code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
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"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
_a = logging.get_logger(__name__)
_a = {
'post_extract_proj': 'feature_projection.projection',
'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
'fc2': 'encoder.layers.*.feed_forward.output_dense',
'final_layer_norm': 'encoder.layers.*.final_layer_norm',
'encoder.layer_norm': 'encoder.layer_norm',
'encoder.layer_norm_for_extract': 'layer_norm_for_extract',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'label_embs_concat': 'label_embeddings_concat',
'mask_emb': 'masked_spec_embed',
'spk_proj': 'speaker_proj',
}
_a = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
'label_embeddings_concat',
'speaker_proj',
'layer_norm_for_extract',
]
def _A ( UpperCamelCase_ : int, UpperCamelCase_ : Any, UpperCamelCase_ : int, UpperCamelCase_ : Union[str, Any], UpperCamelCase_ : Any) -> List[Any]:
'''simple docstring'''
for attribute in key.split("."):
__lowercase = getattr(__a, __a)
if weight_type is not None:
__lowercase = getattr(__a, __a).shape
else:
__lowercase = 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":
__lowercase = value
elif weight_type == "weight_g":
__lowercase = value
elif weight_type == "weight_v":
__lowercase = value
elif weight_type == "bias":
__lowercase = value
else:
__lowercase = value
logger.info(F"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.""")
def _A ( UpperCamelCase_ : int, UpperCamelCase_ : Tuple) -> Union[str, Any]:
'''simple docstring'''
__lowercase = []
__lowercase = fairseq_model.state_dict()
__lowercase = hf_model.unispeech_sat.feature_extractor
for name, value in fairseq_dict.items():
__lowercase = False
if "conv_layers" in name:
load_conv_layer(
__a, __a, __a, __a, hf_model.config.feat_extract_norm == "group", )
__lowercase = True
else:
for key, mapped_key in MAPPING.items():
__lowercase = '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model.")[-1] == name.split(".")[0]:
if "layer_norm_for_extract" in name and (".".join(name.split(".")[:-1]) != key):
# special case since naming is very similar
continue
__lowercase = True
if "*" in mapped_key:
__lowercase = name.split(__a)[0].split(".")[-2]
__lowercase = mapped_key.replace("*", __a)
if "weight_g" in name:
__lowercase = '''weight_g'''
elif "weight_v" in name:
__lowercase = '''weight_v'''
elif "bias" in name:
__lowercase = '''bias'''
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowercase = '''weight'''
else:
__lowercase = 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 ( UpperCamelCase_ : Any, UpperCamelCase_ : str, UpperCamelCase_ : List[Any], UpperCamelCase_ : List[str], UpperCamelCase_ : str) -> List[Any]:
'''simple docstring'''
__lowercase = full_name.split("conv_layers.")[-1]
__lowercase = name.split(".")
__lowercase = int(items[0])
__lowercase = 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.""")
__lowercase = 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.""")
__lowercase = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""")
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found.""")
__lowercase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.""")
__lowercase = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""")
else:
unused_weights.append(__a)
@torch.no_grad()
def _A ( UpperCamelCase_ : Optional[int], UpperCamelCase_ : List[str], UpperCamelCase_ : Dict=None, UpperCamelCase_ : Any=None, UpperCamelCase_ : Union[str, Any]=True) -> str:
'''simple docstring'''
if config_path is not None:
__lowercase = UniSpeechSatConfig.from_pretrained(__a)
else:
__lowercase = UniSpeechSatConfig()
__lowercase = ''''''
if is_finetuned:
__lowercase = UniSpeechSatForCTC(__a)
else:
__lowercase = UniSpeechSatForPreTraining(__a)
__lowercase = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path], arg_overrides={"data": "/".join(dict_path.split("/")[:-1])})
__lowercase = model[0].eval()
recursively_load_weights(__a, __a)
hf_wavavec.save_pretrained(__a)
if __name__ == "__main__":
_a = 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 = parser.parse_args()
convert_unispeech_sat_checkpoint(
args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
)
| 17 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
a__ = logging.get_logger(__name__)
a__ = {
'''junnyu/roformer_chinese_small''': '''https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json''',
'''junnyu/roformer_chinese_base''': '''https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json''',
'''junnyu/roformer_chinese_char_small''': (
'''https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'''
),
'''junnyu/roformer_chinese_char_base''': (
'''https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'''
),
'''junnyu/roformer_small_discriminator''': (
'''https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'''
),
'''junnyu/roformer_small_generator''': (
'''https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'''
),
# See all RoFormer models at https://huggingface.co/models?filter=roformer
}
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = "roformer"
def __init__( self , _a=5_0_0_0_0 , _a=None , _a=7_6_8 , _a=1_2 , _a=1_2 , _a=3_0_7_2 , _a="gelu" , _a=0.1 , _a=0.1 , _a=1_5_3_6 , _a=2 , _a=0.02 , _a=1e-1_2 , _a=0 , _a=False , _a=True , **_a , ) -> List[str]:
super().__init__(pad_token_id=_a , **_a )
_a : Tuple = vocab_size
_a : List[Any] = hidden_size if embedding_size is None else embedding_size
_a : Any = hidden_size
_a : Any = num_hidden_layers
_a : List[Any] = num_attention_heads
_a : str = hidden_act
_a : Any = intermediate_size
_a : Dict = hidden_dropout_prob
_a : Optional[Any] = attention_probs_dropout_prob
_a : str = max_position_embeddings
_a : Dict = type_vocab_size
_a : List[Any] = initializer_range
_a : Dict = layer_norm_eps
_a : Dict = rotary_value
_a : Dict = use_cache
class UpperCAmelCase_ ( __lowercase ):
"""simple docstring"""
@property
def __lowercase ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_a : Dict = {0: '''batch''', 1: '''choice''', 2: '''sequence'''}
else:
_a : List[Any] = {0: '''batch''', 1: '''sequence'''}
_a : Union[str, Any] = {0: '''batch''', 1: '''sequence'''}
return OrderedDict(
[
('''input_ids''', dynamic_axis),
('''attention_mask''', dynamic_axis),
('''token_type_ids''', dynamic_axis),
] )
| 235 | 0 |
"""simple docstring"""
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning things up.
#
# The main idea is:
#
# ./trainer-benchmark.py --base-cmd '<cmd args that don't change>' \
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1' \
# --target-metric-key train_samples_per_second
#
# The variations can be any command line argument that you want to compare and not just dtype as in
# the example.
#
# --variations allows you to compare variations in multiple dimensions.
#
# as the first dimention has 2 options and the second 3 in our example, this will run the trainer 6
# times adding one of:
#
# 1. --tf32 0 --fp16 0
# 2. --tf32 0 --fp16 1
# 3. --tf32 0 --bf16 1
# 4. --tf32 1 --fp16 0
# 5. --tf32 1 --fp16 1
# 6. --tf32 1 --bf16 1
#
# and print the results. This is just a cartesian product - and more than 2 dimensions can be used.
#
# If you want to rely on defaults, this:
# --variations '--tf32 0|--tf32 1' '--fp16 0|--fp16 1|--bf16 1'
# is identical to this:
# --variations '--tf32 0|--tf32 1' '|--fp16|--bf16'
#
# the leading empty variation in the 2nd dimension is a valid variation.
#
# So here we get the following 6 variations:
#
# 1. --tf32 0
# 2. --tf32 0 --fp16
# 3. --tf32 0 --bf16
# 4. --tf32 1
# 5. --tf32 1 --fp16
# 6. --tf32 1 --bf16
#
# In this particular case we don't know what the default tf32 setting is as it's normally
# pytorch-version dependent). That's why it's best to do an explicit setting of each variation:
# `--tf32 0|--tf32 1`
#
# Here is a full example of a train:
#
# CUDA_VISIBLE_DEVICES=0 python ./scripts/benchmark/trainer-benchmark.py \
# --base-cmd \
# ' examples/pytorch/translation/run_translation.py --model_name_or_path t5-small \
# --output_dir output_dir --do_train --label_smoothing 0.1 --logging_strategy no \
# --save_strategy no --per_device_train_batch_size 32 --max_source_length 512 \
# --max_target_length 512 --num_train_epochs 1 --overwrite_output_dir \
# --source_lang en --target_lang ro --dataset_name wmt16 --dataset_config "ro-en" \
# --source_prefix "translate English to Romanian: " --warmup_steps 50 \
# --max_train_samples 20000 --dataloader_num_workers 2 ' \
# --target-metric-key train_samples_per_second --repeat-times 1 --variations \
# '|--fp16|--bf16' '--tf32 0|--tf32 1' --report-metric-keys train_loss \
# --repeat-times 1 --base-variation '--tf32 0'
#
# and here is a possible output:
#
#
# | Variation | Train | Diff | Train |
# | | samples | % | loss |
# | | per | | |
# | | second | | |
# |:----------------|----------:|-------:|--------:|
# | --tf32 0 | 285.11 | 0 | 2.51 |
# | --tf32 1 | 342.09 | 20 | 2.51 |
# | --fp16 --tf32 0 | 423.49 | 49 | 2.51 |
# | --fp16 --tf32 1 | 423.13 | 48 | 2.51 |
# | --bf16 --tf32 0 | 416.80 | 46 | 2.52 |
# | --bf16 --tf32 1 | 415.87 | 46 | 2.52 |
#
#
# So you can quickly compare the different outcomes.
#
# Typically running each experiment once is enough, but if the environment is unstable you can
# re-run each multiple times, e.g., 3 using --repeat-times 3 and it will report the averaged results.
#
# By default it'll use the lowest result as the base line to use as 100% and then compare the rest to
# it as can be seen from the table above, but you can also specify which combination is the one to use as
# the baseline, e.g., to change to another entry use: --base-variation '--tf32 1 --fp16 0'
#
# --target-metric-key is there to tell the program which metrics to compare - the different metric keys are
# inside output_dir/all_results.json. e.g., to measure eval performance instead of train use:
# --target-metric-key eval_samples_per_second
# but of course you will need to adjust the --base-cmd value in the example to perform evaluation as
# well (as currently it doesn't)
#
import argparse
import datetime
import io
import itertools
import json
import math
import os
import platform
import re
import shlex
import subprocess
import sys
from pathlib import Path
from statistics import fmean
import pandas as pd
import torch
from tqdm import tqdm
import transformers
lowercase_ = float("""nan""")
class SCREAMING_SNAKE_CASE :
def __init__( self : List[Any] , a : int )-> Any:
"""simple docstring"""
lowercase__ = sys.stdout
lowercase__ = open(SCREAMING_SNAKE_CASE_ , 'a' )
def __getattr__( self : List[str] , a : List[str] )-> Dict:
"""simple docstring"""
return getattr(self.stdout , SCREAMING_SNAKE_CASE_ )
def SCREAMING_SNAKE_CASE_ ( self : Union[str, Any] , a : List[str] )-> Tuple:
"""simple docstring"""
self.stdout.write(SCREAMING_SNAKE_CASE_ )
# strip tqdm codes
self.file.write(re.sub(R'^.*\r' , '' , SCREAMING_SNAKE_CASE_ , 0 , re.M ) )
def __UpperCamelCase (_SCREAMING_SNAKE_CASE=80 , _SCREAMING_SNAKE_CASE=False ) -> Any:
lowercase__ = []
# deal with critical env vars
lowercase__ = ['CUDA_VISIBLE_DEVICES']
for key in env_keys:
lowercase__ = os.environ.get(snake_case__ , snake_case__ )
if val is not None:
cmd.append(F"""{key}={val}""" )
# python executable (not always needed if the script is executable)
lowercase__ = sys.executable if full_python_path else sys.executable.split('/' )[-1]
cmd.append(snake_case__ )
# now the normal args
cmd += list(map(shlex.quote , sys.argv ) )
# split up into up to MAX_WIDTH lines with shell multi-line escapes
lowercase__ = []
lowercase__ = ''
while len(snake_case__ ) > 0:
current_line += F"""{cmd.pop(0 )} """
if len(snake_case__ ) == 0 or len(snake_case__ ) + len(cmd[0] ) + 1 > max_width - 1:
lines.append(snake_case__ )
lowercase__ = ''
return "\\\n".join(snake_case__ )
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> str:
# unwrap multi-line input
lowercase__ = re.sub(R'[\\\n]+' , ' ' , args.base_cmd )
# remove --output_dir if any and set our own
lowercase__ = re.sub('--output_dir\s+[^\s]+' , '' , args.base_cmd )
args.base_cmd += F""" --output_dir {output_dir}"""
# ensure we have --overwrite_output_dir
lowercase__ = re.sub('--overwrite_output_dir\s+' , '' , args.base_cmd )
args.base_cmd += " --overwrite_output_dir"
return [sys.executable] + shlex.split(args.base_cmd )
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Optional[Any]:
# Enable to debug everything but the run itself, to do it fast and see the progress.
# This is useful for debugging the output formatting quickly - we can remove it later once
# everybody is happy with the output
if 0:
import random
from time import sleep
sleep(0 )
return dict(
{k: random.uniform(0 , 100 ) for k in metric_keys} , **{target_metric_key: random.choice([nan, 1_0.3_1, 1_0_0.2, 5_5.6_6_6_6, 2_2_2.2_2_2_2_2_2_2_2] )} , )
lowercase__ = subprocess.run(snake_case__ , capture_output=snake_case__ , text=snake_case__ )
if verbose:
print('STDOUT' , result.stdout )
print('STDERR' , result.stderr )
# save the streams
lowercase__ = variation.replace(' ' , '-' )
with open(Path(snake_case__ ) / F"""log.{prefix}.stdout.txt""" , 'w' ) as f:
f.write(result.stdout )
with open(Path(snake_case__ ) / F"""log.{prefix}.stderr.txt""" , 'w' ) as f:
f.write(result.stderr )
if result.returncode != 0:
if verbose:
print('failed' )
return {target_metric_key: nan}
with io.open(F"""{output_dir}/all_results.json""" , 'r' , encoding='utf-8' ) as f:
lowercase__ = json.load(snake_case__ )
# filter out just the keys we want
return {k: v for k, v in metrics.items() if k in metric_keys}
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , ) -> str:
lowercase__ = []
lowercase__ = []
lowercase__ = F"""{id}: {variation:<{longest_variation_len}}"""
lowercase__ = F"""{preamble}: """
lowercase__ = set(report_metric_keys + [target_metric_key] )
for i in tqdm(range(snake_case__ ) , desc=snake_case__ , leave=snake_case__ ):
lowercase__ = process_run_single(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )
lowercase__ = single_run_metrics[target_metric_key]
if not math.isnan(snake_case__ ):
metrics.append(snake_case__ )
results.append(snake_case__ )
outcome += "✓"
else:
outcome += "✘"
lowercase__ = F"""\33[2K\r{outcome}"""
if len(snake_case__ ) > 0:
lowercase__ = {k: fmean([x[k] for x in metrics] ) for k in metrics[0].keys()}
lowercase__ = round(mean_metrics[target_metric_key] , 2 )
lowercase__ = F"""{outcome} {mean_target}"""
if len(snake_case__ ) > 1:
results_str += F""" {tuple(round(snake_case__ , 2 ) for x in results )}"""
print(snake_case__ )
lowercase__ = variation
return mean_metrics
else:
print(snake_case__ )
return {variation_key: variation, target_metric_key: nan}
def __UpperCamelCase () -> str:
lowercase__ = torch.cuda.get_device_properties(torch.device('cuda' ) )
return F"""
Datetime : {datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S" )}
Software:
transformers: {transformers.__version__}
torch : {torch.__version__}
cuda : {torch.version.cuda}
python : {platform.python_version()}
Hardware:
{torch.cuda.device_count()} GPUs : {properties.name}, {properties.total_memory/2**30:0.2f}GB
"""
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Dict:
lowercase__ = pd.DataFrame(snake_case__ )
lowercase__ = 'variation'
lowercase__ = 'diff_%'
lowercase__ = nan
if base_variation is not None and len(df[df[variation_key] == base_variation] ):
# this may still return nan
lowercase__ = df.loc[df[variation_key] == base_variation][target_metric_key].item()
if math.isnan(snake_case__ ):
# as a fallback, use the minimal value as the sentinel
lowercase__ = df.loc[df[target_metric_key] != nan][target_metric_key].min()
# create diff column if possible
if not math.isnan(snake_case__ ):
lowercase__ = df.apply(
lambda _SCREAMING_SNAKE_CASE : round(100 * (r[target_metric_key] - sentinel_value) / sentinel_value )
if not math.isnan(r[target_metric_key] )
else 0 , axis='columns' , )
# re-order columns
lowercase__ = [variation_key, target_metric_key, diff_key, *report_metric_keys]
lowercase__ = df.reindex(snake_case__ , axis='columns' ) # reorder cols
# capitalize
lowercase__ = df.rename(str.capitalize , axis='columns' )
# make the cols as narrow as possible
lowercase__ = df.rename(lambda _SCREAMING_SNAKE_CASE : c.replace('_' , '<br>' ) , axis='columns' )
lowercase__ = df.rename(lambda _SCREAMING_SNAKE_CASE : c.replace('_' , '\n' ) , axis='columns' )
lowercase__ = ['', 'Copy between the cut-here-lines and paste as is to github or a forum']
report += ["----------8<-----------------8<--------"]
report += ["*** Results:", df_github.to_markdown(index=snake_case__ , floatfmt='.2f' )]
report += ["```"]
report += ["*** Setup:", get_versions()]
report += ["*** The benchmark command line was:", get_original_command()]
report += ["```"]
report += ["----------8<-----------------8<--------"]
report += ["*** Results (console):", df_console.to_markdown(index=snake_case__ , floatfmt='.2f' )]
print('\n\n'.join(snake_case__ ) )
def __UpperCamelCase () -> Any:
lowercase__ = argparse.ArgumentParser()
parser.add_argument(
'--base-cmd' , default=snake_case__ , type=snake_case__ , required=snake_case__ , help='Base cmd' , )
parser.add_argument(
'--variations' , default=snake_case__ , type=snake_case__ , nargs='+' , required=snake_case__ , help='Multi-dimensional variations, example: \'|--fp16|--bf16\' \'|--tf32\'' , )
parser.add_argument(
'--base-variation' , default=snake_case__ , type=snake_case__ , help='Baseline variation to compare to. if None the minimal target value will be used to compare against' , )
parser.add_argument(
'--target-metric-key' , default=snake_case__ , type=snake_case__ , required=snake_case__ , help='Target metric key in output_dir/all_results.json, e.g., train_samples_per_second' , )
parser.add_argument(
'--report-metric-keys' , default='' , type=snake_case__ , help='Report metric keys - other metric keys from output_dir/all_results.json to report, e.g., train_loss. Use a single argument e.g., \'train_loss train_samples' , )
parser.add_argument(
'--repeat-times' , default=1 , type=snake_case__ , help='How many times to re-run each variation - an average will be reported' , )
parser.add_argument(
'--output_dir' , default='output_benchmark' , type=snake_case__ , help='The output directory where all the benchmark reports will go to and additionally this directory will be used to override --output_dir in the script that is being benchmarked' , )
parser.add_argument(
'--verbose' , default=snake_case__ , action='store_true' , help='Whether to show the outputs of each run or just the benchmark progress' , )
lowercase__ = parser.parse_args()
lowercase__ = args.output_dir
Path(snake_case__ ).mkdir(exist_ok=snake_case__ )
lowercase__ = get_base_command(snake_case__ , snake_case__ )
# split each dimension into its --foo variations
lowercase__ = [list(map(str.strip , re.split(R'\|' , snake_case__ ) ) ) for x in args.variations]
# build a cartesian product of dimensions and convert those back into cmd-line arg strings,
# while stripping white space for inputs that were empty
lowercase__ = list(map(str.strip , map(' '.join , itertools.product(*snake_case__ ) ) ) )
lowercase__ = max(len(snake_case__ ) for x in variations )
# split wanted keys
lowercase__ = args.report_metric_keys.split()
# capture prints into a log file for convenience
lowercase__ = F"""benchmark-report-{datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S" )}.txt"""
print(F"""\nNote: each run's output is also logged under {output_dir}/log.*.std*.txt""" )
print(F"""and this script's output is also piped into {report_fn}""" )
lowercase__ = Tee(snake_case__ )
print(F"""\n*** Running {len(snake_case__ )} benchmarks:""" )
print(F"""Base command: {" ".join(snake_case__ )}""" )
lowercase__ = 'variation'
lowercase__ = []
for id, variation in enumerate(tqdm(snake_case__ , desc='Total completion: ' , leave=snake_case__ ) ):
lowercase__ = base_cmd + variation.split()
results.append(
process_run(
id + 1 , snake_case__ , snake_case__ , snake_case__ , snake_case__ , args.target_metric_key , snake_case__ , args.repeat_times , snake_case__ , args.verbose , ) )
process_results(snake_case__ , args.target_metric_key , snake_case__ , args.base_variation , snake_case__ )
if __name__ == "__main__":
main()
| 365 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
def __init__( self : List[str] , a : Callable , a : Optional[Features] = None , a : str = None , a : bool = False , a : bool = False , a : Optional[dict] = None , a : Optional[int] = None , **a : str , )-> Tuple:
"""simple docstring"""
super().__init__(
features=a , cache_dir=a , keep_in_memory=a , streaming=a , num_proc=a , **a , )
lowercase__ = Generator(
cache_dir=a , features=a , generator=a , gen_kwargs=a , **a , )
def SCREAMING_SNAKE_CASE_ ( self : Any )-> Dict:
"""simple docstring"""
if self.streaming:
lowercase__ = self.builder.as_streaming_dataset(split='train' )
# Build regular (map-style) dataset
else:
lowercase__ = None
lowercase__ = None
lowercase__ = None
lowercase__ = None
self.builder.download_and_prepare(
download_config=a , download_mode=a , verification_mode=a , base_path=a , num_proc=self.num_proc , )
lowercase__ = self.builder.as_dataset(
split='train' , verification_mode=a , in_memory=self.keep_in_memory )
return dataset
| 269 | 0 |
"""simple docstring"""
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
'xlnet-large-cased': 'https://huggingface.co/xlnet-large-cased/resolve/main/config.json',
}
class UpperCAmelCase_ ( A__ ):
lowercase__ = '''xlnet'''
lowercase__ = ['''mems''']
lowercase__ = {
'''n_token''': '''vocab_size''', # Backward compatibility
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''n_head''',
'''num_hidden_layers''': '''n_layer''',
}
def __init__( self : List[str] , snake_case_ : Union[str, Any]=32_000 , snake_case_ : Any=1_024 , snake_case_ : Dict=24 , snake_case_ : Optional[Any]=16 , snake_case_ : List[str]=4_096 , snake_case_ : int="gelu" , snake_case_ : Tuple=True , snake_case_ : Union[str, Any]="bi" , snake_case_ : Any=0.02 , snake_case_ : Optional[Any]=1e-12 , snake_case_ : Optional[Any]=0.1 , snake_case_ : Optional[int]=512 , snake_case_ : Tuple=None , snake_case_ : Optional[Any]=True , snake_case_ : Optional[int]=False , snake_case_ : List[Any]=False , snake_case_ : int=-1 , snake_case_ : int=False , snake_case_ : Union[str, Any]="last" , snake_case_ : Any=True , snake_case_ : List[str]="tanh" , snake_case_ : Optional[int]=0.1 , snake_case_ : Optional[int]=5 , snake_case_ : str=5 , snake_case_ : List[str]=5 , snake_case_ : Any=1 , snake_case_ : List[Any]=2 , **snake_case_ : Optional[Any] , ) -> List[Any]:
'''simple docstring'''
A__ = vocab_size
A__ = d_model
A__ = n_layer
A__ = n_head
if d_model % n_head != 0:
raise ValueError(F"""'d_model % n_head' ({d_model % n_head}) should be equal to 0""" )
if "d_head" in kwargs:
if kwargs["d_head"] != d_model // n_head:
raise ValueError(
F"""`d_head` ({kwargs["d_head"]}) should be equal to `d_model // n_head` ({d_model // n_head})""" )
A__ = d_model // n_head
A__ = ff_activation
A__ = d_inner
A__ = untie_r
A__ = attn_type
A__ = initializer_range
A__ = layer_norm_eps
A__ = dropout
A__ = mem_len
A__ = reuse_len
A__ = bi_data
A__ = clamp_len
A__ = same_length
A__ = summary_type
A__ = summary_use_proj
A__ = summary_activation
A__ = summary_last_dropout
A__ = start_n_top
A__ = end_n_top
A__ = bos_token_id
A__ = pad_token_id
A__ = eos_token_id
if "use_cache" in kwargs:
warnings.warn(
"The `use_cache` argument is deprecated and will be removed in a future version, use `use_mems_eval`"
" instead." , snake_case_ , )
A__ = kwargs["use_cache"]
A__ = use_mems_eval
A__ = use_mems_train
super().__init__(pad_token_id=snake_case_ , bos_token_id=snake_case_ , eos_token_id=snake_case_ , **snake_case_ )
@property
def __magic_name__ ( self : Optional[Any] ) -> Any:
'''simple docstring'''
logger.info(F"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
return -1
@max_position_embeddings.setter
def __magic_name__ ( self : Optional[int] , snake_case_ : Dict ) -> Optional[Any]:
'''simple docstring'''
raise NotImplementedError(
F"""The model {self.model_type} is one of the few models that has no sequence length limit.""" )
| 247 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
A__ : List[Any] = logging.get_logger(__name__)
A__ : str = {'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}
A__ : int = {
'vocab_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/vocab.txt',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/vocab.txt',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/vocab.txt'
),
'distilbert-base-german-cased': 'https://huggingface.co/distilbert-base-german-cased/resolve/main/vocab.txt',
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/vocab.txt'
),
},
'tokenizer_file': {
'distilbert-base-uncased': 'https://huggingface.co/distilbert-base-uncased/resolve/main/tokenizer.json',
'distilbert-base-uncased-distilled-squad': (
'https://huggingface.co/distilbert-base-uncased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-cased': 'https://huggingface.co/distilbert-base-cased/resolve/main/tokenizer.json',
'distilbert-base-cased-distilled-squad': (
'https://huggingface.co/distilbert-base-cased-distilled-squad/resolve/main/tokenizer.json'
),
'distilbert-base-german-cased': (
'https://huggingface.co/distilbert-base-german-cased/resolve/main/tokenizer.json'
),
'distilbert-base-multilingual-cased': (
'https://huggingface.co/distilbert-base-multilingual-cased/resolve/main/tokenizer.json'
),
},
}
A__ : Optional[Any] = {
'distilbert-base-uncased': 5_12,
'distilbert-base-uncased-distilled-squad': 5_12,
'distilbert-base-cased': 5_12,
'distilbert-base-cased-distilled-squad': 5_12,
'distilbert-base-german-cased': 5_12,
'distilbert-base-multilingual-cased': 5_12,
}
A__ : List[str] = {
'distilbert-base-uncased': {'do_lower_case': True},
'distilbert-base-uncased-distilled-squad': {'do_lower_case': True},
'distilbert-base-cased': {'do_lower_case': False},
'distilbert-base-cased-distilled-squad': {'do_lower_case': False},
'distilbert-base-german-cased': {'do_lower_case': False},
'distilbert-base-multilingual-cased': {'do_lower_case': False},
}
class _UpperCAmelCase ( A__ ):
"""simple docstring"""
lowercase__ = VOCAB_FILES_NAMES
lowercase__ = PRETRAINED_VOCAB_FILES_MAP
lowercase__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ = PRETRAINED_INIT_CONFIGURATION
lowercase__ = ["""input_ids""", """attention_mask"""]
lowercase__ = DistilBertTokenizer
def __init__( self : List[Any], lowerCamelCase : List[Any]=None, lowerCamelCase : Dict=None, lowerCamelCase : str=True, lowerCamelCase : Optional[int]="[UNK]", lowerCamelCase : Optional[Any]="[SEP]", lowerCamelCase : List[Any]="[PAD]", lowerCamelCase : Any="[CLS]", lowerCamelCase : Union[str, Any]="[MASK]", lowerCamelCase : str=True, lowerCamelCase : int=None, **lowerCamelCase : Union[str, Any], ):
'''simple docstring'''
super().__init__(
lowerCamelCase, tokenizer_file=lowerCamelCase, do_lower_case=lowerCamelCase, unk_token=lowerCamelCase, sep_token=lowerCamelCase, pad_token=lowerCamelCase, cls_token=lowerCamelCase, mask_token=lowerCamelCase, tokenize_chinese_chars=lowerCamelCase, strip_accents=lowerCamelCase, **lowerCamelCase, )
lowercase__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''', lowerCamelCase ) != do_lower_case
or normalizer_state.get('''strip_accents''', lowerCamelCase ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''', lowerCamelCase ) != tokenize_chinese_chars
):
lowercase__ = getattr(lowerCamelCase, normalizer_state.pop('''type''' ) )
lowercase__ = do_lower_case
lowercase__ = strip_accents
lowercase__ = tokenize_chinese_chars
lowercase__ = normalizer_class(**lowerCamelCase )
lowercase__ = do_lower_case
def lowercase__ ( self : str, lowerCamelCase : Optional[Any], lowerCamelCase : List[Any]=None ):
'''simple docstring'''
lowercase__ = [self.cls_token_id] + token_ids_a + [self.sep_token_id]
if token_ids_a:
output += token_ids_a + [self.sep_token_id]
return output
def lowercase__ ( self : Union[str, Any], lowerCamelCase : List[int], lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
lowercase__ = [self.sep_token_id]
lowercase__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowercase__ ( self : str, lowerCamelCase : str, lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
lowercase__ = self._tokenizer.model.save(lowerCamelCase, name=lowerCamelCase )
return tuple(lowerCamelCase )
| 207 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCamelCase_ : str = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Union[str, Any] = ["""YolosFeatureExtractor"""]
lowerCamelCase_ : Dict = ["""YolosImageProcessor"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase_ : Dict = [
"""YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""YolosForObjectDetection""",
"""YolosModel""",
"""YolosPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_yolos import YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP, YolosConfig, YolosOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_yolos import YolosFeatureExtractor
from .image_processing_yolos import YolosImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_yolos import (
YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST,
YolosForObjectDetection,
YolosModel,
YolosPreTrainedModel,
)
else:
import sys
lowerCamelCase_ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__) | 215 |
"""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
lowerCamelCase_ : Any = random.Random()
def _A ( lowercase , lowercase=1.0 , lowercase=None , lowercase=None ):
"""simple docstring"""
if rng is None:
a =global_rng
a =[]
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 __A ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , __A , __A=7 , __A=400 , __A=2000 , __A=10 , __A=160 , __A=8 , __A=0.0 , __A=4000 , __A=False , __A=True , ) -> Optional[Any]:
a =parent
a =batch_size
a =min_seq_length
a =max_seq_length
a =(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
a =padding_value
a =sampling_rate
a =return_attention_mask
a =do_normalize
a =feature_size
a =chunk_length
a =hop_length
def SCREAMING_SNAKE_CASE ( self ) -> str:
return {
"feature_size": self.feature_size,
"hop_length": self.hop_length,
"chunk_length": self.chunk_length,
"padding_value": self.padding_value,
"sampling_rate": self.sampling_rate,
"return_attention_mask": self.return_attention_mask,
"do_normalize": self.do_normalize,
}
def SCREAMING_SNAKE_CASE ( self , __A=False , __A=False ) -> str:
def _flatten(__A ):
return list(itertools.chain(*__A ) )
if equal_length:
a =[floats_list((self.max_seq_length, self.feature_size) ) for _ in range(self.batch_size )]
else:
# make sure that inputs increase in size
a =[
floats_list((x, self.feature_size) )
for x in range(self.min_seq_length , self.max_seq_length , self.seq_length_diff )
]
if numpify:
a =[np.asarray(__A ) for x in speech_inputs]
return speech_inputs
@require_torch
@require_torchaudio
class __A ( _SCREAMING_SNAKE_CASE, unittest.TestCase ):
"""simple docstring"""
__lowerCAmelCase = WhisperFeatureExtractor if is_speech_available() else None
def SCREAMING_SNAKE_CASE ( self ) -> Dict:
a =WhisperFeatureExtractionTester(self )
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
a =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
a =feat_extract_first.save_pretrained(__A )[0]
check_json_file_has_correct_format(__A )
a =self.feature_extraction_class.from_pretrained(__A )
a =feat_extract_first.to_dict()
a =feat_extract_second.to_dict()
a =feat_extract_first.mel_filters
a =feat_extract_second.mel_filters
self.assertTrue(np.allclose(__A , __A ) )
self.assertEqual(__A , __A )
def SCREAMING_SNAKE_CASE ( self ) -> str:
a =self.feature_extraction_class(**self.feat_extract_dict )
with tempfile.TemporaryDirectory() as tmpdirname:
a =os.path.join(__A , '''feat_extract.json''' )
feat_extract_first.to_json_file(__A )
a =self.feature_extraction_class.from_json_file(__A )
a =feat_extract_first.to_dict()
a =feat_extract_second.to_dict()
a =feat_extract_first.mel_filters
a =feat_extract_second.mel_filters
self.assertTrue(np.allclose(__A , __A ) )
self.assertEqual(__A , __A )
def SCREAMING_SNAKE_CASE ( self ) -> Optional[int]:
# Tests that all call wrap to encode_plus and batch_encode_plus
a =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
# create three inputs of length 800, 1000, and 1200
a =[floats_list((1, x) )[0] for x in range(800 , 1400 , 200 )]
a =[np.asarray(__A ) for speech_input in speech_inputs]
# Test feature size
a =feature_extractor(__A , 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
a =feature_extractor(speech_inputs[0] , return_tensors='''np''' ).input_features
a =feature_extractor(np_speech_inputs[0] , return_tensors='''np''' ).input_features
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
# Test batched
a =feature_extractor(__A , return_tensors='''np''' ).input_features
a =feature_extractor(__A , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__A , __A ):
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
# Test 2-D numpy arrays are batched.
a =[floats_list((1, x) )[0] for x in (800, 800, 800)]
a =np.asarray(__A )
a =feature_extractor(__A , return_tensors='''np''' ).input_features
a =feature_extractor(__A , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__A , __A ):
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
# Test truncation required
a =[floats_list((1, x) )[0] for x in range(200 , (feature_extractor.n_samples + 500) , 200 )]
a =[np.asarray(__A ) for speech_input in speech_inputs]
a =[x[: feature_extractor.n_samples] for x in speech_inputs]
a =[np.asarray(__A ) for speech_input in speech_inputs_truncated]
a =feature_extractor(__A , return_tensors='''np''' ).input_features
a =feature_extractor(__A , return_tensors='''np''' ).input_features
for enc_seq_a, enc_seq_a in zip(__A , __A ):
self.assertTrue(np.allclose(__A , __A , atol=1E-3 ) )
def SCREAMING_SNAKE_CASE ( self ) -> Tuple:
import torch
a =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
a =np.random.rand(100 , 32 ).astype(np.floataa )
a =np_speech_inputs.tolist()
for inputs in [py_speech_inputs, np_speech_inputs]:
a =feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''np''' )
self.assertTrue(np_processed.input_features.dtype == np.floataa )
a =feature_extractor.pad([{'''input_features''': inputs}] , return_tensors='''pt''' )
self.assertTrue(pt_processed.input_features.dtype == torch.floataa )
def SCREAMING_SNAKE_CASE ( self , __A ) -> Dict:
a =load_dataset('''hf-internal-testing/librispeech_asr_dummy''' , '''clean''' , split='''validation''' )
# automatic decoding with librispeech
a =ds.sort('''id''' ).select(range(__A ) )[:num_samples]['''audio''']
return [x["array"] for x in speech_samples]
def SCREAMING_SNAKE_CASE ( self ) -> Any:
# fmt: off
a =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
a =self._load_datasamples(1 )
a =WhisperFeatureExtractor()
a =feature_extractor(__A , return_tensors='''pt''' ).input_features
self.assertEqual(input_features.shape , (1, 80, 3000) )
self.assertTrue(torch.allclose(input_features[0, 0, :30] , __A , atol=1E-4 ) )
def SCREAMING_SNAKE_CASE ( self ) -> Union[str, Any]:
a =self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict() )
a =self._load_datasamples(1 )[0]
a =((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535 # Rescale to [0, 65535] to show issue
a =feat_extract.zero_mean_unit_var_norm([audio] , attention_mask=__A )[0]
self.assertTrue(np.all(np.mean(__A ) < 1E-3 ) )
self.assertTrue(np.all(np.abs(np.var(__A ) - 1 ) < 1E-3 ) ) | 215 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_tensorflow_text_available():
from transformers.models.bert import TFBertTokenizer
a = ['bert-base-uncased', 'bert-base-cased']
a = 'hf-internal-testing/tiny-bert-tf-only'
if is_tf_available():
class SCREAMING_SNAKE_CASE__ ( tf.keras.Model ):
def __init__( self : Optional[Any] , lowerCAmelCase : Union[str, Any] ):
super().__init__()
lowerCAmelCase = tokenizer
lowerCAmelCase = AutoConfig.from_pretrained(lowerCAmelCase )
lowerCAmelCase = TFAutoModel.from_config(lowerCAmelCase )
def __lowercase ( self : List[str] , lowerCAmelCase : Optional[int] ):
lowerCAmelCase = self.tokenizer(lowerCAmelCase )
lowerCAmelCase = self.bert(**lowerCAmelCase )
return out["pooler_output"]
@require_tf
@require_tensorflow_text
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def __lowercase ( self : Optional[Any] ):
super().setUp()
lowerCAmelCase = [
BertTokenizer.from_pretrained(lowerCAmelCase ) for checkpoint in (TOKENIZER_CHECKPOINTS * 2)
] # repeat for when fast_bert_tokenizer=false
lowerCAmelCase = [TFBertTokenizer.from_pretrained(lowerCAmelCase ) for checkpoint in TOKENIZER_CHECKPOINTS] + [
TFBertTokenizer.from_pretrained(lowerCAmelCase , use_fast_bert_tokenizer=lowerCAmelCase )
for checkpoint in TOKENIZER_CHECKPOINTS
]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
lowerCAmelCase = [
"""This is a straightforward English test sentence.""",
"""This one has some weird characters\rto\nsee\r\nif those\u00E9break things.""",
"""Now we're going to add some Chinese: 一 二 三 一二三""",
"""And some much more rare Chinese: 齉 堃 齉堃""",
"""Je vais aussi écrire en français pour tester les accents""",
"""Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ""",
]
lowerCAmelCase = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def __lowercase ( self : Optional[int] ):
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in (self.test_sentences, self.paired_sentences):
lowerCAmelCase = tokenizer(lowerCAmelCase , return_tensors="""tf""" , padding="""longest""" )
lowerCAmelCase = tf_tokenizer(lowerCAmelCase )
for key in python_outputs.keys():
self.assertTrue(tf.reduce_all(python_outputs[key].shape == tf_outputs[key].shape ) )
self.assertTrue(tf.reduce_all(tf.cast(python_outputs[key] , tf.intaa ) == tf_outputs[key] ) )
@slow
def __lowercase ( self : List[str] ):
for tf_tokenizer in self.tf_tokenizers:
lowerCAmelCase = tf_tokenizer(self.paired_sentences )
lowerCAmelCase = tf_tokenizer(
text=[sentence[0] for sentence in self.paired_sentences] , text_pair=[sentence[1] for sentence in self.paired_sentences] , )
for key in merged_outputs.keys():
self.assertTrue(tf.reduce_all(tf.cast(merged_outputs[key] , tf.intaa ) == separated_outputs[key] ) )
@slow
def __lowercase ( self : str ):
for tf_tokenizer in self.tf_tokenizers:
lowerCAmelCase = tf.function(lowerCAmelCase )
for test_inputs in (self.test_sentences, self.paired_sentences):
lowerCAmelCase = tf.constant(lowerCAmelCase )
lowerCAmelCase = compiled_tokenizer(lowerCAmelCase )
lowerCAmelCase = tf_tokenizer(lowerCAmelCase )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def __lowercase ( self : List[str] ):
for tf_tokenizer in self.tf_tokenizers:
lowerCAmelCase = ModelToSave(tokenizer=lowerCAmelCase )
lowerCAmelCase = tf.convert_to_tensor(self.test_sentences )
lowerCAmelCase = model(lowerCAmelCase ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
lowerCAmelCase = Path(lowerCAmelCase ) / """saved.model"""
model.save(lowerCAmelCase )
lowerCAmelCase = tf.keras.models.load_model(lowerCAmelCase )
lowerCAmelCase = loaded_model(lowerCAmelCase )
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertLessEqual(tf.reduce_max(tf.abs(out - loaded_output ) ) , 1e-5 )
| 155 |
"""simple docstring"""
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a = logging.get_logger(__name__)
a = {
'huggingface/time-series-transformer-tourism-monthly': (
'https://huggingface.co/huggingface/time-series-transformer-tourism-monthly/resolve/main/config.json'
),
# See all TimeSeriesTransformer models at https://huggingface.co/models?filter=time_series_transformer
}
class SCREAMING_SNAKE_CASE__ ( _a ):
_a = 'time_series_transformer'
_a = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
'num_hidden_layers': 'encoder_layers',
}
def __init__( self : Optional[Any] , lowerCAmelCase : Optional[int] = None , lowerCAmelCase : Optional[int] = None , lowerCAmelCase : str = "student_t" , lowerCAmelCase : str = "nll" , lowerCAmelCase : int = 1 , lowerCAmelCase : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowerCAmelCase : Optional[Union[str, bool]] = "mean" , lowerCAmelCase : int = 0 , lowerCAmelCase : int = 0 , lowerCAmelCase : int = 0 , lowerCAmelCase : int = 0 , lowerCAmelCase : Optional[List[int]] = None , lowerCAmelCase : Optional[List[int]] = None , lowerCAmelCase : int = 32 , lowerCAmelCase : int = 32 , lowerCAmelCase : int = 2 , lowerCAmelCase : int = 2 , lowerCAmelCase : int = 2 , lowerCAmelCase : int = 2 , lowerCAmelCase : bool = True , lowerCAmelCase : str = "gelu" , lowerCAmelCase : int = 64 , lowerCAmelCase : float = 0.1 , lowerCAmelCase : float = 0.1 , lowerCAmelCase : float = 0.1 , lowerCAmelCase : float = 0.1 , lowerCAmelCase : float = 0.1 , lowerCAmelCase : int = 100 , lowerCAmelCase : float = 0.02 , lowerCAmelCase : List[Any]=True , **lowerCAmelCase : List[str] , ):
# time series specific configuration
lowerCAmelCase = prediction_length
lowerCAmelCase = context_length or prediction_length
lowerCAmelCase = distribution_output
lowerCAmelCase = loss
lowerCAmelCase = input_size
lowerCAmelCase = num_time_features
lowerCAmelCase = lags_sequence
lowerCAmelCase = scaling
lowerCAmelCase = num_dynamic_real_features
lowerCAmelCase = num_static_real_features
lowerCAmelCase = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(lowerCAmelCase ) != num_static_categorical_features:
raise ValueError(
"""The cardinality should be a list of the same length as `num_static_categorical_features`""" )
lowerCAmelCase = cardinality
else:
lowerCAmelCase = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(lowerCAmelCase ) != num_static_categorical_features:
raise ValueError(
"""The embedding dimension should be a list of the same length as `num_static_categorical_features`""" )
lowerCAmelCase = embedding_dimension
else:
lowerCAmelCase = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
lowerCAmelCase = num_parallel_samples
# Transformer architecture configuration
lowerCAmelCase = input_size * len(lowerCAmelCase ) + self._number_of_features
lowerCAmelCase = d_model
lowerCAmelCase = encoder_attention_heads
lowerCAmelCase = decoder_attention_heads
lowerCAmelCase = encoder_ffn_dim
lowerCAmelCase = decoder_ffn_dim
lowerCAmelCase = encoder_layers
lowerCAmelCase = decoder_layers
lowerCAmelCase = dropout
lowerCAmelCase = attention_dropout
lowerCAmelCase = activation_dropout
lowerCAmelCase = encoder_layerdrop
lowerCAmelCase = decoder_layerdrop
lowerCAmelCase = activation_function
lowerCAmelCase = init_std
lowerCAmelCase = use_cache
super().__init__(is_encoder_decoder=lowerCAmelCase , **lowerCAmelCase )
@property
def __lowercase ( self : List[Any] ):
return (
sum(self.embedding_dimension )
+ self.num_dynamic_real_features
+ self.num_time_features
+ self.num_static_real_features
+ self.input_size * 2 # the log1p(abs(loc)) and log(scale) features
)
| 155 | 1 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import BertGenerationDecoder, BertGenerationEncoder
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : List[str] ,lowerCamelCase__ : Any ,lowerCamelCase__ : Union[str, Any]=13 ,lowerCamelCase__ : Tuple=7 ,lowerCamelCase__ : List[str]=True ,lowerCamelCase__ : Dict=True ,lowerCamelCase__ : Dict=99 ,lowerCamelCase__ : List[Any]=32 ,lowerCamelCase__ : Union[str, Any]=5 ,lowerCamelCase__ : Tuple=4 ,lowerCamelCase__ : Optional[Any]=37 ,lowerCamelCase__ : int="gelu" ,lowerCamelCase__ : int=0.1 ,lowerCamelCase__ : Optional[Any]=0.1 ,lowerCamelCase__ : Any=50 ,lowerCamelCase__ : int=0.02 ,lowerCamelCase__ : Any=True ,lowerCamelCase__ : Optional[int]=None ,) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = parent
SCREAMING_SNAKE_CASE = batch_size
SCREAMING_SNAKE_CASE = seq_length
SCREAMING_SNAKE_CASE = is_training
SCREAMING_SNAKE_CASE = use_input_mask
SCREAMING_SNAKE_CASE = vocab_size
SCREAMING_SNAKE_CASE = hidden_size
SCREAMING_SNAKE_CASE = num_hidden_layers
SCREAMING_SNAKE_CASE = num_attention_heads
SCREAMING_SNAKE_CASE = intermediate_size
SCREAMING_SNAKE_CASE = hidden_act
SCREAMING_SNAKE_CASE = hidden_dropout_prob
SCREAMING_SNAKE_CASE = attention_probs_dropout_prob
SCREAMING_SNAKE_CASE = max_position_embeddings
SCREAMING_SNAKE_CASE = initializer_range
SCREAMING_SNAKE_CASE = use_labels
SCREAMING_SNAKE_CASE = scope
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE = None
if self.use_input_mask:
SCREAMING_SNAKE_CASE = random_attention_mask([self.batch_size, self.seq_length] )
if self.use_labels:
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
SCREAMING_SNAKE_CASE = self.get_config()
return config, input_ids, input_mask, token_labels
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Dict:
'''simple docstring'''
return BertGenerationConfig(
vocab_size=self.vocab_size ,hidden_size=self.hidden_size ,num_hidden_layers=self.num_hidden_layers ,num_attention_heads=self.num_attention_heads ,intermediate_size=self.intermediate_size ,hidden_act=self.hidden_act ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,is_decoder=lowerCamelCase__ ,initializer_range=self.initializer_range ,)
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ) -> Tuple:
'''simple docstring'''
(
SCREAMING_SNAKE_CASE
) = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
SCREAMING_SNAKE_CASE = ids_tensor([self.batch_size, self.seq_length] ,vocab_size=2 )
return (
config,
input_ids,
input_mask,
token_labels,
encoder_hidden_states,
encoder_attention_mask,
)
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Dict ,**lowerCamelCase__ : Optional[Any] ,) -> Optional[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = BertGenerationEncoder(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ )
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : int ,lowerCamelCase__ : str ,lowerCamelCase__ : Union[str, Any] ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : List[Any] ,**lowerCamelCase__ : Optional[int] ,) -> Union[str, Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = BertGenerationEncoder(config=lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(
lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,encoder_hidden_states=lowerCamelCase__ ,encoder_attention_mask=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = model(
lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,encoder_hidden_states=lowerCamelCase__ ,)
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, self.seq_length, self.hidden_size) )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : Tuple ,lowerCamelCase__ : Optional[Any] ,lowerCamelCase__ : Dict ,lowerCamelCase__ : List[Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : Optional[Any] ,**lowerCamelCase__ : Dict ,) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = BertGenerationDecoder(config=lowerCamelCase__ ).to(lowerCamelCase__ ).eval()
# first forward pass
SCREAMING_SNAKE_CASE = model(
lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,encoder_hidden_states=lowerCamelCase__ ,encoder_attention_mask=lowerCamelCase__ ,use_cache=lowerCamelCase__ ,)
SCREAMING_SNAKE_CASE = outputs.past_key_values
# create hypothetical multiple next token and extent to next_input_ids
SCREAMING_SNAKE_CASE = ids_tensor((self.batch_size, 3) ,config.vocab_size )
SCREAMING_SNAKE_CASE = ids_tensor((self.batch_size, 3) ,vocab_size=2 )
# append to next input_ids and
SCREAMING_SNAKE_CASE = torch.cat([input_ids, next_tokens] ,dim=-1 )
SCREAMING_SNAKE_CASE = torch.cat([input_mask, next_mask] ,dim=-1 )
SCREAMING_SNAKE_CASE = model(
lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,encoder_hidden_states=lowerCamelCase__ ,encoder_attention_mask=lowerCamelCase__ ,output_hidden_states=lowerCamelCase__ ,)['''hidden_states'''][0]
SCREAMING_SNAKE_CASE = model(
lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,encoder_hidden_states=lowerCamelCase__ ,encoder_attention_mask=lowerCamelCase__ ,past_key_values=lowerCamelCase__ ,output_hidden_states=lowerCamelCase__ ,)['''hidden_states'''][0]
# select random slice
SCREAMING_SNAKE_CASE = ids_tensor((1,) ,output_from_past.shape[-1] ).item()
SCREAMING_SNAKE_CASE = output_from_no_past[:, -3:, random_slice_idx].detach()
SCREAMING_SNAKE_CASE = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1] )
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCamelCase__ ,lowerCamelCase__ ,atol=1e-3 ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ,lowerCamelCase__ : Any ,lowerCamelCase__ : Optional[int] ,lowerCamelCase__ : List[str] ,lowerCamelCase__ : List[str] ,*lowerCamelCase__ : Union[str, Any] ,) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = BertGenerationDecoder(lowerCamelCase__ )
model.to(lowerCamelCase__ )
model.eval()
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ ,attention_mask=lowerCamelCase__ ,labels=lowerCamelCase__ )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.seq_length, self.vocab_size) )
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE = {'''input_ids''': input_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_torch
class UpperCamelCase__ ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , unittest.TestCase ):
'''simple docstring'''
__snake_case : Dict = (BertGenerationEncoder, BertGenerationDecoder) if is_torch_available() else ()
__snake_case : Optional[Any] = (BertGenerationDecoder,) if is_torch_available() else ()
__snake_case : Any = (
{"feature-extraction": BertGenerationEncoder, "text-generation": BertGenerationDecoder}
if is_torch_available()
else {}
)
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = BertGenerationEncoderTester(self )
SCREAMING_SNAKE_CASE = ConfigTester(self ,config_class=lowerCamelCase__ ,hidden_size=37 )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> Optional[Any]:
'''simple docstring'''
self.config_tester.run_common_tests()
def SCREAMING_SNAKE_CASE__ ( self : str ) -> List[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs()
SCREAMING_SNAKE_CASE = '''bert'''
self.model_tester.create_and_check_model(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_model_as_decoder(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> Any:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : int ) -> Dict:
'''simple docstring'''
(
SCREAMING_SNAKE_CASE
) = self.model_tester.prepare_config_and_inputs_for_decoder()
SCREAMING_SNAKE_CASE = None
self.model_tester.create_and_check_model_as_decoder(
lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ ,)
def SCREAMING_SNAKE_CASE__ ( self : str ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self.model_tester.prepare_config_and_inputs_for_decoder()
self.model_tester.create_and_check_for_causal_lm(*lowerCamelCase__ )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Any ) -> Optional[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
self.assertIsNotNone(lowerCamelCase__ )
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = BertGenerationEncoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
SCREAMING_SNAKE_CASE = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )[0]
SCREAMING_SNAKE_CASE = torch.Size([1, 8, 1024] )
self.assertEqual(output.shape ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.tensor(
[[[0.1775, 0.0083, -0.0321], [1.6002, 0.1287, 0.3912], [2.1473, 0.5791, 0.6066]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,lowerCamelCase__ ,atol=1e-4 ) )
@require_torch
class UpperCamelCase__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def SCREAMING_SNAKE_CASE__ ( self : Dict ) -> int:
'''simple docstring'''
SCREAMING_SNAKE_CASE = BertGenerationDecoder.from_pretrained("""google/bert_for_seq_generation_L-24_bbc_encoder""" )
SCREAMING_SNAKE_CASE = torch.tensor([[101, 7592, 1010, 2026, 3899, 2003, 10140, 102]] )
with torch.no_grad():
SCREAMING_SNAKE_CASE = model(lowerCamelCase__ )[0]
SCREAMING_SNAKE_CASE = torch.Size([1, 8, 50358] )
self.assertEqual(output.shape ,lowerCamelCase__ )
SCREAMING_SNAKE_CASE = torch.tensor(
[[[-0.5788, -2.5994, -3.7054], [0.0438, 4.7997, 1.8795], [1.5862, 6.6409, 4.4638]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] ,lowerCamelCase__ ,atol=1e-4 ) )
| 357 |
class UpperCamelCase__ :
'''simple docstring'''
def __init__( self : int ) -> str:
'''simple docstring'''
SCREAMING_SNAKE_CASE = {}
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ) -> None:
'''simple docstring'''
print(self.vertex )
for i in self.vertex:
print(lowerCamelCase__ ,""" -> """ ,""" -> """.join([str(lowerCamelCase__ ) for j in self.vertex[i]] ) )
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ,lowerCamelCase__ : int ,lowerCamelCase__ : int ) -> None:
'''simple docstring'''
if from_vertex in self.vertex:
self.vertex[from_vertex].append(lowerCamelCase__ )
else:
# else make a new vertex
SCREAMING_SNAKE_CASE = [to_vertex]
def SCREAMING_SNAKE_CASE__ ( self : str ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [False] * len(self.vertex )
# call the recursive helper function
for i in range(len(self.vertex ) ):
if not visited[i]:
self.dfs_recursive(lowerCamelCase__ ,lowerCamelCase__ )
def SCREAMING_SNAKE_CASE__ ( self : Any ,lowerCamelCase__ : int ,lowerCamelCase__ : list ) -> None:
'''simple docstring'''
SCREAMING_SNAKE_CASE = True
print(lowerCamelCase__ ,end=""" """ )
# Recur for all the vertices that are adjacent to this node
for i in self.vertex:
if not visited[i]:
self.dfs_recursive(lowerCamelCase__ ,lowerCamelCase__ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = Graph()
g.add_edge(0, 1)
g.add_edge(0, 2)
g.add_edge(1, 2)
g.add_edge(2, 0)
g.add_edge(2, 3)
g.add_edge(3, 3)
g.print_graph()
print("""DFS:""")
g.dfs()
# OUTPUT:
# 0 -> 1 -> 2
# 1 -> 2
# 2 -> 0 -> 3
# 3 -> 3
# DFS:
# 0 1 2 3
| 193 | 0 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a :List[str] = logging.get_logger(__name__)
a :str = "▁"
a :List[str] = {"vocab_file": "sentencepiece.bpe.model", "monolingual_vocab_file": "dict.txt"}
a :str = {
"vocab_file": {
"vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/sentencepiece.bpe.model",
},
"monolingual_vocab_file": {
"vinai/bartpho-syllable": "https://huggingface.co/vinai/bartpho-syllable/resolve/main/dict.txt",
},
}
a :Optional[int] = {"vinai/bartpho-syllable": 1_024}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Union[str, Any] = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Optional[Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :Optional[int] = ["""input_ids""", """attention_mask"""]
def __init__( self , _a , _a , _a="<s>" , _a="</s>" , _a="</s>" , _a="<s>" , _a="<unk>" , _a="<pad>" , _a="<mask>" , _a = None , **_a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = AddedToken(_a , lstrip=_a , rstrip=_a ) if isinstance(_a , _a ) else mask_token
SCREAMING_SNAKE_CASE__ : Dict = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_a , eos_token=_a , unk_token=_a , sep_token=_a , cls_token=_a , pad_token=_a , mask_token=_a , sp_model_kwargs=self.sp_model_kwargs , **_a , )
SCREAMING_SNAKE_CASE__ : int = vocab_file
SCREAMING_SNAKE_CASE__ : Optional[Any] = monolingual_vocab_file
SCREAMING_SNAKE_CASE__ : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(str(_a ) )
# Load the reduced vocab
# Keep order of special tokens for backward compatibility
SCREAMING_SNAKE_CASE__ : Any = {}
SCREAMING_SNAKE_CASE__ : str = 0
for token in [bos_token, pad_token, eos_token, unk_token, sep_token, cls_token]:
if str(_a ) not in self.fairseq_tokens_to_ids:
SCREAMING_SNAKE_CASE__ : Optional[int] = cnt
cnt += 1
with open(_a , """r""" , encoding="""utf-8""" ) as f:
for line in f.readlines():
SCREAMING_SNAKE_CASE__ : Any = line.strip().split()[0]
SCREAMING_SNAKE_CASE__ : Union[str, Any] = len(self.fairseq_tokens_to_ids )
if str(_a ) not in self.fairseq_tokens_to_ids:
SCREAMING_SNAKE_CASE__ : str = len(self.fairseq_tokens_to_ids )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {v: k for k, v in self.fairseq_tokens_to_ids.items()}
def __getstate__( self ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__dict__.copy()
SCREAMING_SNAKE_CASE__ : List[Any] = None
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.sp_model.serialized_model_proto()
return state
def __setstate__( self , _a ) -> List[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE__ : List[str] = {}
SCREAMING_SNAKE_CASE__ : Dict = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.LoadFromSerializedProto(self.sp_model_proto )
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : Tuple = [self.cls_token_id]
SCREAMING_SNAKE_CASE__ : List[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def _a ( self , _a , _a = None , _a = False ) -> List[int]:
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_a , token_ids_a=_a , already_has_special_tokens=_a )
if token_ids_a is None:
return [1] + ([0] * len(_a )) + [1]
return [1] + ([0] * len(_a )) + [1, 1] + ([0] * len(_a )) + [1]
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = [self.sep_token_id]
SCREAMING_SNAKE_CASE__ : List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def _a ( self ) -> Any:
"""simple docstring"""
return len(self.fairseq_ids_to_tokens )
def _a ( self ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def _a ( self , _a ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(_a , out_type=_a )
def _a ( self , _a ) -> Optional[Any]:
"""simple docstring"""
if token in self.fairseq_tokens_to_ids:
return self.fairseq_tokens_to_ids[token]
else:
return self.unk_token_id
def _a ( self , _a ) -> Optional[int]:
"""simple docstring"""
return self.fairseq_ids_to_tokens[index]
def _a ( self , _a ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[str] = """""".join(_a ).replace(_a , """ """ ).strip()
return out_string
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
if not os.path.isdir(_a ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
SCREAMING_SNAKE_CASE__ : int = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
SCREAMING_SNAKE_CASE__ : List[str] = os.path.join(
_a , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""monolingual_vocab_file"""] , )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_a ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _a )
elif not os.path.isfile(self.vocab_file ):
with open(_a , """wb""" ) as fi:
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.sp_model.serialized_model_proto()
fi.write(_a )
if os.path.abspath(self.monolingual_vocab_file ) != os.path.abspath(
_a ) and os.path.isfile(self.monolingual_vocab_file ):
copyfile(self.monolingual_vocab_file , _a )
elif not os.path.isfile(self.monolingual_vocab_file ):
with open(_a , """w""" , encoding="""utf-8""" ) as fp:
for token in self.fairseq_tokens_to_ids:
if token not in self.all_special_tokens:
fp.write(f'''{str(_a )} \n''' )
return out_vocab_file, out_monolingual_vocab_file
| 132 |
"""simple docstring"""
import os
import sys
a :Union[str, Any] = os.path.join(os.path.dirname(__file__), "src")
sys.path.append(SRC_DIR)
from transformers import (
AutoConfig,
AutoModel,
AutoModelForCausalLM,
AutoModelForMaskedLM,
AutoModelForQuestionAnswering,
AutoModelForSequenceClassification,
AutoTokenizer,
add_start_docstrings,
)
a :int = [
"torch",
"numpy",
"tokenizers",
"filelock",
"requests",
"tqdm",
"regex",
"sentencepiece",
"sacremoses",
"importlib_metadata",
"huggingface_hub",
]
@add_start_docstrings(AutoConfig.__doc__ )
def _lowercase ( *__lowerCAmelCase , **__lowerCAmelCase ) -> Optional[Any]:
return AutoConfig.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )
@add_start_docstrings(AutoTokenizer.__doc__ )
def _lowercase ( *__lowerCAmelCase , **__lowerCAmelCase ) -> Union[str, Any]:
return AutoTokenizer.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )
@add_start_docstrings(AutoModel.__doc__ )
def _lowercase ( *__lowerCAmelCase , **__lowerCAmelCase ) -> Dict:
return AutoModel.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )
@add_start_docstrings(AutoModelForCausalLM.__doc__ )
def _lowercase ( *__lowerCAmelCase , **__lowerCAmelCase ) -> Optional[int]:
return AutoModelForCausalLM.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )
@add_start_docstrings(AutoModelForMaskedLM.__doc__ )
def _lowercase ( *__lowerCAmelCase , **__lowerCAmelCase ) -> List[str]:
return AutoModelForMaskedLM.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )
@add_start_docstrings(AutoModelForSequenceClassification.__doc__ )
def _lowercase ( *__lowerCAmelCase , **__lowerCAmelCase ) -> str:
return AutoModelForSequenceClassification.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )
@add_start_docstrings(AutoModelForQuestionAnswering.__doc__ )
def _lowercase ( *__lowerCAmelCase , **__lowerCAmelCase ) -> int:
return AutoModelForQuestionAnswering.from_pretrained(*__lowerCAmelCase , **__lowerCAmelCase )
| 132 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
A__ : List[str] = {
'config': [
'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
'OnnxConfig',
'OnnxConfigWithPast',
'OnnxSeq2SeqConfigWithPast',
'PatchingSpec',
],
'convert': ['export', 'validate_model_outputs'],
'features': ['FeaturesManager'],
'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}
if TYPE_CHECKING:
from .config import (
EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
OnnxConfig,
OnnxConfigWithPast,
OnnxSeqaSeqConfigWithPast,
PatchingSpec,
)
from .convert import export, validate_model_outputs
from .features import FeaturesManager
from .utils import ParameterFormat, compute_serialized_parameters_size
else:
import sys
A__ : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 209 |
"""simple docstring"""
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _snake_case ( lowerCamelCase__ : Any ) -> Union[str, Any]: # picklable for multiprocessing
return i + 1
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
def _snake_case ( ) -> List[Any]:
with parallel_backend("spark" ):
assert ParallelBackendConfig.backend_name == "spark"
lowerCamelCase_ : Optional[Any] =[1, 2, 3]
with pytest.raises(lowerCamelCase__ ):
with parallel_backend("unsupported backend" ):
map_nested(lowerCamelCase__ , lowerCamelCase__ , num_proc=2 )
with pytest.raises(lowerCamelCase__ ):
with parallel_backend("unsupported backend" ):
map_nested(lowerCamelCase__ , lowerCamelCase__ , num_proc=-1 )
@require_dill_gt_0_3_2
@require_joblibspark
@require_not_windows
@pytest.mark.parametrize("num_proc" , [2, -1] )
def _snake_case ( lowerCamelCase__ : Tuple ) -> Optional[Any]:
lowerCamelCase_ : str =[1, 2]
lowerCamelCase_ : List[str] ={"a": 1, "b": 2}
lowerCamelCase_ : List[str] ={"a": [1, 2], "b": [3, 4]}
lowerCamelCase_ : Optional[int] ={"a": {"1": 1}, "b": 2}
lowerCamelCase_ : int ={"a": 1, "b": 2, "c": 3, "d": 4}
lowerCamelCase_ : Optional[int] =[2, 3]
lowerCamelCase_ : List[Any] ={"a": 2, "b": 3}
lowerCamelCase_ : int ={"a": [2, 3], "b": [4, 5]}
lowerCamelCase_ : str ={"a": {"1": 2}, "b": 3}
lowerCamelCase_ : Dict ={"a": 2, "b": 3, "c": 4, "d": 5}
with parallel_backend("spark" ):
assert map_nested(lowerCamelCase__ , lowerCamelCase__ , num_proc=lowerCamelCase__ ) == expected_map_nested_sa
assert map_nested(lowerCamelCase__ , lowerCamelCase__ , num_proc=lowerCamelCase__ ) == expected_map_nested_sa
assert map_nested(lowerCamelCase__ , lowerCamelCase__ , num_proc=lowerCamelCase__ ) == expected_map_nested_sa
assert map_nested(lowerCamelCase__ , lowerCamelCase__ , num_proc=lowerCamelCase__ ) == expected_map_nested_sa
assert map_nested(lowerCamelCase__ , lowerCamelCase__ , num_proc=lowerCamelCase__ ) == expected_map_nested_sa
| 209 | 1 |
'''simple docstring'''
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ , ) -> float | int:
"""simple docstring"""
for nxt, d in graph[v]:
if nxt in visited_forward:
continue
A_ : Optional[int] = cst_fwd.get(a_ , np.inf )
A_ : Dict = cst_fwd[v] + d
if new_cost_f < old_cost_f:
queue.put((new_cost_f, nxt) )
A_ : str = new_cost_f
A_ : List[Any] = v
if nxt in visited_backward:
if cst_fwd[v] + d + cst_bwd[nxt] < shortest_distance:
A_ : List[str] = cst_fwd[v] + d + cst_bwd[nxt]
return shortest_distance
def UpperCAmelCase ( a_ , a_ , a_ , a_ ) -> int:
"""simple docstring"""
A_ : Tuple = -1
A_ : Union[str, Any] = set()
A_ : Tuple = set()
A_ : List[str] = {source: 0}
A_ : Union[str, Any] = {destination: 0}
A_ : Optional[Any] = {source: None}
A_ : Any = {destination: None}
A_ : PriorityQueue[Any] = PriorityQueue()
A_ : PriorityQueue[Any] = PriorityQueue()
A_ : Any = np.inf
queue_forward.put((0, source) )
queue_backward.put((0, destination) )
if source == destination:
return 0
while not queue_forward.empty() and not queue_backward.empty():
A_ , A_ : Dict = queue_forward.get()
visited_forward.add(a_ )
A_ , A_ : Optional[Any] = queue_backward.get()
visited_backward.add(a_ )
A_ : int = pass_and_relaxation(
a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ , )
A_ : Any = pass_and_relaxation(
a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ , a_ , )
if cst_fwd[v_fwd] + cst_bwd[v_bwd] >= shortest_distance:
break
if shortest_distance != np.inf:
A_ : Optional[int] = shortest_distance
return shortest_path_distance
UpperCamelCase__ : int = {
'B': [['C', 1]],
'C': [['D', 1]],
'D': [['F', 1]],
'E': [['B', 1], ['G', 2]],
'F': [],
'G': [['F', 1]],
}
UpperCamelCase__ : Optional[int] = {
'B': [['E', 1]],
'C': [['B', 1]],
'D': [['C', 1]],
'F': [['D', 1], ['G', 1]],
'E': [[None, np.inf]],
'G': [['E', 2]],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
| 344 |
'''simple docstring'''
import datasets
from .evaluate import evaluate
UpperCamelCase__ : int = '\\n@inproceedings{Rajpurkar2016SQuAD10,\n title={SQuAD: 100, 000+ Questions for Machine Comprehension of Text},\n author={Pranav Rajpurkar and Jian Zhang and Konstantin Lopyrev and Percy Liang},\n booktitle={EMNLP},\n year={2016}\n}\n'
UpperCamelCase__ : Any = '\nThis metric wrap the official scoring script for version 1 of the Stanford Question Answering Dataset (SQuAD).\n\nStanford Question Answering Dataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by\ncrowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span,\nfrom the corresponding reading passage, or the question might be unanswerable.\n'
UpperCamelCase__ : Optional[Any] = '\nComputes SQuAD scores (F1 and EM).\nArgs:\n predictions: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair as given in the references (see below)\n - \'prediction_text\': the text of the answer\n references: List of question-answers dictionaries with the following key-values:\n - \'id\': id of the question-answer pair (see above),\n - \'answers\': a Dict in the SQuAD dataset format\n {\n \'text\': list of possible texts for the answer, as a list of strings\n \'answer_start\': list of start positions for the answer, as a list of ints\n }\n Note that answer_start values are not taken into account to compute the metric.\nReturns:\n \'exact_match\': Exact match (the normalized answer exactly match the gold answer)\n \'f1\': The F-score of predicted tokens versus the gold answer\nExamples:\n\n >>> predictions = [{\'prediction_text\': \'1976\', \'id\': \'56e10a3be3433e1400422b22\'}]\n >>> references = [{\'answers\': {\'answer_start\': [97], \'text\': [\'1976\']}, \'id\': \'56e10a3be3433e1400422b22\'}]\n >>> squad_metric = datasets.load_metric("squad")\n >>> results = squad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class _lowerCAmelCase ( datasets.Metric ):
"""simple docstring"""
def UpperCAmelCase_ ( self ) -> str:
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": {"""id""": datasets.Value("""string""" ), """prediction_text""": datasets.Value("""string""" )},
"""references""": {
"""id""": datasets.Value("""string""" ),
"""answers""": datasets.features.Sequence(
{
"""text""": datasets.Value("""string""" ),
"""answer_start""": datasets.Value("""int32""" ),
} ),
},
} ) , codebase_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""] , reference_urls=["""https://rajpurkar.github.io/SQuAD-explorer/"""] , )
def UpperCAmelCase_ ( self , _lowerCamelCase , _lowerCamelCase ) -> List[Any]:
A_ : Optional[Any] = {prediction["""id"""]: prediction["""prediction_text"""] for prediction in predictions}
A_ : List[Any] = [
{
"""paragraphs""": [
{
"""qas""": [
{
"""answers""": [{"""text""": answer_text} for answer_text in ref["""answers"""]["""text"""]],
"""id""": ref["""id"""],
}
for ref in references
]
}
]
}
]
A_ : int = evaluate(dataset=_lowerCamelCase , predictions=_lowerCamelCase )
return score
| 344 | 1 |
"""simple docstring"""
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
snake_case_ = None
try:
import msvcrt
except ImportError:
snake_case_ = None
try:
import fcntl
except ImportError:
snake_case_ = None
# Backward compatibility
# ------------------------------------------------
try:
TimeoutError
except NameError:
snake_case_ = OSError
# Data
# ------------------------------------------------
snake_case_ = [
"""Timeout""",
"""BaseFileLock""",
"""WindowsFileLock""",
"""UnixFileLock""",
"""SoftFileLock""",
"""FileLock""",
]
snake_case_ = """3.0.12"""
snake_case_ = None
def _lowerCAmelCase ( ):
global _logger
UpperCAmelCase = _logger or logging.getLogger(__name__ )
return _logger
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :int , lowercase_ :List[Any] ) -> Optional[int]:
UpperCAmelCase = lock_file
return None
def __str__( self :Dict ) -> Optional[int]:
UpperCAmelCase = f"""The file lock '{self.lock_file}' could not be acquired."""
return temp
class A_ :
"""simple docstring"""
def __init__( self :List[Any] , lowercase_ :Dict ) -> Dict:
UpperCAmelCase = lock
return None
def __enter__( self :Optional[int] ) -> Optional[int]:
return self.lock
def __exit__( self :Optional[int] , lowercase_ :Tuple , lowercase_ :Any , lowercase_ :Union[str, Any] ) -> List[Any]:
self.lock.release()
return None
class A_ :
"""simple docstring"""
def __init__( self :List[str] , lowercase_ :str , lowercase_ :str=-1 , lowercase_ :List[Any]=None ) -> Dict:
UpperCAmelCase = max_filename_length if max_filename_length is not None else 2_55
# Hash the filename if it's too long
UpperCAmelCase = self.hash_filename_if_too_long(lowercase_ , lowercase_ )
# The path to the lock file.
UpperCAmelCase = lock_file
# The file descriptor for the *_lock_file* as it is returned by the
# os.open() function.
# This file lock is only NOT None, if the object currently holds the
# lock.
UpperCAmelCase = None
# The default timeout value.
UpperCAmelCase = timeout
# We use this lock primarily for the lock counter.
UpperCAmelCase = threading.Lock()
# The lock counter is used for implementing the nested locking
# mechanism. Whenever the lock is acquired, the counter is increased and
# the lock is only released, when this value is 0 again.
UpperCAmelCase = 0
return None
@property
def UpperCAmelCase__ ( self :Optional[Any] ) -> int:
return self._lock_file
@property
def UpperCAmelCase__ ( self :Union[str, Any] ) -> Any:
return self._timeout
@timeout.setter
def UpperCAmelCase__ ( self :Union[str, Any] , lowercase_ :Any ) -> Tuple:
UpperCAmelCase = float(lowercase_ )
return None
def UpperCAmelCase__ ( self :Any ) -> List[str]:
raise NotImplementedError()
def UpperCAmelCase__ ( self :Union[str, Any] ) -> List[str]:
raise NotImplementedError()
@property
def UpperCAmelCase__ ( self :Optional[Any] ) -> Union[str, Any]:
return self._lock_file_fd is not None
def UpperCAmelCase__ ( self :Union[str, Any] , lowercase_ :Dict=None , lowercase_ :int=0.05 ) -> Union[str, Any]:
# Use the default timeout, if no timeout is provided.
if timeout is None:
UpperCAmelCase = self.timeout
# Increment the number right at the beginning.
# We can still undo it, if something fails.
with self._thread_lock:
self._lock_counter += 1
UpperCAmelCase = id(self )
UpperCAmelCase = self._lock_file
UpperCAmelCase = time.time()
try:
while True:
with self._thread_lock:
if not self.is_locked:
logger().debug(f"""Attempting to acquire lock {lock_id} on {lock_filename}""" )
self._acquire()
if self.is_locked:
logger().debug(f"""Lock {lock_id} acquired on {lock_filename}""" )
break
elif timeout >= 0 and time.time() - start_time > timeout:
logger().debug(f"""Timeout on acquiring lock {lock_id} on {lock_filename}""" )
raise Timeout(self._lock_file )
else:
logger().debug(
f"""Lock {lock_id} not acquired on {lock_filename}, waiting {poll_intervall} seconds ...""" )
time.sleep(lowercase_ )
except: # noqa
# Something did go wrong, so decrement the counter.
with self._thread_lock:
UpperCAmelCase = max(0 , self._lock_counter - 1 )
raise
return _Acquire_ReturnProxy(lock=self )
def UpperCAmelCase__ ( self :Optional[int] , lowercase_ :List[str]=False ) -> int:
with self._thread_lock:
if self.is_locked:
self._lock_counter -= 1
if self._lock_counter == 0 or force:
UpperCAmelCase = id(self )
UpperCAmelCase = self._lock_file
logger().debug(f"""Attempting to release lock {lock_id} on {lock_filename}""" )
self._release()
UpperCAmelCase = 0
logger().debug(f"""Lock {lock_id} released on {lock_filename}""" )
return None
def __enter__( self :Any ) -> Tuple:
self.acquire()
return self
def __exit__( self :Dict , lowercase_ :Optional[int] , lowercase_ :Union[str, Any] , lowercase_ :Union[str, Any] ) -> Any:
self.release()
return None
def __del__( self :Any ) -> int:
self.release(force=lowercase_ )
return None
def UpperCAmelCase__ ( self :List[str] , lowercase_ :str , lowercase_ :int ) -> str:
UpperCAmelCase = os.path.basename(lowercase_ )
if len(lowercase_ ) > max_length and max_length > 0:
UpperCAmelCase = os.path.dirname(lowercase_ )
UpperCAmelCase = str(hash(lowercase_ ) )
UpperCAmelCase = filename[: max_length - len(lowercase_ ) - 8] + '...' + hashed_filename + '.lock'
return os.path.join(lowercase_ , lowercase_ )
else:
return path
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :Tuple , lowercase_ :Tuple , lowercase_ :List[Any]=-1 , lowercase_ :Union[str, Any]=None ) -> Optional[Any]:
from .file_utils import relative_to_absolute_path
super().__init__(lowercase_ , timeout=lowercase_ , max_filename_length=lowercase_ )
UpperCAmelCase = '\\\\?\\' + relative_to_absolute_path(self.lock_file )
def UpperCAmelCase__ ( self :List[str] ) -> str:
UpperCAmelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
try:
UpperCAmelCase = os.open(self._lock_file , lowercase_ )
except OSError:
pass
else:
try:
msvcrt.locking(lowercase_ , msvcrt.LK_NBLCK , 1 )
except OSError:
os.close(lowercase_ )
else:
UpperCAmelCase = fd
return None
def UpperCAmelCase__ ( self :int ) -> Union[str, Any]:
UpperCAmelCase = self._lock_file_fd
UpperCAmelCase = None
msvcrt.locking(lowercase_ , msvcrt.LK_UNLCK , 1 )
os.close(lowercase_ )
try:
os.remove(self._lock_file )
# Probably another instance of the application
# that acquired the file lock.
except OSError:
pass
return None
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def __init__( self :Optional[Any] , lowercase_ :str , lowercase_ :List[str]=-1 , lowercase_ :str=None ) -> str:
UpperCAmelCase = os.statvfs(os.path.dirname(lowercase_ ) ).f_namemax
super().__init__(lowercase_ , timeout=lowercase_ , max_filename_length=lowercase_ )
def UpperCAmelCase__ ( self :str ) -> Dict:
UpperCAmelCase = os.O_RDWR | os.O_CREAT | os.O_TRUNC
UpperCAmelCase = os.open(self._lock_file , lowercase_ )
try:
fcntl.flock(lowercase_ , fcntl.LOCK_EX | fcntl.LOCK_NB )
except OSError:
os.close(lowercase_ )
else:
UpperCAmelCase = fd
return None
def UpperCAmelCase__ ( self :List[Any] ) -> List[Any]:
# Do not remove the lockfile:
#
# https://github.com/benediktschmitt/py-filelock/issues/31
# https://stackoverflow.com/questions/17708885/flock-removing-locked-file-without-race-condition
UpperCAmelCase = self._lock_file_fd
UpperCAmelCase = None
fcntl.flock(lowercase_ , fcntl.LOCK_UN )
os.close(lowercase_ )
return None
class A_ ( SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
def UpperCAmelCase__ ( self :List[Any] ) -> List[str]:
UpperCAmelCase = os.O_WRONLY | os.O_CREAT | os.O_EXCL | os.O_TRUNC
try:
UpperCAmelCase = os.open(self._lock_file , lowercase_ )
except OSError:
pass
else:
UpperCAmelCase = fd
return None
def UpperCAmelCase__ ( self :Optional[int] ) -> List[Any]:
os.close(self._lock_file_fd )
UpperCAmelCase = None
try:
os.remove(self._lock_file )
# The file is already deleted and that's what we want.
except OSError:
pass
return None
snake_case_ = None
if msvcrt:
snake_case_ = WindowsFileLock
elif fcntl:
snake_case_ = UnixFileLock
else:
snake_case_ = SoftFileLock
if warnings is not None:
warnings.warn("""only soft file lock is available""")
| 355 |
"""simple docstring"""
import math
def _lowerCAmelCase ( lowercase_ ):
assert isinstance(lowercase_ , lowercase_ ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or not number % 2:
# Negatives, 0, 1 and all even numbers are not primes
return False
UpperCAmelCase = range(3 , int(math.sqrt(lowercase_ ) + 1 ) , 2 )
return not any(not number % i for i in odd_numbers )
def _lowerCAmelCase ( lowercase_ , lowercase_=1 , **lowercase_ ):
UpperCAmelCase = factor * value
UpperCAmelCase = value
while not is_prime(lowercase_ ):
value += 1 if not ("desc" in kwargs and kwargs["desc"] is True) else -1
if value == first_value_val:
return next_prime(value + 1 , **lowercase_ )
return value
| 181 | 0 |
"""simple docstring"""
import numpy as np
def __a ( __lowerCamelCase ):
return 1 / (1 + np.exp(-vector ))
def __a ( __lowerCamelCase ):
return vector * sigmoid(__lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 61 |
"""simple docstring"""
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class A_ (lowercase__ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Optional[int] = (DDIMParallelScheduler,)
SCREAMING_SNAKE_CASE__ : Optional[Any] = (("""eta""", 0.0), ("""num_inference_steps""", 50))
def UpperCamelCase__ ( self , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : int = {
"num_train_timesteps": 1000,
"beta_start": 0.00_01,
"beta_end": 0.02,
"beta_schedule": "linear",
"clip_sample": True,
}
config.update(**lowercase_ )
return config
def UpperCamelCase__ ( self , **lowercase_ ):
"""simple docstring"""
UpperCAmelCase_ : Dict = self.scheduler_classes[0]
UpperCAmelCase_ : Union[str, Any] = self.get_scheduler_config(**lowercase_ )
UpperCAmelCase_ : int = scheduler_class(**lowercase_ )
UpperCAmelCase_ , UpperCAmelCase_ : str = 10, 0.0
UpperCAmelCase_ : Optional[int] = self.dummy_model()
UpperCAmelCase_ : str = self.dummy_sample_deter
scheduler.set_timesteps(lowercase_ )
for t in scheduler.timesteps:
UpperCAmelCase_ : Dict = model(lowercase_ , lowercase_ )
UpperCAmelCase_ : Dict = scheduler.step(lowercase_ , lowercase_ , lowercase_ , lowercase_ ).prev_sample
return sample
def UpperCamelCase__ ( self ):
"""simple docstring"""
for timesteps in [100, 500, 1000]:
self.check_over_configs(num_train_timesteps=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for steps_offset in [0, 1]:
self.check_over_configs(steps_offset=lowercase_ )
UpperCAmelCase_ : str = self.scheduler_classes[0]
UpperCAmelCase_ : List[str] = self.get_scheduler_config(steps_offset=1 )
UpperCAmelCase_ : List[str] = scheduler_class(**lowercase_ )
scheduler.set_timesteps(5 )
assert torch.equal(scheduler.timesteps , torch.LongTensor([801, 601, 401, 201, 1] ) )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for beta_start, beta_end in zip([0.00_01, 0.0_01, 0.01, 0.1] , [0.0_02, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=lowercase_ , beta_end=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for timestep_spacing in ["trailing", "leading"]:
self.check_over_configs(timestep_spacing=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for rescale_betas_zero_snr in [True, False]:
self.check_over_configs(rescale_betas_zero_snr=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.check_over_configs(thresholding=lowercase_ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "v_prediction"]:
self.check_over_configs(
thresholding=lowercase_ , prediction_type=lowercase_ , sample_max_value=lowercase_ , )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for t in [1, 10, 49]:
self.check_over_forward(time_step=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for t, num_inference_steps in zip([1, 10, 50] , [10, 50, 500] ):
self.check_over_forward(time_step=lowercase_ , num_inference_steps=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
for t, eta in zip([1, 10, 49] , [0.0, 0.5, 1.0] ):
self.check_over_forward(time_step=lowercase_ , eta=lowercase_ )
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = self.scheduler_classes[0]
UpperCAmelCase_ : List[str] = self.get_scheduler_config()
UpperCAmelCase_ : List[str] = scheduler_class(**lowercase_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(420 , 400 ) - 0.1_47_71 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(980 , 960 ) - 0.3_24_60 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(0 , 0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 , 486 ) - 0.0_09_79 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 , 998 ) - 0.02 ) ) < 1E-5
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = self.scheduler_classes[0]
UpperCAmelCase_ : Optional[int] = self.get_scheduler_config()
UpperCAmelCase_ : List[str] = scheduler_class(**lowercase_ )
UpperCAmelCase_ , UpperCAmelCase_ : Tuple = 10, 0.0
scheduler.set_timesteps(lowercase_ )
UpperCAmelCase_ : Union[str, Any] = self.dummy_model()
UpperCAmelCase_ : List[str] = self.dummy_sample_deter
UpperCAmelCase_ : Any = self.dummy_sample_deter + 0.1
UpperCAmelCase_ : int = self.dummy_sample_deter - 0.1
UpperCAmelCase_ : List[Any] = samplea.shape[0]
UpperCAmelCase_ : int = torch.stack([samplea, samplea, samplea] , dim=0 )
UpperCAmelCase_ : int = torch.arange(lowercase_ )[0:3, None].repeat(1 , lowercase_ )
UpperCAmelCase_ : int = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
UpperCAmelCase_ : Optional[Any] = scheduler.batch_step_no_noise(lowercase_ , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) , lowercase_ )
UpperCAmelCase_ : List[Any] = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : str = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 11_47.79_04 ) < 1E-2
assert abs(result_mean.item() - 0.49_82 ) < 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : Tuple = self.full_loop()
UpperCAmelCase_ : int = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : List[str] = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 1_72.00_67 ) < 1E-2
assert abs(result_mean.item() - 0.22_39_67 ) < 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
UpperCAmelCase_ : List[str] = self.full_loop(prediction_type="v_prediction" )
UpperCAmelCase_ : str = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : Dict = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 52.53_02 ) < 1E-2
assert abs(result_mean.item() - 0.06_84 ) < 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
# We specify different beta, so that the first alpha is 0.99
UpperCAmelCase_ : List[str] = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 )
UpperCAmelCase_ : Dict = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : Tuple = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 1_49.82_95 ) < 1E-2
assert abs(result_mean.item() - 0.19_51 ) < 1E-3
def UpperCamelCase__ ( self ):
"""simple docstring"""
# We specify different beta, so that the first alpha is 0.99
UpperCAmelCase_ : int = self.full_loop(set_alpha_to_one=lowercase_ , beta_start=0.01 )
UpperCAmelCase_ : List[Any] = torch.sum(torch.abs(lowercase_ ) )
UpperCAmelCase_ : Dict = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 1_49.07_84 ) < 1E-2
assert abs(result_mean.item() - 0.19_41 ) < 1E-3
| 61 | 1 |
"""simple docstring"""
from __future__ import annotations
def __lowerCAmelCase ( lowercase : float , lowercase : float , lowercase : float ) -> str:
"""simple docstring"""
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if resistance < 0:
raise ValueError("Resistance cannot be negative" )
if voltage == 0:
return {"voltage": float(current * resistance )}
elif current == 0:
return {"current": voltage / resistance}
elif resistance == 0:
return {"resistance": voltage / current}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 370 |
"""simple docstring"""
import argparse
import logging
import pickle
from collections import Counter
logging.basicConfig(
format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s""", datefmt="""%m/%d/%Y %H:%M:%S""", level=logging.INFO
)
__snake_case = logging.getLogger(__name__)
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser(
description="""Token Counts for smoothing the masking probabilities in MLM (cf XLM/word2vec)"""
)
parser.add_argument(
"""--data_file""", type=str, default="""data/dump.bert-base-uncased.pickle""", help="""The binarized dataset."""
)
parser.add_argument(
"""--token_counts_dump""", type=str, default="""data/token_counts.bert-base-uncased.pickle""", help="""The dump file."""
)
parser.add_argument("""--vocab_size""", default=30522, type=int)
__snake_case = parser.parse_args()
logger.info(F'''Loading data from {args.data_file}''')
with open(args.data_file, """rb""") as fp:
__snake_case = pickle.load(fp)
logger.info("""Counting occurrences for MLM.""")
