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 |
|---|---|---|---|---|
from dataclasses import dataclass, field
from typing import Optional
@dataclass
class __snake_case :
_a : Optional[str]= field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of model to be trained."} )
_a : Optional[str]= field(
default="./" , metadata={"help": "Save dir where model repo is cloned and models updates are saved to."} )
_a : Optional[str]= field(
default="codeparrot/codeparrot-clean-train" , metadata={"help": "Name or path of training dataset."} )
_a : Optional[str]= field(
default="codeparrot/codeparrot-clean-valid" , metadata={"help": "Name or path of validation dataset."} )
_a : Optional[int]= field(default=2 , metadata={"help": "Batch size for training."} )
_a : Optional[int]= field(default=2 , metadata={"help": "Batch size for evaluation."} )
_a : Optional[float]= field(default=0.1 , metadata={"help": "Value of weight decay."} )
_a : Optional[int]= field(
default=1_0000 , metadata={"help": "Size of buffer used to shuffle streaming dataset."} )
_a : Optional[float]= field(default=2E-4 , metadata={"help": "Learning rate fo training."} )
_a : Optional[str]= field(default="cosine" , metadata={"help": "Learning rate."} )
_a : Optional[int]= field(
default=750 , metadata={"help": "Number of warmup steps in the learning rate schedule."} )
_a : Optional[int]= field(
default=16 , metadata={"help": "Number of gradient accumulation steps."} )
_a : Optional[bool]= field(
default=lowerCAmelCase , metadata={"help": "Use gradient checkpointing to reduce memory footprint."} )
_a : Optional[int]= field(default=5_0000 , metadata={"help": "Maximum number of training steps."} )
_a : Optional[int]= field(
default=-1 , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} )
_a : Optional[int]= field(default=1024 , metadata={"help": "Sequence lengths used for training."} )
_a : Optional[int]= field(default=1 , metadata={"help": "Training seed."} )
_a : Optional[int]= field(
default=1024 , metadata={"help": "Interval to save checkpoints. Measured as number of forward passes not training steps."} , )
_a : Optional[str]= field(
default=lowerCAmelCase , metadata={"help": "States path if the training should continue from a checkpoint folder."} )
_a : Optional[bool]= field(default=lowerCAmelCase , metadata={"help": "If True the data is pretokenized."} )
@dataclass
class __snake_case :
_a : Optional[str]= field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of model to be evaluated."} )
_a : Optional[str]= field(
default="codeparrot/codeparrot-clean-valid" , metadata={"help": "Name or path of validation dataset."} )
_a : Optional[int]= field(default=2 , metadata={"help": "Batch size used for evaluation."} )
_a : Optional[int]= field(
default=-1 , metadata={"help": "Maximum number of evaluation steps. If -1 the full dataset is evaluated."} )
_a : Optional[int]= field(default=1024 , metadata={"help": "Length of sequences to be evaluated."} )
_a : Optional[int]= field(default=1 , metadata={"help": "Random seed used for evaluation."} )
@dataclass
class __snake_case :
_a : Optional[str]= field(
default="codeparrot/codeparrot" , metadata={"help": "Model name or path of model to be evaluated."} )
_a : Optional[int]= field(default=lowerCAmelCase , metadata={"help": "Number of workers used for code evaluation."} )
_a : Optional[int]= field(
default=lowerCAmelCase , metadata={"help": "The number of human-eval tasks to run. If not included all tasks are evaluated."} , )
_a : Optional[bool]= field(
default=lowerCAmelCase , metadata={"help": "Sample from the language model's output distribution."} )
_a : Optional[float]= field(default=0.2 , metadata={"help": "Sampling temperature used for generation."} )
_a : Optional[int]= field(default=256 , metadata={"help": "Maximum number of newly generated tokens."} )
_a : Optional[int]= field(default=0 , metadata={"help": "Top-k parameter used for generation."} )
_a : Optional[float]= field(default=0.95 , metadata={"help": "Top-p parameter used for nucleus sampling."} )
_a : Optional[int]= field(default=10 , metadata={"help": "Number of generations to run in parallel."} )
_a : Optional[int]= field(
default=200 , metadata={"help": "Number of completions to generate for each sample."} )
_a : Optional[int]= field(default=1 , metadata={"help": "Random seed used for evaluation."} )
_a : Optional[str]= field(
default="eval_results.json" , metadata={"help": "Random seed used for evaluation."} )
_a : Optional[str]= field(
default="0" , metadata={"help": "Allow `code_eval` to execute Python code on machine"} )
_a : Optional[int]= field(
default=-1 , metadata={
"help": (
"Determine which device to run the `text-generation` Pipeline on. -1 is CPU and any zero or positive"
" number corresponds to which GPU device id to run on."
)
} , )
@dataclass
class __snake_case :
_a : Optional[int]= field(
default=lowerCAmelCase , metadata={
"help": "The number of CPU cores to use for parallel preprocessing. Default uses the maximum available."
} , )
_a : Optional[str]= field(
default="transformersbook/codeparrot" , metadata={"help": "Folder or name of dataset to process."} )
_a : Optional[str]= field(
default="codeparrot-clean" , metadata={"help": "Folder to save processed processed dataset."} )
_a : Optional[int]= field(
default=10_0000 , metadata={"help": "Number of files to save per JSON output file."} )
_a : Optional[str]= field(default="content" , metadata={"help": "Column containing text data to process."} )
_a : Optional[float]= field(
default=1000 , metadata={"help": "Maximum line length in file, otherwise file is filtered."} )
_a : Optional[float]= field(
default=100 , metadata={"help": "Maximum mean line length in file, otherwise file is filtered."} )
_a : Optional[float]= field(
default=0.25 , metadata={"help": "Maximum fraction of non-alphanumeric characters, otherwise file is filtered."} )
_a : Optional[float]= field(
default=1.5 , metadata={"help": "Minimum character token ratio for the file, otherwise file is filtered."} )
_a : Optional[float]= field(
default=0.7 , metadata={"help": "Probability for filtering config, test and uncommon files."} )
_a : Optional[str]= field(
default="codeparrot/codeparrot" , metadata={"help": "Name or path to the tokenizer."} , )
_a : Optional[bool]= field(
default=lowerCAmelCase , metadata={"help": "If True, near-duplicate samples are removed."} )
_a : Optional[float]= field(
default=0.85 , metadata={"help": "Jaccard threshold for near-duplicate samples."} )
@dataclass
class __snake_case :
_a : Optional[str]= field(
default="gpt2" , metadata={"help": "Base tokenizer to build new tokenizer from."} )
_a : Optional[str]= field(
default="transformersbook/codeparrot-train" , metadata={"help": "Dataset to train tokenizer on."} )
_a : Optional[str]= field(default="content" , metadata={"help": "Column containing text data to process."} )
_a : Optional[int]= field(default=20_0000 , metadata={"help": "Number of examples to train tokenizer on."} )
_a : Optional[int]= field(
default=3_2768 , metadata={"help": "Number of examples to train the tokenizer on."} )
_a : Optional[str]= field(default="codeparrot" , metadata={"help": "Name of new tokenizer."} )
_a : Optional[bool]= field(default=lowerCAmelCase , metadata={"help": "Push saved tokenizer to the hub."} )
@dataclass
class __snake_case :
_a : Optional[str]= field(
default="codeparrot/codeparrot" , metadata={"help": "Name or path to the tokenizer."} )
_a : Optional[str]= field(
default="codeparrot/codeparrot-clean-train" , metadata={"help": "Name or path to the dataset to pretokenize."} )
_a : Optional[str]= field(
default="tokenized-codeparrot-train" , metadata={"help": "Repo name of the pretokenized data."} )
_a : Optional[int]= field(default=lowerCAmelCase , metadata={"help": "Number of workers used for code evaluation."} )
@dataclass
class __snake_case :
_a : Optional[str]= field(
default="gpt2-large" , metadata={"help": "Configuration to use for model initialization."} )
_a : Optional[str]= field(
default="codeparrot/codeparrot" , metadata={"help": "Tokenizer attached to model."} )
_a : Optional[str]= field(default="codeparrot" , metadata={"help": "Name of the created model."} )
_a : Optional[bool]= field(default=lowerCAmelCase , metadata={"help": "Push saved tokenizer to the hub."} )
| 20 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A ) -> Optional[Any]:
super().__init__()
lowerCAmelCase_ :int = nn.ModuleList(__A )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A = None , __A = None , __A = None , __A = None , __A = False , __A = True , ) -> Union[ControlNetOutput, Tuple]:
for i, (image, scale, controlnet) in enumerate(zip(__A , __A , self.nets ) ):
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = controlnet(
__A , __A , __A , __A , __A , __A , __A , __A , __A , __A , __A , )
# merge samples
if i == 0:
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = down_samples, mid_sample
else:
lowerCAmelCase_ :str = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(__A , __A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def __lowerCAmelCase ( self , __A , __A = True , __A = None , __A = False , __A = None , ) -> Optional[Any]:
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Dict = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
__A , is_main_process=__A , save_function=__A , safe_serialization=__A , variant=__A , )
idx += 1
lowerCAmelCase_ :Any = model_path_to_save + f"""_{idx}"""
@classmethod
def __lowerCAmelCase ( cls , __A , **__A ) -> List[Any]:
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Dict = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
lowerCAmelCase_ :List[Any] = pretrained_model_path
while os.path.isdir(__A ):
lowerCAmelCase_ :Tuple = ControlNetModel.from_pretrained(__A , **__A )
controlnets.append(__A )
idx += 1
lowerCAmelCase_ :Dict = pretrained_model_path + f"""_{idx}"""
logger.info(f"""{len(__A )} controlnets loaded from {pretrained_model_path}.""" )
if len(__A ) == 0:
raise ValueError(
f"""No ControlNets found under {os.path.dirname(__A )}. Expected at least {pretrained_model_path + "_0"}.""" )
return cls(__A )
| 84 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..models.whisper import WhisperForConditionalGeneration, WhisperProcessor
from .base import PipelineTool
class _lowerCamelCase( _a ):
lowercase_ : int = """openai/whisper-base"""
lowercase_ : Union[str, Any] = (
"""This is a tool that transcribes an audio into text. It takes an input named `audio` and returns the """
"""transcribed text."""
)
lowercase_ : Any = """transcriber"""
lowercase_ : List[Any] = WhisperProcessor
lowercase_ : List[str] = WhisperForConditionalGeneration
lowercase_ : Any = ["""audio"""]
lowercase_ : Union[str, Any] = ["""text"""]
def UpperCamelCase ( self, lowerCamelCase) -> Tuple:
"""simple docstring"""
return self.pre_processor(lowerCamelCase, return_tensors='pt').input_features
def UpperCamelCase ( self, lowerCamelCase) -> List[str]:
"""simple docstring"""
return self.model.generate(inputs=lowerCamelCase)
def UpperCamelCase ( self, lowerCamelCase) -> Optional[Any]:
"""simple docstring"""
return self.pre_processor.batch_decode(lowerCamelCase, skip_special_tokens=lowerCamelCase)[0]
| 21 |
"""simple docstring"""
from PIL import Image
def _snake_case ( lowercase__ : Image , lowercase__ : float ) -> Image:
'''simple docstring'''
def brightness(lowercase__ : int ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(lowercase__ )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
__UpperCAmelCase = change_brightness(img, 1_00)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 84 | 0 |
'''simple docstring'''
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE :int = logging.get_logger(__name__)
def UpperCAmelCase_ ( __lowercase : Optional[Any] , __lowercase : str , __lowercase : Tuple , __lowercase : Tuple ) -> List[Any]:
'''simple docstring'''
_UpperCAmelCase = original_name.split("." )[0]
_UpperCAmelCase = key.split("." )
_UpperCAmelCase = int(key_list[key_list.index(__lowercase ) - 2] )
_UpperCAmelCase = int(key_list[key_list.index(__lowercase ) - 1] )
_UpperCAmelCase = orig_block_num - offset
_UpperCAmelCase = key.replace(f'{orig_block_num}.{layer_num}.{original_name}' , f'block.{new_block_num}.{layer_num}.{new_name}' )
return key
def UpperCAmelCase_ ( __lowercase : List[str] ) -> Any:
'''simple docstring'''
_UpperCAmelCase = OrderedDict()
_UpperCAmelCase , _UpperCAmelCase = 0, 0
for key, value in state_dict.items():
if key.startswith("network" ):
_UpperCAmelCase = key.replace("network" , "poolformer.encoder" )
if "proj" in key:
# Works for the first embedding as well as the internal embedding layers
if key.endswith("bias" ) and "patch_embed" not in key:
patch_emb_offset += 1
_UpperCAmelCase = key[: key.find("proj" )]
_UpperCAmelCase = key.replace(__lowercase , f'patch_embeddings.{total_embed_found}.' )
_UpperCAmelCase = key.replace("proj" , "projection" )
if key.endswith("bias" ):
total_embed_found += 1
if "patch_embeddings" in key:
_UpperCAmelCase = "poolformer.encoder." + key
if "mlp.fc1" in key:
_UpperCAmelCase = replace_key_with_offset(__lowercase , __lowercase , "mlp.fc1" , "output.conv1" )
if "mlp.fc2" in key:
_UpperCAmelCase = replace_key_with_offset(__lowercase , __lowercase , "mlp.fc2" , "output.conv2" )
if "norm1" in key:
_UpperCAmelCase = replace_key_with_offset(__lowercase , __lowercase , "norm1" , "before_norm" )
if "norm2" in key:
_UpperCAmelCase = replace_key_with_offset(__lowercase , __lowercase , "norm2" , "after_norm" )
if "layer_scale_1" in key:
_UpperCAmelCase = replace_key_with_offset(__lowercase , __lowercase , "layer_scale_1" , "layer_scale_1" )
if "layer_scale_2" in key:
_UpperCAmelCase = replace_key_with_offset(__lowercase , __lowercase , "layer_scale_2" , "layer_scale_2" )
if "head" in key:
_UpperCAmelCase = key.replace("head" , "classifier" )
_UpperCAmelCase = value
return new_state_dict
def UpperCAmelCase_ ( ) -> Any:
'''simple docstring'''
_UpperCAmelCase = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase = Image.open(requests.get(__lowercase , stream=__lowercase ).raw )
return image
@torch.no_grad()
def UpperCAmelCase_ ( __lowercase : int , __lowercase : Dict , __lowercase : Dict ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase = PoolFormerConfig()
# set attributes based on model_name
_UpperCAmelCase = "huggingface/label-files"
_UpperCAmelCase = model_name[-3:]
_UpperCAmelCase = 1000
_UpperCAmelCase = "imagenet-1k-id2label.json"
_UpperCAmelCase = (1, 1000)
# set config attributes
_UpperCAmelCase = json.load(open(hf_hub_download(__lowercase , __lowercase , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase = {int(__lowercase ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
if size == "s12":
_UpperCAmelCase = [2, 2, 6, 2]
_UpperCAmelCase = [64, 128, 320, 512]
_UpperCAmelCase = 4.0
_UpperCAmelCase = 0.9
elif size == "s24":
_UpperCAmelCase = [4, 4, 12, 4]
_UpperCAmelCase = [64, 128, 320, 512]
_UpperCAmelCase = 4.0
_UpperCAmelCase = 0.9
elif size == "s36":
_UpperCAmelCase = [6, 6, 18, 6]
_UpperCAmelCase = [64, 128, 320, 512]
_UpperCAmelCase = 4.0
_UpperCAmelCase = 1E-6
_UpperCAmelCase = 0.9
elif size == "m36":
_UpperCAmelCase = [6, 6, 18, 6]
_UpperCAmelCase = [96, 192, 384, 768]
_UpperCAmelCase = 4.0
_UpperCAmelCase = 1E-6
_UpperCAmelCase = 0.95
elif size == "m48":
_UpperCAmelCase = [8, 8, 24, 8]
_UpperCAmelCase = [96, 192, 384, 768]
_UpperCAmelCase = 4.0
_UpperCAmelCase = 1E-6
_UpperCAmelCase = 0.95
else:
raise ValueError(f'Size {size} not supported' )
# load image processor
_UpperCAmelCase = PoolFormerImageProcessor(crop_pct=__lowercase )
# Prepare image
_UpperCAmelCase = prepare_img()
_UpperCAmelCase = image_processor(images=__lowercase , return_tensors="pt" ).pixel_values
logger.info(f'Converting model {model_name}...' )
# load original state dict
_UpperCAmelCase = torch.load(__lowercase , map_location=torch.device("cpu" ) )
# rename keys
_UpperCAmelCase = rename_keys(__lowercase )
# create HuggingFace model and load state dict
_UpperCAmelCase = PoolFormerForImageClassification(__lowercase )
model.load_state_dict(__lowercase )
model.eval()
# Define image processor
_UpperCAmelCase = PoolFormerImageProcessor(crop_pct=__lowercase )
_UpperCAmelCase = image_processor(images=prepare_img() , return_tensors="pt" ).pixel_values
# forward pass
_UpperCAmelCase = model(__lowercase )
_UpperCAmelCase = outputs.logits
# define expected logit slices for different models
if size == "s12":
_UpperCAmelCase = torch.tensor([-0.3045, -0.6758, -0.4869] )
elif size == "s24":
_UpperCAmelCase = torch.tensor([0.4402, -0.1374, -0.8045] )
elif size == "s36":
_UpperCAmelCase = torch.tensor([-0.6080, -0.5133, -0.5898] )
elif size == "m36":
_UpperCAmelCase = torch.tensor([0.3952, 0.2263, -1.2668] )
elif size == "m48":
_UpperCAmelCase = torch.tensor([0.1167, -0.0656, -0.3423] )
else:
raise ValueError(f'Size {size} not supported' )
# verify logits
assert logits.shape == expected_shape
assert torch.allclose(logits[0, :3] , __lowercase , atol=1E-2 )
# finally, save model and image processor
logger.info(f'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(__lowercase ).mkdir(exist_ok=__lowercase )
model.save_pretrained(__lowercase )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(__lowercase )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE :int = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''poolformer_s12''',
type=str,
help='''Name of the model you\'d like to convert.''',
)
parser.add_argument(
'''--checkpoint_path''', default=None, type=str, help='''Path to the original PyTorch checkpoint (.pth file).'''
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
__SCREAMING_SNAKE_CASE :Optional[Any] = parser.parse_args()
convert_poolformer_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path)
| 22 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _SCREAMING_SNAKE_CASE :
def __lowerCAmelCase ( self ) -> Tuple:
torch.manual_seed(0 )
lowerCAmelCase_ :int = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :List[Any] = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCAmelCase_ :str = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__A , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase_ :int = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowerCAmelCase ( self ) -> List[str]:
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , class_embed_type="""timestep""" , mid_block_scale_factor=1.4_1_4 , time_embedding_act_fn="""gelu""" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCAmelCase_ :str = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__A , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[int] = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , )
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Dict = self.get_dummy_components()
lowerCAmelCase_ :Tuple = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Any = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Optional[int] = inputs["""prompt"""]
lowerCAmelCase_ :Optional[int] = inputs["""generator"""]
lowerCAmelCase_ :Any = inputs["""num_inference_steps"""]
lowerCAmelCase_ :Optional[int] = inputs["""output_type"""]
if "image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""image"""]
else:
lowerCAmelCase_ :int = None
if "mask_image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""mask_image"""]
else:
lowerCAmelCase_ :int = None
if "original_image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""original_image"""]
else:
lowerCAmelCase_ :List[Any] = None
lowerCAmelCase_ , lowerCAmelCase_ :int = pipe.encode_prompt(__A )
# inputs with prompt converted to embeddings
lowerCAmelCase_ :List[str] = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowerCAmelCase_ :int = image
if mask_image is not None:
lowerCAmelCase_ :Tuple = mask_image
if original_image is not None:
lowerCAmelCase_ :Optional[Any] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(__A , __A , __A )
lowerCAmelCase_ :Optional[int] = pipe(**__A )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__A )
lowerCAmelCase_ :Optional[int] = self.pipeline_class.from_pretrained(__A )
pipe_loaded.to(__A )
pipe_loaded.set_progress_bar_config(disable=__A )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__A , __A ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
lowerCAmelCase_ :Dict = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Union[str, Any] = inputs["""generator"""]
lowerCAmelCase_ :Any = inputs["""num_inference_steps"""]
lowerCAmelCase_ :Tuple = inputs["""output_type"""]
# inputs with prompt converted to embeddings
lowerCAmelCase_ :Tuple = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowerCAmelCase_ :Optional[int] = image
if mask_image is not None:
lowerCAmelCase_ :str = mask_image
if original_image is not None:
lowerCAmelCase_ :Tuple = original_image
lowerCAmelCase_ :Union[str, Any] = pipe_loaded(**__A )[0]
lowerCAmelCase_ :Dict = np.abs(to_np(__A ) - to_np(__A ) ).max()
self.assertLess(__A , 1E-4 )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Any = self.get_dummy_components()
lowerCAmelCase_ :Optional[int] = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[int] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Dict = pipe(**__A )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__A )
lowerCAmelCase_ :Any = self.pipeline_class.from_pretrained(__A )
pipe_loaded.to(__A )
pipe_loaded.set_progress_bar_config(disable=__A )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :str = pipe_loaded(**__A )[0]
lowerCAmelCase_ :Dict = np.abs(to_np(__A ) - to_np(__A ) ).max()
self.assertLess(__A , 1E-4 )
| 84 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__: List[Any] = logging.get_logger(__name__)
UpperCamelCase__: List[Any] = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class SCREAMING_SNAKE_CASE( A__ ):
"""simple docstring"""
lowerCamelCase__ = """lxmert"""
lowerCamelCase__ = {}
def __init__( self : Tuple , __snake_case : int=30522 , __snake_case : Union[str, Any]=768 , __snake_case : List[str]=12 , __snake_case : Any=9500 , __snake_case : int=1600 , __snake_case : Any=400 , __snake_case : Dict=3072 , __snake_case : int="gelu" , __snake_case : List[Any]=0.1 , __snake_case : Any=0.1 , __snake_case : Optional[Any]=512 , __snake_case : str=2 , __snake_case : Optional[Any]=0.02 , __snake_case : Optional[Any]=1E-12 , __snake_case : Dict=9 , __snake_case : Any=5 , __snake_case : int=5 , __snake_case : Tuple=2048 , __snake_case : Union[str, Any]=4 , __snake_case : Optional[Any]=6.67 , __snake_case : Optional[int]=True , __snake_case : str=True , __snake_case : List[Any]=True , __snake_case : List[Any]=True , __snake_case : int=True , __snake_case : Dict=True , __snake_case : int=True , **__snake_case : int , ) -> Optional[int]:
UpperCAmelCase : List[str] = vocab_size
UpperCAmelCase : Optional[Any] = hidden_size
UpperCAmelCase : Tuple = num_attention_heads
UpperCAmelCase : List[str] = hidden_act
UpperCAmelCase : Optional[Any] = intermediate_size
UpperCAmelCase : Tuple = hidden_dropout_prob
UpperCAmelCase : Dict = attention_probs_dropout_prob
UpperCAmelCase : Tuple = max_position_embeddings
UpperCAmelCase : str = type_vocab_size
UpperCAmelCase : Dict = initializer_range
UpperCAmelCase : List[Any] = layer_norm_eps
UpperCAmelCase : List[Any] = num_qa_labels
UpperCAmelCase : Optional[Any] = num_object_labels
UpperCAmelCase : Optional[int] = num_attr_labels
UpperCAmelCase : List[Any] = l_layers
UpperCAmelCase : Optional[Any] = x_layers
UpperCAmelCase : Optional[Any] = r_layers
UpperCAmelCase : Union[str, Any] = visual_feat_dim
UpperCAmelCase : Dict = visual_pos_dim
UpperCAmelCase : Optional[int] = visual_loss_normalizer
UpperCAmelCase : Any = task_matched
UpperCAmelCase : List[Any] = task_mask_lm
UpperCAmelCase : List[str] = task_obj_predict
UpperCAmelCase : List[Any] = task_qa
UpperCAmelCase : Any = visual_obj_loss
UpperCAmelCase : Any = visual_attr_loss
UpperCAmelCase : Dict = visual_feat_loss
UpperCAmelCase : Union[str, Any] = {'''vision''': r_layers, '''cross_encoder''': x_layers, '''language''': l_layers}
super().__init__(**__snake_case )
| 23 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2""" , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :int = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :List[Any] = jax.device_count()
lowerCAmelCase_ :Optional[Any] = num_samples * [prompt]
lowerCAmelCase_ :int = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Optional[Any] = replicate(__A )
lowerCAmelCase_ :Union[str, Any] = shard(__A )
lowerCAmelCase_ :Optional[Any] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :Tuple = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Union[str, Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Optional[int] = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Union[str, Any] = """stabilityai/stable-diffusion-2"""
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = FlaxDPMSolverMultistepScheduler.from_pretrained(__A , subfolder="""scheduler""" )
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = FlaxStableDiffusionPipeline.from_pretrained(
__A , scheduler=__A , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :Optional[int] = scheduler_params
lowerCAmelCase_ :List[Any] = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :Tuple = jax.device_count()
lowerCAmelCase_ :str = num_samples * [prompt]
lowerCAmelCase_ :Union[str, Any] = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Tuple = replicate(__A )
lowerCAmelCase_ :Optional[int] = shard(__A )
lowerCAmelCase_ :List[str] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :List[Any] = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Optional[Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Dict = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 84 | 0 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
snake_case_ = logging.get_logger(__name__)
@add_end_docstrings(_UpperCAmelCase )
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
def __init__(self : Optional[int] , *a__ : Any , **a__ : Dict ):
"""simple docstring"""
super().__init__(*a__ , **a__ )
requires_backends(self , '''vision''' )
self.check_model_type(a__ )
def __call__(self : Optional[int] , a__ : Union[str, List[str], "Image.Image", List["Image.Image"]] , **a__ : Tuple ):
"""simple docstring"""
return super().__call__(a__ , **a__ )
def a (self : Dict , **a__ : Any ):
"""simple docstring"""
return {}, {}, {}
def a (self : List[str] , a__ : Any ):
"""simple docstring"""
__snake_case = load_image(a__ )
__snake_case = image.size
__snake_case = self.image_processor(images=a__ , return_tensors=self.framework )
return model_inputs
def a (self : int , a__ : List[Any] ):
"""simple docstring"""
__snake_case = self.model(**a__ )
return model_outputs
def a (self : int , a__ : str ):
"""simple docstring"""
__snake_case = model_outputs.predicted_depth
__snake_case = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1 ) , size=self.image_size[::-1] , mode='''bicubic''' , align_corners=a__ )
__snake_case = prediction.squeeze().cpu().numpy()
__snake_case = (output * 255 / np.max(a__ )).astype('''uint8''' )
__snake_case = Image.fromarray(a__ )
__snake_case = {}
__snake_case = predicted_depth
__snake_case = depth
return output_dict
| 24 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _snake_case ( ) -> Generator[int, None, None]:
'''simple docstring'''
lowerCAmelCase_ :dict[int, int] = {}
lowerCAmelCase_ :int = 2
while True:
lowerCAmelCase_ :List[Any] = factor_map.pop(lowercase__ , lowercase__ )
if factor:
lowerCAmelCase_ :Optional[int] = factor + prime
while x in factor_map:
x += factor
lowerCAmelCase_ :List[str] = factor
else:
lowerCAmelCase_ :Optional[int] = prime
yield prime
prime += 1
def _snake_case ( lowercase__ : float = 1E10 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = sieve()
lowerCAmelCase_ :str = 1
while True:
lowerCAmelCase_ :int = next(lowercase__ )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(lowercase__ )
n += 2
if __name__ == "__main__":
print(solution())
| 84 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import torch
from diffusers import (
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin
@skip_mps
class lowerCAmelCase_ (a__ , a__ , unittest.TestCase ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] = IFPipeline
__UpperCamelCase : Dict = TEXT_TO_IMAGE_PARAMS - {'''width''', '''height''', '''latents'''}
__UpperCamelCase : Any = TEXT_TO_IMAGE_BATCH_PARAMS
__UpperCamelCase : Union[str, Any] = PipelineTesterMixin.required_optional_params - {'''latents'''}
def __magic_name__ (self ) -> Union[str, Any]:
"""simple docstring"""
return self._get_dummy_components()
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__=0 ) -> List[Any]:
"""simple docstring"""
if str(SCREAMING_SNAKE_CASE__ ).startswith("""mps""" ):
SCREAMING_SNAKE_CASE__ : Dict = torch.manual_seed(SCREAMING_SNAKE_CASE__ )
else:
SCREAMING_SNAKE_CASE__ : Any = torch.Generator(device=SCREAMING_SNAKE_CASE__ ).manual_seed(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
self._test_save_load_optional_components()
@unittest.skipIf(torch_device != """cuda""" , reason="""float16 requires CUDA""" )
def __magic_name__ (self ) -> List[str]:
"""simple docstring"""
super().test_save_load_floataa(expected_max_diff=1E-1 )
def __magic_name__ (self ) -> List[Any]:
"""simple docstring"""
self._test_attention_slicing_forward_pass(expected_max_diff=1E-2 )
def __magic_name__ (self ) -> Tuple:
"""simple docstring"""
self._test_save_load_local()
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
self._test_inference_batch_single_identical(
expected_max_diff=1E-2 , )
@unittest.skipIf(
torch_device != """cuda""" or not is_xformers_available() , reason="""XFormers attention is only available with CUDA and `xformers` installed""" , )
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3 )
@slow
@require_torch_gpu
class lowerCAmelCase_ (unittest.TestCase ):
"""simple docstring"""
def __magic_name__ (self ) -> Dict:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __magic_name__ (self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = IFPipeline.from_pretrained("""DeepFloyd/IF-I-XL-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa )
SCREAMING_SNAKE_CASE__ : Dict = IFSuperResolutionPipeline.from_pretrained(
"""DeepFloyd/IF-II-L-v1.0""" , variant="""fp16""" , torch_dtype=torch.floataa , text_encoder=SCREAMING_SNAKE_CASE__ , tokenizer=SCREAMING_SNAKE_CASE__ )
# pre compute text embeddings and remove T5 to save memory
pipe_a.text_encoder.to("""cuda""" )
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ : Union[str, Any] = pipe_a.encode_prompt("""anime turtle""" , device="""cuda""" )
del pipe_a.tokenizer
del pipe_a.text_encoder
gc.collect()
SCREAMING_SNAKE_CASE__ : List[str] = None
SCREAMING_SNAKE_CASE__ : Union[str, Any] = None
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# img2img
SCREAMING_SNAKE_CASE__ : Union[str, Any] = IFImgaImgPipeline(**pipe_a.components )
SCREAMING_SNAKE_CASE__ : Optional[Any] = IFImgaImgSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_imgaimg(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
pipe_a.remove_all_hooks()
pipe_a.remove_all_hooks()
# inpainting
SCREAMING_SNAKE_CASE__ : Optional[Any] = IFInpaintingPipeline(**pipe_a.components )
SCREAMING_SNAKE_CASE__ : int = IFInpaintingSuperResolutionPipeline(**pipe_a.components )
pipe_a.enable_model_cpu_offload()
pipe_a.enable_model_cpu_offload()
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
pipe_a.unet.set_attn_processor(AttnAddedKVProcessor() )
self._test_if_inpainting(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
"""simple docstring"""
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : Tuple = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , generator=SCREAMING_SNAKE_CASE__ , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : int = output.images[0]
assert image.shape == (64, 64, 3)
SCREAMING_SNAKE_CASE__ : Any = torch.cuda.max_memory_allocated()
assert mem_bytes < 13 * 10**9
SCREAMING_SNAKE_CASE__ : Optional[int] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy""" )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# pipeline 2
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : int = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Optional[int] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : List[str] = output.images[0]
assert image.shape == (2_56, 2_56, 3)
SCREAMING_SNAKE_CASE__ : List[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
SCREAMING_SNAKE_CASE__ : int = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> str:
"""simple docstring"""
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : int = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Tuple = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Any = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , generator=SCREAMING_SNAKE_CASE__ , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : Dict = output.images[0]
assert image.shape == (64, 64, 3)
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
SCREAMING_SNAKE_CASE__ : str = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy""" )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# pipeline 2
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[Any] = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Optional[Any] = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , original_image=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : List[Any] = output.images[0]
assert image.shape == (2_56, 2_56, 3)
SCREAMING_SNAKE_CASE__ : Optional[Any] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
SCREAMING_SNAKE_CASE__ : str = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def __magic_name__ (self , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) -> Union[str, Any]:
"""simple docstring"""
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(1 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : Dict = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : List[str] = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , mask_image=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , generator=SCREAMING_SNAKE_CASE__ , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : List[Any] = output.images[0]
assert image.shape == (64, 64, 3)
SCREAMING_SNAKE_CASE__ : Tuple = torch.cuda.max_memory_allocated()
assert mem_bytes < 10 * 10**9
SCREAMING_SNAKE_CASE__ : Optional[Any] = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy""" )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# pipeline 2
_start_torch_memory_measurement()
SCREAMING_SNAKE_CASE__ : int = torch.Generator(device="""cpu""" ).manual_seed(0 )
SCREAMING_SNAKE_CASE__ : Dict = floats_tensor((1, 3, 64, 64) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(0 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = floats_tensor((1, 3, 2_56, 2_56) , rng=random.Random(1 ) ).to(SCREAMING_SNAKE_CASE__ )
SCREAMING_SNAKE_CASE__ : int = pipe_a(
prompt_embeds=SCREAMING_SNAKE_CASE__ , negative_prompt_embeds=SCREAMING_SNAKE_CASE__ , image=SCREAMING_SNAKE_CASE__ , mask_image=SCREAMING_SNAKE_CASE__ , original_image=SCREAMING_SNAKE_CASE__ , generator=SCREAMING_SNAKE_CASE__ , num_inference_steps=2 , output_type="""np""" , )
SCREAMING_SNAKE_CASE__ : Dict = output.images[0]
assert image.shape == (2_56, 2_56, 3)
SCREAMING_SNAKE_CASE__ : List[str] = torch.cuda.max_memory_allocated()
assert mem_bytes < 4 * 10**9
SCREAMING_SNAKE_CASE__ : Any = load_numpy(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy""" )
assert_mean_pixel_difference(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
def lowercase_ ( ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
| 25 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
# TODO: is there an appropriate internal test set?
UpperCAmelCase_ :List[Any] = "ssube/stable-diffusion-x4-upscaler-onnx"
def __lowerCAmelCase ( self , __A=0 ) -> Optional[int]:
lowerCAmelCase_ :Optional[Any] = floats_tensor((1, 3, 128, 128) , rng=random.Random(__A ) )
lowerCAmelCase_ :List[Any] = torch.manual_seed(__A )
lowerCAmelCase_ :Tuple = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Dict = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :int = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Tuple = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :int = self.get_dummy_inputs()
lowerCAmelCase_ :List[str] = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :str = np.array(
[0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Optional[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Union[str, Any] = pipe(**__A ).images
lowerCAmelCase_ :Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Tuple = np.array(
[0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Union[str, Any] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Optional[Any] = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Tuple = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :List[str] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Optional[int] = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Dict = pipe(**__A ).images
lowerCAmelCase_ :Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Dict = np.array(
[0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@property
def __lowerCAmelCase ( self ) -> List[Any]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Optional[int] = ort.SessionOptions()
lowerCAmelCase_ :Dict = False
return options
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase_ :Optional[Any] = init_image.resize((128, 128) )
# using the PNDM scheduler by default
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = """A fantasy landscape, trending on artstation"""
lowerCAmelCase_ :List[Any] = torch.manual_seed(0 )
lowerCAmelCase_ :str = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=10 , generator=__A , output_type="""np""" , )
lowerCAmelCase_ :Dict = output.images
lowerCAmelCase_ :List[str] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Optional[Any] = np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase_ :List[str] = init_image.resize((128, 128) )
lowerCAmelCase_ :Any = LMSDiscreteScheduler.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , subfolder="""scheduler""" )
lowerCAmelCase_ :Optional[Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , scheduler=__A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Any = """A fantasy landscape, trending on artstation"""
lowerCAmelCase_ :Optional[Any] = torch.manual_seed(0 )
lowerCAmelCase_ :List[str] = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=20 , generator=__A , output_type="""np""" , )
lowerCAmelCase_ :int = output.images
lowerCAmelCase_ :List[Any] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Union[str, Any] = np.array(
[0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
| 84 | 0 |
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def lowerCAmelCase_ ( snake_case_ ):
_A : Dict = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
_A : Tuple = key.replace("""module.encoder""","""glpn.encoder""" )
if key.startswith("""module.decoder""" ):
_A : str = key.replace("""module.decoder""","""decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
_A : List[str] = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
_A : List[Any] = key.replace(f'''patch_embed{idx}''',f'''patch_embeddings.{int(snake_case_ )-1}''' )
if "norm" in key:
_A : Any = key.replace("""norm""","""layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
_A : List[str] = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
_A : List[str] = key.replace(f'''layer_norm{idx}''',f'''layer_norm.{int(snake_case_ )-1}''' )
if "layer_norm1" in key:
_A : Union[str, Any] = key.replace("""layer_norm1""","""layer_norm_1""" )
if "layer_norm2" in key:
_A : Any = key.replace("""layer_norm2""","""layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
_A : Optional[int] = key[key.find("""block""" ) + len("""block""" )]
_A : List[str] = key.replace(f'''block{idx}''',f'''block.{int(snake_case_ )-1}''' )
if "attn.q" in key:
_A : str = key.replace("""attn.q""","""attention.self.query""" )
if "attn.proj" in key:
_A : Union[str, Any] = key.replace("""attn.proj""","""attention.output.dense""" )
if "attn" in key:
_A : int = key.replace("""attn""","""attention.self""" )
if "fc1" in key:
_A : Optional[int] = key.replace("""fc1""","""dense1""" )
if "fc2" in key:
_A : Optional[Any] = key.replace("""fc2""","""dense2""" )
if "linear_pred" in key:
_A : Optional[int] = key.replace("""linear_pred""","""classifier""" )
if "linear_fuse" in key:
_A : List[Any] = key.replace("""linear_fuse.conv""","""linear_fuse""" )
_A : List[str] = key.replace("""linear_fuse.bn""","""batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
_A : Optional[Any] = key[key.find("""linear_c""" ) + len("""linear_c""" )]
_A : List[str] = key.replace(f'''linear_c{idx}''',f'''linear_c.{int(snake_case_ )-1}''' )
if "bot_conv" in key:
_A : List[Any] = key.replace("""bot_conv""","""0.convolution""" )
if "skip_conv1" in key:
_A : Union[str, Any] = key.replace("""skip_conv1""","""1.convolution""" )
if "skip_conv2" in key:
_A : Union[str, Any] = key.replace("""skip_conv2""","""2.convolution""" )
if "fusion1" in key:
_A : int = key.replace("""fusion1""","""1.fusion""" )
if "fusion2" in key:
_A : str = key.replace("""fusion2""","""2.fusion""" )
if "fusion3" in key:
_A : Optional[Any] = key.replace("""fusion3""","""3.fusion""" )
if "fusion" in key and "conv" in key:
_A : Any = key.replace("""conv""","""convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
_A : List[Any] = key.replace("""module.last_layer_depth""","""head.head""" )
_A : int = value
return new_state_dict
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
# for each of the encoder blocks:
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
_A : Optional[Any] = state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight''' )
_A : int = state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias''' )
# next, add keys and values (in that order) to the state dict
_A : Union[str, Any] = kv_weight[
: config.hidden_sizes[i], :
]
_A : int = kv_bias[: config.hidden_sizes[i]]
_A : Dict = kv_weight[
config.hidden_sizes[i] :, :
]
_A : Optional[int] = kv_bias[config.hidden_sizes[i] :]
def lowerCAmelCase_ ( ):
_A : List[str] = """http://images.cocodataset.org/val2017/000000039769.jpg"""
_A : List[str] = Image.open(requests.get(snake_case_,stream=snake_case_ ).raw )
return image
@torch.no_grad()
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_=False,snake_case_=None ):
_A : str = GLPNConfig(hidden_sizes=[64, 128, 320, 512],decoder_hidden_size=64,depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
_A : Tuple = GLPNImageProcessor()
# prepare image
_A : List[Any] = prepare_img()
_A : str = image_processor(images=snake_case_,return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
_A : int = torch.load(snake_case_,map_location=torch.device("""cpu""" ) )
# rename keys
_A : Tuple = rename_keys(snake_case_ )
# key and value matrices need special treatment
read_in_k_v(snake_case_,snake_case_ )
# create HuggingFace model and load state dict
_A : int = GLPNForDepthEstimation(snake_case_ )
model.load_state_dict(snake_case_ )
model.eval()
# forward pass
_A : str = model(snake_case_ )
_A : Any = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
_A : List[str] = torch.tensor(
[[4.41_47, 4.08_73, 4.06_73], [3.78_90, 3.28_81, 3.15_25], [3.76_74, 3.54_23, 3.49_13]] )
elif "kitti" in model_name:
_A : List[str] = torch.tensor(
[[3.42_91, 2.78_65, 2.51_51], [3.28_41, 2.70_21, 2.35_02], [3.11_47, 2.46_25, 2.24_81]] )
else:
raise ValueError(f'''Unknown model name: {model_name}''' )
_A : Optional[int] = torch.Size([1, 480, 640] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3],snake_case_,atol=1e-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(snake_case_,snake_case_ ),organization="""nielsr""",commit_message="""Add model""",use_temp_dir=snake_case_,)
image_processor.push_to_hub(
repo_path_or_name=Path(snake_case_,snake_case_ ),organization="""nielsr""",commit_message="""Add image processor""",use_temp_dir=snake_case_,)
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path",
default=None,
type=str,
help="Path to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
parser.add_argument(
"--model_name",
default="glpn-kitti",
type=str,
help="Name of the model in case you're pushing to the hub.",
)
_snake_case = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 26 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
] )
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Dict:
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=__A , )
assert hasattr(self , """env""" )
def __lowerCAmelCase ( self , __A ) -> Any:
# configuration for running training on smdistributed Model Parallel
lowerCAmelCase_ :Union[str, Any] = {
"""enabled""": True,
"""processes_per_host""": 8,
}
lowerCAmelCase_ :Tuple = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
lowerCAmelCase_ :Any = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
lowerCAmelCase_ :Any = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=__A , instance_type=self.instance_type , debugger_hook_config=__A , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=__A , py_version="""py36""" , )
def __lowerCAmelCase ( self , __A ) -> List[Any]:
TrainingJobAnalytics(__A ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def __lowerCAmelCase ( self , __A ) -> List[str]:
# create estimator
lowerCAmelCase_ :Any = self.create_estimator(__A )
# run training
estimator.fit()
# result dataframe
lowerCAmelCase_ :Optional[Any] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase_ :List[str] = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
lowerCAmelCase_ :Optional[int] = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase_ :Optional[int] = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , __A )
| 84 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowercase : int = logging.get_logger(__name__)
__lowercase : Union[str, Any] = {
'google/bigbird-roberta-base': 'https://huggingface.co/google/bigbird-roberta-base/resolve/main/config.json',
'google/bigbird-roberta-large': 'https://huggingface.co/google/bigbird-roberta-large/resolve/main/config.json',
'google/bigbird-base-trivia-itc': 'https://huggingface.co/google/bigbird-base-trivia-itc/resolve/main/config.json',
# See all BigBird models at https://huggingface.co/models?filter=big_bird
}
class __UpperCamelCase ( lowerCAmelCase_ ):
A_ = "big_bird"
def __init__( self , __a=5_0358 , __a=768 , __a=12 , __a=12 , __a=3072 , __a="gelu_new" , __a=0.1 , __a=0.1 , __a=4096 , __a=2 , __a=0.02 , __a=1E-1_2 , __a=True , __a=0 , __a=1 , __a=2 , __a=66 , __a="block_sparse" , __a=True , __a=False , __a=64 , __a=3 , __a=None , **__a , ):
'''simple docstring'''
super().__init__(
pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , sep_token_id=__a , **__a , )
__a : int = vocab_size
__a : Tuple = max_position_embeddings
__a : int = hidden_size
__a : Optional[Any] = num_hidden_layers
__a : Optional[Any] = num_attention_heads
__a : Dict = intermediate_size
__a : Tuple = hidden_act
__a : str = hidden_dropout_prob
__a : Union[str, Any] = attention_probs_dropout_prob
__a : Any = initializer_range
__a : Optional[int] = type_vocab_size
__a : str = layer_norm_eps
__a : List[Any] = use_cache
__a : Union[str, Any] = rescale_embeddings
__a : Tuple = attention_type
__a : Any = use_bias
__a : List[Any] = block_size
__a : Optional[Any] = num_random_blocks
__a : List[Any] = classifier_dropout
class __UpperCamelCase ( lowerCAmelCase_ ):
@property
def __UpperCAmelCase ( self ):
'''simple docstring'''
if self.task == "multiple-choice":
__a : List[str] = {0: 'batch', 1: 'choice', 2: 'sequence'}
else:
__a : Dict = {0: 'batch', 1: 'sequence'}
return OrderedDict(
[
('input_ids', dynamic_axis),
('attention_mask', dynamic_axis),
] )
| 27 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 1_0 ) -> str:
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or n < 0:
raise ValueError("""Invalid input""" )
lowerCAmelCase_ :List[str] = 1_0**n
lowerCAmelCase_ :int = 2_8_4_3_3 * (pow(2 , 7_8_3_0_4_5_7 , lowercase__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(10) = }""")
| 84 | 0 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
class SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = ["""image_processor"""]
_SCREAMING_SNAKE_CASE = """SamImageProcessor"""
def __init__( self : List[str] , UpperCamelCase__ : Union[str, Any] ):
"""simple docstring"""
super().__init__(UpperCamelCase__ )
UpperCamelCase = self.image_processor
UpperCamelCase = -1_0
UpperCamelCase = self.image_processor.size['longest_edge']
def __call__( self : Optional[int] , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : str=None , UpperCamelCase__ : str=None , UpperCamelCase__ : Optional[Any]=None , UpperCamelCase__ : Optional[Union[str, TensorType]] = None , **UpperCamelCase__ : Union[str, Any] , ):
"""simple docstring"""
UpperCamelCase = self.image_processor(
UpperCamelCase__ , return_tensors=UpperCamelCase__ , **UpperCamelCase__ , )
# pop arguments that are not used in the foward but used nevertheless
UpperCamelCase = encoding_image_processor['original_sizes']
if hasattr(UpperCamelCase__ , 'numpy' ): # Checks if Torch or TF tensor
UpperCamelCase = original_sizes.numpy()
UpperCamelCase , UpperCamelCase , UpperCamelCase = self._check_and_preprocess_points(
input_points=UpperCamelCase__ , input_labels=UpperCamelCase__ , input_boxes=UpperCamelCase__ , )
UpperCamelCase = self._normalize_and_convert(
UpperCamelCase__ , UpperCamelCase__ , input_points=UpperCamelCase__ , input_labels=UpperCamelCase__ , input_boxes=UpperCamelCase__ , return_tensors=UpperCamelCase__ , )
return encoding_image_processor
def A ( self : str , UpperCamelCase__ : List[str] , UpperCamelCase__ : Any , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : Tuple=None , UpperCamelCase__ : Any="pt" , ):
"""simple docstring"""
if input_points is not None:
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
UpperCamelCase = [
self._normalize_coordinates(self.target_size , UpperCamelCase__ , original_sizes[0] ) for point in input_points
]
else:
UpperCamelCase = [
self._normalize_coordinates(self.target_size , UpperCamelCase__ , UpperCamelCase__ )
for point, original_size in zip(UpperCamelCase__ , UpperCamelCase__ )
]
# check that all arrays have the same shape
if not all(point.shape == input_points[0].shape for point in input_points ):
if input_labels is not None:
UpperCamelCase , UpperCamelCase = self._pad_points_and_labels(UpperCamelCase__ , UpperCamelCase__ )
UpperCamelCase = np.array(UpperCamelCase__ )
if input_labels is not None:
UpperCamelCase = np.array(UpperCamelCase__ )
if input_boxes is not None:
if len(UpperCamelCase__ ) != len(UpperCamelCase__ ):
UpperCamelCase = [
self._normalize_coordinates(self.target_size , UpperCamelCase__ , original_sizes[0] , is_bounding_box=UpperCamelCase__ )
for box in input_boxes
]
else:
UpperCamelCase = [
self._normalize_coordinates(self.target_size , UpperCamelCase__ , UpperCamelCase__ , is_bounding_box=UpperCamelCase__ )
for box, original_size in zip(UpperCamelCase__ , UpperCamelCase__ )
]
UpperCamelCase = np.array(UpperCamelCase__ )
if input_boxes is not None:
if return_tensors == "pt":
UpperCamelCase = torch.from_numpy(UpperCamelCase__ )
# boxes batch size of 1 by default
UpperCamelCase = input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
elif return_tensors == "tf":
UpperCamelCase = tf.convert_to_tensor(UpperCamelCase__ )
# boxes batch size of 1 by default
UpperCamelCase = tf.expand_dims(UpperCamelCase__ , 1 ) if len(input_boxes.shape ) != 3 else input_boxes
encoding_image_processor.update({'input_boxes': input_boxes} )
if input_points is not None:
if return_tensors == "pt":
UpperCamelCase = torch.from_numpy(UpperCamelCase__ )
# point batch size of 1 by default
UpperCamelCase = input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
elif return_tensors == "tf":
UpperCamelCase = tf.convert_to_tensor(UpperCamelCase__ )
# point batch size of 1 by default
UpperCamelCase = tf.expand_dims(UpperCamelCase__ , 1 ) if len(input_points.shape ) != 4 else input_points
encoding_image_processor.update({'input_points': input_points} )
if input_labels is not None:
if return_tensors == "pt":
UpperCamelCase = torch.from_numpy(UpperCamelCase__ )
# point batch size of 1 by default
UpperCamelCase = input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
elif return_tensors == "tf":
UpperCamelCase = tf.convert_to_tensor(UpperCamelCase__ )
# point batch size of 1 by default
UpperCamelCase = tf.expand_dims(UpperCamelCase__ , 1 ) if len(input_labels.shape ) != 3 else input_labels
encoding_image_processor.update({'input_labels': input_labels} )
return encoding_image_processor
def A ( self : Any , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : List[Any] ):
"""simple docstring"""
UpperCamelCase = max([point.shape[0] for point in input_points] )
UpperCamelCase = []
for i, point in enumerate(UpperCamelCase__ ):
if point.shape[0] != expected_nb_points:
UpperCamelCase = np.concatenate(
[point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value] , axis=0 )
UpperCamelCase = np.append(input_labels[i] , [self.point_pad_value] )
processed_input_points.append(UpperCamelCase__ )
UpperCamelCase = processed_input_points
return input_points, input_labels
def A ( self : Tuple , UpperCamelCase__ : int , UpperCamelCase__ : np.ndarray , UpperCamelCase__ : Optional[Any] , UpperCamelCase__ : int=False ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = original_size
UpperCamelCase , UpperCamelCase = self.image_processor._get_preprocess_shape(UpperCamelCase__ , longest_edge=UpperCamelCase__ )
UpperCamelCase = deepcopy(UpperCamelCase__ ).astype(UpperCamelCase__ )
if is_bounding_box:
UpperCamelCase = coords.reshape(-1 , 2 , 2 )
UpperCamelCase = coords[..., 0] * (new_w / old_w)
UpperCamelCase = coords[..., 1] * (new_h / old_h)
if is_bounding_box:
UpperCamelCase = coords.reshape(-1 , 4 )
return coords
def A ( self : List[Any] , UpperCamelCase__ : List[Any]=None , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : Optional[int]=None , ):
"""simple docstring"""
if input_points is not None:
if hasattr(UpperCamelCase__ , 'numpy' ): # Checks for TF or Torch tensor
UpperCamelCase = input_points.numpy().tolist()
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or not isinstance(input_points[0] , UpperCamelCase__ ):
raise ValueError('Input points must be a list of list of floating points.' )
UpperCamelCase = [np.array(UpperCamelCase__ ) for input_point in input_points]
else:
UpperCamelCase = None
if input_labels is not None:
if hasattr(UpperCamelCase__ , 'numpy' ):
UpperCamelCase = input_labels.numpy().tolist()
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ) or not isinstance(input_labels[0] , UpperCamelCase__ ):
raise ValueError('Input labels must be a list of list integers.' )
UpperCamelCase = [np.array(UpperCamelCase__ ) for label in input_labels]
else:
UpperCamelCase = None
if input_boxes is not None:
if hasattr(UpperCamelCase__ , 'numpy' ):
UpperCamelCase = input_boxes.numpy().tolist()
if (
not isinstance(UpperCamelCase__ , UpperCamelCase__ )
or not isinstance(input_boxes[0] , UpperCamelCase__ )
or not isinstance(input_boxes[0][0] , UpperCamelCase__ )
):
raise ValueError('Input boxes must be a list of list of list of floating points.' )
UpperCamelCase = [np.array(UpperCamelCase__ ).astype(np.floataa ) for box in input_boxes]
else:
UpperCamelCase = None
return input_points, input_labels, input_boxes
@property
def A ( self : Tuple ):
"""simple docstring"""
UpperCamelCase = self.image_processor.model_input_names
return list(dict.fromkeys(UpperCamelCase__ ) )
def A ( self : Optional[int] , *UpperCamelCase__ : List[Any] , **UpperCamelCase__ : Optional[Any] ):
"""simple docstring"""
return self.image_processor.post_process_masks(*UpperCamelCase__ , **UpperCamelCase__ )
| 28 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__UpperCAmelCase = 'src/transformers'
__UpperCAmelCase = 'docs/source/en/tasks'
def _snake_case ( lowercase__ : str , lowercase__ : List[str] , lowercase__ : Any ) -> str:
'''simple docstring'''
with open(lowercase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCAmelCase_ :List[Any] = f.readlines()
# Find the start prompt.
lowerCAmelCase_ :Tuple = 0
while not lines[start_index].startswith(lowercase__ ):
start_index += 1
start_index += 1
lowerCAmelCase_ :Dict = start_index
while not lines[end_index].startswith(lowercase__ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
__UpperCAmelCase = direct_transformers_import(TRANSFORMERS_PATH)
__UpperCAmelCase = {
'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__UpperCAmelCase = {
'summarization.md': ('nllb',),
'translation.md': ('nllb',),
}
def _snake_case ( lowercase__ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = TASK_GUIDE_TO_MODELS[task_guide]
lowerCAmelCase_ :List[Any] = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowercase__ , set() )
lowerCAmelCase_ :Union[str, Any] = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([f"""[{name}](../model_doc/{code})""" for code, name in model_names.items()] ) + "\n"
def _snake_case ( lowercase__ : int , lowercase__ : str=False ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = _find_text_in_file(
filename=os.path.join(lowercase__ , lowercase__ ) , start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->""" , end_prompt="""<!--End of the generated tip-->""" , )
lowerCAmelCase_ :int = get_model_list_for_task(lowercase__ )
if current_list != new_list:
if overwrite:
with open(os.path.join(lowercase__ , lowercase__ ) , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
f"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
""" to fix this.""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__UpperCAmelCase = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 84 | 0 |
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def lowercase__ ( __snake_case : Any , __snake_case : Any=10 ):
'''simple docstring'''
UpperCAmelCase_ : List[Any] = []
for _ in range(__snake_case ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def lowercase__ ( __snake_case : List[str] , __snake_case : int=10 ):
'''simple docstring'''
UpperCAmelCase_ : Optional[Any] = []
for step in range(__snake_case ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
UpperCAmelCase_ : List[str] = os.path.join(__snake_case , 'schedule.bin' )
torch.save(scheduler.state_dict() , __snake_case )
UpperCAmelCase_ : str = torch.load(__snake_case )
scheduler.load_state_dict(__snake_case )
return lrs
@require_torch
class lowerCamelCase (unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> List[str]:
self.assertEqual(len(_UpperCamelCase ) , len(_UpperCamelCase ) )
for a, b in zip(_UpperCamelCase , _UpperCamelCase ):
self.assertAlmostEqual(_UpperCamelCase , _UpperCamelCase , delta=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Tuple:
UpperCAmelCase_ : List[str] = torch.tensor([0.1, -0.2, -0.1] , requires_grad=_UpperCamelCase )
UpperCAmelCase_ : List[str] = torch.tensor([0.4, 0.2, -0.5] )
UpperCAmelCase_ : Dict = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
UpperCAmelCase_ : int = AdamW(params=[w] , lr=2E-1 , weight_decay=0.0 )
for _ in range(1_0_0 ):
UpperCAmelCase_ : Optional[int] = criterion(_UpperCamelCase , _UpperCamelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
def __UpperCAmelCase ( self ) -> Optional[Any]:
UpperCAmelCase_ : Union[str, Any] = torch.tensor([0.1, -0.2, -0.1] , requires_grad=_UpperCamelCase )
UpperCAmelCase_ : Dict = torch.tensor([0.4, 0.2, -0.5] )
UpperCAmelCase_ : int = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
UpperCAmelCase_ : str = Adafactor(
params=[w] , lr=1E-2 , eps=(1E-30, 1E-3) , clip_threshold=1.0 , decay_rate=-0.8 , betaa=_UpperCamelCase , weight_decay=0.0 , relative_step=_UpperCamelCase , scale_parameter=_UpperCamelCase , warmup_init=_UpperCamelCase , )
for _ in range(1_0_0_0 ):
UpperCAmelCase_ : List[str] = criterion(_UpperCamelCase , _UpperCamelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() , [0.4, 0.2, -0.5] , tol=1E-2 )
@require_torch
class lowerCamelCase (unittest.TestCase ):
'''simple docstring'''
_snake_case : Union[str, Any] = nn.Linear(5_0 , 5_0 ) if is_torch_available() else None
_snake_case : Union[str, Any] = AdamW(m.parameters() , lr=10.0 ) if is_torch_available() else None
_snake_case : Dict = 1_0
def __UpperCAmelCase ( self , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase , _UpperCamelCase=None ) -> str:
self.assertEqual(len(_UpperCamelCase ) , len(_UpperCamelCase ) )
for a, b in zip(_UpperCamelCase , _UpperCamelCase ):
self.assertAlmostEqual(_UpperCamelCase , _UpperCamelCase , delta=_UpperCamelCase , msg=_UpperCamelCase )
def __UpperCAmelCase ( self ) -> Tuple:
UpperCAmelCase_ : Union[str, Any] = {'num_warmup_steps': 2, 'num_training_steps': 1_0}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
UpperCAmelCase_ : List[str] = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{'num_warmup_steps': 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, 'num_cycles': 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, 'power': 2.0, 'lr_end': 1E-7},
[0.0, 5.0, 10.0, 7.6_56, 5.6_25, 3.9_06, 2.5, 1.4_06, 0.6_25, 0.1_56],
),
get_inverse_sqrt_schedule: (
{'num_warmup_steps': 2},
[0.0, 5.0, 10.0, 8.1_65, 7.0_71, 6.3_25, 5.7_74, 5.3_45, 5.0, 4.7_14],
),
}
for scheduler_func, data in scheds.items():
UpperCAmelCase_ , UpperCAmelCase_ : Union[str, Any] = data
UpperCAmelCase_ : Any = scheduler_func(self.optimizer , **_UpperCamelCase )
self.assertEqual(len([scheduler.get_lr()[0]] ) , 1 )
UpperCAmelCase_ : Optional[int] = unwrap_schedule(_UpperCamelCase , self.num_steps )
self.assertListAlmostEqual(
_UpperCamelCase , _UpperCamelCase , tol=1E-2 , msg=f"failed for {scheduler_func} in normal scheduler" , )
UpperCAmelCase_ : Dict = scheduler_func(self.optimizer , **_UpperCamelCase )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(_UpperCamelCase ) # wrap to test picklability of the schedule
UpperCAmelCase_ : Union[str, Any] = unwrap_and_save_reload_schedule(_UpperCamelCase , self.num_steps )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase , msg=f"failed for {scheduler_func} in save and reload" )
class lowerCamelCase :
'''simple docstring'''
def __init__( self , _UpperCamelCase ) -> List[str]:
UpperCAmelCase_ : Dict = fn
def __call__( self , *_UpperCamelCase , **_UpperCamelCase ) -> Optional[Any]:
return self.fn(*_UpperCamelCase , **_UpperCamelCase )
@classmethod
def __UpperCAmelCase ( self , _UpperCamelCase ) -> List[str]:
UpperCAmelCase_ : Optional[int] = list(map(self , scheduler.lr_lambdas ) )
| 29 |
"""simple docstring"""
def _snake_case ( lowercase__ : list[int] ) -> list[list[int]]:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = []
if len(lowercase__ ) == 1:
return [nums.copy()]
for _ in range(len(lowercase__ ) ):
lowerCAmelCase_ :Optional[Any] = nums.pop(0 )
lowerCAmelCase_ :str = permute(lowercase__ )
for perm in permutations:
perm.append(lowercase__ )
result.extend(lowercase__ )
nums.append(lowercase__ )
return result
def _snake_case ( lowercase__ : Tuple ) -> List[str]:
'''simple docstring'''
def backtrack(lowercase__ : str ):
if start == len(lowercase__ ) - 1:
output.append(nums[:] )
else:
for i in range(lowercase__ , len(lowercase__ ) ):
lowerCAmelCase_ , lowerCAmelCase_ :str = nums[i], nums[start]
backtrack(start + 1 )
lowerCAmelCase_ , lowerCAmelCase_ :str = nums[i], nums[start] # backtrack
lowerCAmelCase_ :int = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
__UpperCAmelCase = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 84 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.local_sgd import LocalSGD
########################################################################
# This is a fully working simple example to use Accelerate
# with LocalSGD, which is a method to synchronize model
# parameters every K batches. It is different, but complementary
# to gradient accumulation.
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__a = 1_6
__a = 3_2
def a ( snake_case__: Accelerator , snake_case__: int = 16 ):
'''simple docstring'''
lowercase_ = AutoTokenizer.from_pretrained('''bert-base-cased''' )
lowercase_ = load_dataset('''glue''' , '''mrpc''' )
def tokenize_function(snake_case__: int ):
# max_length=None => use the model max length (it's actually the default)
lowercase_ = tokenizer(examples['''sentence1'''] , examples['''sentence2'''] , truncation=snake_case__ , max_length=snake_case__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
lowercase_ = datasets.map(
snake_case__ , batched=snake_case__ , remove_columns=['''idx''', '''sentence1''', '''sentence2'''] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
lowercase_ = tokenized_datasets.rename_column('''label''' , '''labels''' )
def collate_fn(snake_case__: Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase_ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
lowercase_ = 16
elif accelerator.mixed_precision != "no":
lowercase_ = 8
else:
lowercase_ = None
return tokenizer.pad(
snake_case__ , padding='''longest''' , max_length=snake_case__ , pad_to_multiple_of=snake_case__ , return_tensors='''pt''' , )
# Instantiate dataloaders.
lowercase_ = DataLoader(
tokenized_datasets['''train'''] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__ )
lowercase_ = DataLoader(
tokenized_datasets['''validation'''] , shuffle=snake_case__ , collate_fn=snake_case__ , batch_size=snake_case__ )
return train_dataloader, eval_dataloader
# For testing only
if os.environ.get('TESTING_MOCKED_DATALOADERS', None) == "1":
from accelerate.test_utils.training import mocked_dataloaders
__a = mocked_dataloaders # noqa: F811
def a ( snake_case__: int , snake_case__: Tuple ):
'''simple docstring'''
# For testing only
if os.environ.get('''TESTING_MOCKED_DATALOADERS''' , snake_case__ ) == "1":
lowercase_ = 2
# New Code #
lowercase_ = int(args.gradient_accumulation_steps )
lowercase_ = int(args.local_sgd_steps )
# Initialize accelerator
lowercase_ = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , gradient_accumulation_steps=snake_case__ )
if accelerator.distributed_type not in [DistributedType.NO, DistributedType.MULTI_CPU, DistributedType.MULTI_GPU]:
raise NotImplementedError('''LocalSGD is supported only for CPUs and GPUs (no DeepSpeed or MegatronLM)''' )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase_ = config['''lr''']
lowercase_ = int(config['''num_epochs'''] )
lowercase_ = int(config['''seed'''] )
lowercase_ = int(config['''batch_size'''] )
lowercase_ = evaluate.load('''glue''' , '''mrpc''' )
set_seed(snake_case__ )
lowercase_ , lowercase_ = get_dataloaders(snake_case__ , snake_case__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase_ = AutoModelForSequenceClassification.from_pretrained('''bert-base-cased''' , return_dict=snake_case__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
lowercase_ = model.to(accelerator.device )
# Instantiate optimizer
lowercase_ = AdamW(params=model.parameters() , lr=snake_case__ )
# Instantiate scheduler
lowercase_ = get_linear_schedule_with_warmup(
optimizer=snake_case__ , num_warmup_steps=100 , num_training_steps=(len(snake_case__ ) * num_epochs) , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ = accelerator.prepare(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
# Now we train the model
for epoch in range(snake_case__ ):
model.train()
with LocalSGD(
accelerator=snake_case__ , model=snake_case__ , local_sgd_steps=snake_case__ , enabled=local_sgd_steps is not None ) as local_sgd:
for step, batch in enumerate(snake_case__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
# New code #
# We use the new `accumulate` context manager to perform gradient accumulation
# We also currently do not support TPUs nor advise it as bugs were found on the XLA side when running our tests.
with accelerator.accumulate(snake_case__ ):
lowercase_ = model(**snake_case__ )
lowercase_ = output.loss
accelerator.backward(snake_case__ )
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
# LocalSGD-specific line
local_sgd.step()
model.eval()
for step, batch in enumerate(snake_case__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
lowercase_ = model(**snake_case__ )
lowercase_ = outputs.logits.argmax(dim=-1 )
lowercase_ , lowercase_ = accelerator.gather_for_metrics((predictions, batch['''labels''']) )
metric.add_batch(
predictions=snake_case__ , references=snake_case__ , )
lowercase_ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , snake_case__ )
def a ( ):
'''simple docstring'''
lowercase_ = argparse.ArgumentParser(description='''Simple example of training script.''' )
parser.add_argument(
'''--mixed_precision''' , type=snake_case__ , default=snake_case__ , choices=['''no''', '''fp16''', '''bf16''', '''fp8'''] , help='''Whether to use mixed precision. Choose'''
'''between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'''
'''and an Nvidia Ampere GPU.''' , )
# New Code #
parser.add_argument(
'''--gradient_accumulation_steps''' , type=snake_case__ , default=1 , help='''The number of minibatches to be ran before gradients are accumulated.''' , )
parser.add_argument(
'''--local_sgd_steps''' , type=snake_case__ , default=8 , help='''Number of local SGD steps or None to disable local SGD''' )
parser.add_argument('''--cpu''' , action='''store_true''' , help='''If passed, will train on the CPU.''' )
lowercase_ = parser.parse_args()
lowercase_ = {'''lr''': 2e-5, '''num_epochs''': 3, '''seed''': 42, '''batch_size''': 16}
training_function(snake_case__ , snake_case__ )
if __name__ == "__main__":
main()
| 30 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :Any = BioGptTokenizer
UpperCAmelCase_ :str = False
def __lowerCAmelCase ( self ) -> List[Any]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ :Optional[Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
lowerCAmelCase_ :str = dict(zip(__A , range(len(__A ) ) ) )
lowerCAmelCase_ :int = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
lowerCAmelCase_ :Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase_ :Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(__A ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(__A ) )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
lowerCAmelCase_ :List[Any] = """lower newer"""
lowerCAmelCase_ :Tuple = """lower newer"""
return input_text, output_text
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :List[str] = BioGptTokenizer(self.vocab_file , self.merges_file )
lowerCAmelCase_ :Union[str, Any] = """lower"""
lowerCAmelCase_ :Any = ["""low""", """er</w>"""]
lowerCAmelCase_ :Union[str, Any] = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
lowerCAmelCase_ :Dict = tokens + ["""<unk>"""]
lowerCAmelCase_ :List[str] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
@slow
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Optional[Any] = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
lowerCAmelCase_ :List[Any] = tokenizer.encode("""sequence builders""" , add_special_tokens=__A )
lowerCAmelCase_ :List[str] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=__A )
lowerCAmelCase_ :Optional[int] = tokenizer.build_inputs_with_special_tokens(__A )
lowerCAmelCase_ :List[str] = tokenizer.build_inputs_with_special_tokens(__A , __A )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 84 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__SCREAMING_SNAKE_CASE : Dict = {
"""configuration_data2vec_audio""": ["""DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Data2VecAudioConfig"""],
"""configuration_data2vec_text""": [
"""DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecTextConfig""",
"""Data2VecTextOnnxConfig""",
],
"""configuration_data2vec_vision""": [
"""DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Data2VecVisionConfig""",
"""Data2VecVisionOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__SCREAMING_SNAKE_CASE : Tuple = [
"""DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecAudioForAudioFrameClassification""",
"""Data2VecAudioForCTC""",
"""Data2VecAudioForSequenceClassification""",
"""Data2VecAudioForXVector""",
"""Data2VecAudioModel""",
"""Data2VecAudioPreTrainedModel""",
]
__SCREAMING_SNAKE_CASE : str = [
"""DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecTextForCausalLM""",
"""Data2VecTextForMaskedLM""",
"""Data2VecTextForMultipleChoice""",
"""Data2VecTextForQuestionAnswering""",
"""Data2VecTextForSequenceClassification""",
"""Data2VecTextForTokenClassification""",
"""Data2VecTextModel""",
"""Data2VecTextPreTrainedModel""",
]
__SCREAMING_SNAKE_CASE : Tuple = [
"""DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""Data2VecVisionForImageClassification""",
"""Data2VecVisionForMaskedImageModeling""",
"""Data2VecVisionForSemanticSegmentation""",
"""Data2VecVisionModel""",
"""Data2VecVisionPreTrainedModel""",
]
if is_tf_available():
__SCREAMING_SNAKE_CASE : Optional[Any] = [
"""TFData2VecVisionForImageClassification""",
"""TFData2VecVisionForSemanticSegmentation""",
"""TFData2VecVisionModel""",
"""TFData2VecVisionPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_dataavec_audio import DATA2VEC_AUDIO_PRETRAINED_CONFIG_ARCHIVE_MAP, DataaVecAudioConfig
from .configuration_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecTextConfig,
DataaVecTextOnnxConfig,
)
from .configuration_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_CONFIG_ARCHIVE_MAP,
DataaVecVisionConfig,
DataaVecVisionOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_dataavec_audio import (
DATA2VEC_AUDIO_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecAudioForAudioFrameClassification,
DataaVecAudioForCTC,
DataaVecAudioForSequenceClassification,
DataaVecAudioForXVector,
DataaVecAudioModel,
DataaVecAudioPreTrainedModel,
)
from .modeling_dataavec_text import (
DATA2VEC_TEXT_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecTextForCausalLM,
DataaVecTextForMaskedLM,
DataaVecTextForMultipleChoice,
DataaVecTextForQuestionAnswering,
DataaVecTextForSequenceClassification,
DataaVecTextForTokenClassification,
DataaVecTextModel,
DataaVecTextPreTrainedModel,
)
from .modeling_dataavec_vision import (
DATA2VEC_VISION_PRETRAINED_MODEL_ARCHIVE_LIST,
DataaVecVisionForImageClassification,
DataaVecVisionForMaskedImageModeling,
DataaVecVisionForSemanticSegmentation,
DataaVecVisionModel,
DataaVecVisionPreTrainedModel,
)
if is_tf_available():
from .modeling_tf_dataavec_vision import (
TFDataaVecVisionForImageClassification,
TFDataaVecVisionForSemanticSegmentation,
TFDataaVecVisionModel,
TFDataaVecVisionPreTrainedModel,
)
else:
import sys
__SCREAMING_SNAKE_CASE : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 31 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "bert-generation"
def __init__( self , __A=5_0358 , __A=1024 , __A=24 , __A=16 , __A=4096 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0.0_2 , __A=1E-12 , __A=0 , __A=2 , __A=1 , __A="absolute" , __A=True , **__A , ) -> Tuple:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :Any = vocab_size
lowerCAmelCase_ :List[Any] = hidden_size
lowerCAmelCase_ :Optional[int] = num_hidden_layers
lowerCAmelCase_ :int = num_attention_heads
lowerCAmelCase_ :List[Any] = hidden_act
lowerCAmelCase_ :Optional[Any] = intermediate_size
lowerCAmelCase_ :List[Any] = hidden_dropout_prob
lowerCAmelCase_ :int = attention_probs_dropout_prob
lowerCAmelCase_ :Tuple = max_position_embeddings
lowerCAmelCase_ :List[str] = initializer_range
lowerCAmelCase_ :Union[str, Any] = layer_norm_eps
lowerCAmelCase_ :List[str] = position_embedding_type
lowerCAmelCase_ :Optional[int] = use_cache
| 84 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : List[Any] = logging.get_logger(__name__)
UpperCAmelCase_ : int = {
'MIT/ast-finetuned-audioset-10-10-0.4593': (
'https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593/resolve/main/config.json'
),
}
class SCREAMING_SNAKE_CASE__ ( lowercase__ ):
snake_case__ : Union[str, Any] = '''audio-spectrogram-transformer'''
def __init__( self : Dict , SCREAMING_SNAKE_CASE__ : Tuple=7_6_8 , SCREAMING_SNAKE_CASE__ : Optional[int]=1_2 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=1_2 , SCREAMING_SNAKE_CASE__ : int=3_0_7_2 , SCREAMING_SNAKE_CASE__ : List[Any]="gelu" , SCREAMING_SNAKE_CASE__ : List[Any]=0.0 , SCREAMING_SNAKE_CASE__ : List[str]=0.0 , SCREAMING_SNAKE_CASE__ : Any=0.02 , SCREAMING_SNAKE_CASE__ : Dict=1E-12 , SCREAMING_SNAKE_CASE__ : Optional[Any]=1_6 , SCREAMING_SNAKE_CASE__ : str=True , SCREAMING_SNAKE_CASE__ : Dict=1_0 , SCREAMING_SNAKE_CASE__ : int=1_0 , SCREAMING_SNAKE_CASE__ : Any=1_0_2_4 , SCREAMING_SNAKE_CASE__ : Optional[int]=1_2_8 , **SCREAMING_SNAKE_CASE__ : str , ) -> Optional[int]:
super().__init__(**SCREAMING_SNAKE_CASE__ )
a_ : Dict = hidden_size
a_ : Any = num_hidden_layers
a_ : Union[str, Any] = num_attention_heads
a_ : Optional[Any] = intermediate_size
a_ : Optional[Any] = hidden_act
a_ : Dict = hidden_dropout_prob
a_ : int = attention_probs_dropout_prob
a_ : List[str] = initializer_range
a_ : List[Any] = layer_norm_eps
a_ : int = patch_size
a_ : int = qkv_bias
a_ : int = frequency_stride
a_ : Union[str, Any] = time_stride
a_ : str = max_length
a_ : List[Any] = num_mel_bins
| 32 |
"""simple docstring"""
def _snake_case ( lowercase__ : List[Any] , lowercase__ : int , lowercase__ : Optional[int] , lowercase__ : Any ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [False] * len(lowercase__ )
lowerCAmelCase_ :str = []
queue.append(lowercase__ )
lowerCAmelCase_ :Any = True
while queue:
lowerCAmelCase_ :Optional[int] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(lowercase__ )
lowerCAmelCase_ :Union[str, Any] = True
lowerCAmelCase_ :int = u
return visited[t]
def _snake_case ( lowercase__ : int , lowercase__ : Optional[int] , lowercase__ : str ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = [-1] * (len(lowercase__ ))
lowerCAmelCase_ :str = 0
while bfs(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
lowerCAmelCase_ :List[str] = float("""Inf""" )
lowerCAmelCase_ :List[str] = sink
while s != source:
# Find the minimum value in select path
lowerCAmelCase_ :Any = min(lowercase__ , graph[parent[s]][s] )
lowerCAmelCase_ :Union[str, Any] = parent[s]
max_flow += path_flow
lowerCAmelCase_ :Tuple = sink
while v != source:
lowerCAmelCase_ :List[str] = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowerCAmelCase_ :Union[str, Any] = parent[v]
return max_flow
__UpperCAmelCase = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__UpperCAmelCase , __UpperCAmelCase = 0, 5
print(ford_fulkerson(graph, source, sink))
| 84 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__A : List[str] = logging.get_logger(__name__)
__A : List[str] = {
'''microsoft/focalnet-tiny''': '''https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json''',
}
class _UpperCAmelCase ( _A , _A ):
SCREAMING_SNAKE_CASE_ : str = "focalnet"
def __init__( self : Tuple , A : Tuple=2_24 , A : Tuple=4 , A : Union[str, Any]=3 , A : Optional[Any]=96 , A : List[Any]=False , A : int=[1_92, 3_84, 7_68, 7_68] , A : Tuple=[2, 2, 6, 2] , A : Union[str, Any]=[2, 2, 2, 2] , A : int=[3, 3, 3, 3] , A : Optional[int]="gelu" , A : Optional[Any]=4.0 , A : Optional[Any]=0.0 , A : Any=0.1 , A : Optional[int]=False , A : Any=1e-4 , A : Optional[int]=False , A : List[Any]=False , A : Dict=False , A : Dict=0.02 , A : Optional[Any]=1e-5 , A : Tuple=32 , A : Dict=None , A : int=None , **A : List[str] , ) -> Union[str, Any]:
super().__init__(**A )
lowercase_ : Dict = image_size
lowercase_ : int = patch_size
lowercase_ : List[str] = num_channels
lowercase_ : Union[str, Any] = embed_dim
lowercase_ : Union[str, Any] = use_conv_embed
lowercase_ : List[Any] = hidden_sizes
lowercase_ : Union[str, Any] = depths
lowercase_ : Tuple = focal_levels
lowercase_ : List[Any] = focal_windows
lowercase_ : Optional[Any] = hidden_act
lowercase_ : List[Any] = mlp_ratio
lowercase_ : Any = hidden_dropout_prob
lowercase_ : str = drop_path_rate
lowercase_ : int = use_layerscale
lowercase_ : List[Any] = layerscale_value
lowercase_ : Optional[Any] = use_post_layernorm
lowercase_ : List[Any] = use_post_layernorm_in_modulation
lowercase_ : int = normalize_modulator
lowercase_ : Tuple = initializer_range
lowercase_ : Tuple = layer_norm_eps
lowercase_ : Tuple = encoder_stride
lowercase_ : Union[str, Any] = ['''stem'''] + [F'''stage{idx}''' for idx in range(1 , len(self.depths ) + 1 )]
lowercase_ , lowercase_ : Optional[int] = get_aligned_output_features_output_indices(
out_features=A , out_indices=A , stage_names=self.stage_names )
| 33 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = 1_0
lowerCAmelCase_ :Optional[int] = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
lowerCAmelCase_ :int = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
"""id""": list(range(lowercase__ ) ),
} , features=lowercase__ , )
return dataset
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple , lowercase__ : int ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=lowercase__ )
return filename
# FILE_CONTENT + files
__UpperCAmelCase = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
lowerCAmelCase_ :List[Any] = FILE_CONTENT
with open(lowercase__ , """w""" ) as f:
f.write(lowercase__ )
return filename
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[Any] ) -> Tuple:
'''simple docstring'''
import bza
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
lowerCAmelCase_ :Tuple = bytes(lowercase__ , """utf-8""" )
with bza.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[Any] ) -> Dict:
'''simple docstring'''
import gzip
lowerCAmelCase_ :int = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
lowerCAmelCase_ :Tuple = bytes(lowercase__ , """utf-8""" )
with gzip.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Optional[int]:
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
lowerCAmelCase_ :List[Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
lowerCAmelCase_ :int = bytes(lowercase__ , """utf-8""" )
with lza.frame.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict , lowercase__ : Optional[int] ) -> Any:
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
lowerCAmelCase_ :Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(lowercase__ , """w""" ) as archive:
archive.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
import tarfile
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> str:
'''simple docstring'''
import lzma
lowerCAmelCase_ :Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
lowerCAmelCase_ :Optional[Any] = bytes(lowercase__ , """utf-8""" )
with lzma.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : List[Any] ) -> Any:
'''simple docstring'''
import zipfile
lowerCAmelCase_ :Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> Tuple:
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
lowerCAmelCase_ :Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
lowerCAmelCase_ :Any = bytes(lowercase__ , """utf-8""" )
with zstd.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
lowerCAmelCase_ :Any = textwrap.dedent(
"""\
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>
<tmx version=\"1.4\">
<header segtype=\"sentence\" srclang=\"ca\" />
<body>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>""" )
with open(lowercase__ , """w""" ) as f:
f.write(lowercase__ )
return filename
__UpperCAmelCase = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
__UpperCAmelCase = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
__UpperCAmelCase = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
__UpperCAmelCase = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
__UpperCAmelCase = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> Union[str, Any]:
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Tuple = datasets.Dataset.from_dict(lowercase__ )
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(lowercase__ ) ) as con:
lowerCAmelCase_ :Union[str, Any] = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> int:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(lowercase__ , """w""" , newline="""""" ) as f:
lowerCAmelCase_ :Optional[int] = csv.DictWriter(lowercase__ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(lowercase__ , """w""" , newline="""""" ) as f:
lowerCAmelCase_ :Dict = csv.DictWriter(lowercase__ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str , lowercase__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
import bza
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(lowercase__ , """rb""" ) as f:
lowerCAmelCase_ :Union[str, Any] = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : List[str] , lowercase__ : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(lowercase__ , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Tuple , lowercase__ : str ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
lowerCAmelCase_ :Optional[Any] = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(lowercase__ , """wb""" ) as f:
lowerCAmelCase_ :Optional[int] = pq.ParquetWriter(lowercase__ , schema=lowercase__ )
lowerCAmelCase_ :List[str] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(lowercase__ ) )] for k in DATA[0]} , schema=lowercase__ )
writer.write_table(lowercase__ )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
lowerCAmelCase_ :Union[str, Any] = {"""data""": DATA}
with open(lowercase__ , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
lowerCAmelCase_ :Optional[Any] = {"""data""": DATA_DICT_OF_LISTS}
with open(lowercase__ , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int , lowercase__ : Dict ) -> Optional[int]:
'''simple docstring'''
import gzip
lowerCAmelCase_ :Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(lowercase__ , """rb""" ) as orig_file:
with gzip.open(lowercase__ , """wb""" ) as zipped_file:
zipped_file.writelines(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : List[Any] ) -> Any:
'''simple docstring'''
import gzip
lowerCAmelCase_ :Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(lowercase__ , """rb""" ) as orig_file:
with gzip.open(lowercase__ , """wb""" ) as zipped_file:
zipped_file.writelines(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : Optional[int] , lowercase__ : List[Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""nested""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : List[Any] , lowercase__ : List[str] ) -> int:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : str , lowercase__ : List[str] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict , lowercase__ : str , lowercase__ : List[str] , lowercase__ : int ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.join("""nested""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :str = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Dict = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple , lowercase__ : Tuple , lowercase__ : List[str] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Any , lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(lowercase__ , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(lowercase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> int:
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> Tuple:
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
return data_dir
| 84 | 0 |
'''simple docstring'''
def snake_case_ (_a : list ):
def merge(_a : list , _a : list ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
return list(_merge() )
if len(_a ) <= 1:
return collection
UpperCAmelCase = len(_a ) // 2
return merge(merge_sort(collection[:mid] ) , merge_sort(collection[mid:] ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
A =input('Enter numbers separated by a comma:\n').strip()
A =[int(item) for item in user_input.split(',')]
print(*merge_sort(unsorted), sep=',')
| 34 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Optional[Any] = "data2vec-text"
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.0_2 , __A=1E-12 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , __A=None , **__A , ) -> Tuple:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :Dict = vocab_size
lowerCAmelCase_ :Dict = hidden_size
lowerCAmelCase_ :int = num_hidden_layers
lowerCAmelCase_ :List[Any] = num_attention_heads
lowerCAmelCase_ :Any = hidden_act
lowerCAmelCase_ :Optional[int] = intermediate_size
lowerCAmelCase_ :str = hidden_dropout_prob
lowerCAmelCase_ :Any = attention_probs_dropout_prob
lowerCAmelCase_ :str = max_position_embeddings
lowerCAmelCase_ :int = type_vocab_size
lowerCAmelCase_ :Tuple = initializer_range
lowerCAmelCase_ :List[Any] = layer_norm_eps
lowerCAmelCase_ :List[Any] = position_embedding_type
lowerCAmelCase_ :List[Any] = use_cache
lowerCAmelCase_ :List[Any] = classifier_dropout
class _SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase_ :List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowerCAmelCase_ :List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 84 | 0 |
'''simple docstring'''
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.models.roberta.modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
)
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def __init__( self : Tuple , snake_case_ : Tuple , snake_case_ : Dict=13 , snake_case_ : str=7 , snake_case_ : List[str]=True , snake_case_ : Optional[Any]=True , snake_case_ : List[Any]=True , snake_case_ : Union[str, Any]=True , snake_case_ : Dict=99 , snake_case_ : Optional[int]=32 , snake_case_ : Optional[Any]=5 , snake_case_ : Dict=4 , snake_case_ : Optional[Any]=37 , snake_case_ : str="gelu" , snake_case_ : Optional[Any]=0.1 , snake_case_ : Union[str, Any]=0.1 , snake_case_ : List[str]=512 , snake_case_ : Union[str, Any]=16 , snake_case_ : Optional[Any]=2 , snake_case_ : Any=0.02 , snake_case_ : Dict=4 , ):
snake_case__ : Optional[Any] = parent
snake_case__ : str = batch_size
snake_case__ : Any = seq_length
snake_case__ : Dict = is_training
snake_case__ : int = use_attention_mask
snake_case__ : Any = use_token_type_ids
snake_case__ : List[Any] = use_labels
snake_case__ : List[Any] = vocab_size
snake_case__ : int = hidden_size
snake_case__ : List[str] = num_hidden_layers
snake_case__ : str = num_attention_heads
snake_case__ : List[str] = intermediate_size
snake_case__ : List[str] = hidden_act
snake_case__ : Any = hidden_dropout_prob
snake_case__ : List[Any] = attention_probs_dropout_prob
snake_case__ : List[Any] = max_position_embeddings
snake_case__ : Any = type_vocab_size
snake_case__ : Dict = type_sequence_label_size
snake_case__ : Optional[Any] = initializer_range
snake_case__ : Tuple = num_choices
def lowerCamelCase ( self : Any ):
snake_case__ : Tuple = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case__ : Optional[int] = None
if self.use_attention_mask:
snake_case__ : Dict = random_attention_mask([self.batch_size, self.seq_length] )
snake_case__ : Tuple = None
if self.use_token_type_ids:
snake_case__ : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case__ : Optional[Any] = RobertaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=snake_case_ , initializer_range=self.initializer_range , )
return config, input_ids, token_type_ids, attention_mask
def lowerCamelCase ( self : List[Any] ):
snake_case__ : Union[str, Any] = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ , snake_case__ : List[str] = config_and_inputs
snake_case__ : List[Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": attention_mask}
return config, inputs_dict
def lowerCamelCase ( self : List[str] ):
snake_case__ : Union[str, Any] = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ , snake_case__ : Dict = config_and_inputs
snake_case__ : List[str] = True
snake_case__ : Union[str, Any] = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
snake_case__ : Dict = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
token_type_ids,
encoder_hidden_states,
encoder_attention_mask,
)
@require_flax
class UpperCAmelCase_ ( _a , unittest.TestCase ):
"""simple docstring"""
lowercase = True
lowercase = (
(
FlaxRobertaModel,
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCamelCase ( self : Dict ):
snake_case__ : Dict = FlaxRobertaModelTester(self )
@slow
def lowerCamelCase ( self : Optional[int] ):
for model_class_name in self.all_model_classes:
snake_case__ : Any = model_class_name.from_pretrained("""roberta-base""" , from_pt=snake_case_ )
snake_case__ : Optional[int] = model(np.ones((1, 1) ) )
self.assertIsNotNone(snake_case_ )
| 35 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( lowercase__ : Dict , lowercase__ : Dict , lowercase__ : str , lowercase__ : Tuple="attention" ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
lowerCAmelCase_ :Union[str, Any] = params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
lowerCAmelCase_ :Any = params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
lowerCAmelCase_ :Optional[int] = params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
return k, o, q, v
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Any , lowercase__ : int , lowercase__ : Any=False ) -> int:
'''simple docstring'''
if split_mlp_wi:
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
lowerCAmelCase_ :List[str] = params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
lowerCAmelCase_ :Tuple = (wi_a, wi_a)
else:
lowerCAmelCase_ :List[Any] = params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]
lowerCAmelCase_ :Dict = params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
return wi, wo
def _snake_case ( lowercase__ : Any , lowercase__ : Dict , lowercase__ : Union[str, Any] , lowercase__ : Optional[int] ) -> Tuple:
'''simple docstring'''
return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]
def _snake_case ( lowercase__ : dict , *, lowercase__ : int , lowercase__ : bool ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = traverse_util.flatten_dict(variables["""target"""] )
lowerCAmelCase_ :Tuple = {"""/""".join(lowercase__ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
lowerCAmelCase_ :Any = """encoder/layers_0/mlp/wi_0/kernel""" in old
print("""Split MLP:""" , lowercase__ )
lowerCAmelCase_ :List[Any] = collections.OrderedDict()
