl""" , igf_data_file="""igf_context_pairs.jbl""" , )
# Load train data for secondary learner
SCREAMING_SNAKE_CASE = joblib.load("""data/IGF_values.jbl""" )
# Train secondary learner
SCREAMING_SNAKE_CASE = training_secondary_learner(
_SCREAMING_SNAKE_CASE , secondary_learner_max_epochs=15 , secondary_learner_batch_size=1_28 , eval_freq=1_00 , igf_model_path="""igf_model.pt""" , )
# load pretrained gpt2 model
SCREAMING_SNAKE_CASE = GPTaLMHeadModel.from_pretrained("""gpt2""" )
set_seed(42 )
# Generate train and test data to train and evaluate gpt2 model
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = generate_datasets(
context_len=32 , file="""data/tokenized_stories_train_wikitext103.jbl""" , number=1_00 , min_len=10_26 , trim=_SCREAMING_SNAKE_CASE )
# fine-tuning of the gpt2 model using igf (Information Gain Filtration)
finetune(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , context_len=32 , max_steps=10_00 , batch_size=16 , threshold=1.0 , recopy_model=_SCREAMING_SNAKE_CASE , secondary_learner=_SCREAMING_SNAKE_CASE , eval_interval=10 , finetuned_model_name="""gpt2_finetuned.pt""" , )
if __name__ == "__main__":
main()
| 296 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __lowercase ( _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> List[str]:
'''simple docstring'''
return field(default_factory=lambda: default , metadata=_SCREAMING_SNAKE_CASE )
@dataclass
class UpperCamelCase__ :
'''simple docstring'''
__snake_case : List[str] = list_field(
default=[] , metadata={
"help": (
"Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"
" of all available models"
)
} , )
__snake_case : List[int] = list_field(
default=[8] , metadata={"help": "List of batch sizes for which memory and time performance will be evaluated"} )
__snake_case : List[int] = list_field(
default=[8, 32, 128, 512] , metadata={"help": "List of sequence lengths for which memory and time performance will be evaluated"} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to benchmark inference of model. Inference can be disabled via --no-inference."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to run on available tpu devices. TPU can be disabled via --no-tpu."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Use FP16 to accelerate inference."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Benchmark training of model"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Verbose memory tracing"} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."} , )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": "Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"
} , )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Trace memory line by line"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Save result to a CSV file"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Save all print statements in a log file"} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Whether to print environment information"} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": (
"Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"
" multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"
" for debugging / testing and on TPU."
)
} , )
__snake_case : str = field(
default=F"inference_time_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving time results to csv."} , )
__snake_case : str = field(
default=F"inference_memory_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving memory results to csv."} , )
__snake_case : str = field(
default=F"train_time_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving time results to csv for training."} , )
__snake_case : str = field(
default=F"train_memory_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving memory results to csv for training."} , )
__snake_case : str = field(
default=F"env_info_{round(time() )}.csv" , metadata={"help": "CSV filename used if saving environment information."} , )
__snake_case : str = field(
default=F"log_{round(time() )}.csv" , metadata={"help": "Log filename used if print statements are saved in log."} , )
__snake_case : int = field(default=3 , metadata={"help": "Times an experiment will be run."} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={
"help": (
"Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"
" model weights."
)
} , )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
warnings.warn(
F"""The class {self.__class__} is deprecated. Hugging Face Benchmarking utils"""
""" are deprecated in general and it is advised to use external Benchmarking libraries """
""" to benchmark Transformer models.""" ,lowerCamelCase__ ,)
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Any:
'''simple docstring'''
return json.dumps(dataclasses.asdict(self ) ,indent=2 )
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
if len(self.models ) <= 0:
raise ValueError(
"""Please make sure you provide at least one model name / model identifier, *e.g.* `--models"""
""" bert-base-cased` or `args.models = ['bert-base-cased'].""" )
return self.models
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> int:
'''simple docstring'''
if not self.multi_process:
return False
elif self.is_tpu:
logger.info("""Multiprocessing is currently not possible on TPU.""" )
return False
else:
return True
| 296 | 1 |
def __lowercase ( _SCREAMING_SNAKE_CASE = 10 ) -> str:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or n < 0:
raise ValueError("""Invalid input""" )
SCREAMING_SNAKE_CASE = 10**n
SCREAMING_SNAKE_CASE = 2_84_33 * (pow(2 , 7_83_04_57 , _SCREAMING_SNAKE_CASE )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(1_0) = }''')
| 296 |
import math
import unittest
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
assert isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(_SCREAMING_SNAKE_CASE ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Tuple:
'''simple docstring'''
self.assertTrue(is_prime(2 ) )
self.assertTrue(is_prime(3 ) )
self.assertTrue(is_prime(5 ) )
self.assertTrue(is_prime(7 ) )
self.assertTrue(is_prime(11 ) )
self.assertTrue(is_prime(13 ) )
self.assertTrue(is_prime(17 ) )
self.assertTrue(is_prime(19 ) )
self.assertTrue(is_prime(23 ) )
self.assertTrue(is_prime(29 ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
is_prime(-19 )
self.assertFalse(
is_prime(0 ) ,"""Zero doesn't have any positive factors, primes must have exactly two.""" ,)
self.assertFalse(
is_prime(1 ) ,"""One only has 1 positive factor, primes must have exactly two.""" ,)
self.assertFalse(is_prime(2 * 2 ) )
self.assertFalse(is_prime(2 * 3 ) )
self.assertFalse(is_prime(3 * 3 ) )
self.assertFalse(is_prime(3 * 5 ) )
self.assertFalse(is_prime(3 * 5 * 7 ) )
if __name__ == "__main__":
unittest.main()
| 296 | 1 |
import math
from datetime import datetime, timedelta
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> datetime:
'''simple docstring'''
SCREAMING_SNAKE_CASE = year % 19
SCREAMING_SNAKE_CASE = year % 4
SCREAMING_SNAKE_CASE = year % 7
SCREAMING_SNAKE_CASE = math.floor(year / 1_00 )
SCREAMING_SNAKE_CASE = math.floor((13 + 8 * leap_day_inhibits) / 25 )
SCREAMING_SNAKE_CASE = leap_day_inhibits / 4
SCREAMING_SNAKE_CASE = (
15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 30
SCREAMING_SNAKE_CASE = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
SCREAMING_SNAKE_CASE = (19 * metonic_cycle + secular_moon_shift) % 30
# PHM -> Paschal Full Moon
SCREAMING_SNAKE_CASE = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 29 and days_from_phm_to_sunday == 6:
return datetime(_SCREAMING_SNAKE_CASE , 4 , 19 )
elif days_to_add == 28 and days_from_phm_to_sunday == 6:
return datetime(_SCREAMING_SNAKE_CASE , 4 , 18 )
else:
return datetime(_SCREAMING_SNAKE_CASE , 3 , 22 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (1_9_9_4, 2_0_0_0, 2_0_1_0, 2_0_2_1, 2_0_2_3):
SCREAMING_SNAKE_CASE_ = """will be""" if year > datetime.now().year else """was"""
print(F'''Easter in {year} {tense} {gauss_easter(year)}''')
| 296 |
import random
class UpperCamelCase__ :
'''simple docstring'''
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : str ) -> tuple[list[int], list[int]]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [ord(lowerCamelCase__ ) for i in text]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = []
for i in plain:
SCREAMING_SNAKE_CASE = random.randint(1 ,300 )
SCREAMING_SNAKE_CASE = (i + k) * k
cipher.append(lowerCamelCase__ )
key.append(lowerCamelCase__ )
return cipher, key
@staticmethod
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : list[int] ,lowerCamelCase__ : list[int] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
for i in range(len(lowerCamelCase__ ) ):
SCREAMING_SNAKE_CASE = int((cipher[i] - (key[i]) ** 2) / key[i] )
plain.append(chr(lowerCamelCase__ ) )
return "".join(lowerCamelCase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = Onepad().encrypt("""Hello""")
print(c, k)
print(Onepad().decrypt(c, k))
| 296 | 1 |
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
SCREAMING_SNAKE_CASE = F"""Input value of [number={number}] must be an integer"""
raise TypeError(_SCREAMING_SNAKE_CASE )
if number < 0:
return False
SCREAMING_SNAKE_CASE = number * number
while number > 0:
if number % 10 != number_square % 10:
return False
number //= 10
number_square //= 10
return True
if __name__ == "__main__":
import doctest
doctest.testmod()
| 296 |
from typing import List
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""snap-research/efficientformer-l1-300""": (
"""https://huggingface.co/snap-research/efficientformer-l1-300/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = "efficientformer"
def __init__( self : Optional[int] ,lowerCamelCase__ : List[int] = [3, 2, 6, 4] ,lowerCamelCase__ : List[int] = [48, 96, 224, 448] ,lowerCamelCase__ : List[bool] = [True, True, True, True] ,lowerCamelCase__ : int = 448 ,lowerCamelCase__ : int = 32 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : int = 7 ,lowerCamelCase__ : int = 5 ,lowerCamelCase__ : int = 8 ,lowerCamelCase__ : int = 4 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 16 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 3 ,lowerCamelCase__ : int = 2 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 1 ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : float = 1e-5 ,lowerCamelCase__ : str = "gelu" ,lowerCamelCase__ : float = 0.02 ,lowerCamelCase__ : float = 1e-1_2 ,lowerCamelCase__ : int = 224 ,lowerCamelCase__ : float = 1e-0_5 ,**lowerCamelCase__ : str ,) -> None:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = hidden_sizes
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = depths
SCREAMING_SNAKE_CASE = mlp_expansion_ratio
SCREAMING_SNAKE_CASE = downsamples
SCREAMING_SNAKE_CASE = dim
SCREAMING_SNAKE_CASE = key_dim
SCREAMING_SNAKE_CASE = attention_ratio
SCREAMING_SNAKE_CASE = resolution
SCREAMING_SNAKE_CASE = pool_size
SCREAMING_SNAKE_CASE = downsample_patch_size
SCREAMING_SNAKE_CASE = downsample_stride
SCREAMING_SNAKE_CASE = downsample_pad
SCREAMING_SNAKE_CASE = drop_path_rate
SCREAMING_SNAKE_CASE = num_metaad_blocks
SCREAMING_SNAKE_CASE = distillation
SCREAMING_SNAKE_CASE = use_layer_scale
SCREAMING_SNAKE_CASE = layer_scale_init_value
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = batch_norm_eps
| 296 | 1 |
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""post_extract_proj""": """feature_projection.projection""",
