code
stringlengths 82
53.2k
| code_codestyle
int64 0
721
| style_context
stringlengths 91
41.9k
| style_context_codestyle
int64 0
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| label
int64 0
1
|
|---|---|---|---|---|
import math
import qiskit
def lowerCAmelCase_ (lowercase__ : int = 1 , lowercase__ : int = 1 , lowercase__ : int = 1 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
if (
isinstance(lowerCamelCase__ , lowerCamelCase__ )
or isinstance(lowerCamelCase__ , lowerCamelCase__ )
or isinstance(lowerCamelCase__ , lowerCamelCase__ )
):
raise TypeError('''inputs must be integers.''' )
if (input_a < 0) or (input_a < 0) or (carry_in < 0):
raise ValueError('''inputs must be positive.''' )
if (
(math.floor(lowerCamelCase__ ) != input_a)
or (math.floor(lowerCamelCase__ ) != input_a)
or (math.floor(lowerCamelCase__ ) != carry_in)
):
raise ValueError('''inputs must be exact integers.''' )
if (input_a > 2) or (input_a > 2) or (carry_in > 2):
raise ValueError('''inputs must be less or equal to 2.''' )
# build registers
lowerCAmelCase__ = qiskit.QuantumRegister(4 , '''qr''' )
lowerCAmelCase__ = qiskit.ClassicalRegister(2 , '''cr''' )
# list the entries
lowerCAmelCase__ = [input_a, input_a, carry_in]
lowerCAmelCase__ = qiskit.QuantumCircuit(lowerCamelCase__ , lowerCamelCase__ )
for i in range(0 , 3 ):
if entry[i] == 2:
quantum_circuit.h(lowerCamelCase__ ) # for hadamard entries
elif entry[i] == 1:
quantum_circuit.x(lowerCamelCase__ ) # for 1 entries
elif entry[i] == 0:
quantum_circuit.i(lowerCamelCase__ ) # for 0 entries
# build the circuit
quantum_circuit.ccx(0 , 1 , 3 ) # ccx = toffoli gate
quantum_circuit.cx(0 , 1 )
quantum_circuit.ccx(1 , 2 , 3 )
quantum_circuit.cx(1 , 2 )
quantum_circuit.cx(0 , 1 )
quantum_circuit.measure([2, 3] , lowerCamelCase__ ) # measure the last two qbits
lowerCAmelCase__ = qiskit.Aer.get_backend('''aer_simulator''' )
lowerCAmelCase__ = qiskit.execute(lowerCamelCase__ , lowerCamelCase__ , shots=10_00 )
return job.result().get_counts(lowerCamelCase__ )
if __name__ == "__main__":
print(F'''Total sum count for state is: {quantum_full_adder(1, 1, 1)}''')
| 668
|
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
lowercase_ : Tuple = 16
lowercase_ : Tuple = 32
def _lowerCAmelCase ( lowerCamelCase__ : Accelerator, lowerCamelCase__ : int = 1_6, lowerCamelCase__ : str = "bert-base-cased" ) -> Dict:
_SCREAMING_SNAKE_CASE : str = AutoTokenizer.from_pretrained(lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : Dict = load_dataset("glue", "mrpc" )
def tokenize_function(lowerCamelCase__ : Any ):
# max_length=None => use the model max length (it's actually the default)
_SCREAMING_SNAKE_CASE : int = 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
_SCREAMING_SNAKE_CASE : Any = datasets.map(
lowerCamelCase__, batched=lowerCamelCase__, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=lowerCamelCase__ )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_SCREAMING_SNAKE_CASE : Optional[Any] = tokenized_datasets.rename_column("label", "labels" )
def collate_fn(lowerCamelCase__ : Tuple ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(lowerCamelCase__, padding="max_length", max_length=1_2_8, return_tensors="pt" )
return tokenizer.pad(lowerCamelCase__, padding="longest", return_tensors="pt" )
# Instantiate dataloaders.
_SCREAMING_SNAKE_CASE : Tuple = DataLoader(
tokenized_datasets["train"], shuffle=lowerCamelCase__, collate_fn=lowerCamelCase__, batch_size=lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : int = DataLoader(
tokenized_datasets["validation"], shuffle=lowerCamelCase__, collate_fn=lowerCamelCase__, batch_size=lowerCamelCase__ )
return train_dataloader, eval_dataloader
def _lowerCAmelCase ( lowerCamelCase__ : Any, lowerCamelCase__ : Optional[Any], lowerCamelCase__ : Optional[int], lowerCamelCase__ : List[str] ) -> List[Any]:
model.eval()
_SCREAMING_SNAKE_CASE : Tuple = 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 )
with torch.no_grad():
_SCREAMING_SNAKE_CASE : List[str] = model(**lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : List[Any] = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Optional[int] = accelerator.gather(
(predictions, batch["labels"]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(lowerCamelCase__ ) - 1:
_SCREAMING_SNAKE_CASE : List[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_SCREAMING_SNAKE_CASE : Any = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=lowerCamelCase__, references=lowerCamelCase__, )
_SCREAMING_SNAKE_CASE : str = metric.compute()
return eval_metric["accuracy"]
def _lowerCAmelCase ( lowerCamelCase__ : str, lowerCamelCase__ : Any ) -> int:
# Initialize accelerator
_SCREAMING_SNAKE_CASE : Optional[Any] = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_SCREAMING_SNAKE_CASE : Optional[Any] = config["lr"]
_SCREAMING_SNAKE_CASE : str = int(config["num_epochs"] )
_SCREAMING_SNAKE_CASE : Dict = int(config["seed"] )
_SCREAMING_SNAKE_CASE : Dict = int(config["batch_size"] )
_SCREAMING_SNAKE_CASE : str = args.model_name_or_path
set_seed(lowerCamelCase__ )
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Optional[int] = get_dataloaders(lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_SCREAMING_SNAKE_CASE : Any = AutoModelForSequenceClassification.from_pretrained(lowerCamelCase__, return_dict=lowerCamelCase__ )
# Instantiate optimizer
_SCREAMING_SNAKE_CASE : Any = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_SCREAMING_SNAKE_CASE : Union[str, Any] = optimizer_cls(params=model.parameters(), lr=lowerCamelCase__ )
if accelerator.state.deepspeed_plugin is not None:
_SCREAMING_SNAKE_CASE : Dict = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
_SCREAMING_SNAKE_CASE : Optional[Any] = 1
_SCREAMING_SNAKE_CASE : str = (len(lowerCamelCase__ ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_SCREAMING_SNAKE_CASE : Dict = get_linear_schedule_with_warmup(
optimizer=lowerCamelCase__, num_warmup_steps=0, num_training_steps=lowerCamelCase__, )
else:
_SCREAMING_SNAKE_CASE : Union[str, Any] = DummyScheduler(lowerCamelCase__, total_num_steps=lowerCamelCase__, warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE : Dict = accelerator.prepare(
lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ )
# We need to keep track of how many total steps we have iterated over
_SCREAMING_SNAKE_CASE : str = 0
# We also need to keep track of the stating epoch so files are named properly
_SCREAMING_SNAKE_CASE : Dict = 0
_SCREAMING_SNAKE_CASE : List[Any] = evaluate.load("glue", "mrpc" )
_SCREAMING_SNAKE_CASE : int = num_epochs
if args.partial_train_epoch is not None:
_SCREAMING_SNAKE_CASE : List[Any] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
_SCREAMING_SNAKE_CASE : str = args.resume_from_checkpoint.split("epoch_" )[1]
_SCREAMING_SNAKE_CASE : Union[str, Any] = ""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
_SCREAMING_SNAKE_CASE : Tuple = int(lowerCamelCase__ ) + 1
_SCREAMING_SNAKE_CASE : Tuple = evaluation_loop(lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ )
accelerator.print("resumed checkpoint performance:", lowerCamelCase__ )
accelerator.print("resumed checkpoint's scheduler's lr:", lr_scheduler.get_lr()[0] )
accelerator.print("resumed optimizers's lr:", optimizer.param_groups[0]["lr"] )
with open(os.path.join(args.output_dir, f'''state_{starting_epoch-1}.json''' ), "r" ) as f:
_SCREAMING_SNAKE_CASE : List[Any] = json.load(lowerCamelCase__ )
assert resumed_state["accuracy"] == accuracy, "Accuracy mismatch, loading from checkpoint failed"
assert (
resumed_state["lr"] == lr_scheduler.get_lr()[0]
), "Scheduler learning rate mismatch, loading from checkpoint failed"
assert (
resumed_state["optimizer_lr"] == optimizer.param_groups[0]["lr"]
), "Optimizer learning rate mismatch, loading from checkpoint failed"
assert resumed_state["epoch"] == starting_epoch - 1, "Epoch mismatch, loading from checkpoint failed"
return
# Now we train the model
_SCREAMING_SNAKE_CASE : int = {}
for epoch in range(lowerCamelCase__, lowerCamelCase__ ):
model.train()
for step, batch in enumerate(lowerCamelCase__ ):
_SCREAMING_SNAKE_CASE : int = model(**lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : int = outputs.loss
_SCREAMING_SNAKE_CASE : Union[str, Any] = loss / gradient_accumulation_steps
accelerator.backward(lowerCamelCase__ )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
_SCREAMING_SNAKE_CASE : int = f'''epoch_{epoch}'''
_SCREAMING_SNAKE_CASE : Optional[Any] = os.path.join(args.output_dir, lowerCamelCase__ )
accelerator.save_state(lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : Tuple = evaluation_loop(lowerCamelCase__, lowerCamelCase__, lowerCamelCase__, lowerCamelCase__ )
_SCREAMING_SNAKE_CASE : Union[str, Any] = accuracy
_SCREAMING_SNAKE_CASE : Tuple = lr_scheduler.get_lr()[0]
_SCREAMING_SNAKE_CASE : str = optimizer.param_groups[0]["lr"]
_SCREAMING_SNAKE_CASE : int = epoch
_SCREAMING_SNAKE_CASE : Optional[Any] = overall_step
accelerator.print(f'''epoch {epoch}:''', lowerCamelCase__ )
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir, f'''state_{epoch}.json''' ), "w" ) as f:
json.dump(lowerCamelCase__, lowerCamelCase__ )
def _lowerCAmelCase ( ) -> List[str]:
_SCREAMING_SNAKE_CASE : Union[str, Any] = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
parser.add_argument(
"--model_name_or_path", type=lowerCamelCase__, default="bert-base-cased", help="Path to pretrained model or model identifier from huggingface.co/models.", required=lowerCamelCase__, )
parser.add_argument(
"--output_dir", type=lowerCamelCase__, default=".", help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.", )
parser.add_argument(
"--resume_from_checkpoint", type=lowerCamelCase__, default=lowerCamelCase__, help="If the training should continue from a checkpoint folder.", )
parser.add_argument(
"--partial_train_epoch", type=lowerCamelCase__, default=lowerCamelCase__, help="If passed, the training will stop after this number of epochs.", )
parser.add_argument(
"--num_epochs", type=lowerCamelCase__, default=2, help="Number of train epochs.", )
_SCREAMING_SNAKE_CASE : List[Any] = parser.parse_args()
_SCREAMING_SNAKE_CASE : Dict = {"lr": 2E-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
training_function(lowerCamelCase__, lowerCamelCase__ )
if __name__ == "__main__":
main()
| 572
| 0
|
"""simple docstring"""
from binascii import hexlify
from hashlib import shaaaa
from os import urandom
# RFC 3526 - More Modular Exponential (MODP) Diffie-Hellman groups for
# Internet Key Exchange (IKE) https://tools.ietf.org/html/rfc3526
_UpperCamelCase = {
# 1536-bit
5: {
"prime": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA237327FFFFFFFFFFFFFFFF""",
base=16,
),
"generator": 2,
},
# 2048-bit
14: {
"prime": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AACAA68FFFFFFFFFFFFFFFF""",
base=16,
),
"generator": 2,
},
# 3072-bit
15: {
"prime": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A93AD2CAFFFFFFFFFFFFFFFF""",
base=16,
),
"generator": 2,
},
# 4096-bit
16: {
"prime": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
+ """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
+ """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
+ """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
+ """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
+ """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934063199"""
+ """FFFFFFFFFFFFFFFF""",
base=16,
),
"generator": 2,
},
# 6144-bit
17: {
"prime": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD129024E08"""
+ """8A67CC74020BBEA63B139B22514A08798E3404DDEF9519B3CD3A431B"""
+ """302B0A6DF25F14374FE1356D6D51C245E485B576625E7EC6F44C42E9"""
+ """A637ED6B0BFF5CB6F406B7EDEE386BFB5A899FA5AE9F24117C4B1FE6"""
+ """49286651ECE45B3DC2007CB8A163BF0598DA48361C55D39A69163FA8"""
+ """FD24CF5F83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3BE39E772C"""
+ """180E86039B2783A2EC07A28FB5C55DF06F4C52C9DE2BCBF695581718"""
+ """3995497CEA956AE515D2261898FA051015728E5A8AAAC42DAD33170D"""
+ """04507A33A85521ABDF1CBA64ECFB850458DBEF0A8AEA71575D060C7D"""
+ """B3970F85A6E1E4C7ABF5AE8CDB0933D71E8C94E04A25619DCEE3D226"""
+ """1AD2EE6BF12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB3143DB5BFC"""
+ """E0FD108E4B82D120A92108011A723C12A787E6D788719A10BDBA5B26"""
+ """99C327186AF4E23C1A946834B6150BDA2583E9CA2AD44CE8DBBBC2DB"""
+ """04DE8EF92E8EFC141FBECAA6287C59474E6BC05D99B2964FA090C3A2"""
+ """233BA186515BE7ED1F612970CEE2D7AFB81BDD762170481CD0069127"""
+ """D5B05AA993B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
+ """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BDF8FF9406"""
+ """AD9E530EE5DB382F413001AEB06A53ED9027D831179727B0865A8918"""
+ """DA3EDBEBCF9B14ED44CE6CBACED4BB1BDB7F1447E6CC254B33205151"""
+ """2BD7AF426FB8F401378CD2BF5983CA01C64B92ECF032EA15D1721D03"""
+ """F482D7CE6E74FEF6D55E702F46980C82B5A84031900B1C9E59E7C97F"""
+ """BEC7E8F323A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
+ """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE32806A1D58B"""
+ """B7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55CDA56C9EC2EF29632"""
+ """387FE8D76E3C0468043E8F663F4860EE12BF2D5B0B7474D6E694F91E"""
+ """6DCC4024FFFFFFFFFFFFFFFF""",
base=16,
),
"generator": 2,
},
# 8192-bit
18: {
"prime": int(
"""FFFFFFFFFFFFFFFFC90FDAA22168C234C4C6628B80DC1CD1"""
+ """29024E088A67CC74020BBEA63B139B22514A08798E3404DD"""
+ """EF9519B3CD3A431B302B0A6DF25F14374FE1356D6D51C245"""
+ """E485B576625E7EC6F44C42E9A637ED6B0BFF5CB6F406B7ED"""
+ """EE386BFB5A899FA5AE9F24117C4B1FE649286651ECE45B3D"""
+ """C2007CB8A163BF0598DA48361C55D39A69163FA8FD24CF5F"""
+ """83655D23DCA3AD961C62F356208552BB9ED529077096966D"""
+ """670C354E4ABC9804F1746C08CA18217C32905E462E36CE3B"""
+ """E39E772C180E86039B2783A2EC07A28FB5C55DF06F4C52C9"""
+ """DE2BCBF6955817183995497CEA956AE515D2261898FA0510"""
+ """15728E5A8AAAC42DAD33170D04507A33A85521ABDF1CBA64"""
+ """ECFB850458DBEF0A8AEA71575D060C7DB3970F85A6E1E4C7"""
+ """ABF5AE8CDB0933D71E8C94E04A25619DCEE3D2261AD2EE6B"""
+ """F12FFA06D98A0864D87602733EC86A64521F2B18177B200C"""
+ """BBE117577A615D6C770988C0BAD946E208E24FA074E5AB31"""
+ """43DB5BFCE0FD108E4B82D120A92108011A723C12A787E6D7"""
+ """88719A10BDBA5B2699C327186AF4E23C1A946834B6150BDA"""
+ """2583E9CA2AD44CE8DBBBC2DB04DE8EF92E8EFC141FBECAA6"""
+ """287C59474E6BC05D99B2964FA090C3A2233BA186515BE7ED"""
+ """1F612970CEE2D7AFB81BDD762170481CD0069127D5B05AA9"""
+ """93B4EA988D8FDDC186FFB7DC90A6C08F4DF435C934028492"""
+ """36C3FAB4D27C7026C1D4DCB2602646DEC9751E763DBA37BD"""
+ """F8FF9406AD9E530EE5DB382F413001AEB06A53ED9027D831"""
+ """179727B0865A8918DA3EDBEBCF9B14ED44CE6CBACED4BB1B"""
+ """DB7F1447E6CC254B332051512BD7AF426FB8F401378CD2BF"""
+ """5983CA01C64B92ECF032EA15D1721D03F482D7CE6E74FEF6"""
+ """D55E702F46980C82B5A84031900B1C9E59E7C97FBEC7E8F3"""
+ """23A97A7E36CC88BE0F1D45B7FF585AC54BD407B22B4154AA"""
+ """CC8F6D7EBF48E1D814CC5ED20F8037E0A79715EEF29BE328"""
+ """06A1D58BB7C5DA76F550AA3D8A1FBFF0EB19CCB1A313D55C"""
+ """DA56C9EC2EF29632387FE8D76E3C0468043E8F663F4860EE"""
+ """12BF2D5B0B7474D6E694F91E6DBE115974A3926F12FEE5E4"""
+ """38777CB6A932DF8CD8BEC4D073B931BA3BC832B68D9DD300"""
+ """741FA7BF8AFC47ED2576F6936BA424663AAB639C5AE4F568"""
+ """3423B4742BF1C978238F16CBE39D652DE3FDB8BEFC848AD9"""
+ """22222E04A4037C0713EB57A81A23F0C73473FC646CEA306B"""
+ """4BCBC8862F8385DDFA9D4B7FA2C087E879683303ED5BDD3A"""
+ """062B3CF5B3A278A66D2A13F83F44F82DDF310EE074AB6A36"""
+ """4597E899A0255DC164F31CC50846851DF9AB48195DED7EA1"""