__snake_case = Counter()
for tk_ids in data:
counter.update(tk_ids)
__snake_case = [0] * args.vocab_size
for k, v in counter.items():
__snake_case = v
logger.info(F'''Dump to {args.token_counts_dump}''')
with open(args.token_counts_dump, """wb""") as handle:
pickle.dump(counts, handle, protocol=pickle.HIGHEST_PROTOCOL)
| 112 | 0 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : int ) -> Optional[int]:
# A local function to see if a dot lands in the circle.
def is_in_circle(SCREAMING_SNAKE_CASE : float , SCREAMING_SNAKE_CASE : float ) -> bool:
__lowercase = sqrt((x**2) + (y**2) )
# Our circle has a radius of 1, so a distance
# greater than 1 would land outside the circle.
return distance_from_centre <= 1
# The proportion of guesses that landed in the circle
__lowercase = mean(
int(is_in_circle(uniform(-1.0 , 1.0 ) , uniform(-1.0 , 1.0 ) ) )
for _ in range(SCREAMING_SNAKE_CASE ) )
# The ratio of the area for circle to square is pi/4.
__lowercase = proportion * 4
print(F"""The estimated value of pi is {pi_estimate}""" )
print(F"""The numpy value of pi is {pi}""" )
print(F"""The total error is {abs(pi - pi_estimate )}""" )
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : Callable[[float], float] , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : float = 1.0 , ) -> float:
return mean(
function_to_integrate(uniform(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) ) for _ in range(SCREAMING_SNAKE_CASE ) ) * (max_value - min_value)
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : float = 0.0 , SCREAMING_SNAKE_CASE : float = 1.0 ) -> None:
def identity_function(SCREAMING_SNAKE_CASE : float ) -> float:
return x
__lowercase = area_under_curve_estimator(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowercase = (max_value * max_value - min_value * min_value) / 2
print('******************' )
print(F"""Estimating area under y=x where x varies from {min_value} to {max_value}""" )
print(F"""Estimated value is {estimated_value}""" )
print(F"""Expected value is {expected_value}""" )
print(F"""Total error is {abs(estimated_value - expected_value )}""" )
print('******************' )
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : int ) -> None:
def function_to_integrate(SCREAMING_SNAKE_CASE : float ) -> float:
return sqrt(4.0 - x * x )
__lowercase = area_under_curve_estimator(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , 0.0 , 2.0 )
print('******************' )
print('Estimating pi using area_under_curve_estimator' )
print(F"""Estimated value is {estimated_value}""" )
print(F"""Expected value is {pi}""" )
print(F"""Total error is {abs(estimated_value - pi )}""" )
print('******************' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 325 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
SCREAMING_SNAKE_CASE__ = get_logger(__name__)
class A__ ( enum.Enum ):
lowerCAmelCase__ : Dict = "all_checks"
lowerCAmelCase__ : List[Any] = "basic_checks"
lowerCAmelCase__ : Dict = "no_checks"
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Optional[dict] , SCREAMING_SNAKE_CASE : dict , SCREAMING_SNAKE_CASE : Optional[Any]=None ) -> Optional[Any]:
if expected_checksums is None:
logger.info('Unable to verify checksums.' )
return
if len(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) > 0:
raise ExpectedMoreDownloadedFiles(str(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) )
if len(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) > 0:
raise UnexpectedDownloadedFile(str(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) )
__lowercase = [url for url in expected_checksums if expected_checksums[url] != recorded_checksums[url]]
__lowercase = ' for ' + verification_name if verification_name is not None else ''
if len(SCREAMING_SNAKE_CASE ) > 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 A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
class A__ ( lowerCAmelCase__ ):
pass
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Optional[dict] , SCREAMING_SNAKE_CASE : dict ) -> Optional[int]:
if expected_splits is None:
logger.info('Unable to verify splits sizes.' )
return
if len(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) > 0:
raise ExpectedMoreSplits(str(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) )
if len(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) > 0:
raise UnexpectedSplits(str(set(SCREAMING_SNAKE_CASE ) - set(SCREAMING_SNAKE_CASE ) ) )
__lowercase = [
{'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(SCREAMING_SNAKE_CASE ) > 0:
raise NonMatchingSplitsSizesError(str(SCREAMING_SNAKE_CASE ) )
logger.info('All the splits matched successfully.' )
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : str , SCREAMING_SNAKE_CASE : bool = True ) -> dict:
if record_checksum:
__lowercase = shaaaa()
with open(SCREAMING_SNAKE_CASE , 'rb' ) as f:
for chunk in iter(lambda: f.read(1 << 20 ) , b'' ):
m.update(SCREAMING_SNAKE_CASE )
__lowercase = m.hexdigest()
else:
__lowercase = None
return {"num_bytes": os.path.getsize(SCREAMING_SNAKE_CASE ), "checksum": checksum}
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : Optional[int] ) -> Dict:
if dataset_size and config.IN_MEMORY_MAX_SIZE:
return dataset_size < config.IN_MEMORY_MAX_SIZE
else:
return False
| 325 | 1 |
'''simple docstring'''
import math
from collections.abc import Callable
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : Callable[[float], float] , _UpperCamelCase : float , _UpperCamelCase : float ) -> float:
'''simple docstring'''
UpperCamelCase__ = xa
UpperCamelCase__ = xa
while True:
if x_n == x_na or function(_UpperCamelCase ) == function(_UpperCamelCase ):
raise ZeroDivisionError("float division by zero, could not find root" )
UpperCamelCase__ = x_na - (
function(_UpperCamelCase ) / ((function(_UpperCamelCase ) - function(_UpperCamelCase )) / (x_na - x_n))
)
if abs(x_na - x_na ) < 10**-5:
return x_na
UpperCamelCase__ = x_na
UpperCamelCase__ = x_na
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : float ) -> float:
'''simple docstring'''
return math.pow(_UpperCamelCase , 3 ) - (2 * x) - 5
if __name__ == "__main__":
print(intersection(f, 3, 3.5)) | 31 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__( _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : float , ) -> tuple:
'''simple docstring'''
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("You cannot supply more or less than 2 values" )
elif electron_conc < 0:
raise ValueError("Electron concentration cannot be negative in a semiconductor" )
elif hole_conc < 0:
raise ValueError("Hole concentration cannot be negative in a semiconductor" )
elif intrinsic_conc < 0:
raise ValueError(
"Intrinsic concentration cannot be negative in a semiconductor" )
elif electron_conc == 0:
return (
"electron_conc",
intrinsic_conc**2 / hole_conc,
)
elif hole_conc == 0:
return (
"hole_conc",
intrinsic_conc**2 / electron_conc,
)
elif intrinsic_conc == 0:
return (
"intrinsic_conc",
(electron_conc * hole_conc) ** 0.5,
)
else:
return (-1, -1)
if __name__ == "__main__":
import doctest
doctest.testmod() | 31 | 1 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwinvaForImageClassification, SwinvaForMaskedImageModeling, SwinvaModel
from transformers.models.swinva.modeling_swinva import SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class A :
"""simple docstring"""
def __init__( self : Any,lowercase_ : List[str],lowercase_ : Any=1_3,lowercase_ : Union[str, Any]=3_2,lowercase_ : Dict=2,lowercase_ : str=3,lowercase_ : List[str]=1_6,lowercase_ : Optional[Any]=[1, 2, 1],lowercase_ : List[str]=[2, 2, 4],lowercase_ : Optional[int]=2,lowercase_ : str=2.0,lowercase_ : Optional[int]=True,lowercase_ : Any=0.0,lowercase_ : Optional[int]=0.0,lowercase_ : Union[str, Any]=0.1,lowercase_ : int="gelu",lowercase_ : List[Any]=False,lowercase_ : Any=True,lowercase_ : Any=0.02,lowercase_ : str=1E-5,lowercase_ : Tuple=True,lowercase_ : Dict=None,lowercase_ : int=True,lowercase_ : Union[str, Any]=1_0,lowercase_ : Tuple=8,)-> List[Any]:
'''simple docstring'''
A__ = parent
A__ = batch_size
A__ = image_size
A__ = patch_size
A__ = num_channels
A__ = embed_dim
A__ = depths
A__ = num_heads
A__ = window_size
A__ = mlp_ratio
A__ = qkv_bias
A__ = hidden_dropout_prob
A__ = attention_probs_dropout_prob
A__ = drop_path_rate
A__ = hidden_act
A__ = use_absolute_embeddings
A__ = patch_norm
A__ = layer_norm_eps
A__ = initializer_range
A__ = is_training
A__ = scope
A__ = use_labels
A__ = type_sequence_label_size
A__ = encoder_stride
def snake_case__ ( self : Dict )-> Any:
'''simple docstring'''
A__ = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
A__ = None
if self.use_labels:
A__ = ids_tensor([self.batch_size],self.type_sequence_label_size )
A__ = self.get_config()
return config, pixel_values, labels
def snake_case__ ( self : Union[str, Any] )-> Tuple:
'''simple docstring'''
return SwinvaConfig(
image_size=self.image_size,patch_size=self.patch_size,num_channels=self.num_channels,embed_dim=self.embed_dim,depths=self.depths,num_heads=self.num_heads,window_size=self.window_size,mlp_ratio=self.mlp_ratio,qkv_bias=self.qkv_bias,hidden_dropout_prob=self.hidden_dropout_prob,attention_probs_dropout_prob=self.attention_probs_dropout_prob,drop_path_rate=self.drop_path_rate,hidden_act=self.hidden_act,use_absolute_embeddings=self.use_absolute_embeddings,path_norm=self.patch_norm,layer_norm_eps=self.layer_norm_eps,initializer_range=self.initializer_range,encoder_stride=self.encoder_stride,)
def snake_case__ ( self : str,lowercase_ : str,lowercase_ : Any,lowercase_ : int )-> Dict:
'''simple docstring'''
A__ = SwinvaModel(config=lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_ )
A__ = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
A__ = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape,(self.batch_size, expected_seq_len, expected_dim) )
def snake_case__ ( self : Tuple,lowercase_ : int,lowercase_ : Dict,lowercase_ : List[str] )-> Optional[int]:
'''simple docstring'''
A__ = SwinvaForMaskedImageModeling(config=lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_ )
self.parent.assertEqual(
result.logits.shape,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
A__ = 1
A__ = SwinvaForMaskedImageModeling(lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
A__ = model(lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, 1, self.image_size, self.image_size) )
def snake_case__ ( self : List[Any],lowercase_ : str,lowercase_ : Tuple,lowercase_ : Dict )-> Tuple:
'''simple docstring'''
A__ = self.type_sequence_label_size
A__ = SwinvaForImageClassification(lowercase_ )
model.to(lowercase_ )
model.eval()
A__ = model(lowercase_,labels=lowercase_ )
self.parent.assertEqual(result.logits.shape,(self.batch_size, self.type_sequence_label_size) )
def snake_case__ ( self : int )-> str:
'''simple docstring'''
A__ = self.prepare_config_and_inputs()
A__ , A__ , A__ = config_and_inputs
A__ = {'pixel_values': pixel_values}
return config, inputs_dict
@require_torch
class A ( _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = (
(SwinvaModel, SwinvaForImageClassification, SwinvaForMaskedImageModeling) if is_torch_available() else ()
)
lowerCamelCase = (
{'feature-extraction': SwinvaModel, 'image-classification': SwinvaForImageClassification}
if is_torch_available()
else {}
)
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
lowerCamelCase = False
def snake_case__ ( self : Tuple )-> Tuple:
'''simple docstring'''
A__ = SwinvaModelTester(self )
A__ = ConfigTester(self,config_class=lowercase_,embed_dim=3_7 )
def snake_case__ ( self : Dict )-> Tuple:
'''simple docstring'''
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def snake_case__ ( self : int )-> Optional[Any]:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase_ )
@unittest.skip(reason='Got `CUDA error: misaligned address` with PyTorch 2.0.0.' )
def snake_case__ ( self : int )-> Optional[int]:
'''simple docstring'''
pass
@unittest.skip(reason='Swinv2 does not use inputs_embeds' )
def snake_case__ ( self : Any )-> Dict:
'''simple docstring'''
pass
def snake_case__ ( self : int )-> List[Any]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(lowercase_ )
self.assertIsInstance(model.get_input_embeddings(),(nn.Module) )
A__ = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase_,nn.Linear ) )
def snake_case__ ( self : Any )-> int:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A__ = model_class(lowercase_ )
A__ = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
A__ = [*signature.parameters.keys()]
A__ = ['pixel_values']
self.assertListEqual(arg_names[:1],lowercase_ )
def snake_case__ ( self : List[str] )-> Tuple:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = True
for model_class in self.all_model_classes:
A__ = True
A__ = False
A__ = True
A__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowercase_,lowercase_ ) )
A__ = outputs.attentions
A__ = len(self.model_tester.depths )
self.assertEqual(len(lowercase_ ),lowercase_ )
# check that output_attentions also work using config
del inputs_dict["output_attentions"]
A__ = True
A__ = config.window_size**2
A__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowercase_,lowercase_ ) )
A__ = outputs.attentions
self.assertEqual(len(lowercase_ ),lowercase_ )
self.assertListEqual(
list(attentions[0].shape[-3:] ),[self.model_tester.num_heads[0], window_size_squared, window_size_squared],)
A__ = len(lowercase_ )
# Check attention is always last and order is fine
A__ = True
A__ = True
A__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowercase_,lowercase_ ) )
if hasattr(self.model_tester,'num_hidden_states_types' ):
A__ = self.model_tester.num_hidden_states_types
else:
# also another +1 for reshaped_hidden_states
A__ = 2
self.assertEqual(out_len + added_hidden_states,len(lowercase_ ) )
A__ = outputs.attentions
self.assertEqual(len(lowercase_ ),lowercase_ )
self.assertListEqual(
list(self_attentions[0].shape[-3:] ),[self.model_tester.num_heads[0], window_size_squared, window_size_squared],)
def snake_case__ ( self : Dict,lowercase_ : Tuple,lowercase_ : Optional[Any],lowercase_ : Optional[int],lowercase_ : Dict )-> List[Any]:
'''simple docstring'''
A__ = model_class(lowercase_ )
model.to(lowercase_ )
model.eval()
with torch.no_grad():
A__ = model(**self._prepare_for_class(lowercase_,lowercase_ ) )
A__ = outputs.hidden_states
A__ = getattr(
self.model_tester,'expected_num_hidden_layers',len(self.model_tester.depths ) + 1 )
self.assertEqual(len(lowercase_ ),lowercase_ )
# Swinv2 has a different seq_length
A__ = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
A__ = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
A__ = outputs.reshaped_hidden_states
self.assertEqual(len(lowercase_ ),lowercase_ )
A__ , A__ , A__ , A__ = reshaped_hidden_states[0].shape
A__ = (
reshaped_hidden_states[0].view(lowercase_,lowercase_,height * width ).permute(0,2,1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ),[num_patches, self.model_tester.embed_dim],)
def snake_case__ ( self : int )-> List[Any]:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes:
A__ = True
self.check_hidden_states_output(lowercase_,lowercase_,lowercase_,lowercase_ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
self.check_hidden_states_output(lowercase_,lowercase_,lowercase_,lowercase_ )
def snake_case__ ( self : Union[str, Any] )-> Dict:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = 3
A__ = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
A__ = (
config.patch_size
if isinstance(config.patch_size,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
A__ = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
A__ = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes:
A__ = True
self.check_hidden_states_output(lowercase_,lowercase_,lowercase_,(padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
A__ = True
self.check_hidden_states_output(lowercase_,lowercase_,lowercase_,(padded_height, padded_width) )
def snake_case__ ( self : Optional[Any] )-> int:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*lowercase_ )
def snake_case__ ( self : Optional[int] )-> Tuple:
'''simple docstring'''
A__ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase_ )
@slow
def snake_case__ ( self : str )-> Any:
'''simple docstring'''
for model_name in SWINV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
A__ = SwinvaModel.from_pretrained(lowercase_ )
self.assertIsNotNone(lowercase_ )
def snake_case__ ( self : Union[str, Any] )-> Tuple:
'''simple docstring'''
A__ , A__ = self.model_tester.prepare_config_and_inputs_for_common()
A__ = _config_zero_init(lowercase_ )
for model_class in self.all_model_classes:
A__ = model_class(config=lowercase_ )
for name, param in model.named_parameters():
if "embeddings" not in name and "logit_scale" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item(),[0.0, 1.0],msg=F'Parameter {name} of model {model_class} seems not properly initialized',)
@require_vision
@require_torch
class A ( unittest.TestCase ):
"""simple docstring"""
@cached_property
def snake_case__ ( self : int )-> Dict:
'''simple docstring'''
return (
AutoImageProcessor.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' )
if is_vision_available()
else None
)
@slow
def snake_case__ ( self : List[Any] )-> Any:
'''simple docstring'''
A__ = SwinvaForImageClassification.from_pretrained('microsoft/swinv2-tiny-patch4-window8-256' ).to(
lowercase_ )
A__ = self.default_image_processor
A__ = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
A__ = image_processor(images=lowercase_,return_tensors='pt' ).to(lowercase_ )
# forward pass
with torch.no_grad():
A__ = model(**lowercase_ )
# verify the logits
A__ = torch.Size((1, 1_0_0_0) )
self.assertEqual(outputs.logits.shape,lowercase_ )
A__ = torch.tensor([-0.3_947, -0.4_306, 0.0_026] ).to(lowercase_ )
self.assertTrue(torch.allclose(outputs.logits[0, :3],lowercase_,atol=1E-4 ) )
| 7 |
import os
from huggingface_hub.constants import HUGGINGFACE_HUB_CACHE, hf_cache_home
_UpperCAmelCase : Optional[int] = HUGGINGFACE_HUB_CACHE
_UpperCAmelCase : List[str] = "config.json"
_UpperCAmelCase : Union[str, Any] = "diffusion_pytorch_model.bin"
_UpperCAmelCase : List[Any] = "diffusion_flax_model.msgpack"
_UpperCAmelCase : Optional[Any] = "model.onnx"
_UpperCAmelCase : int = "diffusion_pytorch_model.safetensors"
_UpperCAmelCase : Optional[Any] = "weights.pb"
_UpperCAmelCase : Tuple = "https://huggingface.co"
_UpperCAmelCase : Union[str, Any] = default_cache_path
_UpperCAmelCase : Optional[Any] = "diffusers_modules"
_UpperCAmelCase : List[Any] = os.getenv("HF_MODULES_CACHE", os.path.join(hf_cache_home, "modules"))
_UpperCAmelCase : Tuple = ["fp16", "non-ema"]
_UpperCAmelCase : Any = ".self_attn"
| 222 | 0 |
def UpperCamelCase ( snake_case__ : int ) -> int:
if n == 1 or not isinstance(snake_case__ , snake_case__ ):
return 0
elif n == 2:
return 1
else:
UpperCamelCase : Optional[Any] = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def UpperCamelCase ( snake_case__ : int ) -> int:
UpperCamelCase : Union[str, Any] = 0
UpperCamelCase : str = 2
while digits < n:
index += 1
UpperCamelCase : str = len(str(fibonacci(snake_case__ ) ) )
return index
def UpperCamelCase ( snake_case__ : int = 1000 ) -> int:
return fibonacci_digits_index(snake_case__ )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 103 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class lowerCAmelCase_ ( unittest.TestCase ):
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=13, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=224, SCREAMING_SNAKE_CASE_=30, SCREAMING_SNAKE_CASE_=400, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5], SCREAMING_SNAKE_CASE_=[0.5, 0.5, 0.5], ) -> List[str]:
UpperCamelCase : Optional[int] = size if size is not None else {'height': 18, 'width': 18}
UpperCamelCase : List[Any] = parent
UpperCamelCase : List[Any] = batch_size
UpperCamelCase : int = num_channels
UpperCamelCase : int = image_size
UpperCamelCase : List[Any] = min_resolution
UpperCamelCase : int = max_resolution
UpperCamelCase : Any = do_resize
UpperCamelCase : Optional[int] = size
UpperCamelCase : List[str] = do_normalize
UpperCamelCase : Optional[Any] = image_mean
UpperCamelCase : Tuple = image_std
def snake_case_ ( self ) -> List[Any]:
return {
"image_mean": self.image_mean,
"image_std": self.image_std,
"do_normalize": self.do_normalize,
"do_resize": self.do_resize,
"size": self.size,
}
@require_torch
@require_vision
class lowerCAmelCase_ ( a__ , unittest.TestCase ):
UpperCAmelCase__ : Optional[Any] = ViTImageProcessor if is_vision_available() else None
def snake_case_ ( self ) -> Any:
UpperCamelCase : Dict = EfficientFormerImageProcessorTester(self )
@property
def snake_case_ ( self ) -> List[Any]:
return self.image_proc_tester.prepare_image_processor_dict()
def snake_case_ ( self ) -> Optional[int]:
UpperCamelCase : Optional[int] = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_, 'image_mean' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_, 'image_std' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_, 'do_normalize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_, 'do_resize' ) )
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_, 'size' ) )
def snake_case_ ( self ) -> Any:
pass
def snake_case_ ( self ) -> int:
# Initialize image_processor
UpperCamelCase : Union[str, Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
UpperCamelCase : List[str] = prepare_image_inputs(self.image_proc_tester, equal_resolution=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_, Image.Image )
# Test not batched input
UpperCamelCase : str = image_processor(image_inputs[0], return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
# Test batched
UpperCamelCase : Optional[Any] = image_processor(SCREAMING_SNAKE_CASE_, return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
def snake_case_ ( self ) -> str:
# Initialize image_processor
UpperCamelCase : List[str] = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
UpperCamelCase : Union[str, Any] = prepare_image_inputs(self.image_proc_tester, equal_resolution=SCREAMING_SNAKE_CASE_, numpify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_, np.ndarray )
# Test not batched input
UpperCamelCase : Dict = image_processor(image_inputs[0], return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
# Test batched
UpperCamelCase : Dict = image_processor(SCREAMING_SNAKE_CASE_, return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
def snake_case_ ( self ) -> Tuple:
# Initialize image_processor
UpperCamelCase : Any = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
UpperCamelCase : int = prepare_image_inputs(self.image_proc_tester, equal_resolution=SCREAMING_SNAKE_CASE_, torchify=SCREAMING_SNAKE_CASE_ )
for image in image_inputs:
self.assertIsInstance(SCREAMING_SNAKE_CASE_, torch.Tensor )
# Test not batched input
UpperCamelCase : Optional[int] = image_processor(image_inputs[0], return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
1,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
# Test batched
UpperCamelCase : int = image_processor(SCREAMING_SNAKE_CASE_, return_tensors='pt' ).pixel_values
self.assertEqual(
encoded_images.shape, (
self.image_proc_tester.batch_size,
self.image_proc_tester.num_channels,
self.image_proc_tester.size['height'],
self.image_proc_tester.size['width'],
), )
| 103 | 1 |
'''simple docstring'''
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
A =subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
A =subprocess.check_output(f"""git diff --name-only {fork_point_sha}""".split()).decode('utf-8').split()
A ='|'.join(sys.argv[1:])
A =re.compile(rf"""^({joined_dirs}).*?\.py$""")
A =[x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
| 34 |
'''simple docstring'''
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
A =logging.getLogger(__name__)
A ='Hello world! cécé herlolip'
A =namedtuple(
'BertAbsConfig',
[
'temp_dir',
'large',
'use_bert_emb',
'finetune_bert',
'encoder',
'share_emb',
'max_pos',
'enc_layers',
'enc_hidden_size',
'enc_heads',
'enc_ff_size',
'enc_dropout',
'dec_layers',
'dec_hidden_size',
'dec_heads',
'dec_ff_size',
'dec_dropout',
],
)
def snake_case_ (_a : List[Any] , _a : Any ):
UpperCAmelCase = BertAbsConfig(
temp_dir='''.''' , finetune_bert=_a , large=_a , share_emb=_a , use_bert_emb=_a , encoder='''bert''' , max_pos=5_1_2 , enc_layers=6 , enc_hidden_size=5_1_2 , enc_heads=8 , enc_ff_size=5_1_2 , enc_dropout=0.2 , dec_layers=6 , dec_hidden_size=7_6_8 , dec_heads=8 , dec_ff_size=2_0_4_8 , dec_dropout=0.2 , )
UpperCAmelCase = torch.load(_a , lambda _a , _a : storage )
UpperCAmelCase = AbsSummarizer(_a , torch.device('''cpu''' ) , _a )
original.eval()
UpperCAmelCase = BertAbsSummarizer(_a , torch.device('''cpu''' ) )
new_model.eval()
# -------------------
# Convert the weights
# -------------------
logging.info('''convert the model''' )
new_model.bert.load_state_dict(original.bert.state_dict() )
new_model.decoder.load_state_dict(original.decoder.state_dict() )
new_model.generator.load_state_dict(original.generator.state_dict() )
# ----------------------------------
# Make sure the outpus are identical
# ----------------------------------
logging.info('''Make sure that the models\' outputs are identical''' )
UpperCAmelCase = BertTokenizer.from_pretrained('''bert-base-uncased''' )
# prepare the model inputs
UpperCAmelCase = tokenizer.encode('''This is sample éàalj\'-.''' )
encoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
UpperCAmelCase = tokenizer.encode('''This is sample 3 éàalj\'-.''' )
decoder_input_ids.extend([tokenizer.pad_token_id] * (5_1_2 - len(_a )) )
UpperCAmelCase = torch.tensor(_a ).unsqueeze(0 )
# failsafe to make sure the weights reset does not affect the
# loaded weights.
assert torch.max(torch.abs(original.generator[0].weight - new_model.generator[0].weight ) ) == 0
# forward pass
UpperCAmelCase = encoder_input_ids
UpperCAmelCase = decoder_input_ids
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = UpperCAmelCase = None
UpperCAmelCase = None
# The original model does not apply the geneator layer immediatly but rather in
# the beam search (where it combines softmax + linear layer). Since we already
# apply the softmax in our generation process we only apply the linear layer here.
# We make sure that the outputs of the full stack are identical
UpperCAmelCase = original(_a , _a , _a , _a , _a , _a , _a )[0]
UpperCAmelCase = original.generator(_a )
UpperCAmelCase = new_model(
_a , _a , _a , _a , _a )[0]
UpperCAmelCase = new_model.generator(_a )
UpperCAmelCase = torch.max(torch.abs(output_converted_model - output_original_model ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.max(torch.abs(output_converted_generator - output_original_generator ) ).item()
print('''Maximum absolute difference beween weights: {:.2f}'''.format(_a ) )
UpperCAmelCase = torch.allclose(_a , _a , atol=1E-3 )
if are_identical:
logging.info('''all weights are equal up to 1e-3''' )
else:
raise ValueError('''the weights are different. The new model is likely different from the original one.''' )
# The model has been saved with torch.save(model) and this is bound to the exact
# directory structure. We save the state_dict instead.
logging.info('''saving the model\'s state dictionary''' )
torch.save(
new_model.state_dict() , '''./bertabs-finetuned-cnndm-extractive-abstractive-summarization/pytorch_model.bin''' )
if __name__ == "__main__":
A =argparse.ArgumentParser()
parser.add_argument(
'--bertabs_checkpoint_path',
default=None,
type=str,
required=True,
help='Path the official PyTorch dump.',
)
parser.add_argument(
'--pytorch_dump_folder_path',
default=None,
type=str,
required=True,
help='Path to the output PyTorch model.',
)
A =parser.parse_args()
convert_bertabs_checkpoints(
args.bertabs_checkpoint_path,
args.pytorch_dump_folder_path,
)
| 34 | 1 |
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import transformers
from transformers import AutoModel, AutoModelForSeqaSeqLM, AutoTokenizer
_A = 'bart'
_A = True
@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE__ )
def _UpperCAmelCase ( ):
if LOAD_DENSE_INDEX:
__UpperCamelCase =AutoTokenizer.from_pretrained('yjernite/retribert-base-uncased' )
__UpperCamelCase =AutoModel.from_pretrained('yjernite/retribert-base-uncased' ).to('cuda:0' )
__UpperCamelCase =qar_model.eval()
else:
__UpperCamelCase , __UpperCamelCase =(None, None)
if MODEL_TYPE == "bart":
__UpperCamelCase =AutoTokenizer.from_pretrained('yjernite/bart_eli5' )
__UpperCamelCase =AutoModelForSeqaSeqLM.from_pretrained('yjernite/bart_eli5' ).to('cuda:0' )
__UpperCamelCase =torch.load('seq2seq_models/eli5_bart_model_blm_2.pth' )
sas_model.load_state_dict(save_dict['model'] )
__UpperCamelCase =sas_model.eval()
else:
__UpperCamelCase , __UpperCamelCase =make_qa_sas_model(
model_name='t5-small' , from_file='seq2seq_models/eli5_t5_model_1024_4.pth' , device='cuda:0' )
return (qar_tokenizer, qar_model, sas_tokenizer, sas_model)
@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE__ )
def _UpperCAmelCase ( ):
if LOAD_DENSE_INDEX:
__UpperCamelCase =faiss.StandardGpuResources()
__UpperCamelCase =datasets.load_dataset(path='wiki_snippets' , name='wiki40b_en_100_0' )['train']
__UpperCamelCase =np.memmap(
'wiki40b_passages_reps_32_l-8_h-768_b-512-512.dat' , dtype='float32' , mode='r' , shape=(wikiaab_passages.num_rows, 1_28) , )
__UpperCamelCase =faiss.IndexFlatIP(1_28 )
__UpperCamelCase =faiss.index_cpu_to_gpu(SCREAMING_SNAKE_CASE__ , 1 , SCREAMING_SNAKE_CASE__ )
wikiaab_gpu_index_flat.add(SCREAMING_SNAKE_CASE__ ) # TODO fix for larger GPU
else:
__UpperCamelCase , __UpperCamelCase =(None, None)
__UpperCamelCase =Elasticsearch([{'host': 'localhost', 'port': '9200'}] )
return (wikiaab_passages, wikiaab_gpu_index_flat, es_client)
@st.cache(allow_output_mutation=SCREAMING_SNAKE_CASE__ )
def _UpperCAmelCase ( ):
__UpperCamelCase =datasets.load_dataset('eli5' , name='LFQA_reddit' )
__UpperCamelCase =elia['train_eli5']
__UpperCamelCase =np.memmap(
'eli5_questions_reps.dat' , dtype='float32' , mode='r' , shape=(elia_train.num_rows, 1_28) )
__UpperCamelCase =faiss.IndexFlatIP(1_28 )
eli5_train_q_index.add(SCREAMING_SNAKE_CASE__ )
return (elia_train, eli5_train_q_index)
_A , _A , _A = load_indexes()
_A , _A , _A , _A = load_models()
_A , _A = load_train_data()
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Dict=10 ):
__UpperCamelCase =embed_questions_for_retrieval([question] , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCamelCase , __UpperCamelCase =eli5_train_q_index.search(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
__UpperCamelCase =[elia_train[int(SCREAMING_SNAKE_CASE__ )] for i in I[0]]
return nn_examples
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : Optional[int] , SCREAMING_SNAKE_CASE__ : List[Any]="wiki40b" , SCREAMING_SNAKE_CASE__ : Optional[int]="dense" , SCREAMING_SNAKE_CASE__ : Optional[Any]=10 ):
if source == "none":
__UpperCamelCase , __UpperCamelCase =(' <P> '.join(['' for _ in range(11 )] ).strip(), [])
else:
if method == "dense":
__UpperCamelCase , __UpperCamelCase =query_qa_dense_index(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
else:
__UpperCamelCase , __UpperCamelCase =query_es_index(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , index_name='english_wiki40b_snippets_100w' , n_results=SCREAMING_SNAKE_CASE__ , )
__UpperCamelCase =[
(res['article_title'], res['section_title'].strip(), res['score'], res['passage_text']) for res in hit_lst
]
__UpperCamelCase ='question: {} context: {}'.format(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
return question_doc, support_list
@st.cache(
hash_funcs={
torch.Tensor: (lambda SCREAMING_SNAKE_CASE__ : None),
transformers.models.bart.tokenization_bart.BartTokenizer: (lambda SCREAMING_SNAKE_CASE__ : None),
} )
def _UpperCAmelCase ( SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : Union[str, Any] , SCREAMING_SNAKE_CASE__ : str=64 , SCREAMING_SNAKE_CASE__ : Dict=2_56 , SCREAMING_SNAKE_CASE__ : Optional[Any]=False , SCREAMING_SNAKE_CASE__ : List[Any]=2 , SCREAMING_SNAKE_CASE__ : Any=0.95 , SCREAMING_SNAKE_CASE__ : Union[str, Any]=0.8 ):
with torch.no_grad():
__UpperCamelCase =qa_sas_generate(
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , num_answers=1 , num_beams=SCREAMING_SNAKE_CASE__ , min_len=SCREAMING_SNAKE_CASE__ , max_len=SCREAMING_SNAKE_CASE__ , do_sample=SCREAMING_SNAKE_CASE__ , temp=SCREAMING_SNAKE_CASE__ , top_p=SCREAMING_SNAKE_CASE__ , top_k=SCREAMING_SNAKE_CASE__ , max_input_length=10_24 , device='cuda:0' , )[0]
return (answer, support_list)
st.title('Long Form Question Answering with ELI5')
# Start sidebar
_A = '<img src=\'https://huggingface.co/front/assets/huggingface_logo.svg\'>'
_A = '\n<html>\n <head>\n <style>\n .img-container {\n padding-left: 90px;\n padding-right: 90px;\n padding-top: 50px;\n padding-bottom: 50px;\n background-color: #f0f3f9;\n }\n </style>\n </head>\n <body>\n <span class="img-container"> <!-- Inline parent element -->\n %s\n </span>\n </body>\n</html>\n' % (
header_html,
)
st.sidebar.markdown(
header_full,
unsafe_allow_html=True,
)
# Long Form QA with ELI5 and Wikipedia
_A = '\nThis demo presents a model trained to [provide long-form answers to open-domain questions](https://yjernite.github.io/lfqa.html).\nFirst, a document retriever fetches a set of relevant Wikipedia passages given the question from the [Wiki40b](https://research.google/pubs/pub49029/) dataset,\na pre-processed fixed snapshot of Wikipedia.\n'
st.sidebar.markdown(description, unsafe_allow_html=True)
_A = [
'Answer the question',
'View the retrieved document only',
'View the most similar ELI5 question and answer',
'Show me everything, please!',
]
_A = st.sidebar.checkbox('Demo options')
if demo_options:
_A = st.sidebar.selectbox(
'',
action_list,
index=3,
)
_A = action_list.index(action_st)
_A = st.sidebar.selectbox(
'',
['Show full text of passages', 'Show passage section titles'],
index=0,
)
_A = show_type == 'Show full text of passages'
else:
_A = 3
_A = True
_A = st.sidebar.checkbox('Retrieval options')
if retrieval_options:
_A = '\n ### Information retriever options\n\n The **sparse** retriever uses ElasticSearch, while the **dense** retriever uses max-inner-product search between a question and passage embedding\n trained using the [ELI5](https://arxiv.org/abs/1907.09190) questions-answer pairs.\n The answer is then generated by sequence to sequence model which takes the question and retrieved document as input.\n '
st.sidebar.markdown(retriever_info)
_A = st.sidebar.selectbox('Which Wikipedia format should the model use?', ['wiki40b', 'none'])
_A = st.sidebar.selectbox('Which Wikipedia indexer should the model use?', ['dense', 'sparse', 'mixed'])
else:
_A = 'wiki40b'
_A = 'dense'
_A = 'beam'
_A = 2
_A = 64
_A = 256
_A = None
_A = None
_A = st.sidebar.checkbox('Generation options')
if generate_options:
_A = '\n ### Answer generation options\n\n The sequence-to-sequence model was initialized with [BART](https://huggingface.co/facebook/bart-large)\n weights and fine-tuned on the ELI5 QA pairs and retrieved documents. You can use the model for greedy decoding with\n **beam** search, or **sample** from the decoder\'s output probabilities.\n '
st.sidebar.markdown(generate_info)
_A = st.sidebar.selectbox('Would you like to use beam search or sample an answer?', ['beam', 'sampled'])
_A = st.sidebar.slider(
'Minimum generation length', min_value=8, max_value=256, value=64, step=8, format=None, key=None
)
_A = st.sidebar.slider(
'Maximum generation length', min_value=64, max_value=512, value=256, step=16, format=None, key=None
)
if sampled == "beam":
_A = st.sidebar.slider('Beam size', min_value=1, max_value=8, value=2, step=None, format=None, key=None)
else:
_A = st.sidebar.slider(
'Nucleus sampling p', min_value=0.1, max_value=1.0, value=0.95, step=0.01, format=None, key=None
)
_A = st.sidebar.slider(
'Temperature', min_value=0.1, max_value=1.0, value=0.7, step=0.01, format=None, key=None
)
_A = None
# start main text
_A = [
'<MY QUESTION>',
'How do people make chocolate?',
'Why do we get a fever when we are sick?',
'How can different animals perceive different colors?',
'What is natural language processing?',
'What\'s the best way to treat a sunburn?',
'What exactly are vitamins ?',
'How does nuclear energy provide electricity?',
'What\'s the difference between viruses and bacteria?',
'Why are flutes classified as woodwinds when most of them are made out of metal ?',
'Why do people like drinking coffee even though it tastes so bad?',
'What happens when wine ages? How does it make the wine taste better?',
'If an animal is an herbivore, where does it get the protein that it needs to survive if it only eats grass?',
'How can we set a date to the beginning or end of an artistic period? Doesn\'t the change happen gradually?',
'How does New Zealand have so many large bird predators?',
]
_A = st.selectbox(
'What would you like to ask? ---- select <MY QUESTION> to enter a new query',
questions_list,
index=1,
)
if question_s == "<MY QUESTION>":
_A = st.text_input('Enter your question here:', '')
else:
_A = question_s
if st.button('Show me!'):
if action in [0, 1, 3]:
if index_type == "mixed":
_A , _A = make_support(question, source=wiki_source, method='dense', n_results=10)
_A , _A = make_support(question, source=wiki_source, method='sparse', n_results=10)
_A = []
for res_d, res_s in zip(support_list_dense, support_list_sparse):
if tuple(res_d) not in support_list:
support_list += [tuple(res_d)]
if tuple(res_s) not in support_list:
support_list += [tuple(res_s)]
_A = support_list[:10]
_A = '<P> ' + ' <P> '.join([res[-1] for res in support_list])
else:
_A , _A = make_support(question, source=wiki_source, method=index_type, n_results=10)
if action in [0, 3]:
_A , _A = answer_question(
question_doc,
sas_model,
sas_tokenizer,
min_len=min_len,
max_len=int(max_len),
sampling=(sampled == 'sampled'),
n_beams=n_beams,
top_p=top_p,
temp=temp,
)
st.markdown('### The model generated answer is:')
st.write(answer)
if action in [0, 1, 3] and wiki_source != "none":
st.markdown('--- \n ### The model is drawing information from the following Wikipedia passages:')
for i, res in enumerate(support_list):
_A = 'https://en.wikipedia.org/wiki/{}'.format(res[0].replace(' ', '_'))
_A = res[1].strip()
if sec_titles == "":
_A = '[{}]({})'.format(res[0], wiki_url)
else:
_A = sec_titles.split(' & ')
_A = ' & '.join(
['[{}]({}#{})'.format(sec.strip(), wiki_url, sec.strip().replace(' ', '_')) for sec in sec_list]
)
st.markdown(
'{0:02d} - **Article**: {1:<18} <br> _Section_: {2}'.format(i + 1, res[0], sections),
unsafe_allow_html=True,
)
if show_passages:
st.write(
'> <span style="font-family:arial; font-size:10pt;">' + res[-1] + '</span>', unsafe_allow_html=True
)
if action in [2, 3]:
_A = find_nearest_training(question)
_A = nn_train_list[0]
st.markdown(
'--- \n ### The most similar question in the ELI5 training set was: \n\n {}'.format(train_exple['title'])
)
_A = [
'{}. {}'.format(i + 1, ' \n'.join([line.strip() for line in ans.split('\n') if line.strip() != '']))
for i, (ans, sc) in enumerate(zip(train_exple['answers']['text'], train_exple['answers']['score']))
if i == 0 or sc > 2
]
st.markdown('##### Its answers were: \n\n {}'.format('\n'.join(answers_st)))
_A = '\n---\n\n**Disclaimer**\n\n*The intent of this app is to provide some (hopefully entertaining) insights into the behavior of a current LFQA system.\nEvaluating biases of such a model and ensuring factual generations are still very much open research problems.\nTherefore, until some significant progress is achieved, we caution against using the generated answers for practical purposes.*\n'
st.sidebar.markdown(disclaimer, unsafe_allow_html=True)
| 350 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
_A = {'configuration_vit': ['VIT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'ViTConfig', 'ViTOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = ['ViTFeatureExtractor']
_A = ['ViTImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'VIT_PRETRAINED_MODEL_ARCHIVE_LIST',
'ViTForImageClassification',
'ViTForMaskedImageModeling',
'ViTModel',
'ViTPreTrainedModel',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'TFViTForImageClassification',
'TFViTModel',
'TFViTPreTrainedModel',
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_A = [
'FlaxViTForImageClassification',
'FlaxViTModel',
'FlaxViTPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_vit import VIT_PRETRAINED_CONFIG_ARCHIVE_MAP, ViTConfig, ViTOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_vit import ViTFeatureExtractor
from .image_processing_vit import ViTImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vit import (
VIT_PRETRAINED_MODEL_ARCHIVE_LIST,
ViTForImageClassification,
ViTForMaskedImageModeling,
ViTModel,
ViTPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_vit import TFViTForImageClassification, TFViTModel, TFViTPreTrainedModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_vit import FlaxViTForImageClassification, FlaxViTModel, FlaxViTPreTrainedModel
else:
import sys
_A = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 117 | 0 |
"""simple docstring"""
import unittest
from transformers import is_flax_available
from transformers.testing_utils import require_flax, require_sentencepiece, require_tokenizers, require_torch, slow
if is_flax_available():
import optax
from flax.training.common_utils import onehot
from transformers import AutoTokenizer, FlaxMTaForConditionalGeneration
from transformers.models.ta.modeling_flax_ta import shift_tokens_right
@require_torch
@require_sentencepiece
@require_tokenizers
@require_flax
class A__ ( unittest.TestCase ):
'''simple docstring'''
@slow
def _SCREAMING_SNAKE_CASE ( self: Union[str, Any]) -> str:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = FlaxMTaForConditionalGeneration.from_pretrained("google/mt5-small")
__lowerCAmelCase : Optional[Any] = AutoTokenizer.from_pretrained("google/mt5-small")
__lowerCAmelCase : Tuple = tokenizer("Hello there" , return_tensors="np").input_ids
__lowerCAmelCase : Dict = tokenizer("Hi I am" , return_tensors="np").input_ids
__lowerCAmelCase : str = shift_tokens_right(_SCREAMING_SNAKE_CASE , model.config.pad_token_id , model.config.decoder_start_token_id)
__lowerCAmelCase : Optional[int] = model(_SCREAMING_SNAKE_CASE , decoder_input_ids=_SCREAMING_SNAKE_CASE).logits
__lowerCAmelCase : int = optax.softmax_cross_entropy(_SCREAMING_SNAKE_CASE , onehot(_SCREAMING_SNAKE_CASE , logits.shape[-1])).mean()
__lowerCAmelCase : List[str] = -(labels.shape[-1] * loss.item())
__lowerCAmelCase : str = -84.9127
self.assertTrue(abs(mtf_score - EXPECTED_SCORE) < 1e-4) | 269 |
"""simple docstring"""
import re
from pathlib import Path
from unittest import TestCase
import pytest
@pytest.mark.integration
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def _SCREAMING_SNAKE_CASE ( self: Optional[int] , _SCREAMING_SNAKE_CASE: str) -> Dict:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , encoding="utf-8") as input_file:
__lowerCAmelCase : List[str] = re.compile(r"(?!.*\b(?:encoding|rb|w|wb|w+|wb+|ab|ab+)\b)(?<=\s)(open)\((.*)\)")
__lowerCAmelCase : List[Any] = input_file.read()
__lowerCAmelCase : Any = regexp.search(_SCREAMING_SNAKE_CASE)
return match
def _SCREAMING_SNAKE_CASE ( self: List[Any] , _SCREAMING_SNAKE_CASE: str) -> Optional[Any]:
"""simple docstring"""
with open(_SCREAMING_SNAKE_CASE , encoding="utf-8") as input_file:
__lowerCAmelCase : Any = re.compile(r"#[^\r\n]*print\(|\"[^\r\n]*print\(|\"\"\".*?print\(.*?\"\"\"|(print\()" , re.DOTALL)
__lowerCAmelCase : Optional[int] = input_file.read()
# use `re.finditer` to handle the case where the ignored groups would be matched first by `re.search`
__lowerCAmelCase : int = regexp.finditer(_SCREAMING_SNAKE_CASE)
__lowerCAmelCase : Union[str, Any] = [match for match in matches if match is not None and match.group(1) is not None]
return matches[0] if matches else None
def _SCREAMING_SNAKE_CASE ( self: str) -> List[Any]:
"""simple docstring"""
__lowerCAmelCase : Optional[Any] = Path("./datasets")
__lowerCAmelCase : Optional[int] = list(dataset_paths.absolute().glob("**/*.py"))
for dataset in dataset_files:
if self._no_encoding_on_file_open(str(_SCREAMING_SNAKE_CASE)):
raise AssertionError(F"""open(...) must use utf-8 encoding in {dataset}""")
def _SCREAMING_SNAKE_CASE ( self: Optional[Any]) -> Optional[int]:
"""simple docstring"""
__lowerCAmelCase : Dict = Path("./datasets")
__lowerCAmelCase : Union[str, Any] = list(dataset_paths.absolute().glob("**/*.py"))
for dataset in dataset_files:
if self._no_print_statements(str(_SCREAMING_SNAKE_CASE)):
raise AssertionError(F"""print statement found in {dataset}. Use datasets.logger/logging instead.""") | 269 | 1 |
from __future__ import annotations
from math import pow, sqrt
def _UpperCamelCase ( UpperCamelCase_ : float , UpperCamelCase_ : float , UpperCamelCase_ : float ) -> dict[str, float]:
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError('One and only one argument must be 0' )
if resistance == 0:
return {"resistance": sqrt(pow(lowercase__ , 2 ) - pow(lowercase__ , 2 ) )}
elif reactance == 0:
return {"reactance": sqrt(pow(lowercase__ , 2 ) - pow(lowercase__ , 2 ) )}
elif impedance == 0:
return {"impedance": sqrt(pow(lowercase__ , 2 ) + pow(lowercase__ , 2 ) )}
else:
raise ValueError('Exactly one argument must be 0' )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 351 |
from __future__ import annotations
from collections.abc import Generator
import requests
from bsa import BeautifulSoup
__snake_case : Any = """https://www.indeed.co.in/jobs?q=mobile+app+development&l="""
def _UpperCamelCase ( UpperCamelCase_ : str = "mumbai" ) -> Generator[tuple[str, str], None, None]:
"""simple docstring"""
lowerCAmelCase__ = BeautifulSoup(requests.get(url + location ).content , 'html.parser' )
# This attribute finds out all the specifics listed in a job
for job in soup.find_all('div' , attrs={'data-tn-component': 'organicJob'} ):
lowerCAmelCase__ = job.find('a' , attrs={'data-tn-element': 'jobTitle'} ).text.strip()
lowerCAmelCase__ = job.find('span' , {'class': 'company'} ).text.strip()
yield job_title, company_name
if __name__ == "__main__":
for i, job in enumerate(fetch_jobs("""Bangalore"""), 1):
print(f'Job {i:>2} is {job[0]} at {job[1]}')
| 122 | 0 |
from __future__ import annotations
class _UpperCamelCase :
'''simple docstring'''
def __init__( self : Optional[Any] , a : int = 0 ) -> Optional[Any]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[str] = key
def __UpperCamelCase ( self : Optional[int] , a : str , a : int ) -> list[str]:
"""simple docstring"""
assert isinstance(a , a ) and isinstance(a , a )
SCREAMING_SNAKE_CASE : Optional[int] = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(a ) ^ key ) for ch in content]
def __UpperCamelCase ( self : List[Any] , a : str , a : int ) -> list[str]:
"""simple docstring"""
assert isinstance(a , a ) and isinstance(a , a )
SCREAMING_SNAKE_CASE : Optional[int] = key or self.__key or 1
# make sure key is an appropriate size
key %= 255
return [chr(ord(a ) ^ key ) for ch in content]
def __UpperCamelCase ( self : Optional[Any] , a : str , a : int = 0 ) -> str:
"""simple docstring"""
assert isinstance(a , a ) and isinstance(a , a )
SCREAMING_SNAKE_CASE : Dict = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
SCREAMING_SNAKE_CASE : Optional[Any] = ""
for ch in content:
ans += chr(ord(a ) ^ key )
return ans
def __UpperCamelCase ( self : int , a : str , a : int = 0 ) -> str:
"""simple docstring"""
assert isinstance(a , a ) and isinstance(a , a )
SCREAMING_SNAKE_CASE : str = key or self.__key or 1
# make sure key can be any size
while key > 255:
key -= 255
# This will be returned
SCREAMING_SNAKE_CASE : int = ""
for ch in content:
ans += chr(ord(a ) ^ key )
return ans
def __UpperCamelCase ( self : str , a : str , a : int = 0 ) -> bool:
"""simple docstring"""
assert isinstance(a , a ) and isinstance(a , a )
try:
with open(a ) as fin, open("encrypt.out" , "w+" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.encrypt_string(a , a ) )
except OSError:
return False
return True
def __UpperCamelCase ( self : Optional[int] , a : str , a : int ) -> bool:
"""simple docstring"""
assert isinstance(a , a ) and isinstance(a , a )
try:
with open(a ) as fin, open("decrypt.out" , "w+" ) as fout:
# actual encrypt-process
for line in fin:
fout.write(self.decrypt_string(a , a ) )
except OSError:
return False
return True
# Tests
# crypt = XORCipher()
# key = 67
# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))
# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))
# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))
# if (crypt.encrypt_file("test.txt",key)):
# print("encrypt successful")
# else:
# print("encrypt unsuccessful")
# if (crypt.decrypt_file("encrypt.out",key)):
# print("decrypt successful")
# else:
# print("decrypt unsuccessful") | 76 |
'''simple docstring'''
def __UpperCAmelCase ( A : int ) -> list:
# bit count represents no. of bits in the gray code
if bit_count < 0:
raise ValueError('''The given input must be positive''' )
# get the generated string sequence
UpperCAmelCase_ : int = gray_code_sequence_string(A )
#
# convert them to integers
for i in range(len(A ) ):
UpperCAmelCase_ : List[str] = int(sequence[i] , 2 )
return sequence
def __UpperCAmelCase ( A : int ) -> list:
# The approach is a recursive one
# Base case achieved when either n = 0 or n=1
if bit_count == 0:
return ["0"]
if bit_count == 1:
return ["0", "1"]
UpperCAmelCase_ : Tuple = 1 << bit_count # defines the length of the sequence
# 1<< n is equivalent to 2^n
# recursive answer will generate answer for n-1 bits
UpperCAmelCase_ : List[str] = gray_code_sequence_string(bit_count - 1 )
UpperCAmelCase_ : int = []
# append 0 to first half of the smaller sequence generated
for i in range(seq_len // 2 ):
UpperCAmelCase_ : Union[str, Any] = '''0''' + smaller_sequence[i]
sequence.append(A )
# append 1 to second half ... start from the end of the list
for i in reversed(range(seq_len // 2 ) ):
UpperCAmelCase_ : Dict = '''1''' + smaller_sequence[i]
sequence.append(A )
return sequence
if __name__ == "__main__":
import doctest
doctest.testmod()
| 304 | 0 |
'''simple docstring'''
import inspect
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel, VQModel
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class snake_case__ ( __SCREAMING_SNAKE_CASE ):
"""simple docstring"""
def __init__( self : Dict , UpperCamelCase__ : VQModel , UpperCamelCase__ : UNetaDModel , UpperCamelCase__ : DDIMScheduler ) -> str:
"""simple docstring"""
super().__init__()
self.register_modules(vqvae=UpperCamelCase__ , unet=UpperCamelCase__ , scheduler=UpperCamelCase__ )
@torch.no_grad()
def __call__( self : Tuple , UpperCamelCase__ : int = 1 , UpperCamelCase__ : Optional[Union[torch.Generator, List[torch.Generator]]] = None , UpperCamelCase__ : float = 0.0 , UpperCamelCase__ : int = 50 , UpperCamelCase__ : Optional[str] = "pil" , UpperCamelCase__ : bool = True , **UpperCamelCase__ : Union[str, Any] , ) -> Union[Tuple, ImagePipelineOutput]:
"""simple docstring"""
snake_case : Optional[Any] = randn_tensor(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) , generator=UpperCamelCase__ , )
snake_case : List[Any] = latents.to(self.device )
# scale the initial noise by the standard deviation required by the scheduler
snake_case : Optional[Any] = latents * self.scheduler.init_noise_sigma
self.scheduler.set_timesteps(UpperCamelCase__ )
# prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
snake_case : str = '''eta''' in set(inspect.signature(self.scheduler.step ).parameters.keys() )
snake_case : Optional[int] = {}
if accepts_eta:
snake_case : Optional[int] = eta
for t in self.progress_bar(self.scheduler.timesteps ):
snake_case : Dict = self.scheduler.scale_model_input(UpperCamelCase__ , UpperCamelCase__ )
# predict the noise residual
snake_case : Union[str, Any] = self.unet(UpperCamelCase__ , UpperCamelCase__ ).sample
# compute the previous noisy sample x_t -> x_t-1
snake_case : Optional[int] = self.scheduler.step(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , **UpperCamelCase__ ).prev_sample
# decode the image latents with the VAE
snake_case : Optional[Any] = self.vqvae.decode(UpperCamelCase__ ).sample
snake_case : Union[str, Any] = (image / 2 + 0.5).clamp(0 , 1 )
snake_case : Dict = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
snake_case : Optional[int] = self.numpy_to_pil(UpperCamelCase__ )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=UpperCamelCase__ )
| 352 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
PNDMScheduler,
StableDiffusionLDMaDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import nightly, 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
enable_full_determinism()
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
lowerCamelCase = StableDiffusionLDMaDPipeline
lowerCamelCase = TEXT_TO_IMAGE_PARAMS
lowerCamelCase = TEXT_TO_IMAGE_BATCH_PARAMS
lowerCamelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
def lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
torch.manual_seed(0 )
snake_case : str = 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 , )
snake_case : Dict = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule='''scaled_linear''' , clip_sample=UpperCamelCase__ , set_alpha_to_one=UpperCamelCase__ , )
torch.manual_seed(0 )
snake_case : int = AutoencoderKL(
block_out_channels=[32, 64] , in_channels=6 , out_channels=6 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
snake_case : Tuple = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1e-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , )
snake_case : int = CLIPTextModel(UpperCamelCase__ )
snake_case : Tuple = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
snake_case : str = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def lowerCAmelCase ( self : Optional[int] , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : str=0 ) -> Optional[int]:
"""simple docstring"""
if str(UpperCamelCase__ ).startswith('''mps''' ):
snake_case : Optional[int] = torch.manual_seed(UpperCamelCase__ )
else:
snake_case : Optional[int] = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
snake_case : Tuple = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''output_type''': '''numpy''',
}
return inputs
def lowerCAmelCase ( self : Any ) -> str:
"""simple docstring"""
snake_case : int = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case : List[Any] = self.get_dummy_components()
snake_case : str = StableDiffusionLDMaDPipeline(**UpperCamelCase__ )
snake_case : Union[str, Any] = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : List[str] = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : Tuple = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : int = output.rgb, output.depth
snake_case : str = rgb[0, -3:, -3:, -1]
snake_case : Union[str, Any] = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
snake_case : int = np.array(
[0.37_338_176, 0.70_247, 0.74_203_193, 0.51_643_604, 0.58_256_793, 0.60_932_136, 0.4_181_095, 0.48_355_877, 0.46_535_262] )
snake_case : str = np.array([103.46_727, 85.812_004, 87.849_236] )
assert np.abs(image_slice_rgb.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(image_slice_depth.flatten() - expected_slice_depth ).max() < 1e-2
def lowerCAmelCase ( self : Dict ) -> int:
"""simple docstring"""
snake_case : int = self.get_dummy_components()
snake_case : Any = StableDiffusionLDMaDPipeline(**UpperCamelCase__ )
snake_case : Optional[Any] = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : int = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : str = 3 * [inputs['''prompt''']]
# forward
snake_case : Union[str, Any] = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Tuple = rgb_slice_a[0, -3:, -3:, -1]
snake_case : List[str] = depth_slice_a[0, -3:, -1]
snake_case : int = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : Optional[int] = 3 * [inputs.pop('''prompt''' )]
snake_case : Dict = ldmad_pipe.tokenizer(
UpperCamelCase__ , padding='''max_length''' , max_length=ldmad_pipe.tokenizer.model_max_length , truncation=UpperCamelCase__ , return_tensors='''pt''' , )
snake_case : Optional[Any] = text_inputs['''input_ids'''].to(UpperCamelCase__ )
snake_case : Any = ldmad_pipe.text_encoder(UpperCamelCase__ )[0]
snake_case : Tuple = prompt_embeds
# forward
snake_case : List[Any] = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Dict = output.rgb, output.depth
snake_case : Any = rgb_slice_a[0, -3:, -3:, -1]
snake_case : Dict = depth_slice_a[0, -3:, -1]
assert np.abs(rgb_slice_a.flatten() - rgb_slice_a.flatten() ).max() < 1e-4
assert np.abs(depth_slice_a.flatten() - depth_slice_a.flatten() ).max() < 1e-4
def lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
snake_case : str = '''cpu''' # ensure determinism for the device-dependent torch.Generator
snake_case : Dict = self.get_dummy_components()
snake_case : List[str] = PNDMScheduler(skip_prk_steps=UpperCamelCase__ )
snake_case : Optional[Any] = StableDiffusionLDMaDPipeline(**UpperCamelCase__ )
snake_case : Dict = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Dict = self.get_dummy_inputs(UpperCamelCase__ )
snake_case : str = '''french fries'''
snake_case : List[str] = ldmad_pipe(**UpperCamelCase__ , negative_prompt=UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Union[str, Any] = rgb[0, -3:, -3:, -1]
snake_case : int = depth[0, -3:, -1]
assert rgb.shape == (1, 64, 64, 3)
assert depth.shape == (1, 64, 64)
snake_case : Dict = np.array(
[0.37_044, 0.71_811_503, 0.7_223_251, 0.48_603_675, 0.5_638_391, 0.6_364_948, 0.42_833_704, 0.4_901_315, 0.47_926_217] )
snake_case : Any = np.array([107.84_738, 84.62_802, 89.962_135] )
assert np.abs(rgb_slice.flatten() - expected_slice_rgb ).max() < 1e-2
assert np.abs(depth_slice.flatten() - expected_slice_depth ).max() < 1e-2
@slow
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Optional[int] ) -> List[str]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : Union[str, Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Union[str, Any]="cpu" , UpperCamelCase__ : Optional[int]=torch.floataa , UpperCamelCase__ : Union[str, Any]=0 ) -> Any:
"""simple docstring"""
snake_case : Any = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
snake_case : Optional[Any] = np.random.RandomState(UpperCamelCase__ ).standard_normal((1, 4, 64, 64) )
snake_case : Any = torch.from_numpy(UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ )
snake_case : List[Any] = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
snake_case : Dict = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' )
snake_case : Optional[Any] = ldmad_pipe.to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Any = self.get_inputs(UpperCamelCase__ )
snake_case : str = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : List[Any] = output.rgb, output.depth
snake_case : Optional[Any] = rgb[0, -3:, -3:, -1].flatten()
snake_case : Tuple = rgb[0, -3:, -1].flatten()
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512)
snake_case : Optional[Any] = np.array(
[0.53_805_465, 0.56_707_305, 0.5_486_515, 0.57_012_236, 0.5_814_511, 0.56_253_487, 0.54_843_014, 0.55_092_263, 0.6_459_706] )
snake_case : str = np.array(
[0.9_263_781, 0.6_678_672, 0.5_486_515, 0.92_202_145, 0.67_831_135, 0.56_253_487, 0.9_241_694, 0.7_551_478, 0.6_459_706] )
assert np.abs(rgb_slice - expected_slice_rgb ).max() < 3e-3
assert np.abs(depth_slice - expected_slice_depth ).max() < 3e-3
@nightly
@require_torch_gpu
class snake_case__ ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : Optional[Any] , UpperCamelCase__ : Any , UpperCamelCase__ : Any="cpu" , UpperCamelCase__ : Optional[int]=torch.floataa , UpperCamelCase__ : Optional[int]=0 ) -> str:
"""simple docstring"""
snake_case : Dict = torch.Generator(device=UpperCamelCase__ ).manual_seed(UpperCamelCase__ )
snake_case : Optional[Any] = np.random.RandomState(UpperCamelCase__ ).standard_normal((1, 4, 64, 64) )
snake_case : int = torch.from_numpy(UpperCamelCase__ ).to(device=UpperCamelCase__ , dtype=UpperCamelCase__ )
snake_case : List[str] = {
'''prompt''': '''a photograph of an astronaut riding a horse''',
'''latents''': latents,
'''generator''': generator,
'''num_inference_steps''': 50,
'''guidance_scale''': 7.5,
'''output_type''': '''numpy''',
}
return inputs
def lowerCAmelCase ( self : Optional[Any] ) -> Tuple:
"""simple docstring"""
snake_case : Optional[Any] = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d''' ).to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : Dict = self.get_inputs(UpperCamelCase__ )
snake_case : str = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Union[str, Any] = 0.495_586
snake_case : Tuple = 0.33_795_515
snake_case : Dict = 112.48_518
snake_case : Optional[int] = 98.489_746
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
def lowerCAmelCase ( self : Dict ) -> Dict:
"""simple docstring"""
snake_case : Optional[int] = StableDiffusionLDMaDPipeline.from_pretrained('''Intel/ldm3d-4c''' ).to(UpperCamelCase__ )
ldmad_pipe.set_progress_bar_config(disable=UpperCamelCase__ )
snake_case : int = self.get_inputs(UpperCamelCase__ )
snake_case : List[Any] = ldmad_pipe(**UpperCamelCase__ )
snake_case ,snake_case : Union[str, Any] = output.rgb, output.depth
snake_case : Tuple = 0.4_194_127
snake_case : Optional[Any] = 0.35_375_586
snake_case : Any = 0.5_638_502
snake_case : int = 0.34_686_103
assert rgb.shape == (1, 512, 512, 3)
assert depth.shape == (1, 512, 512, 1)
assert np.abs(expected_rgb_mean - rgb.mean() ) < 1e-3
assert np.abs(expected_rgb_std - rgb.std() ) < 1e-3
assert np.abs(expected_depth_mean - depth.mean() ) < 1e-3
assert np.abs(expected_depth_std - depth.std() ) < 1e-3
| 83 | 0 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_trust_remote_code
from ...image_processing_utils import ImageProcessingMixin
from ...utils import CONFIG_NAME, IMAGE_PROCESSOR_NAME, get_file_from_repo, logging
from .auto_factory import _LazyAutoMapping
from .configuration_auto import (
CONFIG_MAPPING_NAMES,
AutoConfig,
model_type_to_module_name,
replace_list_option_in_docstrings,
)
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = OrderedDict(
[
('''align''', '''EfficientNetImageProcessor'''),
('''beit''', '''BeitImageProcessor'''),
('''bit''', '''BitImageProcessor'''),
('''blip''', '''BlipImageProcessor'''),
('''blip-2''', '''BlipImageProcessor'''),
('''bridgetower''', '''BridgeTowerImageProcessor'''),
('''chinese_clip''', '''ChineseCLIPImageProcessor'''),
('''clip''', '''CLIPImageProcessor'''),
('''clipseg''', '''ViTImageProcessor'''),
('''conditional_detr''', '''ConditionalDetrImageProcessor'''),
('''convnext''', '''ConvNextImageProcessor'''),
('''convnextv2''', '''ConvNextImageProcessor'''),
('''cvt''', '''ConvNextImageProcessor'''),
('''data2vec-vision''', '''BeitImageProcessor'''),
('''deformable_detr''', '''DeformableDetrImageProcessor'''),
('''deit''', '''DeiTImageProcessor'''),
('''deta''', '''DetaImageProcessor'''),
('''detr''', '''DetrImageProcessor'''),
('''dinat''', '''ViTImageProcessor'''),
('''donut-swin''', '''DonutImageProcessor'''),
('''dpt''', '''DPTImageProcessor'''),
('''efficientformer''', '''EfficientFormerImageProcessor'''),
('''efficientnet''', '''EfficientNetImageProcessor'''),
('''flava''', '''FlavaImageProcessor'''),
('''focalnet''', '''BitImageProcessor'''),
('''git''', '''CLIPImageProcessor'''),
('''glpn''', '''GLPNImageProcessor'''),
('''groupvit''', '''CLIPImageProcessor'''),
('''imagegpt''', '''ImageGPTImageProcessor'''),
('''instructblip''', '''BlipImageProcessor'''),
('''layoutlmv2''', '''LayoutLMv2ImageProcessor'''),
('''layoutlmv3''', '''LayoutLMv3ImageProcessor'''),
('''levit''', '''LevitImageProcessor'''),
('''mask2former''', '''Mask2FormerImageProcessor'''),
('''maskformer''', '''MaskFormerImageProcessor'''),
('''mgp-str''', '''ViTImageProcessor'''),
('''mobilenet_v1''', '''MobileNetV1ImageProcessor'''),
('''mobilenet_v2''', '''MobileNetV2ImageProcessor'''),
('''mobilevit''', '''MobileViTImageProcessor'''),
('''mobilevit''', '''MobileViTImageProcessor'''),
('''mobilevitv2''', '''MobileViTImageProcessor'''),
('''nat''', '''ViTImageProcessor'''),
('''oneformer''', '''OneFormerImageProcessor'''),
('''owlvit''', '''OwlViTImageProcessor'''),
('''perceiver''', '''PerceiverImageProcessor'''),
('''pix2struct''', '''Pix2StructImageProcessor'''),
('''poolformer''', '''PoolFormerImageProcessor'''),
('''regnet''', '''ConvNextImageProcessor'''),
('''resnet''', '''ConvNextImageProcessor'''),
('''sam''', '''SamImageProcessor'''),
('''segformer''', '''SegformerImageProcessor'''),
('''swiftformer''', '''ViTImageProcessor'''),
('''swin''', '''ViTImageProcessor'''),
('''swin2sr''', '''Swin2SRImageProcessor'''),
('''swinv2''', '''ViTImageProcessor'''),
('''table-transformer''', '''DetrImageProcessor'''),
('''timesformer''', '''VideoMAEImageProcessor'''),
('''tvlt''', '''TvltImageProcessor'''),
('''upernet''', '''SegformerImageProcessor'''),
('''van''', '''ConvNextImageProcessor'''),
('''videomae''', '''VideoMAEImageProcessor'''),
('''vilt''', '''ViltImageProcessor'''),
('''vit''', '''ViTImageProcessor'''),
('''vit_hybrid''', '''ViTHybridImageProcessor'''),
('''vit_mae''', '''ViTImageProcessor'''),
('''vit_msn''', '''ViTImageProcessor'''),
('''xclip''', '''CLIPImageProcessor'''),
('''yolos''', '''YolosImageProcessor'''),
]
)
lowerCAmelCase__ = _LazyAutoMapping(CONFIG_MAPPING_NAMES, IMAGE_PROCESSOR_MAPPING_NAMES)
def a__ ( SCREAMING_SNAKE_CASE : str ):
'''simple docstring'''
for module_name, extractors in IMAGE_PROCESSOR_MAPPING_NAMES.items():
if class_name in extractors:
lowerCAmelCase : Optional[int] = model_type_to_module_name(SCREAMING_SNAKE_CASE )
lowerCAmelCase : str = importlib.import_module(f""".{module_name}""" , "transformers.models" )
try:
return getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
except AttributeError:
continue
for _, extractor in IMAGE_PROCESSOR_MAPPING._extra_content.items():
if getattr(SCREAMING_SNAKE_CASE , "__name__" , SCREAMING_SNAKE_CASE ) == class_name:
return extractor
# We did not fine the class, but maybe it's because a dep is missing. In that case, the class will be in the main
# init and we return the proper dummy to get an appropriate error message.
lowerCAmelCase : Optional[int] = importlib.import_module("transformers" )
if hasattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return getattr(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return None
def a__ ( SCREAMING_SNAKE_CASE : Union[str, os.PathLike] , SCREAMING_SNAKE_CASE : Optional[Union[str, os.PathLike]] = None , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : bool = False , SCREAMING_SNAKE_CASE : Optional[Dict[str, str]] = None , SCREAMING_SNAKE_CASE : Optional[Union[bool, str]] = None , SCREAMING_SNAKE_CASE : Optional[str] = None , SCREAMING_SNAKE_CASE : bool = False , **SCREAMING_SNAKE_CASE : Union[str, Any] , ):
'''simple docstring'''
lowerCAmelCase : Dict = get_file_from_repo(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , cache_dir=SCREAMING_SNAKE_CASE , force_download=SCREAMING_SNAKE_CASE , resume_download=SCREAMING_SNAKE_CASE , proxies=SCREAMING_SNAKE_CASE , use_auth_token=SCREAMING_SNAKE_CASE , revision=SCREAMING_SNAKE_CASE , local_files_only=SCREAMING_SNAKE_CASE , )
if resolved_config_file is None:
logger.info(
"Could not locate the image processor configuration file, will try to use the model config instead." )
return {}
with open(SCREAMING_SNAKE_CASE , encoding="utf-8" ) as reader:
return json.load(SCREAMING_SNAKE_CASE )
class SCREAMING_SNAKE_CASE__ :
"""simple docstring"""
def __init__( self ):
"""simple docstring"""
raise EnvironmentError(
"AutoImageProcessor is designed to be instantiated "
"using the `AutoImageProcessor.from_pretrained(pretrained_model_name_or_path)` method." )
@classmethod
@replace_list_option_in_docstrings(snake_case__ )
def lowercase__ ( cls , snake_case__ , **snake_case__ ):
"""simple docstring"""
lowerCAmelCase : Any = kwargs.pop("config" , snake_case__ )
lowerCAmelCase : List[str] = kwargs.pop("trust_remote_code" , snake_case__ )
lowerCAmelCase : List[Any] = True
lowerCAmelCase , lowerCAmelCase : Tuple = ImageProcessingMixin.get_image_processor_dict(snake_case__ , **snake_case__ )
lowerCAmelCase : int = config_dict.get("image_processor_type" , snake_case__ )
lowerCAmelCase : str = None
if "AutoImageProcessor" in config_dict.get("auto_map" , {} ):
lowerCAmelCase : Dict = config_dict["auto_map"]["AutoImageProcessor"]
# If we still don't have the image processor class, check if we're loading from a previous feature extractor config
# and if so, infer the image processor class from there.
if image_processor_class is None and image_processor_auto_map is None:
lowerCAmelCase : str = config_dict.pop("feature_extractor_type" , snake_case__ )
if feature_extractor_class is not None:
logger.warning(
"Could not find image processor class in the image processor config or the model config. Loading"
" based on pattern matching with the model's feature extractor configuration." )
lowerCAmelCase : List[str] = feature_extractor_class.replace("FeatureExtractor" , "ImageProcessor" )
if "AutoFeatureExtractor" in config_dict.get("auto_map" , {} ):
lowerCAmelCase : List[Any] = config_dict["auto_map"]["AutoFeatureExtractor"]
lowerCAmelCase : List[Any] = feature_extractor_auto_map.replace("FeatureExtractor" , "ImageProcessor" )
logger.warning(
"Could not find image processor auto map in the image processor config or the model config."
" Loading based on pattern matching with the model's feature extractor configuration." )
# If we don't find the image processor class in the image processor config, let's try the model config.
if image_processor_class is None and image_processor_auto_map is None:
if not isinstance(snake_case__ , snake_case__ ):
lowerCAmelCase : Dict = AutoConfig.from_pretrained(snake_case__ , **snake_case__ )
# It could be in `config.image_processor_type``
lowerCAmelCase : str = getattr(snake_case__ , "image_processor_type" , snake_case__ )
if hasattr(snake_case__ , "auto_map" ) and "AutoImageProcessor" in config.auto_map:
lowerCAmelCase : Union[str, Any] = config.auto_map["AutoImageProcessor"]
if image_processor_class is not None:
lowerCAmelCase : Union[str, Any] = image_processor_class_from_name(snake_case__ )
lowerCAmelCase : List[Any] = image_processor_auto_map is not None
lowerCAmelCase : int = image_processor_class is not None or type(snake_case__ ) in IMAGE_PROCESSOR_MAPPING
lowerCAmelCase : int = resolve_trust_remote_code(
snake_case__ , snake_case__ , snake_case__ , snake_case__ )
if has_remote_code and trust_remote_code:
lowerCAmelCase : List[Any] = get_class_from_dynamic_module(
snake_case__ , snake_case__ , **snake_case__ )
lowerCAmelCase : str = kwargs.pop("code_revision" , snake_case__ )
if os.path.isdir(snake_case__ ):
image_processor_class.register_for_auto_class()
return image_processor_class.from_dict(snake_case__ , **snake_case__ )
elif image_processor_class is not None:
return image_processor_class.from_dict(snake_case__ , **snake_case__ )
# Last try: we use the IMAGE_PROCESSOR_MAPPING.
elif type(snake_case__ ) in IMAGE_PROCESSOR_MAPPING:
lowerCAmelCase : Optional[Any] = IMAGE_PROCESSOR_MAPPING[type(snake_case__ )]
return image_processor_class.from_dict(snake_case__ , **snake_case__ )
raise ValueError(
f"""Unrecognized image processor in {pretrained_model_name_or_path}. Should have a """
f"""`image_processor_type` key in its {IMAGE_PROCESSOR_NAME} of {CONFIG_NAME}, or one of the following """
f"""`model_type` keys in its {CONFIG_NAME}: {", ".join(c for c in IMAGE_PROCESSOR_MAPPING_NAMES.keys() )}""" )
@staticmethod
def lowercase__ ( snake_case__ , snake_case__ ):
"""simple docstring"""
IMAGE_PROCESSOR_MAPPING.register(snake_case__ , snake_case__ )
| 108 |
"""simple docstring"""
from __future__ import annotations
def lowercase (SCREAMING_SNAKE_CASE_ : str ) -> list[int]:
return [ord(SCREAMING_SNAKE_CASE_ ) - 96 for elem in plain]
def lowercase (SCREAMING_SNAKE_CASE_ : list[int] ) -> str:
return "".join(chr(elem + 96 ) for elem in encoded )
def lowercase () -> None:
SCREAMING_SNAKE_CASE = encode(input('-> ' ).strip().lower() )
print('Encoded: ' , SCREAMING_SNAKE_CASE_ )
print('Decoded:' , decode(SCREAMING_SNAKE_CASE_ ) )
if __name__ == "__main__":
main()
| 113 | 0 |
"""simple docstring"""
from ...processing_utils import ProcessorMixin
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = ["image_processor", "feature_extractor"]
__UpperCamelCase = "TvltImageProcessor"
__UpperCamelCase = "TvltFeatureExtractor"
def __init__( self : int , lowercase_ : Optional[Any] , lowercase_ : Optional[Any]):
'''simple docstring'''
super().__init__(image_processor=lowercase_ , feature_extractor=lowercase_)
SCREAMING_SNAKE_CASE_ : Union[str, Any] = image_processor
SCREAMING_SNAKE_CASE_ : Optional[Any] = feature_extractor
def __call__( self : Any , lowercase_ : str=None , lowercase_ : Optional[Any]=None , lowercase_ : Optional[Any]=None , lowercase_ : str=None , lowercase_ : int=False , lowercase_ : Union[str, Any]=False , *lowercase_ : List[Any] , **lowercase_ : List[str] , ):
'''simple docstring'''
if images is None and audio is None:
raise ValueError('''You need to specify either an `images` or `audio` input to process.''')
SCREAMING_SNAKE_CASE_ : Any = None
if images is not None:
SCREAMING_SNAKE_CASE_ : Tuple = self.image_processor(lowercase_ , mask_pixel=lowercase_ , *lowercase_ , **lowercase_)
if images_mixed is not None:
SCREAMING_SNAKE_CASE_ : Optional[int] = self.image_processor(lowercase_ , is_mixed=lowercase_ , *lowercase_ , **lowercase_)
if audio is not None:
SCREAMING_SNAKE_CASE_ : Any = self.feature_extractor(
lowercase_ , *lowercase_ , sampling_rate=lowercase_ , mask_audio=lowercase_ , **lowercase_)
SCREAMING_SNAKE_CASE_ : List[Any] = {}
if audio is not None:
output_dict.update(lowercase_)
if images is not None:
output_dict.update(lowercase_)
if images_mixed_dict is not None:
output_dict.update(lowercase_)
return output_dict
@property
def _SCREAMING_SNAKE_CASE ( self : List[Any]):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Optional[Any] = self.image_processor.model_input_names
SCREAMING_SNAKE_CASE_ : Dict = self.feature_extractor.model_input_names
return list(dict.fromkeys(image_processor_input_names + feature_extractor_input_names))
| 318 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase_ : Dict = logging.get_logger(__name__)
UpperCAmelCase_ : List[str] = {
"""RWKV/rwkv-4-169m-pile""": """https://huggingface.co/RWKV/rwkv-4-169m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-430m-pile""": """https://huggingface.co/RWKV/rwkv-4-430m-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-1b5-pile""": """https://huggingface.co/RWKV/rwkv-4-1b5-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-3b-pile""": """https://huggingface.co/RWKV/rwkv-4-3b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-7b-pile""": """https://huggingface.co/RWKV/rwkv-4-7b-pile/resolve/main/config.json""",
"""RWKV/rwkv-4-14b-pile""": """https://huggingface.co/RWKV/rwkv-4-14b-pile/resolve/main/config.json""",
"""RWKV/rwkv-raven-1b5""": """https://huggingface.co/RWKV/rwkv-raven-1b5/resolve/main/config.json""",
"""RWKV/rwkv-raven-3b""": """https://huggingface.co/RWKV/rwkv-raven-3b/resolve/main/config.json""",
"""RWKV/rwkv-raven-7b""": """https://huggingface.co/RWKV/rwkv-raven-7b/resolve/main/config.json""",
"""RWKV/rwkv-raven-14b""": """https://huggingface.co/RWKV/rwkv-raven-14b/resolve/main/config.json""",
}
class lowerCAmelCase__ ( UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "rwkv"
__UpperCamelCase = {"max_position_embeddings": "context_length"}
def __init__( self : Union[str, Any] , lowercase_ : Any=50277 , lowercase_ : str=1024 , lowercase_ : List[str]=4096 , lowercase_ : Optional[Any]=32 , lowercase_ : Any=None , lowercase_ : Any=None , lowercase_ : List[Any]=1e-5 , lowercase_ : Union[str, Any]=0 , lowercase_ : Union[str, Any]=0 , lowercase_ : int=6 , lowercase_ : Tuple=False , lowercase_ : Any=True , **lowercase_ : Any , ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : Union[str, Any] = vocab_size
SCREAMING_SNAKE_CASE_ : Any = context_length
SCREAMING_SNAKE_CASE_ : int = hidden_size
SCREAMING_SNAKE_CASE_ : int = num_hidden_layers
SCREAMING_SNAKE_CASE_ : List[str] = attention_hidden_size if attention_hidden_size is not None else hidden_size
SCREAMING_SNAKE_CASE_ : int = intermediate_size if intermediate_size is not None else 4 * hidden_size
SCREAMING_SNAKE_CASE_ : int = layer_norm_epsilon
SCREAMING_SNAKE_CASE_ : Optional[int] = rescale_every
SCREAMING_SNAKE_CASE_ : Dict = use_cache
SCREAMING_SNAKE_CASE_ : Dict = bos_token_id
SCREAMING_SNAKE_CASE_ : Any = eos_token_id
super().__init__(
tie_word_embeddings=lowercase_ , bos_token_id=lowercase_ , eos_token_id=lowercase_ , **lowercase_)
| 318 | 1 |
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
########################################################################
# This is a fully working simple example to use Accelerate,
# specifically showcasing the experiment tracking capability,
# and builds off the `nlp_example.py` script.
#
# 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 help focus on the differences in the code, building `DataLoaders`
# was refactored into its own function.
# New additions from the base script can be found quickly by
# looking for the # New Code # tags
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
UpperCamelCase = 16
UpperCamelCase = 32
def lowercase_ ( _lowerCamelCase : Accelerator , _lowerCamelCase : int = 16):
lowercase__ : List[Any] = AutoTokenizer.from_pretrained("bert-base-cased")
lowercase__ : Optional[int] = load_dataset("glue" , "mrpc")
def tokenize_function(_lowerCamelCase : Dict):
# max_length=None => use the model max length (it's actually the default)
lowercase__ : Tuple = tokenizer(examples["sentence1"] , examples["sentence2"] , truncation=_lowerCamelCase , max_length=_lowerCamelCase)
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__ : Optional[int] = datasets.map(
_lowerCamelCase , batched=_lowerCamelCase , 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__ : int = tokenized_datasets.rename_column("label" , "labels")
def collate_fn(_lowerCamelCase : int):
# On TPU it's best to pad everything to the same length or training will be very slow.
lowercase__ : str = 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__ : List[Any] = 16
elif accelerator.mixed_precision != "no":
lowercase__ : Any = 8
else:
lowercase__ : Tuple = None
return tokenizer.pad(
_lowerCamelCase , padding="longest" , max_length=_lowerCamelCase , pad_to_multiple_of=_lowerCamelCase , return_tensors="pt" , )
# Instantiate dataloaders.
lowercase__ : Dict = DataLoader(
tokenized_datasets["train"] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase)
lowercase__ : Optional[int] = DataLoader(
tokenized_datasets["validation"] , shuffle=_lowerCamelCase , collate_fn=_lowerCamelCase , batch_size=_lowerCamelCase)
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
UpperCamelCase = mocked_dataloaders # noqa: F811
def lowercase_ ( _lowerCamelCase : Dict , _lowerCamelCase : Optional[Any]):
# For testing only
if os.environ.get("TESTING_MOCKED_DATALOADERS" , _lowerCamelCase) == "1":
lowercase__ : int = 2
# Initialize Accelerator
# New Code #
# We pass in "all" to `log_with` to grab all available trackers in the environment
# Note: If using a custom `Tracker` class, should be passed in here such as:
# >>> log_with = ["all", MyCustomTrackerClassInstance()]
if args.with_tracking:
lowercase__ : int = Accelerator(
cpu=args.cpu , mixed_precision=args.mixed_precision , log_with="all" , project_dir=args.project_dir)
else:
lowercase__ : List[Any] = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision)
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
lowercase__ : Any = config["lr"]
lowercase__ : List[str] = int(config["num_epochs"])
lowercase__ : List[str] = int(config["seed"])
lowercase__ : int = int(config["batch_size"])
set_seed(_lowerCamelCase)
lowercase__ , lowercase__ : Optional[Any] = get_dataloaders(_lowerCamelCase , _lowerCamelCase)
lowercase__ : List[str] = evaluate.load("glue" , "mrpc")
# If the batch size is too big we use gradient accumulation
lowercase__ : Tuple = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
lowercase__ : Optional[int] = batch_size // MAX_GPU_BATCH_SIZE
lowercase__ : Union[str, Any] = MAX_GPU_BATCH_SIZE
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
lowercase__ : Tuple = AutoModelForSequenceClassification.from_pretrained("bert-base-cased" , return_dict=_lowerCamelCase)
# 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__ : Any = model.to(accelerator.device)
# Instantiate optimizer
lowercase__ : List[str] = AdamW(params=model.parameters() , lr=_lowerCamelCase)
# Instantiate scheduler
lowercase__ : Optional[int] = get_linear_schedule_with_warmup(
optimizer=_lowerCamelCase , num_warmup_steps=100 , num_training_steps=(len(_lowerCamelCase) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
lowercase__ , lowercase__ , lowercase__ , lowercase__ , lowercase__ : Optional[Any] = accelerator.prepare(
_lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase)
# New Code #
# We need to initialize the trackers we use. Overall configurations can also be stored
if args.with_tracking:
lowercase__ : Dict = os.path.split(_lowerCamelCase)[-1].split(".")[0]
accelerator.init_trackers(_lowerCamelCase , _lowerCamelCase)
# Now we train the model
for epoch in range(_lowerCamelCase):
model.train()
# New Code #
# For our tracking example, we will log the total loss of each epoch
if args.with_tracking:
lowercase__ : Optional[Any] = 0
for step, batch in enumerate(_lowerCamelCase):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device)
lowercase__ : Dict = model(**_lowerCamelCase)
lowercase__ : Tuple = outputs.loss
# New Code #
if args.with_tracking:
total_loss += loss.detach().float()
lowercase__ : str = loss / gradient_accumulation_steps
accelerator.backward(_lowerCamelCase)
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(_lowerCamelCase):
# We could avoid this line since we set the accelerator with `device_placement=True` (the default).
batch.to(accelerator.device)
with torch.no_grad():
lowercase__ : Tuple = model(**_lowerCamelCase)
lowercase__ : str = outputs.logits.argmax(dim=-1)
lowercase__ , lowercase__ : Tuple = accelerator.gather_for_metrics((predictions, batch["labels"]))
metric.add_batch(
predictions=_lowerCamelCase , references=_lowerCamelCase , )
lowercase__ : Dict = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f'''epoch {epoch}:''' , _lowerCamelCase)
# New Code #
# To actually log, we call `Accelerator.log`
# The values passed can be of `str`, `int`, `float` or `dict` of `str` to `float`/`int`
if args.with_tracking:
accelerator.log(
{
"accuracy": eval_metric["accuracy"],
"f1": eval_metric["f1"],
"train_loss": total_loss.item() / len(_lowerCamelCase),
"epoch": epoch,
} , step=_lowerCamelCase , )
# New Code #
# When a run is finished, you should call `accelerator.end_training()`
# to close all of the open trackers
if args.with_tracking:
accelerator.end_training()
def lowercase_ ( ):
lowercase__ : str = argparse.ArgumentParser(description="Simple example of training script.")
parser.add_argument(
"--mixed_precision" , type=_lowerCamelCase , default=_lowerCamelCase , choices=["no", "fp16", "bf16", "fp8"] , help="Whether to use mixed precision. Choose"
"between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10."