"""encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
"""self_attn.k_proj""": """encoder.layers.*.attention.k_proj""",
"""self_attn.v_proj""": """encoder.layers.*.attention.v_proj""",
"""self_attn.q_proj""": """encoder.layers.*.attention.q_proj""",
"""self_attn.out_proj""": """encoder.layers.*.attention.out_proj""",
"""self_attn_layer_norm""": """encoder.layers.*.layer_norm""",
"""fc1""": """encoder.layers.*.feed_forward.intermediate_dense""",
"""fc2""": """encoder.layers.*.feed_forward.output_dense""",
"""final_layer_norm""": """encoder.layers.*.final_layer_norm""",
"""encoder.layer_norm""": """encoder.layer_norm""",
"""encoder.layer_norm_for_extract""": """layer_norm_for_extract""",
"""w2v_model.layer_norm""": """feature_projection.layer_norm""",
"""quantizer.weight_proj""": """quantizer.weight_proj""",
"""quantizer.vars""": """quantizer.codevectors""",
"""project_q""": """project_q""",
"""final_proj""": """project_hid""",
"""w2v_encoder.proj""": """lm_head""",
"""label_embs_concat""": """label_embeddings_concat""",
"""mask_emb""": """masked_spec_embed""",
"""spk_proj""": """speaker_proj""",
}
SCREAMING_SNAKE_CASE_ = [
"""lm_head""",
"""quantizer.weight_proj""",
"""quantizer.codevectors""",
"""project_q""",
"""project_hid""",
"""label_embeddings_concat""",
"""speaker_proj""",
"""layer_norm_for_extract""",
]
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
for attribute in key.split(""".""" ):
SCREAMING_SNAKE_CASE = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
if weight_type is not None:
SCREAMING_SNAKE_CASE = getattr(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).shape
else:
SCREAMING_SNAKE_CASE = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
SCREAMING_SNAKE_CASE = value
elif weight_type == "weight_g":
SCREAMING_SNAKE_CASE = value
elif weight_type == "weight_v":
SCREAMING_SNAKE_CASE = value
elif weight_type == "bias":
SCREAMING_SNAKE_CASE = value
else:
SCREAMING_SNAKE_CASE = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = fairseq_model.state_dict()
SCREAMING_SNAKE_CASE = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
SCREAMING_SNAKE_CASE = False
if "conv_layers" in name:
load_conv_layer(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , hf_model.config.feat_extract_norm == """group""" , )
SCREAMING_SNAKE_CASE = True
else:
for key, mapped_key in MAPPING.items():
SCREAMING_SNAKE_CASE = """unispeech_sat.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
if "layer_norm_for_extract" in name and (".".join(name.split(""".""" )[:-1] ) != key):
# special case since naming is very similar
continue
SCREAMING_SNAKE_CASE = True
if "*" in mapped_key:
SCREAMING_SNAKE_CASE = name.split(_SCREAMING_SNAKE_CASE )[0].split(""".""" )[-2]
SCREAMING_SNAKE_CASE = mapped_key.replace("""*""" , _SCREAMING_SNAKE_CASE )
if "weight_g" in name:
SCREAMING_SNAKE_CASE = """weight_g"""
elif "weight_v" in name:
SCREAMING_SNAKE_CASE = """weight_v"""
elif "bias" in name:
SCREAMING_SNAKE_CASE = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
SCREAMING_SNAKE_CASE = """weight"""
else:
SCREAMING_SNAKE_CASE = None
set_recursively(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
continue
if not is_used:
unused_weights.append(_SCREAMING_SNAKE_CASE )
logger.warning(F"""Unused weights: {unused_weights}""" )
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = full_name.split("""conv_layers.""" )[-1]
SCREAMING_SNAKE_CASE = name.split(""".""" )
SCREAMING_SNAKE_CASE = int(items[0] )
SCREAMING_SNAKE_CASE = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
SCREAMING_SNAKE_CASE = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
SCREAMING_SNAKE_CASE = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""" )
SCREAMING_SNAKE_CASE = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""" )
SCREAMING_SNAKE_CASE = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=True ) -> List[Any]:
'''simple docstring'''
if config_path is not None:
SCREAMING_SNAKE_CASE = UniSpeechSatConfig.from_pretrained(_SCREAMING_SNAKE_CASE )
else:
SCREAMING_SNAKE_CASE = UniSpeechSatConfig()
SCREAMING_SNAKE_CASE = """"""
if is_finetuned:
SCREAMING_SNAKE_CASE = UniSpeechSatForCTC(_SCREAMING_SNAKE_CASE )
else:
SCREAMING_SNAKE_CASE = UniSpeechSatForPreTraining(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
SCREAMING_SNAKE_CASE = model[0].eval()
recursively_load_weights(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
hf_wavavec.save_pretrained(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--checkpoint_path""", default=None, type=str, help="""Path to fairseq checkpoint""")
parser.add_argument("""--dict_path""", default=None, type=str, help="""Path to dict of fine-tuned model""")
parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
parser.add_argument(
"""--not_finetuned""", action="""store_true""", help="""Whether the model to convert is a fine-tuned model or not"""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 296 |
import importlib
import shutil
import threading
import warnings
from typing import List
import fsspec
import fsspec.asyn
from . import compression
from .hffilesystem import HfFileSystem
SCREAMING_SNAKE_CASE_ = importlib.util.find_spec("""s3fs""") is not None
if _has_safs:
from .safilesystem import SaFileSystem # noqa: F401
SCREAMING_SNAKE_CASE_ = [
compression.BzaFileSystem,
compression.GzipFileSystem,
compression.LzaFileSystem,
compression.XzFileSystem,
compression.ZstdFileSystem,
]
# Register custom filesystems
for fs_class in COMPRESSION_FILESYSTEMS + [HfFileSystem]:
if fs_class.protocol in fsspec.registry and fsspec.registry[fs_class.protocol] is not fs_class:
warnings.warn(F'''A filesystem protocol was already set for {fs_class.protocol} and will be overwritten.''')
fsspec.register_implementation(fs_class.protocol, fs_class, clobber=True)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> str:
'''simple docstring'''
if "://" in dataset_path:
SCREAMING_SNAKE_CASE = dataset_path.split("""://""" )[1]
return dataset_path
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
if fs is not None and fs.protocol != "file":
return True
else:
return False
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = not is_remote_filesystem(_SCREAMING_SNAKE_CASE )
if is_local:
# LocalFileSystem.mv does copy + rm, it is more efficient to simply move a local directory
shutil.move(fs._strip_protocol(_SCREAMING_SNAKE_CASE ) , fs._strip_protocol(_SCREAMING_SNAKE_CASE ) )
else:
fs.mv(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , recursive=_SCREAMING_SNAKE_CASE )
def __lowercase ( ) -> None:
'''simple docstring'''
if hasattr(fsspec.asyn , """reset_lock""" ):
# for future fsspec>2022.05.0
fsspec.asyn.reset_lock()
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = threading.Lock()
| 296 | 1 |
import re
from flax.core.frozen_dict import freeze
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.experimental import PartitionSpec as P
# Sentinels
SCREAMING_SNAKE_CASE_ = object()
# For specifying empty leaf dict `{}`
SCREAMING_SNAKE_CASE_ = object()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = tuple((re.compile(x + """$""" ) for x in qs) )
for i in range(len(_SCREAMING_SNAKE_CASE ) - len(_SCREAMING_SNAKE_CASE ) + 1 ):
SCREAMING_SNAKE_CASE = [x.match(_SCREAMING_SNAKE_CASE ) for x, y in zip(_SCREAMING_SNAKE_CASE , ks[i:] )]
if matches and all(_SCREAMING_SNAKE_CASE ):
return True
return False
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
'''simple docstring'''
def replace(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
for rule, replacement in rules:
if _match(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
return replacement
return val
return replace
def __lowercase ( ) -> Optional[Any]:
'''simple docstring'''
return [
# embeddings
(("transformer", "wpe", "embedding"), P("""mp""" , _SCREAMING_SNAKE_CASE )),
(("transformer", "wte", "embedding"), P("""mp""" , _SCREAMING_SNAKE_CASE )),
# atention
(("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(_SCREAMING_SNAKE_CASE , """mp""" )),
(("attention", "out_proj", "kernel"), P("""mp""" , _SCREAMING_SNAKE_CASE )),
(("attention", "out_proj", "bias"), None),
# mlp
(("mlp", "c_fc", "kernel"), P(_SCREAMING_SNAKE_CASE , """mp""" )),
(("mlp", "c_fc", "bias"), P("""mp""" )),
(("mlp", "c_proj", "kernel"), P("""mp""" , _SCREAMING_SNAKE_CASE )),
(("mlp", "c_proj", "bias"), None),
# layer norms
((r"ln_\d+", "bias"), None),
((r"\d+", r"ln_\d+", "scale"), None),
(("ln_f", "bias"), None),
(("ln_f", "scale"), None),
]
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = _get_partition_rules()
SCREAMING_SNAKE_CASE = _replacement_rules(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = {k: _unmatched for k in flatten_dict(_SCREAMING_SNAKE_CASE )}
SCREAMING_SNAKE_CASE = {k: replace(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) for k, v in initd.items()}
assert _unmatched not in result.values(), "Incomplete partition spec."
return freeze(unflatten_dict(_SCREAMING_SNAKE_CASE ) )
| 296 |
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@property
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=3 ,out_channels=3 ,down_block_types=("""DownBlock2D""", """AttnDownBlock2D""") ,up_block_types=("""AttnUpBlock2D""", """UpBlock2D""") ,)
return model
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.dummy_uncond_unet
SCREAMING_SNAKE_CASE = KarrasVeScheduler()
SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=2 ,generator=lowerCamelCase__ ,output_type="""numpy""" ,return_dict=lowerCamelCase__ )[0]
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
SCREAMING_SNAKE_CASE = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
SCREAMING_SNAKE_CASE = np.array([0.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
@slow
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """google/ncsnpp-celebahq-256"""
SCREAMING_SNAKE_CASE = UNetaDModel.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = KarrasVeScheduler()
SCREAMING_SNAKE_CASE = KarrasVePipeline(unet=lowerCamelCase__ ,scheduler=lowerCamelCase__ )
pipe.to(lowerCamelCase__ )
pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(num_inference_steps=20 ,generator=lowerCamelCase__ ,output_type="""numpy""" ).images
SCREAMING_SNAKE_CASE = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
SCREAMING_SNAKE_CASE = np.array([0.578, 0.5811, 0.5924, 0.5809, 0.587, 0.5886, 0.5861, 0.5802, 0.586] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 296 | 1 |
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
random.seed(_SCREAMING_SNAKE_CASE )
np.random.seed(_SCREAMING_SNAKE_CASE )
torch.manual_seed(_SCREAMING_SNAKE_CASE )
torch.cuda.manual_seed_all(_SCREAMING_SNAKE_CASE )
# ^^ safe to call this function even if cuda is not available
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : int ,lowerCamelCase__ : Iterable[torch.nn.Parameter] ,lowerCamelCase__ : float = 0.9999 ,lowerCamelCase__ : float = 0.0 ,lowerCamelCase__ : int = 0 ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : Union[float, int] = 1.0 ,lowerCamelCase__ : Union[float, int] = 2 / 3 ,lowerCamelCase__ : Optional[Any] = None ,lowerCamelCase__ : Dict[str, Any] = None ,**lowerCamelCase__ : str ,) -> List[Any]:
'''simple docstring'''
if isinstance(lowerCamelCase__ ,torch.nn.Module ):
SCREAMING_SNAKE_CASE = (
"""Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
"""Please pass the parameters of the module instead."""