+ """B1D510BD7EE74D73FAF36BC31ECFA268359046F4EB879F92"""
+ """4009438B481C6CD7889A002ED5EE382BC9190DA6FC026E47"""
+ """9558E4475677E9AA9E3050E2765694DFC81F56E880B96E71"""
+ """60C980DD98EDD3DFFFFFFFFFFFFFFFFF""",
base=16,
),
"generator": 2,
},
}
class lowerCamelCase__ :
def __init__( self ,A = 14 ):
if group not in primes:
raise ValueError("""Unsupported Group""" )
UpperCAmelCase = primes[group]['''prime''']
UpperCAmelCase = primes[group]['''generator''']
UpperCAmelCase = int(hexlify(urandom(32 ) ) ,base=16 )
def _UpperCamelCase ( self ):
return hex(self.__private_key )[2:]
def _UpperCamelCase ( self ):
UpperCAmelCase = pow(self.generator ,self.__private_key ,self.prime )
return hex(lowerCamelCase__ )[2:]
def _UpperCamelCase ( self ,A ):
return (
2 <= key <= self.prime - 2
and pow(lowerCamelCase__ ,(self.prime - 1) // 2 ,self.prime ) == 1
)
def _UpperCamelCase ( self ,A ):
UpperCAmelCase = int(lowerCamelCase__ ,base=16 )
if not self.is_valid_public_key(lowerCamelCase__ ):
raise ValueError("""Invalid public key""" )
UpperCAmelCase = pow(lowerCamelCase__ ,self.__private_key ,self.prime )
return shaaaa(str(lowerCamelCase__ ).encode() ).hexdigest()
@staticmethod
def _UpperCamelCase ( A ,A ):
return (
2 <= remote_public_key_str <= prime - 2
and pow(lowerCamelCase__ ,(prime - 1) // 2 ,lowerCamelCase__ ) == 1
)
@staticmethod
def _UpperCamelCase ( A ,A ,A = 14 ):
UpperCAmelCase = int(lowerCamelCase__ ,base=16 )
UpperCAmelCase = int(lowerCamelCase__ ,base=16 )
UpperCAmelCase = primes[group]['''prime''']
if not DiffieHellman.is_valid_public_key_static(lowerCamelCase__ ,lowerCamelCase__ ):
raise ValueError("""Invalid public key""" )
UpperCAmelCase = pow(lowerCamelCase__ ,lowerCamelCase__ ,lowerCamelCase__ )
return shaaaa(str(lowerCamelCase__ ).encode() ).hexdigest()
if __name__ == "__main__":
import doctest
doctest.testmod()
| 717
|
"""simple docstring"""
def _a ( _snake_case = 10 , _snake_case = 22 ):
"""simple docstring"""
UpperCAmelCase = range(1 , _snake_case )
UpperCAmelCase = range(1 , _snake_case )
return sum(
1 for power in powers for base in bases if len(str(base**power ) ) == power )
if __name__ == "__main__":
print(F"""{solution(10, 22) = }""")
| 74
| 0
|
'''simple docstring'''
import torch
from diffusers import DDPMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class SCREAMING_SNAKE_CASE ( __a ):
'''simple docstring'''
__UpperCamelCase = (DDPMParallelScheduler,)
def _UpperCamelCase ( self , **SCREAMING_SNAKE_CASE__ ):
'''simple docstring'''
snake_case: Tuple = {
'num_train_timesteps': 10_00,
'beta_start': 0.00_01,
'beta_end': 0.02,
'beta_schedule': 'linear',
'variance_type': 'fixed_small',
'clip_sample': True,
}
config.update(**lowercase_ )
return config
def _UpperCamelCase ( self ):
'''simple docstring'''
for timesteps in [1, 5, 1_00, 10_00]:
self.check_over_configs(num_train_timesteps=lowercase_ )
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 variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_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'''
self.check_over_configs(thresholding=lowercase_ )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=lowercase_ , prediction_type=lowercase_ , sample_max_value=lowercase_ , )
def _UpperCamelCase ( self ):
'''simple docstring'''
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=lowercase_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
for t in [0, 5_00, 9_99]:
self.check_over_forward(time_step=lowercase_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: List[str] = self.scheduler_classes[0]
snake_case: int = self.get_scheduler_config()
snake_case: Any = scheduler_class(**lowercase_ )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(4_87 ) - 0.0_09_79 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(9_99 ) - 0.02 ) ) < 1E-5
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.scheduler_classes[0]
snake_case: Union[str, Any] = self.get_scheduler_config()
snake_case: str = scheduler_class(**lowercase_ )
snake_case: Optional[Any] = len(lowercase_ )
snake_case: Union[str, Any] = self.dummy_model()
snake_case: List[Any] = self.dummy_sample_deter
snake_case: List[Any] = self.dummy_sample_deter + 0.1
snake_case: str = self.dummy_sample_deter - 0.1
snake_case: List[Any] = samplea.shape[0]
snake_case: List[str] = torch.stack([samplea, samplea, samplea] , dim=0 )
snake_case: Any = torch.arange(lowercase_ )[0:3, None].repeat(1 , lowercase_ )
snake_case: Optional[int] = model(samples.flatten(0 , 1 ) , timesteps.flatten(0 , 1 ) )
snake_case: int = scheduler.batch_step_no_noise(lowercase_ , timesteps.flatten(0 , 1 ) , samples.flatten(0 , 1 ) )
snake_case: List[Any] = torch.sum(torch.abs(lowercase_ ) )
snake_case: Tuple = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 11_53.18_33 ) < 1E-2
assert abs(result_mean.item() - 0.50_05 ) < 1E-3
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.scheduler_classes[0]
snake_case: List[Any] = self.get_scheduler_config()
snake_case: Dict = scheduler_class(**lowercase_ )
snake_case: List[Any] = len(lowercase_ )
snake_case: Tuple = self.dummy_model()
snake_case: Union[str, Any] = self.dummy_sample_deter
snake_case: Optional[int] = torch.manual_seed(0 )
for t in reversed(range(lowercase_ ) ):
# 1. predict noise residual
snake_case: int = model(lowercase_ , lowercase_ )
# 2. predict previous mean of sample x_t-1
snake_case: str = scheduler.step(lowercase_ , lowercase_ , lowercase_ , generator=lowercase_ ).prev_sample
snake_case: str = pred_prev_sample
snake_case: str = torch.sum(torch.abs(lowercase_ ) )
snake_case: List[str] = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 2_58.96_06 ) < 1E-2
assert abs(result_mean.item() - 0.33_72 ) < 1E-3
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Any = self.scheduler_classes[0]
snake_case: str = self.get_scheduler_config(prediction_type='v_prediction' )
snake_case: List[str] = scheduler_class(**lowercase_ )
snake_case: List[str] = len(lowercase_ )
snake_case: Union[str, Any] = self.dummy_model()
snake_case: Union[str, Any] = self.dummy_sample_deter
snake_case: Tuple = torch.manual_seed(0 )
for t in reversed(range(lowercase_ ) ):
# 1. predict noise residual
snake_case: Optional[int] = model(lowercase_ , lowercase_ )
# 2. predict previous mean of sample x_t-1
snake_case: Optional[int] = scheduler.step(lowercase_ , lowercase_ , lowercase_ , generator=lowercase_ ).prev_sample
snake_case: Tuple = pred_prev_sample
snake_case: Union[str, Any] = torch.sum(torch.abs(lowercase_ ) )
snake_case: List[Any] = torch.mean(torch.abs(lowercase_ ) )
assert abs(result_sum.item() - 2_02.02_96 ) < 1E-2
assert abs(result_mean.item() - 0.26_31 ) < 1E-3
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.scheduler_classes[0]
snake_case: List[Any] = self.get_scheduler_config()
snake_case: Union[str, Any] = scheduler_class(**lowercase_ )
snake_case: Tuple = [1_00, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=lowercase_ )
snake_case: List[Any] = scheduler.timesteps
for i, timestep in enumerate(lowercase_ ):
if i == len(lowercase_ ) - 1:
snake_case: Any = -1
else:
snake_case: str = timesteps[i + 1]
snake_case: Dict = scheduler.previous_timestep(lowercase_ )
snake_case: Union[str, Any] = prev_t.item()
self.assertEqual(lowercase_ , lowercase_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.scheduler_classes[0]
snake_case: int = self.get_scheduler_config()
snake_case: Dict = scheduler_class(**lowercase_ )
snake_case: List[Any] = [1_00, 87, 50, 51, 0]
with self.assertRaises(lowercase_ , msg='`custom_timesteps` must be in descending order.' ):
scheduler.set_timesteps(timesteps=lowercase_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.scheduler_classes[0]
snake_case: List[str] = self.get_scheduler_config()
snake_case: Tuple = scheduler_class(**lowercase_ )
snake_case: Any = [1_00, 87, 50, 1, 0]
snake_case: Tuple = len(lowercase_ )
with self.assertRaises(lowercase_ , msg='Can only pass one of `num_inference_steps` or `custom_timesteps`.' ):
scheduler.set_timesteps(num_inference_steps=lowercase_ , timesteps=lowercase_ )
def _UpperCamelCase ( self ):
'''simple docstring'''
snake_case: Dict = self.scheduler_classes[0]
snake_case: int = self.get_scheduler_config()
snake_case: List[Any] = scheduler_class(**lowercase_ )
snake_case: List[Any] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
lowercase_ , msg='`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}' , ):
scheduler.set_timesteps(timesteps=lowercase_ )
| 329
|
'''simple docstring'''
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
__snake_case = logging.get_logger(__name__)
@dataclass
class _a ( __a ):
"""simple docstring"""
A_ = [
'''no_inference''',
'''no_cuda''',
'''no_tpu''',
'''no_speed''',
'''no_memory''',
'''no_env_print''',
'''no_multi_process''',
]
def __init__( self : int , **lowercase_ : int ):
'''simple docstring'''
for deprecated_arg in self.deprecated_args:
if deprecated_arg in kwargs:
lowercase_ = deprecated_arg[3:]
setattr(self , lowercase_ , not kwargs.pop(lowercase_ ) )
logger.warning(
F"""{deprecated_arg} is depreciated. Please use --no_{positive_arg} or"""
F""" {positive_arg}={kwargs[positive_arg]}""" )
lowercase_ = kwargs.pop("""torchscript""" , self.torchscript )
lowercase_ = kwargs.pop("""torch_xla_tpu_print_metrics""" , self.torch_xla_tpu_print_metrics )
lowercase_ = kwargs.pop("""fp16_opt_level""" , self.fpaa_opt_level )
super().__init__(**lowercase_ )
A_ = field(default=__a , metadata={'''help''': '''Trace the models using torchscript'''} )
A_ = field(default=__a , metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''} )
A_ = field(
default='''O1''' , metadata={
'''help''': (
'''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
'''See details at https://nvidia.github.io/apex/amp.html'''
)
} , )
@cached_property
def lowerCamelCase__ ( self : Dict ):
'''simple docstring'''
requires_backends(self , ["""torch"""] )
logger.info("""PyTorch: setting up devices""" )
if not self.cuda:
lowercase_ = torch.device("""cpu""" )
lowercase_ = 0
elif is_torch_tpu_available():
lowercase_ = xm.xla_device()
lowercase_ = 0
else:
lowercase_ = torch.device("""cuda""" if torch.cuda.is_available() else """cpu""" )
lowercase_ = torch.cuda.device_count()
return device, n_gpu
@property
def lowerCamelCase__ ( self : List[str] ):
'''simple docstring'''
return is_torch_tpu_available() and self.tpu
@property
def lowerCamelCase__ ( self : Tuple ):
'''simple docstring'''
requires_backends(self , ["""torch"""] )
# TODO(PVP): currently only single GPU is supported
return torch.cuda.current_device()
@property
def lowerCamelCase__ ( self : Dict ):
'''simple docstring'''
requires_backends(self , ["""torch"""] )
return self._setup_devices[0]
@property
def lowerCamelCase__ ( self : Any ):
'''simple docstring'''
requires_backends(self , ["""torch"""] )
return self._setup_devices[1]
@property
def lowerCamelCase__ ( self : Optional[int] ):
'''simple docstring'''
return self.n_gpu > 0
| 451
| 0
|
def SCREAMING_SNAKE_CASE ( lowercase_ , lowercase_ , lowercase_ = 0 , lowercase_ = 0 ) -> Union[str, Any]:
"""simple docstring"""
A__ = right or len(lowercase_ ) - 1
if left > right:
return -1
elif list_data[left] == key:
return left
elif list_data[right] == key:
return right
else:
return search(lowercase_ , lowercase_ , left + 1 , right - 1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 721
|
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelForSequenceClassification,
AutoTokenizer,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")
require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/text-classification/requirements.txt""")
_lowerCamelCase : Any = logging.getLogger(__name__)
@dataclass
class UpperCamelCase_ :
'''simple docstring'''
UpperCAmelCase__ = field(
default=128 , metadata={
'''help''': (
'''The maximum total input sequence length after tokenization. Sequences longer '''
'''than this will be truncated, sequences shorter will be padded.'''
)
} , )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Overwrite the cached preprocessed datasets or not.'''} )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , 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.'''
)
} , )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of training examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
'''value if set.'''
)
} , )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={
'''help''': (
'''For debugging purposes or quicker training, truncate the number of prediction examples to this '''
'''value if set.'''
)
} , )
@dataclass
class UpperCamelCase_ :
'''simple docstring'''
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''} )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Evaluation language. Also train language if `train_language` is set to None.'''} )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Train language if it is different from the evaluation language.'''} )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''} )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Pretrained tokenizer name or path if not the same as model_name'''} )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co'''} , )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''arg to indicate if tokenizer should do lower case in AutoTokenizer.from_pretrained()'''} , )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Whether to use one of the fast tokenizer (backed by the tokenizers library) or not.'''} , )
UpperCAmelCase__ = field(
default='''main''' , metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''} , )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={
'''help''': (
'''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
'''with private models).'''
)
} , )
UpperCAmelCase__ = field(
default=UpperCAmelCase__ , metadata={'''help''': '''Will enable to load a pretrained model whose head dimensions are different.'''} , )
def SCREAMING_SNAKE_CASE ( ) -> str:
"""simple docstring"""
A__ = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments) )
A__ , A__ , A__ = parser.parse_args_into_dataclasses()
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
# information sent is the one passed as arguments along with your Python/PyTorch versions.
send_example_telemetry('''run_xnli''' , lowercase_ )
# Setup logging
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''' , datefmt='''%m/%d/%Y %H:%M:%S''' , handlers=[logging.StreamHandler(sys.stdout )] , )
if training_args.should_log:
# The default of training_args.log_level is passive, so we set log level at info here to have that default.
transformers.utils.logging.set_verbosity_info()
A__ = training_args.get_process_log_level()
logger.setLevel(lowercase_ )
datasets.utils.logging.set_verbosity(lowercase_ )
transformers.utils.logging.set_verbosity(lowercase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
+ f"""distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}""" )
logger.info(f"""Training/evaluation parameters {training_args}""" )
# Detecting last checkpoint.
A__ = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
A__ = get_last_checkpoint(training_args.output_dir )
if last_checkpoint is None and len(os.listdir(training_args.output_dir ) ) > 0:
raise ValueError(
f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
'''Use --overwrite_output_dir to overcome.''' )
elif last_checkpoint is not None:
logger.info(
f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
'''the `--output_dir` or add `--overwrite_output_dir` to train from scratch.''' )
# Set seed before initializing model.
set_seed(training_args.seed )