"and an Nvidia Ampere GPU." , )
parser.add_argument("--cpu" , action="store_true" , help="If passed, will train on the CPU.")
parser.add_argument(
"--with_tracking" , action="store_true" , help="Whether to load in all available experiment trackers from the environment and use them for logging." , )
parser.add_argument(
"--project_dir" , type=_lowerCamelCase , default="logs" , help="Location on where to store experiment tracking logs` and relevent project information" , )
lowercase__ : int = parser.parse_args()
lowercase__ : Tuple = {"lr": 2E-5, "num_epochs": 3, "seed": 42, "batch_size": 16}
training_function(_lowerCamelCase , _lowerCamelCase)
if __name__ == "__main__":
main()
| 87 |
"""simple docstring"""
from __future__ import annotations
class _UpperCAmelCase :
def __init__( self : Tuple , _lowercase : str , _lowercase : str ):
__UpperCAmelCase , __UpperCAmelCase = text, pattern
__UpperCAmelCase , __UpperCAmelCase = len(_lowercase ), len(_lowercase )
def a ( self : Optional[int] , _lowercase : str ):
for i in range(self.patLen - 1 , -1 , -1 ):
if char == self.pattern[i]:
return i
return -1
def a ( self : int , _lowercase : int ):
for i in range(self.patLen - 1 , -1 , -1 ):
if self.pattern[i] != self.text[current_pos + i]:
return current_pos + i
return -1
def a ( self : Optional[Any] ):
# searches pattern in text and returns index positions
__UpperCAmelCase = []
for i in range(self.textLen - self.patLen + 1 ):
__UpperCAmelCase = self.mismatch_in_text(_lowercase )
if mismatch_index == -1:
positions.append(_lowercase )
else:
__UpperCAmelCase = self.match_in_pattern(self.text[mismatch_index] )
__UpperCAmelCase = (
mismatch_index - match_index
) # shifting index lgtm [py/multiple-definition]
return positions
_lowercase : str = 'ABAABA'
_lowercase : Tuple = 'AB'
_lowercase : Dict = BoyerMooreSearch(text, pattern)
_lowercase : Any = bms.bad_character_heuristic()
if len(positions) == 0:
print('No match found')
else:
print('Pattern found in following positions: ')
print(positions)
| 332 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel
from diffusers import DDIMScheduler, LDMPipeline, UNetaDModel, VQModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class snake_case__(unittest.TestCase ):
"""simple docstring"""
@property
def snake_case ( self : Any ):
torch.manual_seed(0 )
lowercase__ : Tuple = UNetaDModel(
block_out_channels=(32, 64) , layers_per_block=2 , sample_size=32 , in_channels=3 , out_channels=3 , down_block_types=("DownBlock2D", "AttnDownBlock2D") , up_block_types=("AttnUpBlock2D", "UpBlock2D") , )
return model
@property
def snake_case ( self : List[str] ):
torch.manual_seed(0 )
lowercase__ : Optional[int] = VQModel(
block_out_channels=[32, 64] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=3 , )
return model
@property
def snake_case ( self : Dict ):
torch.manual_seed(0 )
lowercase__ : str = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=32 , intermediate_size=37 , layer_norm_eps=1E-0_5 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_000 , )
return CLIPTextModel(SCREAMING_SNAKE_CASE )
def snake_case ( self : str ):
lowercase__ : Any = self.dummy_uncond_unet
lowercase__ : Dict = DDIMScheduler()
lowercase__ : Optional[Any] = self.dummy_vq_model
lowercase__ : Union[str, Any] = LDMPipeline(unet=SCREAMING_SNAKE_CASE , vqvae=SCREAMING_SNAKE_CASE , scheduler=SCREAMING_SNAKE_CASE )
ldm.to(SCREAMING_SNAKE_CASE )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
lowercase__ : int = torch.manual_seed(0 )
lowercase__ : Optional[int] = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="numpy" ).images
lowercase__ : str = torch.manual_seed(0 )
lowercase__ : List[Any] = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=2 , output_type="numpy" , return_dict=SCREAMING_SNAKE_CASE )[0]
lowercase__ : Any = image[0, -3:, -3:, -1]
lowercase__ : Optional[Any] = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowercase__ : List[Any] = np.array([0.8_512, 0.818, 0.6_411, 0.6_808, 0.4_465, 0.5_618, 0.46, 0.6_231, 0.5_172] )
lowercase__ : Optional[Any] = 1E-2 if torch_device != "mps" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < tolerance
@slow
@require_torch
class snake_case__(unittest.TestCase ):
"""simple docstring"""
def snake_case ( self : Optional[Any] ):
lowercase__ : int = LDMPipeline.from_pretrained("CompVis/ldm-celebahq-256" )
ldm.to(SCREAMING_SNAKE_CASE )
ldm.set_progress_bar_config(disable=SCREAMING_SNAKE_CASE )
lowercase__ : Dict = torch.manual_seed(0 )
lowercase__ : Tuple = ldm(generator=SCREAMING_SNAKE_CASE , num_inference_steps=5 , output_type="numpy" ).images
lowercase__ : Tuple = image[0, -3:, -3:, -1]
assert image.shape == (1, 256, 256, 3)
lowercase__ : Optional[Any] = np.array([0.4_399, 0.44_975, 0.46_825, 0.474, 0.4_359, 0.4_581, 0.45_095, 0.4_341, 0.4_447] )
lowercase__ : int = 1E-2 if torch_device != "mps" else 3E-2
assert np.abs(image_slice.flatten() - expected_slice ).max() < tolerance
| 121 |
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
lowerCAmelCase__ = 1.054571817e-34 # unit of ℏ : J * s
lowerCAmelCase__ = 3e8 # unit of c : m * s^-1
def __lowerCamelCase ( lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ):
"""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:
lowercase__ : Optional[Any] = (REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (
240 * (distance) ** 4
)
return {"force": force}
elif area == 0:
lowercase__ : str = (240 * force * (distance) ** 4) / (
REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2
)
return {"area": area}
elif distance == 0:
lowercase__ : Tuple = (
(REDUCED_PLANCK_CONSTANT * SPEED_OF_LIGHT * pi**2 * area) / (240 * force)
) ** (1 / 4)
return {"distance": distance}
raise ValueError("One and only one argument must be 0" )
# Run doctest
if __name__ == "__main__":
import doctest
doctest.testmod()
| 121 | 1 |
def a ( lowerCamelCase_ = 400_0000 ):
'''simple docstring'''
lowercase__ = [0, 1]
lowercase__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
lowercase__ = 0
for j in range(len(SCREAMING_SNAKE_CASE_ ) - 1 ):
if fib[j] % 2 == 0:
total += fib[j]
return total
if __name__ == "__main__":
print(F"{solution() = }")
| 207 |
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 = logging.get_logger(__name__)
_snake_case = {
'''google/mobilenet_v2_1.4_224''': '''https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json''',
'''google/mobilenet_v2_1.0_224''': '''https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json''',
'''google/mobilenet_v2_0.75_160''': '''https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json''',
'''google/mobilenet_v2_0.35_96''': '''https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json''',
# See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
__A : Dict = "mobilenet_v2"
def __init__( self , __A=3 , __A=224 , __A=1.0 , __A=8 , __A=8 , __A=6 , __A=32 , __A=True , __A=True , __A="relu6" , __A=True , __A=0.8 , __A=0.02 , __A=0.001 , __A=255 , **__A , ):
"""simple docstring"""
super().__init__(**__A )
if depth_multiplier <= 0:
raise ValueError("depth_multiplier must be greater than zero." )
lowerCamelCase : str = num_channels
lowerCamelCase : Any = image_size
lowerCamelCase : Union[str, Any] = depth_multiplier
lowerCamelCase : Tuple = depth_divisible_by
lowerCamelCase : Dict = min_depth
lowerCamelCase : Dict = expand_ratio
lowerCamelCase : Optional[Any] = output_stride
lowerCamelCase : int = first_layer_is_expansion
lowerCamelCase : Union[str, Any] = finegrained_output
lowerCamelCase : Optional[Any] = hidden_act
lowerCamelCase : Optional[Any] = tf_padding
lowerCamelCase : Optional[Any] = classifier_dropout_prob
lowerCamelCase : Dict = initializer_range
lowerCamelCase : str = layer_norm_eps
lowerCamelCase : Optional[Any] = semantic_loss_ignore_index
class UpperCAmelCase_ ( UpperCamelCase ):
'''simple docstring'''
__A : Union[str, Any] = version.parse("1.11" )
@property
def _snake_case ( self ):
"""simple docstring"""
return OrderedDict([("pixel_values", {0: "batch"})] )
@property
def _snake_case ( self ):
"""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 _snake_case ( self ):
"""simple docstring"""
return 1e-4
| 283 | 0 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {
'facebook/encodec_24khz': 'https://huggingface.co/facebook/encodec_24khz/resolve/main/config.json',
'facebook/encodec_48khz': 'https://huggingface.co/facebook/encodec_48khz/resolve/main/config.json',
}
class lowerCAmelCase_ ( _lowerCAmelCase ):
UpperCAmelCase__ : Any = "encodec"
def __init__( self, SCREAMING_SNAKE_CASE_=[1.5, 3.0, 6.0, 12.0, 24.0], SCREAMING_SNAKE_CASE_=2_4000, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=128, SCREAMING_SNAKE_CASE_=32, SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=[8, 5, 4, 2], SCREAMING_SNAKE_CASE_="weight_norm", SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_="reflect", SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=2, SCREAMING_SNAKE_CASE_=1.0, SCREAMING_SNAKE_CASE_=1024, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=True, **SCREAMING_SNAKE_CASE_, ) -> Optional[int]:
UpperCamelCase : List[Any] = target_bandwidths
UpperCamelCase : str = sampling_rate
UpperCamelCase : Any = audio_channels
UpperCamelCase : List[str] = normalize
UpperCamelCase : List[Any] = chunk_length_s
UpperCamelCase : List[Any] = overlap
UpperCamelCase : Any = hidden_size
UpperCamelCase : str = num_filters
UpperCamelCase : Any = num_residual_layers
UpperCamelCase : int = upsampling_ratios
UpperCamelCase : Tuple = norm_type
UpperCamelCase : Union[str, Any] = kernel_size
UpperCamelCase : str = last_kernel_size
UpperCamelCase : Union[str, Any] = residual_kernel_size
UpperCamelCase : str = dilation_growth_rate
UpperCamelCase : int = use_causal_conv
UpperCamelCase : int = pad_mode
UpperCamelCase : List[Any] = compress
UpperCamelCase : Dict = num_lstm_layers
UpperCamelCase : List[Any] = trim_right_ratio
UpperCamelCase : List[Any] = codebook_size
UpperCamelCase : List[Any] = codebook_dim if codebook_dim is not None else hidden_size
UpperCamelCase : Optional[Any] = use_conv_shortcut
if self.norm_type not in ["weight_norm", "time_group_norm"]:
raise ValueError(
F"""self.norm_type must be one of `\"weight_norm\"`, `\"time_group_norm\"`), got {self.norm_type}""" )
super().__init__(**SCREAMING_SNAKE_CASE_ )
@property
def snake_case_ ( self ) -> Optional[int]:
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate )
@property
def snake_case_ ( self ) -> Optional[int]:
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1, int((1.0 - self.overlap) * self.chunk_length ) )
@property
def snake_case_ ( self ) -> int:
UpperCamelCase : Optional[int] = np.prod(self.upsampling_ratios )
return math.ceil(self.sampling_rate / hop_length )
@property
def snake_case_ ( self ) -> int:
return int(1000 * self.target_bandwidths[-1] // (self.frame_rate * 10) )
| 366 |
import os
import re
import unicodedata
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import is_torch_available, logging
if is_torch_available():
import torch
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__UpperCAmelCase = logging.get_logger(__name__)
__UpperCAmelCase = {'''vocab_file''': '''spiece.model'''}
__UpperCAmelCase = {
'''vocab_file''': {
'''AI-Sweden/gpt-sw3-126m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-126m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-350m''': '''https://huggingface.co/AI-Sweden/gpt-sw3-350m/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-1.6b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-1.6b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-6.7b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-6.7b/resolve/main/spiece.model''',
'''AI-Sweden/gpt-sw3-20b''': '''https://huggingface.co/AI-Sweden/gpt-sw3-20b/resolve/main/spiece.model''',
}
}
__UpperCAmelCase = {
'''AI-Sweden/gpt-sw3-126m''': 2_048,
'''AI-Sweden/gpt-sw3-350m''': 2_048,
'''AI-Sweden/gpt-sw3-1.6b''': 2_048,
'''AI-Sweden/gpt-sw3-6.7b''': 2_048,
'''AI-Sweden/gpt-sw3-20b''': 2_048,
}
class lowerCAmelCase_ ( a__ ):
UpperCAmelCase__ : Union[str, Any] = VOCAB_FILES_NAMES
UpperCAmelCase__ : Any = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ : int = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ : Optional[int] = ["input_ids", "attention_mask"]
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=False, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_ = None, **SCREAMING_SNAKE_CASE_, ) -> None:
UpperCamelCase : Optional[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
UpperCamelCase : Dict = kwargs.get('name_or_path' )
if name_or_path is None:
logger.warning(
'name_or_path not provided, will work for all GPTSw3 models except gpt-sw3-7b,'
' you are testing the model, this can safely be ignored' )
UpperCamelCase : Tuple = 'None'
# Default definitions for our 2 tokenizer versions, with None-checks to enable proper testing
UpperCamelCase : str = '<|endoftext|>' if eos_token is None else eos_token
UpperCamelCase : Tuple = '<unk>' if unk_token is None else unk_token
if "gpt-sw3-7b" in name_or_path:
UpperCamelCase : str = unk_token if pad_token is None else pad_token
UpperCamelCase : List[str] = eos_token if bos_token is None else bos_token
else:
UpperCamelCase : List[Any] = '<pad>' if pad_token is None else pad_token
UpperCamelCase : Dict = '<s>' if bos_token is None else bos_token
super().__init__(
do_lower_case=SCREAMING_SNAKE_CASE_, remove_space=SCREAMING_SNAKE_CASE_, keep_accents=SCREAMING_SNAKE_CASE_, bos_token=SCREAMING_SNAKE_CASE_, eos_token=SCREAMING_SNAKE_CASE_, unk_token=SCREAMING_SNAKE_CASE_, pad_token=SCREAMING_SNAKE_CASE_, sp_model_kwargs=self.sp_model_kwargs, **SCREAMING_SNAKE_CASE_, )
UpperCamelCase : List[str] = do_lower_case
UpperCamelCase : List[str] = remove_space
UpperCamelCase : List[Any] = keep_accents
UpperCamelCase : List[str] = vocab_file
UpperCamelCase : Union[str, Any] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(SCREAMING_SNAKE_CASE_ )
# Used for whitespace normalization in input texts
# fmt : off
UpperCamelCase : Dict = {' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', '', ''}
# fmt : on
# Regular expression to remove non-printing characters (e.g. some unicode control chars) in preprocessing
UpperCamelCase : List[Any] = re.compile(
F"""[{"".join(map(SCREAMING_SNAKE_CASE_, list(range(0, 9 ) ) + list(range(11, 32 ) ) + list(range(127, 160 ) ) + [160, 173, 8203] ) )}]""" )
def __getstate__( self ) -> Tuple:
UpperCamelCase : List[Any] = self.__dict__.copy()
UpperCamelCase : Optional[int] = None
return state
def __setstate__( self, SCREAMING_SNAKE_CASE_ ) -> Any:
UpperCamelCase : Any = d
# for backward compatibility
if not hasattr(self, 'sp_model_kwargs' ):
UpperCamelCase : Optional[int] = {}
UpperCamelCase : int = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
@property
# Copied from transformers.models.albert.tokenization_albert.AlbertTokenizer.vocab_size
def snake_case_ ( self ) -> int:
return len(self.sp_model )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> str:
UpperCamelCase : Dict = self.non_printing_characters_re.sub('', SCREAMING_SNAKE_CASE_ )
# Normalize whitespaces
UpperCamelCase : Any = ''.join([char if char not in self.whitespaces else ' ' for char in text] )
# NFC Unicode normalization
UpperCamelCase : Dict = unicodedata.normalize('NFC', SCREAMING_SNAKE_CASE_ )
return text
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, **SCREAMING_SNAKE_CASE_ ) -> List[str]:
UpperCamelCase : Any = self.preprocess_text(SCREAMING_SNAKE_CASE_ )
return self.sp_model.encode(SCREAMING_SNAKE_CASE_, out_type=SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> int:
return self.sp_model.PieceToId(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> str:
return self.sp_model.IdToPiece(SCREAMING_SNAKE_CASE_ )
@staticmethod
def snake_case_ ( SCREAMING_SNAKE_CASE_ ) -> str:
return out_string
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> str:
UpperCamelCase : Optional[Any] = []
UpperCamelCase : List[Any] = ''
UpperCamelCase : str = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
# TODO: Check if this is needed, as it ensures that decode(encode(doc)) != doc by adding extra whitespace in the decoded document
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_ ) + token
UpperCamelCase : Dict = True
UpperCamelCase : Optional[Any] = []
else:
current_sub_tokens.append(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : str = False
out_string += self.sp_model.decode(SCREAMING_SNAKE_CASE_ )
return out_string
def snake_case_ ( self ) -> Dict[str, int]:
UpperCamelCase : Tuple = {self.convert_ids_to_tokens(SCREAMING_SNAKE_CASE_ ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None ) -> Tuple[str]:
if not os.path.isdir(SCREAMING_SNAKE_CASE_ ):
logger.error(F"""Vocabulary path ({save_directory}) should be a directory""" )
return
UpperCamelCase : List[str] = os.path.join(
SCREAMING_SNAKE_CASE_, (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file'] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(SCREAMING_SNAKE_CASE_ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, SCREAMING_SNAKE_CASE_ )
elif not os.path.isfile(self.vocab_file ):
with open(SCREAMING_SNAKE_CASE_, 'wb' ) as fi:
UpperCamelCase : Any = self.sp_model.serialized_model_proto()
fi.write(SCREAMING_SNAKE_CASE_ )
return (out_vocab_file,)
def snake_case_ ( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = False ) -> Union[List[int], List[List[int]], "torch.Tensor"]:
if isinstance(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ ):
UpperCamelCase : List[str] = self.preprocess_text(SCREAMING_SNAKE_CASE_ )
UpperCamelCase : Optional[Any] = self.sp_model.encode(SCREAMING_SNAKE_CASE_ )
else:
UpperCamelCase : Union[str, Any] = [self.preprocess_text(SCREAMING_SNAKE_CASE_ ) for t in text]
UpperCamelCase : Any = self.sp_model.encode(SCREAMING_SNAKE_CASE_ )
if return_tensors is True or return_tensors == "pt":
UpperCamelCase : List[Any] = torch.tensor(SCREAMING_SNAKE_CASE_ )
return token_ids
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> str:
return self.sp_model.decode(SCREAMING_SNAKE_CASE_ )
def snake_case_ ( self, SCREAMING_SNAKE_CASE_ ) -> List[int]:
UpperCamelCase : List[Any] = [F"""User: {text}""" if is_user else F"""Bot: {text}""" for is_user, text in conversation.iter_texts()]
UpperCamelCase : Optional[Any] = (
F"""{self.eos_token}{self.bos_token}""" + F"""{self.bos_token}""".join(SCREAMING_SNAKE_CASE_ ) + F"""{self.bos_token}Bot:"""
)
return self.encode(text=SCREAMING_SNAKE_CASE_ )
| 103 | 0 |
def UpperCamelCase ( _A = 2000000 ):
"""simple docstring"""
__magic_name__ : Union[str, Any] = [0 for i in range(n + 1 )]
__magic_name__ : Optional[int] = 1
__magic_name__ : Dict = 1
for i in range(2, int(n**0.5 ) + 1 ):
if primality_list[i] == 0:
for j in range(i * i, n + 1, _A ):
__magic_name__ : Union[str, Any] = 1
__magic_name__ : Optional[int] = 0
for i in range(_A ):
if primality_list[i] == 0:
sum_of_primes += i
return sum_of_primes
if __name__ == "__main__":
print(F"""{solution() = }""")
| 342 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class snake_case__ ( _lowerCAmelCase , unittest.TestCase ):
lowercase__ : Optional[Any] = MgpstrTokenizer
lowercase__ : int = False
lowercase__ : Any = {}
lowercase__ : Optional[int] = False
def __magic_name__ ( self ) -> Optional[Any]:
super().setUp()
# fmt: off
__magic_name__ : List[str] = ["""[GO]""", """[s]""", """0""", """1""", """2""", """3""", """4""", """5""", """6""", """7""", """8""", """9""", """a""", """b""", """c""", """d""", """e""", """f""", """g""", """h""", """i""", """j""", """k""", """l""", """m""", """n""", """o""", """p""", """q""", """r""", """s""", """t""", """u""", """v""", """w""", """x""", """y""", """z"""]
# fmt: on
__magic_name__ : List[Any] = dict(zip(lowerCAmelCase__ , range(len(lowerCAmelCase__ ) ) ) )
__magic_name__ : Any = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES["""vocab_file"""] )
with open(self.vocab_file , """w""" , encoding="""utf-8""" ) as fp:
fp.write(json.dumps(lowerCAmelCase__ ) + """\n""" )
def __magic_name__ ( self , **lowerCAmelCase__ ) -> Optional[int]:
return MgpstrTokenizer.from_pretrained(self.tmpdirname , **lowerCAmelCase__ )
def __magic_name__ ( self , lowerCAmelCase__ ) -> Optional[int]:
__magic_name__ : List[str] = """tester"""
__magic_name__ : int = """tester"""
return input_text, output_text
@unittest.skip("""MGP-STR always lower cases letters.""" )
def __magic_name__ ( self ) -> str:
pass
def __magic_name__ ( self ) -> List[str]:
__magic_name__ : List[Any] = self.get_tokenizers(do_lower_case=lowerCAmelCase__ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
__magic_name__ : Dict = """[SPECIAL_TOKEN]"""
tokenizer.add_special_tokens({"""cls_token""": special_token} )
__magic_name__ : List[str] = tokenizer.encode([special_token] , add_special_tokens=lowerCAmelCase__ )
self.assertEqual(len(lowerCAmelCase__ ) , 1 )
__magic_name__ : Tuple = tokenizer.decode(lowerCAmelCase__ , skip_special_tokens=lowerCAmelCase__ )
self.assertTrue(special_token not in decoded )
def __magic_name__ ( self ) -> Union[str, Any]:
__magic_name__ : int = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
__magic_name__ ,__magic_name__ : Optional[Any] = self.get_input_output_texts(lowerCAmelCase__ )
__magic_name__ : List[Any] = tokenizer.tokenize(lowerCAmelCase__ )
__magic_name__ : Any = tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )
__magic_name__ : Union[str, Any] = tokenizer.encode(lowerCAmelCase__ , add_special_tokens=lowerCAmelCase__ )
self.assertListEqual(lowerCAmelCase__ , lowerCAmelCase__ )
__magic_name__ : List[Any] = tokenizer.convert_ids_to_tokens(lowerCAmelCase__ )
self.assertNotEqual(len(lowerCAmelCase__ ) , 0 )
__magic_name__ : Optional[int] = tokenizer.decode(lowerCAmelCase__ )
self.assertIsInstance(lowerCAmelCase__ , lowerCAmelCase__ )
self.assertEqual(text_a.replace(""" """ , """""" ) , lowerCAmelCase__ )
@unittest.skip("""MGP-STR tokenizer only handles one sequence.""" )
def __magic_name__ ( self ) -> Tuple:
pass
@unittest.skip("""inputs cannot be pretokenized in MgpstrTokenizer""" )
def __magic_name__ ( self ) -> Optional[Any]:
pass
| 342 | 1 |
'''simple docstring'''
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def SCREAMING_SNAKE_CASE__ ( snake_case : Any , snake_case : str ) -> str:
"""simple docstring"""
assert isinstance(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
assert dataset.num_rows == 4
assert dataset.num_columns == 3
assert dataset.column_names == ["col_1", "col_2", "col_3"]
for feature, expected_dtype in expected_features.items():
assert dataset.features[feature].dtype == expected_dtype
@require_sqlalchemy
@pytest.mark.parametrize('keep_in_memory' , [False, True] )
def SCREAMING_SNAKE_CASE__ ( snake_case : List[Any] , snake_case : int , snake_case : List[str] , snake_case : Dict ) -> Union[str, Any]:
"""simple docstring"""
a : Optional[Any] = tmp_path / """cache"""
a : Tuple = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
with assert_arrow_memory_increases() if keep_in_memory else assert_arrow_memory_doesnt_increase():
a : List[Any] = SqlDatasetReader(
'dataset' , 'sqlite:///' + sqlite_path , cache_dir=__SCREAMING_SNAKE_CASE , keep_in_memory=__SCREAMING_SNAKE_CASE ).read()
_check_sql_dataset(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
@require_sqlalchemy
@pytest.mark.parametrize(
'features' , [
None,
{'col_1': 'string', 'col_2': 'int64', 'col_3': 'float64'},
{'col_1': 'string', 'col_2': 'string', 'col_3': 'string'},
{'col_1': 'int32', 'col_2': 'int32', 'col_3': 'int32'},
{'col_1': 'float32', 'col_2': 'float32', 'col_3': 'float32'},
] , )
def SCREAMING_SNAKE_CASE__ ( snake_case : List[Any] , snake_case : str , snake_case : Any , snake_case : Optional[Any] ) -> List[Any]:
"""simple docstring"""
a : Dict = tmp_path / """cache"""
a : Union[str, Any] = {"""col_1""": """string""", """col_2""": """int64""", """col_3""": """float64"""}
a : Tuple = features.copy() if features else default_expected_features
a : Optional[int] = (
Features({feature: Value(__SCREAMING_SNAKE_CASE ) for feature, dtype in features.items()} ) if features is not None else None
)
a : Any = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , features=__SCREAMING_SNAKE_CASE , cache_dir=__SCREAMING_SNAKE_CASE ).read()
_check_sql_dataset(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE__ ( snake_case : List[Any] ) -> Optional[Any]:
"""simple docstring"""
with contextlib.closing(sqlitea.connect(__SCREAMING_SNAKE_CASE ) ) as con:
a : List[str] = con.cursor()
cur.execute('SELECT * FROM dataset' )
for row in cur:
yield row
@require_sqlalchemy
def SCREAMING_SNAKE_CASE__ ( snake_case : List[str] , snake_case : Optional[Any] , snake_case : Union[str, Any] ) -> str:
"""simple docstring"""
a : str = tmp_path / """cache"""
a : int = os.path.join(__SCREAMING_SNAKE_CASE , 'tmp.sql' )
a : Tuple = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , cache_dir=__SCREAMING_SNAKE_CASE ).read()
SqlDatasetWriter(__SCREAMING_SNAKE_CASE , 'dataset' , 'sqlite:///' + output_sqlite_path , num_proc=1 ).write()
a : int = iter_sql_file(__SCREAMING_SNAKE_CASE )
a : int = iter_sql_file(__SCREAMING_SNAKE_CASE )
for rowa, rowa in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
assert rowa == rowa
@require_sqlalchemy
def SCREAMING_SNAKE_CASE__ ( snake_case : int , snake_case : Union[str, Any] , snake_case : Optional[Any] ) -> List[Any]:
"""simple docstring"""
a : int = tmp_path / """cache"""
a : Optional[Any] = os.path.join(__SCREAMING_SNAKE_CASE , 'tmp.sql' )
a : Optional[int] = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , cache_dir=__SCREAMING_SNAKE_CASE ).read()
SqlDatasetWriter(__SCREAMING_SNAKE_CASE , 'dataset' , 'sqlite:///' + output_sqlite_path , num_proc=2 ).write()
a : List[str] = iter_sql_file(__SCREAMING_SNAKE_CASE )
a : Optional[Any] = iter_sql_file(__SCREAMING_SNAKE_CASE )
for rowa, rowa in zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ):
assert rowa == rowa
@require_sqlalchemy
def SCREAMING_SNAKE_CASE__ ( snake_case : List[str] , snake_case : Any , snake_case : str ) -> Union[str, Any]:
"""simple docstring"""
a : List[str] = tmp_path / """cache"""
a : Union[str, Any] = os.path.join(__SCREAMING_SNAKE_CASE , 'tmp.sql' )
a : Any = SqlDatasetReader('dataset' , 'sqlite:///' + sqlite_path , cache_dir=__SCREAMING_SNAKE_CASE ).read()
with pytest.raises(__SCREAMING_SNAKE_CASE ):
SqlDatasetWriter(__SCREAMING_SNAKE_CASE , 'dataset' , 'sqlite:///' + output_sqlite_path , num_proc=0 ).write()
| 370 | '''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( snake_case : int | float | str , snake_case : int | float | str ) -> list[str]:
"""simple docstring"""
if nth_term == "":
return [""]
a : Dict = int(snake_case )
a : Optional[int] = int(snake_case )
a : list[str] = []
for temp in range(int(snake_case ) ):
series.append(F"""1 / {pow(temp + 1 , int(snake_case ) )}""" if series else '1' )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
UpperCamelCase : Optional[int] = int(input("""Enter the last number (nth term) of the P-Series"""))
UpperCamelCase : List[Any] = int(input("""Enter the power for P-Series"""))
print("""Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p""")
print(p_series(nth_term, power))
| 345 | 0 |
"""simple docstring"""
import copy
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
A_ : int = logging.get_logger(__name__)
A_ : str = {
"microsoft/conditional-detr-resnet-50": (
"https://huggingface.co/microsoft/conditional-detr-resnet-50/resolve/main/config.json"
),
}
class lowerCamelCase (A__ ):
lowerCamelCase__ : Optional[Any] = 'conditional_detr'
lowerCamelCase__ : List[Any] = ['past_key_values']
lowerCamelCase__ : List[Any] = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self : Optional[Any] , __UpperCAmelCase : List[str]=True , __UpperCAmelCase : List[str]=None , __UpperCAmelCase : List[str]=3 , __UpperCAmelCase : int=3_0_0 , __UpperCAmelCase : Tuple=6 , __UpperCAmelCase : Tuple=2_0_4_8 , __UpperCAmelCase : int=8 , __UpperCAmelCase : List[Any]=6 , __UpperCAmelCase : int=2_0_4_8 , __UpperCAmelCase : Optional[Any]=8 , __UpperCAmelCase : List[str]=0.0 , __UpperCAmelCase : Union[str, Any]=0.0 , __UpperCAmelCase : Tuple=True , __UpperCAmelCase : List[str]="relu" , __UpperCAmelCase : Any=2_5_6 , __UpperCAmelCase : Union[str, Any]=0.1 , __UpperCAmelCase : int=0.0 , __UpperCAmelCase : Optional[int]=0.0 , __UpperCAmelCase : int=0.02 , __UpperCAmelCase : Any=1.0 , __UpperCAmelCase : Tuple=False , __UpperCAmelCase : List[Any]="sine" , __UpperCAmelCase : Tuple="resnet50" , __UpperCAmelCase : Optional[Any]=True , __UpperCAmelCase : Optional[Any]=False , __UpperCAmelCase : Union[str, Any]=2 , __UpperCAmelCase : str=5 , __UpperCAmelCase : Union[str, Any]=2 , __UpperCAmelCase : Tuple=1 , __UpperCAmelCase : Tuple=1 , __UpperCAmelCase : Union[str, Any]=2 , __UpperCAmelCase : Optional[int]=5 , __UpperCAmelCase : Dict=2 , __UpperCAmelCase : Optional[int]=0.25 , **__UpperCAmelCase : Dict , ) -> Tuple:
if backbone_config is not None and use_timm_backbone:
raise ValueError("""You can't specify both `backbone_config` and `use_timm_backbone`.""" )
if not use_timm_backbone:
if backbone_config is None:
logger.info("""`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.""" )
SCREAMING_SNAKE_CASE__ = CONFIG_MAPPING["""resnet"""](out_features=["""stage4"""] )
elif isinstance(__UpperCAmelCase , __UpperCAmelCase ):
SCREAMING_SNAKE_CASE__ = backbone_config.get("""model_type""" )
SCREAMING_SNAKE_CASE__ = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE__ = config_class.from_dict(__UpperCAmelCase )
SCREAMING_SNAKE_CASE__ = use_timm_backbone
SCREAMING_SNAKE_CASE__ = backbone_config
SCREAMING_SNAKE_CASE__ = num_channels
SCREAMING_SNAKE_CASE__ = num_queries
SCREAMING_SNAKE_CASE__ = d_model
SCREAMING_SNAKE_CASE__ = encoder_ffn_dim
SCREAMING_SNAKE_CASE__ = encoder_layers
SCREAMING_SNAKE_CASE__ = encoder_attention_heads
SCREAMING_SNAKE_CASE__ = decoder_ffn_dim
SCREAMING_SNAKE_CASE__ = decoder_layers
SCREAMING_SNAKE_CASE__ = decoder_attention_heads
SCREAMING_SNAKE_CASE__ = dropout
SCREAMING_SNAKE_CASE__ = attention_dropout
SCREAMING_SNAKE_CASE__ = activation_dropout
SCREAMING_SNAKE_CASE__ = activation_function
SCREAMING_SNAKE_CASE__ = init_std
SCREAMING_SNAKE_CASE__ = init_xavier_std
SCREAMING_SNAKE_CASE__ = encoder_layerdrop
SCREAMING_SNAKE_CASE__ = decoder_layerdrop
SCREAMING_SNAKE_CASE__ = encoder_layers
SCREAMING_SNAKE_CASE__ = auxiliary_loss
SCREAMING_SNAKE_CASE__ = position_embedding_type
SCREAMING_SNAKE_CASE__ = backbone
SCREAMING_SNAKE_CASE__ = use_pretrained_backbone
SCREAMING_SNAKE_CASE__ = dilation
# Hungarian matcher
SCREAMING_SNAKE_CASE__ = class_cost
SCREAMING_SNAKE_CASE__ = bbox_cost
SCREAMING_SNAKE_CASE__ = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE__ = mask_loss_coefficient
SCREAMING_SNAKE_CASE__ = dice_loss_coefficient
SCREAMING_SNAKE_CASE__ = cls_loss_coefficient
SCREAMING_SNAKE_CASE__ = bbox_loss_coefficient
SCREAMING_SNAKE_CASE__ = giou_loss_coefficient
SCREAMING_SNAKE_CASE__ = focal_alpha
super().__init__(is_encoder_decoder=__UpperCAmelCase , **__UpperCAmelCase )
@property
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> int:
return self.encoder_attention_heads
@property
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> int:
return self.d_model
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE__ = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
SCREAMING_SNAKE_CASE__ = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE__ = self.__class__.model_type
return output
class lowerCamelCase (A__ ):
lowerCamelCase__ : Dict = version.parse('1.11' )
@property
def SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
("""pixel_mask""", {0: """batch"""}),
] )
@property
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> float:
return 1e-5
@property
def SCREAMING_SNAKE_CASE ( self : int ) -> int:
return 1_2
| 165 |
"""simple docstring"""
def A ( snake_case__ = 10_00 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = 1, 1
SCREAMING_SNAKE_CASE__ = 2
while True:
SCREAMING_SNAKE_CASE__ = 0
SCREAMING_SNAKE_CASE__ = fa + fa
SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ = fa, f
index += 1
for _ in str(snake_case__ ):
i += 1
if i == n:
break
return index
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 165 | 1 |
'''simple docstring'''
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('hub/hopper-medium-v2/unet/hor32', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/unet/hor128', exist_ok=True)
os.makedirs('hub/hopper-medium-v2/value_function', exist_ok=True)
def lowerCamelCase__ ( _A ):
if hor == 128:
a : int = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
a : List[str] = (32, 128, 256)
a : Dict = ('UpResnetBlock1D', 'UpResnetBlock1D')
elif hor == 32:
a : int = ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D')
a : Any = (32, 64, 128, 256)
a : Dict = ('UpResnetBlock1D', 'UpResnetBlock1D', 'UpResnetBlock1D')
a : int = torch.load(f"""/Users/bglickenhaus/Documents/diffuser/temporal_unet-hopper-mediumv2-hor{hor}.torch""" )
a : Optional[Any] = model.state_dict()
a : Any = {
'down_block_types': down_block_types,
'block_out_channels': block_out_channels,
'up_block_types': up_block_types,
'layers_per_block': 1,
'use_timestep_embedding': True,
'out_block_type': 'OutConv1DBlock',
'norm_num_groups': 8,
'downsample_each_block': False,
'in_channels': 14,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'flip_sin_to_cos': False,
'freq_shift': 1,
'sample_size': 6_5536,
'mid_block_type': 'MidResTemporalBlock1D',
'act_fn': 'mish',
}
a : str = UNetaDModel(**_A )
print(f"""length of state dict: {len(state_dict.keys() )}""" )
print(f"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
a : List[str] = dict(zip(model.state_dict().keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
a : List[str] = state_dict.pop(_A )
hf_value_function.load_state_dict(_A )
torch.save(hf_value_function.state_dict() , f"""hub/hopper-medium-v2/unet/hor{hor}/diffusion_pytorch_model.bin""" )
with open(f"""hub/hopper-medium-v2/unet/hor{hor}/config.json""" , 'w' ) as f:
json.dump(_A , _A )
def lowerCamelCase__ ( ):
a : str = {
'in_channels': 14,
'down_block_types': ('DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D', 'DownResnetBlock1D'),
'up_block_types': (),
'out_block_type': 'ValueFunction',
'mid_block_type': 'ValueFunctionMidBlock1D',
'block_out_channels': (32, 64, 128, 256),
'layers_per_block': 1,
'downsample_each_block': True,
'sample_size': 6_5536,
'out_channels': 14,
'extra_in_channels': 0,
'time_embedding_type': 'positional',
'use_timestep_embedding': True,
'flip_sin_to_cos': False,
'freq_shift': 1,
'norm_num_groups': 8,
'act_fn': 'mish',
}
a : Optional[int] = torch.load('/Users/bglickenhaus/Documents/diffuser/value_function-hopper-mediumv2-hor32.torch' )
a : str = model
a : Dict = UNetaDModel(**_A )
print(f"""length of state dict: {len(state_dict.keys() )}""" )
print(f"""length of value function dict: {len(hf_value_function.state_dict().keys() )}""" )
a : Union[str, Any] = dict(zip(state_dict.keys() , hf_value_function.state_dict().keys() ) )
for k, v in mapping.items():
a : int = state_dict.pop(_A )
hf_value_function.load_state_dict(_A )
torch.save(hf_value_function.state_dict() , 'hub/hopper-medium-v2/value_function/diffusion_pytorch_model.bin' )
with open('hub/hopper-medium-v2/value_function/config.json' , 'w' ) as f:
json.dump(_A , _A )
if __name__ == "__main__":
unet(3_2)
# unet(128)
value_function() | 362 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokenizer,
JumanppTokenizer,
MecabTokenizer,
SudachiTokenizer,
WordpieceTokenizer,
)
from transformers.testing_utils import custom_tokenizers, require_jumanpp, require_sudachi
from ...test_tokenization_common import TokenizerTesterMixin
@custom_tokenizers
class a__( lowerCamelCase__ , unittest.TestCase ):
lowercase__ = BertJapaneseTokenizer
lowercase__ = False
lowercase__ = True
def lowercase_ ( self : int ):
super().setUp()
a : List[Any] = [
'[UNK]',
'[CLS]',
'[SEP]',
'こんにちは',
'こん',
'にちは',
'ばんは',
'##こん',
'##にちは',
'##ばんは',
'世界',
'##世界',
'、',
'##、',
'。',
'##。',
]
a : Dict = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def lowercase_ ( self : Any , __snake_case : str ):
a : Union[str, Any] = 'こんにちは、世界。 \nこんばんは、世界。'
a : List[Any] = 'こんにちは 、 世界 。 こんばんは 、 世界 。'
return input_text, output_text
def lowercase_ ( self : Optional[Any] , __snake_case : Optional[Any] ):
a , a : List[str] = self.get_input_output_texts(__snake_case )
a : Optional[int] = tokenizer.encode(__snake_case , add_special_tokens=__snake_case )
a : str = tokenizer.decode(__snake_case , clean_up_tokenization_spaces=__snake_case )
return text, ids
def lowercase_ ( self : Optional[Any] ):
pass # TODO add if relevant
def lowercase_ ( self : List[Any] ):
pass # TODO add if relevant
def lowercase_ ( self : Dict ):
pass # TODO add if relevant
def lowercase_ ( self : List[Any] ):
a : Optional[int] = self.tokenizer_class(self.vocab_file )
a : Optional[int] = tokenizer.tokenize('こんにちは、世界。\nこんばんは、世界。' )
self.assertListEqual(__snake_case , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
def lowercase_ ( self : Union[str, Any] ):
a : Tuple = self.tokenizer_class(self.vocab_file , word_tokenizer_type='mecab' )
self.assertIsNotNone(__snake_case )
a : List[str] = 'こんにちは、世界。\nこんばんは、世界。'
a : Tuple = tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
a : Optional[int] = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(__snake_case , 'wb' ) as handle:
pickle.dump(__snake_case , __snake_case )
with open(__snake_case , 'rb' ) as handle:
a : Optional[Any] = pickle.load(__snake_case )
a : Tuple = tokenizer_new.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
def lowercase_ ( self : Dict ):
a : List[str] = MecabTokenizer(mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップルストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowercase_ ( self : List[Any] ):
try:
a : int = MecabTokenizer(mecab_dic='unidic_lite' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowercase_ ( self : Any ):
try:
a : Union[str, Any] = MecabTokenizer(mecab_dic='unidic' )
except ModuleNotFoundError:
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowercase_ ( self : str ):
a : Tuple = MecabTokenizer(do_lower_case=__snake_case , mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップルストア', 'で', 'iphone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
def lowercase_ ( self : Union[str, Any] ):
try:
a : Any = MecabTokenizer(
do_lower_case=__snake_case , normalize_text=__snake_case , mecab_option='-d /usr/local/lib/mecab/dic/jumandic' )
except RuntimeError:
# if dict doesn't exist in the system, previous code raises this error.
return
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れた', '\u3000', '。'] , )
def lowercase_ ( self : List[Any] ):
a : Dict = MecabTokenizer(normalize_text=__snake_case , mecab_dic='ipadic' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱｯﾌﾟﾙストア', 'で', 'iPhone', '８', 'が', '発売', 'さ', 'れ', 'た', ' ', '。'] , )
@require_sudachi
def lowercase_ ( self : str ):
a : Optional[int] = self.tokenizer_class(self.vocab_file , word_tokenizer_type='sudachi' )
self.assertIsNotNone(__snake_case )
a : List[Any] = 'こんにちは、世界。\nこんばんは、世界。'
a : int = tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
a : Tuple = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(__snake_case , 'wb' ) as handle:
pickle.dump(__snake_case , __snake_case )
with open(__snake_case , 'rb' ) as handle:
a : Optional[int] = pickle.load(__snake_case )
a : List[Any] = tokenizer_new.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
@require_sudachi
def lowercase_ ( self : List[Any] ):
a : Optional[Any] = SudachiTokenizer(sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'アップル', 'ストア', 'で', 'iPhone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '] , )
@require_sudachi
def lowercase_ ( self : Any ):
a : str = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='A' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国', '人', '参政', '権'] )
@require_sudachi
def lowercase_ ( self : Optional[Any] ):
a : Optional[int] = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='B' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国人', '参政権'] )
@require_sudachi
def lowercase_ ( self : Optional[Any] ):
a : Dict = SudachiTokenizer(sudachi_dict_type='core' , sudachi_split_mode='C' )
self.assertListEqual(tokenizer.tokenize('外国人参政権' ) , ['外国人参政権'] )
@require_sudachi
def lowercase_ ( self : Dict ):
a : Optional[int] = SudachiTokenizer(do_lower_case=__snake_case , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'アップル', 'ストア', 'で', 'iphone', '8', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', ' ', '。', ' ', ' '] , )
@require_sudachi
def lowercase_ ( self : Tuple ):
a : int = SudachiTokenizer(normalize_text=__snake_case , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , [' ', '\t', 'ｱｯﾌﾟﾙ', 'ストア', 'で', 'iPhone', '８', ' ', 'が', ' ', ' ', '\n ', '発売', 'さ', 'れ', 'た', '\u3000', '。', ' ', ' '] , )
@require_sudachi
def lowercase_ ( self : Union[str, Any] ):
a : List[str] = SudachiTokenizer(trim_whitespace=__snake_case , sudachi_dict_type='core' )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れ', 'た', '。'] , )
@require_jumanpp
def lowercase_ ( self : List[Any] ):
a : Optional[int] = self.tokenizer_class(self.vocab_file , word_tokenizer_type='jumanpp' )
self.assertIsNotNone(__snake_case )
a : str = 'こんにちは、世界。\nこんばんは、世界。'
a : Tuple = tokenizer.tokenize(__snake_case )
self.assertListEqual(__snake_case , ['こんにちは', '、', '世界', '。', 'こん', '##ばんは', '、', '世界', '。'] )
self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case ) , [3, 12, 10, 14, 4, 9, 12, 10, 14] )
a : Optional[Any] = os.path.join(self.tmpdirname , 'tokenizer.bin' )
with open(__snake_case , 'wb' ) as handle:
pickle.dump(__snake_case , __snake_case )
with open(__snake_case , 'rb' ) as handle:
a : List[str] = pickle.load(__snake_case )
a : Any = tokenizer_new.tokenize(__snake_case )
self.assertListEqual(__snake_case , __snake_case )
@require_jumanpp
def lowercase_ ( self : List[str] ):
a : Any = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def lowercase_ ( self : List[str] ):
a : List[Any] = JumanppTokenizer(do_lower_case=__snake_case )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iphone', '8', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def lowercase_ ( self : Any ):
a : List[Any] = JumanppTokenizer(normalize_text=__snake_case )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['ｱ', 'ｯ', 'ﾌ', 'ﾟ', 'ﾙ', 'ストア', 'で', 'iPhone', '８', '\u3000', 'が', '\u3000', '\u3000', '\u3000', '発売', 'さ', 'れた', '\u3000', '。'] , )
@require_jumanpp
def lowercase_ ( self : Any ):
a : str = JumanppTokenizer(trim_whitespace=__snake_case )
self.assertListEqual(
tokenizer.tokenize(' \tｱｯﾌﾟﾙストアでiPhone８ が \n 発売された 。 ' ) , ['アップル', 'ストア', 'で', 'iPhone', '8', 'が', '発売', 'さ', 'れた', '。'] , )
@require_jumanpp
def lowercase_ ( self : Tuple ):
a : int = JumanppTokenizer()
self.assertListEqual(
tokenizer.tokenize('ありがとうございますm(_ _)ｍ見つけるのが大変です。' ) , ['ありがとう', 'ございます', 'm(_ _)m', '見つける', 'の', 'が', '大変です', '。'] , )
def lowercase_ ( self : Any ):
a : int = ['[UNK]', '[CLS]', '[SEP]', 'こんにちは', 'こん', 'にちは', 'ばんは', '##こん', '##にちは', '##ばんは']
a : Optional[int] = {}
for i, token in enumerate(__snake_case ):
a : Dict = i
a : Optional[Any] = WordpieceTokenizer(vocab=__snake_case , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('こんにちは' ) , ['こんにちは'] )
self.assertListEqual(tokenizer.tokenize('こんばんは' ) , ['こん', '##ばんは'] )
self.assertListEqual(tokenizer.tokenize('こんばんは こんばんにちは こんにちは' ) , ['こん', '##ばんは', '[UNK]', 'こんにちは'] )
def lowercase_ ( self : Tuple ):
a : List[Any] = BertJapaneseTokenizer.from_pretrained('nlp-waseda/roberta-base-japanese-with-auto-jumanpp' )
a : List[Any] = tokenizer.subword_tokenizer
a : List[str] = subword_tokenizer.tokenize('国境 の 長い トンネル を 抜ける と 雪国 であった 。' )
self.assertListEqual(__snake_case , ['▁国境', '▁の', '▁長い', '▁トンネル', '▁を', '▁抜ける', '▁と', '▁雪', '国', '▁であった', '▁。'] )
a : Union[str, Any] = subword_tokenizer.tokenize('こんばんは こんばん にち は こんにちは' )
self.assertListEqual(__snake_case , ['▁こん', 'ばん', 'は', '▁こん', 'ばん', '▁に', 'ち', '▁は', '▁こんにちは'] )
def lowercase_ ( self : Union[str, Any] ):
a : Optional[Any] = self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese' )
a : Dict = tokenizer.encode('ありがとう。' , add_special_tokens=__snake_case )
a : str = tokenizer.encode('どういたしまして。' , add_special_tokens=__snake_case )
a : Optional[int] = tokenizer.build_inputs_with_special_tokens(__snake_case )
a : Union[str, Any] = tokenizer.build_inputs_with_special_tokens(__snake_case , __snake_case )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class a__( lowerCamelCase__ , unittest.TestCase ):
lowercase__ = BertJapaneseTokenizer
lowercase__ = False
def lowercase_ ( self : List[Any] ):
super().setUp()
a : List[Any] = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
a : List[Any] = os.path.join(self.tmpdirname , VOCAB_FILES_NAMES['vocab_file'] )
with open(self.vocab_file , 'w' , encoding='utf-8' ) as vocab_writer:
vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
def lowercase_ ( self : Optional[Any] , **__snake_case : List[Any] ):
return BertJapaneseTokenizer.from_pretrained(self.tmpdirname , subword_tokenizer_type='character' , **__snake_case )
def lowercase_ ( self : Tuple , __snake_case : List[str] ):
a : int = 'こんにちは、世界。 \nこんばんは、世界。'
a : Optional[Any] = 'こ ん に ち は 、 世 界 。 こ ん ば ん は 、 世 界 。'
return input_text, output_text
def lowercase_ ( self : str ):
pass # TODO add if relevant
def lowercase_ ( self : List[str] ):
pass # TODO add if relevant
def lowercase_ ( self : Any ):
pass # TODO add if relevant
def lowercase_ ( self : Any ):
a : Optional[int] = self.tokenizer_class(self.vocab_file , subword_tokenizer_type='character' )
a : Tuple = tokenizer.tokenize('こんにちは、世界。 \nこんばんは、世界。' )
self.assertListEqual(
__snake_case , ['こ', 'ん', 'に', 'ち', 'は', '、', '世', '界', '。', 'こ', 'ん', 'ば', 'ん', 'は', '、', '世', '界', '。'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(__snake_case ) , [3, 4, 5, 6, 7, 11, 9, 10, 12, 3, 4, 8, 4, 7, 11, 9, 10, 12] )
def lowercase_ ( self : Any ):
a : Union[str, Any] = ['[UNK]', '[CLS]', '[SEP]', 'こ', 'ん', 'に', 'ち', 'は', 'ば', '世', '界', '、', '。']
a : Optional[Any] = {}
for i, token in enumerate(__snake_case ):
a : Tuple = i
a : Optional[int] = CharacterTokenizer(vocab=__snake_case , unk_token='[UNK]' )
self.assertListEqual(tokenizer.tokenize('' ) , [] )
self.assertListEqual(tokenizer.tokenize('こんにちは' ) , ['こ', 'ん', 'に', 'ち', 'は'] )
self.assertListEqual(tokenizer.tokenize('こんにちほ' ) , ['こ', 'ん', 'に', 'ち', '[UNK]'] )
def lowercase_ ( self : Tuple ):
a : List[Any] = self.tokenizer_class.from_pretrained('cl-tohoku/bert-base-japanese-char' )
a : Optional[int] = tokenizer.encode('ありがとう。' , add_special_tokens=__snake_case )
a : List[str] = tokenizer.encode('どういたしまして。' , add_special_tokens=__snake_case )
a : Optional[int] = tokenizer.build_inputs_with_special_tokens(__snake_case )
a : Dict = tokenizer.build_inputs_with_special_tokens(__snake_case , __snake_case )
# 2 is for "[CLS]", 3 is for "[SEP]"
assert encoded_sentence == [2] + text + [3]
assert encoded_pair == [2] + text + [3] + text_a + [3]
@custom_tokenizers
class a__( unittest.TestCase ):
def lowercase_ ( self : List[str] ):
a : List[Any] = 'cl-tohoku/bert-base-japanese'
a : Dict = AutoTokenizer.from_pretrained(__snake_case )
self.assertIsInstance(__snake_case , __snake_case )
class a__( unittest.TestCase ):
def lowercase_ ( self : Union[str, Any] ):
a : List[str] = 'cl-tohoku/bert-base-japanese'
with self.assertLogs('transformers' , level='WARNING' ) as cm:
BertTokenizer.from_pretrained(__snake_case )
self.assertTrue(
cm.records[0].message.startswith(
'The tokenizer class you load from this checkpoint is not the same type as the class this function'
' is called from.' ) )
a : Dict = 'bert-base-cased'
with self.assertLogs('transformers' , level='WARNING' ) as cm:
BertJapaneseTokenizer.from_pretrained(__snake_case )
self.assertTrue(
cm.records[0].message.startswith(
'The tokenizer class you load from this checkpoint is not the same type as the class this function'
' is called from.' ) ) | 96 | 0 |
'''simple docstring'''
a__ : List[Any] = {
'Pillow': 'Pillow<10.0.0',
'accelerate': 'accelerate>=0.20.3',
'av': 'av==9.2.0',
'beautifulsoup4': 'beautifulsoup4',
'black': 'black~=23.1',
'codecarbon': 'codecarbon==1.2.0',
'cookiecutter': 'cookiecutter==1.7.3',
'dataclasses': 'dataclasses',
'datasets': 'datasets!=2.5.0',
'decord': 'decord==0.6.0',
'deepspeed': 'deepspeed>=0.9.3',
'diffusers': 'diffusers',
'dill': 'dill<0.3.5',
'evaluate': 'evaluate>=0.2.0',
'fairscale': 'fairscale>0.3',
'faiss-cpu': 'faiss-cpu',
'fastapi': 'fastapi',
'filelock': 'filelock',
'flax': 'flax>=0.4.1,<=0.7.0',
'ftfy': 'ftfy',
'fugashi': 'fugashi>=1.0',
'GitPython': 'GitPython<3.1.19',
'hf-doc-builder': 'hf-doc-builder>=0.3.0',
'huggingface-hub': 'huggingface-hub>=0.14.1,<1.0',
'importlib_metadata': 'importlib_metadata',
'ipadic': 'ipadic>=1.0.0,<2.0',
'isort': 'isort>=5.5.4',
'jax': 'jax>=0.2.8,!=0.3.2,<=0.4.13',
'jaxlib': 'jaxlib>=0.1.65,<=0.4.13',
'jieba': 'jieba',
'kenlm': 'kenlm',
'keras-nlp': 'keras-nlp>=0.3.1',
'librosa': 'librosa',
'nltk': 'nltk',
'natten': 'natten>=0.14.6',
'numpy': 'numpy>=1.17',
'onnxconverter-common': 'onnxconverter-common',
'onnxruntime-tools': 'onnxruntime-tools>=1.4.2',
'onnxruntime': 'onnxruntime>=1.4.0',
'opencv-python': 'opencv-python',
'optuna': 'optuna',
'optax': 'optax>=0.0.8,<=0.1.4',
'packaging': 'packaging>=20.0',
'parameterized': 'parameterized',
'phonemizer': 'phonemizer',
'protobuf': 'protobuf',
'psutil': 'psutil',
'pyyaml': 'pyyaml>=5.1',
'pydantic': 'pydantic<2',
'pytest': 'pytest>=7.2.0',
'pytest-timeout': 'pytest-timeout',
'pytest-xdist': 'pytest-xdist',
'python': 'python>=3.8.0',
'ray[tune]': 'ray[tune]',
'regex': 'regex!=2019.12.17',
'requests': 'requests',
'rhoknp': 'rhoknp>=1.1.0,<1.3.1',
'rjieba': 'rjieba',
'rouge-score': 'rouge-score!=0.0.7,!=0.0.8,!=0.1,!=0.1.1',
'ruff': 'ruff>=0.0.241,<=0.0.259',
'sacrebleu': 'sacrebleu>=1.4.12,<2.0.0',
'sacremoses': 'sacremoses',
'safetensors': 'safetensors>=0.3.1',
'sagemaker': 'sagemaker>=2.31.0',
'scikit-learn': 'scikit-learn',
'sentencepiece': 'sentencepiece>=0.1.91,!=0.1.92',
'sigopt': 'sigopt',
'starlette': 'starlette',
'sudachipy': 'sudachipy>=0.6.6',
'sudachidict_core': 'sudachidict_core>=20220729',
'tensorflow-cpu': 'tensorflow-cpu>=2.6,<2.14',
'tensorflow': 'tensorflow>=2.6,<2.14',
'tensorflow-text': 'tensorflow-text<2.14',
'tf2onnx': 'tf2onnx',
'timeout-decorator': 'timeout-decorator',
'timm': 'timm',
'tokenizers': 'tokenizers>=0.11.1,!=0.11.3,<0.14',
'torch': 'torch>=1.9,!=1.12.0',
'torchaudio': 'torchaudio',
'torchvision': 'torchvision',
'pyctcdecode': 'pyctcdecode>=0.4.0',
'tqdm': 'tqdm>=4.27',
'unidic': 'unidic>=1.0.2',
'unidic_lite': 'unidic_lite>=1.0.7',
'urllib3': 'urllib3<2.0.0',
'uvicorn': 'uvicorn',
}
| 80 |
# 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.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool
__magic_name__: Tuple = {
"Acehnese Arabic": "ace_Arab",
"Acehnese Latin": "ace_Latn",
"Mesopotamian Arabic": "acm_Arab",
"Ta'izzi-Adeni Arabic": "acq_Arab",
"Tunisian Arabic": "aeb_Arab",
"Afrikaans": "afr_Latn",
"South Levantine Arabic": "ajp_Arab",
"Akan": "aka_Latn",
"Amharic": "amh_Ethi",
"North Levantine Arabic": "apc_Arab",
"Modern Standard Arabic": "arb_Arab",
"Modern Standard Arabic Romanized": "arb_Latn",
"Najdi Arabic": "ars_Arab",
"Moroccan Arabic": "ary_Arab",
"Egyptian Arabic": "arz_Arab",
"Assamese": "asm_Beng",
"Asturian": "ast_Latn",
"Awadhi": "awa_Deva",
"Central Aymara": "ayr_Latn",
"South Azerbaijani": "azb_Arab",
"North Azerbaijani": "azj_Latn",
"Bashkir": "bak_Cyrl",
"Bambara": "bam_Latn",
"Balinese": "ban_Latn",
"Belarusian": "bel_Cyrl",
"Bemba": "bem_Latn",
"Bengali": "ben_Beng",
"Bhojpuri": "bho_Deva",
"Banjar Arabic": "bjn_Arab",
"Banjar Latin": "bjn_Latn",
"Standard Tibetan": "bod_Tibt",
"Bosnian": "bos_Latn",
"Buginese": "bug_Latn",
"Bulgarian": "bul_Cyrl",
"Catalan": "cat_Latn",
"Cebuano": "ceb_Latn",
"Czech": "ces_Latn",
"Chokwe": "cjk_Latn",
"Central Kurdish": "ckb_Arab",
"Crimean Tatar": "crh_Latn",
"Welsh": "cym_Latn",
"Danish": "dan_Latn",
"German": "deu_Latn",
"Southwestern Dinka": "dik_Latn",
"Dyula": "dyu_Latn",
"Dzongkha": "dzo_Tibt",
"Greek": "ell_Grek",
"English": "eng_Latn",
"Esperanto": "epo_Latn",
"Estonian": "est_Latn",
"Basque": "eus_Latn",
"Ewe": "ewe_Latn",
"Faroese": "fao_Latn",
"Fijian": "fij_Latn",
"Finnish": "fin_Latn",
"Fon": "fon_Latn",
"French": "fra_Latn",
"Friulian": "fur_Latn",
"Nigerian Fulfulde": "fuv_Latn",
"Scottish Gaelic": "gla_Latn",
"Irish": "gle_Latn",
"Galician": "glg_Latn",
"Guarani": "grn_Latn",
"Gujarati": "guj_Gujr",
"Haitian Creole": "hat_Latn",
"Hausa": "hau_Latn",
"Hebrew": "heb_Hebr",
"Hindi": "hin_Deva",
"Chhattisgarhi": "hne_Deva",
"Croatian": "hrv_Latn",
"Hungarian": "hun_Latn",
"Armenian": "hye_Armn",
"Igbo": "ibo_Latn",
"Ilocano": "ilo_Latn",
"Indonesian": "ind_Latn",
"Icelandic": "isl_Latn",
"Italian": "ita_Latn",
"Javanese": "jav_Latn",
"Japanese": "jpn_Jpan",
"Kabyle": "kab_Latn",
"Jingpho": "kac_Latn",
"Kamba": "kam_Latn",
"Kannada": "kan_Knda",
"Kashmiri Arabic": "kas_Arab",
"Kashmiri Devanagari": "kas_Deva",
"Georgian": "kat_Geor",
"Central Kanuri Arabic": "knc_Arab",
"Central Kanuri Latin": "knc_Latn",
"Kazakh": "kaz_Cyrl",
"Kabiyè": "kbp_Latn",
"Kabuverdianu": "kea_Latn",
"Khmer": "khm_Khmr",
"Kikuyu": "kik_Latn",
"Kinyarwanda": "kin_Latn",
"Kyrgyz": "kir_Cyrl",
"Kimbundu": "kmb_Latn",
"Northern Kurdish": "kmr_Latn",
"Kikongo": "kon_Latn",
"Korean": "kor_Hang",
"Lao": "lao_Laoo",
"Ligurian": "lij_Latn",
"Limburgish": "lim_Latn",
"Lingala": "lin_Latn",
"Lithuanian": "lit_Latn",
"Lombard": "lmo_Latn",
"Latgalian": "ltg_Latn",
"Luxembourgish": "ltz_Latn",
"Luba-Kasai": "lua_Latn",
"Ganda": "lug_Latn",
"Luo": "luo_Latn",
"Mizo": "lus_Latn",
"Standard Latvian": "lvs_Latn",
"Magahi": "mag_Deva",
"Maithili": "mai_Deva",
"Malayalam": "mal_Mlym",
"Marathi": "mar_Deva",
"Minangkabau Arabic ": "min_Arab",
"Minangkabau Latin": "min_Latn",
"Macedonian": "mkd_Cyrl",
"Plateau Malagasy": "plt_Latn",
"Maltese": "mlt_Latn",
"Meitei Bengali": "mni_Beng",
"Halh Mongolian": "khk_Cyrl",
"Mossi": "mos_Latn",
"Maori": "mri_Latn",
"Burmese": "mya_Mymr",
"Dutch": "nld_Latn",
"Norwegian Nynorsk": "nno_Latn",
"Norwegian Bokmål": "nob_Latn",
"Nepali": "npi_Deva",
"Northern Sotho": "nso_Latn",
"Nuer": "nus_Latn",
"Nyanja": "nya_Latn",
"Occitan": "oci_Latn",
"West Central Oromo": "gaz_Latn",
"Odia": "ory_Orya",
"Pangasinan": "pag_Latn",
"Eastern Panjabi": "pan_Guru",
"Papiamento": "pap_Latn",
"Western Persian": "pes_Arab",
"Polish": "pol_Latn",
"Portuguese": "por_Latn",
"Dari": "prs_Arab",
"Southern Pashto": "pbt_Arab",
"Ayacucho Quechua": "quy_Latn",
"Romanian": "ron_Latn",
"Rundi": "run_Latn",
"Russian": "rus_Cyrl",
"Sango": "sag_Latn",
"Sanskrit": "san_Deva",
"Santali": "sat_Olck",
"Sicilian": "scn_Latn",
"Shan": "shn_Mymr",
"Sinhala": "sin_Sinh",
"Slovak": "slk_Latn",
"Slovenian": "slv_Latn",
"Samoan": "smo_Latn",
"Shona": "sna_Latn",
"Sindhi": "snd_Arab",
"Somali": "som_Latn",
"Southern Sotho": "sot_Latn",
"Spanish": "spa_Latn",
"Tosk Albanian": "als_Latn",
"Sardinian": "srd_Latn",
"Serbian": "srp_Cyrl",
"Swati": "ssw_Latn",
"Sundanese": "sun_Latn",
"Swedish": "swe_Latn",
"Swahili": "swh_Latn",
"Silesian": "szl_Latn",
"Tamil": "tam_Taml",
"Tatar": "tat_Cyrl",
"Telugu": "tel_Telu",
"Tajik": "tgk_Cyrl",
"Tagalog": "tgl_Latn",
"Thai": "tha_Thai",
"Tigrinya": "tir_Ethi",
"Tamasheq Latin": "taq_Latn",
"Tamasheq Tifinagh": "taq_Tfng",
"Tok Pisin": "tpi_Latn",
"Tswana": "tsn_Latn",
"Tsonga": "tso_Latn",
"Turkmen": "tuk_Latn",
"Tumbuka": "tum_Latn",
"Turkish": "tur_Latn",
"Twi": "twi_Latn",
"Central Atlas Tamazight": "tzm_Tfng",
"Uyghur": "uig_Arab",
"Ukrainian": "ukr_Cyrl",
"Umbundu": "umb_Latn",
"Urdu": "urd_Arab",
"Northern Uzbek": "uzn_Latn",
"Venetian": "vec_Latn",
"Vietnamese": "vie_Latn",
"Waray": "war_Latn",
"Wolof": "wol_Latn",
"Xhosa": "xho_Latn",
"Eastern Yiddish": "ydd_Hebr",
"Yoruba": "yor_Latn",
"Yue Chinese": "yue_Hant",
"Chinese Simplified": "zho_Hans",
"Chinese Traditional": "zho_Hant",
"Standard Malay": "zsm_Latn",
"Zulu": "zul_Latn",
}
class snake_case__ ( _lowerCAmelCase ):
lowercase__ : List[str] = '''facebook/nllb-200-distilled-600M'''
lowercase__ : List[Any] = (
'''This is a tool that translates text from a language to another. It takes three inputs: `text`, which should '''
'''be the text to translate, `src_lang`, which should be the language of the text to translate and `tgt_lang`, '''
'''which should be the language for the desired ouput language. Both `src_lang` and `tgt_lang` are written in '''
'''plain English, such as \'Romanian\', or \'Albanian\'. It returns the text translated in `tgt_lang`.'''
)
lowercase__ : List[str] = '''translator'''
lowercase__ : Optional[Any] = AutoTokenizer
lowercase__ : int = AutoModelForSeqaSeqLM
lowercase__ : List[Any] = LANGUAGE_CODES
lowercase__ : str = ['''text''', '''text''', '''text''']
lowercase__ : Any = ['''text''']
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[str]:
if src_lang not in self.lang_to_code:
raise ValueError(F'{src_lang} is not a supported language.' )
if tgt_lang not in self.lang_to_code:
raise ValueError(F'{tgt_lang} is not a supported language.' )
__magic_name__ : Tuple = self.lang_to_code[src_lang]
__magic_name__ : Dict = self.lang_to_code[tgt_lang]
return self.pre_processor._build_translation_inputs(
lowerCAmelCase__ , return_tensors="""pt""" , src_lang=lowerCAmelCase__ , tgt_lang=lowerCAmelCase__ )
def __magic_name__ ( self , lowerCAmelCase__ ) -> Dict:
return self.model.generate(**lowerCAmelCase__ )
def __magic_name__ ( self , lowerCAmelCase__ ) -> Dict:
return self.post_processor.decode(outputs[0].tolist() , skip_special_tokens=lowerCAmelCase__ )
| 342 | 0 |
"""simple docstring"""
import argparse
import datetime
def lowercase__ ( lowercase_ ) -> Optional[int]:
"""simple docstring"""
_UpperCamelCase : int = {
"0": "Sunday",
"1": "Monday",
"2": "Tuesday",
"3": "Wednesday",
"4": "Thursday",
"5": "Friday",
"6": "Saturday",
}
_UpperCamelCase : Union[str, Any] = {0: 1, 1: 2, 2: 3, 3: 4, 4: 5, 5: 6, 6: 0}
# Validate
if not 0 < len(lowercase_ ) < 11:
raise ValueError("Must be 10 characters long" )
# Get month
_UpperCamelCase : Any = int(date_input[0] + date_input[1] )
# Validate
if not 0 < m < 13:
raise ValueError("Month must be between 1 - 12" )
_UpperCamelCase : int = date_input[2]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'" )
# Get day
_UpperCamelCase : List[Any] = int(date_input[3] + date_input[4] )
# Validate
if not 0 < d < 32:
raise ValueError("Date must be between 1 - 31" )
# Get second separator
_UpperCamelCase : Union[str, Any] = date_input[5]
# Validate
if sep_a not in ["-", "/"]:
raise ValueError("Date separator must be '-' or '/'" )
# Get year
_UpperCamelCase : Dict = int(date_input[6] + date_input[7] + date_input[8] + date_input[9] )
# Arbitrary year range
if not 45 < y < 8_500:
raise ValueError(
"Year out of range. There has to be some sort of limit...right?" )
# Get datetime obj for validation
_UpperCamelCase : Dict = datetime.date(int(lowercase_ ) ,int(lowercase_ ) ,int(lowercase_ ) )
# Start math
if m <= 2:
_UpperCamelCase : str = y - 1
_UpperCamelCase : Union[str, Any] = m + 12
# maths var
_UpperCamelCase : Union[str, Any] = int(str(lowercase_ )[:2] )
_UpperCamelCase : str = int(str(lowercase_ )[2:] )
_UpperCamelCase : List[Any] = int(2.6 * m - 5.39 )
_UpperCamelCase : int = int(c / 4 )
_UpperCamelCase : List[Any] = int(k / 4 )
_UpperCamelCase : List[str] = int(d + k )
_UpperCamelCase : str = int(t + u + v + x )
_UpperCamelCase : Union[str, Any] = int(z - (2 * c) )
_UpperCamelCase : List[str] = round(w % 7 )
# End math
# Validate math
if f != convert_datetime_days[dt_ck.weekday()]:
raise AssertionError("The date was evaluated incorrectly. Contact developer." )
# Response
_UpperCamelCase : int = F'''Your date {date_input}, is a {days[str(lowercase_ )]}!'''
return response
if __name__ == "__main__":
import doctest
doctest.testmod()
lowerCamelCase__ = argparse.ArgumentParser(
description=(
"Find out what day of the week nearly any date is or was. Enter "
"date as a string in the mm-dd-yyyy or mm/dd/yyyy format"
)
)
parser.add_argument(
"date_input", type=str, help="Date as a string (mm-dd-yyyy or mm/dd/yyyy)"
)
lowerCamelCase__ = parser.parse_args()
zeller(args.date_input)
| 358 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"facebook/xlm-roberta-xl": "https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json",
"facebook/xlm-roberta-xxl": "https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json",
# See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ :List[Any] = "xlm-roberta-xl"
def __init__( self : Any , __a : Tuple=25_0880 , __a : Optional[Any]=2560 , __a : List[str]=36 , __a : Any=32 , __a : Dict=1_0240 , __a : Optional[Any]="gelu" , __a : int=0.1 , __a : Tuple=0.1 , __a : str=514 , __a : Any=1 , __a : List[Any]=0.02 , __a : List[str]=1e-0_5 , __a : Optional[Any]=1 , __a : List[Any]=0 , __a : Tuple=2 , __a : int="absolute" , __a : Dict=True , __a : Dict=None , **__a : Tuple , ) -> str:
super().__init__(pad_token_id=__a , bos_token_id=__a , eos_token_id=__a , **__a )
_UpperCamelCase : Any = vocab_size
_UpperCamelCase : Optional[int] = hidden_size
_UpperCamelCase : str = num_hidden_layers
_UpperCamelCase : Optional[int] = num_attention_heads
_UpperCamelCase : List[str] = hidden_act
_UpperCamelCase : Union[str, Any] = intermediate_size
_UpperCamelCase : str = hidden_dropout_prob
_UpperCamelCase : str = attention_probs_dropout_prob
_UpperCamelCase : Dict = max_position_embeddings
_UpperCamelCase : Optional[Any] = type_vocab_size
_UpperCamelCase : str = initializer_range
_UpperCamelCase : Any = layer_norm_eps
_UpperCamelCase : Any = position_embedding_type
_UpperCamelCase : Union[str, Any] = use_cache
_UpperCamelCase : Optional[Any] = classifier_dropout
class __SCREAMING_SNAKE_CASE ( _UpperCamelCase ):
'''simple docstring'''
@property
def __SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_UpperCamelCase : Any = {0: "batch", 1: "choice", 2: "sequence"}
else:
_UpperCamelCase : Dict = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 310 | 0 |
'''simple docstring'''
import os
import string
import sys
_A : Optional[Any] =1 << 8
_A : Union[str, Any] ={
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'''right''': 67 + ARROW_KEY_FLAG,
'''left''': 68 + ARROW_KEY_FLAG,
'''mod_int''': 91,
'''undefined''': sys.maxsize,
'''interrupt''': 3,
'''insert''': 50,
'''delete''': 51,
'''pg_up''': 53,
'''pg_down''': 54,
}
_A : Optional[Any] =KEYMAP['''up''']
_A : Optional[Any] =KEYMAP['''left''']
if sys.platform == "win32":
_A : List[str] =[]
_A : Union[str, Any] ={
b'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
b'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
b'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
b'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
b'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
b'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
b'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
b'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
}
for i in range(10):
_A : List[str] =ord(str(i))
def SCREAMING_SNAKE_CASE_ () -> int:
if os.name == "nt":
import msvcrt
lowerCamelCase__ : str = """mbcs"""
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(UpperCamelCase ) == 0:
# Read the keystroke
lowerCamelCase__ : List[Any] = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
lowerCamelCase__ : Optional[int] = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
lowerCamelCase__ : Tuple = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
WIN_CH_BUFFER.append(UpperCamelCase )
if ord(UpperCamelCase ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(126 ) )
lowerCamelCase__ : str = chr(KEYMAP["""esc"""] )
except KeyError:
lowerCamelCase__ : List[str] = cha[1]
else:
lowerCamelCase__ : Any = ch.decode(UpperCamelCase )
else:
lowerCamelCase__ : str = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
lowerCamelCase__ : List[Any] = sys.stdin.fileno()
lowerCamelCase__ : Any = termios.tcgetattr(UpperCamelCase )
try:
tty.setraw(UpperCamelCase )
lowerCamelCase__ : Dict = sys.stdin.read(1 )
finally:
termios.tcsetattr(UpperCamelCase , termios.TCSADRAIN , UpperCamelCase )
return ch
def SCREAMING_SNAKE_CASE_ () -> Optional[int]:
lowerCamelCase__ : Dict = get_raw_chars()
if ord(UpperCamelCase ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(UpperCamelCase ) == KEYMAP["esc"]:
lowerCamelCase__ : List[str] = get_raw_chars()
if ord(UpperCamelCase ) == KEYMAP["mod_int"]:
lowerCamelCase__ : str = get_raw_chars()
if ord(UpperCamelCase ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(UpperCamelCase ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(UpperCamelCase ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 41 |
from __future__ import annotations
import os
from collections.abc import Mapping
_UpperCAmelCase : Tuple = tuple[int, int]
class lowercase :
def __init__( self , A_ , A_ ) -> None:
"""simple docstring"""
UpperCamelCase = vertices
UpperCamelCase = {
(min(A_ ), max(A_ )): weight for edge, weight in edges.items()
}
def __UpperCamelCase ( self , A_ , A_ ) -> None:
"""simple docstring"""
self.vertices.add(edge[0] )
self.vertices.add(edge[1] )
UpperCamelCase = weight
def __UpperCamelCase ( self ) -> Graph:
"""simple docstring"""
UpperCamelCase = Graph({min(self.vertices )} , {} )
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
while len(subgraph.vertices ) < len(self.vertices ):
UpperCamelCase = max(self.edges.values() ) + 1
for edge, weight in self.edges.items():
if (edge[0] in subgraph.vertices) ^ (edge[1] in subgraph.vertices):
if weight < min_weight:
UpperCamelCase = edge
UpperCamelCase = weight
subgraph.add_edge(A_ , A_ )
return subgraph
def A ( lowercase = "p107_network.txt" ) -> int:
'''simple docstring'''
UpperCamelCase = os.path.abspath(os.path.dirname(lowercase ) )
UpperCamelCase = os.path.join(lowercase , lowercase )
UpperCamelCase = {}
UpperCamelCase = 42
UpperCamelCase = 42
UpperCamelCase = 42
with open(lowercase ) as f:
UpperCamelCase = f.read().strip().split('\n' )
UpperCamelCase = [line.split(',' ) for line in data]
for edgea in range(1 , len(lowercase ) ):
for edgea in range(lowercase ):
if adjaceny_matrix[edgea][edgea] != "-":
UpperCamelCase = int(adjaceny_matrix[edgea][edgea] )
UpperCamelCase = Graph(set(range(len(lowercase ) ) ) , lowercase )
UpperCamelCase = graph.prims_algorithm()
UpperCamelCase = sum(graph.edges.values() )
UpperCamelCase = sum(subgraph.edges.values() )
return initial_total - optimal_total
if __name__ == "__main__":
print(F'''{solution() = }''')
| 222 | 0 |
import unittest
from accelerate import debug_launcher
from accelerate.test_utils import require_cpu, test_ops, test_script
@require_cpu
class __SCREAMING_SNAKE_CASE ( unittest.TestCase):
def UpperCamelCase__ ( self ):
"""simple docstring"""
debug_launcher(test_script.main )
def UpperCamelCase__ ( self ):
"""simple docstring"""
debug_launcher(test_ops.main )
| 122 |
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@require_sentencepiece
@slow # see https://github.com/huggingface/transformers/issues/11457
class __SCREAMING_SNAKE_CASE ( __lowercase , unittest.TestCase):
_SCREAMING_SNAKE_CASE : Tuple = BarthezTokenizer
_SCREAMING_SNAKE_CASE : int = BarthezTokenizerFast
_SCREAMING_SNAKE_CASE : Dict = True
_SCREAMING_SNAKE_CASE : Tuple = True
def UpperCamelCase__ ( self ):
"""simple docstring"""
super().setUp()
lowerCAmelCase__ = BarthezTokenizerFast.from_pretrained('moussaKam/mbarthez' )
tokenizer.save_pretrained(self.tmpdirname )
tokenizer.save_pretrained(self.tmpdirname , legacy_format=_UpperCamelCase )
lowerCAmelCase__ = tokenizer
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = '<pad>'
lowerCAmelCase__ = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(_UpperCamelCase ) , _UpperCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(_UpperCamelCase ) , _UpperCamelCase )
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(_UpperCamelCase ) , 10_11_22 )
def UpperCamelCase__ ( self ):
"""simple docstring"""
self.assertEqual(self.get_tokenizer().vocab_size , 10_11_22 )
@require_torch
def UpperCamelCase__ ( self ):
"""simple docstring"""
lowerCAmelCase__ = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
lowerCAmelCase__ = [0, 57, 30_18, 7_03_07, 91, 2]
lowerCAmelCase__ = self.tokenizer(
_UpperCamelCase , max_length=len(_UpperCamelCase ) , padding=_UpperCamelCase , truncation=_UpperCamelCase , return_tensors='pt' )
self.assertIsInstance(_UpperCamelCase , _UpperCamelCase )
self.assertEqual((2, 6) , batch.input_ids.shape )
self.assertEqual((2, 6) , batch.attention_mask.shape )
lowerCAmelCase__ = batch.input_ids.tolist()[0]
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
def UpperCamelCase__ ( self ):
"""simple docstring"""
if not self.test_rust_tokenizer:
return
lowerCAmelCase__ = self.get_tokenizer()
lowerCAmelCase__ = self.get_rust_tokenizer()
lowerCAmelCase__ = 'I was born in 92000, and this is falsé.'
lowerCAmelCase__ = tokenizer.tokenize(_UpperCamelCase )
lowerCAmelCase__ = rust_tokenizer.tokenize(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
lowerCAmelCase__ = tokenizer.encode(_UpperCamelCase , add_special_tokens=_UpperCamelCase )
lowerCAmelCase__ = rust_tokenizer.encode(_UpperCamelCase , add_special_tokens=_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
lowerCAmelCase__ = self.get_rust_tokenizer()
lowerCAmelCase__ = tokenizer.encode(_UpperCamelCase )
lowerCAmelCase__ = rust_tokenizer.encode(_UpperCamelCase )
self.assertListEqual(_UpperCamelCase , _UpperCamelCase )
@slow
def UpperCamelCase__ ( self ):
"""simple docstring"""
# fmt: off
lowerCAmelCase__ = {'input_ids': [[0, 4_90, 1_43_28, 45_07, 3_54, 47, 4_36_69, 95, 25, 7_81_17, 2_02_15, 1_97_79, 1_90, 22, 4_00, 4, 3_53_43, 8_03_10, 6_03, 86, 2_49_37, 1_05, 3_34_38, 9_47_62, 1_96, 3_96_42, 7, 15, 1_59_33, 1_73, 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], [0, 1_05_34, 87, 25, 66, 33_58, 1_96, 5_52_89, 8, 8_29_61, 81, 22_04, 7_52_03, 7, 15, 7_63, 1_29_56, 2_16, 1_78, 1_43_28, 95_95, 13_77, 6_96_93, 7, 4_48, 7_10_21, 1_96, 1_81_06, 14_37, 1_39_74, 1_08, 90_83, 4, 4_93_15, 7, 39, 86, 13_26, 27_93, 4_63_33, 4, 4_48, 1_96, 7_45_88, 7, 4_93_15, 7, 39, 21, 8_22, 3_84_70, 74, 21, 6_67_23, 6_24_80, 8, 2_20_50, 5, 2]], '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, 0, 0, 0, 0, 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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]} # noqa: E501
# fmt: on
# moussaKam/mbarthez is a french model. So we also use french texts.
lowerCAmelCase__ = [
'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
'telles que la traduction et la synthèse de texte.',
]
self.tokenizer_integration_test_util(
expected_encoding=_UpperCamelCase , model_name='moussaKam/mbarthez' , revision='c2e4ecbca5e3cd2c37fe1ac285ca4fbdf1366fb6' , sequences=_UpperCamelCase , )
| 122 | 1 |
import operator
def SCREAMING_SNAKE_CASE_ ( __A : list , __A : bool = False , __A : list | None = None ) -> list:
"""simple docstring"""
a_ : Union[str, Any] = operator.lt if reverse else operator.gt
a_ : List[str] = solution or []
if not arr:
return solution
a_ : str = [arr.pop(0 )]
for i, item in enumerate(__A ):
if _operator(__A , sublist[-1] ):
sublist.append(__A )
arr.pop(__A )
# merging sublist into solution list
if not solution:
solution.extend(__A )
else:
while sublist:
a_ : List[Any] = sublist.pop(0 )
for i, xx in enumerate(__A ):
if not _operator(__A , __A ):
solution.insert(__A , __A )
break
else:
solution.append(__A )
strand_sort(__A , __A , __A )
return solution
if __name__ == "__main__":
assert strand_sort([4, 3, 5, 1, 2]) == [1, 2, 3, 4, 5]
assert strand_sort([4, 3, 5, 1, 2], reverse=True) == [5, 4, 3, 2, 1]
| 32 |
UpperCAmelCase_ : Optional[int] = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
UpperCAmelCase_ : str = {
0: 'Sunday',
1: 'Monday',
2: 'Tuesday',
3: 'Wednesday',
4: 'Thursday',
5: 'Friday',
6: 'Saturday',
}
def SCREAMING_SNAKE_CASE_ ( __A : int , __A : int , __A : int ) -> str:
"""simple docstring"""
assert len(str(__A ) ) > 2, "year should be in YYYY format"
assert 1 <= month <= 12, "month should be between 1 to 12"
assert 1 <= day <= 31, "day should be between 1 to 31"
# Doomsday algorithm:
a_ : List[str] = year // 1_00
a_ : Optional[int] = (5 * (century % 4) + 2) % 7
a_ : List[str] = year % 1_00
a_ : str = centurian % 12
a_ : List[str] = (
(centurian // 12) + centurian_m + (centurian_m // 4) + century_anchor
) % 7
a_ : Any = (
DOOMSDAY_NOT_LEAP[month - 1]
if (year % 4 != 0) or (centurian == 0 and (year % 4_00) == 0)
else DOOMSDAY_LEAP[month - 1]
)
a_ : Any = (dooms_day + day - day_anchor) % 7
return WEEK_DAY_NAMES[week_day]
if __name__ == "__main__":
import doctest
doctest.testmod()
| 32 | 1 |
from __future__ import annotations
import math
import random
from typing import Any
class A__ :
def __init__( self : Tuple ):
'''simple docstring'''
lowerCAmelCase__ : list[Any] = []
lowerCAmelCase__ : int = 0
lowerCAmelCase__ : int = 0
def _lowerCamelCase ( self : int ):
'''simple docstring'''
return self.head == self.tail
def _lowerCamelCase ( self : Tuple , a : Any ):
'''simple docstring'''
self.data.append(a )
lowerCAmelCase__ : int = self.tail + 1
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
lowerCAmelCase__ : Optional[int] = self.data[self.head]
lowerCAmelCase__ : Optional[int] = self.head + 1
return ret
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
return self.tail - self.head
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
print(self.data )
print('**************' )
print(self.data[self.head : self.tail] )
class A__ :
def __init__( self : List[Any] , a : Any ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = data
lowerCAmelCase__ : MyNode | None = None
lowerCAmelCase__ : MyNode | None = None
lowerCAmelCase__ : int = 1
def _lowerCamelCase ( self : List[Any] ):
'''simple docstring'''
return self.data
def _lowerCamelCase ( self : List[str] ):
'''simple docstring'''
return self.left
def _lowerCamelCase ( self : Any ):
'''simple docstring'''
return self.right
def _lowerCamelCase ( self : Optional[int] ):
'''simple docstring'''
return self.height
def _lowerCamelCase ( self : List[Any] , a : Any ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = data
def _lowerCamelCase ( self : List[Any] , a : MyNode | None ):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = node
def _lowerCamelCase ( self : List[str] , a : MyNode | None ):
'''simple docstring'''
lowerCAmelCase__ : List[str] = node
def _lowerCamelCase ( self : List[Any] , a : int ):
'''simple docstring'''
lowerCAmelCase__ : Dict = height
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> int:
if node is None:
return 0
return node.get_height()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> int:
if a > b:
return a
return b
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> MyNode:
print('left rotation node:' , node.get_data() )
lowerCAmelCase__ : Optional[Any] = node.get_left()
assert ret is not None
node.set_left(ret.get_right() )
ret.set_right(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Union[str, Any] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Tuple = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(SCREAMING_SNAKE_CASE_ )
return ret
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> MyNode:
print('right rotation node:' , node.get_data() )
lowerCAmelCase__ : str = node.get_right()
assert ret is not None
node.set_right(ret.get_left() )
ret.set_left(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Optional[int] = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : Dict = my_max(get_height(ret.get_right() ) , get_height(ret.get_left() ) ) + 1
ret.set_height(SCREAMING_SNAKE_CASE_ )
return ret
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> MyNode:
lowerCAmelCase__ : Any = node.get_left()
assert left_child is not None
node.set_left(left_rotation(SCREAMING_SNAKE_CASE_ ) )
return right_rotation(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> MyNode:
lowerCAmelCase__ : Dict = node.get_right()
assert right_child is not None
node.set_right(right_rotation(SCREAMING_SNAKE_CASE_ ) )
return left_rotation(SCREAMING_SNAKE_CASE_ )
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> MyNode | None:
if node is None:
return MyNode(SCREAMING_SNAKE_CASE_ )
if data < node.get_data():
node.set_left(insert_node(node.get_left() , SCREAMING_SNAKE_CASE_ ) )
if (
get_height(node.get_left() ) - get_height(node.get_right() ) == 2
): # an unbalance detected
lowerCAmelCase__ : Optional[int] = node.get_left()
assert left_child is not None
if (
data < left_child.get_data()
): # new node is the left child of the left child
lowerCAmelCase__ : Tuple = right_rotation(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase__ : str = lr_rotation(SCREAMING_SNAKE_CASE_ )
else:
node.set_right(insert_node(node.get_right() , SCREAMING_SNAKE_CASE_ ) )
if get_height(node.get_right() ) - get_height(node.get_left() ) == 2:
lowerCAmelCase__ : Tuple = node.get_right()
assert right_child is not None
if data < right_child.get_data():
lowerCAmelCase__ : Tuple = rl_rotation(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase__ : Union[str, Any] = left_rotation(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : int = my_max(get_height(node.get_right() ) , get_height(node.get_left() ) ) + 1
node.set_height(SCREAMING_SNAKE_CASE_ )
return node
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> Any:
while True:
lowerCAmelCase__ : Optional[int] = root.get_right()
if right_child is None:
break
lowerCAmelCase__ : Optional[Any] = right_child
return root.get_data()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ ) -> Any:
while True:
lowerCAmelCase__ : Tuple = root.get_left()
if left_child is None:
break
lowerCAmelCase__ : List[Any] = left_child
return root.get_data()
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> MyNode | None:
lowerCAmelCase__ : str = root.get_left()
lowerCAmelCase__ : Dict = root.get_right()
if root.get_data() == data:
if left_child is not None and right_child is not None:
lowerCAmelCase__ : Union[str, Any] = get_left_most(SCREAMING_SNAKE_CASE_ )
root.set_data(SCREAMING_SNAKE_CASE_ )
root.set_right(del_node(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
elif left_child is not None:
lowerCAmelCase__ : Optional[Any] = left_child
elif right_child is not None:
lowerCAmelCase__ : Optional[int] = right_child
else:
return None
elif root.get_data() > data:
if left_child is None:
print('No such data' )
return root
else:
root.set_left(del_node(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
else: # root.get_data() < data
if right_child is None:
return root
else:
root.set_right(del_node(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) )
if get_height(SCREAMING_SNAKE_CASE_ ) - get_height(SCREAMING_SNAKE_CASE_ ) == 2:
assert right_child is not None
if get_height(right_child.get_right() ) > get_height(right_child.get_left() ):
lowerCAmelCase__ : Dict = left_rotation(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase__ : List[str] = rl_rotation(SCREAMING_SNAKE_CASE_ )
elif get_height(SCREAMING_SNAKE_CASE_ ) - get_height(SCREAMING_SNAKE_CASE_ ) == -2:
assert left_child is not None
if get_height(left_child.get_left() ) > get_height(left_child.get_right() ):
lowerCAmelCase__ : Optional[int] = right_rotation(SCREAMING_SNAKE_CASE_ )
else:
lowerCAmelCase__ : Dict = lr_rotation(SCREAMING_SNAKE_CASE_ )
lowerCAmelCase__ : str = my_max(get_height(root.get_right() ) , get_height(root.get_left() ) ) + 1
root.set_height(SCREAMING_SNAKE_CASE_ )
return root
class A__ :
def __init__( self : Any ):
'''simple docstring'''
lowerCAmelCase__ : MyNode | None = None
def _lowerCamelCase ( self : Union[str, Any] ):
'''simple docstring'''
return get_height(self.root )
def _lowerCamelCase ( self : List[Any] , a : Any ):
'''simple docstring'''
print('insert:' + str(a ) )
lowerCAmelCase__ : Optional[Any] = insert_node(self.root , a )
def _lowerCamelCase ( self : Tuple , a : Any ):
'''simple docstring'''
print('delete:' + str(a ) )
if self.root is None:
print('Tree is empty!' )
return
lowerCAmelCase__ : List[str] = del_node(self.root , a )
def __str__( self : Dict , ): # a level traversale, gives a more intuitive look on the tree
'''simple docstring'''
lowerCAmelCase__ : int = ''
lowerCAmelCase__ : int = MyQueue()
q.push(self.root )
lowerCAmelCase__ : Any = self.get_height()
if layer == 0:
return output
lowerCAmelCase__ : Optional[Any] = 0
while not q.is_empty():
lowerCAmelCase__ : Any = q.pop()
lowerCAmelCase__ : str = ' ' * int(math.pow(2 , layer - 1 ) )
output += space
if node is None:
output += "*"
q.push(a )
q.push(a )
else:
output += str(node.get_data() )
q.push(node.get_left() )
q.push(node.get_right() )
output += space
lowerCAmelCase__ : str = cnt + 1
for i in range(100 ):
if cnt == math.pow(2 , a ) - 1:
lowerCAmelCase__ : Dict = layer - 1
if layer == 0:
output += "\n*************************************"
return output
output += "\n"
break
output += "\n*************************************"
return output
def lowerCAmelCase__ ( ) -> None:
import doctest
doctest.testmod()
if __name__ == "__main__":
_test()
lowerCamelCase__ = AVLtree()
lowerCamelCase__ = list(range(10))
random.shuffle(lst)
for i in lst:
t.insert(i)
print(str(t))
random.shuffle(lst)
for i in lst:
t.del_node(i)
print(str(t)) | 307 |
import json
import os
from datetime import date
from pathlib import Path
from tabulate import DataRow, TableFormat, tabulate
lowerCamelCase__ = TableFormat(
lineabove=None,
linebelowheader=None,
linebetweenrows=None,
linebelow=None,
headerrow=DataRow("""""", """|""", """|"""),
datarow=DataRow("""""", """|""", """|"""),
padding=1,
with_header_hide=None,
)
lowerCamelCase__ = []
lowerCamelCase__ = []
lowerCamelCase__ = {"""type""": """section""", """text""": {"""type""": """plain_text""", """text""": """No failed tests! 🤗""", """emoji""": True}}
lowerCamelCase__ = [
{
"""type""": """header""",
"""text""": {
"""type""": """plain_text""",
"""text""": F"""🤗 Accelerate nightly {os.environ.get("TEST_TYPE", "")} test results""",
"""emoji""": True,
},
}
]
lowerCamelCase__ = 0
for log in Path().glob("""*.log"""):
lowerCamelCase__ = 0
with open(log, """r""") as f:
for line in f:
lowerCamelCase__ = json.loads(line)
if line.get("""nodeid""", """""") != "":
lowerCamelCase__ = line["""nodeid"""]
if line.get("""duration""", None) is not None:
lowerCamelCase__ = F"""{line["duration"]:.4f}"""
if line.get("""outcome""", """""") == "failed":
section_num_failed += 1
failed.append([test, duration, log.name.split("""_""")[0]])
total_num_failed += 1
group_info.append([str(log), section_num_failed, failed])
lowerCamelCase__ = []
log.unlink()
lowerCamelCase__ = """"""
lowerCamelCase__ = []
if total_num_failed > 0:
for name, num_failed, failed_tests in group_info:
if num_failed > 0:
if num_failed == 1:
message += F"*{name[1:]}: {num_failed} failed test*\n"
else:
message += F"*{name[1:]}: {num_failed} failed tests*\n"
lowerCamelCase__ = []
lowerCamelCase__ = {}
for test in failed_tests:
lowerCamelCase__ = test[0].split("""::""")
lowerCamelCase__ = data[0].split("""/""")[-1]
if data[0] not in filesafailed:
lowerCamelCase__ = [data[1:]]
else:
filesafailed[data[0]] += [data[1:]]
failed_table.append(data)
lowerCamelCase__ = [test[0] for test in failed_table]
lowerCamelCase__ = list(set(files))
# Count number of instances in failed_tests
lowerCamelCase__ = []
for file in individual_files:
table.append([file, len(filesafailed[file])])
lowerCamelCase__ = tabulate(
table,
headers=["""Test Location""", """Num Failed"""],
tablefmt=hf_table_format,
stralign="""right""",
)
message += F"\n```\n{failed_table}\n```"
all_filesafailed.append(filesafailed)
if len(message) > 3000:
lowerCamelCase__ = """Too many failed tests, please see the full report in the Action results."""
lowerCamelCase__ = len(err) + 10
lowerCamelCase__ = message[: 3000 - offset] + F"""\n...\n```\n{err}"""
print(F"""### {message}""")
else:
lowerCamelCase__ = """No failed tests! 🤗"""
print(F"""## {message}""")
payload.append(no_error_payload)
if os.environ.get("""TEST_TYPE""", """""") != "":
from slack_sdk import WebClient
lowerCamelCase__ = WebClient(token=os.environ["""SLACK_API_TOKEN"""])
if message != "No failed tests! 🤗":
lowerCamelCase__ = {
"""type""": """section""",
"""text""": {
"""type""": """mrkdwn""",
"""text""": message,
},
}
payload.append(md_report)
lowerCamelCase__ = {
"""type""": """section""",
"""text""": {
"""type""": """mrkdwn""",
"""text""": """*For more details:*""",
},
"""accessory""": {
"""type""": """button""",
"""text""": {
"""type""": """plain_text""",
"""text""": """Check Action results""",
"""emoji""": True,
},
"""url""": F"""https://github.com/{os.environ["GITHUB_REPOSITORY"]}/actions/runs/{os.environ["GITHUB_RUN_ID"]}""",
},
}
payload.append(action_button)
lowerCamelCase__ = {
"""type""": """context""",
"""elements""": [
{
"""type""": """plain_text""",
"""text""": F"""Nightly {os.environ.get("TEST_TYPE")} test results for {date.today()}""",
}
],
}
payload.append(date_report)
lowerCamelCase__ = client.chat_postMessage(channel="""#accelerate-ci-daily""", text=message, blocks=payload)
lowerCamelCase__ = response.data["""ts"""]
for failed_file in all_filesafailed:
for test_location, test_failures in failed_file.items():
# Keep only the first instance of the test name
lowerCamelCase__ = """"""
for i, row in enumerate(test_failures):
if row[0] != test_class:
lowerCamelCase__ = row[0]
else:
lowerCamelCase__ = """"""
lowerCamelCase__ = {
"""type""": """section""",
"""text""": {
"""type""": """mrkdwn""",
"""text""": F"""Test location: {test_location}\n```\n{tabulate(test_failures, headers=["Class", "Test"], tablefmt=hf_table_format, stralign="right")}\n```""",
},
}
client.chat_postMessage(
channel="""#accelerate-ci-daily""",
thread_ts=ts,
blocks=[payload],
) | 307 | 1 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
a__ : int = logging.get_logger(__name__)
a__ : Optional[Any] = {
'''SenseTime/deformable-detr''': '''https://huggingface.co/sensetime/deformable-detr/resolve/main/config.json''',
# See all Deformable DETR models at https://huggingface.co/models?filter=deformable-detr
}
class a_ ( a__ ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Tuple = 'deformable_detr'
__SCREAMING_SNAKE_CASE : Union[str, Any] = {
'hidden_size': 'd_model',
'num_attention_heads': 'encoder_attention_heads',
}
def __init__( self , _lowerCamelCase=True , _lowerCamelCase=None , _lowerCamelCase=3 , _lowerCamelCase=300 , _lowerCamelCase=1024 , _lowerCamelCase=6 , _lowerCamelCase=1024 , _lowerCamelCase=8 , _lowerCamelCase=6 , _lowerCamelCase=1024 , _lowerCamelCase=8 , _lowerCamelCase=0.0 , _lowerCamelCase=True , _lowerCamelCase="relu" , _lowerCamelCase=256 , _lowerCamelCase=0.1 , _lowerCamelCase=0.0 , _lowerCamelCase=0.0 , _lowerCamelCase=0.0_2 , _lowerCamelCase=1.0 , _lowerCamelCase=True , _lowerCamelCase=False , _lowerCamelCase="sine" , _lowerCamelCase="resnet50" , _lowerCamelCase=True , _lowerCamelCase=False , _lowerCamelCase=4 , _lowerCamelCase=4 , _lowerCamelCase=4 , _lowerCamelCase=False , _lowerCamelCase=300 , _lowerCamelCase=False , _lowerCamelCase=1 , _lowerCamelCase=5 , _lowerCamelCase=2 , _lowerCamelCase=1 , _lowerCamelCase=1 , _lowerCamelCase=5 , _lowerCamelCase=2 , _lowerCamelCase=0.1 , _lowerCamelCase=0.2_5 , _lowerCamelCase=False , **_lowerCamelCase , ) ->Optional[Any]:
if backbone_config is not None and use_timm_backbone:
raise ValueError('''You can\'t specify both `backbone_config` and `use_timm_backbone`.''' )
if not use_timm_backbone:
if backbone_config is None:
logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.''' )
SCREAMING_SNAKE_CASE : Dict = CONFIG_MAPPING['''resnet'''](out_features=['''stage4'''] )
elif isinstance(_lowerCamelCase , _lowerCamelCase ):
SCREAMING_SNAKE_CASE : List[Any] = backbone_config.get('''model_type''' )
SCREAMING_SNAKE_CASE : Optional[Any] = CONFIG_MAPPING[backbone_model_type]
SCREAMING_SNAKE_CASE : int = config_class.from_dict(_lowerCamelCase )
SCREAMING_SNAKE_CASE : str = use_timm_backbone
SCREAMING_SNAKE_CASE : Optional[int] = backbone_config
SCREAMING_SNAKE_CASE : Union[str, Any] = num_channels
SCREAMING_SNAKE_CASE : Optional[Any] = num_queries
SCREAMING_SNAKE_CASE : Optional[int] = max_position_embeddings
SCREAMING_SNAKE_CASE : Optional[int] = d_model
SCREAMING_SNAKE_CASE : str = encoder_ffn_dim
SCREAMING_SNAKE_CASE : str = encoder_layers
SCREAMING_SNAKE_CASE : str = encoder_attention_heads
SCREAMING_SNAKE_CASE : Optional[int] = decoder_ffn_dim
SCREAMING_SNAKE_CASE : int = decoder_layers
SCREAMING_SNAKE_CASE : Union[str, Any] = decoder_attention_heads
SCREAMING_SNAKE_CASE : List[str] = dropout
SCREAMING_SNAKE_CASE : Optional[int] = attention_dropout
SCREAMING_SNAKE_CASE : str = activation_dropout
SCREAMING_SNAKE_CASE : Optional[int] = activation_function
SCREAMING_SNAKE_CASE : Optional[int] = init_std
SCREAMING_SNAKE_CASE : List[str] = init_xavier_std
SCREAMING_SNAKE_CASE : Optional[Any] = encoder_layerdrop
SCREAMING_SNAKE_CASE : Union[str, Any] = auxiliary_loss
SCREAMING_SNAKE_CASE : List[Any] = position_embedding_type
SCREAMING_SNAKE_CASE : str = backbone
SCREAMING_SNAKE_CASE : Dict = use_pretrained_backbone
SCREAMING_SNAKE_CASE : Dict = dilation
# deformable attributes
SCREAMING_SNAKE_CASE : str = num_feature_levels
SCREAMING_SNAKE_CASE : Optional[Any] = encoder_n_points
SCREAMING_SNAKE_CASE : Any = decoder_n_points
SCREAMING_SNAKE_CASE : str = two_stage
SCREAMING_SNAKE_CASE : List[str] = two_stage_num_proposals
SCREAMING_SNAKE_CASE : Dict = with_box_refine
if two_stage is True and with_box_refine is False:
raise ValueError('''If two_stage is True, with_box_refine must be True.''' )
# Hungarian matcher
SCREAMING_SNAKE_CASE : int = class_cost
SCREAMING_SNAKE_CASE : Union[str, Any] = bbox_cost
SCREAMING_SNAKE_CASE : Optional[int] = giou_cost
# Loss coefficients
SCREAMING_SNAKE_CASE : Dict = mask_loss_coefficient
SCREAMING_SNAKE_CASE : Union[str, Any] = dice_loss_coefficient
SCREAMING_SNAKE_CASE : str = bbox_loss_coefficient
SCREAMING_SNAKE_CASE : Tuple = giou_loss_coefficient
SCREAMING_SNAKE_CASE : Optional[int] = eos_coefficient
SCREAMING_SNAKE_CASE : Tuple = focal_alpha
SCREAMING_SNAKE_CASE : Optional[int] = disable_custom_kernels
super().__init__(is_encoder_decoder=_lowerCamelCase , **_lowerCamelCase )
@property
def __lowerCAmelCase ( self ) ->int:
return self.encoder_attention_heads
@property
def __lowerCAmelCase ( self ) ->int:
return self.d_model
def __lowerCAmelCase ( self ) ->Any:
SCREAMING_SNAKE_CASE : str = copy.deepcopy(self.__dict__ )
if self.backbone_config is not None:
SCREAMING_SNAKE_CASE : Optional[int] = self.backbone_config.to_dict()
SCREAMING_SNAKE_CASE : Any = self.__class__.model_type
return output
| 313 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTImageProcessor, ViTMSNConfig, ViTMSNModel
from transformers.image_utils import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
torch.set_grad_enabled(False)
def UpperCAmelCase_( a__ , a__=False ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F"""module.blocks.{i}.norm1.weight""", F"""vit.encoder.layer.{i}.layernorm_before.weight""") )
rename_keys.append((F"""module.blocks.{i}.norm1.bias""", F"""vit.encoder.layer.{i}.layernorm_before.bias""") )
rename_keys.append(
(F"""module.blocks.{i}.attn.proj.weight""", F"""vit.encoder.layer.{i}.attention.output.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.attn.proj.bias""", F"""vit.encoder.layer.{i}.attention.output.dense.bias""") )
rename_keys.append((F"""module.blocks.{i}.norm2.weight""", F"""vit.encoder.layer.{i}.layernorm_after.weight""") )
rename_keys.append((F"""module.blocks.{i}.norm2.bias""", F"""vit.encoder.layer.{i}.layernorm_after.bias""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc1.weight""", F"""vit.encoder.layer.{i}.intermediate.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc1.bias""", F"""vit.encoder.layer.{i}.intermediate.dense.bias""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc2.weight""", F"""vit.encoder.layer.{i}.output.dense.weight""") )
rename_keys.append((F"""module.blocks.{i}.mlp.fc2.bias""", F"""vit.encoder.layer.{i}.output.dense.bias""") )
# projection layer + position embeddings
rename_keys.extend(
[
('''module.cls_token''', '''vit.embeddings.cls_token'''),
('''module.patch_embed.proj.weight''', '''vit.embeddings.patch_embeddings.projection.weight'''),
('''module.patch_embed.proj.bias''', '''vit.embeddings.patch_embeddings.projection.bias'''),
('''module.pos_embed''', '''vit.embeddings.position_embeddings'''),
] )
if base_model:
# layernorm + pooler
rename_keys.extend(
[
('''module.norm.weight''', '''layernorm.weight'''),
('''module.norm.bias''', '''layernorm.bias'''),
] )
# if just the base model, we should remove "vit" from all keys that start with "vit"
SCREAMING_SNAKE_CASE : 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'''),
] )
return rename_keys
def UpperCAmelCase_( a__ , a__ , a__=False ):
"""simple docstring"""
for i in range(config.num_hidden_layers ):
if base_model:
SCREAMING_SNAKE_CASE : Any = ''''''
else:
SCREAMING_SNAKE_CASE : Optional[int] = '''vit.'''