)
deprecate(
"""passing a `torch.nn.Module` to `ExponentialMovingAverage`""" ,"""1.0.0""" ,lowerCamelCase__ ,standard_warn=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = parameters.parameters()
# set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
SCREAMING_SNAKE_CASE = True
if kwargs.get("""max_value""" ,lowerCamelCase__ ) is not None:
SCREAMING_SNAKE_CASE = """The `max_value` argument is deprecated. Please use `decay` instead."""
deprecate("""max_value""" ,"""1.0.0""" ,lowerCamelCase__ ,standard_warn=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = kwargs["""max_value"""]
if kwargs.get("""min_value""" ,lowerCamelCase__ ) is not None:
SCREAMING_SNAKE_CASE = """The `min_value` argument is deprecated. Please use `min_decay` instead."""
deprecate("""min_value""" ,"""1.0.0""" ,lowerCamelCase__ ,standard_warn=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = kwargs["""min_value"""]
SCREAMING_SNAKE_CASE = list(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = [p.clone().detach() for p in parameters]
if kwargs.get("""device""" ,lowerCamelCase__ ) is not None:
SCREAMING_SNAKE_CASE = """The `device` argument is deprecated. Please use `to` instead."""
deprecate("""device""" ,"""1.0.0""" ,lowerCamelCase__ ,standard_warn=lowerCamelCase__ )
self.to(device=kwargs["""device"""] )
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = decay
SCREAMING_SNAKE_CASE = min_decay
SCREAMING_SNAKE_CASE = update_after_step
SCREAMING_SNAKE_CASE = use_ema_warmup
SCREAMING_SNAKE_CASE = inv_gamma
SCREAMING_SNAKE_CASE = power
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = None # set in `step()`
SCREAMING_SNAKE_CASE = model_cls
SCREAMING_SNAKE_CASE = model_config
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Any ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Optional[Any] ) -> "EMAModel":
'''simple docstring'''
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = model_cls.load_config(lowerCamelCase__ ,return_unused_kwargs=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model_cls.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = cls(model.parameters() ,model_cls=lowerCamelCase__ ,model_config=model.config )
ema_model.load_state_dict(lowerCamelCase__ )
return ema_model
def SCREAMING_SNAKE_CASE__ ( self : str ,lowerCamelCase__ : Tuple ) -> List[str]:
'''simple docstring'''
if self.model_cls is None:
raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__.""" )
if self.model_config is None:
raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__.""" )
SCREAMING_SNAKE_CASE = self.model_cls.from_config(self.model_config )
SCREAMING_SNAKE_CASE = self.state_dict()
state_dict.pop("""shadow_params""" ,lowerCamelCase__ )
model.register_to_config(**lowerCamelCase__ )
self.copy_to(model.parameters() )
model.save_pretrained(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : int ) -> float:
'''simple docstring'''
SCREAMING_SNAKE_CASE = max(0 ,optimization_step - self.update_after_step - 1 )
if step <= 0:
return 0.0
if self.use_ema_warmup:
SCREAMING_SNAKE_CASE = 1 - (1 + step / self.inv_gamma) ** -self.power
else:
SCREAMING_SNAKE_CASE = (1 + step) / (10 + step)
SCREAMING_SNAKE_CASE = min(lowerCamelCase__ ,self.decay )
# make sure decay is not smaller than min_decay
SCREAMING_SNAKE_CASE = max(lowerCamelCase__ ,self.min_decay )
return cur_decay_value
@torch.no_grad()
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : Iterable[torch.nn.Parameter] ) -> str:
'''simple docstring'''
if isinstance(lowerCamelCase__ ,torch.nn.Module ):
SCREAMING_SNAKE_CASE = (
"""Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
"""Please pass the parameters of the module instead."""
)
deprecate(
"""passing a `torch.nn.Module` to `ExponentialMovingAverage.step`""" ,"""1.0.0""" ,lowerCamelCase__ ,standard_warn=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = parameters.parameters()
SCREAMING_SNAKE_CASE = list(lowerCamelCase__ )
self.optimization_step += 1
# Compute the decay factor for the exponential moving average.
SCREAMING_SNAKE_CASE = self.get_decay(self.optimization_step )
SCREAMING_SNAKE_CASE = decay
SCREAMING_SNAKE_CASE = 1 - decay
SCREAMING_SNAKE_CASE = contextlib.nullcontext
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
import deepspeed
for s_param, param in zip(self.shadow_params ,lowerCamelCase__ ):
if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
SCREAMING_SNAKE_CASE = deepspeed.zero.GatheredParameters(lowerCamelCase__ ,modifier_rank=lowerCamelCase__ )
with context_manager():
if param.requires_grad:
s_param.sub_(one_minus_decay * (s_param - param) )
else:
s_param.copy_(lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Iterable[torch.nn.Parameter] ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = list(lowerCamelCase__ )
for s_param, param in zip(self.shadow_params ,lowerCamelCase__ ):
param.data.copy_(s_param.to(param.device ).data )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : Optional[int]=None ,lowerCamelCase__ : str=None ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [
p.to(device=lowerCamelCase__ ,dtype=lowerCamelCase__ ) if p.is_floating_point() else p.to(device=lowerCamelCase__ )
for p in self.shadow_params
]
def SCREAMING_SNAKE_CASE__ ( self : str ) -> dict:
'''simple docstring'''
return {
"decay": self.decay,
"min_decay": self.min_decay,
"optimization_step": self.optimization_step,
"update_after_step": self.update_after_step,
"use_ema_warmup": self.use_ema_warmup,
"inv_gamma": self.inv_gamma,
"power": self.power,
"shadow_params": self.shadow_params,
}
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : Iterable[torch.nn.Parameter] ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [param.detach().cpu().clone() for param in parameters]
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Iterable[torch.nn.Parameter] ) -> None:
'''simple docstring'''
if self.temp_stored_params is None:
raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`""" )
for c_param, param in zip(self.temp_stored_params ,lowerCamelCase__ ):
param.data.copy_(c_param.data )
# Better memory-wise.
SCREAMING_SNAKE_CASE = None
def SCREAMING_SNAKE_CASE__ ( self : Tuple ,lowerCamelCase__ : dict ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = copy.deepcopy(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = state_dict.get("""decay""" ,self.decay )
if self.decay < 0.0 or self.decay > 1.0:
raise ValueError("""Decay must be between 0 and 1""" )
SCREAMING_SNAKE_CASE = state_dict.get("""min_decay""" ,self.min_decay )
if not isinstance(self.min_decay ,lowerCamelCase__ ):
raise ValueError("""Invalid min_decay""" )
SCREAMING_SNAKE_CASE = state_dict.get("""optimization_step""" ,self.optimization_step )
if not isinstance(self.optimization_step ,lowerCamelCase__ ):
raise ValueError("""Invalid optimization_step""" )
SCREAMING_SNAKE_CASE = state_dict.get("""update_after_step""" ,self.update_after_step )
if not isinstance(self.update_after_step ,lowerCamelCase__ ):
raise ValueError("""Invalid update_after_step""" )
SCREAMING_SNAKE_CASE = state_dict.get("""use_ema_warmup""" ,self.use_ema_warmup )
if not isinstance(self.use_ema_warmup ,lowerCamelCase__ ):
raise ValueError("""Invalid use_ema_warmup""" )
SCREAMING_SNAKE_CASE = state_dict.get("""inv_gamma""" ,self.inv_gamma )
if not isinstance(self.inv_gamma ,(float, int) ):
raise ValueError("""Invalid inv_gamma""" )
SCREAMING_SNAKE_CASE = state_dict.get("""power""" ,self.power )
if not isinstance(self.power ,(float, int) ):
raise ValueError("""Invalid power""" )
SCREAMING_SNAKE_CASE = state_dict.get("""shadow_params""" ,lowerCamelCase__ )
if shadow_params is not None:
SCREAMING_SNAKE_CASE = shadow_params
if not isinstance(self.shadow_params ,lowerCamelCase__ ):
raise ValueError("""shadow_params must be a list""" )
if not all(isinstance(lowerCamelCase__ ,torch.Tensor ) for p in self.shadow_params ):
raise ValueError("""shadow_params must all be Tensors""" )
| 296 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Union[str, Any] ,lowerCamelCase__ : Callable ,lowerCamelCase__ : Optional[Features] = None ,lowerCamelCase__ : str = None ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : bool = False ,lowerCamelCase__ : Optional[dict] = None ,lowerCamelCase__ : Optional[int] = None ,**lowerCamelCase__ : Optional[Any] ,) -> List[str]:
'''simple docstring'''
super().__init__(
features=lowerCamelCase__ ,cache_dir=lowerCamelCase__ ,keep_in_memory=lowerCamelCase__ ,streaming=lowerCamelCase__ ,num_proc=lowerCamelCase__ ,**lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = Generator(
cache_dir=lowerCamelCase__ ,features=lowerCamelCase__ ,generator=lowerCamelCase__ ,gen_kwargs=lowerCamelCase__ ,**lowerCamelCase__ ,)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
if self.streaming:
SCREAMING_SNAKE_CASE = self.builder.as_streaming_dataset(split="""train""" )
# Build regular (map-style) dataset
else:
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = None
self.builder.download_and_prepare(
download_config=lowerCamelCase__ ,download_mode=lowerCamelCase__ ,verification_mode=lowerCamelCase__ ,base_path=lowerCamelCase__ ,num_proc=self.num_proc ,)
SCREAMING_SNAKE_CASE = self.builder.as_dataset(
split="""train""" ,verification_mode=lowerCamelCase__ ,in_memory=self.keep_in_memory )
return dataset
| 296 | 1 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[str] = TextToVideoSDPipeline
__snake_case : int = TEXT_TO_IMAGE_PARAMS
__snake_case : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
__snake_case : Dict = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""") ,up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""") ,cross_attention_dim=32 ,attention_head_dim=4 ,)
SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.00085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=lowerCamelCase__ ,set_alpha_to_one=lowerCamelCase__ ,)
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = 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 )
SCREAMING_SNAKE_CASE = 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=1000 ,hidden_act="""gelu""" ,projection_dim=512 ,)
SCREAMING_SNAKE_CASE = CLIPTextModel(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : int=0 ) -> List[Any]:
'''simple docstring'''
if str(lowerCamelCase__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCamelCase__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe.to(lowerCamelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """np"""
SCREAMING_SNAKE_CASE = sd_pipe(**lowerCamelCase__ ).frames
SCREAMING_SNAKE_CASE = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
SCREAMING_SNAKE_CASE = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=1e-2 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=25 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=2 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 296 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Optional[float] = field(
default=0.0 , metadata={"help": "The label smoothing epsilon to apply (if not zero)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "Whether to SortishSamler or not."} )