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
# Downloading and loading xnli dataset from the hub.
if training_args.do_train:
if model_args.train_language is None:
A__ = load_dataset(
'''xnli''' , model_args.language , split='''train''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
else:
A__ = load_dataset(
'''xnli''' , model_args.train_language , split='''train''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
A__ = train_dataset.features['''label'''].names
if training_args.do_eval:
A__ = load_dataset(
'''xnli''' , model_args.language , split='''validation''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
A__ = eval_dataset.features['''label'''].names
if training_args.do_predict:
A__ = load_dataset(
'''xnli''' , model_args.language , split='''test''' , cache_dir=model_args.cache_dir , use_auth_token=True if model_args.use_auth_token else None , )
A__ = predict_dataset.features['''label'''].names
# Labels
A__ = len(lowercase_ )
# Load pretrained model and tokenizer
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
A__ = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowercase_ , idalabel={str(lowercase_ ): label for i, label in enumerate(lowercase_ )} , labelaid={label: i for i, label in enumerate(lowercase_ )} , finetuning_task='''xnli''' , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
A__ = AutoTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , do_lower_case=model_args.do_lower_case , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
A__ = AutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('''.ckpt''' in model_args.model_name_or_path ) , config=lowercase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , ignore_mismatched_sizes=model_args.ignore_mismatched_sizes , )
# Preprocessing the datasets
# Padding strategy
if data_args.pad_to_max_length:
A__ = '''max_length'''
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
A__ = False
def preprocess_function(lowercase_ ):
# Tokenize the texts
return tokenizer(
examples['''premise'''] , examples['''hypothesis'''] , padding=lowercase_ , max_length=data_args.max_seq_length , truncation=lowercase_ , )
if training_args.do_train:
if data_args.max_train_samples is not None:
A__ = min(len(lowercase_ ) , data_args.max_train_samples )
A__ = train_dataset.select(range(lowercase_ ) )
with training_args.main_process_first(desc='''train dataset map pre-processing''' ):
A__ = train_dataset.map(
lowercase_ , batched=lowercase_ , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on train dataset''' , )
# Log a few random samples from the training set:
for index in random.sample(range(len(lowercase_ ) ) , 3 ):
logger.info(f"""Sample {index} of the training set: {train_dataset[index]}.""" )
if training_args.do_eval:
if data_args.max_eval_samples is not None:
A__ = min(len(lowercase_ ) , data_args.max_eval_samples )
A__ = eval_dataset.select(range(lowercase_ ) )
with training_args.main_process_first(desc='''validation dataset map pre-processing''' ):
A__ = eval_dataset.map(
lowercase_ , batched=lowercase_ , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on validation dataset''' , )
if training_args.do_predict:
if data_args.max_predict_samples is not None:
A__ = min(len(lowercase_ ) , data_args.max_predict_samples )
A__ = predict_dataset.select(range(lowercase_ ) )
with training_args.main_process_first(desc='''prediction dataset map pre-processing''' ):
A__ = predict_dataset.map(
lowercase_ , batched=lowercase_ , load_from_cache_file=not data_args.overwrite_cache , desc='''Running tokenizer on prediction dataset''' , )
# Get the metric function
A__ = evaluate.load('''xnli''' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowercase_ ):
A__ = p.predictions[0] if isinstance(p.predictions , lowercase_ ) else p.predictions
A__ = np.argmax(lowercase_ , axis=1 )
return metric.compute(predictions=lowercase_ , references=p.label_ids )
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
A__ = default_data_collator
elif training_args.fpaa:
A__ = DataCollatorWithPadding(lowercase_ , pad_to_multiple_of=8 )
else:
A__ = None
# Initialize our Trainer
A__ = Trainer(
model=lowercase_ , args=lowercase_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowercase_ , tokenizer=lowercase_ , data_collator=lowercase_ , )
# Training
if training_args.do_train:
A__ = None
if training_args.resume_from_checkpoint is not None:
A__ = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
A__ = last_checkpoint
A__ = trainer.train(resume_from_checkpoint=lowercase_ )
A__ = train_result.metrics
A__ = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowercase_ )
)
A__ = min(lowercase_ , len(lowercase_ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('''train''' , lowercase_ )
trainer.save_metrics('''train''' , lowercase_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('''*** Evaluate ***''' )
A__ = trainer.evaluate(eval_dataset=lowercase_ )
A__ = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowercase_ )
A__ = min(lowercase_ , len(lowercase_ ) )
trainer.log_metrics('''eval''' , lowercase_ )
trainer.save_metrics('''eval''' , lowercase_ )
# Prediction
if training_args.do_predict:
logger.info('''*** Predict ***''' )
A__ , A__ , A__ = trainer.predict(lowercase_ , metric_key_prefix='''predict''' )
A__ = (
data_args.max_predict_samples if data_args.max_predict_samples is not None else len(lowercase_ )
)
A__ = min(lowercase_ , len(lowercase_ ) )
trainer.log_metrics('''predict''' , lowercase_ )
trainer.save_metrics('''predict''' , lowercase_ )
A__ = np.argmax(lowercase_ , axis=1 )
A__ = os.path.join(training_args.output_dir , '''predictions.txt''' )
if trainer.is_world_process_zero():
with open(lowercase_ , '''w''' ) as writer:
writer.write('''index\tprediction\n''' )
for index, item in enumerate(lowercase_ ):
A__ = label_list[item]
writer.write(f"""{index}\t{item}\n""" )
if __name__ == "__main__":
main()
| 177
| 0
|
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
a :Any = logging.get_logger(__name__)
a :Optional[Any] = "▁"
a :List[str] = {
"vocab_file": "vocab.json",
"spm_file": "sentencepiece.bpe.model",
"tokenizer_config_file": "tokenizer_config.json",
}
a :Optional[Any] = {
"vocab_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json",
},
"spm_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_config_file": {
"facebook/m2m100_418M": "https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json",
"facebook/m2m100_1.2B": "https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json",
},
}
a :Optional[int] = {
"facebook/m2m100_418M": 1_024,
}
# fmt: off
a :Tuple = {
"m2m100": ["af", "am", "ar", "ast", "az", "ba", "be", "bg", "bn", "br", "bs", "ca", "ceb", "cs", "cy", "da", "de", "el", "en", "es", "et", "fa", "ff", "fi", "fr", "fy", "ga", "gd", "gl", "gu", "ha", "he", "hi", "hr", "ht", "hu", "hy", "id", "ig", "ilo", "is", "it", "ja", "jv", "ka", "kk", "km", "kn", "ko", "lb", "lg", "ln", "lo", "lt", "lv", "mg", "mk", "ml", "mn", "mr", "ms", "my", "ne", "nl", "no", "ns", "oc", "or", "pa", "pl", "ps", "pt", "ro", "ru", "sd", "si", "sk", "sl", "so", "sq", "sr", "ss", "su", "sv", "sw", "ta", "th", "tl", "tn", "tr", "uk", "ur", "uz", "vi", "wo", "xh", "yi", "yo", "zh", "zu"],
"wmt21": ["en", "ha", "is", "ja", "cs", "ru", "zh", "de"]
}
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :Any = VOCAB_FILES_NAMES
_SCREAMING_SNAKE_CASE :Union[str, Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_SCREAMING_SNAKE_CASE :List[Any] = PRETRAINED_VOCAB_FILES_MAP
_SCREAMING_SNAKE_CASE :Optional[int] = ["""input_ids""", """attention_mask"""]
_SCREAMING_SNAKE_CASE :List[int] = []
_SCREAMING_SNAKE_CASE :List[int] = []
def __init__( self , _a , _a , _a=None , _a=None , _a="<s>" , _a="</s>" , _a="</s>" , _a="<pad>" , _a="<unk>" , _a="m2m100" , _a = None , _a=8 , **_a , ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = {} if sp_model_kwargs is None else sp_model_kwargs
SCREAMING_SNAKE_CASE__ : List[str] = language_codes
SCREAMING_SNAKE_CASE__ : Dict = FAIRSEQ_LANGUAGE_CODES[language_codes]
SCREAMING_SNAKE_CASE__ : Tuple = {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}
SCREAMING_SNAKE_CASE__ : Optional[int] = kwargs.get("""additional_special_tokens""" , [] )
kwargs["additional_special_tokens"] += [
self.get_lang_token(_a )
for lang_code in fairseq_language_code
if self.get_lang_token(_a ) not in kwargs["additional_special_tokens"]
]
super().__init__(
src_lang=_a , tgt_lang=_a , bos_token=_a , eos_token=_a , sep_token=_a , unk_token=_a , pad_token=_a , language_codes=_a , sp_model_kwargs=self.sp_model_kwargs , num_madeup_words=_a , **_a , )
SCREAMING_SNAKE_CASE__ : List[str] = vocab_file
SCREAMING_SNAKE_CASE__ : List[Any] = load_json(_a )
SCREAMING_SNAKE_CASE__ : List[Any] = {v: k for k, v in self.encoder.items()}
SCREAMING_SNAKE_CASE__ : str = spm_file
SCREAMING_SNAKE_CASE__ : List[Any] = load_spm(_a , self.sp_model_kwargs )
SCREAMING_SNAKE_CASE__ : Optional[int] = len(self.encoder )
SCREAMING_SNAKE_CASE__ : Union[str, Any] = {
self.get_lang_token(_a ): self.encoder_size + i for i, lang_code in enumerate(_a )
}
SCREAMING_SNAKE_CASE__ : List[str] = {lang_code: self.encoder_size + i for i, lang_code in enumerate(_a )}
SCREAMING_SNAKE_CASE__ : Dict = {v: k for k, v in self.lang_token_to_id.items()}
SCREAMING_SNAKE_CASE__ : List[Any] = src_lang if src_lang is not None else """en"""
SCREAMING_SNAKE_CASE__ : int = tgt_lang
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_lang_id(self._src_lang )
self.set_src_lang_special_tokens(self._src_lang )
SCREAMING_SNAKE_CASE__ : Tuple = num_madeup_words
@property
def _a ( self ) -> int:
"""simple docstring"""
return len(self.encoder ) + len(self.lang_token_to_id )
@property
def _a ( self ) -> str:
"""simple docstring"""
return self._src_lang
@src_lang.setter
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = new_src_lang
self.set_src_lang_special_tokens(self._src_lang )
def _a ( self , _a ) -> List[str]:
"""simple docstring"""
return self.sp_model.encode(_a , out_type=_a )
def _a ( self , _a ) -> Dict:
"""simple docstring"""
if token in self.lang_token_to_id:
return self.lang_token_to_id[token]
return self.encoder.get(_a , self.encoder[self.unk_token] )
def _a ( self , _a ) -> str:
"""simple docstring"""
if index in self.id_to_lang_token:
return self.id_to_lang_token[index]
return self.decoder.get(_a , self.unk_token )
def _a ( self , _a ) -> Tuple:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
SCREAMING_SNAKE_CASE__ : Any = """"""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_a ) + token
SCREAMING_SNAKE_CASE__ : Optional[Any] = []
else:
current_sub_tokens.append(_a )
out_string += self.sp_model.decode(_a )
return out_string.strip()
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 )
SCREAMING_SNAKE_CASE__ : Tuple = [1] * len(self.prefix_tokens )
SCREAMING_SNAKE_CASE__ : Tuple = [1] * len(self.suffix_tokens )
if token_ids_a is None:
return prefix_ones + ([0] * len(_a )) + suffix_ones
return prefix_ones + ([0] * len(_a )) + ([0] * len(_a )) + suffix_ones
def _a ( self , _a , _a = None ) -> List[int]:
"""simple docstring"""
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + self.suffix_tokens
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens
def _a ( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : str = {self.convert_ids_to_tokens(_a ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self ) -> Dict:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.__dict__.copy()
SCREAMING_SNAKE_CASE__ : Optional[int] = None
return state
def __setstate__( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[Any] = d
# for backward compatibility
if not hasattr(self , """sp_model_kwargs""" ):
SCREAMING_SNAKE_CASE__ : List[Any] = {}
SCREAMING_SNAKE_CASE__ : Optional[Any] = load_spm(self.spm_file , self.sp_model_kwargs )
def _a ( self , _a , _a = None ) -> Tuple[str]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = Path(_a )
if not save_dir.is_dir():
raise OSError(f'''{save_directory} should be a directory''' )
SCREAMING_SNAKE_CASE__ : Tuple = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""vocab_file"""]
)
SCREAMING_SNAKE_CASE__ : List[str] = save_dir / (
(filename_prefix + """-""" if filename_prefix else """""") + self.vocab_files_names["""spm_file"""]
)
save_json(self.encoder , _a )
if os.path.abspath(self.spm_file ) != os.path.abspath(_a ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , _a )
elif not os.path.isfile(self.spm_file ):
with open(_a , """wb""" ) as fi:
SCREAMING_SNAKE_CASE__ : int = self.sp_model.serialized_model_proto()
fi.write(_a )
return (str(_a ), str(_a ))
def _a ( self , _a , _a = "en" , _a = None , _a = "ro" , **_a , ) -> BatchEncoding:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = src_lang
SCREAMING_SNAKE_CASE__ : Optional[Any] = tgt_lang
self.set_src_lang_special_tokens(self.src_lang )
return super().prepare_seqaseq_batch(_a , _a , **_a )
def _a ( self , _a , _a , _a , **_a ) -> Union[str, Any]:
"""simple docstring"""
if src_lang is None or tgt_lang is None:
raise ValueError("""Translation requires a `src_lang` and a `tgt_lang` for this model""" )
SCREAMING_SNAKE_CASE__ : Optional[Any] = src_lang
SCREAMING_SNAKE_CASE__ : int = self(_a , add_special_tokens=_a , **_a )
SCREAMING_SNAKE_CASE__ : str = self.get_lang_id(_a )
SCREAMING_SNAKE_CASE__ : Any = tgt_lang_id
return inputs
def _a ( self ) -> Dict:
"""simple docstring"""
self.set_src_lang_special_tokens(self.src_lang )
def _a ( self ) -> List[Any]:
"""simple docstring"""
self.set_tgt_lang_special_tokens(self.tgt_lang )
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Tuple = self.get_lang_token(_a )
SCREAMING_SNAKE_CASE__ : str = self.lang_token_to_id[lang_token]
SCREAMING_SNAKE_CASE__ : Optional[int] = [self.cur_lang_id]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [self.eos_token_id]
def _a ( self , _a ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : List[Any] = self.get_lang_token(_a )
SCREAMING_SNAKE_CASE__ : Any = self.lang_token_to_id[lang_token]
SCREAMING_SNAKE_CASE__ : Dict = [self.cur_lang_id]
SCREAMING_SNAKE_CASE__ : List[Any] = [self.eos_token_id]
def _a ( self , _a ) -> str:
"""simple docstring"""
return self.lang_code_to_token[lang]
def _a ( self , _a ) -> int:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Optional[int] = self.get_lang_token(_a )
return self.lang_token_to_id[lang_token]
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> sentencepiece.SentencePieceProcessor:
SCREAMING_SNAKE_CASE__ : Dict = sentencepiece.SentencePieceProcessor(**__lowerCAmelCase )
spm.Load(str(__lowerCAmelCase ) )
return spm
def _lowercase ( __lowerCAmelCase ) -> Union[Dict, List]:
with open(__lowerCAmelCase , """r""" ) as f:
return json.load(__lowerCAmelCase )
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase ) -> None:
with open(__lowerCAmelCase , """w""" ) as f:
json.dump(__lowerCAmelCase , __lowerCAmelCase , indent=2 )
| 680
|
"""simple docstring"""
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.configuration_utils import ConfigMixin, register_to_config
from diffusers.schedulers.scheduling_utils import SchedulerMixin
from diffusers.utils import BaseOutput, deprecate
@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->DDIM
class __a (UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :torch.FloatTensor
_SCREAMING_SNAKE_CASE :Optional[torch.FloatTensor] = None
def _lowercase ( __lowerCAmelCase , __lowerCAmelCase=0.999 , __lowerCAmelCase="cosine" , ) -> Union[str, Any]:
if alpha_transform_type == "cosine":
def alpha_bar_fn(__lowerCAmelCase ):
return math.cos((t + 0.008) / 1.008 * math.pi / 2 ) ** 2
elif alpha_transform_type == "exp":
def alpha_bar_fn(__lowerCAmelCase ):
return math.exp(t * -12.0 )
else:
raise ValueError(F'''Unsupported alpha_tranform_type: {alpha_transform_type}''' )
SCREAMING_SNAKE_CASE__ : List[Any] = []
for i in range(__lowerCAmelCase ):
SCREAMING_SNAKE_CASE__ : List[str] = i / num_diffusion_timesteps
SCREAMING_SNAKE_CASE__ : int = (i + 1) / num_diffusion_timesteps
betas.append(min(1 - alpha_bar_fn(__lowerCAmelCase ) / alpha_bar_fn(__lowerCAmelCase ) , __lowerCAmelCase ) )
return torch.tensor(__lowerCAmelCase , dtype=torch.floataa )
class __a (UpperCamelCase_ , UpperCamelCase_):
'''simple docstring'''
_SCREAMING_SNAKE_CASE :List[Any] = 1
@register_to_config
def __init__( self , _a = 1_000 , _a = 0.0_001 , _a = 0.02 , _a = "linear" , _a = None , _a = True , _a = True , _a = 0 , _a = "epsilon" , _a = 1.0 , **_a , ) -> Dict:
"""simple docstring"""
if kwargs.get("""set_alpha_to_one""" , _a ) is not None:
SCREAMING_SNAKE_CASE__ : Tuple = (
"""The `set_alpha_to_one` argument is deprecated. Please use `set_alpha_to_zero` instead."""
)
deprecate("""set_alpha_to_one""" , """1.0.0""" , _a , standard_warn=_a )
SCREAMING_SNAKE_CASE__ : Tuple = kwargs["""set_alpha_to_one"""]
if trained_betas is not None:
SCREAMING_SNAKE_CASE__ : Dict = torch.tensor(_a , dtype=torch.floataa )
elif beta_schedule == "linear":
SCREAMING_SNAKE_CASE__ : Union[str, Any] = torch.linspace(_a , _a , _a , dtype=torch.floataa )
elif beta_schedule == "scaled_linear":
# this schedule is very specific to the latent diffusion model.
SCREAMING_SNAKE_CASE__ : Optional[int] = (
torch.linspace(beta_start**0.5 , beta_end**0.5 , _a , dtype=torch.floataa ) ** 2
)
elif beta_schedule == "squaredcos_cap_v2":
# Glide cosine schedule
SCREAMING_SNAKE_CASE__ : Tuple = betas_for_alpha_bar(_a )
else:
raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''' )
SCREAMING_SNAKE_CASE__ : Optional[int] = 1.0 - self.betas
SCREAMING_SNAKE_CASE__ : List[Any] = torch.cumprod(self.alphas , dim=0 )