# Shared embeddings.
lowerCAmelCase_ :Optional[int] = old["""token_embedder/embedding"""]
# Encoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :str = tax_attention_lookup(lowercase__ , lowercase__ , """encoder""" , """attention""" )
lowerCAmelCase_ :Optional[Any] = layer_norm
lowerCAmelCase_ :Any = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Tuple = q.T
lowerCAmelCase_ :str = v.T
# Block i, layer 1 (MLP).
lowerCAmelCase_ :Dict = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Any = tax_mlp_lookup(lowercase__ , lowercase__ , """encoder""" , lowercase__ )
lowerCAmelCase_ :Union[str, Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :List[Any] = wi[0].T
lowerCAmelCase_ :Dict = wi[1].T
else:
lowerCAmelCase_ :int = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Tuple = old[
"""encoder/relpos_bias/rel_embedding"""
].T
lowerCAmelCase_ :List[str] = old["""encoder/encoder_norm/scale"""]
if not is_encoder_only:
# Decoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :Optional[Any] = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_self_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """self_attention""" )
lowerCAmelCase_ :List[Any] = layer_norm
lowerCAmelCase_ :List[str] = k.T
lowerCAmelCase_ :Any = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :Dict = v.T
# Block i, layer 1 (Cross Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_cross_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Tuple = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """encoder_decoder_attention""" )
lowerCAmelCase_ :Optional[int] = layer_norm
lowerCAmelCase_ :str = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :int = v.T
# Block i, layer 2 (MLP).
lowerCAmelCase_ :Any = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Dict = tax_mlp_lookup(lowercase__ , lowercase__ , """decoder""" , lowercase__ )
lowerCAmelCase_ :List[Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :Any = wi[0].T
lowerCAmelCase_ :Any = wi[1].T
else:
lowerCAmelCase_ :Tuple = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Optional[Any] = old["""decoder/decoder_norm/scale"""]
lowerCAmelCase_ :Optional[Any] = old[
"""decoder/relpos_bias/rel_embedding"""
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
lowerCAmelCase_ :Tuple = old["""decoder/logits_dense/kernel"""].T
return new
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : bool ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Optional[int] = state_dict["""shared.weight"""]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Tuple = state_dict["""shared.weight"""]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
lowerCAmelCase_ :Any = state_dict["""shared.weight"""]
return state_dict
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : str , lowercase__ : List[Any] , lowercase__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = checkpoints.load_tax_checkpoint(lowercase__ )
lowerCAmelCase_ :Optional[int] = convert_tax_to_pytorch(lowercase__ , num_layers=config.num_layers , is_encoder_only=lowercase__ )
lowerCAmelCase_ :Union[str, Any] = make_state_dict(lowercase__ , lowercase__ )
model.load_state_dict(lowercase__ , strict=lowercase__ )
def _snake_case ( lowercase__ : List[Any] , lowercase__ : Optional[Any] , lowercase__ : str , lowercase__ : bool = False ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Any = TaConfig.from_json_file(lowercase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
lowerCAmelCase_ :List[Any] = TaEncoderModel(lowercase__ )
else:
lowerCAmelCase_ :List[str] = TaForConditionalGeneration(lowercase__ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowercase__ )
# Verify that we can load the checkpoint.
model.from_pretrained(lowercase__ )
print("""Done""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
)
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.'
)
parser.add_argument(
'--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
)
__UpperCAmelCase = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 84 | 0 |
import argparse
import torch
from transformers import (
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForAudioFrameClassification,
WavaVecaForSequenceClassification,
WavaVecaForXVector,
logging,
)
logging.set_verbosity_info()
_snake_case = logging.get_logger(__name__)
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : List[str] = WavaVecaForSequenceClassification.from_pretrained(_lowerCamelCase , config=_lowerCamelCase )
_lowerCAmelCase : Tuple = downstream_dict["projector.weight"]
_lowerCAmelCase : Optional[int] = downstream_dict["projector.bias"]
_lowerCAmelCase : List[Any] = downstream_dict["model.post_net.linear.weight"]
_lowerCAmelCase : Tuple = downstream_dict["model.post_net.linear.bias"]
return model
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Any = WavaVecaForAudioFrameClassification.from_pretrained(_lowerCamelCase , config=_lowerCamelCase )
_lowerCAmelCase : List[Any] = downstream_dict["model.linear.weight"]
_lowerCAmelCase : Dict = downstream_dict["model.linear.bias"]
return model
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Tuple = WavaVecaForXVector.from_pretrained(_lowerCamelCase , config=_lowerCamelCase )
_lowerCAmelCase : List[str] = downstream_dict["connector.weight"]
_lowerCAmelCase : List[Any] = downstream_dict["connector.bias"]
for i, kernel_size in enumerate(hf_config.tdnn_kernel ):
_lowerCAmelCase : List[Any] = downstream_dict[
F"model.framelevel_feature_extractor.module.{i}.kernel.weight"
]
_lowerCAmelCase : Optional[Any] = downstream_dict[F"model.framelevel_feature_extractor.module.{i}.kernel.bias"]
_lowerCAmelCase : List[Any] = downstream_dict["model.utterancelevel_feature_extractor.linear1.weight"]
_lowerCAmelCase : Tuple = downstream_dict["model.utterancelevel_feature_extractor.linear1.bias"]
_lowerCAmelCase : Optional[Any] = downstream_dict["model.utterancelevel_feature_extractor.linear2.weight"]
_lowerCAmelCase : List[str] = downstream_dict["model.utterancelevel_feature_extractor.linear2.bias"]
_lowerCAmelCase : str = downstream_dict["objective.W"]
return model
@torch.no_grad()
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Union[str, Any] = torch.load(_lowerCamelCase , map_location="cpu" )
_lowerCAmelCase : str = checkpoint["Downstream"]
_lowerCAmelCase : Any = WavaVecaConfig.from_pretrained(_lowerCamelCase )
_lowerCAmelCase : List[str] = WavaVecaFeatureExtractor.from_pretrained(
_lowerCamelCase , return_attention_mask=_lowerCamelCase , do_normalize=_lowerCamelCase )
_lowerCAmelCase : str = hf_config.architectures[0]
if arch.endswith("ForSequenceClassification" ):
_lowerCAmelCase : List[Any] = convert_classification(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
elif arch.endswith("ForAudioFrameClassification" ):
_lowerCAmelCase : str = convert_diarization(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
elif arch.endswith("ForXVector" ):
_lowerCAmelCase : Optional[Any] = convert_xvector(_lowerCamelCase , _lowerCamelCase , _lowerCamelCase )
else:
raise NotImplementedError(F"S3PRL weights conversion is not supported for {arch}" )
if hf_config.use_weighted_layer_sum:
_lowerCAmelCase : Dict = checkpoint["Featurizer"]["weights"]
hf_feature_extractor.save_pretrained(_lowerCamelCase )
hf_model.save_pretrained(_lowerCamelCase )
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument(
"--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
)
parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
_snake_case = parser.parse_args()
convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
| 36 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( lowercase__ : Optional[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :str = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
lowerCAmelCase_ :Union[str, Any] = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
lowerCAmelCase_ :Any = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
lowerCAmelCase_ :List[str] = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
lowerCAmelCase_ :Tuple = key.replace(f"""patch_embed{idx}""" , f"""patch_embeddings.{int(lowercase__ )-1}""" )
if "norm" in key:
lowerCAmelCase_ :Dict = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
lowerCAmelCase_ :str = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
lowerCAmelCase_ :str = key.replace(f"""layer_norm{idx}""" , f"""layer_norm.{int(lowercase__ )-1}""" )
if "layer_norm1" in key:
lowerCAmelCase_ :Optional[Any] = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
lowerCAmelCase_ :str = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
lowerCAmelCase_ :List[str] = key[key.find("""block""" ) + len("""block""" )]
lowerCAmelCase_ :int = key.replace(f"""block{idx}""" , f"""block.{int(lowercase__ )-1}""" )
if "attn.q" in key:
lowerCAmelCase_ :Tuple = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
lowerCAmelCase_ :Optional[int] = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
lowerCAmelCase_ :str = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
lowerCAmelCase_ :List[Any] = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
lowerCAmelCase_ :Optional[Any] = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
lowerCAmelCase_ :List[str] = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
lowerCAmelCase_ :str = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
lowerCAmelCase_ :Any = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
lowerCAmelCase_ :str = key[key.find("""linear_c""" ) + len("""linear_c""" )]
lowerCAmelCase_ :Optional[int] = key.replace(f"""linear_c{idx}""" , f"""linear_c.{int(lowercase__ )-1}""" )
if "bot_conv" in key:
lowerCAmelCase_ :Union[str, Any] = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
lowerCAmelCase_ :int = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
lowerCAmelCase_ :str = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
lowerCAmelCase_ :Any = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
lowerCAmelCase_ :List[str] = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
lowerCAmelCase_ :Dict = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
lowerCAmelCase_ :Any = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
lowerCAmelCase_ :Tuple = key.replace("""module.last_layer_depth""" , """head.head""" )
lowerCAmelCase_ :List[Any] = value
return new_state_dict
def _snake_case ( lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
lowerCAmelCase_ :Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
lowerCAmelCase_ :Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
lowerCAmelCase_ :Optional[Any] = kv_weight[
: config.hidden_sizes[i], :
]
lowerCAmelCase_ :Union[str, Any] = kv_bias[: config.hidden_sizes[i]]
lowerCAmelCase_ :List[Any] = kv_weight[
config.hidden_sizes[i] :, :
]
lowerCAmelCase_ :int = kv_bias[config.hidden_sizes[i] :]
def _snake_case ( ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase_ :Optional[Any] = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw )
return image
@torch.no_grad()
def _snake_case ( lowercase__ : List[Any] , lowercase__ : str , lowercase__ : Dict=False , lowercase__ : List[Any]=None ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2] , decoder_hidden_size=6_4 , depths=[3, 8, 2_7, 3] )
# load image processor (only resize + rescale)
lowerCAmelCase_ :Union[str, Any] = GLPNImageProcessor()
# prepare image
lowerCAmelCase_ :List[Any] = prepare_img()
lowerCAmelCase_ :int = image_processor(images=lowercase__ , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
lowerCAmelCase_ :Tuple = torch.load(lowercase__ , map_location=torch.device("""cpu""" ) )
# rename keys
lowerCAmelCase_ :Union[str, Any] = rename_keys(lowercase__ )
# key and value matrices need special treatment
read_in_k_v(lowercase__ , lowercase__ )
# create HuggingFace model and load state dict
lowerCAmelCase_ :List[Any] = GLPNForDepthEstimation(lowercase__ )
model.load_state_dict(lowercase__ )
model.eval()
# forward pass
lowerCAmelCase_ :Dict = model(lowercase__ )
lowerCAmelCase_ :Tuple = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
lowerCAmelCase_ :Optional[Any] = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
elif "kitti" in model_name:
lowerCAmelCase_ :Any = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
else:
raise ValueError(f"""Unknown model name: {model_name}""" )
lowerCAmelCase_ :Union[str, Any] = torch.Size([1, 4_8_0, 6_4_0] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , lowercase__ , atol=1E-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(lowercase__ , lowercase__ ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=lowercase__ , )
image_processor.push_to_hub(
repo_path_or_name=Path(lowercase__ , lowercase__ ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=lowercase__ , )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
__UpperCAmelCase = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 84 | 0 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv('''TEST_SAGEMAKER''' , '''False''' ) ) is not True , reason='''Skipping test because should only be run when releasing minor transformers version''' , )
@pytest.mark.usefixtures('''sm_env''' )
@parameterized_class(
[
{
'''framework''': '''pytorch''',
'''script''': '''run_glue.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.p3.16xlarge''',
'''results''': {'''train_runtime''': 6_5_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
},
{
'''framework''': '''pytorch''',
'''script''': '''run_ddp.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.p3.16xlarge''',
'''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.7, '''eval_loss''': 0.6},
},
{
'''framework''': '''tensorflow''',
'''script''': '''run_tf_dist.py''',
'''model_name_or_path''': '''distilbert-base-cased''',
'''instance_type''': '''ml.p3.16xlarge''',
'''results''': {'''train_runtime''': 6_0_0, '''eval_accuracy''': 0.6, '''eval_loss''': 0.7},
},
] )
class lowerCAmelCase_( unittest.TestCase ):
'''simple docstring'''
def UpperCAmelCase_ ( self ) -> Optional[Any]:
if self.framework == "pytorch":
subprocess.run(
F"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() ,encoding="""utf-8""" ,check=__UpperCAmelCase ,)
assert hasattr(self ,"""env""" )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> Optional[Any]:
lowerCAmelCase__ : Optional[int] = F"""{self.env.base_job_name}-{instance_count}-{'ddp' if 'ddp' in self.script else 'smd'}"""
# distributed data settings
lowerCAmelCase__ : Any = {"""smdistributed""": {"""dataparallel""": {"""enabled""": True}}} if self.script != """run_ddp.py""" else None
# creates estimator
return HuggingFace(
entry_point=self.script ,source_dir=self.env.test_path ,role=self.env.role ,image_uri=self.env.image_uri ,base_job_name=__UpperCAmelCase ,instance_count=__UpperCAmelCase ,instance_type=self.instance_type ,debugger_hook_config=__UpperCAmelCase ,hyperparameters={**self.env.distributed_hyperparameters, """model_name_or_path""": self.model_name_or_path} ,metric_definitions=self.env.metric_definitions ,distribution=__UpperCAmelCase ,py_version="""py36""" ,)
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> Optional[Any]:
TrainingJobAnalytics(__UpperCAmelCase ).export_csv(F"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(2,)] )
def UpperCAmelCase_ ( self ,__UpperCAmelCase ) -> Any:
# create estimator
lowerCAmelCase__ : List[Any] = self.create_estimator(__UpperCAmelCase )
# run training
estimator.fit()
# result dataframe
lowerCAmelCase__ : Any = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase__ : int = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
lowerCAmelCase__ : List[Any] = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase__ : List[str] = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" ,99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F"""{estimator.latest_training_job.name}.json""" ,"""w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} ,__UpperCAmelCase )
| 37 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoCBertForCausalLM',
'RoCBertForMaskedLM',
'RoCBertForMultipleChoice',
'RoCBertForPreTraining',
'RoCBertForQuestionAnswering',
'RoCBertForSequenceClassification',
'RoCBertForTokenClassification',
'RoCBertLayer',
'RoCBertModel',
'RoCBertPreTrainedModel',
'load_tf_weights_in_roc_bert',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 84 | 0 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
UpperCAmelCase_ : Tuple = logging.get_logger(__name__)
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase :Union[str, Any] = R"""\w+[.]\d+"""
UpperCamelCase :int = re.findall(__magic_name__ , __magic_name__ )
for pat in pats:
UpperCamelCase :List[str] = key.replace(__magic_name__ , """_""".join(pat.split(""".""" ) ) )
return key
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Optional[int] ) -> Tuple:
"""simple docstring"""
UpperCamelCase :Optional[Any] = pt_tuple_key[:-1] + ("""scale""",)
if (
any("""norm""" in str_ for str_ in pt_tuple_key )
and (pt_tuple_key[-1] == "bias")
and (pt_tuple_key[:-1] + ("bias",) not in random_flax_state_dict)
and (pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict)
):
UpperCamelCase :List[str] = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
elif pt_tuple_key[-1] in ["weight", "gamma"] and pt_tuple_key[:-1] + ("scale",) in random_flax_state_dict:
UpperCamelCase :Optional[Any] = pt_tuple_key[:-1] + ("""scale""",)
return renamed_pt_tuple_key, pt_tensor
# embedding
if pt_tuple_key[-1] == "weight" and pt_tuple_key[:-1] + ("embedding",) in random_flax_state_dict:
UpperCamelCase :Optional[Any] = pt_tuple_key[:-1] + ("""embedding""",)
return renamed_pt_tuple_key, pt_tensor
# conv layer
UpperCamelCase :Dict = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4:
UpperCamelCase :Any = pt_tensor.transpose(2 , 3 , 1 , 0 )
return renamed_pt_tuple_key, pt_tensor
# linear layer
UpperCamelCase :Dict = pt_tuple_key[:-1] + ("""kernel""",)
if pt_tuple_key[-1] == "weight":
UpperCamelCase :Optional[Any] = pt_tensor.T
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm weight
UpperCamelCase :str = pt_tuple_key[:-1] + ("""weight""",)
if pt_tuple_key[-1] == "gamma":
return renamed_pt_tuple_key, pt_tensor
# old PyTorch layer norm bias
UpperCamelCase :Optional[int] = pt_tuple_key[:-1] + ("""bias""",)
if pt_tuple_key[-1] == "beta":
return renamed_pt_tuple_key, pt_tensor
return pt_tuple_key, pt_tensor
def SCREAMING_SNAKE_CASE_ ( __magic_name__ : List[Any] , __magic_name__ : List[Any] , __magic_name__ : Tuple=42 ) -> Union[str, Any]:
"""simple docstring"""
UpperCamelCase :Any = {k: v.numpy() for k, v in pt_state_dict.items()}
# Step 2: Since the model is stateless, get random Flax params
UpperCamelCase :Tuple = flax_model.init_weights(PRNGKey(__magic_name__ ) )
UpperCamelCase :Any = flatten_dict(__magic_name__ )
UpperCamelCase :Optional[int] = {}
# Need to change some parameters name to match Flax names
for pt_key, pt_tensor in pt_state_dict.items():
UpperCamelCase :Union[str, Any] = rename_key(__magic_name__ )
UpperCamelCase :Optional[Any] = tuple(renamed_pt_key.split(""".""" ) )
# Correctly rename weight parameters
UpperCamelCase , UpperCamelCase :Tuple = rename_key_and_reshape_tensor(__magic_name__ , __magic_name__ , __magic_name__ )
if flax_key in random_flax_state_dict:
if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
raise ValueError(
f"""PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape """
f"""{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.""" )
# also add unexpected weight so that warning is thrown
UpperCamelCase :List[Any] = jnp.asarray(__magic_name__ )
return unflatten_dict(__magic_name__ )
| 38 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "levit"
def __init__( self , __A=224 , __A=3 , __A=3 , __A=2 , __A=1 , __A=16 , __A=[128, 256, 384] , __A=[4, 8, 12] , __A=[4, 4, 4] , __A=[16, 16, 16] , __A=0 , __A=[2, 2, 2] , __A=[2, 2, 2] , __A=0.0_2 , **__A , ) -> Any:
super().__init__(**__A )
lowerCAmelCase_ :Tuple = image_size
lowerCAmelCase_ :Optional[int] = num_channels
lowerCAmelCase_ :Union[str, Any] = kernel_size
lowerCAmelCase_ :Optional[Any] = stride
lowerCAmelCase_ :Optional[int] = padding
lowerCAmelCase_ :Optional[Any] = hidden_sizes
lowerCAmelCase_ :Optional[int] = num_attention_heads
lowerCAmelCase_ :int = depths
lowerCAmelCase_ :List[str] = key_dim
lowerCAmelCase_ :str = drop_path_rate
lowerCAmelCase_ :Optional[int] = patch_size
lowerCAmelCase_ :Union[str, Any] = attention_ratio
lowerCAmelCase_ :Dict = mlp_ratio
lowerCAmelCase_ :Any = initializer_range
lowerCAmelCase_ :Optional[int] = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Tuple = version.parse("1.11" )
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __lowerCAmelCase ( self ) -> float:
return 1E-4
| 84 | 0 |
from packaging import version
from .import_utils import is_accelerate_available
if is_accelerate_available():
import accelerate
def __A ( __lowerCAmelCase )-> Dict:
"""simple docstring"""
if not is_accelerate_available():
return method
_UpperCAmelCase = version.parse(accelerate.__version__ ).base_version
if version.parse(__lowerCAmelCase ) < version.parse('0.17.0' ):
return method
def wrapper(self , *__lowerCAmelCase , **__lowerCAmelCase ):
if hasattr(self , '_hf_hook' ) and hasattr(self._hf_hook , 'pre_forward' ):
self._hf_hook.pre_forward(self )
return method(self , *__lowerCAmelCase , **__lowerCAmelCase )
return wrapper
| 39 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( lowercase__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = FileLock(str(tmpdir / """foo.lock""" ) )
lowerCAmelCase_ :Union[str, Any] = FileLock(str(tmpdir / """foo.lock""" ) )
lowerCAmelCase_ :Dict = 0.01
with locka.acquire():
with pytest.raises(lowercase__ ):
lowerCAmelCase_ :List[Any] = time.time()
locka.acquire(lowercase__ )
assert time.time() - _start > timeout
def _snake_case ( lowercase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = """a""" * 1_0_0_0 + """.lock"""
lowerCAmelCase_ :Optional[Any] = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith(""".lock""" )
assert not locka._lock_file.endswith(lowercase__ )
assert len(os.path.basename(locka._lock_file ) ) <= 2_5_5
lowerCAmelCase_ :Any = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(lowercase__ ):
locka.acquire(0 )
| 84 | 0 |
"""simple docstring"""
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf,
require_torch,
require_torch_gpu,
slow,
)
from .test_pipelines_common import ANY
@is_pipeline_test
class _A ( unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase : str = MODEL_FOR_MASKED_LM_MAPPING
UpperCAmelCase : Any = TF_MODEL_FOR_MASKED_LM_MAPPING
def __snake_case ( self : Tuple):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
if is_torch_available():
import torch
torch.cuda.empty_cache()
@require_tf
def __snake_case ( self : Optional[int]):
a : Any = pipeline(task="fill-mask" , model="sshleifer/tiny-distilroberta-base" , top_k=2 , framework="tf")
a : Any = unmasker("My name is <mask>")
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6) , [
{"sequence": "My name is grouped", "score": 2.1e-05, "token": 38015, "token_str": " grouped"},
{"sequence": "My name is accuser", "score": 2.1e-05, "token": 25506, "token_str": " accuser"},
] , )
a : Any = unmasker("The largest city in France is <mask>")
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6) , [
{
"sequence": "The largest city in France is grouped",
"score": 2.1e-05,
"token": 38015,
"token_str": " grouped",
},
{
"sequence": "The largest city in France is accuser",
"score": 2.1e-05,
"token": 25506,
"token_str": " accuser",
},
] , )
a : Dict = unmasker("My name is <mask>" , targets=[" Patrick", " Clara", " Teven"] , top_k=3)
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6) , [
{"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
{"sequence": "My name is Patrick", "score": 2e-05, "token": 3499, "token_str": " Patrick"},
{"sequence": "My name is Te", "score": 1.9e-05, "token": 2941, "token_str": " Te"},
] , )
@require_torch
def __snake_case ( self : Optional[int]):
a : str = pipeline(task="fill-mask" , model="sshleifer/tiny-distilroberta-base" , top_k=2 , framework="pt")
a : Optional[Any] = unmasker("My name is <mask>")
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6) , [
{"sequence": "My name is Maul", "score": 2.2e-05, "token": 35676, "token_str": " Maul"},
{"sequence": "My name isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
] , )
a : Dict = unmasker("The largest city in France is <mask>")
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6) , [
{
"sequence": "The largest city in France is Maul",
"score": 2.2e-05,
"token": 35676,
"token_str": " Maul",
},
{"sequence": "The largest city in France isELS", "score": 2.2e-05, "token": 16416, "token_str": "ELS"},
] , )
a : Optional[Any] = unmasker("My name is <mask>" , targets=[" Patrick", " Clara", " Teven"] , top_k=3)
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6) , [
{"sequence": "My name is Patrick", "score": 2.1e-05, "token": 3499, "token_str": " Patrick"},
{"sequence": "My name is Te", "score": 2e-05, "token": 2941, "token_str": " Te"},
{"sequence": "My name is Clara", "score": 2e-05, "token": 13606, "token_str": " Clara"},
] , )
a : Optional[int] = unmasker("My name is <mask> <mask>" , top_k=2)
self.assertEqual(
nested_simplify(__UpperCAmelCase , decimals=6) , [
[
{
"score": 2.2e-05,
"token": 35676,
"token_str": " Maul",
"sequence": "<s>My name is Maul<mask></s>",
},
{"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name isELS<mask></s>"},
],
[
{
"score": 2.2e-05,
"token": 35676,
"token_str": " Maul",
"sequence": "<s>My name is<mask> Maul</s>",
},
{"score": 2.2e-05, "token": 16416, "token_str": "ELS", "sequence": "<s>My name is<mask>ELS</s>"},
],
] , )
@require_torch_gpu
def __snake_case ( self : int):
a : int = pipeline("fill-mask" , model="hf-internal-testing/tiny-random-distilbert" , device=0 , framework="pt")
# convert model to fp16
pipe.model.half()
a : Tuple = pipe("Paris is the [MASK] of France.")
# We actually don't care about the result, we just want to make sure
# it works, meaning the float16 tensor got casted back to float32
# for postprocessing.
self.assertIsInstance(__UpperCAmelCase , __UpperCAmelCase)
@slow
@require_torch
def __snake_case ( self : Dict):
a : Any = pipeline(task="fill-mask" , model="distilroberta-base" , top_k=2 , framework="pt")
self.run_large_test(__UpperCAmelCase)
@slow
@require_tf
def __snake_case ( self : Optional[int]):
a : List[Any] = pipeline(task="fill-mask" , model="distilroberta-base" , top_k=2 , framework="tf")
self.run_large_test(__UpperCAmelCase)
def __snake_case ( self : int , __UpperCAmelCase : Optional[int]):
a : Tuple = unmasker("My name is <mask>")
self.assertEqual(
nested_simplify(__UpperCAmelCase) , [
{"sequence": "My name is John", "score": 0.008, "token": 610, "token_str": " John"},
{"sequence": "My name is Chris", "score": 0.007, "token": 1573, "token_str": " Chris"},
] , )
a : List[str] = unmasker("The largest city in France is <mask>")
self.assertEqual(
nested_simplify(__UpperCAmelCase) , [
{
"sequence": "The largest city in France is Paris",
"score": 0.251,
"token": 2201,
"token_str": " Paris",
},
{
"sequence": "The largest city in France is Lyon",
"score": 0.214,
"token": 12790,
"token_str": " Lyon",
},
] , )
a : int = unmasker("My name is <mask>" , targets=[" Patrick", " Clara", " Teven"] , top_k=3)
self.assertEqual(
nested_simplify(__UpperCAmelCase) , [
{"sequence": "My name is Patrick", "score": 0.005, "token": 3499, "token_str": " Patrick"},
{"sequence": "My name is Clara", "score": 0.000, "token": 13606, "token_str": " Clara"},
{"sequence": "My name is Te", "score": 0.000, "token": 2941, "token_str": " Te"},
] , )
@require_torch
def __snake_case ( self : Union[str, Any]):
a : Any = pipeline(task="fill-mask" , model="sshleifer/tiny-distilroberta-base" , framework="pt")
a : Dict = None
a : str = None
self.run_pipeline_test(__UpperCAmelCase , [])
@require_tf
def __snake_case ( self : Tuple):
a : Optional[int] = pipeline(task="fill-mask" , model="sshleifer/tiny-distilroberta-base" , framework="tf")
a : Tuple = None
a : Optional[Any] = None
self.run_pipeline_test(__UpperCAmelCase , [])
def __snake_case ( self : Dict , __UpperCAmelCase : Tuple , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[Any]):
if tokenizer is None or tokenizer.mask_token_id is None:
self.skipTest("The provided tokenizer has no mask token, (probably reformer or wav2vec2)")
a : Optional[Any] = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase)
a : Dict = [
f'''This is another {tokenizer.mask_token} test''',
]
return fill_masker, examples
def __snake_case ( self : Optional[int] , __UpperCAmelCase : Tuple , __UpperCAmelCase : Optional[Any]):
a : Union[str, Any] = fill_masker.tokenizer
a : Union[str, Any] = fill_masker.model
a : Union[str, Any] = fill_masker(
f'''This is a {tokenizer.mask_token}''' , )
self.assertEqual(
__UpperCAmelCase , [
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
] , )
a : List[Any] = fill_masker([f'''This is a {tokenizer.mask_token}'''])
self.assertEqual(
__UpperCAmelCase , [
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
] , )
a : List[str] = fill_masker([f'''This is a {tokenizer.mask_token}''', f'''Another {tokenizer.mask_token} great test.'''])
self.assertEqual(
__UpperCAmelCase , [
[
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
],
[
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
],
] , )
with self.assertRaises(__UpperCAmelCase):
fill_masker([None])
# No mask_token is not supported
with self.assertRaises(__UpperCAmelCase):
fill_masker("This is")
self.run_test_top_k(__UpperCAmelCase , __UpperCAmelCase)
self.run_test_targets(__UpperCAmelCase , __UpperCAmelCase)
self.run_test_top_k_targets(__UpperCAmelCase , __UpperCAmelCase)
self.fill_mask_with_duplicate_targets_and_top_k(__UpperCAmelCase , __UpperCAmelCase)
self.fill_mask_with_multiple_masks(__UpperCAmelCase , __UpperCAmelCase)
def __snake_case ( self : Union[str, Any] , __UpperCAmelCase : str , __UpperCAmelCase : int):
a : str = tokenizer.get_vocab()
a : Any = sorted(vocab.keys())[:2]
# Pipeline argument
a : List[str] = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , targets=__UpperCAmelCase)
a : Optional[int] = fill_masker(f'''This is a {tokenizer.mask_token}''')
self.assertEqual(
__UpperCAmelCase , [
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
] , )
a : int = {vocab[el] for el in targets}
self.assertEqual({el["token"] for el in outputs} , __UpperCAmelCase)
a : Tuple = [tokenizer.decode([x]) for x in target_ids]
self.assertEqual({el["token_str"] for el in outputs} , set(__UpperCAmelCase))
# Call argument
a : Union[str, Any] = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase)
a : str = fill_masker(f'''This is a {tokenizer.mask_token}''' , targets=__UpperCAmelCase)
self.assertEqual(
__UpperCAmelCase , [
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
] , )
a : Any = {vocab[el] for el in targets}
self.assertEqual({el["token"] for el in outputs} , __UpperCAmelCase)
a : List[Any] = [tokenizer.decode([x]) for x in target_ids]
self.assertEqual({el["token_str"] for el in outputs} , set(__UpperCAmelCase))
# Score equivalence
a : Any = fill_masker(f'''This is a {tokenizer.mask_token}''' , targets=__UpperCAmelCase)
a : Optional[Any] = [top_mask["token_str"] for top_mask in outputs]
a : List[Any] = [top_mask["score"] for top_mask in outputs]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(__UpperCAmelCase) == set(__UpperCAmelCase):
a : int = fill_masker(f'''This is a {tokenizer.mask_token}''' , targets=__UpperCAmelCase)
a : List[str] = [top_mask["score"] for top_mask in unmasked_targets]
self.assertEqual(nested_simplify(__UpperCAmelCase) , nested_simplify(__UpperCAmelCase))
# Raises with invalid
with self.assertRaises(__UpperCAmelCase):
a : int = fill_masker(f'''This is a {tokenizer.mask_token}''' , targets=[])
# For some tokenizers, `""` is actually in the vocabulary and the expected error won't raised
if "" not in tokenizer.get_vocab():
with self.assertRaises(__UpperCAmelCase):
a : List[Any] = fill_masker(f'''This is a {tokenizer.mask_token}''' , targets=[""])
with self.assertRaises(__UpperCAmelCase):
a : Union[str, Any] = fill_masker(f'''This is a {tokenizer.mask_token}''' , targets="")
def __snake_case ( self : int , __UpperCAmelCase : Optional[Any] , __UpperCAmelCase : str):
a : List[str] = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase , top_k=2)
a : str = fill_masker(f'''This is a {tokenizer.mask_token}''')
self.assertEqual(
__UpperCAmelCase , [
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
] , )
a : Union[str, Any] = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase)
a : Optional[int] = fill_masker(f'''This is a {tokenizer.mask_token}''' , top_k=2)
self.assertEqual(
__UpperCAmelCase , [
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
] , )
self.assertEqual(nested_simplify(__UpperCAmelCase) , nested_simplify(__UpperCAmelCase))
def __snake_case ( self : Any , __UpperCAmelCase : Optional[int] , __UpperCAmelCase : str):
a : Tuple = tokenizer.get_vocab()
a : List[str] = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase)
# top_k=2, ntargets=3
a : Dict = sorted(vocab.keys())[:3]
a : int = fill_masker(f'''This is a {tokenizer.mask_token}''' , top_k=2 , targets=__UpperCAmelCase)
# If we use the most probably targets, and filter differently, we should still
# have the same results
a : Union[str, Any] = [el["token_str"] for el in sorted(__UpperCAmelCase , key=lambda __UpperCAmelCase: x["score"] , reverse=__UpperCAmelCase)]
# For some BPE tokenizers, `</w>` is removed during decoding, so `token_str` won't be the same as in `targets`.
if set(__UpperCAmelCase).issubset(__UpperCAmelCase):
a : Tuple = fill_masker(f'''This is a {tokenizer.mask_token}''' , top_k=3 , targets=__UpperCAmelCase)
# They should yield exactly the same result
self.assertEqual(nested_simplify(__UpperCAmelCase) , nested_simplify(__UpperCAmelCase))
def __snake_case ( self : int , __UpperCAmelCase : Any , __UpperCAmelCase : Any):
a : str = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase)
a : str = tokenizer.get_vocab()
# String duplicates + id duplicates
a : List[Any] = sorted(vocab.keys())[:3]
a : Dict = [targets[0], targets[1], targets[0], targets[2], targets[1]]
a : Union[str, Any] = fill_masker(f'''My name is {tokenizer.mask_token}''' , targets=__UpperCAmelCase , top_k=10)
# The target list contains duplicates, so we can't output more
# than them
self.assertEqual(len(__UpperCAmelCase) , 3)
def __snake_case ( self : Optional[int] , __UpperCAmelCase : Union[str, Any] , __UpperCAmelCase : List[Any]):
a : Dict = FillMaskPipeline(model=__UpperCAmelCase , tokenizer=__UpperCAmelCase)
a : Tuple = fill_masker(
f'''This is a {tokenizer.mask_token} {tokenizer.mask_token} {tokenizer.mask_token}''' , top_k=2)
self.assertEqual(
__UpperCAmelCase , [
[
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
],
[
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
],
[
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
{"sequence": ANY(__UpperCAmelCase), "score": ANY(__UpperCAmelCase), "token": ANY(__UpperCAmelCase), "token_str": ANY(__UpperCAmelCase)},
],
] , )
| 40 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__UpperCAmelCase = 1.054571817e-34 # unit of ℏ : J * s
__UpperCAmelCase = 3e8 # unit of c : m * s^-1
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float ) -> dict[str, float]:
'''simple docstring'''
if (force, area, distance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if force < 0:
raise ValueError("""Magnitude of force can not be negative""" )
if distance < 0:
raise ValueError("""Distance can not be negative""" )
if area < 0:
raise ValueError("""Area can not be negative""" )
if force == 0:
lowerCAmelCase_ :Union[str, Any] = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
2_4_0 * (distance) ** 4
)
return {"force": force}
elif area == 0:
lowerCAmelCase_ :Optional[Any] = (2_4_0 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
lowerCAmelCase_ :Any = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError("""One and only one argument must be 0""" )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 0 |
'''simple docstring'''
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available():
from transformers import WhisperFeatureExtractor
if is_torch_available():
import torch
_A : Union[str, Any] =random.Random()
def SCREAMING_SNAKE_CASE_ (UpperCamelCase , UpperCamelCase=1.0 , UpperCamelCase=None , UpperCamelCase=None ) -> int:
if rng is None:
lowerCamelCase__ : Dict = global_rng
lowerCamelCase__ : str = []
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 _lowercase ( unittest.TestCase ):
def __init__( self: int , UpperCamelCase__: List[Any] , UpperCamelCase__: Tuple=7 , UpperCamelCase__: List[str]=400 , UpperCamelCase__: Union[str, Any]=2_000 , UpperCamelCase__: Optional[Any]=10 , UpperCamelCase__: Any=160 , UpperCamelCase__: Union[str, Any]=8 , UpperCamelCase__: int=0.0 , UpperCamelCase__: Optional[Any]=4_000 , UpperCamelCase__: List[str]=False , UpperCamelCase__: str=True , ):
lowerCamelCase__ : List[Any] = parent
lowerCamelCase__ : Union[str, Any] = batch_size
lowerCamelCase__ : str = min_seq_length
lowerCamelCase__ : List[Any] = max_seq_length
lowerCamelCase__ : Union[str, Any] = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
lowerCamelCase__ : List[Any] = padding_value
lowerCamelCase__ : Any = sampling_rate
lowerCamelCase__ : Dict = return_attention_mask
lowerCamelCase__ : Union[str, Any] = do_normalize
lowerCamelCase__ : Optional[int] = feature_size
lowerCamelCase__ : Union[str, Any] = chunk_length
lowerCamelCase__ : Dict = hop_length
def lowerCamelCase_ ( self: List[Any] ):
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 lowerCamelCase_ ( self: List[Any] , UpperCamelCase__: List[Any]=False , UpperCamelCase__: int=False ):
def _flatten(UpperCamelCase__: List[str] ):
return list(itertools.chain(*UpperCamelCase__ ) )
if equal_length:
lowerCamelCase__ : List[str] = [floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
lowerCamelCase__ : Union[str, Any] = [
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
lowerCamelCase__ : Any = [np.asarray(UpperCamelCase__ ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class _lowercase ( _lowercase , unittest.TestCase ):
a = WhisperFeatureExtractor if is_speech_available() else None
def lowerCamelCase_ ( self: Tuple ):
lowerCamelCase__ : Union[str, Any] = WhisperFeatureExtractionTester(self )
def lowerCamelCase_ ( self: List[str] ):
lowerCamelCase__ : Dict = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase__ : List[Any] = feat_extract_first.save_pretrained(UpperCamelCase__ )[0]
check_json_file_has_correct_format(UpperCamelCase__ )
lowerCamelCase__ : Dict = self.feature_extraction_class.from_pretrained(UpperCamelCase__ )
lowerCamelCase__ : int = feat_extract_first.to_dict()
lowerCamelCase__ : List[str] = feat_extract_second.to_dict()
lowerCamelCase__ : List[str] = feat_extract_first.mel_filters
lowerCamelCase__ : Dict = feat_extract_second.mel_filters
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( self: Any ):
lowerCamelCase__ : Tuple = self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCamelCase__ : Tuple = os.path.join(UpperCamelCase__ , """feat_extract.json""" )
feat_extract_first.to_json_file(UpperCamelCase__ )
lowerCamelCase__ : int = self.feature_extraction_class.from_json_file(UpperCamelCase__ )
lowerCamelCase__ : Optional[int] = feat_extract_first.to_dict()
lowerCamelCase__ : Dict = feat_extract_second.to_dict()
lowerCamelCase__ : Union[str, Any] = feat_extract_first.mel_filters
lowerCamelCase__ : Tuple = feat_extract_second.mel_filters
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ ) )
self.assertEqual(UpperCamelCase__ , UpperCamelCase__ )
def lowerCamelCase_ ( self: Union[str, Any] ):
# Tests that all call wrap to encode_plus and batch_encode_plus
lowerCamelCase__ : Any = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
lowerCamelCase__ : int = [floats_list((1, x) )[0] for x in range(800 , 1_400 , 200 )]
lowerCamelCase__ : str = [np.asarray(UpperCamelCase__ ) for speech_input in speech_inputs]
# Test feature size
lowerCamelCase__ : Dict = feature_extractor(UpperCamelCase__ , 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
lowerCamelCase__ : Any = feature_extractor(speech_inputs[0] , return_tensors="""np""" ).input_features
lowerCamelCase__ : Dict = feature_extractor(np_speech_inputs[0] , return_tensors="""np""" ).input_features
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 ) )
# Test batched
lowerCamelCase__ : List[Any] = feature_extractor(UpperCamelCase__ , return_tensors="""np""" ).input_features
lowerCamelCase__ : Optional[Any] = feature_extractor(UpperCamelCase__ , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 ) )
# Test 2-D numpy arrays are batched.
lowerCamelCase__ : List[str] = [floats_list((1, x) )[0] for x in (800, 800, 800)]
lowerCamelCase__ : str = np.asarray(UpperCamelCase__ )
lowerCamelCase__ : Union[str, Any] = feature_extractor(UpperCamelCase__ , return_tensors="""np""" ).input_features
lowerCamelCase__ : List[str] = feature_extractor(UpperCamelCase__ , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 ) )
# Test truncation required
lowerCamelCase__ : Union[str, Any] = [floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
lowerCamelCase__ : int = [np.asarray(UpperCamelCase__ ) for speech_input in speech_inputs]
lowerCamelCase__ : Dict = [x[: feature_extractor.n_samples] for x in speech_inputs]
lowerCamelCase__ : Dict = [np.asarray(UpperCamelCase__ ) for speech_input in speech_inputs_truncated]
lowerCamelCase__ : str = feature_extractor(UpperCamelCase__ , return_tensors="""np""" ).input_features
lowerCamelCase__ : Union[str, Any] = feature_extractor(UpperCamelCase__ , return_tensors="""np""" ).input_features
for enc_seq_a, enc_seq_a in zip(UpperCamelCase__ , UpperCamelCase__ ):
self.assertTrue(np.allclose(UpperCamelCase__ , UpperCamelCase__ , atol=1e-3 ) )
def lowerCamelCase_ ( self: Optional[Any] ):
import torch
lowerCamelCase__ : int = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase__ : Dict = np.random.rand(100 , 32 ).astype(np.floataa )
lowerCamelCase__ : Optional[int] = np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
lowerCamelCase__ : Tuple = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""np""" )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
lowerCamelCase__ : int = feature_extractor.pad([{"""input_features""": inputs}] , return_tensors="""pt""" )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def lowerCamelCase_ ( self: Optional[int] , UpperCamelCase__: List[Any] ):
lowerCamelCase__ : List[Any] = load_dataset("""hf-internal-testing/librispeech_asr_dummy""" , """clean""" , split="""validation""" )
# automatic decoding with librispeech
lowerCamelCase__ : List[Any] = ds.sort("""id""" ).select(range(UpperCamelCase__ ) )[:num_samples]["""audio"""]
return [x["array"] for x in speech_samples]
def lowerCamelCase_ ( self: str ):
# fmt: off
lowerCamelCase__ : Any = torch.tensor(
[
0.1_193, -0.0_946, -0.1_098, -0.0_196, 0.0_225, -0.0_690, -0.1_736, 0.0_951,
0.0_971, -0.0_817, -0.0_702, 0.0_162, 0.0_260, 0.0_017, -0.0_192, -0.1_678,
0.0_709, -0.1_867, -0.0_655, -0.0_274, -0.0_234, -0.1_884, -0.0_516, -0.0_554,
-0.0_274, -0.1_425, -0.1_423, 0.0_837, 0.0_377, -0.0_854
] )
# fmt: on
lowerCamelCase__ : Optional[int] = self._load_datasamples(1 )
lowerCamelCase__ : Optional[Any] = WhisperFeatureExtractor()
lowerCamelCase__ : Dict = feature_extractor(UpperCamelCase__ , return_tensors="""pt""" ).input_features
self.assertEqual(input_features.shape , (1, 80, 3_000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , UpperCamelCase__ , atol=1e-4 ) )
def lowerCamelCase_ ( self: Union[str, Any] ):
lowerCamelCase__ : Dict = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
lowerCamelCase__ : Dict = self._load_datasamples(1 )[0]
lowerCamelCase__ : List[str] = ((audio - audio.min()) / (audio.max() - audio.min())) * 65_535 # Rescale to [0, 65535] to show issue
lowerCamelCase__ : Any = feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=UpperCamelCase__ )[0]
self.assertTrue(np.all(np.mean(UpperCamelCase__ ) < 1e-3 ) )
self.assertTrue(np.all(np.abs(np.var(UpperCamelCase__ ) - 1 ) < 1e-3 ) )
| 41 |
"""simple docstring"""
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> int:
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("""String lengths must match!""" )
lowerCAmelCase_ :Optional[int] = 0
for chara, chara in zip(lowercase__ , lowercase__ ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 0 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __A ) -> list[int]: # This function is recursive
_snake_case = len(__A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
_snake_case = array[0]
_snake_case = False
_snake_case = 1
_snake_case = []
while not is_found and i < array_length:
if array[i] < pivot:
_snake_case = True
_snake_case = [element for element in array[i:] if element >= array[i]]
_snake_case = longest_subsequence(__A )
if len(__A ) > len(__A ):
_snake_case = temp_array
else:
i += 1
_snake_case = [element for element in array[1:] if element >= pivot]
_snake_case = [pivot, *longest_subsequence(__A )]
if len(__A ) > len(__A ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 42 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A ) -> Optional[Any]:
super().__init__()
lowerCAmelCase_ :int = nn.ModuleList(__A )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A = None , __A = None , __A = None , __A = None , __A = False , __A = True , ) -> Union[ControlNetOutput, Tuple]:
for i, (image, scale, controlnet) in enumerate(zip(__A , __A , self.nets ) ):
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = controlnet(
__A , __A , __A , __A , __A , __A , __A , __A , __A , __A , __A , )
# merge samples
if i == 0:
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = down_samples, mid_sample
else:
lowerCAmelCase_ :str = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(__A , __A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def __lowerCAmelCase ( self , __A , __A = True , __A = None , __A = False , __A = None , ) -> Optional[Any]:
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Dict = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
__A , is_main_process=__A , save_function=__A , safe_serialization=__A , variant=__A , )
idx += 1
lowerCAmelCase_ :Any = model_path_to_save + f"""_{idx}"""
@classmethod
def __lowerCAmelCase ( cls , __A , **__A ) -> List[Any]:
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Dict = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
lowerCAmelCase_ :List[Any] = pretrained_model_path
while os.path.isdir(__A ):
lowerCAmelCase_ :Tuple = ControlNetModel.from_pretrained(__A , **__A )
controlnets.append(__A )
idx += 1
lowerCAmelCase_ :Dict = pretrained_model_path + f"""_{idx}"""
logger.info(f"""{len(__A )} controlnets loaded from {pretrained_model_path}.""" )
if len(__A ) == 0:
raise ValueError(
f"""No ControlNets found under {os.path.dirname(__A )}. Expected at least {pretrained_model_path + "_0"}.""" )
return cls(__A )
| 84 | 0 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_validate_point(SCREAMING_SNAKE_CASE )
_validate_point(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) != len(SCREAMING_SNAKE_CASE ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
return float(sum(abs(a - b ) for a, b in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) ) )
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if point:
if isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
for item in point:
if not isinstance(SCREAMING_SNAKE_CASE , (int, float) ):
__UpperCamelCase :Optional[int] = (
'''Expected a list of numbers as input, found '''
f"""{type(SCREAMING_SNAKE_CASE ).__name__}"""
)
raise TypeError(SCREAMING_SNAKE_CASE )
else:
__UpperCamelCase :List[str] = f"""Expected a list of numbers as input, found {type(SCREAMING_SNAKE_CASE ).__name__}"""
raise TypeError(SCREAMING_SNAKE_CASE )
else:
raise ValueError('''Missing an input''' )
def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
_validate_point(SCREAMING_SNAKE_CASE )
_validate_point(SCREAMING_SNAKE_CASE )
if len(SCREAMING_SNAKE_CASE ) != len(SCREAMING_SNAKE_CASE ):
raise ValueError('''Both points must be in the same n-dimensional space''' )
return float(sum(abs(x - y ) for x, y in zip(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 43 |
"""simple docstring"""
from PIL import Image
def _snake_case ( lowercase__ : Image , lowercase__ : float ) -> Image:
'''simple docstring'''
def brightness(lowercase__ : int ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(lowercase__ )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
__UpperCAmelCase = change_brightness(img, 1_00)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 84 | 0 |
"""simple docstring"""
_a : Tuple = [
999,
800,
799,
600,
599,
500,
400,
399,
377,
355,
333,
311,
288,
266,
244,
222,
200,
199,
177,
155,
133,
111,
88,
66,
44,
22,
0,
]
_a : List[Any] = [
999,
976,
952,
928,
905,
882,
858,
857,
810,
762,
715,
714,
572,
429,
428,
286,
285,
238,
190,
143,
142,
118,
95,
71,
47,
24,
0,
]
_a : Any = [
999,
988,
977,
966,
955,
944,
933,
922,
911,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
350,
300,
299,
266,
233,
200,
199,
179,
159,
140,
120,
100,
99,
88,
77,
66,
55,
44,
33,
22,
11,
0,
]
_a : Dict = [
999,
995,
992,
989,
985,
981,
978,
975,
971,
967,
964,
961,
957,
956,
951,
947,
942,
937,
933,
928,
923,
919,
914,
913,
908,
903,
897,
892,
887,
881,
876,
871,
870,
864,
858,
852,
846,
840,
834,
828,
827,
820,
813,
806,
799,
792,
785,
784,
777,
770,
763,
756,
749,
742,
741,
733,
724,
716,
707,
699,
698,
688,
677,
666,
656,
655,
645,
634,
623,
613,
612,
598,
584,
570,
569,
555,
541,
527,
526,
505,
484,
483,
462,
440,
439,
396,
395,
352,
351,
308,
307,
264,
263,
220,
219,
176,
132,
88,
44,
0,
]
_a : Optional[Any] = [
999,
997,
995,
992,
990,
988,
986,
984,
981,
979,
977,
975,
972,
970,
968,
966,
964,
961,
959,
957,
956,
954,
951,
949,
946,
944,
941,
939,
936,
934,
931,
929,
926,
924,
921,
919,
916,
914,
913,
910,
907,
905,
902,
899,
896,
893,
891,
888,
885,
882,
879,
877,
874,
871,
870,
867,
864,
861,
858,
855,
852,
849,
846,
843,
840,
837,
834,
831,
828,
827,
824,
821,
817,
814,
811,
808,
804,
801,
798,
795,
791,
788,
785,
784,
780,
777,
774,
770,
766,
763,
760,
756,
752,
749,
746,
742,
741,
737,
733,
730,
726,
722,
718,
714,
710,
707,
703,
699,
698,
694,
690,
685,
681,
677,
673,
669,
664,
660,
656,
655,
650,
646,
641,
636,
632,
627,
622,
618,
613,
612,
607,
602,
596,
591,
586,
580,
575,
570,
569,
563,
557,
551,
545,
539,
533,
527,
526,
519,
512,
505,
498,
491,
484,
483,
474,
466,
457,
449,
440,
439,
428,
418,
407,
396,
395,
381,
366,
352,
351,
330,
308,
307,
286,
264,
263,
242,
220,
219,
176,
175,
132,
131,
88,
44,
0,
]
_a : Optional[int] = [
999,
991,
982,
974,
966,
958,
950,
941,
933,
925,
916,
908,
900,
899,
874,
850,
825,
800,
799,
700,
600,
500,
400,
300,
200,
100,
0,
]
_a : Union[str, Any] = [
999,
992,
985,
978,
971,
964,
957,
949,
942,
935,
928,
921,
914,
907,
900,
899,
879,
859,
840,
820,
800,
799,
766,
733,
700,
699,
650,
600,
599,
500,
499,
400,
399,
300,
299,
200,
199,
100,
99,
0,
]
_a : int = [
999,
996,
992,
989,
985,
982,
979,
975,
972,
968,
965,
961,
958,
955,
951,
948,
944,
941,
938,
934,
931,
927,
924,
920,
917,
914,
910,
907,
903,
900,
899,
891,
884,
876,
869,
861,
853,
846,
838,
830,
823,
815,
808,
800,
799,
788,
777,
766,
755,
744,
733,
722,
711,
700,
699,
688,
677,
666,
655,
644,
633,
622,
611,
600,
599,
585,
571,
557,
542,
528,
514,
500,
499,
485,
471,
457,
442,
428,
414,
400,
399,
379,
359,
340,
320,
300,
299,
279,
259,
240,
220,
200,
199,
166,
133,
100,
99,
66,
33,
0,
]
| 44 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _SCREAMING_SNAKE_CASE :
def __lowerCAmelCase ( self ) -> Tuple:
torch.manual_seed(0 )
lowerCAmelCase_ :int = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :List[Any] = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCAmelCase_ :str = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__A , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase_ :int = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowerCAmelCase ( self ) -> List[str]:
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , class_embed_type="""timestep""" , mid_block_scale_factor=1.4_1_4 , time_embedding_act_fn="""gelu""" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCAmelCase_ :str = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__A , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[int] = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , )
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Dict = self.get_dummy_components()
lowerCAmelCase_ :Tuple = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Any = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Optional[int] = inputs["""prompt"""]
lowerCAmelCase_ :Optional[int] = inputs["""generator"""]
lowerCAmelCase_ :Any = inputs["""num_inference_steps"""]
lowerCAmelCase_ :Optional[int] = inputs["""output_type"""]
if "image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""image"""]
else:
lowerCAmelCase_ :int = None
if "mask_image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""mask_image"""]
else:
lowerCAmelCase_ :int = None
if "original_image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""original_image"""]
else:
lowerCAmelCase_ :List[Any] = None
lowerCAmelCase_ , lowerCAmelCase_ :int = pipe.encode_prompt(__A )
# inputs with prompt converted to embeddings
lowerCAmelCase_ :List[str] = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowerCAmelCase_ :int = image
if mask_image is not None:
lowerCAmelCase_ :Tuple = mask_image
if original_image is not None:
lowerCAmelCase_ :Optional[Any] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(__A , __A , __A )
lowerCAmelCase_ :Optional[int] = pipe(**__A )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__A )
lowerCAmelCase_ :Optional[int] = self.pipeline_class.from_pretrained(__A )
pipe_loaded.to(__A )
pipe_loaded.set_progress_bar_config(disable=__A )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__A , __A ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
lowerCAmelCase_ :Dict = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Union[str, Any] = inputs["""generator"""]
lowerCAmelCase_ :Any = inputs["""num_inference_steps"""]
lowerCAmelCase_ :Tuple = inputs["""output_type"""]
# inputs with prompt converted to embeddings
lowerCAmelCase_ :Tuple = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowerCAmelCase_ :Optional[int] = image
if mask_image is not None:
lowerCAmelCase_ :str = mask_image
if original_image is not None:
lowerCAmelCase_ :Tuple = original_image
lowerCAmelCase_ :Union[str, Any] = pipe_loaded(**__A )[0]
lowerCAmelCase_ :Dict = np.abs(to_np(__A ) - to_np(__A ) ).max()
self.assertLess(__A , 1E-4 )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Any = self.get_dummy_components()
lowerCAmelCase_ :Optional[int] = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[int] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Dict = pipe(**__A )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__A )
lowerCAmelCase_ :Any = self.pipeline_class.from_pretrained(__A )
pipe_loaded.to(__A )
pipe_loaded.set_progress_bar_config(disable=__A )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :str = pipe_loaded(**__A )[0]
lowerCAmelCase_ :Dict = np.abs(to_np(__A ) - to_np(__A ) ).max()
self.assertLess(__A , 1E-4 )
| 84 | 0 |
"""simple docstring"""
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
lowercase_ = datasets.utils.logging.get_logger(__name__)
@dataclass
class __lowerCAmelCase ( datasets.BuilderConfig ):
'''simple docstring'''
__UpperCAmelCase : Optional[datasets.Features] = None
__UpperCAmelCase : str = "utf-8"
__UpperCAmelCase : Optional[str] = None
__UpperCAmelCase : Optional[str] = None
__UpperCAmelCase : bool = True # deprecated
__UpperCAmelCase : Optional[int] = None # deprecated
__UpperCAmelCase : int = 1_0 << 2_0 # 10MB
__UpperCAmelCase : Optional[bool] = None
class __lowerCAmelCase ( datasets.ArrowBasedBuilder ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = JsonConfig
def __UpperCAmelCase ( self ):
if self.config.block_size is not None:
logger.warning('''The JSON loader parameter `block_size` is deprecated. Please use `chunksize` instead''' )
__a = self.config.block_size
if self.config.use_threads is not True:
logger.warning(
'''The JSON loader parameter `use_threads` is deprecated and doesn\'t have any effect anymore.''' )
if self.config.newlines_in_values is not None:
raise ValueError('''The JSON loader parameter `newlines_in_values` is no longer supported''' )
return datasets.DatasetInfo(features=self.config.features )
def __UpperCAmelCase ( self , _a ):
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}''' )
__a = dl_manager.download_and_extract(self.config.data_files )
if isinstance(_a , (str, list, tuple) ):
__a = data_files
if isinstance(_a , _a ):
__a = [files]
__a = [dl_manager.iter_files(_a ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={'''files''': files} )]
__a = []
for split_name, files in data_files.items():
if isinstance(_a , _a ):
__a = [files]
__a = [dl_manager.iter_files(_a ) for file in files]
splits.append(datasets.SplitGenerator(name=_a , gen_kwargs={'''files''': files} ) )
return splits
def __UpperCAmelCase ( self , _a ):
if self.config.features is not None:
# adding missing columns
for column_name in set(self.config.features ) - set(pa_table.column_names ):
__a = self.config.features.arrow_schema.field(_a ).type
__a = pa_table.append_column(_a , pa.array([None] * len(_a ) , type=_a ) )
# more expensive cast to support nested structures with keys in a different order
# allows str <-> int/float or str to Audio for example
__a = table_cast(_a , self.config.features.arrow_schema )
return pa_table
def __UpperCAmelCase ( self , _a ):
for file_idx, file in enumerate(itertools.chain.from_iterable(_a ) ):
# If the file is one json object and if we need to look at the list of items in one specific field
if self.config.field is not None:
with open(_a , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
__a = json.load(_a )
# We keep only the field we are interested in
__a = dataset[self.config.field]
# We accept two format: a list of dicts or a dict of lists
if isinstance(_a , (list, tuple) ):
__a = set().union(*[row.keys() for row in dataset] )
__a = {col: [row.get(_a ) for row in dataset] for col in keys}
else:
__a = dataset
__a = pa.Table.from_pydict(_a )
yield file_idx, self._cast_table(_a )
# If the file has one json object per line
else:
with open(_a , '''rb''' ) as f:
__a = 0
# Use block_size equal to the chunk size divided by 32 to leverage multithreading
# Set a default minimum value of 16kB if the chunk size is really small
__a = max(self.config.chunksize // 32 , 16 << 10 )
__a = (
self.config.encoding_errors if self.config.encoding_errors is not None else '''strict'''
)
while True:
__a = f.read(self.config.chunksize )
if not batch:
break
# Finish current line
try:
batch += f.readline()
except (AttributeError, io.UnsupportedOperation):
batch += readline(_a )
# PyArrow only accepts utf-8 encoded bytes
if self.config.encoding != "utf-8":
__a = batch.decode(self.config.encoding , errors=_a ).encode('''utf-8''' )
try:
while True:
try:
__a = paj.read_json(
io.BytesIO(_a ) , read_options=paj.ReadOptions(block_size=_a ) )
break
except (pa.ArrowInvalid, pa.ArrowNotImplementedError) as e:
if (
isinstance(_a , pa.ArrowInvalid )
and "straddling" not in str(_a )
or block_size > len(_a )
):
raise
else:
# Increase the block size in case it was too small.
# The block size will be reset for the next file.
logger.debug(
f'''Batch of {len(_a )} bytes couldn\'t be parsed with block_size={block_size}. Retrying with block_size={block_size * 2}.''' )
block_size *= 2
except pa.ArrowInvalid as e:
try:
with open(
_a , encoding=self.config.encoding , errors=self.config.encoding_errors ) as f:
__a = json.load(_a )
except json.JSONDecodeError:
logger.error(f'''Failed to read file \'{file}\' with error {type(_a )}: {e}''' )
raise e
# If possible, parse the file as a list of json objects and exit the loop
if isinstance(_a , _a ): # list is the only sequence type supported in JSON
try:
__a = set().union(*[row.keys() for row in dataset] )
__a = {col: [row.get(_a ) for row in dataset] for col in keys}
__a = pa.Table.from_pydict(_a )
except (pa.ArrowInvalid, AttributeError) as e:
logger.error(f'''Failed to read file \'{file}\' with error {type(_a )}: {e}''' )
raise ValueError(f'''Not able to read records in the JSON file at {file}.''' ) from None
yield file_idx, self._cast_table(_a )
break
else:
logger.error(f'''Failed to read file \'{file}\' with error {type(_a )}: {e}''' )
raise ValueError(
f'''Not able to read records in the JSON file at {file}. '''
f'''You should probably indicate the field of the JSON file containing your records. '''
f'''This JSON file contain the following fields: {str(list(dataset.keys() ) )}. '''
f'''Select the correct one and provide it as `field=\'XXX\'` to the dataset loading method. ''' ) from None
# 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(_a )
batch_idx += 1
| 45 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2""" , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :int = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :List[Any] = jax.device_count()
lowerCAmelCase_ :Optional[Any] = num_samples * [prompt]
lowerCAmelCase_ :int = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Optional[Any] = replicate(__A )
lowerCAmelCase_ :Union[str, Any] = shard(__A )
lowerCAmelCase_ :Optional[Any] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :Tuple = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Union[str, Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Optional[int] = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Union[str, Any] = """stabilityai/stable-diffusion-2"""
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = FlaxDPMSolverMultistepScheduler.from_pretrained(__A , subfolder="""scheduler""" )
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = FlaxStableDiffusionPipeline.from_pretrained(
__A , scheduler=__A , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :Optional[int] = scheduler_params
lowerCAmelCase_ :List[Any] = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :Tuple = jax.device_count()
lowerCAmelCase_ :str = num_samples * [prompt]
lowerCAmelCase_ :Union[str, Any] = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Tuple = replicate(__A )
lowerCAmelCase_ :Optional[int] = shard(__A )
lowerCAmelCase_ :List[str] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :List[Any] = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Optional[Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Dict = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 84 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import YolosForObjectDetection, YolosModel
from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowercase :
def __init__( self , lowercase , lowercase=13 , lowercase=[30, 30] , lowercase=2 , lowercase=3 , lowercase=True , lowercase=True , lowercase=32 , lowercase=5 , lowercase=4 , lowercase=37 , lowercase="gelu" , lowercase=0.1 , lowercase=0.1 , lowercase=10 , lowercase=0.02 , lowercase=3 , lowercase=None , lowercase=8 , lowercase=10 , ) -> Dict:
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = image_size
lowerCAmelCase = patch_size
lowerCAmelCase = num_channels
lowerCAmelCase = is_training
lowerCAmelCase = use_labels
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = type_sequence_label_size
lowerCAmelCase = initializer_range
lowerCAmelCase = num_labels
lowerCAmelCase = scope
lowerCAmelCase = n_targets
lowerCAmelCase = num_detection_tokens
# we set the expected sequence length (which is used in several tests)
# expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
lowerCAmelCase = (image_size[1] // patch_size) * (image_size[0] // patch_size)
lowerCAmelCase = num_patches + 1 + self.num_detection_tokens
def _snake_case ( self ) -> Optional[int]:
lowerCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]] )
lowerCAmelCase = None
if self.use_labels:
# labels is a list of Dict (each Dict being the labels for a given example in the batch)
lowerCAmelCase = []
for i in range(self.batch_size ):
lowerCAmelCase = {}
lowerCAmelCase = torch.randint(
high=self.num_labels , size=(self.n_targets,) , device=lowercase )
lowerCAmelCase = torch.rand(self.n_targets , 4 , device=lowercase )
labels.append(lowercase )
lowerCAmelCase = self.get_config()
return config, pixel_values, labels
def _snake_case ( self ) -> List[Any]:
return YolosConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=lowercase , initializer_range=self.initializer_range , num_detection_tokens=self.num_detection_tokens , num_labels=self.num_labels , )
def _snake_case ( self , lowercase , lowercase , lowercase ) -> Any:
lowerCAmelCase = YolosModel(config=lowercase )
model.to(lowercase )
model.eval()
lowerCAmelCase = model(lowercase )
self.parent.assertEqual(
result.last_hidden_state.shape , (self.batch_size, self.expected_seq_len, self.hidden_size) )
def _snake_case ( self , lowercase , lowercase , lowercase ) -> Tuple:
lowerCAmelCase = YolosForObjectDetection(lowercase )
model.to(lowercase )
model.eval()
lowerCAmelCase = model(pixel_values=lowercase )
lowerCAmelCase = model(lowercase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
lowerCAmelCase = model(pixel_values=lowercase , labels=lowercase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_detection_tokens, self.num_labels + 1) )
self.parent.assertEqual(result.pred_boxes.shape , (self.batch_size, self.num_detection_tokens, 4) )
def _snake_case ( self ) -> List[str]:
lowerCAmelCase = self.prepare_config_and_inputs()
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase = config_and_inputs
lowerCAmelCase = {"""pixel_values""": pixel_values}
return config, inputs_dict
@require_torch
class lowercase ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
_SCREAMING_SNAKE_CASE = (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
_SCREAMING_SNAKE_CASE = (
{'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
)
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
_SCREAMING_SNAKE_CASE = False
def _snake_case ( self , lowercase , lowercase , lowercase=False ) -> Union[str, Any]:
lowerCAmelCase = super()._prepare_for_class(lowercase , lowercase , return_labels=lowercase )
if return_labels:
if model_class.__name__ == "YolosForObjectDetection":
lowerCAmelCase = []
for i in range(self.model_tester.batch_size ):
lowerCAmelCase = {}
lowerCAmelCase = torch.ones(
size=(self.model_tester.n_targets,) , device=lowercase , dtype=torch.long )
lowerCAmelCase = torch.ones(
self.model_tester.n_targets , 4 , device=lowercase , dtype=torch.float )
labels.append(lowercase )
lowerCAmelCase = labels
return inputs_dict
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = YolosModelTester(self )
lowerCAmelCase = ConfigTester(self , config_class=lowercase , has_text_modality=lowercase , hidden_size=37 )
def _snake_case ( self ) -> str:
self.config_tester.run_common_tests()
def _snake_case ( self ) -> Optional[Any]:
# YOLOS does not use inputs_embeds
pass
def _snake_case ( self ) -> Optional[int]:
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(lowercase )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowerCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase , nn.Linear ) )
def _snake_case ( self ) -> List[Any]:
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(lowercase )
lowerCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowerCAmelCase = [*signature.parameters.keys()]
lowerCAmelCase = ["""pixel_values"""]
self.assertListEqual(arg_names[:1] , lowercase )
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase )
def _snake_case ( self ) -> Any:
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
lowerCAmelCase = True
# in YOLOS, the seq_len is different
lowerCAmelCase = self.model_tester.expected_seq_len
for model_class in self.all_model_classes:
lowerCAmelCase = True
lowerCAmelCase = False
lowerCAmelCase = True
lowerCAmelCase = model_class(lowercase )
model.to(lowercase )
model.eval()
with torch.no_grad():
lowerCAmelCase = model(**self._prepare_for_class(lowercase , lowercase ) )
lowerCAmelCase = outputs.attentions
self.assertEqual(len(lowercase ) , self.model_tester.num_hidden_layers )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
lowerCAmelCase = True
lowerCAmelCase = model_class(lowercase )
model.to(lowercase )
model.eval()
with torch.no_grad():
lowerCAmelCase = model(**self._prepare_for_class(lowercase , lowercase ) )
lowerCAmelCase = outputs.attentions
self.assertEqual(len(lowercase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
lowerCAmelCase = len(lowercase )
# Check attention is always last and order is fine
lowerCAmelCase = True
lowerCAmelCase = True
lowerCAmelCase = model_class(lowercase )
model.to(lowercase )
model.eval()
with torch.no_grad():
lowerCAmelCase = model(**self._prepare_for_class(lowercase , lowercase ) )
lowerCAmelCase = 1
self.assertEqual(out_len + added_hidden_states , len(lowercase ) )
lowerCAmelCase = outputs.attentions
self.assertEqual(len(lowercase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads, seq_len, seq_len] , )
def _snake_case ( self ) -> Optional[Any]:
def check_hidden_states_output(lowercase , lowercase , lowercase ):
lowerCAmelCase = model_class(lowercase )
model.to(lowercase )
model.eval()
with torch.no_grad():
lowerCAmelCase = model(**self._prepare_for_class(lowercase , lowercase ) )
lowerCAmelCase = outputs.hidden_states
lowerCAmelCase = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(lowercase ) , lowercase )
# YOLOS has a different seq_length
lowerCAmelCase = self.model_tester.expected_seq_len
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) , [seq_length, self.model_tester.hidden_size] , )
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowerCAmelCase = True
check_hidden_states_output(lowercase , lowercase , lowercase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
lowerCAmelCase = True
check_hidden_states_output(lowercase , lowercase , lowercase )
def _snake_case ( self ) -> Union[str, Any]:
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_object_detection(*lowercase )
@slow
def _snake_case ( self ) -> Dict:
for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowerCAmelCase = YolosModel.from_pretrained(lowercase )
self.assertIsNotNone(lowercase )
def UpperCAmelCase__ ( ):
'''simple docstring'''
lowerCAmelCase = Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
return image
@require_torch
@require_vision
class lowercase ( unittest.TestCase ):
@cached_property
def _snake_case ( self ) -> List[str]:
return AutoImageProcessor.from_pretrained("""hustvl/yolos-small""" ) if is_vision_available() else None
@slow
def _snake_case ( self ) -> List[str]:
lowerCAmelCase = YolosForObjectDetection.from_pretrained("""hustvl/yolos-small""" ).to(lowercase )
lowerCAmelCase = self.default_image_processor
lowerCAmelCase = prepare_img()
lowerCAmelCase = image_processor(images=lowercase , return_tensors="""pt""" ).to(lowercase )
# forward pass
with torch.no_grad():
lowerCAmelCase = model(inputs.pixel_values )
# verify outputs
lowerCAmelCase = torch.Size((1, 100, 92) )
self.assertEqual(outputs.logits.shape , lowercase )
lowerCAmelCase = torch.tensor(
[[-24.0_248, -10.3_024, -14.8_290], [-42.0_392, -16.8_200, -27.4_334], [-27.2_743, -11.8_154, -18.7_148]] , device=lowercase , )
lowerCAmelCase = torch.tensor(
[[0.2_559, 0.5_455, 0.4_706], [0.2_989, 0.7_279, 0.1_875], [0.7_732, 0.4_017, 0.4_462]] , device=lowercase )
self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] , lowercase , atol=1e-4 ) )
self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3] , lowercase , atol=1e-4 ) )
# verify postprocessing
lowerCAmelCase = image_processor.post_process_object_detection(
lowercase , threshold=0.3 , target_sizes=[image.size[::-1]] )[0]
lowerCAmelCase = torch.tensor([0.9_994, 0.9_790, 0.9_964, 0.9_972, 0.9_861] ).to(lowercase )
lowerCAmelCase = [75, 75, 17, 63, 17]
lowerCAmelCase = torch.tensor([335.0_609, 79.3_848, 375.4_216, 187.2_495] ).to(lowercase )
self.assertEqual(len(results["""scores"""] ) , 5 )
self.assertTrue(torch.allclose(results["""scores"""] , lowercase , atol=1e-4 ) )
self.assertSequenceEqual(results["""labels"""].tolist() , lowercase )
self.assertTrue(torch.allclose(results["""boxes"""][0, :] , lowercase ) )
| 46 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _snake_case ( ) -> Generator[int, None, None]:
'''simple docstring'''
lowerCAmelCase_ :dict[int, int] = {}
lowerCAmelCase_ :int = 2
while True:
lowerCAmelCase_ :List[Any] = factor_map.pop(lowercase__ , lowercase__ )
if factor:
lowerCAmelCase_ :Optional[int] = factor + prime
while x in factor_map:
x += factor
lowerCAmelCase_ :List[str] = factor
else:
lowerCAmelCase_ :Optional[int] = prime
yield prime
prime += 1
def _snake_case ( lowercase__ : float = 1E10 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = sieve()
lowerCAmelCase_ :str = 1
while True:
lowerCAmelCase_ :int = next(lowercase__ )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(lowercase__ )
n += 2
if __name__ == "__main__":
print(solution())
| 84 | 0 |
'''simple docstring'''
lowerCamelCase : Optional[Any] = 2_5_6
# Modulus to hash a string
lowerCamelCase : int = 1_0_0_0_0_0_3
def _lowerCAmelCase ( _UpperCamelCase : str , _UpperCamelCase : str ) -> bool:
"""simple docstring"""
_SCREAMING_SNAKE_CASE =len(_UpperCamelCase )
_SCREAMING_SNAKE_CASE =len(_UpperCamelCase )
if p_len > t_len:
return False
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =0
_SCREAMING_SNAKE_CASE =1
# Calculating the hash of pattern and substring of text
for i in range(_UpperCamelCase ):
_SCREAMING_SNAKE_CASE =(ord(pattern[i] ) + p_hash * alphabet_size) % modulus
_SCREAMING_SNAKE_CASE =(ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
_SCREAMING_SNAKE_CASE =(modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
_SCREAMING_SNAKE_CASE =(
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def _lowerCAmelCase ( ) -> None:
"""simple docstring"""
_SCREAMING_SNAKE_CASE ='abc1abc12'
_SCREAMING_SNAKE_CASE ='alskfjaldsabc1abc1abc12k23adsfabcabc'
_SCREAMING_SNAKE_CASE ='alskfjaldsk23adsfabcabc'
assert rabin_karp(_UpperCamelCase , _UpperCamelCase ) and not rabin_karp(_UpperCamelCase , _UpperCamelCase )
# Test 2)
_SCREAMING_SNAKE_CASE ='ABABX'
_SCREAMING_SNAKE_CASE ='ABABZABABYABABX'
assert rabin_karp(_UpperCamelCase , _UpperCamelCase )
# Test 3)
_SCREAMING_SNAKE_CASE ='AAAB'
_SCREAMING_SNAKE_CASE ='ABAAAAAB'
assert rabin_karp(_UpperCamelCase , _UpperCamelCase )
# Test 4)
_SCREAMING_SNAKE_CASE ='abcdabcy'
_SCREAMING_SNAKE_CASE ='abcxabcdabxabcdabcdabcy'
assert rabin_karp(_UpperCamelCase , _UpperCamelCase )
# Test 5)
_SCREAMING_SNAKE_CASE ='Lü'
_SCREAMING_SNAKE_CASE ='Lüsai'
assert rabin_karp(_UpperCamelCase , _UpperCamelCase )
_SCREAMING_SNAKE_CASE ='Lue'
assert not rabin_karp(_UpperCamelCase , _UpperCamelCase )
print('Success.' )
if __name__ == "__main__":
test_rabin_karp()
| 47 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
# TODO: is there an appropriate internal test set?
UpperCAmelCase_ :List[Any] = "ssube/stable-diffusion-x4-upscaler-onnx"
def __lowerCAmelCase ( self , __A=0 ) -> Optional[int]:
lowerCAmelCase_ :Optional[Any] = floats_tensor((1, 3, 128, 128) , rng=random.Random(__A ) )
lowerCAmelCase_ :List[Any] = torch.manual_seed(__A )
lowerCAmelCase_ :Tuple = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Dict = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :int = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Tuple = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :int = self.get_dummy_inputs()
lowerCAmelCase_ :List[str] = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :str = np.array(
[0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Optional[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Union[str, Any] = pipe(**__A ).images
lowerCAmelCase_ :Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Tuple = np.array(
[0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Union[str, Any] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Optional[Any] = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Tuple = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :List[str] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Optional[int] = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Dict = pipe(**__A ).images
lowerCAmelCase_ :Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Dict = np.array(
[0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@property
def __lowerCAmelCase ( self ) -> List[Any]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Optional[int] = ort.SessionOptions()
lowerCAmelCase_ :Dict = False
return options
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase_ :Optional[Any] = init_image.resize((128, 128) )
# using the PNDM scheduler by default
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = """A fantasy landscape, trending on artstation"""
lowerCAmelCase_ :List[Any] = torch.manual_seed(0 )
lowerCAmelCase_ :str = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=10 , generator=__A , output_type="""np""" , )
lowerCAmelCase_ :Dict = output.images
lowerCAmelCase_ :List[str] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Optional[Any] = np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase_ :List[str] = init_image.resize((128, 128) )
lowerCAmelCase_ :Any = LMSDiscreteScheduler.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , subfolder="""scheduler""" )
lowerCAmelCase_ :Optional[Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , scheduler=__A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Any = """A fantasy landscape, trending on artstation"""
lowerCAmelCase_ :Optional[Any] = torch.manual_seed(0 )
lowerCAmelCase_ :List[str] = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=20 , generator=__A , output_type="""np""" , )
lowerCAmelCase_ :int = output.images
lowerCAmelCase_ :List[Any] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Union[str, Any] = np.array(
[0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
| 84 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
SCREAMING_SNAKE_CASE__ : Optional[int] = get_tests_dir('fixtures/test_sentencepiece.model')
@require_sentencepiece
@require_tokenizers
class UpperCamelCase__ (lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
lowerCamelCase_ : str = XLMRobertaTokenizer
lowerCamelCase_ : str = XLMRobertaTokenizerFast
lowerCamelCase_ : int = True
lowerCamelCase_ : List[Any] = True
def _lowercase ( self ) -> Optional[int]:
super().setUp()
# We have a SentencePiece fixture for testing
lowerCamelCase : Any = XLMRobertaTokenizer(UpperCamelCase__ , keep_accents=UpperCamelCase__ )
tokenizer.save_pretrained(self.tmpdirname )
def _lowercase ( self ) -> List[str]:
lowerCamelCase : Optional[int] = "<pad>"
lowerCamelCase : Union[str, Any] = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__ ) , UpperCamelCase__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__ ) , UpperCamelCase__ )
def _lowercase ( self ) -> Union[str, Any]:
lowerCamelCase : str = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , "<s>" )
self.assertEqual(vocab_keys[1] , "<pad>" )
self.assertEqual(vocab_keys[-1] , "<mask>" )
self.assertEqual(len(UpperCamelCase__ ) , 1002 )
def _lowercase ( self ) -> Dict:
self.assertEqual(self.get_tokenizer().vocab_size , 1002 )
def _lowercase ( self ) -> Any:
lowerCamelCase : List[str] = XLMRobertaTokenizer(UpperCamelCase__ , keep_accents=UpperCamelCase__ )
lowerCamelCase : str = tokenizer.tokenize("This is a test" )
self.assertListEqual(UpperCamelCase__ , ["▁This", "▁is", "▁a", "▁t", "est"] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(UpperCamelCase__ ) , [value + tokenizer.fairseq_offset for value in [285, 46, 10, 170, 382]] , )
lowerCamelCase : int = tokenizer.tokenize("I was born in 92000, and this is falsé." )
self.assertListEqual(
UpperCamelCase__ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"9",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"é",
".",
] , )
lowerCamelCase : Optional[int] = tokenizer.convert_tokens_to_ids(UpperCamelCase__ )
self.assertListEqual(
UpperCamelCase__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
lowerCamelCase : Any = tokenizer.convert_ids_to_tokens(UpperCamelCase__ )
self.assertListEqual(
UpperCamelCase__ , [
SPIECE_UNDERLINE + "I",
SPIECE_UNDERLINE + "was",
SPIECE_UNDERLINE + "b",
"or",
"n",
SPIECE_UNDERLINE + "in",
SPIECE_UNDERLINE + "",
"<unk>",
"2",
"0",
"0",
"0",
",",
SPIECE_UNDERLINE + "and",
SPIECE_UNDERLINE + "this",
SPIECE_UNDERLINE + "is",
SPIECE_UNDERLINE + "f",
"al",
"s",
"<unk>",
".",
] , )
def _lowercase ( self ) -> Any:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
lowerCamelCase : Dict = (self.rust_tokenizer_class, "hf-internal-testing/tiny-xlm-roberta", {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})''' ):
lowerCamelCase : str = self.rust_tokenizer_class.from_pretrained(UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase : Any = self.tokenizer_class.from_pretrained(UpperCamelCase__ , **UpperCamelCase__ )
lowerCamelCase : Optional[Any] = tempfile.mkdtemp()
lowerCamelCase : Dict = tokenizer_r.save_pretrained(UpperCamelCase__ )
lowerCamelCase : Tuple = tokenizer_p.save_pretrained(UpperCamelCase__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
lowerCamelCase : Any = tuple(f for f in tokenizer_r_files if "tokenizer.json" not in f )
self.assertSequenceEqual(UpperCamelCase__ , UpperCamelCase__ )
# Checks everything loads correctly in the same way
lowerCamelCase : int = tokenizer_r.from_pretrained(UpperCamelCase__ )
lowerCamelCase : Any = tokenizer_p.from_pretrained(UpperCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase__ , UpperCamelCase__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(UpperCamelCase__ )
# Save tokenizer rust, legacy_format=True
lowerCamelCase : str = tempfile.mkdtemp()
lowerCamelCase : int = tokenizer_r.save_pretrained(UpperCamelCase__ , legacy_format=UpperCamelCase__ )
lowerCamelCase : List[str] = tokenizer_p.save_pretrained(UpperCamelCase__ )
# Checks it save with the same files
self.assertSequenceEqual(UpperCamelCase__ , UpperCamelCase__ )
# Checks everything loads correctly in the same way
lowerCamelCase : Optional[int] = tokenizer_r.from_pretrained(UpperCamelCase__ )
lowerCamelCase : Any = tokenizer_p.from_pretrained(UpperCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase__ , UpperCamelCase__ ) )
shutil.rmtree(UpperCamelCase__ )
# Save tokenizer rust, legacy_format=False
lowerCamelCase : List[str] = tempfile.mkdtemp()
lowerCamelCase : Any = tokenizer_r.save_pretrained(UpperCamelCase__ , legacy_format=UpperCamelCase__ )
lowerCamelCase : int = tokenizer_p.save_pretrained(UpperCamelCase__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any("tokenizer.json" in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
lowerCamelCase : List[str] = tokenizer_r.from_pretrained(UpperCamelCase__ )
lowerCamelCase : Optional[Any] = tokenizer_p.from_pretrained(UpperCamelCase__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(UpperCamelCase__ , UpperCamelCase__ ) )
shutil.rmtree(UpperCamelCase__ )
@cached_property
def _lowercase ( self ) -> List[str]:
return XLMRobertaTokenizer.from_pretrained("xlm-roberta-base" )
def _lowercase ( self ) -> List[Any]:
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(UpperCamelCase__ , f.name )
lowerCamelCase : Optional[Any] = XLMRobertaTokenizer(f.name , keep_accents=UpperCamelCase__ )
lowerCamelCase : Optional[Any] = pickle.dumps(UpperCamelCase__ )
pickle.loads(UpperCamelCase__ )
def _lowercase ( self ) -> int:
if not self.test_rust_tokenizer:
return
lowerCamelCase : Dict = self.get_tokenizer()
lowerCamelCase : Optional[Any] = self.get_rust_tokenizer()
lowerCamelCase : int = "I was born in 92000, and this is falsé."