__snake_case : bool = field(
default=lowerCAmelCase_ , metadata={"help": "Whether to use generate to calculate generative metrics (ROUGE, BLEU)."} )
__snake_case : bool = field(default=lowerCAmelCase_ , metadata={"help": "whether to use adafactor"} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Encoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Decoder layer dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(default=lowerCAmelCase_ , metadata={"help": "Dropout probability. Goes into model.config."} )
__snake_case : Optional[float] = field(
default=lowerCAmelCase_ , metadata={"help": "Attention dropout probability. Goes into model.config."} )
__snake_case : Optional[str] = field(
default="linear" , metadata={"help": F"Which lr scheduler to use. Selected in {sorted(arg_to_scheduler.keys() )}"} , )
| 296 | 1 |
import os
import unittest
from tempfile import TemporaryDirectory
import torch
import torch.nn as nn
from accelerate.utils import (
OffloadedWeightsLoader,
extract_submodules_state_dict,
load_offloaded_weight,
offload_state_dict,
offload_weight,
)
class UpperCamelCase__ ( nn.Module ):
'''simple docstring'''
def __init__( self : Optional[int] ) -> List[Any]:
'''simple docstring'''
super().__init__()
SCREAMING_SNAKE_CASE = nn.Linear(3 ,4 )
SCREAMING_SNAKE_CASE = nn.BatchNormad(4 )
SCREAMING_SNAKE_CASE = nn.Linear(4 ,5 )
def SCREAMING_SNAKE_CASE__ ( self : Dict ,lowerCamelCase__ : Tuple ) -> Optional[Any]:
'''simple docstring'''
return self.lineara(self.batchnorm(self.lineara(lowerCamelCase__ ) ) )
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : int ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ModelForTest()
with TemporaryDirectory() as tmp_dir:
offload_state_dict(lowerCamelCase__ ,model.state_dict() )
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""index.json""" )
self.assertTrue(os.path.isfile(lowerCamelCase__ ) )
# TODO: add tests on what is inside the index
for key in ["linear1.weight", "linear1.bias", "linear2.weight", "linear2.bias"]:
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,F"""{key}.dat""" )
self.assertTrue(os.path.isfile(lowerCamelCase__ ) )
# TODO: add tests on the fact weights are properly loaded
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [torch.floataa, torch.floataa, torch.bfloataa]
for dtype in dtypes:
SCREAMING_SNAKE_CASE = torch.randn(2 ,3 ,dtype=lowerCamelCase__ )
with TemporaryDirectory() as tmp_dir:
SCREAMING_SNAKE_CASE = offload_weight(lowerCamelCase__ ,"""weight""" ,lowerCamelCase__ ,{} )
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""weight.dat""" )
self.assertTrue(os.path.isfile(lowerCamelCase__ ) )
self.assertDictEqual(lowerCamelCase__ ,{"""weight""": {"""shape""": [2, 3], """dtype""": str(lowerCamelCase__ ).split(""".""" )[1]}} )
SCREAMING_SNAKE_CASE = load_offloaded_weight(lowerCamelCase__ ,index["""weight"""] )
self.assertTrue(torch.equal(lowerCamelCase__ ,lowerCamelCase__ ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ModelForTest()
SCREAMING_SNAKE_CASE = model.state_dict()
SCREAMING_SNAKE_CASE = {k: v for k, v in state_dict.items() if """linear2""" not in k}
SCREAMING_SNAKE_CASE = {k: v for k, v in state_dict.items() if """linear2""" in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = OffloadedWeightsLoader(state_dict=lowerCamelCase__ ,save_folder=lowerCamelCase__ )
# Every key is there with the right value
self.assertEqual(sorted(lowerCamelCase__ ) ,sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(lowerCamelCase__ ,weight_map[key] ) )
SCREAMING_SNAKE_CASE = {k: v for k, v in state_dict.items() if """weight""" in k}
SCREAMING_SNAKE_CASE = {k: v for k, v in state_dict.items() if """weight""" not in k}
with TemporaryDirectory() as tmp_dir:
offload_state_dict(lowerCamelCase__ ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = OffloadedWeightsLoader(state_dict=lowerCamelCase__ ,save_folder=lowerCamelCase__ )
# Every key is there with the right value
self.assertEqual(sorted(lowerCamelCase__ ) ,sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(lowerCamelCase__ ,weight_map[key] ) )
with TemporaryDirectory() as tmp_dir:
offload_state_dict(lowerCamelCase__ ,lowerCamelCase__ )
# Duplicates are removed
SCREAMING_SNAKE_CASE = OffloadedWeightsLoader(state_dict=lowerCamelCase__ ,save_folder=lowerCamelCase__ )
# Every key is there with the right value
self.assertEqual(sorted(lowerCamelCase__ ) ,sorted(state_dict.keys() ) )
for key, param in state_dict.items():
self.assertTrue(torch.allclose(lowerCamelCase__ ,weight_map[key] ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {"""a.1""": 0, """a.10""": 1, """a.2""": 2}
SCREAMING_SNAKE_CASE = extract_submodules_state_dict(lowerCamelCase__ ,["""a.1""", """a.2"""] )
self.assertDictEqual(lowerCamelCase__ ,{"""a.1""": 0, """a.2""": 2} )
SCREAMING_SNAKE_CASE = {"""a.1.a""": 0, """a.10.a""": 1, """a.2.a""": 2}
SCREAMING_SNAKE_CASE = extract_submodules_state_dict(lowerCamelCase__ ,["""a.1""", """a.2"""] )
self.assertDictEqual(lowerCamelCase__ ,{"""a.1.a""": 0, """a.2.a""": 2} )
| 296 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_available, load_numpy, skip_mps, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[str] = TextToVideoSDPipeline
__snake_case : int = TEXT_TO_IMAGE_PARAMS
__snake_case : Tuple = TEXT_TO_IMAGE_BATCH_PARAMS
# No `output_type`.
__snake_case : Dict = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
] )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[int]:
'''simple docstring'''
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = UNetaDConditionModel(
block_out_channels=(32, 64, 64, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("""CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """CrossAttnDownBlock3D""", """DownBlock3D""") ,up_block_types=("""UpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""", """CrossAttnUpBlock3D""") ,cross_attention_dim=32 ,attention_head_dim=4 ,)
SCREAMING_SNAKE_CASE = DDIMScheduler(
beta_start=0.00085 ,beta_end=0.012 ,beta_schedule="""scaled_linear""" ,clip_sample=lowerCamelCase__ ,set_alpha_to_one=lowerCamelCase__ ,)
torch.manual_seed(0 )
SCREAMING_SNAKE_CASE = 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 )
SCREAMING_SNAKE_CASE = 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=1000 ,hidden_act="""gelu""" ,projection_dim=512 ,)
SCREAMING_SNAKE_CASE = CLIPTextModel(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )
SCREAMING_SNAKE_CASE = {
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
"""text_encoder""": text_encoder,
"""tokenizer""": tokenizer,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : int=0 ) -> List[Any]:
'''simple docstring'''
if str(lowerCamelCase__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE = torch.manual_seed(lowerCamelCase__ )
else:
SCREAMING_SNAKE_CASE = torch.Generator(device=lowerCamelCase__ ).manual_seed(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""guidance_scale""": 6.0,
"""output_type""": """pt""",
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
SCREAMING_SNAKE_CASE = self.get_dummy_components()
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = sd_pipe.to(lowerCamelCase__ )
sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.get_dummy_inputs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = """np"""
SCREAMING_SNAKE_CASE = sd_pipe(**lowerCamelCase__ ).frames
SCREAMING_SNAKE_CASE = frames[0][-3:, -3:, -1]
assert frames[0].shape == (64, 64, 3)
SCREAMING_SNAKE_CASE = np.array([158.0, 160.0, 153.0, 125.0, 100.0, 121.0, 111.0, 93.0, 113.0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
self._test_attention_slicing_forward_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=3e-3 )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() ,reason="""XFormers attention is only available with CUDA and `xformers` installed""" ,)
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=lowerCamelCase__ ,expected_max_diff=1e-2 )
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> List[Any]:
'''simple docstring'''
pass
@unittest.skip(reason="""Batching needs to be properly figured out first for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
pass
@unittest.skip(reason="""`num_images_per_prompt` argument is not supported for this pipeline.""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Tuple:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> Optional[Any]:
'''simple docstring'''
return super().test_progress_bar()
@slow
@skip_mps
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=25 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/text_to_video/video_2step.npy""" )
SCREAMING_SNAKE_CASE = TextToVideoSDPipeline.from_pretrained("""damo-vilab/text-to-video-ms-1.7b""" )
SCREAMING_SNAKE_CASE = pipe.to("""cuda""" )
SCREAMING_SNAKE_CASE = """Spiderman is surfing"""
SCREAMING_SNAKE_CASE = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE = pipe(lowerCamelCase__ ,generator=lowerCamelCase__ ,num_inference_steps=2 ,output_type="""pt""" ).frames
SCREAMING_SNAKE_CASE = video_frames.cpu().numpy()
assert np.abs(expected_video - video ).mean() < 5e-2
| 296 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[List[np.ndarray], torch.FloatTensor]
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipeline_text_to_video_synth import TextToVideoSDPipeline
from .pipeline_text_to_video_synth_imgaimg import VideoToVideoSDPipeline # noqa: F401
from .pipeline_text_to_video_zero import TextToVideoZeroPipeline
| 296 |
def __lowercase ( _SCREAMING_SNAKE_CASE = 10 ) -> str:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) or n < 0:
raise ValueError("""Invalid input""" )
SCREAMING_SNAKE_CASE = 10**n
SCREAMING_SNAKE_CASE = 2_84_33 * (pow(2 , 7_83_04_57 , _SCREAMING_SNAKE_CASE )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F'''{solution(1_0) = }''')
| 296 | 1 |
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : List[str] = ["input_features"]
def __init__( self : str ,lowerCamelCase__ : Union[str, Any]=80 ,lowerCamelCase__ : str=16000 ,lowerCamelCase__ : List[Any]=160 ,lowerCamelCase__ : Optional[int]=30 ,lowerCamelCase__ : Optional[int]=400 ,lowerCamelCase__ : Any=0.0 ,lowerCamelCase__ : Tuple=False ,**lowerCamelCase__ : str ,) -> int:
'''simple docstring'''
super().__init__(
feature_size=lowerCamelCase__ ,sampling_rate=lowerCamelCase__ ,padding_value=lowerCamelCase__ ,return_attention_mask=lowerCamelCase__ ,**lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = n_fft
SCREAMING_SNAKE_CASE = hop_length
SCREAMING_SNAKE_CASE = chunk_length
SCREAMING_SNAKE_CASE = chunk_length * sampling_rate
SCREAMING_SNAKE_CASE = self.n_samples // hop_length
SCREAMING_SNAKE_CASE = sampling_rate
SCREAMING_SNAKE_CASE = mel_filter_bank(
num_frequency_bins=1 + n_fft // 2 ,num_mel_filters=lowerCamelCase__ ,min_frequency=0.0 ,max_frequency=8000.0 ,sampling_rate=lowerCamelCase__ ,norm="""slaney""" ,mel_scale="""slaney""" ,)
def SCREAMING_SNAKE_CASE__ ( self : List[str] ,lowerCamelCase__ : np.array ) -> np.ndarray:
'''simple docstring'''
SCREAMING_SNAKE_CASE = spectrogram(
lowerCamelCase__ ,window_function(self.n_fft ,"""hann""" ) ,frame_length=self.n_fft ,hop_length=self.hop_length ,power=2.0 ,mel_filters=self.mel_filters ,log_mel="""log10""" ,)