# At every step in inverted ddim, we are looking into the next alphas_cumprod
# For the final step, there is no next alphas_cumprod, and the index is out of bounds
# `set_alpha_to_zero` decides whether we set this parameter simply to zero
# in this case, self.step() just output the predicted noise
# or whether we use the final alpha of the "non-previous" one.
SCREAMING_SNAKE_CASE__ : Any = torch.tensor(0.0 ) if set_alpha_to_zero else self.alphas_cumprod[-1]
# standard deviation of the initial noise distribution
SCREAMING_SNAKE_CASE__ : Tuple = 1.0
# setable values
SCREAMING_SNAKE_CASE__ : Dict = None
SCREAMING_SNAKE_CASE__ : List[str] = torch.from_numpy(np.arange(0 , _a ).copy().astype(np.intaa ) )
def _a ( self , _a , _a = None ) -> torch.FloatTensor:
"""simple docstring"""
return sample
def _a ( self , _a , _a = None ) -> Optional[int]:
"""simple docstring"""
if num_inference_steps > self.config.num_train_timesteps:
raise ValueError(
f'''`num_inference_steps`: {num_inference_steps} cannot be larger than `self.config.train_timesteps`:'''
f''' {self.config.num_train_timesteps} as the unet model trained with this scheduler can only handle'''
f''' maximal {self.config.num_train_timesteps} timesteps.''' )
SCREAMING_SNAKE_CASE__ : List[str] = num_inference_steps
SCREAMING_SNAKE_CASE__ : Optional[Any] = self.config.num_train_timesteps // self.num_inference_steps
# creates integer timesteps by multiplying by ratio
# casting to int to avoid issues when num_inference_step is power of 3
SCREAMING_SNAKE_CASE__ : str = (np.arange(0 , _a ) * step_ratio).round().copy().astype(np.intaa )
SCREAMING_SNAKE_CASE__ : Tuple = torch.from_numpy(_a ).to(_a )
self.timesteps += self.config.steps_offset
def _a ( self , _a , _a , _a , _a = 0.0 , _a = False , _a = None , _a = True , ) -> Union[DDIMSchedulerOutput, Tuple]:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : int = timestep + self.config.num_train_timesteps // self.num_inference_steps
# 2. compute alphas, betas
# change original implementation to exactly match noise levels for analogous forward process
SCREAMING_SNAKE_CASE__ : Optional[int] = self.alphas_cumprod[timestep]
SCREAMING_SNAKE_CASE__ : Optional[int] = (
self.alphas_cumprod[prev_timestep]
if prev_timestep < self.config.num_train_timesteps
else self.final_alpha_cumprod
)
SCREAMING_SNAKE_CASE__ : Any = 1 - alpha_prod_t
# 3. compute predicted original sample from predicted noise also called
# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
if self.config.prediction_type == "epsilon":
SCREAMING_SNAKE_CASE__ : int = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
SCREAMING_SNAKE_CASE__ : List[Any] = model_output
elif self.config.prediction_type == "sample":
SCREAMING_SNAKE_CASE__ : Dict = model_output
SCREAMING_SNAKE_CASE__ : int = (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
elif self.config.prediction_type == "v_prediction":
SCREAMING_SNAKE_CASE__ : Dict = (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
SCREAMING_SNAKE_CASE__ : str = (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
else:
raise ValueError(
f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or'''
""" `v_prediction`""" )
# 4. Clip or threshold "predicted x_0"
if self.config.clip_sample:
SCREAMING_SNAKE_CASE__ : Tuple = pred_original_sample.clamp(
-self.config.clip_sample_range , self.config.clip_sample_range )
# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
SCREAMING_SNAKE_CASE__ : Any = (1 - alpha_prod_t_prev) ** 0.5 * pred_epsilon
# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
SCREAMING_SNAKE_CASE__ : Dict = alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction
if not return_dict:
return (prev_sample, pred_original_sample)
return DDIMSchedulerOutput(prev_sample=_a , pred_original_sample=_a )
def __len__( self ) -> Dict:
"""simple docstring"""
return self.config.num_train_timesteps
| 680
| 1
|
"""simple docstring"""
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
A: int = logging.get_logger(__name__) # pylint: disable=invalid-name
def _snake_case ( UpperCamelCase : Union[List, PIL.Image.Image, torch.Tensor] ):
warnings.warn(
"""The preprocess method is deprecated and will be removed in a future version. Please"""
""" use VaeImageProcessor.preprocess instead""" , UpperCamelCase , )
if isinstance(UpperCamelCase , torch.Tensor ):
return image
elif isinstance(UpperCamelCase , PIL.Image.Image ):
UpperCAmelCase : Optional[int] = [image]
if isinstance(image[0] , PIL.Image.Image ):
UpperCAmelCase , UpperCAmelCase : Dict = image[0].size
UpperCAmelCase , UpperCAmelCase : Dict = (x - x % 8 for x in (w, h)) # resize to integer multiple of 8
UpperCAmelCase : Any = [np.array(i.resize((w, h) , resample=PIL_INTERPOLATION["""lanczos"""] ) )[None, :] for i in image]
UpperCAmelCase : Tuple = np.concatenate(UpperCamelCase , axis=0 )
UpperCAmelCase : Dict = np.array(UpperCamelCase ).astype(np.floataa ) / 255.0
UpperCAmelCase : List[Any] = image.transpose(0 , 3 , 1 , 2 )
UpperCAmelCase : Dict = 2.0 * image - 1.0
UpperCAmelCase : str = torch.from_numpy(UpperCamelCase )
elif isinstance(image[0] , torch.Tensor ):
UpperCAmelCase : Optional[int] = torch.cat(UpperCamelCase , dim=0 )
return image
def _snake_case ( UpperCamelCase : Union[List, PIL.Image.Image, torch.Tensor] ):
if isinstance(UpperCamelCase , torch.Tensor ):
return mask
elif isinstance(UpperCamelCase , PIL.Image.Image ):
UpperCAmelCase : List[str] = [mask]
if isinstance(mask[0] , PIL.Image.Image ):
UpperCAmelCase , UpperCAmelCase : int = mask[0].size
UpperCAmelCase , UpperCAmelCase : int = (x - x % 32 for x in (w, h)) # resize to integer multiple of 32
UpperCAmelCase : Tuple = [np.array(m.convert("""L""" ).resize((w, h) , resample=PIL_INTERPOLATION["""nearest"""] ) )[None, :] for m in mask]
UpperCAmelCase : List[Any] = np.concatenate(UpperCamelCase , axis=0 )
UpperCAmelCase : str = mask.astype(np.floataa ) / 255.0
UpperCAmelCase : int = 0
UpperCAmelCase : Dict = 1
UpperCAmelCase : List[str] = torch.from_numpy(UpperCamelCase )
elif isinstance(mask[0] , torch.Tensor ):
UpperCAmelCase : Dict = torch.cat(UpperCamelCase , dim=0 )
return mask
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : UNetaDModel
__lowerCAmelCase : RePaintScheduler
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> int:
'''simple docstring'''
super().__init__()
self.register_modules(unet=_SCREAMING_SNAKE_CASE , scheduler=_SCREAMING_SNAKE_CASE )
@torch.no_grad()
def __call__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = 250 , _SCREAMING_SNAKE_CASE = 0.0 , _SCREAMING_SNAKE_CASE = 10 , _SCREAMING_SNAKE_CASE = 10 , _SCREAMING_SNAKE_CASE = None , _SCREAMING_SNAKE_CASE = "pil" , _SCREAMING_SNAKE_CASE = True , ) -> Union[ImagePipelineOutput, Tuple]:
'''simple docstring'''
UpperCAmelCase : List[str] = image
UpperCAmelCase : Optional[Any] = _preprocess_image(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : List[str] = original_image.to(device=self.device , dtype=self.unet.dtype )
UpperCAmelCase : Dict = _preprocess_mask(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Optional[Any] = mask_image.to(device=self.device , dtype=self.unet.dtype )
UpperCAmelCase : int = original_image.shape[0]
# sample gaussian noise to begin the loop
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and len(_SCREAMING_SNAKE_CASE ) != batch_size:
raise ValueError(
F"You have passed a list of generators of length {len(_SCREAMING_SNAKE_CASE )}, but requested an effective batch"
F" size of {batch_size}. Make sure the batch size matches the length of the generators." )
UpperCAmelCase : Any = original_image.shape
UpperCAmelCase : Optional[Any] = randn_tensor(_SCREAMING_SNAKE_CASE , generator=_SCREAMING_SNAKE_CASE , device=self.device , dtype=self.unet.dtype )
# set step values
self.scheduler.set_timesteps(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , self.device )
UpperCAmelCase : Union[str, Any] = eta
UpperCAmelCase : str = self.scheduler.timesteps[0] + 1
UpperCAmelCase : Any = generator[0] if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) else generator
for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
if t < t_last:
# predict the noise residual
UpperCAmelCase : Optional[int] = self.unet(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).sample
# compute previous image: x_t -> x_t-1
UpperCAmelCase : Tuple = self.scheduler.step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ).prev_sample
else:
# compute the reverse: x_t-1 -> x_t
UpperCAmelCase : Union[str, Any] = self.scheduler.undo_step(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCAmelCase : Any = t
UpperCAmelCase : Dict = (image / 2 + 0.5).clamp(0 , 1 )
UpperCAmelCase : Tuple = image.cpu().permute(0 , 2 , 3 , 1 ).numpy()
if output_type == "pil":
UpperCAmelCase : Union[str, Any] = self.numpy_to_pil(_SCREAMING_SNAKE_CASE )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=_SCREAMING_SNAKE_CASE )
| 359
|
"""simple docstring"""
import json
import os
from typing import Optional
import numpy as np
from ...feature_extraction_utils import BatchFeature
from ...processing_utils import ProcessorMixin
from ...utils import logging
from ...utils.hub import get_file_from_repo
from ..auto import AutoTokenizer
A: List[Any] = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( UpperCAmelCase__ ):
__lowerCAmelCase : Optional[int] = 'AutoTokenizer'
__lowerCAmelCase : str = ['tokenizer']
__lowerCAmelCase : Any = {
'semantic_prompt': 1,
'coarse_prompt': 2,
'fine_prompt': 2,
}
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ) -> int:
'''simple docstring'''
super().__init__(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Optional[int] = speaker_embeddings
@classmethod
def SCREAMING_SNAKE_CASE ( cls , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json" , **_SCREAMING_SNAKE_CASE ) -> List[Any]:
'''simple docstring'''
if speaker_embeddings_dict_path is not None:
UpperCAmelCase : Any = get_file_from_repo(
_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , subfolder=kwargs.pop("""subfolder""" , _SCREAMING_SNAKE_CASE ) , cache_dir=kwargs.pop("""cache_dir""" , _SCREAMING_SNAKE_CASE ) , force_download=kwargs.pop("""force_download""" , _SCREAMING_SNAKE_CASE ) , proxies=kwargs.pop("""proxies""" , _SCREAMING_SNAKE_CASE ) , resume_download=kwargs.pop("""resume_download""" , _SCREAMING_SNAKE_CASE ) , local_files_only=kwargs.pop("""local_files_only""" , _SCREAMING_SNAKE_CASE ) , use_auth_token=kwargs.pop("""use_auth_token""" , _SCREAMING_SNAKE_CASE ) , revision=kwargs.pop("""revision""" , _SCREAMING_SNAKE_CASE ) , )
if speaker_embeddings_path is None:
logger.warning(
F"`{os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )}` does not exists\n , no preloaded speaker embeddings will be used - Make sure to provide a correct path to the json\n dictionnary if wanted, otherwise set `speaker_embeddings_dict_path=None`." )
UpperCAmelCase : Optional[int] = None
else:
with open(_SCREAMING_SNAKE_CASE ) as speaker_embeddings_json:
UpperCAmelCase : List[str] = json.load(_SCREAMING_SNAKE_CASE )
else:
UpperCAmelCase : List[str] = None
UpperCAmelCase : List[Any] = AutoTokenizer.from_pretrained(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
return cls(tokenizer=_SCREAMING_SNAKE_CASE , speaker_embeddings=_SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE="speaker_embeddings_path.json" , _SCREAMING_SNAKE_CASE="speaker_embeddings" , _SCREAMING_SNAKE_CASE = False , **_SCREAMING_SNAKE_CASE , ) -> Union[str, Any]:
'''simple docstring'''
if self.speaker_embeddings is not None:
os.makedirs(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , """v2""" ) , exist_ok=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : str = {}
UpperCAmelCase : Union[str, Any] = save_directory
for prompt_key in self.speaker_embeddings:
if prompt_key != "repo_or_path":
UpperCAmelCase : Optional[Any] = self._load_voice_preset(_SCREAMING_SNAKE_CASE )
UpperCAmelCase : Any = {}
for key in self.speaker_embeddings[prompt_key]:
np.save(
os.path.join(
embeddings_dict["""repo_or_path"""] , _SCREAMING_SNAKE_CASE , F"{prompt_key}_{key}" ) , voice_preset[key] , allow_pickle=_SCREAMING_SNAKE_CASE , )
UpperCAmelCase : Optional[int] = os.path.join(_SCREAMING_SNAKE_CASE , F"{prompt_key}_{key}.npy" )
UpperCAmelCase : Tuple = tmp_dict
with open(os.path.join(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) , """w""" ) as fp:
json.dump(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
super().save_pretrained(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE = None , **_SCREAMING_SNAKE_CASE ) -> Dict:
'''simple docstring'''
UpperCAmelCase : Optional[int] = self.speaker_embeddings[voice_preset]
UpperCAmelCase : List[Any] = {}
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset_paths:
raise ValueError(
F"Voice preset unrecognized, missing {key} as a key in self.speaker_embeddings[{voice_preset}]." )
UpperCAmelCase : List[str] = get_file_from_repo(
self.speaker_embeddings.get("""repo_or_path""" , """/""" ) , voice_preset_paths[key] , subfolder=kwargs.pop("""subfolder""" , _SCREAMING_SNAKE_CASE ) , cache_dir=kwargs.pop("""cache_dir""" , _SCREAMING_SNAKE_CASE ) , force_download=kwargs.pop("""force_download""" , _SCREAMING_SNAKE_CASE ) , proxies=kwargs.pop("""proxies""" , _SCREAMING_SNAKE_CASE ) , resume_download=kwargs.pop("""resume_download""" , _SCREAMING_SNAKE_CASE ) , local_files_only=kwargs.pop("""local_files_only""" , _SCREAMING_SNAKE_CASE ) , use_auth_token=kwargs.pop("""use_auth_token""" , _SCREAMING_SNAKE_CASE ) , revision=kwargs.pop("""revision""" , _SCREAMING_SNAKE_CASE ) , )
if path is None:
raise ValueError(
F"`{os.path.join(self.speaker_embeddings.get('repo_or_path' , '/' ) , voice_preset_paths[key] )}` does not exists\n , no preloaded voice preset will be used - Make sure to provide correct paths to the {voice_preset}\n embeddings." )
UpperCAmelCase : List[str] = np.load(_SCREAMING_SNAKE_CASE )
return voice_preset_dict
def SCREAMING_SNAKE_CASE ( self , _SCREAMING_SNAKE_CASE = None ) -> List[str]:
'''simple docstring'''
for key in ["semantic_prompt", "coarse_prompt", "fine_prompt"]:
if key not in voice_preset:
raise ValueError(F"Voice preset unrecognized, missing {key} as a key." )
if not isinstance(voice_preset[key] , np.ndarray ):
raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray." )
if len(voice_preset[key].shape ) != self.preset_shape[key]:
raise ValueError(F"{key} voice preset must be a {str(self.preset_shape[key] )}D ndarray." )
def __call__( self , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="pt" , _SCREAMING_SNAKE_CASE=256 , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True , _SCREAMING_SNAKE_CASE=False , **_SCREAMING_SNAKE_CASE , ) -> Optional[int]:
'''simple docstring'''
if voice_preset is not None and not isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
if (
isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
and self.speaker_embeddings is not None
and voice_preset in self.speaker_embeddings
):
UpperCAmelCase : Dict = self._load_voice_preset(_SCREAMING_SNAKE_CASE )
else:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) and not voice_preset.endswith(""".npz""" ):
UpperCAmelCase : Tuple = voice_preset + """.npz"""
UpperCAmelCase : Union[str, Any] = np.load(_SCREAMING_SNAKE_CASE )
if voice_preset is not None:
self._validate_voice_preset_dict(_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
UpperCAmelCase : List[Any] = BatchFeature(data=_SCREAMING_SNAKE_CASE , tensor_type=_SCREAMING_SNAKE_CASE )
UpperCAmelCase : List[str] = self.tokenizer(
_SCREAMING_SNAKE_CASE , return_tensors=_SCREAMING_SNAKE_CASE , padding="""max_length""" , max_length=_SCREAMING_SNAKE_CASE , return_attention_mask=_SCREAMING_SNAKE_CASE , return_token_type_ids=_SCREAMING_SNAKE_CASE , add_special_tokens=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE , )
if voice_preset is not None:
UpperCAmelCase : List[Any] = voice_preset
return encoded_text
| 359
| 1
|
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def UpperCamelCase ( lowercase_ : Any , lowercase_ : Optional[Any] , lowercase_ : Optional[int] ) -> tuple[complex, complex]:
'''simple docstring'''
if a == 0:
raise ValueError('''Coefficient \'a\' must not be zero.''' )
lowercase =b * b - 4 * a * c
lowercase =(-b + sqrt(lowercase_ )) / (2 * a)
lowercase =(-b - sqrt(lowercase_ )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def UpperCamelCase ( ) -> Optional[Any]:
'''simple docstring'''
lowercase , lowercase =quadratic_roots(a=5 , b=6 , c=1 )
print(f'The solutions are: {solutiona} and {solutiona}' )
if __name__ == "__main__":
main()
| 72
|
"""simple docstring"""
import os
import pytest
from attr import dataclass
__UpperCAmelCase ="""us-east-1""" # defaults region
@dataclass
class lowerCAmelCase__ :
lowercase__ : str
lowercase__ : List[Any] = """arn:aws:iam::558105141721:role/sagemaker_execution_role"""
lowercase__ : Union[str, Any] = {
"""task_name""": """mnli""",
"""per_device_train_batch_size""": 16,
"""per_device_eval_batch_size""": 16,
"""do_train""": True,
"""do_eval""": True,
"""do_predict""": True,
"""output_dir""": """/opt/ml/model""",
"""overwrite_output_dir""": True,
"""max_steps""": 5_00,
"""save_steps""": 55_00,
}
lowercase__ : List[str] = {**hyperparameters, """max_steps""": 10_00}
@property
def lowercase_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
{"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
]
else:
return [
{"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
{"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
{"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
]
@property
def lowercase_ ( self ):
'''simple docstring'''
return f"""{self.framework}-transfromers-test"""
@property
def lowercase_ ( self ):
'''simple docstring'''
return f"""./tests/sagemaker/scripts/{self.framework}"""
@property
def lowercase_ ( self ):
'''simple docstring'''
if self.framework == "pytorch":
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
else:
return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"
@pytest.fixture(scope="class" )
def __a ( A ) -> int:
'''simple docstring'''
A__ = SageMakerTestEnvironment(framework=request.cls.framework )
| 337
| 0
|
'''simple docstring'''
import datasets
from .evaluate import evaluate
snake_case_ : Union[str, Any] = '\\n@article{hendrycks2021cuad,\n title={CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review},\n author={Dan Hendrycks and Collin Burns and Anya Chen and Spencer Ball},\n journal={arXiv preprint arXiv:2103.06268},\n year={2021}\n}\n'
snake_case_ : Tuple = '\nThis metric wrap the official scoring script for version 1 of the Contract\nUnderstanding Atticus Dataset (CUAD).\nContract Understanding Atticus Dataset (CUAD) v1 is a corpus of more than 13,000 labels in 510\ncommercial legal contracts that have been manually labeled to identify 41 categories of important\nclauses that lawyers look for when reviewing contracts in connection with corporate transactions.\n'
snake_case_ : Any = '\nComputes CUAD scores (EM, F1, AUPR, Precision@80%Recall, and Precision@90%Recall).\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\': list of possible texts for the answer, as a list of strings\n depending on a threshold on the confidence probability of each prediction.\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 CUAD 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\n \'aupr\': Area Under the Precision-Recall curve\n \'prec_at_80_recall\': Precision at 80% recall\n \'prec_at_90_recall\': Precision at 90% recall\nExamples:\n >>> predictions = [{\'prediction_text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\'], \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> references = [{\'answers\': {\'answer_start\': [143, 49], \'text\': [\'The seller:\', \'The buyer/End-User: Shenzhen LOHAS Supply Chain Management Co., Ltd.\']}, \'id\': \'LohaCompanyltd_20191209_F-1_EX-10.16_11917878_EX-10.16_Supply Agreement__Parties\'}]\n >>> cuad_metric = datasets.load_metric("cuad")\n >>> results = cuad_metric.compute(predictions=predictions, references=references)\n >>> print(results)\n {\'exact_match\': 100.0, \'f1\': 100.0, \'aupr\': 0.0, \'prec_at_80_recall\': 1.0, \'prec_at_90_recall\': 1.0}\n'
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION, _KWARGS_DESCRIPTION )
class lowercase__ ( datasets.Metric ):
'''simple docstring'''
def UpperCAmelCase ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''predictions''': {
'''id''': datasets.Value('''string''' ),
'''prediction_text''': datasets.features.Sequence(datasets.Value('''string''' ) ),
},
'''references''': {
'''id''': datasets.Value('''string''' ),
'''answers''': datasets.features.Sequence(
{
'''text''': datasets.Value('''string''' ),
'''answer_start''': datasets.Value('''int32''' ),
} ),
},
} ) , codebase_urls=['''https://www.atticusprojectai.org/cuad'''] , reference_urls=['''https://www.atticusprojectai.org/cuad'''] , )
def UpperCAmelCase ( self , lowerCamelCase__ , lowerCamelCase__ ):
'''simple docstring'''
UpperCamelCase = {prediction['''id''']: prediction['''prediction_text'''] for prediction in predictions}
UpperCamelCase = [
{
'''paragraphs''': [
{
'''qas''': [
{
'''answers''': [{'''text''': answer_text} for answer_text in ref['''answers''']['''text''']],
'''id''': ref['''id'''],
}
for ref in references
]
}
]
}
]
UpperCamelCase = evaluate(dataset=lowerCamelCase__ , predictions=lowerCamelCase__ )
return score
| 350
|
'''simple docstring'''
def __snake_case ( _UpperCAmelCase : list[list[float]]):
UpperCamelCase = []
for data in source_data:
for i, el in enumerate(_UpperCAmelCase):
if len(_UpperCAmelCase) < i + 1:
data_lists.append([])
data_lists[i].append(float(_UpperCAmelCase))
return data_lists
def __snake_case ( _UpperCAmelCase : list[list[float]], _UpperCAmelCase : list[int]):
UpperCamelCase = []
for dlist, weight in zip(_UpperCAmelCase, _UpperCAmelCase):
UpperCamelCase = min(_UpperCAmelCase)
UpperCamelCase = max(_UpperCAmelCase)
UpperCamelCase = []
# for weight 0 score is 1 - actual score
if weight == 0:
for item in dlist:
try:
score.append(1 - ((item - mind) / (maxd - mind)))
except ZeroDivisionError:
score.append(1)
elif weight == 1:
for item in dlist:
try:
score.append((item - mind) / (maxd - mind))
except ZeroDivisionError:
score.append(0)
# weight not 0 or 1
else:
UpperCamelCase = f'Invalid weight of {weight:f} provided'
raise ValueError(_UpperCAmelCase)
score_lists.append(_UpperCAmelCase)
return score_lists
def __snake_case ( _UpperCAmelCase : list[list[float]]):
UpperCamelCase = [0 for i in range(len(score_lists[0]))]
for slist in score_lists:
for j, ele in enumerate(_UpperCAmelCase):
UpperCamelCase = final_scores[j] + ele
return final_scores
def __snake_case ( _UpperCAmelCase : list[list[float]], _UpperCAmelCase : list[int]):
UpperCamelCase = get_data(_UpperCAmelCase)
UpperCamelCase = calculate_each_score(_UpperCAmelCase, _UpperCAmelCase)
UpperCamelCase = generate_final_scores(_UpperCAmelCase)
# append scores to source data
for i, ele in enumerate(_UpperCAmelCase):
source_data[i].append(_UpperCAmelCase)
return source_data
| 350
| 1
|
'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
UpperCAmelCase_ : Union[str, Any] = '''\
'''
UpperCAmelCase_ : int = '''
Perplexity (PPL) is one of the most common metrics for evaluating language models.