lowerCamelCase : List[str] = tokenizer.tokenize(UpperCamelCase__ )
lowerCamelCase : List[Any] = rust_tokenizer.tokenize(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : str = tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ )
lowerCamelCase : Union[str, Any] = rust_tokenizer.encode(UpperCamelCase__ , add_special_tokens=UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
lowerCamelCase : Optional[Any] = self.get_rust_tokenizer()
lowerCamelCase : List[str] = tokenizer.encode(UpperCamelCase__ )
lowerCamelCase : int = rust_tokenizer.encode(UpperCamelCase__ )
self.assertListEqual(UpperCamelCase__ , UpperCamelCase__ )
@slow
def _lowercase ( self ) -> Dict:
lowerCamelCase : str = "Hello World!"
lowerCamelCase : Optional[Any] = [0, 3_5378, 6661, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(UpperCamelCase__ , self.big_tokenizer.encode(UpperCamelCase__ ) )
@slow
def _lowercase ( self ) -> int:
lowerCamelCase : List[str] = (
"This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
" add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth"
)
lowerCamelCase : Union[str, Any] = [
0,
3293,
83,
10,
4552,
4989,
7986,
678,
10,
5915,
111,
17_9459,
12_4850,
4,
6044,
237,
12,
6,
5,
6,
4,
6780,
705,
15,
1388,
44,
378,
1_0114,
711,
152,
20,
6,
5,
2_2376,
642,
1221,
1_5190,
3_4153,
450,
5608,
959,
1119,
5_7702,
136,
186,
47,
1098,
2_9367,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
6044,
237,
6284,
5_0901,
528,
31,
90,
34,
927,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(UpperCamelCase__ , self.big_tokenizer.encode(UpperCamelCase__ ) )
@slow
def _lowercase ( self ) -> Any:
# fmt: off
lowerCamelCase : int = {"input_ids": [[0, 1_1062, 8_2772, 7, 15, 8_2772, 538, 5_1529, 237, 1_7198, 1290, 206, 9, 21_5175, 1314, 136, 1_7198, 1290, 206, 9, 5_6359, 42, 12_2009, 9, 1_6466, 16, 8_7344, 4537, 9, 4717, 7_8381, 6, 15_9958, 7, 15, 2_4480, 618, 4, 527, 2_2693, 5428, 4, 2777, 2_4480, 9874, 4, 4_3523, 594, 4, 803, 1_8392, 3_3189, 18, 4, 4_3523, 2_4447, 1_2399, 100, 2_4955, 8_3658, 9626, 14_4057, 15, 839, 2_2335, 16, 136, 2_4955, 8_3658, 8_3479, 15, 3_9102, 724, 16, 678, 645, 2789, 1328, 4589, 42, 12_2009, 11_5774, 23, 805, 1328, 4_6876, 7, 136, 5_3894, 1940, 4_2227, 4_1159, 1_7721, 823, 425, 4, 2_7512, 9_8722, 206, 136, 5531, 4970, 919, 1_7336, 5, 2], [0, 2_0080, 618, 83, 8_2775, 47, 479, 9, 1517, 73, 5_3894, 333, 8_0581, 11_0117, 1_8811, 5256, 1295, 51, 15_2526, 297, 7986, 390, 12_4416, 538, 3_5431, 214, 98, 1_5044, 2_5737, 136, 7108, 4_3701, 23, 756, 13_5355, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 581, 6_3773, 11_9455, 6, 14_7797, 8_8203, 7, 645, 70, 21, 3285, 1_0269, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCamelCase__ , model_name="xlm-roberta-base" , revision="d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3" , )
| 48 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
] )
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Dict:
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=__A , )
assert hasattr(self , """env""" )
def __lowerCAmelCase ( self , __A ) -> Any:
# configuration for running training on smdistributed Model Parallel
lowerCAmelCase_ :Union[str, Any] = {
"""enabled""": True,
"""processes_per_host""": 8,
}
lowerCAmelCase_ :Tuple = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
lowerCAmelCase_ :Any = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
lowerCAmelCase_ :Any = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=__A , instance_type=self.instance_type , debugger_hook_config=__A , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=__A , py_version="""py36""" , )
def __lowerCAmelCase ( self , __A ) -> List[Any]:
TrainingJobAnalytics(__A ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def __lowerCAmelCase ( self , __A ) -> List[str]:
# create estimator
lowerCAmelCase_ :Any = self.create_estimator(__A )
# run training
estimator.fit()
# result dataframe
lowerCAmelCase_ :Optional[Any] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase_ :List[str] = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
lowerCAmelCase_ :Optional[int] = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase_ :Optional[int] = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , __A )
| 84 | 0 |
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
__snake_case :int = logging.get_logger(__name__)
__snake_case :Optional[Any] = {
'''google/mobilenet_v1_1.0_224''': '''https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v1_0.75_192''': '''https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json''',
# See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : List[str] = '''mobilenet_v1'''
def __init__( self : int , __SCREAMING_SNAKE_CASE : List[Any]=3 , __SCREAMING_SNAKE_CASE : Tuple=224 , __SCREAMING_SNAKE_CASE : Any=1.0 , __SCREAMING_SNAKE_CASE : List[Any]=8 , __SCREAMING_SNAKE_CASE : Dict="relu6" , __SCREAMING_SNAKE_CASE : Optional[int]=True , __SCREAMING_SNAKE_CASE : Any=0.9_99 , __SCREAMING_SNAKE_CASE : Optional[int]=0.02 , __SCREAMING_SNAKE_CASE : Dict=0.0_01 , **__SCREAMING_SNAKE_CASE : str , ):
'''simple docstring'''
super().__init__(**__SCREAMING_SNAKE_CASE)
if depth_multiplier <= 0:
raise ValueError('''depth_multiplier must be greater than zero.''')
__a = num_channels
__a = image_size
__a = depth_multiplier
__a = min_depth
__a = hidden_act
__a = tf_padding
__a = classifier_dropout_prob
__a = initializer_range
__a = layer_norm_eps
class _A ( __UpperCAmelCase ):
UpperCamelCase__ : List[str] = version.parse('''1.11''' )
@property
def _lowerCamelCase ( self : Union[str, Any]):
'''simple docstring'''
return OrderedDict([('''pixel_values''', {0: '''batch'''})])
@property
def _lowerCamelCase ( self : List[str]):
'''simple docstring'''
if self.task == "image-classification":
return OrderedDict([('''logits''', {0: '''batch'''})])
else:
return OrderedDict([('''last_hidden_state''', {0: '''batch'''}), ('''pooler_output''', {0: '''batch'''})])
@property
def _lowerCamelCase ( self : Dict):
'''simple docstring'''
return 1E-4
| 49 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 1_0 ) -> str:
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or n < 0:
raise ValueError("""Invalid input""" )
lowerCAmelCase_ :List[str] = 1_0**n
lowerCAmelCase_ :int = 2_8_4_3_3 * (pow(2 , 7_8_3_0_4_5_7 , lowercase__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(10) = }""")
| 84 | 0 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase , _UpperCAmelCase = None ) -> list[list[str]]:
lowerCamelCase__ : int = word_bank or []
# create a table
lowerCamelCase__ : int = len(_UpperCAmelCase ) + 1
lowerCamelCase__ : list[list[list[str]]] = []
for _ in range(_UpperCAmelCase ):
table.append([] )
# seed value
lowerCamelCase__ : str = [[]] # because empty string has empty combination
# iterate through the indices
for i in range(_UpperCAmelCase ):
# condition
if table[i] != []:
for word in word_bank:
# slice condition
if target[i : i + len(_UpperCAmelCase )] == word:
lowerCamelCase__ : list[list[str]] = [
[word, *way] for way in table[i]
]
# adds the word to every combination the current position holds
# now,push that combination to the table[i+len(word)]
table[i + len(_UpperCAmelCase )] += new_combinations
# combinations are in reverse order so reverse for better output
for combination in table[len(_UpperCAmelCase )]:
combination.reverse()
return table[len(_UpperCAmelCase )]
if __name__ == "__main__":
print(all_construct("""jwajalapa""", ["""jwa""", """j""", """w""", """a""", """la""", """lapa"""]))
print(all_construct("""rajamati""", ["""s""", """raj""", """amat""", """raja""", """ma""", """i""", """t"""]))
print(
all_construct(
"""hexagonosaurus""",
["""h""", """ex""", """hex""", """ag""", """ago""", """ru""", """auru""", """rus""", """go""", """no""", """o""", """s"""],
)
)
| 50 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__UpperCAmelCase = 'src/transformers'
__UpperCAmelCase = 'docs/source/en/tasks'
def _snake_case ( lowercase__ : str , lowercase__ : List[str] , lowercase__ : Any ) -> str:
'''simple docstring'''
with open(lowercase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCAmelCase_ :List[Any] = f.readlines()
# Find the start prompt.
lowerCAmelCase_ :Tuple = 0
while not lines[start_index].startswith(lowercase__ ):
start_index += 1
start_index += 1
lowerCAmelCase_ :Dict = start_index
while not lines[end_index].startswith(lowercase__ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
__UpperCAmelCase = direct_transformers_import(TRANSFORMERS_PATH)
__UpperCAmelCase = {
'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__UpperCAmelCase = {
'summarization.md': ('nllb',),
'translation.md': ('nllb',),
}
def _snake_case ( lowercase__ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = TASK_GUIDE_TO_MODELS[task_guide]
lowerCAmelCase_ :List[Any] = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowercase__ , set() )
lowerCAmelCase_ :Union[str, Any] = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([f"""[{name}](../model_doc/{code})""" for code, name in model_names.items()] ) + "\n"
def _snake_case ( lowercase__ : int , lowercase__ : str=False ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = _find_text_in_file(
filename=os.path.join(lowercase__ , lowercase__ ) , start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->""" , end_prompt="""<!--End of the generated tip-->""" , )
lowerCAmelCase_ :int = get_model_list_for_task(lowercase__ )
if current_list != new_list:
if overwrite:
with open(os.path.join(lowercase__ , lowercase__ ) , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
f"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
""" to fix this.""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__UpperCAmelCase = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 84 | 0 |
class __snake_case :
def __init__( self : Optional[int] , _snake_case : str = "" , _snake_case : bool = False):
"""simple docstring"""
UpperCAmelCase_ = {}
# A node will be a leaf if the tree contains its word
UpperCAmelCase_ = is_leaf
UpperCAmelCase_ = prefix
def lowerCamelCase ( self : Union[str, Any] , _snake_case : str):
"""simple docstring"""
UpperCAmelCase_ = 0
for q, w in zip(self.prefix , _snake_case):
if q != w:
break
x += 1
return self.prefix[:x], self.prefix[x:], word[x:]
def lowerCamelCase ( self : Optional[Any] , _snake_case : list[str]):
"""simple docstring"""
for word in words:
self.insert(_snake_case)
def lowerCamelCase ( self : List[Any] , _snake_case : str):
"""simple docstring"""
if self.prefix == word:
UpperCAmelCase_ = True
# Case 2: The node has no edges that have a prefix to the word
# Solution: We create an edge from the current node to a new one
# containing the word
elif word[0] not in self.nodes:
UpperCAmelCase_ = RadixNode(prefix=_snake_case , is_leaf=_snake_case)
else:
UpperCAmelCase_ = self.nodes[word[0]]
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = incoming_node.match(
_snake_case)
# Case 3: The node prefix is equal to the matching
# Solution: We insert remaining word on the next node
if remaining_prefix == "":
self.nodes[matching_string[0]].insert(_snake_case)
# Case 4: The word is greater equal to the matching
# Solution: Create a node in between both nodes, change
# prefixes and add the new node for the remaining word
else:
UpperCAmelCase_ = remaining_prefix
UpperCAmelCase_ = self.nodes[matching_string[0]]
UpperCAmelCase_ = RadixNode(_snake_case , _snake_case)
UpperCAmelCase_ = aux_node
if remaining_word == "":
UpperCAmelCase_ = True
else:
self.nodes[matching_string[0]].insert(_snake_case)
def lowerCamelCase ( self : Any , _snake_case : str):
"""simple docstring"""
UpperCAmelCase_ = self.nodes.get(word[0] , _snake_case)
if not incoming_node:
return False
else:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = incoming_node.match(
_snake_case)
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# This applies when the word and the prefix are equal
elif remaining_word == "":
return incoming_node.is_leaf
# We have word remaining so we check the next node
else:
return incoming_node.find(_snake_case)
def lowerCamelCase ( self : Optional[int] , _snake_case : str):
"""simple docstring"""
UpperCAmelCase_ = self.nodes.get(word[0] , _snake_case)
if not incoming_node:
return False
else:
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ = incoming_node.match(
_snake_case)
# If there is remaining prefix, the word can't be on the tree
if remaining_prefix != "":
return False
# We have word remaining so we check the next node
elif remaining_word != "":
return incoming_node.delete(_snake_case)
else:
# If it is not a leaf, we don't have to delete
if not incoming_node.is_leaf:
return False
else:
# We delete the nodes if no edges go from it
if len(incoming_node.nodes) == 0:
del self.nodes[word[0]]
# We merge the current node with its only child
if len(self.nodes) == 1 and not self.is_leaf:
UpperCAmelCase_ = list(self.nodes.values())[0]
UpperCAmelCase_ = merging_node.is_leaf
self.prefix += merging_node.prefix
UpperCAmelCase_ = merging_node.nodes
# If there is more than 1 edge, we just mark it as non-leaf
elif len(incoming_node.nodes) > 1:
UpperCAmelCase_ = False
# If there is 1 edge, we merge it with its child
else:
UpperCAmelCase_ = list(incoming_node.nodes.values())[0]
UpperCAmelCase_ = merging_node.is_leaf
incoming_node.prefix += merging_node.prefix
UpperCAmelCase_ = merging_node.nodes
return True
def lowerCamelCase ( self : Tuple , _snake_case : int = 0):
"""simple docstring"""
if self.prefix != "":
print('''-''' * height , self.prefix , ''' (leaf)''' if self.is_leaf else '''''')
for value in self.nodes.values():
value.print_tree(height + 1)
def A () -> bool:
"""simple docstring"""
UpperCAmelCase_ = '''banana bananas bandana band apple all beast'''.split()
UpperCAmelCase_ = RadixNode()
root.insert_many(__A )
assert all(root.find(__A ) for word in words )
assert not root.find('''bandanas''' )
assert not root.find('''apps''' )
root.delete('''all''' )
assert not root.find('''all''' )
root.delete('''banana''' )
assert not root.find('''banana''' )
assert root.find('''bananas''' )
return True
def A () -> None:
"""simple docstring"""
assert test_trie()
def A () -> None:
"""simple docstring"""
UpperCAmelCase_ = RadixNode()
UpperCAmelCase_ = '''banana bananas bandanas bandana band apple all beast'''.split()
root.insert_many(__A )
print('''Words:''' , __A )
print('''Tree:''' )
root.print_tree()
if __name__ == "__main__":
main()
| 51 |
"""simple docstring"""
def _snake_case ( lowercase__ : list[int] ) -> list[list[int]]:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = []
if len(lowercase__ ) == 1:
return [nums.copy()]
for _ in range(len(lowercase__ ) ):
lowerCAmelCase_ :Optional[Any] = nums.pop(0 )
lowerCAmelCase_ :str = permute(lowercase__ )
for perm in permutations:
perm.append(lowercase__ )
result.extend(lowercase__ )
nums.append(lowercase__ )
return result
def _snake_case ( lowercase__ : Tuple ) -> List[str]:
'''simple docstring'''
def backtrack(lowercase__ : str ):
if start == len(lowercase__ ) - 1:
output.append(nums[:] )
else:
for i in range(lowercase__ , len(lowercase__ ) ):
lowerCAmelCase_ , lowerCAmelCase_ :str = nums[i], nums[start]
backtrack(start + 1 )
lowerCAmelCase_ , lowerCAmelCase_ :str = nums[i], nums[start] # backtrack
lowerCAmelCase_ :int = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
__UpperCAmelCase = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 84 | 0 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1 / sqrt(2 ) ) -> IIRFilter:
UpperCamelCase : Optional[int] = tau * frequency / samplerate
UpperCamelCase : Tuple = sin(_lowerCAmelCase )
UpperCamelCase : List[Any] = cos(_lowerCAmelCase )
UpperCamelCase : str = _sin / (2 * q_factor)
UpperCamelCase : List[str] = (1 - _cos) / 2
UpperCamelCase : Any = 1 - _cos
UpperCamelCase : int = 1 + alpha
UpperCamelCase : List[Any] = -2 * _cos
UpperCamelCase : Optional[Any] = 1 - alpha
UpperCamelCase : Optional[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1 / sqrt(2 ) ) -> IIRFilter:
UpperCamelCase : Union[str, Any] = tau * frequency / samplerate
UpperCamelCase : Dict = sin(_lowerCAmelCase )
UpperCamelCase : int = cos(_lowerCAmelCase )
UpperCamelCase : Dict = _sin / (2 * q_factor)
UpperCamelCase : str = (1 + _cos) / 2
UpperCamelCase : List[str] = -1 - _cos
UpperCamelCase : Any = 1 + alpha
UpperCamelCase : List[str] = -2 * _cos
UpperCamelCase : Optional[Any] = 1 - alpha
UpperCamelCase : Dict = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1 / sqrt(2 ) ) -> IIRFilter:
UpperCamelCase : Union[str, Any] = tau * frequency / samplerate
UpperCamelCase : Tuple = sin(_lowerCAmelCase )
UpperCamelCase : List[Any] = cos(_lowerCAmelCase )
UpperCamelCase : str = _sin / (2 * q_factor)
UpperCamelCase : List[str] = _sin / 2
UpperCamelCase : Dict = 0
UpperCamelCase : str = -ba
UpperCamelCase : Tuple = 1 + alpha
UpperCamelCase : int = -2 * _cos
UpperCamelCase : Any = 1 - alpha
UpperCamelCase : List[str] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1 / sqrt(2 ) ) -> IIRFilter:
UpperCamelCase : List[Any] = tau * frequency / samplerate
UpperCamelCase : List[str] = sin(_lowerCAmelCase )
UpperCamelCase : Optional[int] = cos(_lowerCAmelCase )
UpperCamelCase : int = _sin / (2 * q_factor)
UpperCamelCase : str = 1 - alpha
UpperCamelCase : Union[str, Any] = -2 * _cos
UpperCamelCase : Optional[Any] = 1 + alpha
UpperCamelCase : Any = IIRFilter(2 )
filt.set_coefficients([ba, ba, ba] , [ba, ba, ba] )
return filt
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1 / sqrt(2 ) , ) -> IIRFilter:
UpperCamelCase : Optional[Any] = tau * frequency / samplerate
UpperCamelCase : Optional[int] = sin(_lowerCAmelCase )
UpperCamelCase : Any = cos(_lowerCAmelCase )
UpperCamelCase : Tuple = _sin / (2 * q_factor)
UpperCamelCase : Any = 10 ** (gain_db / 40)
UpperCamelCase : Optional[Any] = 1 + alpha * big_a
UpperCamelCase : int = -2 * _cos
UpperCamelCase : List[Any] = 1 - alpha * big_a
UpperCamelCase : Any = 1 + alpha / big_a
UpperCamelCase : List[Any] = -2 * _cos
UpperCamelCase : List[str] = 1 - alpha / big_a
UpperCamelCase : Optional[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1 / sqrt(2 ) , ) -> IIRFilter:
UpperCamelCase : List[Any] = tau * frequency / samplerate
UpperCamelCase : int = sin(_lowerCAmelCase )
UpperCamelCase : Tuple = cos(_lowerCAmelCase )
UpperCamelCase : Union[str, Any] = _sin / (2 * q_factor)
UpperCamelCase : Optional[Any] = 10 ** (gain_db / 40)
UpperCamelCase : Tuple = (big_a + 1) - (big_a - 1) * _cos
UpperCamelCase : Any = (big_a + 1) + (big_a - 1) * _cos
UpperCamelCase : str = (big_a - 1) - (big_a + 1) * _cos
UpperCamelCase : List[Any] = (big_a - 1) + (big_a + 1) * _cos
UpperCamelCase : Tuple = 2 * sqrt(_lowerCAmelCase ) * alpha
UpperCamelCase : str = big_a * (pmc + aaa)
UpperCamelCase : int = 2 * big_a * mpc
UpperCamelCase : Optional[Any] = big_a * (pmc - aaa)
UpperCamelCase : Any = ppmc + aaa
UpperCamelCase : Optional[Any] = -2 * pmpc
UpperCamelCase : int = ppmc - aaa
UpperCamelCase : Optional[Any] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
def A_ ( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1 / sqrt(2 ) , ) -> IIRFilter:
UpperCamelCase : Optional[int] = tau * frequency / samplerate
UpperCamelCase : Union[str, Any] = sin(_lowerCAmelCase )
UpperCamelCase : Optional[int] = cos(_lowerCAmelCase )
UpperCamelCase : Optional[int] = _sin / (2 * q_factor)
UpperCamelCase : int = 10 ** (gain_db / 40)
UpperCamelCase : Union[str, Any] = (big_a + 1) - (big_a - 1) * _cos
UpperCamelCase : str = (big_a + 1) + (big_a - 1) * _cos
UpperCamelCase : Tuple = (big_a - 1) - (big_a + 1) * _cos
UpperCamelCase : Union[str, Any] = (big_a - 1) + (big_a + 1) * _cos
UpperCamelCase : Dict = 2 * sqrt(_lowerCAmelCase ) * alpha
UpperCamelCase : str = big_a * (ppmc + aaa)
UpperCamelCase : Any = -2 * big_a * pmpc
UpperCamelCase : int = big_a * (ppmc - aaa)
UpperCamelCase : str = pmc + aaa
UpperCamelCase : Dict = 2 * mpc
UpperCamelCase : List[Any] = pmc - aaa
UpperCamelCase : List[str] = IIRFilter(2 )
filt.set_coefficients([aa, aa, aa] , [ba, ba, ba] )
return filt
| 52 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :Any = BioGptTokenizer
UpperCAmelCase_ :str = False
def __lowerCAmelCase ( self ) -> List[Any]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ :Optional[Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
lowerCAmelCase_ :str = dict(zip(__A , range(len(__A ) ) ) )
lowerCAmelCase_ :int = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
lowerCAmelCase_ :Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase_ :Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(__A ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(__A ) )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
lowerCAmelCase_ :List[Any] = """lower newer"""
lowerCAmelCase_ :Tuple = """lower newer"""
return input_text, output_text
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :List[str] = BioGptTokenizer(self.vocab_file , self.merges_file )
lowerCAmelCase_ :Union[str, Any] = """lower"""
lowerCAmelCase_ :Any = ["""low""", """er</w>"""]
lowerCAmelCase_ :Union[str, Any] = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
lowerCAmelCase_ :Dict = tokens + ["""<unk>"""]
lowerCAmelCase_ :List[str] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
@slow
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Optional[Any] = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
lowerCAmelCase_ :List[Any] = tokenizer.encode("""sequence builders""" , add_special_tokens=__A )
lowerCAmelCase_ :List[str] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=__A )
lowerCAmelCase_ :Optional[int] = tokenizer.build_inputs_with_special_tokens(__A )
lowerCAmelCase_ :List[str] = tokenizer.build_inputs_with_special_tokens(__A , __A )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 84 | 0 |
'''simple docstring'''
def lowercase__ ( __lowercase : float , __lowercase : float , __lowercase : int ) -> float:
"""simple docstring"""
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(__lowercase , __lowercase ):
raise Exception('Years to repay must be an integer > 0' )
# Yearly rate is divided by 12 to get monthly rate
__UpperCamelCase = rate_per_annum / 12
# Years to repay is multiplied by 12 to get number of payments as payment is monthly
__UpperCamelCase = years_to_repay * 12
return (
principal
* rate_per_month
* (1 + rate_per_month) ** number_of_payments
/ ((1 + rate_per_month) ** number_of_payments - 1)
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 53 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "bert-generation"
def __init__( self , __A=5_0358 , __A=1024 , __A=24 , __A=16 , __A=4096 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0.0_2 , __A=1E-12 , __A=0 , __A=2 , __A=1 , __A="absolute" , __A=True , **__A , ) -> Tuple:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :Any = vocab_size
lowerCAmelCase_ :List[Any] = hidden_size
lowerCAmelCase_ :Optional[int] = num_hidden_layers
lowerCAmelCase_ :int = num_attention_heads
lowerCAmelCase_ :List[Any] = hidden_act
lowerCAmelCase_ :Optional[Any] = intermediate_size
lowerCAmelCase_ :List[Any] = hidden_dropout_prob
lowerCAmelCase_ :int = attention_probs_dropout_prob
lowerCAmelCase_ :Tuple = max_position_embeddings
lowerCAmelCase_ :List[str] = initializer_range
lowerCAmelCase_ :Union[str, Any] = layer_norm_eps
lowerCAmelCase_ :List[str] = position_embedding_type
lowerCAmelCase_ :Optional[int] = use_cache
| 84 | 0 |
"""simple docstring"""
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from huggingface_hub import HfFolder, Repository, create_repo, delete_repo
from requests.exceptions import HTTPError
import transformers
from transformers import (
CONFIG_MAPPING,
FEATURE_EXTRACTOR_MAPPING,
PROCESSOR_MAPPING,
TOKENIZER_MAPPING,
AutoConfig,
AutoFeatureExtractor,
AutoProcessor,
AutoTokenizer,
BertTokenizer,
ProcessorMixin,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
)
from transformers.testing_utils import TOKEN, USER, get_tests_dir, is_staging_test
from transformers.tokenization_utils import TOKENIZER_CONFIG_FILE
from transformers.utils import FEATURE_EXTRACTOR_NAME, is_tokenizers_available
sys.path.append(str(Path(__file__).parent.parent.parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
from test_module.custom_feature_extraction import CustomFeatureExtractor # noqa E402
from test_module.custom_processing import CustomProcessor # noqa E402
from test_module.custom_tokenization import CustomTokenizer # noqa E402
a__ : Dict = get_tests_dir('''fixtures/dummy_feature_extractor_config.json''')
a__ : Dict = get_tests_dir('''fixtures/vocab.json''')
a__ : List[Any] = get_tests_dir('''fixtures''')
class UpperCamelCase_ ( unittest.TestCase):
"""simple docstring"""
snake_case__ : Any = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
def UpperCAmelCase_ ( self : Any ) -> Optional[int]:
__SCREAMING_SNAKE_CASE = 0
def UpperCAmelCase_ ( self : List[Any] ) -> str:
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Any:
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = WavaVecaConfig()
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained("facebook/wav2vec2-base-960h" )
# save in new folder
model_config.save_pretrained(UpperCAmelCase__ )
processor.save_pretrained(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained(UpperCAmelCase__ )
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Any ) -> Any:
with tempfile.TemporaryDirectory() as tmpdirname:
# copy relevant files
copyfile(UpperCAmelCase__ , os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) )
copyfile(UpperCAmelCase__ , os.path.join(UpperCAmelCase__ , "vocab.json" ) )
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained(UpperCAmelCase__ )
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
def UpperCAmelCase_ ( self : List[Any] ) -> Any:
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor()
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h" )
__SCREAMING_SNAKE_CASE = WavaVecaProcessor(UpperCAmelCase__ , UpperCAmelCase__ )
# save in new folder
processor.save_pretrained(UpperCAmelCase__ )
# drop `processor_class` in tokenizer
with open(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) , "r" ) as f:
__SCREAMING_SNAKE_CASE = json.load(UpperCAmelCase__ )
config_dict.pop("processor_class" )
with open(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) , "w" ) as f:
f.write(json.dumps(UpperCAmelCase__ ) )
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained(UpperCAmelCase__ )
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Dict ) -> Tuple:
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = WavaVecaFeatureExtractor()
__SCREAMING_SNAKE_CASE = AutoTokenizer.from_pretrained("facebook/wav2vec2-base-960h" )
__SCREAMING_SNAKE_CASE = WavaVecaProcessor(UpperCAmelCase__ , UpperCAmelCase__ )
# save in new folder
processor.save_pretrained(UpperCAmelCase__ )
# drop `processor_class` in feature extractor
with open(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) , "r" ) as f:
__SCREAMING_SNAKE_CASE = json.load(UpperCAmelCase__ )
config_dict.pop("processor_class" )
with open(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) , "w" ) as f:
f.write(json.dumps(UpperCAmelCase__ ) )
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained(UpperCAmelCase__ )
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
def UpperCAmelCase_ ( self : List[str] ) -> List[str]:
with tempfile.TemporaryDirectory() as tmpdirname:
__SCREAMING_SNAKE_CASE = WavaVecaConfig(processor_class="Wav2Vec2Processor" )
model_config.save_pretrained(UpperCAmelCase__ )
# copy relevant files
copyfile(UpperCAmelCase__ , os.path.join(UpperCAmelCase__ , "vocab.json" ) )
# create emtpy sample processor
with open(os.path.join(UpperCAmelCase__ , UpperCAmelCase__ ) , "w" ) as f:
f.write("{}" )
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained(UpperCAmelCase__ )
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
def UpperCAmelCase_ ( self : Union[str, Any] ) -> int:
# If remote code is not set, we will time out when asking whether to load the model.
with self.assertRaises(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" )
# If remote code is disabled, we can't load this config.
with self.assertRaises(UpperCAmelCase__ ):
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" , trust_remote_code=UpperCAmelCase__ )
self.assertTrue(processor.special_attribute_present )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
__SCREAMING_SNAKE_CASE = processor.feature_extractor
self.assertTrue(feature_extractor.special_attribute_present )
self.assertEqual(feature_extractor.__class__.__name__ , "NewFeatureExtractor" )
__SCREAMING_SNAKE_CASE = processor.tokenizer
self.assertTrue(tokenizer.special_attribute_present )
if is_tokenizers_available():
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizerFast" )
# Test we can also load the slow version
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=UpperCAmelCase__ , use_fast=UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = new_processor.tokenizer
self.assertTrue(new_tokenizer.special_attribute_present )
self.assertEqual(new_tokenizer.__class__.__name__ , "NewTokenizer" )
else:
self.assertEqual(tokenizer.__class__.__name__ , "NewTokenizer" )
def UpperCAmelCase_ ( self : Any ) -> Optional[Any]:
try:
AutoConfig.register("custom" , UpperCAmelCase__ )
AutoFeatureExtractor.register(UpperCAmelCase__ , UpperCAmelCase__ )
AutoTokenizer.register(UpperCAmelCase__ , slow_tokenizer_class=UpperCAmelCase__ )
AutoProcessor.register(UpperCAmelCase__ , UpperCAmelCase__ )
# Trying to register something existing in the Transformers library will raise an error
with self.assertRaises(UpperCAmelCase__ ):
AutoProcessor.register(UpperCAmelCase__ , UpperCAmelCase__ )
# Now that the config is registered, it can be used as any other config with the auto-API
__SCREAMING_SNAKE_CASE = CustomFeatureExtractor.from_pretrained(UpperCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , "vocab.txt" )
with open(UpperCAmelCase__ , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
__SCREAMING_SNAKE_CASE = CustomTokenizer(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = CustomProcessor(UpperCAmelCase__ , UpperCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained(UpperCAmelCase__ )
self.assertIsInstance(UpperCAmelCase__ , UpperCAmelCase__ )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def UpperCAmelCase_ ( self : List[Any] ) -> List[str]:
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
snake_case__ : Optional[Any] = False
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
snake_case__ : int = False
class UpperCamelCase_ ( UpperCamelCase):
"""simple docstring"""
snake_case__ : Any = "AutoFeatureExtractor"
snake_case__ : Tuple = "AutoTokenizer"
snake_case__ : List[Any] = False
try:
AutoConfig.register("custom" , UpperCAmelCase__ )
AutoFeatureExtractor.register(UpperCAmelCase__ , UpperCAmelCase__ )
AutoTokenizer.register(UpperCAmelCase__ , slow_tokenizer_class=UpperCAmelCase__ )
AutoProcessor.register(UpperCAmelCase__ , UpperCAmelCase__ )
# If remote code is not set, the default is to use local classes.
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained("hf-internal-testing/test_dynamic_processor" )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote code is disabled, we load the local ones.
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=UpperCAmelCase__ )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertFalse(processor.special_attribute_present )
self.assertFalse(processor.feature_extractor.special_attribute_present )
self.assertFalse(processor.tokenizer.special_attribute_present )
# If remote is enabled, we load from the Hub.
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained(
"hf-internal-testing/test_dynamic_processor" , trust_remote_code=UpperCAmelCase__ )
self.assertEqual(processor.__class__.__name__ , "NewProcessor" )
self.assertTrue(processor.special_attribute_present )
self.assertTrue(processor.feature_extractor.special_attribute_present )
self.assertTrue(processor.tokenizer.special_attribute_present )
finally:
if "custom" in CONFIG_MAPPING._extra_content:
del CONFIG_MAPPING._extra_content["custom"]
if CustomConfig in FEATURE_EXTRACTOR_MAPPING._extra_content:
del FEATURE_EXTRACTOR_MAPPING._extra_content[CustomConfig]
if CustomConfig in TOKENIZER_MAPPING._extra_content:
del TOKENIZER_MAPPING._extra_content[CustomConfig]
if CustomConfig in PROCESSOR_MAPPING._extra_content:
del PROCESSOR_MAPPING._extra_content[CustomConfig]
def UpperCAmelCase_ ( self : str ) -> Optional[Any]:
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-bert" )
self.assertEqual(processor.__class__.__name__ , "BertTokenizerFast" )
def UpperCAmelCase_ ( self : Optional[Any] ) -> Union[str, Any]:
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained("hf-internal-testing/tiny-random-convnext" )
self.assertEqual(processor.__class__.__name__ , "ConvNextImageProcessor" )
@is_staging_test
class UpperCamelCase_ ( unittest.TestCase):
"""simple docstring"""
snake_case__ : Optional[Any] = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "bla", "blou"]
@classmethod
def UpperCAmelCase_ ( cls : Tuple ) -> int:
__SCREAMING_SNAKE_CASE = TOKEN
HfFolder.save_token(UpperCAmelCase__ )
@classmethod
def UpperCAmelCase_ ( cls : int ) -> Tuple:
try:
delete_repo(token=cls._token , repo_id="test-processor" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-processor-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="test-dynamic-processor" )
except HTTPError:
pass
def UpperCAmelCase_ ( self : Dict ) -> Any:
__SCREAMING_SNAKE_CASE = WavaVecaProcessor.from_pretrained(UpperCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(UpperCAmelCase__ , "test-processor" ) , push_to_hub=UpperCAmelCase__ , use_auth_token=self._token )
__SCREAMING_SNAKE_CASE = WavaVecaProcessor.from_pretrained(F"""{USER}/test-processor""" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(UpperCAmelCase__ , getattr(new_processor.feature_extractor , UpperCAmelCase__ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def UpperCAmelCase_ ( self : List[str] ) -> Tuple:
__SCREAMING_SNAKE_CASE = WavaVecaProcessor.from_pretrained(UpperCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
processor.save_pretrained(
os.path.join(UpperCAmelCase__ , "test-processor-org" ) , push_to_hub=UpperCAmelCase__ , use_auth_token=self._token , organization="valid_org" , )
__SCREAMING_SNAKE_CASE = WavaVecaProcessor.from_pretrained("valid_org/test-processor-org" )
for k, v in processor.feature_extractor.__dict__.items():
self.assertEqual(UpperCAmelCase__ , getattr(new_processor.feature_extractor , UpperCAmelCase__ ) )
self.assertDictEqual(new_processor.tokenizer.get_vocab() , processor.tokenizer.get_vocab() )
def UpperCAmelCase_ ( self : Optional[int] ) -> int:
CustomFeatureExtractor.register_for_auto_class()
CustomTokenizer.register_for_auto_class()
CustomProcessor.register_for_auto_class()
__SCREAMING_SNAKE_CASE = CustomFeatureExtractor.from_pretrained(UpperCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
__SCREAMING_SNAKE_CASE = os.path.join(UpperCAmelCase__ , "vocab.txt" )
with open(UpperCAmelCase__ , "w" , encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
__SCREAMING_SNAKE_CASE = CustomTokenizer(UpperCAmelCase__ )
__SCREAMING_SNAKE_CASE = CustomProcessor(UpperCAmelCase__ , UpperCAmelCase__ )
with tempfile.TemporaryDirectory() as tmp_dir:
create_repo(F"""{USER}/test-dynamic-processor""" , token=self._token )
__SCREAMING_SNAKE_CASE = Repository(UpperCAmelCase__ , clone_from=F"""{USER}/test-dynamic-processor""" , token=self._token )
processor.save_pretrained(UpperCAmelCase__ )
# This has added the proper auto_map field to the feature extractor config
self.assertDictEqual(
processor.feature_extractor.auto_map , {
"AutoFeatureExtractor": "custom_feature_extraction.CustomFeatureExtractor",
"AutoProcessor": "custom_processing.CustomProcessor",
} , )
# This has added the proper auto_map field to the tokenizer config
with open(os.path.join(UpperCAmelCase__ , "tokenizer_config.json" ) ) as f:
__SCREAMING_SNAKE_CASE = json.load(UpperCAmelCase__ )
self.assertDictEqual(
tokenizer_config["auto_map"] , {
"AutoTokenizer": ["custom_tokenization.CustomTokenizer", None],
"AutoProcessor": "custom_processing.CustomProcessor",
} , )
# The code has been copied from fixtures
self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase__ , "custom_feature_extraction.py" ) ) )
self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase__ , "custom_tokenization.py" ) ) )
self.assertTrue(os.path.isfile(os.path.join(UpperCAmelCase__ , "custom_processing.py" ) ) )
repo.push_to_hub()
__SCREAMING_SNAKE_CASE = AutoProcessor.from_pretrained(F"""{USER}/test-dynamic-processor""" , trust_remote_code=UpperCAmelCase__ )
# Can't make an isinstance check because the new_processor is from the CustomProcessor class of a dynamic module
self.assertEqual(new_processor.__class__.__name__ , "CustomProcessor" )
| 54 |
"""simple docstring"""
def _snake_case ( lowercase__ : List[Any] , lowercase__ : int , lowercase__ : Optional[int] , lowercase__ : Any ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [False] * len(lowercase__ )
lowerCAmelCase_ :str = []
queue.append(lowercase__ )
lowerCAmelCase_ :Any = True
while queue:
lowerCAmelCase_ :Optional[int] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(lowercase__ )
lowerCAmelCase_ :Union[str, Any] = True
lowerCAmelCase_ :int = u
return visited[t]
def _snake_case ( lowercase__ : int , lowercase__ : Optional[int] , lowercase__ : str ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = [-1] * (len(lowercase__ ))
lowerCAmelCase_ :str = 0
while bfs(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
lowerCAmelCase_ :List[str] = float("""Inf""" )
lowerCAmelCase_ :List[str] = sink
while s != source:
# Find the minimum value in select path
lowerCAmelCase_ :Any = min(lowercase__ , graph[parent[s]][s] )
lowerCAmelCase_ :Union[str, Any] = parent[s]
max_flow += path_flow
lowerCAmelCase_ :Tuple = sink
while v != source:
lowerCAmelCase_ :List[str] = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowerCAmelCase_ :Union[str, Any] = parent[v]
return max_flow
__UpperCAmelCase = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__UpperCAmelCase , __UpperCAmelCase = 0, 5
print(ford_fulkerson(graph, source, sink))
| 84 | 0 |
'''simple docstring'''
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def __snake_case ( ):
lowerCamelCase_ = ArgumentParser(
description=(
"PyTorch TPU distributed training launch helper utility that will spawn up multiple distributed processes"
) )
# Optional arguments for the launch helper
parser.add_argument("--num_cores" , type=UpperCAmelCase_ , default=1 , help="Number of TPU cores to use (1 or 8)." )
# positional
parser.add_argument(
"training_script" , type=UpperCAmelCase_ , help=(
"The full path to the single TPU training "
"program/script to be launched in parallel, "
"followed by all the arguments for the "
"training script"
) , )
# rest from the training program
parser.add_argument("training_script_args" , nargs=UpperCAmelCase_ )
return parser.parse_args()
def __snake_case ( ):
lowerCamelCase_ = parse_args()
# Import training_script as a module.
lowerCamelCase_ = Path(args.training_script )
sys.path.append(str(script_fpath.parent.resolve() ) )
lowerCamelCase_ = script_fpath.stem
lowerCamelCase_ = importlib.import_module(UpperCAmelCase_ )
# Patch sys.argv
lowerCamelCase_ = [args.training_script] + args.training_script_args + ["--tpu_num_cores", str(args.num_cores )]
xmp.spawn(mod._mp_fn , args=() , nprocs=args.num_cores )
if __name__ == "__main__":
main()
| 55 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = 1_0
lowerCAmelCase_ :Optional[int] = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
lowerCAmelCase_ :int = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
"""id""": list(range(lowercase__ ) ),
} , features=lowercase__ , )
return dataset
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple , lowercase__ : int ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=lowercase__ )
return filename
# FILE_CONTENT + files
__UpperCAmelCase = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
lowerCAmelCase_ :List[Any] = FILE_CONTENT
with open(lowercase__ , """w""" ) as f:
f.write(lowercase__ )
return filename
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[Any] ) -> Tuple:
'''simple docstring'''
import bza
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
lowerCAmelCase_ :Tuple = bytes(lowercase__ , """utf-8""" )
with bza.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[Any] ) -> Dict:
'''simple docstring'''
import gzip
lowerCAmelCase_ :int = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
lowerCAmelCase_ :Tuple = bytes(lowercase__ , """utf-8""" )
with gzip.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Optional[int]:
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
lowerCAmelCase_ :List[Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
lowerCAmelCase_ :int = bytes(lowercase__ , """utf-8""" )
with lza.frame.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict , lowercase__ : Optional[int] ) -> Any:
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
lowerCAmelCase_ :Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(lowercase__ , """w""" ) as archive:
archive.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
import tarfile
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> str:
'''simple docstring'''
import lzma
lowerCAmelCase_ :Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
lowerCAmelCase_ :Optional[Any] = bytes(lowercase__ , """utf-8""" )
with lzma.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : List[Any] ) -> Any:
'''simple docstring'''
import zipfile
lowerCAmelCase_ :Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> Tuple:
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
lowerCAmelCase_ :Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
lowerCAmelCase_ :Any = bytes(lowercase__ , """utf-8""" )
with zstd.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
lowerCAmelCase_ :Any = textwrap.dedent(
"""\
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>
<tmx version=\"1.4\">
<header segtype=\"sentence\" srclang=\"ca\" />
<body>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>""" )
with open(lowercase__ , """w""" ) as f:
f.write(lowercase__ )
return filename
__UpperCAmelCase = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
__UpperCAmelCase = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
__UpperCAmelCase = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
__UpperCAmelCase = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
__UpperCAmelCase = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> Union[str, Any]:
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Tuple = datasets.Dataset.from_dict(lowercase__ )
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(lowercase__ ) ) as con:
lowerCAmelCase_ :Union[str, Any] = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> int:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(lowercase__ , """w""" , newline="""""" ) as f:
lowerCAmelCase_ :Optional[int] = csv.DictWriter(lowercase__ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(lowercase__ , """w""" , newline="""""" ) as f:
lowerCAmelCase_ :Dict = csv.DictWriter(lowercase__ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str , lowercase__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
import bza
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(lowercase__ , """rb""" ) as f:
lowerCAmelCase_ :Union[str, Any] = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : List[str] , lowercase__ : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(lowercase__ , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Tuple , lowercase__ : str ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
lowerCAmelCase_ :Optional[Any] = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(lowercase__ , """wb""" ) as f:
lowerCAmelCase_ :Optional[int] = pq.ParquetWriter(lowercase__ , schema=lowercase__ )
lowerCAmelCase_ :List[str] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(lowercase__ ) )] for k in DATA[0]} , schema=lowercase__ )
writer.write_table(lowercase__ )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
lowerCAmelCase_ :Union[str, Any] = {"""data""": DATA}
with open(lowercase__ , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
lowerCAmelCase_ :Optional[Any] = {"""data""": DATA_DICT_OF_LISTS}
with open(lowercase__ , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int , lowercase__ : Dict ) -> Optional[int]:
'''simple docstring'''
import gzip
lowerCAmelCase_ :Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(lowercase__ , """rb""" ) as orig_file:
with gzip.open(lowercase__ , """wb""" ) as zipped_file:
zipped_file.writelines(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : List[Any] ) -> Any:
'''simple docstring'''
import gzip
lowerCAmelCase_ :Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(lowercase__ , """rb""" ) as orig_file:
with gzip.open(lowercase__ , """wb""" ) as zipped_file:
zipped_file.writelines(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : Optional[int] , lowercase__ : List[Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""nested""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : List[Any] , lowercase__ : List[str] ) -> int:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : str , lowercase__ : List[str] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict , lowercase__ : str , lowercase__ : List[str] , lowercase__ : int ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.join("""nested""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :str = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Dict = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple , lowercase__ : Tuple , lowercase__ : List[str] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Any , lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(lowercase__ , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(lowercase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> int:
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> Tuple:
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
return data_dir
| 84 | 0 |
'''simple docstring'''
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> np.array:
'''simple docstring'''
snake_case_ = F"{sampling_rate}"
snake_case_ = '''1'''
snake_case_ = '''f32le'''
snake_case_ = [
'''ffmpeg''',
'''-i''',
'''pipe:0''',
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
try:
with subprocess.Popen(__UpperCAmelCase, stdin=subprocess.PIPE, stdout=subprocess.PIPE ) as ffmpeg_process:
snake_case_ = ffmpeg_process.communicate(__UpperCAmelCase )
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
snake_case_ = output_stream[0]
snake_case_ = np.frombuffer(__UpperCAmelCase, np.floataa )
if audio.shape[0] == 0:
raise ValueError('''Malformed soundfile''' )
return audio
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = "f32le", ) -> List[Any]:
'''simple docstring'''
snake_case_ = F"{sampling_rate}"
snake_case_ = '''1'''
if format_for_conversion == "s16le":
snake_case_ = 2
elif format_for_conversion == "f32le":
snake_case_ = 4
else:
raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`" )
snake_case_ = platform.system()
if system == "Linux":
snake_case_ = '''alsa'''
snake_case_ = '''default'''
elif system == "Darwin":
snake_case_ = '''avfoundation'''
snake_case_ = ''':0'''
elif system == "Windows":
snake_case_ = '''dshow'''
snake_case_ = '''default'''
snake_case_ = [
'''ffmpeg''',
'''-f''',
format_,
'''-i''',
input_,
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-fflags''',
'''nobuffer''',
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
snake_case_ = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
snake_case_ = _ffmpeg_stream(__UpperCAmelCase, __UpperCAmelCase )
for item in iterator:
yield item
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = None, __UpperCAmelCase = None, __UpperCAmelCase = "f32le", ) -> Dict:
'''simple docstring'''
if stream_chunk_s is not None:
snake_case_ = stream_chunk_s
else:
snake_case_ = chunk_length_s
snake_case_ = ffmpeg_microphone(__UpperCAmelCase, __UpperCAmelCase, format_for_conversion=__UpperCAmelCase )
if format_for_conversion == "s16le":
snake_case_ = np.intaa
snake_case_ = 2
elif format_for_conversion == "f32le":
snake_case_ = np.floataa
snake_case_ = 4
else:
raise ValueError(F"Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`" )
if stride_length_s is None:
snake_case_ = chunk_length_s / 6
snake_case_ = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(__UpperCAmelCase, (int, float) ):
snake_case_ = [stride_length_s, stride_length_s]
snake_case_ = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
snake_case_ = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
snake_case_ = datetime.datetime.now()
snake_case_ = datetime.timedelta(seconds=__UpperCAmelCase )
for item in chunk_bytes_iter(__UpperCAmelCase, __UpperCAmelCase, stride=(stride_left, stride_right), stream=__UpperCAmelCase ):
# Put everything back in numpy scale
snake_case_ = np.frombuffer(item['''raw'''], dtype=__UpperCAmelCase )
snake_case_ = (
item['''stride'''][0] // size_of_sample,
item['''stride'''][1] // size_of_sample,
)
snake_case_ = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase, __UpperCAmelCase = False ) -> Any:
'''simple docstring'''
snake_case_ = b''''''
snake_case_ ,snake_case_ = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
F"Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}" )
snake_case_ = 0
for raw in iterator:
acc += raw
if stream and len(__UpperCAmelCase ) < chunk_len:
snake_case_ = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(__UpperCAmelCase ) >= chunk_len:
# We are flushing the accumulator
snake_case_ = (_stride_left, stride_right)
snake_case_ = {'''raw''': acc[:chunk_len], '''stride''': stride}
if stream:
snake_case_ = False
yield item
snake_case_ = stride_left
snake_case_ = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(__UpperCAmelCase ) > stride_left:
snake_case_ = {'''raw''': acc, '''stride''': (_stride_left, 0)}
if stream:
snake_case_ = False
yield item
def __magic_name__ ( __UpperCAmelCase, __UpperCAmelCase ) -> Optional[int]:
'''simple docstring'''
snake_case_ = 2**24 # 16Mo
try:
with subprocess.Popen(__UpperCAmelCase, stdout=subprocess.PIPE, bufsize=__UpperCAmelCase ) as ffmpeg_process:
while True:
snake_case_ = ffmpeg_process.stdout.read(__UpperCAmelCase )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error
| 56 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Optional[Any] = "data2vec-text"
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.0_2 , __A=1E-12 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , __A=None , **__A , ) -> Tuple:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :Dict = vocab_size
lowerCAmelCase_ :Dict = hidden_size
lowerCAmelCase_ :int = num_hidden_layers
lowerCAmelCase_ :List[Any] = num_attention_heads
lowerCAmelCase_ :Any = hidden_act
lowerCAmelCase_ :Optional[int] = intermediate_size
lowerCAmelCase_ :str = hidden_dropout_prob
lowerCAmelCase_ :Any = attention_probs_dropout_prob
lowerCAmelCase_ :str = max_position_embeddings
lowerCAmelCase_ :int = type_vocab_size
lowerCAmelCase_ :Tuple = initializer_range
lowerCAmelCase_ :List[Any] = layer_norm_eps
lowerCAmelCase_ :List[Any] = position_embedding_type
lowerCAmelCase_ :List[Any] = use_cache
lowerCAmelCase_ :List[Any] = classifier_dropout
class _SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase_ :List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowerCAmelCase_ :List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 84 | 0 |
"""simple docstring"""
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
A : Any = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code that will be used in the tests.
# If BertLMPredictionHead is changed in modeling_bert.py, this code needs to be manually updated.
A : Dict = " def __init__(self, config):\n super().__init__()\n self.transform = BertPredictionHeadTransform(config)\n\n # The output weights are the same as the input embeddings, but there is\n # an output-only bias for each token.\n self.decoder = nn.Linear(config.hidden_size, config.vocab_size, bias=False)\n\n self.bias = nn.Parameter(torch.zeros(config.vocab_size))\n\n # Need a link between the two variables so that the bias is correctly resized with `resize_token_embeddings`\n self.decoder.bias = self.bias\n\n def forward(self, hidden_states):\n hidden_states = self.transform(hidden_states)\n hidden_states = self.decoder(hidden_states)\n return hidden_states\n"
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def snake_case ( self ):
__lowerCAmelCase = tempfile.mkdtemp()
os.makedirs(os.path.join(self.transformer_dir , "models/bert/" ) )
__lowerCAmelCase = self.transformer_dir
shutil.copy(
os.path.join(__a , "src/transformers/models/bert/modeling_bert.py" ) , os.path.join(self.transformer_dir , "models/bert/modeling_bert.py" ) , )
def snake_case ( self ):
__lowerCAmelCase = "src/transformers"
shutil.rmtree(self.transformer_dir )
def snake_case ( self , __a , __a , __a , __a=None ):
__lowerCAmelCase = comment + f"\nclass {class_name}(nn.Module):\n" + class_code
if overwrite_result is not None:
__lowerCAmelCase = comment + f"\nclass {class_name}(nn.Module):\n" + overwrite_result
__lowerCAmelCase = black.Mode(target_versions={black.TargetVersion.PYaa} , line_length=1_19 )
__lowerCAmelCase = black.format_str(__a , mode=__a )
__lowerCAmelCase = os.path.join(self.transformer_dir , "new_code.py" )
with open(__a , "w" , newline="\n" ) as f:
f.write(__a )
if overwrite_result is None:
self.assertTrue(len(check_copies.is_copy_consistent(__a ) ) == 0 )
else:
check_copies.is_copy_consistent(f.name , overwrite=__a )
with open(__a , "r" ) as f:
self.assertTrue(f.read() , __a )
def snake_case ( self ):
__lowerCAmelCase = check_copies.find_code_in_transformers("models.bert.modeling_bert.BertLMPredictionHead" )
self.assertEqual(__a , __a )
def snake_case ( self ):
# Base copy consistency
self.check_copy_consistency(
"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead" , "BertLMPredictionHead" , REFERENCE_CODE + "\n" , )
# With no empty line at the end
self.check_copy_consistency(
"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead" , "BertLMPredictionHead" , __a , )
# Copy consistency with rename
self.check_copy_consistency(
"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel" , "TestModelLMPredictionHead" , re.sub("Bert" , "TestModel" , __a ) , )
# Copy consistency with a really long name
__lowerCAmelCase = "TestModelWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
self.check_copy_consistency(
f"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->{long_class_name}" , f"{long_class_name}LMPredictionHead" , re.sub("Bert" , __a , __a ) , )
# Copy consistency with overwrite
self.check_copy_consistency(
"# Copied from transformers.models.bert.modeling_bert.BertLMPredictionHead with Bert->TestModel" , "TestModelLMPredictionHead" , __a , overwrite_result=re.sub("Bert" , "TestModel" , __a ) , )
def snake_case ( self ):
__lowerCAmelCase = check_copies.LOCALIZED_READMES["README_zh-hans.md"]
__lowerCAmelCase = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
" Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
" Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
" Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut.\n1."
" **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (from HuggingFace),"
" released together with the paper [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
" lighter](https://arxiv.org/abs/1910.01108) by Victor Sanh, Lysandre Debut and Thomas Wolf. The same"
" method has been applied to compress GPT2 into"
" [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
" [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
" Multilingual BERT into"
" [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
" version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)**"
" (from Google Research/Stanford University) released with the paper [ELECTRA: Pre-training text encoders"
" as discriminators rather than generators](https://arxiv.org/abs/2003.10555) by Kevin Clark, Minh-Thang"
" Luong, Quoc V. Le, Christopher D. Manning."
)
__lowerCAmelCase = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
__lowerCAmelCase = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n1."
" **[DistilBERT](https://huggingface.co/transformers/model_doc/distilbert.html)** (来自 HuggingFace) 伴随论文"
" [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and"
" lighter](https://arxiv.org/abs/1910.01108) 由 Victor Sanh, Lysandre Debut and Thomas Wolf 发布。 The same"
" method has been applied to compress GPT2 into"
" [DistilGPT2](https://github.com/huggingface/transformers/tree/main/examples/distillation), RoBERTa into"
" [DistilRoBERTa](https://github.com/huggingface/transformers/tree/main/examples/distillation),"
" Multilingual BERT into"
" [DistilmBERT](https://github.com/huggingface/transformers/tree/main/examples/distillation) and a German"
" version of DistilBERT.\n1. **[ELECTRA](https://huggingface.co/transformers/model_doc/electra.html)** (来自"
" Google Research/Stanford University) 伴随论文 [ELECTRA: Pre-training text encoders as discriminators rather"
" than generators](https://arxiv.org/abs/2003.10555) 由 Kevin Clark, Minh-Thang Luong, Quoc V. Le,"
" Christopher D. Manning 发布。\n"
)
__lowerCAmelCase , __lowerCAmelCase = check_copies.convert_to_localized_md(
__a , __a , localized_readme["format_model_list"] )
self.assertFalse(__a )
self.assertEqual(__a , __a )
__lowerCAmelCase , __lowerCAmelCase = check_copies.convert_to_localized_md(
__a , __a , localized_readme["format_model_list"] )
# Check whether the number of models is equal to README.md after conversion.
self.assertTrue(__a )
__lowerCAmelCase = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (from Google Research and the"
" Toyota Technological Institute at Chicago) released with the paper [ALBERT: A Lite BERT for"
" Self-supervised Learning of Language Representations](https://arxiv.org/abs/1909.11942), by Zhenzhong"
" Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut."
)
__lowerCAmelCase = (
"1. **[ALBERT](https://huggingface.co/transformers/main/model_doc/albert.html)** (来自 Google Research and"
" the Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
__lowerCAmelCase = (
"1. **[ALBERT](https://huggingface.co/transformers/model_doc/albert.html)** (来自 Google Research and the"
" Toyota Technological Institute at Chicago) 伴随论文 [ALBERT: A Lite BERT for Self-supervised Learning of"
" Language Representations](https://arxiv.org/abs/1909.11942), 由 Zhenzhong Lan, Mingda Chen, Sebastian"
" Goodman, Kevin Gimpel, Piyush Sharma, Radu Soricut 发布。\n"
)
__lowerCAmelCase , __lowerCAmelCase = check_copies.convert_to_localized_md(
__a , __a , localized_readme["format_model_list"] )
# Check if the model link is synchronized.
self.assertEqual(__a , __a )
| 57 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( lowercase__ : Dict , lowercase__ : Dict , lowercase__ : str , lowercase__ : Tuple="attention" ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
lowerCAmelCase_ :Union[str, Any] = params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
lowerCAmelCase_ :Any = params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
lowerCAmelCase_ :Optional[int] = params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
return k, o, q, v
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Any , lowercase__ : int , lowercase__ : Any=False ) -> int:
'''simple docstring'''
if split_mlp_wi:
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
lowerCAmelCase_ :List[str] = params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
lowerCAmelCase_ :Tuple = (wi_a, wi_a)
else:
lowerCAmelCase_ :List[Any] = params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]
lowerCAmelCase_ :Dict = params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
return wi, wo
def _snake_case ( lowercase__ : Any , lowercase__ : Dict , lowercase__ : Union[str, Any] , lowercase__ : Optional[int] ) -> Tuple:
'''simple docstring'''
return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]
def _snake_case ( lowercase__ : dict , *, lowercase__ : int , lowercase__ : bool ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = traverse_util.flatten_dict(variables["""target"""] )
lowerCAmelCase_ :Tuple = {"""/""".join(lowercase__ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
lowerCAmelCase_ :Any = """encoder/layers_0/mlp/wi_0/kernel""" in old
print("""Split MLP:""" , lowercase__ )
lowerCAmelCase_ :List[Any] = collections.OrderedDict()
# Shared embeddings.
lowerCAmelCase_ :Optional[int] = old["""token_embedder/embedding"""]
# Encoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :str = tax_attention_lookup(lowercase__ , lowercase__ , """encoder""" , """attention""" )
lowerCAmelCase_ :Optional[Any] = layer_norm
lowerCAmelCase_ :Any = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Tuple = q.T
lowerCAmelCase_ :str = v.T
# Block i, layer 1 (MLP).
lowerCAmelCase_ :Dict = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Any = tax_mlp_lookup(lowercase__ , lowercase__ , """encoder""" , lowercase__ )
lowerCAmelCase_ :Union[str, Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :List[Any] = wi[0].T
lowerCAmelCase_ :Dict = wi[1].T
else:
lowerCAmelCase_ :int = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Tuple = old[
"""encoder/relpos_bias/rel_embedding"""
].T
lowerCAmelCase_ :List[str] = old["""encoder/encoder_norm/scale"""]
if not is_encoder_only:
# Decoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :Optional[Any] = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_self_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """self_attention""" )
lowerCAmelCase_ :List[Any] = layer_norm
lowerCAmelCase_ :List[str] = k.T
lowerCAmelCase_ :Any = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :Dict = v.T
# Block i, layer 1 (Cross Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_cross_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Tuple = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """encoder_decoder_attention""" )
lowerCAmelCase_ :Optional[int] = layer_norm
lowerCAmelCase_ :str = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :int = v.T
# Block i, layer 2 (MLP).
lowerCAmelCase_ :Any = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Dict = tax_mlp_lookup(lowercase__ , lowercase__ , """decoder""" , lowercase__ )
lowerCAmelCase_ :List[Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :Any = wi[0].T
lowerCAmelCase_ :Any = wi[1].T
else:
lowerCAmelCase_ :Tuple = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Optional[Any] = old["""decoder/decoder_norm/scale"""]
lowerCAmelCase_ :Optional[Any] = old[
"""decoder/relpos_bias/rel_embedding"""
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
lowerCAmelCase_ :Tuple = old["""decoder/logits_dense/kernel"""].T
return new
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : bool ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Optional[int] = state_dict["""shared.weight"""]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Tuple = state_dict["""shared.weight"""]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
lowerCAmelCase_ :Any = state_dict["""shared.weight"""]
return state_dict
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : str , lowercase__ : List[Any] , lowercase__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = checkpoints.load_tax_checkpoint(lowercase__ )
lowerCAmelCase_ :Optional[int] = convert_tax_to_pytorch(lowercase__ , num_layers=config.num_layers , is_encoder_only=lowercase__ )
lowerCAmelCase_ :Union[str, Any] = make_state_dict(lowercase__ , lowercase__ )
model.load_state_dict(lowercase__ , strict=lowercase__ )
def _snake_case ( lowercase__ : List[Any] , lowercase__ : Optional[Any] , lowercase__ : str , lowercase__ : bool = False ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Any = TaConfig.from_json_file(lowercase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
lowerCAmelCase_ :List[Any] = TaEncoderModel(lowercase__ )
else:
lowerCAmelCase_ :List[str] = TaForConditionalGeneration(lowercase__ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowercase__ )
# Verify that we can load the checkpoint.
model.from_pretrained(lowercase__ )
print("""Done""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
)
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.'