SCREAMING_SNAKE_CASE = log_spec[:, :-1]
SCREAMING_SNAKE_CASE = np.maximum(lowerCamelCase__ ,log_spec.max() - 8.0 )
SCREAMING_SNAKE_CASE = (log_spec + 4.0) / 4.0
return log_spec
@staticmethod
# Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ : List[np.ndarray] ,lowerCamelCase__ : List[np.ndarray] ,lowerCamelCase__ : float = 0.0 ) -> List[np.ndarray]:
'''simple docstring'''
if attention_mask is not None:
SCREAMING_SNAKE_CASE = np.array(lowerCamelCase__ ,np.intaa )
SCREAMING_SNAKE_CASE = []
for vector, length in zip(lowerCamelCase__ ,attention_mask.sum(-1 ) ):
SCREAMING_SNAKE_CASE = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7 )
if length < normed_slice.shape[0]:
SCREAMING_SNAKE_CASE = padding_value
normed_input_values.append(lowerCamelCase__ )
else:
SCREAMING_SNAKE_CASE = [(x - x.mean()) / np.sqrt(x.var() + 1e-7 ) for x in input_values]
return normed_input_values
def __call__( self : Dict ,lowerCamelCase__ : Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]] ,lowerCamelCase__ : bool = True ,lowerCamelCase__ : Optional[int] = None ,lowerCamelCase__ : Optional[Union[str, TensorType]] = None ,lowerCamelCase__ : Optional[bool] = None ,lowerCamelCase__ : Optional[str] = "max_length" ,lowerCamelCase__ : Optional[int] = None ,lowerCamelCase__ : Optional[int] = None ,lowerCamelCase__ : Optional[bool] = None ,**lowerCamelCase__ : int ,) -> BatchFeature:
'''simple docstring'''
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
F"""The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"""
F""" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"""
F""" was sampled with {self.sampling_rate} and not {sampling_rate}.""" )
else:
logger.warning(
"""It is strongly recommended to pass the `sampling_rate` argument to this function. """
"""Failing to do so can result in silent errors that might be hard to debug.""" )
SCREAMING_SNAKE_CASE = isinstance(lowerCamelCase__ ,np.ndarray ) and len(raw_speech.shape ) > 1
if is_batched_numpy and len(raw_speech.shape ) > 2:
raise ValueError(F"""Only mono-channel audio is supported for input to {self}""" )
SCREAMING_SNAKE_CASE = is_batched_numpy or (
isinstance(lowerCamelCase__ ,(list, tuple) ) and (isinstance(raw_speech[0] ,(np.ndarray, tuple, list) ))
)
if is_batched:
SCREAMING_SNAKE_CASE = [np.asarray([speech] ,dtype=np.floataa ).T for speech in raw_speech]
elif not is_batched and not isinstance(lowerCamelCase__ ,np.ndarray ):
SCREAMING_SNAKE_CASE = np.asarray(lowerCamelCase__ ,dtype=np.floataa )
elif isinstance(lowerCamelCase__ ,np.ndarray ) and raw_speech.dtype is np.dtype(np.floataa ):
SCREAMING_SNAKE_CASE = raw_speech.astype(np.floataa )
# always return batch
if not is_batched:
SCREAMING_SNAKE_CASE = [np.asarray([raw_speech] ).T]
SCREAMING_SNAKE_CASE = BatchFeature({"""input_features""": raw_speech} )
# convert into correct format for padding
SCREAMING_SNAKE_CASE = self.pad(
lowerCamelCase__ ,padding=lowerCamelCase__ ,max_length=max_length if max_length else self.n_samples ,truncation=lowerCamelCase__ ,pad_to_multiple_of=lowerCamelCase__ ,return_attention_mask=return_attention_mask or do_normalize ,)
# zero-mean and unit-variance normalization
if do_normalize:
SCREAMING_SNAKE_CASE = self.zero_mean_unit_var_norm(
padded_inputs["""input_features"""] ,attention_mask=padded_inputs["""attention_mask"""] ,padding_value=self.padding_value ,)
SCREAMING_SNAKE_CASE = np.stack(padded_inputs["""input_features"""] ,axis=0 )
# make sure list is in array format
SCREAMING_SNAKE_CASE = padded_inputs.get("""input_features""" ).transpose(2 ,0 ,1 )
SCREAMING_SNAKE_CASE = [self._np_extract_fbank_features(lowerCamelCase__ ) for waveform in input_features[0]]
if isinstance(input_features[0] ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ,dtype=np.floataa ) for feature in input_features]
else:
SCREAMING_SNAKE_CASE = input_features
if return_attention_mask:
# rescale from sample (48000) to feature (3000)
SCREAMING_SNAKE_CASE = padded_inputs["""attention_mask"""][:, :: self.hop_length]
if return_tensors is not None:
SCREAMING_SNAKE_CASE = padded_inputs.convert_to_tensors(lowerCamelCase__ )
return padded_inputs
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Dict[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 296 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {
"""configuration_llama""": ["""LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """LlamaConfig"""],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizer"""]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ["""LlamaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
"""LlamaForCausalLM""",
"""LlamaModel""",
"""LlamaPreTrainedModel""",
"""LlamaForSequenceClassification""",
]
if TYPE_CHECKING:
from .configuration_llama import LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP, LlamaConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama import LlamaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_llama_fast import LlamaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_llama import LlamaForCausalLM, LlamaForSequenceClassification, LlamaModel, LlamaPreTrainedModel
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 296 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : Optional[int] ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : Optional[Any]=7 ,lowerCamelCase__ : Dict=3 ,lowerCamelCase__ : List[str]=18 ,lowerCamelCase__ : Tuple=30 ,lowerCamelCase__ : Optional[Any]=400 ,lowerCamelCase__ : int=True ,lowerCamelCase__ : Tuple=None ,lowerCamelCase__ : int=True ,lowerCamelCase__ : Optional[int]=None ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : Dict=[0.5, 0.5, 0.5] ,lowerCamelCase__ : Dict=[0.5, 0.5, 0.5] ,) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = size if size is not None else {"""shortest_edge""": 18}
SCREAMING_SNAKE_CASE = crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = min_resolution
SCREAMING_SNAKE_CASE = max_resolution
SCREAMING_SNAKE_CASE = do_resize
SCREAMING_SNAKE_CASE = size
SCREAMING_SNAKE_CASE = do_center_crop
SCREAMING_SNAKE_CASE = crop_size
SCREAMING_SNAKE_CASE = do_normalize
SCREAMING_SNAKE_CASE = image_mean
SCREAMING_SNAKE_CASE = image_std
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> List[Any]:
'''simple docstring'''
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"do_center_crop": self.do_center_crop,
"size": self.size,
"crop_size": self.crop_size,
}
@require_torch
@require_vision
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : Optional[Any] = LevitImageProcessor if is_vision_available() else None
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = LevitImageProcessingTester(self )
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> str:
'''simple docstring'''
return self.image_processor_tester.prepare_image_processor_dict()
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(lowerCamelCase__ ,"""image_mean""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""image_std""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""do_normalize""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""do_resize""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""do_center_crop""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""size""" ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size ,{"""shortest_edge""": 18} )
self.assertEqual(image_processor.crop_size ,{"""height""": 18, """width""": 18} )
SCREAMING_SNAKE_CASE = self.image_processing_class.from_dict(self.image_processor_dict ,size=42 ,crop_size=84 )
self.assertEqual(image_processor.size ,{"""shortest_edge""": 42} )
self.assertEqual(image_processor.crop_size ,{"""height""": 84, """width""": 84} )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Dict:
'''simple docstring'''
pass
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ ,Image.Image )
# Test not batched input
SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
SCREAMING_SNAKE_CASE = image_processing(lowerCamelCase__ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCamelCase__ ,numpify=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ ,np.ndarray )
# Test not batched input
SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
SCREAMING_SNAKE_CASE = image_processing(lowerCamelCase__ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
SCREAMING_SNAKE_CASE = prepare_image_inputs(self.image_processor_tester ,equal_resolution=lowerCamelCase__ ,torchify=lowerCamelCase__ )
for image in image_inputs:
self.assertIsInstance(lowerCamelCase__ ,torch.Tensor )
# Test not batched input
SCREAMING_SNAKE_CASE = image_processing(image_inputs[0] ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
1,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
# Test batched
SCREAMING_SNAKE_CASE = image_processing(lowerCamelCase__ ,return_tensors="""pt""" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
self.image_processor_tester.crop_size["""height"""],
self.image_processor_tester.crop_size["""width"""],
) ,)
| 296 |
from pathlib import Path
import fire
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = Path(_SCREAMING_SNAKE_CASE )
dest_dir.mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
for path in src_dir.iterdir():
SCREAMING_SNAKE_CASE = [x.rstrip() for x in list(path.open().readlines() )][:n]
SCREAMING_SNAKE_CASE = dest_dir.joinpath(path.name )
print(_SCREAMING_SNAKE_CASE )
dest_path.open("""w""" ).write("""\n""".join(_SCREAMING_SNAKE_CASE ) )
if __name__ == "__main__":
fire.Fire(minify)
| 296 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
SCREAMING_SNAKE_CASE = [1_44, 1_92, 2_40]
SCREAMING_SNAKE_CASE = [16, 32, 64, 96, 1_28, 1_60, 6_40]
elif "mobilevit_xs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [96, 1_20, 1_44]
SCREAMING_SNAKE_CASE = [16, 32, 48, 64, 80, 96, 3_84]
elif "mobilevit_xxs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [64, 80, 96]
SCREAMING_SNAKE_CASE = [16, 16, 24, 48, 64, 80, 3_20]
SCREAMING_SNAKE_CASE = 0.05
SCREAMING_SNAKE_CASE = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = 5_12
SCREAMING_SNAKE_CASE = 16
SCREAMING_SNAKE_CASE = 21
SCREAMING_SNAKE_CASE = """pascal-voc-id2label.json"""
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
SCREAMING_SNAKE_CASE = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
SCREAMING_SNAKE_CASE = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.weight""" , """.layernorm.weight""" )
if F""".global_rep.{i}.bias""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.bias""" , """.layernorm.bias""" )
if ".global_rep." in name:
SCREAMING_SNAKE_CASE = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
SCREAMING_SNAKE_CASE = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
SCREAMING_SNAKE_CASE = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
SCREAMING_SNAKE_CASE = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
SCREAMING_SNAKE_CASE = """mobilevit.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Dict:
'''simple docstring'''
if base_model:
SCREAMING_SNAKE_CASE = """"""
else:
SCREAMING_SNAKE_CASE = """mobilevit."""