It is defined as the exponentiated average negative log-likelihood of a sequence.
For more information, see https://huggingface.co/docs/transformers/perplexity
'''
UpperCAmelCase_ : str = '''
Args:
model_id (str): model used for calculating Perplexity
NOTE: Perplexity can only be calculated for causal language models.
This includes models such as gpt2, causal variations of bert,
causal versions of t5, and more (the full list can be found
in the AutoModelForCausalLM documentation here:
https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )
input_texts (list of str): input text, each separate text snippet
is one list entry.
batch_size (int): the batch size to run texts through the model. Defaults to 16.
add_start_token (bool): whether to add the start token to the texts,
so the perplexity can include the probability of the first word. Defaults to True.
device (str): device to run on, defaults to \'cuda\' when available
Returns:
perplexity: dictionary containing the perplexity scores for the texts
in the input list, as well as the mean perplexity. If one of the input texts is
longer than the max input length of the model, then it is truncated to the
max length for the perplexity computation.
Examples:
Example 1:
>>> perplexity = datasets.load_metric("perplexity")
>>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]
>>> results = perplexity.compute(model_id=\'gpt2\',
... add_start_token=False,
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
[\'perplexities\', \'mean_perplexity\']
>>> print(round(results["mean_perplexity"], 2))
78.22
>>> print(round(results["perplexities"][0], 2))
11.11
Example 2:
>>> perplexity = datasets.load_metric("perplexity")
>>> input_texts = datasets.load_dataset("wikitext",
... "wikitext-2-raw-v1",
... split="test")["text"][:50] # doctest:+ELLIPSIS
[...]
>>> input_texts = [s for s in input_texts if s!=\'\']
>>> results = perplexity.compute(model_id=\'gpt2\',
... input_texts=input_texts) # doctest:+ELLIPSIS
>>> print(list(results.keys()))
[\'perplexities\', \'mean_perplexity\']
>>> print(round(results["mean_perplexity"], 2))
60.35
>>> print(round(results["perplexities"][0], 2))
81.12
'''
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION)
class lowerCAmelCase ( datasets.Metric):
def lowerCAmelCase ( self ) -> str:
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'''input_texts''': datasets.Value('''string''' ),
} ) , reference_urls=['''https://huggingface.co/docs/transformers/perplexity'''] , )
def lowerCAmelCase ( self , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 16 , __SCREAMING_SNAKE_CASE = True , __SCREAMING_SNAKE_CASE=None ) -> Any:
'''simple docstring'''
if device is not None:
assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
if device == "gpu":
__snake_case = '''cuda'''
else:
__snake_case = '''cuda''' if torch.cuda.is_available() else '''cpu'''
__snake_case = AutoModelForCausalLM.from_pretrained(__SCREAMING_SNAKE_CASE )
__snake_case = model.to(__SCREAMING_SNAKE_CASE )
__snake_case = AutoTokenizer.from_pretrained(__SCREAMING_SNAKE_CASE )
# if batch_size > 1 (which generally leads to padding being required), and
# if there is not an already assigned pad_token, assign an existing
# special token to also be the padding token
if tokenizer.pad_token is None and batch_size > 1:
__snake_case = list(tokenizer.special_tokens_map_extended.values() )
# check that the model already has at least one special token defined
assert (
len(__SCREAMING_SNAKE_CASE ) > 0
), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
# assign one of the special tokens to also be the pad token
tokenizer.add_special_tokens({'''pad_token''': existing_special_tokens[0]} )
if add_start_token:
# leave room for <BOS> token to be added:
assert (
tokenizer.bos_token is not None
), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
__snake_case = model.config.max_length - 1
else:
__snake_case = model.config.max_length
__snake_case = tokenizer(
__SCREAMING_SNAKE_CASE , add_special_tokens=__SCREAMING_SNAKE_CASE , padding=__SCREAMING_SNAKE_CASE , truncation=__SCREAMING_SNAKE_CASE , max_length=__SCREAMING_SNAKE_CASE , return_tensors='''pt''' , return_attention_mask=__SCREAMING_SNAKE_CASE , ).to(__SCREAMING_SNAKE_CASE )
__snake_case = encodings['''input_ids''']
__snake_case = encodings['''attention_mask''']
# check that each input is long enough:
if add_start_token:
assert torch.all(torch.ge(attn_masks.sum(1 ) , 1 ) ), "Each input text must be at least one token long."
else:
assert torch.all(
torch.ge(attn_masks.sum(1 ) , 2 ) ), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."
__snake_case = []
__snake_case = CrossEntropyLoss(reduction='''none''' )
for start_index in logging.tqdm(range(0 , len(__SCREAMING_SNAKE_CASE ) , __SCREAMING_SNAKE_CASE ) ):
__snake_case = min(start_index + batch_size , len(__SCREAMING_SNAKE_CASE ) )
__snake_case = encoded_texts[start_index:end_index]
__snake_case = attn_masks[start_index:end_index]
if add_start_token:
__snake_case = torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0 ) ).to(__SCREAMING_SNAKE_CASE )
__snake_case = torch.cat([bos_tokens_tensor, encoded_batch] , dim=1 )
__snake_case = torch.cat(
[torch.ones(bos_tokens_tensor.size() , dtype=torch.intaa ).to(__SCREAMING_SNAKE_CASE ), attn_mask] , dim=1 )
__snake_case = encoded_batch
with torch.no_grad():
__snake_case = model(__SCREAMING_SNAKE_CASE , attention_mask=__SCREAMING_SNAKE_CASE ).logits
__snake_case = out_logits[..., :-1, :].contiguous()
__snake_case = labels[..., 1:].contiguous()
__snake_case = attn_mask[..., 1:].contiguous()
__snake_case = torch.expa(
(loss_fct(shift_logits.transpose(1 , 2 ) , __SCREAMING_SNAKE_CASE ) * shift_attention_mask_batch).sum(1 )
/ shift_attention_mask_batch.sum(1 ) )
ppls += perplexity_batch.tolist()
return {"perplexities": ppls, "mean_perplexity": np.mean(__SCREAMING_SNAKE_CASE )}
| 24
|
'''simple docstring'''
class UpperCAmelCase :
def __init__( self : List[str] , __snake_case : str ) -> Union[str, Any]:
_lowerCAmelCase = val
_lowerCAmelCase = None
_lowerCAmelCase = None
def lowercase__ ( self : Optional[Any] , __snake_case : int ) -> Optional[int]:
if self.val:
if val < self.val:
if self.left is None:
_lowerCAmelCase = Node(__snake_case )
else:
self.left.insert(__snake_case )
elif val > self.val:
if self.right is None:
_lowerCAmelCase = Node(__snake_case )
else:
self.right.insert(__snake_case )
else:
_lowerCAmelCase = val
def UpperCamelCase__ ( lowerCAmelCase , lowerCAmelCase ):
"""simple docstring"""
if root:
inorder(root.left , lowerCAmelCase )
res.append(root.val )
inorder(root.right , lowerCAmelCase )
def UpperCamelCase__ ( lowerCAmelCase ):
"""simple docstring"""
if len(lowerCAmelCase ) == 0:
return arr
_lowerCAmelCase = Node(arr[0] )
for i in range(1 , len(lowerCAmelCase ) ):
root.insert(arr[i] )
# Traverse BST in order.
_lowerCAmelCase = []
inorder(lowerCAmelCase , lowerCAmelCase )
return res
if __name__ == "__main__":
print(tree_sort([10, 1, 3, 2, 9, 14, 13]))
| 207
| 0
|
'''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE = """"""
__SCREAMING_SNAKE_CASE = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
__SCREAMING_SNAKE_CASE = None # compression type in fsspec. ex: "gzip"
__SCREAMING_SNAKE_CASE = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self : str , a_ : Union[str, Any] = "" , a_ : Optional[Any] = None , a_ : List[str] = None , **a_ : Optional[int] ):
"""simple docstring"""
super().__init__(self , **_lowercase )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
__snake_case = fsspec.open(
_lowercase , mode="rb" , protocol=_lowercase , compression=self.compression , client_kwargs={
"requote_redirect_url": False, # see https://github.com/huggingface/datasets/pull/5459
"trust_env": True, # Enable reading proxy env variables.
**(target_options or {}).pop("client_kwargs" , {} ), # To avoid issues if it was already passed.
} , **(target_options or {}) , )
__snake_case = os.path.basename(self.file.path.split("::" )[0] )
__snake_case = (
self.compressed_name[: self.compressed_name.rindex("." )]
if """.""" in self.compressed_name
else self.compressed_name
)
__snake_case = None
@classmethod
def A ( cls : List[str] , a_ : Tuple ):
"""simple docstring"""
return super()._strip_protocol(_lowercase ).lstrip("/" )
def A ( self : List[str] ):
"""simple docstring"""
if self.dir_cache is None:
__snake_case = {**self.file.fs.info(self.file.path ), """name""": self.uncompressed_name}
__snake_case = {f["""name"""]: f}
def A ( self : Tuple , a_ : int ):
"""simple docstring"""
return self.file.open().read()
def A ( self : List[str] , a_ : Optional[Any] , a_ : Optional[int] = "rb" , a_ : int=None , a_ : Any=True , a_ : Union[str, Any]=None , **a_ : List[Any] , ):
"""simple docstring"""
__snake_case = self._strip_protocol(_lowercase )
if mode != "rb":
raise ValueError(f'''Tried to read with mode {mode} on file {self.file.path} opened with mode \'rb\'''' )
return self.file.open()
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE = """bz2"""
__SCREAMING_SNAKE_CASE = """bz2"""
__SCREAMING_SNAKE_CASE = """.bz2"""
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE = """gzip"""
__SCREAMING_SNAKE_CASE = """gzip"""
__SCREAMING_SNAKE_CASE = """.gz"""
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE = """lz4"""
__SCREAMING_SNAKE_CASE = """lz4"""
__SCREAMING_SNAKE_CASE = """.lz4"""
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE = """xz"""
__SCREAMING_SNAKE_CASE = """xz"""
__SCREAMING_SNAKE_CASE = """.xz"""
class SCREAMING_SNAKE_CASE__ ( SCREAMING_SNAKE_CASE__ ):
__SCREAMING_SNAKE_CASE = """zstd"""
__SCREAMING_SNAKE_CASE = """zstd"""
__SCREAMING_SNAKE_CASE = """.zst"""
def __init__( self : str , a_ : Union[str, Any] , a_ : Dict = "rb" , a_ : Dict = None , a_ : List[Any] = None , a_ : str = DEFAULT_BLOCK_SIZE , **a_ : str , ):
"""simple docstring"""
super().__init__(
fo=_lowercase , mode=_lowercase , target_protocol=_lowercase , target_options=_lowercase , block_size=_lowercase , **_lowercase , )
# We need to wrap the zstd decompressor to avoid this error in fsspec==2021.7.0 and zstandard==0.15.2:
#
# File "/Users/user/.virtualenvs/hf-datasets/lib/python3.7/site-packages/fsspec/core.py", line 145, in open
# out.close = close
# AttributeError: 'zstd.ZstdDecompressionReader' object attribute 'close' is read-only
#
# see https://github.com/intake/filesystem_spec/issues/725
__snake_case = self.file.__enter__
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[Any] , a_ : List[str] ):
"""simple docstring"""
__snake_case = file_
def __enter__( self : List[Any] ):
"""simple docstring"""
self._file.__enter__()
return self
def __exit__( self : List[str] , *a_ : Union[str, Any] , **a_ : Optional[int] ):
"""simple docstring"""
self._file.__exit__(*_lowercase , **_lowercase )
def __iter__( self : Tuple ):
"""simple docstring"""
return iter(self._file )
def A ( self : str ):
"""simple docstring"""
return next(self._file )
def __getattr__( self : str , a_ : List[str] ):
"""simple docstring"""
return getattr(self._file , _lowercase )
def fixed_enter(*a_ : Union[str, Any] , **a_ : List[str] ):
return WrappedFile(_enter(*_lowercase , **_lowercase ) )
__snake_case = fixed_enter
| 721
|
'''simple docstring'''
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
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 MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import DPTImageProcessor
class SCREAMING_SNAKE_CASE__ :
def __init__( self : str , a_ : Tuple , a_ : Optional[Any]=2 , a_ : str=32 , a_ : Dict=16 , a_ : List[str]=3 , a_ : Dict=True , a_ : Optional[int]=True , a_ : List[str]=32 , a_ : int=4 , a_ : str=[0, 1, 2, 3] , a_ : Any=4 , a_ : Optional[int]=37 , a_ : Any="gelu" , a_ : Optional[int]=0.1 , a_ : Optional[Any]=0.1 , a_ : Union[str, Any]=0.02 , a_ : Union[str, Any]=3 , a_ : Any=[1, 384, 24, 24] , a_ : Optional[Any]=True , a_ : Optional[int]=None , ):
"""simple docstring"""
__snake_case = parent
__snake_case = batch_size
__snake_case = image_size
__snake_case = patch_size
__snake_case = num_channels
__snake_case = is_training
__snake_case = use_labels
__snake_case = hidden_size
__snake_case = num_hidden_layers
__snake_case = backbone_out_indices
__snake_case = num_attention_heads
__snake_case = intermediate_size
__snake_case = hidden_act
__snake_case = hidden_dropout_prob
__snake_case = attention_probs_dropout_prob
__snake_case = initializer_range
__snake_case = num_labels
__snake_case = backbone_featmap_shape
__snake_case = scope
__snake_case = is_hybrid
# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
__snake_case = (image_size // patch_size) ** 2
__snake_case = num_patches + 1
def A ( self : int ):
"""simple docstring"""
__snake_case = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
__snake_case = None
if self.use_labels:
__snake_case = ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
__snake_case = self.get_config()
return config, pixel_values, labels
def A ( self : Optional[Any] ):
"""simple docstring"""
__snake_case = {
"global_padding": "same",
"layer_type": "bottleneck",
"depths": [3, 4, 9],
"out_features": ["stage1", "stage2", "stage3"],
"embedding_dynamic_padding": True,
"hidden_sizes": [96, 192, 384, 768],
"num_groups": 2,
}
return DPTConfig(
image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , backbone_out_indices=self.backbone_out_indices , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=a_ , initializer_range=self.initializer_range , is_hybrid=self.is_hybrid , backbone_config=a_ , backbone_featmap_shape=self.backbone_featmap_shape , )
def A ( self : int , a_ : Union[str, Any] , a_ : List[str] , a_ : List[str] ):
"""simple docstring"""
__snake_case = DPTModel(config=a_ )
model.to(a_ )
model.eval()
__snake_case = model(a_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A ( self : List[Any] , a_ : List[Any] , a_ : Union[str, Any] , a_ : List[str] ):
"""simple docstring"""
__snake_case = self.num_labels
__snake_case = DPTForDepthEstimation(a_ )
model.to(a_ )
model.eval()
__snake_case = model(a_ )
self.parent.assertEqual(result.predicted_depth.shape , (self.batch_size, self.image_size, self.image_size) )
def A ( self : Optional[Any] , a_ : List[str] , a_ : int , a_ : Tuple ):
"""simple docstring"""
__snake_case = self.num_labels
__snake_case = DPTForSemanticSegmentation(a_ )
model.to(a_ )
model.eval()
__snake_case = model(a_ , labels=a_ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size, self.image_size) )
def A ( self : List[Any] ):
"""simple docstring"""
__snake_case = self.prepare_config_and_inputs()
__snake_case , __snake_case , __snake_case = config_and_inputs
__snake_case = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase , _UpperCamelCase , unittest.TestCase ):
__SCREAMING_SNAKE_CASE = (DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
__SCREAMING_SNAKE_CASE = (
{
"""depth-estimation""": DPTForDepthEstimation,
"""feature-extraction""": DPTModel,
"""image-segmentation""": DPTForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
__SCREAMING_SNAKE_CASE = False
def A ( self : Optional[Any] ):
"""simple docstring"""
__snake_case = DPTModelTester(self )
__snake_case = ConfigTester(self , config_class=a_ , has_text_modality=a_ , hidden_size=37 )
def A ( self : Optional[Any] ):
"""simple docstring"""
self.config_tester.run_common_tests()
@unittest.skip(reason="DPT does not use inputs_embeds" )
def A ( self : Any ):
"""simple docstring"""
pass
def A ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case = model_class(a_ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
__snake_case = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(a_ , nn.Linear ) )
def A ( self : List[str] ):
"""simple docstring"""
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
__snake_case = model_class(a_ )
__snake_case = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
__snake_case = [*signature.parameters.keys()]
__snake_case = ["pixel_values"]
self.assertListEqual(arg_names[:1] , a_ )
def A ( self : int ):
"""simple docstring"""
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*a_ )
def A ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_depth_estimation(*a_ )
def A ( self : Union[str, Any] ):
"""simple docstring"""
__snake_case = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*a_ )
def A ( self : Optional[int] ):
"""simple docstring"""
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case = True
if model_class in get_values(a_ ):
continue
__snake_case = model_class(a_ )
model.to(a_ )
model.train()
__snake_case = self._prepare_for_class(a_ , a_ , return_labels=a_ )
__snake_case = model(**a_ ).loss
loss.backward()