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
SCREAMING_SNAKE_CASE : Optional[Any] = state_dict.pop(F"""module.blocks.{i}.attn.qkv.weight""" )
SCREAMING_SNAKE_CASE : List[str] = state_dict.pop(F"""module.blocks.{i}.attn.qkv.bias""" )
# next, add query, keys and values (in that order) to the state dict
SCREAMING_SNAKE_CASE : List[str] = in_proj_weight[
: config.hidden_size, :
]
SCREAMING_SNAKE_CASE : Any = in_proj_bias[: config.hidden_size]
SCREAMING_SNAKE_CASE : int = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
SCREAMING_SNAKE_CASE : List[Any] = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
SCREAMING_SNAKE_CASE : List[str] = in_proj_weight[
-config.hidden_size :, :
]
SCREAMING_SNAKE_CASE : Optional[Any] = in_proj_bias[-config.hidden_size :]
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = ['''head.weight''', '''head.bias''']
for k in ignore_keys:
state_dict.pop(a__ , a__ )
def UpperCAmelCase_( a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = [
'''module.fc.fc1.weight''',
'''module.fc.fc1.bias''',
'''module.fc.bn1.weight''',
'''module.fc.bn1.bias''',
'''module.fc.bn1.running_mean''',
'''module.fc.bn1.running_var''',
'''module.fc.bn1.num_batches_tracked''',
'''module.fc.fc2.weight''',
'''module.fc.fc2.bias''',
'''module.fc.bn2.weight''',
'''module.fc.bn2.bias''',
'''module.fc.bn2.running_mean''',
'''module.fc.bn2.running_var''',
'''module.fc.bn2.num_batches_tracked''',
'''module.fc.fc3.weight''',
'''module.fc.fc3.bias''',
]
for k in ignore_keys:
state_dict.pop(a__ , a__ )
def UpperCAmelCase_( a__ , a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : int = dct.pop(a__ )
SCREAMING_SNAKE_CASE : Optional[Any] = val
def UpperCAmelCase_( a__ , a__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = ViTMSNConfig()
SCREAMING_SNAKE_CASE : Optional[int] = 1_000
SCREAMING_SNAKE_CASE : str = '''datasets/huggingface/label-files'''
SCREAMING_SNAKE_CASE : List[str] = '''imagenet-1k-id2label.json'''
SCREAMING_SNAKE_CASE : Union[str, Any] = json.load(open(hf_hub_download(a__ , a__ ) , '''r''' ) )
SCREAMING_SNAKE_CASE : List[Any] = {int(a__ ): v for k, v in idalabel.items()}
SCREAMING_SNAKE_CASE : str = idalabel
SCREAMING_SNAKE_CASE : int = {v: k for k, v in idalabel.items()}
if "s16" in checkpoint_url:
SCREAMING_SNAKE_CASE : Tuple = 384
SCREAMING_SNAKE_CASE : Any = 1_536
SCREAMING_SNAKE_CASE : List[str] = 6
elif "l16" in checkpoint_url:
SCREAMING_SNAKE_CASE : Optional[int] = 1_024
SCREAMING_SNAKE_CASE : Optional[int] = 4_096
SCREAMING_SNAKE_CASE : Tuple = 24
SCREAMING_SNAKE_CASE : Union[str, Any] = 16
SCREAMING_SNAKE_CASE : Dict = 0.1
elif "b4" in checkpoint_url:
SCREAMING_SNAKE_CASE : str = 4
elif "l7" in checkpoint_url:
SCREAMING_SNAKE_CASE : Union[str, Any] = 7
SCREAMING_SNAKE_CASE : Union[str, Any] = 1_024
SCREAMING_SNAKE_CASE : List[Any] = 4_096
SCREAMING_SNAKE_CASE : List[Any] = 24
SCREAMING_SNAKE_CASE : Tuple = 16
SCREAMING_SNAKE_CASE : Union[str, Any] = 0.1
SCREAMING_SNAKE_CASE : Union[str, Any] = ViTMSNModel(a__ )
SCREAMING_SNAKE_CASE : Optional[int] = torch.hub.load_state_dict_from_url(a__ , map_location='''cpu''' )['''target_encoder''']
SCREAMING_SNAKE_CASE : Any = ViTImageProcessor(size=config.image_size )
remove_projection_head(a__ )
SCREAMING_SNAKE_CASE : Any = create_rename_keys(a__ , base_model=a__ )
for src, dest in rename_keys:
rename_key(a__ , a__ , a__ )
read_in_q_k_v(a__ , a__ , base_model=a__ )
model.load_state_dict(a__ )
model.eval()
SCREAMING_SNAKE_CASE : Any = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
SCREAMING_SNAKE_CASE : Dict = Image.open(requests.get(a__ , stream=a__ ).raw )
SCREAMING_SNAKE_CASE : Optional[int] = ViTImageProcessor(
size=config.image_size , image_mean=a__ , image_std=a__ )
SCREAMING_SNAKE_CASE : int = image_processor(images=a__ , return_tensors='''pt''' )
# forward pass
torch.manual_seed(2 )
SCREAMING_SNAKE_CASE : Tuple = model(**a__ )
SCREAMING_SNAKE_CASE : str = outputs.last_hidden_state
# The following Colab Notebook was used to generate these outputs:
# https://colab.research.google.com/gist/sayakpaul/3672419a04f5997827503fd84079bdd1/scratchpad.ipynb
if "s16" in checkpoint_url:
SCREAMING_SNAKE_CASE : Tuple = torch.tensor([[-1.0_915, -1.4_876, -1.1_809]] )
elif "b16" in checkpoint_url:
SCREAMING_SNAKE_CASE : Tuple = torch.tensor([[14.2_889, -18.9_045, 11.7_281]] )
elif "l16" in checkpoint_url:
SCREAMING_SNAKE_CASE : Union[str, Any] = torch.tensor([[41.5_028, -22.8_681, 45.6_475]] )
elif "b4" in checkpoint_url:
SCREAMING_SNAKE_CASE : str = torch.tensor([[-4.3_868, 5.2_932, -0.4_137]] )
else:
SCREAMING_SNAKE_CASE : List[str] = torch.tensor([[-0.1_792, -0.6_465, 2.4_263]] )
# verify logits
assert torch.allclose(last_hidden_state[:, 0, :3] , a__ , atol=1e-4 )
print(F"""Saving model to {pytorch_dump_folder_path}""" )
model.save_pretrained(a__ )
print(F"""Saving image processor to {pytorch_dump_folder_path}""" )
image_processor.save_pretrained(a__ )
if __name__ == "__main__":
a__ : Dict = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--checkpoint_url''',
default='''https://dl.fbaipublicfiles.com/msn/vits16_800ep.pth.tar''',
type=str,
help='''URL of the checkpoint you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
)
a__ : Any = parser.parse_args()
convert_vit_msn_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 313 | 1 |
from sklearn.metrics import fa_score
import datasets
_UpperCAmelCase : Optional[Any] = """
The F1 score is the harmonic mean of the precision and recall. It can be computed with the equation:
F1 = 2 * (precision * recall) / (precision + recall)
"""
_UpperCAmelCase : Union[str, Any] = """
Args:
predictions (`list` of `int`): Predicted labels.
references (`list` of `int`): Ground truth labels.
labels (`list` of `int`): The set of labels to include when `average` is not set to `'binary'`, and the order of the labels if `average` is `None`. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class. Labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels in `predictions` and `references` are used in sorted order. Defaults to None.
pos_label (`int`): The class to be considered the positive class, in the case where `average` is set to `binary`. Defaults to 1.
average (`string`): This parameter is required for multiclass/multilabel targets. If set to `None`, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data. Defaults to `'binary'`.
- 'binary': Only report results for the class specified by `pos_label`. This is applicable only if the classes found in `predictions` and `references` are binary.
- 'micro': Calculate metrics globally by counting the total true positives, false negatives and false positives.
- 'macro': Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
- 'weighted': Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters `'macro'` to account for label imbalance. This option can result in an F-score that is not between precision and recall.
- 'samples': Calculate metrics for each instance, and find their average (only meaningful for multilabel classification).
sample_weight (`list` of `float`): Sample weights Defaults to None.
Returns:
f1 (`float` or `array` of `float`): F1 score or list of f1 scores, depending on the value passed to `average`. Minimum possible value is 0. Maximum possible value is 1. Higher f1 scores are better.
Examples:
Example 1-A simple binary example
>>> f1_metric = datasets.load_metric(\"f1\")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0])
>>> print(results)
{'f1': 0.5}
Example 2-The same simple binary example as in Example 1, but with `pos_label` set to `0`.
>>> f1_metric = datasets.load_metric(\"f1\")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], pos_label=0)
>>> print(round(results['f1'], 2))
0.67
Example 3-The same simple binary example as in Example 1, but with `sample_weight` included.
>>> f1_metric = datasets.load_metric(\"f1\")
>>> results = f1_metric.compute(references=[0, 1, 0, 1, 0], predictions=[0, 0, 1, 1, 0], sample_weight=[0.9, 0.5, 3.9, 1.2, 0.3])
>>> print(round(results['f1'], 2))
0.35
Example 4-A multiclass example, with different values for the `average` input.
>>> predictions = [0, 2, 1, 0, 0, 1]
>>> references = [0, 1, 2, 0, 1, 2]
>>> results = f1_metric.compute(predictions=predictions, references=references, average=\"macro\")
>>> print(round(results['f1'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average=\"micro\")
>>> print(round(results['f1'], 2))
0.33
>>> results = f1_metric.compute(predictions=predictions, references=references, average=\"weighted\")
>>> print(round(results['f1'], 2))
0.27
>>> results = f1_metric.compute(predictions=predictions, references=references, average=None)
>>> print(results)
{'f1': array([0.8, 0. , 0. ])}
"""
_UpperCAmelCase : str = """
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P.
and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and
Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
journal={Journal of Machine Learning Research},
volume={12},
pages={2825--2830},
year={2011}
}
"""
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase ( datasets.Metric ):
def a ( self ):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Sequence(datasets.Value('int32' ) ),
'references': datasets.Sequence(datasets.Value('int32' ) ),
}
if self.config_name == 'multilabel'
else {
'predictions': datasets.Value('int32' ),
'references': datasets.Value('int32' ),
} ) , reference_urls=['https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html'] , )
def a ( self , snake_case , snake_case , snake_case=None , snake_case=1 , snake_case="binary" , snake_case=None ):
snake_case_ = fa_score(
snake_case , snake_case , labels=snake_case , pos_label=snake_case , average=snake_case , sample_weight=snake_case )
return {"f1": float(snake_case ) if score.size == 1 else score}
| 200 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Tuple = {
"""configuration_xlm_roberta_xl""": [
"""XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""XLMRobertaXLConfig""",
"""XLMRobertaXLOnnxConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_UpperCAmelCase : int = [
"""XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""XLMRobertaXLForCausalLM""",
"""XLMRobertaXLForMaskedLM""",
"""XLMRobertaXLForMultipleChoice""",
"""XLMRobertaXLForQuestionAnswering""",
"""XLMRobertaXLForSequenceClassification""",
"""XLMRobertaXLForTokenClassification""",
"""XLMRobertaXLModel""",
"""XLMRobertaXLPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP,
XLMRobertaXLConfig,
XLMRobertaXLOnnxConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm_roberta_xl import (
XLM_ROBERTA_XL_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMRobertaXLForCausalLM,
XLMRobertaXLForMaskedLM,
XLMRobertaXLForMultipleChoice,
XLMRobertaXLForQuestionAnswering,
XLMRobertaXLForSequenceClassification,
XLMRobertaXLForTokenClassification,
XLMRobertaXLModel,
XLMRobertaXLPreTrainedModel,
)
else:
import sys
_UpperCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure)
| 200 | 1 |
import unittest
from typing import Tuple
import torch
from diffusers.utils import floats_tensor, randn_tensor, torch_all_close, torch_device
from diffusers.utils.testing_utils import require_torch
@require_torch
class __UpperCAmelCase :
@property
def __magic_name__ ( self : List[Any] ):
return self.get_dummy_input()
@property
def __magic_name__ ( self : str ):
if self.block_type == "down":
return (4, 3_2, 1_6, 1_6)
elif self.block_type == "mid":
return (4, 3_2, 3_2, 3_2)
elif self.block_type == "up":
return (4, 3_2, 6_4, 6_4)
raise ValueError(F'''\'{self.block_type}\' is not a supported block_type. Set it to \'up\', \'mid\', or \'down\'.''' )
def __magic_name__ ( self : Any, __A : Tuple=True, __A : Optional[int]=False, __A : Tuple=False, __A : Union[str, Any]=False, ):
UpperCAmelCase : Optional[Any] = 4
UpperCAmelCase : int = 3_2
UpperCAmelCase : int = (3_2, 3_2)
UpperCAmelCase : List[Any] = torch.manual_seed(0 )
UpperCAmelCase : Dict = torch.device(__A )
UpperCAmelCase : Union[str, Any] = (batch_size, num_channels) + sizes
UpperCAmelCase : Tuple = randn_tensor(__A, generator=__A, device=__A )
UpperCAmelCase : Optional[Any] = {'''hidden_states''': hidden_states}
if include_temb:
UpperCAmelCase : Optional[int] = 1_2_8
UpperCAmelCase : Union[str, Any] = randn_tensor((batch_size, temb_channels), generator=__A, device=__A )
if include_res_hidden_states_tuple:
UpperCAmelCase : Dict = torch.manual_seed(1 )
UpperCAmelCase : List[Any] = (randn_tensor(__A, generator=__A, device=__A ),)
if include_encoder_hidden_states:
UpperCAmelCase : Tuple = floats_tensor((batch_size, 3_2, 3_2) ).to(__A )
if include_skip_sample:
UpperCAmelCase : Optional[int] = randn_tensor(((batch_size, 3) + sizes), generator=__A, device=__A )
return dummy_input
def __magic_name__ ( self : List[Any] ):
UpperCAmelCase : List[Any] = {
'''in_channels''': 3_2,
'''out_channels''': 3_2,
'''temb_channels''': 1_2_8,
}
if self.block_type == "up":
UpperCAmelCase : Optional[Any] = 3_2
if self.block_type == "mid":
init_dict.pop('''out_channels''' )
UpperCAmelCase : Optional[int] = self.dummy_input
return init_dict, inputs_dict
def __magic_name__ ( self : str, __A : Dict ):
UpperCAmelCase , UpperCAmelCase : Tuple = self.prepare_init_args_and_inputs_for_common()
UpperCAmelCase : Optional[int] = self.block_class(**__A )
unet_block.to(__A )
unet_block.eval()
with torch.no_grad():
UpperCAmelCase : Tuple = unet_block(**__A )
if isinstance(__A, __A ):
UpperCAmelCase : int = output[0]
self.assertEqual(output.shape, self.output_shape )
UpperCAmelCase : Optional[int] = output[0, -1, -3:, -3:]
UpperCAmelCase : Union[str, Any] = torch.tensor(__A ).to(__A )
assert torch_all_close(output_slice.flatten(), __A, atol=5E-3 )
@unittest.skipIf(torch_device == '''mps''', '''Training is not supported in mps''' )
def __magic_name__ ( self : Any ):
UpperCAmelCase , UpperCAmelCase : List[Any] = self.prepare_init_args_and_inputs_for_common()
UpperCAmelCase : Any = self.block_class(**__A )
model.to(__A )
model.train()
UpperCAmelCase : Tuple = model(**__A )
if isinstance(__A, __A ):
UpperCAmelCase : Dict = output[0]
UpperCAmelCase : List[str] = torch.device(__A )
UpperCAmelCase : str = randn_tensor(output.shape, device=__A )
UpperCAmelCase : Dict = torch.nn.functional.mse_loss(__A, __A )
loss.backward()
| 336 |
import argparse
import collections
import json
from pathlib import Path
import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTImageProcessor,
MobileViTVaConfig,
MobileViTVaForImageClassification,
MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging
logging.set_verbosity_info()
_lowerCamelCase : Optional[int] = logging.get_logger(__name__)
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Optional[Any]:
print('''Loading config file...''' )
def flatten_yaml_as_dict(UpperCAmelCase : Tuple , UpperCAmelCase : Any="" , UpperCAmelCase : Dict="." ):
UpperCAmelCase : List[str] = []
for k, v in d.items():
UpperCAmelCase : List[Any] = parent_key + sep + k if parent_key else k
if isinstance(UpperCAmelCase , collections.abc.MutableMapping ):
items.extend(flatten_yaml_as_dict(UpperCAmelCase , UpperCAmelCase , sep=UpperCAmelCase ).items() )
else:
items.append((new_key, v) )
return dict(UpperCAmelCase )
UpperCAmelCase : List[str] = argparse.Namespace()
with open(UpperCAmelCase , '''r''' ) as yaml_file:
try:
UpperCAmelCase : List[str] = yaml.load(UpperCAmelCase , Loader=yaml.FullLoader )
UpperCAmelCase : Optional[int] = flatten_yaml_as_dict(UpperCAmelCase )
for k, v in flat_cfg.items():
setattr(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
except yaml.YAMLError as exc:
logger.error('''Error while loading config file: {}. Error message: {}'''.format(UpperCAmelCase , str(UpperCAmelCase ) ) )
return config
def a__ ( UpperCAmelCase : List[str] , UpperCAmelCase : int ) -> List[Any]:
UpperCAmelCase : int = MobileViTVaConfig()
UpperCAmelCase : str = False
# dataset
if task_name.startswith('''imagenet1k_''' ):
UpperCAmelCase : Any = 1_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : Any = 384
else:
UpperCAmelCase : Tuple = 256
UpperCAmelCase : int = '''imagenet-1k-id2label.json'''
elif task_name.startswith('''imagenet21k_to_1k_''' ):
UpperCAmelCase : Optional[Any] = 21_000
if int(task_name.strip().split('''_''' )[-1] ) == 384:
UpperCAmelCase : str = 384
else:
UpperCAmelCase : Dict = 256
UpperCAmelCase : List[Any] = '''imagenet-22k-id2label.json'''
elif task_name.startswith('''ade20k_''' ):
UpperCAmelCase : Optional[Any] = 151
UpperCAmelCase : Tuple = 512
UpperCAmelCase : Tuple = '''ade20k-id2label.json'''
UpperCAmelCase : Tuple = True
elif task_name.startswith('''voc_''' ):
UpperCAmelCase : Dict = 21
UpperCAmelCase : str = 512
UpperCAmelCase : Union[str, Any] = '''pascal-voc-id2label.json'''
UpperCAmelCase : Dict = True
# orig_config
UpperCAmelCase : List[Any] = load_orig_config_file(UpperCAmelCase )
assert getattr(UpperCAmelCase , '''model.classification.name''' , -1 ) == "mobilevit_v2", "Invalid model"
UpperCAmelCase : Tuple = getattr(UpperCAmelCase , '''model.classification.mitv2.width_multiplier''' , 1.0 )
assert (
getattr(UpperCAmelCase , '''model.classification.mitv2.attn_norm_layer''' , -1 ) == "layer_norm_2d"
), "Norm layers other than layer_norm_2d is not supported"
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.classification.activation.name''' , '''swish''' )
# config.image_size == getattr(orig_config, 'sampler.bs.crop_size_width', 256)
if is_segmentation_model:
UpperCAmelCase : str = getattr(UpperCAmelCase , '''model.segmentation.output_stride''' , 16 )
if "_deeplabv3" in task_name:
UpperCAmelCase : int = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_rates''' , [12, 24, 36] )
UpperCAmelCase : Any = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_out_channels''' , 512 )
UpperCAmelCase : Optional[Any] = getattr(UpperCAmelCase , '''model.segmentation.deeplabv3.aspp_dropout''' , 0.1 )
# id2label
UpperCAmelCase : Union[str, Any] = '''huggingface/label-files'''
UpperCAmelCase : List[Any] = json.load(open(hf_hub_download(UpperCAmelCase , UpperCAmelCase , repo_type='''dataset''' ) , '''r''' ) )
UpperCAmelCase : Any = {int(UpperCAmelCase ): v for k, v in idalabel.items()}
UpperCAmelCase : int = idalabel
UpperCAmelCase : Optional[int] = {v: k for k, v in idalabel.items()}
return config
def a__ ( UpperCAmelCase : Dict , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : Optional[int] ) -> List[str]:
UpperCAmelCase : Union[str, Any] = dct.pop(UpperCAmelCase )
UpperCAmelCase : List[str] = val
def a__ ( UpperCAmelCase : Union[str, Any] , UpperCAmelCase : int=False ) -> Union[str, Any]:
if base_model:
UpperCAmelCase : Dict = ''''''
else:
UpperCAmelCase : Dict = '''mobilevitv2.'''
UpperCAmelCase : Optional[int] = []
for k in state_dict.keys():
if k[:8] == "encoder.":
UpperCAmelCase : List[str] = k[8:]
else:
UpperCAmelCase : Dict = k
if ".block." in k:
UpperCAmelCase : List[Any] = k_new.replace('''.block.''' , '''.''' )
if ".conv." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.conv.''' , '''.convolution.''' )
if ".norm." in k:
UpperCAmelCase : List[str] = k_new.replace('''.norm.''' , '''.normalization.''' )
if "conv_1." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''conv_1.''' , f'''{model_prefix}conv_stem.''' )
for i in [1, 2]:
if f'''layer_{i}.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.''' , f'''{model_prefix}encoder.layer.{i-1}.layer.''' )
if ".exp_1x1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''.exp_1x1.''' , '''.expand_1x1.''' )
if ".red_1x1." in k:
UpperCAmelCase : int = k_new.replace('''.red_1x1.''' , '''.reduce_1x1.''' )
for i in [3, 4, 5]:
if f'''layer_{i}.0.''' in k:
UpperCAmelCase : Any = k_new.replace(f'''layer_{i}.0.''' , f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.''' )
if f'''layer_{i}.1.local_rep.0.''' in k:
UpperCAmelCase : str = k_new.replace(f'''layer_{i}.1.local_rep.0.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.''' )
if f'''layer_{i}.1.local_rep.1.''' in k:
UpperCAmelCase : int = k_new.replace(f'''layer_{i}.1.local_rep.1.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.''' )
for i in [3, 4, 5]:
if i == 3:
UpperCAmelCase : Dict = [0, 1]
elif i == 4:
UpperCAmelCase : Dict = [0, 1, 2, 3]
elif i == 5:
UpperCAmelCase : int = [0, 1, 2]
for j in j_in:
if f'''layer_{i}.1.global_rep.{j}.''' in k:
UpperCAmelCase : Optional[Any] = k_new.replace(
f'''layer_{i}.1.global_rep.{j}.''' , f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.''' )
if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
UpperCAmelCase : Any = k_new.replace(
f'''layer_{i}.1.global_rep.{j+1}.''' , f'''{model_prefix}encoder.layer.{i-1}.layernorm.''' )
if f'''layer_{i}.1.conv_proj.''' in k:
UpperCAmelCase : Union[str, Any] = k_new.replace(f'''layer_{i}.1.conv_proj.''' , f'''{model_prefix}encoder.layer.{i-1}.conv_projection.''' )
if "pre_norm_attn.0." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''pre_norm_attn.0.''' , '''layernorm_before.''' )
if "pre_norm_attn.1." in k:
UpperCAmelCase : Optional[Any] = k_new.replace('''pre_norm_attn.1.''' , '''attention.''' )
if "pre_norm_ffn.0." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.0.''' , '''layernorm_after.''' )
if "pre_norm_ffn.1." in k:
UpperCAmelCase : List[Any] = k_new.replace('''pre_norm_ffn.1.''' , '''ffn.conv1.''' )
if "pre_norm_ffn.3." in k:
UpperCAmelCase : Any = k_new.replace('''pre_norm_ffn.3.''' , '''ffn.conv2.''' )
if "classifier.1." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''classifier.1.''' , '''classifier.''' )
if "seg_head." in k:
UpperCAmelCase : Union[str, Any] = k_new.replace('''seg_head.''' , '''segmentation_head.''' )
if ".aspp_layer." in k:
UpperCAmelCase : Tuple = k_new.replace('''.aspp_layer.''' , '''.''' )
if ".aspp_pool." in k:
UpperCAmelCase : Optional[int] = k_new.replace('''.aspp_pool.''' , '''.''' )
rename_keys.append((k, k_new) )
return rename_keys
def a__ ( UpperCAmelCase : Union[str, Any] ) -> Any:
UpperCAmelCase : str = []
for k in state_dict.keys():
if k.startswith('''seg_head.aux_head.''' ):
keys_to_ignore.append(UpperCAmelCase )
for k in keys_to_ignore:
state_dict.pop(UpperCAmelCase , UpperCAmelCase )
def a__ ( ) -> Union[str, Any]:
UpperCAmelCase : int = '''http://images.cocodataset.org/val2017/000000039769.jpg'''
# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
UpperCAmelCase : List[str] = Image.open(requests.get(UpperCAmelCase , stream=UpperCAmelCase ).raw )
return im
@torch.no_grad()
def a__ ( UpperCAmelCase : int , UpperCAmelCase : int , UpperCAmelCase : Union[str, Any] , UpperCAmelCase : List[Any] ) -> Union[str, Any]:
UpperCAmelCase : Union[str, Any] = get_mobilevitva_config(UpperCAmelCase , UpperCAmelCase )
# load original state_dict
UpperCAmelCase : List[str] = torch.load(UpperCAmelCase , map_location='''cpu''' )
# load huggingface model
if task_name.startswith('''ade20k_''' ) or task_name.startswith('''voc_''' ):
UpperCAmelCase : str = MobileViTVaForSemanticSegmentation(UpperCAmelCase ).eval()
UpperCAmelCase : str = False
else:
UpperCAmelCase : Union[str, Any] = MobileViTVaForImageClassification(UpperCAmelCase ).eval()
UpperCAmelCase : Any = False
# remove and rename some keys of load the original model
UpperCAmelCase : Optional[Any] = checkpoint
remove_unused_keys(UpperCAmelCase )
UpperCAmelCase : Optional[Any] = create_rename_keys(UpperCAmelCase , base_model=UpperCAmelCase )
for rename_key_src, rename_key_dest in rename_keys:
rename_key(UpperCAmelCase , UpperCAmelCase , UpperCAmelCase )
# load modified state_dict
model.load_state_dict(UpperCAmelCase )
# Check outputs on an image, prepared by MobileViTImageProcessor
UpperCAmelCase : Dict = MobileViTImageProcessor(crop_size=config.image_size , size=config.image_size + 32 )
UpperCAmelCase : Any = image_processor(images=prepare_img() , return_tensors='''pt''' )
UpperCAmelCase : Union[str, Any] = model(**UpperCAmelCase )
# verify classification model
if task_name.startswith('''imagenet''' ):
UpperCAmelCase : Optional[Any] = outputs.logits
UpperCAmelCase : int = logits.argmax(-1 ).item()
print('''Predicted class:''' , model.config.idalabel[predicted_class_idx] )
if task_name.startswith('''imagenet1k_256''' ) and config.width_multiplier == 1.0:
# expected_logits for base variant
UpperCAmelCase : str = torch.tensor([-1.6_336E00, -7.3_204E-02, -5.1_883E-01] )
assert torch.allclose(logits[0, :3] , UpperCAmelCase , atol=1E-4 )
Path(UpperCAmelCase ).mkdir(exist_ok=UpperCAmelCase )
print(f'''Saving model {task_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(UpperCAmelCase )
print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(UpperCAmelCase )
if __name__ == "__main__":
_lowerCamelCase : str = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--task",
default="imagenet1k_256",
type=str,
help=(
"Name of the task for which the MobileViTV2 model you'd like to convert is trained on . "
"\n Classification (ImageNet-1k)\n - MobileViTV2 (256x256) : imagenet1k_256\n - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384\n - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :\n imagenet21k_to_1k_256\n - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on\n ImageNet-1k 384x384) : imagenet21k_to_1k_384\n Segmentation\n - ADE20K Dataset : ade20k_deeplabv3\n - Pascal VOC 2012 Dataset: voc_deeplabv3\n "
),
choices=[
"imagenet1k_256",
"imagenet1k_384",
"imagenet21k_to_1k_256",
"imagenet21k_to_1k_384",
"ade20k_deeplabv3",
"voc_deeplabv3",
],
)
parser.add_argument(
"--orig_checkpoint_path", required=True, type=str, help="Path to the original state dict (.pt file)."
)
parser.add_argument("--orig_config_path", required=True, type=str, help="Path to the original config file.")
parser.add_argument(
"--pytorch_dump_folder_path", required=True, type=str, help="Path to the output PyTorch model directory."
)
_lowerCamelCase : Optional[int] = parser.parse_args()
convert_mobilevitva_checkpoint(
args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
)
| 336 | 1 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
UpperCAmelCase__ = argparse.ArgumentParser()
parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
parser.add_argument(
"""--txt2img_unclip""",
default="""kakaobrain/karlo-v1-alpha""",
type=str,
required=False,
help="""The pretrained txt2img unclip.""",
)
UpperCAmelCase__ = parser.parse_args()
UpperCAmelCase__ = UnCLIPPipeline.from_pretrained(args.txtaimg_unclip)
UpperCAmelCase__ = CLIPImageProcessor()
UpperCAmelCase__ = CLIPVisionModelWithProjection.from_pretrained("""openai/clip-vit-large-patch14""")
UpperCAmelCase__ = UnCLIPImageVariationPipeline(
decoder=txtaimg.decoder,
text_encoder=txtaimg.text_encoder,
tokenizer=txtaimg.tokenizer,
text_proj=txtaimg.text_proj,
feature_extractor=feature_extractor,
image_encoder=image_encoder,
super_res_first=txtaimg.super_res_first,
super_res_last=txtaimg.super_res_last,
decoder_scheduler=txtaimg.decoder_scheduler,
super_res_scheduler=txtaimg.super_res_scheduler,
)
imgaimg.save_pretrained(args.dump_path)
| 365 | """simple docstring"""
import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING:
from ... import PreTrainedTokenizerBase, TensorType
UpperCAmelCase__ = logging.get_logger(__name__)
class a ( lowerCAmelCase_ ):
_snake_case : List[Any] = 'vision-encoder-decoder'
_snake_case : Optional[int] = True
def __init__( self : int , **__lowerCAmelCase : Any ):
super().__init__(**__lowerCAmelCase )
if "encoder" not in kwargs or "decoder" not in kwargs:
raise ValueError(
f'''A configuraton of type {self.model_type} cannot be instantiated because '''
f'''not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}''' )
_UpperCAmelCase = kwargs.pop("""encoder""" )
_UpperCAmelCase = encoder_config.pop("""model_type""" )
_UpperCAmelCase = kwargs.pop("""decoder""" )
_UpperCAmelCase = decoder_config.pop("""model_type""" )
_UpperCAmelCase = AutoConfig.for_model(__lowerCAmelCase , **__lowerCAmelCase )
_UpperCAmelCase = AutoConfig.for_model(__lowerCAmelCase , **__lowerCAmelCase )
_UpperCAmelCase = True
@classmethod
def lowerCAmelCase_ ( cls : int , __lowerCAmelCase : PretrainedConfig , __lowerCAmelCase : PretrainedConfig , **__lowerCAmelCase : str ):
logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config""" )
_UpperCAmelCase = True
_UpperCAmelCase = True
return cls(encoder=encoder_config.to_dict() , decoder=decoder_config.to_dict() , **__lowerCAmelCase )
def lowerCAmelCase_ ( self : int ):
_UpperCAmelCase = copy.deepcopy(self.__dict__ )
_UpperCAmelCase = self.encoder.to_dict()
_UpperCAmelCase = self.decoder.to_dict()
_UpperCAmelCase = self.__class__.model_type
return output
class a ( lowerCAmelCase_ ):
_snake_case : Union[str, Any] = version.parse('1.11' )
@property
def lowerCAmelCase_ ( self : int ):
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowerCAmelCase_ ( self : Tuple ):
return 1e-4
@property
def lowerCAmelCase_ ( self : Dict ):
return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}} )
class a ( lowerCAmelCase_ ):
@property
def lowerCAmelCase_ ( self : Any ):
_UpperCAmelCase = OrderedDict()
_UpperCAmelCase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
_UpperCAmelCase = {0: """batch""", 1: """past_decoder_sequence + sequence"""}
_UpperCAmelCase = {0: """batch""", 1: """encoder_sequence"""}
return common_inputs
def lowerCAmelCase_ ( self : Union[str, Any] , __lowerCAmelCase : "PreTrainedTokenizerBase" , __lowerCAmelCase : int = -1 , __lowerCAmelCase : int = -1 , __lowerCAmelCase : bool = False , __lowerCAmelCase : Optional["TensorType"] = None , ):
import torch
_UpperCAmelCase = OrderedDict()
_UpperCAmelCase = super().generate_dummy_inputs(
__lowerCAmelCase , batch_size=__lowerCAmelCase , seq_length=__lowerCAmelCase , is_pair=__lowerCAmelCase , framework=__lowerCAmelCase )
_UpperCAmelCase , _UpperCAmelCase = dummy_input["""input_ids"""].shape
_UpperCAmelCase = (batch, encoder_sequence, self._config.encoder_hidden_size)
_UpperCAmelCase = dummy_input.pop("""input_ids""" )
_UpperCAmelCase = dummy_input.pop("""attention_mask""" )
_UpperCAmelCase = torch.zeros(__lowerCAmelCase )
return common_inputs
class a ( lowerCAmelCase_ ):
@property
def lowerCAmelCase_ ( self : Tuple ):
pass
def lowerCAmelCase_ ( self : List[Any] , __lowerCAmelCase : PretrainedConfig ):
return VisionEncoderDecoderEncoderOnnxConfig(__lowerCAmelCase )
def lowerCAmelCase_ ( self : int , __lowerCAmelCase : PretrainedConfig , __lowerCAmelCase : PretrainedConfig , __lowerCAmelCase : str = "default" ):
_UpperCAmelCase = encoder_config.hidden_size
return VisionEncoderDecoderDecoderOnnxConfig(__lowerCAmelCase , __lowerCAmelCase )
| 30 | 0 |
import functools
from typing import Any
def _a ( a :str , a :list[str] ) -> bool:
# Validation
if not isinstance(a , a ) or len(a ) == 0:
raise ValueError('''the string should be not empty string''' )
if not isinstance(a , a ) or not all(
isinstance(a , a ) and len(a ) > 0 for item in words ):
raise ValueError('''the words should be a list of non-empty strings''' )
# Build trie
a = {}
a = '''WORD_KEEPER'''
for word in words:
a = trie
for c in word:
if c not in trie_node:
a = {}
a = trie_node[c]
a = True
a = len(a )
# Dynamic programming method
@functools.cache
def is_breakable(a :int ) -> bool:
if index == len_string:
return True
a = trie
for i in range(a , a ):
a = trie_node.get(string[i] , a )
if trie_node is None:
return False
if trie_node.get(a , a ) and is_breakable(i + 1 ):
return True
return False
return is_breakable(0 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 0 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''camembert-base''': '''https://huggingface.co/camembert-base/resolve/main/config.json''',
'''umberto-commoncrawl-cased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json'''
),
'''umberto-wikipedia-uncased-v1''': (
'''https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json'''
),
}
class _snake_case ( a__ ):
snake_case__ = "camembert"
def __init__( self : Union[str, Any] , UpperCAmelCase : List[Any]=30522 , UpperCAmelCase : Optional[int]=768 , UpperCAmelCase : Union[str, Any]=12 , UpperCAmelCase : Tuple=12 , UpperCAmelCase : Tuple=3072 , UpperCAmelCase : int="gelu" , UpperCAmelCase : Optional[int]=0.1 , UpperCAmelCase : int=0.1 , UpperCAmelCase : Tuple=512 , UpperCAmelCase : Tuple=2 , UpperCAmelCase : int=0.0_2 , UpperCAmelCase : Tuple=1E-12 , UpperCAmelCase : Union[str, Any]=1 , UpperCAmelCase : int=0 , UpperCAmelCase : int=2 , UpperCAmelCase : str="absolute" , UpperCAmelCase : Dict=True , UpperCAmelCase : int=None , **UpperCAmelCase : List[str] , ):
super().__init__(pad_token_id=UpperCAmelCase , bos_token_id=UpperCAmelCase , eos_token_id=UpperCAmelCase , **UpperCAmelCase )
__lowerCamelCase : Any = vocab_size
__lowerCamelCase : Optional[int] = hidden_size
__lowerCamelCase : int = num_hidden_layers
__lowerCamelCase : int = num_attention_heads
__lowerCamelCase : int = hidden_act
__lowerCamelCase : Union[str, Any] = intermediate_size
__lowerCamelCase : Optional[int] = hidden_dropout_prob
__lowerCamelCase : List[Any] = attention_probs_dropout_prob
__lowerCamelCase : Dict = max_position_embeddings
__lowerCamelCase : Tuple = type_vocab_size
__lowerCamelCase : Any = initializer_range
__lowerCamelCase : str = layer_norm_eps
__lowerCamelCase : List[Any] = position_embedding_type
__lowerCamelCase : Dict = use_cache
__lowerCamelCase : List[Any] = classifier_dropout
class _snake_case ( a__ ):
@property
def lowerCamelCase__ ( self : int ):
if self.task == "multiple-choice":
__lowerCamelCase : List[str] = {0: "batch", 1: "choice", 2: "sequence"}
else:
__lowerCamelCase : Tuple = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] ) | 135 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import (
DDIMScheduler,
KandinskyVaaControlnetPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class lowercase_ (lowerCamelCase__ , unittest.TestCase ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = KandinskyVaaControlnetPipeline
SCREAMING_SNAKE_CASE : Any = ['image_embeds', 'negative_image_embeds', 'hint']
SCREAMING_SNAKE_CASE : str = ['image_embeds', 'negative_image_embeds', 'hint']
SCREAMING_SNAKE_CASE : List[Any] = [
'generator',
'height',
'width',
'latents',
'guidance_scale',
'num_inference_steps',
'return_dict',
'guidance_scale',
'num_images_per_prompt',
'output_type',
'return_dict',
]
SCREAMING_SNAKE_CASE : List[str] = False
@property
def SCREAMING_SNAKE_CASE ( self : List[str] ):
return 3_2
@property
def SCREAMING_SNAKE_CASE ( self : List[str] ):
return 3_2
@property
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
return self.time_input_dim
@property
def SCREAMING_SNAKE_CASE ( self : int ):
return self.time_input_dim * 4
@property
def SCREAMING_SNAKE_CASE ( self : str ):
return 1_0_0
@property
def SCREAMING_SNAKE_CASE ( self : Any ):
torch.manual_seed(0 )
__lowercase = {
'''in_channels''': 8,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image_hint''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
__lowercase = UNetaDConditionModel(**lowercase__ )
return model
@property
def SCREAMING_SNAKE_CASE ( self : Union[str, Any] ):
return {
"block_out_channels": [3_2, 3_2, 6_4, 6_4],
"down_block_types": [
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"DownEncoderBlock2D",
"AttnDownEncoderBlock2D",
],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 1_2,
"out_channels": 3,
"up_block_types": ["AttnUpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D", "UpDecoderBlock2D"],
"vq_embed_dim": 4,
}
@property
def SCREAMING_SNAKE_CASE ( self : Tuple ):
torch.manual_seed(0 )
__lowercase = VQModel(**self.dummy_movq_kwargs )
return model
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = self.dummy_unet
__lowercase = self.dummy_movq
__lowercase = DDIMScheduler(
num_train_timesteps=1_0_0_0 ,beta_schedule='''linear''' ,beta_start=0.0_0_0_8_5 ,beta_end=0.0_1_2 ,clip_sample=lowercase__ ,set_alpha_to_one=lowercase__ ,steps_offset=1 ,prediction_type='''epsilon''' ,thresholding=lowercase__ ,)
__lowercase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def SCREAMING_SNAKE_CASE ( self : Dict ,lowercase__ : Optional[Any] ,lowercase__ : Any=0 ):
__lowercase = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(lowercase__ ) ).to(lowercase__ )
__lowercase = floats_tensor((1, self.text_embedder_hidden_size) ,rng=random.Random(seed + 1 ) ).to(
lowercase__ )
# create hint
__lowercase = floats_tensor((1, 3, 6_4, 6_4) ,rng=random.Random(lowercase__ ) ).to(lowercase__ )
if str(lowercase__ ).startswith('''mps''' ):
__lowercase = torch.manual_seed(lowercase__ )
else:
__lowercase = torch.Generator(device=lowercase__ ).manual_seed(lowercase__ )
__lowercase = {
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''hint''': hint,
'''generator''': generator,
'''height''': 6_4,
'''width''': 6_4,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def SCREAMING_SNAKE_CASE ( self : Optional[Any] ):
__lowercase = '''cpu'''
__lowercase = self.get_dummy_components()
__lowercase = self.pipeline_class(**lowercase__ )
__lowercase = pipe.to(lowercase__ )
pipe.set_progress_bar_config(disable=lowercase__ )
__lowercase = pipe(**self.get_dummy_inputs(lowercase__ ) )
__lowercase = output.images
__lowercase = pipe(
**self.get_dummy_inputs(lowercase__ ) ,return_dict=lowercase__ ,)[0]
__lowercase = image[0, -3:, -3:, -1]
__lowercase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 6_4, 6_4, 3)
__lowercase = np.array(
[0.6_9_5_9_8_2_6, 0.8_6_8_2_7_9, 0.7_5_5_8_0_9_2, 0.6_8_7_6_9_4_6_7, 0.8_5_8_0_5_8_0_4, 0.6_5_9_7_7_4_9_6, 0.4_4_8_8_5_3_0_2, 0.5_9_5_9_1_1_1, 0.4_2_5_1_5_9_5] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_slice.flatten()}"
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), F" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"
@slow
@require_torch_gpu
class lowercase_ (unittest.TestCase ):
"""simple docstring"""
def SCREAMING_SNAKE_CASE ( self : List[str] ):
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE ( self : List[Any] ):
__lowercase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_controlnet_robotcat_fp16.npy''' )
__lowercase = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/hint_image_cat.png''' )
__lowercase = torch.from_numpy(np.array(lowercase__ ) ).float() / 2_5_5.0
__lowercase = hint.permute(2 ,0 ,1 ).unsqueeze(0 )
__lowercase = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' ,torch_dtype=torch.floataa )
pipe_prior.to(lowercase__ )
__lowercase = KandinskyVaaControlnetPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-controlnet-depth''' ,torch_dtype=torch.floataa )
__lowercase = pipeline.to(lowercase__ )
pipeline.set_progress_bar_config(disable=lowercase__ )
__lowercase = '''A robot, 4k photo'''
__lowercase = torch.Generator(device='''cuda''' ).manual_seed(0 )
__lowercase , __lowercase = pipe_prior(
lowercase__ ,generator=lowercase__ ,num_inference_steps=5 ,negative_prompt='''''' ,).to_tuple()
__lowercase = torch.Generator(device='''cuda''' ).manual_seed(0 )
__lowercase = pipeline(
image_embeds=lowercase__ ,negative_image_embeds=lowercase__ ,hint=lowercase__ ,generator=lowercase__ ,num_inference_steps=1_0_0 ,output_type='''np''' ,)
__lowercase = output.images[0]
assert image.shape == (5_1_2, 5_1_2, 3)
assert_mean_pixel_difference(lowercase__ ,lowercase__ )
| 355 |
'''simple docstring'''
lowerCAmelCase__ = '''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
def _A ( ):
"""simple docstring"""
__lowercase = input('''Enter message: ''' )
__lowercase = input('''Enter key [alphanumeric]: ''' )
__lowercase = input('''Encrypt/Decrypt [e/d]: ''' )
if mode.lower().startswith('''e''' ):
__lowercase = '''encrypt'''
__lowercase = encrypt_message(A__ , A__ )
elif mode.lower().startswith('''d''' ):
__lowercase = '''decrypt'''
__lowercase = decrypt_message(A__ , A__ )
print(F"\n{mode.title()}ed message:" )
print(A__ )
def _A ( A__ , A__ ):
"""simple docstring"""
return translate_message(A__ , A__ , '''encrypt''' )
def _A ( A__ , A__ ):
"""simple docstring"""
return translate_message(A__ , A__ , '''decrypt''' )
def _A ( A__ , A__ , A__ ):
"""simple docstring"""
__lowercase = []
__lowercase = 0
__lowercase = key.upper()
for symbol in message:
__lowercase = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(A__ )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(A__ ):
__lowercase = 0
else:
translated.append(A__ )
return "".join(A__ )
if __name__ == "__main__":
main()
| 52 | 0 |
'''simple docstring'''
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
PipelineKarrasSchedulerTesterMixin,
PipelineLatentTesterMixin,
PipelineTesterMixin,
assert_mean_pixel_difference,
)
enable_full_determinism()
class snake_case ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , unittest.TestCase ):
"""simple docstring"""
_lowerCamelCase = StableUnCLIPPipeline
_lowerCamelCase = TEXT_TO_IMAGE_PARAMS
_lowerCamelCase = TEXT_TO_IMAGE_BATCH_PARAMS
_lowerCamelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
_lowerCamelCase = TEXT_TO_IMAGE_IMAGE_PARAMS
# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
_lowerCamelCase = False
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = 32
lowerCamelCase_ = embedder_hidden_size
# prior components
torch.manual_seed(0 )
lowerCamelCase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
lowerCamelCase_ = CLIPTextModelWithProjection(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=__UpperCAmelCase , projection_dim=__UpperCAmelCase , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
lowerCamelCase_ = PriorTransformer(
num_attention_heads=2 , attention_head_dim=12 , embedding_dim=__UpperCAmelCase , num_layers=1 , )
torch.manual_seed(0 )
lowerCamelCase_ = DDPMScheduler(
variance_type="fixed_small_log" , prediction_type="sample" , num_train_timesteps=1000 , clip_sample=__UpperCAmelCase , clip_sample_range=5.0 , beta_schedule="squaredcos_cap_v2" , )
# regular denoising components
torch.manual_seed(0 )
lowerCamelCase_ = StableUnCLIPImageNormalizer(embedding_dim=__UpperCAmelCase )
lowerCamelCase_ = DDPMScheduler(beta_schedule="squaredcos_cap_v2" )
torch.manual_seed(0 )
lowerCamelCase_ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
torch.manual_seed(0 )
lowerCamelCase_ = CLIPTextModel(
CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=__UpperCAmelCase , projection_dim=32 , intermediate_size=37 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1000 , ) )
torch.manual_seed(0 )
lowerCamelCase_ = UNetaDConditionModel(
sample_size=32 , in_channels=4 , out_channels=4 , down_block_types=("CrossAttnDownBlock2D", "DownBlock2D") , up_block_types=("UpBlock2D", "CrossAttnUpBlock2D") , block_out_channels=(32, 64) , attention_head_dim=(2, 4) , class_embed_type="projection" , projection_class_embeddings_input_dim=embedder_projection_dim * 2 , cross_attention_dim=__UpperCAmelCase , layers_per_block=1 , upcast_attention=__UpperCAmelCase , use_linear_projection=__UpperCAmelCase , )
torch.manual_seed(0 )
lowerCamelCase_ = DDIMScheduler(
beta_schedule="scaled_linear" , beta_start=0.00_085 , beta_end=0.012 , prediction_type="v_prediction" , set_alpha_to_one=__UpperCAmelCase , steps_offset=1 , )
torch.manual_seed(0 )
lowerCamelCase_ = AutoencoderKL()
lowerCamelCase_ = {
# prior components
"""prior_tokenizer""": prior_tokenizer,
"""prior_text_encoder""": prior_text_encoder,
"""prior""": prior,
"""prior_scheduler""": prior_scheduler,
# image noising components
"""image_normalizer""": image_normalizer,
"""image_noising_scheduler""": image_noising_scheduler,
# regular denoising components
"""tokenizer""": tokenizer,
"""text_encoder""": text_encoder,
"""unet""": unet,
"""scheduler""": scheduler,
"""vae""": vae,
}
return components
def snake_case ( self , UpperCamelCase , UpperCamelCase=0 ):
"""simple docstring"""
if str(__UpperCAmelCase ).startswith("mps" ):
lowerCamelCase_ = torch.manual_seed(__UpperCAmelCase )
else:
lowerCamelCase_ = torch.Generator(device=__UpperCAmelCase ).manual_seed(__UpperCAmelCase )
lowerCamelCase_ = {
"""prompt""": """A painting of a squirrel eating a burger""",
"""generator""": generator,
"""num_inference_steps""": 2,
"""prior_num_inference_steps""": 2,
"""output_type""": """numpy""",
}
return inputs
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = torch_device == """cpu"""
self._test_attention_slicing_forward_pass(test_max_difference=__UpperCAmelCase )
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = torch_device in ["""cpu""", """mps"""]
self._test_inference_batch_single_identical(test_max_difference=__UpperCAmelCase )
@slow
@require_torch_gpu
class snake_case ( unittest.TestCase ):
"""simple docstring"""
def snake_case ( self ):
"""simple docstring"""
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case ( self ):
"""simple docstring"""
lowerCamelCase_ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy" )
lowerCamelCase_ = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
# stable unclip will oom when integration tests are run on a V100,
# so turn on memory savings
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowerCamelCase_ = torch.Generator(device="cpu" ).manual_seed(0 )
lowerCamelCase_ = pipe("anime turle" , generator=__UpperCAmelCase , output_type="np" )
lowerCamelCase_ = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__UpperCAmelCase , __UpperCAmelCase )
def snake_case ( self ):
"""simple docstring"""
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
lowerCamelCase_ = StableUnCLIPPipeline.from_pretrained("fusing/stable-unclip-2-1-l" , torch_dtype=torch.floataa )
lowerCamelCase_ = pipe.to(__UpperCAmelCase )
pipe.set_progress_bar_config(disable=__UpperCAmelCase )
pipe.enable_attention_slicing()
pipe.enable_sequential_cpu_offload()
lowerCamelCase_ = pipe(
"anime turtle" , prior_num_inference_steps=2 , num_inference_steps=2 , output_type="np" , )
lowerCamelCase_ = torch.cuda.max_memory_allocated()
# make sure that less than 7 GB is allocated
assert mem_bytes < 7 * 10**9
| 55 |
'''simple docstring'''
# coding=utf-8
# Copyright 2020 The HuggingFace Inc. team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# this script dumps information about the environment
import os
import sys
import transformers
_lowerCAmelCase = '''3'''
print('''Python version:''', sys.version)
print('''transformers version:''', transformers.__version__)
try:
import torch
print('''Torch version:''', torch.__version__)
print('''Cuda available:''', torch.cuda.is_available())
print('''Cuda version:''', torch.version.cuda)
print('''CuDNN version:''', torch.backends.cudnn.version())
print('''Number of GPUs available:''', torch.cuda.device_count())
print('''NCCL version:''', torch.cuda.nccl.version())
except ImportError:
print('''Torch version:''', None)
try:
import deepspeed
print('''DeepSpeed version:''', deepspeed.__version__)
except ImportError:
print('''DeepSpeed version:''', None)
try:
import tensorflow as tf
print('''TensorFlow version:''', tf.__version__)
print('''TF GPUs available:''', bool(tf.config.list_physical_devices('''GPU''')))
print('''Number of TF GPUs available:''', len(tf.config.list_physical_devices('''GPU''')))
except ImportError:
print('''TensorFlow version:''', None)
| 37 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase :int = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase :int = [
"""NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""NllbMoeForConditionalGeneration""",
"""NllbMoeModel""",
"""NllbMoePreTrainedModel""",
"""NllbMoeTop2Router""",
"""NllbMoeSparseMLP""",
]
if TYPE_CHECKING:
from .configuration_nllb_moe import (
NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
NllbMoeConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_nllb_moe import (
NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
NllbMoeForConditionalGeneration,
NllbMoeModel,
NllbMoePreTrainedModel,
NllbMoeSparseMLP,
NllbMoeTopaRouter,
)
else:
import sys
__UpperCAmelCase :Any = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__) | 352 |
'''simple docstring'''
import os
import sys
import unittest
__UpperCAmelCase :Optional[int] = 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_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase :Dict = os.path.join(git_repo_path, "src", "diffusers")
class a ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase__ ( self : Any ) -> int:
__UpperCAmelCase : Optional[Any] = find_backend(''' if not is_torch_available():''' )
self.assertEqual(snake_case , '''torch''' )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
__UpperCAmelCase : Union[str, Any] = find_backend(''' if not (is_torch_available() and is_transformers_available()):''' )
self.assertEqual(snake_case , '''torch_and_transformers''' )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
__UpperCAmelCase : List[str] = find_backend(
''' if not (is_torch_available() and is_transformers_available() and is_onnx_available()):''' )
self.assertEqual(snake_case , '''torch_and_transformers_and_onnx''' )
def lowerCamelCase__ ( self : Optional[int] ) -> int:
__UpperCAmelCase : Tuple = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn('''torch''' , snake_case )
self.assertIn('''torch_and_transformers''' , snake_case )
self.assertIn('''flax_and_transformers''' , snake_case )
self.assertIn('''torch_and_transformers_and_onnx''' , snake_case )
# Likewise, we can't assert on the exact content of a key
self.assertIn('''UNet2DModel''' , objects['''torch'''] )
self.assertIn('''FlaxUNet2DConditionModel''' , objects['''flax'''] )
self.assertIn('''StableDiffusionPipeline''' , objects['''torch_and_transformers'''] )
self.assertIn('''FlaxStableDiffusionPipeline''' , objects['''flax_and_transformers'''] )
self.assertIn('''LMSDiscreteScheduler''' , objects['''torch_and_scipy'''] )
self.assertIn('''OnnxStableDiffusionPipeline''' , objects['''torch_and_transformers_and_onnx'''] )
def lowerCamelCase__ ( self : Optional[int] ) -> List[Any]:
__UpperCAmelCase : str = create_dummy_object('''CONSTANT''' , '''\'torch\'''' )
self.assertEqual(snake_case , '''\nCONSTANT = None\n''' )
__UpperCAmelCase : Union[str, Any] = create_dummy_object('''function''' , '''\'torch\'''' )
self.assertEqual(
snake_case , '''\ndef function(*args, **kwargs):\n requires_backends(function, \'torch\')\n''' )
__UpperCAmelCase : Optional[int] = '''
class FakeClass(metaclass=DummyObject):
_backends = \'torch\'
def __init__(self, *args, **kwargs):
requires_backends(self, \'torch\')
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, \'torch\')
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, \'torch\')
'''
__UpperCAmelCase : Optional[Any] = create_dummy_object('''FakeClass''' , '''\'torch\'''' )
self.assertEqual(snake_case , snake_case )
def lowerCamelCase__ ( self : int ) -> List[Any]:
__UpperCAmelCase : List[str] = '''# This file is autogenerated by the command `make fix-copies`, do not edit.
from ..utils import DummyObject, requires_backends
CONSTANT = None
def function(*args, **kwargs):
requires_backends(function, ["torch"])
class FakeClass(metaclass=DummyObject):
_backends = ["torch"]
def __init__(self, *args, **kwargs):
requires_backends(self, ["torch"])
@classmethod
def from_config(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
@classmethod
def from_pretrained(cls, *args, **kwargs):
requires_backends(cls, ["torch"])
'''
__UpperCAmelCase : Optional[int] = create_dummy_files({'''torch''': ['''CONSTANT''', '''function''', '''FakeClass''']} )
self.assertEqual(dummy_files['''torch'''] , snake_case ) | 240 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def __lowerCAmelCase ( UpperCamelCase__ ) -> Tuple:
__lowerCamelCase = filter(lambda UpperCamelCase__ : p.requires_grad , model.parameters() )
__lowerCamelCase = sum([np.prod(p.size() ) for p in model_parameters] )
return params
__UpperCAmelCase =logging.getLogger(__name__)
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
if metric == "rouge2":
__lowerCamelCase = '''{val_avg_rouge2:.4f}-{step_count}'''
elif metric == "bleu":
__lowerCamelCase = '''{val_avg_bleu:.4f}-{step_count}'''
elif metric == "em":
__lowerCamelCase = '''{val_avg_em:.4f}-{step_count}'''
elif metric == "loss":
__lowerCamelCase = '''{val_avg_loss:.4f}-{step_count}'''
else:
raise NotImplementedError(
f"""seq2seq callbacks only support rouge2 and bleu, got {metric}, You can make your own by adding to this"""
''' function.''' )
__lowerCamelCase = ModelCheckpoint(
dirpath=UpperCamelCase__ , filename=UpperCamelCase__ , monitor=f"""val_{metric}""" , mode='''max''' , save_top_k=1 , every_n_epochs=1 , )
return checkpoint_callback
def __lowerCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ) -> Optional[int]:
return EarlyStopping(
monitor=f"""val_{metric}""" , mode='''min''' if '''loss''' in metric else '''max''' , patience=UpperCamelCase__ , verbose=UpperCamelCase__ , )
class a__ ( pl.Callback ):
def SCREAMING_SNAKE_CASE__ ( self : Any , a : str , a : List[Any] ):
"""simple docstring"""
__lowerCamelCase = {f"""lr_group_{i}""": param['''lr'''] for i, param in enumerate(pl_module.trainer.optimizers[0].param_groups )}
pl_module.logger.log_metrics(a )
@rank_zero_only
def SCREAMING_SNAKE_CASE__ ( self : Dict , a : pl.Trainer , a : pl.LightningModule , a : str , a : List[Any]=True ):
"""simple docstring"""
logger.info(f"""***** {type_path} results at step {trainer.global_step:05d} *****""" )
__lowerCamelCase = trainer.callback_metrics
trainer.logger.log_metrics({k: v for k, v in metrics.items() if k not in ['''log''', '''progress_bar''', '''preds''']} )
# Log results
__lowerCamelCase = Path(pl_module.hparams.output_dir )
if type_path == "test":
__lowerCamelCase = od / '''test_results.txt'''
__lowerCamelCase = od / '''test_generations.txt'''
else:
# this never gets hit. I prefer not to save intermediate generations, and results are in metrics.json
# If people want this it will be easy enough to add back.
__lowerCamelCase = od / f"""{type_path}_results/{trainer.global_step:05d}.txt"""
__lowerCamelCase = od / f"""{type_path}_generations/{trainer.global_step:05d}.txt"""
results_file.parent.mkdir(exist_ok=a )
generations_file.parent.mkdir(exist_ok=a )
with open(a , '''a+''' ) as writer:
for key in sorted(a ):
if key in ["log", "progress_bar", "preds"]:
continue
__lowerCamelCase = metrics[key]
if isinstance(a , torch.Tensor ):
__lowerCamelCase = val.item()
__lowerCamelCase = f"""{key}: {val:.6f}\n"""
writer.write(a )
if not save_generations:
return
if "preds" in metrics:
__lowerCamelCase = '''\n'''.join(metrics['''preds'''] )
generations_file.open('''w+''' ).write(a )
@rank_zero_only
def SCREAMING_SNAKE_CASE__ ( self : str , a : Dict , a : List[str] ):
"""simple docstring"""
try:
__lowerCamelCase = pl_module.model.model.num_parameters()
except AttributeError:
__lowerCamelCase = pl_module.model.num_parameters()
__lowerCamelCase = count_trainable_parameters(a )
# mp stands for million parameters
trainer.logger.log_metrics({'''n_params''': npars, '''mp''': npars / 1e6, '''grad_mp''': n_trainable_pars / 1e6} )
@rank_zero_only
def SCREAMING_SNAKE_CASE__ ( self : int , a : pl.Trainer , a : pl.LightningModule ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
return self._write_logs(a , a , '''test''' )
@rank_zero_only
def SCREAMING_SNAKE_CASE__ ( self : Tuple , a : pl.Trainer , a : Optional[int] ):
"""simple docstring"""
save_json(pl_module.metrics , pl_module.metrics_save_path )
# Uncommenting this will save val generations
# return self._write_logs(trainer, pl_module, "valid")
| 67 | '''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
__UpperCAmelCase =["gpt2"]
__UpperCAmelCase ="gpt2"
if is_tf_available():
class a__ ( tf.Module ):
def __init__( self : str , a : Union[str, Any] ):
"""simple docstring"""
super().__init__()
__lowerCamelCase = tokenizer
__lowerCamelCase = AutoConfig.from_pretrained(a )
__lowerCamelCase = TFGPTaLMHeadModel.from_config(a )
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name='''text''' ),) )
def SCREAMING_SNAKE_CASE__ ( self : str , a : Tuple ):
"""simple docstring"""
__lowerCamelCase = self.tokenizer(a )
__lowerCamelCase = tokenized['''input_ids'''].to_tensor()
__lowerCamelCase = tf.cast(input_ids_dense > 0 , tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
__lowerCamelCase = self.model(input_ids=a , attention_mask=a )['''logits''']
return outputs
@require_tf
@require_keras_nlp
class a__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
super().setUp()
__lowerCamelCase = [GPTaTokenizer.from_pretrained(a ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
__lowerCamelCase = [TFGPTaTokenizer.from_pretrained(a ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
__lowerCamelCase = [
'''This is a straightforward English test sentence.''',
'''This one has some weird characters\rto\nsee\r\nif those\u00E9break things.''',
'''Now we\'re going to add some Chinese: 一 二 三 一二三''',
'''And some much more rare Chinese: 齉 堃 齉堃''',
'''Je vais aussi écrire en français pour tester les accents''',
'''Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ''',
]
__lowerCamelCase = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in self.test_sentences:
__lowerCamelCase = tokenizer([test_inputs] , return_tensors='''tf''' )
__lowerCamelCase = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
__lowerCamelCase = python_outputs[key].numpy()
__lowerCamelCase = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
self.assertTrue(tf.reduce_all(tf.cast(a , tf.intaa ) == tf_outputs_values ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Optional[Any] ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
__lowerCamelCase = tf.function(a )
for test_inputs in self.test_sentences:
__lowerCamelCase = tf.constant(a )
__lowerCamelCase = compiled_tokenizer(a )
__lowerCamelCase = tf_tokenizer(a )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
__lowerCamelCase = ModelToSave(tokenizer=a )
__lowerCamelCase = tf.convert_to_tensor([self.test_sentences[0]] )
__lowerCamelCase = model.serving(a ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
__lowerCamelCase = Path(a ) / '''saved.model'''
tf.saved_model.save(a , a , signatures={'''serving_default''': model.serving} )
__lowerCamelCase = tf.saved_model.load(a )
__lowerCamelCase = loaded_model.signatures['''serving_default'''](a )['''output_0''']
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
__lowerCamelCase = tf.convert_to_tensor([self.test_sentences[0]] )
__lowerCamelCase = tf_tokenizer(a ) # Build model with some sample inputs
__lowerCamelCase = tf_tokenizer.get_config()
__lowerCamelCase = TFGPTaTokenizer.from_config(a )
__lowerCamelCase = model_from_config(a )
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )
@slow
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
__lowerCamelCase = 12_31_23
for max_length in [3, 5, 10_24]:
__lowerCamelCase = tf.convert_to_tensor([self.test_sentences[0]] )
__lowerCamelCase = tf_tokenizer(a , max_length=a )
__lowerCamelCase = out['''input_ids'''].numpy().shape[1]
assert out_length == max_length
| 67 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowercase__ :str = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ :Optional[Any] = [
"XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"XLMForMultipleChoice",
"XLMForQuestionAnswering",
"XLMForQuestionAnsweringSimple",
"XLMForSequenceClassification",
"XLMForTokenClassification",
"XLMModel",
"XLMPreTrainedModel",
"XLMWithLMHeadModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowercase__ :List[str] = [
"TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFXLMForMultipleChoice",
"TFXLMForQuestionAnsweringSimple",
"TFXLMForSequenceClassification",
"TFXLMForTokenClassification",
"TFXLMMainLayer",
"TFXLMModel",
"TFXLMPreTrainedModel",
"TFXLMWithLMHeadModel",
]
if TYPE_CHECKING:
from .configuration_xlm import XLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XLMConfig, XLMOnnxConfig
from .tokenization_xlm import XLMTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlm import (
XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
XLMForMultipleChoice,
XLMForQuestionAnswering,
XLMForQuestionAnsweringSimple,
XLMForSequenceClassification,
XLMForTokenClassification,
XLMModel,
XLMPreTrainedModel,
XLMWithLMHeadModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlm import (
TF_XLM_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLMForMultipleChoice,
TFXLMForQuestionAnsweringSimple,
TFXLMForSequenceClassification,
TFXLMForTokenClassification,
TFXLMMainLayer,
TFXLMModel,
TFXLMPreTrainedModel,
TFXLMWithLMHeadModel,
)
else:
import sys
lowercase__ :List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 369 |
import numpy as np
import datasets
lowercase__ :Dict = "\nCompute the Mahalanobis Distance\n\nMahalonobis distance is the distance between a point and a distribution.\nAnd not between two distinct points. It is effectively a multivariate equivalent of the Euclidean distance.\nIt was introduced by Prof. P. C. Mahalanobis in 1936\nand has been used in various statistical applications ever since\n[source: https://www.machinelearningplus.com/statistics/mahalanobis-distance/]\n"
lowercase__ :List[Any] = "\\n@article{de2000mahalanobis,\n title={The mahalanobis distance},\n author={De Maesschalck, Roy and Jouan-Rimbaud, Delphine and Massart, D{\'e}sir{\'e} L},\n journal={Chemometrics and intelligent laboratory systems},\n volume={50},\n number={1},\n pages={1--18},\n year={2000},\n publisher={Elsevier}\n}\n"
lowercase__ :Dict = "\nArgs:\n X: List of datapoints to be compared with the `reference_distribution`.\n reference_distribution: List of datapoints from the reference distribution we want to compare to.\nReturns:\n mahalanobis: The Mahalonobis distance for each datapoint in `X`.\nExamples:\n\n >>> mahalanobis_metric = datasets.load_metric(\"mahalanobis\")\n >>> results = mahalanobis_metric.compute(reference_distribution=[[0, 1], [1, 0]], X=[[0, 1]])\n >>> print(results)\n {'mahalanobis': array([0.5])}\n"
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class lowercase ( datasets.Metric ):
def A__ ( self):
return datasets.MetricInfo(
description=_DESCRIPTION ,citation=_CITATION ,inputs_description=_KWARGS_DESCRIPTION ,features=datasets.Features(
{
'''X''': datasets.Sequence(datasets.Value('''float''' ,id='''sequence''') ,id='''X'''),
}) ,)
def A__ ( self ,A__ ,A__):
# convert to numpy arrays
lowercase = np.array(A__)
lowercase = np.array(A__)
# Assert that arrays are 2D
if len(X.shape) != 2:
raise ValueError('''Expected `X` to be a 2D vector''')
if len(reference_distribution.shape) != 2:
raise ValueError('''Expected `reference_distribution` to be a 2D vector''')
if reference_distribution.shape[0] < 2:
raise ValueError(
'''Expected `reference_distribution` to be a 2D vector with more than one element in the first dimension''')
# Get mahalanobis distance for each prediction
lowercase = X - np.mean(A__)
lowercase = np.cov(reference_distribution.T)
try:
lowercase = np.linalg.inv(A__)
except np.linalg.LinAlgError:
lowercase = np.linalg.pinv(A__)
lowercase = np.dot(A__ ,A__)
lowercase = np.dot(A__ ,X_minus_mu.T).diagonal()
return {"mahalanobis": mahal_dist}
| 97 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_torch_available
a_ = {'configuration_speech_encoder_decoder': ['SpeechEncoderDecoderConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['SpeechEncoderDecoderModel']
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a_ = ['FlaxSpeechEncoderDecoderModel']
if TYPE_CHECKING:
from .configuration_speech_encoder_decoder import SpeechEncoderDecoderConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_speech_encoder_decoder import SpeechEncoderDecoderModel
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_speech_encoder_decoder import FlaxSpeechEncoderDecoderModel
else:
import sys
a_ = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 175 | import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('.')