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if key[:8] == "encoder.":
SCREAMING_SNAKE_CASE = key[8:]
if "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[0][6:] ) - 1
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
SCREAMING_SNAKE_CASE = layer.transformer.layer[transformer_num].attention.attention.all_head_size
SCREAMING_SNAKE_CASE = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[:dim]
SCREAMING_SNAKE_CASE = val[dim : dim * 2]
SCREAMING_SNAKE_CASE = val[-dim:]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_mobilevit_config(_SCREAMING_SNAKE_CASE )
# load original state_dict
SCREAMING_SNAKE_CASE = torch.load(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = MobileViTForSemanticSegmentation(_SCREAMING_SNAKE_CASE ).eval()
else:
SCREAMING_SNAKE_CASE = MobileViTForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
SCREAMING_SNAKE_CASE = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileViTImageProcessor
SCREAMING_SNAKE_CASE = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
[[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
[[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
[[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
[[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
[[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
[[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
else:
assert logits.shape == (1, 10_00)
if mobilevit_name == "mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
elif mobilevit_name == "mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
elif mobilevit_name == "mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
SCREAMING_SNAKE_CASE = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
SCREAMING_SNAKE_CASE = model_mapping[mobilevit_name]
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
model.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--mobilevit_name""",
default="""mobilevit_s""",
type=str,
help=(
"""Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
""" 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
),
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 296 |
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transformers.models.bert.configuration_bert import BertConfig
from transformers.models.roberta.configuration_roberta import RobertaConfig
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, get_tests_dir
sys.path.append(str(Path(__file__).parent.parent.parent.parent / """utils"""))
from test_module.custom_configuration import CustomConfig # noqa E402
SCREAMING_SNAKE_CASE_ = get_tests_dir("""fixtures/dummy-config.json""")
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = 0
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
'''simple docstring'''
self.assertIsNotNone(transformers.models.auto.__spec__ )
self.assertIsNotNone(importlib.util.find_spec("""transformers.models.auto""" ) )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base-uncased""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AutoConfig.for_model("""roberta""" )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> int:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_dir:
# This model name contains bert and roberta, but roberta ends up being picked.
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""fake-roberta""" )
os.makedirs(lowerCamelCase__ ,exist_ok=lowerCamelCase__ )
with open(os.path.join(lowerCamelCase__ ,"""config.json""" ) ,"""w""" ) as f:
f.write(json.dumps({} ) )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertEqual(type(lowerCamelCase__ ) ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
try:
AutoConfig.register("""custom""" ,lowerCamelCase__ )
# Wrong model type will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""model""" ,lowerCamelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(lowerCamelCase__ ):
AutoConfig.register("""bert""" ,lowerCamelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
SCREAMING_SNAKE_CASE = CustomConfig()
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ )
self.assertIsInstance(lowerCamelCase__ ,lowerCamelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""bert-base is not a local folder and is not a valid model identifier""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""bert-base""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> str:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,R"""aaaaaa is not a valid git identifier \(branch name, tag name or commit id\)""" ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,revision="""aaaaaa""" )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> List[Any]:
'''simple docstring'''
with self.assertRaisesRegex(
lowerCamelCase__ ,"""hf-internal-testing/no-config-test-repo does not appear to have a file named config.json.""" ,):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/no-config-test-repo""" )
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
# Test config can be reloaded.
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained(lowerCamelCase__ ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(reloaded_config.__class__.__name__ ,"""NewModelConfig""" )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Union[str, Any]:
'''simple docstring'''
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = "new-model"
try:
AutoConfig.register("""new-model""" ,lowerCamelCase__ )
# If remote code is not set, the default is to use local
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote code is disabled, we load the local one.
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfigLocal""" )
# If remote is enabled, we load from the Hub
SCREAMING_SNAKE_CASE = AutoConfig.from_pretrained("""hf-internal-testing/test_dynamic_model""" ,trust_remote_code=lowerCamelCase__ )
self.assertEqual(config.__class__.__name__ ,"""NewModelConfig""" )
finally:
if "new-model" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["new-model"]
| 296 | 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
from ..auto import CONFIG_MAPPING
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/table-transformer-detection""": (
"""https://huggingface.co/microsoft/table-transformer-detection/resolve/main/config.json"""
),
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Union[str, Any] = "table-transformer"
__snake_case : Union[str, Any] = ["past_key_values"]
__snake_case : List[Any] = {
"hidden_size": "d_model",
"num_attention_heads": "encoder_attention_heads",
}
def __init__( self : Optional[int] ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : Optional[Any]=None ,lowerCamelCase__ : List[Any]=3 ,lowerCamelCase__ : Optional[int]=100 ,lowerCamelCase__ : List[Any]=6 ,lowerCamelCase__ : Dict=2048 ,lowerCamelCase__ : List[Any]=8 ,lowerCamelCase__ : Dict=6 ,lowerCamelCase__ : Dict=2048 ,lowerCamelCase__ : Any=8 ,lowerCamelCase__ : Optional[int]=0.0 ,lowerCamelCase__ : int=0.0 ,lowerCamelCase__ : List[Any]=True ,lowerCamelCase__ : Optional[int]="relu" ,lowerCamelCase__ : Tuple=256 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : Optional[Any]=0.0 ,lowerCamelCase__ : Tuple=0.0 ,lowerCamelCase__ : List[Any]=0.02 ,lowerCamelCase__ : int=1.0 ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : Optional[Any]="sine" ,lowerCamelCase__ : List[str]="resnet50" ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : List[str]=False ,lowerCamelCase__ : int=1 ,lowerCamelCase__ : Dict=5 ,lowerCamelCase__ : Tuple=2 ,lowerCamelCase__ : Union[str, Any]=1 ,lowerCamelCase__ : str=1 ,lowerCamelCase__ : Any=5 ,lowerCamelCase__ : Tuple=2 ,lowerCamelCase__ : str=0.1 ,**lowerCamelCase__ : List[str] ,) -> Optional[int]:
'''simple docstring'''
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
SCREAMING_SNAKE_CASE = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(lowerCamelCase__ ,lowerCamelCase__ ):
SCREAMING_SNAKE_CASE = backbone_config.get("""model_type""" )
SCREAMING_SNAKE_CASE = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE = config_class.from_dict(lowerCamelCase__ )
# set timm attributes to None
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = None, None, None
SCREAMING_SNAKE_CASE = use_timm_backbone
SCREAMING_SNAKE_CASE = backbone_config
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = num_queries
SCREAMING_SNAKE_CASE = d_model
SCREAMING_SNAKE_CASE = encoder_ffn_dim
SCREAMING_SNAKE_CASE = encoder_layers
SCREAMING_SNAKE_CASE = encoder_attention_heads
SCREAMING_SNAKE_CASE = decoder_ffn_dim
SCREAMING_SNAKE_CASE = decoder_layers
SCREAMING_SNAKE_CASE = decoder_attention_heads
SCREAMING_SNAKE_CASE = dropout
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = activation_dropout
SCREAMING_SNAKE_CASE = activation_function
SCREAMING_SNAKE_CASE = init_std
SCREAMING_SNAKE_CASE = init_xavier_std
SCREAMING_SNAKE_CASE = encoder_layerdrop
SCREAMING_SNAKE_CASE = decoder_layerdrop
SCREAMING_SNAKE_CASE = encoder_layers
SCREAMING_SNAKE_CASE = auxiliary_loss
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = backbone
SCREAMING_SNAKE_CASE = use_pretrained_backbone
SCREAMING_SNAKE_CASE = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE = class_cost
SCREAMING_SNAKE_CASE = bbox_cost
SCREAMING_SNAKE_CASE = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE = mask_loss_coefficient
SCREAMING_SNAKE_CASE = dice_loss_coefficient