)
parser.add_argument(
'--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
)
__UpperCAmelCase = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 84 | 0 |
'''simple docstring'''
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
lowercase_ = logging.get_logger(__name__)
class a_ ( snake_case_ ):
'''simple docstring'''
UpperCamelCase = ['''input_features''']
def __init__( self , A=80 , A=1_6000 , A=160 , A=30 , A=400 , A=0.0 , A=False , **A , ) -> Dict:
super().__init__(
feature_size=A , sampling_rate=A , padding_value=A , return_attention_mask=A , **A , )
_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=A , min_frequency=0.0 , max_frequency=8000.0 , sampling_rate=A , norm="""slaney""" , mel_scale="""slaney""" , )
def snake_case_( self , A ) -> np.ndarray:
_SCREAMING_SNAKE_CASE = spectrogram(
A , 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(A , 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 snake_case_( A , A , A = 0.0 ) -> List[np.ndarray]:
if attention_mask is not None:
_SCREAMING_SNAKE_CASE = np.array(A , np.intaa )
_SCREAMING_SNAKE_CASE = []
for vector, length in zip(A , 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(A )
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 , A , A = True , A = None , A = None , A = None , A = "max_length" , A = None , A = None , A = None , **A , ) -> BatchFeature:
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(A , 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(A , (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(A , np.ndarray ):
_SCREAMING_SNAKE_CASE = np.asarray(A , dtype=np.floataa )
elif isinstance(A , 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(
A , padding=A , max_length=max_length if max_length else self.n_samples , truncation=A , pad_to_multiple_of=A , 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(A ) for waveform in input_features[0]]
if isinstance(input_features[0] , A ):
_SCREAMING_SNAKE_CASE = [np.asarray(A , 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(A )
return padded_inputs
def snake_case_( self ) -> Dict[str, Any]:
_SCREAMING_SNAKE_CASE = copy.deepcopy(self.__dict__ )
_SCREAMING_SNAKE_CASE = self.__class__.__name__
if "mel_filters" in output:
del output["mel_filters"]
return output
| 58 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( lowercase__ : Optional[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :str = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
lowerCAmelCase_ :Union[str, Any] = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
lowerCAmelCase_ :Any = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
lowerCAmelCase_ :List[str] = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
lowerCAmelCase_ :Tuple = key.replace(f"""patch_embed{idx}""" , f"""patch_embeddings.{int(lowercase__ )-1}""" )
if "norm" in key:
lowerCAmelCase_ :Dict = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
lowerCAmelCase_ :str = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
lowerCAmelCase_ :str = key.replace(f"""layer_norm{idx}""" , f"""layer_norm.{int(lowercase__ )-1}""" )
if "layer_norm1" in key:
lowerCAmelCase_ :Optional[Any] = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
lowerCAmelCase_ :str = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
lowerCAmelCase_ :List[str] = key[key.find("""block""" ) + len("""block""" )]
lowerCAmelCase_ :int = key.replace(f"""block{idx}""" , f"""block.{int(lowercase__ )-1}""" )
if "attn.q" in key:
lowerCAmelCase_ :Tuple = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
lowerCAmelCase_ :Optional[int] = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
lowerCAmelCase_ :str = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
lowerCAmelCase_ :List[Any] = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
lowerCAmelCase_ :Optional[Any] = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
lowerCAmelCase_ :List[str] = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
lowerCAmelCase_ :str = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
lowerCAmelCase_ :Any = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
lowerCAmelCase_ :str = key[key.find("""linear_c""" ) + len("""linear_c""" )]
lowerCAmelCase_ :Optional[int] = key.replace(f"""linear_c{idx}""" , f"""linear_c.{int(lowercase__ )-1}""" )
if "bot_conv" in key:
lowerCAmelCase_ :Union[str, Any] = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
lowerCAmelCase_ :int = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
lowerCAmelCase_ :str = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
lowerCAmelCase_ :Any = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
lowerCAmelCase_ :List[str] = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
lowerCAmelCase_ :Dict = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
lowerCAmelCase_ :Any = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
lowerCAmelCase_ :Tuple = key.replace("""module.last_layer_depth""" , """head.head""" )
lowerCAmelCase_ :List[Any] = value
return new_state_dict
def _snake_case ( lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
lowerCAmelCase_ :Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
lowerCAmelCase_ :Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
lowerCAmelCase_ :Optional[Any] = kv_weight[
: config.hidden_sizes[i], :
]
lowerCAmelCase_ :Union[str, Any] = kv_bias[: config.hidden_sizes[i]]
lowerCAmelCase_ :List[Any] = kv_weight[
config.hidden_sizes[i] :, :
]
lowerCAmelCase_ :int = kv_bias[config.hidden_sizes[i] :]
def _snake_case ( ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase_ :Optional[Any] = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw )
return image
@torch.no_grad()
def _snake_case ( lowercase__ : List[Any] , lowercase__ : str , lowercase__ : Dict=False , lowercase__ : List[Any]=None ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2] , decoder_hidden_size=6_4 , depths=[3, 8, 2_7, 3] )
# load image processor (only resize + rescale)
lowerCAmelCase_ :Union[str, Any] = GLPNImageProcessor()
# prepare image
lowerCAmelCase_ :List[Any] = prepare_img()
lowerCAmelCase_ :int = image_processor(images=lowercase__ , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
lowerCAmelCase_ :Tuple = torch.load(lowercase__ , map_location=torch.device("""cpu""" ) )
# rename keys
lowerCAmelCase_ :Union[str, Any] = rename_keys(lowercase__ )
# key and value matrices need special treatment
read_in_k_v(lowercase__ , lowercase__ )
# create HuggingFace model and load state dict
lowerCAmelCase_ :List[Any] = GLPNForDepthEstimation(lowercase__ )
model.load_state_dict(lowercase__ )
model.eval()
# forward pass
lowerCAmelCase_ :Dict = model(lowercase__ )
lowerCAmelCase_ :Tuple = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
lowerCAmelCase_ :Optional[Any] = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
elif "kitti" in model_name:
lowerCAmelCase_ :Any = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
else:
raise ValueError(f"""Unknown model name: {model_name}""" )
lowerCAmelCase_ :Union[str, Any] = torch.Size([1, 4_8_0, 6_4_0] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , lowercase__ , atol=1E-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(lowercase__ , lowercase__ ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=lowercase__ , )
image_processor.push_to_hub(
repo_path_or_name=Path(lowercase__ , lowercase__ ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=lowercase__ , )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
__UpperCAmelCase = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 84 | 0 |
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version
from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging
__lowerCamelCase = (
"""https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)
__lowerCamelCase = logging.get_logger(__name__) # pylint: disable=invalid-name
def UpperCamelCase ( ):
snake_case : Optional[Any] = "https://pypi.org/pypi/diffusers/json"
snake_case : List[str] = json.loads(request.urlopen(__lowerCamelCase ).read() )["releases"].keys()
return sorted(__lowerCamelCase , key=lambda __lowerCamelCase : version.Version(__lowerCamelCase ) )
def UpperCamelCase ( ):
# This function has already been executed if HF_MODULES_CACHE already is in the Python path.
if HF_MODULES_CACHE in sys.path:
return
sys.path.append(__lowerCamelCase )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
snake_case : int = Path(__lowerCamelCase ) / "__init__.py"
if not init_path.exists():
init_path.touch()
def UpperCamelCase ( __lowerCamelCase : Union[str, os.PathLike] ):
init_hf_modules()
snake_case : Union[str, Any] = Path(__lowerCamelCase ) / name
# If the parent module does not exist yet, recursively create it.
if not dynamic_module_path.parent.exists():
create_dynamic_module(dynamic_module_path.parent )
os.makedirs(__lowerCamelCase , exist_ok=__lowerCamelCase )
snake_case : Dict = dynamic_module_path / "__init__.py"
if not init_path.exists():
init_path.touch()
def UpperCamelCase ( __lowerCamelCase : Union[str, Any] ):
with open(__lowerCamelCase , "r" , encoding="utf-8" ) as f:
snake_case : Tuple = f.read()
# Imports of the form `import .xxx`
snake_case : Any = re.findall("^\s*import\s+\.(\S+)\s*$" , __lowerCamelCase , flags=re.MULTILINE )
# Imports of the form `from .xxx import yyy`
relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import" , __lowerCamelCase , flags=re.MULTILINE )
# Unique-ify
return list(set(__lowerCamelCase ) )
def UpperCamelCase ( __lowerCamelCase : int ):
snake_case : Union[str, Any] = False
snake_case : str = [module_file]
snake_case : str = []
# Let's recurse through all relative imports
while not no_change:
snake_case : int = []
for f in files_to_check:
new_imports.extend(get_relative_imports(__lowerCamelCase ) )
snake_case : Any = Path(__lowerCamelCase ).parent
snake_case : str = [str(module_path / m ) for m in new_imports]
snake_case : Tuple = [f for f in new_import_files if f not in all_relative_imports]
snake_case : Optional[int] = [f"""{f}.py""" for f in new_import_files]
snake_case : Optional[Any] = len(__lowerCamelCase ) == 0
all_relative_imports.extend(__lowerCamelCase )
return all_relative_imports
def UpperCamelCase ( __lowerCamelCase : Union[str, Any] ):
with open(__lowerCamelCase , "r" , encoding="utf-8" ) as f:
snake_case : int = f.read()
# Imports of the form `import xxx`
snake_case : Union[str, Any] = re.findall("^\s*import\s+(\S+)\s*$" , __lowerCamelCase , flags=re.MULTILINE )
# Imports of the form `from xxx import yyy`
imports += re.findall("^\s*from\s+(\S+)\s+import" , __lowerCamelCase , flags=re.MULTILINE )
# Only keep the top-level module
snake_case : Dict = [imp.split("." )[0] for imp in imports if not imp.startswith("." )]
# Unique-ify and test we got them all
snake_case : Optional[Any] = list(set(__lowerCamelCase ) )
snake_case : Union[str, Any] = []
for imp in imports:
try:
importlib.import_module(__lowerCamelCase )
except ImportError:
missing_packages.append(__lowerCamelCase )
if len(__lowerCamelCase ) > 0:
raise ImportError(
"This modeling file requires the following packages that were not found in your environment: "
f"""{', '.join(__lowerCamelCase )}. Run `pip install {' '.join(__lowerCamelCase )}`""" )
return get_relative_imports(__lowerCamelCase )
def UpperCamelCase ( __lowerCamelCase : int , __lowerCamelCase : Any ):
snake_case : List[str] = module_path.replace(os.path.sep , "." )
snake_case : List[Any] = importlib.import_module(__lowerCamelCase )
if class_name is None:
return find_pipeline_class(__lowerCamelCase )
return getattr(__lowerCamelCase , __lowerCamelCase )
def UpperCamelCase ( __lowerCamelCase : List[str] ):
from ..pipelines import DiffusionPipeline
snake_case : List[str] = dict(inspect.getmembers(__lowerCamelCase , inspect.isclass ) )
snake_case : str = None
for cls_name, cls in cls_members.items():
if (
cls_name != DiffusionPipeline.__name__
and issubclass(cls , __lowerCamelCase )
and cls.__module__.split("." )[0] != "diffusers"
):
if pipeline_class is not None:
raise ValueError(
f"""Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:"""
f""" {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in"""
f""" {loaded_module}.""" )
snake_case : Dict = cls
return pipeline_class
def UpperCamelCase ( __lowerCamelCase : Union[str, os.PathLike] , __lowerCamelCase : str , __lowerCamelCase : Optional[Union[str, os.PathLike]] = None , __lowerCamelCase : bool = False , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[Dict[str, str]] = None , __lowerCamelCase : Optional[Union[bool, str]] = None , __lowerCamelCase : Optional[str] = None , __lowerCamelCase : bool = False , ):
snake_case : List[str] = str(__lowerCamelCase )
snake_case : Optional[Any] = os.path.join(__lowerCamelCase , __lowerCamelCase )
if os.path.isfile(__lowerCamelCase ):
snake_case : Optional[int] = module_file_or_url
snake_case : List[str] = "local"
elif pretrained_model_name_or_path.count("/" ) == 0:
snake_case : Optional[Any] = get_diffusers_versions()
# cut ".dev0"
snake_case : str = "v" + ".".join(__version__.split("." )[:3] )
# retrieve github version that matches
if revision is None:
snake_case : Dict = latest_version if latest_version[1:] in available_versions else "main"
logger.info(f"""Defaulting to latest_version: {revision}.""" )
elif revision in available_versions:
snake_case : Dict = f"""v{revision}"""
elif revision == "main":
snake_case : str = revision
else:
raise ValueError(
f"""`custom_revision`: {revision} does not exist. Please make sure to choose one of"""
f""" {', '.join(available_versions + ['main'] )}.""" )
# community pipeline on GitHub
snake_case : Any = COMMUNITY_PIPELINES_URL.format(revision=__lowerCamelCase , pipeline=__lowerCamelCase )
try:
snake_case : List[Any] = cached_download(
__lowerCamelCase , cache_dir=__lowerCamelCase , force_download=__lowerCamelCase , proxies=__lowerCamelCase , resume_download=__lowerCamelCase , local_files_only=__lowerCamelCase , use_auth_token=__lowerCamelCase , )
snake_case : Tuple = "git"
snake_case : Optional[int] = pretrained_model_name_or_path + ".py"
except EnvironmentError:
logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
else:
try:
# Load from URL or cache if already cached
snake_case : List[Any] = hf_hub_download(
__lowerCamelCase , __lowerCamelCase , cache_dir=__lowerCamelCase , force_download=__lowerCamelCase , proxies=__lowerCamelCase , resume_download=__lowerCamelCase , local_files_only=__lowerCamelCase , use_auth_token=__lowerCamelCase , )
snake_case : Union[str, Any] = os.path.join("local" , "--".join(pretrained_model_name_or_path.split("/" ) ) )
except EnvironmentError:
logger.error(f"""Could not locate the {module_file} inside {pretrained_model_name_or_path}.""" )
raise
# Check we have all the requirements in our environment
snake_case : List[str] = check_imports(__lowerCamelCase )
# Now we move the module inside our cached dynamic modules.
snake_case : Tuple = DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
create_dynamic_module(__lowerCamelCase )
snake_case : Union[str, Any] = Path(__lowerCamelCase ) / full_submodule
if submodule == "local" or submodule == "git":
# We always copy local files (we could hash the file to see if there was a change, and give them the name of
# that hash, to only copy when there is a modification but it seems overkill for now).
# The only reason we do the copy is to avoid putting too many folders in sys.path.
shutil.copy(__lowerCamelCase , submodule_path / module_file )
for module_needed in modules_needed:
snake_case : str = f"""{module_needed}.py"""
shutil.copy(os.path.join(__lowerCamelCase , __lowerCamelCase ) , submodule_path / module_needed )
else:
# Get the commit hash
# TODO: we will get this info in the etag soon, so retrieve it from there and not here.
if isinstance(__lowerCamelCase , __lowerCamelCase ):
snake_case : Any = use_auth_token
elif use_auth_token is True:
snake_case : Dict = HfFolder.get_token()
else:
snake_case : Optional[Any] = None
snake_case : Optional[Any] = model_info(__lowerCamelCase , revision=__lowerCamelCase , token=__lowerCamelCase ).sha
# The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
# benefit of versioning.
snake_case : Any = submodule_path / commit_hash
snake_case : List[Any] = full_submodule + os.path.sep + commit_hash
create_dynamic_module(__lowerCamelCase )
if not (submodule_path / module_file).exists():
shutil.copy(__lowerCamelCase , submodule_path / module_file )
# Make sure we also have every file with relative
for module_needed in modules_needed:
if not (submodule_path / module_needed).exists():
get_cached_module_file(
__lowerCamelCase , f"""{module_needed}.py""" , cache_dir=__lowerCamelCase , force_download=__lowerCamelCase , resume_download=__lowerCamelCase , proxies=__lowerCamelCase , use_auth_token=__lowerCamelCase , revision=__lowerCamelCase , local_files_only=__lowerCamelCase , )
return os.path.join(__lowerCamelCase , __lowerCamelCase )
def UpperCamelCase ( __lowerCamelCase : Union[str, os.PathLike] , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None , __lowerCamelCase : Optional[Union[str, os.PathLike]] = None , __lowerCamelCase : bool = False , __lowerCamelCase : bool = False , __lowerCamelCase : Optional[Dict[str, str]] = None , __lowerCamelCase : Optional[Union[bool, str]] = None , __lowerCamelCase : Optional[str] = None , __lowerCamelCase : bool = False , **__lowerCamelCase : Tuple , ):
snake_case : int = get_cached_module_file(
__lowerCamelCase , __lowerCamelCase , cache_dir=__lowerCamelCase , force_download=__lowerCamelCase , resume_download=__lowerCamelCase , proxies=__lowerCamelCase , use_auth_token=__lowerCamelCase , revision=__lowerCamelCase , local_files_only=__lowerCamelCase , )
return get_class_in_module(__lowerCamelCase , final_module.replace(".py" , "" ) )
| 59 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoCBertForCausalLM',
'RoCBertForMaskedLM',
'RoCBertForMultipleChoice',
'RoCBertForPreTraining',
'RoCBertForQuestionAnswering',
'RoCBertForSequenceClassification',
'RoCBertForTokenClassification',
'RoCBertLayer',
'RoCBertModel',
'RoCBertPreTrainedModel',
'load_tf_weights_in_roc_bert',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 84 | 0 |
"""simple docstring"""
import math
def _snake_case ( _snake_case : List[Any] , _snake_case : Any ):
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(_snake_case )
else:
if x == 0: # 0 raised to any number is 0
return 0
elif y == 0:
return 1 # any number raised to 0 is 1
raise AssertionError('''This should never happen''' )
if __name__ == "__main__": # Main function
# Read two numbers from input and typecast them to int using map function.
# Here x is the base and y is the power.
snake_case__ : List[Any] = '''Enter the base and the power separated by a comma: '''
snake_case__ , snake_case__ : Optional[int] = map(int, input(prompt).split(''','''))
snake_case__ , snake_case__ : str = map(int, input(prompt).split(''','''))
# We find the log of each number, using the function res(), which takes two
# arguments.
snake_case__ : str = res(xa, ya)
snake_case__ : Tuple = res(xa, ya)
# We check for the largest number
if resa > resa:
print('''Largest number is''', xa, '''^''', ya)
elif resa > resa:
print('''Largest number is''', xa, '''^''', ya)
else:
print('''Both are equal''')
| 60 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "levit"
def __init__( self , __A=224 , __A=3 , __A=3 , __A=2 , __A=1 , __A=16 , __A=[128, 256, 384] , __A=[4, 8, 12] , __A=[4, 4, 4] , __A=[16, 16, 16] , __A=0 , __A=[2, 2, 2] , __A=[2, 2, 2] , __A=0.0_2 , **__A , ) -> Any:
super().__init__(**__A )
lowerCAmelCase_ :Tuple = image_size
lowerCAmelCase_ :Optional[int] = num_channels
lowerCAmelCase_ :Union[str, Any] = kernel_size
lowerCAmelCase_ :Optional[Any] = stride
lowerCAmelCase_ :Optional[int] = padding
lowerCAmelCase_ :Optional[Any] = hidden_sizes
lowerCAmelCase_ :Optional[int] = num_attention_heads
lowerCAmelCase_ :int = depths
lowerCAmelCase_ :List[str] = key_dim
lowerCAmelCase_ :str = drop_path_rate
lowerCAmelCase_ :Optional[int] = patch_size
lowerCAmelCase_ :Union[str, Any] = attention_ratio
lowerCAmelCase_ :Dict = mlp_ratio
lowerCAmelCase_ :Any = initializer_range
lowerCAmelCase_ :Optional[int] = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Tuple = version.parse("1.11" )
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __lowerCAmelCase ( self ) -> float:
return 1E-4
| 84 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_a = logging.get_logger(__name__)
_a = {
'alibaba-damo/mgp-str-base': 'https://huggingface.co/alibaba-damo/mgp-str-base/resolve/main/config.json',
}
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple = """mgp-str"""
def __init__( self , lowercase_=[32, 128] , lowercase_=4 , lowercase_=3 , lowercase_=27 , lowercase_=38 , lowercase_=5_0257 , lowercase_=3_0522 , lowercase_=768 , lowercase_=12 , lowercase_=12 , lowercase_=4.0 , lowercase_=True , lowercase_=False , lowercase_=1E-5 , lowercase_=0.0 , lowercase_=0.0 , lowercase_=0.0 , lowercase_=False , lowercase_=0.02 , **lowercase_ , ):
"""simple docstring"""
super().__init__(**lowercase_ )
UpperCAmelCase_ : List[str] = image_size
UpperCAmelCase_ : int = patch_size
UpperCAmelCase_ : Tuple = num_channels
UpperCAmelCase_ : Dict = max_token_length
UpperCAmelCase_ : Optional[int] = num_character_labels
UpperCAmelCase_ : Optional[int] = num_bpe_labels
UpperCAmelCase_ : Optional[int] = num_wordpiece_labels
UpperCAmelCase_ : Optional[int] = hidden_size
UpperCAmelCase_ : int = num_hidden_layers
UpperCAmelCase_ : List[str] = num_attention_heads
UpperCAmelCase_ : str = mlp_ratio
UpperCAmelCase_ : Any = distilled
UpperCAmelCase_ : Union[str, Any] = layer_norm_eps
UpperCAmelCase_ : Dict = drop_rate
UpperCAmelCase_ : str = qkv_bias
UpperCAmelCase_ : Tuple = attn_drop_rate
UpperCAmelCase_ : List[Any] = drop_path_rate
UpperCAmelCase_ : Optional[Any] = output_aa_attentions
UpperCAmelCase_ : List[str] = initializer_range
| 61 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( lowercase__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = FileLock(str(tmpdir / """foo.lock""" ) )
lowerCAmelCase_ :Union[str, Any] = FileLock(str(tmpdir / """foo.lock""" ) )
lowerCAmelCase_ :Dict = 0.01
with locka.acquire():
with pytest.raises(lowercase__ ):
lowerCAmelCase_ :List[Any] = time.time()
locka.acquire(lowercase__ )
assert time.time() - _start > timeout
def _snake_case ( lowercase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = """a""" * 1_0_0_0 + """.lock"""
lowerCAmelCase_ :Optional[Any] = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith(""".lock""" )
assert not locka._lock_file.endswith(lowercase__ )
assert len(os.path.basename(locka._lock_file ) ) <= 2_5_5
lowerCAmelCase_ :Any = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(lowercase__ ):
locka.acquire(0 )
| 84 | 0 |
import sys
_A = (
'73167176531330624919225119674426574742355349194934'
'96983520312774506326239578318016984801869478851843'
'85861560789112949495459501737958331952853208805511'
'12540698747158523863050715693290963295227443043557'
'66896648950445244523161731856403098711121722383113'
'62229893423380308135336276614282806444486645238749'
'30358907296290491560440772390713810515859307960866'
'70172427121883998797908792274921901699720888093776'
'65727333001053367881220235421809751254540594752243'
'52584907711670556013604839586446706324415722155397'
'53697817977846174064955149290862569321978468622482'
'83972241375657056057490261407972968652414535100474'
'82166370484403199890008895243450658541227588666881'
'16427171479924442928230863465674813919123162824586'
'17866458359124566529476545682848912883142607690042'
'24219022671055626321111109370544217506941658960408'
'07198403850962455444362981230987879927244284909188'
'84580156166097919133875499200524063689912560717606'
'05886116467109405077541002256983155200055935729725'
'71636269561882670428252483600823257530420752963450'
)
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : str = N ):
__UpperCamelCase =-sys.maxsize - 1
for i in range(len(SCREAMING_SNAKE_CASE__ ) - 12 ):
__UpperCamelCase =1
for j in range(13 ):
product *= int(n[i + j] )
if product > largest_product:
__UpperCamelCase =product
return largest_product
if __name__ == "__main__":
print(f"""{solution() = }""")
| 62 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__UpperCAmelCase = 1.054571817e-34 # unit of ℏ : J * s
__UpperCAmelCase = 3e8 # unit of c : m * s^-1
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float ) -> dict[str, float]:
'''simple docstring'''
if (force, area, distance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if force < 0:
raise ValueError("""Magnitude of force can not be negative""" )
if distance < 0:
raise ValueError("""Distance can not be negative""" )
if area < 0:
raise ValueError("""Area can not be negative""" )
if force == 0:
lowerCAmelCase_ :Union[str, Any] = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
2_4_0 * (distance) ** 4
)
return {"force": force}
elif area == 0:
lowerCAmelCase_ :Optional[Any] = (2_4_0 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
lowerCAmelCase_ :Any = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError("""One and only one argument must be 0""" )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import DetrImageProcessor
class __SCREAMING_SNAKE_CASE (unittest.TestCase ):
"""simple docstring"""
def __init__( self : List[str] , __a : Dict , __a : List[str]=7 , __a : int=3 , __a : Optional[int]=30 , __a : Optional[Any]=4_00 , __a : List[str]=True , __a : Tuple=None , __a : Optional[int]=True , __a : Dict=1 / 2_55 , __a : Any=True , __a : Optional[int]=[0.5, 0.5, 0.5] , __a : Any=[0.5, 0.5, 0.5] , __a : Any=True , ):
# by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
_a = size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
_a = parent
_a = batch_size
_a = num_channels
_a = min_resolution
_a = max_resolution
_a = do_resize
_a = size
_a = do_rescale
_a = rescale_factor
_a = do_normalize
_a = image_mean
_a = image_std
_a = do_pad
def UpperCamelCase__ ( self : Optional[int] ):
return {
"do_resize": self.do_resize,
"size": self.size,
"do_rescale": self.do_rescale,
"rescale_factor": self.rescale_factor,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_pad": self.do_pad,
}
def UpperCamelCase__ ( self : List[str] , __a : List[Any] , __a : List[str]=False ):
if not batched:
_a = image_inputs[0]
if isinstance(__a , Image.Image ):
_a , _a = image.size
else:
_a , _a = image.shape[1], image.shape[2]
if w < h:
_a = int(self.size["shortest_edge"] * h / w )
_a = self.size["shortest_edge"]
elif w > h:
_a = self.size["shortest_edge"]
_a = int(self.size["shortest_edge"] * w / h )
else:
_a = self.size["shortest_edge"]
_a = self.size["shortest_edge"]
else:
_a = []
for image in image_inputs:
_a , _a = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
_a = max(__a , key=lambda __a : item[0] )[0]
_a = max(__a , key=lambda __a : item[1] )[1]
return expected_height, expected_width
@require_torch
@require_vision
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ , unittest.TestCase ):
"""simple docstring"""
__a =DetrImageProcessor if is_vision_available() else None
def UpperCamelCase__ ( self : Optional[Any] ):
_a = DetrImageProcessingTester(self )
@property
def UpperCamelCase__ ( self : Optional[Any] ):
return self.image_processor_tester.prepare_image_processor_dict()
def UpperCamelCase__ ( self : Dict ):
_a = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(__a , "image_mean" ) )
self.assertTrue(hasattr(__a , "image_std" ) )
self.assertTrue(hasattr(__a , "do_normalize" ) )
self.assertTrue(hasattr(__a , "do_rescale" ) )
self.assertTrue(hasattr(__a , "rescale_factor" ) )
self.assertTrue(hasattr(__a , "do_resize" ) )
self.assertTrue(hasattr(__a , "size" ) )
self.assertTrue(hasattr(__a , "do_pad" ) )
def UpperCamelCase__ ( self : Tuple ):
_a = self.image_processing_class.from_dict(self.image_processor_dict )
self.assertEqual(image_processor.size , {"shortest_edge": 18, "longest_edge": 13_33} )
self.assertEqual(image_processor.do_pad , __a )
_a = self.image_processing_class.from_dict(
self.image_processor_dict , size=42 , max_size=84 , pad_and_return_pixel_mask=__a )
self.assertEqual(image_processor.size , {"shortest_edge": 42, "longest_edge": 84} )
self.assertEqual(image_processor.do_pad , __a )
def UpperCamelCase__ ( self : Dict ):
pass
def UpperCamelCase__ ( self : Union[str, Any] ):
# Initialize image_processing
_a = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a )
for image in image_inputs:
self.assertIsInstance(__a , Image.Image )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
_a , _a = self.image_processor_tester.get_expected_values(__a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_a , _a = self.image_processor_tester.get_expected_values(__a , batched=__a )
_a = image_processing(__a , return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase__ ( self : int ):
# Initialize image_processing
_a = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , numpify=__a )
for image in image_inputs:
self.assertIsInstance(__a , np.ndarray )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
_a , _a = self.image_processor_tester.get_expected_values(__a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_a = image_processing(__a , return_tensors="pt" ).pixel_values
_a , _a = self.image_processor_tester.get_expected_values(__a , batched=__a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
def UpperCamelCase__ ( self : List[str] ):
# Initialize image_processing
_a = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
_a = prepare_image_inputs(self.image_processor_tester , equal_resolution=__a , torchify=__a )
for image in image_inputs:
self.assertIsInstance(__a , torch.Tensor )
# Test not batched input
_a = image_processing(image_inputs[0] , return_tensors="pt" ).pixel_values
_a , _a = self.image_processor_tester.get_expected_values(__a )
self.assertEqual(
encoded_images.shape , (1, self.image_processor_tester.num_channels, expected_height, expected_width) , )
# Test batched
_a = image_processing(__a , return_tensors="pt" ).pixel_values
_a , _a = self.image_processor_tester.get_expected_values(__a , batched=__a )
self.assertEqual(
encoded_images.shape , (
self.image_processor_tester.batch_size,
self.image_processor_tester.num_channels,
expected_height,
expected_width,
) , )
@slow
def UpperCamelCase__ ( self : List[Any] ):
# prepare image and target
_a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt" , "r" ) as f:
_a = json.loads(f.read() )
_a = {"image_id": 3_97_69, "annotations": target}
# encode them
_a = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50" )
_a = image_processing(images=__a , annotations=__a , return_tensors="pt" )
# verify pixel values
_a = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , __a )
_a = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , __a , atol=1e-4 ) )
# verify area
_a = torch.tensor([5887.9600, 11250.2061, 489353.8438, 837122.7500, 147967.5156, 165732.3438] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , __a ) )
# verify boxes
_a = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , __a )
_a = torch.tensor([0.5503, 0.2765, 0.0604, 0.2215] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , __a , atol=1e-3 ) )
# verify image_id
_a = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , __a ) )
# verify is_crowd
_a = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , __a ) )
# verify class_labels
_a = torch.tensor([75, 75, 63, 65, 17, 17] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , __a ) )
# verify orig_size
_a = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , __a ) )
# verify size
_a = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , __a ) )
@slow
def UpperCamelCase__ ( self : List[str] ):
# prepare image, target and masks_path
_a = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt" , "r" ) as f:
_a = json.loads(f.read() )
_a = {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}
_a = pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic" )
# encode them
_a = DetrImageProcessor.from_pretrained("facebook/detr-resnet-50-panoptic" )
_a = image_processing(images=__a , annotations=__a , masks_path=__a , return_tensors="pt" )
# verify pixel values
_a = torch.Size([1, 3, 8_00, 10_66] )
self.assertEqual(encoding["pixel_values"].shape , __a )
_a = torch.tensor([0.2796, 0.3138, 0.3481] )
self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3] , __a , atol=1e-4 ) )
# verify area
_a = torch.tensor([147979.6875, 165527.0469, 484638.5938, 11292.9375, 5879.6562, 7634.1147] )
self.assertTrue(torch.allclose(encoding["labels"][0]["area"] , __a ) )
# verify boxes
_a = torch.Size([6, 4] )
self.assertEqual(encoding["labels"][0]["boxes"].shape , __a )
_a = torch.tensor([0.2625, 0.5437, 0.4688, 0.8625] )
self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0] , __a , atol=1e-3 ) )
# verify image_id
_a = torch.tensor([3_97_69] )
self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"] , __a ) )
# verify is_crowd
_a = torch.tensor([0, 0, 0, 0, 0, 0] )
self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"] , __a ) )
# verify class_labels
_a = torch.tensor([17, 17, 63, 75, 75, 93] )
self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"] , __a ) )
# verify masks
_a = 82_28_73
self.assertEqual(encoding["labels"][0]["masks"].sum().item() , __a )
# verify orig_size
_a = torch.tensor([4_80, 6_40] )
self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"] , __a ) )
# verify size
_a = torch.tensor([8_00, 10_66] )
self.assertTrue(torch.allclose(encoding["labels"][0]["size"] , __a ) )
| 63 |
"""simple docstring"""
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> int:
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("""String lengths must match!""" )
lowerCAmelCase_ :Optional[int] = 0
for chara, chara in zip(lowercase__ , lowercase__ ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 0 |
"""simple docstring"""
import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class lowercase( __a ):
'''simple docstring'''
lowercase__ = 42
lowercase__ = None
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : List[Any]=0.9_99 , snake_case__ : Dict="cosine" , ):
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(snake_case__ : Dict ):
return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(snake_case__ : Union[str, Any] ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}" )
_snake_case : int = []
for i in range(snake_case__ ):
_snake_case : Tuple = i / num_diffusion_timesteps
_snake_case : Union[str, Any] = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(snake_case__ ) / alpha_bar_fn(snake_case__ ) , snake_case__ ) )
return torch.tensor(snake_case__ , dtype=torch.floataa )
class lowercase( __a , __a ):
'''simple docstring'''
@register_to_config
def __init__( self: List[str], a_: int = 1_000, a_: str = "fixed_small_log", a_: bool = True, a_: Optional[float] = 1.0, a_: str = "epsilon", a_: str = "squaredcos_cap_v2", ):
'''simple docstring'''
if beta_schedule != "squaredcos_cap_v2":
raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'""" )
_snake_case : Tuple = betas_for_alpha_bar(a_ )
_snake_case : List[str] = 1.0 - self.betas
_snake_case : Optional[int] = torch.cumprod(self.alphas, dim=0 )
_snake_case : Tuple = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
_snake_case : Optional[Any] = 1.0
# setable values
_snake_case : str = None
_snake_case : Tuple = torch.from_numpy(np.arange(0, a_ )[::-1].copy() )
_snake_case : int = variance_type
def UpperCamelCase_ ( self: Dict, a_: torch.FloatTensor, a_: Optional[int] = None ):
'''simple docstring'''
return sample
def UpperCamelCase_ ( self: Any, a_: int, a_: Union[str, torch.device] = None ):
'''simple docstring'''
_snake_case : List[Any] = num_inference_steps
_snake_case : Any = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
_snake_case : Optional[int] = (np.arange(0, a_ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
_snake_case : Optional[Any] = torch.from_numpy(a_ ).to(a_ )
def UpperCamelCase_ ( self: List[Any], a_: Tuple, a_: Dict=None, a_: Union[str, Any]=None, a_: Tuple=None ):
'''simple docstring'''
if prev_timestep is None:
_snake_case : Optional[Any] = t - 1
_snake_case : Dict = self.alphas_cumprod[t]
_snake_case : List[Any] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
_snake_case : List[str] = 1 - alpha_prod_t
_snake_case : List[Any] = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
_snake_case : int = self.betas[t]
else:
_snake_case : Union[str, Any] = 1 - alpha_prod_t / alpha_prod_t_prev
# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
# and sample from it to get previous sample
# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
_snake_case : int = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
_snake_case : Any = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
_snake_case : Optional[int] = torch.log(torch.clamp(a_, min=1E-20 ) )
_snake_case : int = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
_snake_case : Optional[Any] = variance.log()
_snake_case : List[str] = beta.log()
_snake_case : Optional[int] = (predicted_variance + 1) / 2
_snake_case : Dict = frac * max_log + (1 - frac) * min_log
return variance
def UpperCamelCase_ ( self: Optional[Any], a_: torch.FloatTensor, a_: int, a_: torch.FloatTensor, a_: Optional[int] = None, a_: Tuple=None, a_: bool = True, ):
'''simple docstring'''
_snake_case : List[Any] = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
_snake_case , _snake_case : str = torch.split(a_, sample.shape[1], dim=1 )
else:
_snake_case : Dict = None
# 1. compute alphas, betas
if prev_timestep is None:
_snake_case : Optional[Any] = t - 1
_snake_case : Optional[int] = self.alphas_cumprod[t]
_snake_case : Optional[int] = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
_snake_case : Optional[Any] = 1 - alpha_prod_t
_snake_case : int = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
_snake_case : str = self.betas[t]
_snake_case : int = self.alphas[t]
else:
_snake_case : Dict = 1 - alpha_prod_t / alpha_prod_t_prev
_snake_case : str = 1 - beta
# 2. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
if self.config.prediction_type == "epsilon":
_snake_case : List[str] = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
_snake_case : Tuple = model_output
else:
raise ValueError(
f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
""" for the UnCLIPScheduler.""" )
# 3. Clip "predicted x_0"
if self.config.clip_sample:
_snake_case : int = torch.clamp(
a_, -self.config.clip_sample_range, self.config.clip_sample_range )
# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_snake_case : List[Any] = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
_snake_case : Any = alpha ** 0.5 * beta_prod_t_prev / beta_prod_t
# 5. Compute predicted previous sample µ_t
# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
_snake_case : Dict = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
_snake_case : Any = 0
if t > 0:
_snake_case : str = randn_tensor(
model_output.shape, dtype=model_output.dtype, generator=a_, device=model_output.device )
_snake_case : Union[str, Any] = self._get_variance(
a_, predicted_variance=a_, prev_timestep=a_, )
if self.variance_type == "fixed_small_log":
_snake_case : Tuple = variance
elif self.variance_type == "learned_range":
_snake_case : int = (0.5 * variance).exp()
else:
raise ValueError(
f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
""" for the UnCLIPScheduler.""" )
_snake_case : List[Any] = variance * variance_noise
_snake_case : Any = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=a_, pred_original_sample=a_ )
def UpperCamelCase_ ( self: Union[str, Any], a_: torch.FloatTensor, a_: torch.FloatTensor, a_: torch.IntTensor, ):
'''simple docstring'''
_snake_case : List[Any] = self.alphas_cumprod.to(device=original_samples.device, dtype=original_samples.dtype )
_snake_case : str = timesteps.to(original_samples.device )
_snake_case : Union[str, Any] = alphas_cumprod[timesteps] ** 0.5
_snake_case : List[str] = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
_snake_case : Optional[Any] = sqrt_alpha_prod.unsqueeze(-1 )
_snake_case : Tuple = (1 - alphas_cumprod[timesteps]) ** 0.5
_snake_case : Optional[Any] = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
_snake_case : str = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
_snake_case : Any = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 64 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A ) -> Optional[Any]:
super().__init__()
lowerCAmelCase_ :int = nn.ModuleList(__A )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A = None , __A = None , __A = None , __A = None , __A = False , __A = True , ) -> Union[ControlNetOutput, Tuple]:
for i, (image, scale, controlnet) in enumerate(zip(__A , __A , self.nets ) ):
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = controlnet(
__A , __A , __A , __A , __A , __A , __A , __A , __A , __A , __A , )
# merge samples
if i == 0:
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = down_samples, mid_sample
else:
lowerCAmelCase_ :str = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(__A , __A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def __lowerCAmelCase ( self , __A , __A = True , __A = None , __A = False , __A = None , ) -> Optional[Any]:
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Dict = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
__A , is_main_process=__A , save_function=__A , safe_serialization=__A , variant=__A , )
idx += 1
lowerCAmelCase_ :Any = model_path_to_save + f"""_{idx}"""
@classmethod
def __lowerCAmelCase ( cls , __A , **__A ) -> List[Any]:
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Dict = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
lowerCAmelCase_ :List[Any] = pretrained_model_path
while os.path.isdir(__A ):
lowerCAmelCase_ :Tuple = ControlNetModel.from_pretrained(__A , **__A )
controlnets.append(__A )
idx += 1
lowerCAmelCase_ :Dict = pretrained_model_path + f"""_{idx}"""
logger.info(f"""{len(__A )} controlnets loaded from {pretrained_model_path}.""" )
if len(__A ) == 0:
raise ValueError(
f"""No ControlNets found under {os.path.dirname(__A )}. Expected at least {pretrained_model_path + "_0"}.""" )
return cls(__A )
| 84 | 0 |
import argparse
import os.path as osp
import re
import torch
from safetensors.torch import load_file, save_file
# =================#
# UNet Conversion #
# =================#
UpperCamelCase__ = [
# (stable-diffusion, HF Diffusers)
('time_embed.0.weight', 'time_embedding.linear_1.weight'),
('time_embed.0.bias', 'time_embedding.linear_1.bias'),
('time_embed.2.weight', 'time_embedding.linear_2.weight'),
('time_embed.2.bias', 'time_embedding.linear_2.bias'),
('input_blocks.0.0.weight', 'conv_in.weight'),
('input_blocks.0.0.bias', 'conv_in.bias'),
('out.0.weight', 'conv_norm_out.weight'),
('out.0.bias', 'conv_norm_out.bias'),
('out.2.weight', 'conv_out.weight'),
('out.2.bias', 'conv_out.bias'),
]
UpperCamelCase__ = [
# (stable-diffusion, HF Diffusers)
('in_layers.0', 'norm1'),
('in_layers.2', 'conv1'),
('out_layers.0', 'norm2'),
('out_layers.3', 'conv2'),
('emb_layers.1', 'time_emb_proj'),
('skip_connection', 'conv_shortcut'),
]
UpperCamelCase__ = []
# hardcoded number of downblocks and resnets/attentions...
# would need smarter logic for other networks.
for i in range(4):
# loop over downblocks/upblocks
for j in range(2):
# loop over resnets/attentions for downblocks
UpperCamelCase__ = f'''down_blocks.{i}.resnets.{j}.'''
UpperCamelCase__ = f'''input_blocks.{3*i + j + 1}.0.'''
unet_conversion_map_layer.append((sd_down_res_prefix, hf_down_res_prefix))
if i < 3:
# no attention layers in down_blocks.3
UpperCamelCase__ = f'''down_blocks.{i}.attentions.{j}.'''
UpperCamelCase__ = f'''input_blocks.{3*i + j + 1}.1.'''
unet_conversion_map_layer.append((sd_down_atn_prefix, hf_down_atn_prefix))
for j in range(3):
# loop over resnets/attentions for upblocks
UpperCamelCase__ = f'''up_blocks.{i}.resnets.{j}.'''
UpperCamelCase__ = f'''output_blocks.{3*i + j}.0.'''
unet_conversion_map_layer.append((sd_up_res_prefix, hf_up_res_prefix))
if i > 0:
# no attention layers in up_blocks.0
UpperCamelCase__ = f'''up_blocks.{i}.attentions.{j}.'''
UpperCamelCase__ = f'''output_blocks.{3*i + j}.1.'''
unet_conversion_map_layer.append((sd_up_atn_prefix, hf_up_atn_prefix))
if i < 3:
# no downsample in down_blocks.3
UpperCamelCase__ = f'''down_blocks.{i}.downsamplers.0.conv.'''
UpperCamelCase__ = f'''input_blocks.{3*(i+1)}.0.op.'''
unet_conversion_map_layer.append((sd_downsample_prefix, hf_downsample_prefix))
# no upsample in up_blocks.3
UpperCamelCase__ = f'''up_blocks.{i}.upsamplers.0.'''
UpperCamelCase__ = f'''output_blocks.{3*i + 2}.{1 if i == 0 else 2}.'''
unet_conversion_map_layer.append((sd_upsample_prefix, hf_upsample_prefix))
UpperCamelCase__ = 'mid_block.attentions.0.'
UpperCamelCase__ = 'middle_block.1.'
unet_conversion_map_layer.append((sd_mid_atn_prefix, hf_mid_atn_prefix))
for j in range(2):
UpperCamelCase__ = f'''mid_block.resnets.{j}.'''
UpperCamelCase__ = f'''middle_block.{2*j}.'''
unet_conversion_map_layer.append((sd_mid_res_prefix, hf_mid_res_prefix))
def lowerCAmelCase_ ( __A ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ = {k: k for k in unet_state_dict.keys()}
for sd_name, hf_name in unet_conversion_map:
UpperCAmelCase__ = sd_name
for k, v in mapping.items():
if "resnets" in k:
for sd_part, hf_part in unet_conversion_map_resnet:
UpperCAmelCase__ = v.replace(__A, __A )
UpperCAmelCase__ = v
for k, v in mapping.items():
for sd_part, hf_part in unet_conversion_map_layer:
UpperCAmelCase__ = v.replace(__A, __A )
UpperCAmelCase__ = v
UpperCAmelCase__ = {v: unet_state_dict[k] for k, v in mapping.items()}
return new_state_dict
# ================#
# VAE Conversion #
# ================#
UpperCamelCase__ = [
# (stable-diffusion, HF Diffusers)
('nin_shortcut', 'conv_shortcut'),
('norm_out', 'conv_norm_out'),
('mid.attn_1.', 'mid_block.attentions.0.'),
]
for i in range(4):
# down_blocks have two resnets
for j in range(2):
UpperCamelCase__ = f'''encoder.down_blocks.{i}.resnets.{j}.'''
UpperCamelCase__ = f'''encoder.down.{i}.block.{j}.'''
vae_conversion_map.append((sd_down_prefix, hf_down_prefix))
if i < 3:
UpperCamelCase__ = f'''down_blocks.{i}.downsamplers.0.'''
UpperCamelCase__ = f'''down.{i}.downsample.'''
vae_conversion_map.append((sd_downsample_prefix, hf_downsample_prefix))
UpperCamelCase__ = f'''up_blocks.{i}.upsamplers.0.'''
UpperCamelCase__ = f'''up.{3-i}.upsample.'''
vae_conversion_map.append((sd_upsample_prefix, hf_upsample_prefix))
# up_blocks have three resnets
# also, up blocks in hf are numbered in reverse from sd
for j in range(3):
UpperCamelCase__ = f'''decoder.up_blocks.{i}.resnets.{j}.'''
UpperCamelCase__ = f'''decoder.up.{3-i}.block.{j}.'''
vae_conversion_map.append((sd_up_prefix, hf_up_prefix))
# this part accounts for mid blocks in both the encoder and the decoder
for i in range(2):
UpperCamelCase__ = f'''mid_block.resnets.{i}.'''
UpperCamelCase__ = f'''mid.block_{i+1}.'''
vae_conversion_map.append((sd_mid_res_prefix, hf_mid_res_prefix))
UpperCamelCase__ = [
# (stable-diffusion, HF Diffusers)
('norm.', 'group_norm.'),
('q.', 'query.'),
('k.', 'key.'),
('v.', 'value.'),
('proj_out.', 'proj_attn.'),
]
def lowerCAmelCase_ ( __A ) -> Dict:
'''simple docstring'''
return w.reshape(*w.shape, 1, 1 )
def lowerCAmelCase_ ( __A ) -> str:
'''simple docstring'''
UpperCAmelCase__ = {k: k for k in vae_state_dict.keys()}
for k, v in mapping.items():
for sd_part, hf_part in vae_conversion_map:
UpperCAmelCase__ = v.replace(__A, __A )
UpperCAmelCase__ = v
for k, v in mapping.items():
if "attentions" in k:
for sd_part, hf_part in vae_conversion_map_attn:
UpperCAmelCase__ = v.replace(__A, __A )
UpperCAmelCase__ = v
UpperCAmelCase__ = {v: vae_state_dict[k] for k, v in mapping.items()}
UpperCAmelCase__ = ["q", "k", "v", "proj_out"]
for k, v in new_state_dict.items():
for weight_name in weights_to_convert:
if f"""mid.attn_1.{weight_name}.weight""" in k:
print(f"""Reshaping {k} for SD format""" )
UpperCAmelCase__ = reshape_weight_for_sd(__A )
return new_state_dict
# =========================#
# Text Encoder Conversion #
# =========================#
UpperCamelCase__ = [
# (stable-diffusion, HF Diffusers)
('resblocks.', 'text_model.encoder.layers.'),
('ln_1', 'layer_norm1'),
('ln_2', 'layer_norm2'),
('.c_fc.', '.fc1.'),
('.c_proj.', '.fc2.'),
('.attn', '.self_attn'),
('ln_final.', 'transformer.text_model.final_layer_norm.'),
('token_embedding.weight', 'transformer.text_model.embeddings.token_embedding.weight'),
('positional_embedding', 'transformer.text_model.embeddings.position_embedding.weight'),
]
UpperCamelCase__ = {re.escape(x[1]): x[0] for x in textenc_conversion_lst}
UpperCamelCase__ = re.compile('|'.join(protected.keys()))
# Ordering is from https://github.com/pytorch/pytorch/blob/master/test/cpp/api/modules.cpp
UpperCamelCase__ = {'q': 0, 'k': 1, 'v': 2}
def lowerCAmelCase_ ( __A ) -> Union[str, Any]:
'''simple docstring'''
UpperCAmelCase__ = {}
UpperCAmelCase__ = {}
UpperCAmelCase__ = {}
for k, v in text_enc_dict.items():
if (
k.endswith(".self_attn.q_proj.weight" )
or k.endswith(".self_attn.k_proj.weight" )
or k.endswith(".self_attn.v_proj.weight" )
):
UpperCAmelCase__ = k[: -len(".q_proj.weight" )]
UpperCAmelCase__ = k[-len("q_proj.weight" )]
if k_pre not in capture_qkv_weight:
UpperCAmelCase__ = [None, None, None]
UpperCAmelCase__ = v
continue
if (
k.endswith(".self_attn.q_proj.bias" )
or k.endswith(".self_attn.k_proj.bias" )
or k.endswith(".self_attn.v_proj.bias" )
):
UpperCAmelCase__ = k[: -len(".q_proj.bias" )]
UpperCAmelCase__ = k[-len("q_proj.bias" )]
if k_pre not in capture_qkv_bias:
UpperCAmelCase__ = [None, None, None]
UpperCAmelCase__ = v
continue
UpperCAmelCase__ = textenc_pattern.sub(lambda __A : protected[re.escape(m.group(0 ) )], __A )
UpperCAmelCase__ = v
for k_pre, tensors in capture_qkv_weight.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
UpperCAmelCase__ = textenc_pattern.sub(lambda __A : protected[re.escape(m.group(0 ) )], __A )
UpperCAmelCase__ = torch.cat(__A )
for k_pre, tensors in capture_qkv_bias.items():
if None in tensors:
raise Exception("CORRUPTED MODEL: one of the q-k-v values for the text encoder was missing" )
UpperCAmelCase__ = textenc_pattern.sub(lambda __A : protected[re.escape(m.group(0 ) )], __A )
UpperCAmelCase__ = torch.cat(__A )
return new_state_dict
def lowerCAmelCase_ ( __A ) -> Tuple:
'''simple docstring'''
return text_enc_dict
if __name__ == "__main__":
UpperCamelCase__ = argparse.ArgumentParser()
parser.add_argument('--model_path', default=None, type=str, required=True, help='Path to the model to convert.')
parser.add_argument('--checkpoint_path', default=None, type=str, required=True, help='Path to the output model.')
parser.add_argument('--half', action='store_true', help='Save weights in half precision.')
parser.add_argument(
'--use_safetensors', action='store_true', help='Save weights use safetensors, default is ckpt.'
)
UpperCamelCase__ = parser.parse_args()
assert args.model_path is not None, "Must provide a model path!"
assert args.checkpoint_path is not None, "Must provide a checkpoint path!"
# Path for safetensors
UpperCamelCase__ = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.safetensors')
UpperCamelCase__ = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.safetensors')
UpperCamelCase__ = osp.join(args.model_path, 'text_encoder', 'model.safetensors')
# Load models from safetensors if it exists, if it doesn't pytorch
if osp.exists(unet_path):
UpperCamelCase__ = load_file(unet_path, device='cpu')
else:
UpperCamelCase__ = osp.join(args.model_path, 'unet', 'diffusion_pytorch_model.bin')
UpperCamelCase__ = torch.load(unet_path, map_location='cpu')
if osp.exists(vae_path):
UpperCamelCase__ = load_file(vae_path, device='cpu')
else:
UpperCamelCase__ = osp.join(args.model_path, 'vae', 'diffusion_pytorch_model.bin')
UpperCamelCase__ = torch.load(vae_path, map_location='cpu')
if osp.exists(text_enc_path):
UpperCamelCase__ = load_file(text_enc_path, device='cpu')
else:
UpperCamelCase__ = osp.join(args.model_path, 'text_encoder', 'pytorch_model.bin')
UpperCamelCase__ = torch.load(text_enc_path, map_location='cpu')
# Convert the UNet model
UpperCamelCase__ = convert_unet_state_dict(unet_state_dict)
UpperCamelCase__ = {'model.diffusion_model.' + k: v for k, v in unet_state_dict.items()}
# Convert the VAE model
UpperCamelCase__ = convert_vae_state_dict(vae_state_dict)
UpperCamelCase__ = {'first_stage_model.' + k: v for k, v in vae_state_dict.items()}
# Easiest way to identify v2.0 model seems to be that the text encoder (OpenCLIP) is deeper
UpperCamelCase__ = 'text_model.encoder.layers.22.layer_norm2.bias' in text_enc_dict
if is_vaa_model:
# Need to add the tag 'transformer' in advance so we can knock it out from the final layer-norm
UpperCamelCase__ = {'transformer.' + k: v for k, v in text_enc_dict.items()}
UpperCamelCase__ = convert_text_enc_state_dict_vaa(text_enc_dict)
UpperCamelCase__ = {'cond_stage_model.model.' + k: v for k, v in text_enc_dict.items()}
else:
UpperCamelCase__ = convert_text_enc_state_dict(text_enc_dict)
UpperCamelCase__ = {'cond_stage_model.transformer.' + k: v for k, v in text_enc_dict.items()}
# Put together new checkpoint
UpperCamelCase__ = {**unet_state_dict, **vae_state_dict, **text_enc_dict}
if args.half:
UpperCamelCase__ = {k: v.half() for k, v in state_dict.items()}
if args.use_safetensors:
save_file(state_dict, args.checkpoint_path)
else:
UpperCamelCase__ = {'state_dict': state_dict}
torch.save(state_dict, args.checkpoint_path)
| 65 |
"""simple docstring"""
from PIL import Image
def _snake_case ( lowercase__ : Image , lowercase__ : float ) -> Image:
'''simple docstring'''
def brightness(lowercase__ : int ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(lowercase__ )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
__UpperCAmelCase = change_brightness(img, 1_00)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 84 | 0 |
"""simple docstring"""
from random import randint
from tempfile import TemporaryFile
import numpy as np
def A_ ( _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :Dict = 0
if start < end:
snake_case_ :Dict = randint(_lowercase, _lowercase )
snake_case_ :List[Any] = a[end]
snake_case_ :str = a[pivot]
snake_case_ :List[Any] = temp
snake_case_, snake_case_ :Any = _in_place_partition(_lowercase, _lowercase, _lowercase )
count += _in_place_quick_sort(_lowercase, _lowercase, p - 1 )
count += _in_place_quick_sort(_lowercase, p + 1, _lowercase )
return count
def A_ ( _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
snake_case_ :Dict = 0
snake_case_ :int = randint(_lowercase, _lowercase )
snake_case_ :Optional[int] = a[end]
snake_case_ :List[str] = a[pivot]
snake_case_ :Any = temp
snake_case_ :str = start - 1
for index in range(_lowercase, _lowercase ):
count += 1
if a[index] < a[end]: # check if current val is less than pivot value
snake_case_ :Optional[int] = new_pivot_index + 1
snake_case_ :Tuple = a[new_pivot_index]
snake_case_ :Tuple = a[index]
snake_case_ :Tuple = temp
snake_case_ :int = a[new_pivot_index + 1]
snake_case_ :Union[str, Any] = a[end]
snake_case_ :str = temp
return new_pivot_index + 1, count
__a = TemporaryFile()
__a = 1_00 # 1000 elements are to be sorted
__a , __a = 0, 1 # mean and standard deviation
__a = np.random.normal(mu, sigma, p)
np.save(outfile, X)
print("The array is")
print(X)
outfile.seek(0) # using the same array
__a = np.load(outfile)
__a = len(M) - 1
__a = _in_place_quick_sort(M, 0, r)
print(
"No of Comparisons for 100 elements selected from a standard normal distribution"
"is :"
)
print(z)
| 66 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _SCREAMING_SNAKE_CASE :
def __lowerCAmelCase ( self ) -> Tuple:
torch.manual_seed(0 )
lowerCAmelCase_ :int = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :List[Any] = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCAmelCase_ :str = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__A , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase_ :int = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowerCAmelCase ( self ) -> List[str]:
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , class_embed_type="""timestep""" , mid_block_scale_factor=1.4_1_4 , time_embedding_act_fn="""gelu""" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCAmelCase_ :str = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__A , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[int] = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , )
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Dict = self.get_dummy_components()
lowerCAmelCase_ :Tuple = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Any = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Optional[int] = inputs["""prompt"""]
lowerCAmelCase_ :Optional[int] = inputs["""generator"""]
lowerCAmelCase_ :Any = inputs["""num_inference_steps"""]
lowerCAmelCase_ :Optional[int] = inputs["""output_type"""]
if "image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""image"""]
else:
lowerCAmelCase_ :int = None
if "mask_image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""mask_image"""]
else:
lowerCAmelCase_ :int = None
if "original_image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""original_image"""]
else:
lowerCAmelCase_ :List[Any] = None
lowerCAmelCase_ , lowerCAmelCase_ :int = pipe.encode_prompt(__A )
# inputs with prompt converted to embeddings
lowerCAmelCase_ :List[str] = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowerCAmelCase_ :int = image
if mask_image is not None:
lowerCAmelCase_ :Tuple = mask_image
if original_image is not None:
lowerCAmelCase_ :Optional[Any] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(__A , __A , __A )
lowerCAmelCase_ :Optional[int] = pipe(**__A )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__A )
lowerCAmelCase_ :Optional[int] = self.pipeline_class.from_pretrained(__A )
pipe_loaded.to(__A )
pipe_loaded.set_progress_bar_config(disable=__A )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__A , __A ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
lowerCAmelCase_ :Dict = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Union[str, Any] = inputs["""generator"""]
lowerCAmelCase_ :Any = inputs["""num_inference_steps"""]
lowerCAmelCase_ :Tuple = inputs["""output_type"""]
# inputs with prompt converted to embeddings
lowerCAmelCase_ :Tuple = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowerCAmelCase_ :Optional[int] = image
if mask_image is not None:
lowerCAmelCase_ :str = mask_image
if original_image is not None:
lowerCAmelCase_ :Tuple = original_image
lowerCAmelCase_ :Union[str, Any] = pipe_loaded(**__A )[0]
lowerCAmelCase_ :Dict = np.abs(to_np(__A ) - to_np(__A ) ).max()
self.assertLess(__A , 1E-4 )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Any = self.get_dummy_components()
lowerCAmelCase_ :Optional[int] = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[int] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Dict = pipe(**__A )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__A )
lowerCAmelCase_ :Any = self.pipeline_class.from_pretrained(__A )
pipe_loaded.to(__A )
pipe_loaded.set_progress_bar_config(disable=__A )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :str = pipe_loaded(**__A )[0]
lowerCAmelCase_ :Dict = np.abs(to_np(__A ) - to_np(__A ) ).max()
self.assertLess(__A , 1E-4 )
| 84 | 0 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__UpperCAmelCase =True
except (ImportError, ModuleNotFoundError):
__UpperCAmelCase =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def __lowerCAmelCase ( UpperCamelCase__ ) -> str:
re.sub('''<n>''' , '''''' , UpperCamelCase__ ) # remove pegasus newline char
assert NLTK_AVAILABLE, "nltk must be installed to separate newlines between sentences. (pip install nltk)"
return "\n".join(nltk.sent_tokenize(UpperCamelCase__ ) )
| 67 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2""" , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :int = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :List[Any] = jax.device_count()
lowerCAmelCase_ :Optional[Any] = num_samples * [prompt]
lowerCAmelCase_ :int = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Optional[Any] = replicate(__A )
lowerCAmelCase_ :Union[str, Any] = shard(__A )
lowerCAmelCase_ :Optional[Any] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :Tuple = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Union[str, Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Optional[int] = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Union[str, Any] = """stabilityai/stable-diffusion-2"""
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = FlaxDPMSolverMultistepScheduler.from_pretrained(__A , subfolder="""scheduler""" )
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = FlaxStableDiffusionPipeline.from_pretrained(
__A , scheduler=__A , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :Optional[int] = scheduler_params
lowerCAmelCase_ :List[Any] = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :Tuple = jax.device_count()
lowerCAmelCase_ :str = num_samples * [prompt]
lowerCAmelCase_ :Union[str, Any] = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Tuple = replicate(__A )
lowerCAmelCase_ :Optional[int] = shard(__A )
lowerCAmelCase_ :List[str] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :List[Any] = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Optional[Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Dict = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 84 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase__ = {
"""configuration_longformer""": [
"""LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""LongformerConfig""",
"""LongformerOnnxConfig""",
],
"""tokenization_longformer""": ["""LongformerTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = ["""LongformerTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""LongformerForMaskedLM""",
"""LongformerForMultipleChoice""",
"""LongformerForQuestionAnswering""",
"""LongformerForSequenceClassification""",
"""LongformerForTokenClassification""",
"""LongformerModel""",
"""LongformerPreTrainedModel""",
"""LongformerSelfAttention""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFLongformerForMaskedLM""",
"""TFLongformerForMultipleChoice""",
"""TFLongformerForQuestionAnswering""",
"""TFLongformerForSequenceClassification""",
"""TFLongformerForTokenClassification""",
"""TFLongformerModel""",
"""TFLongformerPreTrainedModel""",
"""TFLongformerSelfAttention""",
]
if TYPE_CHECKING:
from .configuration_longformer import (
LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
LongformerConfig,
LongformerOnnxConfig,
)
from .tokenization_longformer import LongformerTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_longformer_fast import LongformerTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_longformer import (
LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
LongformerForMaskedLM,
LongformerForMultipleChoice,
LongformerForQuestionAnswering,
LongformerForSequenceClassification,
LongformerForTokenClassification,
LongformerModel,
LongformerPreTrainedModel,
LongformerSelfAttention,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_longformer import (
TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
TFLongformerForMaskedLM,
TFLongformerForMultipleChoice,
TFLongformerForQuestionAnswering,
TFLongformerForSequenceClassification,
TFLongformerForTokenClassification,
TFLongformerModel,
TFLongformerPreTrainedModel,
TFLongformerSelfAttention,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 68 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _snake_case ( ) -> Generator[int, None, None]:
'''simple docstring'''
lowerCAmelCase_ :dict[int, int] = {}
lowerCAmelCase_ :int = 2
while True:
lowerCAmelCase_ :List[Any] = factor_map.pop(lowercase__ , lowercase__ )
if factor:
lowerCAmelCase_ :Optional[int] = factor + prime
while x in factor_map:
x += factor
lowerCAmelCase_ :List[str] = factor
else:
lowerCAmelCase_ :Optional[int] = prime
yield prime
prime += 1
def _snake_case ( lowercase__ : float = 1E10 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = sieve()
lowerCAmelCase_ :str = 1
while True:
lowerCAmelCase_ :int = next(lowercase__ )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(lowercase__ )
n += 2
if __name__ == "__main__":
print(solution())
| 84 | 0 |
"""simple docstring"""
import itertools
import os
from collections import Counter, defaultdict
from concurrent.futures import ThreadPoolExecutor, as_completed
import numpy as np
import datasets
from .execute import check_correctness
__UpperCamelCase = '''\
@misc{chen2021evaluating,
title={Evaluating Large Language Models Trained on Code},
author={Mark Chen and Jerry Tworek and Heewoo Jun and Qiming Yuan \
and Henrique Ponde de Oliveira Pinto and Jared Kaplan and Harri Edwards \
and Yuri Burda and Nicholas Joseph and Greg Brockman and Alex Ray \
and Raul Puri and Gretchen Krueger and Michael Petrov and Heidy Khlaaf \
and Girish Sastry and Pamela Mishkin and Brooke Chan and Scott Gray \
and Nick Ryder and Mikhail Pavlov and Alethea Power and Lukasz Kaiser \
and Mohammad Bavarian and Clemens Winter and Philippe Tillet \
and Felipe Petroski Such and Dave Cummings and Matthias Plappert \
and Fotios Chantzis and Elizabeth Barnes and Ariel Herbert-Voss \
and William Hebgen Guss and Alex Nichol and Alex Paino and Nikolas Tezak \
and Jie Tang and Igor Babuschkin and Suchir Balaji and Shantanu Jain \
and William Saunders and Christopher Hesse and Andrew N. Carr \
and Jan Leike and Josh Achiam and Vedant Misra and Evan Morikawa \
and Alec Radford and Matthew Knight and Miles Brundage and Mira Murati \
and Katie Mayer and Peter Welinder and Bob McGrew and Dario Amodei \
and Sam McCandlish and Ilya Sutskever and Wojciech Zaremba},
year={2021},
eprint={2107.03374},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
'''
__UpperCamelCase = '''\
This metric implements the evaluation harness for the HumanEval problem solving dataset
described in the paper "Evaluating Large Language Models Trained on Code"
(https://arxiv.org/abs/2107.03374).
'''
__UpperCamelCase = '''
Calculates how good are predictions given some references, using certain scores
Args:
predictions: list of candidates to evaluate. Each candidates should be a list
of strings with several code candidates to solve the problem.
references: a list with a test for each prediction. Each test should evaluate the
correctness of a code candidate.
k: number of code candidates to consider in the evaluation (Default: [1, 10, 100])
num_workers: number of workers used to evaluate the canidate programs (Default: 4).
timeout:
Returns:
pass_at_k: dict with pass rates for each k
results: dict with granular results of each unittest
Examples:
>>> code_eval = datasets.load_metric("code_eval")
>>> test_cases = ["assert add(2,3)==5"]
>>> candidates = [["def add(a,b): return a*b", "def add(a, b): return a+b"]]
>>> pass_at_k, results = code_eval.compute(references=test_cases, predictions=candidates, k=[1, 2])
>>> print(pass_at_k)
{\'pass@1\': 0.5, \'pass@2\': 1.0}
'''
__UpperCamelCase = '''
################################################################################
!!!WARNING!!!
################################################################################
The "code_eval" metric executes untrusted model-generated code in Python.
Although it is highly unlikely that model-generated code will do something
overtly malicious in response to this test suite, model-generated code may act
destructively due to a lack of model capability or alignment.
Users are strongly encouraged to sandbox this evaluation suite so that it
does not perform destructive actions on their host or network. For more
information on how OpenAI sandboxes its code, see the paper "Evaluating Large
Language Models Trained on Code" (https://arxiv.org/abs/2107.03374).
Once you have read this disclaimer and taken appropriate precautions,
set the environment variable HF_ALLOW_CODE_EVAL="1". Within Python you can to this
with:
>>> import os
>>> os.environ["HF_ALLOW_CODE_EVAL"] = "1"
################################################################################\
'''
__UpperCamelCase = '''The MIT License
Copyright (c) OpenAI (https://openai.com)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase ( datasets.Metric ):
def a_ ( self) -> List[str]:
return datasets.MetricInfo(
# This is the description that will appear on the metrics page.
description=_DESCRIPTION, citation=_CITATION, inputs_description=_KWARGS_DESCRIPTION, features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('string')),
'references': datasets.Value('string'),
}), homepage='https://github.com/openai/human-eval', codebase_urls=['https://github.com/openai/human-eval'], reference_urls=['https://github.com/openai/human-eval'], license=_LICENSE, )
def a_ ( self, lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__=[1, 10, 100], lowerCAmelCase__=4, lowerCAmelCase__=3.0) -> Dict:
if os.getenv('HF_ALLOW_CODE_EVAL', 0) != "1":
raise ValueError(_WARNING)
if os.name == "nt":
raise NotImplementedError('This metric is currently not supported on Windows.')
with ThreadPoolExecutor(max_workers=lowerCAmelCase__) as executor:
snake_case_ = []
snake_case_ = Counter()
snake_case_ = 0
snake_case_ = defaultdict(lowerCAmelCase__)
for task_id, (candidates, test_case) in enumerate(zip(lowerCAmelCase__, lowerCAmelCase__)):
for candidate in candidates:
snake_case_ = candidate + '\n' + test_case
snake_case_ = (test_program, timeout, task_id, completion_id[task_id])
snake_case_ = executor.submit(lowerCAmelCase__, *lowerCAmelCase__)
futures.append(lowerCAmelCase__)
completion_id[task_id] += 1
n_samples += 1
for future in as_completed(lowerCAmelCase__):
snake_case_ = future.result()
results[result["task_id"]].append((result['completion_id'], result))
snake_case_ , snake_case_ = [], []
for result in results.values():
result.sort()
snake_case_ = [r[1]['passed'] for r in result]
total.append(len(lowerCAmelCase__))
correct.append(sum(lowerCAmelCase__))
snake_case_ = np.array(lowerCAmelCase__)
snake_case_ = np.array(lowerCAmelCase__)
snake_case_ = k
snake_case_ = {f'pass@{k}': estimate_pass_at_k(lowerCAmelCase__, lowerCAmelCase__, lowerCAmelCase__).mean() for k in ks if (total >= k).all()}
return pass_at_k, results
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> Any:
def estimator(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) -> float:
if n - c < k:
return 1.0
return 1.0 - np.prod(1.0 - k / np.arange(n - c + 1 , n + 1 ) )
if isinstance(UpperCAmelCase , UpperCAmelCase ):
snake_case_ = itertools.repeat(UpperCAmelCase , len(UpperCAmelCase ) )
else:
assert len(UpperCAmelCase ) == len(UpperCAmelCase )
snake_case_ = iter(UpperCAmelCase )
return np.array([estimator(int(UpperCAmelCase ) , int(UpperCAmelCase ) , UpperCAmelCase ) for n, c in zip(UpperCAmelCase , UpperCAmelCase )] )
| 69 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
# TODO: is there an appropriate internal test set?
UpperCAmelCase_ :List[Any] = "ssube/stable-diffusion-x4-upscaler-onnx"
def __lowerCAmelCase ( self , __A=0 ) -> Optional[int]:
lowerCAmelCase_ :Optional[Any] = floats_tensor((1, 3, 128, 128) , rng=random.Random(__A ) )
lowerCAmelCase_ :List[Any] = torch.manual_seed(__A )
lowerCAmelCase_ :Tuple = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Dict = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :int = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Tuple = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :int = self.get_dummy_inputs()
lowerCAmelCase_ :List[str] = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :str = np.array(
[0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Optional[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Union[str, Any] = pipe(**__A ).images
lowerCAmelCase_ :Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Tuple = np.array(
[0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Union[str, Any] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Optional[Any] = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Tuple = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :List[str] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Optional[int] = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Dict = pipe(**__A ).images
lowerCAmelCase_ :Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Dict = np.array(
[0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@property
def __lowerCAmelCase ( self ) -> List[Any]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Optional[int] = ort.SessionOptions()
lowerCAmelCase_ :Dict = False
return options
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase_ :Optional[Any] = init_image.resize((128, 128) )
# using the PNDM scheduler by default
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = """A fantasy landscape, trending on artstation"""
lowerCAmelCase_ :List[Any] = torch.manual_seed(0 )
lowerCAmelCase_ :str = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=10 , generator=__A , output_type="""np""" , )
lowerCAmelCase_ :Dict = output.images
lowerCAmelCase_ :List[str] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Optional[Any] = np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase_ :List[str] = init_image.resize((128, 128) )
lowerCAmelCase_ :Any = LMSDiscreteScheduler.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , subfolder="""scheduler""" )
lowerCAmelCase_ :Optional[Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , scheduler=__A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Any = """A fantasy landscape, trending on artstation"""
lowerCAmelCase_ :Optional[Any] = torch.manual_seed(0 )
lowerCAmelCase_ :List[str] = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=20 , generator=__A , output_type="""np""" , )
lowerCAmelCase_ :int = output.images
lowerCAmelCase_ :List[Any] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Union[str, Any] = np.array(
[0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
| 84 | 0 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConformerConfig,
WavaVecaConformerForCTC,
WavaVecaConformerForPreTraining,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaProcessor,
logging,
)
logging.set_verbosity_info()
A__ : Tuple =logging.get_logger(__name__)
A__ : List[Any] ={
'''post_extract_proj''': '''feature_projection.projection''',
'''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
'''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
'''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
'''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
'''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
'''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
'''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
'''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
'''self_attn.rotary_emb''': '''encoder.embed_positions''',
'''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
'''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
'''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
'''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
'''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
'''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
'''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
'''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
'''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
'''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
'''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
'''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
'''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
'''encoder.layer_norm''': '''encoder.layer_norm''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
A__ : List[Any] =[
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
for attribute in key.split(""".""" ):
_lowerCAmelCase = getattr(lowerCAmelCase , lowerCAmelCase )
if weight_type is not None:
_lowerCAmelCase = getattr(lowerCAmelCase , lowerCAmelCase ).shape
else:
_lowerCAmelCase = 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":
_lowerCAmelCase = value
elif weight_type == "weight_g":
_lowerCAmelCase = value
elif weight_type == "weight_v":
_lowerCAmelCase = value
elif weight_type == "bias":
_lowerCAmelCase = value
elif weight_type == "running_mean":
_lowerCAmelCase = value
elif weight_type == "running_var":
_lowerCAmelCase = value
elif weight_type == "num_batches_tracked":
_lowerCAmelCase = value
elif weight_type == "inv_freq":
_lowerCAmelCase = value
else:
_lowerCAmelCase = value
logger.info(f"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}." )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = []
_lowerCAmelCase = fairseq_model.state_dict()
_lowerCAmelCase = hf_model.wavaveca_conformer.feature_extractor
for name, value in fairseq_dict.items():
_lowerCAmelCase = False
if "conv_layers" in name:
load_conv_layer(
lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , hf_model.config.feat_extract_norm == """group""" , )
_lowerCAmelCase = True
else:
for key, mapped_key in MAPPING.items():
_lowerCAmelCase = """wav2vec2_conformer.""" + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("""w2v_model.""" )[-1] == name.split(""".""" )[0]:
_lowerCAmelCase = True
if "*" in mapped_key:
_lowerCAmelCase = name.split(lowerCAmelCase )[0].split(""".""" )[-2]
_lowerCAmelCase = mapped_key.replace("""*""" , lowerCAmelCase )
if "pos_bias_u" in name:
_lowerCAmelCase = None
elif "pos_bias_v" in name:
_lowerCAmelCase = None
elif "weight_g" in name:
_lowerCAmelCase = """weight_g"""
elif "weight_v" in name:
_lowerCAmelCase = """weight_v"""
elif "bias" in name:
_lowerCAmelCase = """bias"""
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
_lowerCAmelCase = """weight"""
elif "running_mean" in name:
_lowerCAmelCase = """running_mean"""
elif "inv_freq" in name:
_lowerCAmelCase = """inv_freq"""
elif "running_var" in name:
_lowerCAmelCase = """running_var"""
elif "num_batches_tracked" in name:
_lowerCAmelCase = """num_batches_tracked"""
else:
_lowerCAmelCase = None
set_recursively(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase )
continue
if not is_used:
unused_weights.append(lowerCAmelCase )
logger.warning(f"Unused weights: {unused_weights}" )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = full_name.split("""conv_layers.""" )[-1]
_lowerCAmelCase = name.split(""".""" )
_lowerCAmelCase = int(items[0] )
_lowerCAmelCase = 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." )
_lowerCAmelCase = 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." )
_lowerCAmelCase = value
logger.info(f"Feat extract conv layer {layer_id} was initialized from {full_name}." )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found." )
_lowerCAmelCase = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
f"{full_name} has size {value.shape}, but"
f" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found." )
_lowerCAmelCase = value
logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}." )
else:
unused_weights.append(lowerCAmelCase )
@torch.no_grad()
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase=None , lowerCAmelCase=None , lowerCAmelCase=True ):
"""simple docstring"""
if config_path is not None:
_lowerCAmelCase = WavaVecaConformerConfig.from_pretrained(lowerCAmelCase , hidden_act="""swish""" )
else:
_lowerCAmelCase = WavaVecaConformerConfig()
if "rope" in checkpoint_path:
_lowerCAmelCase = """rotary"""
if is_finetuned:
if dict_path:
_lowerCAmelCase = Dictionary.load(lowerCAmelCase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
_lowerCAmelCase = target_dict.pad_index
_lowerCAmelCase = target_dict.bos_index
_lowerCAmelCase = target_dict.eos_index
_lowerCAmelCase = len(target_dict.symbols )
_lowerCAmelCase = os.path.join(lowerCAmelCase , """vocab.json""" )
if not os.path.isdir(lowerCAmelCase ):
logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(lowerCAmelCase ) )
return
os.makedirs(lowerCAmelCase , exist_ok=lowerCAmelCase )
_lowerCAmelCase = target_dict.indices
# fairseq has the <pad> and <s> switched
_lowerCAmelCase = 0
_lowerCAmelCase = 1
with open(lowerCAmelCase , """w""" , encoding="""utf-8""" ) as vocab_handle:
json.dump(lowerCAmelCase , lowerCAmelCase )
_lowerCAmelCase = WavaVecaCTCTokenizer(
lowerCAmelCase , unk_token=target_dict.unk_word , pad_token=target_dict.pad_word , bos_token=target_dict.bos_word , eos_token=target_dict.eos_word , word_delimiter_token="""|""" , do_lower_case=lowerCAmelCase , )
_lowerCAmelCase = True if config.feat_extract_norm == """layer""" else False
_lowerCAmelCase = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=lowerCAmelCase , return_attention_mask=lowerCAmelCase , )
_lowerCAmelCase = WavaVecaProcessor(feature_extractor=lowerCAmelCase , tokenizer=lowerCAmelCase )
processor.save_pretrained(lowerCAmelCase )
_lowerCAmelCase = WavaVecaConformerForCTC(lowerCAmelCase )
else:
_lowerCAmelCase = WavaVecaConformerForPreTraining(lowerCAmelCase )
if is_finetuned:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"""data""": """/""".join(dict_path.split("""/""" )[:-1] )} )
else:
_lowerCAmelCase = argparse.Namespace(task="""audio_pretraining""" )
_lowerCAmelCase = fairseq.tasks.setup_task(lowerCAmelCase )
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] , task=lowerCAmelCase )
_lowerCAmelCase = model[0].eval()
recursively_load_weights(lowerCAmelCase , lowerCAmelCase , not is_finetuned )
hf_wavavec.save_pretrained(lowerCAmelCase )
if __name__ == "__main__":
A__ : str =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'''
)
A__ : List[Any] =parser.parse_args()
convert_wavaveca_conformer_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 70 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
] )
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Dict:
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=__A , )
assert hasattr(self , """env""" )
def __lowerCAmelCase ( self , __A ) -> Any:
# configuration for running training on smdistributed Model Parallel
lowerCAmelCase_ :Union[str, Any] = {
"""enabled""": True,
"""processes_per_host""": 8,
}
lowerCAmelCase_ :Tuple = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
lowerCAmelCase_ :Any = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
lowerCAmelCase_ :Any = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=__A , instance_type=self.instance_type , debugger_hook_config=__A , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=__A , py_version="""py36""" , )
def __lowerCAmelCase ( self , __A ) -> List[Any]:
TrainingJobAnalytics(__A ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def __lowerCAmelCase ( self , __A ) -> List[str]:
# create estimator
lowerCAmelCase_ :Any = self.create_estimator(__A )
# run training
estimator.fit()
# result dataframe
lowerCAmelCase_ :Optional[Any] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase_ :List[str] = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
lowerCAmelCase_ :Optional[int] = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase_ :Optional[int] = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , __A )
| 84 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ :Tuple = {'''configuration_sew''': ['''SEW_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''SEWConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ :List[str] = [
'''SEW_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''SEWForCTC''',
'''SEWForSequenceClassification''',
'''SEWModel''',
'''SEWPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_sew import SEW_PRETRAINED_CONFIG_ARCHIVE_MAP, SEWConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_sew import (
SEW_PRETRAINED_MODEL_ARCHIVE_LIST,
SEWForCTC,
SEWForSequenceClassification,
SEWModel,
SEWPreTrainedModel,
)
else:
import sys
A_ :int = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 71 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 1_0 ) -> str:
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or n < 0:
raise ValueError("""Invalid input""" )
lowerCAmelCase_ :List[str] = 1_0**n
lowerCAmelCase_ :int = 2_8_4_3_3 * (pow(2 , 7_8_3_0_4_5_7 , lowercase__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(10) = }""")
| 84 | 0 |
"""simple docstring"""
import numpy as np
def snake_case_ ( A_ : np.ndarray, A_ : float ):
'''simple docstring'''
return np.where(vector > 0, A_, (alpha * (np.exp(A_ ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 72 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__UpperCAmelCase = 'src/transformers'
__UpperCAmelCase = 'docs/source/en/tasks'
def _snake_case ( lowercase__ : str , lowercase__ : List[str] , lowercase__ : Any ) -> str:
'''simple docstring'''
with open(lowercase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCAmelCase_ :List[Any] = f.readlines()
# Find the start prompt.
lowerCAmelCase_ :Tuple = 0
while not lines[start_index].startswith(lowercase__ ):
start_index += 1
start_index += 1
lowerCAmelCase_ :Dict = start_index
while not lines[end_index].startswith(lowercase__ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
__UpperCAmelCase = direct_transformers_import(TRANSFORMERS_PATH)
__UpperCAmelCase = {
'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__UpperCAmelCase = {
'summarization.md': ('nllb',),
'translation.md': ('nllb',),
}
def _snake_case ( lowercase__ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = TASK_GUIDE_TO_MODELS[task_guide]
lowerCAmelCase_ :List[Any] = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowercase__ , set() )
lowerCAmelCase_ :Union[str, Any] = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([f"""[{name}](../model_doc/{code})""" for code, name in model_names.items()] ) + "\n"
def _snake_case ( lowercase__ : int , lowercase__ : str=False ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = _find_text_in_file(
filename=os.path.join(lowercase__ , lowercase__ ) , start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->""" , end_prompt="""<!--End of the generated tip-->""" , )
lowerCAmelCase_ :int = get_model_list_for_task(lowercase__ )
if current_list != new_list:
if overwrite:
with open(os.path.join(lowercase__ , lowercase__ ) , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
f"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
""" to fix this.""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__UpperCAmelCase = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 84 | 0 |
import numpy as np
from scipy.spatial.distance import cdist
from sklearn.metrics import fa_score
import datasets
a ="""\
@inproceedings{kakwani2020indicnlpsuite,
title={{IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages}},
author={Divyanshu Kakwani and Anoop Kunchukuttan and Satish Golla and Gokul N.C. and Avik Bhattacharyya and Mitesh M. Khapra and Pratyush Kumar},
year={2020},
booktitle={Findings of EMNLP},
}
"""
a ="""\
IndicGLUE is a natural language understanding benchmark for Indian languages. It contains a wide
variety of tasks and covers 11 major Indian languages - as, bn, gu, hi, kn, ml, mr, or, pa, ta, te.
"""
a ="""
Compute IndicGLUE evaluation metric associated to each IndicGLUE dataset.
Args:
predictions: list of predictions to score (as int64),
except for 'cvit-mkb-clsr' where each prediction is a vector (of float32).
references: list of ground truth labels corresponding to the predictions (as int64),
except for 'cvit-mkb-clsr' where each reference is a vector (of float32).