def __lowercase ( lowerCamelCase : Any ):
UpperCamelCase_ : Union[str, Any] = test_file.split(os.path.sep )
if components[0:2] != ["tests", "models"]:
raise ValueError(
'`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got '
F"{test_file} instead." )
UpperCamelCase_ : str = components[-1]
if not test_fn.endswith('py' ):
raise ValueError(F"`test_file` should be a python file. Got {test_fn} instead." )
if not test_fn.startswith('test_modeling_' ):
raise ValueError(
F"`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead." )
UpperCamelCase_ : Union[str, Any] = components[:-1] + [test_fn.replace('.py' , '' )]
UpperCamelCase_ : List[Any] = '.'.join(lowerCamelCase )
return test_module_path
def __lowercase ( lowerCamelCase : Optional[Any] ):
UpperCamelCase_ : List[Any] = get_module_path(lowerCamelCase )
UpperCamelCase_ : Union[str, Any] = importlib.import_module(lowerCamelCase )
return test_module
def __lowercase ( lowerCamelCase : List[str] ):
UpperCamelCase_ : int = []
UpperCamelCase_ : Tuple = get_test_module(lowerCamelCase )
for attr in dir(lowerCamelCase ):
if attr.endswith('ModelTester' ):
tester_classes.append(getattr(lowerCamelCase , lowerCamelCase ) )
# sort with class names
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x.__name__ )
def __lowercase ( lowerCamelCase : str ):
UpperCamelCase_ : List[str] = []
UpperCamelCase_ : Union[str, Any] = get_test_module(lowerCamelCase )
for attr in dir(lowerCamelCase ):
UpperCamelCase_ : Dict = getattr(lowerCamelCase , lowerCamelCase )
# (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
# `all_model_classes` is not empty (which also excludes other special classes).
UpperCamelCase_ : Optional[int] = getattr(lowerCamelCase , 'all_model_classes' , [] )
if len(lowerCamelCase ) > 0:
test_classes.append(lowerCamelCase )
# sort with class names
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x.__name__ )
def __lowercase ( lowerCamelCase : Dict ):
UpperCamelCase_ : int = get_test_classes(lowerCamelCase )
UpperCamelCase_ : List[Any] = set()
for test_class in test_classes:
model_classes.update(test_class.all_model_classes )
# sort with class names
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x.__name__ )
def __lowercase ( lowerCamelCase : Tuple ):
UpperCamelCase_ : int = test_class()
if hasattr(lowerCamelCase , 'setUp' ):
test.setUp()
UpperCamelCase_ : List[Any] = None
if hasattr(lowerCamelCase , 'model_tester' ):
# `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
if test.model_tester is not None:
UpperCamelCase_ : Optional[Any] = test.model_tester.__class__
return model_tester
def __lowercase ( lowerCamelCase : Tuple , lowerCamelCase : Dict ):
UpperCamelCase_ : Optional[Any] = get_test_classes(lowerCamelCase )
UpperCamelCase_ : Tuple = []
for test_class in test_classes:
if model_class in test_class.all_model_classes:
target_test_classes.append(lowerCamelCase )
# sort with class names
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x.__name__ )
def __lowercase ( lowerCamelCase : Any , lowerCamelCase : Tuple ):
UpperCamelCase_ : List[Any] = get_test_classes_for_model(lowerCamelCase , lowerCamelCase )
UpperCamelCase_ : int = []
for test_class in test_classes:
UpperCamelCase_ : Tuple = get_model_tester_from_test_class(lowerCamelCase )
if tester_class is not None:
tester_classes.append(lowerCamelCase )
# sort with class names
return sorted(lowerCamelCase , key=lambda lowerCamelCase : x.__name__ )
def __lowercase ( lowerCamelCase : str ):
UpperCamelCase_ : Tuple = get_test_classes(lowerCamelCase )
UpperCamelCase_ : Tuple = {test_class: get_model_tester_from_test_class(lowerCamelCase ) for test_class in test_classes}
return test_tester_mapping
def __lowercase ( lowerCamelCase : Any ):
UpperCamelCase_ : List[str] = get_model_classes(lowerCamelCase )
UpperCamelCase_ : int = {
model_class: get_test_classes_for_model(lowerCamelCase , lowerCamelCase ) for model_class in model_classes
}
return model_test_mapping
def __lowercase ( lowerCamelCase : Tuple ):
UpperCamelCase_ : Tuple = get_model_classes(lowerCamelCase )
UpperCamelCase_ : Optional[Any] = {
model_class: get_tester_classes_for_model(lowerCamelCase , lowerCamelCase ) for model_class in model_classes
}
return model_to_tester_mapping
def __lowercase ( lowerCamelCase : Any ):
if isinstance(lowerCamelCase , lowerCamelCase ):
return o
elif isinstance(lowerCamelCase , lowerCamelCase ):
return o.__name__
elif isinstance(lowerCamelCase , (list, tuple) ):
return [to_json(lowerCamelCase ) for x in o]
elif isinstance(lowerCamelCase , lowerCamelCase ):
return {to_json(lowerCamelCase ): to_json(lowerCamelCase ) for k, v in o.items()}
else:
return o
| 175 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since the output of this script is fed into Makefile commands it doesn't print a newline after the results
import re
import subprocess
import sys
SCREAMING_SNAKE_CASE_ = subprocess.check_output('git merge-base main HEAD'.split()).decode('utf-8')
SCREAMING_SNAKE_CASE_ = subprocess.check_output(F'''git diff --name-only {fork_point_sha}'''.split()).decode('utf-8').split()
SCREAMING_SNAKE_CASE_ = '|'.join(sys.argv[1:])
SCREAMING_SNAKE_CASE_ = re.compile(RF'''^({joined_dirs}).*?\.py$''')
SCREAMING_SNAKE_CASE_ = [x for x in modified_files if regex.match(x)]
print(' '.join(relevant_modified_files), end='')
| 189 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils import is_flax_available, is_tf_available, is_torch_available, is_torch_fx_proxy
if is_flax_available():
import jax.numpy as jnp
class a ( UpperCAmelCase ):
def __get__( self , A_ , A_=None ):
'''simple docstring'''
if obj is None:
return self
if self.fget is None:
raise AttributeError("unreadable attribute" )
_UpperCAmelCase : Optional[int] = "__cached_" + self.fget.__name__
_UpperCAmelCase : Union[str, Any] = getattr(A_ , A_ , A_ )
if cached is None:
_UpperCAmelCase : Dict = self.fget(A_ )
setattr(A_ , A_ , A_ )
return cached
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Optional[Any] ) -> int:
_UpperCAmelCase : str = val.lower()
if val in {"y", "yes", "t", "true", "on", "1"}:
return 1
if val in {"n", "no", "f", "false", "off", "0"}:
return 0
raise ValueError(F'invalid truth value {val!r}' )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Any ) -> int:
if is_torch_fx_proxy(lowerCAmelCase ):
return True
if is_torch_available():
import torch
if isinstance(lowerCAmelCase , torch.Tensor ):
return True
if is_tf_available():
import tensorflow as tf
if isinstance(lowerCAmelCase , tf.Tensor ):
return True
if is_flax_available():
import jax.numpy as jnp
from jax.core import Tracer
if isinstance(lowerCAmelCase , (jnp.ndarray, Tracer) ):
return True
return isinstance(lowerCAmelCase , np.ndarray )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Any ) -> Dict:
return isinstance(lowerCAmelCase , np.ndarray )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Optional[Any] ) -> Any:
return _is_numpy(lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Tuple ) -> Optional[int]:
import torch
return isinstance(lowerCAmelCase , torch.Tensor )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Any ) -> Optional[int]:
return False if not is_torch_available() else _is_torch(lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[Any] ) -> List[Any]:
import torch
return isinstance(lowerCAmelCase , torch.device )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int ) -> Tuple:
return False if not is_torch_available() else _is_torch_device(lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[str] ) -> Tuple:
import torch
if isinstance(lowerCAmelCase , lowerCAmelCase ):
if hasattr(lowerCAmelCase , lowerCAmelCase ):
_UpperCAmelCase : Any = getattr(lowerCAmelCase , lowerCAmelCase )
else:
return False
return isinstance(lowerCAmelCase , torch.dtype )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Any ) -> int:
return False if not is_torch_available() else _is_torch_dtype(lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[Any] ) -> Optional[Any]:
import tensorflow as tf
return isinstance(lowerCAmelCase , tf.Tensor )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str ) -> Optional[Any]:
return False if not is_tf_available() else _is_tensorflow(lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Optional[int] ) -> Any:
import tensorflow as tf
# the `is_symbolic_tensor` predicate is only available starting with TF 2.14
if hasattr(lowerCAmelCase , "is_symbolic_tensor" ):
return tf.is_symbolic_tensor(lowerCAmelCase )
return type(lowerCAmelCase ) == tf.Tensor
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Tuple ) -> Optional[Any]:
return False if not is_tf_available() else _is_tf_symbolic_tensor(lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Optional[Any] ) -> List[str]:
import jax.numpy as jnp # noqa: F811
return isinstance(lowerCAmelCase , jnp.ndarray )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[Any] ) -> str:
return False if not is_flax_available() else _is_jax(lowerCAmelCase )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str ) -> Tuple:
if isinstance(lowerCAmelCase , (dict, UserDict) ):
return {k: to_py_obj(lowerCAmelCase ) for k, v in obj.items()}
elif isinstance(lowerCAmelCase , (list, tuple) ):
return [to_py_obj(lowerCAmelCase ) for o in obj]
elif is_tf_tensor(lowerCAmelCase ):
return obj.numpy().tolist()
elif is_torch_tensor(lowerCAmelCase ):
return obj.detach().cpu().tolist()
elif is_jax_tensor(lowerCAmelCase ):
return np.asarray(lowerCAmelCase ).tolist()
elif isinstance(lowerCAmelCase , (np.ndarray, np.number) ): # tolist also works on 0d np arrays
return obj.tolist()
else:
return obj
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Optional[Any] ) -> List[Any]:
if isinstance(lowerCAmelCase , (dict, UserDict) ):
return {k: to_numpy(lowerCAmelCase ) for k, v in obj.items()}
elif isinstance(lowerCAmelCase , (list, tuple) ):
return np.array(lowerCAmelCase )
elif is_tf_tensor(lowerCAmelCase ):
return obj.numpy()
elif is_torch_tensor(lowerCAmelCase ):
return obj.detach().cpu().numpy()
elif is_jax_tensor(lowerCAmelCase ):
return np.asarray(lowerCAmelCase )
else:
return obj
class a ( UpperCAmelCase ):
def _UpperCAmelCase ( self ):
'''simple docstring'''
_UpperCAmelCase : Optional[Any] = fields(self )
# Safety and consistency checks
if not len(A_ ):
raise ValueError(f'{self.__class__.__name__} has no fields.' )
if not all(field.default is None for field in class_fields[1:] ):
raise ValueError(f'{self.__class__.__name__} should not have more than one required field.' )
_UpperCAmelCase : Any = getattr(self , class_fields[0].name )
_UpperCAmelCase : List[str] = all(getattr(self , field.name ) is None for field in class_fields[1:] )
if other_fields_are_none and not is_tensor(A_ ):
if isinstance(A_ , A_ ):
_UpperCAmelCase : Union[str, Any] = first_field.items()
_UpperCAmelCase : Optional[int] = True
else:
try:
_UpperCAmelCase : Tuple = iter(A_ )
_UpperCAmelCase : Any = True
except TypeError:
_UpperCAmelCase : str = False
# if we provided an iterator as first field and the iterator is a (key, value) iterator
# set the associated fields
if first_field_iterator:
for idx, element in enumerate(A_ ):
if (
not isinstance(A_ , (list, tuple) )
or not len(A_ ) == 2
or not isinstance(element[0] , A_ )
):
if idx == 0:
# If we do not have an iterator of key/values, set it as attribute
_UpperCAmelCase : str = first_field
else:
# If we have a mixed iterator, raise an error
raise ValueError(
f'Cannot set key/value for {element}. It needs to be a tuple (key, value).' )
break
setattr(self , element[0] , element[1] )
if element[1] is not None:
_UpperCAmelCase : List[str] = element[1]
elif first_field is not None:
_UpperCAmelCase : Tuple = first_field
else:
for field in class_fields:
_UpperCAmelCase : int = getattr(self , field.name )
if v is not None:
_UpperCAmelCase : Union[str, Any] = v
def __delitem__( self , *A_ , **A_ ):
'''simple docstring'''
raise Exception(f'You cannot use ``__delitem__`` on a {self.__class__.__name__} instance.' )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
raise Exception(f'You cannot use ``setdefault`` on a {self.__class__.__name__} instance.' )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
raise Exception(f'You cannot use ``pop`` on a {self.__class__.__name__} instance.' )
def _UpperCAmelCase ( self , *A_ , **A_ ):
'''simple docstring'''
raise Exception(f'You cannot use ``update`` on a {self.__class__.__name__} instance.' )
def __getitem__( self , A_ ):
'''simple docstring'''
if isinstance(A_ , A_ ):
_UpperCAmelCase : Optional[int] = dict(self.items() )
return inner_dict[k]
else:
return self.to_tuple()[k]
def __setattr__( self , A_ , A_ ):
'''simple docstring'''
if name in self.keys() and value is not None:
# Don't call self.__setitem__ to avoid recursion errors
super().__setitem__(A_ , A_ )
super().__setattr__(A_ , A_ )
def __setitem__( self , A_ , A_ ):
'''simple docstring'''
super().__setitem__(A_ , A_ )
# Don't call self.__setattr__ to avoid recursion errors
super().__setattr__(A_ , A_ )
def _UpperCAmelCase ( self ):
'''simple docstring'''
return tuple(self[k] for k in self.keys() )
class a ( UpperCAmelCase , UpperCAmelCase ):
@classmethod
def _UpperCAmelCase ( cls , A_ ):
'''simple docstring'''
raise ValueError(
f'{value} is not a valid {cls.__name__}, please select one of {list(cls._valueamember_map_.keys() )}' )
class a ( UpperCAmelCase ):
_lowercase = "longest"
_lowercase = "max_length"
_lowercase = "do_not_pad"
class a ( UpperCAmelCase ):
_lowercase = "pt"
_lowercase = "tf"
_lowercase = "np"
_lowercase = "jax"
class a :
def __init__( self , A_ ):
'''simple docstring'''
_UpperCAmelCase : Tuple = context_managers
_UpperCAmelCase : Dict = ExitStack()
def __enter__( self ):
'''simple docstring'''
for context_manager in self.context_managers:
self.stack.enter_context(A_ )
def __exit__( self , *A_ , **A_ ):
'''simple docstring'''
self.stack.__exit__(*A_ , **A_ )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: int ) -> Optional[Any]:
_UpperCAmelCase : Optional[Any] = infer_framework(lowerCAmelCase )
if framework == "tf":
_UpperCAmelCase : Union[str, Any] = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_UpperCAmelCase : Optional[int] = inspect.signature(model_class.forward ) # PyTorch models
else:
_UpperCAmelCase : Dict = inspect.signature(model_class.__call__ ) # Flax models
for p in signature.parameters:
if p == "return_loss" and signature.parameters[p].default is True:
return True
return False
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[str] ) -> List[str]:
_UpperCAmelCase : List[Any] = model_class.__name__
_UpperCAmelCase : Dict = infer_framework(lowerCAmelCase )
if framework == "tf":
_UpperCAmelCase : Dict = inspect.signature(model_class.call ) # TensorFlow models
elif framework == "pt":
_UpperCAmelCase : Dict = inspect.signature(model_class.forward ) # PyTorch models
else:
_UpperCAmelCase : Tuple = inspect.signature(model_class.__call__ ) # Flax models
if "QuestionAnswering" in model_name:
return [p for p in signature.parameters if "label" in p or p in ("start_positions", "end_positions")]
else:
return [p for p in signature.parameters if "label" in p]
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: MutableMapping , lowerCAmelCase: str = "" , lowerCAmelCase: str = "." ) -> List[Any]:
def _flatten_dict(lowerCAmelCase: int , lowerCAmelCase: Tuple="" , lowerCAmelCase: List[str]="." ):
for k, v in d.items():
_UpperCAmelCase : Optional[int] = str(lowerCAmelCase ) + delimiter + str(lowerCAmelCase ) if parent_key else k
if v and isinstance(lowerCAmelCase , lowerCAmelCase ):
yield from flatten_dict(lowerCAmelCase , lowerCAmelCase , delimiter=lowerCAmelCase ).items()
else:
yield key, v
return dict(_flatten_dict(lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) )
@contextmanager
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Union[str, Any] , lowerCAmelCase: bool = False ) -> List[Any]:
if use_temp_dir:
with tempfile.TemporaryDirectory() as tmp_dir:
yield tmp_dir
else:
yield working_dir
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Dict , lowerCAmelCase: Tuple=None ) -> List[str]:
if is_numpy_array(lowerCAmelCase ):
return np.transpose(lowerCAmelCase , axes=lowerCAmelCase )
elif is_torch_tensor(lowerCAmelCase ):
return array.T if axes is None else array.permute(*lowerCAmelCase )
elif is_tf_tensor(lowerCAmelCase ):
import tensorflow as tf
return tf.transpose(lowerCAmelCase , perm=lowerCAmelCase )
elif is_jax_tensor(lowerCAmelCase ):
return jnp.transpose(lowerCAmelCase , axes=lowerCAmelCase )
else:
raise ValueError(F'Type not supported for transpose: {type(lowerCAmelCase )}.' )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Optional[Any] , lowerCAmelCase: Any ) -> int:
if is_numpy_array(lowerCAmelCase ):
return np.reshape(lowerCAmelCase , lowerCAmelCase )
elif is_torch_tensor(lowerCAmelCase ):
return array.reshape(*lowerCAmelCase )
elif is_tf_tensor(lowerCAmelCase ):
import tensorflow as tf
return tf.reshape(lowerCAmelCase , lowerCAmelCase )
elif is_jax_tensor(lowerCAmelCase ):
return jnp.reshape(lowerCAmelCase , lowerCAmelCase )
else:
raise ValueError(F'Type not supported for reshape: {type(lowerCAmelCase )}.' )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: Union[str, Any]=None ) -> Union[str, Any]:
if is_numpy_array(lowerCAmelCase ):
return np.squeeze(lowerCAmelCase , axis=lowerCAmelCase )
elif is_torch_tensor(lowerCAmelCase ):
return array.squeeze() if axis is None else array.squeeze(dim=lowerCAmelCase )
elif is_tf_tensor(lowerCAmelCase ):
import tensorflow as tf
return tf.squeeze(lowerCAmelCase , axis=lowerCAmelCase )
elif is_jax_tensor(lowerCAmelCase ):
return jnp.squeeze(lowerCAmelCase , axis=lowerCAmelCase )
else:
raise ValueError(F'Type not supported for squeeze: {type(lowerCAmelCase )}.' )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Dict , lowerCAmelCase: List[str] ) -> Union[str, Any]:
if is_numpy_array(lowerCAmelCase ):
return np.expand_dims(lowerCAmelCase , lowerCAmelCase )
elif is_torch_tensor(lowerCAmelCase ):
return array.unsqueeze(dim=lowerCAmelCase )
elif is_tf_tensor(lowerCAmelCase ):
import tensorflow as tf
return tf.expand_dims(lowerCAmelCase , axis=lowerCAmelCase )
elif is_jax_tensor(lowerCAmelCase ):
return jnp.expand_dims(lowerCAmelCase , axis=lowerCAmelCase )
else:
raise ValueError(F'Type not supported for expand_dims: {type(lowerCAmelCase )}.' )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[Any] ) -> int:
if is_numpy_array(lowerCAmelCase ):
return np.size(lowerCAmelCase )
elif is_torch_tensor(lowerCAmelCase ):
return array.numel()
elif is_tf_tensor(lowerCAmelCase ):
import tensorflow as tf
return tf.size(lowerCAmelCase )
elif is_jax_tensor(lowerCAmelCase ):
return array.size
else:
raise ValueError(F'Type not supported for expand_dims: {type(lowerCAmelCase )}.' )
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: List[str] , lowerCAmelCase: List[Any] ) -> List[Any]:
for key, value in auto_map.items():
if isinstance(lowerCAmelCase , (tuple, list) ):
_UpperCAmelCase : List[Any] = [F'{repo_id}--{v}' if (v is not None and "--" not in v) else v for v in value]
elif value is not None and "--" not in value:
_UpperCAmelCase : Tuple = F'{repo_id}--{value}'
return auto_map
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: Any ) -> List[Any]:
for base_class in inspect.getmro(lowerCAmelCase ):
_UpperCAmelCase : int = base_class.__module__
_UpperCAmelCase : Dict = base_class.__name__
if module.startswith("tensorflow" ) or module.startswith("keras" ) or name == "TFPreTrainedModel":
return "tf"
elif module.startswith("torch" ) or name == "PreTrainedModel":
return "pt"
elif module.startswith("flax" ) or module.startswith("jax" ) or name == "FlaxPreTrainedModel":
return "flax"
else:
raise TypeError(F'Could not infer framework from class {model_class}.' )
| 189 | 1 |
"""simple docstring"""
import os
import string
import sys
__UpperCamelCase : Optional[Any] = 1 << 8
__UpperCamelCase : Dict = {
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 2_7,
'''up''': 6_5 + ARROW_KEY_FLAG,
'''down''': 6_6 + ARROW_KEY_FLAG,
'''right''': 6_7 + ARROW_KEY_FLAG,
'''left''': 6_8 + ARROW_KEY_FLAG,
'''mod_int''': 9_1,
'''undefined''': sys.maxsize,
'''interrupt''': 3,
'''insert''': 5_0,
'''delete''': 5_1,
'''pg_up''': 5_3,
'''pg_down''': 5_4,
}
__UpperCamelCase : Tuple = KEYMAP['''up''']
__UpperCamelCase : List[str] = KEYMAP['''left''']
if sys.platform == "win32":
__UpperCamelCase : Optional[int] = []
__UpperCamelCase : Optional[int] = {
b'''\xe0H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
b'''\x00H''': KEYMAP['''up'''] - ARROW_KEY_FLAG,
b'''\xe0P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
b'''\x00P''': KEYMAP['''down'''] - ARROW_KEY_FLAG,
b'''\xe0M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
b'''\x00M''': KEYMAP['''right'''] - ARROW_KEY_FLAG,
b'''\xe0K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
b'''\x00K''': KEYMAP['''left'''] - ARROW_KEY_FLAG,
}
for i in range(1_0):
__UpperCamelCase : List[str] = ord(str(i))
def __SCREAMING_SNAKE_CASE ( ):
if os.name == "nt":
import msvcrt
lowerCAmelCase__ : str = '''mbcs'''
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(A_ ) == 0:
# Read the keystroke
lowerCAmelCase__ : Optional[Any] = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
lowerCAmelCase__ : Optional[int] = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
lowerCAmelCase__ : str = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP['''mod_int'''] ) )
WIN_CH_BUFFER.append(A_ )
if ord(A_ ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(1_26 ) )
lowerCAmelCase__ : str = chr(KEYMAP['''esc'''] )
except KeyError:
lowerCAmelCase__ : List[Any] = cha[1]
else:
lowerCAmelCase__ : int = ch.decode(A_ )
else:
lowerCAmelCase__ : Optional[Any] = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
lowerCAmelCase__ : Optional[Any] = sys.stdin.fileno()
lowerCAmelCase__ : List[Any] = termios.tcgetattr(A_ )
try:
tty.setraw(A_ )
lowerCAmelCase__ : Tuple = sys.stdin.read(1 )
finally:
termios.tcsetattr(A_ , termios.TCSADRAIN , A_ )
return ch
def __SCREAMING_SNAKE_CASE ( ):
lowerCAmelCase__ : Tuple = get_raw_chars()
if ord(A_ ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(A_ ) == KEYMAP["esc"]:
lowerCAmelCase__ : Optional[int] = get_raw_chars()
if ord(A_ ) == KEYMAP["mod_int"]:
lowerCAmelCase__ : Dict = get_raw_chars()
if ord(A_ ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(A_ ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(A_ ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 106 |
"""simple docstring"""
from typing import Optional
from urllib.parse import quote
import huggingface_hub as hfh
from packaging import version
def snake_case_ ( A_ : str, A_ : str, A_ : Optional[str] = None ):
'''simple docstring'''
if version.parse(hfh.__version__ ).release < version.parse('''0.11.0''' ).release:
# old versions of hfh don't url-encode the file path
_lowerCamelCase : Optional[Any] = quote(A_ )
return hfh.hf_hub_url(A_, A_, repo_type='''dataset''', revision=A_ )
| 72 | 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.
import numpy as np
import torch
from ..models.clipseg import CLIPSegForImageSegmentation
from ..utils import is_vision_available, requires_backends
from .base import PipelineTool
if is_vision_available():
from PIL import Image
class snake_case_ ( UpperCamelCase__ ):
A_ = (
'''This is a tool that creates a segmentation mask of an image according to a label. It cannot create an image.'''
'''It takes two arguments named `image` which should be the original image, and `label` which should be a text '''
'''describing the elements what should be identified in the segmentation mask. The tool returns the mask.'''
)
A_ = '''CIDAS/clipseg-rd64-refined'''
A_ = '''image_segmenter'''
A_ = CLIPSegForImageSegmentation
A_ = ['''image''', '''text''']
A_ = ['''image''']
def __init__( self : List[str] , *_snake_case : Tuple , **_snake_case : str )->Dict:
'''simple docstring'''
requires_backends(self , ["""vision"""] )
super().__init__(*UpperCamelCase_ , **UpperCamelCase_ )
def UpperCAmelCase__ ( self : Optional[Any] , _snake_case : "Image" , _snake_case : str )->int:
'''simple docstring'''
return self.pre_processor(text=[label] , images=[image] , padding=UpperCamelCase_ , return_tensors="""pt""" )
def UpperCAmelCase__ ( self : Optional[int] , _snake_case : List[str] )->Optional[Any]:
'''simple docstring'''
with torch.no_grad():
__lowerCAmelCase : Optional[int] = self.model(**UpperCamelCase_ ).logits
return logits
def UpperCAmelCase__ ( self : Any , _snake_case : str )->Dict:
'''simple docstring'''
__lowerCAmelCase : str = outputs.cpu().detach().numpy()
__lowerCAmelCase : Union[str, Any] = 0
__lowerCAmelCase : Union[str, Any] = 1
return Image.fromarray((array * 255).astype(np.uinta ) ) | 355 |
_UpperCAmelCase = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
_UpperCAmelCase = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :dict[int, list[int]] , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :list[bool] ) -> list[int]:
__lowerCAmelCase : str = True
__lowerCAmelCase : str = []
for neighbour in graph[vert]:
if not visited[neighbour]:
order += topology_sort(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
order.append(SCREAMING_SNAKE_CASE )
return order
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :dict[int, list[int]] , SCREAMING_SNAKE_CASE :int , SCREAMING_SNAKE_CASE :list[bool] ) -> list[int]:
__lowerCAmelCase : Optional[Any] = True
__lowerCAmelCase : Union[str, Any] = [vert]
for neighbour in reversed_graph[vert]:
if not visited[neighbour]:
component += find_components(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
return component
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :dict[int, list[int]] ) -> list[list[int]]:
__lowerCAmelCase : Optional[Any] = len(SCREAMING_SNAKE_CASE ) * [False]
__lowerCAmelCase : dict[int, list[int]] = {vert: [] for vert in range(len(SCREAMING_SNAKE_CASE ) )}
for vert, neighbours in graph.items():
for neighbour in neighbours:
reversed_graph[neighbour].append(SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Tuple = []
for i, was_visited in enumerate(SCREAMING_SNAKE_CASE ):
if not was_visited:
order += topology_sort(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
__lowerCAmelCase : Optional[Any] = []
__lowerCAmelCase : Any = len(SCREAMING_SNAKE_CASE ) * [False]
for i in range(len(SCREAMING_SNAKE_CASE ) ):
__lowerCAmelCase : Optional[int] = order[len(SCREAMING_SNAKE_CASE ) - i - 1]
if not visited[vert]:
__lowerCAmelCase : Any = find_components(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
components_list.append(SCREAMING_SNAKE_CASE )
return components_list | 232 | 0 |
from __future__ import annotations
__UpperCAmelCase = [-10, -5, 0, 5, 5.1, 11, 13, 21, 3, 4, -21, -10, -5, -1, 0]
__UpperCAmelCase = [-5, 0, 5, 5.1, 11, 13, 21, -1, 4, -1, -10, -5, -1, 0, -1]
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase )
for i in range(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = -1
for j in range(i + 1, __lowerCamelCase ):
if arr[i] < arr[j]:
SCREAMING_SNAKE_CASE_ = arr[j]
break
result.append(__lowerCamelCase )
return result
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = []
for i, outer in enumerate(__lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = -1
for inner in arr[i + 1 :]:
if outer < inner:
SCREAMING_SNAKE_CASE_ = inner
break
result.append(__lowerCamelCase )
return result
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase )
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = [-1] * arr_size
for index in reversed(range(__lowerCamelCase ) ):
if stack:
while stack[-1] <= arr[index]:
stack.pop()
if not stack:
break
if stack:
SCREAMING_SNAKE_CASE_ = stack[-1]
stack.append(arr[index] )
return result
if __name__ == "__main__":
from doctest import testmod
from timeit import timeit
testmod()
print(next_greatest_element_slow(arr))
print(next_greatest_element_fast(arr))
print(next_greatest_element(arr))
__UpperCAmelCase = (
"from __main__ import arr, next_greatest_element_slow, "
"next_greatest_element_fast, next_greatest_element"
)
print(
"next_greatest_element_slow():",
timeit("next_greatest_element_slow(arr)", setup=setup),
)
print(
"next_greatest_element_fast():",
timeit("next_greatest_element_fast(arr)", setup=setup),
)
print(
" next_greatest_element():",
timeit("next_greatest_element(arr)", setup=setup),
)
| 299 |
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = []
SCREAMING_SNAKE_CASE_ = {
'''^''': 3,
'''*''': 2,
'''/''': 2,
'''%''': 2,
'''+''': 1,
'''-''': 1,
} # Priority of each operator
SCREAMING_SNAKE_CASE_ = len(__lowerCamelCase ) if (len(__lowerCamelCase ) > 7) else 7
# Print table header for output
print(
'''Symbol'''.center(8 ), '''Stack'''.center(__lowerCamelCase ), '''Postfix'''.center(__lowerCamelCase ), sep=''' | ''', )
print('''-''' * (print_width * 3 + 7) )
for x in infix:
if x.isalpha() or x.isdigit():
post_fix.append(__lowerCamelCase ) # if x is Alphabet / Digit, add it to Postfix
elif x == "(":
stack.append(__lowerCamelCase ) # if x is "(" push to Stack
elif x == ")": # if x is ")" pop stack until "(" is encountered
while stack[-1] != "(":
post_fix.append(stack.pop() ) # Pop stack & add the content to Postfix
stack.pop()
else:
if len(__lowerCamelCase ) == 0:
stack.append(__lowerCamelCase ) # If stack is empty, push x to stack
else: # while priority of x is not > priority of element in the stack
while len(__lowerCamelCase ) > 0 and priority[x] <= priority[stack[-1]]:
post_fix.append(stack.pop() ) # pop stack & add to Postfix
stack.append(__lowerCamelCase ) # push x to stack
print(
x.center(8 ), (''''''.join(__lowerCamelCase )).ljust(__lowerCamelCase ), (''''''.join(__lowerCamelCase )).ljust(__lowerCamelCase ), sep=''' | ''', ) # Output in tabular format
while len(__lowerCamelCase ) > 0: # while stack is not empty
post_fix.append(stack.pop() ) # pop stack & add to Postfix
print(
''' '''.center(8 ), (''''''.join(__lowerCamelCase )).ljust(__lowerCamelCase ), (''''''.join(__lowerCamelCase )).ljust(__lowerCamelCase ), sep=''' | ''', ) # Output in tabular format
return "".join(__lowerCamelCase ) # return Postfix as str
def A__ ( __lowerCamelCase ):
SCREAMING_SNAKE_CASE_ = list(infix[::-1] ) # reverse the infix equation
for i in range(len(__lowerCamelCase ) ):
if infix[i] == "(":
SCREAMING_SNAKE_CASE_ = ''')''' # change "(" to ")"
elif infix[i] == ")":
SCREAMING_SNAKE_CASE_ = '''(''' # change ")" to "("
return (infix_2_postfix(''''''.join(__lowerCamelCase ) ))[
::-1
] # call infix_2_postfix on Infix, return reverse of Postfix
if __name__ == "__main__":
__UpperCAmelCase = input("\nEnter an Infix Equation = ") # Input an Infix equation
__UpperCAmelCase = "".join(Infix.split()) # Remove spaces from the input
print("\n\t", Infix, "(Infix) -> ", infix_2_prefix(Infix), "(Prefix)")
| 299 | 1 |
"""simple docstring"""
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM,
AutoTokenizer,
DataCollatorForWholeWordMask,
HfArgumentParser,
Trainer,
TrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
__snake_case : Optional[int] = logging.getLogger(__name__)
__snake_case : Optional[int] = list(MODEL_FOR_MASKED_LM_MAPPING.keys())
__snake_case : List[Any] = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
@dataclass
class A__ :
'''simple docstring'''
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'If training from scratch, pass a model type from the list: ' + ', '.join(__SCREAMING_SNAKE_CASE )} , )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'Override some existing default config settings when a model is trained from scratch. Example: '
'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Pretrained config name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'} )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'} , )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'} , )
SCREAMING_SNAKE_CASE = field(
default='main' , metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'} , )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
'with private models).'
)
} , )
def _SCREAMING_SNAKE_CASE ( self: Tuple) -> Tuple:
"""simple docstring"""
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
raise ValueError(
"--config_overrides can't be used in combination with --config_name or --model_name_or_path")
@dataclass
class A__ :
'''simple docstring'''
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'The name of the dataset to use (via the datasets library).'} )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'} )
SCREAMING_SNAKE_CASE = field(default=__SCREAMING_SNAKE_CASE , metadata={'help': 'The input training data file (a text file).'} )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'An optional input evaluation data file to evaluate the perplexity on (a text file).'} , )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'An optional input train ref data file for whole word masking in Chinese.'} , )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'An optional input validation ref data file for whole word masking in Chinese.'} , )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'Overwrite the cached training and evaluation sets'} )
SCREAMING_SNAKE_CASE = field(
default=5 , metadata={
'help': 'The percentage of the train set used as validation set in case there\'s no validation split'
} , )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated. Default to the max input length of the model.'
)
} , )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={'help': 'The number of processes to use for the preprocessing.'} , )
SCREAMING_SNAKE_CASE = field(
default=0.15 , metadata={'help': 'Ratio of tokens to mask for masked language modeling loss'} )
SCREAMING_SNAKE_CASE = field(
default=__SCREAMING_SNAKE_CASE , metadata={
'help': (
'Whether to pad all samples to `max_seq_length`. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch.'
)
} , )
def _SCREAMING_SNAKE_CASE ( self: List[Any]) -> List[Any]:
"""simple docstring"""
if self.train_file is not None:
__lowerCAmelCase : Union[str, Any] = self.train_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
if self.validation_file is not None:
__lowerCAmelCase : List[Any] = self.validation_file.split(".")[-1]
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
def _lowercase ( __snake_case ,__snake_case ) -> int:
with open(__snake_case ,"r" ,encoding="utf-8" ) as f:
__lowerCAmelCase : List[str] = [json.loads(__snake_case ) for line in f.read().splitlines() if (len(__snake_case ) > 0 and not line.isspace())]
assert len(__snake_case ) == len(__snake_case )
__lowerCAmelCase : Tuple = {c: dataset[c] for c in dataset.column_names}
__lowerCAmelCase : List[str] = refs
return Dataset.from_dict(__snake_case )
def _lowercase ( ) -> Tuple:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
__lowerCAmelCase : List[str] = 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 : str = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : Union[str, Any] = parser.parse_args_into_dataclasses()
# Detecting last checkpoint.
__lowerCAmelCase : Tuple = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
__lowerCAmelCase : Dict = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
"Use --overwrite_output_dir to overcome." )
elif last_checkpoint is not None:
logger.info(
F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." )
# 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 )] ,)
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank ) else logging.WARN )
# 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}""" )
# 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" ,__snake_case )
# 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.dataset_name is not None:
# Downloading and loading a dataset from the hub.
__lowerCAmelCase : Dict = load_dataset(data_args.dataset_name ,data_args.dataset_config_name )
if "validation" not in datasets.keys():
__lowerCAmelCase : List[Any] = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=F"""train[:{data_args.validation_split_percentage}%]""" ,)
__lowerCAmelCase : Optional[Any] = load_dataset(
data_args.dataset_name ,data_args.dataset_config_name ,split=F"""train[{data_args.validation_split_percentage}%:]""" ,)
else:
__lowerCAmelCase : int = {}
if data_args.train_file is not None:
__lowerCAmelCase : Dict = data_args.train_file
if data_args.validation_file is not None:
__lowerCAmelCase : List[Any] = data_args.validation_file
__lowerCAmelCase : Tuple = data_args.train_file.split("." )[-1]
if extension == "txt":
__lowerCAmelCase : int = "text"
__lowerCAmelCase : str = load_dataset(__snake_case ,data_files=__snake_case )
# 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 : Union[str, Any] = {
"cache_dir": model_args.cache_dir,
"revision": model_args.model_revision,
"use_auth_token": True if model_args.use_auth_token else None,
}
if model_args.config_name:
__lowerCAmelCase : Union[str, Any] = AutoConfig.from_pretrained(model_args.config_name ,**__snake_case )
elif model_args.model_name_or_path:
__lowerCAmelCase : int = AutoConfig.from_pretrained(model_args.model_name_or_path ,**__snake_case )
else:
__lowerCAmelCase : Dict = CONFIG_MAPPING[model_args.model_type]()
logger.warning("You are instantiating a new config instance from scratch." )
if model_args.config_overrides is not None:
logger.info(F"""Overriding config: {model_args.config_overrides}""" )
config.update_from_string(model_args.config_overrides )
logger.info(F"""New config: {config}""" )
__lowerCAmelCase : Optional[Any] = {
"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,
}
if model_args.tokenizer_name:
__lowerCAmelCase : Any = AutoTokenizer.from_pretrained(model_args.tokenizer_name ,**__snake_case )
elif model_args.model_name_or_path:
__lowerCAmelCase : Any = AutoTokenizer.from_pretrained(model_args.model_name_or_path ,**__snake_case )
else:
raise ValueError(
"You are instantiating a new tokenizer from scratch. This is not supported by this script."
"You can do it from another script, save it, and load it from here, using --tokenizer_name." )
if model_args.model_name_or_path:
__lowerCAmelCase : Dict = AutoModelForMaskedLM.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 ,)
else:
logger.info("Training new model from scratch" )
__lowerCAmelCase : Optional[int] = AutoModelForMaskedLM.from_config(__snake_case )
model.resize_token_embeddings(len(__snake_case ) )
# Preprocessing the datasets.
# First we tokenize all the texts.
if training_args.do_train:
__lowerCAmelCase : Any = datasets["train"].column_names
else:
__lowerCAmelCase : Optional[int] = datasets["validation"].column_names
__lowerCAmelCase : Dict = "text" if "text" in column_names else column_names[0]
__lowerCAmelCase : Union[str, Any] = "max_length" if data_args.pad_to_max_length else False
def tokenize_function(__snake_case ):
# Remove empty lines
__lowerCAmelCase : Union[str, Any] = [line for line in examples["text"] if len(__snake_case ) > 0 and not line.isspace()]
return tokenizer(examples["text"] ,padding=__snake_case ,truncation=__snake_case ,max_length=data_args.max_seq_length )
__lowerCAmelCase : Dict = datasets.map(
__snake_case ,batched=__snake_case ,num_proc=data_args.preprocessing_num_workers ,remove_columns=[text_column_name] ,load_from_cache_file=not data_args.overwrite_cache ,)
# Add the chinese references if provided
if data_args.train_ref_file is not None:
__lowerCAmelCase : str = add_chinese_references(tokenized_datasets["train"] ,data_args.train_ref_file )
if data_args.validation_ref_file is not None:
__lowerCAmelCase : int = add_chinese_references(
tokenized_datasets["validation"] ,data_args.validation_ref_file )
# If we have ref files, need to avoid it removed by trainer
__lowerCAmelCase : List[str] = data_args.train_ref_file or data_args.validation_ref_file
if has_ref:
__lowerCAmelCase : List[Any] = False
# Data collator
# This one will take care of randomly masking the tokens.
__lowerCAmelCase : List[Any] = DataCollatorForWholeWordMask(tokenizer=__snake_case ,mlm_probability=data_args.mlm_probability )
# Initialize our Trainer
__lowerCAmelCase : Union[str, Any] = Trainer(
model=__snake_case ,args=__snake_case ,train_dataset=tokenized_datasets["train"] if training_args.do_train else None ,eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None ,tokenizer=__snake_case ,data_collator=__snake_case ,)
# Training
if training_args.do_train:
if last_checkpoint is not None:
__lowerCAmelCase : List[str] = last_checkpoint
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path ):
__lowerCAmelCase : Optional[int] = model_args.model_name_or_path
else:
__lowerCAmelCase : Optional[int] = None
__lowerCAmelCase : List[str] = trainer.train(resume_from_checkpoint=__snake_case )
trainer.save_model() # Saves the tokenizer too for easy upload
__lowerCAmelCase : int = os.path.join(training_args.output_dir ,"train_results.txt" )
if trainer.is_world_process_zero():
with open(__snake_case ,"w" ) as writer:
logger.info("***** Train results *****" )
for key, value in sorted(train_result.metrics.items() ):
logger.info(F""" {key} = {value}""" )
writer.write(F"""{key} = {value}\n""" )
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model
trainer.state.save_to_json(os.path.join(training_args.output_dir ,"trainer_state.json" ) )
# Evaluation
__lowerCAmelCase : Optional[int] = {}
if training_args.do_eval:
logger.info("*** Evaluate ***" )
__lowerCAmelCase : int = trainer.evaluate()
__lowerCAmelCase : Optional[Any] = math.exp(eval_output["eval_loss"] )
__lowerCAmelCase : Dict = perplexity
__lowerCAmelCase : Union[str, Any] = os.path.join(training_args.output_dir ,"eval_results_mlm_wwm.txt" )
if trainer.is_world_process_zero():
with open(__snake_case ,"w" ) as writer:
logger.info("***** Eval results *****" )
for key, value in sorted(results.items() ):
logger.info(F""" {key} = {value}""" )
writer.write(F"""{key} = {value}\n""" )
return results
def _lowercase ( __snake_case ) -> Tuple:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main() | 58 |
"""simple docstring"""
from __future__ import annotations
from bisect import bisect_left
from functools import total_ordering
from heapq import merge
@total_ordering
class A__ ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def __lt__( self: List[Any] , _SCREAMING_SNAKE_CASE: Union[str, Any]) -> Dict:
"""simple docstring"""
return self[-1] < other[-1]
def __eq__( self: int , _SCREAMING_SNAKE_CASE: Any) -> Tuple:
"""simple docstring"""
return self[-1] == other[-1]
def _lowercase ( __snake_case ) -> list:
__lowerCAmelCase : list[Stack] = []
# sort into stacks
for element in collection:
__lowerCAmelCase : Dict = Stack([element] )
__lowerCAmelCase : str = bisect_left(__snake_case ,__snake_case )
if i != len(__snake_case ):
stacks[i].append(__snake_case )
else:
stacks.append(__snake_case )
# use a heap-based merge to merge stack efficiently
__lowerCAmelCase : Union[str, Any] = merge(*(reversed(__snake_case ) for stack in stacks) )
return collection
if __name__ == "__main__":
__snake_case : Union[str, Any] = input('Enter numbers separated by a comma:\n').strip()
__snake_case : Optional[int] = [int(item) for item in user_input.split(',')]
print(patience_sort(unsorted)) | 58 | 1 |
"""simple docstring"""
import os
import string
import sys
A_ : Dict = 1 << 8
A_ : Any = {
"tab": ord("\t"),
"newline": ord("\r"),
"esc": 27,
"up": 65 + ARROW_KEY_FLAG,
"down": 66 + ARROW_KEY_FLAG,
"right": 67 + ARROW_KEY_FLAG,
"left": 68 + ARROW_KEY_FLAG,
"mod_int": 91,
"undefined": sys.maxsize,
"interrupt": 3,
"insert": 50,
"delete": 51,
"pg_up": 53,
"pg_down": 54,
}
A_ : Optional[Any] = KEYMAP["up"]
A_ : int = KEYMAP["left"]
if sys.platform == "win32":
A_ : Dict = []
A_ : Optional[int] = {
b"\xe0H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\x00H": KEYMAP["up"] - ARROW_KEY_FLAG,
b"\xe0P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\x00P": KEYMAP["down"] - ARROW_KEY_FLAG,
b"\xe0M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\x00M": KEYMAP["right"] - ARROW_KEY_FLAG,
b"\xe0K": KEYMAP["left"] - ARROW_KEY_FLAG,
b"\x00K": KEYMAP["left"] - ARROW_KEY_FLAG,
}
for i in range(10):
A_ : Optional[Any] = ord(str(i))
def A ( ):
'''simple docstring'''
if os.name == "nt":
import msvcrt
SCREAMING_SNAKE_CASE__ = """mbcs"""
# Flush the keyboard buffer
while msvcrt.kbhit():
msvcrt.getch()
if len(_SCREAMING_SNAKE_CASE ) == 0:
# Read the keystroke
SCREAMING_SNAKE_CASE__ = msvcrt.getch()
# If it is a prefix char, get second part
if ch in (b"\x00", b"\xe0"):
SCREAMING_SNAKE_CASE__ = ch + msvcrt.getch()
# Translate actual Win chars to bullet char types
try:
SCREAMING_SNAKE_CASE__ = chr(WIN_KEYMAP[cha] )
WIN_CH_BUFFER.append(chr(KEYMAP["""mod_int"""] ) )
WIN_CH_BUFFER.append(_SCREAMING_SNAKE_CASE )
if ord(_SCREAMING_SNAKE_CASE ) in (
KEYMAP["insert"] - 1 << 9,
KEYMAP["delete"] - 1 << 9,
KEYMAP["pg_up"] - 1 << 9,
KEYMAP["pg_down"] - 1 << 9,
):
WIN_CH_BUFFER.append(chr(1_26 ) )
SCREAMING_SNAKE_CASE__ = chr(KEYMAP["""esc"""] )
except KeyError:
SCREAMING_SNAKE_CASE__ = cha[1]
else:
SCREAMING_SNAKE_CASE__ = ch.decode(_SCREAMING_SNAKE_CASE )
else:
SCREAMING_SNAKE_CASE__ = WIN_CH_BUFFER.pop(0 )
elif os.name == "posix":
import termios
import tty
SCREAMING_SNAKE_CASE__ = sys.stdin.fileno()
SCREAMING_SNAKE_CASE__ = termios.tcgetattr(_SCREAMING_SNAKE_CASE )
try:
tty.setraw(_SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE__ = sys.stdin.read(1 )
finally:
termios.tcsetattr(_SCREAMING_SNAKE_CASE , termios.TCSADRAIN , _SCREAMING_SNAKE_CASE )
return ch
def A ( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = get_raw_chars()
if ord(_SCREAMING_SNAKE_CASE ) in [KEYMAP["interrupt"], KEYMAP["newline"]]:
return char
elif ord(_SCREAMING_SNAKE_CASE ) == KEYMAP["esc"]:
SCREAMING_SNAKE_CASE__ = get_raw_chars()
if ord(_SCREAMING_SNAKE_CASE ) == KEYMAP["mod_int"]:
SCREAMING_SNAKE_CASE__ = get_raw_chars()
if ord(_SCREAMING_SNAKE_CASE ) >= KEYMAP["arrow_begin"] - ARROW_KEY_FLAG and ord(_SCREAMING_SNAKE_CASE ) <= KEYMAP["arrow_end"] - ARROW_KEY_FLAG:
return chr(ord(_SCREAMING_SNAKE_CASE ) + ARROW_KEY_FLAG )
else:
return KEYMAP["undefined"]
else:
return get_raw_chars()
else:
if char in string.printable:
return char
else:
return KEYMAP["undefined"]
| 165 |
'''simple docstring'''
import os
import tempfile
import unittest
from pathlib import Path
from transformers import AutoConfig, is_torch_available
from transformers.testing_utils import require_torch, torch_device
if is_torch_available():
from transformers import PyTorchBenchmark, PyTorchBenchmarkArguments
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def lowercase (self , UpperCAmelCase ) -> Union[str, Any]:
for model_result in results.values():
for batch_size, sequence_length in zip(model_result["""bs"""] , model_result["""ss"""] ):
_snake_case = model_result["""result"""][batch_size][sequence_length]
self.assertIsNotNone(UpperCAmelCase )
def lowercase (self ) -> Optional[int]:
_snake_case = """sshleifer/tiny-gpt2"""
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase )
_snake_case = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase (self ) -> Dict:
_snake_case = """sgugger/tiny-distilbert-classification"""
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , only_pretrain_model=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase )
_snake_case = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase (self ) -> Optional[Any]:
_snake_case = """sshleifer/tiny-gpt2"""
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , torchscript=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase )
_snake_case = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
@unittest.skipIf(torch_device == """cpu""" , """Cant do half precision""" )
def lowercase (self ) -> Optional[int]:
_snake_case = """sshleifer/tiny-gpt2"""
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , fpaa=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase )
_snake_case = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase (self ) -> Union[str, Any]:
_snake_case = """sshleifer/tiny-gpt2"""
_snake_case = AutoConfig.from_pretrained(UpperCAmelCase )
# set architectures equal to `None`
_snake_case = None
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase , configs=[config] )
_snake_case = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase (self ) -> Optional[int]:
_snake_case = """sshleifer/tiny-gpt2"""
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase )
_snake_case = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
@unittest.skipIf(torch_device == """cpu""" , """Can't do half precision""" )
def lowercase (self ) -> Tuple:
_snake_case = """sshleifer/tiny-gpt2"""
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , fpaa=UpperCAmelCase , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase )
_snake_case = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowercase (self ) -> Union[str, Any]:
_snake_case = """sshleifer/tiny-gpt2"""
_snake_case = AutoConfig.from_pretrained(UpperCAmelCase )
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase , configs=[config] )
_snake_case = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase (self ) -> Dict:
_snake_case = """sshleifer/tinier_bart"""
_snake_case = AutoConfig.from_pretrained(UpperCAmelCase )
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase , configs=[config] )
_snake_case = benchmark.run()
self.check_results_dict_not_empty(results.time_inference_result )
self.check_results_dict_not_empty(results.memory_inference_result )
def lowercase (self ) -> Any:
_snake_case = """sshleifer/tiny-gpt2"""
_snake_case = AutoConfig.from_pretrained(UpperCAmelCase )
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase , configs=[config] )
_snake_case = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowercase (self ) -> int:
_snake_case = """sshleifer/tinier_bart"""
_snake_case = AutoConfig.from_pretrained(UpperCAmelCase )
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase , configs=[config] )
_snake_case = benchmark.run()
self.check_results_dict_not_empty(results.time_train_result )
self.check_results_dict_not_empty(results.memory_train_result )
def lowercase (self ) -> str:
_snake_case = """sshleifer/tiny-gpt2"""
with tempfile.TemporaryDirectory() as tmp_dir:
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , save_to_csv=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , inference_time_csv_file=os.path.join(UpperCAmelCase , """inf_time.csv""" ) , train_memory_csv_file=os.path.join(UpperCAmelCase , """train_mem.csv""" ) , inference_memory_csv_file=os.path.join(UpperCAmelCase , """inf_mem.csv""" ) , train_time_csv_file=os.path.join(UpperCAmelCase , """train_time.csv""" ) , env_info_csv_file=os.path.join(UpperCAmelCase , """env.csv""" ) , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase )
benchmark.run()
self.assertTrue(Path(os.path.join(UpperCAmelCase , """inf_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(UpperCAmelCase , """train_time.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(UpperCAmelCase , """inf_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(UpperCAmelCase , """train_mem.csv""" ) ).exists() )
self.assertTrue(Path(os.path.join(UpperCAmelCase , """env.csv""" ) ).exists() )
def lowercase (self ) -> int:
_snake_case = """sshleifer/tiny-gpt2"""
def _check_summary_is_not_empty(UpperCAmelCase ):
self.assertTrue(hasattr(UpperCAmelCase , """sequential""" ) )
self.assertTrue(hasattr(UpperCAmelCase , """cumulative""" ) )
self.assertTrue(hasattr(UpperCAmelCase , """current""" ) )
self.assertTrue(hasattr(UpperCAmelCase , """total""" ) )
with tempfile.TemporaryDirectory() as tmp_dir:
_snake_case = PyTorchBenchmarkArguments(
models=[MODEL_ID] , training=UpperCAmelCase , inference=UpperCAmelCase , sequence_lengths=[8] , batch_sizes=[1] , log_filename=os.path.join(UpperCAmelCase , """log.txt""" ) , log_print=UpperCAmelCase , trace_memory_line_by_line=UpperCAmelCase , multi_process=UpperCAmelCase , )
_snake_case = PyTorchBenchmark(UpperCAmelCase )
_snake_case = benchmark.run()
_check_summary_is_not_empty(result.inference_summary )
_check_summary_is_not_empty(result.train_summary )
self.assertTrue(Path(os.path.join(UpperCAmelCase , """log.txt""" ) ).exists() ) | 341 | 0 |
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> List[str]:
print("""\nThe shortest path matrix using Floyd Warshall algorithm\n""")
for i in range(SCREAMING_SNAKE_CASE__):
for j in range(SCREAMING_SNAKE_CASE__):
if dist[i][j] != float("""inf"""):
print(int(dist[i][j]) , end="""\t""")
else:
print("""INF""" , end="""\t""")
print()
def A__ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__) -> Dict:
__snake_case: List[Any] = [[float("""inf""") for _ in range(SCREAMING_SNAKE_CASE__)] for _ in range(SCREAMING_SNAKE_CASE__)]
for i in range(SCREAMING_SNAKE_CASE__):
for j in range(SCREAMING_SNAKE_CASE__):
__snake_case: str = graph[i][j]
# check vertex k against all other vertices (i, j)
for k in range(SCREAMING_SNAKE_CASE__):
# looping through rows of graph array
for i in range(SCREAMING_SNAKE_CASE__):
# looping through columns of graph array
for j in range(SCREAMING_SNAKE_CASE__):
if (
dist[i][k] != float("""inf""")
and dist[k][j] != float("""inf""")
and dist[i][k] + dist[k][j] < dist[i][j]
):
__snake_case: Optional[int] = dist[i][k] + dist[k][j]
_print_dist(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__)
return dist, v
if __name__ == "__main__":
__UpperCAmelCase : int = int(input("Enter number of vertices: "))
__UpperCAmelCase : Optional[Any] = int(input("Enter number of edges: "))
__UpperCAmelCase : List[str] = [[float("inf") for i in range(v)] for j in range(v)]
for i in range(v):
__UpperCAmelCase : Union[str, Any] = 0.0
# src and dst are indices that must be within the array size graph[e][v]
# failure to follow this will result in an error
for i in range(e):
print("\nEdge ", i + 1)
__UpperCAmelCase : Optional[Any] = int(input("Enter source:"))
__UpperCAmelCase : Optional[int] = int(input("Enter destination:"))
__UpperCAmelCase : Union[str, Any] = float(input("Enter weight:"))
__UpperCAmelCase : str = weight
floyd_warshall(graph, v)
# Example Input
# Enter number of vertices: 3
# Enter number of edges: 2
# # generated graph from vertex and edge inputs
# [[inf, inf, inf], [inf, inf, inf], [inf, inf, inf]]
# [[0.0, inf, inf], [inf, 0.0, inf], [inf, inf, 0.0]]
# specify source, destination and weight for edge #1
# Edge 1
# Enter source:1
# Enter destination:2
# Enter weight:2
# specify source, destination and weight for edge #2
# Edge 2
# Enter source:2
# Enter destination:1
# Enter weight:1
# # Expected Output from the vertice, edge and src, dst, weight inputs!!
# 0 INF INF
# INF 0 2
# INF 1 0
| 293 |
from __future__ import annotations
import numpy as np
def A__ ( SCREAMING_SNAKE_CASE__) -> List[str]:
return np.maximum(0 , SCREAMING_SNAKE_CASE__)
if __name__ == "__main__":
print(np.array(relu([-1, 0, 5]))) # --> [0, 0, 5]
| 293 | 1 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from transformers import CLIPImageProcessor, CLIPVisionModel
from ...models import PriorTransformer
from ...pipelines import DiffusionPipeline
from ...schedulers import HeunDiscreteScheduler
from ...utils import (
BaseOutput,
is_accelerate_available,
logging,
randn_tensor,
replace_example_docstring,
)
from .renderer import ShapERenderer
_snake_case = logging.get_logger(__name__) # pylint: disable=invalid-name
_snake_case = "\n Examples:\n ```py\n >>> from PIL import Image\n >>> import torch\n >>> from diffusers import DiffusionPipeline\n >>> from diffusers.utils import export_to_gif, load_image\n\n >>> device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n >>> repo = \"openai/shap-e-img2img\"\n >>> pipe = DiffusionPipeline.from_pretrained(repo, torch_dtype=torch.float16)\n >>> pipe = pipe.to(device)\n\n >>> guidance_scale = 3.0\n >>> image_url = \"https://hf.co/datasets/diffusers/docs-images/resolve/main/shap-e/corgi.png\"\n >>> image = load_image(image_url).convert(\"RGB\")\n\n >>> images = pipe(\n ... image,\n ... guidance_scale=guidance_scale,\n ... num_inference_steps=64,\n ... frame_size=256,\n ... ).images\n\n >>> gif_path = export_to_gif(images[0], \"corgi_3d.gif\")\n ```\n"
@dataclass
class lowercase ( UpperCamelCase__ ):
_a = 42
class lowercase ( UpperCamelCase__ ):
def __init__( self , _a , _a , _a , _a , _a , ) -> List[Any]:
super().__init__()
self.register_modules(
prior=_a , image_encoder=_a , image_processor=_a , scheduler=_a , renderer=_a , )
def a__ ( self , _a , _a , _a , _a , _a , _a ) -> str:
if latents is None:
_A : str = randn_tensor(_a , generator=_a , device=_a , dtype=_a )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
_A : Union[str, Any] = latents.to(_a )
_A : int = latents * scheduler.init_noise_sigma
return latents
def a__ ( self , _a=0 ) -> Optional[Any]:
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
_A : str = torch.device(F'''cuda:{gpu_id}''' )
_A : Any = [self.image_encoder, self.prior]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(_a , _a )
@property
def a__ ( self ) -> List[Any]:
if self.device != torch.device("""meta""" ) or not hasattr(self.image_encoder , """_hf_hook""" ):
return self.device
for module in self.image_encoder.modules():
if (
hasattr(_a , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
def a__ ( self , _a , _a , _a , _a , ) -> Tuple:
if isinstance(_a , _a ) and isinstance(image[0] , torch.Tensor ):
_A : int = torch.cat(_a , axis=0 ) if image[0].ndim == 4 else torch.stack(_a , axis=0 )
if not isinstance(_a , torch.Tensor ):
_A : Dict = self.image_processor(_a , return_tensors="""pt""" ).pixel_values[0].unsqueeze(0 )
_A : int = image.to(dtype=self.image_encoder.dtype , device=_a )
_A : List[Any] = self.image_encoder(_a )["""last_hidden_state"""]
_A : List[Any] = image_embeds[:, 1:, :].contiguous() # batch_size, dim, 256
_A : Dict = image_embeds.repeat_interleave(_a , dim=0 )
if do_classifier_free_guidance:
_A : str = torch.zeros_like(_a )
# For classifier free guidance, we need to do two forward passes.
# Here we concatenate the unconditional and text embeddings into a single batch
# to avoid doing two forward passes
_A : List[str] = torch.cat([negative_image_embeds, image_embeds] )
return image_embeds
@torch.no_grad()
@replace_example_docstring(_a )
def __call__( self , _a , _a = 1 , _a = 25 , _a = None , _a = None , _a = 4.0 , _a = 64 , _a = "pil" , _a = True , ) -> Union[str, Any]:
if isinstance(_a , PIL.Image.Image ):
_A : List[Any] = 1
elif isinstance(_a , torch.Tensor ):
_A : Any = image.shape[0]
elif isinstance(_a , _a ) and isinstance(image[0] , (torch.Tensor, PIL.Image.Image) ):
_A : Union[str, Any] = len(_a )
else:
raise ValueError(
F'''`image` has to be of type `PIL.Image.Image`, `torch.Tensor`, `List[PIL.Image.Image]` or `List[torch.Tensor]` but is {type(_a )}''' )
_A : Optional[int] = self._execution_device
_A : Tuple = batch_size * num_images_per_prompt
_A : List[Any] = guidance_scale > 1.0
_A : Optional[Any] = self._encode_image(_a , _a , _a , _a )
# prior
self.scheduler.set_timesteps(_a , device=_a )
_A : Optional[int] = self.scheduler.timesteps
_A : List[str] = self.prior.config.num_embeddings
_A : int = self.prior.config.embedding_dim
_A : Optional[Any] = self.prepare_latents(
(batch_size, num_embeddings * embedding_dim) , image_embeds.dtype , _a , _a , _a , self.scheduler , )
# YiYi notes: for testing only to match ldm, we can directly create a latents with desired shape: batch_size, num_embeddings, embedding_dim
_A : List[Any] = latents.reshape(latents.shape[0] , _a , _a )
for i, t in enumerate(self.progress_bar(_a ) ):
# expand the latents if we are doing classifier free guidance
_A : List[str] = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
_A : int = self.scheduler.scale_model_input(_a , _a )
_A : Tuple = self.prior(
_a , timestep=_a , proj_embedding=_a , ).predicted_image_embedding
# remove the variance
_A , _A : Optional[Any] = noise_pred.split(
scaled_model_input.shape[2] , dim=2 ) # batch_size, num_embeddings, embedding_dim
if do_classifier_free_guidance is not None:
_A , _A : Dict = noise_pred.chunk(2 )
_A : Tuple = noise_pred_uncond + guidance_scale * (noise_pred - noise_pred_uncond)
_A : int = self.scheduler.step(
_a , timestep=_a , sample=_a , ).prev_sample
if output_type == "latent":
return ShapEPipelineOutput(images=_a )
_A : List[str] = []
for i, latent in enumerate(_a ):
print()
_A : List[str] = self.renderer.decode(
latent[None, :] , _a , size=_a , ray_batch_size=4096 , n_coarse_samples=64 , n_fine_samples=128 , )
images.append(_a )
_A : List[Any] = torch.stack(_a )
if output_type not in ["np", "pil"]:
raise ValueError(F'''Only the output types `pil` and `np` are supported not output_type={output_type}''' )
_A : List[str] = images.cpu().numpy()
if output_type == "pil":
_A : List[Any] = [self.numpy_to_pil(_a ) for image in images]
# Offload last model to CPU
if hasattr(self , """final_offload_hook""" ) and self.final_offload_hook is not None:
self.final_offload_hook.offload()
if not return_dict:
return (images,)
return ShapEPipelineOutput(images=_a )
| 26 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase = {'''configuration_xlnet''': ['''XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''XLNetConfig''']}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = ['''XLNetTokenizer''']
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = ['''XLNetTokenizerFast''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = [
'''XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''XLNetForMultipleChoice''',
'''XLNetForQuestionAnswering''',
'''XLNetForQuestionAnsweringSimple''',
'''XLNetForSequenceClassification''',
'''XLNetForTokenClassification''',
'''XLNetLMHeadModel''',
'''XLNetModel''',
'''XLNetPreTrainedModel''',
'''load_tf_weights_in_xlnet''',
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCamelCase = [
'''TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''TFXLNetForMultipleChoice''',
'''TFXLNetForQuestionAnsweringSimple''',
'''TFXLNetForSequenceClassification''',
'''TFXLNetForTokenClassification''',
'''TFXLNetLMHeadModel''',
'''TFXLNetMainLayer''',
'''TFXLNetModel''',
'''TFXLNetPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_xlnet import XLNET_PRETRAINED_CONFIG_ARCHIVE_MAP, XLNetConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet import XLNetTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_xlnet_fast import XLNetTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_xlnet import (
XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
XLNetForMultipleChoice,
XLNetForQuestionAnswering,
XLNetForQuestionAnsweringSimple,
XLNetForSequenceClassification,
XLNetForTokenClassification,
XLNetLMHeadModel,
XLNetModel,
XLNetPreTrainedModel,
load_tf_weights_in_xlnet,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_xlnet import (
TF_XLNET_PRETRAINED_MODEL_ARCHIVE_LIST,
TFXLNetForMultipleChoice,
TFXLNetForQuestionAnsweringSimple,
TFXLNetForSequenceClassification,
TFXLNetForTokenClassification,
TFXLNetLMHeadModel,
TFXLNetMainLayer,
TFXLNetModel,
TFXLNetPreTrainedModel,
)
else:
import sys
lowerCamelCase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 188 | 0 |
'''simple docstring'''
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
__A =logging.get_logger(__name__)
class _snake_case ( a__ ):
lowerCAmelCase :Union[str, Any] = ['''input_values''', '''padding_mask''']
def __init__( self , _lowerCamelCase = 1 , _lowerCamelCase = 2_4000 , _lowerCamelCase = 0.0 , _lowerCamelCase = None , _lowerCamelCase = None , **_lowerCamelCase , ):
super().__init__(feature_size=_lowerCamelCase , sampling_rate=_lowerCamelCase , padding_value=_lowerCamelCase , **_lowerCamelCase)
UpperCAmelCase__ : Tuple = chunk_length_s
UpperCAmelCase__ : List[Any] = overlap
@property
def snake_case__ ( self):
if self.chunk_length_s is None:
return None
else:
return int(self.chunk_length_s * self.sampling_rate)
@property
def snake_case__ ( self):
if self.chunk_length_s is None or self.overlap is None:
return None
else:
return max(1 , int((1.0 - self.overlap) * self.chunk_length))
def __call__( self , _lowerCamelCase , _lowerCamelCase = None , _lowerCamelCase = False , _lowerCamelCase = None , _lowerCamelCase = None , _lowerCamelCase = None , ):
if sampling_rate is not None:
if sampling_rate != self.sampling_rate:
raise ValueError(
f'''The model corresponding to this feature extractor: {self} was trained using a sampling rate of'''
f''' {self.sampling_rate}. Please make sure that the provided audio input was sampled with'''
f''' {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.""")
if padding and truncation:
raise ValueError("""Both padding and truncation were set. Make sure you only set one.""")
elif padding is None:
# by default let's pad the inputs
UpperCAmelCase__ : str = True
UpperCAmelCase__ : Optional[Any] = bool(
isinstance(_lowerCamelCase , (list, tuple)) and (isinstance(raw_audio[0] , (np.ndarray, tuple, list))))
if is_batched:
UpperCAmelCase__ : Optional[Any] = [np.asarray(_lowerCamelCase , dtype=np.floataa).T for audio in raw_audio]
elif not is_batched and not isinstance(_lowerCamelCase , np.ndarray):
UpperCAmelCase__ : Tuple = np.asarray(_lowerCamelCase , dtype=np.floataa)
elif isinstance(_lowerCamelCase , np.ndarray) and raw_audio.dtype is np.dtype(np.floataa):
UpperCAmelCase__ : Dict = raw_audio.astype(np.floataa)
# always return batch
if not is_batched:
UpperCAmelCase__ : List[str] = [np.asarray(_lowerCamelCase).T]
# verify inputs are valid
for idx, example in enumerate(_lowerCamelCase):
if example.ndim > 2:
raise ValueError(f'''Expected input shape (channels, length) but got shape {example.shape}''')
if self.feature_size == 1 and example.ndim != 1:
raise ValueError(f'''Expected mono audio but example has {example.shape[-1]} channels''')
if self.feature_size == 2 and example.shape[-1] != 2:
raise ValueError(f'''Expected stereo audio but example has {example.shape[-1]} channels''')
UpperCAmelCase__ : List[str] = None
UpperCAmelCase__ : Tuple = BatchFeature({"""input_values""": raw_audio})
if self.chunk_stride is not None and self.chunk_length is not None and max_length is None:
if truncation:
UpperCAmelCase__ : Union[str, Any] = min(array.shape[0] for array in raw_audio)
UpperCAmelCase__ : Any = int(np.floor(max_length / self.chunk_stride))
UpperCAmelCase__ : Optional[int] = (nb_step - 1) * self.chunk_stride + self.chunk_length
elif padding:
UpperCAmelCase__ : Any = max(array.shape[0] for array in raw_audio)
UpperCAmelCase__ : str = int(np.ceil(max_length / self.chunk_stride))
UpperCAmelCase__ : Tuple = (nb_step - 1) * self.chunk_stride + self.chunk_length
UpperCAmelCase__ : Dict = """max_length"""
else:
UpperCAmelCase__ : Optional[Any] = input_values
# normal padding on batch
if padded_inputs is None:
UpperCAmelCase__ : str = self.pad(
_lowerCamelCase , max_length=_lowerCamelCase , truncation=_lowerCamelCase , padding=_lowerCamelCase , return_attention_mask=_lowerCamelCase , )
if padding:
UpperCAmelCase__ : Union[str, Any] = padded_inputs.pop("""attention_mask""")
UpperCAmelCase__ : Any = []
for example in padded_inputs.pop("""input_values"""):
if self.feature_size == 1:
UpperCAmelCase__ : Any = example[..., None]
input_values.append(example.T)
UpperCAmelCase__ : List[Any] = input_values
if return_tensors is not None:
UpperCAmelCase__ : Union[str, Any] = padded_inputs.convert_to_tensors(_lowerCamelCase)
return padded_inputs | 283 |
'''simple docstring'''
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
__A ='\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n authors={Xu, Wei and Napoles, Courtney and Pavlick, Ellie and Chen, Quanze and Callison-Burch, Chris},\n journal = {Transactions of the Association for Computational Linguistics},\n volume = {4},\n year={2016},\n url = {https://www.aclweb.org/anthology/Q16-1029},\n pages = {401--415\n},\n@inproceedings{post-2018-call,\n title = "A Call for Clarity in Reporting {BLEU} Scores",\n author = "Post, Matt",\n booktitle = "Proceedings of the Third Conference on Machine Translation: Research Papers",\n month = oct,\n year = "2018",\n address = "Belgium, Brussels",\n publisher = "Association for Computational Linguistics",\n url = "https://www.aclweb.org/anthology/W18-6319",\n pages = "186--191",\n}\n'
__A ='\\nWIKI_SPLIT is the combination of three metrics SARI, EXACT and SACREBLEU\nIt can be used to evaluate the quality of machine-generated texts.\n'
__A ='\nCalculates sari score (between 0 and 100) given a list of source and predicted\nsentences, and a list of lists of reference sentences. It also computes the BLEU score as well as the exact match score.\nArgs:\n sources: list of source sentences where each sentence should be a string.\n predictions: list of predicted sentences where each sentence should be a string.\n references: list of lists of reference sentences where each sentence should be a string.\nReturns:\n sari: sari score\n sacrebleu: sacrebleu score\n exact: exact score\n\nExamples:\n >>> sources=["About 95 species are currently accepted ."]\n >>> predictions=["About 95 you now get in ."]\n >>> references=[["About 95 species are currently known ."]]\n >>> wiki_split = datasets.load_metric("wiki_split")\n >>> results = wiki_split.compute(sources=sources, predictions=predictions, references=references)\n >>> print(results)\n {\'sari\': 21.805555555555557, \'sacrebleu\': 14.535768424205482, \'exact\': 0.0}\n'
def _UpperCamelCase ( UpperCamelCase__ ):
def remove_articles(UpperCamelCase__ ):
UpperCAmelCase__ : Tuple = re.compile(R"""\b(a|an|the)\b""" , re.UNICODE )
return re.sub(UpperCamelCase__ , """ """ , UpperCamelCase__ )
def white_space_fix(UpperCamelCase__ ):
return " ".join(text.split() )
def remove_punc(UpperCamelCase__ ):
UpperCAmelCase__ : int = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(UpperCamelCase__ ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(UpperCamelCase__ ) ) ) )
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
return int(normalize_answer(UpperCamelCase__ ) == normalize_answer(UpperCamelCase__ ) )
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : Any = [any(compute_exact(UpperCamelCase__ , UpperCamelCase__ ) for ref in refs ) for pred, refs in zip(UpperCamelCase__ , UpperCamelCase__ )]
return (sum(UpperCamelCase__ ) / len(UpperCamelCase__ )) * 1_0_0
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : List[Any] = [rgram for rgrams in rgramslist for rgram in rgrams]
UpperCAmelCase__ : List[Any] = Counter(UpperCamelCase__ )
UpperCAmelCase__ : str = Counter(UpperCamelCase__ )
UpperCAmelCase__ : Dict = Counter()
for sgram, scount in sgramcounter.items():
UpperCAmelCase__ : Dict = scount * numref
UpperCAmelCase__ : int = Counter(UpperCamelCase__ )
UpperCAmelCase__ : Optional[int] = Counter()
for cgram, ccount in cgramcounter.items():
UpperCAmelCase__ : Union[str, Any] = ccount * numref
# KEEP
UpperCAmelCase__ : str = sgramcounter_rep & cgramcounter_rep
UpperCAmelCase__ : List[Any] = keepgramcounter_rep & rgramcounter
UpperCAmelCase__ : Dict = sgramcounter_rep & rgramcounter
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : Union[str, Any] = 0
for keepgram in keepgramcountergood_rep:
keeptmpscorea += keepgramcountergood_rep[keepgram] / keepgramcounter_rep[keepgram]
# Fix an alleged bug [2] in the keep score computation.
# keeptmpscore2 += keepgramcountergood_rep[keepgram] / keepgramcounterall_rep[keepgram]
keeptmpscorea += keepgramcountergood_rep[keepgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase__ : List[str] = 1
UpperCAmelCase__ : Optional[Any] = 1
if len(UpperCamelCase__ ) > 0:
UpperCAmelCase__ : Optional[int] = keeptmpscorea / len(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
# Fix an alleged bug [2] in the keep score computation.
# keepscore_recall = keeptmpscore2 / len(keepgramcounterall_rep)
UpperCAmelCase__ : Any = keeptmpscorea / sum(keepgramcounterall_rep.values() )
UpperCAmelCase__ : Any = 0
if keepscore_precision > 0 or keepscore_recall > 0:
UpperCAmelCase__ : str = 2 * keepscore_precision * keepscore_recall / (keepscore_precision + keepscore_recall)
# DELETION
UpperCAmelCase__ : str = sgramcounter_rep - cgramcounter_rep
UpperCAmelCase__ : Optional[Any] = delgramcounter_rep - rgramcounter
UpperCAmelCase__ : List[str] = sgramcounter_rep - rgramcounter
UpperCAmelCase__ : str = 0
UpperCAmelCase__ : List[Any] = 0
for delgram in delgramcountergood_rep:
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounter_rep[delgram]
deltmpscorea += delgramcountergood_rep[delgram] / delgramcounterall_rep[delgram]
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase__ : Union[str, Any] = 1
if len(UpperCamelCase__ ) > 0:
UpperCAmelCase__ : Optional[Any] = deltmpscorea / len(UpperCamelCase__ )
# ADDITION
UpperCAmelCase__ : Tuple = set(UpperCamelCase__ ) - set(UpperCamelCase__ )
UpperCAmelCase__ : Optional[Any] = set(UpperCamelCase__ ) & set(UpperCamelCase__ )
UpperCAmelCase__ : List[str] = set(UpperCamelCase__ ) - set(UpperCamelCase__ )
UpperCAmelCase__ : str = 0
for addgram in addgramcountergood:
addtmpscore += 1
# Define 0/0=1 instead of 0 to give higher scores for predictions that match
# a target exactly.
UpperCAmelCase__ : List[Any] = 1
UpperCAmelCase__ : List[Any] = 1
if len(UpperCamelCase__ ) > 0:
UpperCAmelCase__ : Optional[int] = addtmpscore / len(UpperCamelCase__ )
if len(UpperCamelCase__ ) > 0:
UpperCAmelCase__ : int = addtmpscore / len(UpperCamelCase__ )
UpperCAmelCase__ : Tuple = 0
if addscore_precision > 0 or addscore_recall > 0:
UpperCAmelCase__ : int = 2 * addscore_precision * addscore_recall / (addscore_precision + addscore_recall)
return (keepscore, delscore_precision, addscore)
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase__ : Dict = len(UpperCamelCase__ )
UpperCAmelCase__ : Tuple = ssent.split(""" """ )
UpperCAmelCase__ : Optional[int] = csent.split(""" """ )
UpperCAmelCase__ : Union[str, Any] = []
UpperCAmelCase__ : Tuple = []
UpperCAmelCase__ : Any = []
UpperCAmelCase__ : Optional[Any] = []
UpperCAmelCase__ : Any = []
UpperCAmelCase__ : Tuple = []
UpperCAmelCase__ : Optional[Any] = []
UpperCAmelCase__ : Union[str, Any] = []
UpperCAmelCase__ : Dict = []
UpperCAmelCase__ : List[Any] = []
for rsent in rsents:
UpperCAmelCase__ : List[str] = rsent.split(""" """ )
UpperCAmelCase__ : Dict = []
UpperCAmelCase__ : str = []
UpperCAmelCase__ : Dict = []
ragramslist.append(UpperCamelCase__ )
for i in range(0 , len(UpperCamelCase__ ) - 1 ):
if i < len(UpperCamelCase__ ) - 1:
UpperCAmelCase__ : Optional[int] = ragrams[i] + """ """ + ragrams[i + 1]
ragrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 2:
UpperCAmelCase__ : Union[str, Any] = ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2]
ragrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 3:
UpperCAmelCase__ : Any = ragrams[i] + """ """ + ragrams[i + 1] + """ """ + ragrams[i + 2] + """ """ + ragrams[i + 3]
ragrams.append(UpperCamelCase__ )
ragramslist.append(UpperCamelCase__ )
ragramslist.append(UpperCamelCase__ )
ragramslist.append(UpperCamelCase__ )
for i in range(0 , len(UpperCamelCase__ ) - 1 ):
if i < len(UpperCamelCase__ ) - 1:
UpperCAmelCase__ : Optional[int] = sagrams[i] + """ """ + sagrams[i + 1]
sagrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 2:
UpperCAmelCase__ : Dict = sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2]
sagrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 3:
UpperCAmelCase__ : str = sagrams[i] + """ """ + sagrams[i + 1] + """ """ + sagrams[i + 2] + """ """ + sagrams[i + 3]
sagrams.append(UpperCamelCase__ )
for i in range(0 , len(UpperCamelCase__ ) - 1 ):
if i < len(UpperCamelCase__ ) - 1:
UpperCAmelCase__ : Dict = cagrams[i] + """ """ + cagrams[i + 1]
cagrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 2:
UpperCAmelCase__ : int = cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2]
cagrams.append(UpperCamelCase__ )
if i < len(UpperCamelCase__ ) - 3:
UpperCAmelCase__ : List[Any] = cagrams[i] + """ """ + cagrams[i + 1] + """ """ + cagrams[i + 2] + """ """ + cagrams[i + 3]
cagrams.append(UpperCamelCase__ )
((UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__)) : Optional[Any] = SARIngram(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
((UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__)) : str = SARIngram(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
((UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__)) : Any = SARIngram(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
((UpperCAmelCase__) , (UpperCAmelCase__) , (UpperCAmelCase__)) : Optional[int] = SARIngram(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
UpperCAmelCase__ : Tuple = sum([keepascore, keepascore, keepascore, keepascore] ) / 4
UpperCAmelCase__ : Union[str, Any] = sum([delascore, delascore, delascore, delascore] ) / 4
UpperCAmelCase__ : Dict = sum([addascore, addascore, addascore, addascore] ) / 4
UpperCAmelCase__ : List[Any] = (avgkeepscore + avgdelscore + avgaddscore) / 3
return finalscore
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ = True , UpperCamelCase__ = "13a" , UpperCamelCase__ = True ):
# Normalization is requried for the ASSET dataset (one of the primary
# datasets in sentence simplification) to allow using space
# to split the sentence. Even though Wiki-Auto and TURK datasets,
# do not require normalization, we do it for consistency.
# Code adapted from the EASSE library [1] written by the authors of the ASSET dataset.
# [1] https://github.com/feralvam/easse/blob/580bba7e1378fc8289c663f864e0487188fe8067/easse/utils/preprocessing.py#L7
if lowercase:
UpperCAmelCase__ : List[str] = sentence.lower()
if tokenizer in ["13a", "intl"]:
if version.parse(sacrebleu.__version__ ).major >= 2:
UpperCAmelCase__ : Tuple = sacrebleu.metrics.bleu._get_tokenizer(UpperCamelCase__ )()(UpperCamelCase__ )
else:
UpperCAmelCase__ : Tuple = sacrebleu.TOKENIZERS[tokenizer]()(UpperCamelCase__ )
elif tokenizer == "moses":
UpperCAmelCase__ : Union[str, Any] = sacremoses.MosesTokenizer().tokenize(UpperCamelCase__ , return_str=UpperCamelCase__ , escape=UpperCamelCase__ )
elif tokenizer == "penn":
UpperCAmelCase__ : Dict = sacremoses.MosesTokenizer().penn_tokenize(UpperCamelCase__ , return_str=UpperCamelCase__ )
else:
UpperCAmelCase__ : List[Any] = sentence
if not return_str:
UpperCAmelCase__ : List[str] = normalized_sent.split()
return normalized_sent
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
if not (len(UpperCamelCase__ ) == len(UpperCamelCase__ ) == len(UpperCamelCase__ )):
raise ValueError("""Sources length must match predictions and references lengths.""" )
UpperCAmelCase__ : Optional[int] = 0
for src, pred, refs in zip(UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
sari_score += SARIsent(normalize(UpperCamelCase__ ) , normalize(UpperCamelCase__ ) , [normalize(UpperCamelCase__ ) for sent in refs] )
UpperCAmelCase__ : Optional[int] = sari_score / len(UpperCamelCase__ )
return 1_0_0 * sari_score
def _UpperCamelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__="exp" , UpperCamelCase__=None , UpperCamelCase__=False , UpperCamelCase__=False , UpperCamelCase__=False , ):
UpperCAmelCase__ : int = len(references[0] )
if any(len(UpperCamelCase__ ) != references_per_prediction for refs in references ):
raise ValueError("""Sacrebleu requires the same number of references for each prediction""" )
UpperCAmelCase__ : int = [[refs[i] for refs in references] for i in range(UpperCamelCase__ )]
UpperCAmelCase__ : int = sacrebleu.corpus_bleu(
UpperCamelCase__ , UpperCamelCase__ , smooth_method=UpperCamelCase__ , smooth_value=UpperCamelCase__ , force=UpperCamelCase__ , lowercase=UpperCamelCase__ , use_effective_order=UpperCamelCase__ , )
return output.score
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class _snake_case ( datasets.Metric ):
def snake_case__ ( self):
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
"""predictions""": datasets.Value("""string""" , id="""sequence"""),
"""references""": datasets.Sequence(datasets.Value("""string""" , id="""sequence""") , id="""references"""),
}) , codebase_urls=[
"""https://github.com/huggingface/transformers/blob/master/src/transformers/data/metrics/squad_metrics.py""",
"""https://github.com/cocoxu/simplification/blob/master/SARI.py""",
"""https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/sari_hook.py""",
"""https://github.com/mjpost/sacreBLEU""",
] , reference_urls=[
"""https://www.aclweb.org/anthology/Q16-1029.pdf""",
"""https://github.com/mjpost/sacreBLEU""",
"""https://en.wikipedia.org/wiki/BLEU""",
"""https://towardsdatascience.com/evaluating-text-output-in-nlp-bleu-at-your-own-risk-e8609665a213""",
] , )
def snake_case__ ( self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase):
UpperCAmelCase__ : Union[str, Any] = {}
result.update({"""sari""": compute_sari(sources=_lowerCamelCase , predictions=_lowerCamelCase , references=_lowerCamelCase)})
result.update({"""sacrebleu""": compute_sacrebleu(predictions=_lowerCamelCase , references=_lowerCamelCase)})
result.update({"""exact""": compute_em(predictions=_lowerCamelCase , references=_lowerCamelCase)})
return result | 283 | 1 |
import math
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A : Any = logging.get_logger(__name__)
A : List[Any] = {
'facebook/data2vec-base-960h': 'https://huggingface.co/facebook/data2vec-audio-base-960h/resolve/main/config.json',
# See all Data2VecAudio models at https://huggingface.co/models?filter=data2vec-audio
}
class A ( UpperCAmelCase__ ):
'''simple docstring'''
A__ = '''data2vec-audio'''
def __init__(self : Dict , _UpperCAmelCase : Optional[int]=32 , _UpperCAmelCase : str=768 , _UpperCAmelCase : Optional[int]=12 , _UpperCAmelCase : Optional[Any]=12 , _UpperCAmelCase : Any=3072 , _UpperCAmelCase : List[Any]="gelu" , _UpperCAmelCase : Union[str, Any]=0.1 , _UpperCAmelCase : Dict=0.1 , _UpperCAmelCase : Optional[int]=0.1 , _UpperCAmelCase : Optional[int]=0.0 , _UpperCAmelCase : Tuple=0.1 , _UpperCAmelCase : List[Any]=0.1 , _UpperCAmelCase : Union[str, Any]=0.02 , _UpperCAmelCase : Tuple=1E-5 , _UpperCAmelCase : Optional[int]="gelu" , _UpperCAmelCase : Any=(512, 512, 512, 512, 512, 512, 512) , _UpperCAmelCase : List[str]=(5, 2, 2, 2, 2, 2, 2) , _UpperCAmelCase : Tuple=(10, 3, 3, 3, 3, 2, 2) , _UpperCAmelCase : Optional[int]=False , _UpperCAmelCase : int=16 , _UpperCAmelCase : Tuple=19 , _UpperCAmelCase : str=5 , _UpperCAmelCase : Union[str, Any]=0.05 , _UpperCAmelCase : Union[str, Any]=10 , _UpperCAmelCase : int=2 , _UpperCAmelCase : int=0.0 , _UpperCAmelCase : Any=10 , _UpperCAmelCase : Any=0 , _UpperCAmelCase : Any="sum" , _UpperCAmelCase : Tuple=False , _UpperCAmelCase : List[str]=False , _UpperCAmelCase : List[str]=256 , _UpperCAmelCase : Tuple=(512, 512, 512, 512, 1500) , _UpperCAmelCase : Optional[int]=(5, 3, 3, 1, 1) , _UpperCAmelCase : List[Any]=(1, 2, 3, 1, 1) , _UpperCAmelCase : Any=512 , _UpperCAmelCase : Any=0 , _UpperCAmelCase : Any=1 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : Any=False , _UpperCAmelCase : Optional[int]=3 , _UpperCAmelCase : Union[str, Any]=2 , _UpperCAmelCase : Any=3 , _UpperCAmelCase : Union[str, Any]=None , **_UpperCAmelCase : str , ) -> Optional[Any]:
"""simple docstring"""
super().__init__(**_UpperCAmelCase , pad_token_id=_UpperCAmelCase , bos_token_id=_UpperCAmelCase , eos_token_id=_UpperCAmelCase )
lowercase__ = hidden_size
lowercase__ = feat_extract_activation
lowercase__ = list(_UpperCAmelCase )
lowercase__ = list(_UpperCAmelCase )
lowercase__ = list(_UpperCAmelCase )
lowercase__ = conv_bias
lowercase__ = num_conv_pos_embeddings
lowercase__ = num_conv_pos_embedding_groups
lowercase__ = conv_pos_kernel_size
lowercase__ = len(self.conv_dim )
lowercase__ = num_hidden_layers
lowercase__ = intermediate_size
lowercase__ = hidden_act
lowercase__ = num_attention_heads
lowercase__ = hidden_dropout
lowercase__ = attention_dropout
lowercase__ = activation_dropout
lowercase__ = feat_proj_dropout
lowercase__ = final_dropout
lowercase__ = layerdrop
lowercase__ = layer_norm_eps
lowercase__ = initializer_range
lowercase__ = vocab_size
lowercase__ = use_weighted_layer_sum
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
"""Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =="""
""" `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ="""
f''' {len(self.conv_dim )}`, `len(config.conv_stride) = {len(self.conv_stride )}`,'''
f''' `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase__ = mask_time_prob
lowercase__ = mask_time_length
lowercase__ = mask_time_min_masks
lowercase__ = mask_feature_prob
lowercase__ = mask_feature_length
lowercase__ = mask_feature_min_masks
# ctc loss
lowercase__ = ctc_loss_reduction
lowercase__ = ctc_zero_infinity
# adapter
lowercase__ = add_adapter
lowercase__ = adapter_kernel_size
lowercase__ = adapter_stride
lowercase__ = num_adapter_layers
lowercase__ = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowercase__ = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowercase__ = list(_UpperCAmelCase )
lowercase__ = list(_UpperCAmelCase )
lowercase__ = list(_UpperCAmelCase )
lowercase__ = xvector_output_dim
@property
def lowerCamelCase__ (self : Optional[Any] ) -> Dict:
"""simple docstring"""
return math.prod(self.conv_stride )
| 305 |
def UpperCamelCase ( __magic_name__ : List[Any] ) -> Optional[int]:
"""simple docstring"""
lowercase__ = [0] * len(__magic_name__ )
lowercase__ = []
lowercase__ = [1] * len(__magic_name__ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i in range(len(__magic_name__ ) ):
if indegree[i] == 0:
queue.append(__magic_name__ )
while queue:
lowercase__ = queue.pop(0 )
for x in graph[vertex]:
indegree[x] -= 1
if long_dist[vertex] + 1 > long_dist[x]:
lowercase__ = long_dist[vertex] + 1
if indegree[x] == 0:
queue.append(__magic_name__ )
print(max(__magic_name__ ) )
# Adjacency list of Graph
A : Union[str, Any] = {0: [2, 3, 4], 1: [2, 7], 2: [5], 3: [5, 7], 4: [7], 5: [6], 6: [7], 7: []}
longest_distance(graph)
| 305 | 1 |
import os
import time
import numpy as np
import onnxruntime as ort
snake_case_ = '''1'''
snake_case_ = '''0'''
snake_case_ = '''1'''
snake_case_ = ort.SessionOptions()
snake_case_ = ort.GraphOptimizationLevel.ORT_DISABLE_ALL
print('''Create inference session...''')
snake_case_ = ['''TensorrtExecutionProvider''', '''CUDAExecutionProvider''']
snake_case_ = ort.InferenceSession('''model.onnx''', sess_options=sess_opt, providers=execution_provider)
snake_case_ = ort.RunOptions()
snake_case_ = 128
snake_case_ = 1
snake_case_ = np.ones((batch, sequence), dtype=np.intaa)
snake_case_ = np.ones((batch, sequence), dtype=np.intaa)
snake_case_ = np.ones((batch, sequence), dtype=np.intaa)
print('''Warm up phase...''')
sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print('''Start inference...''')