SCREAMING_SNAKE_CASE = bbox_loss_coefficient
SCREAMING_SNAKE_CASE = giou_loss_coefficient
SCREAMING_SNAKE_CASE = eos_coefficient
super().__init__(is_encoder_decoder=lowerCamelCase__ ,**lowerCamelCase__ )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> int:
'''simple docstring'''
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> int:
'''simple docstring'''
return self.d_model
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : int = version.parse("1.11" )
@property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ) -> float:
'''simple docstring'''
return 1e-5
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> int:
'''simple docstring'''
return 12
| 296 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
"""microsoft/git-base""": """https://huggingface.co/microsoft/git-base/resolve/main/config.json""",
}
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Dict = "git_vision_model"
def __init__( self : List[Any] ,lowerCamelCase__ : Dict=768 ,lowerCamelCase__ : Union[str, Any]=3072 ,lowerCamelCase__ : Optional[int]=12 ,lowerCamelCase__ : Tuple=12 ,lowerCamelCase__ : Tuple=3 ,lowerCamelCase__ : Optional[Any]=224 ,lowerCamelCase__ : Union[str, Any]=16 ,lowerCamelCase__ : List[Any]="quick_gelu" ,lowerCamelCase__ : Optional[Any]=1e-5 ,lowerCamelCase__ : str=0.0 ,lowerCamelCase__ : Optional[int]=0.02 ,**lowerCamelCase__ : Union[str, Any] ,) -> Optional[int]:
'''simple docstring'''
super().__init__(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = num_channels
SCREAMING_SNAKE_CASE = patch_size
SCREAMING_SNAKE_CASE = image_size
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = attention_dropout
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = hidden_act
@classmethod
def SCREAMING_SNAKE_CASE__ ( cls : Tuple ,lowerCamelCase__ : Union[str, os.PathLike] ,**lowerCamelCase__ : int ) -> "PretrainedConfig":
'''simple docstring'''
cls._set_token_in_kwargs(lowerCamelCase__ )
SCREAMING_SNAKE_CASE, SCREAMING_SNAKE_CASE = cls.get_config_dict(lowerCamelCase__ ,**lowerCamelCase__ )
# get the vision config dict if we are loading from GITConfig
if config_dict.get("""model_type""" ) == "git":
SCREAMING_SNAKE_CASE = config_dict["""vision_config"""]
if "model_type" in config_dict and hasattr(cls ,"""model_type""" ) and config_dict["model_type"] != cls.model_type:
logger.warning(
F"""You are using a model of type {config_dict['model_type']} to instantiate a model of type """
F"""{cls.model_type}. This is not supported for all configurations of models and can yield errors.""" )
return cls.from_dict(lowerCamelCase__ ,**lowerCamelCase__ )
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
__snake_case : Dict = "git"
def __init__( self : Optional[int] ,lowerCamelCase__ : int=None ,lowerCamelCase__ : str=30522 ,lowerCamelCase__ : Tuple=768 ,lowerCamelCase__ : Union[str, Any]=6 ,lowerCamelCase__ : str=12 ,lowerCamelCase__ : List[str]=3072 ,lowerCamelCase__ : Dict="gelu" ,lowerCamelCase__ : Tuple=0.1 ,lowerCamelCase__ : Any=0.1 ,lowerCamelCase__ : List[str]=1024 ,lowerCamelCase__ : List[str]=0.02 ,lowerCamelCase__ : str=1e-1_2 ,lowerCamelCase__ : Optional[int]=0 ,lowerCamelCase__ : Optional[int]="absolute" ,lowerCamelCase__ : Optional[Any]=True ,lowerCamelCase__ : str=False ,lowerCamelCase__ : int=101 ,lowerCamelCase__ : int=102 ,lowerCamelCase__ : Dict=None ,**lowerCamelCase__ : List[Any] ,) -> Optional[Any]:
'''simple docstring'''
super().__init__(bos_token_id=lowerCamelCase__ ,eos_token_id=lowerCamelCase__ ,pad_token_id=lowerCamelCase__ ,**lowerCamelCase__ )
if vision_config is None:
SCREAMING_SNAKE_CASE = {}
logger.info("""vision_config is None. initializing the GitVisionConfig with default values.""" )
SCREAMING_SNAKE_CASE = GitVisionConfig(**lowerCamelCase__ )
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = layer_norm_eps
SCREAMING_SNAKE_CASE = position_embedding_type
SCREAMING_SNAKE_CASE = use_cache
SCREAMING_SNAKE_CASE = tie_word_embeddings
SCREAMING_SNAKE_CASE = num_image_with_embedding
SCREAMING_SNAKE_CASE = bos_token_id
SCREAMING_SNAKE_CASE = eos_token_id
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
SCREAMING_SNAKE_CASE = self.vision_config.to_dict()
SCREAMING_SNAKE_CASE = self.__class__.model_type
return output
| 296 | 1 |
from math import asin, atan, cos, radians, sin, sqrt, tan
SCREAMING_SNAKE_CASE_ = 637_8137.0
SCREAMING_SNAKE_CASE_ = 635_6752.31_4245
SCREAMING_SNAKE_CASE_ = 6_3_7_8_1_3_7
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
SCREAMING_SNAKE_CASE = (AXIS_A - AXIS_B) / AXIS_A
SCREAMING_SNAKE_CASE = atan((1 - flattening) * tan(radians(_SCREAMING_SNAKE_CASE ) ) )
SCREAMING_SNAKE_CASE = atan((1 - flattening) * tan(radians(_SCREAMING_SNAKE_CASE ) ) )
SCREAMING_SNAKE_CASE = radians(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = radians(_SCREAMING_SNAKE_CASE )
# Equation
SCREAMING_SNAKE_CASE = sin((phi_a - phi_a) / 2 )
SCREAMING_SNAKE_CASE = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
SCREAMING_SNAKE_CASE = sqrt(sin_sq_phi + (cos(_SCREAMING_SNAKE_CASE ) * cos(_SCREAMING_SNAKE_CASE ) * sin_sq_lambda) )
return 2 * RADIUS * asin(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 296 |
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
SCREAMING_SNAKE_CASE_ = random.Random()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=1.0 , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None ) -> Tuple:
'''simple docstring'''
if rng is None:
SCREAMING_SNAKE_CASE = global_rng
SCREAMING_SNAKE_CASE = []
for batch_idx in range(shape[0] ):
values.append([] )
for _ in range(shape[1] ):
values[-1].append(rng.random() * scale )
return values
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
def __init__( self : List[Any] ,lowerCamelCase__ : str ,lowerCamelCase__ : Optional[int]=7 ,lowerCamelCase__ : Optional[Any]=400 ,lowerCamelCase__ : List[str]=2000 ,lowerCamelCase__ : List[str]=2048 ,lowerCamelCase__ : Any=128 ,lowerCamelCase__ : List[str]=1 ,lowerCamelCase__ : str=512 ,lowerCamelCase__ : Optional[Any]=30 ,lowerCamelCase__ : Tuple=44100 ,) -> Tuple:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = min_seq_length
SCREAMING_SNAKE_CASE = max_seq_length
SCREAMING_SNAKE_CASE = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
SCREAMING_SNAKE_CASE = spectrogram_length
SCREAMING_SNAKE_CASE = feature_size
SCREAMING_SNAKE_CASE = num_audio_channels
SCREAMING_SNAKE_CASE = hop_length
SCREAMING_SNAKE_CASE = chunk_length
SCREAMING_SNAKE_CASE = sampling_rate
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[int]:
'''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 SCREAMING_SNAKE_CASE__ ( self : Optional[int] ,lowerCamelCase__ : Tuple=False ,lowerCamelCase__ : Union[str, Any]=False ) -> str:
'''simple docstring'''
def _flatten(lowerCamelCase__ : List[Any] ):
return list(itertools.chain(*lowerCamelCase__ ) )
if equal_length:
SCREAMING_SNAKE_CASE = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
SCREAMING_SNAKE_CASE = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length ,self.max_seq_length ,self.seq_length_diff )
]
if numpify:
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class UpperCamelCase__ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
__snake_case : List[Any] = TvltFeatureExtractor
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = TvltFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
self.assertTrue(hasattr(lowerCamelCase__ ,"""spectrogram_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""feature_size""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""num_audio_channels""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""hop_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""chunk_length""" ) )
self.assertTrue(hasattr(lowerCamelCase__ ,"""sampling_rate""" ) )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = feat_extract_first.save_pretrained(lowerCamelCase__ )[0]
check_json_file_has_correct_format(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_pretrained(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
SCREAMING_SNAKE_CASE = os.path.join(lowerCamelCase__ ,"""feat_extract.json""" )
feat_extract_first.to_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = self.feature_extraction_class.from_json_file(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feat_extract_first.to_dict()
SCREAMING_SNAKE_CASE = feat_extract_second.to_dict()
SCREAMING_SNAKE_CASE = dict_first.pop("""mel_filters""" )
SCREAMING_SNAKE_CASE = dict_second.pop("""mel_filters""" )
self.assertTrue(np.allclose(lowerCamelCase__ ,lowerCamelCase__ ) )
self.assertEqual(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.feature_extraction_class(**self.feat_extract_dict )
# create three inputs of length 800, 1000, and 1200
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in range(800 ,1400 ,200 )]
SCREAMING_SNAKE_CASE = [np.asarray(lowerCamelCase__ ) for speech_input in speech_inputs]
# Test not batched input
SCREAMING_SNAKE_CASE = 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
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,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
SCREAMING_SNAKE_CASE = feature_extractor(
lowerCamelCase__ ,return_tensors="""np""" ,sampling_rate=44100 ,mask_audio=lowerCamelCase__ ).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.