def A ( self : int ):
"""simple docstring"""
for model_class in self.all_model_classes:
if model_class.__name__ == "DPTForDepthEstimation":
continue
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case = False
__snake_case = True
if model_class in get_values(a_ ) or not model_class.supports_gradient_checkpointing:
continue
__snake_case = model_class(a_ )
model.to(a_ )
model.gradient_checkpointing_enable()
model.train()
__snake_case = self._prepare_for_class(a_ , a_ , return_labels=a_ )
__snake_case = model(**a_ ).loss
loss.backward()
def A ( self : Dict ):
"""simple docstring"""
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case = _config_zero_init(a_ )
for model_class in self.all_model_classes:
__snake_case = model_class(config=a_ )
# Skip the check for the backbone
__snake_case = []
for name, module in model.named_modules():
if module.__class__.__name__ == "DPTViTHybridEmbeddings":
__snake_case = [f'''{name}.{key}''' for key in module.state_dict().keys()]
break
for name, param in model.named_parameters():
if param.requires_grad:
if name in backbone_params:
continue
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=f'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def A ( self : Tuple ):
"""simple docstring"""
pass
@slow
def A ( self : int ):
"""simple docstring"""
for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
__snake_case = DPTModel.from_pretrained(a_ )
self.assertIsNotNone(a_ )
def A ( self : int ):
"""simple docstring"""
__snake_case , __snake_case = self.model_tester.prepare_config_and_inputs_for_common()
__snake_case = "add"
with self.assertRaises(a_ ):
__snake_case = DPTForDepthEstimation(a_ )
def __UpperCAmelCase ( ) -> Union[str, Any]:
__snake_case = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
@slow
class SCREAMING_SNAKE_CASE__ ( unittest.TestCase ):
def A ( self : Dict ):
"""simple docstring"""
__snake_case = DPTImageProcessor.from_pretrained("Intel/dpt-hybrid-midas" )
__snake_case = DPTForDepthEstimation.from_pretrained("Intel/dpt-hybrid-midas" ).to(a_ )
__snake_case = prepare_img()
__snake_case = image_processor(images=a_ , return_tensors="pt" ).to(a_ )
# forward pass
with torch.no_grad():
__snake_case = model(**a_ )
__snake_case = outputs.predicted_depth
# verify the predicted depth
__snake_case = torch.Size((1, 384, 384) )
self.assertEqual(predicted_depth.shape , a_ )
__snake_case = torch.tensor(
[[[5.6437, 5.6146, 5.6511], [5.4371, 5.5649, 5.5958], [5.5215, 5.5184, 5.5293]]] ).to(a_ )
self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 100 , a_ , atol=1e-4 ) )
| 680
| 0
|
"""simple docstring"""
def lowerCamelCase_ (UpperCamelCase__ : Any ):
stooge(UpperCamelCase__ , 0 , len(UpperCamelCase__ ) - 1 )
return arr
def lowerCamelCase_ (UpperCamelCase__ : List[Any] , UpperCamelCase__ : List[Any] , UpperCamelCase__ : Optional[int] ):
if i >= h:
return
# If first element is smaller than the last then swap them
if arr[i] > arr[h]:
_UpperCAmelCase , _UpperCAmelCase : Dict = arr[h], arr[i]
# If there are more than 2 elements in the array
if h - i + 1 > 2:
_UpperCAmelCase : List[Any] = (int)((h - i + 1) / 3 )
# Recursively sort first 2/3 elements
stooge(UpperCamelCase__ , UpperCamelCase__ , (h - t) )
# Recursively sort last 2/3 elements
stooge(UpperCamelCase__ , i + t , (UpperCamelCase__) )
# Recursively sort first 2/3 elements
stooge(UpperCamelCase__ , UpperCamelCase__ , (h - t) )
if __name__ == "__main__":
_lowerCAmelCase :Optional[Any] = input('Enter numbers separated by a comma:\n').strip()
_lowerCAmelCase :str = [int(item) for item in user_input.split(',')]
print(stooge_sort(unsorted))
| 506
|
"""simple docstring"""
import qiskit
def lowerCamelCase_ (UpperCamelCase__ : int , UpperCamelCase__ : int ):
_UpperCAmelCase : Any = qiskit.Aer.get_backend('''aer_simulator''' )
# Create a Quantum Circuit acting on the q register
_UpperCAmelCase : Tuple = qiskit.QuantumCircuit(UpperCamelCase__ , UpperCamelCase__ )
# Apply X (NOT) Gate to Qubits 0 & 1
circuit.x(0 )
circuit.x(1 )
# Map the quantum measurement to the classical bits
circuit.measure([0, 1] , [0, 1] )
# Execute the circuit on the qasm simulator
_UpperCAmelCase : Dict = qiskit.execute(UpperCamelCase__ , UpperCamelCase__ , shots=1000 )
# Return the histogram data of the results of the experiment.
return job.result().get_counts(UpperCamelCase__ )
if __name__ == "__main__":
_lowerCAmelCase :Tuple = single_qubit_measure(2, 2)
print(f"Total count for various states are: {counts}")
| 506
| 1
|
"""simple docstring"""
import numpy as np
def UpperCAmelCase ( snake_case : np.ndarray ):
return 1 / (1 + np.exp(-vector ))
def UpperCAmelCase ( snake_case : np.ndarray ):
return vector * sigmoid(snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 700
|
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
import torch
from ..models.auto.modeling_auto import MODEL_FOR_DEPTH_ESTIMATION_MAPPING
UpperCamelCase__ = logging.get_logger(__name__)
@add_end_docstrings(UpperCamelCase_ )
class a__ ( UpperCamelCase_ ):
def __init__( self : int ,*a__ : Optional[Any] ,**a__ : Union[str, Any]) -> Tuple:
"""simple docstring"""
super().__init__(*a__ ,**a__)
requires_backends(self ,'''vision''')
self.check_model_type(a__)
def __call__( self : str ,a__ : Union[str, List[str], "Image.Image", List["Image.Image"]] ,**a__ : List[str]) -> Optional[int]:
"""simple docstring"""
return super().__call__(a__ ,**a__)
def __UpperCamelCase ( self : Union[str, Any] ,**a__ : List[Any]) -> Any:
"""simple docstring"""
return {}, {}, {}
def __UpperCamelCase ( self : Tuple ,a__ : Optional[int]) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase:List[str] = load_image(a__)
_lowerCAmelCase:int = image.size
_lowerCAmelCase:int = self.image_processor(images=a__ ,return_tensors=self.framework)
return model_inputs
def __UpperCamelCase ( self : Dict ,a__ : List[str]) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase:Any = self.model(**a__)
return model_outputs
def __UpperCamelCase ( self : List[Any] ,a__ : Dict) -> Any:
"""simple docstring"""
_lowerCAmelCase:Optional[int] = model_outputs.predicted_depth
_lowerCAmelCase:Union[str, Any] = torch.nn.functional.interpolate(
predicted_depth.unsqueeze(1) ,size=self.image_size[::-1] ,mode='''bicubic''' ,align_corners=a__)
_lowerCAmelCase:List[str] = prediction.squeeze().cpu().numpy()
_lowerCAmelCase:Any = (output * 255 / np.max(a__)).astype('''uint8''')
_lowerCAmelCase:Dict = Image.fromarray(a__)
_lowerCAmelCase:Tuple = {}
_lowerCAmelCase:Optional[int] = predicted_depth
_lowerCAmelCase:str = depth
return output_dict
| 439
| 0
|
"""simple docstring"""
import os
from pathlib import Path
def __A () ->List[str]:
"""simple docstring"""
from torch.utils.cpp_extension import load
lowerCAmelCase__ :List[Any] = Path(_SCREAMING_SNAKE_CASE ).resolve().parent.parent.parent / 'kernels' / 'deformable_detr'
lowerCAmelCase__ :int = [
root / filename
for filename in [
'vision.cpp',
os.path.join('cpu' , 'ms_deform_attn_cpu.cpp' ),
os.path.join('cuda' , 'ms_deform_attn_cuda.cu' ),
]
]
load(
'MultiScaleDeformableAttention' , _SCREAMING_SNAKE_CASE , with_cuda=_SCREAMING_SNAKE_CASE , extra_include_paths=[str(_SCREAMING_SNAKE_CASE )] , extra_cflags=['-DWITH_CUDA=1'] , extra_cuda_cflags=[
'-DCUDA_HAS_FP16=1',
'-D__CUDA_NO_HALF_OPERATORS__',
'-D__CUDA_NO_HALF_CONVERSIONS__',
'-D__CUDA_NO_HALF2_OPERATORS__',
] , )
import MultiScaleDeformableAttention as MSDA
return MSDA
| 93
|
import qiskit
def lowercase ( SCREAMING_SNAKE_CASE = 2 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
SCREAMING_SNAKE_CASE_ = qubits
# Using Aer's simulator
SCREAMING_SNAKE_CASE_ = qiskit.Aer.get_backend('aer_simulator' )
# Creating a Quantum Circuit acting on the q register
SCREAMING_SNAKE_CASE_ = qiskit.QuantumCircuit(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )
# Adding a H gate on qubit 0 (now q0 in superposition)
circuit.h(0 )
for i in range(1 , SCREAMING_SNAKE_CASE ):
# Adding CX (CNOT) gate
circuit.cx(i - 1 , SCREAMING_SNAKE_CASE )
# Mapping the quantum measurement to the classical bits
circuit.measure(list(range(SCREAMING_SNAKE_CASE ) ) , list(range(SCREAMING_SNAKE_CASE ) ) )
# Now measuring any one qubit would affect other qubits to collapse
# their super position and have same state as the measured one.
# Executing the circuit on the simulator
SCREAMING_SNAKE_CASE_ = qiskit.execute(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , shots=10_00 )
return job.result().get_counts(SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(f"""Total count for various states are: {quantum_entanglement(3)}""")
| 205
| 0
|
import warnings
from ...utils import logging
from .image_processing_segformer import SegformerImageProcessor
SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
class __UpperCAmelCase ( __A ):
"""simple docstring"""
def __init__( self , *__A , **__A ):
warnings.warn(
"""The class SegformerFeatureExtractor is deprecated and will be removed in version 5 of Transformers."""
""" Please use SegformerImageProcessor instead.""" , __A , )
super().__init__(*__A , **__A )
| 209
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE = {'configuration_yolos': ['YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'YolosConfig', 'YolosOnnxConfig']}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = ['YolosFeatureExtractor']
SCREAMING_SNAKE_CASE = ['YolosImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE = [
'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
SCREAMING_SNAKE_CASE = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 209
| 1
|
"""simple docstring"""
def lowerCAmelCase ( __UpperCamelCase ):
'''simple docstring'''
UpperCAmelCase__ : List[str] = len(A__ )
UpperCAmelCase__ : Union[str, Any] = len(matrix[0] )
UpperCAmelCase__ : Optional[int] = min(A__ , A__ )
for row in range(A__ ):
# Check if diagonal element is not zero
if matrix[row][row] != 0:
# Eliminate all the elements below the diagonal
for col in range(row + 1 , A__ ):
UpperCAmelCase__ : int = matrix[col][row] / matrix[row][row]
for i in range(A__ , A__ ):
matrix[col][i] -= multiplier * matrix[row][i]
else:
# Find a non-zero diagonal element to swap rows
UpperCAmelCase__ : int = True
for i in range(row + 1 , A__ ):
if matrix[i][row] != 0:
UpperCAmelCase__ : List[Any] = matrix[i], matrix[row]
UpperCAmelCase__ : Dict = False
break
if reduce:
rank -= 1
for i in range(A__ ):
UpperCAmelCase__ : List[Any] = matrix[i][rank]
# Reduce the row pointer by one to stay on the same row
row -= 1
return rank
if __name__ == "__main__":
import doctest
doctest.testmod()
| 65
|
'''simple docstring'''
from __future__ import annotations
def UpperCamelCase_ ( A__ : list , A__ : int , A__ : int , A__ : int ):
'''simple docstring'''
lowerCAmelCase_ : int = []
lowerCAmelCase_, lowerCAmelCase_ : Any = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
lowerCAmelCase_ : Dict = result + left + right
return input_list
def UpperCamelCase_ ( A__ : list ):
'''simple docstring'''
if len(A__ ) <= 1:
return input_list
lowerCAmelCase_ : str = list(A__ )
# iteration for two-way merging
lowerCAmelCase_ : Dict = 2
while p <= len(A__ ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(A__ ) , A__ ):
lowerCAmelCase_ : Tuple = i
lowerCAmelCase_ : List[str] = i + p - 1
lowerCAmelCase_ : Optional[Any] = (low + high + 1) // 2
lowerCAmelCase_ : List[str] = merge(A__ , A__ , A__ , A__ )
# final merge of last two parts
if p * 2 >= len(A__ ):
lowerCAmelCase_ : Union[str, Any] = i
lowerCAmelCase_ : Dict = merge(A__ , 0 , A__ , len(A__ ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
__A : int = input("Enter numbers separated by a comma:\n").strip()
if user_input == "":
__A : str = []
else:
__A : Optional[Any] = [int(item.strip()) for item in user_input.split(",")]
print(iter_merge_sort(unsorted))
| 275
| 0
|
'''simple docstring'''
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
lowerCAmelCase__ = get_tests_dir() + "/test_data/fsmt/fsmt_val_data.json"
with io.open(filename, "r", encoding="utf-8") as f:
lowerCAmelCase__ = json.load(f)
@require_torch
class lowercase ( unittest.TestCase ):
def _snake_case ( self , _snake_case) -> str:
return FSMTTokenizer.from_pretrained(_snake_case)
def _snake_case ( self , _snake_case) -> Optional[int]:
UpperCAmelCase_ : Optional[Any] = FSMTForConditionalGeneration.from_pretrained(_snake_case).to(_snake_case)
if torch_device == "cuda":
model.half()
return model
@parameterized.expand(
[
['en-ru', 26.0],
['ru-en', 22.0],
['en-de', 22.0],
['de-en', 29.0],
])
@slow
def _snake_case ( self , _snake_case , _snake_case) -> Optional[int]:
# note: this test is not testing the best performance since it only evals a small batch
# but it should be enough to detect a regression in the output quality
UpperCAmelCase_ : Optional[int] = F"""facebook/wmt19-{pair}"""
UpperCAmelCase_ : Any = self.get_tokenizer(_snake_case)
UpperCAmelCase_ : Union[str, Any] = self.get_model(_snake_case)
UpperCAmelCase_ : Any = bleu_data[pair]['src']
UpperCAmelCase_ : Optional[int] = bleu_data[pair]['tgt']
UpperCAmelCase_ : Union[str, Any] = tokenizer(_snake_case , return_tensors='pt' , truncation=_snake_case , padding='longest').to(_snake_case)
UpperCAmelCase_ : Tuple = model.generate(
input_ids=batch.input_ids , num_beams=8 , )
UpperCAmelCase_ : List[Any] = tokenizer.batch_decode(
_snake_case , skip_special_tokens=_snake_case , clean_up_tokenization_spaces=_snake_case)
UpperCAmelCase_ : List[str] = calculate_bleu(_snake_case , _snake_case)
print(_snake_case)
self.assertGreaterEqual(scores['bleu'] , _snake_case)
| 471
|
'''simple docstring'''
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def SCREAMING_SNAKE_CASE( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Tuple:
# load base model
UpperCAmelCase_ : List[str] = StableDiffusionPipeline.from_pretrained(UpperCamelCase ,torch_dtype=torch.floataa )
# load LoRA weight from .safetensors
UpperCAmelCase_ : Union[str, Any] = load_file(UpperCamelCase )
UpperCAmelCase_ : Any = []
# directly update weight in diffusers model
for key in state_dict:
# it is suggested to print out the key, it usually will be something like below
# "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"
# as we have set the alpha beforehand, so just skip
if ".alpha" in key or key in visited:
continue
if "text" in key:
UpperCAmelCase_ : Optional[Any] = key.split('.' )[0].split(LORA_PREFIX_TEXT_ENCODER + '_' )[-1].split('_' )
UpperCAmelCase_ : str = pipeline.text_encoder
else:
UpperCAmelCase_ : List[str] = key.split('.' )[0].split(LORA_PREFIX_UNET + '_' )[-1].split('_' )
UpperCAmelCase_ : List[str] = pipeline.unet
# find the target layer
UpperCAmelCase_ : Dict = layer_infos.pop(0 )
while len(UpperCamelCase ) > -1:
try:
UpperCAmelCase_ : List[str] = curr_layer.__getattr__(UpperCamelCase )
if len(UpperCamelCase ) > 0:
UpperCAmelCase_ : Tuple = layer_infos.pop(0 )
elif len(UpperCamelCase ) == 0:
break
except Exception:
if len(UpperCamelCase ) > 0:
temp_name += "_" + layer_infos.pop(0 )
else:
UpperCAmelCase_ : List[Any] = layer_infos.pop(0 )
UpperCAmelCase_ : str = []
if "lora_down" in key:
pair_keys.append(key.replace('lora_down' ,'lora_up' ) )
pair_keys.append(UpperCamelCase )
else:
pair_keys.append(UpperCamelCase )
pair_keys.append(key.replace('lora_up' ,'lora_down' ) )
# update weight
if len(state_dict[pair_keys[0]].shape ) == 4:
UpperCAmelCase_ : Dict = state_dict[pair_keys[0]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
UpperCAmelCase_ : List[str] = state_dict[pair_keys[1]].squeeze(3 ).squeeze(2 ).to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(UpperCamelCase ,UpperCamelCase ).unsqueeze(2 ).unsqueeze(3 )
else:
UpperCAmelCase_ : Optional[int] = state_dict[pair_keys[0]].to(torch.floataa )
UpperCAmelCase_ : Tuple = state_dict[pair_keys[1]].to(torch.floataa )
curr_layer.weight.data += alpha * torch.mm(UpperCamelCase ,UpperCamelCase )
# update visited list
for item in pair_keys:
visited.append(UpperCamelCase )
return pipeline
if __name__ == "__main__":
lowerCAmelCase__ = argparse.ArgumentParser()
parser.add_argument(
"--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
)
parser.add_argument(
"--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
)
parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
parser.add_argument(
"--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
)
parser.add_argument(
"--lora_prefix_text_encoder",
default="lora_te",
type=str,
help="The prefix of text encoder weight in safetensors",
)
parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
parser.add_argument(
"--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