Returns: depending on the IndicGLUE subset, one or several of:
\"accuracy\": Accuracy
\"f1\": F1 score
\"precision\": Precision@10
Examples:
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'wnli') # 'wnli' or any of [\"copa\", \"sna\", \"csqa\", \"wstp\", \"inltkh\", \"bbca\", \"iitp-mr\", \"iitp-pr\", \"actsa-sc\", \"md\"]
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0}
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'wiki-ner')
>>> references = [0, 1]
>>> predictions = [0, 1]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'accuracy': 1.0, 'f1': 1.0}
>>> indic_glue_metric = datasets.load_metric('indic_glue', 'cvit-mkb-clsr')
>>> references = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> predictions = [[0.5, 0.5, 0.5], [0.1, 0.2, 0.3]]
>>> results = indic_glue_metric.compute(predictions=predictions, references=references)
>>> print(results)
{'precision@10': 1.0}
"""
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
return float((preds == labels).mean() )
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> List[str]:
__lowerCamelCase : Optional[Any] = simple_accuracy(lowerCamelCase__ , lowerCamelCase__ )
__lowerCamelCase : Tuple = float(fa_score(y_true=lowerCamelCase__ , y_pred=lowerCamelCase__ ) )
return {
"accuracy": acc,
"f1": fa,
}
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> Optional[Any]:
__lowerCamelCase : Any = np.array(lowerCamelCase__ )
__lowerCamelCase : List[Any] = np.array(lowerCamelCase__ )
__lowerCamelCase : Any = en_sentvecs.shape[0]
# mean centering
__lowerCamelCase : Union[str, Any] = en_sentvecs - np.mean(lowerCamelCase__ , axis=0 )
__lowerCamelCase : Dict = in_sentvecs - np.mean(lowerCamelCase__ , axis=0 )
__lowerCamelCase : Optional[int] = cdist(lowerCamelCase__ , lowerCamelCase__ , 'cosine' )
__lowerCamelCase : Optional[Any] = np.array(range(lowerCamelCase__ ) )
__lowerCamelCase : Dict = sim.argsort(axis=1 )[:, :1_0]
__lowerCamelCase : Optional[int] = np.any(preds == actual[:, None] , axis=1 )
return float(matches.mean() )
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class A_ ( datasets.Metric ):
def lowerCAmelCase ( self : Optional[Any]):
if self.config_name not in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"cvit-mkb-clsr",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
"wiki-ner",
]:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]')
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'predictions': datasets.Value('int64')
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32')),
'references': datasets.Value('int64')
if self.config_name != 'cvit-mkb-clsr'
else datasets.Sequence(datasets.Value('float32')),
}) ,codebase_urls=[] ,reference_urls=[] ,format='numpy' if self.config_name != 'cvit-mkb-clsr' else None ,)
def lowerCAmelCase ( self : Any ,SCREAMING_SNAKE_CASE__ : Tuple ,SCREAMING_SNAKE_CASE__ : Optional[Any]):
if self.config_name == "cvit-mkb-clsr":
return {"precision@10": precision_at_aa(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)}
elif self.config_name in ["wiki-ner"]:
return acc_and_fa(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)
elif self.config_name in [
"wnli",
"copa",
"sna",
"csqa",
"wstp",
"inltkh",
"bbca",
"iitp-mr",
"iitp-pr",
"actsa-sc",
"md",
]:
return {"accuracy": simple_accuracy(SCREAMING_SNAKE_CASE__ ,SCREAMING_SNAKE_CASE__)}
else:
raise KeyError(
'You should supply a configuration name selected in '
'["wnli", "copa", "sna", "csqa", "wstp", "inltkh", "bbca", '
'"cvit-mkb-clsr", "iitp-mr", "iitp-pr", "actsa-sc", "md", '
'"wiki-ner"]')
| 73 |
"""simple docstring"""
def _snake_case ( lowercase__ : list[int] ) -> list[list[int]]:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = []
if len(lowercase__ ) == 1:
return [nums.copy()]
for _ in range(len(lowercase__ ) ):
lowerCAmelCase_ :Optional[Any] = nums.pop(0 )
lowerCAmelCase_ :str = permute(lowercase__ )
for perm in permutations:
perm.append(lowercase__ )
result.extend(lowercase__ )
nums.append(lowercase__ )
return result
def _snake_case ( lowercase__ : Tuple ) -> List[str]:
'''simple docstring'''
def backtrack(lowercase__ : str ):
if start == len(lowercase__ ) - 1:
output.append(nums[:] )
else:
for i in range(lowercase__ , len(lowercase__ ) ):
lowerCAmelCase_ , lowerCAmelCase_ :str = nums[i], nums[start]
backtrack(start + 1 )
lowerCAmelCase_ , lowerCAmelCase_ :str = nums[i], nums[start] # backtrack
lowerCAmelCase_ :int = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
__UpperCAmelCase = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 84 | 0 |
"""simple docstring"""
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def _snake_case ( snake_case__ : List[Any] , snake_case__ : Optional[int]=0.999 , snake_case__ : Union[str, Any]="cosine" , ):
if alpha_transform_type == "cosine":
def alpha_bar_fn(snake_case__ : Union[str, Any] ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(snake_case__ : Dict ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'Unsupported alpha_tranform_type: {alpha_transform_type}' )
A = []
for i in range(snake_case__ ):
A = i / num_diffusion_timesteps
A = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(snake_case__ ) / alpha_bar_fn(snake_case__ ) , snake_case__ ) )
return torch.tensor(snake_case__ , dtype=torch.floataa )
class lowerCAmelCase_ ( _lowercase , _lowercase ):
'''simple docstring'''
_lowerCamelCase: Optional[int] = [e.name for e in KarrasDiffusionSchedulers]
_lowerCamelCase: Optional[Any] = 2
@register_to_config
def __init__( self : str ,A_ : int = 1000 ,A_ : float = 0.0_00_85 ,A_ : float = 0.0_12 ,A_ : str = "linear" ,A_ : Optional[Union[np.ndarray, List[float]]] = None ,A_ : str = "epsilon" ,A_ : Optional[bool] = False ,A_ : Optional[bool] = False ,A_ : float = 1.0 ,A_ : str = "linspace" ,A_ : int = 0 ,) -> List[str]:
if trained_betas is not None:
A = torch.tensor(A_ ,dtype=torch.floataa )
elif beta_schedule == "linear":
A = torch.linspace(A_ ,A_ ,A_ ,dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
A = (
torch.linspace(beta_start**0.5 ,beta_end**0.5 ,A_ ,dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
A = betas_for_alpha_bar(A_ ,alpha_transform_type='cosine' )
elif beta_schedule == "exp":
A = betas_for_alpha_bar(A_ ,alpha_transform_type='exp' )
else:
raise NotImplementedError(F'{beta_schedule} does is not implemented for {self.__class__}' )
A = 1.0 - self.betas
A = torch.cumprod(self.alphas ,dim=0 )
# set all values
self.set_timesteps(A_ ,A_ ,A_ )
A = use_karras_sigmas
def _SCREAMING_SNAKE_CASE ( self : int ,A_ : Tuple ,A_ : Tuple=None ) -> Tuple:
if schedule_timesteps is None:
A = self.timesteps
A = (schedule_timesteps == timestep).nonzero()
# The sigma index that is taken for the **very** first `step`
# is always the second index (or the last index if there is only 1)
# This way we can ensure we don't accidentally skip a sigma in
# case we start in the middle of the denoising schedule (e.g. for image-to-image)
if len(self._index_counter ) == 0:
A = 1 if len(A_ ) > 1 else 0
else:
A = timestep.cpu().item() if torch.is_tensor(A_ ) else timestep
A = self._index_counter[timestep_int]
return indices[pos].item()
@property
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> List[Any]:
# standard deviation of the initial noise distribution
if self.config.timestep_spacing in ["linspace", "trailing"]:
return self.sigmas.max()
return (self.sigmas.max() ** 2 + 1) ** 0.5
def _SCREAMING_SNAKE_CASE ( self : List[Any] ,A_ : torch.FloatTensor ,A_ : Union[float, torch.FloatTensor] ,) -> torch.FloatTensor:
A = self.index_for_timestep(A_ )
A = self.sigmas[step_index]
A = sample / ((sigma**2 + 1) ** 0.5)
return sample
def _SCREAMING_SNAKE_CASE ( self : str ,A_ : int ,A_ : Union[str, torch.device] = None ,A_ : Optional[int] = None ,) -> Optional[Any]:
A = num_inference_steps
A = num_train_timesteps or self.config.num_train_timesteps
# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
if self.config.timestep_spacing == "linspace":
A = np.linspace(0 ,num_train_timesteps - 1 ,A_ ,dtype=A_ )[::-1].copy()
elif self.config.timestep_spacing == "leading":
A = num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
A = (np.arange(0 ,A_ ) * step_ratio).round()[::-1].copy().astype(A_ )
timesteps += self.config.steps_offset
elif self.config.timestep_spacing == "trailing":
A = num_train_timesteps / self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
A = (np.arange(A_ ,0 ,-step_ratio )).round().copy().astype(A_ )
timesteps -= 1
else:
raise ValueError(
F'{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.' )
A = np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5 )
A = np.log(A_ )
A = np.interp(A_ ,np.arange(0 ,len(A_ ) ) ,A_ )
if self.config.use_karras_sigmas:
A = self._convert_to_karras(in_sigmas=A_ ,num_inference_steps=self.num_inference_steps )
A = np.array([self._sigma_to_t(A_ ,A_ ) for sigma in sigmas] )
A = np.concatenate([sigmas, [0.0]] ).astype(np.floataa )
A = torch.from_numpy(A_ ).to(device=A_ )
A = torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2 ), sigmas[-1:]] )
A = torch.from_numpy(A_ )
A = torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2 )] )
if str(A_ ).startswith('mps' ):
# mps does not support float64
A = timesteps.to(A_ ,dtype=torch.floataa )
else:
A = timesteps.to(device=A_ )
# empty dt and derivative
A = None
A = None
# for exp beta schedules, such as the one for `pipeline_shap_e.py`
# we need an index counter
A = defaultdict(A_ )
def _SCREAMING_SNAKE_CASE ( self : Any ,A_ : Optional[Any] ,A_ : List[str] ) -> Dict:
# get log sigma
A = np.log(A_ )
# get distribution
A = log_sigma - log_sigmas[:, np.newaxis]
# get sigmas range
A = np.cumsum((dists >= 0) ,axis=0 ).argmax(axis=0 ).clip(max=log_sigmas.shape[0] - 2 )
A = low_idx + 1
A = log_sigmas[low_idx]
A = log_sigmas[high_idx]
# interpolate sigmas
A = (low - log_sigma) / (low - high)
A = np.clip(A_ ,0 ,1 )
# transform interpolation to time range
A = (1 - w) * low_idx + w * high_idx
A = t.reshape(sigma.shape )
return t
def _SCREAMING_SNAKE_CASE ( self : List[str] ,A_ : torch.FloatTensor ,A_ : int ) -> torch.FloatTensor:
A = in_sigmas[-1].item()
A = in_sigmas[0].item()
A = 7.0 # 7.0 is the value used in the paper
A = np.linspace(0 ,1 ,A_ )
A = sigma_min ** (1 / rho)
A = sigma_max ** (1 / rho)
A = (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
return sigmas
@property
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Dict:
return self.dt is None
def _SCREAMING_SNAKE_CASE ( self : Tuple ,A_ : Union[torch.FloatTensor, np.ndarray] ,A_ : Union[float, torch.FloatTensor] ,A_ : Union[torch.FloatTensor, np.ndarray] ,A_ : bool = True ,) -> Union[SchedulerOutput, Tuple]:
A = self.index_for_timestep(A_ )
# advance index counter by 1
A = timestep.cpu().item() if torch.is_tensor(A_ ) else timestep
self._index_counter[timestep_int] += 1
if self.state_in_first_order:
A = self.sigmas[step_index]
A = self.sigmas[step_index + 1]
else:
# 2nd order / Heun's method
A = self.sigmas[step_index - 1]
A = self.sigmas[step_index]
# currently only gamma=0 is supported. This usually works best anyways.
# We can support gamma in the future but then need to scale the timestep before
# passing it to the model which requires a change in API
A = 0
A = sigma * (gamma + 1) # Note: sigma_hat == sigma for now
# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
if self.config.prediction_type == "epsilon":
A = sigma_hat if self.state_in_first_order else sigma_next
A = sample - sigma_input * model_output
elif self.config.prediction_type == "v_prediction":
A = sigma_hat if self.state_in_first_order else sigma_next
A = model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
sample / (sigma_input**2 + 1)
)
elif self.config.prediction_type == "sample":
A = model_output
else:
raise ValueError(
F'prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`' )
if self.config.clip_sample:
A = pred_original_sample.clamp(
-self.config.clip_sample_range ,self.config.clip_sample_range )
if self.state_in_first_order:
# 2. Convert to an ODE derivative for 1st order
A = (sample - pred_original_sample) / sigma_hat
# 3. delta timestep
A = sigma_next - sigma_hat
# store for 2nd order step
A = derivative
A = dt
A = sample
else:
# 2. 2nd order / Heun's method
A = (sample - pred_original_sample) / sigma_next
A = (self.prev_derivative + derivative) / 2
# 3. take prev timestep & sample
A = self.dt
A = self.sample
# free dt and derivative
# Note, this puts the scheduler in "first order mode"
A = None
A = None
A = None
A = sample + derivative * dt
if not return_dict:
return (prev_sample,)
return SchedulerOutput(prev_sample=A_ )
def _SCREAMING_SNAKE_CASE ( self : int ,A_ : torch.FloatTensor ,A_ : torch.FloatTensor ,A_ : torch.FloatTensor ,) -> torch.FloatTensor:
# Make sure sigmas and timesteps have the same device and dtype as original_samples
A = self.sigmas.to(device=original_samples.device ,dtype=original_samples.dtype )
if original_samples.device.type == "mps" and torch.is_floating_point(A_ ):
# mps does not support float64
A = self.timesteps.to(original_samples.device ,dtype=torch.floataa )
A = timesteps.to(original_samples.device ,dtype=torch.floataa )
else:
A = self.timesteps.to(original_samples.device )
A = timesteps.to(original_samples.device )
A = [self.index_for_timestep(A_ ,A_ ) for t in timesteps]
A = sigmas[step_indices].flatten()
while len(sigma.shape ) < len(original_samples.shape ):
A = sigma.unsqueeze(-1 )
A = original_samples + noise * sigma
return noisy_samples
def __len__( self : Dict ) -> int:
return self.config.num_train_timesteps | 74 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :Any = BioGptTokenizer
UpperCAmelCase_ :str = False
def __lowerCAmelCase ( self ) -> List[Any]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ :Optional[Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
lowerCAmelCase_ :str = dict(zip(__A , range(len(__A ) ) ) )
lowerCAmelCase_ :int = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
lowerCAmelCase_ :Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase_ :Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(__A ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(__A ) )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
lowerCAmelCase_ :List[Any] = """lower newer"""
lowerCAmelCase_ :Tuple = """lower newer"""
return input_text, output_text
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :List[str] = BioGptTokenizer(self.vocab_file , self.merges_file )
lowerCAmelCase_ :Union[str, Any] = """lower"""
lowerCAmelCase_ :Any = ["""low""", """er</w>"""]
lowerCAmelCase_ :Union[str, Any] = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
lowerCAmelCase_ :Dict = tokens + ["""<unk>"""]
lowerCAmelCase_ :List[str] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
@slow
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Optional[Any] = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
lowerCAmelCase_ :List[Any] = tokenizer.encode("""sequence builders""" , add_special_tokens=__A )
lowerCAmelCase_ :List[str] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=__A )
lowerCAmelCase_ :Optional[int] = tokenizer.build_inputs_with_special_tokens(__A )
lowerCAmelCase_ :List[str] = tokenizer.build_inputs_with_special_tokens(__A , __A )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 84 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
a_ : int = logging.get_logger(__name__)
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
warnings.warn(
'''The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'''
''' use FlavaImageProcessor instead.''', lowerCAmelCase, )
super().__init__(*lowerCAmelCase, **lowerCAmelCase )
| 75 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "bert-generation"
def __init__( self , __A=5_0358 , __A=1024 , __A=24 , __A=16 , __A=4096 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0.0_2 , __A=1E-12 , __A=0 , __A=2 , __A=1 , __A="absolute" , __A=True , **__A , ) -> Tuple:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :Any = vocab_size
lowerCAmelCase_ :List[Any] = hidden_size
lowerCAmelCase_ :Optional[int] = num_hidden_layers
lowerCAmelCase_ :int = num_attention_heads
lowerCAmelCase_ :List[Any] = hidden_act
lowerCAmelCase_ :Optional[Any] = intermediate_size
lowerCAmelCase_ :List[Any] = hidden_dropout_prob
lowerCAmelCase_ :int = attention_probs_dropout_prob
lowerCAmelCase_ :Tuple = max_position_embeddings
lowerCAmelCase_ :List[str] = initializer_range
lowerCAmelCase_ :Union[str, Any] = layer_norm_eps
lowerCAmelCase_ :List[str] = position_embedding_type
lowerCAmelCase_ :Optional[int] = use_cache
| 84 | 0 |
from __future__ import annotations
def lowerCamelCase__ ( _a , _a):
# Checks if the entire collection has been sorted
if len(_a) <= 1 or n <= 1:
return
insert_next(_a , n - 1)
rec_insertion_sort(_a , n - 1)
def lowerCamelCase__ ( _a , _a):
# Checks order between adjacent elements
if index >= len(_a) or collection[index - 1] <= collection[index]:
return
# Swaps adjacent elements since they are not in ascending order
SCREAMING_SNAKE_CASE ,SCREAMING_SNAKE_CASE : List[Any] = (
collection[index],
collection[index - 1],
)
insert_next(_a , index + 1)
if __name__ == "__main__":
a_ = input('Enter integers separated by spaces: ')
a_ = [int(num) for num in numbers.split()]
rec_insertion_sort(number_list, len(number_list))
print(number_list) | 76 |
"""simple docstring"""
def _snake_case ( lowercase__ : List[Any] , lowercase__ : int , lowercase__ : Optional[int] , lowercase__ : Any ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [False] * len(lowercase__ )
lowerCAmelCase_ :str = []
queue.append(lowercase__ )
lowerCAmelCase_ :Any = True
while queue:
lowerCAmelCase_ :Optional[int] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(lowercase__ )
lowerCAmelCase_ :Union[str, Any] = True
lowerCAmelCase_ :int = u
return visited[t]
def _snake_case ( lowercase__ : int , lowercase__ : Optional[int] , lowercase__ : str ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = [-1] * (len(lowercase__ ))
lowerCAmelCase_ :str = 0
while bfs(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
lowerCAmelCase_ :List[str] = float("""Inf""" )
lowerCAmelCase_ :List[str] = sink
while s != source:
# Find the minimum value in select path
lowerCAmelCase_ :Any = min(lowercase__ , graph[parent[s]][s] )
lowerCAmelCase_ :Union[str, Any] = parent[s]
max_flow += path_flow
lowerCAmelCase_ :Tuple = sink
while v != source:
lowerCAmelCase_ :List[str] = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowerCAmelCase_ :Union[str, Any] = parent[v]
return max_flow
__UpperCAmelCase = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__UpperCAmelCase , __UpperCAmelCase = 0, 5
print(ford_fulkerson(graph, source, sink))
| 84 | 0 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
UNetaDConditionModel,
VideoToVideoSDPipeline,
)
from diffusers.utils import floats_tensor, is_xformers_available, skip_mps
from diffusers.utils.testing_utils import enable_full_determinism, slow, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
@skip_mps
class UpperCAmelCase_ ( _a , unittest.TestCase):
lowerCamelCase__ : str = VideoToVideoSDPipeline
lowerCamelCase__ : Optional[int] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS.union({"video"}) - {"image", "width", "height"}
lowerCamelCase__ : Tuple = TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS.union({"video"}) - {"image"}
lowerCamelCase__ : Union[str, Any] = PipelineTesterMixin.required_optional_params - {"latents"}
lowerCamelCase__ : List[Any] = False
# No `output_type`.
lowerCamelCase__ : List[Any] = frozenset(
[
"num_inference_steps",
"generator",
"latents",
"return_dict",
"callback",
"callback_steps",
])
def _UpperCAmelCase ( self ) -> Union[str, Any]:
torch.manual_seed(0 )
lowercase__ : Optional[int] = UNetaDConditionModel(
block_out_channels=(3_2, 6_4, 6_4, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=('CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'CrossAttnDownBlock3D', 'DownBlock3D') , up_block_types=('UpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D', 'CrossAttnUpBlock3D') , cross_attention_dim=3_2 , attention_head_dim=4 , )
lowercase__ : Union[str, Any] = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='scaled_linear' , clip_sample=a , set_alpha_to_one=a , )
torch.manual_seed(0 )
lowercase__ : Tuple = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] , up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
lowercase__ : Optional[int] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act='gelu' , projection_dim=5_1_2 , )
lowercase__ : Union[str, Any] = CLIPTextModel(a )
lowercase__ : Optional[int] = CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )
lowercase__ : List[str] = {
'unet': unet,
'scheduler': scheduler,
'vae': vae,
'text_encoder': text_encoder,
'tokenizer': tokenizer,
}
return components
def _UpperCAmelCase ( self , a , a=0 ) -> Union[str, Any]:
# 3 frames
lowercase__ : List[Any] = floats_tensor((1, 3, 3, 3_2, 3_2) , rng=random.Random(a ) ).to(a )
if str(a ).startswith('mps' ):
lowercase__ : Tuple = torch.manual_seed(a )
else:
lowercase__ : Any = torch.Generator(device=a ).manual_seed(a )
lowercase__ : Any = {
'prompt': 'A painting of a squirrel eating a burger',
'video': video,
'generator': generator,
'num_inference_steps': 2,
'guidance_scale': 6.0,
'output_type': 'pt',
}
return inputs
def _UpperCAmelCase ( self ) -> Union[str, Any]:
lowercase__ : List[Any] = 'cpu' # ensure determinism for the device-dependent torch.Generator
lowercase__ : Optional[Any] = self.get_dummy_components()
lowercase__ : List[Any] = VideoToVideoSDPipeline(**a )
lowercase__ : Optional[Any] = sd_pipe.to(a )
sd_pipe.set_progress_bar_config(disable=a )
lowercase__ : List[Any] = self.get_dummy_inputs(a )
lowercase__ : Any = 'np'
lowercase__ : Any = sd_pipe(**a ).frames
lowercase__ : List[Any] = frames[0][-3:, -3:, -1]
assert frames[0].shape == (3_2, 3_2, 3)
lowercase__ : Optional[Any] = np.array([1_0_6, 1_1_7, 1_1_3, 1_7_4, 1_3_7, 1_1_2, 1_4_8, 1_5_1, 1_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
@unittest.skipIf(
torch_device != 'cuda' or not is_xformers_available() , reason='XFormers attention is only available with CUDA and `xformers` installed' , )
def _UpperCAmelCase ( self ) -> int:
self._test_xformers_attention_forwardGenerator_pass(test_mean_pixel_difference=a , expected_max_diff=5e-3 )
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def _UpperCAmelCase ( self ) -> Dict:
pass
@unittest.skip(reason='Batching needs to be properly figured out first for this pipeline.' )
def _UpperCAmelCase ( self ) -> int:
pass
@unittest.skip(reason='`num_images_per_prompt` argument is not supported for this pipeline.' )
def _UpperCAmelCase ( self ) -> int:
pass
def _UpperCAmelCase ( self ) -> Union[str, Any]:
return super().test_progress_bar()
@slow
@skip_mps
class UpperCAmelCase_ ( unittest.TestCase):
def _UpperCAmelCase ( self ) -> int:
lowercase__ : Dict = VideoToVideoSDPipeline.from_pretrained('cerspense/zeroscope_v2_XL' , torch_dtype=torch.floataa )
pipe.enable_model_cpu_offload()
# 10 frames
lowercase__ : Any = torch.Generator(device='cpu' ).manual_seed(0 )
lowercase__ : int = torch.randn((1, 1_0, 3, 1_0_2_4, 5_7_6) , generator=a )
lowercase__ : Optional[int] = video.to('cuda' )
lowercase__ : Optional[int] = 'Spiderman is surfing'
lowercase__ : Union[str, Any] = pipe(a , video=a , generator=a , num_inference_steps=3 , output_type='pt' ).frames
lowercase__ : Any = np.array([-1.0_458_984, -1.1_279_297, -0.9_663_086, -0.91_503_906, -0.75_097_656] )
assert np.abs(video_frames.cpu().numpy()[0, 0, 0, 0, -5:] - expected_array ).sum() < 1e-2
| 77 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = 1_0
lowerCAmelCase_ :Optional[int] = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
lowerCAmelCase_ :int = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
"""id""": list(range(lowercase__ ) ),
} , features=lowercase__ , )
return dataset
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple , lowercase__ : int ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=lowercase__ )
return filename
# FILE_CONTENT + files
__UpperCAmelCase = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
lowerCAmelCase_ :List[Any] = FILE_CONTENT
with open(lowercase__ , """w""" ) as f:
f.write(lowercase__ )
return filename
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[Any] ) -> Tuple:
'''simple docstring'''
import bza
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
lowerCAmelCase_ :Tuple = bytes(lowercase__ , """utf-8""" )
with bza.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[Any] ) -> Dict:
'''simple docstring'''
import gzip
lowerCAmelCase_ :int = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
lowerCAmelCase_ :Tuple = bytes(lowercase__ , """utf-8""" )
with gzip.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Optional[int]:
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
lowerCAmelCase_ :List[Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
lowerCAmelCase_ :int = bytes(lowercase__ , """utf-8""" )
with lza.frame.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict , lowercase__ : Optional[int] ) -> Any:
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
lowerCAmelCase_ :Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(lowercase__ , """w""" ) as archive:
archive.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
import tarfile
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> str:
'''simple docstring'''
import lzma
lowerCAmelCase_ :Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
lowerCAmelCase_ :Optional[Any] = bytes(lowercase__ , """utf-8""" )
with lzma.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : List[Any] ) -> Any:
'''simple docstring'''
import zipfile
lowerCAmelCase_ :Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> Tuple:
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
lowerCAmelCase_ :Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
lowerCAmelCase_ :Any = bytes(lowercase__ , """utf-8""" )
with zstd.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
lowerCAmelCase_ :Any = textwrap.dedent(
"""\
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>
<tmx version=\"1.4\">
<header segtype=\"sentence\" srclang=\"ca\" />
<body>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>""" )
with open(lowercase__ , """w""" ) as f:
f.write(lowercase__ )
return filename
__UpperCAmelCase = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
__UpperCAmelCase = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
__UpperCAmelCase = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
__UpperCAmelCase = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
__UpperCAmelCase = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> Union[str, Any]:
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Tuple = datasets.Dataset.from_dict(lowercase__ )
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(lowercase__ ) ) as con:
lowerCAmelCase_ :Union[str, Any] = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> int:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(lowercase__ , """w""" , newline="""""" ) as f:
lowerCAmelCase_ :Optional[int] = csv.DictWriter(lowercase__ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(lowercase__ , """w""" , newline="""""" ) as f:
lowerCAmelCase_ :Dict = csv.DictWriter(lowercase__ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str , lowercase__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
import bza
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(lowercase__ , """rb""" ) as f:
lowerCAmelCase_ :Union[str, Any] = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : List[str] , lowercase__ : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(lowercase__ , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Tuple , lowercase__ : str ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
lowerCAmelCase_ :Optional[Any] = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(lowercase__ , """wb""" ) as f:
lowerCAmelCase_ :Optional[int] = pq.ParquetWriter(lowercase__ , schema=lowercase__ )
lowerCAmelCase_ :List[str] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(lowercase__ ) )] for k in DATA[0]} , schema=lowercase__ )
writer.write_table(lowercase__ )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
lowerCAmelCase_ :Union[str, Any] = {"""data""": DATA}
with open(lowercase__ , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
lowerCAmelCase_ :Optional[Any] = {"""data""": DATA_DICT_OF_LISTS}
with open(lowercase__ , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int , lowercase__ : Dict ) -> Optional[int]:
'''simple docstring'''
import gzip
lowerCAmelCase_ :Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(lowercase__ , """rb""" ) as orig_file:
with gzip.open(lowercase__ , """wb""" ) as zipped_file:
zipped_file.writelines(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : List[Any] ) -> Any:
'''simple docstring'''
import gzip
lowerCAmelCase_ :Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(lowercase__ , """rb""" ) as orig_file:
with gzip.open(lowercase__ , """wb""" ) as zipped_file:
zipped_file.writelines(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : Optional[int] , lowercase__ : List[Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""nested""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : List[Any] , lowercase__ : List[str] ) -> int:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : str , lowercase__ : List[str] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict , lowercase__ : str , lowercase__ : List[str] , lowercase__ : int ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.join("""nested""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :str = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Dict = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple , lowercase__ : Tuple , lowercase__ : List[str] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Any , lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(lowercase__ , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(lowercase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> int:
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> Tuple:
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
return data_dir
| 84 | 0 |
"""simple docstring"""
from collections.abc import Sequence
def _lowerCAmelCase ( lowercase_ , lowercase_ = False ):
if not arr:
return 0
UpperCAmelCase = 0 if allow_empty_subarrays else float('-inf' )
UpperCAmelCase = 0.0
for num in arr:
UpperCAmelCase = max(0 if allow_empty_subarrays else num , curr_sum + num )
UpperCAmelCase = max(lowercase_ , lowercase_ )
return max_sum
if __name__ == "__main__":
from doctest import testmod
testmod()
snake_case_ = [-2, 1, -3, 4, -1, 2, 1, -5, 4]
print(f'''{max_subarray_sum(nums) = }''')
| 78 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Optional[Any] = "data2vec-text"
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.0_2 , __A=1E-12 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , __A=None , **__A , ) -> Tuple:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :Dict = vocab_size
lowerCAmelCase_ :Dict = hidden_size
lowerCAmelCase_ :int = num_hidden_layers
lowerCAmelCase_ :List[Any] = num_attention_heads
lowerCAmelCase_ :Any = hidden_act
lowerCAmelCase_ :Optional[int] = intermediate_size
lowerCAmelCase_ :str = hidden_dropout_prob
lowerCAmelCase_ :Any = attention_probs_dropout_prob
lowerCAmelCase_ :str = max_position_embeddings
lowerCAmelCase_ :int = type_vocab_size
lowerCAmelCase_ :Tuple = initializer_range
lowerCAmelCase_ :List[Any] = layer_norm_eps
lowerCAmelCase_ :List[Any] = position_embedding_type
lowerCAmelCase_ :List[Any] = use_cache
lowerCAmelCase_ :List[Any] = classifier_dropout
class _SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase_ :List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowerCAmelCase_ :List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 84 | 0 |
'''simple docstring'''
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class _UpperCAmelCase ( enum.Enum ):
"""simple docstring"""
snake_case = '''all_checks'''
snake_case = '''basic_checks'''
snake_case = '''no_checks'''
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
def __lowercase ( __lowercase , __lowercase , __lowercase=None ) -> Dict:
'''simple docstring'''
if expected_checksums is None:
logger.info("Unable to verify checksums." )
return
if len(set(__lowercase ) - set(__lowercase ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(__lowercase ) - set(__lowercase ) ) )
if len(set(__lowercase ) - set(__lowercase ) ) > 0:
raise UnexpectedDownloadedFile(str(set(__lowercase ) - set(__lowercase ) ) )
_A = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
_A = " for " + verification_name if verification_name is not None else ""
if len(__lowercase ) > 0:
raise NonMatchingChecksumError(
F'''Checksums didn\'t match{for_verification_name}:\n'''
F'''{bad_urls}\n'''
"Set `verification_mode='no_checks'` to skip checksums verification and ignore this error" )
logger.info("All the checksums matched successfully" + for_verification_name )
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
class _UpperCAmelCase ( snake_case_ ):
"""simple docstring"""
def __lowercase ( __lowercase , __lowercase ) -> List[str]:
'''simple docstring'''
if expected_splits is None:
logger.info("Unable to verify splits sizes." )
return
if len(set(__lowercase ) - set(__lowercase ) ) > 0:
raise ExpectedMoreSplits(str(set(__lowercase ) - set(__lowercase ) ) )
if len(set(__lowercase ) - set(__lowercase ) ) > 0:
raise UnexpectedSplits(str(set(__lowercase ) - set(__lowercase ) ) )
_A = [
{"expected": expected_splits[name], "recorded": recorded_splits[name]}
for name in expected_splits
if expected_splits[name].num_examples != recorded_splits[name].num_examples
]
if len(__lowercase ) > 0:
raise NonMatchingSplitsSizesError(str(__lowercase ) )
logger.info("All the splits matched successfully." )
def __lowercase ( __lowercase , __lowercase = True ) -> dict:
'''simple docstring'''
if record_checksum:
_A = shaaaa()
with open(__lowercase , "rb" ) as f:
for chunk in iter(lambda: f.read(1 << 20 ) , B"" ):
m.update(__lowercase )
_A = m.hexdigest()
else:
_A = None
return {"num_bytes": os.path.getsize(__lowercase ), "checksum": checksum}
def __lowercase ( __lowercase ) -> str:
'''simple docstring'''
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 79 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( lowercase__ : Dict , lowercase__ : Dict , lowercase__ : str , lowercase__ : Tuple="attention" ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
lowerCAmelCase_ :Union[str, Any] = params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
lowerCAmelCase_ :Any = params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
lowerCAmelCase_ :Optional[int] = params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
return k, o, q, v
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Any , lowercase__ : int , lowercase__ : Any=False ) -> int:
'''simple docstring'''
if split_mlp_wi:
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
lowerCAmelCase_ :List[str] = params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
lowerCAmelCase_ :Tuple = (wi_a, wi_a)
else:
lowerCAmelCase_ :List[Any] = params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]
lowerCAmelCase_ :Dict = params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
return wi, wo
def _snake_case ( lowercase__ : Any , lowercase__ : Dict , lowercase__ : Union[str, Any] , lowercase__ : Optional[int] ) -> Tuple:
'''simple docstring'''
return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]
def _snake_case ( lowercase__ : dict , *, lowercase__ : int , lowercase__ : bool ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = traverse_util.flatten_dict(variables["""target"""] )
lowerCAmelCase_ :Tuple = {"""/""".join(lowercase__ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
lowerCAmelCase_ :Any = """encoder/layers_0/mlp/wi_0/kernel""" in old
print("""Split MLP:""" , lowercase__ )
lowerCAmelCase_ :List[Any] = collections.OrderedDict()
# Shared embeddings.
lowerCAmelCase_ :Optional[int] = old["""token_embedder/embedding"""]
# Encoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :str = tax_attention_lookup(lowercase__ , lowercase__ , """encoder""" , """attention""" )
lowerCAmelCase_ :Optional[Any] = layer_norm
lowerCAmelCase_ :Any = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Tuple = q.T
lowerCAmelCase_ :str = v.T
# Block i, layer 1 (MLP).
lowerCAmelCase_ :Dict = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Any = tax_mlp_lookup(lowercase__ , lowercase__ , """encoder""" , lowercase__ )
lowerCAmelCase_ :Union[str, Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :List[Any] = wi[0].T
lowerCAmelCase_ :Dict = wi[1].T
else:
lowerCAmelCase_ :int = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Tuple = old[
"""encoder/relpos_bias/rel_embedding"""
].T
lowerCAmelCase_ :List[str] = old["""encoder/encoder_norm/scale"""]
if not is_encoder_only:
# Decoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :Optional[Any] = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_self_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """self_attention""" )
lowerCAmelCase_ :List[Any] = layer_norm
lowerCAmelCase_ :List[str] = k.T
lowerCAmelCase_ :Any = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :Dict = v.T
# Block i, layer 1 (Cross Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_cross_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Tuple = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """encoder_decoder_attention""" )
lowerCAmelCase_ :Optional[int] = layer_norm
lowerCAmelCase_ :str = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :int = v.T
# Block i, layer 2 (MLP).
lowerCAmelCase_ :Any = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Dict = tax_mlp_lookup(lowercase__ , lowercase__ , """decoder""" , lowercase__ )
lowerCAmelCase_ :List[Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :Any = wi[0].T
lowerCAmelCase_ :Any = wi[1].T
else:
lowerCAmelCase_ :Tuple = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Optional[Any] = old["""decoder/decoder_norm/scale"""]
lowerCAmelCase_ :Optional[Any] = old[
"""decoder/relpos_bias/rel_embedding"""
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
lowerCAmelCase_ :Tuple = old["""decoder/logits_dense/kernel"""].T
return new
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : bool ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Optional[int] = state_dict["""shared.weight"""]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Tuple = state_dict["""shared.weight"""]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
lowerCAmelCase_ :Any = state_dict["""shared.weight"""]
return state_dict
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : str , lowercase__ : List[Any] , lowercase__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = checkpoints.load_tax_checkpoint(lowercase__ )
lowerCAmelCase_ :Optional[int] = convert_tax_to_pytorch(lowercase__ , num_layers=config.num_layers , is_encoder_only=lowercase__ )
lowerCAmelCase_ :Union[str, Any] = make_state_dict(lowercase__ , lowercase__ )
model.load_state_dict(lowercase__ , strict=lowercase__ )
def _snake_case ( lowercase__ : List[Any] , lowercase__ : Optional[Any] , lowercase__ : str , lowercase__ : bool = False ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Any = TaConfig.from_json_file(lowercase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
lowerCAmelCase_ :List[Any] = TaEncoderModel(lowercase__ )
else:
lowerCAmelCase_ :List[str] = TaForConditionalGeneration(lowercase__ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowercase__ )
# Verify that we can load the checkpoint.
model.from_pretrained(lowercase__ )
print("""Done""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
)
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.'
)
parser.add_argument(
'--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
)
__UpperCAmelCase = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 84 | 0 |
'''simple docstring'''
from datasets.utils.patching import _PatchedModuleObj, patch_submodule
from . import _test_patching
def _UpperCamelCase ( ) -> List[Any]:
'''simple docstring'''
import os as original_os
from os import path as original_path
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
UpperCamelCase__ = "__test_patch_submodule_mock__"
with patch_submodule(_test_patching , "os.path.join" , __A ):
# Every way to access os.path.join must be patched, and the rest must stay untouched
# check os.path.join
assert isinstance(_test_patching.os , _PatchedModuleObj )
assert isinstance(_test_patching.os.path , _PatchedModuleObj )
assert _test_patching.os.path.join is mock
# check path.join
assert isinstance(_test_patching.path , _PatchedModuleObj )
assert _test_patching.path.join is mock
# check join
assert _test_patching.join is mock
# check that the other attributes are untouched
assert _test_patching.os.rename is original_rename
assert _test_patching.path.dirname is original_dirname
assert _test_patching.os.path.dirname is original_dirname
# Even renamed modules or objects must be patched
# check renamed_os.path.join
assert isinstance(_test_patching.renamed_os , _PatchedModuleObj )
assert isinstance(_test_patching.renamed_os.path , _PatchedModuleObj )
assert _test_patching.renamed_os.path.join is mock
# check renamed_path.join
assert isinstance(_test_patching.renamed_path , _PatchedModuleObj )
assert _test_patching.renamed_path.join is mock
# check renamed_join
assert _test_patching.renamed_join is mock
# check that the other attributes are untouched
assert _test_patching.renamed_os.rename is original_rename
assert _test_patching.renamed_path.dirname is original_dirname
assert _test_patching.renamed_os.path.dirname is original_dirname
# check that everthing is back to normal when the patch is over
assert _test_patching.os is original_os
assert _test_patching.path is original_path
assert _test_patching.join is original_join
assert _test_patching.renamed_os is original_os
assert _test_patching.renamed_path is original_path
assert _test_patching.renamed_join is original_join
def _UpperCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
assert _test_patching.open is open
UpperCamelCase__ = "__test_patch_submodule_builtin_mock__"
# _test_patching has "open" in its globals
assert _test_patching.open is open
with patch_submodule(_test_patching , "open" , __A ):
assert _test_patching.open is mock
# check that everthing is back to normal when the patch is over
assert _test_patching.open is open
def _UpperCamelCase ( ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ = "__test_patch_submodule_missing_mock__"
with patch_submodule(_test_patching , "pandas.read_csv" , __A ):
pass
def _UpperCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ = "__test_patch_submodule_missing_builtin_mock__"
# _test_patching doesn't have "len" in its globals
assert getattr(_test_patching , "len" , __A ) is None
with patch_submodule(_test_patching , "len" , __A ):
assert _test_patching.len is mock
assert _test_patching.len is len
def _UpperCamelCase ( ) -> Union[str, Any]:
'''simple docstring'''
UpperCamelCase__ = "__test_patch_submodule_start_and_stop_mock__"
UpperCamelCase__ = patch_submodule(_test_patching , "open" , __A )
assert _test_patching.open is open
patch.start()
assert _test_patching.open is mock
patch.stop()
assert _test_patching.open is open
def _UpperCamelCase ( ) -> List[str]:
'''simple docstring'''
from os import rename as original_rename
from os.path import dirname as original_dirname
from os.path import join as original_join
UpperCamelCase__ = "__test_patch_submodule_successive_join__"
UpperCamelCase__ = "__test_patch_submodule_successive_dirname__"
UpperCamelCase__ = "__test_patch_submodule_successive_rename__"
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
with patch_submodule(_test_patching , "os.path.join" , __A ):
with patch_submodule(_test_patching , "os.rename" , __A ):
with patch_submodule(_test_patching , "os.path.dirname" , __A ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
# try another order
with patch_submodule(_test_patching , "os.rename" , __A ):
with patch_submodule(_test_patching , "os.path.join" , __A ):
with patch_submodule(_test_patching , "os.path.dirname" , __A ):
assert _test_patching.os.path.join is mock_join
assert _test_patching.os.path.dirname is mock_dirname
assert _test_patching.os.rename is mock_rename
assert _test_patching.os.path.join is original_join
assert _test_patching.os.path.dirname is original_dirname
assert _test_patching.os.rename is original_rename
def _UpperCamelCase ( ) -> Dict:
'''simple docstring'''
UpperCamelCase__ = "__test_patch_submodule_doesnt_exist_mock__"
with patch_submodule(_test_patching , "__module_that_doesn_exist__.__attribute_that_doesn_exist__" , __A ):
pass
with patch_submodule(_test_patching , "os.__attribute_that_doesn_exist__" , __A ):
pass
| 80 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( lowercase__ : Optional[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :str = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
lowerCAmelCase_ :Union[str, Any] = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
lowerCAmelCase_ :Any = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
lowerCAmelCase_ :List[str] = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
lowerCAmelCase_ :Tuple = key.replace(f"""patch_embed{idx}""" , f"""patch_embeddings.{int(lowercase__ )-1}""" )
if "norm" in key:
lowerCAmelCase_ :Dict = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
lowerCAmelCase_ :str = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
lowerCAmelCase_ :str = key.replace(f"""layer_norm{idx}""" , f"""layer_norm.{int(lowercase__ )-1}""" )
if "layer_norm1" in key:
lowerCAmelCase_ :Optional[Any] = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
lowerCAmelCase_ :str = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
lowerCAmelCase_ :List[str] = key[key.find("""block""" ) + len("""block""" )]
lowerCAmelCase_ :int = key.replace(f"""block{idx}""" , f"""block.{int(lowercase__ )-1}""" )
if "attn.q" in key:
lowerCAmelCase_ :Tuple = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
lowerCAmelCase_ :Optional[int] = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
lowerCAmelCase_ :str = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
lowerCAmelCase_ :List[Any] = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
lowerCAmelCase_ :Optional[Any] = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
lowerCAmelCase_ :List[str] = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
lowerCAmelCase_ :str = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
lowerCAmelCase_ :Any = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
lowerCAmelCase_ :str = key[key.find("""linear_c""" ) + len("""linear_c""" )]
lowerCAmelCase_ :Optional[int] = key.replace(f"""linear_c{idx}""" , f"""linear_c.{int(lowercase__ )-1}""" )
if "bot_conv" in key:
lowerCAmelCase_ :Union[str, Any] = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
lowerCAmelCase_ :int = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
lowerCAmelCase_ :str = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
lowerCAmelCase_ :Any = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
lowerCAmelCase_ :List[str] = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
lowerCAmelCase_ :Dict = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
lowerCAmelCase_ :Any = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
lowerCAmelCase_ :Tuple = key.replace("""module.last_layer_depth""" , """head.head""" )
lowerCAmelCase_ :List[Any] = value
return new_state_dict
def _snake_case ( lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
lowerCAmelCase_ :Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
lowerCAmelCase_ :Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
lowerCAmelCase_ :Optional[Any] = kv_weight[
: config.hidden_sizes[i], :
]
lowerCAmelCase_ :Union[str, Any] = kv_bias[: config.hidden_sizes[i]]
lowerCAmelCase_ :List[Any] = kv_weight[
config.hidden_sizes[i] :, :
]
lowerCAmelCase_ :int = kv_bias[config.hidden_sizes[i] :]
def _snake_case ( ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase_ :Optional[Any] = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw )
return image
@torch.no_grad()
def _snake_case ( lowercase__ : List[Any] , lowercase__ : str , lowercase__ : Dict=False , lowercase__ : List[Any]=None ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2] , decoder_hidden_size=6_4 , depths=[3, 8, 2_7, 3] )
# load image processor (only resize + rescale)
lowerCAmelCase_ :Union[str, Any] = GLPNImageProcessor()
# prepare image
lowerCAmelCase_ :List[Any] = prepare_img()
lowerCAmelCase_ :int = image_processor(images=lowercase__ , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
lowerCAmelCase_ :Tuple = torch.load(lowercase__ , map_location=torch.device("""cpu""" ) )
# rename keys
lowerCAmelCase_ :Union[str, Any] = rename_keys(lowercase__ )
# key and value matrices need special treatment
read_in_k_v(lowercase__ , lowercase__ )
# create HuggingFace model and load state dict
lowerCAmelCase_ :List[Any] = GLPNForDepthEstimation(lowercase__ )
model.load_state_dict(lowercase__ )
model.eval()
# forward pass
lowerCAmelCase_ :Dict = model(lowercase__ )
lowerCAmelCase_ :Tuple = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
lowerCAmelCase_ :Optional[Any] = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
elif "kitti" in model_name:
lowerCAmelCase_ :Any = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
else:
raise ValueError(f"""Unknown model name: {model_name}""" )
lowerCAmelCase_ :Union[str, Any] = torch.Size([1, 4_8_0, 6_4_0] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , lowercase__ , atol=1E-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(lowercase__ , lowercase__ ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=lowercase__ , )
image_processor.push_to_hub(
repo_path_or_name=Path(lowercase__ , lowercase__ ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=lowercase__ , )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
__UpperCAmelCase = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 84 | 0 |
"""simple docstring"""
def _A ( lowercase ):
"""simple docstring"""
if not isinstance(lowercase , lowercase ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
a =0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod() | 81 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoCBertForCausalLM',
'RoCBertForMaskedLM',
'RoCBertForMultipleChoice',
'RoCBertForPreTraining',
'RoCBertForQuestionAnswering',
'RoCBertForSequenceClassification',
'RoCBertForTokenClassification',
'RoCBertLayer',
'RoCBertModel',
'RoCBertPreTrainedModel',
'load_tf_weights_in_roc_bert',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 84 | 0 |
import logging
import os
import sys
from dataclasses import dataclass, field
from itertools import chain
from typing import Optional, Union
import datasets
import numpy as np
import torch
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForMultipleChoice,
AutoTokenizer,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import PaddingStrategy, check_min_version, send_example_telemetry
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
A__ = logging.getLogger(__name__)
@dataclass
class __lowerCAmelCase :
__lowerCamelCase = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
__lowerCamelCase = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
@dataclass
class __lowerCAmelCase :
__lowerCamelCase = field(default=lowerCamelCase__ , metadata={'''help''': '''The input training data file (a text file).'''} )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. If passed, sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': (
'''Whether to pad all samples to the maximum sentence length. '''
'''If False, will pad the samples dynamically when batching to the maximum length in the batch. More '''
'''efficient on GPU but very bad for TPU.'''
)
} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
__lowerCamelCase = field(
default=lowerCamelCase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
def snake_case ( self ):
"""simple docstring"""
if self.train_file is not None:
_lowerCAmelCase = self.train_file.split(""".""" )[-1]
assert extension in ["csv", "json"], "`train_file` should be a csv or a json file."
if self.validation_file is not None:
_lowerCAmelCase = self.validation_file.split(""".""" )[-1]
assert extension in ["csv", "json"], "`validation_file` should be a csv or a json file."
@dataclass
class __lowerCAmelCase :
__lowerCamelCase = 42
__lowerCamelCase = True
__lowerCamelCase = None
__lowerCamelCase = None
def __call__( self , _snake_case ):
"""simple docstring"""
_lowerCAmelCase = """label""" if """label""" in features[0].keys() else """labels"""
_lowerCAmelCase = [feature.pop(_snake_case ) for feature in features]
_lowerCAmelCase = len(_snake_case )
_lowerCAmelCase = len(features[0]["""input_ids"""] )
_lowerCAmelCase = [
[{k: v[i] for k, v in feature.items()} for i in range(_snake_case )] for feature in features
]
_lowerCAmelCase = list(chain(*_snake_case ) )
_lowerCAmelCase = self.tokenizer.pad(
_snake_case , padding=self.padding , max_length=self.max_length , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors="""pt""" , )
# Un-flatten
_lowerCAmelCase = {k: v.view(_snake_case , _snake_case , -1 ) for k, v in batch.items()}
# Add back labels
_lowerCAmelCase = torch.tensor(_snake_case , dtype=torch.intaa )
return batch
def _UpperCAmelCase ( ):
"""simple docstring"""
_lowerCAmelCase = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
if len(sys.argv ) == 2 and sys.argv[1].endswith(""".json""" ):
# If we pass only one argument to the script and it's the path to a json file,
# let's parse it to get our arguments.
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry("""run_swag""" , snake_case , snake_case )
# Setup logging
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""" , datefmt="""%m/%d/%Y %H:%M:%S""" , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
_lowerCAmelCase = training_args.get_process_log_level()
logger.setLevel(snake_case )
datasets.utils.logging.set_verbosity(snake_case )
transformers.utils.logging.set_verbosity(snake_case )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ F'distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}' )
logger.info(F'Training/evaluation parameters {training_args}' )
# Detecting last checkpoint.
_lowerCAmelCase = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_lowerCAmelCase = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F'Output directory ({training_args.output_dir}) already exists and is not empty. '
"""Use --overwrite_output_dir to overcome.""" )
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
F'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '
"""the `--output_dir` or add `--overwrite_output_dir` to train from scratch.""" )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
# (the dataset will be downloaded automatically from the datasets Hub).
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
# 'text' is found. You can easily tweak this behavior (see below).
# In distributed training, the load_dataset function guarantee that only one local process can concurrently
# download the dataset.
if data_args.train_file is not None or data_args.validation_file is not None:
_lowerCAmelCase = {}
if data_args.train_file is not None:
_lowerCAmelCase = data_args.train_file
if data_args.validation_file is not None:
_lowerCAmelCase = data_args.validation_file
_lowerCAmelCase = data_args.train_file.split(""".""" )[-1]
_lowerCAmelCase = load_dataset(
snake_case , data_files=snake_case , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
# Downloading and loading the swag dataset from the hub.
_lowerCAmelCase = load_dataset(
"""swag""" , """regular""" , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Load pretrained model and tokenizer
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowerCAmelCase = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_lowerCAmelCase = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
_lowerCAmelCase = AutoModelForMultipleChoice.from_pretrained(
model_args.model_name_or_path , from_tf=bool(""".ckpt""" in model_args.model_name_or_path ) , config=snake_case , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# When using your own dataset or a different dataset from swag, you will probably need to change this.
_lowerCAmelCase = [F'ending{i}' for i in range(4 )]
_lowerCAmelCase = """sent1"""
_lowerCAmelCase = """sent2"""
if data_args.max_seq_length is None:
_lowerCAmelCase = tokenizer.model_max_length
if max_seq_length > 10_24:
logger.warning(
"""The chosen tokenizer supports a `model_max_length` that is longer than the default `block_size` value"""
""" of 1024. If you would like to use a longer `block_size` up to `tokenizer.model_max_length` you can"""
""" override this default with `--block_size xxx`.""" )
_lowerCAmelCase = 10_24
else:
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F'The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'
F'model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.' )
_lowerCAmelCase = min(data_args.max_seq_length , tokenizer.model_max_length )
# Preprocessing the datasets.
def preprocess_function(snake_case ):
_lowerCAmelCase = [[context] * 4 for context in examples[context_name]]
_lowerCAmelCase = examples[question_header_name]
_lowerCAmelCase = [
[F'{header} {examples[end][i]}' for end in ending_names] for i, header in enumerate(snake_case )
]
# Flatten out
_lowerCAmelCase = list(chain(*snake_case ) )
_lowerCAmelCase = list(chain(*snake_case ) )
# Tokenize
_lowerCAmelCase = tokenizer(
snake_case , snake_case , truncation=snake_case , max_length=snake_case , padding="""max_length""" if data_args.pad_to_max_length else False , )
# Un-flatten
return {k: [v[i : i + 4] for i in range(0 , len(snake_case ) , 4 )] for k, v in tokenized_examples.items()}
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError("""--do_train requires a train dataset""" )
_lowerCAmelCase = raw_datasets["""train"""]
if data_args.max_train_samples is not None:
_lowerCAmelCase = min(len(snake_case ) , data_args.max_train_samples )
_lowerCAmelCase = train_dataset.select(range(snake_case ) )
with training_args.main_process_first(desc="""train dataset map pre-processing""" ):
_lowerCAmelCase = train_dataset.map(
snake_case , batched=snake_case , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
if training_args.do_eval:
if "validation" not in raw_datasets:
raise ValueError("""--do_eval requires a validation dataset""" )
_lowerCAmelCase = raw_datasets["""validation"""]
if data_args.max_eval_samples is not None:
_lowerCAmelCase = min(len(snake_case ) , data_args.max_eval_samples )
_lowerCAmelCase = eval_dataset.select(range(snake_case ) )
with training_args.main_process_first(desc="""validation dataset map pre-processing""" ):
_lowerCAmelCase = eval_dataset.map(
snake_case , batched=snake_case , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , )
# Data collator
_lowerCAmelCase = (
default_data_collator
if data_args.pad_to_max_length
else DataCollatorForMultipleChoice(tokenizer=snake_case , pad_to_multiple_of=8 if training_args.fpaa else None )
)
# Metric
def compute_metrics(snake_case ):
_lowerCAmelCase , _lowerCAmelCase = eval_predictions
_lowerCAmelCase = np.argmax(snake_case , axis=1 )
return {"accuracy": (preds == label_ids).astype(np.floataa ).mean().item()}
# Initialize our Trainer
_lowerCAmelCase = Trainer(
model=snake_case , args=snake_case , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , tokenizer=snake_case , data_collator=snake_case , compute_metrics=snake_case , )
# Training
if training_args.do_train:
_lowerCAmelCase = None
if training_args.resume_from_checkpoint is not None:
_lowerCAmelCase = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_lowerCAmelCase = last_checkpoint
_lowerCAmelCase = trainer.train(resume_from_checkpoint=snake_case )
trainer.save_model() # Saves the tokenizer too for easy upload
_lowerCAmelCase = train_result.metrics
_lowerCAmelCase = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(snake_case )
)
_lowerCAmelCase = min(snake_case , len(snake_case ) )
trainer.log_metrics("""train""" , snake_case )
trainer.save_metrics("""train""" , snake_case )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info("""*** Evaluate ***""" )
_lowerCAmelCase = trainer.evaluate()
_lowerCAmelCase = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(snake_case )
_lowerCAmelCase = min(snake_case , len(snake_case ) )
trainer.log_metrics("""eval""" , snake_case )
trainer.save_metrics("""eval""" , snake_case )
_lowerCAmelCase = {
"""finetuned_from""": model_args.model_name_or_path,
"""tasks""": """multiple-choice""",
"""dataset_tags""": """swag""",
"""dataset_args""": """regular""",
"""dataset""": """SWAG""",
"""language""": """en""",
}
if training_args.push_to_hub:
trainer.push_to_hub(**snake_case )
else:
trainer.create_model_card(**snake_case )
def _UpperCAmelCase ( snake_case ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 82 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "levit"
def __init__( self , __A=224 , __A=3 , __A=3 , __A=2 , __A=1 , __A=16 , __A=[128, 256, 384] , __A=[4, 8, 12] , __A=[4, 4, 4] , __A=[16, 16, 16] , __A=0 , __A=[2, 2, 2] , __A=[2, 2, 2] , __A=0.0_2 , **__A , ) -> Any:
super().__init__(**__A )
lowerCAmelCase_ :Tuple = image_size
lowerCAmelCase_ :Optional[int] = num_channels
lowerCAmelCase_ :Union[str, Any] = kernel_size
lowerCAmelCase_ :Optional[Any] = stride
lowerCAmelCase_ :Optional[int] = padding
lowerCAmelCase_ :Optional[Any] = hidden_sizes
lowerCAmelCase_ :Optional[int] = num_attention_heads
lowerCAmelCase_ :int = depths
lowerCAmelCase_ :List[str] = key_dim
lowerCAmelCase_ :str = drop_path_rate
lowerCAmelCase_ :Optional[int] = patch_size
lowerCAmelCase_ :Union[str, Any] = attention_ratio
lowerCAmelCase_ :Dict = mlp_ratio
lowerCAmelCase_ :Any = initializer_range
lowerCAmelCase_ :Optional[int] = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Tuple = version.parse("1.11" )
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __lowerCAmelCase ( self ) -> float:
return 1E-4
| 84 | 0 |
'''simple docstring'''
from typing import List
import numpy as np
def A__ ( UpperCAmelCase_ ):
_UpperCamelCase : Optional[Any] = {key: len(UpperCAmelCase_ ) for key, value in gen_kwargs.items() if isinstance(UpperCAmelCase_ , UpperCAmelCase_ )}
if len(set(lists_lengths.values() ) ) > 1:
raise RuntimeError(
(
'Sharding is ambiguous for this dataset: '
+ 'we found several data sources lists of different lengths, and we don\'t know over which list we should parallelize:\n'
+ '\n'.join(f'\t- key {key} has length {length}' for key, length in lists_lengths.items() )
+ '\nTo fix this, check the \'gen_kwargs\' and make sure to use lists only for data sources, '
+ 'and use tuples otherwise. In the end there should only be one single list, or several lists with the same length.'
) )
_UpperCamelCase : Union[str, Any] = max(lists_lengths.values() , default=0 )
return max(1 , UpperCAmelCase_ )
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
_UpperCamelCase : Dict = []
for group_idx in range(UpperCAmelCase_ ):
_UpperCamelCase : Tuple = num_shards // max_num_jobs + (group_idx < (num_shards % max_num_jobs))
if num_shards_to_add == 0:
break
_UpperCamelCase : Union[str, Any] = shards_indices_per_group[-1].stop if shards_indices_per_group else 0
_UpperCamelCase : Union[str, Any] = range(UpperCAmelCase_ , start + num_shards_to_add )
shards_indices_per_group.append(UpperCAmelCase_ )
return shards_indices_per_group
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
_UpperCamelCase : List[str] = _number_of_shards_in_gen_kwargs(UpperCAmelCase_ )
if num_shards == 1:
return [dict(UpperCAmelCase_ )]
else:
_UpperCamelCase : str = _distribute_shards(num_shards=UpperCAmelCase_ , max_num_jobs=UpperCAmelCase_ )
return [
{
key: [value[shard_idx] for shard_idx in shard_indices_per_group[group_idx]]
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ )
else value
for key, value in gen_kwargs.items()
}
for group_idx in range(len(UpperCAmelCase_ ) )
]
def A__ ( UpperCAmelCase_ ):
return {
key: [value for gen_kwargs in gen_kwargs_list for value in gen_kwargs[key]]
if isinstance(gen_kwargs_list[0][key] , UpperCAmelCase_ )
else gen_kwargs_list[0][key]
for key in gen_kwargs_list[0]
}
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
_UpperCamelCase : Optional[Any] = {len(UpperCAmelCase_ ) for value in gen_kwargs.values() if isinstance(UpperCAmelCase_ , UpperCAmelCase_ )}
_UpperCamelCase : Tuple = {}
for size in list_sizes:
_UpperCamelCase : str = list(range(UpperCAmelCase_ ) )
rng.shuffle(indices_per_size[size] )
# Now let's copy the gen_kwargs and shuffle the lists based on their sizes
_UpperCamelCase : Tuple = dict(UpperCAmelCase_ )
for key, value in shuffled_kwargs.items():
if isinstance(UpperCAmelCase_ , UpperCAmelCase_ ):
_UpperCamelCase : Tuple = [value[i] for i in indices_per_size[len(UpperCAmelCase_ )]]
return shuffled_kwargs
| 83 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( lowercase__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = FileLock(str(tmpdir / """foo.lock""" ) )
lowerCAmelCase_ :Union[str, Any] = FileLock(str(tmpdir / """foo.lock""" ) )
lowerCAmelCase_ :Dict = 0.01
with locka.acquire():
with pytest.raises(lowercase__ ):
lowerCAmelCase_ :List[Any] = time.time()
locka.acquire(lowercase__ )
assert time.time() - _start > timeout
def _snake_case ( lowercase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = """a""" * 1_0_0_0 + """.lock"""
lowerCAmelCase_ :Optional[Any] = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith(""".lock""" )
assert not locka._lock_file.endswith(lowercase__ )
assert len(os.path.basename(locka._lock_file ) ) <= 2_5_5
lowerCAmelCase_ :Any = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(lowercase__ ):
locka.acquire(0 )
| 84 | 0 |
'''simple docstring'''
import math
from datetime import datetime, timedelta
def UpperCamelCase_( snake_case : int ):
'''simple docstring'''
snake_case_ = year % 1_9
snake_case_ = year % 4
snake_case_ = year % 7
snake_case_ = math.floor(year / 1_0_0 )
snake_case_ = math.floor((1_3 + 8 * leap_day_inhibits) / 2_5 )
snake_case_ = leap_day_inhibits / 4
snake_case_ = (
1_5 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
) % 3_0
snake_case_ = (4 + leap_day_inhibits - leap_day_reinstall_number) % 7
# days to be added to March 21
snake_case_ = (1_9 * metonic_cycle + secular_moon_shift) % 3_0
# PHM -> Paschal Full Moon
snake_case_ = (
2 * julian_leap_year
+ 4 * non_leap_year
+ 6 * days_to_add
+ century_starting_point
) % 7
if days_to_add == 2_9 and days_from_phm_to_sunday == 6:
return datetime(snake_case , 4 , 1_9 )
elif days_to_add == 2_8 and days_from_phm_to_sunday == 6:
return datetime(snake_case , 4 , 1_8 )
else:
return datetime(snake_case , 3 , 2_2 ) + timedelta(
days=int(days_to_add + days_from_phm_to_sunday ) )
if __name__ == "__main__":
for year in (1994, 2000, 2010, 2021, 2023):
_SCREAMING_SNAKE_CASE : Any = "will be" if year > datetime.now().year else "was"
print(F"Easter in {year} {tense} {gauss_easter(year)}")
| 85 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__UpperCAmelCase = 1.054571817e-34 # unit of ℏ : J * s
__UpperCAmelCase = 3e8 # unit of c : m * s^-1
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float ) -> dict[str, float]:
'''simple docstring'''
if (force, area, distance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if force < 0:
raise ValueError("""Magnitude of force can not be negative""" )
if distance < 0:
raise ValueError("""Distance can not be negative""" )
if area < 0:
raise ValueError("""Area can not be negative""" )
if force == 0:
lowerCAmelCase_ :Union[str, Any] = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
2_4_0 * (distance) ** 4
)
return {"force": force}
elif area == 0:
lowerCAmelCase_ :Optional[Any] = (2_4_0 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
lowerCAmelCase_ :Any = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError("""One and only one argument must be 0""" )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 0 |
"""simple docstring"""
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configuration_fsmt import FSMTConfig
from transformers.optimization import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
from transformers.trainer_pt_utils import get_tpu_sampler
from transformers.training_args import ParallelMode
from transformers.utils import is_torch_tpu_available
if is_fairscale_available():
from fairscale.optim import OSS
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"""linear""": get_linear_schedule_with_warmup,
"""cosine""": get_cosine_schedule_with_warmup,
"""cosine_w_restarts""": get_cosine_with_hard_restarts_schedule_with_warmup,
"""polynomial""": get_polynomial_decay_schedule_with_warmup,
"""constant""": get_constant_schedule,
"""constant_w_warmup""": get_constant_schedule_with_warmup,
}
class A__ ( _lowerCamelCase):
def __init__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , *_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE ):
super().__init__(*_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if config is None:
assert isinstance(self.model , _SCREAMING_SNAKE_CASE ), (
"If no `config` is passed the model to be trained has to be of type `PreTrainedModel`, but is"
f" {self.model.__class__}"
)
__lowerCAmelCase : Any = self.model.config
else:
__lowerCAmelCase : int = config
__lowerCAmelCase : Any = data_args
__lowerCAmelCase : int = self.config.tgt_vocab_size if isinstance(self.config , _SCREAMING_SNAKE_CASE ) else self.config.vocab_size
if self.args.label_smoothing != 0 or (self.data_args is not None and self.data_args.ignore_pad_token_for_loss):
assert self.config.pad_token_id is not None, (
"Make sure that `config.pad_token_id` is correcly defined when ignoring `pad_token` for loss"
" calculation or doing label smoothing."
)
if self.config.pad_token_id is None and self.config.eos_token_id is not None:
logger.warning(
f"The `config.pad_token_id` is `None`. Using `config.eos_token_id` = {self.config.eos_token_id} for"
' padding..' )
if self.args.label_smoothing == 0:
__lowerCAmelCase : int = torch.nn.CrossEntropyLoss(ignore_index=self.config.pad_token_id )
else:
# dynamically import label_smoothed_nll_loss
from utils import label_smoothed_nll_loss
__lowerCAmelCase : int = label_smoothed_nll_loss
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
if self.optimizer is None:
__lowerCAmelCase : Optional[int] = ['bias', 'LayerNorm.weight']
__lowerCAmelCase : Dict = [
{
'params': [p for n, p in self.model.named_parameters() if not any(nd in n for nd in no_decay )],
'weight_decay': self.args.weight_decay,
},
{
'params': [p for n, p in self.model.named_parameters() if any(nd in n for nd in no_decay )],
'weight_decay': 0.0,
},
]
__lowerCAmelCase : List[Any] = Adafactor if self.args.adafactor else AdamW
if self.args.adafactor:
__lowerCAmelCase : int = Adafactor
__lowerCAmelCase : List[Any] = {'scale_parameter': False, 'relative_step': False}
else:
__lowerCAmelCase : Any = AdamW
__lowerCAmelCase : int = {
'betas': (self.args.adam_betaa, self.args.adam_betaa),
'eps': self.args.adam_epsilon,
}
__lowerCAmelCase : List[Any] = self.args.learning_rate
if self.sharded_ddp:
__lowerCAmelCase : Optional[Any] = OSS(
params=_SCREAMING_SNAKE_CASE , optim=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
else:
__lowerCAmelCase : int = optimizer_cls(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
if self.lr_scheduler is None:
__lowerCAmelCase : Any = self._get_lr_scheduler(_SCREAMING_SNAKE_CASE )
else: # ignoring --lr_scheduler
logger.warning('scheduler is passed to `Seq2SeqTrainer`, `--lr_scheduler` arg is ignored.' )
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : int = arg_to_scheduler[self.args.lr_scheduler]
if self.args.lr_scheduler == "constant":
__lowerCAmelCase : Union[str, Any] = schedule_func(self.optimizer )
elif self.args.lr_scheduler == "constant_w_warmup":
__lowerCAmelCase : Optional[Any] = schedule_func(self.optimizer , num_warmup_steps=self.args.warmup_steps )
else:
__lowerCAmelCase : List[str] = schedule_func(
self.optimizer , num_warmup_steps=self.args.warmup_steps , num_training_steps=_SCREAMING_SNAKE_CASE )
return scheduler
def __lowerCamelCase ( self ):
if isinstance(self.train_dataset , torch.utils.data.IterableDataset ):
return None
elif is_torch_tpu_available():
return get_tpu_sampler(self.train_dataset )
else:
if self.args.sortish_sampler:
self.train_dataset.make_sortish_sampler(
self.args.per_device_train_batch_size , distributed=(self.args.parallel_mode == ParallelMode.DISTRIBUTED) , )
return (
RandomSampler(self.train_dataset )
if self.args.local_rank == -1
else DistributedSampler(self.train_dataset )
)
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if self.args.label_smoothing == 0:
if self.data_args is not None and self.data_args.ignore_pad_token_for_loss:
# force training to ignore pad token
__lowerCAmelCase : Optional[int] = model(**_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE )[0]
__lowerCAmelCase : Optional[Any] = self.loss_fn(logits.view(-1 , logits.shape[-1] ) , labels.view(-1 ) )
else:
# compute usual loss via models
__lowerCAmelCase , __lowerCAmelCase : Union[str, Any] = model(**_SCREAMING_SNAKE_CASE , labels=_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE )[:2]
else:
# compute label smoothed loss
__lowerCAmelCase : int = model(**_SCREAMING_SNAKE_CASE , use_cache=_SCREAMING_SNAKE_CASE )[0]
__lowerCAmelCase : Optional[int] = torch.nn.functional.log_softmax(_SCREAMING_SNAKE_CASE , dim=-1 )
__lowerCAmelCase , __lowerCAmelCase : Union[str, Any] = self.loss_fn(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , self.args.label_smoothing , ignore_index=self.config.pad_token_id )
return loss, logits
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
__lowerCAmelCase : Tuple = inputs.pop('labels' )
__lowerCAmelCase , __lowerCAmelCase : Any = self._compute_loss(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
return loss
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , ):
__lowerCAmelCase : Tuple = self._prepare_inputs(_SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = {
'max_length': self.data_args.val_max_target_length
if self.data_args is not None
else self.config.max_length,
'num_beams': self.data_args.eval_beams if self.data_args is not None else self.config.num_beams,
}
if self.args.predict_with_generate and not self.args.prediction_loss_only:
__lowerCAmelCase : Tuple = self.model.generate(
inputs['input_ids'] , attention_mask=inputs['attention_mask'] , **_SCREAMING_SNAKE_CASE , )
# in case the batch is shorter than max length, the output should be padded
if generated_tokens.shape[-1] < gen_kwargs["max_length"]:
__lowerCAmelCase : Optional[Any] = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE , gen_kwargs['max_length'] )
__lowerCAmelCase : Any = inputs.pop('labels' )
with torch.no_grad():
# compute loss on predict data
__lowerCAmelCase , __lowerCAmelCase : List[Any] = self._compute_loss(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = loss.mean().detach()
if self.args.prediction_loss_only:
return (loss, None, None)
__lowerCAmelCase : str = generated_tokens if self.args.predict_with_generate else logits
if labels.shape[-1] < gen_kwargs["max_length"]:
__lowerCAmelCase : Tuple = self._pad_tensors_to_max_len(_SCREAMING_SNAKE_CASE , gen_kwargs['max_length'] )
return (loss, logits, labels)
def __lowerCamelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# If PAD token is not defined at least EOS token has to be defined
__lowerCAmelCase : Any = self.config.pad_token_id if self.config.pad_token_id is not None else self.config.eos_token_id
if pad_token_id is None:
raise ValueError(
'Make sure that either `config.pad_token_id` or `config.eos_token_id` is defined if tensor has to be'
f" padded to `max_length`={max_length}" )
__lowerCAmelCase : Tuple = pad_token_id * torch.ones(
(tensor.shape[0], max_length) , dtype=tensor.dtype , device=tensor.device )
__lowerCAmelCase : Dict = tensor
return padded_tensor | 86 |
"""simple docstring"""
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> int:
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("""String lengths must match!""" )
lowerCAmelCase_ :Optional[int] = 0
for chara, chara in zip(lowercase__ , lowercase__ ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...file_utils import TensorType, is_torch_available
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging
UpperCamelCase = logging.get_logger(__name__)
UpperCamelCase = {
'''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/config.json''',
# See all BlenderbotSmall models at https://huggingface.co/models?filter=blenderbot_small
}
class snake_case_ ( __A ):
__A : List[Any] = "blenderbot-small"
__A : Tuple = ["past_key_values"]
__A : Union[str, Any] = {"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}
def __init__( self : Any , lowercase_ : Any=5_02_65 , lowercase_ : Optional[Any]=5_12 , lowercase_ : Optional[int]=8 , lowercase_ : Tuple=20_48 , lowercase_ : Any=16 , lowercase_ : Optional[int]=8 , lowercase_ : Any=20_48 , lowercase_ : Any=16 , lowercase_ : Tuple=0.0 , lowercase_ : Optional[Any]=0.0 , lowercase_ : Union[str, Any]=True , lowercase_ : Optional[Any]=True , lowercase_ : int="gelu" , lowercase_ : str=5_12 , lowercase_ : str=0.1 , lowercase_ : Optional[int]=0.0 , lowercase_ : Union[str, Any]=0.0 , lowercase_ : Union[str, Any]=0.02 , lowercase_ : str=1 , lowercase_ : int=False , lowercase_ : Optional[int]=0 , lowercase_ : Tuple=1 , lowercase_ : int=2 , lowercase_ : List[str]=2 , **lowercase_ : Tuple , ) -> Union[str, Any]:
lowercase__ : Any = vocab_size
lowercase__ : int = max_position_embeddings
lowercase__ : Optional[Any] = d_model
lowercase__ : List[str] = encoder_ffn_dim
lowercase__ : List[str] = encoder_layers
lowercase__ : List[Any] = encoder_attention_heads
lowercase__ : List[str] = decoder_ffn_dim
lowercase__ : Optional[Any] = decoder_layers
lowercase__ : Union[str, Any] = decoder_attention_heads
lowercase__ : int = dropout
lowercase__ : Optional[int] = attention_dropout
lowercase__ : Dict = activation_dropout
lowercase__ : Union[str, Any] = activation_function
lowercase__ : Dict = init_std
lowercase__ : int = encoder_layerdrop
lowercase__ : List[str] = decoder_layerdrop
lowercase__ : str = use_cache
lowercase__ : Dict = encoder_layers
lowercase__ : int = scale_embedding # scale factor will be sqrt(d_model) if True
super().__init__(
pad_token_id=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , is_encoder_decoder=lowercase_ , decoder_start_token_id=lowercase_ , forced_eos_token_id=lowercase_ , **lowercase_ , )
class snake_case_ ( __A ):
@property
def __UpperCamelCase ( self : Any ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
lowercase__ : str = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
lowercase__ : Tuple = {0: "batch"}
lowercase__ : Any = {0: "batch", 1: "past_decoder_sequence + sequence"}
else:
lowercase__ : Dict = {0: "batch", 1: "decoder_sequence"}
lowercase__ : Tuple = {0: "batch", 1: "decoder_sequence"}
if self.use_past:
self.fill_with_past_key_values_(lowercase_ , direction="inputs" )
elif self.task == "causal-lm":
# TODO: figure this case out.
lowercase__ : Optional[int] = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
] )
if self.use_past:
lowercase__ , lowercase__ : Any = self.num_layers
for i in range(lowercase_ ):
lowercase__ : List[str] = {0: "batch", 2: "past_sequence + sequence"}
lowercase__ : Any = {0: "batch", 2: "past_sequence + sequence"}
else:
lowercase__ : int = OrderedDict(
[
("input_ids", {0: "batch", 1: "encoder_sequence"}),
("attention_mask", {0: "batch", 1: "encoder_sequence"}),
("decoder_input_ids", {0: "batch", 1: "decoder_sequence"}),
("decoder_attention_mask", {0: "batch", 1: "decoder_sequence"}),
] )
return common_inputs
@property
def __UpperCamelCase ( self : str ) -> Mapping[str, Mapping[int, str]]:
if self.task in ["default", "seq2seq-lm"]:
lowercase__ : Dict = super().outputs
else:
lowercase__ : List[str] = super(lowercase_ , self ).outputs
if self.use_past:
lowercase__ , lowercase__ : Optional[Any] = self.num_layers
for i in range(lowercase_ ):
lowercase__ : Dict = {0: "batch", 2: "past_sequence + sequence"}
lowercase__ : List[Any] = {0: "batch", 2: "past_sequence + sequence"}
return common_outputs
def __UpperCamelCase ( self : Tuple , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
lowercase__ : Optional[int] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
# Generate decoder inputs
lowercase__ : str = seq_length if not self.use_past else 1
lowercase__ : Optional[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
lowercase__ : Union[str, Any] = {F'''decoder_{name}''': tensor for name, tensor in decoder_inputs.items()}
lowercase__ : Union[str, Any] = dict(**lowercase_ , **lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
lowercase__ , lowercase__ : Union[str, Any] = common_inputs["input_ids"].shape
lowercase__ : Optional[int] = common_inputs["decoder_input_ids"].shape[1]
lowercase__ , lowercase__ : List[str] = self.num_attention_heads
lowercase__ : Dict = (
batch,
num_encoder_attention_heads,
encoder_seq_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase__ : List[str] = decoder_seq_length + 3
lowercase__ : Union[str, Any] = (
batch,
num_decoder_attention_heads,
decoder_past_length,
self._config.hidden_size // num_decoder_attention_heads,
)
lowercase__ : Tuple = torch.cat(
[common_inputs["decoder_attention_mask"], torch.ones(lowercase_ , lowercase_ )] , dim=1 )
lowercase__ : Union[str, Any] = []
# If the number of encoder and decoder layers are present in the model configuration, both are considered
lowercase__ , lowercase__ : List[str] = self.num_layers
lowercase__ : List[Any] = min(lowercase_ , lowercase_ )
lowercase__ : List[Any] = max(lowercase_ , lowercase_ ) - min_num_layers
lowercase__ : int = "encoder" if num_encoder_layers > num_decoder_layers else "decoder"
for _ in range(lowercase_ ):
common_inputs["past_key_values"].append(
(
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
torch.zeros(lowercase_ ),
) )