snake_case_ = time.time()
snake_case_ = 2_000
snake_case_ = {}
for iter in range(max_iters):
snake_case_ = sess.run(
None,
{
sess.get_inputs()[0].name: input_ids,
sess.get_inputs()[1].name: attention_mask,
sess.get_inputs()[2].name: token_type_ids,
},
run_options=run_opt,
)
print('''Average Inference Time = {:.3f} ms'''.format((time.time() - start_time) * 1_000 / max_iters))
| 355 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 216 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A =logging.get_logger(__name__)
A ={
"google/efficientnet-b7": "https://huggingface.co/google/efficientnet-b7/resolve/main/config.json",
}
class _a ( UpperCamelCase__ ):
__a : Tuple = """efficientnet"""
def __init__( self : Any , lowercase : int = 3 , lowercase : int = 600 , lowercase : float = 2.0 , lowercase : float = 3.1 , lowercase : int = 8 , lowercase : List[int] = [3, 3, 5, 3, 5, 5, 3] , lowercase : List[int] = [32, 16, 24, 40, 80, 112, 192] , lowercase : List[int] = [16, 24, 40, 80, 112, 192, 320] , lowercase : List[int] = [] , lowercase : List[int] = [1, 2, 2, 2, 1, 2, 1] , lowercase : List[int] = [1, 2, 2, 3, 3, 4, 1] , lowercase : List[int] = [1, 6, 6, 6, 6, 6, 6] , lowercase : float = 0.25 , lowercase : str = "swish" , lowercase : int = 2_560 , lowercase : str = "mean" , lowercase : float = 0.02 , lowercase : float = 0.001 , lowercase : float = 0.99 , lowercase : float = 0.5 , lowercase : float = 0.2 , **lowercase : Dict , ):
'''simple docstring'''
super().__init__(**lowercase_ )
UpperCAmelCase = num_channels
UpperCAmelCase = image_size
UpperCAmelCase = width_coefficient
UpperCAmelCase = depth_coefficient
UpperCAmelCase = depth_divisor
UpperCAmelCase = kernel_sizes
UpperCAmelCase = in_channels
UpperCAmelCase = out_channels
UpperCAmelCase = depthwise_padding
UpperCAmelCase = strides
UpperCAmelCase = num_block_repeats
UpperCAmelCase = expand_ratios
UpperCAmelCase = squeeze_expansion_ratio
UpperCAmelCase = hidden_act
UpperCAmelCase = hidden_dim
UpperCAmelCase = pooling_type
UpperCAmelCase = initializer_range
UpperCAmelCase = batch_norm_eps
UpperCAmelCase = batch_norm_momentum
UpperCAmelCase = dropout_rate
UpperCAmelCase = drop_connect_rate
UpperCAmelCase = sum(lowercase_ ) * 4
class _a ( UpperCamelCase__ ):
__a : str = version.parse("""1.11""" )
@property
def A ( self : Tuple ):
'''simple docstring'''
return OrderedDict(
[
('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
] )
@property
def A ( self : str ):
'''simple docstring'''
return 1E-5
| 34 |
'''simple docstring'''
def UpperCamelCase ( _lowerCamelCase : int | float | str ):
try:
A__ = float(_lowerCamelCase )
except ValueError:
raise ValueError("Please enter a valid number" )
A__ = decimal - int(_lowerCamelCase )
if fractional_part == 0:
return int(_lowerCamelCase ), 1
else:
A__ = len(str(_lowerCamelCase ).split("." )[1] )
A__ = int(decimal * (10**number_of_frac_digits) )
A__ = 10**number_of_frac_digits
A__, A__ = denominator, numerator
while True:
A__ = dividend % divisor
if remainder == 0:
break
A__, A__ = divisor, remainder
A__, A__ = numerator / divisor, denominator / divisor
return int(_lowerCamelCase ), int(_lowerCamelCase )
if __name__ == "__main__":
print(f"""{decimal_to_fraction(2) = }""")
print(f"""{decimal_to_fraction(89.0) = }""")
print(f"""{decimal_to_fraction("67") = }""")
print(f"""{decimal_to_fraction("45.0") = }""")
print(f"""{decimal_to_fraction(1.5) = }""")
print(f"""{decimal_to_fraction("6.25") = }""")
print(f"""{decimal_to_fraction("78td") = }""")
| 237 | 0 |
import requests
def _lowerCAmelCase ( A__: str , A__: str ):
'''simple docstring'''
UpperCAmelCase = {'''Content-Type''': '''application/json'''}
UpperCAmelCase = requests.post(A__ , json={'''text''': message_body} , headers=A__ )
if response.status_code != 200:
UpperCAmelCase = (
'''Request to slack returned an error '''
F"""{response.status_code}, the response is:\n{response.text}"""
)
raise ValueError(A__ )
if __name__ == "__main__":
# Set the slack url to the one provided by Slack when you create the webhook at
# https://my.slack.com/services/new/incoming-webhook/
send_slack_message("<YOUR MESSAGE BODY>", "<SLACK CHANNEL URL>")
| 354 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
__magic_name__ = {"configuration_beit": ["BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "BeitConfig", "BeitOnnxConfig"]}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = ["BeitFeatureExtractor"]
__magic_name__ = ["BeitImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
"BEIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"BeitForImageClassification",
"BeitForMaskedImageModeling",
"BeitForSemanticSegmentation",
"BeitModel",
"BeitPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__magic_name__ = [
"FlaxBeitForImageClassification",
"FlaxBeitForMaskedImageModeling",
"FlaxBeitModel",
"FlaxBeitPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_beit import BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP, BeitConfig, BeitOnnxConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_beit import BeitFeatureExtractor
from .image_processing_beit import BeitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_beit import (
BEIT_PRETRAINED_MODEL_ARCHIVE_LIST,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
BeitPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_beit import (
FlaxBeitForImageClassification,
FlaxBeitForMaskedImageModeling,
FlaxBeitModel,
FlaxBeitPreTrainedModel,
)
else:
import sys
__magic_name__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 152 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ (self ) -> Any:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ (self ) -> Tuple:
__UpperCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4''' )
__UpperCAmelCase = sd_pipe.to(lowercase__ )
sd_pipe.set_progress_bar_config(disable=lowercase__ )
sd_pipe.set_scheduler('''sample_euler''' )
__UpperCAmelCase = '''A painting of a squirrel eating a burger'''
__UpperCAmelCase = torch.manual_seed(0 )
__UpperCAmelCase = sd_pipe([prompt] , generator=lowercase__ , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
__UpperCAmelCase = output.images
__UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCAmelCase = np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCAmelCase_ (self ) -> Dict:
__UpperCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
__UpperCAmelCase = sd_pipe.to(lowercase__ )
sd_pipe.set_progress_bar_config(disable=lowercase__ )
sd_pipe.set_scheduler('''sample_euler''' )
__UpperCAmelCase = '''A painting of a squirrel eating a burger'''
__UpperCAmelCase = torch.manual_seed(0 )
__UpperCAmelCase = sd_pipe([prompt] , generator=lowercase__ , guidance_scale=9.0 , num_inference_steps=20 , output_type='''np''' )
__UpperCAmelCase = output.images
__UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCAmelCase = np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-1
def lowerCAmelCase_ (self ) -> Optional[int]:
__UpperCAmelCase = StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base''' )
__UpperCAmelCase = sd_pipe.to(lowercase__ )
sd_pipe.set_progress_bar_config(disable=lowercase__ )
sd_pipe.set_scheduler('''sample_dpmpp_2m''' )
__UpperCAmelCase = '''A painting of a squirrel eating a burger'''
__UpperCAmelCase = torch.manual_seed(0 )
__UpperCAmelCase = sd_pipe(
[prompt] , generator=lowercase__ , guidance_scale=7.5 , num_inference_steps=15 , output_type='''np''' , use_karras_sigmas=lowercase__ , )
__UpperCAmelCase = output.images
__UpperCAmelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 512, 512, 3)
__UpperCAmelCase = np.array(
[0.11381689, 0.12112921, 0.1389457, 0.12549606, 0.1244964, 0.10831517, 0.11562866, 0.10867816, 0.10499048] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
| 333 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
from transformers.testing_utils import (
require_torch,
require_vision,
slow,
torch_device,
)
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from torch import nn
from transformers import SwiftFormerForImageClassification, SwiftFormerModel
from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import ViTImageProcessor
class A_ :
'''simple docstring'''
def __init__(self , lowercase__ , lowercase__=13 , lowercase__=3 , lowercase__=True , lowercase__=True , lowercase__=0.1 , lowercase__=0.1 , lowercase__=224 , lowercase__=1_000 , lowercase__=[3, 3, 6, 4] , lowercase__=[48, 56, 112, 220] , ) -> int:
__UpperCAmelCase = parent
__UpperCAmelCase = batch_size
__UpperCAmelCase = num_channels
__UpperCAmelCase = is_training
__UpperCAmelCase = use_labels
__UpperCAmelCase = hidden_dropout_prob
__UpperCAmelCase = attention_probs_dropout_prob
__UpperCAmelCase = num_labels
__UpperCAmelCase = image_size
__UpperCAmelCase = layer_depths
__UpperCAmelCase = embed_dims
def lowerCAmelCase_ (self ) -> str:
__UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__UpperCAmelCase = None
if self.use_labels:
__UpperCAmelCase = ids_tensor([self.batch_size] , self.num_labels )
__UpperCAmelCase = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase_ (self ) -> Optional[Any]:
return SwiftFormerConfig(
depths=self.layer_depths , embed_dims=self.embed_dims , mlp_ratio=4 , downsamples=[True, True, True, True] , hidden_act='''gelu''' , num_labels=self.num_labels , down_patch_size=3 , down_stride=2 , down_pad=1 , drop_rate=0.0 , drop_path_rate=0.0 , use_layer_scale=lowercase__ , layer_scale_init_value=1E-5 , )
def lowerCAmelCase_ (self , lowercase__ , lowercase__ , lowercase__ ) -> int:
__UpperCAmelCase = SwiftFormerModel(config=lowercase__ )
model.to(lowercase__ )
model.eval()
__UpperCAmelCase = model(lowercase__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dims[-1], 7, 7) )
def lowerCAmelCase_ (self , lowercase__ , lowercase__ , lowercase__ ) -> List[Any]:
__UpperCAmelCase = self.num_labels
__UpperCAmelCase = SwiftFormerForImageClassification(lowercase__ )
model.to(lowercase__ )
model.eval()
__UpperCAmelCase = model(lowercase__ , labels=lowercase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
__UpperCAmelCase = SwiftFormerForImageClassification(lowercase__ )
model.to(lowercase__ )
model.eval()
__UpperCAmelCase = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__UpperCAmelCase = model(lowercase__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase_ (self ) -> Optional[int]:
((__UpperCAmelCase) , (__UpperCAmelCase) , (__UpperCAmelCase)) = self.prepare_config_and_inputs()
__UpperCAmelCase = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_torch
class A_ ( _a , _a , unittest.TestCase ):
'''simple docstring'''
a__ = (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
a__ = (
{"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
if is_torch_available()
else {}
)
a__ = False
a__ = False
a__ = False
a__ = False
a__ = False
def lowerCAmelCase_ (self ) -> List[str]:
__UpperCAmelCase = SwiftFormerModelTester(self )
__UpperCAmelCase = ConfigTester(
self , config_class=lowercase__ , has_text_modality=lowercase__ , hidden_size=37 , num_attention_heads=12 , num_hidden_layers=12 , )
def lowerCAmelCase_ (self ) -> Dict:
self.config_tester.run_common_tests()
@unittest.skip(reason='''SwiftFormer does not use inputs_embeds''' )
def lowerCAmelCase_ (self ) -> List[Any]:
pass
def lowerCAmelCase_ (self ) -> Any:
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase = model_class(lowercase__ )
__UpperCAmelCase = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(lowercase__ , nn.Linear ) )
def lowerCAmelCase_ (self ) -> Optional[int]:
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase = model_class(lowercase__ )
__UpperCAmelCase = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__UpperCAmelCase = [*signature.parameters.keys()]
__UpperCAmelCase = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , lowercase__ )
def lowerCAmelCase_ (self ) -> Optional[int]:
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowercase__ )
def lowerCAmelCase_ (self ) -> Optional[int]:
__UpperCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*lowercase__ )
@slow
def lowerCAmelCase_ (self ) -> Any:
for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase = SwiftFormerModel.from_pretrained(lowercase__ )
self.assertIsNotNone(lowercase__ )
@unittest.skip(reason='''SwiftFormer does not output attentions''' )
def lowerCAmelCase_ (self ) -> List[str]:
pass
def lowerCAmelCase_ (self ) -> Union[str, Any]:
def check_hidden_states_output(lowercase__ , lowercase__ , lowercase__ ):
__UpperCAmelCase = model_class(lowercase__ )
model.to(lowercase__ )
model.eval()
with torch.no_grad():
__UpperCAmelCase = model(**self._prepare_for_class(lowercase__ , lowercase__ ) )
__UpperCAmelCase = outputs.hidden_states
__UpperCAmelCase = 8
self.assertEqual(len(lowercase__ ) , lowercase__ ) # TODO
# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
# with the width and height being successively divided by 2, after every 2 blocks
for i in range(len(lowercase__ ) ):
self.assertEqual(
hidden_states[i].shape , torch.Size(
[
self.model_tester.batch_size,
self.model_tester.embed_dims[i // 2],
(self.model_tester.image_size // 4) // 2 ** (i // 2),
(self.model_tester.image_size // 4) // 2 ** (i // 2),
] ) , )
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__UpperCAmelCase = True
check_hidden_states_output(lowercase__ , lowercase__ , lowercase__ )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
__UpperCAmelCase = True
check_hidden_states_output(lowercase__ , lowercase__ , lowercase__ )
def lowerCAmelCase_ (self ) -> Tuple:
def _config_zero_init(lowercase__ ):
__UpperCAmelCase = copy.deepcopy(lowercase__ )
for key in configs_no_init.__dict__.keys():
if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
setattr(lowercase__ , lowercase__ , 1E-10 )
if isinstance(getattr(lowercase__ , lowercase__ , lowercase__ ) , lowercase__ ):
__UpperCAmelCase = _config_zero_init(getattr(lowercase__ , lowercase__ ) )
setattr(lowercase__ , lowercase__ , lowercase__ )
return configs_no_init
__UpperCAmelCase , __UpperCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
__UpperCAmelCase = _config_zero_init(lowercase__ )
for model_class in self.all_model_classes:
__UpperCAmelCase = model_class(config=lowercase__ )
for name, param in model.named_parameters():
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9) / 1E9).round().item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.''' )
def lowerCAmelCase_ (self ) -> Optional[Any]:
pass
def __a ( ) -> Any:
'''simple docstring'''
__UpperCAmelCase = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_torch
@require_vision
class A_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def lowerCAmelCase_ (self ) -> str:
return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs''' ) if is_vision_available() else None
@slow
def lowerCAmelCase_ (self ) -> Tuple:
__UpperCAmelCase = SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs''' ).to(lowercase__ )
__UpperCAmelCase = self.default_image_processor
__UpperCAmelCase = prepare_img()
__UpperCAmelCase = image_processor(images=lowercase__ , return_tensors='''pt''' ).to(lowercase__ )
# forward pass
with torch.no_grad():
__UpperCAmelCase = model(**lowercase__ )
# verify the logits
__UpperCAmelCase = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape , lowercase__ )
__UpperCAmelCase = torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]] ).to(lowercase__ )
self.assertTrue(torch.allclose(outputs.logits[0, :3] , lowercase__ , atol=1E-4 ) )
| 333 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : Optional[Any] = logging.get_logger(__name__)
_snake_case : Union[str, Any] = {
"transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}
class a (_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : Optional[Any] = "transfo-xl"
__UpperCAmelCase : Union[str, Any] = ["mems"]
__UpperCAmelCase : str = {
"n_token": "vocab_size",
"hidden_size": "d_model",
"num_attention_heads": "n_head",
"num_hidden_layers": "n_layer",
}
def __init__( self : Optional[Any] , lowerCamelCase : List[Any]=267735 , lowerCamelCase : Dict=[20000, 40000, 200000] , lowerCamelCase : Tuple=1024 , lowerCamelCase : List[str]=1024 , lowerCamelCase : Optional[int]=16 , lowerCamelCase : Optional[int]=64 , lowerCamelCase : Tuple=4096 , lowerCamelCase : Union[str, Any]=4 , lowerCamelCase : List[str]=False , lowerCamelCase : Optional[Any]=18 , lowerCamelCase : Optional[int]=1600 , lowerCamelCase : Tuple=1000 , lowerCamelCase : Union[str, Any]=True , lowerCamelCase : Dict=True , lowerCamelCase : List[Any]=0 , lowerCamelCase : List[str]=-1 , lowerCamelCase : Tuple=True , lowerCamelCase : List[str]=0.1 , lowerCamelCase : Optional[int]=0.0 , lowerCamelCase : List[str]=True , lowerCamelCase : Union[str, Any]="normal" , lowerCamelCase : int=0.01 , lowerCamelCase : Dict=0.01 , lowerCamelCase : Tuple=0.02 , lowerCamelCase : List[str]=1E-5 , lowerCamelCase : List[Any]=0 , **lowerCamelCase : str , ) -> Optional[Any]:
__snake_case : List[Any] = vocab_size
__snake_case : Dict = []
self.cutoffs.extend(_a )
if proj_share_all_but_first:
__snake_case : List[str] = [False] + [True] * len(self.cutoffs )
else:
__snake_case : Optional[Any] = [False] + [False] * len(self.cutoffs )
__snake_case : Optional[int] = d_model
__snake_case : str = d_embed
__snake_case : Optional[Any] = d_head
__snake_case : Optional[int] = d_inner
__snake_case : List[str] = div_val
__snake_case : List[str] = pre_lnorm
__snake_case : Union[str, Any] = n_layer
__snake_case : Optional[int] = n_head
__snake_case : str = mem_len
__snake_case : int = same_length
__snake_case : Dict = attn_type
__snake_case : int = clamp_len
__snake_case : Optional[int] = sample_softmax
__snake_case : List[Any] = adaptive
__snake_case : Optional[int] = dropout
__snake_case : Optional[int] = dropatt
__snake_case : Optional[Any] = untie_r
__snake_case : List[str] = init
__snake_case : Any = init_range
__snake_case : Optional[int] = proj_init_std
__snake_case : List[Any] = init_std
__snake_case : List[Any] = layer_norm_epsilon
super().__init__(eos_token_id=_a , **_a )
@property
def __snake_case ( self : Dict ) -> List[str]:
# Message copied from Transformer-XL documentation
logger.info(F'The model {self.model_type} is one of the few models that has no sequence length limit.' )
return -1
@max_position_embeddings.setter
def __snake_case ( self : List[Any] , lowerCamelCase : Optional[Any] ) -> Tuple:
# Message copied from Transformer-XL documentation
raise NotImplementedError(
F'The model {self.model_type} is one of the few models that has no sequence length limit.' )
| 369 |
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
if index == r:
for j in range(__lowerCamelCase ):
print(data[j] , end=" " )
print(" " )
return
# When no more elements are there to put in data[]
if i >= n:
return
# current is included, put next at next location
__snake_case : Union[str, Any] = arr[i]
combination_util(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , index + 1 , __lowerCamelCase , i + 1 )
# current is excluded, replace it with
# next (Note that i+1 is passed, but
# index is not changed)
combination_util(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , i + 1 )
# The main function that prints all combinations
# of size r in arr[] of size n. This function
# mainly uses combinationUtil()
def lowerCAmelCase_ ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ):
# A temporary array to store all combination one by one
__snake_case : Union[str, Any] = [0] * r
# Print all combination using temporary array 'data[]'
combination_util(__lowerCamelCase , __lowerCamelCase , __lowerCamelCase , 0 , __lowerCamelCase , 0 )
if __name__ == "__main__":
# Driver code to check the function above
_snake_case : List[str] = [10, 20, 30, 40, 50]
print_combination(arr, len(arr), 3)
# This code is contributed by Ambuj sahu
| 134 | 0 |
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def __UpperCAmelCase ( __a : str ,__a : float | Decimal ,__a : float = 10**-10 ) -> float:
"""simple docstring"""
_a : Any = a
while True:
_a : int = Decimal(__a ) - (
Decimal(eval(__a ) ) / Decimal(eval(str(diff(__a ) ) ) ) # noqa: S307
)
# This number dictates the accuracy of the answer
if abs(eval(__a ) ) < precision: # noqa: S307
return float(__a )
# Let's Execute
if __name__ == "__main__":
# Find root of trigonometric function
# Find value of pi
print(f'''The root of sin(x) = 0 is {newton_raphson("sin(x)", 2)}''')
# Find root of polynomial
print(f'''The root of x**2 - 5*x + 2 = 0 is {newton_raphson("x**2 - 5*x + 2", 0.4)}''')
# Find Square Root of 5
print(f'''The root of log(x) - 1 = 0 is {newton_raphson("log(x) - 1", 2)}''')
# Exponential Roots
print(f'''The root of exp(x) - 1 = 0 is {newton_raphson("exp(x) - 1", 0)}''')
| 235 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a__ = {'''configuration_mmbt''': ['''MMBTConfig''']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a__ = ['''MMBTForClassification''', '''MMBTModel''', '''ModalEmbeddings''']
if TYPE_CHECKING:
from .configuration_mmbt import MMBTConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mmbt import MMBTForClassification, MMBTModel, ModalEmbeddings
else:
import sys
a__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 235 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase : Any = logging.get_logger(__name__)
lowerCAmelCase : Tuple = {
"""microsoft/focalnet-tiny""": """https://huggingface.co/microsoft/focalnet-tiny/resolve/main/config.json""",
}
class __magic_name__ ( UpperCAmelCase__ , UpperCAmelCase__ ):
'''simple docstring'''
__UpperCamelCase = "focalnet"
def __init__( self , _a=224 , _a=4 , _a=3 , _a=96 , _a=False , _a=[192, 384, 768, 768] , _a=[2, 2, 6, 2] , _a=[2, 2, 2, 2] , _a=[3, 3, 3, 3] , _a="gelu" , _a=4.0 , _a=0.0 , _a=0.1 , _a=False , _a=1e-4 , _a=False , _a=False , _a=False , _a=0.02 , _a=1e-5 , _a=32 , _a=None , _a=None , **_a , ):
"""simple docstring"""
super().__init__(**_a )
lowerCamelCase = image_size
lowerCamelCase = patch_size
lowerCamelCase = num_channels
lowerCamelCase = embed_dim
lowerCamelCase = use_conv_embed
lowerCamelCase = hidden_sizes
lowerCamelCase = depths
lowerCamelCase = focal_levels
lowerCamelCase = focal_windows
lowerCamelCase = hidden_act
lowerCamelCase = mlp_ratio
lowerCamelCase = hidden_dropout_prob
lowerCamelCase = drop_path_rate
lowerCamelCase = use_layerscale
lowerCamelCase = layerscale_value
lowerCamelCase = use_post_layernorm
lowerCamelCase = use_post_layernorm_in_modulation
lowerCamelCase = normalize_modulator
lowerCamelCase = initializer_range
lowerCamelCase = layer_norm_eps
lowerCamelCase = encoder_stride
lowerCamelCase = ["""stem"""] + [f'stage{idx}' for idx in range(1 , len(self.depths ) + 1 )]
lowerCamelCase , lowerCamelCase = get_aligned_output_features_output_indices(
out_features=_a , out_indices=_a , stage_names=self.stage_names )
| 168 |
"""simple docstring"""
from __future__ import annotations
class __magic_name__ :
'''simple docstring'''
def __init__( self , _a ):
"""simple docstring"""
lowerCamelCase = TypeError(
"""Matrices must be formed from a list of zero or more lists containing at """
"""least one and the same number of values, each of which must be of type """
"""int or float.""" )
if len(_a ) != 0:
lowerCamelCase = len(rows[0] )
if cols == 0:
raise error
for row in rows:
if len(_a ) != cols:
raise error
for value in row:
if not isinstance(_a , (int, float) ):
raise error
lowerCamelCase = rows
else:
lowerCamelCase = []
def _lowerCAmelCase ( self ):
"""simple docstring"""
return [[row[i] for row in self.rows] for i in range(len(self.rows[0] ) )]
@property
def _lowerCAmelCase ( self ):
"""simple docstring"""
return len(self.rows )
@property
def _lowerCAmelCase ( self ):
"""simple docstring"""
return len(self.rows[0] )
@property
def _lowerCAmelCase ( self ):
"""simple docstring"""
return (self.num_rows, self.num_columns)
@property
def _lowerCAmelCase ( self ):
"""simple docstring"""
return self.order[0] == self.order[1]
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = [
[0 if column_num != row_num else 1 for column_num in range(self.num_rows )]
for row_num in range(self.num_rows )
]
return Matrix(_a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
if not self.is_square:
return 0
if self.order == (0, 0):
return 1
if self.order == (1, 1):
return int(self.rows[0][0] )
if self.order == (2, 2):
return int(
(self.rows[0][0] * self.rows[1][1])
- (self.rows[0][1] * self.rows[1][0]) )
else:
return sum(
self.rows[0][column] * self.cofactors().rows[0][column]
for column in range(self.num_columns ) )
def _lowerCAmelCase ( self ):
"""simple docstring"""
return bool(self.determinant() )
def _lowerCAmelCase ( self , _a , _a ):
"""simple docstring"""
lowerCamelCase = [
[
self.rows[other_row][other_column]
for other_column in range(self.num_columns )
if other_column != column
]
for other_row in range(self.num_rows )
if other_row != row
]
return Matrix(_a ).determinant()
def _lowerCAmelCase ( self , _a , _a ):
"""simple docstring"""
if (row + column) % 2 == 0:
return self.get_minor(_a , _a )
return -1 * self.get_minor(_a , _a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
return Matrix(
[
[self.get_minor(_a , _a ) for column in range(self.num_columns )]
for row in range(self.num_rows )
] )
def _lowerCAmelCase ( self ):
"""simple docstring"""
return Matrix(
[
[
self.minors().rows[row][column]
if (row + column) % 2 == 0
else self.minors().rows[row][column] * -1
for column in range(self.minors().num_columns )
]
for row in range(self.minors().num_rows )
] )
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = [
[self.cofactors().rows[column][row] for column in range(self.num_columns )]
for row in range(self.num_rows )
]
return Matrix(_a )
def _lowerCAmelCase ( self ):
"""simple docstring"""
lowerCamelCase = self.determinant()
if not determinant:
raise TypeError("""Only matrices with a non-zero determinant have an inverse""" )
return self.adjugate() * (1 / determinant)
def __repr__( self ):
"""simple docstring"""
return str(self.rows )
def __str__( self ):
"""simple docstring"""
if self.num_rows == 0:
return "[]"
if self.num_rows == 1:
return "[[" + ". ".join(str(self.rows[0] ) ) + "]]"
return (
"["
+ "\n ".join(
[
"""[""" + """. """.join([str(_a ) for value in row] ) + """.]"""
for row in self.rows
] )
+ "]"
)
def _lowerCAmelCase ( self , _a , _a = None ):
"""simple docstring"""
lowerCamelCase = TypeError("""Row must be a list containing all ints and/or floats""" )
if not isinstance(_a , _a ):
raise type_error
for value in row:
if not isinstance(_a , (int, float) ):
raise type_error
if len(_a ) != self.num_columns:
raise ValueError(
"""Row must be equal in length to the other rows in the matrix""" )
if position is None:
self.rows.append(_a )
else:
lowerCamelCase = self.rows[0:position] + [row] + self.rows[position:]
def _lowerCAmelCase ( self , _a , _a = None ):
"""simple docstring"""
lowerCamelCase = TypeError(
"""Column must be a list containing all ints and/or floats""" )
if not isinstance(_a , _a ):
raise type_error
for value in column:
if not isinstance(_a , (int, float) ):
raise type_error
if len(_a ) != self.num_rows:
raise ValueError(
"""Column must be equal in length to the other columns in the matrix""" )
if position is None:
lowerCamelCase = [self.rows[i] + [column[i]] for i in range(self.num_rows )]
else:
lowerCamelCase = [
self.rows[i][0:position] + [column[i]] + self.rows[i][position:]
for i in range(self.num_rows )
]
def __eq__( self , _a ):
"""simple docstring"""
if not isinstance(_a , _a ):
return NotImplemented
return self.rows == other.rows
def __ne__( self , _a ):
"""simple docstring"""
return not self == other
def __neg__( self ):
"""simple docstring"""
return self * -1
def __add__( self , _a ):
"""simple docstring"""
if self.order != other.order:
raise ValueError("""Addition requires matrices of the same order""" )
return Matrix(
[
[self.rows[i][j] + other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __sub__( self , _a ):
"""simple docstring"""
if self.order != other.order:
raise ValueError("""Subtraction requires matrices of the same order""" )
return Matrix(
[
[self.rows[i][j] - other.rows[i][j] for j in range(self.num_columns )]
for i in range(self.num_rows )
] )
def __mul__( self , _a ):
"""simple docstring"""
if isinstance(_a , (int, float) ):
return Matrix(
[[int(element * other ) for element in row] for row in self.rows] )
elif isinstance(_a , _a ):
if self.num_columns != other.num_rows:
raise ValueError(
"""The number of columns in the first matrix must """
"""be equal to the number of rows in the second""" )
return Matrix(
[
[Matrix.dot_product(_a , _a ) for column in other.columns()]
for row in self.rows
] )
else:
raise TypeError(
"""A Matrix can only be multiplied by an int, float, or another matrix""" )
def __pow__( self , _a ):
"""simple docstring"""
if not isinstance(_a , _a ):
raise TypeError("""A Matrix can only be raised to the power of an int""" )
if not self.is_square:
raise ValueError("""Only square matrices can be raised to a power""" )
if other == 0:
return self.identity()
if other < 0:
if self.is_invertable():
return self.inverse() ** (-other)
raise ValueError(
"""Only invertable matrices can be raised to a negative power""" )
lowerCamelCase = self
for _ in range(other - 1 ):
result *= self
return result
@classmethod
def _lowerCAmelCase ( cls , _a , _a ):
"""simple docstring"""
return sum(row[i] * column[i] for i in range(len(_a ) ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 168 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
def lowerCamelCase ( _UpperCamelCase : Any , _UpperCamelCase : Optional[Any] ) -> int:
'''simple docstring'''
__UpperCAmelCase : int = b.T
__UpperCAmelCase : Union[str, Any] = np.sum(np.square(_UpperCamelCase ) , axis=1 )
__UpperCAmelCase : List[Any] = np.sum(np.square(_UpperCamelCase ) , axis=0 )
__UpperCAmelCase : int = np.matmul(_UpperCamelCase , _UpperCamelCase )
__UpperCAmelCase : Tuple = aa[:, None] - 2 * ab + ba[None, :]
return d
def lowerCamelCase ( _UpperCamelCase : Tuple , _UpperCamelCase : Optional[Any] ) -> Optional[int]:
'''simple docstring'''
__UpperCAmelCase : Tuple = x.reshape(-1 , 3 )
__UpperCAmelCase : Optional[Any] = squared_euclidean_distance(_UpperCamelCase , _UpperCamelCase )
return np.argmin(_UpperCamelCase , axis=1 )
class lowerCamelCase__ ( A ):
"""simple docstring"""
__a = ["""pixel_values"""]
def __init__( self : List[str] , UpperCamelCase : Optional[Union[List[List[int]], np.ndarray]] = None , UpperCamelCase : bool = True , UpperCamelCase : Dict[str, int] = None , UpperCamelCase : PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase : bool = True , UpperCamelCase : bool = True , **UpperCamelCase : int , ):
'''simple docstring'''
super().__init__(**UpperCamelCase )
__UpperCAmelCase : Optional[Any] = size if size is not None else {"""height""": 256, """width""": 256}
__UpperCAmelCase : Tuple = get_size_dict(UpperCamelCase )
__UpperCAmelCase : Union[str, Any] = np.array(UpperCamelCase ) if clusters is not None else None
__UpperCAmelCase : int = do_resize
__UpperCAmelCase : List[str] = size
__UpperCAmelCase : Dict = resample
__UpperCAmelCase : int = do_normalize
__UpperCAmelCase : List[Any] = do_color_quantize
def lowerCamelCase__ ( self : List[str] , UpperCamelCase : np.ndarray , UpperCamelCase : Dict[str, int] , UpperCamelCase : PILImageResampling = PILImageResampling.BILINEAR , UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , **UpperCamelCase : str , ):
'''simple docstring'''
__UpperCAmelCase : Any = get_size_dict(UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}''' )
return resize(
UpperCamelCase , size=(size["""height"""], size["""width"""]) , resample=UpperCamelCase , data_format=UpperCamelCase , **UpperCamelCase )
def lowerCamelCase__ ( self : Dict , UpperCamelCase : np.ndarray , UpperCamelCase : Optional[Union[str, ChannelDimension]] = None , ):
'''simple docstring'''
__UpperCAmelCase : Optional[Any] = rescale(image=UpperCamelCase , scale=1 / 127.5 , data_format=UpperCamelCase )
__UpperCAmelCase : Optional[Any] = image - 1
return image
def lowerCamelCase__ ( self : Dict , UpperCamelCase : ImageInput , UpperCamelCase : bool = None , UpperCamelCase : Dict[str, int] = None , UpperCamelCase : PILImageResampling = None , UpperCamelCase : bool = None , UpperCamelCase : Optional[bool] = None , UpperCamelCase : Optional[Union[List[List[int]], np.ndarray]] = None , UpperCamelCase : Optional[Union[str, TensorType]] = None , UpperCamelCase : Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST , **UpperCamelCase : Optional[int] , ):
'''simple docstring'''
__UpperCAmelCase : List[str] = do_resize if do_resize is not None else self.do_resize
__UpperCAmelCase : Any = size if size is not None else self.size
__UpperCAmelCase : Any = get_size_dict(UpperCamelCase )
__UpperCAmelCase : Optional[Any] = resample if resample is not None else self.resample
__UpperCAmelCase : Any = do_normalize if do_normalize is not None else self.do_normalize
__UpperCAmelCase : Any = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
__UpperCAmelCase : Tuple = clusters if clusters is not None else self.clusters
__UpperCAmelCase : Tuple = np.array(UpperCamelCase )
__UpperCAmelCase : int = make_list_of_images(UpperCamelCase )
if not valid_images(UpperCamelCase ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_color_quantize and clusters is None:
raise ValueError("""Clusters must be specified if do_color_quantize is True.""" )
# All transformations expect numpy arrays.
__UpperCAmelCase : Tuple = [to_numpy_array(UpperCamelCase ) for image in images]
if do_resize:
__UpperCAmelCase : Dict = [self.resize(image=UpperCamelCase , size=UpperCamelCase , resample=UpperCamelCase ) for image in images]
if do_normalize:
__UpperCAmelCase : Any = [self.normalize(image=UpperCamelCase ) for image in images]
if do_color_quantize:
__UpperCAmelCase : str = [to_channel_dimension_format(UpperCamelCase , ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
__UpperCAmelCase : Optional[int] = np.array(UpperCamelCase )
__UpperCAmelCase : Any = color_quantize(UpperCamelCase , UpperCamelCase ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
__UpperCAmelCase : Dict = images.shape[0]
__UpperCAmelCase : Optional[Any] = images.reshape(UpperCamelCase , -1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
__UpperCAmelCase : int = list(UpperCamelCase )
else:
__UpperCAmelCase : List[Any] = [to_channel_dimension_format(UpperCamelCase , UpperCamelCase ) for image in images]
__UpperCAmelCase : List[str] = {"""input_ids""": images}
return BatchFeature(data=UpperCamelCase , tensor_type=UpperCamelCase )
| 115 |
"""simple docstring"""
import re
def lowerCamelCase ( _UpperCamelCase : str ) -> str:
'''simple docstring'''
if len(re.findall("""[ATCG]""" , _UpperCamelCase ) ) != len(_UpperCamelCase ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" , """TAGC""" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 115 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import numpy
import tensorflow as tf
from transformers import (
TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST,
TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST,
BertConfig,
DPRConfig,
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
class snake_case__ :
def __init__( self , lowerCAmelCase__ , lowerCAmelCase__=13 , lowerCAmelCase__=7 , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=True , lowerCAmelCase__=99 , lowerCAmelCase__=32 , lowerCAmelCase__=2 , lowerCAmelCase__=4 , lowerCAmelCase__=37 , lowerCAmelCase__="gelu" , lowerCAmelCase__=0.1 , lowerCAmelCase__=0.1 , lowerCAmelCase__=5_12 , lowerCAmelCase__=16 , lowerCAmelCase__=2 , lowerCAmelCase__=0.0_2 , lowerCAmelCase__=3 , lowerCAmelCase__=4 , lowerCAmelCase__=None , lowerCAmelCase__=0 , ) -> Optional[Any]:
__magic_name__ : Dict = parent
__magic_name__ : Union[str, Any] = batch_size
__magic_name__ : Optional[Any] = seq_length
__magic_name__ : Optional[Any] = is_training
__magic_name__ : Union[str, Any] = use_input_mask
__magic_name__ : List[Any] = use_token_type_ids
__magic_name__ : int = use_labels
__magic_name__ : Optional[int] = vocab_size
__magic_name__ : Dict = hidden_size
__magic_name__ : Any = num_hidden_layers
__magic_name__ : Optional[Any] = num_attention_heads
__magic_name__ : Any = intermediate_size
__magic_name__ : List[str] = hidden_act
__magic_name__ : List[Any] = hidden_dropout_prob
__magic_name__ : Tuple = attention_probs_dropout_prob
__magic_name__ : Optional[Any] = max_position_embeddings
__magic_name__ : Optional[int] = type_vocab_size
__magic_name__ : Optional[Any] = type_sequence_label_size
__magic_name__ : Optional[Any] = initializer_range
__magic_name__ : List[str] = num_labels
__magic_name__ : List[Any] = num_choices
__magic_name__ : List[str] = scope
__magic_name__ : str = projection_dim
def __magic_name__( self ) -> int:
__magic_name__ : int = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__magic_name__ : str = None
if self.use_input_mask:
# follow test_modeling_tf_ctrl.py
__magic_name__ : Optional[Any] = random_attention_mask([self.batch_size, self.seq_length] )
__magic_name__ : Any = None
if self.use_token_type_ids:
__magic_name__ : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__magic_name__ : int = None
__magic_name__ : Optional[int] = None
__magic_name__ : Any = None
if self.use_labels:
__magic_name__ : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__magic_name__ : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__magic_name__ : Tuple = ids_tensor([self.batch_size] , self.num_choices )
__magic_name__ : Dict = BertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , is_decoder=lowerCAmelCase__ , initializer_range=self.initializer_range , )
__magic_name__ : Optional[int] = DPRConfig(projection_dim=self.projection_dim , **config.to_dict() )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def __magic_name__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
__magic_name__ : Any = TFDPRContextEncoder(config=lowerCAmelCase__ )
__magic_name__ : Any = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
__magic_name__ : Dict = model(lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
__magic_name__ : Dict = model(lowerCAmelCase__ )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def __magic_name__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> int:
__magic_name__ : Any = TFDPRQuestionEncoder(config=lowerCAmelCase__ )
__magic_name__ : Any = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
__magic_name__ : Optional[int] = model(lowerCAmelCase__ , token_type_ids=lowerCAmelCase__ )
__magic_name__ : Tuple = model(lowerCAmelCase__ )
self.parent.assertEqual(result.pooler_output.shape , (self.batch_size, self.projection_dim or self.hidden_size) )
def __magic_name__( self , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ) -> List[Any]:
__magic_name__ : Any = TFDPRReader(config=lowerCAmelCase__ )
__magic_name__ : Optional[int] = model(lowerCAmelCase__ , attention_mask=lowerCAmelCase__ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.relevance_logits.shape , (self.batch_size,) )
def __magic_name__( self ) -> int:
__magic_name__ : Optional[int] = self.prepare_config_and_inputs()
(
__magic_name__
) : Union[str, Any] = config_and_inputs
__magic_name__ : List[Any] = {"""input_ids""": input_ids}
return config, inputs_dict
@require_tf
class snake_case__ ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
lowercase__ : Union[str, Any] = (
(
TFDPRContextEncoder,
TFDPRQuestionEncoder,
TFDPRReader,
)
if is_tf_available()
else ()
)
lowercase__ : Tuple = {'''feature-extraction''': TFDPRQuestionEncoder} if is_tf_available() else {}
lowercase__ : Optional[int] = False
lowercase__ : str = False
lowercase__ : Dict = False
lowercase__ : int = False
lowercase__ : List[str] = False
def __magic_name__( self ) -> Optional[Any]:
__magic_name__ : Optional[Any] = TFDPRModelTester(self )
__magic_name__ : str = ConfigTester(self , config_class=lowerCAmelCase__ , hidden_size=37 )
def __magic_name__( self ) -> Dict:
self.config_tester.run_common_tests()
def __magic_name__( self ) -> str:
__magic_name__ : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_context_encoder(*lowerCAmelCase__ )
def __magic_name__( self ) -> Any:
__magic_name__ : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_question_encoder(*lowerCAmelCase__ )
def __magic_name__( self ) -> List[str]:
__magic_name__ : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_dpr_reader(*lowerCAmelCase__ )
@slow
def __magic_name__( self ) -> Tuple:
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : Optional[int] = TFDPRContextEncoder.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
for model_name in TF_DPR_CONTEXT_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : Dict = TFDPRContextEncoder.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
for model_name in TF_DPR_QUESTION_ENCODER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : Optional[Any] = TFDPRQuestionEncoder.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
for model_name in TF_DPR_READER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__magic_name__ : Union[str, Any] = TFDPRReader.from_pretrained(lowerCAmelCase__ )
self.assertIsNotNone(lowerCAmelCase__ )
@require_tf
class snake_case__ ( unittest.TestCase ):
@slow
def __magic_name__( self ) -> str:
__magic_name__ : Any = TFDPRQuestionEncoder.from_pretrained("""facebook/dpr-question_encoder-single-nq-base""" )
__magic_name__ : List[str] = tf.constant(
[[1_01, 75_92, 10_10, 20_03, 20_26, 38_99, 1_01_40, 10_29, 1_02]] ) # [CLS] hello, is my dog cute? [SEP]
__magic_name__ : Optional[int] = model(lowerCAmelCase__ )[0] # embedding shape = (1, 768)
# compare the actual values for a slice.
__magic_name__ : Any = tf.constant(
[
[
0.0_3_2_3_6_2_5_3,
0.1_2_7_5_3_3_3_5,
0.1_6_8_1_8_5_0_9,
0.0_0_2_7_9_7_8_6,
0.3_8_9_6_9_3_3,
0.2_4_2_6_4_9_4_5,
0.2_1_7_8_9_7_1,
-0.0_2_3_3_5_2_2_7,
-0.0_8_4_8_1_9_5_9,
-0.1_4_3_2_4_1_1_7,
]
] )
self.assertTrue(numpy.allclose(output[:, :10].numpy() , expected_slice.numpy() , atol=1e-4 ) )
| 366 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet import XLNetTokenizer
else:
__magic_name__: Any = None
__magic_name__: Dict = logging.get_logger(__name__)
__magic_name__: Any = {"vocab_file": "spiece.model", "tokenizer_file": "tokenizer.json"}
__magic_name__: str = {
"vocab_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/spiece.model",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/spiece.model",
},
"tokenizer_file": {
"xlnet-base-cased": "https://huggingface.co/xlnet-base-cased/resolve/main/tokenizer.json",
"xlnet-large-cased": "https://huggingface.co/xlnet-large-cased/resolve/main/tokenizer.json",
},
}
__magic_name__: Optional[Any] = {
"xlnet-base-cased": None,
"xlnet-large-cased": None,
}
__magic_name__: Optional[Any] = "▁"
# Segments (not really needed)
__magic_name__: List[Any] = 0
__magic_name__: Dict = 1
__magic_name__: List[str] = 2
__magic_name__: List[Any] = 3
__magic_name__: Optional[int] = 4
class snake_case__ ( _lowerCAmelCase ):
lowercase__ : Dict = VOCAB_FILES_NAMES
lowercase__ : Union[str, Any] = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Tuple = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : List[str] = '''left'''
lowercase__ : List[str] = XLNetTokenizer
def __init__( self , lowerCAmelCase__=None , lowerCAmelCase__=None , lowerCAmelCase__=False , lowerCAmelCase__=True , lowerCAmelCase__=False , lowerCAmelCase__="<s>" , lowerCAmelCase__="</s>" , lowerCAmelCase__="<unk>" , lowerCAmelCase__="<sep>" , lowerCAmelCase__="<pad>" , lowerCAmelCase__="<cls>" , lowerCAmelCase__="<mask>" , lowerCAmelCase__=["<eop>", "<eod>"] , **lowerCAmelCase__ , ) -> Tuple:
# Mask token behave like a normal word, i.e. include the space before it
__magic_name__ : Optional[int] = AddedToken(lowerCAmelCase__ , lstrip=lowerCAmelCase__ , rstrip=lowerCAmelCase__ ) if isinstance(lowerCAmelCase__ , lowerCAmelCase__ ) else mask_token
super().__init__(
vocab_file=lowerCAmelCase__ , tokenizer_file=lowerCAmelCase__ , do_lower_case=lowerCAmelCase__ , remove_space=lowerCAmelCase__ , keep_accents=lowerCAmelCase__ , bos_token=lowerCAmelCase__ , eos_token=lowerCAmelCase__ , unk_token=lowerCAmelCase__ , sep_token=lowerCAmelCase__ , pad_token=lowerCAmelCase__ , cls_token=lowerCAmelCase__ , mask_token=lowerCAmelCase__ , additional_special_tokens=lowerCAmelCase__ , **lowerCAmelCase__ , )
__magic_name__ : List[str] = 3
__magic_name__ : str = do_lower_case
__magic_name__ : Union[str, Any] = remove_space
__magic_name__ : str = keep_accents
__magic_name__ : Tuple = vocab_file
__magic_name__ : List[Any] = False if not self.vocab_file else True
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> List[int]:
__magic_name__ : Any = [self.sep_token_id]
__magic_name__ : Any = [self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> List[int]:
__magic_name__ : List[str] = [self.sep_token_id]
__magic_name__ : Optional[Any] = [2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def __magic_name__ ( self , lowerCAmelCase__ , lowerCAmelCase__ = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"""Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
"""tokenizer.""" )
if not os.path.isdir(lowerCAmelCase__ ):
logger.error(F'Vocabulary path ({save_directory}) should be a directory' )
return
__magic_name__ : List[str] = os.path.join(
lowerCAmelCase__ , (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase__ ):
copyfile(self.vocab_file , lowerCAmelCase__ )
return (out_vocab_file,)
| 138 | 0 |
"""simple docstring"""
def a__ ( lowerCAmelCase , lowerCAmelCase ) -> Optional[Any]:
UpperCAmelCase__ : str = 0
while b > 0:
if b & 1:
res += a
a += a
b >>= 1
return res
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> Tuple:
UpperCAmelCase__ : List[str] = 0
while b > 0:
if b & 1:
UpperCAmelCase__ : Optional[Any] = ((res % c) + (a % c)) % c
a += a
b >>= 1
return res
| 171 |
"""simple docstring"""
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers.models.trocr.modeling_trocr import TrOCRDecoder, TrOCRForCausalLM
@require_torch
class lowerCamelCase :
'''simple docstring'''
def __init__(self , _lowerCamelCase , _lowerCamelCase=99 , _lowerCamelCase=13 , _lowerCamelCase=16 , _lowerCamelCase=7 , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=True , _lowerCamelCase=False , _lowerCamelCase=True , _lowerCamelCase=2 , _lowerCamelCase=32 , _lowerCamelCase=4 , _lowerCamelCase=4 , _lowerCamelCase=30 , _lowerCamelCase=0 , _lowerCamelCase=1 , _lowerCamelCase=2 , _lowerCamelCase=None , ):
"""simple docstring"""
UpperCAmelCase__ : int = parent
UpperCAmelCase__ : Optional[Any] = batch_size
UpperCAmelCase__ : Union[str, Any] = decoder_seq_length
# For common tests
UpperCAmelCase__ : int = self.decoder_seq_length
UpperCAmelCase__ : Optional[Any] = is_training
UpperCAmelCase__ : Optional[Any] = use_attention_mask
UpperCAmelCase__ : List[Any] = use_labels
UpperCAmelCase__ : Optional[Any] = vocab_size
UpperCAmelCase__ : List[Any] = d_model
UpperCAmelCase__ : List[str] = d_model
UpperCAmelCase__ : Dict = decoder_layers
UpperCAmelCase__ : Any = decoder_layers
UpperCAmelCase__ : Tuple = decoder_ffn_dim
UpperCAmelCase__ : Any = decoder_attention_heads
UpperCAmelCase__ : List[str] = decoder_attention_heads
UpperCAmelCase__ : List[str] = eos_token_id
UpperCAmelCase__ : int = bos_token_id
UpperCAmelCase__ : Optional[int] = pad_token_id
UpperCAmelCase__ : Any = decoder_start_token_id
UpperCAmelCase__ : Dict = use_cache
UpperCAmelCase__ : Optional[Any] = max_position_embeddings
UpperCAmelCase__ : Optional[int] = None
UpperCAmelCase__ : Dict = decoder_seq_length
UpperCAmelCase__ : str = 2
UpperCAmelCase__ : List[str] = 1
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
UpperCAmelCase__ : List[str] = None
if self.use_attention_mask:
UpperCAmelCase__ : Union[str, Any] = ids_tensor([self.batch_size, self.decoder_seq_length] , vocab_size=2 )
UpperCAmelCase__ : Any = None
if self.use_labels:
UpperCAmelCase__ : List[Any] = ids_tensor([self.batch_size, self.decoder_seq_length] , self.vocab_size )
UpperCAmelCase__ : Union[str, Any] = TrOCRConfig(
vocab_size=self.vocab_size , d_model=self.d_model , decoder_layers=self.decoder_layers , decoder_ffn_dim=self.decoder_ffn_dim , decoder_attention_heads=self.decoder_attention_heads , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , use_cache=self.use_cache , pad_token_id=self.pad_token_id , decoder_start_token_id=self.decoder_start_token_id , max_position_embeddings=self.max_position_embeddings , )
return (config, input_ids, attention_mask, lm_labels)
def _a (self , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , ):
"""simple docstring"""
UpperCAmelCase__ : List[str] = True
UpperCAmelCase__ : str = TrOCRDecoder(config=_lowerCamelCase ).to(_lowerCamelCase ).eval()
UpperCAmelCase__ : Any = input_ids[:2]
input_ids[input_ids == 0] += 1
# first forward pass
UpperCAmelCase__ : Optional[Any] = model(_lowerCamelCase , use_cache=_lowerCamelCase )
UpperCAmelCase__ : str = model(_lowerCamelCase )
UpperCAmelCase__ : Optional[int] = model(_lowerCamelCase , use_cache=_lowerCamelCase )
self.parent.assertTrue(len(_lowerCamelCase ) == len(_lowerCamelCase ) )
self.parent.assertTrue(len(_lowerCamelCase ) == len(_lowerCamelCase ) + 1 )
UpperCAmelCase__ : List[Any] = outputs["""past_key_values"""]
# create hypothetical next token and extent to next_input_ids
UpperCAmelCase__ : List[str] = ids_tensor((2, 1) , config.vocab_size - 1 ) + 1
# append to next input_ids and
UpperCAmelCase__ : Any = torch.cat([input_ids, next_tokens] , dim=-1 )
UpperCAmelCase__ : int = model(_lowerCamelCase )["""last_hidden_state"""]
UpperCAmelCase__ : Dict = model(_lowerCamelCase , past_key_values=_lowerCamelCase )["""last_hidden_state"""]
# select random slice
UpperCAmelCase__ : Any = ids_tensor((1,) , output_from_past.shape[-1] ).item()
UpperCAmelCase__ : int = output_from_no_past[:, next_input_ids.shape[-1] - 1, random_slice_idx].detach()
UpperCAmelCase__ : Dict = output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
assert torch.allclose(_lowerCamelCase , _lowerCamelCase , atol=1e-3 )
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Tuple = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Any = config_and_inputs
UpperCAmelCase__ : Optional[int] = {"""input_ids""": input_ids, """attention_mask""": attention_mask}
return config, inputs_dict
@require_torch
class lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE = (TrOCRDecoder, TrOCRForCausalLM) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = (TrOCRForCausalLM,) if is_torch_available() else ()
SCREAMING_SNAKE_CASE = {'text-generation': TrOCRForCausalLM} if is_torch_available() else {}
SCREAMING_SNAKE_CASE = True
SCREAMING_SNAKE_CASE = False
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Union[str, Any] = TrOCRStandaloneDecoderModelTester(self , is_training=_lowerCamelCase )
UpperCAmelCase__ : Union[str, Any] = ConfigTester(self , config_class=_lowerCamelCase )
def _a (self ):
"""simple docstring"""
pass
def _a (self ):
"""simple docstring"""
pass
def _a (self ):
"""simple docstring"""
pass
def _a (self ):
"""simple docstring"""
self.config_tester.run_common_tests()
def _a (self ):
"""simple docstring"""
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past(*_lowerCamelCase )
def _a (self ):
"""simple docstring"""
return
@unittest.skip("""The model doesn't support left padding""" ) # and it's not used enough to be worth fixing :)
def _a (self ):
"""simple docstring"""
pass
| 171 | 1 |
"""simple docstring"""
# Lint as: python3
# pylint: enable=line-too-long
# pylint: disable=g-import-not-at-top,g-bad-import-order,wrong-import-position
__lowercase = """2.13.1"""
import platform
import pyarrow
from packaging import version
if version.parse(platform.python_version()) < version.parse("""3.7"""):
raise ImportWarning(
"""To use `datasets`, Python>=3.7 is required, and the current version of Python doesn't match this condition."""
)
if version.parse(pyarrow.__version__).major < 8:
raise ImportWarning(
"""To use `datasets`, the module `pyarrow>=8.0.0` is required, and the current version of `pyarrow` doesn't match this condition.\n"""
"""If you are running this in a Google Colab, you should probably just restart the runtime to use the right version of `pyarrow`."""
)
del platform
del pyarrow
del version
from .arrow_dataset import Dataset
from .arrow_reader import ReadInstruction
from .builder import ArrowBasedBuilder, BeamBasedBuilder, BuilderConfig, DatasetBuilder, GeneratorBasedBuilder
from .combine import concatenate_datasets, interleave_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .download import *
from .features import *
from .fingerprint import disable_caching, enable_caching, is_caching_enabled, set_caching_enabled
from .info import DatasetInfo, MetricInfo
from .inspect import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
list_datasets,
list_metrics,
)
from .iterable_dataset import IterableDataset
from .load import load_dataset, load_dataset_builder, load_from_disk, load_metric
from .metric import Metric
from .splits import (
NamedSplit,
NamedSplitAll,
Split,
SplitBase,
SplitDict,
SplitGenerator,
SplitInfo,
SubSplitInfo,
percent,
)
from .tasks import *
from .utils import *
from .utils import logging
# deprecated modules
from datasets import arrow_dataset as _arrow_dataset # isort:skip
from datasets import utils as _utils # isort:skip
from datasets.utils import download_manager as _deprecated_download_manager # isort:skip
__lowercase = concatenate_datasets
__lowercase = DownloadConfig
__lowercase = DownloadManager
__lowercase = DownloadMode
__lowercase = DownloadConfig
__lowercase = DownloadMode
__lowercase = DownloadManager
del _arrow_dataset, _utils, _deprecated_download_manager
| 226 |
"""simple docstring"""
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
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_config_docstrings.py
__lowercase = """src/transformers"""
# This is to make sure the transformers module imported is the one in the repo.
__lowercase = direct_transformers_import(PATH_TO_TRANSFORMERS)
__lowercase = transformers.models.auto.configuration_auto.CONFIG_MAPPING
__lowercase = {
# used to compute the property `self.chunk_length`
"""EncodecConfig""": ["""overlap"""],
# used as `self.bert_model = BertModel(config, ...)`
"""DPRConfig""": True,
# not used in modeling files, but it's an important information
"""FSMTConfig""": ["""langs"""],
# used internally in the configuration class file
"""GPTNeoConfig""": ["""attention_types"""],
# used internally in the configuration class file
"""EsmConfig""": ["""is_folding_model"""],
# used during training (despite we don't have training script for these models yet)
"""Mask2FormerConfig""": ["""ignore_value"""],
# `ignore_value` used during training (despite we don't have training script for these models yet)
# `norm` used in conversion script (despite not using in the modeling file)
"""OneFormerConfig""": ["""ignore_value""", """norm"""],
# used during preprocessing and collation, see `collating_graphormer.py`
"""GraphormerConfig""": ["""spatial_pos_max"""],
# used internally in the configuration class file
"""T5Config""": ["""feed_forward_proj"""],
# used internally in the configuration class file
# `tokenizer_class` get default value `T5Tokenizer` intentionally
"""MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
"""UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
# used internally in the configuration class file
"""LongT5Config""": ["""feed_forward_proj"""],
# used internally in the configuration class file
"""SwitchTransformersConfig""": ["""feed_forward_proj"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""BioGptConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""GLPNConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""SegformerConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""CvtConfig""": ["""layer_norm_eps"""],
# having default values other than `1e-5` - we can't fix them without breaking
"""PerceiverConfig""": ["""layer_norm_eps"""],
# used internally to calculate the feature size
"""InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
# used internally to calculate the feature size
"""TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
# used internally to calculate the feature size
"""AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
# used internally to calculate `mlp_dim`
"""SamVisionConfig""": ["""mlp_ratio"""],
# For (head) training, but so far not implemented
"""ClapAudioConfig""": ["""num_classes"""],
# Not used, but providing useful information to users
"""SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}
# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
{
"""CLIPSegConfig""": True,
"""DeformableDetrConfig""": True,
"""DetaConfig""": True,
"""DinatConfig""": True,
"""DonutSwinConfig""": True,
"""EfficientFormerConfig""": True,
"""FSMTConfig""": True,
"""JukeboxConfig""": True,
"""LayoutLMv2Config""": True,
"""MaskFormerSwinConfig""": True,
"""MT5Config""": True,
"""NatConfig""": True,
"""OneFormerConfig""": True,
"""PerceiverConfig""": True,
"""RagConfig""": True,
"""SpeechT5Config""": True,
"""SwinConfig""": True,
"""Swin2SRConfig""": True,
"""Swinv2Config""": True,
"""SwitchTransformersConfig""": True,
"""TableTransformerConfig""": True,
"""TapasConfig""": True,
"""TransfoXLConfig""": True,
"""UniSpeechConfig""": True,
"""UniSpeechSatConfig""": True,
"""WavLMConfig""": True,
"""WhisperConfig""": True,
# TODO: @Arthur (for `alignment_head` and `alignment_layer`)
"""JukeboxPriorConfig""": True,
# TODO: @Younes (for `is_decoder`)
"""Pix2StructTextConfig""": True,
}
)
def lowercase ( A_ , A_ , A_ , A_ )-> Optional[Any]:
'''simple docstring'''
a : List[str] = False
for attribute in attributes:
for modeling_source in source_strings:
# check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
if (
F'''config.{attribute}''' in modeling_source
or F'''getattr(config, "{attribute}"''' in modeling_source
or F'''getattr(self.config, "{attribute}"''' in modeling_source
):
a : str = True
# Deal with multi-line cases
elif (
re.search(
RF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"''' , A_ , )
is not None
):
a : List[Any] = True
# `SequenceSummary` is called with `SequenceSummary(config)`
elif attribute in [
"summary_type",
"summary_use_proj",
"summary_activation",
"summary_last_dropout",
"summary_proj_to_labels",
"summary_first_dropout",
]:
if "SequenceSummary" in modeling_source:
a : str = True
if attribute_used:
break
if attribute_used:
break
# common and important attributes, even if they do not always appear in the modeling files
a : Tuple = [
"bos_index",
"eos_index",
"pad_index",
"unk_index",
"mask_index",
"image_size",
"use_cache",
"out_features",
"out_indices",
]
a : str = ["encoder_no_repeat_ngram_size"]
# Special cases to be allowed
a : int = True
if not attribute_used:
a : Dict = False
for attribute in attributes:
# Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
if attribute in ["is_encoder_decoder"] and default_value is True:
a : Optional[int] = True
elif attribute in ["tie_word_embeddings"] and default_value is False:
a : List[Any] = True
# Allow cases without checking the default value in the configuration class
elif attribute in attributes_to_allow + attributes_used_in_generation:
a : str = True
elif attribute.endswith("_token_id" ):
a : str = True
# configuration class specific cases
if not case_allowed:
a : Union[str, Any] = SPECIAL_CASES_TO_ALLOW.get(config_class.__name__ , [] )
a : Optional[Any] = allowed_cases is True or attribute in allowed_cases
return attribute_used or case_allowed
def lowercase ( A_ )-> Tuple:
'''simple docstring'''
a : Optional[int] = dict(inspect.signature(config_class.__init__ ).parameters )
a : Any = [x for x in list(signature.keys() ) if x not in ["self", "kwargs"]]
a : str = [signature[param].default for param in parameter_names]
# If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
# as one variant is used, the test should pass
a : Dict = {}
if len(config_class.attribute_map ) > 0:
a : int = {v: k for k, v in config_class.attribute_map.items()}
# Get the path to modeling source files
a : int = inspect.getsourcefile(A_ )
a : Union[str, Any] = os.path.dirname(A_ )
# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
a : Optional[Any] = [os.path.join(A_ , A_ ) for fn in os.listdir(A_ ) if fn.startswith("modeling_" )]
# Get the source code strings
a : Tuple = []
for path in modeling_paths:
if os.path.isfile(A_ ):
with open(A_ ) as fp:
modeling_sources.append(fp.read() )
a : Optional[Any] = []
for config_param, default_value in zip(A_ , A_ ):
# `attributes` here is all the variant names for `config_param`
a : str = [config_param]
# some configuration classes have non-empty `attribute_map`, and both names could be used in the
# corresponding modeling files. As long as one of them appears, it is fine.
if config_param in reversed_attribute_map:
attributes.append(reversed_attribute_map[config_param] )
if not check_attribute_being_used(A_ , A_ , A_ , A_ ):
unused_attributes.append(attributes[0] )
return sorted(A_ )
def lowercase ( )-> str:
'''simple docstring'''
a : List[Any] = {}
for _config_class in list(CONFIG_MAPPING.values() ):
# Skip deprecated models
if "models.deprecated" in _config_class.__module__:
continue
# Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
a : str = [
cls
for name, cls in inspect.getmembers(
inspect.getmodule(_config_class ) , lambda A_ : inspect.isclass(A_ )
and issubclass(A_ , A_ )
and inspect.getmodule(A_ ) == inspect.getmodule(_config_class ) , )
]
for config_class in config_classes_in_module:
a : Union[str, Any] = check_config_attributes_being_used(A_ )
if len(A_ ) > 0:
a : Dict = unused_attributes
if len(A_ ) > 0:
a : Union[str, Any] = "The following configuration classes contain unused attributes in the corresponding modeling files:\n"
for name, attributes in configs_with_unused_attributes.items():
error += F'''{name}: {attributes}\n'''
raise ValueError(A_ )
if __name__ == "__main__":
check_config_attributes()
| 226 | 1 |
'''simple docstring'''
from math import factorial, radians
def a__ ( lowerCAmelCase__ , lowerCAmelCase__ = 18 , lowerCAmelCase__ = 10 ) -> float:
UpperCAmelCase__ : int = angle_in_degrees - ((angle_in_degrees // 3_6_0.0) * 3_6_0.0)
# Converting from degrees to radians
UpperCAmelCase__ : int = radians(lowerCAmelCase__ )
UpperCAmelCase__ : str = angle_in_radians
UpperCAmelCase__ : Dict = 3
UpperCAmelCase__ : Dict = -1
for _ in range(lowerCAmelCase__ ):
result += (b * (angle_in_radians**a)) / factorial(lowerCAmelCase__ )
UpperCAmelCase__ : Dict = -b # One positive term and the next will be negative and so on...
a += 2 # Increased by 2 for every term.
return round(lowerCAmelCase__ , lowerCAmelCase__ )
if __name__ == "__main__":
__import__('''doctest''').testmod()
| 181 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFCvtForImageClassification, TFCvtModel
from transformers.models.cvt.modeling_tf_cvt import TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class lowerCamelCase_ ( __a ):
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : str = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(_A , '''embed_dim''' ) )
self.parent.assertTrue(hasattr(_A , '''num_heads''' ) )
class lowerCamelCase_ :
def __init__( self : int , _A : Tuple , _A : Any=13 , _A : Optional[int]=64 , _A : Optional[Any]=3 , _A : List[str]=[16, 48, 96] , _A : int=[1, 3, 6] , _A : Optional[int]=[1, 2, 10] , _A : int=[7, 3, 3] , _A : Union[str, Any]=[4, 2, 2] , _A : Dict=[2, 1, 1] , _A : Optional[Any]=[2, 2, 2] , _A : Optional[Any]=[False, False, True] , _A : List[Any]=[0.0, 0.0, 0.0] , _A : str=0.0_2 , _A : Tuple=1e-12 , _A : Union[str, Any]=True , _A : Optional[Any]=True , _A : Optional[int]=2 , ):
'''simple docstring'''
UpperCAmelCase__ : Dict = parent
UpperCAmelCase__ : List[str] = batch_size
UpperCAmelCase__ : Optional[int] = image_size
UpperCAmelCase__ : List[str] = patch_sizes
UpperCAmelCase__ : Any = patch_stride
UpperCAmelCase__ : Tuple = patch_padding
UpperCAmelCase__ : int = is_training
UpperCAmelCase__ : Dict = use_labels
UpperCAmelCase__ : List[Any] = num_labels
UpperCAmelCase__ : Optional[Any] = num_channels
UpperCAmelCase__ : Optional[int] = embed_dim
UpperCAmelCase__ : int = num_heads
UpperCAmelCase__ : Any = stride_kv
UpperCAmelCase__ : str = depth
UpperCAmelCase__ : List[Any] = cls_token
UpperCAmelCase__ : List[Any] = attention_drop_rate
UpperCAmelCase__ : Optional[int] = initializer_range
UpperCAmelCase__ : Optional[int] = layer_norm_eps
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : int = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
UpperCAmelCase__ : Any = None
if self.use_labels:
# create a random int32 tensor of given shape
UpperCAmelCase__ : List[Any] = ids_tensor([self.batch_size] , self.num_labels )
UpperCAmelCase__ : List[Any] = self.get_config()
return config, pixel_values, labels
def lowercase_ ( self : Any ):
'''simple docstring'''
return CvtConfig(
image_size=self.image_size , num_labels=self.num_labels , num_channels=self.num_channels , embed_dim=self.embed_dim , num_heads=self.num_heads , patch_sizes=self.patch_sizes , patch_padding=self.patch_padding , patch_stride=self.patch_stride , stride_kv=self.stride_kv , depth=self.depth , cls_token=self.cls_token , attention_drop_rate=self.attention_drop_rate , initializer_range=self.initializer_range , )
def lowercase_ ( self : Optional[int] , _A : List[Any] , _A : Tuple , _A : Dict ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = TFCvtModel(config=_A )
UpperCAmelCase__ : List[str] = model(_A , training=_A )
UpperCAmelCase__ : int = (self.image_size, self.image_size)
UpperCAmelCase__ , UpperCAmelCase__ : Optional[int] = image_size[0], image_size[1]
for i in range(len(self.depth ) ):
UpperCAmelCase__ : Union[str, Any] = floor(((height + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
UpperCAmelCase__ : Optional[Any] = floor(((width + 2 * self.patch_padding[i] - self.patch_sizes[i]) / self.patch_stride[i]) + 1 )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.embed_dim[-1], height, width) )
def lowercase_ ( self : Optional[Any] , _A : Optional[Any] , _A : List[Any] , _A : int ):
'''simple docstring'''
UpperCAmelCase__ : str = self.num_labels
UpperCAmelCase__ : Union[str, Any] = TFCvtForImageClassification(_A )
UpperCAmelCase__ : Any = model(_A , labels=_A , training=_A )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = self.prepare_config_and_inputs()
UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ : Dict = config_and_inputs
UpperCAmelCase__ : Optional[Any] = {'''pixel_values''': pixel_values}
return config, inputs_dict
@require_tf
class lowerCamelCase_ ( __a , __a , unittest.TestCase ):
lowerCAmelCase__ = (TFCvtModel, TFCvtForImageClassification) if is_tf_available() else ()
lowerCAmelCase__ = (
{'feature-extraction': TFCvtModel, 'image-classification': TFCvtForImageClassification}
if is_tf_available()
else {}
)
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
lowerCAmelCase__ = False
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = TFCvtModelTester(self )
UpperCAmelCase__ : Tuple = TFCvtConfigTester(self , config_class=_A , has_text_modality=_A , hidden_size=37 )
def lowercase_ ( self : Any ):
'''simple docstring'''
self.config_tester.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
@unittest.skip(reason='''Cvt does not output attentions''' )
def lowercase_ ( self : Any ):
'''simple docstring'''
pass
@unittest.skip(reason='''Cvt does not use inputs_embeds''' )
def lowercase_ ( self : str ):
'''simple docstring'''
pass
@unittest.skip(reason='''Cvt does not support input and output embeddings''' )
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
pass
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
def lowercase_ ( self : List[str] ):
'''simple docstring'''
super().test_dataset_conversion()
@unittest.skipIf(
not is_tf_available() or len(tf.config.list_physical_devices('''GPU''' ) ) == 0 , reason='''TF does not support backprop for grouped convolutions on CPU.''' , )
@slow
def lowercase_ ( self : Optional[int] ):
'''simple docstring'''
super().test_keras_fit()
@unittest.skip(reason='''Get `Failed to determine best cudnn convolution algo.` error after using TF 2.12+cuda 11.8''' )
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
UpperCAmelCase__ : List[Any] = tf.keras.mixed_precision.Policy('''mixed_float16''' )
tf.keras.mixed_precision.set_global_policy(_A )
super().test_keras_fit()
tf.keras.mixed_precision.set_global_policy('''float32''' )
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
UpperCAmelCase__ , UpperCAmelCase__ : str = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : str = model_class(_A )
UpperCAmelCase__ : Optional[int] = inspect.signature(model.call )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
UpperCAmelCase__ : List[Any] = [*signature.parameters.keys()]
UpperCAmelCase__ : Any = ['''pixel_values''']
self.assertListEqual(arg_names[:1] , _A )
def lowercase_ ( self : Any ):
'''simple docstring'''
def check_hidden_states_output(_A : Dict , _A : Optional[Any] , _A : Dict ):
UpperCAmelCase__ : str = model_class(_A )
UpperCAmelCase__ : List[str] = model(**self._prepare_for_class(_A , _A ) )
UpperCAmelCase__ : Tuple = outputs.hidden_states
UpperCAmelCase__ : int = len(self.model_tester.depth )
self.assertEqual(len(_A ) , _A )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-3:] ) , [
self.model_tester.embed_dim[0],
self.model_tester.image_size // 4,
self.model_tester.image_size // 4,
] , )
UpperCAmelCase__ , UpperCAmelCase__ : int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
UpperCAmelCase__ : Tuple = True
check_hidden_states_output(_A , _A , _A )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
UpperCAmelCase__ : List[str] = True
check_hidden_states_output(_A , _A , _A )
def lowercase_ ( self : Tuple ):
'''simple docstring'''
UpperCAmelCase__ : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_A )
def lowercase_ ( self : str ):
'''simple docstring'''
UpperCAmelCase__ : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*_A )
@slow
def lowercase_ ( self : Optional[Any] ):
'''simple docstring'''
for model_name in TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
UpperCAmelCase__ : Optional[int] = TFCvtModel.from_pretrained(_A )
self.assertIsNotNone(_A )
def a__ ( ) -> Any:
UpperCAmelCase__ : str = Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''' )
return image
@require_tf
@require_vision
class lowerCamelCase_ ( unittest.TestCase ):
@cached_property
def lowercase_ ( self : Union[str, Any] ):
'''simple docstring'''
return AutoImageProcessor.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowercase_ ( self : Any ):
'''simple docstring'''
UpperCAmelCase__ : Dict = TFCvtForImageClassification.from_pretrained(TF_CVT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
UpperCAmelCase__ : Union[str, Any] = self.default_image_processor
UpperCAmelCase__ : Optional[Any] = prepare_img()
UpperCAmelCase__ : Tuple = image_processor(images=_A , return_tensors='''tf''' )
# forward pass
UpperCAmelCase__ : Optional[Any] = model(**_A )
# verify the logits
UpperCAmelCase__ : Union[str, Any] = tf.TensorShape((1, 1_000) )
self.assertEqual(outputs.logits.shape , _A )
UpperCAmelCase__ : Union[str, Any] = tf.constant([0.9_2_8_5, 0.9_0_1_5, -0.3_1_5_0] )
self.assertTrue(np.allclose(outputs.logits[0, :3].numpy() , _A , atol=1e-4 ) )
| 181 | 1 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : List[Any] = 'char'
A_ : Optional[int] = 'bpe'
A_ : Optional[int] = 'wp'
__snake_case = (DecodeType.CHARACTER, DecodeType.BPE, DecodeType.WORDPIECE)
class __lowerCamelCase ( a__ ):
'''simple docstring'''
A_ : str = ['image_processor', 'char_tokenizer']
A_ : List[str] = 'ViTImageProcessor'
A_ : int = 'MgpstrTokenizer'
def __init__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ) -> Dict:
_a = None
if "feature_extractor" in kwargs:
warnings.warn(
'''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
''' instead.''' , __UpperCAmelCase , )
_a = kwargs.pop('''feature_extractor''' )
_a = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError('''You need to specify an `image_processor`.''' )
if tokenizer is None:
raise ValueError('''You need to specify a `tokenizer`.''' )
_a = tokenizer
_a = AutoTokenizer.from_pretrained('''gpt2''' )
_a = AutoTokenizer.from_pretrained('''bert-base-uncased''' )
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
def __call__( self , __UpperCAmelCase=None , __UpperCAmelCase=None , __UpperCAmelCase=None , **__UpperCAmelCase ) -> int:
if images is None and text is None:
raise ValueError('''You need to specify either an `images` or `text` input to process.''' )
if images is not None:
_a = self.image_processor(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if text is not None:
_a = self.char_tokenizer(__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase )
if text is None:
return inputs
elif images is None:
return encodings
else:
_a = encodings['''input_ids''']
return inputs
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Union[str, Any]:
_a , _a , _a = sequences
_a = char_preds.size(0 )
_a , _a = self._decode_helper(__UpperCAmelCase , '''char''' )
_a , _a = self._decode_helper(__UpperCAmelCase , '''bpe''' )
_a , _a = self._decode_helper(__UpperCAmelCase , '''wp''' )
_a = []
_a = []
for i in range(__UpperCAmelCase ):
_a = [char_scores[i], bpe_scores[i], wp_scores[i]]
_a = [char_strs[i], bpe_strs[i], wp_strs[i]]
_a = scores.index(max(__UpperCAmelCase ) )
final_strs.append(strs[max_score_index] )
final_scores.append(scores[max_score_index] )
_a = {}
_a = final_strs
_a = final_scores
_a = char_strs
_a = bpe_strs
_a = wp_strs
return out
def _UpperCAmelCase ( self , __UpperCAmelCase , __UpperCAmelCase ) -> str:
if format == DecodeType.CHARACTER:
_a = self.char_decode
_a = 1
_a = '''[s]'''
elif format == DecodeType.BPE:
_a = self.bpe_decode
_a = 2
_a = '''#'''
elif format == DecodeType.WORDPIECE:
_a = self.wp_decode
_a = 102
_a = '''[SEP]'''
else:
raise ValueError(F'Format {format} is not supported.' )
_a , _a = [], []
_a = pred_logits.size(0 )
_a = pred_logits.size(1 )
_a , _a = pred_logits.topk(1 , dim=-1 , largest=__UpperCAmelCase , sorted=__UpperCAmelCase )
_a = preds_index.view(-1 , __UpperCAmelCase )[:, 1:]
_a = decoder(__UpperCAmelCase )
_a , _a = torch.nn.functional.softmax(__UpperCAmelCase , dim=2 ).max(dim=2 )
_a = preds_max_prob[:, 1:]
for index in range(__UpperCAmelCase ):
_a = preds_str[index].find(__UpperCAmelCase )
_a = preds_str[index][:pred_eos]
_a = preds_index[index].cpu().tolist()
_a = pred_index.index(__UpperCAmelCase ) if eos_token in pred_index else -1
_a = preds_max_prob[index][: pred_eos_index + 1]
_a = pred_max_prob.cumprod(dim=0 )[-1] if pred_max_prob.nelement() != 0 else 0.0
dec_strs.append(__UpperCAmelCase )
conf_scores.append(__UpperCAmelCase )
return dec_strs, conf_scores
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Optional[Any]:
_a = [seq.replace(''' ''' , '''''' ) for seq in self.char_tokenizer.batch_decode(__UpperCAmelCase )]
return decode_strs
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Dict:
return self.bpe_tokenizer.batch_decode(__UpperCAmelCase )
def _UpperCAmelCase ( self , __UpperCAmelCase ) -> Union[str, Any]:
_a = [seq.replace(''' ''' , '''''' ) for seq in self.wp_tokenizer.batch_decode(__UpperCAmelCase )]
return decode_strs | 353 |
"""simple docstring"""
from pathlib import Path
import fire
from tqdm import tqdm
def A_ ( _lowerCAmelCase : str="ro", _lowerCAmelCase : Optional[Any]="en", _lowerCAmelCase : Union[str, Any]="wmt16", _lowerCAmelCase : int=None ):
"""simple docstring"""
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''run pip install datasets''' )
_a = f'{src_lang}-{tgt_lang}'
print(f'Converting {dataset}-{pair}' )
_a = datasets.load_dataset(_lowerCAmelCase, _lowerCAmelCase )
if save_dir is None:
_a = f'{dataset}-{pair}'
_a = Path(_lowerCAmelCase )
save_dir.mkdir(exist_ok=_lowerCAmelCase )
for split in ds.keys():
print(f'Splitting {split} with {ds[split].num_rows} records' )
# to save to val.source, val.target like summary datasets
_a = '''val''' if split == '''validation''' else split
_a = save_dir.joinpath(f'{fn}.source' )
_a = save_dir.joinpath(f'{fn}.target' )
_a = src_path.open('''w+''' )
_a = tgt_path.open('''w+''' )
# reader is the bottleneck so writing one record at a time doesn't slow things down
for x in tqdm(ds[split] ):
_a = x['''translation''']
src_fp.write(ex[src_lang] + '''\n''' )
tgt_fp.write(ex[tgt_lang] + '''\n''' )
print(f'Saved {dataset} dataset to {save_dir}' )
if __name__ == "__main__":
fire.Fire(download_wmt_dataset) | 153 | 0 |
"""simple docstring"""
import argparse
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
# - single CPU or single GPU
# - multi GPUS (using PyTorch distributed mode)
# - (multi) TPUs
# - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################
__lowerCamelCase = 16
__lowerCamelCase = 32
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ = 16 ):
"""simple docstring"""
A__ = AutoTokenizer.from_pretrained('bert-base-cased' )
A__ = load_dataset('glue' , 'mrpc' )
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
A__ = tokenizer(examples['sentence1'] , examples['sentence2'] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
# starting with the main process first:
with accelerator.main_process_first():
A__ = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=['idx', 'sentence1', 'sentence2'] , )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
A__ = tokenized_datasets.rename_column('label' , 'labels' )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
A__ = 128 if accelerator.distributed_type == DistributedType.TPU else None
# When using mixed precision we want round multiples of 8/16
if accelerator.mixed_precision == "fp8":
A__ = 16
elif accelerator.mixed_precision != "no":
A__ = 8
else:
A__ = None
return tokenizer.pad(
UpperCamelCase__ , padding='longest' , max_length=UpperCamelCase__ , pad_to_multiple_of=UpperCamelCase__ , return_tensors='pt' , )
# Instantiate dataloaders.
A__ = DataLoader(
tokenized_datasets['train'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ , drop_last=UpperCamelCase__ )
A__ = DataLoader(
tokenized_datasets['validation'] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=UpperCamelCase__ , drop_last=(accelerator.mixed_precision == 'fp8') , )
return train_dataloader, eval_dataloader
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
A__ = Accelerator(cpu=args.cpu , mixed_precision=args.mixed_precision )
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
A__ = config['lr']
A__ = int(config['num_epochs'] )
A__ = int(config['seed'] )
A__ = int(config['batch_size'] )
A__ = evaluate.load('glue' , 'mrpc' )
# If the batch size is too big we use gradient accumulation
A__ = 1
if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
A__ = batch_size // MAX_GPU_BATCH_SIZE
A__ = MAX_GPU_BATCH_SIZE
set_seed(UpperCamelCase__ )
A__ , A__ = get_dataloaders(UpperCamelCase__ , UpperCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
A__ = AutoModelForSequenceClassification.from_pretrained('bert-base-cased' , return_dict=UpperCamelCase__ )
# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
A__ = model.to(accelerator.device )
# Instantiate optimizer
A__ = AdamW(params=model.parameters() , lr=UpperCamelCase__ )
# Instantiate scheduler
A__ = get_linear_schedule_with_warmup(
optimizer=UpperCamelCase__ , num_warmup_steps=100 , num_training_steps=(len(UpperCamelCase__ ) * num_epochs) // gradient_accumulation_steps , )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
A__ , A__ , A__ , A__ , A__ = accelerator.prepare(
UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ )
# Now we train the model
for epoch in range(UpperCamelCase__ ):
model.train()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
A__ = model(**UpperCamelCase__ )
A__ = outputs.loss
A__ = loss / gradient_accumulation_steps
accelerator.backward(UpperCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
model.eval()
for step, batch in enumerate(UpperCamelCase__ ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
A__ = model(**UpperCamelCase__ )
A__ = outputs.logits.argmax(dim=-1 )
A__ , A__ = accelerator.gather_for_metrics((predictions, batch['labels']) )
metric.add_batch(
predictions=UpperCamelCase__ , references=UpperCamelCase__ , )
A__ = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'''epoch {epoch}:''' , UpperCamelCase__ )
def UpperCAmelCase ( ):
"""simple docstring"""
A__ = argparse.ArgumentParser(description='Simple example of training script.' )
parser.add_argument(
'--mixed_precision' , type=UpperCamelCase__ , default=UpperCamelCase__ , choices=['no', 'fp16', 'bf16', 'fp8'] , help='Whether to use mixed precision. Choose'
'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
'and an Nvidia Ampere GPU.' , )
parser.add_argument('--cpu' , action='store_true' , help='If passed, will train on the CPU.' )
A__ = parser.parse_args()
A__ = {'lr': 2E-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
training_function(UpperCamelCase__ , UpperCamelCase__ )
if __name__ == "__main__":
main()
| 221 | """simple docstring"""
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowerCamelCase = {
"configuration_vivit": ["VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP", "VivitConfig"],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = ["VivitImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase = [
"VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST",
"VivitModel",
"VivitPreTrainedModel",
"VivitForVideoClassification",
]
if TYPE_CHECKING:
from .configuration_vivit import VIVIT_PRETRAINED_CONFIG_ARCHIVE_MAP, VivitConfig
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_vivit import VivitImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_vivit import (
VIVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
VivitForVideoClassification,
VivitModel,
VivitPreTrainedModel,
)
else:
import sys
__lowerCamelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 221 | 1 |
'''simple docstring'''
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
snake_case_ : List[Any] = logging.get_logger('transformers.models.speecht5')
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ ):
hf_model.apply_weight_norm()
_UpperCamelCase : Any = checkpoint['input_conv.weight_g']
_UpperCamelCase : Optional[int] = checkpoint['input_conv.weight_v']
_UpperCamelCase : str = checkpoint['input_conv.bias']
for i in range(len(config.upsample_rates ) ):
_UpperCamelCase : List[str] = checkpoint[f'upsamples.{i}.1.weight_g']
_UpperCamelCase : List[str] = checkpoint[f'upsamples.{i}.1.weight_v']
_UpperCamelCase : Optional[Any] = checkpoint[f'upsamples.{i}.1.bias']
for i in range(len(config.upsample_rates ) * len(config.resblock_kernel_sizes ) ):
for j in range(len(config.resblock_dilation_sizes ) ):
_UpperCamelCase : Optional[Any] = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_g']
_UpperCamelCase : List[Any] = checkpoint[f'blocks.{i}.convs1.{j}.1.weight_v']
_UpperCamelCase : Any = checkpoint[f'blocks.{i}.convs1.{j}.1.bias']
_UpperCamelCase : Union[str, Any] = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_g']
_UpperCamelCase : Optional[int] = checkpoint[f'blocks.{i}.convs2.{j}.1.weight_v']
_UpperCamelCase : str = checkpoint[f'blocks.{i}.convs2.{j}.1.bias']
_UpperCamelCase : int = checkpoint['output_conv.1.weight_g']
_UpperCamelCase : List[str] = checkpoint['output_conv.1.weight_v']
_UpperCamelCase : List[Any] = checkpoint['output_conv.1.bias']
hf_model.remove_weight_norm()
@torch.no_grad()
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_=None , UpperCAmelCase_=None , ):
if config_path is not None:
_UpperCamelCase : List[Any] = SpeechTaHifiGanConfig.from_pretrained(UpperCAmelCase_ )
else:
_UpperCamelCase : Tuple = SpeechTaHifiGanConfig()
_UpperCamelCase : int = SpeechTaHifiGan(UpperCAmelCase_ )
_UpperCamelCase : Optional[int] = torch.load(UpperCAmelCase_ )
load_weights(orig_checkpoint['model']['generator'] , UpperCAmelCase_ , UpperCAmelCase_ )
_UpperCamelCase : int = np.load(UpperCAmelCase_ )
_UpperCamelCase : Any = stats[0].reshape(-1 )
_UpperCamelCase : Dict = stats[1].reshape(-1 )
_UpperCamelCase : Tuple = torch.from_numpy(UpperCAmelCase_ ).float()
_UpperCamelCase : int = torch.from_numpy(UpperCAmelCase_ ).float()
model.save_pretrained(UpperCAmelCase_ )
if repo_id:
print('Pushing to the hub...' )
model.push_to_hub(UpperCAmelCase_ )
if __name__ == "__main__":
snake_case_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument('--checkpoint_path', required=True, default=None, type=str, help='Path to original checkpoint')
parser.add_argument('--stats_path', required=True, default=None, type=str, help='Path to stats.npy file')
parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
parser.add_argument(
'--pytorch_dump_folder_path', required=True, default=None, type=str, help='Path to the output PyTorch model.'