)
parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")
lowerCAmelCase__ = parser.parse_args()
lowerCAmelCase__ = args.base_model_path
lowerCAmelCase__ = args.checkpoint_path
lowerCAmelCase__ = args.dump_path
lowerCAmelCase__ = args.lora_prefix_unet
lowerCAmelCase__ = args.lora_prefix_text_encoder
lowerCAmelCase__ = args.alpha
lowerCAmelCase__ = convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)
lowerCAmelCase__ = pipe.to(args.device)
pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
| 471
| 1
|
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def lowerCAmelCase__(__snake_case ,__snake_case ) -> np.array:
'''simple docstring'''
lowerCamelCase__ = F'{sampling_rate}'
lowerCamelCase__ = '''1'''
lowerCamelCase__ = '''f32le'''
lowerCamelCase__ = [
'''ffmpeg''',
'''-i''',
'''pipe:0''',
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
try:
with subprocess.Popen(__snake_case ,stdin=subprocess.PIPE ,stdout=subprocess.PIPE ) as ffmpeg_process:
lowerCamelCase__ = ffmpeg_process.communicate(__snake_case )
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to load audio files from filename''' ) from error
lowerCamelCase__ = output_stream[0]
lowerCamelCase__ = np.frombuffer(__snake_case ,np.floataa )
if audio.shape[0] == 0:
raise ValueError('''Malformed soundfile''' )
return audio
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case = "f32le" ,) -> str:
'''simple docstring'''
lowerCamelCase__ = F'{sampling_rate}'
lowerCamelCase__ = '''1'''
if format_for_conversion == "s16le":
lowerCamelCase__ = 2
elif format_for_conversion == "f32le":
lowerCamelCase__ = 4
else:
raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )
lowerCamelCase__ = platform.system()
if system == "Linux":
lowerCamelCase__ = '''alsa'''
lowerCamelCase__ = '''default'''
elif system == "Darwin":
lowerCamelCase__ = '''avfoundation'''
lowerCamelCase__ = ''':0'''
elif system == "Windows":
lowerCamelCase__ = '''dshow'''
lowerCamelCase__ = '''default'''
lowerCamelCase__ = [
'''ffmpeg''',
'''-f''',
format_,
'''-i''',
input_,
'''-ac''',
ac,
'''-ar''',
ar,
'''-f''',
format_for_conversion,
'''-fflags''',
'''nobuffer''',
'''-hide_banner''',
'''-loglevel''',
'''quiet''',
'''pipe:1''',
]
lowerCamelCase__ = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
lowerCamelCase__ = _ffmpeg_stream(__snake_case ,__snake_case )
for item in iterator:
yield item
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case = None ,__snake_case = None ,__snake_case = "f32le" ,) -> Optional[Any]:
'''simple docstring'''
if stream_chunk_s is not None:
lowerCamelCase__ = stream_chunk_s
else:
lowerCamelCase__ = chunk_length_s
lowerCamelCase__ = ffmpeg_microphone(__snake_case ,__snake_case ,format_for_conversion=__snake_case )
if format_for_conversion == "s16le":
lowerCamelCase__ = np.intaa
lowerCamelCase__ = 2
elif format_for_conversion == "f32le":
lowerCamelCase__ = np.floataa
lowerCamelCase__ = 4
else:
raise ValueError(F'Unhandled format `{format_for_conversion}`. Please use `s16le` or `f32le`' )
if stride_length_s is None:
lowerCamelCase__ = chunk_length_s / 6
lowerCamelCase__ = int(round(sampling_rate * chunk_length_s ) ) * size_of_sample
if isinstance(__snake_case ,(int, float) ):
lowerCamelCase__ = [stride_length_s, stride_length_s]
lowerCamelCase__ = int(round(sampling_rate * stride_length_s[0] ) ) * size_of_sample
lowerCamelCase__ = int(round(sampling_rate * stride_length_s[1] ) ) * size_of_sample
lowerCamelCase__ = datetime.datetime.now()
lowerCamelCase__ = datetime.timedelta(seconds=__snake_case )
for item in chunk_bytes_iter(__snake_case ,__snake_case ,stride=(stride_left, stride_right) ,stream=__snake_case ):
# Put everything back in numpy scale
lowerCamelCase__ = np.frombuffer(item['''raw'''] ,dtype=__snake_case )
lowerCamelCase__ = (
item['''stride'''][0] // size_of_sample,
item['''stride'''][1] // size_of_sample,
)
lowerCamelCase__ = sampling_rate
audio_time += delta
if datetime.datetime.now() > audio_time + 10 * delta:
# We're late !! SKIP
continue
yield item
def lowerCAmelCase__(__snake_case ,__snake_case ,__snake_case ,__snake_case = False ) -> Dict:
'''simple docstring'''
lowerCamelCase__ = b''''''
lowerCamelCase__ , lowerCamelCase__ = stride
if stride_left + stride_right >= chunk_len:
raise ValueError(
F'Stride needs to be strictly smaller than chunk_len: ({stride_left}, {stride_right}) vs {chunk_len}' )
lowerCamelCase__ = 0
for raw in iterator:
acc += raw
if stream and len(__snake_case ) < chunk_len:
lowerCamelCase__ = (_stride_left, 0)
yield {"raw": acc[:chunk_len], "stride": stride, "partial": True}
else:
while len(__snake_case ) >= chunk_len:
# We are flushing the accumulator
lowerCamelCase__ = (_stride_left, stride_right)
lowerCamelCase__ = {'''raw''': acc[:chunk_len], '''stride''': stride}
if stream:
lowerCamelCase__ = False
yield item
lowerCamelCase__ = stride_left
lowerCamelCase__ = acc[chunk_len - stride_left - stride_right :]
# Last chunk
if len(__snake_case ) > stride_left:
lowerCamelCase__ = {'''raw''': acc, '''stride''': (_stride_left, 0)}
if stream:
lowerCamelCase__ = False
yield item
def lowerCAmelCase__(__snake_case ,__snake_case ) -> List[str]:
'''simple docstring'''
lowerCamelCase__ = 2**24 # 16Mo
try:
with subprocess.Popen(__snake_case ,stdout=subprocess.PIPE ,bufsize=__snake_case ) as ffmpeg_process:
while True:
lowerCamelCase__ = ffmpeg_process.stdout.read(__snake_case )
if raw == b"":
break
yield raw
except FileNotFoundError as error:
raise ValueError('''ffmpeg was not found but is required to stream audio files from filename''' ) from error
| 481
|
from math import factorial
_a = {str(digit): factorial(digit) for digit in range(10)}
def lowerCAmelCase__(__snake_case ) -> int:
'''simple docstring'''
if not isinstance(__snake_case ,__snake_case ):
raise TypeError('''Parameter number must be int''' )
if number < 0:
raise ValueError('''Parameter number must be greater than or equal to 0''' )
# Converts number in string to iterate on its digits and adds its factorial.
return sum(DIGIT_FACTORIAL[digit] for digit in str(__snake_case ) )
def lowerCAmelCase__(__snake_case = 60 ,__snake_case = 1000000 ) -> int:
'''simple docstring'''
if not isinstance(__snake_case ,__snake_case ) or not isinstance(__snake_case ,__snake_case ):
raise TypeError('''Parameters chain_length and number_limit must be int''' )
if chain_length <= 0 or number_limit <= 0:
raise ValueError(
'''Parameters chain_length and number_limit must be greater than 0''' )
# the counter for the chains with the exact desired length
lowerCamelCase__ = 0
# the cached sizes of the previous chains
lowerCamelCase__ = {}
for start_chain_element in range(1 ,__snake_case ):
# The temporary set will contain the elements of the chain
lowerCamelCase__ = set()
lowerCamelCase__ = 0
# Stop computing the chain when you find a cached size, a repeating item or the
# length is greater then the desired one.
lowerCamelCase__ = start_chain_element
while (
chain_element not in chain_sets_lengths
and chain_element not in chain_set
and chain_set_length <= chain_length
):
chain_set.add(__snake_case )
chain_set_length += 1
lowerCamelCase__ = digit_factorial_sum(__snake_case )
if chain_element in chain_sets_lengths:
chain_set_length += chain_sets_lengths[chain_element]
lowerCamelCase__ = chain_set_length
# If chain contains the exact amount of elements increase the counter
if chain_set_length == chain_length:
chains_counter += 1
return chains_counter
if __name__ == "__main__":
import doctest
doctest.testmod()
print(f"""{solution()}""")
| 481
| 1
|
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.mark.parametrize('revision' , [None, 'v2'] )
def A ( snake_case__ : Optional[int] , snake_case__ : List[str] , snake_case__ : Any ) -> Optional[int]:
'''simple docstring'''
__snake_case = hf_hub_url(repo_id=snake_case__ , path=snake_case__ , revision=snake_case__ )
assert url == f"https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(snake_case__ )}"
| 676
|
from urllib.parse import quote
import pytest
from datasets.utils.hub import hf_hub_url
@pytest.mark.parametrize('repo_id' , ['canonical_dataset_name', 'org-name/dataset-name'] )
@pytest.mark.parametrize('path' , ['filename.csv', 'filename with blanks.csv'] )
@pytest.mark.parametrize('revision' , [None, 'v2'] )
def A ( snake_case__ : Optional[int] , snake_case__ : List[str] , snake_case__ : Any ) -> Optional[int]:
'''simple docstring'''
__snake_case = hf_hub_url(repo_id=snake_case__ , path=snake_case__ , revision=snake_case__ )
assert url == f"https://huggingface.co/datasets/{repo_id}/resolve/{revision or 'main'}/{quote(snake_case__ )}"
| 676
| 1
|
"""simple docstring"""
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCamelCase ( a ):
"""simple docstring"""
_lowerCamelCase : Dict =["image_processor", "tokenizer"]
_lowerCamelCase : int ="ViltImageProcessor"
_lowerCamelCase : int =("BertTokenizer", "BertTokenizerFast")
def __init__( self : str , _lowerCamelCase : Optional[int]=None , _lowerCamelCase : List[Any]=None , **_lowerCamelCase : int ):
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.''' , _lowerCamelCase , )
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`.''' )
super().__init__(_lowerCamelCase , _lowerCamelCase )
A__ = self.image_processor
def __call__( self : Dict , _lowerCamelCase : str , _lowerCamelCase : Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None , _lowerCamelCase : bool = True , _lowerCamelCase : Union[bool, str, PaddingStrategy] = False , _lowerCamelCase : Union[bool, str, TruncationStrategy] = None , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : int = 0 , _lowerCamelCase : Optional[int] = None , _lowerCamelCase : Optional[bool] = None , _lowerCamelCase : Optional[bool] = None , _lowerCamelCase : bool = False , _lowerCamelCase : bool = False , _lowerCamelCase : bool = False , _lowerCamelCase : bool = False , _lowerCamelCase : bool = True , _lowerCamelCase : Optional[Union[str, TensorType]] = None , **_lowerCamelCase : List[str] , ):
A__ = self.tokenizer(
text=_lowerCamelCase , add_special_tokens=_lowerCamelCase , padding=_lowerCamelCase , truncation=_lowerCamelCase , max_length=_lowerCamelCase , stride=_lowerCamelCase , pad_to_multiple_of=_lowerCamelCase , return_token_type_ids=_lowerCamelCase , return_attention_mask=_lowerCamelCase , return_overflowing_tokens=_lowerCamelCase , return_special_tokens_mask=_lowerCamelCase , return_offsets_mapping=_lowerCamelCase , return_length=_lowerCamelCase , verbose=_lowerCamelCase , return_tensors=_lowerCamelCase , **_lowerCamelCase , )
# add pixel_values + pixel_mask
A__ = self.image_processor(_lowerCamelCase , return_tensors=_lowerCamelCase )
encoding.update(_lowerCamelCase )
return encoding
def A__ ( self : Tuple , *_lowerCamelCase : Tuple , **_lowerCamelCase : Optional[Any] ):
return self.tokenizer.batch_decode(*_lowerCamelCase , **_lowerCamelCase )
def A__ ( self : List[Any] , *_lowerCamelCase : int , **_lowerCamelCase : int ):
return self.tokenizer.decode(*_lowerCamelCase , **_lowerCamelCase )
@property
def A__ ( self : Optional[Any] ):
A__ = self.tokenizer.model_input_names
A__ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def A__ ( self : int ):
warnings.warn(
'''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.''' , _lowerCamelCase , )
return self.image_processor_class
@property
def A__ ( self : Any ):
warnings.warn(
'''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.''' , _lowerCamelCase , )
return self.image_processor
| 571
|
"""simple docstring"""
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__snake_case : Optional[int] = logging.get_logger(__name__)
__snake_case : Any = '▁'
__snake_case : Any = {
'vocab_file': 'vocab.json',
'spm_file': 'sentencepiece.bpe.model',
}
__snake_case : Union[str, Any] = {
'vocab_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/vocab.json'
),
},
'spm_file': {
'facebook/s2t-small-librispeech-asr': (
'https://huggingface.co/facebook/s2t-small-librispeech-asr/resolve/main/sentencepiece.bpe.model'
)
},
}
__snake_case : Optional[Any] = {
'facebook/s2t-small-librispeech-asr': 1_024,
}
__snake_case : Tuple = ['pt', 'fr', 'ru', 'nl', 'ro', 'it', 'es', 'de']
__snake_case : Optional[Any] = {'mustc': MUSTC_LANGS}
class UpperCamelCase ( a ):
"""simple docstring"""
_lowerCamelCase : int =VOCAB_FILES_NAMES
_lowerCamelCase : Any =PRETRAINED_VOCAB_FILES_MAP
_lowerCamelCase : Any =MAX_MODEL_INPUT_SIZES
_lowerCamelCase : List[str] =["input_ids", "attention_mask"]
_lowerCamelCase : List[int] =[]
def __init__( self : str , _lowerCamelCase : Optional[int] , _lowerCamelCase : str , _lowerCamelCase : Optional[int]="<s>" , _lowerCamelCase : Optional[int]="</s>" , _lowerCamelCase : List[Any]="<pad>" , _lowerCamelCase : Dict="<unk>" , _lowerCamelCase : int=False , _lowerCamelCase : Any=False , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Optional[Any]=None , _lowerCamelCase : Optional[Dict[str, Any]] = None , **_lowerCamelCase : str , ):
A__ = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , pad_token=_lowerCamelCase , do_upper_case=_lowerCamelCase , do_lower_case=_lowerCamelCase , tgt_lang=_lowerCamelCase , lang_codes=_lowerCamelCase , sp_model_kwargs=self.sp_model_kwargs , **_lowerCamelCase , )
A__ = do_upper_case
A__ = do_lower_case
A__ = load_json(_lowerCamelCase )
A__ = {v: k for k, v in self.encoder.items()}
A__ = spm_file
A__ = load_spm(_lowerCamelCase , self.sp_model_kwargs )
if lang_codes is not None:
A__ = lang_codes
A__ = LANGUAGES[lang_codes]
A__ = [F'''<lang:{lang}>''' for lang in self.langs]
A__ = {lang: self.sp_model.PieceToId(F'''<lang:{lang}>''' ) for lang in self.langs}
A__ = self.lang_tokens
A__ = tgt_lang if tgt_lang is not None else self.langs[0]
self.set_tgt_lang_special_tokens(self._tgt_lang )
else:
A__ = {}
@property
def A__ ( self : Any ):
return len(self.encoder )
@property
def A__ ( self : Dict ):
return self._tgt_lang
@tgt_lang.setter
def A__ ( self : Any , _lowerCamelCase : Optional[Any] ):
A__ = new_tgt_lang
self.set_tgt_lang_special_tokens(_lowerCamelCase )
def A__ ( self : Any , _lowerCamelCase : str ):
A__ = self.lang_code_to_id[tgt_lang]
A__ = [lang_code_id]
def A__ ( self : Dict , _lowerCamelCase : str ):
return self.sp_model.encode(_lowerCamelCase , out_type=_lowerCamelCase )
def A__ ( self : List[str] , _lowerCamelCase : List[str] ):
return self.encoder.get(_lowerCamelCase , self.encoder[self.unk_token] )
def A__ ( self : List[Any] , _lowerCamelCase : int ):
return self.decoder.get(_lowerCamelCase , self.unk_token )
def A__ ( self : int , _lowerCamelCase : List[str] ):
A__ = []
A__ = ''''''
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
A__ = self.sp_model.decode(_lowerCamelCase )
out_string += (decoded.upper() if self.do_upper_case else decoded) + token + " "
A__ = []
else:
current_sub_tokens.append(_lowerCamelCase )
A__ = self.sp_model.decode(_lowerCamelCase )
out_string += decoded.upper() if self.do_upper_case else decoded
return out_string.strip()
def A__ ( self : List[str] , _lowerCamelCase : List[Any] , _lowerCamelCase : List[Any]=None ):
if token_ids_a is None:
return self.prefix_tokens + token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return self.prefix_tokens + token_ids_a + token_ids_a + [self.eos_token_id]
def A__ ( self : Optional[int] , _lowerCamelCase : List[int] , _lowerCamelCase : Optional[List[int]] = None , _lowerCamelCase : bool = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=_lowerCamelCase , token_ids_a=_lowerCamelCase , already_has_special_tokens=_lowerCamelCase )
A__ = [1] * len(self.prefix_tokens )
A__ = [1]
if token_ids_a is None:
return prefix_ones + ([0] * len(_lowerCamelCase )) + suffix_ones
return prefix_ones + ([0] * len(_lowerCamelCase )) + ([0] * len(_lowerCamelCase )) + suffix_ones
def A__ ( self : List[str] ):
A__ = self.encoder.copy()
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__( self : List[str] ):
A__ = self.__dict__.copy()
A__ = None
return state
def __setstate__( self : Tuple , _lowerCamelCase : Dict ):
A__ = d
# for backward compatibility
if not hasattr(self , '''sp_model_kwargs''' ):
A__ = {}
A__ = load_spm(self.spm_file , self.sp_model_kwargs )
def A__ ( self : str , _lowerCamelCase : str , _lowerCamelCase : Optional[str] = None ):
A__ = Path(_lowerCamelCase )
assert save_dir.is_dir(), F'''{save_directory} should be a directory'''
A__ = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''vocab_file''']
)
A__ = save_dir / (
(filename_prefix + '''-''' if filename_prefix else '''''') + self.vocab_files_names['''spm_file''']
)
save_json(self.encoder , _lowerCamelCase )