# TODO: test this.
lowercase__ : str = encoder_shape if remaining_side_name == "encoder" else decoder_shape
for _ in range(lowercase_ , lowercase_ ):
common_inputs["past_key_values"].append((torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) )
return common_inputs
def __UpperCamelCase ( self : Optional[Any] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
lowercase__ : Any = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ )
if self.use_past:
if not is_torch_available():
raise ValueError("Cannot generate dummy past_keys inputs without PyTorch installed." )
else:
import torch
lowercase__ , lowercase__ : str = common_inputs["input_ids"].shape
# Not using the same length for past_key_values
lowercase__ : Dict = seqlen + 2
lowercase__ , lowercase__ : List[str] = self.num_layers
lowercase__ , lowercase__ : Optional[Any] = self.num_attention_heads
lowercase__ : Optional[int] = (
batch,
num_encoder_attention_heads,
past_key_values_length,
self._config.hidden_size // num_encoder_attention_heads,
)
lowercase__ : Optional[int] = common_inputs["attention_mask"].dtype
lowercase__ : List[Any] = torch.cat(
[common_inputs["attention_mask"], torch.ones(lowercase_ , lowercase_ , dtype=lowercase_ )] , dim=1 )
lowercase__ : Dict = [
(torch.zeros(lowercase_ ), torch.zeros(lowercase_ )) for _ in range(lowercase_ )
]
return common_inputs
def __UpperCamelCase ( self : List[Any] , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
# Copied from OnnxConfig.generate_dummy_inputs
# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
lowercase__ : List[Any] = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_batch , num_token_to_add=0 )
# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
lowercase__ : Optional[Any] = tokenizer.num_special_tokens_to_add(lowercase_ )
lowercase__ : List[Any] = compute_effective_axis_dimension(
lowercase_ , fixed_dimension=OnnxConfig.default_fixed_sequence , num_token_to_add=lowercase_ )
# Generate dummy inputs according to compute batch and sequence
lowercase__ : int = [" ".join([tokenizer.unk_token] ) * seq_length] * batch_size
lowercase__ : Union[str, Any] = dict(tokenizer(lowercase_ , return_tensors=lowercase_ ) )
return common_inputs
def __UpperCamelCase ( self : str , lowercase_ : PreTrainedTokenizer , lowercase_ : int = -1 , lowercase_ : int = -1 , lowercase_ : bool = False , lowercase_ : Optional[TensorType] = None , ) -> Mapping[str, Any]:
if self.task in ["default", "seq2seq-lm"]:
lowercase__ : str = self._generate_dummy_inputs_for_default_and_seqaseq_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
elif self.task == "causal-lm":
lowercase__ : List[str] = self._generate_dummy_inputs_for_causal_lm(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
else:
lowercase__ : Optional[Any] = self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
lowercase_ , batch_size=lowercase_ , seq_length=lowercase_ , is_pair=lowercase_ , framework=lowercase_ )
return common_inputs
def __UpperCamelCase ( self : Tuple , lowercase_ : Tuple , lowercase_ : str , lowercase_ : Union[str, Any] , lowercase_ : List[Any] ) -> Any:
if self.task in ["default", "seq2seq-lm"]:
lowercase__ : Dict = super()._flatten_past_key_values_(lowercase_ , lowercase_ , lowercase_ , lowercase_ )
else:
lowercase__ : str = super(lowercase_ , self )._flatten_past_key_values_(
lowercase_ , lowercase_ , lowercase_ , lowercase_ )
| 87 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A ) -> Optional[Any]:
super().__init__()
lowerCAmelCase_ :int = nn.ModuleList(__A )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A = None , __A = None , __A = None , __A = None , __A = False , __A = True , ) -> Union[ControlNetOutput, Tuple]:
for i, (image, scale, controlnet) in enumerate(zip(__A , __A , self.nets ) ):
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = controlnet(
__A , __A , __A , __A , __A , __A , __A , __A , __A , __A , __A , )
# merge samples
if i == 0:
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = down_samples, mid_sample
else:
lowerCAmelCase_ :str = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(__A , __A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def __lowerCAmelCase ( self , __A , __A = True , __A = None , __A = False , __A = None , ) -> Optional[Any]:
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Dict = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
__A , is_main_process=__A , save_function=__A , safe_serialization=__A , variant=__A , )
idx += 1
lowerCAmelCase_ :Any = model_path_to_save + f"""_{idx}"""
@classmethod
def __lowerCAmelCase ( cls , __A , **__A ) -> List[Any]:
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Dict = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
lowerCAmelCase_ :List[Any] = pretrained_model_path
while os.path.isdir(__A ):
lowerCAmelCase_ :Tuple = ControlNetModel.from_pretrained(__A , **__A )
controlnets.append(__A )
idx += 1
lowerCAmelCase_ :Dict = pretrained_model_path + f"""_{idx}"""
logger.info(f"""{len(__A )} controlnets loaded from {pretrained_model_path}.""" )
if len(__A ) == 0:
raise ValueError(
f"""No ControlNets found under {os.path.dirname(__A )}. Expected at least {pretrained_model_path + "_0"}.""" )
return cls(__A )
| 84 | 0 |
import os
__lowerCAmelCase : int = {'I': 1, 'V': 5, 'X': 10, 'L': 50, 'C': 100, 'D': 500, 'M': 1000}
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = 0
__magic_name__ = 0
while index < len(A_ ) - 1:
__magic_name__ = SYMBOLS[numerals[index]]
__magic_name__ = SYMBOLS[numerals[index + 1]]
if current_value < next_value:
total_value -= current_value
else:
total_value += current_value
index += 1
total_value += SYMBOLS[numerals[index]]
return total_value
def a__ ( A_ ):
'''simple docstring'''
__magic_name__ = """"""
__magic_name__ = num // 1000
numerals += m_count * "M"
num %= 1000
__magic_name__ = num // 100
if c_count == 9:
numerals += "CM"
c_count -= 9
elif c_count == 4:
numerals += "CD"
c_count -= 4
if c_count >= 5:
numerals += "D"
c_count -= 5
numerals += c_count * "C"
num %= 100
__magic_name__ = num // 10
if x_count == 9:
numerals += "XC"
x_count -= 9
elif x_count == 4:
numerals += "XL"
x_count -= 4
if x_count >= 5:
numerals += "L"
x_count -= 5
numerals += x_count * "X"
num %= 10
if num == 9:
numerals += "IX"
num -= 9
elif num == 4:
numerals += "IV"
num -= 4
if num >= 5:
numerals += "V"
num -= 5
numerals += num * "I"
return numerals
def a__ ( A_ = "/p089_roman.txt" ):
'''simple docstring'''
__magic_name__ = 0
with open(os.path.dirname(A_ ) + roman_numerals_filename ) as filea:
__magic_name__ = filea.readlines()
for line in lines:
__magic_name__ = line.strip()
__magic_name__ = parse_roman_numerals(A_ )
__magic_name__ = generate_roman_numerals(A_ )
savings += len(A_ ) - len(A_ )
return savings
if __name__ == "__main__":
print(F'''{solution() = }''')
| 88 |
"""simple docstring"""
from PIL import Image
def _snake_case ( lowercase__ : Image , lowercase__ : float ) -> Image:
'''simple docstring'''
def brightness(lowercase__ : int ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(lowercase__ )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
__UpperCAmelCase = change_brightness(img, 1_00)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 84 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {
'''configuration_lilt''': ['''LILT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''LiltConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCAmelCase = [
'''LILT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''LiltForQuestionAnswering''',
'''LiltForSequenceClassification''',
'''LiltForTokenClassification''',
'''LiltModel''',
'''LiltPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
__lowerCAmelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 89 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _SCREAMING_SNAKE_CASE :
def __lowerCAmelCase ( self ) -> Tuple:
torch.manual_seed(0 )
lowerCAmelCase_ :int = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :List[Any] = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCAmelCase_ :str = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__A , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase_ :int = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowerCAmelCase ( self ) -> List[str]:
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , class_embed_type="""timestep""" , mid_block_scale_factor=1.4_1_4 , time_embedding_act_fn="""gelu""" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCAmelCase_ :str = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__A , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[int] = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , )
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Dict = self.get_dummy_components()
lowerCAmelCase_ :Tuple = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Any = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Optional[int] = inputs["""prompt"""]
lowerCAmelCase_ :Optional[int] = inputs["""generator"""]
lowerCAmelCase_ :Any = inputs["""num_inference_steps"""]
lowerCAmelCase_ :Optional[int] = inputs["""output_type"""]
if "image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""image"""]
else:
lowerCAmelCase_ :int = None
if "mask_image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""mask_image"""]
else:
lowerCAmelCase_ :int = None
if "original_image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""original_image"""]
else:
lowerCAmelCase_ :List[Any] = None
lowerCAmelCase_ , lowerCAmelCase_ :int = pipe.encode_prompt(__A )
# inputs with prompt converted to embeddings
lowerCAmelCase_ :List[str] = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowerCAmelCase_ :int = image
if mask_image is not None:
lowerCAmelCase_ :Tuple = mask_image
if original_image is not None:
lowerCAmelCase_ :Optional[Any] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(__A , __A , __A )
lowerCAmelCase_ :Optional[int] = pipe(**__A )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__A )
lowerCAmelCase_ :Optional[int] = self.pipeline_class.from_pretrained(__A )
pipe_loaded.to(__A )
pipe_loaded.set_progress_bar_config(disable=__A )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__A , __A ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
lowerCAmelCase_ :Dict = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Union[str, Any] = inputs["""generator"""]
lowerCAmelCase_ :Any = inputs["""num_inference_steps"""]
lowerCAmelCase_ :Tuple = inputs["""output_type"""]
# inputs with prompt converted to embeddings
lowerCAmelCase_ :Tuple = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowerCAmelCase_ :Optional[int] = image
if mask_image is not None:
lowerCAmelCase_ :str = mask_image
if original_image is not None:
lowerCAmelCase_ :Tuple = original_image
lowerCAmelCase_ :Union[str, Any] = pipe_loaded(**__A )[0]
lowerCAmelCase_ :Dict = np.abs(to_np(__A ) - to_np(__A ) ).max()
self.assertLess(__A , 1E-4 )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Any = self.get_dummy_components()
lowerCAmelCase_ :Optional[int] = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[int] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Dict = pipe(**__A )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__A )
lowerCAmelCase_ :Any = self.pipeline_class.from_pretrained(__A )
pipe_loaded.to(__A )
pipe_loaded.set_progress_bar_config(disable=__A )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :str = pipe_loaded(**__A )[0]
lowerCAmelCase_ :Dict = np.abs(to_np(__A ) - to_np(__A ) ).max()
self.assertLess(__A , 1E-4 )
| 84 | 0 |
from __future__ import annotations
import math
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : list ) -> list:
"""simple docstring"""
if len(UpperCamelCase__ ) != 2 or len(a[0] ) != 2 or len(UpperCamelCase__ ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
__lowerCamelCase = [
[a[0][0] * b[0][0] + a[0][1] * b[1][0], a[0][0] * b[0][1] + a[0][1] * b[1][1]],
[a[1][0] * b[0][0] + a[1][1] * b[1][0], a[1][0] * b[0][1] + a[1][1] * b[1][1]],
]
return new_matrix
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : list ) -> List[str]:
"""simple docstring"""
return [
[matrix_a[row][col] + matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(UpperCamelCase__ ) )
]
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : list ) -> Optional[Any]:
"""simple docstring"""
return [
[matrix_a[row][col] - matrix_b[row][col] for col in range(len(matrix_a[row] ) )]
for row in range(len(UpperCamelCase__ ) )
]
def lowerCamelCase_ ( UpperCamelCase__ : list ) -> tuple[list, list, list, list]:
"""simple docstring"""
if len(UpperCamelCase__ ) % 2 != 0 or len(a[0] ) % 2 != 0:
raise Exception('Odd matrices are not supported!' )
__lowerCamelCase = len(UpperCamelCase__ )
__lowerCamelCase = matrix_length // 2
__lowerCamelCase = [[a[i][j] for j in range(UpperCamelCase__ , UpperCamelCase__ )] for i in range(UpperCamelCase__ )]
__lowerCamelCase = [
[a[i][j] for j in range(UpperCamelCase__ , UpperCamelCase__ )] for i in range(UpperCamelCase__ , UpperCamelCase__ )
]
__lowerCamelCase = [[a[i][j] for j in range(UpperCamelCase__ )] for i in range(UpperCamelCase__ )]
__lowerCamelCase = [[a[i][j] for j in range(UpperCamelCase__ )] for i in range(UpperCamelCase__ , UpperCamelCase__ )]
return top_left, top_right, bot_left, bot_right
def lowerCamelCase_ ( UpperCamelCase__ : list ) -> tuple[int, int]:
"""simple docstring"""
return len(UpperCamelCase__ ), len(matrix[0] )
def lowerCamelCase_ ( UpperCamelCase__ : list ) -> None:
"""simple docstring"""
print('\n'.join(str(UpperCamelCase__ ) for line in matrix ) )
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : list ) -> list:
"""simple docstring"""
if matrix_dimensions(UpperCamelCase__ ) == (2, 2):
return default_matrix_multiplication(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = split_matrix(UpperCamelCase__ )
__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase = split_matrix(UpperCamelCase__ )
__lowerCamelCase = actual_strassen(UpperCamelCase__ , matrix_subtraction(UpperCamelCase__ , UpperCamelCase__ ) )
__lowerCamelCase = actual_strassen(matrix_addition(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ )
__lowerCamelCase = actual_strassen(matrix_addition(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ )
__lowerCamelCase = actual_strassen(UpperCamelCase__ , matrix_subtraction(UpperCamelCase__ , UpperCamelCase__ ) )
__lowerCamelCase = actual_strassen(matrix_addition(UpperCamelCase__ , UpperCamelCase__ ) , matrix_addition(UpperCamelCase__ , UpperCamelCase__ ) )
__lowerCamelCase = actual_strassen(matrix_subtraction(UpperCamelCase__ , UpperCamelCase__ ) , matrix_addition(UpperCamelCase__ , UpperCamelCase__ ) )
__lowerCamelCase = actual_strassen(matrix_subtraction(UpperCamelCase__ , UpperCamelCase__ ) , matrix_addition(UpperCamelCase__ , UpperCamelCase__ ) )
__lowerCamelCase = matrix_addition(matrix_subtraction(matrix_addition(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ ) , UpperCamelCase__ )
__lowerCamelCase = matrix_addition(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = matrix_addition(UpperCamelCase__ , UpperCamelCase__ )
__lowerCamelCase = matrix_subtraction(matrix_subtraction(matrix_addition(UpperCamelCase__ , UpperCamelCase__ ) , UpperCamelCase__ ) , UpperCamelCase__ )
# construct the new matrix from our 4 quadrants
__lowerCamelCase = []
for i in range(len(UpperCamelCase__ ) ):
new_matrix.append(top_left[i] + top_right[i] )
for i in range(len(UpperCamelCase__ ) ):
new_matrix.append(bot_left[i] + bot_right[i] )
return new_matrix
def lowerCamelCase_ ( UpperCamelCase__ : list , UpperCamelCase__ : list ) -> list:
"""simple docstring"""
if matrix_dimensions(UpperCamelCase__ )[1] != matrix_dimensions(UpperCamelCase__ )[0]:
__lowerCamelCase = (
'Unable to multiply these matrices, please check the dimensions.\n'
F"""Matrix A: {matrixa}\n"""
F"""Matrix B: {matrixa}"""
)
raise Exception(UpperCamelCase__ )
__lowerCamelCase = matrix_dimensions(UpperCamelCase__ )
__lowerCamelCase = matrix_dimensions(UpperCamelCase__ )
if dimensiona[0] == dimensiona[1] and dimensiona[0] == dimensiona[1]:
return [matrixa, matrixa]
__lowerCamelCase = max(*UpperCamelCase__ , *UpperCamelCase__ )
__lowerCamelCase = int(math.pow(2 , math.ceil(math.loga(UpperCamelCase__ ) ) ) )
__lowerCamelCase = matrixa
__lowerCamelCase = matrixa
# Adding zeros to the matrices so that the arrays dimensions are the same and also
# power of 2
for i in range(0 , UpperCamelCase__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , UpperCamelCase__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
if i < dimensiona[0]:
for _ in range(dimensiona[1] , UpperCamelCase__ ):
new_matrixa[i].append(0 )
else:
new_matrixa.append([0] * maxim )
__lowerCamelCase = actual_strassen(UpperCamelCase__ , UpperCamelCase__ )
# Removing the additional zeros
for i in range(0 , UpperCamelCase__ ):
if i < dimensiona[0]:
for _ in range(dimensiona[1] , UpperCamelCase__ ):
final_matrix[i].pop()
else:
final_matrix.pop()
return final_matrix
if __name__ == "__main__":
__A = [
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 4, 3, 1],
[2, 3, 6, 7],
[3, 1, 2, 4],
[2, 3, 4, 5],
[6, 2, 3, 1],
]
__A = [[0, 2, 1, 1], [16, 2, 3, 3], [2, 2, 7, 7], [13, 11, 22, 4]]
print(strassen(matrixa, matrixa))
| 90 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2""" , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :int = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :List[Any] = jax.device_count()
lowerCAmelCase_ :Optional[Any] = num_samples * [prompt]
lowerCAmelCase_ :int = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Optional[Any] = replicate(__A )
lowerCAmelCase_ :Union[str, Any] = shard(__A )
lowerCAmelCase_ :Optional[Any] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :Tuple = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Union[str, Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Optional[int] = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Union[str, Any] = """stabilityai/stable-diffusion-2"""
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = FlaxDPMSolverMultistepScheduler.from_pretrained(__A , subfolder="""scheduler""" )
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = FlaxStableDiffusionPipeline.from_pretrained(
__A , scheduler=__A , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :Optional[int] = scheduler_params
lowerCAmelCase_ :List[Any] = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :Tuple = jax.device_count()
lowerCAmelCase_ :str = num_samples * [prompt]
lowerCAmelCase_ :Union[str, Any] = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Tuple = replicate(__A )
lowerCAmelCase_ :Optional[int] = shard(__A )
lowerCAmelCase_ :List[str] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :List[Any] = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Optional[Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Dict = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 84 | 0 |
"""simple docstring"""
import os
import re
import shutil
from argparse import ArgumentParser, Namespace
from datasets.commands import BaseDatasetsCLICommand
from datasets.utils.logging import get_logger
UpperCAmelCase_ : Union[str, Any] = """<<<<<<< This should probably be modified because it mentions: """
UpperCAmelCase_ : Optional[Any] = """=======
>>>>>>>
"""
UpperCAmelCase_ : Union[str, Any] = [
"""TextEncoderConfig""",
"""ByteTextEncoder""",
"""SubwordTextEncoder""",
"""encoder_config""",
"""maybe_build_from_corpus""",
"""manual_dir""",
]
UpperCAmelCase_ : List[str] = [
# (pattern, replacement)
# Order is important here for some replacements
(r"""tfds\.core""", r"""datasets"""),
(r"""tf\.io\.gfile\.GFile""", r"""open"""),
(r"""tf\.([\w\d]+)""", r"""datasets.Value('\1')"""),
(r"""tfds\.features\.Text\(\)""", r"""datasets.Value('string')"""),
(r"""tfds\.features\.Text\(""", r"""datasets.Value('string'),"""),
(r"""features\s*=\s*tfds.features.FeaturesDict\(""", r"""features=datasets.Features("""),
(r"""tfds\.features\.FeaturesDict\(""", r"""dict("""),
(r"""The TensorFlow Datasets Authors""", r"""The TensorFlow Datasets Authors and the HuggingFace Datasets Authors"""),
(r"""tfds\.""", r"""datasets."""),
(r"""dl_manager\.manual_dir""", r"""self.config.data_dir"""),
(r"""self\.builder_config""", r"""self.config"""),
]
def _A (__a ) -> Optional[int]:
"""simple docstring"""
return ConvertCommand(args.tfds_path , args.datasets_directory )
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
@staticmethod
def _SCREAMING_SNAKE_CASE ( lowercase_ : ArgumentParser):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : List[Any] = parser.add_parser(
'''convert''' , help='''Convert a TensorFlow Datasets dataset to a HuggingFace Datasets dataset.''' , )
train_parser.add_argument(
'''--tfds_path''' , type=lowercase_ , required=lowercase_ , help='''Path to a TensorFlow Datasets folder to convert or a single tfds file to convert.''' , )
train_parser.add_argument(
'''--datasets_directory''' , type=lowercase_ , required=lowercase_ , help='''Path to the HuggingFace Datasets folder.''')
train_parser.set_defaults(func=lowercase_)
def __init__( self : Union[str, Any] , lowercase_ : str , lowercase_ : str , *lowercase_ : Union[str, Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Dict = get_logger('''datasets-cli/converting''')
SCREAMING_SNAKE_CASE_ : Optional[Any] = tfds_path
SCREAMING_SNAKE_CASE_ : Tuple = datasets_directory
def _SCREAMING_SNAKE_CASE ( self : str):
'''simple docstring'''
if os.path.isdir(self._tfds_path):
SCREAMING_SNAKE_CASE_ : Union[str, Any] = os.path.abspath(self._tfds_path)
elif os.path.isfile(self._tfds_path):
SCREAMING_SNAKE_CASE_ : List[str] = os.path.dirname(self._tfds_path)
else:
raise ValueError('''--tfds_path is neither a directory nor a file. Please check path.''')
SCREAMING_SNAKE_CASE_ : Any = os.path.abspath(self._datasets_directory)
self._logger.info(F'Converting datasets from {abs_tfds_path} to {abs_datasets_path}')
SCREAMING_SNAKE_CASE_ : Optional[int] = []
SCREAMING_SNAKE_CASE_ : Union[str, Any] = []
SCREAMING_SNAKE_CASE_ : Union[str, Any] = {}
if os.path.isdir(self._tfds_path):
SCREAMING_SNAKE_CASE_ : List[str] = os.listdir(lowercase_)
else:
SCREAMING_SNAKE_CASE_ : Any = [os.path.basename(self._tfds_path)]
for f_name in file_names:
self._logger.info(F'Looking at file {f_name}')
SCREAMING_SNAKE_CASE_ : Tuple = os.path.join(lowercase_ , lowercase_)
SCREAMING_SNAKE_CASE_ : Any = os.path.join(lowercase_ , lowercase_)
if not os.path.isfile(lowercase_) or "__init__" in f_name or "_test" in f_name or ".py" not in f_name:
self._logger.info('''Skipping file''')
continue
with open(lowercase_ , encoding='''utf-8''') as f:
SCREAMING_SNAKE_CASE_ : Any = f.readlines()
SCREAMING_SNAKE_CASE_ : List[str] = []
SCREAMING_SNAKE_CASE_ : Dict = False
SCREAMING_SNAKE_CASE_ : Any = False
SCREAMING_SNAKE_CASE_ : List[str] = []
for line in lines:
SCREAMING_SNAKE_CASE_ : List[Any] = line
# Convert imports
if "import tensorflow.compat.v2 as tf" in out_line:
continue
elif "@tfds.core" in out_line:
continue
elif "builder=self" in out_line:
continue
elif "import tensorflow_datasets.public_api as tfds" in out_line:
SCREAMING_SNAKE_CASE_ : List[str] = '''import datasets\n'''
elif "import tensorflow" in out_line:
# order is important here
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ''''''
continue
elif "from absl import logging" in out_line:
SCREAMING_SNAKE_CASE_ : str = '''from datasets import logging\n'''
elif "getLogger" in out_line:
SCREAMING_SNAKE_CASE_ : List[str] = out_line.replace('''getLogger''' , '''get_logger''')
elif any(expression in out_line for expression in TO_HIGHLIGHT):
SCREAMING_SNAKE_CASE_ : Optional[Any] = True
SCREAMING_SNAKE_CASE_ : Dict = list(filter(lambda lowercase_: e in out_line , lowercase_))
out_lines.append(HIGHLIGHT_MESSAGE_PRE + str(lowercase_) + '''\n''')
out_lines.append(lowercase_)
out_lines.append(lowercase_)
continue
else:
for pattern, replacement in TO_CONVERT:
SCREAMING_SNAKE_CASE_ : List[Any] = re.sub(lowercase_ , lowercase_ , lowercase_)
# Take care of saving utilities (to later move them together with main script)
if "tensorflow_datasets" in out_line:
SCREAMING_SNAKE_CASE_ : Any = re.match(r'''from\stensorflow_datasets.*import\s([^\.\r\n]+)''' , lowercase_)
tfds_imports.extend(imp.strip() for imp in match.group(1).split(''','''))
SCREAMING_SNAKE_CASE_ : str = '''from . import ''' + match.group(1)
# Check we have not forget anything
if "tf." in out_line or "tfds." in out_line or "tensorflow_datasets" in out_line:
raise ValueError(F'Error converting {out_line.strip()}')
if "GeneratorBasedBuilder" in out_line or "BeamBasedBuilder" in out_line:
SCREAMING_SNAKE_CASE_ : List[str] = True
out_lines.append(lowercase_)
if is_builder or "wmt" in f_name:
# We create a new directory for each dataset
SCREAMING_SNAKE_CASE_ : List[Any] = f_name.replace('''.py''' , '''''')
SCREAMING_SNAKE_CASE_ : Any = os.path.join(lowercase_ , lowercase_)
SCREAMING_SNAKE_CASE_ : str = os.path.join(lowercase_ , lowercase_)
os.makedirs(lowercase_ , exist_ok=lowercase_)
self._logger.info(F'Adding directory {output_dir}')
imports_to_builder_map.update({imp: output_dir for imp in tfds_imports})
else:
# Utilities will be moved at the end
utils_files.append(lowercase_)
if needs_manual_update:
with_manual_update.append(lowercase_)
with open(lowercase_ , '''w''' , encoding='''utf-8''') as f:
f.writelines(lowercase_)
self._logger.info(F'Converted in {output_file}')
for utils_file in utils_files:
try:
SCREAMING_SNAKE_CASE_ : Tuple = os.path.basename(lowercase_)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = imports_to_builder_map[f_name.replace('''.py''' , '''''')]
self._logger.info(F'Moving {dest_folder} to {utils_file}')
shutil.copy(lowercase_ , lowercase_)
except KeyError:
self._logger.error(F'Cannot find destination folder for {utils_file}. Please copy manually.')
if with_manual_update:
for file_path in with_manual_update:
self._logger.warning(
F'You need to manually update file {file_path} to remove configurations using \'TextEncoderConfig\'.')
| 91 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _snake_case ( ) -> Generator[int, None, None]:
'''simple docstring'''
lowerCAmelCase_ :dict[int, int] = {}
lowerCAmelCase_ :int = 2
while True:
lowerCAmelCase_ :List[Any] = factor_map.pop(lowercase__ , lowercase__ )
if factor:
lowerCAmelCase_ :Optional[int] = factor + prime
while x in factor_map:
x += factor
lowerCAmelCase_ :List[str] = factor
else:
lowerCAmelCase_ :Optional[int] = prime
yield prime
prime += 1
def _snake_case ( lowercase__ : float = 1E10 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = sieve()
lowerCAmelCase_ :str = 1
while True:
lowerCAmelCase_ :int = next(lowercase__ )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(lowercase__ )
n += 2
if __name__ == "__main__":
print(solution())
| 84 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ = {"""configuration_deit""": ["""DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """DeiTConfig""", """DeiTOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = ["""DeiTFeatureExtractor"""]
UpperCamelCase__ = ["""DeiTImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""DeiTForImageClassification""",
"""DeiTForImageClassificationWithTeacher""",
"""DeiTForMaskedImageModeling""",
"""DeiTModel""",
"""DeiTPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
"""TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFDeiTForImageClassification""",
"""TFDeiTForImageClassificationWithTeacher""",
"""TFDeiTForMaskedImageModeling""",
"""TFDeiTModel""",
"""TFDeiTPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_deit import DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, DeiTConfig, DeiTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_deit import DeiTFeatureExtractor
from .image_processing_deit import DeiTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_deit import (
DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
DeiTForImageClassification,
DeiTForImageClassificationWithTeacher,
DeiTForMaskedImageModeling,
DeiTModel,
DeiTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_deit import (
TF_DEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDeiTForImageClassification,
TFDeiTForImageClassificationWithTeacher,
TFDeiTForMaskedImageModeling,
TFDeiTModel,
TFDeiTPreTrainedModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 92 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
# TODO: is there an appropriate internal test set?
UpperCAmelCase_ :List[Any] = "ssube/stable-diffusion-x4-upscaler-onnx"
def __lowerCAmelCase ( self , __A=0 ) -> Optional[int]:
lowerCAmelCase_ :Optional[Any] = floats_tensor((1, 3, 128, 128) , rng=random.Random(__A ) )
lowerCAmelCase_ :List[Any] = torch.manual_seed(__A )
lowerCAmelCase_ :Tuple = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Dict = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :int = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Tuple = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :int = self.get_dummy_inputs()
lowerCAmelCase_ :List[str] = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :str = np.array(
[0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Optional[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Union[str, Any] = pipe(**__A ).images
lowerCAmelCase_ :Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Tuple = np.array(
[0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Union[str, Any] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Optional[Any] = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Tuple = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :List[str] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Optional[int] = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Dict = pipe(**__A ).images
lowerCAmelCase_ :Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Dict = np.array(
[0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@property
def __lowerCAmelCase ( self ) -> List[Any]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Optional[int] = ort.SessionOptions()
lowerCAmelCase_ :Dict = False
return options
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase_ :Optional[Any] = init_image.resize((128, 128) )
# using the PNDM scheduler by default
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = """A fantasy landscape, trending on artstation"""
lowerCAmelCase_ :List[Any] = torch.manual_seed(0 )
lowerCAmelCase_ :str = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=10 , generator=__A , output_type="""np""" , )
lowerCAmelCase_ :Dict = output.images
lowerCAmelCase_ :List[str] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Optional[Any] = np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase_ :List[str] = init_image.resize((128, 128) )
lowerCAmelCase_ :Any = LMSDiscreteScheduler.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , subfolder="""scheduler""" )
lowerCAmelCase_ :Optional[Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , scheduler=__A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Any = """A fantasy landscape, trending on artstation"""
lowerCAmelCase_ :Optional[Any] = torch.manual_seed(0 )
lowerCAmelCase_ :List[str] = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=20 , generator=__A , output_type="""np""" , )
lowerCAmelCase_ :int = output.images
lowerCAmelCase_ :List[Any] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Union[str, Any] = np.array(
[0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
| 84 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
ViTHybridConfig,
ViTHybridForImageClassification,
ViTHybridImageProcessor,
ViTHybridModel,
)
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
_lowercase : int = logging.get_logger(__name__)
def snake_case_ ( __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Tuple=False ):
"""simple docstring"""
lowercase_ : Tuple = []
# fmt: off
# stem:
rename_keys.append(('''cls_token''', '''vit.embeddings.cls_token''') )
rename_keys.append(('''pos_embed''', '''vit.embeddings.position_embeddings''') )
rename_keys.append(('''patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight''') )
rename_keys.append(('''patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias''') )
# backbone
rename_keys.append(('''patch_embed.backbone.stem.conv.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.convolution.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.weight''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.weight''') )
rename_keys.append(('''patch_embed.backbone.stem.norm.bias''', '''vit.embeddings.patch_embeddings.backbone.bit.embedder.norm.bias''') )
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm1.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm1.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm2.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm2.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.conv3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.conv3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.{layer_idx}.norm3.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.{layer_idx}.norm3.bias''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.conv.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.conv.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.weight''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.weight''') )
rename_keys.append((F'''patch_embed.backbone.stages.{stage_idx}.blocks.0.downsample.norm.bias''', F'''vit.embeddings.patch_embeddings.backbone.bit.encoder.stages.{stage_idx}.layers.0.downsample.norm.bias''') )
# transformer encoder
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'''blocks.{i}.norm1.weight''', F'''vit.encoder.layer.{i}.layernorm_before.weight''') )
rename_keys.append((F'''blocks.{i}.norm1.bias''', F'''vit.encoder.layer.{i}.layernorm_before.bias''') )
rename_keys.append((F'''blocks.{i}.attn.proj.weight''', F'''vit.encoder.layer.{i}.attention.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.attn.proj.bias''', F'''vit.encoder.layer.{i}.attention.output.dense.bias''') )
rename_keys.append((F'''blocks.{i}.norm2.weight''', F'''vit.encoder.layer.{i}.layernorm_after.weight''') )
rename_keys.append((F'''blocks.{i}.norm2.bias''', F'''vit.encoder.layer.{i}.layernorm_after.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.weight''', F'''vit.encoder.layer.{i}.intermediate.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc1.bias''', F'''vit.encoder.layer.{i}.intermediate.dense.bias''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.weight''', F'''vit.encoder.layer.{i}.output.dense.weight''') )
rename_keys.append((F'''blocks.{i}.mlp.fc2.bias''', F'''vit.encoder.layer.{i}.output.dense.bias''') )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''norm.weight''', '''layernorm.weight'''),
('''norm.bias''', '''layernorm.bias'''),
('''pre_logits.fc.weight''', '''pooler.dense.weight'''),
('''pre_logits.fc.bias''', '''pooler.dense.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
lowercase_ : Union[str, Any] = [(pair[0], pair[1][4:]) if pair[1].startswith('''vit''' ) else pair for pair in rename_keys]
else:
# layernorm + classification head
rename_keys.extend(
[
('''norm.weight''', '''vit.layernorm.weight'''),
('''norm.bias''', '''vit.layernorm.bias'''),
('''head.weight''', '''classifier.weight'''),
('''head.bias''', '''classifier.bias'''),
] )
# fmt: on
return rename_keys
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[int] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Any=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
lowercase_ : int = ''''''
else:
lowercase_ : Any = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
lowercase_ : Optional[Any] = state_dict.pop(F'''blocks.{i}.attn.qkv.weight''' )
lowercase_ : int = state_dict.pop(F'''blocks.{i}.attn.qkv.bias''' )
# next, add query, keys and values (in that order) to the state dict
lowercase_ : List[str] = in_proj_weight[
: config.hidden_size, :
]
lowercase_ : List[str] = in_proj_bias[: config.hidden_size]
lowercase_ : List[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
lowercase_ : Optional[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
lowercase_ : List[str] = in_proj_weight[
-config.hidden_size :, :
]
lowercase_ : Tuple = in_proj_bias[-config.hidden_size :]
def snake_case_ ( __SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
lowercase_ : Optional[int] = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def snake_case_ ( __SCREAMING_SNAKE_CASE : Optional[Any] , __SCREAMING_SNAKE_CASE : Dict , __SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
lowercase_ : Union[str, Any] = dct.pop(__SCREAMING_SNAKE_CASE )
lowercase_ : str = val
def snake_case_ ( ):
"""simple docstring"""
lowercase_ : Tuple = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
lowercase_ : Optional[Any] = Image.open(requests.get(__SCREAMING_SNAKE_CASE , stream=__SCREAMING_SNAKE_CASE ).raw )
return im
@torch.no_grad()
def snake_case_ ( __SCREAMING_SNAKE_CASE : Any , __SCREAMING_SNAKE_CASE : Union[str, Any] , __SCREAMING_SNAKE_CASE : int=False ):
"""simple docstring"""
lowercase_ : Optional[int] = BitConfig(
global_padding='''same''' , layer_type='''bottleneck''' , depths=(3, 4, 9) , out_features=['''stage3'''] , embedding_dynamic_padding=__SCREAMING_SNAKE_CASE , )
lowercase_ : Optional[Any] = ViTHybridConfig(backbone_config=__SCREAMING_SNAKE_CASE , image_size=384 , num_labels=1000 )
lowercase_ : Optional[int] = False
# load original model from timm
lowercase_ : Tuple = timm.create_model(__SCREAMING_SNAKE_CASE , pretrained=__SCREAMING_SNAKE_CASE )
timm_model.eval()
# load state_dict of original model, remove and rename some keys
lowercase_ : str = timm_model.state_dict()
if base_model:
remove_classification_head_(__SCREAMING_SNAKE_CASE )
lowercase_ : List[Any] = create_rename_keys(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
for src, dest in rename_keys:
rename_key(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
read_in_q_k_v(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
lowercase_ : Tuple = '''huggingface/label-files'''
lowercase_ : Union[str, Any] = '''imagenet-1k-id2label.json'''
lowercase_ : Union[str, Any] = json.load(open(hf_hub_download(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , repo_type='''dataset''' ) , '''r''' ) )
lowercase_ : Dict = {int(__SCREAMING_SNAKE_CASE ): v for k, v in idalabel.items()}
lowercase_ : Dict = idalabel
lowercase_ : Any = {v: k for k, v in idalabel.items()}
# load HuggingFace model
if vit_name[-5:] == "in21k":
lowercase_ : Optional[int] = ViTHybridModel(__SCREAMING_SNAKE_CASE ).eval()
else:
lowercase_ : int = ViTHybridForImageClassification(__SCREAMING_SNAKE_CASE ).eval()
model.load_state_dict(__SCREAMING_SNAKE_CASE )
# create image processor
lowercase_ : Any = create_transform(**resolve_data_config({} , model=__SCREAMING_SNAKE_CASE ) )
lowercase_ : Optional[Any] = transform.transforms
lowercase_ : int = {
'''bilinear''': PILImageResampling.BILINEAR,
'''bicubic''': PILImageResampling.BICUBIC,
'''nearest''': PILImageResampling.NEAREST,
}
lowercase_ : int = ViTHybridImageProcessor(
do_resize=__SCREAMING_SNAKE_CASE , size={'''shortest_edge''': timm_transforms[0].size} , resample=pillow_resamplings[timm_transforms[0].interpolation.value] , do_center_crop=__SCREAMING_SNAKE_CASE , crop_size={'''height''': timm_transforms[1].size[0], '''width''': timm_transforms[1].size[1]} , do_normalize=__SCREAMING_SNAKE_CASE , image_mean=timm_transforms[-1].mean.tolist() , image_std=timm_transforms[-1].std.tolist() , )
lowercase_ : List[str] = prepare_img()
lowercase_ : Any = transform(__SCREAMING_SNAKE_CASE ).unsqueeze(0 )
lowercase_ : Optional[int] = processor(__SCREAMING_SNAKE_CASE , return_tensors='''pt''' ).pixel_values
# verify pixel values
assert torch.allclose(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
# verify logits
with torch.no_grad():
lowercase_ : Union[str, Any] = model(__SCREAMING_SNAKE_CASE )
lowercase_ : Optional[int] = outputs.logits
print('''Predicted class:''' , logits.argmax(-1 ).item() )
if base_model:
lowercase_ : Dict = timm_model.forward_features(__SCREAMING_SNAKE_CASE )
assert timm_pooled_output.shape == outputs.pooler_output.shape
assert torch.allclose(__SCREAMING_SNAKE_CASE , outputs.pooler_output , atol=1E-3 )
else:
lowercase_ : Union[str, Any] = timm_model(__SCREAMING_SNAKE_CASE )
assert timm_logits.shape == outputs.logits.shape
assert torch.allclose(__SCREAMING_SNAKE_CASE , outputs.logits , atol=1E-3 )
print('''Looks ok!''' )
if pytorch_dump_folder_path is not None:
Path(__SCREAMING_SNAKE_CASE ).mkdir(exist_ok=__SCREAMING_SNAKE_CASE )
print(F'''Saving model {vit_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(__SCREAMING_SNAKE_CASE )
print(F'''Saving processor to {pytorch_dump_folder_path}''' )
processor.save_pretrained(__SCREAMING_SNAKE_CASE )
if push_to_hub:
print(F'''Pushing model and processor to the hub {vit_name}''' )
model.push_to_hub(F'''ybelkada/{vit_name}''' )
processor.push_to_hub(F'''ybelkada/{vit_name}''' )
if __name__ == "__main__":
_lowercase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--vit_name",
default="vit_base_r50_s16_384",
type=str,
help="Name of the hybrid ViT timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
_lowercase : Tuple = parser.parse_args()
convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 93 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
] )
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Dict:
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=__A , )
assert hasattr(self , """env""" )
def __lowerCAmelCase ( self , __A ) -> Any:
# configuration for running training on smdistributed Model Parallel
lowerCAmelCase_ :Union[str, Any] = {
"""enabled""": True,
"""processes_per_host""": 8,
}
lowerCAmelCase_ :Tuple = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
lowerCAmelCase_ :Any = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
lowerCAmelCase_ :Any = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=__A , instance_type=self.instance_type , debugger_hook_config=__A , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=__A , py_version="""py36""" , )
def __lowerCAmelCase ( self , __A ) -> List[Any]:
TrainingJobAnalytics(__A ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def __lowerCAmelCase ( self , __A ) -> List[str]:
# create estimator
lowerCAmelCase_ :Any = self.create_estimator(__A )
# run training
estimator.fit()
# result dataframe
lowerCAmelCase_ :Optional[Any] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase_ :List[str] = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
lowerCAmelCase_ :Optional[int] = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase_ :Optional[int] = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , __A )
| 84 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pytest
from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex
from .utils import require_elasticsearch, require_faiss
snake_case : List[str] = pytest.mark.integration
@require_faiss
class _snake_case ( _snake_case ):
def SCREAMING_SNAKE_CASE__ ( self ):
a :Union[str, Any] = Dataset.from_dict({'''filename''': ['''my_name-train''' + '''_''' + str(_lowerCamelCase ) for x in np.arange(30 ).tolist()]} )
return dset
def SCREAMING_SNAKE_CASE__ ( self ):
import faiss
a :Dataset = self._create_dummy_dataset()
a :str = dset.map(
lambda _lowerCamelCase , _lowerCamelCase : {"vecs": i * np.ones(5 , dtype=np.floataa )} , with_indices=_lowerCamelCase , keep_in_memory=_lowerCamelCase )
a :List[Any] = dset.add_faiss_index('''vecs''' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT )
a , a :List[Any] = dset.get_nearest_examples('''vecs''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
dset.drop_index('''vecs''' )
def SCREAMING_SNAKE_CASE__ ( self ):
import faiss
a :Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' , batch_size=100 , metric_type=faiss.METRIC_INNER_PRODUCT , )
a , a :List[Any] = dset.get_nearest_examples('''vecs''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
def SCREAMING_SNAKE_CASE__ ( self ):
import faiss
a :Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' , metric_type=faiss.METRIC_INNER_PRODUCT , )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_lowerCamelCase ) as tmp_file:
dset.save_faiss_index('''vecs''' , tmp_file.name )
dset.load_faiss_index('''vecs2''' , tmp_file.name )
os.unlink(tmp_file.name )
a , a :List[str] = dset.get_nearest_examples('''vecs2''' , np.ones(5 , dtype=np.floataa ) )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
def SCREAMING_SNAKE_CASE__ ( self ):
a :Dataset = self._create_dummy_dataset()
dset.add_faiss_index_from_external_arrays(
external_arrays=np.ones((30, 5) ) * np.arange(30 ).reshape(-1 , 1 ) , index_name='''vecs''' )
dset.drop_index('''vecs''' )
self.assertRaises(_lowerCamelCase , partial(dset.get_nearest_examples , '''vecs2''' , np.ones(5 , dtype=np.floataa ) ) )
def SCREAMING_SNAKE_CASE__ ( self ):
from elasticsearch import Elasticsearch
a :Dataset = self._create_dummy_dataset()
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
a :Tuple = {'''acknowledged''': True}
mocked_bulk.return_value([(True, None)] * 30 )
a :List[str] = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 29}]}}
a :Tuple = Elasticsearch()
dset.add_elasticsearch_index('''filename''' , es_client=_lowerCamelCase )
a , a :Dict = dset.get_nearest_examples('''filename''' , '''my_name-train_29''' )
self.assertEqual(examples['''filename'''][0] , '''my_name-train_29''' )
@require_faiss
class _snake_case ( _snake_case ):
def SCREAMING_SNAKE_CASE__ ( self ):
import faiss
a :Dict = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
# add vectors
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsNotNone(index.faiss_index )
self.assertEqual(index.faiss_index.ntotal , 5 )
index.add_vectors(np.zeros((5, 5) , dtype=np.floataa ) )
self.assertEqual(index.faiss_index.ntotal , 10 )
# single query
a :Tuple = np.zeros(5 , dtype=np.floataa )
a :str = 1
a , a :int = index.search(_lowerCamelCase )
self.assertRaises(_lowerCamelCase , index.search , query.reshape(-1 , 1 ) )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
# batched queries
a :Tuple = np.eye(5 , dtype=np.floataa )[::-1]
a , a :Dict = index.search_batch(_lowerCamelCase )
self.assertRaises(_lowerCamelCase , index.search_batch , queries[0] )
a :Union[str, Any] = [scores[0] for scores in total_scores]
a :List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_lowerCamelCase ) , 0 )
self.assertListEqual([4, 3, 2, 1, 0] , _lowerCamelCase )
def SCREAMING_SNAKE_CASE__ ( self ):
import faiss
a :List[Any] = FaissIndex(string_factory='''Flat''' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
a :str = FaissIndex(string_factory='''LSH''' )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexLSH )
with self.assertRaises(_lowerCamelCase ):
a :Tuple = FaissIndex(string_factory='''Flat''' , custom_index=faiss.IndexFlat(5 ) )
def SCREAMING_SNAKE_CASE__ ( self ):
import faiss
a :List[str] = faiss.IndexFlat(5 )
a :List[Any] = FaissIndex(custom_index=_lowerCamelCase )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
self.assertIsInstance(index.faiss_index , faiss.IndexFlat )
def SCREAMING_SNAKE_CASE__ ( self ):
import faiss
a :Optional[Any] = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
# see https://bugs.python.org/issue14243 and
# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
with tempfile.NamedTemporaryFile(delete=_lowerCamelCase ) as tmp_file:
index.save(tmp_file.name )
a :int = FaissIndex.load(tmp_file.name )
os.unlink(tmp_file.name )
a :Optional[int] = np.zeros(5 , dtype=np.floataa )
a :Any = 1
a , a :Any = index.search(_lowerCamelCase )
self.assertGreater(scores[0] , 0 )
self.assertEqual(indices[0] , 1 )
@require_faiss
def __lowerCamelCase ( UpperCAmelCase_ : Optional[int] ):
"""simple docstring"""
import faiss
a :Any = FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT )
index.add_vectors(np.eye(5 , dtype=np.floataa ) )
a :Optional[Any] = '''index.faiss'''
a :Union[str, Any] = F'''mock://{index_name}'''
index.save(UpperCAmelCase_ , storage_options=mockfs.storage_options )
a :List[str] = FaissIndex.load(UpperCAmelCase_ , storage_options=mockfs.storage_options )
a :str = np.zeros(5 , dtype=np.floataa )
a :List[str] = 1
a , a :Union[str, Any] = index.search(UpperCAmelCase_ )
assert scores[0] > 0
assert indices[0] == 1
@require_elasticsearch
class _snake_case ( _snake_case ):
def SCREAMING_SNAKE_CASE__ ( self ):
from elasticsearch import Elasticsearch
with patch('''elasticsearch.Elasticsearch.search''' ) as mocked_search, patch(
'''elasticsearch.client.IndicesClient.create''' ) as mocked_index_create, patch('''elasticsearch.helpers.streaming_bulk''' ) as mocked_bulk:
a :Any = Elasticsearch()
a :Optional[Any] = {'''acknowledged''': True}
a :Union[str, Any] = ElasticSearchIndex(es_client=_lowerCamelCase )
mocked_bulk.return_value([(True, None)] * 3 )
index.add_documents(['''foo''', '''bar''', '''foobar'''] )
# single query
a :int = '''foo'''
a :List[str] = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
a , a :Optional[Any] = index.search(_lowerCamelCase )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# single query with timeout
a :Optional[Any] = '''foo'''
a :Optional[Any] = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 0}]}}
a , a :str = index.search(_lowerCamelCase , request_timeout=30 )
self.assertEqual(scores[0] , 1 )
self.assertEqual(indices[0] , 0 )
# batched queries
a :Dict = ['''foo''', '''bar''', '''foobar''']
a :Optional[Any] = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
a , a :List[Any] = index.search_batch(_lowerCamelCase )
a :str = [scores[0] for scores in total_scores]
a :List[Any] = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_lowerCamelCase ) , 0 )
self.assertListEqual([1, 1, 1] , _lowerCamelCase )
# batched queries with timeout
a :Union[str, Any] = ['''foo''', '''bar''', '''foobar''']
a :str = {'''hits''': {'''hits''': [{'''_score''': 1, '''_id''': 1}]}}
a , a :str = index.search_batch(_lowerCamelCase , request_timeout=30 )
a :Union[str, Any] = [scores[0] for scores in total_scores]
a :int = [indices[0] for indices in total_indices]
self.assertGreater(np.min(_lowerCamelCase ) , 0 )
self.assertListEqual([1, 1, 1] , _lowerCamelCase )
| 94 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 1_0 ) -> str:
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or n < 0:
raise ValueError("""Invalid input""" )
lowerCAmelCase_ :List[str] = 1_0**n
lowerCAmelCase_ :int = 2_8_4_3_3 * (pow(2 , 7_8_3_0_4_5_7 , lowercase__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(10) = }""")
| 84 | 0 |
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class __lowerCAmelCase ( UpperCamelCase__):
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__ ) -> Dict:
'''simple docstring'''
a__ : Any =params
a__ : Dict =np.array(lowerCAmelCase__ )
a__ : Any =np.array([len(lowerCAmelCase__ ) for t in data] )
self.check()
self.remove_long_sequences()
self.remove_empty_sequences()
self.remove_unknown_sequences()
self.check()
self.print_statistics()
def __getitem__( self , lowerCAmelCase__ ) -> str:
'''simple docstring'''
return (self.token_ids[index], self.lengths[index])
def __len__( self ) -> int:
'''simple docstring'''
return len(self.lengths )
def _lowercase ( self ) -> Any:
'''simple docstring'''
assert len(self.token_ids ) == len(self.lengths )
assert all(self.lengths[i] == len(self.token_ids[i] ) for i in range(len(self.lengths ) ) )
def _lowercase ( self ) -> List[str]:
'''simple docstring'''
a__ : Any =self.params.max_model_input_size
a__ : List[str] =self.lengths > max_len
logger.info(F'''Splitting {sum(lowerCAmelCase__ )} too long sequences.''' )
def divide_chunks(lowerCAmelCase__ , lowerCAmelCase__ ):
return [l[i : i + n] for i in range(0 , len(lowerCAmelCase__ ) , lowerCAmelCase__ )]
a__ : Optional[int] =[]
a__ : Any =[]
if self.params.mlm:
a__ , a__ : List[str] =self.params.special_tok_ids["cls_token"], self.params.special_tok_ids["sep_token"]
else:
a__ , a__ : Union[str, Any] =self.params.special_tok_ids["bos_token"], self.params.special_tok_ids["eos_token"]
for seq_, len_ in zip(self.token_ids , self.lengths ):
assert (seq_[0] == cls_id) and (seq_[-1] == sep_id), seq_
if len_ <= max_len:
new_tok_ids.append(seq_ )
new_lengths.append(len_ )
else:
a__ : Dict =[]
for sub_s in divide_chunks(seq_ , max_len - 2 ):
if sub_s[0] != cls_id:
a__ : int =np.insert(lowerCAmelCase__ , 0 , lowerCAmelCase__ )
if sub_s[-1] != sep_id:
a__ : Tuple =np.insert(lowerCAmelCase__ , len(lowerCAmelCase__ ) , lowerCAmelCase__ )
assert len(lowerCAmelCase__ ) <= max_len
assert (sub_s[0] == cls_id) and (sub_s[-1] == sep_id), sub_s
sub_seqs.append(lowerCAmelCase__ )
new_tok_ids.extend(lowerCAmelCase__ )
new_lengths.extend([len(lowerCAmelCase__ ) for l in sub_seqs] )
a__ : Dict =np.array(lowerCAmelCase__ )
a__ : Dict =np.array(lowerCAmelCase__ )
def _lowercase ( self ) -> Optional[int]:
'''simple docstring'''
a__ : Optional[Any] =len(self )
a__ : Tuple =self.lengths > 1_1
a__ : str =self.token_ids[indices]
a__ : Optional[Any] =self.lengths[indices]
a__ : Dict =len(self )
logger.info(F'''Remove {init_size - new_size} too short (<=11 tokens) sequences.''' )
def _lowercase ( self ) -> List[Any]:
'''simple docstring'''
if "unk_token" not in self.params.special_tok_ids:
return
else:
a__ : Any =self.params.special_tok_ids["unk_token"]
a__ : Any =len(self )
a__ : int =np.array([np.count_nonzero(a == unk_token_id ) for a in self.token_ids] )
a__ : Optional[Any] =(unk_occs / self.lengths) < 0.5
a__ : Optional[Any] =self.token_ids[indices]
a__ : Optional[Any] =self.lengths[indices]
a__ : Any =len(self )
logger.info(F'''Remove {init_size - new_size} sequences with a high level of unknown tokens (50%).''' )
def _lowercase ( self ) -> Tuple:
'''simple docstring'''
if not self.params.is_master:
return
logger.info(F'''{len(self )} sequences''' )
# data_len = sum(self.lengths)
# nb_unique_tokens = len(Counter(list(chain(*self.token_ids))))
# logger.info(f'{data_len} tokens ({nb_unique_tokens} unique)')
# unk_idx = self.params.special_tok_ids['unk_token']
# nb_unknown = sum([(t==unk_idx).sum() for t in self.token_ids])
# logger.info(f'{nb_unknown} unknown tokens (covering {100*nb_unknown/data_len:.2f}% of the data)')
def _lowercase ( self , lowerCAmelCase__ ) -> List[Any]:
'''simple docstring'''
a__ : List[str] =[t[0] for t in batch]
a__ : Optional[int] =[t[1] for t in batch]
assert len(lowerCAmelCase__ ) == len(lowerCAmelCase__ )
# Max for paddings
a__ : Optional[Any] =max(lowerCAmelCase__ )
# Pad token ids
if self.params.mlm:
a__ : str =self.params.special_tok_ids["pad_token"]
else:
a__ : Dict =self.params.special_tok_ids["unk_token"]
a__ : Any =[list(t.astype(lowerCAmelCase__ ) ) + [pad_idx] * (max_seq_len_ - len(lowerCAmelCase__ )) for t in token_ids]
assert len(tk_ ) == len(lowerCAmelCase__ )
assert all(len(lowerCAmelCase__ ) == max_seq_len_ for t in tk_ )
a__ : List[str] =torch.tensor(tk_ ) # (bs, max_seq_len_)
a__ : List[Any] =torch.tensor(lowerCAmelCase__ ) # (bs)
return tk_t, lg_t
| 95 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__UpperCAmelCase = 'src/transformers'
__UpperCAmelCase = 'docs/source/en/tasks'
def _snake_case ( lowercase__ : str , lowercase__ : List[str] , lowercase__ : Any ) -> str:
'''simple docstring'''
with open(lowercase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCAmelCase_ :List[Any] = f.readlines()
# Find the start prompt.
lowerCAmelCase_ :Tuple = 0
while not lines[start_index].startswith(lowercase__ ):
start_index += 1
start_index += 1
lowerCAmelCase_ :Dict = start_index
while not lines[end_index].startswith(lowercase__ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
__UpperCAmelCase = direct_transformers_import(TRANSFORMERS_PATH)
__UpperCAmelCase = {
'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__UpperCAmelCase = {
'summarization.md': ('nllb',),
'translation.md': ('nllb',),
}
def _snake_case ( lowercase__ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = TASK_GUIDE_TO_MODELS[task_guide]
lowerCAmelCase_ :List[Any] = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowercase__ , set() )
lowerCAmelCase_ :Union[str, Any] = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([f"""[{name}](../model_doc/{code})""" for code, name in model_names.items()] ) + "\n"
def _snake_case ( lowercase__ : int , lowercase__ : str=False ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = _find_text_in_file(
filename=os.path.join(lowercase__ , lowercase__ ) , start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->""" , end_prompt="""<!--End of the generated tip-->""" , )
lowerCAmelCase_ :int = get_model_list_for_task(lowercase__ )
if current_list != new_list:
if overwrite:
with open(os.path.join(lowercase__ , lowercase__ ) , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
f"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
""" to fix this.""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__UpperCAmelCase = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 84 | 0 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
lowercase__ = """src/transformers"""
# Matches is_xxx_available()
lowercase__ = re.compile(R"""is\_([a-z_]*)_available()""")
# Catches a one-line _import_struct = {xxx}
lowercase__ = re.compile(R"""^_import_structure\s+=\s+\{([^\}]+)\}""")
# Catches a line with a key-values pattern: "bla": ["foo", "bar"]
lowercase__ = re.compile(R"""\s+\"\S*\":\s+\[([^\]]*)\]""")
# Catches a line if not is_foo_available
lowercase__ = re.compile(R"""^\s*if\s+not\s+is\_[a-z_]*\_available\(\)""")
# Catches a line _import_struct["bla"].append("foo")
lowercase__ = re.compile(R"""^\s*_import_structure\[\"\S*\"\]\.append\(\"(\S*)\"\)""")
# Catches a line _import_struct["bla"].extend(["foo", "bar"]) or _import_struct["bla"] = ["foo", "bar"]
lowercase__ = re.compile(R"""^\s*_import_structure\[\S*\](?:\.extend\(|\s*=\s+)\[([^\]]*)\]""")
# Catches a line with an object between quotes and a comma: "MyModel",
lowercase__ = re.compile("""^\s+\"([^\"]+)\",""")
# Catches a line with objects between brackets only: ["foo", "bar"],
lowercase__ = re.compile("""^\s+\[([^\]]+)\]""")
# Catches a line with from foo import bar, bla, boo
lowercase__ = re.compile(R"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")
# Catches a line with try:
lowercase__ = re.compile(R"""^\s*try:""")
# Catches a line with else:
lowercase__ = re.compile(R"""^\s*else:""")
def _snake_case ( lowercase__ ):
if _re_test_backend.search(lowercase__ ) is None:
return None
_lowerCamelCase : Optional[Any] = [b[0] for b in _re_backend.findall(lowercase__ )]
backends.sort()
return "_and_".join(lowercase__ )
def _snake_case ( lowercase__ ):
with open(lowercase__ , 'r' , encoding='utf-8' , newline='\n' ) as f:
_lowerCamelCase : Dict = f.readlines()
_lowerCamelCase : Optional[Any] = 0
while line_index < len(lowercase__ ) and not lines[line_index].startswith('_import_structure = {' ):
line_index += 1
# If this is a traditional init, just return.
if line_index >= len(lowercase__ ):
return None
# First grab the objects without a specific backend in _import_structure
_lowerCamelCase : str = []
while not lines[line_index].startswith('if TYPE_CHECKING' ) and find_backend(lines[line_index] ) is None:
_lowerCamelCase : Optional[int] = lines[line_index]
# If we have everything on a single line, let's deal with it.
if _re_one_line_import_struct.search(lowercase__ ):
_lowerCamelCase : Optional[Any] = _re_one_line_import_struct.search(lowercase__ ).groups()[0]
_lowerCamelCase : Optional[Any] = re.findall('\[([^\]]+)\]' , lowercase__ )
for imp in imports:
objects.extend([obj[1:-1] for obj in imp.split(', ' )] )
line_index += 1
continue
_lowerCamelCase : int = _re_import_struct_key_value.search(lowercase__ )
if single_line_import_search is not None:
_lowerCamelCase : Optional[Any] = [obj[1:-1] for obj in single_line_import_search.groups()[0].split(', ' ) if len(lowercase__ ) > 0]
objects.extend(lowercase__ )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
line_index += 1
_lowerCamelCase : Optional[int] = {'none': objects}
# Let's continue with backend-specific objects in _import_structure
while not lines[line_index].startswith('if TYPE_CHECKING' ):
# If the line is an if not is_backend_available, we grab all objects associated.
_lowerCamelCase : Optional[int] = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_lowerCamelCase : Union[str, Any] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_lowerCamelCase : Tuple = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 4 ):
_lowerCamelCase : Optional[int] = lines[line_index]
if _re_import_struct_add_one.search(lowercase__ ) is not None:
objects.append(_re_import_struct_add_one.search(lowercase__ ).groups()[0] )
elif _re_import_struct_add_many.search(lowercase__ ) is not None:
_lowerCamelCase : Dict = _re_import_struct_add_many.search(lowercase__ ).groups()[0].split(', ' )
_lowerCamelCase : str = [obj[1:-1] for obj in imports if len(lowercase__ ) > 0]
objects.extend(lowercase__ )
elif _re_between_brackets.search(lowercase__ ) is not None:
_lowerCamelCase : Optional[Any] = _re_between_brackets.search(lowercase__ ).groups()[0].split(', ' )
_lowerCamelCase : Optional[Any] = [obj[1:-1] for obj in imports if len(lowercase__ ) > 0]
objects.extend(lowercase__ )
elif _re_quote_object.search(lowercase__ ) is not None:
objects.append(_re_quote_object.search(lowercase__ ).groups()[0] )
elif line.startswith(' ' * 8 + '"' ):
objects.append(line[9:-3] )
elif line.startswith(' ' * 12 + '"' ):
objects.append(line[13:-3] )
line_index += 1
_lowerCamelCase : Optional[int] = objects
else:
line_index += 1
# At this stage we are in the TYPE_CHECKING part, first grab the objects without a specific backend
_lowerCamelCase : List[str] = []
while (
line_index < len(lowercase__ )
and find_backend(lines[line_index] ) is None
and not lines[line_index].startswith('else' )
):
_lowerCamelCase : Tuple = lines[line_index]
_lowerCamelCase : Optional[int] = _re_import.search(lowercase__ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 8 ):
objects.append(line[8:-2] )
line_index += 1
_lowerCamelCase : Optional[int] = {'none': objects}
# Let's continue with backend-specific objects
while line_index < len(lowercase__ ):
# If the line is an if is_backend_available, we grab all objects associated.
_lowerCamelCase : Tuple = find_backend(lines[line_index] )
# Check if the backend declaration is inside a try block:
if _re_try.search(lines[line_index - 1] ) is None:
_lowerCamelCase : List[Any] = None
if backend is not None:
line_index += 1
# Scroll until we hit the else block of try-except-else
while _re_else.search(lines[line_index] ) is None:
line_index += 1
line_index += 1
_lowerCamelCase : Tuple = []
# Until we unindent, add backend objects to the list
while len(lines[line_index] ) <= 1 or lines[line_index].startswith(' ' * 8 ):
_lowerCamelCase : List[str] = lines[line_index]
_lowerCamelCase : List[Any] = _re_import.search(lowercase__ )
if single_line_import_search is not None:
objects.extend(single_line_import_search.groups()[0].split(', ' ) )
elif line.startswith(' ' * 12 ):
objects.append(line[12:-2] )
line_index += 1
_lowerCamelCase : Dict = objects
else:
line_index += 1
return import_dict_objects, type_hint_objects
def _snake_case ( lowercase__ , lowercase__ ):
def find_duplicates(lowercase__ ):
return [k for k, v in collections.Counter(lowercase__ ).items() if v > 1]
if list(import_dict_objects.keys() ) != list(type_hint_objects.keys() ):
return ["Both sides of the init do not have the same backends!"]
_lowerCamelCase : Optional[Any] = []
for key in import_dict_objects.keys():
_lowerCamelCase : int = find_duplicates(import_dict_objects[key] )
if duplicate_imports:
errors.append(f'''Duplicate _import_structure definitions for: {duplicate_imports}''' )
_lowerCamelCase : Any = find_duplicates(type_hint_objects[key] )
if duplicate_type_hints:
errors.append(f'''Duplicate TYPE_CHECKING objects for: {duplicate_type_hints}''' )
if sorted(set(import_dict_objects[key] ) ) != sorted(set(type_hint_objects[key] ) ):
_lowerCamelCase : Dict = 'base imports' if key == 'none' else f'''{key} backend'''
errors.append(f'''Differences for {name}:''' )
for a in type_hint_objects[key]:
if a not in import_dict_objects[key]:
errors.append(f''' {a} in TYPE_HINT but not in _import_structure.''' )
for a in import_dict_objects[key]:
if a not in type_hint_objects[key]:
errors.append(f''' {a} in _import_structure but not in TYPE_HINT.''' )
return errors
def _snake_case ( ):
_lowerCamelCase : int = []
for root, _, files in os.walk(lowercase__ ):
if "__init__.py" in files:
_lowerCamelCase : Dict = os.path.join(lowercase__ , '__init__.py' )
_lowerCamelCase : Any = parse_init(lowercase__ )
if objects is not None:
_lowerCamelCase : str = analyze_results(*lowercase__ )
if len(lowercase__ ) > 0:
_lowerCamelCase : Tuple = f'''Problem in {fname}, both halves do not define the same objects.\n{errors[0]}'''
failures.append('\n'.join(lowercase__ ) )
if len(lowercase__ ) > 0:
raise ValueError('\n\n'.join(lowercase__ ) )
def _snake_case ( ):
_lowerCamelCase : Dict = []
for path, directories, files in os.walk(lowercase__ ):
for folder in directories:
# Ignore private modules
if folder.startswith('_' ):
directories.remove(lowercase__ )
continue
# Ignore leftovers from branches (empty folders apart from pycache)
if len(list((Path(lowercase__ ) / folder).glob('*.py' ) ) ) == 0:
continue
_lowerCamelCase : Tuple = str((Path(lowercase__ ) / folder).relative_to(lowercase__ ) )
_lowerCamelCase : str = short_path.replace(os.path.sep , '.' )
submodules.append(lowercase__ )
for fname in files:
if fname == "__init__.py":
continue
_lowerCamelCase : List[str] = str((Path(lowercase__ ) / fname).relative_to(lowercase__ ) )
_lowerCamelCase : int = short_path.replace('.py' , '' ).replace(os.path.sep , '.' )
if len(submodule.split('.' ) ) == 1:
submodules.append(lowercase__ )
return submodules
lowercase__ = [
"""convert_pytorch_checkpoint_to_tf2""",
"""modeling_flax_pytorch_utils""",
]
def _snake_case ( ):
# This is to make sure the transformers module imported is the one in the repo.
_lowerCamelCase : int = importlib.util.spec_from_file_location(
'transformers' , os.path.join(lowercase__ , '__init__.py' ) , submodule_search_locations=[PATH_TO_TRANSFORMERS] , )
_lowerCamelCase : List[str] = spec.loader.load_module()
_lowerCamelCase : Dict = [
module
for module in get_transformers_submodules()
if module not in IGNORE_SUBMODULES and module not in transformers._import_structure.keys()
]
if len(lowercase__ ) > 0:
_lowerCamelCase : List[Any] = '\n'.join(f'''- {module}''' for module in module_not_registered )
raise ValueError(
'The following submodules are not properly registered in the main init of Transformers:\n'
f'''{list_of_modules}\n'''
'Make sure they appear somewhere in the keys of `_import_structure` with an empty list as value.' )
if __name__ == "__main__":
check_all_inits()
check_submodules() | 96 |
"""simple docstring"""
def _snake_case ( lowercase__ : list[int] ) -> list[list[int]]:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = []
if len(lowercase__ ) == 1:
return [nums.copy()]
for _ in range(len(lowercase__ ) ):
lowerCAmelCase_ :Optional[Any] = nums.pop(0 )
lowerCAmelCase_ :str = permute(lowercase__ )
for perm in permutations:
perm.append(lowercase__ )
result.extend(lowercase__ )
nums.append(lowercase__ )
return result
def _snake_case ( lowercase__ : Tuple ) -> List[str]:
'''simple docstring'''
def backtrack(lowercase__ : str ):
if start == len(lowercase__ ) - 1:
output.append(nums[:] )
else:
for i in range(lowercase__ , len(lowercase__ ) ):
lowerCAmelCase_ , lowerCAmelCase_ :str = nums[i], nums[start]
backtrack(start + 1 )
lowerCAmelCase_ , lowerCAmelCase_ :str = nums[i], nums[start] # backtrack
lowerCAmelCase_ :int = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
__UpperCAmelCase = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 84 | 0 |
'''simple docstring'''
import argparse
import fairseq
import torch
from torch import nn
from transformers import (
MBartaaTokenizer,
MBartConfig,
MBartForCausalLM,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
__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''',
'''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
'''quantizer.weight_proj''': '''quantizer.weight_proj''',
'''quantizer.vars''': '''quantizer.codevectors''',
'''project_q''': '''project_q''',
'''final_proj''': '''project_hid''',
'''w2v_encoder.proj''': '''lm_head''',
'''mask_emb''': '''masked_spec_embed''',
}
__snake_case = [
'''lm_head''',
'''quantizer.weight_proj''',
'''quantizer.codevectors''',
'''project_q''',
'''project_hid''',
]
def a ( __a , __a , __a , __a , __a ) -> Dict:
'''simple docstring'''
for attribute in key.split('''.''' ):
UpperCamelCase__ :int = getattr(__a , __a )
if weight_type is not None:
UpperCamelCase__ :Tuple = getattr(__a , __a ).shape
else:
UpperCamelCase__ :Union[str, Any] = hf_pointer.shape
assert hf_shape == value.shape, (
f'''Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'''
f''' {value.shape} for {full_name}'''
)
if weight_type == "weight":
UpperCamelCase__ :str = value
elif weight_type == "weight_g":
UpperCamelCase__ :int = value
elif weight_type == "weight_v":
UpperCamelCase__ :List[str] = value
elif weight_type == "bias":
UpperCamelCase__ :Union[str, Any] = value
else:
UpperCamelCase__ :Dict = value
logger.info(f'''{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.''' )
def a ( __a , __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ :Tuple = []
UpperCamelCase__ :str = fairseq_model.state_dict()
UpperCamelCase__ :List[Any] = hf_model.feature_extractor
UpperCamelCase__ :Dict = hf_model.adapter
for name, value in fairseq_dict.items():
UpperCamelCase__ :Tuple = False
if "conv_layers" in name:
load_conv_layer(
__a , __a , __a , __a , hf_model.config.feat_extract_norm == '''group''' , )
UpperCamelCase__ :Any = True
elif any(x in name for x in ['''adaptor''', '''w2v_encoder.proj.''', '''w2v_proj_ln.'''] ):
load_adapter(__a , __a , __a , __a )
UpperCamelCase__ :List[Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split('''w2v_model.''' )[-1] == name.split('''.''' )[0]:
UpperCamelCase__ :Tuple = True
if "*" in mapped_key:
UpperCamelCase__ :Any = name.split(__a )[0].split('''.''' )[-2]
UpperCamelCase__ :Optional[Any] = mapped_key.replace('''*''' , __a )
if "weight_g" in name:
UpperCamelCase__ :List[Any] = '''weight_g'''
elif "weight_v" in name:
UpperCamelCase__ :Optional[Any] = '''weight_v'''
elif "bias" in name:
UpperCamelCase__ :Tuple = '''bias'''
elif "weight" in name:
UpperCamelCase__ :Optional[Any] = '''weight'''
else:
UpperCamelCase__ :str = None
set_recursively(__a , __a , __a , __a , __a )
continue
if not is_used:
unused_weights.append(__a )
logger.warning(f'''Unused weights: {unused_weights}''' )
def a ( __a , __a , __a , __a , __a ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ :int = full_name.split('''conv_layers.''' )[-1]
UpperCamelCase__ :str = name.split('''.''' )
UpperCamelCase__ :int = int(items[0] )
UpperCamelCase__ :Optional[int] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''
)
UpperCamelCase__ :Any = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''
)
UpperCamelCase__ :List[Any] = value
logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.''' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
f'''{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'''
" found."
)
UpperCamelCase__ :Any = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
f'''{full_name} has size {value.shape}, but'''
f''' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'''
)
UpperCamelCase__ :int = value
logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
else:
unused_weights.append(__a )
def a ( __a , __a , __a , __a ) -> Tuple:
'''simple docstring'''
UpperCamelCase__ :Tuple = full_name.split('''adaptor.''' )[-1]
UpperCamelCase__ :int = name.split('''.''' )
if items[1].isdigit():
UpperCamelCase__ :List[str] = int(items[1] )
else:
UpperCamelCase__ :List[Any] = None
if "adaptor" not in full_name:
if "proj_ln" in full_name:
# has to be layer norm
if "bias" in name:
assert (
value.shape == adapter.proj_layer_norm.bias.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found.'''
UpperCamelCase__ :List[str] = value
logger.info(f'''Adapter proj layer norm bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj_layer_norm.weight.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found.'''
UpperCamelCase__ :int = value
else:
# has to be projection layer
if "bias" in name:
assert (
value.shape == adapter.proj.bias.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found.'''
UpperCamelCase__ :Optional[Any] = value
logger.info(f'''Adapter proj layer bias was initialized from {full_name}.''' )
if "weight" in name:
assert (
value.shape == adapter.proj.weight.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found.'''
UpperCamelCase__ :List[str] = value
logger.info(f'''Adapter proj layer weight was initialized from {full_name}.''' )
elif isinstance(__a , __a ):
if "bias" in name:
assert (
value.shape == adapter.layers[layer_id].conv.bias.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found.'''
UpperCamelCase__ :Dict = value
logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
elif "weight" in name:
assert (
value.shape == adapter.layers[layer_id].conv.weight.data.shape
), f'''{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found.'''
UpperCamelCase__ :str = value
logger.info(f'''Adapter layer {layer_id} bias was initialized from {full_name}.''' )
else:
unused_weights.append(__a )
def a ( __a ) -> Any:
'''simple docstring'''
UpperCamelCase__ , UpperCamelCase__ :Any = emb.weight.shape
UpperCamelCase__ :int = nn.Linear(__a , __a , bias=__a )
UpperCamelCase__ :Dict = emb.weight.data
return lin_layer
@torch.no_grad()
def a ( __a , __a , __a , __a , __a , __a , __a , __a , __a , __a , __a , ) -> List[Any]:
'''simple docstring'''
UpperCamelCase__ :str = WavaVecaConfig.from_pretrained(
__a , add_adapter=__a , adapter_stride=__a , adapter_kernel_size=__a , use_auth_token=__a , output_hidden_size=__a , )
UpperCamelCase__ :Dict = MBartConfig.from_pretrained(__a )
# load model
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ :Union[str, Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={
'''config_yaml''': config_yaml_path,
'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] ),
'''w2v_path''': checkpoint_path,
'''load_pretrained_decoder_from''': None,
} , )
UpperCamelCase__ :List[str] = model[0].eval()
# load feature extractor
UpperCamelCase__ :Optional[Any] = WavaVecaFeatureExtractor.from_pretrained(__a , use_auth_token=__a )
# set weights for wav2vec2 encoder
UpperCamelCase__ :List[str] = WavaVecaModel(__a )
recursively_load_weights_wavaveca(model.encoder , __a )
# load decoder weights
UpperCamelCase__ :Tuple = MBartForCausalLM(__a )
UpperCamelCase__ , UpperCamelCase__ :int = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=__a )
logger.warning(f'''The following keys are missing when loading the decoder weights: {missing_keys}''' )
logger.warning(f'''The following keys are unexpected when loading the decoder weights: {unexpected_keys}''' )
UpperCamelCase__ :str = SpeechEncoderDecoderModel(encoder=__a , decoder=__a )
UpperCamelCase__ :Tuple = False
UpperCamelCase__ :List[Any] = MBartaaTokenizer(__a )
tokenizer.save_pretrained(__a )
UpperCamelCase__ :Dict = hf_wavavec.config.to_dict()
UpperCamelCase__ :Optional[int] = tokenizer.pad_token_id
UpperCamelCase__ :Tuple = tokenizer.bos_token_id
UpperCamelCase__ :Union[str, Any] = tokenizer.eos_token_id
UpperCamelCase__ :Any = '''mbart50'''
UpperCamelCase__ :Optional[Any] = '''wav2vec2'''
UpperCamelCase__ :List[Any] = tokenizer.eos_token_id
UpperCamelCase__ :Optional[int] = 250004
UpperCamelCase__ :Tuple = tokenizer.eos_token_id
UpperCamelCase__ :int = SpeechEncoderDecoderConfig.from_dict(__a )
hf_wavavec.save_pretrained(__a )
feature_extractor.save_pretrained(__a )
if __name__ == "__main__":
__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_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
parser.add_argument(
'''--encoder_config_path''',
default='''facebook/wav2vec2-xls-r-1b''',
type=str,
help='''Path to hf encoder wav2vec2 checkpoint config''',
)
parser.add_argument(
'''--decoder_config_path''',
default='''facebook/mbart-large-50-one-to-many-mmt''',
type=str,
help='''Path to hf decoder checkpoint config''',
)
parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
parser.add_argument('''--encoder_output_dim''', default=1024, type=int, help='''encoder output dim''')
parser.add_argument('''--start_token_id''', default=250004, type=int, help='''`decoder_start_token_id` of model config''')
__snake_case = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
args.config_yaml_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
add_adapter=args.add_adapter,
adapter_kernel_size=args.adapter_kernel_size,
adapter_stride=args.adapter_stride,
decoder_start_token_id=args.start_token_id,
encoder_output_dim=args.encoder_output_dim,
) | 97 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :Any = BioGptTokenizer
UpperCAmelCase_ :str = False
def __lowerCAmelCase ( self ) -> List[Any]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ :Optional[Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
lowerCAmelCase_ :str = dict(zip(__A , range(len(__A ) ) ) )
lowerCAmelCase_ :int = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
lowerCAmelCase_ :Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase_ :Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(__A ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(__A ) )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
lowerCAmelCase_ :List[Any] = """lower newer"""
lowerCAmelCase_ :Tuple = """lower newer"""
return input_text, output_text
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :List[str] = BioGptTokenizer(self.vocab_file , self.merges_file )
lowerCAmelCase_ :Union[str, Any] = """lower"""
lowerCAmelCase_ :Any = ["""low""", """er</w>"""]
lowerCAmelCase_ :Union[str, Any] = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
lowerCAmelCase_ :Dict = tokens + ["""<unk>"""]
lowerCAmelCase_ :List[str] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
@slow
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Optional[Any] = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
lowerCAmelCase_ :List[Any] = tokenizer.encode("""sequence builders""" , add_special_tokens=__A )
lowerCAmelCase_ :List[str] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=__A )
lowerCAmelCase_ :Optional[int] = tokenizer.build_inputs_with_special_tokens(__A )
lowerCAmelCase_ :List[str] = tokenizer.build_inputs_with_special_tokens(__A , __A )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 84 | 0 |
"""simple docstring"""
from PIL import Image
def a_ ( lowerCamelCase , lowerCamelCase ):
UpperCAmelCase__ = (2_5_9 * (level + 2_5_5)) / (2_5_5 * (2_5_9 - level))
def contrast(lowerCamelCase ) -> int:
return int(1_2_8 + factor * (c - 1_2_8) )
return img.point(lowerCamelCase )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change contrast to 170
lowerCAmelCase__ : Any = change_contrast(img, 170)
cont_img.save('image_data/lena_high_contrast.png', format='png')
| 98 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "bert-generation"
def __init__( self , __A=5_0358 , __A=1024 , __A=24 , __A=16 , __A=4096 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0.0_2 , __A=1E-12 , __A=0 , __A=2 , __A=1 , __A="absolute" , __A=True , **__A , ) -> Tuple:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :Any = vocab_size
lowerCAmelCase_ :List[Any] = hidden_size
lowerCAmelCase_ :Optional[int] = num_hidden_layers
lowerCAmelCase_ :int = num_attention_heads
lowerCAmelCase_ :List[Any] = hidden_act
lowerCAmelCase_ :Optional[Any] = intermediate_size
lowerCAmelCase_ :List[Any] = hidden_dropout_prob
lowerCAmelCase_ :int = attention_probs_dropout_prob
lowerCAmelCase_ :Tuple = max_position_embeddings
lowerCAmelCase_ :List[str] = initializer_range
lowerCAmelCase_ :Union[str, Any] = layer_norm_eps
lowerCAmelCase_ :List[str] = position_embedding_type
lowerCAmelCase_ :Optional[int] = use_cache
| 84 | 0 |
import logging
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import librosa
import torch
from datasets import DatasetDict, load_dataset
from packaging import version
from torch import nn
from transformers import (
HfArgumentParser,
Trainer,
TrainingArguments,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaForPreTraining,
is_apex_available,
trainer_utils,
)
from transformers.models.wavaveca.modeling_wavaveca import _compute_mask_indices
if is_apex_available():
from apex import amp
if version.parse(version.parse(torch.__version__).base_version) >= version.parse("""1.6"""):
lowercase : Tuple = True
from torch.cuda.amp import autocast
lowercase : List[Any] = logging.getLogger(__name__)
@dataclass
class A__ :
"""simple docstring"""
__A : str = field(
metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
__A : Optional[str] = field(
default=__UpperCAmelCase , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
__A : Optional[bool] = field(
default=__UpperCAmelCase , metadata={'''help''': '''Whether to freeze the feature extractor layers of the model.'''} )
__A : Optional[bool] = field(
default=__UpperCAmelCase , metadata={'''help''': '''Whether to log verbose messages or not.'''} , )
__A : Optional[float] = field(
default=2.0 , metadata={'''help''': '''Maximum temperature for gumbel softmax.'''} )
__A : Optional[float] = field(
default=0.5 , metadata={'''help''': '''Minimum temperature for gumbel softmax.'''} )
__A : Optional[float] = field(
default=0.99_9995 , metadata={'''help''': '''Decay of gumbel temperature during training.'''} )
def A_ ( A__ , A__ ) -> Tuple:
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' , datefmt='%m/%d/%Y %H:%M:%S' , handlers=[logging.StreamHandler(sys.stdout )] , )
a__ : Optional[int] = logging.WARNING
if model_args.verbose_logging:
a__ : str = logging.DEBUG
elif trainer_utils.is_main_process(training_args.local_rank ):
a__ : int = logging.INFO
logger.setLevel(A__ )
@dataclass
class A__ :
"""simple docstring"""
__A : str = field(
default=__UpperCAmelCase , metadata={'''help''': '''The name of the dataset to use (via the datasets library).'''} )
__A : Optional[str] = field(
default=__UpperCAmelCase , metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''} )
__A : Optional[str] = field(
default='''train''' , metadata={
'''help''': '''The name of the training data set split to use (via the datasets library). Defaults to \'train\''''
} , )
__A : Optional[str] = field(
default='''validation''' , metadata={
'''help''': (
'''The name of the validation data set split to use (via the datasets library). Defaults to \'validation\''''
)
} , )
__A : Optional[str] = field(
default='''file''' , metadata={'''help''': '''Column in the dataset that contains speech file path. Defaults to \'file\''''} , )
__A : bool = field(
default=__UpperCAmelCase , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
__A : Optional[int] = field(
default=1 , metadata={
'''help''': '''The percentage of the train set used as validation set in case there\'s no validation split'''
} , )
__A : Optional[int] = field(
default=__UpperCAmelCase , metadata={'''help''': '''The number of processes to use for the preprocessing.'''} , )
__A : Optional[float] = field(
default=20.0 , metadata={'''help''': '''Filter audio files that are longer than `max_duration_in_seconds` seconds'''} )
@dataclass
class A__ :
"""simple docstring"""
__A : WavaVecaForPreTraining
__A : WavaVecaFeatureExtractor
__A : Union[bool, str] = "longest"
__A : Optional[int] = None
__A : Optional[int] = None
def __call__( self , lowercase) -> Dict[str, torch.Tensor]:
'''simple docstring'''
a__ : Optional[Any] = self.feature_extractor.pad(
lowercase , max_length=self.max_length , padding=self.padding , pad_to_multiple_of=self.pad_to_multiple_of , return_tensors='pt' , )
a__ : str = self.model._get_feat_extract_output_lengths(batch['input_values'].shape[-1])
a__ : List[str] = batch['input_values'].shape[0]
# make sure that no loss is computed on padded inputs
if batch["attention_mask"] is not None:
# compute real output lengths according to convolution formula
a__ : Optional[int] = self.model._get_feat_extract_output_lengths(batch['attention_mask'].sum(-1)).to(
torch.long)
a__ : Optional[Any] = torch.zeros(
(batch_size, mask_indices_seq_length) , dtype=torch.long , device=batch['input_values'].device)
# these two operations makes sure that all values
# before the output lengths indices are attended to
a__ : Tuple = 1
a__ : List[Any] = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool()
# sample randomly masked indices
a__ : Any = _compute_mask_indices(
(batch_size, mask_indices_seq_length) , self.model.config.mask_time_prob , self.model.config.mask_time_length , attention_mask=lowercase , min_masks=2 , )
return batch
class A__ ( __UpperCAmelCase ):
"""simple docstring"""
def __init__( self , *lowercase , lowercase=1 , lowercase=0 , lowercase=1.0 , **lowercase) -> str:
'''simple docstring'''
super().__init__(*lowercase , **lowercase)
a__ : Dict = 0
a__ : int = max_gumbel_temp
a__ : List[Any] = min_gumbel_temp
a__ : List[str] = gumbel_temp_decay
def __lowercase ( self , lowercase , lowercase) -> torch.Tensor:
'''simple docstring'''
model.train()
a__ : Any = self._prepare_inputs(lowercase)
if self.use_amp:
with autocast():
a__ : List[str] = self.compute_loss(lowercase , lowercase)
else:
a__ : Optional[int] = self.compute_loss(lowercase , lowercase)
if self.args.n_gpu > 1 or self.deepspeed:
if model.module.config.ctc_loss_reduction == "mean":
a__ : List[str] = loss.mean()
elif model.module.config.ctc_loss_reduction == "sum":
a__ : List[str] = loss.sum() / (inputs['mask_time_indices']).sum()
else:
raise ValueError(F'{model.config.ctc_loss_reduction} is not valid. Choose one of [\'mean\', \'sum\']')
if self.args.gradient_accumulation_steps > 1:
a__ : Tuple = loss / self.args.gradient_accumulation_steps
if self.use_amp:
self.scaler.scale(lowercase).backward()
elif self.use_apex:
with amp.scale_loss(lowercase , self.optimizer) as scaled_loss:
scaled_loss.backward()
elif self.deepspeed:
self.deepspeed.backward(lowercase)
else:
loss.backward()
self.num_update_step += 1
# make sure gumbel softmax temperature is decayed
if self.args.n_gpu > 1 or self.deepspeed:
model.module.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp))
else:
model.set_gumbel_temperature(
max(self.max_gumbel_temp * self.gumbel_temp_decay**self.num_update_step , self.min_gumbel_temp))
return loss.detach()
def A_ ( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
a__ : Tuple = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
a__ , a__ , a__ : List[Any] = parser.parse_args_into_dataclasses()
configure_logger(A__ , A__ )