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
snake_case_ : List[str] = parser.parse_args()
convert_hifigan_checkpoint(
args.checkpoint_path,
args.stats_path,
args.pytorch_dump_folder_path,
args.config_path,
args.push_to_hub,
)
| 236 |
'''simple docstring'''
from __future__ import annotations
def A__ ( UpperCAmelCase_ ):
if not nums:
return 0
_UpperCamelCase : Any = nums[0]
_UpperCamelCase : Optional[int] = 0
for num in nums[1:]:
_UpperCamelCase , _UpperCamelCase : Optional[Any] = (
max_excluding + num,
max(UpperCAmelCase_ , UpperCAmelCase_ ),
)
return max(UpperCAmelCase_ , UpperCAmelCase_ )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 236 | 1 |
'''simple docstring'''
def a ( __a , __a , __a , __a ) -> str:
'''simple docstring'''
if height >= 1:
move_tower(height - 1 , __a , __a , __a )
move_disk(__a , __a )
move_tower(height - 1 , __a , __a , __a )
def a ( __a , __a ) -> str:
'''simple docstring'''
print('''moving disk from''' , __a , '''to''' , __a )
def a ( ) -> Optional[Any]:
'''simple docstring'''
UpperCamelCase__ :Optional[int] = int(input('''Height of hanoi: ''' ).strip() )
move_tower(__a , '''A''' , '''B''' , '''C''' )
if __name__ == "__main__":
main() | 97 |
'''simple docstring'''
import unittest
from dataclasses import dataclass
import pytest
from accelerate.commands.config.config_args import SageMakerConfig
from accelerate.utils import ComputeEnvironment
from accelerate.utils.launch import _convert_nargs_to_dict
@dataclass
class lowercase ( A__ ):
"""simple docstring"""
_a = ComputeEnvironment.AMAZON_SAGEMAKER
_a = True
_a = 'ml.p3.2xlarge'
_a = 'accelerate_sagemaker_execution_role'
_a = 'hf-sm'
_a = 'us-east-1'
_a = 1
_a = 'accelerate-sagemaker-1'
_a = '1.6'
_a = '4.4'
_a = 'train.py'
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'False',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
_a = [
'--model_name_or_path',
'bert',
'--do_train',
'--do_test',
'False',
'--do_predict',
'--epochs',
'3',
'--learning_rate',
'5e-5',
'--max_steps',
'50.5',
]
class lowercase ( unittest.TestCase ):
"""simple docstring"""
def lowerCAmelCase__ ( self ):
'''simple docstring'''
UpperCamelCase__ :Union[str, Any] = _convert_nargs_to_dict(MockLaunchConfig.success_training_script_args )
assert isinstance(converted_args['''model_name_or_path'''] , UpperCamelCase_ )
assert isinstance(converted_args['''do_train'''] , UpperCamelCase_ )
assert isinstance(converted_args['''epochs'''] , UpperCamelCase_ )
assert isinstance(converted_args['''learning_rate'''] , UpperCamelCase_ )
assert isinstance(converted_args['''max_steps'''] , UpperCamelCase_ )
with pytest.raises(UpperCamelCase_ ):
_convert_nargs_to_dict(MockLaunchConfig.fail_training_script_args ) | 97 | 1 |
def __lowerCAmelCase ( ):
lowercase__ = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
lowercase__ = 6
lowercase__ = 1
lowercase__ = 1901
lowercase__ = 0
while year < 2001:
day += 7
if (year % 4 == 0 and year % 100 != 0) or (year % 400 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
lowercase__ = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
lowercase__ = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
lowercase__ = day - days_per_month[month - 2]
if month > 12:
year += 1
lowercase__ = 1
if year < 2001 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 224 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, BitForImageClassification, BitImageProcessor
from transformers.image_utils import PILImageResampling
from transformers.utils import logging
logging.set_verbosity_info()
lowercase_ = logging.get_logger(__name__)
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = "huggingface/label-files"
lowercase__ = "imagenet-1k-id2label.json"
lowercase__ = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="dataset" ) , "r" ) )
lowercase__ = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
lowercase__ = {v: k for k, v in idalabel.items()}
lowercase__ = "std_conv" if "bit" in model_name else False
# note that when using BiT as backbone for ViT-hybrid checkpoints,
# one needs to additionally set config.layer_type = "bottleneck", config.stem_type = "same",
# config.conv_layer = "std_conv_same"
lowercase__ = BitConfig(
conv_layer=SCREAMING_SNAKE_CASE_ , num_labels=1000 , idalabel=SCREAMING_SNAKE_CASE_ , labelaid=SCREAMING_SNAKE_CASE_ , )
return config
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
if "stem.conv" in name:
lowercase__ = name.replace("stem.conv" , "bit.embedder.convolution" )
if "blocks" in name:
lowercase__ = name.replace("blocks" , "layers" )
if "head.fc" in name:
lowercase__ = name.replace("head.fc" , "classifier.1" )
if name.startswith("norm" ):
lowercase__ = "bit." + name
if "bit" not in name and "classifier" not in name:
lowercase__ = "bit.encoder." + name
return name
def __lowerCAmelCase ( ):
lowercase__ = "http://images.cocodataset.org/val2017/000000039769.jpg"
lowercase__ = Image.open(requests.get(SCREAMING_SNAKE_CASE_ , stream=SCREAMING_SNAKE_CASE_ ).raw )
return im
@torch.no_grad()
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_=False ):
lowercase__ = get_config(SCREAMING_SNAKE_CASE_ )
# load original model from timm
lowercase__ = create_model(SCREAMING_SNAKE_CASE_ , pretrained=SCREAMING_SNAKE_CASE_ )
timm_model.eval()
# load state_dict of original model
lowercase__ = timm_model.state_dict()
for key in state_dict.copy().keys():
lowercase__ = state_dict.pop(SCREAMING_SNAKE_CASE_ )
lowercase__ = val.squeeze() if "head" in key else val
# load HuggingFace model
lowercase__ = BitForImageClassification(SCREAMING_SNAKE_CASE_ )
model.eval()
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
# create image processor
lowercase__ = create_transform(**resolve_data_config({} , model=SCREAMING_SNAKE_CASE_ ) )
lowercase__ = transform.transforms
lowercase__ = {
"bilinear": PILImageResampling.BILINEAR,
"bicubic": PILImageResampling.BICUBIC,
"nearest": PILImageResampling.NEAREST,
}
lowercase__ = BitImageProcessor(
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__ = prepare_img()
lowercase__ = transform(SCREAMING_SNAKE_CASE_ ).unsqueeze(0 )
lowercase__ = 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__ = model(SCREAMING_SNAKE_CASE_ )
lowercase__ = outputs.logits
print("Logits:" , logits[0, :3] )
print("Predicted class:" , model.config.idalabel[logits.argmax(-1 ).item()] )
lowercase__ = 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 {model_name} and processor to {pytorch_dump_folder_path}''' )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
print(f'''Pushing model {model_name} and processor to the hub''' )
model.push_to_hub(f'''ybelkada/{model_name}''' )
processor.push_to_hub(f'''ybelkada/{model_name}''' )
if __name__ == "__main__":
lowercase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default="""resnetv2_50x1_bitm""",
type=str,
help="""Name of the BiT 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 push the model to the hub.""",
)
lowercase_ = parser.parse_args()
convert_bit_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 224 | 1 |
from math import ceil, sqrt
def UpperCamelCase__( UpperCamelCase__ : int = 1_00_00_00 )->Union[str, Any]:
A__ = 0
for outer_width in range(3 , (limit // 4) + 2 ):
if outer_width**2 > limit:
A__ = max(ceil(sqrt(outer_width**2 - limit ) ) , 1 )
else:
A__ = 1
if (outer_width - hole_width_lower_bound) % 2:
hole_width_lower_bound += 1
answer += (outer_width - hole_width_lower_bound - 2) // 2 + 1
return answer
if __name__ == "__main__":
print(F"{solution() = }")
| 193 |
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
lowerCamelCase__ = logging.get_logger(__name__)
@add_end_docstrings(lowerCamelCase__ )
class SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
def __init__( self : Optional[int] , **__lowercase : Dict ):
'''simple docstring'''
super().__init__(**__lowercase )
if self.framework != "pt":
raise ValueError(F"The {self.__class__} is only available in PyTorch." )
# No specific FOR_XXX available yet
def __call__( self : str , __lowercase : Union[np.ndarray, bytes, str] , **__lowercase : int ):
'''simple docstring'''
return super().__call__(__lowercase , **__lowercase )
def UpperCamelCase_ ( self : List[Any] , **__lowercase : Union[str, Any] ):
'''simple docstring'''
__a = {}
if "candidate_labels" in kwargs:
__a = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
__a = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def UpperCamelCase_ ( self : int , __lowercase : Dict , __lowercase : Dict=None , __lowercase : str="This is a sound of {}." ):
'''simple docstring'''
if isinstance(__lowercase , __lowercase ):
if audio.startswith("""http://""" ) or audio.startswith("""https://""" ):
# We need to actually check for a real protocol, otherwise it's impossible to use a local file
# like http_huggingface_co.png
__a = requests.get(__lowercase ).content
else:
with open(__lowercase , """rb""" ) as f:
__a = f.read()
if isinstance(__lowercase , __lowercase ):
__a = ffmpeg_read(__lowercase , self.feature_extractor.sampling_rate )
if not isinstance(__lowercase , np.ndarray ):
raise ValueError("""We expect a numpy ndarray as input""" )
if len(audio.shape ) != 1:
raise ValueError("""We expect a single channel audio input for ZeroShotAudioClassificationPipeline""" )
__a = self.feature_extractor(
[audio] , sampling_rate=self.feature_extractor.sampling_rate , return_tensors="""pt""" )
__a = candidate_labels
__a = [hypothesis_template.format(__lowercase ) for x in candidate_labels]
__a = self.tokenizer(__lowercase , return_tensors=self.framework , padding=__lowercase )
__a = [text_inputs]
return inputs
def UpperCamelCase_ ( self : Any , __lowercase : Any ):
'''simple docstring'''
__a = model_inputs.pop("""candidate_labels""" )
__a = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] , __lowercase ):
__a = text_inputs[0]
else:
# Batching case.
__a = text_inputs[0][0]
__a = self.model(**__lowercase , **__lowercase )
__a = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_audio,
}
return model_outputs
def UpperCamelCase_ ( self : Optional[Any] , __lowercase : Dict ):
'''simple docstring'''
__a = model_outputs.pop("""candidate_labels""" )
__a = model_outputs["""logits"""][0]
if self.framework == "pt":
__a = logits.softmax(dim=0 )
__a = probs.tolist()
else:
raise ValueError("""`tf` framework not supported.""" )
__a = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(__lowercase , __lowercase ) , key=lambda __lowercase : -x[0] )
]
return result
| 302 | 0 |
from __future__ import annotations
def __lowerCamelCase (UpperCAmelCase__ : list[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ):
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = array[indexa], array[indexa]
def __lowerCamelCase (UpperCAmelCase__ : list[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ):
if length > 1:
SCREAMING_SNAKE_CASE = int(length / 2 )
for i in range(UpperCAmelCase__ , low + middle ):
comp_and_swap(UpperCAmelCase__ , UpperCAmelCase__ , i + middle , UpperCAmelCase__ )
bitonic_merge(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
bitonic_merge(UpperCAmelCase__ , low + middle , UpperCAmelCase__ , UpperCAmelCase__ )
def __lowerCamelCase (UpperCAmelCase__ : list[int] , UpperCAmelCase__ : int , UpperCAmelCase__ : int , UpperCAmelCase__ : int ):
if length > 1:
SCREAMING_SNAKE_CASE = int(length / 2 )
bitonic_sort(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , 1 )
bitonic_sort(UpperCAmelCase__ , low + middle , UpperCAmelCase__ , 0 )
bitonic_merge(UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ , UpperCAmelCase__ )
if __name__ == "__main__":
_lowerCamelCase : List[Any] = input('''Enter numbers separated by a comma:\n''').strip()
_lowerCamelCase : Union[str, Any] = [int(item.strip()) for item in user_input.split(''',''')]
bitonic_sort(unsorted, 0, len(unsorted), 1)
print('''\nSorted array in ascending order is: ''', end='''''')
print(*unsorted, sep=''', ''')
bitonic_merge(unsorted, 0, len(unsorted), 0)
print('''Sorted array in descending order is: ''', end='''''')
print(*unsorted, sep=''', ''')
| 206 | import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer
_lowerCamelCase : Union[str, Any] = logging.get_logger(__name__)
_lowerCamelCase : str = {'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}
_lowerCamelCase : int = {
'''vocab_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
},
'''merges_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
},
'''tokenizer_file''': {
'''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
},
}
_lowerCamelCase : List[Any] = {
'''allenai/led-base-16384''': 1_63_84,
}
class lowercase ( a ):
lowercase__ : Tuple = VOCAB_FILES_NAMES
lowercase__ : Any = PRETRAINED_VOCAB_FILES_MAP
lowercase__ : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
lowercase__ : Optional[Any] = LEDTokenizer
lowercase__ : str = ["""input_ids""", """attention_mask"""]
def __init__( self : Union[str, Any] , _UpperCamelCase : Tuple=None , _UpperCamelCase : str=None , _UpperCamelCase : Tuple=None , _UpperCamelCase : List[str]="replace" , _UpperCamelCase : str="<s>" , _UpperCamelCase : List[Any]="</s>" , _UpperCamelCase : List[Any]="</s>" , _UpperCamelCase : List[str]="<s>" , _UpperCamelCase : Tuple="<unk>" , _UpperCamelCase : List[Any]="<pad>" , _UpperCamelCase : Tuple="<mask>" , _UpperCamelCase : List[str]=False , _UpperCamelCase : List[Any]=True , **_UpperCamelCase : Optional[Any] , ) -> Tuple:
'''simple docstring'''
super().__init__(
_UpperCamelCase , _UpperCamelCase , tokenizer_file=_UpperCamelCase , errors=_UpperCamelCase , bos_token=_UpperCamelCase , eos_token=_UpperCamelCase , sep_token=_UpperCamelCase , cls_token=_UpperCamelCase , unk_token=_UpperCamelCase , pad_token=_UpperCamelCase , mask_token=_UpperCamelCase , add_prefix_space=_UpperCamelCase , trim_offsets=_UpperCamelCase , **_UpperCamelCase , )
SCREAMING_SNAKE_CASE = json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
if pre_tok_state.get("add_prefix_space" , _UpperCamelCase ) != add_prefix_space:
SCREAMING_SNAKE_CASE = getattr(_UpperCamelCase , pre_tok_state.pop("type" ) )
SCREAMING_SNAKE_CASE = add_prefix_space
SCREAMING_SNAKE_CASE = pre_tok_class(**_UpperCamelCase )
SCREAMING_SNAKE_CASE = add_prefix_space
# the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
SCREAMING_SNAKE_CASE = "post_processor"
SCREAMING_SNAKE_CASE = getattr(self.backend_tokenizer , _UpperCamelCase , _UpperCamelCase )
if tokenizer_component_instance:
SCREAMING_SNAKE_CASE = json.loads(tokenizer_component_instance.__getstate__() )
# The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
if "sep" in state:
SCREAMING_SNAKE_CASE = tuple(state["sep"] )
if "cls" in state:
SCREAMING_SNAKE_CASE = tuple(state["cls"] )
SCREAMING_SNAKE_CASE = False
if state.get("add_prefix_space" , _UpperCamelCase ) != add_prefix_space:
SCREAMING_SNAKE_CASE = add_prefix_space
SCREAMING_SNAKE_CASE = True
if state.get("trim_offsets" , _UpperCamelCase ) != trim_offsets:
SCREAMING_SNAKE_CASE = trim_offsets
SCREAMING_SNAKE_CASE = True
if changes_to_apply:
SCREAMING_SNAKE_CASE = getattr(_UpperCamelCase , state.pop("type" ) )
SCREAMING_SNAKE_CASE = component_class(**_UpperCamelCase )
setattr(self.backend_tokenizer , _UpperCamelCase , _UpperCamelCase )
@property
# Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
def __snake_case( self : int ) -> str:
'''simple docstring'''
if self._mask_token is None:
if self.verbose:
logger.error("Using mask_token, but it is not set yet." )
return None
return str(self._mask_token )
@mask_token.setter
def __snake_case( self : Optional[int] , _UpperCamelCase : Optional[int] ) -> Dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = AddedToken(_UpperCamelCase , lstrip=_UpperCamelCase , rstrip=_UpperCamelCase ) if isinstance(_UpperCamelCase , _UpperCamelCase ) else value
SCREAMING_SNAKE_CASE = value
def __snake_case( self : List[Any] , *_UpperCamelCase : Optional[int] , **_UpperCamelCase : Union[str, Any] ) -> BatchEncoding:
'''simple docstring'''
SCREAMING_SNAKE_CASE = kwargs.get("is_split_into_words" , _UpperCamelCase )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs." )
return super()._batch_encode_plus(*_UpperCamelCase , **_UpperCamelCase )
def __snake_case( self : int , *_UpperCamelCase : Dict , **_UpperCamelCase : Tuple ) -> BatchEncoding:
'''simple docstring'''
SCREAMING_SNAKE_CASE = kwargs.get("is_split_into_words" , _UpperCamelCase )
if is_split_into_words and not self.add_prefix_space:
raise ValueError(
F"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
"to use it with pretokenized inputs." )
return super()._encode_plus(*_UpperCamelCase , **_UpperCamelCase )
def __snake_case( self : List[Any] , _UpperCamelCase : str , _UpperCamelCase : Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = self._tokenizer.model.save(_UpperCamelCase , name=_UpperCamelCase )
return tuple(_UpperCamelCase )
def __snake_case( self : List[Any] , _UpperCamelCase : List[Any] , _UpperCamelCase : int=None ) -> List[Any]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [self.bos_token_id] + token_ids_a + [self.eos_token_id]
if token_ids_a is None:
return output
return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]
def __snake_case( self : Dict , _UpperCamelCase : List[int] , _UpperCamelCase : Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
SCREAMING_SNAKE_CASE = [self.sep_token_id]
SCREAMING_SNAKE_CASE = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def __snake_case( self : Optional[Any] , _UpperCamelCase : Union[Dict[str, EncodedInput], BatchEncoding] , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : PaddingStrategy = PaddingStrategy.DO_NOT_PAD , _UpperCamelCase : Optional[int] = None , _UpperCamelCase : Optional[bool] = None , ) -> dict:
'''simple docstring'''
SCREAMING_SNAKE_CASE = super()._pad(
encoded_inputs=_UpperCamelCase , max_length=_UpperCamelCase , padding_strategy=_UpperCamelCase , pad_to_multiple_of=_UpperCamelCase , return_attention_mask=_UpperCamelCase , )
# Load from model defaults
if return_attention_mask is None:
SCREAMING_SNAKE_CASE = "attention_mask" in self.model_input_names
if return_attention_mask and "global_attention_mask" in encoded_inputs:
SCREAMING_SNAKE_CASE = encoded_inputs[self.model_input_names[0]]
# `global_attention_mask` need to have the same length as other (sequential) inputs.
SCREAMING_SNAKE_CASE = len(encoded_inputs["global_attention_mask"] ) != len(_UpperCamelCase )
if needs_to_be_padded:
SCREAMING_SNAKE_CASE = len(_UpperCamelCase ) - len(encoded_inputs["global_attention_mask"] )
if self.padding_side == "right":
# Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
SCREAMING_SNAKE_CASE = (
encoded_inputs["global_attention_mask"] + [-1] * difference
)
elif self.padding_side == "left":
SCREAMING_SNAKE_CASE = [-1] * difference + encoded_inputs[
"global_attention_mask"
]
else:
raise ValueError("Invalid padding strategy:" + str(self.padding_side ) )
return encoded_inputs
| 206 | 1 |
import numpy as np
import torch
from torch.utils.data import DataLoader
from accelerate.utils.dataclasses import DistributedType
class A_ :
def __init__( self , _A=2 , _A=3 , _A=6_4 , _A=None ):
'''simple docstring'''
UpperCAmelCase = np.random.default_rng(_A )
UpperCAmelCase = length
UpperCAmelCase = rng.normal(size=(length,) ).astype(np.floataa )
UpperCAmelCase = a * self.x + b + rng.normal(scale=0.1 , size=(length,) ).astype(np.floataa )
def __len__( self ):
'''simple docstring'''
return self.length
def __getitem__( self , _A ):
'''simple docstring'''
return {"x": self.x[i], "y": self.y[i]}
class A_ (torch.nn.Module ):
def __init__( self , _A=0 , _A=0 , _A=False ):
'''simple docstring'''
super().__init__()
UpperCAmelCase = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
UpperCAmelCase = torch.nn.Parameter(torch.tensor([2, 3] ).float() )
UpperCAmelCase = True
def _lowercase ( self , _A=None ):
'''simple docstring'''
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
UpperCAmelCase = False
return x * self.a[0] + self.b[0]
class A_ (torch.nn.Module ):
def __init__( self , _A=0 , _A=0 , _A=False ):
'''simple docstring'''
super().__init__()
UpperCAmelCase = torch.nn.Parameter(torch.tensor(_A ).float() )
UpperCAmelCase = torch.nn.Parameter(torch.tensor(_A ).float() )
UpperCAmelCase = True
def _lowercase ( self , _A=None ):
'''simple docstring'''
if self.first_batch:
print(F"""Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}""" )
UpperCAmelCase = False
return x * self.a + self.b
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ = 16 ) -> Union[str, Any]:
'''simple docstring'''
from datasets import load_dataset
from transformers import AutoTokenizer
UpperCAmelCase = AutoTokenizer.from_pretrained('''bert-base-cased''' )
UpperCAmelCase = {'''train''': '''tests/test_samples/MRPC/train.csv''', '''validation''': '''tests/test_samples/MRPC/dev.csv'''}
UpperCAmelCase = load_dataset('''csv''' , data_files=UpperCamelCase__ )
UpperCAmelCase = datasets['''train'''].unique('''label''' )
UpperCAmelCase = {v: i for i, v in enumerate(UpperCamelCase__ )}
def tokenize_function(UpperCamelCase__ ):
# max_length=None => use the model max length (it's actually the default)
UpperCAmelCase = tokenizer(
examples['''sentence1'''] , examples['''sentence2'''] , truncation=UpperCamelCase__ , max_length=UpperCamelCase__ , padding='''max_length''' )
if "label" in examples:
UpperCAmelCase = [label_to_id[l] for l in examples['''label''']]
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
UpperCAmelCase = datasets.map(
UpperCamelCase__ , batched=UpperCamelCase__ , remove_columns=['''sentence1''', '''sentence2''', '''label'''] , )
def collate_fn(UpperCamelCase__ ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(UpperCamelCase__ , padding='''max_length''' , max_length=128 , return_tensors='''pt''' )
return tokenizer.pad(UpperCamelCase__ , padding='''longest''' , return_tensors='''pt''' )
# Instantiate dataloaders.
UpperCAmelCase = DataLoader(tokenized_datasets['''train'''] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=2 )
UpperCAmelCase = DataLoader(tokenized_datasets['''validation'''] , shuffle=UpperCamelCase__ , collate_fn=UpperCamelCase__ , batch_size=1 )
return train_dataloader, eval_dataloader
| 273 |
import argparse
import torch
from transformers import YosoConfig, YosoForMaskedLM
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ ) -> Union[str, Any]:
'''simple docstring'''
if "model" in orig_key:
UpperCAmelCase = orig_key.replace('''model.''' , '''''' )
if "norm1" in orig_key:
UpperCAmelCase = orig_key.replace('''norm1''' , '''attention.output.LayerNorm''' )
if "norm2" in orig_key:
UpperCAmelCase = orig_key.replace('''norm2''' , '''output.LayerNorm''' )
if "norm" in orig_key:
UpperCAmelCase = orig_key.replace('''norm''' , '''LayerNorm''' )
if "transformer" in orig_key:
UpperCAmelCase = orig_key.split('''.''' )[0].split('''_''' )[-1]
UpperCAmelCase = orig_key.replace(F"""transformer_{layer_num}""" , F"""encoder.layer.{layer_num}""" )
if "mha.attn" in orig_key:
UpperCAmelCase = orig_key.replace('''mha.attn''' , '''attention.self''' )
if "mha" in orig_key:
UpperCAmelCase = orig_key.replace('''mha''' , '''attention''' )
if "W_q" in orig_key:
UpperCAmelCase = orig_key.replace('''W_q''' , '''self.query''' )
if "W_k" in orig_key:
UpperCAmelCase = orig_key.replace('''W_k''' , '''self.key''' )
if "W_v" in orig_key:
UpperCAmelCase = orig_key.replace('''W_v''' , '''self.value''' )
if "ff1" in orig_key:
UpperCAmelCase = orig_key.replace('''ff1''' , '''intermediate.dense''' )
if "ff2" in orig_key:
UpperCAmelCase = orig_key.replace('''ff2''' , '''output.dense''' )
if "ff" in orig_key:
UpperCAmelCase = orig_key.replace('''ff''' , '''output.dense''' )
if "mlm_class" in orig_key:
UpperCAmelCase = orig_key.replace('''mlm.mlm_class''' , '''cls.predictions.decoder''' )
if "mlm" in orig_key:
UpperCAmelCase = orig_key.replace('''mlm''' , '''cls.predictions.transform''' )
if "cls" not in orig_key:
UpperCAmelCase = '''yoso.''' + orig_key
return orig_key
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ ) -> Dict:
'''simple docstring'''
for key in orig_state_dict.copy().keys():
UpperCAmelCase = orig_state_dict.pop(UpperCamelCase__ )
if ("pooler" in key) or ("sen_class" in key):
continue
else:
UpperCAmelCase = val
UpperCAmelCase = orig_state_dict['''cls.predictions.decoder.bias''']
UpperCAmelCase = torch.arange(UpperCamelCase__ ).expand((1, -1) ) + 2
return orig_state_dict
def __SCREAMING_SNAKE_CASE ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ) -> int:
'''simple docstring'''
UpperCAmelCase = torch.load(UpperCamelCase__ , map_location='''cpu''' )['''model_state_dict''']
UpperCAmelCase = YosoConfig.from_json_file(UpperCamelCase__ )
UpperCAmelCase = YosoForMaskedLM(UpperCamelCase__ )
UpperCAmelCase = convert_checkpoint_helper(config.max_position_embeddings , UpperCamelCase__ )
print(model.load_state_dict(UpperCamelCase__ ) )
model.eval()
model.save_pretrained(UpperCamelCase__ )
print(F"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )
if __name__ == "__main__":
__A : Any = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--pytorch_model_path", default=None, type=str, required=True, help="Path to YOSO pytorch checkpoint."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The json file for YOSO model config.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__A : List[str] = parser.parse_args()
convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)
| 273 | 1 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : str ) -> List[Any]:
"""simple docstring"""
return (pointa[0] - pointa[0]) ** 2 + (pointa[1] - pointa[1]) ** 2
def UpperCamelCase_ ( lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : str=0 ) -> Union[str, Any]:
"""simple docstring"""
return sorted(lowerCAmelCase__ , key=lambda lowerCAmelCase__ : x[column] )
def UpperCamelCase_ ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Optional[Any]=float('inf' ) ) -> Optional[int]:
"""simple docstring"""
for i in range(points_counts - 1 ):
for j in range(i + 1 , lowerCAmelCase__ ):
lowerCAmelCase_ : Union[str, Any] = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
lowerCAmelCase_ : Optional[int] = current_dis
return min_dis
def UpperCamelCase_ ( lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any]=float('inf' ) ) -> Dict:
"""simple docstring"""
for i in range(min(6 , points_counts - 1 ) , lowerCAmelCase__ ):
for j in range(max(0 , i - 6 ) , lowerCAmelCase__ ):
lowerCAmelCase_ : Any = euclidean_distance_sqr(points[i] , points[j] )
if current_dis < min_dis:
lowerCAmelCase_ : Union[str, Any] = current_dis
return min_dis
def UpperCamelCase_ ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : int , lowerCAmelCase__ : Optional[int] ) -> Dict:
"""simple docstring"""
if points_counts <= 3:
return dis_between_closest_pair(lowerCAmelCase__ , lowerCAmelCase__ )
# recursion
lowerCAmelCase_ : int = points_counts // 2
lowerCAmelCase_ : Optional[Any] = closest_pair_of_points_sqr(
lowerCAmelCase__ , points_sorted_on_y[:mid] , lowerCAmelCase__ )
lowerCAmelCase_ : Optional[Any] = closest_pair_of_points_sqr(
lowerCAmelCase__ , points_sorted_on_y[mid:] , points_counts - mid )
lowerCAmelCase_ : Any = min(lowerCAmelCase__ , lowerCAmelCase__ )
lowerCAmelCase_ : str = []
for point in points_sorted_on_x:
if abs(point[0] - points_sorted_on_x[mid][0] ) < closest_pair_dis:
cross_strip.append(lowerCAmelCase__ )
lowerCAmelCase_ : List[Any] = dis_between_closest_in_strip(
lowerCAmelCase__ , len(lowerCAmelCase__ ) , lowerCAmelCase__ )
return min(lowerCAmelCase__ , lowerCAmelCase__ )
def UpperCamelCase_ ( lowerCAmelCase__ : str , lowerCAmelCase__ : Tuple ) -> List[Any]:
"""simple docstring"""
lowerCAmelCase_ : List[str] = column_based_sort(lowerCAmelCase__ , column=0 )
lowerCAmelCase_ : Dict = column_based_sort(lowerCAmelCase__ , column=1 )
return (
closest_pair_of_points_sqr(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
) ** 0.5
if __name__ == "__main__":
lowercase__ : List[str] = [(2, 3), (1_2, 3_0), (4_0, 5_0), (5, 1), (1_2, 1_0), (3, 4)]
print("""Distance:""", closest_pair_of_points(points, len(points)))
| 289 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ : List[Any] = logging.get_logger(__name__)
lowercase__ : List[str] = {
"""s-JoL/Open-Llama-V1""": """https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json""",
}
class UpperCamelCase__ ( lowercase_ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = """open-llama"""
def __init__( self : Optional[int] , SCREAMING_SNAKE_CASE_ : Tuple=1_0_0_0_0_0 , SCREAMING_SNAKE_CASE_ : Optional[int]=4_0_9_6 , SCREAMING_SNAKE_CASE_ : List[Any]=1_1_0_0_8 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=3_2 , SCREAMING_SNAKE_CASE_ : Optional[Any]=3_2 , SCREAMING_SNAKE_CASE_ : str="silu" , SCREAMING_SNAKE_CASE_ : Optional[Any]=2_0_4_8 , SCREAMING_SNAKE_CASE_ : Union[str, Any]=0.02 , SCREAMING_SNAKE_CASE_ : int=1E-6 , SCREAMING_SNAKE_CASE_ : List[str]=True , SCREAMING_SNAKE_CASE_ : Dict=0 , SCREAMING_SNAKE_CASE_ : Dict=1 , SCREAMING_SNAKE_CASE_ : Optional[Any]=2 , SCREAMING_SNAKE_CASE_ : str=False , SCREAMING_SNAKE_CASE_ : Optional[int]=True , SCREAMING_SNAKE_CASE_ : List[Any]=0.1 , SCREAMING_SNAKE_CASE_ : Optional[Any]=0.1 , SCREAMING_SNAKE_CASE_ : Dict=True , SCREAMING_SNAKE_CASE_ : Any=True , SCREAMING_SNAKE_CASE_ : Tuple=None , **SCREAMING_SNAKE_CASE_ : Optional[int] , ):
lowerCAmelCase_ : Union[str, Any] = vocab_size
lowerCAmelCase_ : int = max_position_embeddings
lowerCAmelCase_ : Union[str, Any] = hidden_size
lowerCAmelCase_ : Optional[Any] = intermediate_size
lowerCAmelCase_ : List[Any] = num_hidden_layers
lowerCAmelCase_ : Optional[int] = num_attention_heads
lowerCAmelCase_ : List[Any] = hidden_act
lowerCAmelCase_ : List[Any] = initializer_range
lowerCAmelCase_ : List[str] = rms_norm_eps
lowerCAmelCase_ : List[Any] = use_cache
lowerCAmelCase_ : Optional[int] = kwargs.pop(
'use_memorry_efficient_attention' , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : List[Any] = hidden_dropout_prob
lowerCAmelCase_ : List[str] = attention_dropout_prob
lowerCAmelCase_ : Tuple = use_stable_embedding
lowerCAmelCase_ : Optional[Any] = shared_input_output_embedding
lowerCAmelCase_ : Optional[Any] = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=SCREAMING_SNAKE_CASE_ , bos_token_id=SCREAMING_SNAKE_CASE_ , eos_token_id=SCREAMING_SNAKE_CASE_ , tie_word_embeddings=SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ , )
def SCREAMING_SNAKE_CASE__ ( self : List[str] ):
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling , SCREAMING_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_ : int = self.rope_scaling.get('type' , SCREAMING_SNAKE_CASE_ )
lowerCAmelCase_ : Union[str, Any] = self.rope_scaling.get('factor' , SCREAMING_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(SCREAMING_SNAKE_CASE_ , SCREAMING_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}" )
| 289 | 1 |
'''simple docstring'''
import os
import unicodedata
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import SPIECE_UNDERLINE, logging
a_ : str = logging.get_logger(__name__)
a_ : int = {"""vocab_file""": """spiece.model"""}
a_ : Dict = {
"""vocab_file""": {
"""TsinghuaAI/CPM-Generate""": """https://huggingface.co/TsinghuaAI/CPM-Generate/resolve/main/spiece.model""",
}
}
class __UpperCamelCase ( lowerCamelCase__ ):
def __init__( self, lowerCAmelCase, lowerCAmelCase=False, lowerCAmelCase=True, lowerCAmelCase=False, lowerCAmelCase="<s>", lowerCAmelCase="</s>", lowerCAmelCase="<unk>", lowerCAmelCase="<sep>", lowerCAmelCase="<pad>", lowerCAmelCase="<cls>", lowerCAmelCase="<mask>", lowerCAmelCase=["<eop>", "<eod>"], lowerCAmelCase = None, **lowerCAmelCase, ):
"""simple docstring"""
lowerCamelCase_ =AddedToken(lowerCAmelCase, lstrip=lowerCAmelCase, rstrip=lowerCAmelCase ) if isinstance(lowerCAmelCase, lowerCAmelCase ) else mask_token
lowerCamelCase_ ={} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
do_lower_case=lowerCAmelCase, remove_space=lowerCAmelCase, keep_accents=lowerCAmelCase, bos_token=lowerCAmelCase, eos_token=lowerCAmelCase, unk_token=lowerCAmelCase, sep_token=lowerCAmelCase, pad_token=lowerCAmelCase, cls_token=lowerCAmelCase, mask_token=lowerCAmelCase, additional_special_tokens=lowerCAmelCase, sp_model_kwargs=self.sp_model_kwargs, **lowerCAmelCase, )
lowerCamelCase_ =3
lowerCamelCase_ =do_lower_case
lowerCamelCase_ =remove_space
lowerCamelCase_ =keep_accents
lowerCamelCase_ =vocab_file
lowerCamelCase_ =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(lowerCAmelCase )
try:
import jieba
except ModuleNotFoundError as error:
raise error.__class__(
'''You need to install jieba to use CpmTokenizer or CpmTokenizerFast. '''
'''See https://pypi.org/project/jieba/ for installation.''' )
lowerCamelCase_ =jieba
lowerCamelCase_ =str.maketrans(''' \n''', '''\u2582\u2583''' )
@property
# Copied from transformers.models.xlnet.tokenization_xlnet.XLNetTokenizer.vocab_size
def lowercase__ ( self ):
"""simple docstring"""
return len(self.sp_model )
def lowercase__ ( self ):
"""simple docstring"""
lowerCamelCase_ ={self.convert_ids_to_tokens(lowerCAmelCase ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ):
"""simple docstring"""
lowerCamelCase_ =self.__dict__.copy()
lowerCamelCase_ =None
return state
def __setstate__( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =d
# for backward compatibility
if not hasattr(self, '''sp_model_kwargs''' ):
lowerCamelCase_ ={}
lowerCamelCase_ =spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
if self.remove_space:
lowerCamelCase_ =''' '''.join(inputs.strip().split() )
else:
lowerCamelCase_ =inputs
lowerCamelCase_ =outputs.replace('''``''', '''"''' ).replace('''\'\'''', '''"''' )
if not self.keep_accents:
lowerCamelCase_ =unicodedata.normalize('''NFKD''', lowerCAmelCase )
lowerCamelCase_ =''''''.join([c for c in outputs if not unicodedata.combining(lowerCAmelCase )] )
if self.do_lower_case:
lowerCamelCase_ =outputs.lower()
return outputs
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =self.preprocess_text(lowerCAmelCase )
lowerCamelCase_ =self.sp_model.encode(lowerCAmelCase, out_type=lowerCAmelCase )
lowerCamelCase_ =[]
for piece in pieces:
if len(lowerCAmelCase ) > 1 and piece[-1] == str(''',''' ) and piece[-2].isdigit():
lowerCamelCase_ =self.sp_model.EncodeAsPieces(piece[:-1].replace(lowerCAmelCase, '''''' ) )
if piece[0] != SPIECE_UNDERLINE and cur_pieces[0][0] == SPIECE_UNDERLINE:
if len(cur_pieces[0] ) == 1:
lowerCamelCase_ =cur_pieces[1:]
else:
lowerCamelCase_ =cur_pieces[0][1:]
cur_pieces.append(piece[-1] )
new_pieces.extend(lowerCAmelCase )
else:
new_pieces.append(lowerCAmelCase )
return new_pieces
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self.sp_model.PieceToId(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
return self.sp_model.IdToPiece(lowerCAmelCase )
def lowercase__ ( self, lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =''''''.join(lowerCAmelCase ).replace(lowerCAmelCase, ''' ''' ).strip()
return out_string
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
lowerCamelCase_ =[self.sep_token_id]
lowerCamelCase_ =[self.cls_token_id]
if token_ids_a is None:
return token_ids_a + sep + cls
return token_ids_a + sep + token_ids_a + sep + cls
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None, lowerCAmelCase = False ):
"""simple docstring"""
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowerCAmelCase, token_ids_a=lowerCAmelCase, already_has_special_tokens=lowerCAmelCase )
if token_ids_a is not None:
return ([0] * len(lowerCAmelCase )) + [1] + ([0] * len(lowerCAmelCase )) + [1, 1]
return ([0] * len(lowerCAmelCase )) + [1, 1]
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
lowerCamelCase_ =[self.sep_token_id]
lowerCamelCase_ =[2]
if token_ids_a is None:
return len(token_ids_a + sep ) * [0] + cls_segment_id
return len(token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1] + cls_segment_id
def lowercase__ ( self, lowerCAmelCase, lowerCAmelCase = None ):
"""simple docstring"""
if not os.path.isdir(lowerCAmelCase ):
logger.error(f'''Vocabulary path ({save_directory}) should be a directory''' )
return
lowerCamelCase_ =os.path.join(
lowerCAmelCase, (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file'''] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(lowerCAmelCase ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file, lowerCAmelCase )
elif not os.path.isfile(self.vocab_file ):
with open(lowerCAmelCase, '''wb''' ) as fi:
lowerCamelCase_ =self.sp_model.serialized_model_proto()
fi.write(lowerCAmelCase )
return (out_vocab_file,)
def lowercase__ ( self, *lowerCAmelCase, **lowerCAmelCase ):
"""simple docstring"""
lowerCamelCase_ =super()._decode(*lowerCAmelCase, **lowerCAmelCase )
lowerCamelCase_ =text.replace(''' ''', '''''' ).replace('''\u2582''', ''' ''' ).replace('''\u2583''', '''\n''' )
return text
| 75 |
'''simple docstring'''
import argparse
import os
import re
import torch
from flax.traverse_util import flatten_dict
from tax import checkpoints
from transformers import (
AutoTokenizer,
PixaStructConfig,
PixaStructForConditionalGeneration,
PixaStructImageProcessor,
PixaStructProcessor,
PixaStructTextConfig,
PixaStructVisionConfig,
)
def a_ ( __snake_case : Any ) -> int:
"""simple docstring"""
lowerCamelCase_ =checkpoints.load_tax_checkpoint(__snake_case )
lowerCamelCase_ =flatten_dict(__snake_case )
return flax_params
def a_ ( __snake_case : Dict ) -> Optional[int]:
"""simple docstring"""
lowerCamelCase_ ={}
lowerCamelCase_ ={
'''token_embedder''': '''embeddings''',
'''encoder_norm''': '''layernorm''',
'''kernel''': '''weight''',
'''.out''': '''.output''',
'''scale''': '''weight''',
'''embedders_0.pos_embedding''': '''row_embedder.weight''',
'''embedders_1.pos_embedding''': '''column_embedder.weight''',
}
lowerCamelCase_ ={
'''query''': '''attention.query''',
'''key''': '''attention.key''',
'''value''': '''attention.value''',
'''output.dense''': '''output''',
'''encoder_decoder_attention.o''': '''encoder_decoder_attention.attention.o''',
'''pre_self_attention_layer_norm''': '''self_attention.layer_norm''',
'''pre_cross_attention_layer_norm''': '''encoder_decoder_attention.layer_norm''',
'''mlp.''': '''mlp.DenseReluDense.''',
'''pre_mlp_layer_norm''': '''mlp.layer_norm''',
'''self_attention.o''': '''self_attention.attention.o''',
'''decoder.embeddings.embedding''': '''decoder.embed_tokens.weight''',
'''decoder.relpos_bias.rel_embedding''': '''decoder.layer.0.self_attention.attention.relative_attention_bias.weight''',
'''decoder.decoder_norm.weight''': '''decoder.final_layer_norm.weight''',
'''decoder.logits_dense.weight''': '''decoder.lm_head.weight''',
}
for key in flax_dict.keys():
if "target" in key:
# remove the first prefix from the key
lowerCamelCase_ ='''.'''.join(key[1:] )
# rename the key
for old, new in CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "decoder" in new_key:
for old, new in DECODER_CONVERSION_MAPPING.items():
lowerCamelCase_ =new_key.replace(__snake_case , __snake_case )
if "layers" in new_key and "decoder" not in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =new_key.replace('''encoder''' , '''encoder.encoder''' )
elif "layers" in new_key and "decoder" in new_key:
# use regex to replace the layer number
lowerCamelCase_ =re.sub(r'''layers_(\d+)''' , r'''layer.\1''' , __snake_case )
lowerCamelCase_ =flax_dict[key]
lowerCamelCase_ ={}
# convert converted_dict into torch format
for key in converted_dict.keys():
if ("embed_tokens" not in key) and ("embedder" not in key):
lowerCamelCase_ =torch.from_numpy(converted_dict[key].T )
else:
lowerCamelCase_ =torch.from_numpy(converted_dict[key] )
return converted_torch_dict
def a_ ( __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : Any=False , __snake_case : Optional[int]=False ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ =get_flax_param(__snake_case )
if not use_large:
lowerCamelCase_ =PixaStructVisionConfig()
lowerCamelCase_ =PixaStructTextConfig()
else:
lowerCamelCase_ =PixaStructVisionConfig(
hidden_size=1536 , d_ff=3968 , num_attention_heads=24 , num_hidden_layers=18 )
lowerCamelCase_ =PixaStructTextConfig(hidden_size=1536 , d_ff=3968 , num_heads=24 , num_layers=18 )
lowerCamelCase_ =PixaStructConfig(
vision_config=encoder_config.to_dict() , text_config=decoder_config.to_dict() , is_vqa=__snake_case )
lowerCamelCase_ =PixaStructForConditionalGeneration(__snake_case )
lowerCamelCase_ =rename_and_convert_flax_params(__snake_case )
model.load_state_dict(__snake_case )
lowerCamelCase_ =AutoTokenizer.from_pretrained('''ybelkada/test-pix2struct-tokenizer''' )
lowerCamelCase_ =PixaStructImageProcessor()
lowerCamelCase_ =PixaStructProcessor(image_processor=__snake_case , tokenizer=__snake_case )
if use_large:
lowerCamelCase_ =4096
lowerCamelCase_ =True
# mkdir if needed
os.makedirs(__snake_case , exist_ok=__snake_case )
model.save_pretrained(__snake_case )
processor.save_pretrained(__snake_case )
print('''Model saved in {}'''.format(__snake_case ) )
if __name__ == "__main__":
a_ : Optional[int] = argparse.ArgumentParser()
parser.add_argument("""--t5x_checkpoint_path""", default=None, type=str, help="""Path to the original T5x checkpoint.""")
parser.add_argument("""--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
parser.add_argument("""--use_large""", action="""store_true""", help="""Use large model.""")
parser.add_argument("""--is_vqa""", action="""store_true""", help="""Use large model.""")
a_ : Tuple = parser.parse_args()
convert_pixastruct_original_pytorch_checkpoint_to_hf(
args.tax_checkpoint_path, args.pytorch_dump_folder_path, args.use_large
)
| 75 | 1 |
"""simple docstring"""
from __future__ import annotations
def _lowerCAmelCase ( UpperCAmelCase__ : Dict, UpperCAmelCase__ : Union[str, Any], UpperCAmelCase__ : Optional[Any], UpperCAmelCase__ : List[Any] ) ->List[str]: # noqa: E741
while r - l > 1:
A__ : List[str] = (l + r) // 2
if v[m] >= key:
A__ : Any = m
else:
A__ : List[str] = m # noqa: E741
return r
def _lowerCAmelCase ( UpperCAmelCase__ : list[int] ) ->int:
if len(UpperCAmelCase__ ) == 0:
return 0
A__ : Optional[Any] = [0] * len(UpperCAmelCase__ )
A__ : Tuple = 1
A__ : int = v[0]
for i in range(1, len(UpperCAmelCase__ ) ):
if v[i] < tail[0]:
A__ : List[Any] = v[i]
elif v[i] > tail[length - 1]:
A__ : Dict = v[i]
length += 1
else:
A__ : Optional[int] = v[i]
return length
if __name__ == "__main__":
import doctest
doctest.testmod()
| 296 |
"""simple docstring"""
import argparse
import glob
import importlib.util
import os
import re
import black
from doc_builder.style_doc import style_docstrings_in_code
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
A_ = '''src/diffusers'''
A_ = '''.'''
# This is to make sure the diffusers module imported is the one in the repo.
A_ = importlib.util.spec_from_file_location(
'''diffusers''',
os.path.join(DIFFUSERS_PATH, '''__init__.py'''),
submodule_search_locations=[DIFFUSERS_PATH],
)
A_ = spec.loader.load_module()
def _lowerCAmelCase ( UpperCAmelCase__ : Optional[int], UpperCAmelCase__ : Optional[Any] ) ->Any:
return line.startswith(UpperCAmelCase__ ) or len(UpperCAmelCase__ ) <= 1 or re.search(R"""^\s*\)(\s*->.*:|:)\s*$""", UpperCAmelCase__ ) is not None
def _lowerCAmelCase ( UpperCAmelCase__ : List[str] ) ->Union[str, Any]:
A__ : Any = object_name.split(""".""" )
A__ : int = 0
# First let's find the module where our object lives.
A__ : str = parts[i]
while i < len(UpperCAmelCase__ ) and not os.path.isfile(os.path.join(UpperCAmelCase__, f'{module}.py' ) ):
i += 1
if i < len(UpperCAmelCase__ ):
A__ : Union[str, Any] = os.path.join(UpperCAmelCase__, parts[i] )
if i >= len(UpperCAmelCase__ ):
raise ValueError(f'`object_name` should begin with the name of a module of diffusers but got {object_name}.' )
with open(os.path.join(UpperCAmelCase__, f'{module}.py' ), """r""", encoding="""utf-8""", newline="""\n""" ) as f:
A__ : List[Any] = f.readlines()
# Now let's find the class / func in the code!
A__ : Optional[Any] = """"""
A__ : Any = 0
for name in parts[i + 1 :]:
while (
line_index < len(UpperCAmelCase__ ) and re.search(Rf'^{indent}(class|def)\s+{name}(\(|\:)', lines[line_index] ) is None
):
line_index += 1
indent += " "
line_index += 1
if line_index >= len(UpperCAmelCase__ ):
raise ValueError(f' {object_name} does not match any function or class in {module}.' )
# We found the beginning of the class / func, now let's find the end (when the indent diminishes).
A__ : List[Any] = line_index
while line_index < len(UpperCAmelCase__ ) and _should_continue(lines[line_index], UpperCAmelCase__ ):
line_index += 1
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
A__ : List[Any] = lines[start_index:line_index]
return "".join(UpperCAmelCase__ )
A_ = re.compile(r'''^(\s*)#\s*Copied from\s+diffusers\.(\S+\.\S+)\s*($|\S.*$)''')
A_ = re.compile(r'''^\s*(\S+)->(\S+)(\s+.*|$)''')
A_ = re.compile(r'''<FILL\s+[^>]*>''')
def _lowerCAmelCase ( UpperCAmelCase__ : List[str] ) ->Optional[Any]:
A__ : Dict = code.split("""\n""" )
A__ : List[Any] = 0
while idx < len(UpperCAmelCase__ ) and len(lines[idx] ) == 0:
idx += 1
if idx < len(UpperCAmelCase__ ):
return re.search(R"""^(\s*)\S""", lines[idx] ).groups()[0]
return ""
def _lowerCAmelCase ( UpperCAmelCase__ : Optional[Any] ) ->int:
A__ : str = len(get_indent(UpperCAmelCase__ ) ) > 0
if has_indent:
A__ : Union[str, Any] = f'class Bla:\n{code}'
A__ : Optional[Any] = black.Mode(target_versions={black.TargetVersion.PYaa}, line_length=1_1_9, preview=UpperCAmelCase__ )
A__ : Tuple = black.format_str(UpperCAmelCase__, mode=UpperCAmelCase__ )
A__ , A__ : List[Any] = style_docstrings_in_code(UpperCAmelCase__ )
return result[len("""class Bla:\n""" ) :] if has_indent else result
def _lowerCAmelCase ( UpperCAmelCase__ : Any, UpperCAmelCase__ : Dict=False ) ->List[Any]:
with open(UpperCAmelCase__, """r""", encoding="""utf-8""", newline="""\n""" ) as f:
A__ : int = f.readlines()
A__ : Dict = []
A__ : List[str] = 0
# Not a for loop cause `lines` is going to change (if `overwrite=True`).
while line_index < len(UpperCAmelCase__ ):
A__ : Dict = _re_copy_warning.search(lines[line_index] )
if search is None:
line_index += 1
continue
# There is some copied code here, let's retrieve the original.
A__ , A__ , A__ : Dict = search.groups()
A__ : Tuple = find_code_in_diffusers(UpperCAmelCase__ )
A__ : int = get_indent(UpperCAmelCase__ )
A__ : List[str] = line_index + 1 if indent == theoretical_indent else line_index + 2
A__ : Tuple = theoretical_indent
A__ : Optional[Any] = start_index
# Loop to check the observed code, stop when indentation diminishes or if we see a End copy comment.
A__ : Tuple = True
while line_index < len(UpperCAmelCase__ ) and should_continue:
line_index += 1
if line_index >= len(UpperCAmelCase__ ):
break
A__ : Optional[int] = lines[line_index]
A__ : Tuple = _should_continue(UpperCAmelCase__, UpperCAmelCase__ ) and re.search(f'^{indent}# End copy', UpperCAmelCase__ ) is None
# Clean up empty lines at the end (if any).
while len(lines[line_index - 1] ) <= 1:
line_index -= 1
A__ : Dict = lines[start_index:line_index]
A__ : Tuple = """""".join(UpperCAmelCase__ )
# Remove any nested `Copied from` comments to avoid circular copies
A__ : Optional[int] = [line for line in theoretical_code.split("""\n""" ) if _re_copy_warning.search(UpperCAmelCase__ ) is None]
A__ : Optional[Any] = """\n""".join(UpperCAmelCase__ )
# Before comparing, use the `replace_pattern` on the original code.
if len(UpperCAmelCase__ ) > 0:
A__ : int = replace_pattern.replace("""with""", """""" ).split(""",""" )
A__ : List[Any] = [_re_replace_pattern.search(UpperCAmelCase__ ) for p in patterns]
for pattern in patterns:
if pattern is None:
continue
A__ , A__ , A__ : Union[str, Any] = pattern.groups()
A__ : Union[str, Any] = re.sub(UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ )
if option.strip() == "all-casing":
A__ : List[Any] = re.sub(obja.lower(), obja.lower(), UpperCAmelCase__ )
A__ : Tuple = re.sub(obja.upper(), obja.upper(), UpperCAmelCase__ )
# Blackify after replacement. To be able to do that, we need the header (class or function definition)
# from the previous line
A__ : Optional[int] = blackify(lines[start_index - 1] + theoretical_code )
A__ : List[Any] = theoretical_code[len(lines[start_index - 1] ) :]
# Test for a diff and act accordingly.
if observed_code != theoretical_code:
diffs.append([object_name, start_index] )
if overwrite:
A__ : List[Any] = lines[:start_index] + [theoretical_code] + lines[line_index:]
A__ : Tuple = start_index + 1
if overwrite and len(UpperCAmelCase__ ) > 0:
# Warn the user a file has been modified.
print(f'Detected changes, rewriting {filename}.' )
with open(UpperCAmelCase__, """w""", encoding="""utf-8""", newline="""\n""" ) as f:
f.writelines(UpperCAmelCase__ )
return diffs
def _lowerCAmelCase ( UpperCAmelCase__ : bool = False ) ->Any:
A__ : Dict = glob.glob(os.path.join(UpperCAmelCase__, """**/*.py""" ), recursive=UpperCAmelCase__ )
A__ : str = []
for filename in all_files:
A__ : Any = is_copy_consistent(UpperCAmelCase__, UpperCAmelCase__ )
diffs += [f'- {filename}: copy does not match {d[0]} at line {d[1]}' for d in new_diffs]
if not overwrite and len(UpperCAmelCase__ ) > 0:
A__ : Any = """\n""".join(UpperCAmelCase__ )
raise Exception(
"""Found the following copy inconsistencies:\n"""
+ diff
+ """\nRun `make fix-copies` or `python utils/check_copies.py --fix_and_overwrite` to fix them.""" )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument('''--fix_and_overwrite''', action='''store_true''', help='''Whether to fix inconsistencies.''')
A_ = parser.parse_args()
check_copies(args.fix_and_overwrite)
| 296 | 1 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs
from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging
logging.set_verbosity_info()
__A = logging.get_logger()
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
__magic_name__ :nn.Module
__magic_name__ :List[nn.Module] = field(default_factory=lowerCamelCase__ )
__magic_name__ :list = field(default_factory=lowerCamelCase__ )
def snake_case ( self , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :str = len(list(m.modules() ) ) == 1 or isinstance(lowerCAmelCase__ , nn.Convad ) or isinstance(lowerCAmelCase__ , nn.BatchNormad )
if has_not_submodules:
self.traced.append(lowerCAmelCase__ )
def __call__( self , __UpperCAmelCase ):
'''simple docstring'''
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(lowerCAmelCase__ )
[x.remove() for x in self.handles]
return self
@property
def snake_case ( self ):
'''simple docstring'''
return list(filter(lambda __UpperCAmelCase : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class _lowerCAmelCase :
"""simple docstring"""
__magic_name__ :nn.Module
__magic_name__ :nn.Module
__magic_name__ :int = 1
__magic_name__ :List = field(default_factory=lowerCamelCase__ )
__magic_name__ :List = field(default_factory=lowerCamelCase__ )
__magic_name__ :bool = True
def __call__( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Any = Tracker(self.dest )(lowerCAmelCase__ ).parametrized
lowerCAmelCase__ :Dict = Tracker(self.src )(lowerCAmelCase__ ).parametrized
lowerCAmelCase__ :Any = list(filter(lambda __UpperCAmelCase : type(lowerCAmelCase__ ) not in self.src_skip , lowerCAmelCase__ ) )
lowerCAmelCase__ :Tuple = list(filter(lambda __UpperCAmelCase : type(lowerCAmelCase__ ) not in self.dest_skip , lowerCAmelCase__ ) )
if len(lowerCAmelCase__ ) != len(lowerCAmelCase__ ) and self.raise_if_mismatch:
raise Exception(
F"Numbers of operations are different. Source module has {len(lowerCAmelCase__ )} operations while"
F" destination module has {len(lowerCAmelCase__ )}." )
for dest_m, src_m in zip(lowerCAmelCase__ , lowerCAmelCase__ ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(F"Transfered from={src_m} to={dest_m}" )
class _lowerCAmelCase ( nn.Module ):
"""simple docstring"""
def __init__( self , __UpperCAmelCase ):
'''simple docstring'''
super().__init__()
lowerCAmelCase__ :List[Tuple[str, nn.Module]] = []
# - get the stem
feature_blocks.append(('conv1', model.stem) )
# - get all the feature blocks
for k, v in model.trunk_output.named_children():
assert k.startswith('block' ), F"Unexpected layer name {k}"
lowerCAmelCase__ :Any = len(lowerCAmelCase__ ) + 1
feature_blocks.append((F"res{block_index}", v) )
lowerCAmelCase__ :Optional[Any] = nn.ModuleDict(lowerCAmelCase__ )
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
return get_trunk_forward_outputs(
lowerCAmelCase__ , out_feat_keys=lowerCAmelCase__ , feature_blocks=self._feature_blocks , )
class _lowerCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
def snake_case ( self , __UpperCAmelCase ):
'''simple docstring'''
lowerCAmelCase__ :Union[str, Any] = x.split('-' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self , __UpperCAmelCase ):
'''simple docstring'''
if x not in self:
lowerCAmelCase__ :Tuple = self.convert_name_to_timm(lowerCAmelCase__ )
lowerCAmelCase__ :int = partial(lambda: (timm.create_model(lowerCAmelCase__ , pretrained=lowerCAmelCase__ ).eval(), None) )
else:
lowerCAmelCase__ :Dict = super().__getitem__(lowerCAmelCase__ )
return val
class _lowerCAmelCase ( lowerCamelCase__ ):
"""simple docstring"""
def __getitem__( self , __UpperCAmelCase ):
'''simple docstring'''
if "seer" in x and "in1k" not in x:
lowerCAmelCase__ :Optional[int] = RegNetModel
else:
lowerCAmelCase__ :int = RegNetForImageClassification
return val
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) ->Optional[Any]:
"""simple docstring"""
for from_key, to_key in keys:
lowerCAmelCase__ :Any = from_state_dict[from_key].clone()
print(F"Copied key={from_key} to={to_key}" )
return to_state_dict
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = True , ) ->List[str]:
"""simple docstring"""
print(F"Converting {name}..." )
with torch.no_grad():
lowerCAmelCase__ :int = from_model_func()
lowerCAmelCase__ :int = our_model_func(_lowerCamelCase ).eval()
lowerCAmelCase__ :Optional[int] = ModuleTransfer(src=_lowerCamelCase , dest=_lowerCamelCase , raise_if_mismatch=_lowerCamelCase )
lowerCAmelCase__ :str = torch.randn((1, 3, 224, 224) )
module_transfer(_lowerCamelCase )
if from_state_dict is not None:
lowerCAmelCase__ :str = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
lowerCAmelCase__ :List[str] = [("""0.clf.0.weight""", """classifier.1.weight"""), ("""0.clf.0.bias""", """classifier.1.bias""")]
lowerCAmelCase__ :List[str] = manually_copy_vissl_head(_lowerCamelCase , our_model.state_dict() , _lowerCamelCase )
our_model.load_state_dict(_lowerCamelCase )
lowerCAmelCase__ :str = our_model(_lowerCamelCase , output_hidden_states=_lowerCamelCase )
lowerCAmelCase__ :str = (
our_outputs.logits if isinstance(_lowerCamelCase , _lowerCamelCase ) else our_outputs.last_hidden_state
)
lowerCAmelCase__ :str = from_model(_lowerCamelCase )
lowerCAmelCase__ :List[str] = from_output[-1] if type(_lowerCamelCase ) is list else from_output
# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
if "seer" in name and "in1k" in name:
lowerCAmelCase__ :Dict = our_outputs.hidden_states[-1]
assert torch.allclose(_lowerCamelCase , _lowerCamelCase ), "The model logits don't match the original one."
if push_to_hub:
our_model.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add model' , use_temp_dir=_lowerCamelCase , )
lowerCAmelCase__ :List[Any] = 224 if """seer""" not in name else 384
# we can use the convnext one
lowerCAmelCase__ :Any = AutoImageProcessor.from_pretrained('facebook/convnext-base-224-22k-1k' , size=_lowerCamelCase )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='Add image processor' , use_temp_dir=_lowerCamelCase , )
print(F"Pushed {name}" )
def __A (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = True ) ->List[str]:
"""simple docstring"""
lowerCAmelCase__ :List[str] = """imagenet-1k-id2label.json"""
lowerCAmelCase__ :Optional[int] = 1000
lowerCAmelCase__ :str = (1, num_labels)
lowerCAmelCase__ :Tuple = """huggingface/label-files"""
lowerCAmelCase__ :Union[str, Any] = num_labels
lowerCAmelCase__ :Tuple = json.load(open(cached_download(hf_hub_url(_lowerCamelCase , _lowerCamelCase , repo_type='dataset' ) ) , 'r' ) )
lowerCAmelCase__ :List[Any] = {int(_lowerCamelCase ): v for k, v in idalabel.items()}
lowerCAmelCase__ :Dict = idalabel
lowerCAmelCase__ :Union[str, Any] = {v: k for k, v in idalabel.items()}
lowerCAmelCase__ :Optional[int] = partial(_lowerCamelCase , num_labels=_lowerCamelCase , idalabel=_lowerCamelCase , labelaid=_lowerCamelCase )
lowerCAmelCase__ :int = {
"""regnet-x-002""": ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 , layer_type='x' ),
"""regnet-x-004""": ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 160, 384] , groups_width=16 , layer_type='x' ),
"""regnet-x-006""": ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 240, 528] , groups_width=24 , layer_type='x' ),
"""regnet-x-008""": ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 128, 288, 672] , groups_width=16 , layer_type='x' ),
"""regnet-x-016""": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 168, 408, 912] , groups_width=24 , layer_type='x' ),
"""regnet-x-032""": ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 192, 432, 1008] , groups_width=48 , layer_type='x' ),
"""regnet-x-040""": ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 240, 560, 1360] , groups_width=40 , layer_type='x' ),
"""regnet-x-064""": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 392, 784, 1624] , groups_width=56 , layer_type='x' ),
"""regnet-x-080""": ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 240, 720, 1920] , groups_width=120 , layer_type='x' ),
"""regnet-x-120""": ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2240] , groups_width=112 , layer_type='x' ),
"""regnet-x-160""": ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[256, 512, 896, 2048] , groups_width=128 , layer_type='x' ),
"""regnet-x-320""": ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[336, 672, 1344, 2520] , groups_width=168 , layer_type='x' ),
# y variant
"""regnet-y-002""": ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 152, 368] , groups_width=8 ),
"""regnet-y-004""": ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 104, 208, 440] , groups_width=8 ),
"""regnet-y-006""": ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 112, 256, 608] , groups_width=16 ),
"""regnet-y-008""": ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 128, 320, 768] , groups_width=16 ),
"""regnet-y-016""": ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 120, 336, 888] , groups_width=24 ),
"""regnet-y-032""": ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 216, 576, 1512] , groups_width=24 ),
"""regnet-y-040""": ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[128, 192, 512, 1088] , groups_width=64 ),
"""regnet-y-064""": ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[144, 288, 576, 1296] , groups_width=72 ),
"""regnet-y-080""": ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[168, 448, 896, 2016] , groups_width=56 ),
"""regnet-y-120""": ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[224, 448, 896, 2240] , groups_width=112 ),
"""regnet-y-160""": ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[224, 448, 1232, 3024] , groups_width=112 ),
"""regnet-y-320""": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
"""regnet-y-320-seer""": RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
"""regnet-y-640-seer""": RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1968, 4920] , groups_width=328 ),
"""regnet-y-1280-seer""": RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1056, 2904, 7392] , groups_width=264 ),
"""regnet-y-2560-seer""": RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1696, 2544, 5088] , groups_width=640 ),
"""regnet-y-10b-seer""": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2020, 4040, 1_1110, 2_8280] , groups_width=1010 ),
# finetuned on imagenet
"""regnet-y-320-seer-in1k""": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[232, 696, 1392, 3712] , groups_width=232 ),
"""regnet-y-640-seer-in1k""": ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[328, 984, 1968, 4920] , groups_width=328 ),
"""regnet-y-1280-seer-in1k""": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[528, 1056, 2904, 7392] , groups_width=264 ),
"""regnet-y-2560-seer-in1k""": ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[640, 1696, 2544, 5088] , groups_width=640 ),
"""regnet-y-10b-seer-in1k""": ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[2020, 4040, 1_1110, 2_8280] , groups_width=1010 ),
}
lowerCAmelCase__ :List[Any] = NameToOurModelFuncMap()
lowerCAmelCase__ :str = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple[nn.Module, Dict]:
lowerCAmelCase__ :int = torch.hub.load_state_dict_from_url(_lowerCamelCase , model_dir=str(_lowerCamelCase ) , map_location='cpu' )
lowerCAmelCase__ :List[str] = model_func()
# check if we have a head, if yes add it
lowerCAmelCase__ :List[Any] = files["""classy_state_dict"""]["""base_model"""]["""model"""]
lowerCAmelCase__ :List[Any] = model_state_dict["""trunk"""]
model.load_state_dict(_lowerCamelCase )
return model.eval(), model_state_dict["heads"]
# pretrained
lowerCAmelCase__ :Any = partial(
_lowerCamelCase , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
lowerCAmelCase__ :str = partial(
_lowerCamelCase , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
lowerCAmelCase__ :Optional[int] = partial(
_lowerCamelCase , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
lowerCAmelCase__ :int = partial(
_lowerCamelCase , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1010 , w_a=1744 , w_a=620.83 , w_m=2.5_2 ) ) ) , )
# IN1K finetuned
lowerCAmelCase__ :List[Any] = partial(
_lowerCamelCase , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
lowerCAmelCase__ :int = partial(
_lowerCamelCase , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
lowerCAmelCase__ :str = partial(
_lowerCamelCase , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
lowerCAmelCase__ :Optional[int] = partial(
_lowerCamelCase , 'https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=1010 , w_a=1744 , w_a=620.83 , w_m=2.5_2 ) ) ) , )
if model_name:
convert_weight_and_push(
_lowerCamelCase , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , _lowerCamelCase , _lowerCamelCase , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
_lowerCamelCase , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , )
return config, expected_shape
if __name__ == "__main__":
__A = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"""--model_name""",
default=None,
type=str,
help=(
"""The name of the model you wish to convert, it must be one of the supported regnet* architecture,"""
""" currently: regnetx-*, regnety-*. If `None`, all of them will the converted."""
),
)
parser.add_argument(
"""--pytorch_dump_folder_path""",
default=None,
type=Path,
required=True,
help="""Path to the output PyTorch model directory.""",
)
parser.add_argument(
"""--push_to_hub""",
default=True,
type=bool,
required=False,
help="""If True, push model and image processor to the hub.""",
)
__A = parser.parse_args()
__A = args.pytorch_dump_folder_path
pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)
| 293 |
'''simple docstring'''
import string
def lowerCAmelCase_ ( _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : Dict = """"""
for i in sequence:
__SCREAMING_SNAKE_CASE : Any = ord(_lowerCamelCase )
if 65 <= extract <= 90:
output += chr(1_55 - extract )
elif 97 <= extract <= 1_22:
output += chr(2_19 - extract )
else:
output += i
return output
def lowerCAmelCase_ ( _lowerCamelCase: str ):
__SCREAMING_SNAKE_CASE : Optional[Any] = string.ascii_letters
__SCREAMING_SNAKE_CASE : Union[str, Any] = string.ascii_lowercase[::-1] + string.ascii_uppercase[::-1]
return "".join(
letters_reversed[letters.index(_lowerCamelCase )] if c in letters else c for c in sequence )
def lowerCAmelCase_ ( ):
from timeit import timeit
print("""Running performance benchmarks...""" )
__SCREAMING_SNAKE_CASE : Union[str, Any] = """from string import printable ; from __main__ import atbash, atbash_slow"""
print(F"> atbash_slow(): {timeit('atbash_slow(printable)' , setup=_lowerCamelCase )} seconds" )
print(F"> atbash(): {timeit('atbash(printable)' , setup=_lowerCamelCase )} seconds" )
if __name__ == "__main__":
for example in ("ABCDEFGH", "123GGjj", "testStringtest", "with space"):
print(f"{example} encrypted in atbash: {atbash(example)}")
benchmark() | 112 | 0 |
"""simple docstring"""
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
A_ = 0b101_100_111_110_110_010_010_000_011_110_111_011_000_110_011_110
# bin(x)[2:] gives bits of x as str, use int to convert them to 0/1
A_ = [int(bit) for bit in bin(WATERMARK_MESSAGE)[2:]]
class __SCREAMING_SNAKE_CASE :
def __init__( self : List[str] ):
'''simple docstring'''
A__ : List[Any] = WATERMARK_BITS
A__ : int = WatermarkEncoder()
self.encoder.set_watermark("""bits""" , self.watermark )
def _UpperCamelCase ( self : str , snake_case : torch.FloatTensor ):
'''simple docstring'''
if images.shape[-1] < 256:
return images
A__ : Tuple = (255 * (images / 2 + 0.5)).cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
A__ : Any = [self.encoder.encode(_a , """dwtDct""" ) for image in images]
A__ : Optional[Any] = torch.from_numpy(np.array(_a ) ).permute(0 , 3 , 1 , 2 )
A__ : str = torch.clamp(2 * (images / 255 - 0.5) , min=-1.0 , max=1.0 )
return images
| 355 |
"""simple docstring"""
from typing import Any, Dict, List, Union
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 ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING
A_ = logging.get_logger(__name__)
A_ = Dict[str, Any]
A_ = List[Prediction]
@add_end_docstrings(UpperCamelCase )
class __SCREAMING_SNAKE_CASE ( UpperCamelCase ):
def __init__( self : str , *snake_case : Tuple , **snake_case : Tuple ):
'''simple docstring'''
super().__init__(*snake_case , **snake_case )
if self.framework == "tf":
raise ValueError(F'The {self.__class__} is only available in PyTorch.' )
requires_backends(self , """vision""" )
self.check_model_type(
dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items() ) )
def _UpperCamelCase ( self : List[Any] , **snake_case : Optional[int] ):
'''simple docstring'''
A__ : Dict = {}
if "threshold" in kwargs:
A__ : int = kwargs["""threshold"""]
return {}, {}, postprocess_kwargs
def __call__( self : Tuple , *snake_case : Union[str, Any] , **snake_case : Union[str, Any] ):
'''simple docstring'''
return super().__call__(*snake_case , **snake_case )
def _UpperCamelCase ( self : str , snake_case : int ):
'''simple docstring'''
A__ : List[str] = load_image(snake_case )
A__ : int = torch.IntTensor([[image.height, image.width]] )
A__ : Union[str, Any] = self.image_processor(images=[image] , return_tensors="""pt""" )
if self.tokenizer is not None:
A__ : str = self.tokenizer(text=inputs["""words"""] , boxes=inputs["""boxes"""] , return_tensors="""pt""" )
A__ : List[str] = target_size
return inputs
def _UpperCamelCase ( self : Optional[int] , snake_case : List[Any] ):
'''simple docstring'''
A__ : str = model_inputs.pop("""target_size""" )
A__ : Dict = self.model(**snake_case )
A__ : Optional[Any] = outputs.__class__({"""target_size""": target_size, **outputs} )
if self.tokenizer is not None:
A__ : str = model_inputs["""bbox"""]
return model_outputs
def _UpperCamelCase ( self : Tuple , snake_case : Optional[int] , snake_case : int=0.9 ):
'''simple docstring'''
A__ : Any = model_outputs["""target_size"""]
if self.tokenizer is not None:
# This is a LayoutLMForTokenClassification variant.
# The OCR got the boxes and the model classified the words.
A__ , A__ : Tuple = target_size[0].tolist()
def unnormalize(snake_case : Optional[int] ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
] ) )
A__ , A__ : Optional[int] = model_outputs["""logits"""].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
A__ : Optional[Any] = [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
A__ : List[str] = [unnormalize(snake_case ) for bbox in model_outputs["""bbox"""].squeeze(0 )]
A__ : Tuple = ["""score""", """label""", """box"""]
A__ : Any = [dict(zip(snake_case , snake_case ) ) for vals in zip(scores.tolist() , snake_case , snake_case ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
A__ : Union[str, Any] = self.image_processor.post_process_object_detection(snake_case , snake_case , snake_case )
A__ : str = raw_annotations[0]
A__ : str = raw_annotation["""scores"""]
A__ : List[Any] = raw_annotation["""labels"""]
A__ : int = raw_annotation["""boxes"""]
A__ : str = scores.tolist()
A__ : Any = [self.model.config.idalabel[label.item()] for label in labels]
A__ : int = [self._get_bounding_box(snake_case ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
A__ : str = ["""score""", """label""", """box"""]
A__ : Dict = [
dict(zip(snake_case , snake_case ) )
for vals in zip(raw_annotation["""scores"""] , raw_annotation["""labels"""] , raw_annotation["""boxes"""] )
]
return annotation
def _UpperCamelCase ( self : Union[str, Any] , snake_case : "torch.Tensor" ):
'''simple docstring'''
if self.framework != "pt":
raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch.""" )
A__ , A__ , A__ , A__ : Any = box.int().tolist()
A__ : Any = {
"""xmin""": xmin,
"""ymin""": ymin,
"""xmax""": xmax,
"""ymax""": ymax,
}
return bbox
| 296 | 0 |
"""simple docstring"""
from math import asin, atan, cos, radians, sin, sqrt, tan
_a : Any = 6_37_81_37.0
_a : List[str] = 6_35_67_52.31_42_45
_a : Tuple = 6_378_137
def SCREAMING_SNAKE_CASE ( _lowerCamelCase : float ,_lowerCamelCase : float ,_lowerCamelCase : float ,_lowerCamelCase : float ) -> float:
_lowerCAmelCase : Any = (AXIS_A - AXIS_B) / AXIS_A
_lowerCAmelCase : List[str] = atan((1 - flattening) * tan(radians(_lowerCamelCase ) ) )
_lowerCAmelCase : Dict = atan((1 - flattening) * tan(radians(_lowerCamelCase ) ) )
_lowerCAmelCase : Optional[Any] = radians(_lowerCamelCase )
_lowerCAmelCase : int = radians(_lowerCamelCase )
# Equation
_lowerCAmelCase : Dict = sin((phi_a - phi_a) / 2 )
_lowerCAmelCase : Tuple = sin((lambda_a - lambda_a) / 2 )
# Square both values
sin_sq_phi *= sin_sq_phi
sin_sq_lambda *= sin_sq_lambda
_lowerCAmelCase : int = sqrt(sin_sq_phi + (cos(_lowerCamelCase ) * cos(_lowerCamelCase ) * sin_sq_lambda) )
return 2 * RADIUS * asin(_lowerCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 |
"""simple docstring"""
def UpperCAmelCase__ (lowerCAmelCase_ ):
'''simple docstring'''
if upper_limit < 0:
raise ValueError("Limit for the Catalan sequence must be ≥ 0" )
__SCREAMING_SNAKE_CASE = [0] * (upper_limit + 1)
# Base case: C(0) = C(1) = 1
__SCREAMING_SNAKE_CASE = 1
if upper_limit > 0:
__SCREAMING_SNAKE_CASE = 1
# Recurrence relation: C(i) = sum(C(j).C(i-j-1)), from j = 0 to i
for i in range(2 , upper_limit + 1 ):
for j in range(lowerCAmelCase_ ):
catalan_list[i] += catalan_list[j] * catalan_list[i - j - 1]
return catalan_list
if __name__ == "__main__":
print('''\n********* Catalan Numbers Using Dynamic Programming ************\n''')
print('''\n*** Enter -1 at any time to quit ***''')
print('''\nEnter the upper limit (≥ 0) for the Catalan number sequence: ''', end='''''')
try:
while True:
a__ : List[str] = int(input().strip())
if N < 0:
print('''\n********* Goodbye!! ************''')
break
else:
print(F"The Catalan numbers from 0 through {N} are:")
print(catalan_numbers(N))
print('''Try another upper limit for the sequence: ''', end='''''')
except (NameError, ValueError):
print('''\n********* Invalid input, goodbye! ************\n''')
import doctest
doctest.testmod()
| 54 | 0 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
A_ = version.parse(version.parse(torch.__version__).base_version) < version.parse('''1.11''')
def UpperCAmelCase__ (snake_case__ : Tuple , snake_case__ : tuple , snake_case__ : Path , snake_case__ : Tuple , snake_case__ : List[str] , snake_case__ : int , snake_case__ : Union[str, Any] , snake_case__ : Dict=False , ):
"""simple docstring"""
output_path.parent.mkdir(parents=snake_case__ , exist_ok=snake_case__ )
# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
# so we check the torch version for backwards compatibility
if is_torch_less_than_1_11:
export(
snake_case__ , snake_case__ , f=output_path.as_posix() , input_names=snake_case__ , output_names=snake_case__ , dynamic_axes=snake_case__ , do_constant_folding=snake_case__ , use_external_data_format=snake_case__ , enable_onnx_checker=snake_case__ , opset_version=snake_case__ , )
else:
export(
snake_case__ , snake_case__ , f=output_path.as_posix() , input_names=snake_case__ , output_names=snake_case__ , dynamic_axes=snake_case__ , do_constant_folding=snake_case__ , opset_version=snake_case__ , )
@torch.no_grad()
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str , snake_case__ : int , snake_case__ : bool = False ):
"""simple docstring"""
_snake_case : str = torch.floataa if fpaa else torch.floataa
if fpaa and torch.cuda.is_available():
_snake_case : List[Any] = """cuda"""
elif fpaa and not torch.cuda.is_available():
raise ValueError("""`float16` model export is only supported on GPUs with CUDA""" )
else:
_snake_case : Optional[int] = """cpu"""
_snake_case : Dict = StableDiffusionPipeline.from_pretrained(snake_case__ , torch_dtype=snake_case__ ).to(snake_case__ )
_snake_case : List[Any] = Path(snake_case__ )
# TEXT ENCODER
_snake_case : Dict = pipeline.text_encoder.config.max_position_embeddings
_snake_case : List[Any] = pipeline.text_encoder.config.hidden_size
_snake_case : List[Any] = pipeline.tokenizer(
"""A sample prompt""" , padding="""max_length""" , max_length=pipeline.tokenizer.model_max_length , truncation=snake_case__ , return_tensors="""pt""" , )
onnx_export(
pipeline.text_encoder , model_args=(text_input.input_ids.to(device=snake_case__ , dtype=torch.intaa )) , output_path=output_path / """text_encoder""" / """model.onnx""" , ordered_input_names=["""input_ids"""] , output_names=["""last_hidden_state""", """pooler_output"""] , dynamic_axes={
"""input_ids""": {0: """batch""", 1: """sequence"""},
} , opset=snake_case__ , )
del pipeline.text_encoder
# UNET
_snake_case : str = pipeline.unet.config.in_channels
_snake_case : List[str] = pipeline.unet.config.sample_size
_snake_case : str = output_path / """unet""" / """model.onnx"""
onnx_export(
pipeline.unet , model_args=(
torch.randn(2 , snake_case__ , snake_case__ , snake_case__ ).to(device=snake_case__ , dtype=snake_case__ ),
torch.randn(2 ).to(device=snake_case__ , dtype=snake_case__ ),
torch.randn(2 , snake_case__ , snake_case__ ).to(device=snake_case__ , dtype=snake_case__ ),
False,
) , output_path=snake_case__ , ordered_input_names=["""sample""", """timestep""", """encoder_hidden_states""", """return_dict"""] , output_names=["""out_sample"""] , dynamic_axes={
"""sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
"""timestep""": {0: """batch"""},
"""encoder_hidden_states""": {0: """batch""", 1: """sequence"""},
} , opset=snake_case__ , use_external_data_format=snake_case__ , )
_snake_case : str = str(unet_path.absolute().as_posix() )
_snake_case : Optional[int] = os.path.dirname(snake_case__ )
_snake_case : Tuple = onnx.load(snake_case__ )
# clean up existing tensor files
shutil.rmtree(snake_case__ )
os.mkdir(snake_case__ )
# collate external tensor files into one
onnx.save_model(
snake_case__ , snake_case__ , save_as_external_data=snake_case__ , all_tensors_to_one_file=snake_case__ , location="""weights.pb""" , convert_attribute=snake_case__ , )
del pipeline.unet
# VAE ENCODER
_snake_case : Optional[Any] = pipeline.vae
_snake_case : Optional[int] = vae_encoder.config.in_channels
_snake_case : List[Any] = vae_encoder.config.sample_size
# need to get the raw tensor output (sample) from the encoder
_snake_case : Union[str, Any] = lambda snake_case__ , snake_case__ : vae_encoder.encode(snake_case__ , snake_case__ )[0].sample()
onnx_export(
snake_case__ , model_args=(
torch.randn(1 , snake_case__ , snake_case__ , snake_case__ ).to(device=snake_case__ , dtype=snake_case__ ),
False,
) , output_path=output_path / """vae_encoder""" / """model.onnx""" , ordered_input_names=["""sample""", """return_dict"""] , output_names=["""latent_sample"""] , dynamic_axes={
"""sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=snake_case__ , )
# VAE DECODER
_snake_case : List[str] = pipeline.vae
_snake_case : str = vae_decoder.config.latent_channels
_snake_case : Any = vae_decoder.config.out_channels
# forward only through the decoder part
_snake_case : List[str] = vae_encoder.decode
onnx_export(
snake_case__ , model_args=(
torch.randn(1 , snake_case__ , snake_case__ , snake_case__ ).to(device=snake_case__ , dtype=snake_case__ ),
False,
) , output_path=output_path / """vae_decoder""" / """model.onnx""" , ordered_input_names=["""latent_sample""", """return_dict"""] , output_names=["""sample"""] , dynamic_axes={
"""latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
} , opset=snake_case__ , )
del pipeline.vae
# SAFETY CHECKER
if pipeline.safety_checker is not None:
_snake_case : Optional[Any] = pipeline.safety_checker
_snake_case : Tuple = safety_checker.config.vision_config.num_channels
_snake_case : int = safety_checker.config.vision_config.image_size
_snake_case : int = safety_checker.forward_onnx
onnx_export(
pipeline.safety_checker , model_args=(
torch.randn(
1 , snake_case__ , snake_case__ , snake_case__ , ).to(device=snake_case__ , dtype=snake_case__ ),
torch.randn(1 , snake_case__ , snake_case__ , snake_case__ ).to(device=snake_case__ , dtype=snake_case__ ),
) , output_path=output_path / """safety_checker""" / """model.onnx""" , ordered_input_names=["""clip_input""", """images"""] , output_names=["""out_images""", """has_nsfw_concepts"""] , dynamic_axes={
"""clip_input""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
"""images""": {0: """batch""", 1: """height""", 2: """width""", 3: """channels"""},
} , opset=snake_case__ , )
del pipeline.safety_checker
_snake_case : str = OnnxRuntimeModel.from_pretrained(output_path / """safety_checker""" )
_snake_case : Optional[int] = pipeline.feature_extractor
else:
_snake_case : Any = None
_snake_case : Optional[Any] = None
_snake_case : Dict = OnnxStableDiffusionPipeline(
vae_encoder=OnnxRuntimeModel.from_pretrained(output_path / """vae_encoder""" ) , vae_decoder=OnnxRuntimeModel.from_pretrained(output_path / """vae_decoder""" ) , text_encoder=OnnxRuntimeModel.from_pretrained(output_path / """text_encoder""" ) , tokenizer=pipeline.tokenizer , unet=OnnxRuntimeModel.from_pretrained(output_path / """unet""" ) , scheduler=pipeline.scheduler , safety_checker=snake_case__ , feature_extractor=snake_case__ , requires_safety_checker=safety_checker is not None , )
onnx_pipeline.save_pretrained(snake_case__ )
print("""ONNX pipeline saved to""" , snake_case__ )
del pipeline
del onnx_pipeline
_snake_case : int = OnnxStableDiffusionPipeline.from_pretrained(snake_case__ , provider="""CPUExecutionProvider""" )
print("""ONNX pipeline is loadable""" )
if __name__ == "__main__":
A_ = argparse.ArgumentParser()
parser.add_argument(
'''--model_path''',
type=str,
required=True,
help='''Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).''',
)
parser.add_argument('''--output_path''', type=str, required=True, help='''Path to the output model.''')
parser.add_argument(
'''--opset''',
default=14,
type=int,
help='''The version of the ONNX operator set to use.''',
)
parser.add_argument('''--fp16''', action='''store_true''', default=False, help='''Export the models in `float16` mode''')
A_ = parser.parse_args()
convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
| 360 |
"""simple docstring"""
def UpperCAmelCase__ (snake_case__ : int = 1_00_00_00 ):
"""simple docstring"""
_snake_case : Dict = [i - 1 for i in range(limit + 1 )]
for i in range(2 , limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i , limit + 1 , snake_case__ ):
phi[j] -= phi[j] // i
return sum(phi[2 : limit + 1] )
if __name__ == "__main__":
print(solution())
| 132 | 0 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
lowerCamelCase : Optional[Any] = logging.get_logger(__name__)
class A__ ( A__ ):
def __init__( self : str , *_a : Optional[int] , **_a : Dict ) -> None:
'''simple docstring'''
warnings.warn(
'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use FlavaImageProcessor instead.' , _a , )
super().__init__(*_a , **_a )
| 47 |
'''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 SCREAMING_SNAKE_CASE ( _a ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = 42
_SCREAMING_SNAKE_CASE = None
def __lowerCamelCase ( A__ , A__=0.999 , A__="cosine" , ) -> Tuple:
"""simple docstring"""
if alpha_transform_type == "cosine":
def alpha_bar_fn(A__ ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(A__ ):
return math.exp(t * -12.0 )
else:
raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}""" )
UpperCamelCase = []
for i in range(A__ ):
UpperCamelCase = i / num_diffusion_timesteps
UpperCamelCase = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(A__ ) / alpha_bar_fn(A__ ) , A__ ) )
return torch.tensor(A__ , dtype=torch.floataa )
class SCREAMING_SNAKE_CASE ( _a , _a ):
"""simple docstring"""
@register_to_config
def __init__( self : List[str] , UpperCamelCase__ : int = 1_0_0_0 , UpperCamelCase__ : str = "fixed_small_log" , UpperCamelCase__ : bool = True , UpperCamelCase__ : Optional[float] = 1.0 , UpperCamelCase__ : str = "epsilon" , UpperCamelCase__ : str = "squaredcos_cap_v2" , ):
"""simple docstring"""
if beta_schedule != "squaredcos_cap_v2":
raise ValueError('UnCLIPScheduler only supports `beta_schedule`: \'squaredcos_cap_v2\'' )
UpperCamelCase = betas_for_alpha_bar(UpperCamelCase__ )
UpperCamelCase = 1.0 - self.betas
UpperCamelCase = torch.cumprod(self.alphas , dim=0 )
UpperCamelCase = torch.tensor(1.0 )
# standard deviation of the initial noise distribution
UpperCamelCase = 1.0
# setable values
UpperCamelCase = None
UpperCamelCase = torch.from_numpy(np.arange(0 , UpperCamelCase__ )[::-1].copy() )
UpperCamelCase = variance_type
def A ( self : Dict , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : Optional[int] = None ):
"""simple docstring"""
return sample
def A ( self : List[str] , UpperCamelCase__ : int , UpperCamelCase__ : Union[str, torch.device] = None ):
"""simple docstring"""
UpperCamelCase = num_inference_steps
UpperCamelCase = (self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
UpperCamelCase = (np.arange(0 , UpperCamelCase__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
UpperCamelCase = torch.from_numpy(UpperCamelCase__ ).to(UpperCamelCase__ )
def A ( self : Dict , UpperCamelCase__ : Union[str, Any] , UpperCamelCase__ : Union[str, Any]=None , UpperCamelCase__ : Optional[int]=None , UpperCamelCase__ : Tuple=None ):
"""simple docstring"""
if prev_timestep is None:
UpperCamelCase = t - 1
UpperCamelCase = self.alphas_cumprod[t]
UpperCamelCase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCamelCase = 1 - alpha_prod_t
UpperCamelCase = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCamelCase = self.betas[t]
else:
UpperCamelCase = 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
UpperCamelCase = beta_prod_t_prev / beta_prod_t * beta
if variance_type is None:
UpperCamelCase = self.config.variance_type
# hacks - were probably added for training stability
if variance_type == "fixed_small_log":
UpperCamelCase = torch.log(torch.clamp(UpperCamelCase__ , min=1E-2_0 ) )
UpperCamelCase = torch.exp(0.5 * variance )
elif variance_type == "learned_range":
# NOTE difference with DDPM scheduler
UpperCamelCase = variance.log()
UpperCamelCase = beta.log()
UpperCamelCase = (predicted_variance + 1) / 2
UpperCamelCase = frac * max_log + (1 - frac) * min_log
return variance
def A ( self : int , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : int , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : Optional[int] = None , UpperCamelCase__ : str=None , UpperCamelCase__ : bool = True , ):
"""simple docstring"""
UpperCamelCase = timestep
if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
UpperCamelCase , UpperCamelCase = torch.split(UpperCamelCase__ , sample.shape[1] , dim=1 )
else:
UpperCamelCase = None
# 1. compute alphas, betas
if prev_timestep is None:
UpperCamelCase = t - 1
UpperCamelCase = self.alphas_cumprod[t]
UpperCamelCase = self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
UpperCamelCase = 1 - alpha_prod_t
UpperCamelCase = 1 - alpha_prod_t_prev
if prev_timestep == t - 1:
UpperCamelCase = self.betas[t]
UpperCamelCase = self.alphas[t]
else:
UpperCamelCase = 1 - alpha_prod_t / alpha_prod_t_prev
UpperCamelCase = 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":
UpperCamelCase = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
elif self.config.prediction_type == "sample":
UpperCamelCase = 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:
UpperCamelCase = torch.clamp(
UpperCamelCase__ , -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
UpperCamelCase = (alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
UpperCamelCase = 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
UpperCamelCase = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample
# 6. Add noise
UpperCamelCase = 0
if t > 0:
UpperCamelCase = randn_tensor(
model_output.shape , dtype=model_output.dtype , generator=UpperCamelCase__ , device=model_output.device )
UpperCamelCase = self._get_variance(
UpperCamelCase__ , predicted_variance=UpperCamelCase__ , prev_timestep=UpperCamelCase__ , )
if self.variance_type == "fixed_small_log":
UpperCamelCase = variance
elif self.variance_type == "learned_range":
UpperCamelCase = (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.' )
UpperCamelCase = variance * variance_noise
UpperCamelCase = pred_prev_sample + variance
if not return_dict:
return (pred_prev_sample,)
return UnCLIPSchedulerOutput(prev_sample=UpperCamelCase__ , pred_original_sample=UpperCamelCase__ )
def A ( self : int , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : torch.FloatTensor , UpperCamelCase__ : torch.IntTensor , ):
"""simple docstring"""
UpperCamelCase = self.alphas_cumprod.to(device=original_samples.device , dtype=original_samples.dtype )
UpperCamelCase = timesteps.to(original_samples.device )
UpperCamelCase = alphas_cumprod[timesteps] ** 0.5
UpperCamelCase = sqrt_alpha_prod.flatten()
while len(sqrt_alpha_prod.shape ) < len(original_samples.shape ):
UpperCamelCase = sqrt_alpha_prod.unsqueeze(-1 )
UpperCamelCase = (1 - alphas_cumprod[timesteps]) ** 0.5
UpperCamelCase = sqrt_one_minus_alpha_prod.flatten()
while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
UpperCamelCase = sqrt_one_minus_alpha_prod.unsqueeze(-1 )
UpperCamelCase = sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
return noisy_samples
| 28 | 0 |
import numpy as np
def _lowerCAmelCase ( __lowerCAmelCase ) -> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = 0.5 * (1.0 + tf.math.erf(x / tf.cast(tf.sqrt(2.0 ) , x.dtype ) ))
return x * cdf
def _lowerCAmelCase ( __lowerCAmelCase ) -> List[str]:
"""simple docstring"""
snake_case__ : Dict = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Tuple = tf.cast(math.pi , x.dtype )
snake_case__ : str = tf.cast(0.044_715 , x.dtype )
snake_case__ : int = 0.5 * (1.0 + tf.tanh(tf.sqrt(2.0 / pi ) * (x + coeff * tf.pow(__lowerCAmelCase , 3 )) ))
return x * cdf
def _lowerCAmelCase ( __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = tf.convert_to_tensor(__lowerCAmelCase )
return x * tf.tanh(tf.math.softplus(__lowerCAmelCase ) )
def _lowerCAmelCase ( __lowerCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : str = tf.cast(0.044_715 , x.dtype )
snake_case__ : Optional[Any] = tf.cast(0.7_978_845_608 , x.dtype )
return 0.5 * x * (1.0 + tf.tanh(x * coeffa * (1.0 + coeffa * x * x) ))
def _lowerCAmelCase ( __lowerCAmelCase ) -> Union[str, Any]:
"""simple docstring"""
snake_case__ : Optional[int] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Optional[Any] = tf.cast(1.702 , x.dtype )
return x * tf.math.sigmoid(coeff * x )
def _lowerCAmelCase ( __lowerCAmelCase ) -> str:
"""simple docstring"""
return tf.clip_by_value(_gelu(__lowerCAmelCase ) , -10 , 10 )
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase=-1 ) -> Optional[Any]:
"""simple docstring"""
snake_case__ , snake_case__ : str = tf.split(__lowerCAmelCase , 2 , axis=__lowerCAmelCase )
return a * tf.math.sigmoid(__lowerCAmelCase )
if version.parse(tf.version.VERSION) >= version.parse('''2.4'''):
def _lowerCAmelCase ( __lowerCAmelCase ) -> Optional[int]:
"""simple docstring"""
return tf.keras.activations.gelu(__lowerCAmelCase , approximate=__lowerCAmelCase )
A__ = tf.keras.activations.gelu
A__ = approximate_gelu_wrap
else:
A__ = _gelu
A__ = _gelu_new
A__ = {
'''gelu''': gelu,
'''gelu_10''': gelu_aa,
'''gelu_fast''': gelu_fast,
'''gelu_new''': gelu_new,
'''glu''': glu,
'''mish''': mish,
'''quick_gelu''': quick_gelu,
'''relu''': tf.keras.activations.relu,
'''sigmoid''': tf.keras.activations.sigmoid,
'''silu''': tf.keras.activations.swish,
'''swish''': tf.keras.activations.swish,
'''tanh''': tf.keras.activations.tanh,
}
def _lowerCAmelCase ( __lowerCAmelCase ) -> Optional[int]:
"""simple docstring"""
if activation_string in ACTaFN:
return ACTaFN[activation_string]
else:
raise KeyError(f"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys() )}""" )
| 44 | 1 |
import tempfile
import unittest
import numpy as np
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import BertConfig, is_flax_available
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_flax
if is_flax_available():
import os
from flax.core.frozen_dict import unfreeze
from flax.traverse_util import flatten_dict
from transformers import FlaxBertModel
a_ = '0.12' # assumed parallelism: 8
@require_flax
@is_staging_test
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def __UpperCamelCase ( cls : Any ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : List[Any] = TOKEN
HfFolder.save_token(a )
@classmethod
def __UpperCamelCase ( cls : List[str] ) -> Optional[Any]:
"""simple docstring"""
try:
delete_repo(token=cls._token , repo_id="test-model-flax" )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id="valid_org/test-model-flax-org" )
except HTTPError:
pass
def __UpperCamelCase ( self : Union[str, Any] ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Tuple = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE : List[str] = FlaxBertModel(a )
model.push_to_hub("test-model-flax" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE : List[Any] = FlaxBertModel.from_pretrained(F"{USER}/test-model-flax" )
SCREAMING_SNAKE_CASE : Tuple = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE : Dict = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE : Dict = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(a , 1e-3 , msg=F"{key} not identical" )
# Reset repo
delete_repo(token=self._token , repo_id="test-model-flax" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(a , repo_id="test-model-flax" , push_to_hub=a , use_auth_token=self._token )
SCREAMING_SNAKE_CASE : int = FlaxBertModel.from_pretrained(F"{USER}/test-model-flax" )
SCREAMING_SNAKE_CASE : str = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE : Any = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE : str = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(a , 1e-3 , msg=F"{key} not identical" )
def __UpperCamelCase ( self : Optional[int] ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Dict = BertConfig(
vocab_size=99 , hidden_size=32 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=37 )
SCREAMING_SNAKE_CASE : Optional[int] = FlaxBertModel(a )
model.push_to_hub("valid_org/test-model-flax-org" , use_auth_token=self._token )
SCREAMING_SNAKE_CASE : Optional[int] = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE : str = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE : List[Any] = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE : Any = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(a , 1e-3 , msg=F"{key} not identical" )
# Reset repo
delete_repo(token=self._token , repo_id="valid_org/test-model-flax-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(
a , repo_id="valid_org/test-model-flax-org" , push_to_hub=a , use_auth_token=self._token )
SCREAMING_SNAKE_CASE : Tuple = FlaxBertModel.from_pretrained("valid_org/test-model-flax-org" )
SCREAMING_SNAKE_CASE : Optional[int] = flatten_dict(unfreeze(model.params ) )
SCREAMING_SNAKE_CASE : str = flatten_dict(unfreeze(new_model.params ) )
for key in base_params.keys():
SCREAMING_SNAKE_CASE : Optional[int] = (base_params[key] - new_params[key]).sum().item()
self.assertLessEqual(a , 1e-3 , msg=F"{key} not identical" )
def lowerCamelCase__ ( _a , _a):
SCREAMING_SNAKE_CASE : Optional[Any] = True
SCREAMING_SNAKE_CASE : Union[str, Any] = flatten_dict(modela.params)
SCREAMING_SNAKE_CASE : Optional[int] = flatten_dict(modela.params)
for key in flat_params_a.keys():
if np.sum(np.abs(flat_params_a[key] - flat_params_a[key])) > 1E-4:
SCREAMING_SNAKE_CASE : Any = False
return models_are_equal
@require_flax
class _UpperCamelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCamelCase ( self : str ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE : List[Any] = FlaxBertModel(a )
SCREAMING_SNAKE_CASE : Optional[int] = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(a , a ) )
with self.assertRaises(a ):
SCREAMING_SNAKE_CASE : Tuple = FlaxBertModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : int = FlaxBertModel.from_pretrained(a , subfolder=a )
self.assertTrue(check_models_equal(a , a ) )
def __UpperCamelCase ( self : int ) -> str:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Any = BertConfig.from_pretrained("hf-internal-testing/tiny-bert-flax-only" )
SCREAMING_SNAKE_CASE : Dict = FlaxBertModel(a )
SCREAMING_SNAKE_CASE : Tuple = "bert"
with tempfile.TemporaryDirectory() as tmp_dir:
model.save_pretrained(os.path.join(a , a ) , max_shard_size="10KB" )
with self.assertRaises(a ):
SCREAMING_SNAKE_CASE : List[Any] = FlaxBertModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : List[Any] = FlaxBertModel.from_pretrained(a , subfolder=a )
self.assertTrue(check_models_equal(a , a ) )
def __UpperCamelCase ( self : str ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE : str = "bert"
SCREAMING_SNAKE_CASE : Optional[Any] = "hf-internal-testing/tiny-random-bert-subfolder"
with self.assertRaises(a ):
SCREAMING_SNAKE_CASE : str = FlaxBertModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : int = FlaxBertModel.from_pretrained(a , subfolder=a )
self.assertIsNotNone(a )
def __UpperCamelCase ( self : Optional[Any] ) -> Optional[int]:
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[int] = "bert"
SCREAMING_SNAKE_CASE : Optional[int] = "hf-internal-testing/tiny-random-bert-sharded-subfolder"
with self.assertRaises(a ):
SCREAMING_SNAKE_CASE : Union[str, Any] = FlaxBertModel.from_pretrained(a )
SCREAMING_SNAKE_CASE : Optional[int] = FlaxBertModel.from_pretrained(a , subfolder=a )
self.assertIsNotNone(a ) | 76 |
"""simple docstring"""
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax import FlaxTimestepEmbedding, FlaxTimesteps
from .modeling_flax_utils import FlaxModelMixin
from .unet_ad_blocks_flax import (
FlaxCrossAttnDownBlockaD,
FlaxDownBlockaD,
FlaxUNetMidBlockaDCrossAttn,
)
@flax.struct.dataclass
class lowercase__ ( snake_case__ ):
_UpperCAmelCase :jnp.ndarray
_UpperCAmelCase :jnp.ndarray
class lowercase__ ( nn.Module ):
_UpperCAmelCase :int
_UpperCAmelCase :Tuple[int] = (16, 32, 96, 256)
_UpperCAmelCase :jnp.dtype = jnp.floataa
def UpperCAmelCase__ ( self : List[Any] ):
lowerCamelCase_ : Optional[int] =nn.Conv(
self.block_out_channels[0] , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
lowerCamelCase_ : Any =[]
for i in range(len(self.block_out_channels ) - 1 ):
lowerCamelCase_ : Union[str, Any] =self.block_out_channels[i]
lowerCamelCase_ : Any =self.block_out_channels[i + 1]
lowerCamelCase_ : List[str] =nn.Conv(
snake_case__ , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case__ )
lowerCamelCase_ : List[str] =nn.Conv(
snake_case__ , kernel_size=(3, 3) , strides=(2, 2) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
blocks.append(snake_case__ )
lowerCamelCase_ : Union[str, Any] =blocks
lowerCamelCase_ : Optional[Any] =nn.Conv(
self.conditioning_embedding_channels , kernel_size=(3, 3) , padding=((1, 1), (1, 1)) , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self : Tuple , snake_case__ : Union[str, Any] ):
lowerCamelCase_ : int =self.conv_in(snake_case__ )
lowerCamelCase_ : List[Any] =nn.silu(snake_case__ )
for block in self.blocks:
lowerCamelCase_ : Union[str, Any] =block(snake_case__ )
lowerCamelCase_ : List[str] =nn.silu(snake_case__ )
lowerCamelCase_ : Tuple =self.conv_out(snake_case__ )
return embedding
@flax_register_to_config
class lowercase__ ( nn.Module, snake_case__, snake_case__ ):
_UpperCAmelCase :int = 32
_UpperCAmelCase :int = 4
_UpperCAmelCase :Tuple[str] = (
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"CrossAttnDownBlock2D",
"DownBlock2D",
)
_UpperCAmelCase :Union[bool, Tuple[bool]] = False
_UpperCAmelCase :Tuple[int] = (320, 640, 1280, 1280)
_UpperCAmelCase :int = 2
_UpperCAmelCase :Union[int, Tuple[int]] = 8
_UpperCAmelCase :Optional[Union[int, Tuple[int]]] = None
_UpperCAmelCase :int = 1280
_UpperCAmelCase :float = 0.0
_UpperCAmelCase :bool = False
_UpperCAmelCase :jnp.dtype = jnp.floataa
_UpperCAmelCase :bool = True
_UpperCAmelCase :int = 0
_UpperCAmelCase :str = "rgb"
_UpperCAmelCase :Tuple[int] = (16, 32, 96, 256)
def UpperCAmelCase__ ( self : int , snake_case__ : jax.random.KeyArray ):
# init input tensors
lowerCamelCase_ : str =(1, self.in_channels, self.sample_size, self.sample_size)
lowerCamelCase_ : List[Any] =jnp.zeros(snake_case__ , dtype=jnp.floataa )
lowerCamelCase_ : int =jnp.ones((1,) , dtype=jnp.intaa )
lowerCamelCase_ : Union[str, Any] =jnp.zeros((1, 1, self.cross_attention_dim) , dtype=jnp.floataa )
lowerCamelCase_ : Optional[int] =(1, 3, self.sample_size * 8, self.sample_size * 8)
lowerCamelCase_ : Any =jnp.zeros(snake_case__ , dtype=jnp.floataa )
lowerCamelCase_ , lowerCamelCase_ : Any =jax.random.split(snake_case__ )
lowerCamelCase_ : Tuple ={"params": params_rng, "dropout": dropout_rng}
return self.init(snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ )["params"]
def UpperCAmelCase__ ( self : List[Any] ):