SCREAMING_SNAKE_CASE = [floats_list((1, x) )[0] for x in (800, 800, 800)]
SCREAMING_SNAKE_CASE = np.asarray(lowerCamelCase__ )
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,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 SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : str ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" ,"""clean""" ,split="""validation""" )
# automatic decoding with librispeech
SCREAMING_SNAKE_CASE = ds.sort("""id""" ).select(range(lowerCamelCase__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._load_datasamples(1 )
SCREAMING_SNAKE_CASE = TvltFeatureExtractor()
SCREAMING_SNAKE_CASE = feature_extractor(lowerCamelCase__ ,return_tensors="""pt""" ).audio_values
self.assertEquals(audio_values.shape ,(1, 1, 192, 128) )
SCREAMING_SNAKE_CASE = torch.tensor([[-0.3032, -0.2708], [-0.4434, -0.4007]] )
self.assertTrue(torch.allclose(audio_values[0, 0, :2, :2] ,lowerCamelCase__ ,atol=1e-4 ) )
| 296 | 1 |
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> bool:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("""Input list must be a non empty list""" )
if len(_SCREAMING_SNAKE_CASE ) == 1:
return True
SCREAMING_SNAKE_CASE = series[1] - series[0]
for index in range(len(_SCREAMING_SNAKE_CASE ) - 1 ):
if series[index + 1] - series[index] != common_diff:
return False
return True
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> float:
'''simple docstring'''
if not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise ValueError("""Input series is not valid, valid series - [2, 4, 6]""" )
if len(_SCREAMING_SNAKE_CASE ) == 0:
raise ValueError("""Input list must be a non empty list""" )
SCREAMING_SNAKE_CASE = 0
for val in series:
answer += val
return answer / len(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 296 |
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 (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def __lowercase ( _SCREAMING_SNAKE_CASE ) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = MobileViTConfig()
# size of the architecture
if "mobilevit_s" in mobilevit_name:
SCREAMING_SNAKE_CASE = [1_44, 1_92, 2_40]
SCREAMING_SNAKE_CASE = [16, 32, 64, 96, 1_28, 1_60, 6_40]
elif "mobilevit_xs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [96, 1_20, 1_44]
SCREAMING_SNAKE_CASE = [16, 32, 48, 64, 80, 96, 3_84]
elif "mobilevit_xxs" in mobilevit_name:
SCREAMING_SNAKE_CASE = [64, 80, 96]
SCREAMING_SNAKE_CASE = [16, 16, 24, 48, 64, 80, 3_20]
SCREAMING_SNAKE_CASE = 0.05
SCREAMING_SNAKE_CASE = 2.0
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = 5_12
SCREAMING_SNAKE_CASE = 16
SCREAMING_SNAKE_CASE = 21
SCREAMING_SNAKE_CASE = """pascal-voc-id2label.json"""
else:
SCREAMING_SNAKE_CASE = 10_00
SCREAMING_SNAKE_CASE = """imagenet-1k-id2label.json"""
SCREAMING_SNAKE_CASE = """huggingface/label-files"""
SCREAMING_SNAKE_CASE = json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , repo_type="""dataset""" ) , """r""" ) )
SCREAMING_SNAKE_CASE = {int(_SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE = idalabel
SCREAMING_SNAKE_CASE = {v: k for k, v in idalabel.items()}
return config
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
for i in range(1 , 6 ):
if F"""layer_{i}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F"""layer_{i}.""" , F"""encoder.layer.{i - 1}.""" )
if "conv_1." in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_1.""" , """conv_stem.""" )
if ".block." in name:
SCREAMING_SNAKE_CASE = name.replace(""".block.""" , """.""" )
if "exp_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""exp_1x1""" , """expand_1x1""" )
if "red_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""red_1x1""" , """reduce_1x1""" )
if ".local_rep.conv_3x3." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_3x3.""" , """.conv_kxk.""" )
if ".local_rep.conv_1x1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".local_rep.conv_1x1.""" , """.conv_1x1.""" )
if ".norm." in name:
SCREAMING_SNAKE_CASE = name.replace(""".norm.""" , """.normalization.""" )
if ".conv." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv.""" , """.convolution.""" )
if ".conv_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".conv_proj.""" , """.conv_projection.""" )
for i in range(0 , 2 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.layer.{j}.""" )
for i in range(2 , 6 ):
for j in range(0 , 4 ):
if F""".{i}.{j}.""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".{i}.{j}.""" , F""".{i}.""" )
if "expand_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""expand_1x1""" , """downsampling_layer.expand_1x1""" )
if "conv_3x3" in name:
SCREAMING_SNAKE_CASE = name.replace("""conv_3x3""" , """downsampling_layer.conv_3x3""" )
if "reduce_1x1" in name:
SCREAMING_SNAKE_CASE = name.replace("""reduce_1x1""" , """downsampling_layer.reduce_1x1""" )
for i in range(2 , 5 ):
if F""".global_rep.{i}.weight""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.weight""" , """.layernorm.weight""" )
if F""".global_rep.{i}.bias""" in name:
SCREAMING_SNAKE_CASE = name.replace(F""".global_rep.{i}.bias""" , """.layernorm.bias""" )
if ".global_rep." in name:
SCREAMING_SNAKE_CASE = name.replace(""".global_rep.""" , """.transformer.""" )
if ".pre_norm_mha.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.0.""" , """.layernorm_before.""" )
if ".pre_norm_mha.1.out_proj." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_mha.1.out_proj.""" , """.attention.output.dense.""" )
if ".pre_norm_ffn.0." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.0.""" , """.layernorm_after.""" )
if ".pre_norm_ffn.1." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.1.""" , """.intermediate.dense.""" )
if ".pre_norm_ffn.4." in name:
SCREAMING_SNAKE_CASE = name.replace(""".pre_norm_ffn.4.""" , """.output.dense.""" )
if ".transformer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".transformer.""" , """.transformer.layer.""" )
if ".aspp_layer." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_layer.""" , """.""" )
if ".aspp_pool." in name:
SCREAMING_SNAKE_CASE = name.replace(""".aspp_pool.""" , """.""" )
if "seg_head." in name:
SCREAMING_SNAKE_CASE = name.replace("""seg_head.""" , """segmentation_head.""" )
if "segmentation_head.classifier.classifier." in name:
SCREAMING_SNAKE_CASE = name.replace("""segmentation_head.classifier.classifier.""" , """segmentation_head.classifier.""" )
if "classifier.fc." in name:
SCREAMING_SNAKE_CASE = name.replace("""classifier.fc.""" , """classifier.""" )
elif (not base_model) and ("segmentation_head." not in name):
SCREAMING_SNAKE_CASE = """mobilevit.""" + name
return name
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> Dict:
'''simple docstring'''
if base_model:
SCREAMING_SNAKE_CASE = """"""
else:
SCREAMING_SNAKE_CASE = """mobilevit."""
for key in orig_state_dict.copy().keys():
SCREAMING_SNAKE_CASE = orig_state_dict.pop(_SCREAMING_SNAKE_CASE )
if key[:8] == "encoder.":
SCREAMING_SNAKE_CASE = key[8:]
if "qkv" in key:
SCREAMING_SNAKE_CASE = key.split(""".""" )
SCREAMING_SNAKE_CASE = int(key_split[0][6:] ) - 1
SCREAMING_SNAKE_CASE = int(key_split[3] )
SCREAMING_SNAKE_CASE = model.get_submodule(F"""{model_prefix}encoder.layer.{layer_num}""" )
SCREAMING_SNAKE_CASE = layer.transformer.layer[transformer_num].attention.attention.all_head_size
SCREAMING_SNAKE_CASE = (
F"""{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."""
)
if "weight" in key:
SCREAMING_SNAKE_CASE = val[:dim, :]
SCREAMING_SNAKE_CASE = val[dim : dim * 2, :]
SCREAMING_SNAKE_CASE = val[-dim:, :]
else:
SCREAMING_SNAKE_CASE = val[:dim]
SCREAMING_SNAKE_CASE = val[dim : dim * 2]
SCREAMING_SNAKE_CASE = val[-dim:]
else:
SCREAMING_SNAKE_CASE = val
return orig_state_dict
def __lowercase ( ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = """http://images.cocodataset.org/val2017/000000039769.jpg"""
SCREAMING_SNAKE_CASE = Image.open(requests.get(_SCREAMING_SNAKE_CASE , stream=_SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def __lowercase ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = get_mobilevit_config(_SCREAMING_SNAKE_CASE )
# load original state_dict
SCREAMING_SNAKE_CASE = torch.load(_SCREAMING_SNAKE_CASE , map_location="""cpu""" )
# load 🤗 model
if mobilevit_name.startswith("""deeplabv3_""" ):
SCREAMING_SNAKE_CASE = MobileViTForSemanticSegmentation(_SCREAMING_SNAKE_CASE ).eval()
else:
SCREAMING_SNAKE_CASE = MobileViTForImageClassification(_SCREAMING_SNAKE_CASE ).eval()
SCREAMING_SNAKE_CASE = convert_state_dict(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
model.load_state_dict(_SCREAMING_SNAKE_CASE )
# Check outputs on an image, prepared by MobileViTImageProcessor
SCREAMING_SNAKE_CASE = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
SCREAMING_SNAKE_CASE = image_processor(images=prepare_img() , return_tensors="""pt""" )
SCREAMING_SNAKE_CASE = model(**_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE = outputs.logits
if mobilevit_name.startswith("""deeplabv3_""" ):
assert logits.shape == (1, 21, 32, 32)
if mobilevit_name == "deeplabv3_mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.2_065, 6.1_292, 6.2_070], [6.1_079, 6.1_254, 6.1_747], [6.0_042, 6.1_071, 6.1_034]],
[[-6.9_253, -6.8_653, -7.0_398], [-7.3_218, -7.3_983, -7.3_670], [-7.1_961, -7.2_482, -7.1_569]],
[[-4.4_723, -4.4_348, -4.3_769], [-5.3_629, -5.4_632, -5.4_598], [-5.1_587, -5.3_402, -5.5_059]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[5.4_449, 5.5_733, 5.6_314], [5.1_815, 5.3_930, 5.5_963], [5.1_656, 5.4_333, 5.4_853]],
[[-9.4_423, -9.7_766, -9.6_714], [-9.1_581, -9.5_720, -9.5_519], [-9.1_006, -9.6_458, -9.5_703]],
[[-7.7_721, -7.3_716, -7.1_583], [-8.4_599, -8.0_624, -7.7_944], [-8.4_172, -7.8_366, -7.5_025]],
] )
elif mobilevit_name == "deeplabv3_mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor(
[
[[6.9_811, 6.9_743, 7.3_123], [7.1_777, 7.1_931, 7.3_938], [7.5_633, 7.8_050, 7.8_901]],
[[-10.5_536, -10.2_332, -10.2_924], [-10.2_336, -9.8_624, -9.5_964], [-10.8_840, -10.8_158, -10.6_659]],
[[-3.4_938, -3.0_631, -2.8_620], [-3.4_205, -2.8_135, -2.6_875], [-3.4_179, -2.7_945, -2.8_750]],
] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3, :3, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
else:
assert logits.shape == (1, 10_00)
if mobilevit_name == "mobilevit_s":
SCREAMING_SNAKE_CASE = torch.tensor([-0.9_866, 0.2_392, -1.1_241] )
elif mobilevit_name == "mobilevit_xs":
SCREAMING_SNAKE_CASE = torch.tensor([-2.4_761, -0.9_399, -1.9_587] )
elif mobilevit_name == "mobilevit_xxs":
SCREAMING_SNAKE_CASE = torch.tensor([-1.9_364, -1.2_327, -0.4_653] )
else:
raise ValueError(F"""Unknown mobilevit_name: {mobilevit_name}""" )
assert torch.allclose(logits[0, :3] , _SCREAMING_SNAKE_CASE , atol=1E-4 )
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
print(F"""Saving model {mobilevit_name} to {pytorch_dump_folder_path}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if push_to_hub:
SCREAMING_SNAKE_CASE = {
"""mobilevit_s""": """mobilevit-small""",
"""mobilevit_xs""": """mobilevit-x-small""",
"""mobilevit_xxs""": """mobilevit-xx-small""",
"""deeplabv3_mobilevit_s""": """deeplabv3-mobilevit-small""",
"""deeplabv3_mobilevit_xs""": """deeplabv3-mobilevit-x-small""",
"""deeplabv3_mobilevit_xxs""": """deeplabv3-mobilevit-xx-small""",
}
print("""Pushing to the hub...""" )
SCREAMING_SNAKE_CASE = model_mapping[mobilevit_name]
image_processor.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
model.push_to_hub(_SCREAMING_SNAKE_CASE , organization="""apple""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--mobilevit_name""",
default="""mobilevit_s""",
type=str,
help=(
"""Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
""" 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
),
)
parser.add_argument(
"""--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
)
parser.add_argument(
"""--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
)
parser.add_argument(
"""--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
)
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_movilevit_checkpoint(
args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
)
| 296 | 1 |
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