# Downloading and loading a dataset from the hub.
a__ : Dict = load_dataset(data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
if "validation" not in datasets.keys():
# make sure only "validation" and "train" keys remain"
a__ : Tuple = DatasetDict()
a__ : Any = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'{data_args.train_split_name}[:{data_args.validation_split_percentage}%]' , cache_dir=model_args.cache_dir , )
a__ : List[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'{data_args.train_split_name}[{data_args.validation_split_percentage}%:]' , cache_dir=model_args.cache_dir , )
else:
# make sure only "validation" and "train" keys remain"
a__ : List[Any] = DatasetDict()
a__ : str = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split='validation' , cache_dir=model_args.cache_dir , )
a__ : Optional[Any] = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , split=F'{data_args.train_split_name}' , cache_dir=model_args.cache_dir , )
# only normalized-inputs-training is supported
a__ : str = WavaVecaFeatureExtractor.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , do_normalize=A__ )
def prepare_dataset(A__ ):
# check that all files have the correct sampling rate
a__ , a__ : Tuple = librosa.load(batch[data_args.speech_file_column] , sr=feature_extractor.sampling_rate )
return batch
# load audio files into numpy arrays
a__ : Tuple = datasets.map(
A__ , num_proc=data_args.preprocessing_num_workers , remove_columns=datasets['train'].column_names )
# filter audio files that are too long
a__ : Tuple = vectorized_datasets.filter(
lambda A__ : len(data['speech'] ) < int(data_args.max_duration_in_seconds * feature_extractor.sampling_rate ) )
def normalize(A__ ):
return feature_extractor(batch['speech'] , sampling_rate=feature_extractor.sampling_rate )
# normalize and transform to `BatchFeatures`
a__ : Any = vectorized_datasets.map(
A__ , batched=A__ , num_proc=data_args.preprocessing_num_workers , load_from_cache_file=not data_args.overwrite_cache , remove_columns=vectorized_datasets['train'].column_names , )
# pretraining is only supported for "newer" stable layer norm architecture
# apply_spec_augment has to be True, mask_feature_prob has to be 0.0
a__ : Any = WavaVecaConfig.from_pretrained(
model_args.model_name_or_path , cache_dir=model_args.cache_dir , gradient_checkpointing=training_args.gradient_checkpointing , )
if not config.do_stable_layer_norm or config.feat_extract_norm != "layer":
raise ValueError(
'PreTraining is only supported for ``config.do_stable_layer_norm=True`` and'
' ``config.feat_extract_norm=\'layer\'' )
a__ : Any = WavaVecaForPreTraining(A__ )
a__ : int = DataCollatorForWavaVecaPretraining(model=A__ , feature_extractor=A__ )
a__ : Optional[int] = WavaVecaPreTrainer(
model=A__ , data_collator=A__ , args=A__ , train_dataset=vectorized_datasets['train'] , eval_dataset=vectorized_datasets['validation'] , tokenizer=A__ , max_gumbel_temp=model_args.max_gumbel_temperature , min_gumbel_temp=model_args.min_gumbel_temperature , gumbel_temp_decay=model_args.gumbel_temperature_decay , )
trainer.train()
if __name__ == "__main__":
main()
| 99 |
"""simple docstring"""
def _snake_case ( lowercase__ : List[Any] , lowercase__ : int , lowercase__ : Optional[int] , lowercase__ : Any ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = [False] * len(lowercase__ )
lowerCAmelCase_ :str = []
queue.append(lowercase__ )
lowerCAmelCase_ :Any = True
while queue:
lowerCAmelCase_ :Optional[int] = queue.pop(0 )
for ind in range(len(graph[u] ) ):
if visited[ind] is False and graph[u][ind] > 0:
queue.append(lowercase__ )
lowerCAmelCase_ :Union[str, Any] = True
lowerCAmelCase_ :int = u
return visited[t]
def _snake_case ( lowercase__ : int , lowercase__ : Optional[int] , lowercase__ : str ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = [-1] * (len(lowercase__ ))
lowerCAmelCase_ :str = 0
while bfs(lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
lowerCAmelCase_ :List[str] = float("""Inf""" )
lowerCAmelCase_ :List[str] = sink
while s != source:
# Find the minimum value in select path
lowerCAmelCase_ :Any = min(lowercase__ , graph[parent[s]][s] )
lowerCAmelCase_ :Union[str, Any] = parent[s]
max_flow += path_flow
lowerCAmelCase_ :Tuple = sink
while v != source:
lowerCAmelCase_ :List[str] = parent[v]
graph[u][v] -= path_flow
graph[v][u] += path_flow
lowerCAmelCase_ :Union[str, Any] = parent[v]
return max_flow
__UpperCAmelCase = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
__UpperCAmelCase , __UpperCAmelCase = 0, 5
print(ford_fulkerson(graph, source, sink))
| 84 | 0 |
"""simple docstring"""
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
__magic_name__ = {
"sample_size": 32,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": 1000,
"block_out_channels": [32, 64],
"attention_head_dim": 8,
"down_block_types": [
"ResnetDownsampleBlock2D",
"AttnDownBlock2D",
],
"up_block_types": [
"AttnUpBlock2D",
"ResnetUpsampleBlock2D",
],
"resnet_time_scale_shift": "scale_shift",
"upsample_type": "resnet",
"downsample_type": "resnet",
}
__magic_name__ = {
"sample_size": 64,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 3,
"num_class_embeds": 1000,
"block_out_channels": [192, 192 * 2, 192 * 3, 192 * 4],
"attention_head_dim": 64,
"down_block_types": [
"ResnetDownsampleBlock2D",
"AttnDownBlock2D",
"AttnDownBlock2D",
"AttnDownBlock2D",
],
"up_block_types": [
"AttnUpBlock2D",
"AttnUpBlock2D",
"AttnUpBlock2D",
"ResnetUpsampleBlock2D",
],
"resnet_time_scale_shift": "scale_shift",
"upsample_type": "resnet",
"downsample_type": "resnet",
}
__magic_name__ = {
"sample_size": 256,
"in_channels": 3,
"out_channels": 3,
"layers_per_block": 2,
"num_class_embeds": None,
"block_out_channels": [256, 256, 256 * 2, 256 * 2, 256 * 4, 256 * 4],
"attention_head_dim": 64,
"down_block_types": [
"ResnetDownsampleBlock2D",
"ResnetDownsampleBlock2D",
"ResnetDownsampleBlock2D",
"AttnDownBlock2D",
"AttnDownBlock2D",
"AttnDownBlock2D",
],
"up_block_types": [
"AttnUpBlock2D",
"AttnUpBlock2D",
"AttnUpBlock2D",
"ResnetUpsampleBlock2D",
"ResnetUpsampleBlock2D",
"ResnetUpsampleBlock2D",
],
"resnet_time_scale_shift": "default",
"upsample_type": "resnet",
"downsample_type": "resnet",
}
__magic_name__ = {
"num_train_timesteps": 40,
"sigma_min": 0.002,
"sigma_max": 80.0,
}
__magic_name__ = {
"num_train_timesteps": 201,
"sigma_min": 0.002,
"sigma_max": 80.0,
}
__magic_name__ = {
"num_train_timesteps": 151,
"sigma_min": 0.002,
"sigma_max": 80.0,
}
def _lowerCAmelCase ( UpperCamelCase_ ):
if isinstance(UpperCamelCase_ , UpperCamelCase_ ):
return v
if v.lower() in ("yes", "true", "t", "y", "1"):
return True
elif v.lower() in ("no", "false", "f", "n", "0"):
return False
else:
raise argparse.ArgumentTypeError("""boolean value expected""" )
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=False ):
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.in_layers.0.weight"]
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.in_layers.0.bias"]
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.in_layers.2.weight"]
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.in_layers.2.bias"]
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.emb_layers.1.weight"]
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.emb_layers.1.bias"]
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.out_layers.0.weight"]
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.out_layers.0.bias"]
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.out_layers.3.weight"]
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.out_layers.3.bias"]
if has_skip:
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.skip_connection.weight"]
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.skip_connection.bias"]
return new_checkpoint
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_=None ):
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.qkv.weight"].chunk(3 , dim=0 )
__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE ,__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.qkv.bias"].chunk(3 , dim=0 )
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.norm.weight"]
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.norm.bias"]
__SCREAMING_SNAKE_CASE = weight_q.squeeze(-1 ).squeeze(-1 )
__SCREAMING_SNAKE_CASE = bias_q.squeeze(-1 ).squeeze(-1 )
__SCREAMING_SNAKE_CASE = weight_k.squeeze(-1 ).squeeze(-1 )
__SCREAMING_SNAKE_CASE = bias_k.squeeze(-1 ).squeeze(-1 )
__SCREAMING_SNAKE_CASE = weight_v.squeeze(-1 ).squeeze(-1 )
__SCREAMING_SNAKE_CASE = bias_v.squeeze(-1 ).squeeze(-1 )
__SCREAMING_SNAKE_CASE = (
checkpoint[f"{old_prefix}.proj_out.weight"].squeeze(-1 ).squeeze(-1 )
)
__SCREAMING_SNAKE_CASE = checkpoint[f"{old_prefix}.proj_out.bias"].squeeze(-1 ).squeeze(-1 )
return new_checkpoint
def _lowerCAmelCase ( UpperCamelCase_ , UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = torch.load(UpperCamelCase_ , map_location="""cpu""" )
__SCREAMING_SNAKE_CASE = {}
__SCREAMING_SNAKE_CASE = checkpoint["""time_embed.0.weight"""]
__SCREAMING_SNAKE_CASE = checkpoint["""time_embed.0.bias"""]
__SCREAMING_SNAKE_CASE = checkpoint["""time_embed.2.weight"""]
__SCREAMING_SNAKE_CASE = checkpoint["""time_embed.2.bias"""]
if unet_config["num_class_embeds"] is not None:
__SCREAMING_SNAKE_CASE = checkpoint["""label_emb.weight"""]
__SCREAMING_SNAKE_CASE = checkpoint["""input_blocks.0.0.weight"""]
__SCREAMING_SNAKE_CASE = checkpoint["""input_blocks.0.0.bias"""]
__SCREAMING_SNAKE_CASE = unet_config["""down_block_types"""]
__SCREAMING_SNAKE_CASE = unet_config["""layers_per_block"""]
__SCREAMING_SNAKE_CASE = unet_config["""attention_head_dim"""]
__SCREAMING_SNAKE_CASE = unet_config["""block_out_channels"""]
__SCREAMING_SNAKE_CASE = 1
__SCREAMING_SNAKE_CASE = channels_list[0]
for i, layer_type in enumerate(UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = channels_list[i]
__SCREAMING_SNAKE_CASE = current_channels != prev_channels
if layer_type == "ResnetDownsampleBlock2D":
for j in range(UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = f"down_blocks.{i}.resnets.{j}"
__SCREAMING_SNAKE_CASE = f"input_blocks.{current_layer}.0"
__SCREAMING_SNAKE_CASE = True if j == 0 and downsample_block_has_skip else False
__SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , has_skip=UpperCamelCase_ )
current_layer += 1
elif layer_type == "AttnDownBlock2D":
for j in range(UpperCamelCase_ ):
__SCREAMING_SNAKE_CASE = f"down_blocks.{i}.resnets.{j}"
__SCREAMING_SNAKE_CASE = f"input_blocks.{current_layer}.0"
__SCREAMING_SNAKE_CASE = True if j == 0 and downsample_block_has_skip else False
__SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , has_skip=UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = f"down_blocks.{i}.attentions.{j}"
__SCREAMING_SNAKE_CASE = f"input_blocks.{current_layer}.1"
__SCREAMING_SNAKE_CASE = convert_attention(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
current_layer += 1
if i != len(UpperCamelCase_ ) - 1:
__SCREAMING_SNAKE_CASE = f"down_blocks.{i}.downsamplers.0"
__SCREAMING_SNAKE_CASE = f"input_blocks.{current_layer}.0"
__SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
current_layer += 1
__SCREAMING_SNAKE_CASE = current_channels
# hardcoded the mid-block for now
__SCREAMING_SNAKE_CASE = """mid_block.resnets.0"""
__SCREAMING_SNAKE_CASE = """middle_block.0"""
__SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = """mid_block.attentions.0"""
__SCREAMING_SNAKE_CASE = """middle_block.1"""
__SCREAMING_SNAKE_CASE = convert_attention(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = """mid_block.resnets.1"""
__SCREAMING_SNAKE_CASE = """middle_block.2"""
__SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = 0
__SCREAMING_SNAKE_CASE = unet_config["""up_block_types"""]
for i, layer_type in enumerate(UpperCamelCase_ ):
if layer_type == "ResnetUpsampleBlock2D":
for j in range(layers_per_block + 1 ):
__SCREAMING_SNAKE_CASE = f"up_blocks.{i}.resnets.{j}"
__SCREAMING_SNAKE_CASE = f"output_blocks.{current_layer}.0"
__SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , has_skip=UpperCamelCase_ )
current_layer += 1
if i != len(UpperCamelCase_ ) - 1:
__SCREAMING_SNAKE_CASE = f"up_blocks.{i}.upsamplers.0"
__SCREAMING_SNAKE_CASE = f"output_blocks.{current_layer-1}.1"
__SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
elif layer_type == "AttnUpBlock2D":
for j in range(layers_per_block + 1 ):
__SCREAMING_SNAKE_CASE = f"up_blocks.{i}.resnets.{j}"
__SCREAMING_SNAKE_CASE = f"output_blocks.{current_layer}.0"
__SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , has_skip=UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = f"up_blocks.{i}.attentions.{j}"
__SCREAMING_SNAKE_CASE = f"output_blocks.{current_layer}.1"
__SCREAMING_SNAKE_CASE = convert_attention(
UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
current_layer += 1
if i != len(UpperCamelCase_ ) - 1:
__SCREAMING_SNAKE_CASE = f"up_blocks.{i}.upsamplers.0"
__SCREAMING_SNAKE_CASE = f"output_blocks.{current_layer-1}.2"
__SCREAMING_SNAKE_CASE = convert_resnet(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
__SCREAMING_SNAKE_CASE = checkpoint["""out.0.weight"""]
__SCREAMING_SNAKE_CASE = checkpoint["""out.0.bias"""]
__SCREAMING_SNAKE_CASE = checkpoint["""out.2.weight"""]
__SCREAMING_SNAKE_CASE = checkpoint["""out.2.bias"""]
return new_checkpoint
if __name__ == "__main__":
__magic_name__ = argparse.ArgumentParser()
parser.add_argument("--unet_path", default=None, type=str, required=True, help="Path to the unet.pt to convert.")
parser.add_argument(
"--dump_path", default=None, type=str, required=True, help="Path to output the converted UNet model."
)
parser.add_argument("--class_cond", default=True, type=str, help="Whether the model is class-conditional.")
__magic_name__ = parser.parse_args()
__magic_name__ = strabool(args.class_cond)
__magic_name__ = os.path.basename(args.unet_path)
print(F"""Checkpoint: {ckpt_name}""")
# Get U-Net config
if "imagenet64" in ckpt_name:
__magic_name__ = IMAGENET_64_UNET_CONFIG
elif "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__magic_name__ = LSUN_256_UNET_CONFIG
elif "test" in ckpt_name:
__magic_name__ = TEST_UNET_CONFIG
else:
raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")
if not args.class_cond:
__magic_name__ = None
__magic_name__ = con_pt_to_diffuser(args.unet_path, unet_config)
__magic_name__ = UNetaDModel(**unet_config)
image_unet.load_state_dict(converted_unet_ckpt)
# Get scheduler config
if "cd" in ckpt_name or "test" in ckpt_name:
__magic_name__ = CD_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "imagenet64" in ckpt_name:
__magic_name__ = CT_IMAGENET_64_SCHEDULER_CONFIG
elif "ct" in ckpt_name and "256" in ckpt_name and (("bedroom" in ckpt_name) or ("cat" in ckpt_name)):
__magic_name__ = CT_LSUN_256_SCHEDULER_CONFIG
else:
raise ValueError(F"""Checkpoint type {ckpt_name} is not currently supported.""")
__magic_name__ = CMStochasticIterativeScheduler(**scheduler_config)
__magic_name__ = ConsistencyModelPipeline(unet=image_unet, scheduler=cm_scheduler)
consistency_model.save_pretrained(args.dump_path)
| 100 |
"""simple docstring"""
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = 1_0
lowerCAmelCase_ :Optional[int] = datasets.Features(
{
"""tokens""": datasets.Sequence(datasets.Value("""string""" ) ),
"""labels""": datasets.Sequence(datasets.ClassLabel(names=["""negative""", """positive"""] ) ),
"""answers""": datasets.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
"""id""": datasets.Value("""int64""" ),
} )
lowerCAmelCase_ :int = datasets.Dataset.from_dict(
{
"""tokens""": [["""foo"""] * 5] * n,
"""labels""": [[1] * 5] * n,
"""answers""": [{"""answer_start""": [9_7], """text""": ["""1976"""]}] * 1_0,
"""id""": list(range(lowercase__ ) ),
} , features=lowercase__ , )
return dataset
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple , lowercase__ : int ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """file.arrow""" )
dataset.map(cache_file_name=lowercase__ )
return filename
# FILE_CONTENT + files
__UpperCAmelCase = '\\n Text data.\n Second line of data.'
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt"""
lowerCAmelCase_ :List[Any] = FILE_CONTENT
with open(lowercase__ , """w""" ) as f:
f.write(lowercase__ )
return filename
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[Any] ) -> Tuple:
'''simple docstring'''
import bza
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """file.txt.bz2"""
lowerCAmelCase_ :Tuple = bytes(lowercase__ , """utf-8""" )
with bza.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[Any] ) -> Dict:
'''simple docstring'''
import gzip
lowerCAmelCase_ :int = str(tmp_path_factory.mktemp("""data""" ) / """file.txt.gz""" )
lowerCAmelCase_ :Tuple = bytes(lowercase__ , """utf-8""" )
with gzip.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Optional[int]:
'''simple docstring'''
if datasets.config.LZ4_AVAILABLE:
import lza.frame
lowerCAmelCase_ :List[Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.lz4"""
lowerCAmelCase_ :int = bytes(lowercase__ , """utf-8""" )
with lza.frame.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict , lowercase__ : Optional[int] ) -> Any:
'''simple docstring'''
if datasets.config.PY7ZR_AVAILABLE:
import pyazr
lowerCAmelCase_ :Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.7z"""
with pyazr.SevenZipFile(lowercase__ , """w""" ) as archive:
archive.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
import tarfile
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """file.txt.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> str:
'''simple docstring'''
import lzma
lowerCAmelCase_ :Optional[Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.xz"""
lowerCAmelCase_ :Optional[Any] = bytes(lowercase__ , """utf-8""" )
with lzma.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : List[Any] ) -> Any:
'''simple docstring'''
import zipfile
lowerCAmelCase_ :Dict = tmp_path_factory.mktemp("""data""" ) / """file.txt.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> Tuple:
'''simple docstring'''
if datasets.config.ZSTANDARD_AVAILABLE:
import zstandard as zstd
lowerCAmelCase_ :Union[str, Any] = tmp_path_factory.mktemp("""data""" ) / """file.txt.zst"""
lowerCAmelCase_ :Any = bytes(lowercase__ , """utf-8""" )
with zstd.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """file.xml"""
lowerCAmelCase_ :Any = textwrap.dedent(
"""\
<?xml version=\"1.0\" encoding=\"UTF-8\" ?>
<tmx version=\"1.4\">
<header segtype=\"sentence\" srclang=\"ca\" />
<body>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 1</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 1</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 2</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 2</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 3</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 3</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 4</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 4</seg></tuv>
</tu>
<tu>
<tuv xml:lang=\"ca\"><seg>Contingut 5</seg></tuv>
<tuv xml:lang=\"en\"><seg>Content 5</seg></tuv>
</tu>
</body>
</tmx>""" )
with open(lowercase__ , """w""" ) as f:
f.write(lowercase__ )
return filename
__UpperCAmelCase = [
{'col_1': '0', 'col_2': 0, 'col_3': 0.0},
{'col_1': '1', 'col_2': 1, 'col_3': 1.0},
{'col_1': '2', 'col_2': 2, 'col_3': 2.0},
{'col_1': '3', 'col_2': 3, 'col_3': 3.0},
]
__UpperCAmelCase = [
{'col_1': '4', 'col_2': 4, 'col_3': 4.0},
{'col_1': '5', 'col_2': 5, 'col_3': 5.0},
]
__UpperCAmelCase = {
'col_1': ['0', '1', '2', '3'],
'col_2': [0, 1, 2, 3],
'col_3': [0.0, 1.0, 2.0, 3.0],
}
__UpperCAmelCase = [
{'col_3': 0.0, 'col_1': '0', 'col_2': 0},
{'col_3': 1.0, 'col_1': '1', 'col_2': 1},
]
__UpperCAmelCase = [
{'col_1': 's0', 'col_2': 0, 'col_3': 0.0},
{'col_1': 's1', 'col_2': 1, 'col_3': 1.0},
{'col_1': 's2', 'col_2': 2, 'col_3': 2.0},
{'col_1': 's3', 'col_2': 3, 'col_3': 3.0},
]
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> Union[str, Any]:
'''simple docstring'''
return DATA_DICT_OF_LISTS
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Tuple = datasets.Dataset.from_dict(lowercase__ )
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.arrow""" )
dataset.map(cache_file_name=lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.sqlite""" )
with contextlib.closing(sqlitea.connect(lowercase__ ) ) as con:
lowerCAmelCase_ :Union[str, Any] = con.cursor()
cur.execute("""CREATE TABLE dataset(col_1 text, col_2 int, col_3 real)""" )
for item in DATA:
cur.execute("""INSERT INTO dataset(col_1, col_2, col_3) VALUES (?, ?, ?)""" , tuple(item.values() ) )
con.commit()
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> int:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.csv""" )
with open(lowercase__ , """w""" , newline="""""" ) as f:
lowerCAmelCase_ :Optional[int] = csv.DictWriter(lowercase__ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.csv""" )
with open(lowercase__ , """w""" , newline="""""" ) as f:
lowerCAmelCase_ :Dict = csv.DictWriter(lowercase__ , fieldnames=["""col_1""", """col_2""", """col_3"""] )
writer.writeheader()
for item in DATA:
writer.writerow(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str , lowercase__ : Dict ) -> Union[str, Any]:
'''simple docstring'''
import bza
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.bz2"""
with open(lowercase__ , """rb""" ) as f:
lowerCAmelCase_ :Union[str, Any] = f.read()
# data = bytes(FILE_CONTENT, "utf-8")
with bza.open(lowercase__ , """wb""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : List[str] , lowercase__ : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(csv_path.replace(""".csv""" , """.CSV""" ) ) )
f.write(lowercase__ , arcname=os.path.basename(csva_path.replace(""".csv""" , """.CSV""" ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Tuple , lowercase__ : str ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.csv.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.parquet""" )
lowerCAmelCase_ :Optional[Any] = pa.schema(
{
"""col_1""": pa.string(),
"""col_2""": pa.intaa(),
"""col_3""": pa.floataa(),
} )
with open(lowercase__ , """wb""" ) as f:
lowerCAmelCase_ :Optional[int] = pq.ParquetWriter(lowercase__ , schema=lowercase__ )
lowerCAmelCase_ :List[str] = pa.Table.from_pydict({k: [DATA[i][k] for i in range(len(lowercase__ ) )] for k in DATA[0]} , schema=lowercase__ )
writer.write_table(lowercase__ )
writer.close()
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Dict = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
lowerCAmelCase_ :Union[str, Any] = {"""data""": DATA}
with open(lowercase__ , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : str ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.json""" )
lowerCAmelCase_ :Optional[Any] = {"""data""": DATA_DICT_OF_LISTS}
with open(lowercase__ , """w""" ) as f:
json.dump(lowercase__ , lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_312.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA_312:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any ) -> Optional[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = str(tmp_path_factory.mktemp("""data""" ) / """dataset-str.jsonl""" )
with open(lowercase__ , """w""" ) as f:
for item in DATA_STR:
f.write(json.dumps(lowercase__ ) + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : int , lowercase__ : Dict ) -> Optional[int]:
'''simple docstring'''
import gzip
lowerCAmelCase_ :Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt.gz""" )
with open(lowercase__ , """rb""" ) as orig_file:
with gzip.open(lowercase__ , """wb""" ) as zipped_file:
zipped_file.writelines(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : List[Any] ) -> Any:
'''simple docstring'''
import gzip
lowerCAmelCase_ :Optional[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.gz""" )
with open(lowercase__ , """rb""" ) as orig_file:
with gzip.open(lowercase__ , """wb""" ) as zipped_file:
zipped_file.writelines(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : Optional[int] , lowercase__ : List[Any] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""nested""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : List[Any] , lowercase__ : List[str] ) -> int:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.jsonl.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : str , lowercase__ : List[str] ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """dataset.jsonl.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.add(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Dict , lowercase__ : str , lowercase__ : List[str] , lowercase__ : int ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data""" ) / """dataset_nested.jsonl.tar"""
with tarfile.TarFile(lowercase__ , """w""" ) as f:
f.add(lowercase__ , arcname=os.path.join("""nested""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> Tuple:
'''simple docstring'''
lowerCAmelCase_ :str = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :List[Any] = str(tmp_path_factory.mktemp("""data""" ) / """dataset.txt""" )
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :List[str] = str(tmp_path_factory.mktemp("""data""" ) / """dataset2.txt""" )
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[Any] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :Dict = ["""0""", """1""", """2""", """3"""]
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.abc"""
with open(lowercase__ , """w""" ) as f:
for item in data:
f.write(item + """\n""" )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : List[str] , lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Any = tmp_path_factory.mktemp("""data""" ) / """dataset.text.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple , lowercase__ : Tuple , lowercase__ : List[str] ) -> List[str]:
'''simple docstring'''
lowerCAmelCase_ :str = tmp_path_factory.mktemp("""data""" ) / """dataset_with_dir.text.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
f.write(lowercase__ , arcname=os.path.join("""main_dir""" , os.path.basename(lowercase__ ) ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Optional[int] , lowercase__ : Any , lowercase__ : Tuple ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.ext.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename("""unsupported.ext""" ) )
f.write(lowercase__ , arcname=os.path.basename("""unsupported_2.ext""" ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = """\n""".join(["""First""", """Second\u2029with Unicode new line""", """Third"""] )
lowerCAmelCase_ :str = str(tmp_path_factory.mktemp("""data""" ) / """dataset_with_unicode_new_lines.txt""" )
with open(lowercase__ , """w""" , encoding="""utf-8""" ) as f:
f.write(lowercase__ )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> int:
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_image_rgb.jpg""" )
@pytest.fixture(scope="""session""" )
def _snake_case ( ) -> Tuple:
'''simple docstring'''
return os.path.join("""tests""" , """features""" , """data""" , """test_audio_44100.wav""" )
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Any , lowercase__ : Tuple ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = tmp_path_factory.mktemp("""data""" ) / """dataset.img.zip"""
with zipfile.ZipFile(lowercase__ , """w""" ) as f:
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ) )
f.write(lowercase__ , arcname=os.path.basename(lowercase__ ).replace(""".jpg""" , """2.jpg""" ) )
return path
@pytest.fixture(scope="""session""" )
def _snake_case ( lowercase__ : Tuple ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ :int = tmp_path_factory.mktemp("""data_dir""" )
(data_dir / "subdir").mkdir()
with open(data_dir / """subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden file
with open(data_dir / """subdir""" / """.test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
# hidden directory
(data_dir / ".subdir").mkdir()
with open(data_dir / """.subdir""" / """train.txt""" , """w""" ) as f:
f.write("""foo\n""" * 1_0 )
with open(data_dir / """.subdir""" / """test.txt""" , """w""" ) as f:
f.write("""bar\n""" * 1_0 )
return data_dir
| 84 | 0 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_early_stopping_callback
from torch import nn
from torch.utils.data import DataLoader
from transformers import MBartTokenizer, TaForConditionalGeneration
from transformers.models.bart.modeling_bart import shift_tokens_right
from utils import (
ROUGE_KEYS,
LegacySeqaSeqDataset,
SeqaSeqDataset,
assert_all_frozen,
calculate_bleu,
calculate_rouge,
check_output_dir,
flatten_list,
freeze_embeds,
freeze_params,
get_git_info,
label_smoothed_nll_loss,
lmap,
pickle_save,
save_git_info,
save_json,
use_task_specific_params,
)
# need the parent dir module
sys.path.insert(2, str(Path(__file__).resolve().parents[1]))
from lightning_base import BaseTransformer, add_generic_args, generic_train # noqa
lowercase__ :List[Any] = logging.getLogger(__name__)
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowercase_ : Any ='''summarization'''
lowercase_ : Optional[Any] =['''loss''']
lowercase_ : Union[str, Any] =ROUGE_KEYS
lowercase_ : Optional[int] ='''rouge2'''
def __init__( self ,A__ ,**A__):
if hparams.sortish_sampler and hparams.gpus > 1:
lowercase = False
elif hparams.max_tokens_per_batch is not None:
if hparams.gpus > 1:
raise NotImplementedError('''Dynamic Batch size does not work for multi-gpu training''')
if hparams.sortish_sampler:
raise ValueError('''--sortish_sampler and --max_tokens_per_batch may not be used simultaneously''')
super().__init__(A__ ,num_labels=A__ ,mode=self.mode ,**A__)
use_task_specific_params(self.model ,'''summarization''')
save_git_info(self.hparams.output_dir)
lowercase = Path(self.output_dir) / '''metrics.json'''
lowercase = Path(self.output_dir) / '''hparams.pkl'''
pickle_save(self.hparams ,self.hparams_save_path)
lowercase = 0
lowercase = defaultdict(A__)
lowercase = self.config.model_type
lowercase = self.config.tgt_vocab_size if self.model_type == '''fsmt''' else self.config.vocab_size
lowercase = {
"data_dir": self.hparams.data_dir,
"max_source_length": self.hparams.max_source_length,
"prefix": self.model.config.prefix or "",
}
lowercase = {
'''train''': self.hparams.n_train,
'''val''': self.hparams.n_val,
'''test''': self.hparams.n_test,
}
lowercase = {k: v if v >= 0 else None for k, v in n_observations_per_split.items()}
lowercase = {
'''train''': self.hparams.max_target_length,
'''val''': self.hparams.val_max_target_length,
'''test''': self.hparams.test_max_target_length,
}
assert self.target_lens["train"] <= self.target_lens["val"], f'target_lens: {self.target_lens}'
assert self.target_lens["train"] <= self.target_lens["test"], f'target_lens: {self.target_lens}'
if self.hparams.freeze_embeds:
freeze_embeds(self.model)
if self.hparams.freeze_encoder:
freeze_params(self.model.get_encoder())
assert_all_frozen(self.model.get_encoder())
lowercase = get_git_info()['''repo_sha''']
lowercase = hparams.num_workers
lowercase = None # default to config
if self.model.config.decoder_start_token_id is None and isinstance(self.tokenizer ,A__):
lowercase = self.tokenizer.lang_code_to_id[hparams.tgt_lang]
lowercase = self.decoder_start_token_id
lowercase = (
SeqaSeqDataset if hasattr(self.tokenizer ,'''prepare_seq2seq_batch''') else LegacySeqaSeqDataset
)
lowercase = False
lowercase = self.model.config.num_beams if self.hparams.eval_beams is None else self.hparams.eval_beams
if self.hparams.eval_max_gen_length is not None:
lowercase = self.hparams.eval_max_gen_length
else:
lowercase = self.model.config.max_length
lowercase = self.default_val_metric if self.hparams.val_metric is None else self.hparams.val_metric
def A__ ( self ,A__):
lowercase = {
k: self.tokenizer.batch_decode(v.tolist()) if '''mask''' not in k else v.shape for k, v in batch.items()
}
save_json(A__ ,Path(self.output_dir) / '''text_batch.json''')
save_json({k: v.tolist() for k, v in batch.items()} ,Path(self.output_dir) / '''tok_batch.json''')
lowercase = True
return readable_batch
def A__ ( self ,A__ ,**A__):
return self.model(A__ ,**A__)
def A__ ( self ,A__):
lowercase = self.tokenizer.batch_decode(
A__ ,skip_special_tokens=A__ ,clean_up_tokenization_spaces=A__)
return lmap(str.strip ,A__)
def A__ ( self ,A__):
lowercase = self.tokenizer.pad_token_id
lowercase , lowercase = batch['''input_ids'''], batch['''attention_mask''']
lowercase = batch['''labels''']
if isinstance(self.model ,A__):
lowercase = self.model._shift_right(A__)
else:
lowercase = shift_tokens_right(A__ ,A__)
if not self.already_saved_batch: # This would be slightly better if it only happened on rank zero
lowercase = decoder_input_ids
self.save_readable_batch(A__)
lowercase = self(A__ ,attention_mask=A__ ,decoder_input_ids=A__ ,use_cache=A__)
lowercase = outputs['''logits''']
if self.hparams.label_smoothing == 0:
# Same behavior as modeling_bart.py, besides ignoring pad_token_id
lowercase = nn.CrossEntropyLoss(ignore_index=A__)
assert lm_logits.shape[-1] == self.vocab_size
lowercase = ce_loss_fct(lm_logits.view(-1 ,lm_logits.shape[-1]) ,tgt_ids.view(-1))
else:
lowercase = nn.functional.log_softmax(A__ ,dim=-1)
lowercase , lowercase = label_smoothed_nll_loss(
A__ ,A__ ,self.hparams.label_smoothing ,ignore_index=A__)
return (loss,)
@property
def A__ ( self):
return self.tokenizer.pad_token_id
def A__ ( self ,A__ ,A__):
lowercase = self._step(A__)
lowercase = dict(zip(self.loss_names ,A__))
# tokens per batch
lowercase = batch['''input_ids'''].ne(self.pad).sum() + batch['''labels'''].ne(self.pad).sum()
lowercase = batch['''input_ids'''].shape[0]
lowercase = batch['''input_ids'''].eq(self.pad).sum()
lowercase = batch['''input_ids'''].eq(self.pad).float().mean()
# TODO(SS): make a wandb summary metric for this
return {"loss": loss_tensors[0], "log": logs}
def A__ ( self ,A__ ,A__):
return self._generative_step(A__)
def A__ ( self ,A__ ,A__="val"):
self.step_count += 1
lowercase = {k: torch.stack([x[k] for x in outputs]).mean() for k in self.loss_names}
lowercase = losses['''loss''']
lowercase = {
k: np.array([x[k] for x in outputs]).mean() for k in self.metric_names + ['''gen_time''', '''gen_len''']
}
lowercase = (
generative_metrics[self.val_metric] if self.val_metric in generative_metrics else losses[self.val_metric]
)
lowercase = torch.tensor(A__).type_as(A__)
generative_metrics.update({k: v.item() for k, v in losses.items()})
losses.update(A__)
lowercase = {f'{prefix}_avg_{k}': x for k, x in losses.items()}
lowercase = self.step_count
self.metrics[prefix].append(A__) # callback writes this to self.metrics_save_path
lowercase = flatten_list([x['''preds'''] for x in outputs])
return {
"log": all_metrics,
"preds": preds,
f'{prefix}_loss': loss,
f'{prefix}_{self.val_metric}': metric_tensor,
}
def A__ ( self ,A__ ,A__):
return calculate_rouge(A__ ,A__)
def A__ ( self ,A__):
lowercase = time.time()
# parser.add_argument('--eval_max_gen_length', type=int, default=None, help='never generate more than n tokens')
lowercase = self.model.generate(
batch['''input_ids'''] ,attention_mask=batch['''attention_mask'''] ,use_cache=A__ ,decoder_start_token_id=self.decoder_start_token_id ,num_beams=self.eval_beams ,max_length=self.eval_max_length ,)
lowercase = (time.time() - ta) / batch['''input_ids'''].shape[0]
lowercase = self.ids_to_clean_text(A__)
lowercase = self.ids_to_clean_text(batch['''labels'''])
lowercase = self._step(A__)
lowercase = dict(zip(self.loss_names ,A__))
lowercase = self.calc_generative_metrics(A__ ,A__)
lowercase = np.mean(lmap(A__ ,A__))
base_metrics.update(gen_time=A__ ,gen_len=A__ ,preds=A__ ,target=A__ ,**A__)
return base_metrics
def A__ ( self ,A__ ,A__):
return self._generative_step(A__)
def A__ ( self ,A__):
return self.validation_epoch_end(A__ ,prefix='''test''')
def A__ ( self ,A__):
lowercase = self.n_obs[type_path]
lowercase = self.target_lens[type_path]
lowercase = self.dataset_class(
self.tokenizer ,type_path=A__ ,n_obs=A__ ,max_target_length=A__ ,**self.dataset_kwargs ,)
return dataset
def A__ ( self ,A__ ,A__ ,A__ = False):
lowercase = self.get_dataset(A__)
if self.hparams.sortish_sampler and type_path != "test" and type_path != "val":
lowercase = dataset.make_sortish_sampler(A__ ,distributed=self.hparams.gpus > 1)
return DataLoader(
A__ ,batch_size=A__ ,collate_fn=dataset.collate_fn ,shuffle=A__ ,num_workers=self.num_workers ,sampler=A__ ,)
elif self.hparams.max_tokens_per_batch is not None and type_path != "test" and type_path != "val":
lowercase = dataset.make_dynamic_sampler(
self.hparams.max_tokens_per_batch ,distributed=self.hparams.gpus > 1)
return DataLoader(
A__ ,batch_sampler=A__ ,collate_fn=dataset.collate_fn ,num_workers=self.num_workers ,)
else:
return DataLoader(
A__ ,batch_size=A__ ,collate_fn=dataset.collate_fn ,shuffle=A__ ,num_workers=self.num_workers ,sampler=A__ ,)
def A__ ( self):
lowercase = self.get_dataloader('''train''' ,batch_size=self.hparams.train_batch_size ,shuffle=A__)
return dataloader
def A__ ( self):
return self.get_dataloader('''val''' ,batch_size=self.hparams.eval_batch_size)
def A__ ( self):
return self.get_dataloader('''test''' ,batch_size=self.hparams.eval_batch_size)
@staticmethod
def A__ ( A__ ,A__):
BaseTransformer.add_model_specific_args(A__ ,A__)
add_generic_args(A__ ,A__)
parser.add_argument(
'''--max_source_length''' ,default=1_0_2_4 ,type=A__ ,help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) ,)
parser.add_argument(
'''--max_target_length''' ,default=5_6 ,type=A__ ,help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) ,)
parser.add_argument(
'''--val_max_target_length''' ,default=1_4_2 ,type=A__ ,help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) ,)
parser.add_argument(
'''--test_max_target_length''' ,default=1_4_2 ,type=A__ ,help=(
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
) ,)
parser.add_argument('''--freeze_encoder''' ,action='''store_true''')
parser.add_argument('''--freeze_embeds''' ,action='''store_true''')
parser.add_argument('''--sortish_sampler''' ,action='''store_true''' ,default=A__)
parser.add_argument('''--overwrite_output_dir''' ,action='''store_true''' ,default=A__)
parser.add_argument('''--max_tokens_per_batch''' ,type=A__ ,default=A__)
parser.add_argument('''--logger_name''' ,type=A__ ,choices=['''default''', '''wandb''', '''wandb_shared'''] ,default='''default''')
parser.add_argument('''--n_train''' ,type=A__ ,default=-1 ,required=A__ ,help='''# examples. -1 means use all.''')
parser.add_argument('''--n_val''' ,type=A__ ,default=5_0_0 ,required=A__ ,help='''# examples. -1 means use all.''')
parser.add_argument('''--n_test''' ,type=A__ ,default=-1 ,required=A__ ,help='''# examples. -1 means use all.''')
parser.add_argument(
'''--task''' ,type=A__ ,default='''summarization''' ,required=A__ ,help='''# examples. -1 means use all.''')
parser.add_argument('''--label_smoothing''' ,type=A__ ,default=0.0 ,required=A__)
parser.add_argument('''--src_lang''' ,type=A__ ,default='''''' ,required=A__)
parser.add_argument('''--tgt_lang''' ,type=A__ ,default='''''' ,required=A__)
parser.add_argument('''--eval_beams''' ,type=A__ ,default=A__ ,required=A__)
parser.add_argument(
'''--val_metric''' ,type=A__ ,default=A__ ,required=A__ ,choices=['''bleu''', '''rouge2''', '''loss''', None])
parser.add_argument('''--eval_max_gen_length''' ,type=A__ ,default=A__ ,help='''never generate more than n tokens''')
parser.add_argument('''--save_top_k''' ,type=A__ ,default=1 ,required=A__ ,help='''How many checkpoints to save''')
parser.add_argument(
'''--early_stopping_patience''' ,type=A__ ,default=-1 ,required=A__ ,help=(
'''-1 means never early stop. early_stopping_patience is measured in validation checks, not epochs. So'''
''' val_check_interval will effect it.'''
) ,)
return parser
class lowercase ( SCREAMING_SNAKE_CASE__ ):
lowercase_ : Optional[int] ='''translation'''
lowercase_ : int =['''loss''']
lowercase_ : Optional[Any] =['''bleu''']
lowercase_ : Tuple ='''bleu'''
def __init__( self ,A__ ,**A__):
super().__init__(A__ ,**A__)
lowercase = hparams.src_lang
lowercase = hparams.tgt_lang
def A__ ( self ,A__ ,A__):
return calculate_bleu(A__ ,A__)
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__=None ):
'''simple docstring'''
Path(args.output_dir ).mkdir(exist_ok=lowerCAmelCase__ )
check_output_dir(lowerCAmelCase__ , expected_items=3 )
if model is None:
if "summarization" in args.task:
lowercase = SummarizationModule(lowerCAmelCase__ )
else:
lowercase = TranslationModule(lowerCAmelCase__ )
lowercase = Path(args.data_dir ).name
if (
args.logger_name == "default"
or args.fast_dev_run
or str(args.output_dir ).startswith('''/tmp''' )
or str(args.output_dir ).startswith('''/var''' )
):
lowercase = True # don't pollute wandb logs unnecessarily
elif args.logger_name == "wandb":
from pytorch_lightning.loggers import WandbLogger
lowercase = os.environ.get('''WANDB_PROJECT''' , lowerCAmelCase__ )
lowercase = WandbLogger(name=model.output_dir.name , project=lowerCAmelCase__ )
elif args.logger_name == "wandb_shared":
from pytorch_lightning.loggers import WandbLogger
lowercase = WandbLogger(name=model.output_dir.name , project=f'hf_{dataset}' )
if args.early_stopping_patience >= 0:
lowercase = get_early_stopping_callback(model.val_metric , args.early_stopping_patience )
else:
lowercase = False
lowercase = args.val_metric == '''loss'''
lowercase = generic_train(
lowerCAmelCase__ , lowerCAmelCase__ , logging_callback=SeqaSeqLoggingCallback() , checkpoint_callback=get_checkpoint_callback(
args.output_dir , model.val_metric , args.save_top_k , lowerCAmelCase__ ) , early_stopping_callback=lowerCAmelCase__ , logger=lowerCAmelCase__ , )
pickle_save(model.hparams , model.output_dir / '''hparams.pkl''' )
if not args.do_predict:
return model
lowercase = ''''''
lowercase = sorted(glob.glob(os.path.join(args.output_dir , '''*.ckpt''' ) , recursive=lowerCAmelCase__ ) )
if checkpoints:
lowercase = checkpoints[-1]
lowercase = checkpoints[-1]
trainer.logger.log_hyperparams(model.hparams )
# test() without a model tests using the best checkpoint automatically
trainer.test()
return model
if __name__ == "__main__":
lowercase__ :int = argparse.ArgumentParser()
lowercase__ :Union[str, Any] = pl.Trainer.add_argparse_args(parser)
lowercase__ :Optional[Any] = SummarizationModule.add_model_specific_args(parser, os.getcwd())
lowercase__ :Union[str, Any] = parser.parse_args()
main(args)
| 101 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/data2vec-text-base': 'https://huggingface.co/data2vec/resolve/main/config.json',
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Optional[Any] = "data2vec-text"
def __init__( self , __A=3_0522 , __A=768 , __A=12 , __A=12 , __A=3072 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=2 , __A=0.0_2 , __A=1E-12 , __A=1 , __A=0 , __A=2 , __A="absolute" , __A=True , __A=None , **__A , ) -> Tuple:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :Dict = vocab_size
lowerCAmelCase_ :Dict = hidden_size
lowerCAmelCase_ :int = num_hidden_layers
lowerCAmelCase_ :List[Any] = num_attention_heads
lowerCAmelCase_ :Any = hidden_act
lowerCAmelCase_ :Optional[int] = intermediate_size
lowerCAmelCase_ :str = hidden_dropout_prob
lowerCAmelCase_ :Any = attention_probs_dropout_prob
lowerCAmelCase_ :str = max_position_embeddings
lowerCAmelCase_ :int = type_vocab_size
lowerCAmelCase_ :Tuple = initializer_range
lowerCAmelCase_ :List[Any] = layer_norm_eps
lowerCAmelCase_ :List[Any] = position_embedding_type
lowerCAmelCase_ :List[Any] = use_cache
lowerCAmelCase_ :List[Any] = classifier_dropout
class _SCREAMING_SNAKE_CASE ( A__ ):
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
lowerCAmelCase_ :List[Any] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowerCAmelCase_ :List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 84 | 0 |
"""simple docstring"""
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
ControlNetModel,
DDIMScheduler,
StableDiffusionControlNetImgaImgPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion_controlnet import MultiControlNetModel
from diffusers.utils import floats_tensor, load_image, load_numpy, randn_tensor, slow, torch_device
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
)
enable_full_determinism()
class _UpperCAmelCase ( __snake_case, __snake_case, __snake_case, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =StableDiffusionControlNetImgaImgPipeline
lowerCamelCase__ =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
lowerCamelCase__ =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCamelCase__ =IMAGE_TO_IMAGE_IMAGE_PARAMS.union({'control_image'} )
lowerCamelCase__ =IMAGE_TO_IMAGE_IMAGE_PARAMS
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : List[Any] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
torch.manual_seed(0 )
__snake_case : Optional[Any] = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
torch.manual_seed(0 )
__snake_case : Any = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=a_ , set_alpha_to_one=a_ , )
torch.manual_seed(0 )
__snake_case : Any = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
__snake_case : List[str] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
__snake_case : Any = CLIPTextModel(a_ )
__snake_case : List[str] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__snake_case : Tuple = {
'''unet''': unet,
'''controlnet''': controlnet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def SCREAMING_SNAKE_CASE (self , a_ , a_=0 ):
'''simple docstring'''
if str(a_ ).startswith('''mps''' ):
__snake_case : int = torch.manual_seed(a_ )
else:
__snake_case : int = torch.Generator(device=a_ ).manual_seed(a_ )
__snake_case : List[str] = 2
__snake_case : List[str] = randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=a_ , device=torch.device(a_ ) , )
__snake_case : Any = floats_tensor(control_image.shape , rng=random.Random(a_ ) ).to(a_ )
__snake_case : str = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__snake_case : int = Image.fromarray(np.uinta(a_ ) ).convert('''RGB''' ).resize((64, 64) )
__snake_case : Any = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
'''image''': image,
'''control_image''': control_image,
}
return inputs
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
class _UpperCAmelCase ( __snake_case, __snake_case, unittest.TestCase ):
'''simple docstring'''
lowerCamelCase__ =StableDiffusionControlNetImgaImgPipeline
lowerCamelCase__ =TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
lowerCamelCase__ =TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
lowerCamelCase__ =frozenset([] ) # TO_DO: add image_params once refactored VaeImageProcessor.preprocess
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
torch.manual_seed(0 )
__snake_case : Optional[int] = UNetaDConditionModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=32 , )
torch.manual_seed(0 )
def init_weights(a_ ):
if isinstance(a_ , torch.nn.Convad ):
torch.nn.init.normal(m.weight )
m.bias.data.fill_(1.0 )
__snake_case : Dict = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(a_ )
torch.manual_seed(0 )
__snake_case : Any = ControlNetModel(
block_out_channels=(32, 64) , layers_per_block=2 , in_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , cross_attention_dim=32 , conditioning_embedding_out_channels=(16, 32) , )
controlneta.controlnet_down_blocks.apply(a_ )
torch.manual_seed(0 )
__snake_case : List[Any] = DDIMScheduler(
beta_start=0.0_0085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=a_ , set_alpha_to_one=a_ , )
torch.manual_seed(0 )
__snake_case : Union[str, Any] = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
__snake_case : Optional[Any] = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=10_00 , )
__snake_case : Dict = CLIPTextModel(a_ )
__snake_case : List[str] = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
__snake_case : Any = MultiControlNetModel([controlneta, controlneta] )
__snake_case : Any = {
'''unet''': unet,
'''controlnet''': controlnet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def SCREAMING_SNAKE_CASE (self , a_ , a_=0 ):
'''simple docstring'''
if str(a_ ).startswith('''mps''' ):
__snake_case : Dict = torch.manual_seed(a_ )
else:
__snake_case : Optional[int] = torch.Generator(device=a_ ).manual_seed(a_ )
__snake_case : Union[str, Any] = 2
__snake_case : Any = [
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=a_ , device=torch.device(a_ ) , ),
randn_tensor(
(1, 3, 32 * controlnet_embedder_scale_factor, 32 * controlnet_embedder_scale_factor) , generator=a_ , device=torch.device(a_ ) , ),
]
__snake_case : List[str] = floats_tensor(control_image[0].shape , rng=random.Random(a_ ) ).to(a_ )
__snake_case : Union[str, Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
__snake_case : Optional[Any] = Image.fromarray(np.uinta(a_ ) ).convert('''RGB''' ).resize((64, 64) )
__snake_case : Any = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
'''image''': image,
'''control_image''': control_image,
}
return inputs
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : str = self.get_dummy_components()
__snake_case : List[Any] = self.pipeline_class(**a_ )
pipe.to(a_ )
__snake_case : List[Any] = 10.0
__snake_case : List[str] = 4
__snake_case : Optional[Any] = self.get_dummy_inputs(a_ )
__snake_case : int = steps
__snake_case : Union[str, Any] = scale
__snake_case : str = pipe(**a_ )[0]
__snake_case : int = self.get_dummy_inputs(a_ )
__snake_case : Tuple = steps
__snake_case : Any = scale
__snake_case : str = pipe(**a_ , control_guidance_start=0.1 , control_guidance_end=0.2 )[0]
__snake_case : str = self.get_dummy_inputs(a_ )
__snake_case : Union[str, Any] = steps
__snake_case : Tuple = scale
__snake_case : List[str] = pipe(**a_ , control_guidance_start=[0.1, 0.3] , control_guidance_end=[0.2, 0.7] )[0]
__snake_case : int = self.get_dummy_inputs(a_ )
__snake_case : Tuple = steps
__snake_case : List[Any] = scale
__snake_case : str = pipe(**a_ , control_guidance_start=0.4 , control_guidance_end=[0.5, 0.8] )[0]
# make sure that all outputs are different
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
assert np.sum(np.abs(output_a - output_a ) ) > 1E-3
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
return self._test_attention_slicing_forward_pass(expected_max_diff=2E-3 )
@unittest.skipIf(
torch_device != '''cuda''' or not is_xformers_available() , reason='''XFormers attention is only available with CUDA and `xformers` installed''' , )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=2E-3 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
self._test_inference_batch_single_identical(expected_max_diff=2E-3 )
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : str = self.get_dummy_components()
__snake_case : Union[str, Any] = self.pipeline_class(**a_ )
pipe.to(a_ )
pipe.set_progress_bar_config(disable=a_ )
with tempfile.TemporaryDirectory() as tmpdir:
try:
# save_pretrained is not implemented for Multi-ControlNet
pipe.save_pretrained(a_ )
except NotImplementedError:
pass
@slow
@require_torch_gpu
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE (self ):
'''simple docstring'''
__snake_case : Any = ControlNetModel.from_pretrained('''lllyasviel/sd-controlnet-canny''' )
__snake_case : Union[str, Any] = StableDiffusionControlNetImgaImgPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , safety_checker=a_ , controlnet=a_ )
pipe.enable_model_cpu_offload()
pipe.set_progress_bar_config(disable=a_ )
__snake_case : Any = torch.Generator(device='''cpu''' ).manual_seed(0 )
__snake_case : List[Any] = '''evil space-punk bird'''
__snake_case : Union[str, Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png''' ).resize((5_12, 5_12) )
__snake_case : List[str] = load_image(
'''https://huggingface.co/lllyasviel/sd-controlnet-canny/resolve/main/images/bird.png''' ).resize((5_12, 5_12) )
__snake_case : Any = pipe(
a_ , a_ , control_image=a_ , generator=a_ , output_type='''np''' , num_inference_steps=50 , strength=0.6 , )
__snake_case : List[str] = output.images[0]
assert image.shape == (5_12, 5_12, 3)
__snake_case : Union[str, Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/img2img.npy''' )
assert np.abs(expected_image - image ).max() < 9E-2
| 102 |
"""simple docstring"""
import argparse
import collections
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def _snake_case ( lowercase__ : Dict , lowercase__ : Dict , lowercase__ : str , lowercase__ : Tuple="attention" ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
lowerCAmelCase_ :Union[str, Any] = params[f"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
lowerCAmelCase_ :Any = params[f"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
lowerCAmelCase_ :Optional[int] = params[f"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
return k, o, q, v
def _snake_case ( lowercase__ : Optional[Any] , lowercase__ : Any , lowercase__ : int , lowercase__ : Any=False ) -> int:
'''simple docstring'''
if split_mlp_wi:
lowerCAmelCase_ :Tuple = params[f"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
lowerCAmelCase_ :List[str] = params[f"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
lowerCAmelCase_ :Tuple = (wi_a, wi_a)
else:
lowerCAmelCase_ :List[Any] = params[f"""{prefix}/layers_{i}/mlp/wi/kernel"""]
lowerCAmelCase_ :Dict = params[f"""{prefix}/layers_{i}/mlp/wo/kernel"""]
return wi, wo
def _snake_case ( lowercase__ : Any , lowercase__ : Dict , lowercase__ : Union[str, Any] , lowercase__ : Optional[int] ) -> Tuple:
'''simple docstring'''
return params[f"""{prefix}/layers_{i}/{layer_name}/scale"""]
def _snake_case ( lowercase__ : dict , *, lowercase__ : int , lowercase__ : bool ) -> List[Any]:
'''simple docstring'''
lowerCAmelCase_ :Tuple = traverse_util.flatten_dict(variables["""target"""] )
lowerCAmelCase_ :Tuple = {"""/""".join(lowercase__ ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
lowerCAmelCase_ :Any = """encoder/layers_0/mlp/wi_0/kernel""" in old
print("""Split MLP:""" , lowercase__ )
lowerCAmelCase_ :List[Any] = collections.OrderedDict()
# Shared embeddings.
lowerCAmelCase_ :Optional[int] = old["""token_embedder/embedding"""]
# Encoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :str = tax_attention_lookup(lowercase__ , lowercase__ , """encoder""" , """attention""" )
lowerCAmelCase_ :Optional[Any] = layer_norm
lowerCAmelCase_ :Any = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Tuple = q.T
lowerCAmelCase_ :str = v.T
# Block i, layer 1 (MLP).
lowerCAmelCase_ :Dict = tax_layer_norm_lookup(lowercase__ , lowercase__ , """encoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Any = tax_mlp_lookup(lowercase__ , lowercase__ , """encoder""" , lowercase__ )
lowerCAmelCase_ :Union[str, Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :List[Any] = wi[0].T
lowerCAmelCase_ :Dict = wi[1].T
else:
lowerCAmelCase_ :int = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Tuple = old[
"""encoder/relpos_bias/rel_embedding"""
].T
lowerCAmelCase_ :List[str] = old["""encoder/encoder_norm/scale"""]
if not is_encoder_only:
# Decoder.
for i in range(lowercase__ ):
# Block i, layer 0 (Self Attention).
lowerCAmelCase_ :Optional[Any] = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_self_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """self_attention""" )
lowerCAmelCase_ :List[Any] = layer_norm
lowerCAmelCase_ :List[str] = k.T
lowerCAmelCase_ :Any = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :Dict = v.T
# Block i, layer 1 (Cross Attention).
lowerCAmelCase_ :int = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_cross_attention_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :Tuple = tax_attention_lookup(lowercase__ , lowercase__ , """decoder""" , """encoder_decoder_attention""" )
lowerCAmelCase_ :Optional[int] = layer_norm
lowerCAmelCase_ :str = k.T
lowerCAmelCase_ :Tuple = o.T
lowerCAmelCase_ :Any = q.T
lowerCAmelCase_ :int = v.T
# Block i, layer 2 (MLP).
lowerCAmelCase_ :Any = tax_layer_norm_lookup(lowercase__ , lowercase__ , """decoder""" , """pre_mlp_layer_norm""" )
lowerCAmelCase_ , lowerCAmelCase_ :Dict = tax_mlp_lookup(lowercase__ , lowercase__ , """decoder""" , lowercase__ )
lowerCAmelCase_ :List[Any] = layer_norm
if split_mlp_wi:
lowerCAmelCase_ :Any = wi[0].T
lowerCAmelCase_ :Any = wi[1].T
else:
lowerCAmelCase_ :Tuple = wi.T
lowerCAmelCase_ :List[str] = wo.T
lowerCAmelCase_ :Optional[Any] = old["""decoder/decoder_norm/scale"""]
lowerCAmelCase_ :Optional[Any] = old[
"""decoder/relpos_bias/rel_embedding"""
].T
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
lowerCAmelCase_ :Tuple = old["""decoder/logits_dense/kernel"""].T
return new
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : bool ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :Optional[int] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Optional[int] = state_dict["""shared.weight"""]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
lowerCAmelCase_ :Tuple = state_dict["""shared.weight"""]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("""Using shared word embeddings as lm_head.""" )
lowerCAmelCase_ :Any = state_dict["""shared.weight"""]
return state_dict
def _snake_case ( lowercase__ : Union[str, Any] , lowercase__ : str , lowercase__ : List[Any] , lowercase__ : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = checkpoints.load_tax_checkpoint(lowercase__ )
lowerCAmelCase_ :Optional[int] = convert_tax_to_pytorch(lowercase__ , num_layers=config.num_layers , is_encoder_only=lowercase__ )
lowerCAmelCase_ :Union[str, Any] = make_state_dict(lowercase__ , lowercase__ )
model.load_state_dict(lowercase__ , strict=lowercase__ )
def _snake_case ( lowercase__ : List[Any] , lowercase__ : Optional[Any] , lowercase__ : str , lowercase__ : bool = False ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :Any = TaConfig.from_json_file(lowercase__ )
print(f"""Building PyTorch model from configuration: {config}""" )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
lowerCAmelCase_ :List[Any] = TaEncoderModel(lowercase__ )
else:
lowerCAmelCase_ :List[str] = TaForConditionalGeneration(lowercase__ )
# Load weights from tf checkpoint
load_tax_weights_in_ta(lowercase__ , lowercase__ , lowercase__ , lowercase__ )
# Save pytorch-model
print(f"""Save PyTorch model to {pytorch_dump_path}""" )
model.save_pretrained(lowercase__ )
# Verify that we can load the checkpoint.
model.from_pretrained(lowercase__ )
print("""Done""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
# Required parameters
parser.add_argument(
'--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
)
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.'
)
parser.add_argument(
'--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
)
__UpperCAmelCase = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
)
| 84 | 0 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def UpperCamelCase( __UpperCamelCase : Optional[int] ):
lowerCAmelCase_ : Union[str, Any] = int(__UpperCamelCase )
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = t // 3600, (t // 60) % 60, t % 60
return f"""{h}:{m:02d}:{s:02d}""" if h != 0 else f"""{m:02d}:{s:02d}"""
def UpperCamelCase( __UpperCamelCase : Dict ,__UpperCamelCase : Tuple ,__UpperCamelCase : List[str] ,__UpperCamelCase : Optional[int] ,__UpperCamelCase : int=300 ):
# docstyle-ignore
return f"""
<div>
{prefix}
<progress value='{value}' max='{total}' style='width:{width}px; height:20px; vertical-align: middle;'></progress>
{label}
</div>
"""
def UpperCamelCase( __UpperCamelCase : int ):
lowerCAmelCase_ : str = '''<table border="1" class="dataframe">\n'''
html_code += """ <thead>\n <tr style="text-align: left;">\n"""
for i in items[0]:
html_code += f""" <th>{i}</th>\n"""
html_code += " </tr>\n </thead>\n <tbody>\n"
for line in items[1:]:
html_code += " <tr>\n"
for elt in line:
lowerCAmelCase_ : Optional[Any] = f"""{elt:.6f}""" if isinstance(__UpperCamelCase ,__UpperCamelCase ) else str(__UpperCamelCase )
html_code += f""" <td>{elt}</td>\n"""
html_code += " </tr>\n"
html_code += " </tbody>\n</table><p>"
return html_code
class __snake_case :
_a = 5
_a = 0.2
def __init__( self : Optional[Any] , A_ : int , A_ : Optional[str] = None , A_ : bool = True , A_ : Optional["NotebookTrainingTracker"] = None , A_ : int = 3_0_0 , ):
lowerCAmelCase_ : Union[str, Any] = total
lowerCAmelCase_ : int = '''''' if prefix is None else prefix
lowerCAmelCase_ : Optional[Any] = leave
lowerCAmelCase_ : Union[str, Any] = parent
lowerCAmelCase_ : Any = width
lowerCAmelCase_ : Optional[Any] = None
lowerCAmelCase_ : Tuple = None
lowerCAmelCase_ : str = None
def UpperCAmelCase__ ( self : Union[str, Any] , A_ : int , A_ : bool = False , A_ : str = None):
lowerCAmelCase_ : Dict = value
if comment is not None:
lowerCAmelCase_ : List[str] = comment
if self.last_value is None:
lowerCAmelCase_ : List[str] = time.time()
lowerCAmelCase_ : Dict = value
lowerCAmelCase_ : Union[str, Any] = None
lowerCAmelCase_ : str = self.warmup
lowerCAmelCase_ : List[Any] = 1
self.update_bar(A_)
elif value <= self.last_value and not force_update:
return
elif force_update or self.first_calls > 0 or value >= min(self.last_value + self.wait_for , self.total):
if self.first_calls > 0:
self.first_calls -= 1
lowerCAmelCase_ : Tuple = time.time()
lowerCAmelCase_ : List[str] = current_time - self.start_time
# We could have value = self.start_value if the update is called twixe with the same start value.
if value > self.start_value:
lowerCAmelCase_ : int = self.elapsed_time / (value - self.start_value)
else:
lowerCAmelCase_ : str = None
if value >= self.total:
lowerCAmelCase_ : int = self.total
lowerCAmelCase_ : List[str] = None
if not self.leave:
self.close()
elif self.average_time_per_item is not None:
lowerCAmelCase_ : List[str] = self.average_time_per_item * (self.total - value)
self.update_bar(A_)
lowerCAmelCase_ : Tuple = value
lowerCAmelCase_ : Optional[int] = current_time
if self.average_time_per_item is None:
lowerCAmelCase_ : List[Any] = 1
else:
lowerCAmelCase_ : Any = max(int(self.update_every / self.average_time_per_item) , 1)
def UpperCAmelCase__ ( self : int , A_ : Optional[int] , A_ : List[str]=None):
lowerCAmelCase_ : str = ''' ''' * (len(str(self.total)) - len(str(A_))) + str(A_)
if self.elapsed_time is None:
lowerCAmelCase_ : str = F"""[{spaced_value}/{self.total} : < :"""
elif self.predicted_remaining is None:
lowerCAmelCase_ : Tuple = F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time)}"""
else:
lowerCAmelCase_ : Any = (
F"""[{spaced_value}/{self.total} {format_time(self.elapsed_time)} <"""
F""" {format_time(self.predicted_remaining)}"""
)
self.label += F""", {1/self.average_time_per_item:.2f} it/s"""
self.label += "]" if self.comment is None or len(self.comment) == 0 else F""", {self.comment}]"""
self.display()
def UpperCAmelCase__ ( self : Tuple):
lowerCAmelCase_ : Union[str, Any] = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width)
if self.parent is not None:
# If this is a child bar, the parent will take care of the display.
self.parent.display()
return
if self.output is None:
lowerCAmelCase_ : int = disp.display(disp.HTML(self.html_code) , display_id=A_)
else:
self.output.update(disp.HTML(self.html_code))
def UpperCAmelCase__ ( self : Optional[int]):
if self.parent is None and self.output is not None:
self.output.update(disp.HTML(''''''))
class __snake_case ( UpperCamelCase_ ):
def __init__( self : Union[str, Any] , A_ : Optional[int] , A_ : Dict=None):
super().__init__(A_)
lowerCAmelCase_ : int = None if column_names is None else [column_names]
lowerCAmelCase_ : Optional[int] = None
def UpperCAmelCase__ ( self : List[str]):
lowerCAmelCase_ : Optional[Any] = html_progress_bar(self.value , self.total , self.prefix , self.label , self.width)
if self.inner_table is not None:
self.html_code += text_to_html_table(self.inner_table)
if self.child_bar is not None:
self.html_code += self.child_bar.html_code
if self.output is None:
lowerCAmelCase_ : Optional[Any] = disp.display(disp.HTML(self.html_code) , display_id=A_)
else:
self.output.update(disp.HTML(self.html_code))
def UpperCAmelCase__ ( self : List[str] , A_ : Optional[int]):
if self.inner_table is None:
lowerCAmelCase_ : List[Any] = [list(values.keys()), list(values.values())]
else:
lowerCAmelCase_ : List[Any] = self.inner_table[0]
if len(self.inner_table) == 1:
# We give a chance to update the column names at the first iteration
for key in values.keys():
if key not in columns:
columns.append(A_)
lowerCAmelCase_ : str = columns
self.inner_table.append([values[c] for c in columns])
def UpperCAmelCase__ ( self : Optional[Any] , A_ : Optional[Any] , A_ : Any=None , A_ : Optional[int]=3_0_0):
lowerCAmelCase_ : Any = NotebookProgressBar(A_ , prefix=A_ , parent=self , width=A_)
return self.child_bar
def UpperCAmelCase__ ( self : List[str]):
lowerCAmelCase_ : Dict = None
self.display()
class __snake_case ( UpperCamelCase_ ):
def __init__( self : Dict):
lowerCAmelCase_ : str = None
lowerCAmelCase_ : List[str] = None
lowerCAmelCase_ : Tuple = False
def UpperCAmelCase__ ( self : int , A_ : Optional[Any] , A_ : List[Any] , A_ : Union[str, Any] , **A_ : str):
lowerCAmelCase_ : str = '''Epoch''' if args.evaluation_strategy == IntervalStrategy.EPOCH else '''Step'''
lowerCAmelCase_ : Optional[int] = 0
lowerCAmelCase_ : Any = 0
lowerCAmelCase_ : List[str] = [self.first_column] + ['''Training Loss''']
if args.evaluation_strategy != IntervalStrategy.NO:
column_names.append('''Validation Loss''')
lowerCAmelCase_ : str = NotebookTrainingTracker(state.max_steps , A_)
def UpperCAmelCase__ ( self : Optional[int] , A_ : Optional[Any] , A_ : Optional[int] , A_ : List[str] , **A_ : List[Any]):
lowerCAmelCase_ : int = int(state.epoch) if int(state.epoch) == state.epoch else F"""{state.epoch:.2f}"""
self.training_tracker.update(
state.global_step + 1 , comment=F"""Epoch {epoch}/{state.num_train_epochs}""" , force_update=self._force_next_update , )
lowerCAmelCase_ : Dict = False
def UpperCAmelCase__ ( self : Optional[Any] , A_ : List[str] , A_ : Optional[Any] , A_ : int , A_ : List[str]=None , **A_ : List[str]):
if not has_length(A_):
return
if self.prediction_bar is None:
if self.training_tracker is not None:
lowerCAmelCase_ : int = self.training_tracker.add_child(len(A_))
else:
lowerCAmelCase_ : List[str] = NotebookProgressBar(len(A_))
self.prediction_bar.update(1)
else:
self.prediction_bar.update(self.prediction_bar.value + 1)
def UpperCAmelCase__ ( self : List[Any] , A_ : Optional[Any] , A_ : List[str] , A_ : Dict , **A_ : Tuple):
if self.prediction_bar is not None:
self.prediction_bar.close()
lowerCAmelCase_ : List[Any] = None
def UpperCAmelCase__ ( self : List[Any] , A_ : Optional[Any] , A_ : str , A_ : Any , A_ : Tuple=None , **A_ : str):
# Only for when there is no evaluation
if args.evaluation_strategy == IntervalStrategy.NO and "loss" in logs:
lowerCAmelCase_ : List[str] = {'''Training Loss''': logs['''loss''']}
# First column is necessarily Step sine we're not in epoch eval strategy
lowerCAmelCase_ : int = state.global_step
self.training_tracker.write_line(A_)
def UpperCAmelCase__ ( self : int , A_ : Union[str, Any] , A_ : Tuple , A_ : Tuple , A_ : Optional[Any]=None , **A_ : Optional[Any]):
if self.training_tracker is not None:
lowerCAmelCase_ : Any = {'''Training Loss''': '''No log''', '''Validation Loss''': '''No log'''}
for log in reversed(state.log_history):
if "loss" in log:
lowerCAmelCase_ : Dict = log['''loss''']
break
if self.first_column == "Epoch":
lowerCAmelCase_ : str = int(state.epoch)
else:
lowerCAmelCase_ : Optional[int] = state.global_step
lowerCAmelCase_ : Optional[Any] = '''eval'''
for k in metrics:
if k.endswith('''_loss'''):
lowerCAmelCase_ : str = re.sub(r'''\_loss$''' , '''''' , A_)
lowerCAmelCase_ : Any = metrics.pop('''total_flos''' , A_)
lowerCAmelCase_ : List[Any] = metrics.pop('''epoch''' , A_)
lowerCAmelCase_ : Optional[Any] = metrics.pop(F"""{metric_key_prefix}_runtime""" , A_)
lowerCAmelCase_ : List[Any] = metrics.pop(F"""{metric_key_prefix}_samples_per_second""" , A_)
lowerCAmelCase_ : List[Any] = metrics.pop(F"""{metric_key_prefix}_steps_per_second""" , A_)
lowerCAmelCase_ : Tuple = metrics.pop(F"""{metric_key_prefix}_jit_compilation_time""" , A_)
for k, v in metrics.items():
if k == F"""{metric_key_prefix}_loss""":
lowerCAmelCase_ : Optional[int] = v
else:
lowerCAmelCase_ : str = k.split('''_''')
lowerCAmelCase_ : List[str] = ''' '''.join([part.capitalize() for part in splits[1:]])
lowerCAmelCase_ : List[Any] = v
self.training_tracker.write_line(A_)
self.training_tracker.remove_child()
lowerCAmelCase_ : Any = None
# Evaluation takes a long time so we should force the next update.
lowerCAmelCase_ : str = True
def UpperCAmelCase__ ( self : Union[str, Any] , A_ : Optional[int] , A_ : Union[str, Any] , A_ : Any , **A_ : int):
self.training_tracker.update(
state.global_step , comment=F"""Epoch {int(state.epoch)}/{state.num_train_epochs}""" , force_update=A_)
lowerCAmelCase_ : str = None
| 103 |
"""simple docstring"""
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase = logging.get_logger(__name__)
def _snake_case ( lowercase__ : Optional[Any] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :str = OrderedDict()
for key, value in state_dict.items():
if key.startswith("""module.encoder""" ):
lowerCAmelCase_ :Union[str, Any] = key.replace("""module.encoder""" , """glpn.encoder""" )
if key.startswith("""module.decoder""" ):
lowerCAmelCase_ :Any = key.replace("""module.decoder""" , """decoder.stages""" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
lowerCAmelCase_ :List[str] = key[key.find("""patch_embed""" ) + len("""patch_embed""" )]
lowerCAmelCase_ :Tuple = key.replace(f"""patch_embed{idx}""" , f"""patch_embeddings.{int(lowercase__ )-1}""" )
if "norm" in key:
lowerCAmelCase_ :Dict = key.replace("""norm""" , """layer_norm""" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
lowerCAmelCase_ :str = key[key.find("""glpn.encoder.layer_norm""" ) + len("""glpn.encoder.layer_norm""" )]
lowerCAmelCase_ :str = key.replace(f"""layer_norm{idx}""" , f"""layer_norm.{int(lowercase__ )-1}""" )
if "layer_norm1" in key:
lowerCAmelCase_ :Optional[Any] = key.replace("""layer_norm1""" , """layer_norm_1""" )
if "layer_norm2" in key:
lowerCAmelCase_ :str = key.replace("""layer_norm2""" , """layer_norm_2""" )
if "block" in key:
# replace for example block1 by block.0
lowerCAmelCase_ :List[str] = key[key.find("""block""" ) + len("""block""" )]
lowerCAmelCase_ :int = key.replace(f"""block{idx}""" , f"""block.{int(lowercase__ )-1}""" )
if "attn.q" in key:
lowerCAmelCase_ :Tuple = key.replace("""attn.q""" , """attention.self.query""" )
if "attn.proj" in key:
lowerCAmelCase_ :Optional[int] = key.replace("""attn.proj""" , """attention.output.dense""" )
if "attn" in key:
lowerCAmelCase_ :str = key.replace("""attn""" , """attention.self""" )
if "fc1" in key:
lowerCAmelCase_ :List[Any] = key.replace("""fc1""" , """dense1""" )
if "fc2" in key:
lowerCAmelCase_ :Optional[Any] = key.replace("""fc2""" , """dense2""" )
if "linear_pred" in key:
lowerCAmelCase_ :List[str] = key.replace("""linear_pred""" , """classifier""" )
if "linear_fuse" in key:
lowerCAmelCase_ :str = key.replace("""linear_fuse.conv""" , """linear_fuse""" )
lowerCAmelCase_ :Any = key.replace("""linear_fuse.bn""" , """batch_norm""" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
lowerCAmelCase_ :str = key[key.find("""linear_c""" ) + len("""linear_c""" )]
lowerCAmelCase_ :Optional[int] = key.replace(f"""linear_c{idx}""" , f"""linear_c.{int(lowercase__ )-1}""" )
if "bot_conv" in key:
lowerCAmelCase_ :Union[str, Any] = key.replace("""bot_conv""" , """0.convolution""" )
if "skip_conv1" in key:
lowerCAmelCase_ :int = key.replace("""skip_conv1""" , """1.convolution""" )
if "skip_conv2" in key:
lowerCAmelCase_ :str = key.replace("""skip_conv2""" , """2.convolution""" )
if "fusion1" in key:
lowerCAmelCase_ :Any = key.replace("""fusion1""" , """1.fusion""" )
if "fusion2" in key:
lowerCAmelCase_ :List[str] = key.replace("""fusion2""" , """2.fusion""" )
if "fusion3" in key:
lowerCAmelCase_ :Dict = key.replace("""fusion3""" , """3.fusion""" )
if "fusion" in key and "conv" in key:
lowerCAmelCase_ :Any = key.replace("""conv""" , """convolutional_layer""" )
if key.startswith("""module.last_layer_depth""" ):
lowerCAmelCase_ :Tuple = key.replace("""module.last_layer_depth""" , """head.head""" )
lowerCAmelCase_ :List[Any] = value
return new_state_dict
def _snake_case ( lowercase__ : str , lowercase__ : int ) -> str:
'''simple docstring'''
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
lowerCAmelCase_ :Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.weight""" )
lowerCAmelCase_ :Tuple = state_dict.pop(f"""glpn.encoder.block.{i}.{j}.attention.self.kv.bias""" )
# next, add keys and values (in that order) to the state dict
lowerCAmelCase_ :Optional[Any] = kv_weight[
: config.hidden_sizes[i], :
]
lowerCAmelCase_ :Union[str, Any] = kv_bias[: config.hidden_sizes[i]]
lowerCAmelCase_ :List[Any] = kv_weight[
config.hidden_sizes[i] :, :
]
lowerCAmelCase_ :int = kv_bias[config.hidden_sizes[i] :]
def _snake_case ( ) -> Any:
'''simple docstring'''
lowerCAmelCase_ :int = """http://images.cocodataset.org/val2017/000000039769.jpg"""
lowerCAmelCase_ :Optional[Any] = Image.open(requests.get(lowercase__ , stream=lowercase__ ).raw )
return image
@torch.no_grad()
def _snake_case ( lowercase__ : List[Any] , lowercase__ : str , lowercase__ : Dict=False , lowercase__ : List[Any]=None ) -> int:
'''simple docstring'''
lowerCAmelCase_ :int = GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2] , decoder_hidden_size=6_4 , depths=[3, 8, 2_7, 3] )
# load image processor (only resize + rescale)
lowerCAmelCase_ :Union[str, Any] = GLPNImageProcessor()
# prepare image
lowerCAmelCase_ :List[Any] = prepare_img()
lowerCAmelCase_ :int = image_processor(images=lowercase__ , return_tensors="""pt""" ).pixel_values
logger.info("""Converting model...""" )
# load original state dict
lowerCAmelCase_ :Tuple = torch.load(lowercase__ , map_location=torch.device("""cpu""" ) )
# rename keys
lowerCAmelCase_ :Union[str, Any] = rename_keys(lowercase__ )
# key and value matrices need special treatment
read_in_k_v(lowercase__ , lowercase__ )
# create HuggingFace model and load state dict
lowerCAmelCase_ :List[Any] = GLPNForDepthEstimation(lowercase__ )
model.load_state_dict(lowercase__ )
model.eval()
# forward pass
lowerCAmelCase_ :Dict = model(lowercase__ )
lowerCAmelCase_ :Tuple = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
lowerCAmelCase_ :Optional[Any] = torch.tensor(
[[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]] )
elif "kitti" in model_name:
lowerCAmelCase_ :Any = torch.tensor(
[[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]] )
else:
raise ValueError(f"""Unknown model name: {model_name}""" )
lowerCAmelCase_ :Union[str, Any] = torch.Size([1, 4_8_0, 6_4_0] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , lowercase__ , atol=1E-4 )
print("""Looks ok!""" )
# finally, push to hub if required
if push_to_hub:
logger.info("""Pushing model and image processor to the hub...""" )
model.push_to_hub(
repo_path_or_name=Path(lowercase__ , lowercase__ ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=lowercase__ , )
image_processor.push_to_hub(
repo_path_or_name=Path(lowercase__ , lowercase__ ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=lowercase__ , )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument(
'--checkpoint_path',
default=None,
type=str,
help='Path to the original PyTorch checkpoint (.pth file).',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', action='store_true', help='Whether to upload the model to the HuggingFace hub.'