lowerCamelCase_ : Union[str, Any] =self.block_out_channels
lowerCamelCase_ : Optional[int] =block_out_channels[0] * 4
# If `num_attention_heads` is not defined (which is the case for most models)
# it will default to `attention_head_dim`. This looks weird upon first reading it and it is.
# The reason for this behavior is to correct for incorrectly named variables that were introduced
# when this library was created. The incorrect naming was only discovered much later in https://github.com/huggingface/diffusers/issues/2011#issuecomment-1547958131
# Changing `attention_head_dim` to `num_attention_heads` for 40,000+ configurations is too backwards breaking
# which is why we correct for the naming here.
lowerCamelCase_ : int =self.num_attention_heads or self.attention_head_dim
# input
lowerCamelCase_ : Any =nn.Conv(
block_out_channels[0] , kernel_size=(3, 3) , strides=(1, 1) , padding=((1, 1), (1, 1)) , dtype=self.dtype , )
# time
lowerCamelCase_ : Union[str, Any] =FlaxTimesteps(
block_out_channels[0] , flip_sin_to_cos=self.flip_sin_to_cos , freq_shift=self.config.freq_shift )
lowerCamelCase_ : List[Any] =FlaxTimestepEmbedding(snake_case__ , dtype=self.dtype )
lowerCamelCase_ : List[str] =FlaxControlNetConditioningEmbedding(
conditioning_embedding_channels=block_out_channels[0] , block_out_channels=self.conditioning_embedding_out_channels , )
lowerCamelCase_ : Optional[int] =self.only_cross_attention
if isinstance(snake_case__ , snake_case__ ):
lowerCamelCase_ : str =(only_cross_attention,) * len(self.down_block_types )
if isinstance(snake_case__ , snake_case__ ):
lowerCamelCase_ : Any =(num_attention_heads,) * len(self.down_block_types )
# down
lowerCamelCase_ : Optional[int] =[]
lowerCamelCase_ : Optional[Any] =[]
lowerCamelCase_ : str =block_out_channels[0]
lowerCamelCase_ : str =nn.Conv(
snake_case__ , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case__ )
for i, down_block_type in enumerate(self.down_block_types ):
lowerCamelCase_ : Union[str, Any] =output_channel
lowerCamelCase_ : Tuple =block_out_channels[i]
lowerCamelCase_ : List[Any] =i == len(snake_case__ ) - 1
if down_block_type == "CrossAttnDownBlock2D":
lowerCamelCase_ : Tuple =FlaxCrossAttnDownBlockaD(
in_channels=snake_case__ , out_channels=snake_case__ , dropout=self.dropout , num_layers=self.layers_per_block , num_attention_heads=num_attention_heads[i] , add_downsample=not is_final_block , use_linear_projection=self.use_linear_projection , only_cross_attention=only_cross_attention[i] , dtype=self.dtype , )
else:
lowerCamelCase_ : Union[str, Any] =FlaxDownBlockaD(
in_channels=snake_case__ , out_channels=snake_case__ , dropout=self.dropout , num_layers=self.layers_per_block , add_downsample=not is_final_block , dtype=self.dtype , )
down_blocks.append(snake_case__ )
for _ in range(self.layers_per_block ):
lowerCamelCase_ : List[Any] =nn.Conv(
snake_case__ , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case__ )
if not is_final_block:
lowerCamelCase_ : Any =nn.Conv(
snake_case__ , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
controlnet_down_blocks.append(snake_case__ )
lowerCamelCase_ : List[str] =down_blocks
lowerCamelCase_ : int =controlnet_down_blocks
# mid
lowerCamelCase_ : int =block_out_channels[-1]
lowerCamelCase_ : str =FlaxUNetMidBlockaDCrossAttn(
in_channels=snake_case__ , dropout=self.dropout , num_attention_heads=num_attention_heads[-1] , use_linear_projection=self.use_linear_projection , dtype=self.dtype , )
lowerCamelCase_ : List[str] =nn.Conv(
snake_case__ , kernel_size=(1, 1) , padding="VALID" , kernel_init=nn.initializers.zeros_init() , bias_init=nn.initializers.zeros_init() , dtype=self.dtype , )
def __call__( self : Tuple , snake_case__ : Dict , snake_case__ : str , snake_case__ : Union[str, Any] , snake_case__ : int , snake_case__ : float = 1.0 , snake_case__ : bool = True , snake_case__ : bool = False , ):
lowerCamelCase_ : int =self.controlnet_conditioning_channel_order
if channel_order == "bgr":
lowerCamelCase_ : Optional[Any] =jnp.flip(snake_case__ , axis=1 )
# 1. time
if not isinstance(snake_case__ , jnp.ndarray ):
lowerCamelCase_ : Dict =jnp.array([timesteps] , dtype=jnp.intaa )
elif isinstance(snake_case__ , jnp.ndarray ) and len(timesteps.shape ) == 0:
lowerCamelCase_ : Any =timesteps.astype(dtype=jnp.floataa )
lowerCamelCase_ : Optional[Any] =jnp.expand_dims(snake_case__ , 0 )
lowerCamelCase_ : Any =self.time_proj(snake_case__ )
lowerCamelCase_ : Union[str, Any] =self.time_embedding(snake_case__ )
# 2. pre-process
lowerCamelCase_ : List[str] =jnp.transpose(snake_case__ , (0, 2, 3, 1) )
lowerCamelCase_ : Union[str, Any] =self.conv_in(snake_case__ )
lowerCamelCase_ : List[str] =jnp.transpose(snake_case__ , (0, 2, 3, 1) )
lowerCamelCase_ : str =self.controlnet_cond_embedding(snake_case__ )
sample += controlnet_cond
# 3. down
lowerCamelCase_ : List[str] =(sample,)
for down_block in self.down_blocks:
if isinstance(snake_case__ , snake_case__ ):
lowerCamelCase_ , lowerCamelCase_ : Union[str, Any] =down_block(snake_case__ , snake_case__ , snake_case__ , deterministic=not train )
else:
lowerCamelCase_ , lowerCamelCase_ : Optional[int] =down_block(snake_case__ , snake_case__ , deterministic=not train )
down_block_res_samples += res_samples
# 4. mid
lowerCamelCase_ : Optional[int] =self.mid_block(snake_case__ , snake_case__ , snake_case__ , deterministic=not train )
# 5. contronet blocks
lowerCamelCase_ : Dict =()
for down_block_res_sample, controlnet_block in zip(snake_case__ , self.controlnet_down_blocks ):
lowerCamelCase_ : Dict =controlnet_block(snake_case__ )
controlnet_down_block_res_samples += (down_block_res_sample,)
lowerCamelCase_ : List[Any] =controlnet_down_block_res_samples
lowerCamelCase_ : Tuple =self.controlnet_mid_block(snake_case__ )
# 6. scaling
lowerCamelCase_ : Dict =[sample * conditioning_scale for sample in down_block_res_samples]
mid_block_res_sample *= conditioning_scale
if not return_dict:
return (down_block_res_samples, mid_block_res_sample)
return FlaxControlNetOutput(
down_block_res_samples=snake_case__ , mid_block_res_sample=snake_case__ )
| 144 | 0 |
def __lowerCamelCase ( lowerCAmelCase__ ):
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError('multiplicative_persistence() only accepts integral values' )
if num < 0:
raise ValueError('multiplicative_persistence() does not accept negative values' )
lowerCAmelCase__ = 0
lowerCAmelCase__ = str(lowerCAmelCase__ )
while len(lowerCAmelCase__ ) != 1:
lowerCAmelCase__ = [int(lowerCAmelCase__ ) for i in num_string]
lowerCAmelCase__ = 1
for i in range(0 , len(lowerCAmelCase__ ) ):
total *= numbers[i]
lowerCAmelCase__ = str(lowerCAmelCase__ )
steps += 1
return steps
def __lowerCamelCase ( lowerCAmelCase__ ):
if not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise ValueError('additive_persistence() only accepts integral values' )
if num < 0:
raise ValueError('additive_persistence() does not accept negative values' )
lowerCAmelCase__ = 0
lowerCAmelCase__ = str(lowerCAmelCase__ )
while len(lowerCAmelCase__ ) != 1:
lowerCAmelCase__ = [int(lowerCAmelCase__ ) for i in num_string]
lowerCAmelCase__ = 0
for i in range(0 , len(lowerCAmelCase__ ) ):
total += numbers[i]
lowerCAmelCase__ = str(lowerCAmelCase__ )
steps += 1
return steps
if __name__ == "__main__":
import doctest
doctest.testmod()
| 368 | import argparse
import math
import os
import torch
from neural_compressor.utils.pytorch import load
from PIL import Image
from transformers import CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, StableDiffusionPipeline, UNetaDConditionModel
def __lowerCamelCase ( ):
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
'-m' , '--pretrained_model_name_or_path' , type=lowerCAmelCase__ , default=lowerCAmelCase__ , required=lowerCAmelCase__ , help='Path to pretrained model or model identifier from huggingface.co/models.' , )
parser.add_argument(
'-c' , '--caption' , type=lowerCAmelCase__ , default='robotic cat with wings' , help='Text used to generate images.' , )
parser.add_argument(
'-n' , '--images_num' , type=lowerCAmelCase__ , default=4 , help='How much images to generate.' , )
parser.add_argument(
'-s' , '--seed' , type=lowerCAmelCase__ , default=4_2 , help='Seed for random process.' , )
parser.add_argument(
'-ci' , '--cuda_id' , type=lowerCAmelCase__ , default=0 , help='cuda_id.' , )
lowerCAmelCase__ = parser.parse_args()
return args
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ ):
if not len(lowerCAmelCase__ ) == rows * cols:
raise ValueError('The specified number of rows and columns are not correct.' )
lowerCAmelCase__ , lowerCAmelCase__ = imgs[0].size
lowerCAmelCase__ = Image.new('RGB' , size=(cols * w, rows * h) )
lowerCAmelCase__ , lowerCAmelCase__ = grid.size
for i, img in enumerate(lowerCAmelCase__ ):
grid.paste(lowerCAmelCase__ , box=(i % cols * w, i // cols * h) )
return grid
def __lowerCamelCase ( lowerCAmelCase__ , lowerCAmelCase__="robotic cat with wings" , lowerCAmelCase__=7.5 , lowerCAmelCase__=5_0 , lowerCAmelCase__=1 , lowerCAmelCase__=4_2 , ):
lowerCAmelCase__ = torch.Generator(pipeline.device ).manual_seed(lowerCAmelCase__ )
lowerCAmelCase__ = pipeline(
lowerCAmelCase__ , guidance_scale=lowerCAmelCase__ , num_inference_steps=lowerCAmelCase__ , generator=lowerCAmelCase__ , num_images_per_prompt=lowerCAmelCase__ , ).images
lowerCAmelCase__ = int(math.sqrt(lowerCAmelCase__ ) )
lowerCAmelCase__ = image_grid(lowerCAmelCase__ , rows=_rows , cols=num_images_per_prompt // _rows )
return grid, images
lowerCAmelCase__ = parse_args()
# Load models and create wrapper for stable diffusion
lowerCAmelCase__ = CLIPTokenizer.from_pretrained(args.pretrained_model_name_or_path, subfolder='tokenizer')
lowerCAmelCase__ = CLIPTextModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='text_encoder')
lowerCAmelCase__ = AutoencoderKL.from_pretrained(args.pretrained_model_name_or_path, subfolder='vae')
lowerCAmelCase__ = UNetaDConditionModel.from_pretrained(args.pretrained_model_name_or_path, subfolder='unet')
lowerCAmelCase__ = StableDiffusionPipeline.from_pretrained(
args.pretrained_model_name_or_path, text_encoder=text_encoder, vae=vae, unet=unet, tokenizer=tokenizer
)
lowerCAmelCase__ = lambda images, clip_input: (images, False)
if os.path.exists(os.path.join(args.pretrained_model_name_or_path, 'best_model.pt')):
lowerCAmelCase__ = load(args.pretrained_model_name_or_path, model=unet)
unet.eval()
setattr(pipeline, 'unet', unet)
else:
lowerCAmelCase__ = unet.to(torch.device('cuda', args.cuda_id))
lowerCAmelCase__ = pipeline.to(unet.device)
lowerCAmelCase__ , lowerCAmelCase__ = generate_images(pipeline, prompt=args.caption, num_images_per_prompt=args.images_num, seed=args.seed)
grid.save(os.path.join(args.pretrained_model_name_or_path, '{}.png'.format('_'.join(args.caption.split()))))
lowerCAmelCase__ = os.path.join(args.pretrained_model_name_or_path, '_'.join(args.caption.split()))
os.makedirs(dirname, exist_ok=True)
for idx, image in enumerate(images):
image.save(os.path.join(dirname, '{}.png'.format(idx + 1)))
| 119 | 0 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
while b:
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_: Union[str, Any] =b, a % b
return a
def __magic_name__ ( lowercase , lowercase ):
return a if b == 0 else euclidean_gcd_recursive(_a , a % b )
def __magic_name__ ( ):
print(f'''euclidean_gcd(3, 5) = {euclidean_gcd(3 , 5 )}''' )
print(f'''euclidean_gcd(5, 3) = {euclidean_gcd(5 , 3 )}''' )
print(f'''euclidean_gcd(1, 3) = {euclidean_gcd(1 , 3 )}''' )
print(f'''euclidean_gcd(3, 6) = {euclidean_gcd(3 , 6 )}''' )
print(f'''euclidean_gcd(6, 3) = {euclidean_gcd(6 , 3 )}''' )
print(f'''euclidean_gcd_recursive(3, 5) = {euclidean_gcd_recursive(3 , 5 )}''' )
print(f'''euclidean_gcd_recursive(5, 3) = {euclidean_gcd_recursive(5 , 3 )}''' )
print(f'''euclidean_gcd_recursive(1, 3) = {euclidean_gcd_recursive(1 , 3 )}''' )
print(f'''euclidean_gcd_recursive(3, 6) = {euclidean_gcd_recursive(3 , 6 )}''' )
print(f'''euclidean_gcd_recursive(6, 3) = {euclidean_gcd_recursive(6 , 3 )}''' )
if __name__ == "__main__":
main()
| 173 |
'''simple docstring'''
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, BaseOutput, DiffusionPipeline, ImagePipelineOutput
from .mel import Mel
class _a ( __a ):
__a : str = ["""vqvae"""]
def __init__( self : str , lowercase : AutoencoderKL , lowercase : UNetaDConditionModel , lowercase : Mel , lowercase : Union[DDIMScheduler, DDPMScheduler] , ):
'''simple docstring'''
super().__init__()
self.register_modules(unet=lowercase , scheduler=lowercase , mel=lowercase , vqvae=lowercase )
def A ( self : Optional[Any] ):
'''simple docstring'''
return 50 if isinstance(self.scheduler , lowercase ) else 1_000
@torch.no_grad()
def __call__( self : Optional[Any] , lowercase : int = 1 , lowercase : str = None , lowercase : np.ndarray = None , lowercase : int = 0 , lowercase : int = 0 , lowercase : int = None , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : float = 0 , lowercase : torch.Generator = None , lowercase : float = 0 , lowercase : torch.Tensor = None , lowercase : torch.Tensor = None , lowercase : Tuple=True , ):
'''simple docstring'''
UpperCAmelCase = steps or self.get_default_steps()
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = step_generator or generator
# For backwards compatibility
if type(self.unet.config.sample_size ) == int:
UpperCAmelCase = (self.unet.config.sample_size, self.unet.config.sample_size)
if noise is None:
UpperCAmelCase = randn_tensor(
(
batch_size,
self.unet.config.in_channels,
self.unet.config.sample_size[0],
self.unet.config.sample_size[1],
) , generator=lowercase , device=self.device , )
UpperCAmelCase = noise
UpperCAmelCase = None
if audio_file is not None or raw_audio is not None:
self.mel.load_audio(lowercase , lowercase )
UpperCAmelCase = self.mel.audio_slice_to_image(lowercase )
UpperCAmelCase = np.frombuffer(input_image.tobytes() , dtype='''uint8''' ).reshape(
(input_image.height, input_image.width) )
UpperCAmelCase = (input_image / 255) * 2 - 1
UpperCAmelCase = torch.tensor(input_image[np.newaxis, :, :] , dtype=torch.float ).to(self.device )
if self.vqvae is not None:
UpperCAmelCase = self.vqvae.encode(torch.unsqueeze(lowercase , 0 ) ).latent_dist.sample(
generator=lowercase )[0]
UpperCAmelCase = self.vqvae.config.scaling_factor * input_images
if start_step > 0:
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , self.scheduler.timesteps[start_step - 1] )
UpperCAmelCase = (
self.unet.config.sample_size[1] * self.mel.get_sample_rate() / self.mel.x_res / self.mel.hop_length
)
UpperCAmelCase = int(mask_start_secs * pixels_per_second )
UpperCAmelCase = int(mask_end_secs * pixels_per_second )
UpperCAmelCase = self.scheduler.add_noise(lowercase , lowercase , torch.tensor(self.scheduler.timesteps[start_step:] ) )
for step, t in enumerate(self.progress_bar(self.scheduler.timesteps[start_step:] ) ):
if isinstance(self.unet , lowercase ):
UpperCAmelCase = self.unet(lowercase , lowercase , lowercase )['''sample''']
else:
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
if isinstance(self.scheduler , lowercase ):
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , eta=lowercase , generator=lowercase , )['''prev_sample''']
else:
UpperCAmelCase = self.scheduler.step(
model_output=lowercase , timestep=lowercase , sample=lowercase , generator=lowercase , )['''prev_sample''']
if mask is not None:
if mask_start > 0:
UpperCAmelCase = mask[:, step, :, :mask_start]
if mask_end > 0:
UpperCAmelCase = mask[:, step, :, -mask_end:]
if self.vqvae is not None:
# 0.18215 was scaling factor used in training to ensure unit variance
UpperCAmelCase = 1 / self.vqvae.config.scaling_factor * images
UpperCAmelCase = self.vqvae.decode(lowercase )['''sample''']
UpperCAmelCase = (images / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase = images.cpu().permute(0 , 2 , 3 , 1 ).numpy()
UpperCAmelCase = (images * 255).round().astype('''uint8''' )
UpperCAmelCase = list(
(Image.fromarray(_[:, :, 0] ) for _ in images)
if images.shape[3] == 1
else (Image.fromarray(lowercase , mode='''RGB''' ).convert('''L''' ) for _ in images) )
UpperCAmelCase = [self.mel.image_to_audio(lowercase ) for _ in images]
if not return_dict:
return images, (self.mel.get_sample_rate(), audios)
return BaseOutput(**AudioPipelineOutput(np.array(lowercase )[:, np.newaxis, :] ) , **ImagePipelineOutput(lowercase ) )
@torch.no_grad()
def A ( self : Dict , lowercase : List[Image.Image] , lowercase : int = 50 ):
'''simple docstring'''
assert isinstance(self.scheduler , lowercase )
self.scheduler.set_timesteps(lowercase )
UpperCAmelCase = np.array(
[np.frombuffer(image.tobytes() , dtype='''uint8''' ).reshape((1, image.height, image.width) ) for image in images] )
UpperCAmelCase = (sample / 255) * 2 - 1
UpperCAmelCase = torch.Tensor(lowercase ).to(self.device )
for t in self.progress_bar(torch.flip(self.scheduler.timesteps , (0,) ) ):
UpperCAmelCase = t - self.scheduler.config.num_train_timesteps // self.scheduler.num_inference_steps
UpperCAmelCase = self.scheduler.alphas_cumprod[t]
UpperCAmelCase = (
self.scheduler.alphas_cumprod[prev_timestep]
if prev_timestep >= 0
else self.scheduler.final_alpha_cumprod
)
UpperCAmelCase = 1 - alpha_prod_t
UpperCAmelCase = self.unet(lowercase , lowercase )['''sample''']
UpperCAmelCase = (1 - alpha_prod_t_prev) ** 0.5 * model_output
UpperCAmelCase = (sample - pred_sample_direction) * alpha_prod_t_prev ** (-0.5)
UpperCAmelCase = sample * alpha_prod_t ** 0.5 + beta_prod_t ** 0.5 * model_output
return sample
@staticmethod
def A ( lowercase : torch.Tensor , lowercase : torch.Tensor , lowercase : float ):
'''simple docstring'''
UpperCAmelCase = acos(torch.dot(torch.flatten(lowercase ) , torch.flatten(lowercase ) ) / torch.norm(lowercase ) / torch.norm(lowercase ) )
return sin((1 - alpha) * theta ) * xa / sin(lowercase ) + sin(alpha * theta ) * xa / sin(lowercase )
| 34 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def snake_case ( A__ ):
UpperCAmelCase_ : Dict = analyze_text(A__ )
UpperCAmelCase_ : Dict = list(" " + ascii_lowercase )
# what is our total sum of probabilities.
UpperCAmelCase_ : Any = sum(single_char_strings.values() )
# one length string
UpperCAmelCase_ : List[Any] = 0
# for each alpha we go in our dict and if it is in it we calculate entropy
for ch in my_alphas:
if ch in single_char_strings:
UpperCAmelCase_ : List[str] = single_char_strings[ch]
UpperCAmelCase_ : List[Any] = my_str / all_sum
my_fir_sum += prob * math.loga(A__ ) # entropy formula.
# print entropy
print(F"""{round(-1 * my_fir_sum ):.1f}""" )
# two len string
UpperCAmelCase_ : Union[str, Any] = sum(two_char_strings.values() )
UpperCAmelCase_ : Union[str, Any] = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
UpperCAmelCase_ : Tuple = cha + cha
if sequence in two_char_strings:
UpperCAmelCase_ : int = two_char_strings[sequence]
UpperCAmelCase_ : Tuple = int(A__ ) / all_sum
my_sec_sum += prob * math.loga(A__ )
# print second entropy
print(F"""{round(-1 * my_sec_sum ):.1f}""" )
# print the difference between them
print(F"""{round((-1 * my_sec_sum) - (-1 * my_fir_sum) ):.1f}""" )
def snake_case ( A__ ):
UpperCAmelCase_ : Optional[int] = Counter() # type: ignore
UpperCAmelCase_ : Any = Counter() # type: ignore
single_char_strings[text[-1]] += 1
# first case when we have space at start.
two_char_strings[" " + text[0]] += 1
for i in range(0 ,len(A__ ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def snake_case ( ):
import doctest
doctest.testmod()
# text = (
# "Had repulsive dashwoods suspicion sincerity but advantage now him. Remark "
# "easily garret nor nay. Civil those mrs enjoy shy fat merry. You greatest "
# "jointure saw horrible. He private he on be imagine suppose. Fertile "
# "beloved evident through no service elderly is. Blind there if every no so "
# "at. Own neglected you preferred way sincerity delivered his attempted. To "
# "of message cottage windows do besides against uncivil. Delightful "
# "unreserved impossible few estimating men favourable see entreaties. She "
# "propriety immediate was improving. He or entrance humoured likewise "
# "moderate. Much nor game son say feel. Fat make met can must form into "
# "gate. Me we offending prevailed discovery. "
# )
# calculate_prob(text)
if __name__ == "__main__":
main()
| 370 |
"""simple docstring"""
def snake_case ( A__ = 10_00 ):
UpperCAmelCase_ : Optional[Any] = 2**power
UpperCAmelCase_ : Optional[int] = str(A__ )
UpperCAmelCase_ : Tuple = list(A__ )
UpperCAmelCase_ : Any = 0
for i in list_num:
sum_of_num += int(A__ )
return sum_of_num
if __name__ == "__main__":
lowerCamelCase_ = int(input('''Enter the power of 2: ''').strip())
print('''2 ^ ''', power, ''' = ''', 2**power)
lowerCamelCase_ = solution(power)
print('''Sum of the digits is: ''', result)
| 253 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
BertTokenizer,
ViltConfig,
ViltForImageAndTextRetrieval,
ViltForImagesAndTextClassification,
ViltForMaskedLM,
ViltForQuestionAnswering,
ViltImageProcessor,
ViltProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
_UpperCamelCase = logging.get_logger(__name__)
def a_ ( _lowerCAmelCase ,_lowerCAmelCase=False ,_lowerCAmelCase=False ,_lowerCAmelCase=False ) -> Optional[Any]:
__lowerCamelCase : Optional[Any] = []
for i in range(config.num_hidden_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append((F'transformer.blocks.{i}.norm1.weight', F'vilt.encoder.layer.{i}.layernorm_before.weight') )
rename_keys.append((F'transformer.blocks.{i}.norm1.bias', F'vilt.encoder.layer.{i}.layernorm_before.bias') )
rename_keys.append(
(F'transformer.blocks.{i}.attn.proj.weight', F'vilt.encoder.layer.{i}.attention.output.dense.weight') )
rename_keys.append(
(F'transformer.blocks.{i}.attn.proj.bias', F'vilt.encoder.layer.{i}.attention.output.dense.bias') )
rename_keys.append((F'transformer.blocks.{i}.norm2.weight', F'vilt.encoder.layer.{i}.layernorm_after.weight') )
rename_keys.append((F'transformer.blocks.{i}.norm2.bias', F'vilt.encoder.layer.{i}.layernorm_after.bias') )
rename_keys.append(
(F'transformer.blocks.{i}.mlp.fc1.weight', F'vilt.encoder.layer.{i}.intermediate.dense.weight') )
rename_keys.append((F'transformer.blocks.{i}.mlp.fc1.bias', F'vilt.encoder.layer.{i}.intermediate.dense.bias') )
rename_keys.append((F'transformer.blocks.{i}.mlp.fc2.weight', F'vilt.encoder.layer.{i}.output.dense.weight') )
rename_keys.append((F'transformer.blocks.{i}.mlp.fc2.bias', F'vilt.encoder.layer.{i}.output.dense.bias') )
# embeddings
rename_keys.extend(
[
# text embeddings
('text_embeddings.word_embeddings.weight', 'vilt.embeddings.text_embeddings.word_embeddings.weight'),
(
'text_embeddings.position_embeddings.weight',
'vilt.embeddings.text_embeddings.position_embeddings.weight',
),
('text_embeddings.position_ids', 'vilt.embeddings.text_embeddings.position_ids'),
(
'text_embeddings.token_type_embeddings.weight',
'vilt.embeddings.text_embeddings.token_type_embeddings.weight',
),
('text_embeddings.LayerNorm.weight', 'vilt.embeddings.text_embeddings.LayerNorm.weight'),
('text_embeddings.LayerNorm.bias', 'vilt.embeddings.text_embeddings.LayerNorm.bias'),
# patch embeddings
('transformer.cls_token', 'vilt.embeddings.cls_token'),
('transformer.patch_embed.proj.weight', 'vilt.embeddings.patch_embeddings.projection.weight'),
('transformer.patch_embed.proj.bias', 'vilt.embeddings.patch_embeddings.projection.bias'),
('transformer.pos_embed', 'vilt.embeddings.position_embeddings'),
# token type embeddings
('token_type_embeddings.weight', 'vilt.embeddings.token_type_embeddings.weight'),
] )
# final layernorm + pooler
rename_keys.extend(
[
('transformer.norm.weight', 'vilt.layernorm.weight'),
('transformer.norm.bias', 'vilt.layernorm.bias'),
('pooler.dense.weight', 'vilt.pooler.dense.weight'),
('pooler.dense.bias', 'vilt.pooler.dense.bias'),
] )
# classifier head(s)
if vqa_model:
# classification head
rename_keys.extend(
[
('vqa_classifier.0.weight', 'classifier.0.weight'),
('vqa_classifier.0.bias', 'classifier.0.bias'),
('vqa_classifier.1.weight', 'classifier.1.weight'),
('vqa_classifier.1.bias', 'classifier.1.bias'),
('vqa_classifier.3.weight', 'classifier.3.weight'),
('vqa_classifier.3.bias', 'classifier.3.bias'),
] )
elif nlvr_model:
# classification head
rename_keys.extend(
[
('nlvr2_classifier.0.weight', 'classifier.0.weight'),
('nlvr2_classifier.0.bias', 'classifier.0.bias'),
('nlvr2_classifier.1.weight', 'classifier.1.weight'),
('nlvr2_classifier.1.bias', 'classifier.1.bias'),
('nlvr2_classifier.3.weight', 'classifier.3.weight'),
('nlvr2_classifier.3.bias', 'classifier.3.bias'),
] )
else:
pass
return rename_keys
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> int:
for i in range(config.num_hidden_layers ):
__lowerCamelCase : str = 'vilt.'
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
__lowerCamelCase : Tuple = state_dict.pop(F'transformer.blocks.{i}.attn.qkv.weight' )
__lowerCamelCase : Optional[int] = state_dict.pop(F'transformer.blocks.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
__lowerCamelCase : Union[str, Any] = in_proj_weight[
: config.hidden_size, :
]
__lowerCamelCase : Optional[int] = in_proj_bias[: config.hidden_size]
__lowerCamelCase : Tuple = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
__lowerCamelCase : Tuple = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
__lowerCamelCase : List[str] = in_proj_weight[
-config.hidden_size :, :
]
__lowerCamelCase : Optional[Any] = in_proj_bias[-config.hidden_size :]
def a_ ( _lowerCAmelCase ) -> Optional[int]:
__lowerCamelCase : Union[str, Any] = ['head.weight', 'head.bias']
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase ,_lowerCAmelCase )
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ) -> List[str]:
__lowerCamelCase : Optional[int] = dct.pop(_lowerCAmelCase )
__lowerCamelCase : Dict = val
@torch.no_grad()
def a_ ( _lowerCAmelCase ,_lowerCAmelCase ) -> Tuple:
__lowerCamelCase : Union[str, Any] = ViltConfig(image_size=384 ,patch_size=32 ,tie_word_embeddings=_lowerCAmelCase )
__lowerCamelCase : Any = False
__lowerCamelCase : int = False
__lowerCamelCase : Union[str, Any] = False
__lowerCamelCase : Union[str, Any] = False
if "vqa" in checkpoint_url:
__lowerCamelCase : int = True
__lowerCamelCase : Any = 3129
__lowerCamelCase : int = 'huggingface/label-files'
__lowerCamelCase : Tuple = 'vqa2-id2label.json'
__lowerCamelCase : Tuple = json.load(open(hf_hub_download(_lowerCAmelCase ,_lowerCAmelCase ,repo_type='dataset' ) ,'r' ) )
__lowerCamelCase : List[Any] = {int(_lowerCAmelCase ): v for k, v in idalabel.items()}
__lowerCamelCase : str = idalabel
__lowerCamelCase : int = {v: k for k, v in idalabel.items()}
__lowerCamelCase : Optional[int] = ViltForQuestionAnswering(_lowerCAmelCase )
elif "nlvr" in checkpoint_url:
__lowerCamelCase : Dict = True
__lowerCamelCase : str = 2
__lowerCamelCase : Optional[Any] = {0: 'False', 1: 'True'}
__lowerCamelCase : int = {v: k for k, v in config.idalabel.items()}
__lowerCamelCase : Optional[int] = 3
__lowerCamelCase : List[str] = ViltForImagesAndTextClassification(_lowerCAmelCase )
elif "irtr" in checkpoint_url:
__lowerCamelCase : int = True
__lowerCamelCase : Optional[Any] = ViltForImageAndTextRetrieval(_lowerCAmelCase )
elif "mlm_itm" in checkpoint_url:
__lowerCamelCase : Dict = True
__lowerCamelCase : Optional[Any] = ViltForMaskedLM(_lowerCAmelCase )
else:
raise ValueError('Unknown model type' )
# load state_dict of original model, remove and rename some keys
__lowerCamelCase : Tuple = torch.hub.load_state_dict_from_url(_lowerCAmelCase ,map_location='cpu' )['state_dict']
__lowerCamelCase : List[Any] = create_rename_keys(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
for src, dest in rename_keys:
rename_key(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase )
read_in_q_k_v(_lowerCAmelCase ,_lowerCAmelCase )
if mlm_model or irtr_model:
__lowerCamelCase : Any = ['itm_score.fc.weight', 'itm_score.fc.bias']
for k in ignore_keys:
state_dict.pop(_lowerCAmelCase ,_lowerCAmelCase )
# load state dict into HuggingFace model
model.eval()
if mlm_model:
__lowerCamelCase : Optional[Any] = model.load_state_dict(_lowerCAmelCase ,strict=_lowerCAmelCase )
assert missing_keys == ["mlm_score.decoder.bias"]
else:
model.load_state_dict(_lowerCAmelCase )
# Define processor
__lowerCamelCase : List[str] = ViltImageProcessor(size=384 )
__lowerCamelCase : List[str] = BertTokenizer.from_pretrained('bert-base-uncased' )
__lowerCamelCase : List[str] = ViltProcessor(_lowerCAmelCase ,_lowerCAmelCase )
# Forward pass on example inputs (image + text)
if nlvr_model:
__lowerCamelCase : Tuple = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' ,stream=_lowerCAmelCase ).raw )
__lowerCamelCase : List[Any] = Image.open(requests.get('https://lil.nlp.cornell.edu/nlvr/exs/ex0_0.jpg' ,stream=_lowerCAmelCase ).raw )
__lowerCamelCase : Union[str, Any] = (
'The left image contains twice the number of dogs as the right image, and at least two dogs in total are'
' standing.'
)
__lowerCamelCase : Tuple = processor(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors='pt' )
__lowerCamelCase : List[Any] = processor(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors='pt' )
__lowerCamelCase : str = model(
input_ids=encoding_a.input_ids ,pixel_values=encoding_a.pixel_values ,pixel_values_a=encoding_a.pixel_values ,)
else:
__lowerCamelCase : Dict = Image.open(requests.get('http://images.cocodataset.org/val2017/000000039769.jpg' ,stream=_lowerCAmelCase ).raw )
if mlm_model:
__lowerCamelCase : Optional[int] = 'a bunch of [MASK] laying on a [MASK].'
else:
__lowerCamelCase : Optional[Any] = 'How many cats are there?'
__lowerCamelCase : Optional[Any] = processor(_lowerCAmelCase ,_lowerCAmelCase ,return_tensors='pt' )
__lowerCamelCase : Optional[Any] = model(**_lowerCAmelCase )
# Verify outputs
if mlm_model:
__lowerCamelCase : int = torch.Size([1, 11, 30522] )
__lowerCamelCase : int = torch.tensor([-12.5061, -12.5123, -12.5174] )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] ,_lowerCAmelCase ,atol=1E-4 )
# verify masked token prediction equals "cats"
__lowerCamelCase : Optional[Any] = outputs.logits[0, 4, :].argmax(-1 ).item()
assert tokenizer.decode([predicted_id] ) == "cats"
elif vqa_model:
__lowerCamelCase : str = torch.Size([1, 3129] )
__lowerCamelCase : Dict = torch.tensor([-15.9495, -18.1472, -10.3041] )
assert torch.allclose(outputs.logits[0, :3] ,_lowerCAmelCase ,atol=1E-4 )
assert outputs.logits.shape == expected_shape
assert torch.allclose(outputs.logits[0, 0, :3] ,_lowerCAmelCase ,atol=1E-4 )
# verify vqa prediction equals "2"
__lowerCamelCase : Optional[Any] = outputs.logits.argmax(-1 ).item()
assert model.config.idalabel[predicted_idx] == "2"
elif nlvr_model:
__lowerCamelCase : Dict = torch.Size([1, 2] )
__lowerCamelCase : Optional[Any] = torch.tensor([-2.8721, 2.1291] )
assert torch.allclose(outputs.logits[0, :3] ,_lowerCAmelCase ,atol=1E-4 )
assert outputs.logits.shape == expected_shape
Path(_lowerCAmelCase ).mkdir(exist_ok=_lowerCAmelCase )
print(F'Saving model and processor to {pytorch_dump_folder_path}' )
model.save_pretrained(_lowerCAmelCase )
processor.save_pretrained(_lowerCAmelCase )
if __name__ == "__main__":
_UpperCamelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint_url',
default='https://github.com/dandelin/ViLT/releases/download/200k/vilt_200k_mlm_itm.ckpt',
type=str,
help='URL of the checkpoint you\'d like to convert.',
)
parser.add_argument(
'--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
)
_UpperCamelCase = parser.parse_args()
convert_vilt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)
| 208 |
import json
import os
import shutil
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoConfig, BertConfig, GPTaConfig
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import TOKEN, USER, is_staging_test
sys.path.append(str(Path(__file__).parent.parent / '''utils'''))
from test_module.custom_configuration import CustomConfig # noqa E402
_a = {
'''return_dict''': False,
'''output_hidden_states''': True,
'''output_attentions''': True,
'''torchscript''': True,
'''torch_dtype''': '''float16''',
'''use_bfloat16''': True,
'''tf_legacy_loss''': True,
'''pruned_heads''': {'''a''': 1},
'''tie_word_embeddings''': False,
'''is_decoder''': True,
'''cross_attention_hidden_size''': 1_2_8,
'''add_cross_attention''': True,
'''tie_encoder_decoder''': True,
'''max_length''': 5_0,
'''min_length''': 3,
'''do_sample''': True,
'''early_stopping''': True,
'''num_beams''': 3,
'''num_beam_groups''': 3,
'''diversity_penalty''': 0.5,
'''temperature''': 2.0,
'''top_k''': 1_0,
'''top_p''': 0.7,
'''typical_p''': 0.2,
'''repetition_penalty''': 0.8,
'''length_penalty''': 0.8,
'''no_repeat_ngram_size''': 5,
'''encoder_no_repeat_ngram_size''': 5,
'''bad_words_ids''': [1, 2, 3],
'''num_return_sequences''': 3,
'''chunk_size_feed_forward''': 5,
'''output_scores''': True,
'''return_dict_in_generate''': True,
'''forced_bos_token_id''': 2,
'''forced_eos_token_id''': 3,
'''remove_invalid_values''': True,
'''architectures''': ['''BertModel'''],
'''finetuning_task''': '''translation''',
'''id2label''': {0: '''label'''},
'''label2id''': {'''label''': '''0'''},
'''tokenizer_class''': '''BertTokenizerFast''',
'''prefix''': '''prefix''',
'''bos_token_id''': 6,
'''pad_token_id''': 7,
'''eos_token_id''': 8,
'''sep_token_id''': 9,
'''decoder_start_token_id''': 1_0,
'''exponential_decay_length_penalty''': (5, 1.01),
'''suppress_tokens''': [0, 1],
'''begin_suppress_tokens''': 2,
'''task_specific_params''': {'''translation''': '''some_params'''},
'''problem_type''': '''regression''',
}
@is_staging_test
class A_ ( unittest.TestCase ):
@classmethod
def UpperCAmelCase ( cls : Dict ) -> List[str]:
__lowerCAmelCase: str = TOKEN
HfFolder.save_token(UpperCAmelCase )
@classmethod
def UpperCAmelCase ( cls : str ) -> List[Any]:
try:
delete_repo(token=cls._token , repo_id='test-config' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='valid_org/test-config-org' )
except HTTPError:
pass
try:
delete_repo(token=cls._token , repo_id='test-dynamic-config' )
except HTTPError:
pass
def UpperCAmelCase ( self : int ) -> Optional[int]:
__lowerCAmelCase: Any = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('test-config' , use_auth_token=self._token )
__lowerCAmelCase: str = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='test-config' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(UpperCAmelCase , repo_id='test-config' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained(F'''{USER}/test-config''' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : int ) -> Dict:
__lowerCAmelCase: int = BertConfig(
vocab_size=9_9 , hidden_size=3_2 , num_hidden_layers=5 , num_attention_heads=4 , intermediate_size=3_7 )
config.push_to_hub('valid_org/test-config-org' , use_auth_token=self._token )
__lowerCAmelCase: Dict = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
# Reset repo
delete_repo(token=self._token , repo_id='valid_org/test-config-org' )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
config.save_pretrained(
UpperCAmelCase , repo_id='valid_org/test-config-org' , push_to_hub=UpperCAmelCase , use_auth_token=self._token )
__lowerCAmelCase: int = BertConfig.from_pretrained('valid_org/test-config-org' )
for k, v in config.to_dict().items():
if k != "transformers_version":
self.assertEqual(UpperCAmelCase , getattr(UpperCAmelCase , UpperCAmelCase ) )
def UpperCAmelCase ( self : Union[str, Any] ) -> List[str]:
CustomConfig.register_for_auto_class()
__lowerCAmelCase: Any = CustomConfig(attribute=4_2 )
config.push_to_hub('test-dynamic-config' , use_auth_token=self._token )
# This has added the proper auto_map field to the config
self.assertDictEqual(config.auto_map , {'AutoConfig': 'custom_configuration.CustomConfig'} )
__lowerCAmelCase: int = AutoConfig.from_pretrained(F'''{USER}/test-dynamic-config''' , trust_remote_code=UpperCAmelCase )
# Can't make an isinstance check because the new_config is from the FakeConfig class of a dynamic module
self.assertEqual(new_config.__class__.__name__ , 'CustomConfig' )
self.assertEqual(new_config.attribute , 4_2 )
class A_ ( unittest.TestCase ):
def UpperCAmelCase ( self : Union[str, Any] ) -> int:
__lowerCAmelCase: List[Any] = GPTaConfig()
# attempt to modify each of int/float/bool/str config records and verify they were updated
__lowerCAmelCase: Union[str, Any] = c.n_embd + 1 # int
__lowerCAmelCase: str = c.resid_pdrop + 1.0 # float
__lowerCAmelCase: List[Any] = not c.scale_attn_weights # bool
__lowerCAmelCase: List[str] = c.summary_type + 'foo' # str
c.update_from_string(
F'''n_embd={n_embd},resid_pdrop={resid_pdrop},scale_attn_weights={scale_attn_weights},summary_type={summary_type}''' )
self.assertEqual(UpperCAmelCase , c.n_embd , 'mismatch for key: n_embd' )
self.assertEqual(UpperCAmelCase , c.resid_pdrop , 'mismatch for key: resid_pdrop' )
self.assertEqual(UpperCAmelCase , c.scale_attn_weights , 'mismatch for key: scale_attn_weights' )
self.assertEqual(UpperCAmelCase , c.summary_type , 'mismatch for key: summary_type' )
def UpperCAmelCase ( self : Optional[Any] ) -> Any:
__lowerCAmelCase: str = PretrainedConfig()
__lowerCAmelCase: Optional[int] = [key for key in base_config.__dict__ if key not in config_common_kwargs]
# If this part of the test fails, you have arguments to addin config_common_kwargs above.
self.assertListEqual(
UpperCAmelCase , ['is_encoder_decoder', '_name_or_path', '_commit_hash', 'transformers_version'] )
__lowerCAmelCase: int = [key for key, value in config_common_kwargs.items() if value == getattr(UpperCAmelCase , UpperCAmelCase )]
if len(UpperCAmelCase ) > 0:
raise ValueError(
'The following keys are set with the default values in'
' `test_configuration_common.config_common_kwargs` pick another value for them:'
F''' {', '.join(UpperCAmelCase )}.''' )
def UpperCAmelCase ( self : int ) -> Optional[Any]:
with self.assertRaises(UpperCAmelCase ):
# config is in subfolder, the following should not work without specifying the subfolder
__lowerCAmelCase: List[Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' )
__lowerCAmelCase: List[str] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert-subfolder' , subfolder='bert' )
self.assertIsNotNone(UpperCAmelCase )
def UpperCAmelCase ( self : Tuple ) -> List[Any]:
# A mock response for an HTTP head request to emulate server down
__lowerCAmelCase: Union[str, Any] = mock.Mock()
__lowerCAmelCase: str = 5_0_0
__lowerCAmelCase: Optional[Any] = {}
__lowerCAmelCase: Optional[int] = HTTPError
__lowerCAmelCase: List[Any] = {}
# Download this model to make sure it's in the cache.
__lowerCAmelCase: Tuple = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# Under the mock environment we get a 500 error when trying to reach the model.
with mock.patch('requests.Session.request' , return_value=UpperCAmelCase ) as mock_head:
__lowerCAmelCase: Union[str, Any] = BertConfig.from_pretrained('hf-internal-testing/tiny-random-bert' )
# This check we did call the fake head request
mock_head.assert_called()
def UpperCAmelCase ( self : Any ) -> Optional[Any]:
# This test is for deprecated behavior and can be removed in v5
__lowerCAmelCase: Tuple = BertConfig.from_pretrained(
'https://huggingface.co/hf-internal-testing/tiny-random-bert/resolve/main/config.json' )
def UpperCAmelCase ( self : Dict ) -> str:
__lowerCAmelCase: Optional[Any] = AutoConfig.from_pretrained('bert-base-cased' )
__lowerCAmelCase: Optional[Any] = ['config.4.0.0.json']
with tempfile.TemporaryDirectory() as tmp_dir:
configuration.save_pretrained(UpperCAmelCase )
__lowerCAmelCase: Tuple = 2
json.dump(configuration.to_dict() , open(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , 'w' ) )
# This should pick the new configuration file as the version of Transformers is > 4.0.0
__lowerCAmelCase: Dict = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# Will need to be adjusted if we reach v42 and this test is still here.
# Should pick the old configuration file as the version of Transformers is < 4.42.0
__lowerCAmelCase: Dict = ['config.42.0.0.json']
__lowerCAmelCase: Optional[int] = 7_6_8
configuration.save_pretrained(UpperCAmelCase )
shutil.move(os.path.join(UpperCAmelCase , 'config.4.0.0.json' ) , os.path.join(UpperCAmelCase , 'config.42.0.0.json' ) )
__lowerCAmelCase: int = AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 7_6_8 )
def UpperCAmelCase ( self : Union[str, Any] ) -> Dict:
# This repo has two configuration files, one for v4.0.0 and above with a different hidden size.
__lowerCAmelCase: Tuple = 'hf-internal-testing/test-two-configs'
import transformers as new_transformers
__lowerCAmelCase: List[Any] = 'v4.0.0'
__lowerCAmelCase , __lowerCAmelCase: Any = new_transformers.models.auto.AutoConfig.from_pretrained(
UpperCAmelCase , return_unused_kwargs=UpperCAmelCase )
self.assertEqual(new_configuration.hidden_size , 2 )
# This checks `_configuration_file` ia not kept in the kwargs by mistake.
self.assertDictEqual(UpperCAmelCase , {} )
# Testing an older version by monkey-patching the version in the module it's used.
import transformers as old_transformers
__lowerCAmelCase: List[Any] = 'v3.0.0'
__lowerCAmelCase: Union[str, Any] = old_transformers.models.auto.AutoConfig.from_pretrained(UpperCAmelCase )
self.assertEqual(old_configuration.hidden_size , 7_6_8 )
| 322 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from diffusers import DDIMScheduler, KandinskyVaaPipeline, KandinskyVaaPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.utils import floats_tensor, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
enable_full_determinism()
class a__ ( UpperCAmelCase__ , unittest.TestCase ):
lowerCamelCase : int =KandinskyVaaPipeline
lowerCamelCase : Union[str, Any] =[
"image_embeds",
"negative_image_embeds",
]
lowerCamelCase : Tuple =["image_embeds", "negative_image_embeds"]
lowerCamelCase : Optional[int] =[
"generator",
"height",
"width",
"latents",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
lowerCamelCase : Union[str, Any] =False
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
return 32
@property
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
return 32
@property
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
return self.time_input_dim
@property
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
return self.time_input_dim * 4
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
return 1_00
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCamelCase = {
'''in_channels''': 4,
# Out channels is double in channels because predicts mean and variance
'''out_channels''': 8,
'''addition_embed_type''': '''image''',
'''down_block_types''': ('''ResnetDownsampleBlock2D''', '''SimpleCrossAttnDownBlock2D'''),
'''up_block_types''': ('''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''),
'''mid_block_type''': '''UNetMidBlock2DSimpleCrossAttn''',
'''block_out_channels''': (self.block_out_channels_a, self.block_out_channels_a * 2),
'''layers_per_block''': 1,
'''encoder_hid_dim''': self.text_embedder_hidden_size,
'''encoder_hid_dim_type''': '''image_proj''',
'''cross_attention_dim''': self.cross_attention_dim,
'''attention_head_dim''': 4,
'''resnet_time_scale_shift''': '''scale_shift''',
'''class_embed_type''': None,
}
__lowerCamelCase = UNetaDConditionModel(**a )
return model
@property
def SCREAMING_SNAKE_CASE__ ( self : Any ):
"""simple docstring"""
return {
"block_out_channels": [32, 64],
"down_block_types": ["DownEncoderBlock2D", "AttnDownEncoderBlock2D"],
"in_channels": 3,
"latent_channels": 4,
"layers_per_block": 1,
"norm_num_groups": 8,
"norm_type": "spatial",
"num_vq_embeddings": 12,
"out_channels": 3,
"up_block_types": [
"AttnUpDecoderBlock2D",
"UpDecoderBlock2D",
],
"vq_embed_dim": 4,
}
@property
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
torch.manual_seed(0 )
__lowerCamelCase = VQModel(**self.dummy_movq_kwargs )
return model
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
__lowerCamelCase = self.dummy_unet
__lowerCamelCase = self.dummy_movq
__lowerCamelCase = DDIMScheduler(
num_train_timesteps=10_00 , beta_schedule='''linear''' , beta_start=0.0_00_85 , beta_end=0.0_12 , clip_sample=a , set_alpha_to_one=a , steps_offset=1 , prediction_type='''epsilon''' , thresholding=a , )
__lowerCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''movq''': movq,
}
return components
def SCREAMING_SNAKE_CASE__ ( self : str , a : int , a : Optional[int]=0 ):
"""simple docstring"""
__lowerCamelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(a ) ).to(a )
__lowerCamelCase = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
a )
if str(a ).startswith('''mps''' ):
__lowerCamelCase = torch.manual_seed(a )
else:
__lowerCamelCase = torch.Generator(device=a ).manual_seed(a )
__lowerCamelCase = {
'''image_embeds''': image_embeds,
'''negative_image_embeds''': negative_image_embeds,
'''generator''': generator,
'''height''': 64,
'''width''': 64,
'''guidance_scale''': 4.0,
'''num_inference_steps''': 2,
'''output_type''': '''np''',
}
return inputs
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
__lowerCamelCase = '''cpu'''
__lowerCamelCase = self.get_dummy_components()
__lowerCamelCase = self.pipeline_class(**a )
__lowerCamelCase = pipe.to(a )
pipe.set_progress_bar_config(disable=a )
__lowerCamelCase = pipe(**self.get_dummy_inputs(a ) )
__lowerCamelCase = output.images
__lowerCamelCase = pipe(
**self.get_dummy_inputs(a ) , return_dict=a , )[0]
__lowerCamelCase = image[0, -3:, -3:, -1]
__lowerCamelCase = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
__lowerCamelCase = np.array(
[0.6_23_79_76, 1.0, 0.36_44_13_32, 1.0, 0.70_63_96_34, 0.29_87_71_86, 0.85_65_21_25, 0.5_21_68_43, 0.54_45_40_46] )
assert (
np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
), f""" expected_slice {expected_slice}, but got {image_slice.flatten()}"""
assert (
np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 1e-2
), f""" expected_slice {expected_slice}, but got {image_from_tuple_slice.flatten()}"""
@slow
@require_torch_gpu
class a__ ( unittest.TestCase ):
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
__lowerCamelCase = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/kandinskyv22/kandinskyv22_text2img_cat_fp16.npy''' )
__lowerCamelCase = KandinskyVaaPriorPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-prior''' , torch_dtype=torch.floataa )
pipe_prior.to(a )
__lowerCamelCase = KandinskyVaaPipeline.from_pretrained(
'''kandinsky-community/kandinsky-2-2-decoder''' , torch_dtype=torch.floataa )
__lowerCamelCase = pipeline.to(a )
pipeline.set_progress_bar_config(disable=a )
__lowerCamelCase = '''red cat, 4k photo'''
__lowerCamelCase = torch.Generator(device='''cuda''' ).manual_seed(0 )
__lowerCamelCase , __lowerCamelCase = pipe_prior(
a , generator=a , num_inference_steps=5 , negative_prompt='''''' , ).to_tuple()
__lowerCamelCase = torch.Generator(device='''cuda''' ).manual_seed(0 )
__lowerCamelCase = pipeline(
image_embeds=a , negative_image_embeds=a , generator=a , num_inference_steps=1_00 , output_type='''np''' , )
__lowerCamelCase = output.images[0]
assert image.shape == (5_12, 5_12, 3)
assert_mean_pixel_difference(a , a )
| 237 | '''simple docstring'''
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class a__ :
def __init__( self : List[Any] , a : Tuple , a : int , a : int ):
"""simple docstring"""
if dst_width < 0 or dst_height < 0:
raise ValueError('''Destination width/height should be > 0''' )
__lowerCamelCase = img
__lowerCamelCase = img.shape[1]
__lowerCamelCase = img.shape[0]
__lowerCamelCase = dst_width
__lowerCamelCase = dst_height
__lowerCamelCase = self.src_w / self.dst_w
__lowerCamelCase = self.src_h / self.dst_h
__lowerCamelCase = __lowerCamelCase = (
np.ones((self.dst_h, self.dst_w, 3) , np.uinta ) * 2_55
)
def SCREAMING_SNAKE_CASE__ ( self : str ):
"""simple docstring"""
for i in range(self.dst_h ):
for j in range(self.dst_w ):
__lowerCamelCase = self.img[self.get_y(a )][self.get_x(a )]
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] , a : int ):
"""simple docstring"""
return int(self.ratio_x * x )
def SCREAMING_SNAKE_CASE__ ( self : Tuple , a : int ):
"""simple docstring"""
return int(self.ratio_y * y )
if __name__ == "__main__":
__UpperCAmelCase , __UpperCAmelCase =8_0_0, 6_0_0
__UpperCAmelCase =imread("image_data/lena.jpg", 1)
__UpperCAmelCase =NearestNeighbour(im, dst_w, dst_h)
n.process()
imshow(
f'Image resized from: {im.shape[1]}x{im.shape[0]} to {dst_w}x{dst_h}', n.output
)
waitKey(0)
destroyAllWindows()
| 237 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ : Any ={
'''configuration_canine''': ['''CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''CanineConfig'''],
'''tokenization_canine''': ['''CanineTokenizer'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__ : Tuple =[
'''CANINE_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''CanineForMultipleChoice''',
'''CanineForQuestionAnswering''',
'''CanineForSequenceClassification''',
'''CanineForTokenClassification''',
'''CanineLayer''',
'''CanineModel''',
'''CaninePreTrainedModel''',
'''load_tf_weights_in_canine''',
]
if TYPE_CHECKING:
from .configuration_canine import CANINE_PRETRAINED_CONFIG_ARCHIVE_MAP, CanineConfig
from .tokenization_canine import CanineTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_canine import (
CANINE_PRETRAINED_MODEL_ARCHIVE_LIST,
CanineForMultipleChoice,
CanineForQuestionAnswering,
CanineForSequenceClassification,
CanineForTokenClassification,
CanineLayer,
CanineModel,
CaninePreTrainedModel,
load_tf_weights_in_canine,
)
else:
import sys
A__ : Dict =_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 70 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
A__ : List[Any] =pytest.mark.integration
@pytest.mark.parametrize("""path""" , ["""paws""", """csv"""] )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
inspect_dataset(lowerCAmelCase , lowerCAmelCase )
_lowerCAmelCase = path + """.py"""
assert script_name in os.listdir(lowerCAmelCase )
assert "__pycache__" not in os.listdir(lowerCAmelCase )
@pytest.mark.filterwarnings("""ignore:inspect_metric is deprecated:FutureWarning""" )
@pytest.mark.filterwarnings("""ignore:metric_module_factory is deprecated:FutureWarning""" )
@pytest.mark.parametrize("""path""" , ["""accuracy"""] )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
inspect_metric(lowerCAmelCase , lowerCAmelCase )
_lowerCAmelCase = path + """.py"""
assert script_name in os.listdir(lowerCAmelCase )
assert "__pycache__" not in os.listdir(lowerCAmelCase )
@pytest.mark.parametrize(
"""path, config_name, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = get_dataset_config_info(lowerCAmelCase , config_name=lowerCAmelCase )
assert info.config_name == config_name
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
with pytest.raises(lowerCAmelCase ):
get_dataset_config_info(lowerCAmelCase , config_name=lowerCAmelCase )
@pytest.mark.parametrize(
"""path, expected""" , [
("""squad""", """plain_text"""),
("""acronym_identification""", """default"""),
("""lhoestq/squad""", """plain_text"""),
("""lhoestq/test""", """default"""),
("""lhoestq/demo1""", """lhoestq--demo1"""),
("""dalle-mini/wit""", """dalle-mini--wit"""),
] , )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = get_dataset_config_names(lowerCAmelCase )
assert expected in config_names
@pytest.mark.parametrize(
"""path, expected_configs, expected_splits_in_first_config""" , [
("""squad""", ["""plain_text"""], ["""train""", """validation"""]),
("""dalle-mini/wit""", ["""dalle-mini--wit"""], ["""train"""]),
("""paws""", ["""labeled_final""", """labeled_swap""", """unlabeled_final"""], ["""train""", """test""", """validation"""]),
] , )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = get_dataset_infos(lowerCAmelCase )
assert list(infos.keys() ) == expected_configs
_lowerCAmelCase = expected_configs[0]
assert expected_config in infos
_lowerCAmelCase = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits_in_first_config
@pytest.mark.parametrize(
"""path, expected_config, expected_splits""" , [
("""squad""", """plain_text""", ["""train""", """validation"""]),
("""dalle-mini/wit""", """dalle-mini--wit""", ["""train"""]),
("""paws""", """labeled_final""", ["""train""", """test""", """validation"""]),
] , )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
_lowerCAmelCase = get_dataset_infos(lowerCAmelCase )
assert expected_config in infos
_lowerCAmelCase = infos[expected_config]
assert info.config_name == expected_config
assert list(info.splits.keys() ) == expected_splits
@pytest.mark.parametrize(
"""path, config_name, expected_exception""" , [
("""paws""", None, ValueError),
] , )
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
with pytest.raises(lowerCAmelCase ):
get_dataset_split_names(lowerCAmelCase , config_name=lowerCAmelCase )
| 70 | 1 |
import re
from filelock import FileLock
try:
import nltk
lowercase_ = True
except (ImportError, ModuleNotFoundError):
lowercase_ = False
if NLTK_AVAILABLE:
with FileLock('.lock') as lock:
nltk.download('punkt', quiet=True)
def UpperCamelCase__ ( SCREAMING_SNAKE_CASE__ ):
re.sub('<n>' , '' , SCREAMING_SNAKE_CASE__ ) # 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(SCREAMING_SNAKE_CASE__ ) )
| 194 |
import warnings
from .generation import TFGenerationMixin
class A_ ( __UpperCamelCase ):
'''simple docstring'''
warnings.warn(
"""Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
"""be removed in Transformers v5. Import as `from transformers import TFGenerationMixin` instead.""" , __UpperCamelCase , )
| 194 | 1 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_keras_nlp, require_tf, slow
if is_tf_available():
import tensorflow as tf
if is_keras_nlp_available():
from transformers.models.gpta import TFGPTaTokenizer
lowercase_ = ["gpt2"]
lowercase_ = "gpt2"
if is_tf_available():
class __lowerCAmelCase ( tf.Module ):
'''simple docstring'''
def __init__( self , _a ):
super().__init__()
__a = tokenizer
__a = AutoConfig.from_pretrained(_a )
__a = TFGPTaLMHeadModel.from_config(_a )
@tf.function(input_signature=(tf.TensorSpec((None,) , tf.string , name='''text''' ),) )
def __UpperCAmelCase ( self , _a ):
__a = self.tokenizer(_a )
__a = tokenized["input_ids"].to_tensor()
__a = tf.cast(input_ids_dense > 0 , tf.intaa )
# input_mask = tf.reshape(input_mask, [-1, MAX_SEQ_LEN])
__a = self.model(input_ids=_a , attention_mask=_a )["logits"]
return outputs
@require_tf
@require_keras_nlp
class __lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self ):
super().setUp()
__a = [GPTaTokenizer.from_pretrained(_a ) for checkpoint in (TOKENIZER_CHECKPOINTS)]
__a = [TFGPTaTokenizer.from_pretrained(_a ) for checkpoint in TOKENIZER_CHECKPOINTS]
assert len(self.tokenizers ) == len(self.tf_tokenizers )
__a = [
"This is a straightforward English test sentence.",
"This one has some weird characters\rto\nsee\r\nif those\u00E9break things.",
"Now we're going to add some Chinese: 一 二 三 一二三",
"And some much more rare Chinese: 齉 堃 齉堃",
"Je vais aussi écrire en français pour tester les accents",
"Classical Irish also has some unusual characters, so in they go: Gaelaċ, ꝼ",
]
__a = list(zip(self.test_sentences , self.test_sentences[::-1] ) )
def __UpperCAmelCase ( self ):
for tokenizer, tf_tokenizer in zip(self.tokenizers , self.tf_tokenizers ):
for test_inputs in self.test_sentences:
__a = tokenizer([test_inputs] , return_tensors='''tf''' )
__a = tf_tokenizer([test_inputs] )
for key in python_outputs.keys():
# convert them to numpy to avoid messing with ragged tensors
__a = python_outputs[key].numpy()
__a = tf_outputs[key].numpy()
self.assertTrue(tf.reduce_all(python_outputs_values.shape == tf_outputs_values.shape ) )
self.assertTrue(tf.reduce_all(tf.cast(_a , tf.intaa ) == tf_outputs_values ) )
@slow
def __UpperCAmelCase ( self ):
for tf_tokenizer in self.tf_tokenizers:
__a = tf.function(_a )
for test_inputs in self.test_sentences:
__a = tf.constant(_a )
__a = compiled_tokenizer(_a )
__a = tf_tokenizer(_a )
for key in eager_outputs.keys():
self.assertTrue(tf.reduce_all(eager_outputs[key] == compiled_outputs[key] ) )
@slow
def __UpperCAmelCase ( self ):
for tf_tokenizer in self.tf_tokenizers:
__a = ModelToSave(tokenizer=_a )
__a = tf.convert_to_tensor([self.test_sentences[0]] )
__a = model.serving(_a ) # Build model with some sample inputs
with TemporaryDirectory() as tempdir:
__a = Path(_a ) / "saved.model"
tf.saved_model.save(_a , _a , signatures={'''serving_default''': model.serving} )
__a = tf.saved_model.load(_a )
__a = loaded_model.signatures["serving_default"](_a )["output_0"]
# We may see small differences because the loaded model is compiled, so we need an epsilon for the test
self.assertTrue(tf.reduce_all(out == loaded_output ) )
@slow
def __UpperCAmelCase ( self ):
for tf_tokenizer in self.tf_tokenizers:
__a = tf.convert_to_tensor([self.test_sentences[0]] )
__a = tf_tokenizer(_a ) # Build model with some sample inputs
__a = tf_tokenizer.get_config()
__a = TFGPTaTokenizer.from_config(_a )
__a = model_from_config(_a )
for key in from_config_output.keys():
self.assertTrue(tf.reduce_all(from_config_output[key] == out[key] ) )
@slow
def __UpperCAmelCase ( self ):
for tf_tokenizer in self.tf_tokenizers:
# for the test to run
__a = 123_123
for max_length in [3, 5, 1_024]:
__a = tf.convert_to_tensor([self.test_sentences[0]] )
__a = tf_tokenizer(_a , max_length=_a )
__a = out["input_ids"].numpy().shape[1]
assert out_length == max_length
| 45 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
def lowercase ( __magic_name__ ):
'''simple docstring'''
UpperCAmelCase : List[str] = YolosConfig()
# size of the architecture
if "yolos_ti" in yolos_name:
UpperCAmelCase : Tuple = 192
UpperCAmelCase : str = 768
UpperCAmelCase : List[Any] = 12
UpperCAmelCase : List[Any] = 3
UpperCAmelCase : List[Any] = [800, 1333]
UpperCAmelCase : List[str] = False
elif yolos_name == "yolos_s_dWr":
UpperCAmelCase : Union[str, Any] = 330
UpperCAmelCase : Union[str, Any] = 14
UpperCAmelCase : Any = 6
UpperCAmelCase : int = 1320
elif "yolos_s" in yolos_name:
UpperCAmelCase : Union[str, Any] = 384
UpperCAmelCase : Dict = 1536
UpperCAmelCase : str = 12
UpperCAmelCase : List[str] = 6
elif "yolos_b" in yolos_name:
UpperCAmelCase : int = [800, 1344]
UpperCAmelCase : Optional[int] = 91
UpperCAmelCase : int = "huggingface/label-files"
UpperCAmelCase : Union[str, Any] = "coco-detection-id2label.json"
UpperCAmelCase : Optional[Any] = json.load(open(hf_hub_download(__magic_name__ , __magic_name__ , repo_type="dataset" ) , "r" ) )
UpperCAmelCase : str = {int(__magic_name__ ): v for k, v in idalabel.items()}
UpperCAmelCase : str = idalabel
UpperCAmelCase : Union[str, Any] = {v: k for k, v in idalabel.items()}
return config
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ = False ):
'''simple docstring'''
for i in range(config.num_hidden_layers ):
# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
UpperCAmelCase : Tuple = state_dict.pop(F"blocks.{i}.attn.qkv.weight" )
UpperCAmelCase : List[Any] = state_dict.pop(F"blocks.{i}.attn.qkv.bias" )
# next, add query, keys and values (in that order) to the state dict
UpperCAmelCase : str = in_proj_weight[: config.hidden_size, :]
UpperCAmelCase : Optional[int] = in_proj_bias[: config.hidden_size]
UpperCAmelCase : Optional[Any] = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCAmelCase : int = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCAmelCase : str = in_proj_weight[-config.hidden_size :, :]
UpperCAmelCase : Tuple = in_proj_bias[-config.hidden_size :]
def lowercase ( __magic_name__ ):
'''simple docstring'''
if "backbone" in name:
UpperCAmelCase : int = name.replace("backbone" , "vit" )
if "cls_token" in name:
UpperCAmelCase : Dict = name.replace("cls_token" , "embeddings.cls_token" )
if "det_token" in name:
UpperCAmelCase : int = name.replace("det_token" , "embeddings.detection_tokens" )
if "mid_pos_embed" in name:
UpperCAmelCase : Tuple = name.replace("mid_pos_embed" , "encoder.mid_position_embeddings" )
if "pos_embed" in name:
UpperCAmelCase : int = name.replace("pos_embed" , "embeddings.position_embeddings" )
if "patch_embed.proj" in name:
UpperCAmelCase : str = name.replace("patch_embed.proj" , "embeddings.patch_embeddings.projection" )
if "blocks" in name:
UpperCAmelCase : Tuple = name.replace("blocks" , "encoder.layer" )
if "attn.proj" in name:
UpperCAmelCase : Tuple = name.replace("attn.proj" , "attention.output.dense" )
if "attn" in name:
UpperCAmelCase : Any = name.replace("attn" , "attention.self" )
if "norm1" in name:
UpperCAmelCase : int = name.replace("norm1" , "layernorm_before" )
if "norm2" in name:
UpperCAmelCase : List[str] = name.replace("norm2" , "layernorm_after" )
if "mlp.fc1" in name:
UpperCAmelCase : List[str] = name.replace("mlp.fc1" , "intermediate.dense" )
if "mlp.fc2" in name:
UpperCAmelCase : Dict = name.replace("mlp.fc2" , "output.dense" )
if "class_embed" in name:
UpperCAmelCase : Any = name.replace("class_embed" , "class_labels_classifier" )
if "bbox_embed" in name:
UpperCAmelCase : Optional[int] = name.replace("bbox_embed" , "bbox_predictor" )
if "vit.norm" in name:
UpperCAmelCase : Tuple = name.replace("vit.norm" , "vit.layernorm" )
return name
def lowercase ( __magic_name__ , __magic_name__ ):
'''simple docstring'''
for key in orig_state_dict.copy().keys():
UpperCAmelCase : Optional[int] = orig_state_dict.pop(__magic_name__ )
if "qkv" in key:
UpperCAmelCase : str = key.split("." )
UpperCAmelCase : List[Any] = int(key_split[2] )
UpperCAmelCase : int = model.vit.encoder.layer[layer_num].attention.attention.all_head_size
if "weight" in key:
UpperCAmelCase : Optional[int] = val[:dim, :]
UpperCAmelCase : Union[str, Any] = val[
dim : dim * 2, :
]
UpperCAmelCase : Any = val[-dim:, :]
else:
UpperCAmelCase : Tuple = val[:dim]
UpperCAmelCase : List[str] = val[dim : dim * 2]
UpperCAmelCase : Any = val[-dim:]
else:
UpperCAmelCase : Union[str, Any] = val
return orig_state_dict
def lowercase ( ):
'''simple docstring'''
UpperCAmelCase : Union[str, Any] = "http://images.cocodataset.org/val2017/000000039769.jpg"
UpperCAmelCase : Tuple = Image.open(requests.get(__magic_name__ , stream=__magic_name__ ).raw )
return im
@torch.no_grad()
def lowercase ( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = False ):
'''simple docstring'''
UpperCAmelCase : Tuple = get_yolos_config(__magic_name__ )
# load original state_dict
UpperCAmelCase : int = torch.load(__magic_name__ , map_location="cpu" )["model"]
# load 🤗 model
UpperCAmelCase : int = YolosForObjectDetection(__magic_name__ )
model.eval()
UpperCAmelCase : Dict = convert_state_dict(__magic_name__ , __magic_name__ )
model.load_state_dict(__magic_name__ )
# Check outputs on an image, prepared by YolosImageProcessor
UpperCAmelCase : Dict = 800 if yolos_name != "yolos_ti" else 512
UpperCAmelCase : int = YolosImageProcessor(format="coco_detection" , size=__magic_name__ )
UpperCAmelCase : List[Any] = image_processor(images=prepare_img() , return_tensors="pt" )
UpperCAmelCase : List[str] = model(**__magic_name__ )
UpperCAmelCase , UpperCAmelCase : Optional[int] = outputs.logits, outputs.pred_boxes
UpperCAmelCase , UpperCAmelCase : Optional[Any] = None, None
if yolos_name == "yolos_ti":
UpperCAmelCase : str = torch.tensor(
[[-3_9.5_0_2_2, -1_1.9_8_2_0, -1_7.6_8_8_8], [-2_9.9_5_7_4, -9.9_7_6_9, -1_7.7_6_9_1], [-4_2.3_2_8_1, -2_0.7_2_0_0, -3_0.6_2_9_4]] )
UpperCAmelCase : Tuple = torch.tensor(
[[0.4_0_2_1, 0.0_8_3_6, 0.7_9_7_9], [0.0_1_8_4, 0.2_6_0_9, 0.0_3_6_4], [0.1_7_8_1, 0.2_0_0_4, 0.2_0_9_5]] )
elif yolos_name == "yolos_s_200_pre":
UpperCAmelCase : Union[str, Any] = torch.tensor(
[[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]] )
UpperCAmelCase : List[str] = torch.tensor(
[[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]] )
elif yolos_name == "yolos_s_300_pre":
UpperCAmelCase : List[str] = torch.tensor(
[[-3_6.2_2_2_0, -1_4.4_3_8_5, -2_3.5_4_5_7], [-3_5.6_9_7_0, -1_4.7_5_8_3, -2_1.3_9_3_5], [-3_1.5_9_3_9, -1_3.6_0_4_2, -1_6.8_0_4_9]] )
UpperCAmelCase : Dict = torch.tensor(
[[0.7_6_1_4, 0.2_3_1_6, 0.4_7_2_8], [0.7_1_6_8, 0.4_4_9_5, 0.3_8_5_5], [0.4_9_9_6, 0.1_4_6_6, 0.9_9_9_6]] )
elif yolos_name == "yolos_s_dWr":
UpperCAmelCase : Dict = torch.tensor(
[[-4_2.8_6_6_8, -2_4.1_0_4_9, -4_1.1_6_9_0], [-3_4.7_4_5_6, -1_4.1_2_7_4, -2_4.9_1_9_4], [-3_3.7_8_9_8, -1_2.1_9_4_6, -2_5.6_4_9_5]] )
UpperCAmelCase : List[Any] = torch.tensor(
[[0.5_5_8_7, 0.2_7_7_3, 0.0_6_0_5], [0.5_0_0_4, 0.3_0_1_4, 0.9_9_9_4], [0.4_9_9_9, 0.1_5_4_8, 0.9_9_9_4]] )
elif yolos_name == "yolos_base":
UpperCAmelCase : str = torch.tensor(
[[-4_0.6_0_6_4, -2_4.3_0_8_4, -3_2.6_4_4_7], [-5_5.1_9_9_0, -3_0.7_7_1_9, -3_5.5_8_7_7], [-5_1.4_3_1_1, -3_3.3_5_0_7, -3_5.6_4_6_2]] )
UpperCAmelCase : Union[str, Any] = torch.tensor(
[[0.5_5_5_5, 0.2_7_9_4, 0.0_6_5_5], [0.9_0_4_9, 0.2_6_6_4, 0.1_8_9_4], [0.9_1_8_3, 0.1_9_8_4, 0.1_6_3_5]] )
else:
raise ValueError(F"Unknown yolos_name: {yolos_name}" )
assert torch.allclose(logits[0, :3, :3] , __magic_name__ , atol=1e-4 )
assert torch.allclose(pred_boxes[0, :3, :3] , __magic_name__ , atol=1e-4 )
Path(__magic_name__ ).mkdir(exist_ok=__magic_name__ )
print(F"Saving model {yolos_name} to {pytorch_dump_folder_path}" )
model.save_pretrained(__magic_name__ )
print(F"Saving image processor to {pytorch_dump_folder_path}" )
image_processor.save_pretrained(__magic_name__ )
if push_to_hub:
UpperCAmelCase : int = {
"yolos_ti": "yolos-tiny",
"yolos_s_200_pre": "yolos-small",
"yolos_s_300_pre": "yolos-small-300",
"yolos_s_dWr": "yolos-small-dwr",
"yolos_base": "yolos-base",
}
print("Pushing to the hub..." )
UpperCAmelCase : Tuple = model_mapping[yolos_name]
image_processor.push_to_hub(__magic_name__ , organization="hustvl" )
model.push_to_hub(__magic_name__ , organization="hustvl" )
if __name__ == "__main__":
a : List[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--yolos_name",
default="yolos_s_200_pre",
type=str,
help=(
"Name of the YOLOS model you'd like to convert. Should be one of 'yolos_ti', 'yolos_s_200_pre',"
" 'yolos_s_300_pre', 'yolos_s_dWr', 'yolos_base'."
),
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, help="Path to the original state dict (.pth file)."
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
)
a : str = parser.parse_args()
convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)
| 311 | 0 |
'''simple docstring'''
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square(SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int ) -> int:
# BASE CASE
if row >= rows or col >= cols:
return 0
UpperCAmelCase__ = update_area_of_max_square(SCREAMING_SNAKE_CASE__ , col + 1 )
UpperCAmelCase__ = update_area_of_max_square(row + 1 , col + 1 )
UpperCAmelCase__ = update_area_of_max_square(row + 1 , SCREAMING_SNAKE_CASE__ )
if mat[row][col]:
UpperCAmelCase__ = 1 + min([right, diagonal, down] )
UpperCAmelCase__ = max(largest_square_area[0] , SCREAMING_SNAKE_CASE__ )
return sub_problem_sol
else:
return 0
UpperCAmelCase__ = [0]
update_area_of_max_square(0 , 0 )
return largest_square_area[0]
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ):
'''simple docstring'''
def update_area_of_max_square_using_dp_array(
SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ) -> int:
if row >= rows or col >= cols:
return 0
if dp_array[row][col] != -1:
return dp_array[row][col]
UpperCAmelCase__ = update_area_of_max_square_using_dp_array(SCREAMING_SNAKE_CASE__ , col + 1 , SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = update_area_of_max_square_using_dp_array(row + 1 , col + 1 , SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = update_area_of_max_square_using_dp_array(row + 1 , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
if mat[row][col]:
UpperCAmelCase__ = 1 + min([right, diagonal, down] )
UpperCAmelCase__ = max(largest_square_area[0] , SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = sub_problem_sol
return sub_problem_sol
else:
return 0
UpperCAmelCase__ = [0]
UpperCAmelCase__ = [[-1] * cols for _ in range(SCREAMING_SNAKE_CASE__ )]
update_area_of_max_square_using_dp_array(0 , 0 , SCREAMING_SNAKE_CASE__ )
return largest_square_area[0]
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ):
'''simple docstring'''
UpperCAmelCase__ = [[0] * (cols + 1) for _ in range(rows + 1 )]
UpperCAmelCase__ = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
UpperCAmelCase__ = dp_array[row][col + 1]
UpperCAmelCase__ = dp_array[row + 1][col + 1]
UpperCAmelCase__ = dp_array[row + 1][col]
if mat[row][col] == 1:
UpperCAmelCase__ = 1 + min(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = max(dp_array[row][col] , SCREAMING_SNAKE_CASE__ )
else:
UpperCAmelCase__ = 0
return largest_square_area
def _UpperCamelCase ( SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : int , SCREAMING_SNAKE_CASE__ : list[list[int]] ):
'''simple docstring'''
UpperCAmelCase__ = [0] * (cols + 1)
UpperCAmelCase__ = [0] * (cols + 1)
UpperCAmelCase__ = 0
for row in range(rows - 1 , -1 , -1 ):
for col in range(cols - 1 , -1 , -1 ):
UpperCAmelCase__ = current_row[col + 1]
UpperCAmelCase__ = next_row[col + 1]
UpperCAmelCase__ = next_row[col]
if mat[row][col] == 1:
UpperCAmelCase__ = 1 + min(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
UpperCAmelCase__ = max(current_row[col] , SCREAMING_SNAKE_CASE__ )
else:
UpperCAmelCase__ = 0
UpperCAmelCase__ = current_row
return largest_square_area
if __name__ == "__main__":
import doctest
doctest.testmod()
print(largest_square_area_in_matrix_bottom_up(2, 2, [[1, 1], [1, 1]]))
| 61 |
'''simple docstring'''
import importlib.util
import json
import os
import warnings
from dataclasses import dataclass, field
import torch
from ..training_args import TrainingArguments
from ..utils import cached_property, is_sagemaker_dp_enabled, logging
UpperCAmelCase_ = logging.get_logger(__name__)
def _UpperCamelCase ( ):
'''simple docstring'''
UpperCAmelCase__ = os.getenv("""SM_HP_MP_PARAMETERS""" , """{}""" )
try:
# Parse it and check the field "partitions" is included, it is required for model parallel.
UpperCAmelCase__ = json.loads(SCREAMING_SNAKE_CASE__ )
if "partitions" not in smp_options:
return False
except json.JSONDecodeError:
return False
# Get the sagemaker specific framework parameters from mpi_options variable.
UpperCAmelCase__ = os.getenv("""SM_FRAMEWORK_PARAMS""" , """{}""" )
try:
# Parse it and check the field "sagemaker_distributed_dataparallel_enabled".
UpperCAmelCase__ = json.loads(SCREAMING_SNAKE_CASE__ )
if not mpi_options.get("""sagemaker_mpi_enabled""" , SCREAMING_SNAKE_CASE__ ):
return False
except json.JSONDecodeError:
return False
# Lastly, check if the `smdistributed` module is present.
return importlib.util.find_spec("""smdistributed""" ) is not None
if is_sagemaker_model_parallel_available():
import smdistributed.modelparallel.torch as smp
smp.init()
@dataclass
class lowerCAmelCase_ ( lowerCamelCase_ ):
'''simple docstring'''
lowerCAmelCase_ : str = field(
default="""""" , metadata={"""help""": """Used by the SageMaker launcher to send mp-specific args. Ignored in SageMakerTrainer"""} , )
def SCREAMING_SNAKE_CASE__ ( self : Tuple ):
"""simple docstring"""
super().__post_init__()
warnings.warn(
"""`SageMakerTrainingArguments` is deprecated and will be removed in v5 of Transformers. You can use """
"""`TrainingArguments` instead.""" , _UpperCAmelCase , )
@cached_property
def SCREAMING_SNAKE_CASE__ ( self : Union[str, Any] ):
"""simple docstring"""
logger.info("""PyTorch: setting up devices""" )
if torch.distributed.is_available() and torch.distributed.is_initialized() and self.local_rank == -1:
logger.warning(
"""torch.distributed process group is initialized, but local_rank == -1. """
"""In order to use Torch DDP, launch your script with `python -m torch.distributed.launch""" )
if self.no_cuda:
UpperCAmelCase__ = torch.device("""cpu""" )
UpperCAmelCase__ = 0
elif is_sagemaker_model_parallel_available():
UpperCAmelCase__ = smp.local_rank()
UpperCAmelCase__ = torch.device("""cuda""" , _UpperCAmelCase )
UpperCAmelCase__ = 1
elif is_sagemaker_dp_enabled():
import smdistributed.dataparallel.torch.torch_smddp # noqa: F401
torch.distributed.init_process_group(backend="""smddp""" , timeout=self.ddp_timeout_delta )
UpperCAmelCase__ = int(os.getenv("""SMDATAPARALLEL_LOCAL_RANK""" ) )
UpperCAmelCase__ = torch.device("""cuda""" , self.local_rank )
UpperCAmelCase__ = 1
elif self.local_rank == -1:
# if n_gpu is > 1 we'll use nn.DataParallel.
# If you only want to use a specific subset of GPUs use `CUDA_VISIBLE_DEVICES=0`
# Explicitly set CUDA to the first (index 0) CUDA device, otherwise `set_device` will
# trigger an error that a device index is missing. Index 0 takes into account the
# GPUs available in the environment, so `CUDA_VISIBLE_DEVICES=1,2` with `cuda:0`
# will use the first GPU in that env, i.e. GPU#1
UpperCAmelCase__ = torch.device("""cuda:0""" if torch.cuda.is_available() else """cpu""" )
# Sometimes the line in the postinit has not been run before we end up here, so just checking we're not at
# the default value.
UpperCAmelCase__ = torch.cuda.device_count()
else:
# Here, we'll use torch.distributed.
# Initializes the distributed backend which will take care of synchronizing nodes/GPUs
if not torch.distributed.is_initialized():
torch.distributed.init_process_group(backend="""nccl""" , timeout=self.ddp_timeout_delta )
UpperCAmelCase__ = torch.device("""cuda""" , self.local_rank )
UpperCAmelCase__ = 1
if device.type == "cuda":
torch.cuda.set_device(_UpperCAmelCase )
return device
@property
def SCREAMING_SNAKE_CASE__ ( self : List[Any] ):
"""simple docstring"""
if is_sagemaker_model_parallel_available():
return smp.dp_size()
return super().world_size
@property
def SCREAMING_SNAKE_CASE__ ( self : Dict ):
"""simple docstring"""
return not is_sagemaker_model_parallel_available()
@property
def SCREAMING_SNAKE_CASE__ ( self : Optional[int] ):
"""simple docstring"""
return False
| 61 | 1 |
"""simple docstring"""
A: Optional[int] = {
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
1_0: "a",
1_1: "b",
1_2: "c",
1_3: "d",
1_4: "e",
1_5: "f",
}
def _snake_case ( UpperCamelCase : float ):
assert type(UpperCamelCase__ ) in (int, float) and decimal == int(UpperCamelCase__ )
UpperCAmelCase : Union[str, Any] = int(UpperCamelCase__ )
UpperCAmelCase : Tuple = ''''''
UpperCAmelCase : int = False
if decimal < 0:
UpperCAmelCase : Optional[int] = True
decimal *= -1
while decimal > 0:
UpperCAmelCase : str = divmod(UpperCamelCase__ , 16 )
UpperCAmelCase : List[str] = values[remainder] + hexadecimal
UpperCAmelCase : Optional[int] = '''0x''' + hexadecimal
if negative:
UpperCAmelCase : List[Any] = '''-''' + hexadecimal
return hexadecimal
if __name__ == "__main__":
import doctest
doctest.testmod()
| 109 |
'''simple docstring'''
import re
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
return [char.split() for char in re.split(R'''[^ a-z A-Z 0-9 \s]''', str_ )]
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : int =split_input(str_ )
return "".join(
[''''''.join([char.capitalize() for char in sub_str] ) for sub_str in string_split] )
def _a( UpperCamelCase__ : str, UpperCamelCase__ : bool, UpperCamelCase__ : str ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : Any =split_input(UpperCamelCase__ )
if upper:
SCREAMING_SNAKE_CASE__ : int =''''''.join(
[
separator.join([char.upper() for char in sub_str] )
for sub_str in string_split
] )
else:
SCREAMING_SNAKE_CASE__ : Any =''''''.join(
[
separator.join([char.lower() for char in sub_str] )
for sub_str in string_split
] )
return res_str
except IndexError:
return "not valid string"
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
return to_simple_case(UpperCamelCase__ )
def _a( UpperCamelCase__ : str ):
'''simple docstring'''
try:
SCREAMING_SNAKE_CASE__ : List[str] =to_simple_case(UpperCamelCase__ )
return res_str[0].lower() + res_str[1:]
except IndexError:
return "not valid string"
def _a( UpperCamelCase__ : str, UpperCamelCase__ : bool ):
'''simple docstring'''
return to_complex_case(UpperCamelCase__, UpperCamelCase__, '''_''' )
def _a( UpperCamelCase__ : str, UpperCamelCase__ : bool ):
'''simple docstring'''
return to_complex_case(UpperCamelCase__, UpperCamelCase__, '''-''' )
if __name__ == "__main__":
__import__('doctest').testmod() | 152 | 0 |
a : Dict = """ABCDEFGHIJKLMNOPQRSTUVWXYZ"""
def __lowerCamelCase ( ) -> None:
UpperCAmelCase : Optional[int] = input("""Enter message: """ )
UpperCAmelCase : Dict = input("""Enter key [alphanumeric]: """ )
UpperCAmelCase : Optional[Any] = input("""Encrypt/Decrypt [e/d]: """ )
if mode.lower().startswith("""e""" ):
UpperCAmelCase : List[str] = """encrypt"""
UpperCAmelCase : List[str] = encrypt_message(_lowercase , _lowercase )
elif mode.lower().startswith("""d""" ):
UpperCAmelCase : Tuple = """decrypt"""
UpperCAmelCase : str = decrypt_message(_lowercase , _lowercase )
print(F'''\n{mode.title()}ed message:''' )
print(_lowercase )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """encrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase ) -> str:
return translate_message(_lowercase , _lowercase , """decrypt""" )
def __lowerCamelCase ( _lowercase , _lowercase , _lowercase ) -> str:
UpperCAmelCase : Optional[int] = []
UpperCAmelCase : Optional[Any] = 0
UpperCAmelCase : Tuple = key.upper()
for symbol in message:
UpperCAmelCase : Dict = LETTERS.find(symbol.upper() )
if num != -1:
if mode == "encrypt":
num += LETTERS.find(key[key_index] )
elif mode == "decrypt":
num -= LETTERS.find(key[key_index] )
num %= len(_lowercase )
if symbol.isupper():
translated.append(LETTERS[num] )
elif symbol.islower():
translated.append(LETTERS[num].lower() )
key_index += 1
if key_index == len(_lowercase ):
UpperCAmelCase : Optional[int] = 0
else:
translated.append(_lowercase )
return "".join(_lowercase )
if __name__ == "__main__":
main()
| 355 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : List[Any] = {
"""configuration_m2m_100""": ["""M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP""", """M2M100Config""", """M2M100OnnxConfig"""],
"""tokenization_m2m_100""": ["""M2M100Tokenizer"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
a : Any = [
"""M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""M2M100ForConditionalGeneration""",
"""M2M100Model""",
"""M2M100PreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_mam_aaa import M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP, MaMaaaConfig, MaMaaaOnnxConfig
from .tokenization_mam_aaa import MaMaaaTokenizer
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_mam_aaa import (
M2M_100_PRETRAINED_MODEL_ARCHIVE_LIST,
MaMaaaForConditionalGeneration,
MaMaaaModel,
MaMaaaPreTrainedModel,
)
else:
import sys
a : List[Any] = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 338 | 0 |
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