)
parser.add_argument(
'--model_name',
default='glpn-kitti',
type=str,
help='Name of the model in case you\'re pushing to the hub.',
)
__UpperCAmelCase = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 84 | 0 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class lowercase_ :
"""simple docstring"""
def __init__( self : str ,lowercase__ : int | None = None ):
__lowercase = value
__lowercase = None # Added in order to delete a node easier
__lowercase = None
__lowercase = None
def __repr__( self : int ):
from pprint import pformat
if self.left is None and self.right is None:
return str(self.value )
return pformat({F"{self.value}": (self.left, self.right)} ,indent=1 )
class lowercase_ :
"""simple docstring"""
def __init__( self : int ,lowercase__ : Node | None = None ):
__lowercase = root
def __str__( self : List[str] ):
return str(self.root )
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ,lowercase__ : Node ,lowercase__ : Node | None ):
if new_children is not None: # reset its kids
__lowercase = node.parent
if node.parent is not None: # reset its parent
if self.is_right(lowercase__ ): # If it is the right children
__lowercase = new_children
else:
__lowercase = new_children
else:
__lowercase = new_children
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,lowercase__ : Node ):
if node.parent and node.parent.right:
return node == node.parent.right
return False
def SCREAMING_SNAKE_CASE ( self : str ):
return self.root is None
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : Any ):
__lowercase = Node(lowercase__ ) # create a new Node
if self.empty(): # if Tree is empty
__lowercase = new_node # set its root
else: # Tree is not empty
__lowercase = self.root # from root
if parent_node is None:
return
while True: # While we don't get to a leaf
if value < parent_node.value: # We go left
if parent_node.left is None:
__lowercase = new_node # We insert the new node in a leaf
break
else:
__lowercase = parent_node.left
else:
if parent_node.right is None:
__lowercase = new_node
break
else:
__lowercase = parent_node.right
__lowercase = parent_node
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,*lowercase__ : List[str] ):
for value in values:
self.__insert(lowercase__ )
def SCREAMING_SNAKE_CASE ( self : Optional[int] ,lowercase__ : Any ):
if self.empty():
raise IndexError('''Warning: Tree is empty! please use another.''' )
else:
__lowercase = self.root
# use lazy evaluation here to avoid NoneType Attribute error
while node is not None and node.value is not value:
__lowercase = node.left if value < node.value else node.right
return node
def SCREAMING_SNAKE_CASE ( self : Optional[int] ,lowercase__ : Node | None = None ):
if node is None:
if self.root is None:
return None
__lowercase = self.root
if not self.empty():
while node.right is not None:
__lowercase = node.right
return node
def SCREAMING_SNAKE_CASE ( self : int ,lowercase__ : Node | None = None ):
if node is None:
__lowercase = self.root
if self.root is None:
return None
if not self.empty():
__lowercase = self.root
while node.left is not None:
__lowercase = node.left
return node
def SCREAMING_SNAKE_CASE ( self : Any ,lowercase__ : int ):
__lowercase = self.search(lowercase__ ) # Look for the node with that label
if node is not None:
if node.left is None and node.right is None: # If it has no children
self.__reassign_nodes(lowercase__ ,lowercase__ )
elif node.left is None: # Has only right children
self.__reassign_nodes(lowercase__ ,node.right )
elif node.right is None: # Has only left children
self.__reassign_nodes(lowercase__ ,node.left )
else:
__lowercase = self.get_max(
node.left ) # Gets the max value of the left branch
self.remove(tmp_node.value ) # type: ignore
__lowercase = (
tmp_node.value # type: ignore
) # Assigns the value to the node to delete and keep tree structure
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ,lowercase__ : Node | None ):
if node is not None:
yield node # Preorder Traversal
yield from self.preorder_traverse(node.left )
yield from self.preorder_traverse(node.right )
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : str=None ):
if traversal_function is None:
return self.preorder_traverse(self.root )
else:
return traversal_function(self.root )
def SCREAMING_SNAKE_CASE ( self : str ,lowercase__ : list ,lowercase__ : Node | None ):
if node:
self.inorder(lowercase__ ,node.left )
arr.append(node.value )
self.inorder(lowercase__ ,node.right )
def SCREAMING_SNAKE_CASE ( self : Tuple ,lowercase__ : int ,lowercase__ : Node ):
__lowercase = []
self.inorder(lowercase__ ,lowercase__ ) # append all values to list using inorder traversal
return arr[k - 1]
def _A ( A__ ):
"""simple docstring"""
__lowercase = []
if curr_node is not None:
__lowercase = postorder(curr_node.left ) + postorder(curr_node.right ) + [curr_node]
return node_list
def _A ( ):
"""simple docstring"""
__lowercase = (8, 3, 6, 1, 10, 14, 13, 4, 7)
__lowercase = BinarySearchTree()
for i in testlist:
t.insert(A__ )
# Prints all the elements of the list in order traversal
print(A__ )
if t.search(6 ) is not None:
print('''The value 6 exists''' )
else:
print('''The value 6 doesn\'t exist''' )
if t.search(-1 ) is not None:
print('''The value -1 exists''' )
else:
print('''The value -1 doesn\'t exist''' )
if not t.empty():
print('''Max Value: ''' , t.get_max().value ) # type: ignore
print('''Min Value: ''' , t.get_min().value ) # type: ignore
for i in testlist:
t.remove(A__ )
print(A__ )
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
| 104 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenization_roc_bert': ['RoCBertTokenizer'],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
pass
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = [
'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
'RoCBertForCausalLM',
'RoCBertForMaskedLM',
'RoCBertForMultipleChoice',
'RoCBertForPreTraining',
'RoCBertForQuestionAnswering',
'RoCBertForSequenceClassification',
'RoCBertForTokenClassification',
'RoCBertLayer',
'RoCBertModel',
'RoCBertPreTrainedModel',
'load_tf_weights_in_roc_bert',
]
if TYPE_CHECKING:
from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
from .tokenization_roc_bert import RoCBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
raise OptionalDependencyNotAvailable()
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roc_bert import (
ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
RoCBertForCausalLM,
RoCBertForMaskedLM,
RoCBertForMultipleChoice,
RoCBertForPreTraining,
RoCBertForQuestionAnswering,
RoCBertForSequenceClassification,
RoCBertForTokenClassification,
RoCBertLayer,
RoCBertModel,
RoCBertPreTrainedModel,
load_tf_weights_in_roc_bert,
)
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 84 | 0 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class __UpperCamelCase ( a__ , a__ , unittest.TestCase ):
lowerCamelCase : Union[str, Any] =StableDiffusionSAGPipeline
lowerCamelCase : Optional[int] =TEXT_TO_IMAGE_PARAMS
lowerCamelCase : int =TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase : Optional[int] =TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase : Any =TEXT_TO_IMAGE_IMAGE_PARAMS
lowerCamelCase : Tuple =False
def __a ( self ) -> List[str]:
torch.manual_seed(0 )
a : List[Any] = 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 , )
a : Tuple = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=lowerCAmelCase__ , set_alpha_to_one=lowerCAmelCase__ , )
torch.manual_seed(0 )
a : Tuple = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , )
torch.manual_seed(0 )
a : Any = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
a : Any = CLIPTextModel(lowerCAmelCase__ )
a : int = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
a : Optional[Any] = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def __a ( self , lowerCAmelCase__ , lowerCAmelCase__=0 ) -> Optional[int]:
if str(lowerCAmelCase__ ).startswith("mps" ):
a : Optional[int] = torch.manual_seed(lowerCAmelCase__ )
else:
a : Tuple = torch.Generator(device=lowerCAmelCase__ ).manual_seed(lowerCAmelCase__ )
a : Optional[Any] = {
"prompt": ".",
"generator": generator,
"num_inference_steps": 2,
"guidance_scale": 1.0,
"sag_scale": 1.0,
"output_type": "numpy",
}
return inputs
def __a ( self ) -> List[str]:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class __UpperCamelCase ( unittest.TestCase ):
def __a ( self ) -> List[str]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __a ( self ) -> Dict:
a : Any = StableDiffusionSAGPipeline.from_pretrained("CompVis/stable-diffusion-v1-4" )
a : Any = sag_pipe.to(lowerCAmelCase__ )
sag_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
a : str = "."
a : int = torch.manual_seed(0 )
a : int = sag_pipe(
[prompt] , generator=lowerCAmelCase__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" )
a : Optional[Any] = output.images
a : List[Any] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
a : Optional[Any] = np.array([0.1_568, 0.1_738, 0.1_695, 0.1_693, 0.1_507, 0.1_705, 0.1_547, 0.1_751, 0.1_949] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def __a ( self ) -> Optional[int]:
a : Optional[Any] = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
a : Tuple = sag_pipe.to(lowerCAmelCase__ )
sag_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
a : int = "."
a : str = torch.manual_seed(0 )
a : Optional[Any] = sag_pipe(
[prompt] , generator=lowerCAmelCase__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" )
a : Optional[int] = output.images
a : int = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
a : str = np.array([0.3_459, 0.2_876, 0.2_537, 0.3_002, 0.2_671, 0.2_160, 0.3_026, 0.2_262, 0.2_371] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-2
def __a ( self ) -> Dict:
a : Tuple = StableDiffusionSAGPipeline.from_pretrained("stabilityai/stable-diffusion-2-1-base" )
a : Optional[int] = sag_pipe.to(lowerCAmelCase__ )
sag_pipe.set_progress_bar_config(disable=lowerCAmelCase__ )
a : Optional[int] = "."
a : Tuple = torch.manual_seed(0 )
a : Union[str, Any] = sag_pipe(
[prompt] , width=768 , height=512 , generator=lowerCAmelCase__ , guidance_scale=7.5 , sag_scale=1.0 , num_inference_steps=20 , output_type="np" , )
a : List[Any] = output.images
assert image.shape == (1, 512, 768, 3)
| 105 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/levit-128S': 'https://huggingface.co/facebook/levit-128S/resolve/main/config.json',
# See all LeViT models at https://huggingface.co/models?filter=levit
}
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "levit"
def __init__( self , __A=224 , __A=3 , __A=3 , __A=2 , __A=1 , __A=16 , __A=[128, 256, 384] , __A=[4, 8, 12] , __A=[4, 4, 4] , __A=[16, 16, 16] , __A=0 , __A=[2, 2, 2] , __A=[2, 2, 2] , __A=0.0_2 , **__A , ) -> Any:
super().__init__(**__A )
lowerCAmelCase_ :Tuple = image_size
lowerCAmelCase_ :Optional[int] = num_channels
lowerCAmelCase_ :Union[str, Any] = kernel_size
lowerCAmelCase_ :Optional[Any] = stride
lowerCAmelCase_ :Optional[int] = padding
lowerCAmelCase_ :Optional[Any] = hidden_sizes
lowerCAmelCase_ :Optional[int] = num_attention_heads
lowerCAmelCase_ :int = depths
lowerCAmelCase_ :List[str] = key_dim
lowerCAmelCase_ :str = drop_path_rate
lowerCAmelCase_ :Optional[int] = patch_size
lowerCAmelCase_ :Union[str, Any] = attention_ratio
lowerCAmelCase_ :Dict = mlp_ratio
lowerCAmelCase_ :Any = initializer_range
lowerCAmelCase_ :Optional[int] = [
["""Subsample""", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["""Subsample""", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :Tuple = version.parse("1.11" )
@property
def __lowerCAmelCase ( self ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def __lowerCAmelCase ( self ) -> float:
return 1E-4
| 84 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCamelCase : int = logging.get_logger(__name__)
__UpperCamelCase : Any = {
'''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 SCREAMING_SNAKE_CASE ( a_ ):
"""simple docstring"""
lowercase__ = "cvt"
def __init__( self : Tuple ,lowercase_ : Union[str, Any]=3 ,lowercase_ : Dict=[7, 3, 3] ,lowercase_ : Union[str, Any]=[4, 2, 2] ,lowercase_ : Optional[int]=[2, 1, 1] ,lowercase_ : List[str]=[6_4, 1_9_2, 3_8_4] ,lowercase_ : Tuple=[1, 3, 6] ,lowercase_ : Any=[1, 2, 1_0] ,lowercase_ : Any=[4.0, 4.0, 4.0] ,lowercase_ : str=[0.0, 0.0, 0.0] ,lowercase_ : str=[0.0, 0.0, 0.0] ,lowercase_ : int=[0.0, 0.0, 0.1] ,lowercase_ : str=[True, True, True] ,lowercase_ : Optional[Any]=[False, False, True] ,lowercase_ : Any=["dw_bn", "dw_bn", "dw_bn"] ,lowercase_ : Optional[int]=[3, 3, 3] ,lowercase_ : int=[1, 1, 1] ,lowercase_ : Tuple=[2, 2, 2] ,lowercase_ : Dict=[1, 1, 1] ,lowercase_ : List[Any]=[1, 1, 1] ,lowercase_ : List[Any]=0.02 ,lowercase_ : Optional[Any]=1E-12 ,**lowercase_ : str ,):
super().__init__(**lowercase_ )
lowerCAmelCase__ : List[str] = num_channels
lowerCAmelCase__ : Union[str, Any] = patch_sizes
lowerCAmelCase__ : int = patch_stride
lowerCAmelCase__ : Union[str, Any] = patch_padding
lowerCAmelCase__ : Optional[int] = embed_dim
lowerCAmelCase__ : List[str] = num_heads
lowerCAmelCase__ : List[str] = depth
lowerCAmelCase__ : List[Any] = mlp_ratio
lowerCAmelCase__ : List[Any] = attention_drop_rate
lowerCAmelCase__ : str = drop_rate
lowerCAmelCase__ : Union[str, Any] = drop_path_rate
lowerCAmelCase__ : Optional[Any] = qkv_bias
lowerCAmelCase__ : List[str] = cls_token
lowerCAmelCase__ : str = qkv_projection_method
lowerCAmelCase__ : List[str] = kernel_qkv
lowerCAmelCase__ : Optional[Any] = padding_kv
lowerCAmelCase__ : Any = stride_kv
lowerCAmelCase__ : Dict = padding_q
lowerCAmelCase__ : str = stride_q
lowerCAmelCase__ : List[str] = initializer_range
lowerCAmelCase__ : Tuple = layer_norm_eps
| 106 |
"""simple docstring"""
import os
import time
import pytest
from datasets.utils.filelock import FileLock, Timeout
def _snake_case ( lowercase__ : Optional[int] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = FileLock(str(tmpdir / """foo.lock""" ) )
lowerCAmelCase_ :Union[str, Any] = FileLock(str(tmpdir / """foo.lock""" ) )
lowerCAmelCase_ :Dict = 0.01
with locka.acquire():
with pytest.raises(lowercase__ ):
lowerCAmelCase_ :List[Any] = time.time()
locka.acquire(lowercase__ )
assert time.time() - _start > timeout
def _snake_case ( lowercase__ : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
lowerCAmelCase_ :List[Any] = """a""" * 1_0_0_0 + """.lock"""
lowerCAmelCase_ :Optional[Any] = FileLock(str(tmpdir / filename ) )
assert locka._lock_file.endswith(""".lock""" )
assert not locka._lock_file.endswith(lowercase__ )
assert len(os.path.basename(locka._lock_file ) ) <= 2_5_5
lowerCAmelCase_ :Any = FileLock(tmpdir / filename )
with locka.acquire():
with pytest.raises(lowercase__ ):
locka.acquire(0 )
| 84 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
import torch
from ..models.auto import AutoModelForVisualQuestionAnswering, AutoProcessor
from ..utils import requires_backends
from .base import PipelineTool
if TYPE_CHECKING:
from PIL import Image
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : int = """dandelin/vilt-b32-finetuned-vqa"""
SCREAMING_SNAKE_CASE_ : Tuple = (
"""This is a tool that answers a question about an image. It takes an input named `image` which should be the """
"""image containing the information, as well as a `question` which should be the question in English. It """
"""returns a text that is the answer to the question."""
)
SCREAMING_SNAKE_CASE_ : Tuple = """image_qa"""
SCREAMING_SNAKE_CASE_ : List[str] = AutoProcessor
SCREAMING_SNAKE_CASE_ : Any = AutoModelForVisualQuestionAnswering
SCREAMING_SNAKE_CASE_ : str = ["""image""", """text"""]
SCREAMING_SNAKE_CASE_ : Union[str, Any] = ["""text"""]
def __init__( self : int , *__lowerCamelCase : int , **__lowerCamelCase : Dict ) -> str:
requires_backends(self , ["vision"] )
super().__init__(*__lowerCamelCase , **__lowerCamelCase )
def __UpperCAmelCase ( self : Optional[Any] , __lowerCamelCase : "Image" , __lowerCamelCase : str ) -> Union[str, Any]:
return self.pre_processor(__lowerCamelCase , __lowerCamelCase , return_tensors="pt" )
def __UpperCAmelCase ( self : int , __lowerCamelCase : Dict ) -> List[str]:
with torch.no_grad():
return self.model(**__lowerCamelCase ).logits
def __UpperCAmelCase ( self : Optional[int] , __lowerCamelCase : List[Any] ) -> Any:
a = outputs.argmax(-1 ).item()
return self.model.config.idalabel[idx]
| 107 |
"""simple docstring"""
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
__UpperCAmelCase = 1.054571817e-34 # unit of ℏ : J * s
__UpperCAmelCase = 3e8 # unit of c : m * s^-1
def _snake_case ( lowercase__ : float , lowercase__ : float , lowercase__ : float ) -> dict[str, float]:
'''simple docstring'''
if (force, area, distance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if force < 0:
raise ValueError("""Magnitude of force can not be negative""" )
if distance < 0:
raise ValueError("""Distance can not be negative""" )
if area < 0:
raise ValueError("""Area can not be negative""" )
if force == 0:
lowerCAmelCase_ :Union[str, Any] = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
2_4_0 * (distance) ** 4
)
return {"force": force}
elif area == 0:
lowerCAmelCase_ :Optional[Any] = (2_4_0 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
lowerCAmelCase_ :Any = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (2_4_0 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError("""One and only one argument must be 0""" )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {}
class SCREAMING_SNAKE_CASE__ ( lowercase ):
"""simple docstring"""
a : str ="llama"
a : List[str] =["past_key_values"]
def __init__( self , snake_case__=32_000 , snake_case__=4_096 , snake_case__=11_008 , snake_case__=32 , snake_case__=32 , snake_case__=None , snake_case__="silu" , snake_case__=2_048 , snake_case__=0.02 , snake_case__=1e-6 , snake_case__=True , snake_case__=0 , snake_case__=1 , snake_case__=2 , snake_case__=1 , snake_case__=False , snake_case__=None , **snake_case__ , ):
"""simple docstring"""
lowerCAmelCase : Optional[Any] = vocab_size
lowerCAmelCase : str = max_position_embeddings
lowerCAmelCase : str = hidden_size
lowerCAmelCase : Optional[int] = intermediate_size
lowerCAmelCase : Any = num_hidden_layers
lowerCAmelCase : List[str] = num_attention_heads
# for backward compatibility
if num_key_value_heads is None:
lowerCAmelCase : Tuple = num_attention_heads
lowerCAmelCase : Dict = num_key_value_heads
lowerCAmelCase : Optional[Any] = hidden_act
lowerCAmelCase : Optional[Any] = initializer_range
lowerCAmelCase : Any = rms_norm_eps
lowerCAmelCase : List[Any] = pretraining_tp
lowerCAmelCase : int = use_cache
lowerCAmelCase : List[str] = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=snake_case__ , bos_token_id=snake_case__ , eos_token_id=snake_case__ , tie_word_embeddings=snake_case__ , **snake_case__ , )
def lowercase__ ( self ):
"""simple docstring"""
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , snake_case__ ) 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}""" )
lowerCAmelCase : Optional[Any] = self.rope_scaling.get("type" , snake_case__ )
lowerCAmelCase : int = self.rope_scaling.get("factor" , snake_case__ )
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(snake_case__ , snake_case__ ) or rope_scaling_factor <= 1.0:
raise ValueError(f"""`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}""" )
| 108 |
"""simple docstring"""
def _snake_case ( lowercase__ : str , lowercase__ : str ) -> int:
'''simple docstring'''
if len(lowercase__ ) != len(lowercase__ ):
raise ValueError("""String lengths must match!""" )
lowerCAmelCase_ :Optional[int] = 0
for chara, chara in zip(lowercase__ , lowercase__ ):
if chara != chara:
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 84 | 0 |
"""simple docstring"""
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def _snake_case ( UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int , UpperCamelCase : int ):
# prepare kernel
# the kernel size have to be odd
if (ksize % 2) == 0:
UpperCAmelCase : int = ksize + 1
UpperCAmelCase : Any = np.zeros((ksize, ksize) , dtype=np.floataa )
# each value
for y in range(UpperCamelCase ):
for x in range(UpperCamelCase ):
# distance from center
UpperCAmelCase : int = x - ksize // 2
UpperCAmelCase : Optional[int] = y - ksize // 2
# degree to radiant
UpperCAmelCase : List[str] = theta / 180 * np.pi
UpperCAmelCase : str = np.cos(_theta )
UpperCAmelCase : Optional[Any] = np.sin(_theta )
# get kernel x
UpperCAmelCase : List[Any] = cos_theta * px + sin_theta * py
# get kernel y
UpperCAmelCase : Any = -sin_theta * px + cos_theta * py
# fill kernel
UpperCAmelCase : Any = np.exp(
-(_x**2 + gamma**2 * _y**2) / (2 * sigma**2) ) * np.cos(2 * np.pi * _x / lambd + psi )
return gabor
if __name__ == "__main__":
import doctest
doctest.testmod()
# read original image
A: Any = imread("../image_data/lena.jpg")
# turn image in gray scale value
A: Optional[int] = cvtColor(img, COLOR_BGR2GRAY)
# Apply multiple Kernel to detect edges
A: List[str] = np.zeros(gray.shape[:2])
for theta in [0, 3_0, 6_0, 9_0, 1_2_0, 1_5_0]:
A: List[str] = gabor_filter_kernel(1_0, 8, theta, 1_0, 0, 0)
out += filteraD(gray, CV_8UC3, kernel_aa)
A: str = out / out.max() * 2_5_5
A: List[Any] = out.astype(np.uinta)
imshow("Original", gray)
imshow("Gabor filter with 20x20 mask and 6 directions", out)
waitKey(0)
| 109 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE ( A__ ):
def __init__( self , __A ) -> Optional[Any]:
super().__init__()
lowerCAmelCase_ :int = nn.ModuleList(__A )
def __lowerCAmelCase ( self , __A , __A , __A , __A , __A , __A = None , __A = None , __A = None , __A = None , __A = False , __A = True , ) -> Union[ControlNetOutput, Tuple]:
for i, (image, scale, controlnet) in enumerate(zip(__A , __A , self.nets ) ):
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = controlnet(
__A , __A , __A , __A , __A , __A , __A , __A , __A , __A , __A , )
# merge samples
if i == 0:
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = down_samples, mid_sample
else:
lowerCAmelCase_ :str = [
samples_prev + samples_curr
for samples_prev, samples_curr in zip(__A , __A )
]
mid_block_res_sample += mid_sample
return down_block_res_samples, mid_block_res_sample
def __lowerCAmelCase ( self , __A , __A = True , __A = None , __A = False , __A = None , ) -> Optional[Any]:
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Dict = save_directory
for controlnet in self.nets:
controlnet.save_pretrained(
__A , is_main_process=__A , save_function=__A , safe_serialization=__A , variant=__A , )
idx += 1
lowerCAmelCase_ :Any = model_path_to_save + f"""_{idx}"""
@classmethod
def __lowerCAmelCase ( cls , __A , **__A ) -> List[Any]:
lowerCAmelCase_ :int = 0
lowerCAmelCase_ :Dict = []
# load controlnet and append to list until no controlnet directory exists anymore
# first controlnet has to be saved under `./mydirectory/controlnet` to be compliant with `DiffusionPipeline.from_prertained`
# second, third, ... controlnets have to be saved under `./mydirectory/controlnet_1`, `./mydirectory/controlnet_2`, ...
lowerCAmelCase_ :List[Any] = pretrained_model_path
while os.path.isdir(__A ):
lowerCAmelCase_ :Tuple = ControlNetModel.from_pretrained(__A , **__A )
controlnets.append(__A )
idx += 1
lowerCAmelCase_ :Dict = pretrained_model_path + f"""_{idx}"""
logger.info(f"""{len(__A )} controlnets loaded from {pretrained_model_path}.""" )
if len(__A ) == 0:
raise ValueError(
f"""No ControlNets found under {os.path.dirname(__A )}. Expected at least {pretrained_model_path + "_0"}.""" )
return cls(__A )
| 84 | 0 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
AutoTokenizer,
DataCollatorForLanguageModeling,
DataCollatorForPermutationLanguageModeling,
DataCollatorForWholeWordMask,
HfArgumentParser,
LineByLineTextDataset,
LineByLineWithRefDataset,
PreTrainedTokenizer,
TextDataset,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import is_main_process
lowerCAmelCase = logging.getLogger(__name__)
lowerCAmelCase = list(MODEL_WITH_LM_HEAD_MAPPING.keys())
lowerCAmelCase = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class _a :
_lowercase : Optional[str] = field(
default=UpperCamelCase__ , metadata={
'''help''': (
'''The model checkpoint for weights initialization. Leave None if you want to train a model from'''
''' scratch.'''
)
} , )
_lowercase : Optional[str] = field(
default=UpperCamelCase__ , metadata={'''help''': '''If training from scratch, pass a model type from the list: ''' + ''', '''.join(UpperCamelCase__ )} , )
_lowercase : Optional[str] = field(
default=UpperCamelCase__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
_lowercase : Optional[str] = field(
default=UpperCamelCase__ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
_lowercase : Optional[str] = field(
default=UpperCamelCase__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
@dataclass
class _a :
_lowercase : Optional[str] = field(
default=UpperCamelCase__ , metadata={'''help''': '''The input training data file (a text file).'''} )
_lowercase : Optional[str] = field(
default=UpperCamelCase__ , metadata={
'''help''': (
'''The input training data files (multiple files in glob format). '''
'''Very often splitting large files to smaller files can prevent tokenizer going out of memory'''
)
} , )
_lowercase : Optional[str] = field(
default=UpperCamelCase__ , metadata={'''help''': '''An optional input evaluation data file to evaluate the perplexity on (a text file).'''} , )
_lowercase : Optional[str] = field(
default=UpperCamelCase__ , metadata={'''help''': '''An optional input train ref data file for whole word mask in Chinese.'''} , )
_lowercase : Optional[str] = field(
default=UpperCamelCase__ , metadata={'''help''': '''An optional input eval ref data file for whole word mask in Chinese.'''} , )
_lowercase : bool = field(
default=UpperCamelCase__ , metadata={'''help''': '''Whether distinct lines of text in the dataset are to be handled as distinct sequences.'''} , )
_lowercase : bool = field(
default=UpperCamelCase__ , metadata={'''help''': '''Train with masked-language modeling loss instead of language modeling.'''} )
_lowercase : bool = field(default=UpperCamelCase__ , metadata={'''help''': '''Whether ot not to use whole word mask.'''} )
_lowercase : float = field(
default=0.15 , metadata={'''help''': '''Ratio of tokens to mask for masked language modeling loss'''} )
_lowercase : float = field(
default=1 / 6 , metadata={
'''help''': (
'''Ratio of length of a span of masked tokens to surrounding context length for permutation language'''
''' modeling.'''
)
} , )
_lowercase : int = field(
default=5 , metadata={'''help''': '''Maximum length of a span of masked tokens for permutation language modeling.'''} )
_lowercase : int = field(
default=-1 , metadata={
'''help''': (
'''Optional input sequence length after tokenization.'''
'''The training dataset will be truncated in block of this size for training.'''
'''Default to the model max input length for single sentence inputs (take into account special tokens).'''
)
} , )
_lowercase : bool = field(
default=UpperCamelCase__ , metadata={'''help''': '''Overwrite the cached training and evaluation sets'''} )
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False , SCREAMING_SNAKE_CASE = None , ):
"""simple docstring"""
def _dataset(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE=None ):
if args.line_by_line:
if ref_path is not None:
if not args.whole_word_mask or not args.mlm:
raise ValueError('''You need to set world whole masking and mlm to True for Chinese Whole Word Mask''' )
return LineByLineWithRefDataset(
tokenizer=SCREAMING_SNAKE_CASE , file_path=SCREAMING_SNAKE_CASE , block_size=args.block_size , ref_path=SCREAMING_SNAKE_CASE , )
return LineByLineTextDataset(tokenizer=SCREAMING_SNAKE_CASE , file_path=SCREAMING_SNAKE_CASE , block_size=args.block_size )
else:
return TextDataset(
tokenizer=SCREAMING_SNAKE_CASE , file_path=SCREAMING_SNAKE_CASE , block_size=args.block_size , overwrite_cache=args.overwrite_cache , cache_dir=SCREAMING_SNAKE_CASE , )
if evaluate:
return _dataset(args.eval_data_file , args.eval_ref_file )
elif args.train_data_files:
return ConcatDataset([_dataset(SCREAMING_SNAKE_CASE ) for f in glob(args.train_data_files )] )
else:
return _dataset(args.train_data_file , args.train_ref_file )
def _a ( ):
"""simple docstring"""
lowercase__ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
lowercase__ , lowercase__ , lowercase__ = parser.parse_args_into_dataclasses()
if data_args.eval_data_file is None and training_args.do_eval:
raise ValueError(
'''Cannot do evaluation without an evaluation data file. Either supply a file to --eval_data_file '''
'''or remove the --do_eval argument.''' )
if (
os.path.exists(training_args.output_dir )
and os.listdir(training_args.output_dir )
and training_args.do_train
and not training_args.overwrite_output_dir
):
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. Use'
''' --overwrite_output_dir to overcome.''' )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN , )
logger.warning(
'''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s''' , training_args.local_rank , training_args.device , training_args.n_gpu , bool(training_args.local_rank != -1 ) , training_args.fpaa , )
# Set the verbosity to info of the Transformers logger (on main process only):
if is_main_process(training_args.local_rank ):
transformers.utils.logging.set_verbosity_info()
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
logger.info('''Training/evaluation parameters %s''' , SCREAMING_SNAKE_CASE )
# Set seed
set_seed(training_args.seed )
# Load pretrained model and tokenizer
#
# Distributed training:
# The .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
if model_args.config_name:
lowercase__ = AutoConfig.from_pretrained(model_args.config_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
lowercase__ = AutoConfig.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
lowercase__ = CONFIG_MAPPING[model_args.model_type]()
logger.warning('''You are instantiating a new config instance from scratch.''' )
if model_args.tokenizer_name:
lowercase__ = AutoTokenizer.from_pretrained(model_args.tokenizer_name , cache_dir=model_args.cache_dir )
elif model_args.model_name_or_path:
lowercase__ = AutoTokenizer.from_pretrained(model_args.model_name_or_path , cache_dir=model_args.cache_dir )
else:
raise ValueError(
'''You are instantiating a new tokenizer from scratch. This is not supported, but you can do it from another'''
''' script, save it,and load it from here, using --tokenizer_name''' )
if model_args.model_name_or_path:
lowercase__ = AutoModelWithLMHead.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir , )
else:
logger.info('''Training new model from scratch''' )
lowercase__ = AutoModelWithLMHead.from_config(SCREAMING_SNAKE_CASE )
model.resize_token_embeddings(len(SCREAMING_SNAKE_CASE ) )
if config.model_type in ["bert", "roberta", "distilbert", "camembert"] and not data_args.mlm:
raise ValueError(
'''BERT and RoBERTa-like models do not have LM heads but masked LM heads. They must be run using the'''
'''--mlm flag (masked language modeling).''' )
if data_args.block_size <= 0:
lowercase__ = tokenizer.max_len
# Our input block size will be the max possible for the model
else:
lowercase__ = min(data_args.block_size , tokenizer.max_len )
# Get datasets
lowercase__ = (
get_dataset(SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir ) if training_args.do_train else None
)
lowercase__ = (
get_dataset(SCREAMING_SNAKE_CASE , tokenizer=SCREAMING_SNAKE_CASE , evaluate=SCREAMING_SNAKE_CASE , cache_dir=model_args.cache_dir )
if training_args.do_eval
else None
)
if config.model_type == "xlnet":
lowercase__ = DataCollatorForPermutationLanguageModeling(
tokenizer=SCREAMING_SNAKE_CASE , plm_probability=data_args.plm_probability , max_span_length=data_args.max_span_length , )
else:
if data_args.mlm and data_args.whole_word_mask:
lowercase__ = DataCollatorForWholeWordMask(
tokenizer=SCREAMING_SNAKE_CASE , mlm_probability=data_args.mlm_probability )
else:
lowercase__ = DataCollatorForLanguageModeling(
tokenizer=SCREAMING_SNAKE_CASE , mlm=data_args.mlm , mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
lowercase__ = Trainer(
model=SCREAMING_SNAKE_CASE , args=SCREAMING_SNAKE_CASE , data_collator=SCREAMING_SNAKE_CASE , train_dataset=SCREAMING_SNAKE_CASE , eval_dataset=SCREAMING_SNAKE_CASE , prediction_loss_only=SCREAMING_SNAKE_CASE , )
# Training
if training_args.do_train:
lowercase__ = (
model_args.model_name_or_path
if model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path )
else None
)
trainer.train(model_path=SCREAMING_SNAKE_CASE )
trainer.save_model()
# For convenience, we also re-save the tokenizer to the same directory,
# so that you can share your model easily on huggingface.co/models =)
if trainer.is_world_master():
tokenizer.save_pretrained(training_args.output_dir )
# Evaluation
lowercase__ = {}
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
lowercase__ = trainer.evaluate()
lowercase__ = math.exp(eval_output['''eval_loss'''] )
lowercase__ = {'''perplexity''': perplexity}
lowercase__ = os.path.join(training_args.output_dir , '''eval_results_lm.txt''' )
if trainer.is_world_master():
with open(SCREAMING_SNAKE_CASE , '''w''' ) as writer:
logger.info('''***** Eval results *****''' )
for key in sorted(result.keys() ):
logger.info(''' %s = %s''' , SCREAMING_SNAKE_CASE , str(result[key] ) )
writer.write('''%s = %s\n''' % (key, str(result[key] )) )
results.update(SCREAMING_SNAKE_CASE )
return results
def _a ( SCREAMING_SNAKE_CASE ):
"""simple docstring"""
main()
if __name__ == "__main__":
main()
| 110 |
"""simple docstring"""
from PIL import Image
def _snake_case ( lowercase__ : Image , lowercase__ : float ) -> Image:
'''simple docstring'''
def brightness(lowercase__ : int ) -> float:
return 1_2_8 + level + (c - 1_2_8)
if not -255.0 <= level <= 255.0:
raise ValueError("""level must be between -255.0 (black) and 255.0 (white)""" )
return img.point(lowercase__ )
if __name__ == "__main__":
# Load image
with Image.open('image_data/lena.jpg') as img:
# Change brightness to 100
__UpperCAmelCase = change_brightness(img, 1_00)
brigt_img.save('image_data/lena_brightness.png', format='png')
| 84 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_SCREAMING_SNAKE_CASE : Any = logging.get_logger(__name__)
class A__ ( A__ ):
"""simple docstring"""
__magic_name__ = "timm_backbone"
def __init__( self , __snake_case=None , __snake_case=3 , __snake_case=True , __snake_case=True , __snake_case=None , **__snake_case , ):
super().__init__(**__A )
snake_case = backbone
snake_case = num_channels
snake_case = features_only
snake_case = use_pretrained_backbone
snake_case = True
snake_case = out_indices if out_indices is not None else (-1,)
| 127 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class _SCREAMING_SNAKE_CASE :
def __lowerCAmelCase ( self ) -> Tuple:
torch.manual_seed(0 )
lowerCAmelCase_ :int = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :List[Any] = UNetaDConditionModel(
sample_size=32 , layers_per_block=1 , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=3 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCAmelCase_ :str = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__A , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase_ :int = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowerCAmelCase ( self ) -> List[str]:
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = TaEncoderModel.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = AutoTokenizer.from_pretrained("""hf-internal-testing/tiny-random-t5""" )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[Any] = UNetaDConditionModel(
sample_size=32 , layers_per_block=[1, 2] , block_out_channels=[32, 64] , down_block_types=[
"""ResnetDownsampleBlock2D""",
"""SimpleCrossAttnDownBlock2D""",
] , mid_block_type="""UNetMidBlock2DSimpleCrossAttn""" , up_block_types=["""SimpleCrossAttnUpBlock2D""", """ResnetUpsampleBlock2D"""] , in_channels=6 , out_channels=6 , cross_attention_dim=32 , encoder_hid_dim=32 , attention_head_dim=8 , addition_embed_type="""text""" , addition_embed_type_num_heads=2 , cross_attention_norm="""group_norm""" , resnet_time_scale_shift="""scale_shift""" , act_fn="""gelu""" , class_embed_type="""timestep""" , mid_block_scale_factor=1.4_1_4 , time_embedding_act_fn="""gelu""" , time_embedding_dim=32 , )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
lowerCAmelCase_ :str = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , thresholding=__A , dynamic_thresholding_ratio=0.9_5 , sample_max_value=1.0 , prediction_type="""epsilon""" , variance_type="""learned_range""" , )
torch.manual_seed(0 )
lowerCAmelCase_ :Optional[int] = DDPMScheduler(
num_train_timesteps=1000 , beta_schedule="""squaredcos_cap_v2""" , beta_start=0.0_0_0_1 , beta_end=0.0_2 , )
torch.manual_seed(0 )
lowerCAmelCase_ :Dict = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Dict = self.get_dummy_components()
lowerCAmelCase_ :Tuple = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Any = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Optional[int] = inputs["""prompt"""]
lowerCAmelCase_ :Optional[int] = inputs["""generator"""]
lowerCAmelCase_ :Any = inputs["""num_inference_steps"""]
lowerCAmelCase_ :Optional[int] = inputs["""output_type"""]
if "image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""image"""]
else:
lowerCAmelCase_ :int = None
if "mask_image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""mask_image"""]
else:
lowerCAmelCase_ :int = None
if "original_image" in inputs:
lowerCAmelCase_ :List[Any] = inputs["""original_image"""]
else:
lowerCAmelCase_ :List[Any] = None
lowerCAmelCase_ , lowerCAmelCase_ :int = pipe.encode_prompt(__A )
# inputs with prompt converted to embeddings
lowerCAmelCase_ :List[str] = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowerCAmelCase_ :int = image
if mask_image is not None:
lowerCAmelCase_ :Tuple = mask_image
if original_image is not None:
lowerCAmelCase_ :Optional[Any] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(__A , __A , __A )
lowerCAmelCase_ :Optional[int] = pipe(**__A )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__A )
lowerCAmelCase_ :Optional[int] = self.pipeline_class.from_pretrained(__A )
pipe_loaded.to(__A )
pipe_loaded.set_progress_bar_config(disable=__A )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__A , __A ) is None , f"""`{optional_component}` did not stay set to None after loading.""" , )
lowerCAmelCase_ :Dict = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Union[str, Any] = inputs["""generator"""]
lowerCAmelCase_ :Any = inputs["""num_inference_steps"""]
lowerCAmelCase_ :Tuple = inputs["""output_type"""]
# inputs with prompt converted to embeddings
lowerCAmelCase_ :Tuple = {
"""prompt_embeds""": prompt_embeds,
"""negative_prompt_embeds""": negative_prompt_embeds,
"""generator""": generator,
"""num_inference_steps""": num_inference_steps,
"""output_type""": output_type,
}
if image is not None:
lowerCAmelCase_ :Optional[int] = image
if mask_image is not None:
lowerCAmelCase_ :str = mask_image
if original_image is not None:
lowerCAmelCase_ :Tuple = original_image
lowerCAmelCase_ :Union[str, Any] = pipe_loaded(**__A )[0]
lowerCAmelCase_ :Dict = np.abs(to_np(__A ) - to_np(__A ) ).max()
self.assertLess(__A , 1E-4 )
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :Any = self.get_dummy_components()
lowerCAmelCase_ :Optional[int] = self.pipeline_class(**__A )
pipe.to(__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[int] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :Dict = pipe(**__A )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__A )
lowerCAmelCase_ :Any = self.pipeline_class.from_pretrained(__A )
pipe_loaded.to(__A )
pipe_loaded.set_progress_bar_config(disable=__A )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs(__A )
lowerCAmelCase_ :str = pipe_loaded(**__A )[0]
lowerCAmelCase_ :Dict = np.abs(to_np(__A ) - to_np(__A ) ).max()
self.assertLess(__A , 1E-4 )
| 84 | 0 |
"""simple docstring"""
import os
def _lowerCamelCase ( ):
'''simple docstring'''
__lowerCAmelCase = os.path.join(os.path.dirname(lowercase__ ) , "num.txt" )
with open(lowercase__ ) as file_hand:
return str(sum(int(lowercase__ ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 57 |
"""simple docstring"""
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Optional[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = FlaxStableDiffusionPipeline.from_pretrained(
"""stabilityai/stable-diffusion-2""" , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :int = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :List[Any] = jax.device_count()
lowerCAmelCase_ :Optional[Any] = num_samples * [prompt]
lowerCAmelCase_ :int = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Optional[Any] = replicate(__A )
lowerCAmelCase_ :Union[str, Any] = shard(__A )
lowerCAmelCase_ :Optional[Any] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :Tuple = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Union[str, Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :Any = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Optional[int] = jnp.array([0.4_2_3_8, 0.4_4_1_4, 0.4_3_9_5, 0.4_4_5_3, 0.4_6_2_9, 0.4_5_9_0, 0.4_5_3_1, 0.4_5_5_0_8, 0.4_5_1_2] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Union[str, Any] = """stabilityai/stable-diffusion-2"""
lowerCAmelCase_ , lowerCAmelCase_ :Tuple = FlaxDPMSolverMultistepScheduler.from_pretrained(__A , subfolder="""scheduler""" )
lowerCAmelCase_ , lowerCAmelCase_ :List[str] = FlaxStableDiffusionPipeline.from_pretrained(
__A , scheduler=__A , revision="""bf16""" , dtype=jnp.bfloataa , )
lowerCAmelCase_ :Optional[int] = scheduler_params
lowerCAmelCase_ :List[Any] = """A painting of a squirrel eating a burger"""
lowerCAmelCase_ :Tuple = jax.device_count()
lowerCAmelCase_ :str = num_samples * [prompt]
lowerCAmelCase_ :Union[str, Any] = sd_pipe.prepare_inputs(__A )
lowerCAmelCase_ :Tuple = replicate(__A )
lowerCAmelCase_ :Optional[int] = shard(__A )
lowerCAmelCase_ :List[str] = jax.random.PRNGKey(0 )
lowerCAmelCase_ :List[Any] = jax.random.split(__A , jax.device_count() )
lowerCAmelCase_ :Optional[Any] = sd_pipe(__A , __A , __A , num_inference_steps=25 , jit=__A )[0]
assert images.shape == (jax.device_count(), 1, 768, 768, 3)
lowerCAmelCase_ :List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
lowerCAmelCase_ :List[str] = images[0, 253:256, 253:256, -1]
lowerCAmelCase_ :Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
lowerCAmelCase_ :Dict = jnp.array([0.4_3_3_6, 0.4_2_9_6_9, 0.4_4_5_3, 0.4_1_9_9, 0.4_2_9_7, 0.4_5_3_1, 0.4_4_3_4, 0.4_4_3_4, 0.4_2_9_7] )
print(f"""output_slice: {output_slice}""" )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 84 | 0 |
from argparse import ArgumentParser
from .env import EnvironmentCommand
def a_ ( ):
'''simple docstring'''
_lowerCamelCase : str =ArgumentParser('Diffusers CLI tool' , usage='diffusers-cli <command> [<args>]' )
_lowerCamelCase : Tuple =parser.add_subparsers(help='diffusers-cli command helpers' )
# Register commands
EnvironmentCommand.register_subcommand(lowercase__ )
# Let's go
_lowerCamelCase : Tuple =parser.parse_args()
if not hasattr(lowercase__ , 'func' ):
parser.print_help()
exit(1 )
# Run
_lowerCamelCase : List[str] =args.func(lowercase__ )
service.run()
if __name__ == "__main__":
main()
| 199 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _snake_case ( ) -> Generator[int, None, None]:
'''simple docstring'''
lowerCAmelCase_ :dict[int, int] = {}
lowerCAmelCase_ :int = 2
while True:
lowerCAmelCase_ :List[Any] = factor_map.pop(lowercase__ , lowercase__ )
if factor:
lowerCAmelCase_ :Optional[int] = factor + prime
while x in factor_map:
x += factor
lowerCAmelCase_ :List[str] = factor
else:
lowerCAmelCase_ :Optional[int] = prime
yield prime
prime += 1
def _snake_case ( lowercase__ : float = 1E10 ) -> int:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = sieve()
lowerCAmelCase_ :str = 1
while True:
lowerCAmelCase_ :int = next(lowercase__ )
if (2 * prime * n) > limit:
return n
# Ignore the next prime as the reminder will be 2.
next(lowercase__ )
n += 2
if __name__ == "__main__":
print(solution())
| 84 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ = {
"""configuration_mgp_str""": ["""MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MgpstrConfig"""],
"""processing_mgp_str""": ["""MgpstrProcessor"""],
"""tokenization_mgp_str""": ["""MgpstrTokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase__ = [
"""MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""MgpstrModel""",
"""MgpstrPreTrainedModel""",
"""MgpstrForSceneTextRecognition""",
]
if TYPE_CHECKING:
from .configuration_mgp_str import MGP_STR_PRETRAINED_CONFIG_ARCHIVE_MAP, MgpstrConfig
from .processing_mgp_str import MgpstrProcessor
from .tokenization_mgp_str import MgpstrTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mgp_str import (
MGP_STR_PRETRAINED_MODEL_ARCHIVE_LIST,
MgpstrForSceneTextRecognition,
MgpstrModel,
MgpstrPreTrainedModel,
)
else:
import sys
lowerCAmelCase__ = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 68 |
"""simple docstring"""
import random
import unittest
import numpy as np
import torch
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionUpscalePipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
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 _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
# TODO: is there an appropriate internal test set?
UpperCAmelCase_ :List[Any] = "ssube/stable-diffusion-x4-upscaler-onnx"
def __lowerCAmelCase ( self , __A=0 ) -> Optional[int]:
lowerCAmelCase_ :Optional[Any] = floats_tensor((1, 3, 128, 128) , rng=random.Random(__A ) )
lowerCAmelCase_ :List[Any] = torch.manual_seed(__A )
lowerCAmelCase_ :Tuple = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""image""": image,
"""generator""": generator,
"""num_inference_steps""": 3,
"""guidance_scale""": 7.5,
"""output_type""": """numpy""",
}
return inputs
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Optional[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Dict = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1].flatten()
# started as 128, should now be 512
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :int = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Tuple = PNDMScheduler.from_config(pipe.scheduler.config , skip_prk_steps=__A )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :int = self.get_dummy_inputs()
lowerCAmelCase_ :List[str] = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :str = np.array(
[0.6_8_9_8_8_9_2, 0.5_9_2_4_0_5_5_6, 0.5_2_4_9_9_5_2_7, 0.5_8_8_6_6_2_1_5, 0.5_2_2_5_8_2_3_5, 0.5_2_5_7_2_7_1_5, 0.6_2_4_1_4_4_7_3, 0.6_1_7_4_3_8_7, 0.6_2_1_4_9_6_4] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Optional[Any] = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Union[str, Any] = pipe(**__A ).images
lowerCAmelCase_ :Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Tuple = np.array(
[0.7_6_5_9_2_7_8, 0.7_6_4_3_7_6_6_4, 0.7_5_5_7_9_1_0_7, 0.7_6_9_1_1_1_6, 0.7_7_6_6_6_9_8_6, 0.7_7_2_7_6_7_2, 0.7_7_5_8_6_6_4, 0.7_8_1_2_2_2_6, 0.7_6_9_4_2_5_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> Union[str, Any]:
lowerCAmelCase_ :Union[str, Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Union[str, Any] = EulerDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Optional[Any] = pipe(**__A ).images
lowerCAmelCase_ :Any = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Tuple = np.array(
[0.6_9_7_4_7_8_2, 0.6_8_9_0_2_0_9_3, 0.7_0_1_3_5_8_8_5, 0.7_5_8_3_6_1_8, 0.7_8_0_4_5_4_5, 0.7_8_5_4_9_1_2, 0.7_8_6_6_7_4_2_6, 0.7_8_7_4_3_8_6_3, 0.7_8_0_7_0_2_2_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
def __lowerCAmelCase ( self ) -> List[str]:
lowerCAmelCase_ :List[str] = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint , provider="""CPUExecutionProvider""" )
lowerCAmelCase_ :Optional[int] = EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :List[Any] = self.get_dummy_inputs()
lowerCAmelCase_ :Dict = pipe(**__A ).images
lowerCAmelCase_ :Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Dict = np.array(
[0.7_7_4_2_4_4_9_6, 0.7_7_3_6_0_1, 0.7_6_4_5_2_8_8, 0.7_7_6_9_5_9_8, 0.7_7_7_2_7_3_9, 0.7_7_3_8_6_8_8, 0.7_8_1_8_7_2_3_3, 0.7_7_8_7_9_5_8_4, 0.7_6_7_0_4_3] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-1
@nightly
@require_onnxruntime
@require_torch_gpu
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
@property
def __lowerCAmelCase ( self ) -> List[Any]:
return (
"CUDAExecutionProvider",
{
"gpu_mem_limit": "15000000000", # 15GB
"arena_extend_strategy": "kSameAsRequested",
},
)
@property
def __lowerCAmelCase ( self ) -> Tuple:
lowerCAmelCase_ :Optional[int] = ort.SessionOptions()
lowerCAmelCase_ :Dict = False
return options
def __lowerCAmelCase ( self ) -> Optional[Any]:
lowerCAmelCase_ :Optional[Any] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase_ :Optional[Any] = init_image.resize((128, 128) )
# using the PNDM scheduler by default
lowerCAmelCase_ :Tuple = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Union[str, Any] = """A fantasy landscape, trending on artstation"""
lowerCAmelCase_ :List[Any] = torch.manual_seed(0 )
lowerCAmelCase_ :str = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=10 , generator=__A , output_type="""np""" , )
lowerCAmelCase_ :Dict = output.images
lowerCAmelCase_ :List[str] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Optional[Any] = np.array([0.4_8_8_3, 0.4_9_4_7, 0.4_9_8_0, 0.4_9_7_5, 0.4_9_8_2, 0.4_9_8_0, 0.5_0_0_0, 0.5_0_0_6, 0.4_9_7_2] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
def __lowerCAmelCase ( self ) -> Dict:
lowerCAmelCase_ :Optional[int] = load_image(
"""https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
"""/img2img/sketch-mountains-input.jpg""" )
lowerCAmelCase_ :List[str] = init_image.resize((128, 128) )
lowerCAmelCase_ :Any = LMSDiscreteScheduler.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , subfolder="""scheduler""" )
lowerCAmelCase_ :Optional[Any] = OnnxStableDiffusionUpscalePipeline.from_pretrained(
"""ssube/stable-diffusion-x4-upscaler-onnx""" , scheduler=__A , provider=self.gpu_provider , sess_options=self.gpu_options , )
pipe.set_progress_bar_config(disable=__A )
lowerCAmelCase_ :Any = """A fantasy landscape, trending on artstation"""
lowerCAmelCase_ :Optional[Any] = torch.manual_seed(0 )
lowerCAmelCase_ :List[str] = pipe(
prompt=__A , image=__A , guidance_scale=7.5 , num_inference_steps=20 , generator=__A , output_type="""np""" , )
lowerCAmelCase_ :int = output.images
lowerCAmelCase_ :List[Any] = images[0, 255:258, 383:386, -1]
assert images.shape == (1, 512, 512, 3)
lowerCAmelCase_ :Union[str, Any] = np.array(
[0.5_0_1_7_3_7_5_3, 0.5_0_2_2_3_3_5_6, 0.5_0_2_0_3_9, 0.5_0_2_3_3_0_3_6, 0.5_0_2_3_7_2_5, 0.5_0_2_2_6_0_1, 0.5_0_1_8_7_5_8, 0.5_0_2_3_4_0_8_5, 0.5_0_2_4_1_5_6_6] )
# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2E-2
| 84 | 0 |
import sys
def lowerCamelCase_ ( _a : Tuple ):
'''simple docstring'''
UpperCAmelCase_ : str = len(lowercase__ )
UpperCAmelCase_ : Dict = [[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
UpperCAmelCase_ : Any = [[0 for x in range(lowercase__ )] for x in range(lowercase__ )]
for chain_length in range(2 , lowercase__ ):
for a in range(1 , n - chain_length + 1 ):
UpperCAmelCase_ : str = a + chain_length - 1
UpperCAmelCase_ : Dict = sys.maxsize
for c in range(lowercase__ , lowercase__ ):
UpperCAmelCase_ : List[str] = (
matrix[a][c] + matrix[c + 1][b] + array[a - 1] * array[c] * array[b]
)
if cost < matrix[a][b]:
UpperCAmelCase_ : Any = cost
UpperCAmelCase_ : Optional[int] = c
return matrix, sol
def lowerCamelCase_ ( _a : int , _a : Optional[Any] , _a : Dict ):
'''simple docstring'''
if i == j:
print("""A""" + str(lowercase__ ) , end=""" """ )
else:
print("""(""" , end=""" """ )
print_optiomal_solution(lowercase__ , lowercase__ , optimal_solution[i][j] )
print_optiomal_solution(lowercase__ , optimal_solution[i][j] + 1 , lowercase__ )
print(""")""" , end=""" """ )
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCAmelCase_ : Any = [30, 35, 15, 5, 10, 20, 25]
UpperCAmelCase_ : Any = len(lowercase__ )
# Size of matrix created from above array will be
# 30*35 35*15 15*5 5*10 10*20 20*25
UpperCAmelCase_ : Dict = matrix_chain_order(lowercase__ )
print("""No. of Operation required: """ + str(matrix[1][n - 1] ) )
print_optiomal_solution(lowercase__ , 1 , n - 1 )
if __name__ == "__main__":
main()
| 345 |
"""simple docstring"""
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("TEST_SAGEMAKER" , "False" ) ) is not True , reason="Skipping test because should only be run when releasing minor transformers version" , )
@pytest.mark.usefixtures("sm_env" )
@parameterized_class(
[
{
"framework": "pytorch",
"script": "run_glue_model_parallelism.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
{
"framework": "pytorch",
"script": "run_glue.py",
"model_name_or_path": "roberta-large",
"instance_type": "ml.p3dn.24xlarge",
"results": {"train_runtime": 1600, "eval_accuracy": 0.3, "eval_loss": 1.2},
},
] )
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def __lowerCAmelCase ( self ) -> Dict:
if self.framework == "pytorch":
subprocess.run(
f"""cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py""".split() , encoding="""utf-8""" , check=__A , )
assert hasattr(self , """env""" )
def __lowerCAmelCase ( self , __A ) -> Any:
# configuration for running training on smdistributed Model Parallel
lowerCAmelCase_ :Union[str, Any] = {
"""enabled""": True,
"""processes_per_host""": 8,
}
lowerCAmelCase_ :Tuple = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
lowerCAmelCase_ :Any = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
lowerCAmelCase_ :Any = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=f"""{self.env.base_job_name}-{instance_count}-smp-{name_extension}""" , instance_count=__A , instance_type=self.instance_type , debugger_hook_config=__A , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 500,
} , metric_definitions=self.env.metric_definitions , distribution=__A , py_version="""py36""" , )
def __lowerCAmelCase ( self , __A ) -> List[Any]:
TrainingJobAnalytics(__A ).export_csv(f"""{self.env.test_path}/{job_name}_metrics.csv""" )
@parameterized.expand([(1,)] )
def __lowerCAmelCase ( self , __A ) -> List[str]:
# create estimator
lowerCAmelCase_ :Any = self.create_estimator(__A )
# run training
estimator.fit()
# result dataframe
lowerCAmelCase_ :Optional[Any] = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
lowerCAmelCase_ :List[str] = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
lowerCAmelCase_ :Optional[int] = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
lowerCAmelCase_ :Optional[int] = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 99_9999 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(f"""{estimator.latest_training_job.name}.json""" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , __A )
| 84 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers.models.esm.modeling_tf_esm import (
TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
TFEsmModel,
)
class a_ :
def __init__( self , snake_case_ , ):
_lowerCAmelCase : int = parent
_lowerCAmelCase : Tuple = 1_3
_lowerCAmelCase : Optional[Any] = 7
_lowerCAmelCase : List[str] = True
_lowerCAmelCase : Union[str, Any] = True
_lowerCAmelCase : Tuple = True
_lowerCAmelCase : int = 9_9
_lowerCAmelCase : Optional[Any] = 3_2
_lowerCAmelCase : Optional[int] = 2
_lowerCAmelCase : Optional[Any] = 4
_lowerCAmelCase : Any = 3_7
_lowerCAmelCase : List[Any] = """gelu"""
_lowerCAmelCase : Optional[Any] = 0.1
_lowerCAmelCase : Dict = 0.1
_lowerCAmelCase : Union[str, Any] = 5_1_2
_lowerCAmelCase : Union[str, Any] = 1_6
_lowerCAmelCase : Optional[int] = 2
_lowerCAmelCase : str = 0.02
_lowerCAmelCase : List[Any] = 3
_lowerCAmelCase : Union[str, Any] = 4
_lowerCAmelCase : int = None
def __UpperCamelCase ( self ):
_lowerCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase : Any = None
if self.use_input_mask:
_lowerCAmelCase : Union[str, Any] = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase : Tuple = None
_lowerCAmelCase : Tuple = None
_lowerCAmelCase : Optional[Any] = None
if self.use_labels:
_lowerCAmelCase : Union[str, Any] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase : str = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase : Optional[Any] = EsmConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , pad_token_id=1 , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __UpperCamelCase ( self ):
(
_lowerCAmelCase
) : Tuple = self.prepare_config_and_inputs()
_lowerCAmelCase : Union[str, Any] = True
_lowerCAmelCase : Any = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
_lowerCAmelCase : int = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
return (
config,
input_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase : Optional[Any] = TFEsmModel(config=__A )
_lowerCAmelCase : Tuple = {"""input_ids""": input_ids, """attention_mask""": input_mask}
_lowerCAmelCase : List[str] = model(__A )
_lowerCAmelCase : Union[str, Any] = [input_ids, input_mask]
_lowerCAmelCase : int = model(__A )
_lowerCAmelCase : int = model(__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , ):
_lowerCAmelCase : Optional[int] = True
_lowerCAmelCase : Tuple = TFEsmModel(config=__A )
_lowerCAmelCase : List[Any] = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""encoder_hidden_states""": encoder_hidden_states,
"""encoder_attention_mask""": encoder_attention_mask,
}
_lowerCAmelCase : Dict = model(__A )
_lowerCAmelCase : Optional[int] = [input_ids, input_mask]
_lowerCAmelCase : Optional[Any] = model(__A , encoder_hidden_states=__A )
# Also check the case where encoder outputs are not passed
_lowerCAmelCase : int = model(__A , attention_mask=__A )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase : Optional[int] = TFEsmForMaskedLM(config=__A )
_lowerCAmelCase : Tuple = model([input_ids, input_mask] )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __UpperCamelCase ( self , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ , snake_case_ ):
_lowerCAmelCase : Dict = self.num_labels
_lowerCAmelCase : List[str] = TFEsmForTokenClassification(config=__A )
_lowerCAmelCase : str = {"""input_ids""": input_ids, """attention_mask""": input_mask}
_lowerCAmelCase : Tuple = model(__A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __UpperCamelCase ( self ):
_lowerCAmelCase : List[Any] = self.prepare_config_and_inputs()
(
_lowerCAmelCase
) : Optional[int] = config_and_inputs
_lowerCAmelCase : Tuple = {"""input_ids""": input_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class a_ (A__ , A__ , unittest.TestCase ):
__lowerCAmelCase : Any = (
(
TFEsmModel,
TFEsmForMaskedLM,
TFEsmForSequenceClassification,
TFEsmForTokenClassification,
)
if is_tf_available()
else ()
)
__lowerCAmelCase : int = (
{
"feature-extraction": TFEsmModel,
"fill-mask": TFEsmForMaskedLM,
"text-classification": TFEsmForSequenceClassification,
"token-classification": TFEsmForTokenClassification,
"zero-shot": TFEsmForSequenceClassification,
}
if is_tf_available()
else {}
)
__lowerCAmelCase : Any = False
__lowerCAmelCase : List[str] = False
def __UpperCamelCase ( self ):
_lowerCAmelCase : str = TFEsmModelTester(self )
_lowerCAmelCase : List[Any] = ConfigTester(self , config_class=__A , hidden_size=3_7 )
def __UpperCamelCase ( self ):
self.config_tester.run_common_tests()
def __UpperCamelCase ( self ):
_lowerCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__A )
def __UpperCamelCase ( self ):
_lowerCAmelCase : Dict = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*__A )
def __UpperCamelCase ( self ):
_lowerCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__A )
def __UpperCamelCase ( self ):
_lowerCAmelCase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__A )
@slow
def __UpperCamelCase ( self ):
for model_name in TF_ESM_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase : Union[str, Any] = TFEsmModel.from_pretrained(__A )
self.assertIsNotNone(__A )
@unittest.skip("""Protein models do not support embedding resizing.""" )
def __UpperCamelCase ( self ):
pass
@unittest.skip("""Protein models do not support embedding resizing.""" )
def __UpperCamelCase ( self ):
pass
def __UpperCamelCase ( self ):
_lowerCAmelCase : List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
_lowerCAmelCase : Union[str, Any] = model_class(__A )
assert isinstance(model.get_input_embeddings() , tf.keras.layers.Layer )
if model_class is TFEsmForMaskedLM:
# Output embedding test differs from the main test because they're a matrix, not a layer
_lowerCAmelCase : Optional[int] = model.get_bias()
assert isinstance(__A , __A )
for k, v in name.items():
assert isinstance(__A , tf.Variable )
else:
_lowerCAmelCase : Tuple = model.get_output_embeddings()
assert x is None
_lowerCAmelCase : Any = model.get_bias()
assert name is None
@require_tf
class a_ (unittest.TestCase ):
@slow
def __UpperCamelCase ( self ):
_lowerCAmelCase : Any = TFEsmForMaskedLM.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
_lowerCAmelCase : Tuple = tf.constant([[0, 1, 2, 3, 4, 5]] )
_lowerCAmelCase : str = model(__A )[0]
_lowerCAmelCase : str = [1, 6, 3_3]
self.assertEqual(list(output.numpy().shape ) , __A )
# compare the actual values for a slice.
_lowerCAmelCase : int = tf.constant(
[
[
[8.92_1518, -1_0.5_8_9_8_1_4, -6.467_1307],
[-6.396_7156, -1_3.9_1_1_3_7_7, -1.121_1915],
[-7.78_1247, -1_3.9_5_1_5_5_7, -3.74_0592],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-2 ) )
@slow
def __UpperCamelCase ( self ):
_lowerCAmelCase : List[str] = TFEsmModel.from_pretrained("""facebook/esm2_t6_8M_UR50D""" )
_lowerCAmelCase : Dict = tf.constant([[0, 6, 4, 1_3, 5, 4, 1_6, 1_2, 1_1, 7, 2]] )
_lowerCAmelCase : Tuple = model(__A )[0]
# compare the actual values for a slice.
_lowerCAmelCase : int = tf.constant(
[
[
[0.1444_3092, 0.5412_5327, 0.324_7739],
[0.3034_0484, 0.0052_6676, 0.3107_7722],
[0.3227_8043, -0.2498_7096, 0.341_4628],
]
] )
self.assertTrue(numpy.allclose(output[:, :3, :3].numpy() , expected_slice.numpy() , atol=1E-4 ) )
| 309 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 1_0 ) -> str:
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or n < 0:
raise ValueError("""Invalid input""" )
lowerCAmelCase_ :List[str] = 1_0**n
lowerCAmelCase_ :int = 2_8_4_3_3 * (pow(2 , 7_8_3_0_4_5_7 , lowercase__ )) + 1
return str(number % modulus )
if __name__ == "__main__":
from doctest import testmod
testmod()
print(F"""{solution(10) = }""")
| 84 | 0 |
'''simple docstring'''
from collections import defaultdict
def UpperCamelCase( UpperCAmelCase_ ):
UpperCAmelCase : List[Any] = 1
UpperCAmelCase : str = True
for v in tree[start]:
if v not in visited:
ret += dfs(lowercase__ )
if ret % 2 == 0:
cuts.append(lowercase__ )
return ret
def UpperCamelCase( ):
dfs(1 )
if __name__ == "__main__":
lowercase__ , lowercase__ = 10, 9
lowercase__ = defaultdict(list)
lowercase__ = {}
lowercase__ = []
lowercase__ = 0
lowercase__ = [(2, 1), (3, 1), (4, 3), (5, 2), (6, 1), (7, 2), (8, 6), (9, 8), (10, 8)]
for u, v in edges:
tree[u].append(v)
tree[v].append(u)
even_tree()
print(len(cuts) - 1)
| 151 |
"""simple docstring"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__UpperCAmelCase = 'src/transformers'
__UpperCAmelCase = 'docs/source/en/tasks'
def _snake_case ( lowercase__ : str , lowercase__ : List[str] , lowercase__ : Any ) -> str:
'''simple docstring'''
with open(lowercase__ , """r""" , encoding="""utf-8""" , newline="""\n""" ) as f:
lowerCAmelCase_ :List[Any] = f.readlines()
# Find the start prompt.
lowerCAmelCase_ :Tuple = 0
while not lines[start_index].startswith(lowercase__ ):
start_index += 1
start_index += 1
lowerCAmelCase_ :Dict = start_index
while not lines[end_index].startswith(lowercase__ ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
__UpperCAmelCase = direct_transformers_import(TRANSFORMERS_PATH)
__UpperCAmelCase = {
'asr.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
'audio_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
'language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
'image_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
'masked_language_modeling.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
'multiple_choice.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
'object_detection.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
'question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
'semantic_segmentation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
'sequence_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
'summarization.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'token_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
'translation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
'video_classification.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
'document_question_answering.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
'monocular_depth_estimation.md': transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__UpperCAmelCase = {
'summarization.md': ('nllb',),
'translation.md': ('nllb',),
}
def _snake_case ( lowercase__ : List[str] ) -> str:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = TASK_GUIDE_TO_MODELS[task_guide]
lowerCAmelCase_ :List[Any] = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(lowercase__ , set() )
lowerCAmelCase_ :Union[str, Any] = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([f"""[{name}](../model_doc/{code})""" for code, name in model_names.items()] ) + "\n"
def _snake_case ( lowercase__ : int , lowercase__ : str=False ) -> Dict:
'''simple docstring'''
lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ :List[Any] = _find_text_in_file(
filename=os.path.join(lowercase__ , lowercase__ ) , start_prompt="""<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->""" , end_prompt="""<!--End of the generated tip-->""" , )
lowerCAmelCase_ :int = get_model_list_for_task(lowercase__ )
if current_list != new_list:
if overwrite:
with open(os.path.join(lowercase__ , lowercase__ ) , """w""" , encoding="""utf-8""" , newline="""\n""" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
f"""The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`"""
""" to fix this.""" )
if __name__ == "__main__":
__UpperCAmelCase = argparse.ArgumentParser()
parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
__UpperCAmelCase = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 84 | 0 |
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class _snake_case :
def SCREAMING_SNAKE_CASE__ ( self , a) -> int:
raise NotImplementedError()
def SCREAMING_SNAKE_CASE__ ( self) -> Optional[Any]:
raise NotImplementedError()
class _snake_case ( A__ ):
def __init__( self , a , a = False , **a) -> int:
SCREAMING_SNAKE_CASE = tokenizer
SCREAMING_SNAKE_CASE = skip_prompt
SCREAMING_SNAKE_CASE = decode_kwargs
# variables used in the streaming process
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = True
def SCREAMING_SNAKE_CASE__ ( self , a) -> str:
if len(value.shape) > 1 and value.shape[0] > 1:
raise ValueError('TextStreamer only supports batch size 1')
elif len(value.shape) > 1:
SCREAMING_SNAKE_CASE = value[0]
if self.skip_prompt and self.next_tokens_are_prompt:
SCREAMING_SNAKE_CASE = False
return
# Add the new token to the cache and decodes the entire thing.
self.token_cache.extend(value.tolist())
SCREAMING_SNAKE_CASE = self.tokenizer.decode(self.token_cache , **self.decode_kwargs)
# After the symbol for a new line, we flush the cache.
if text.endswith('\n'):
SCREAMING_SNAKE_CASE = text[self.print_len :]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
# If the last token is a CJK character, we print the characters.
elif len(__A) > 0 and self._is_chinese_char(ord(text[-1])):
SCREAMING_SNAKE_CASE = text[self.print_len :]
self.print_len += len(__A)
# Otherwise, prints until the last space char (simple heuristic to avoid printing incomplete words,
# which may change with the subsequent token -- there are probably smarter ways to do this!)
else:
SCREAMING_SNAKE_CASE = text[self.print_len : text.rfind(' ') + 1]
self.print_len += len(__A)
self.on_finalized_text(__A)
def SCREAMING_SNAKE_CASE__ ( self) -> Dict:
# Flush the cache, if it exists
if len(self.token_cache) > 0:
SCREAMING_SNAKE_CASE = self.tokenizer.decode(self.token_cache , **self.decode_kwargs)
SCREAMING_SNAKE_CASE = text[self.print_len :]
SCREAMING_SNAKE_CASE = []
SCREAMING_SNAKE_CASE = 0
else:
SCREAMING_SNAKE_CASE = """"""
SCREAMING_SNAKE_CASE = True
self.on_finalized_text(__A , stream_end=__A)
def SCREAMING_SNAKE_CASE__ ( self , a , a = False) -> Optional[Any]:
print(__A , flush=__A , end='' if not stream_end else None)
def SCREAMING_SNAKE_CASE__ ( self , a) -> Dict:
# This defines a "chinese character" as anything in the CJK Unicode block:
# https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
#
# Note that the CJK Unicode block is NOT all Japanese and Korean characters,
# despite its name. The modern Korean Hangul alphabet is a different block,
# as is Japanese Hiragana and Katakana. Those alphabets are used to write
# space-separated words, so they are not treated specially and handled
# like the all of the other languages.
if (
(cp >= 0x4e_00 and cp <= 0x9f_ff)
or (cp >= 0x34_00 and cp <= 0x4d_bf) #
or (cp >= 0x2_00_00 and cp <= 0x2_a6_df) #
or (cp >= 0x2_a7_00 and cp <= 0x2_b7_3f) #
or (cp >= 0x2_b7_40 and cp <= 0x2_b8_1f) #
or (cp >= 0x2_b8_20 and cp <= 0x2_ce_af) #
or (cp >= 0xf9_00 and cp <= 0xfa_ff)
or (cp >= 0x2_f8_00 and cp <= 0x2_fa_1f) #
): #
return True
return False
class _snake_case ( A__ ):
def __init__( self , a , a = False , a = None , **a) -> Dict:
super().__init__(__A , __A , **__A)
SCREAMING_SNAKE_CASE = Queue()
SCREAMING_SNAKE_CASE = None
SCREAMING_SNAKE_CASE = timeout
def SCREAMING_SNAKE_CASE__ ( self , a , a = False) -> List[str]:
self.text_queue.put(__A , timeout=self.timeout)
if stream_end:
self.text_queue.put(self.stop_signal , timeout=self.timeout)
def __iter__( self) -> Optional[Any]:
return self
def SCREAMING_SNAKE_CASE__ ( self) -> Tuple:
SCREAMING_SNAKE_CASE = self.text_queue.get(timeout=self.timeout)
if value == self.stop_signal:
raise StopIteration()
else:
return value
| 137 |
"""simple docstring"""
def _snake_case ( lowercase__ : list[int] ) -> list[list[int]]:
'''simple docstring'''
lowerCAmelCase_ :Optional[Any] = []
if len(lowercase__ ) == 1:
return [nums.copy()]
for _ in range(len(lowercase__ ) ):
lowerCAmelCase_ :Optional[Any] = nums.pop(0 )
lowerCAmelCase_ :str = permute(lowercase__ )
for perm in permutations:
perm.append(lowercase__ )
result.extend(lowercase__ )
nums.append(lowercase__ )
return result
def _snake_case ( lowercase__ : Tuple ) -> List[str]:
'''simple docstring'''
def backtrack(lowercase__ : str ):
if start == len(lowercase__ ) - 1:
output.append(nums[:] )
else:
for i in range(lowercase__ , len(lowercase__ ) ):
lowerCAmelCase_ , lowerCAmelCase_ :str = nums[i], nums[start]
backtrack(start + 1 )
lowerCAmelCase_ , lowerCAmelCase_ :str = nums[i], nums[start] # backtrack
lowerCAmelCase_ :int = []
backtrack(0 )
return output
if __name__ == "__main__":
import doctest
# use res to print the data in permute2 function
__UpperCAmelCase = permutea([1, 2, 3])
print(res)
doctest.testmod()
| 84 | 0 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowercase_ ( A__ , unittest.TestCase ):
"""simple docstring"""
UpperCAmelCase_ : Any = BioGptTokenizer
UpperCAmelCase_ : str = False
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase = [
"""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>""",
]
lowerCAmelCase = dict(zip(__A , range(len(__A ) ) ) )
lowerCAmelCase = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''vocab_file'''] )
lowerCAmelCase = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['''merges_file'''] )
with open(self.vocab_file , '''w''' ) as fp:
fp.write(json.dumps(__A ) )
with open(self.merges_file , '''w''' ) as fp:
fp.write('''\n'''.join(__A ) )
def SCREAMING_SNAKE_CASE_ ( self , __SCREAMING_SNAKE_CASE ) ->Optional[int]:
lowerCAmelCase = """lower newer"""
lowerCAmelCase = """lower newer"""
return input_text, output_text
def SCREAMING_SNAKE_CASE_ ( self ) ->str:
lowerCAmelCase = BioGptTokenizer(self.vocab_file , self.merges_file )
lowerCAmelCase = """lower"""
lowerCAmelCase = ["""low""", """er</w>"""]
lowerCAmelCase = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
lowerCAmelCase = tokens + ["""<unk>"""]
lowerCAmelCase = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
@slow
def SCREAMING_SNAKE_CASE_ ( self ) ->List[Any]:
lowerCAmelCase = BioGptTokenizer.from_pretrained('''microsoft/biogpt''' )
lowerCAmelCase = tokenizer.encode('''sequence builders''' , add_special_tokens=__A )
lowerCAmelCase = tokenizer.encode('''multi-sequence build''' , add_special_tokens=__A )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(__A )
lowerCAmelCase = tokenizer.build_inputs_with_special_tokens(__A , __A )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 338 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _SCREAMING_SNAKE_CASE ( A__ , unittest.TestCase ):
UpperCAmelCase_ :Any = BioGptTokenizer
UpperCAmelCase_ :str = False
def __lowerCAmelCase ( self ) -> List[Any]:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
lowerCAmelCase_ :Optional[Any] = [
"""l""",
"""o""",
"""w""",
"""e""",
"""r""",
"""s""",
"""t""",
"""i""",
"""d""",
"""n""",
"""w</w>""",
"""r</w>""",
"""t</w>""",
"""lo""",
"""low""",
"""er</w>""",
"""low</w>""",
"""lowest</w>""",
"""newer</w>""",
"""wider</w>""",
"""<unk>""",
]
lowerCAmelCase_ :str = dict(zip(__A , range(len(__A ) ) ) )
lowerCAmelCase_ :int = ["""l o 123""", """lo w 1456""", """e r</w> 1789""", """"""]
lowerCAmelCase_ :Optional[int] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
lowerCAmelCase_ :Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""merges_file"""] )
with open(self.vocab_file , """w""" ) as fp:
fp.write(json.dumps(__A ) )
with open(self.merges_file , """w""" ) as fp:
fp.write("""\n""".join(__A ) )
def __lowerCAmelCase ( self , __A ) -> Optional[int]:
lowerCAmelCase_ :List[Any] = """lower newer"""
lowerCAmelCase_ :Tuple = """lower newer"""
return input_text, output_text
def __lowerCAmelCase ( self ) -> str:
lowerCAmelCase_ :List[str] = BioGptTokenizer(self.vocab_file , self.merges_file )
lowerCAmelCase_ :Union[str, Any] = """lower"""
lowerCAmelCase_ :Any = ["""low""", """er</w>"""]
lowerCAmelCase_ :Union[str, Any] = tokenizer.tokenize(__A )
self.assertListEqual(__A , __A )
lowerCAmelCase_ :Dict = tokens + ["""<unk>"""]
lowerCAmelCase_ :List[str] = [14, 15, 20]
self.assertListEqual(tokenizer.convert_tokens_to_ids(__A ) , __A )
@slow
def __lowerCAmelCase ( self ) -> List[Any]:
lowerCAmelCase_ :Optional[Any] = BioGptTokenizer.from_pretrained("""microsoft/biogpt""" )
lowerCAmelCase_ :List[Any] = tokenizer.encode("""sequence builders""" , add_special_tokens=__A )
lowerCAmelCase_ :List[str] = tokenizer.encode("""multi-sequence build""" , add_special_tokens=__A )
lowerCAmelCase_ :Optional[int] = tokenizer.build_inputs_with_special_tokens(__A )
lowerCAmelCase_ :List[str] = tokenizer.build_inputs_with_special_tokens(__A , __A )
self.assertTrue(encoded_sentence == [2] + text )
self.assertTrue(encoded_pair == [2] + text + [2] + text_a )
| 84 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import HuggingFace
@pytest.mark.skipif(
literal_eval(os.getenv("""TEST_SAGEMAKER""" , """False""" ) ) is not True , reason="""Skipping test because should only be run when releasing minor transformers version""" , )
@pytest.mark.usefixtures("""sm_env""" )
@parameterized_class(
[
{
"""framework""": """pytorch""",
"""script""": """run_glue_model_parallelism.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
{
"""framework""": """pytorch""",
"""script""": """run_glue.py""",
"""model_name_or_path""": """roberta-large""",
"""instance_type""": """ml.p3dn.24xlarge""",
"""results""": {"""train_runtime""": 1600, """eval_accuracy""": 0.3, """eval_loss""": 1.2},
},
] )
class __A( unittest.TestCase ):
"""simple docstring"""
def UpperCAmelCase_ (self ):
if self.framework == "pytorch":
subprocess.run(
F"cp ./examples/pytorch/text-classification/run_glue.py {self.env.test_path}/run_glue.py".split() , encoding="""utf-8""" , check=__A , )
assert hasattr(self , """env""" )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
# configuration for running training on smdistributed Model Parallel
UpperCamelCase__ = {
"""enabled""": True,
"""processes_per_host""": 8,
}
UpperCamelCase__ = {
"""enabled""": True,
"""parameters""": {
"""microbatches""": 4,
"""placement_strategy""": """spread""",
"""pipeline""": """interleaved""",
"""optimize""": """speed""",
"""partitions""": 4,
"""ddp""": True,
},
}
UpperCamelCase__ = {"""smdistributed""": {"""modelparallel""": smp_options}, """mpi""": mpi_options}
UpperCamelCase__ = """trainer""" if self.script == """run_glue.py""" else """smtrainer"""
# creates estimator
return HuggingFace(
entry_point=self.script , source_dir=self.env.test_path , role=self.env.role , image_uri=self.env.image_uri , base_job_name=F"{self.env.base_job_name}-{instance_count}-smp-{name_extension}" , instance_count=__A , instance_type=self.instance_type , debugger_hook_config=__A , hyperparameters={
**self.env.hyperparameters,
"""model_name_or_path""": self.model_name_or_path,
"""max_steps""": 5_00,
} , metric_definitions=self.env.metric_definitions , distribution=__A , py_version="""py36""" , )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
TrainingJobAnalytics(__A ).export_csv(F"{self.env.test_path}/{job_name}_metrics.csv" )
@parameterized.expand([(1,)] )
def UpperCAmelCase_ (self , SCREAMING_SNAKE_CASE_ ):
# create estimator
UpperCamelCase__ = self.create_estimator(__A )
# run training
estimator.fit()
# result dataframe
UpperCamelCase__ = TrainingJobAnalytics(estimator.latest_training_job.name ).dataframe()
# extract kpis
UpperCamelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_accuracy"""]["""value"""] )
UpperCamelCase__ = list(result_metrics_df[result_metrics_df.metric_name == """eval_loss"""]["""value"""] )
# get train time from SageMaker job, this includes starting, preprocessing, stopping
UpperCamelCase__ = (
Session().describe_training_job(estimator.latest_training_job.name ).get("""TrainingTimeInSeconds""" , 99_99_99 )
)
# assert kpis
assert train_runtime <= self.results["train_runtime"]
assert all(t >= self.results["""eval_accuracy"""] for t in eval_accuracy )
assert all(t <= self.results["""eval_loss"""] for t in eval_loss )
# dump tests result into json file to share in PR
with open(F"{estimator.latest_training_job.name}.json" , """w""" ) as outfile:
json.dump({"""train_time""": train_runtime, """eval_accuracy""": eval_accuracy, """eval_loss""": eval_loss} , __A )
| 244 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class _SCREAMING_SNAKE_CASE ( A__ ):
UpperCAmelCase_ :str = "bert-generation"
def __init__( self , __A=5_0358 , __A=1024 , __A=24 , __A=16 , __A=4096 , __A="gelu" , __A=0.1 , __A=0.1 , __A=512 , __A=0.0_2 , __A=1E-12 , __A=0 , __A=2 , __A=1 , __A="absolute" , __A=True , **__A , ) -> Tuple:
super().__init__(pad_token_id=__A , bos_token_id=__A , eos_token_id=__A , **__A )
lowerCAmelCase_ :Any = vocab_size
lowerCAmelCase_ :List[Any] = hidden_size
lowerCAmelCase_ :Optional[int] = num_hidden_layers
lowerCAmelCase_ :int = num_attention_heads
lowerCAmelCase_ :List[Any] = hidden_act
lowerCAmelCase_ :Optional[Any] = intermediate_size
lowerCAmelCase_ :List[Any] = hidden_dropout_prob
lowerCAmelCase_ :int = attention_probs_dropout_prob
lowerCAmelCase_ :Tuple = max_position_embeddings
lowerCAmelCase_ :List[str] = initializer_range
lowerCAmelCase_ :Union[str, Any] = layer_norm_eps
lowerCAmelCase_ :List[str] = position_embedding_type
lowerCAmelCase_ :Optional[int] = use_cache
| 84 | 0 |
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