if os.path.abspath(self.spm_file ) != os.path.abspath(_lowerCamelCase ) and os.path.isfile(self.spm_file ):
copyfile(self.spm_file , _lowerCamelCase )
elif not os.path.isfile(self.spm_file ):
with open(_lowerCamelCase , '''wb''' ) as fi:
A__ = self.sp_model.serialized_model_proto()
fi.write(_lowerCamelCase )
return (str(_lowerCamelCase ), str(_lowerCamelCase ))
def a_ ( __a , __a ):
A__ = sentencepiece.SentencePieceProcessor(**__a )
spm.Load(str(__a ) )
return spm
def a_ ( __a ):
with open(__a , '''r''' ) as f:
return json.load(__a )
def a_ ( __a , __a ):
with open(__a , '''w''' ) as f:
json.dump(__a , __a , indent=2 )
| 571
| 1
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from torch.backends.cuda import sdp_kernel
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class UpperCAmelCase_ ( lowercase__, unittest.TestCase ):
snake_case_ = ConsistencyModelPipeline
snake_case_ = UNCONDITIONAL_IMAGE_GENERATION_PARAMS
snake_case_ = UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS
# Override required_optional_params to remove num_images_per_prompt
snake_case_ = frozenset(
[
"""num_inference_steps""",
"""generator""",
"""latents""",
"""output_type""",
"""return_dict""",
"""callback""",
"""callback_steps""",
] )
@property
def _lowerCamelCase ( self : Tuple ) -> List[Any]:
_lowercase = UNetaDModel.from_pretrained(
"diffusers/consistency-models-test" , subfolder="test_unet" , )
return unet
@property
def _lowerCamelCase ( self : Union[str, Any] ) -> List[Any]:
_lowercase = UNetaDModel.from_pretrained(
"diffusers/consistency-models-test" , subfolder="test_unet_class_cond" , )
return unet
def _lowerCamelCase ( self : int , _lowercase : List[Any]=False ) -> Tuple:
if class_cond:
_lowercase = self.dummy_cond_unet
else:
_lowercase = self.dummy_uncond_unet
# Default to CM multistep sampler
_lowercase = CMStochasticIterativeScheduler(
num_train_timesteps=4_0 , sigma_min=0.0_02 , sigma_max=80.0 , )
_lowercase = {
"unet": unet,
"scheduler": scheduler,
}
return components
def _lowerCamelCase ( self : Optional[Any] , _lowercase : Dict , _lowercase : Optional[Any]=0 ) -> Any:
if str(_lowercase ).startswith("mps" ):
_lowercase = torch.manual_seed(_lowercase )
else:
_lowercase = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
_lowercase = {
"batch_size": 1,
"num_inference_steps": None,
"timesteps": [2_2, 0],
"generator": generator,
"output_type": "np",
}
return inputs
def _lowerCamelCase ( self : Optional[Any] ) -> Any:
_lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowercase = self.get_dummy_components()
_lowercase = ConsistencyModelPipeline(**_lowercase )
_lowercase = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
_lowercase = self.get_dummy_inputs(_lowercase )
_lowercase = pipe(**_lowercase ).images
assert image.shape == (1, 3_2, 3_2, 3)
_lowercase = image[0, -3:, -3:, -1]
_lowercase = np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowerCamelCase ( self : Tuple ) -> List[str]:
_lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowercase = self.get_dummy_components(class_cond=_lowercase )
_lowercase = ConsistencyModelPipeline(**_lowercase )
_lowercase = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
_lowercase = self.get_dummy_inputs(_lowercase )
_lowercase = 0
_lowercase = pipe(**_lowercase ).images
assert image.shape == (1, 3_2, 3_2, 3)
_lowercase = image[0, -3:, -3:, -1]
_lowercase = np.array([0.35_72, 0.62_73, 0.40_31, 0.39_61, 0.43_21, 0.57_30, 0.52_66, 0.47_80, 0.50_04] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowerCamelCase ( self : str ) -> Tuple:
_lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowercase = self.get_dummy_components()
_lowercase = ConsistencyModelPipeline(**_lowercase )
_lowercase = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
_lowercase = self.get_dummy_inputs(_lowercase )
_lowercase = 1
_lowercase = None
_lowercase = pipe(**_lowercase ).images
assert image.shape == (1, 3_2, 3_2, 3)
_lowercase = image[0, -3:, -3:, -1]
_lowercase = np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def _lowerCamelCase ( self : Union[str, Any] ) -> List[Any]:
_lowercase = "cpu" # ensure determinism for the device-dependent torch.Generator
_lowercase = self.get_dummy_components(class_cond=_lowercase )
_lowercase = ConsistencyModelPipeline(**_lowercase )
_lowercase = pipe.to(_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
_lowercase = self.get_dummy_inputs(_lowercase )
_lowercase = 1
_lowercase = None
_lowercase = 0
_lowercase = pipe(**_lowercase ).images
assert image.shape == (1, 3_2, 3_2, 3)
_lowercase = image[0, -3:, -3:, -1]
_lowercase = np.array([0.50_04, 0.50_04, 0.49_94, 0.50_08, 0.49_76, 0.50_18, 0.49_90, 0.49_82, 0.49_87] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@slow
@require_torch_gpu
class UpperCAmelCase_ ( unittest.TestCase ):
def _lowerCamelCase ( self : List[Any] ) -> Optional[int]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def _lowerCamelCase ( self : Tuple , _lowercase : Optional[int]=0 , _lowercase : Tuple=False , _lowercase : List[str]="cpu" , _lowercase : Optional[int]=torch.floataa , _lowercase : Union[str, Any]=(1, 3, 6_4, 6_4) ) -> str:
_lowercase = torch.manual_seed(_lowercase )
_lowercase = {
"num_inference_steps": None,
"timesteps": [2_2, 0],
"class_labels": 0,
"generator": generator,
"output_type": "np",
}
if get_fixed_latents:
_lowercase = self.get_fixed_latents(seed=_lowercase , device=_lowercase , dtype=_lowercase , shape=_lowercase )
_lowercase = latents
return inputs
def _lowerCamelCase ( self : List[Any] , _lowercase : Optional[int]=0 , _lowercase : str="cpu" , _lowercase : Union[str, Any]=torch.floataa , _lowercase : Any=(1, 3, 6_4, 6_4) ) -> Optional[Any]:
if type(_lowercase ) == str:
_lowercase = torch.device(_lowercase )
_lowercase = torch.Generator(device=_lowercase ).manual_seed(_lowercase )
_lowercase = randn_tensor(_lowercase , generator=_lowercase , device=_lowercase , dtype=_lowercase )
return latents
def _lowerCamelCase ( self : Optional[int] ) -> Dict:
_lowercase = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
_lowercase = CMStochasticIterativeScheduler(
num_train_timesteps=4_0 , sigma_min=0.0_02 , sigma_max=80.0 , )
_lowercase = ConsistencyModelPipeline(unet=_lowercase , scheduler=_lowercase )
pipe.to(torch_device=_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
_lowercase = self.get_inputs()
_lowercase = pipe(**_lowercase ).images
assert image.shape == (1, 6_4, 6_4, 3)
_lowercase = image[0, -3:, -3:, -1]
_lowercase = np.array([0.08_88, 0.08_81, 0.06_66, 0.04_79, 0.02_92, 0.01_95, 0.02_01, 0.01_63, 0.02_54] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
def _lowerCamelCase ( self : Any ) -> Optional[int]:
_lowercase = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
_lowercase = CMStochasticIterativeScheduler(
num_train_timesteps=4_0 , sigma_min=0.0_02 , sigma_max=80.0 , )
_lowercase = ConsistencyModelPipeline(unet=_lowercase , scheduler=_lowercase )
pipe.to(torch_device=_lowercase )
pipe.set_progress_bar_config(disable=_lowercase )
_lowercase = self.get_inputs()
_lowercase = 1
_lowercase = None
_lowercase = pipe(**_lowercase ).images
assert image.shape == (1, 6_4, 6_4, 3)
_lowercase = image[0, -3:, -3:, -1]
_lowercase = np.array([0.03_40, 0.01_52, 0.00_63, 0.02_67, 0.02_21, 0.01_07, 0.04_16, 0.01_86, 0.02_17] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2
@require_torch_a
def _lowerCamelCase ( self : str ) -> str:
_lowercase = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
_lowercase = CMStochasticIterativeScheduler(
num_train_timesteps=4_0 , sigma_min=0.0_02 , sigma_max=80.0 , )
_lowercase = ConsistencyModelPipeline(unet=_lowercase , scheduler=_lowercase )
pipe.to(torch_device=_lowercase , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=_lowercase )
_lowercase = self.get_inputs(get_fixed_latents=_lowercase , device=_lowercase )
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=_lowercase , enable_math=_lowercase , enable_mem_efficient=_lowercase ):
_lowercase = pipe(**_lowercase ).images
assert image.shape == (1, 6_4, 6_4, 3)
_lowercase = image[0, -3:, -3:, -1]
_lowercase = np.array([0.18_75, 0.14_28, 0.12_89, 0.21_51, 0.20_92, 0.14_77, 0.18_77, 0.16_41, 0.13_53] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
@require_torch_a
def _lowerCamelCase ( self : Dict ) -> Optional[int]:
_lowercase = UNetaDModel.from_pretrained("diffusers/consistency_models" , subfolder="diffusers_cd_imagenet64_l2" )
_lowercase = CMStochasticIterativeScheduler(
num_train_timesteps=4_0 , sigma_min=0.0_02 , sigma_max=80.0 , )
_lowercase = ConsistencyModelPipeline(unet=_lowercase , scheduler=_lowercase )
pipe.to(torch_device=_lowercase , torch_dtype=torch.floataa )
pipe.set_progress_bar_config(disable=_lowercase )
_lowercase = self.get_inputs(get_fixed_latents=_lowercase , device=_lowercase )
_lowercase = 1
_lowercase = None
# Ensure usage of flash attention in torch 2.0
with sdp_kernel(enable_flash=_lowercase , enable_math=_lowercase , enable_mem_efficient=_lowercase ):
_lowercase = pipe(**_lowercase ).images
assert image.shape == (1, 6_4, 6_4, 3)
_lowercase = image[0, -3:, -3:, -1]
_lowercase = np.array([0.16_63, 0.19_48, 0.22_75, 0.16_80, 0.12_04, 0.12_45, 0.18_58, 0.13_38, 0.20_95] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
| 227
|
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler
__UpperCamelCase : Union[str, Any] = 1_6
__UpperCamelCase : Optional[int] = 3_2
def __UpperCAmelCase ( _snake_case : Accelerator, _snake_case : int = 1_6, _snake_case : str = "bert-base-cased" ):
_lowercase = AutoTokenizer.from_pretrained(_snake_case )
_lowercase = load_dataset("glue", "mrpc" )
def tokenize_function(_snake_case : List[str] ):
# max_length=None => use the model max length (it's actually the default)
_lowercase = tokenizer(examples["sentence1"], examples["sentence2"], truncation=_snake_case, max_length=_snake_case )
return outputs
# Apply the method we just defined to all the examples in all the splits of the dataset
_lowercase = datasets.map(
_snake_case, batched=_snake_case, remove_columns=["idx", "sentence1", "sentence2"], load_from_cache_file=_snake_case )
# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
# transformers library
_lowercase = tokenized_datasets.rename_column("label", "labels" )
def collate_fn(_snake_case : Tuple ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(_snake_case, padding="max_length", max_length=1_2_8, return_tensors="pt" )
return tokenizer.pad(_snake_case, padding="longest", return_tensors="pt" )
# Instantiate dataloaders.
_lowercase = DataLoader(
tokenized_datasets["train"], shuffle=_snake_case, collate_fn=_snake_case, batch_size=_snake_case )
_lowercase = DataLoader(
tokenized_datasets["validation"], shuffle=_snake_case, collate_fn=_snake_case, batch_size=_snake_case )
return train_dataloader, eval_dataloader
def __UpperCAmelCase ( _snake_case : Union[str, Any], _snake_case : Tuple ):
# Initialize accelerator
_lowercase = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
_lowercase = config["lr"]
_lowercase = int(config["num_epochs"] )
_lowercase = int(config["seed"] )
_lowercase = int(config["batch_size"] )
_lowercase = args.model_name_or_path
set_seed(_snake_case )
_lowercase , _lowercase = get_dataloaders(_snake_case, _snake_case, _snake_case )
# Instantiate the model (we build the model here so that the seed also control new weights initialization)
_lowercase = AutoModelForSequenceClassification.from_pretrained(_snake_case, return_dict=_snake_case )
# Instantiate optimizer
_lowercase = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
_lowercase = optimizer_cls(params=model.parameters(), lr=_snake_case )
if accelerator.state.deepspeed_plugin is not None:
_lowercase = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
_lowercase = 1
_lowercase = (len(_snake_case ) * num_epochs) // gradient_accumulation_steps
# Instantiate scheduler
if (
accelerator.state.deepspeed_plugin is None
or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
):
_lowercase = get_linear_schedule_with_warmup(
optimizer=_snake_case, num_warmup_steps=0, num_training_steps=_snake_case, )
else:
_lowercase = DummyScheduler(_snake_case, total_num_steps=_snake_case, warmup_num_steps=0 )
# Prepare everything
# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
# prepare method.
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase = accelerator.prepare(
_snake_case, _snake_case, _snake_case, _snake_case, _snake_case )
# We need to keep track of how many total steps we have iterated over
_lowercase = 0
# We also need to keep track of the stating epoch so files are named properly
_lowercase = 0
# Now we train the model
_lowercase = evaluate.load("glue", "mrpc" )
_lowercase = 0
_lowercase = {}
for epoch in range(_snake_case, _snake_case ):
model.train()
for step, batch in enumerate(_snake_case ):
_lowercase = model(**_snake_case )
_lowercase = outputs.loss
_lowercase = loss / gradient_accumulation_steps
accelerator.backward(_snake_case )
if step % gradient_accumulation_steps == 0:
optimizer.step()
lr_scheduler.step()
optimizer.zero_grad()
overall_step += 1
model.eval()
_lowercase = 0
for step, batch in enumerate(_snake_case ):
# We could avoid this line since we set the accelerator with `device_placement=True`.
batch.to(accelerator.device )
with torch.no_grad():
_lowercase = model(**_snake_case )
_lowercase = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
_lowercase , _lowercase = accelerator.gather(
(predictions, batch["labels"]) ) # If we are in a multiprocess environment, the last batch has duplicates
if accelerator.use_distributed:
if step == len(_snake_case ) - 1:
_lowercase = predictions[: len(eval_dataloader.dataset ) - samples_seen]
_lowercase = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=_snake_case, references=_snake_case, )
_lowercase = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(f"""epoch {epoch}:""", _snake_case )
_lowercase = eval_metric["accuracy"]
if best_performance < eval_metric["accuracy"]:
_lowercase = eval_metric["accuracy"]
if args.performance_lower_bound is not None:
assert (
args.performance_lower_bound <= best_performance
), f"""Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}"""
accelerator.wait_for_everyone()
if accelerator.is_main_process:
with open(os.path.join(args.output_dir, "all_results.json" ), "w" ) as f:
json.dump(_snake_case, _snake_case )
def __UpperCAmelCase ( ):
_lowercase = argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage." )
parser.add_argument(
"--model_name_or_path", type=_snake_case, default="bert-base-cased", help="Path to pretrained model or model identifier from huggingface.co/models.", required=_snake_case, )
parser.add_argument(
"--output_dir", type=_snake_case, default=".", help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory.", )
parser.add_argument(
"--performance_lower_bound", type=_snake_case, default=_snake_case, help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value.", )
parser.add_argument(
"--num_epochs", type=_snake_case, default=3, help="Number of train epochs.", )
_lowercase = parser.parse_args()
_lowercase = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
training_function(_snake_case, _snake_case )
if __name__ == "__main__":
main()
| 227
| 1
|
'''simple docstring'''
import math
def UpperCAmelCase ( A : int ):
SCREAMING_SNAKE_CASE : Tuple = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(A )
def UpperCAmelCase ( A : float = 1 / 12345 ):
SCREAMING_SNAKE_CASE : List[Any] = 0
SCREAMING_SNAKE_CASE : Union[str, Any] = 0
SCREAMING_SNAKE_CASE : str = 3
while True:
SCREAMING_SNAKE_CASE : List[str] = (integer**2 - 1) / 4
# if candidate is an integer, then there is a partition for k
if partition_candidate == int(A ):
SCREAMING_SNAKE_CASE : List[Any] = int(A )
total_partitions += 1
if check_partition_perfect(A ):
perfect_partitions += 1
if perfect_partitions > 0:
if perfect_partitions / total_partitions < max_proportion:
return int(A )
integer += 1
if __name__ == "__main__":
print(f'{solution() = }')
| 527
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
lowerCAmelCase_ : Dict = {
'configuration_encodec': [
'ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP',
'EncodecConfig',
],
'feature_extraction_encodec': ['EncodecFeatureExtractor'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
lowerCAmelCase_ : List[Any] = [
'ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST',
'EncodecModel',
'EncodecPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_encodec import (
ENCODEC_PRETRAINED_CONFIG_ARCHIVE_MAP,
EncodecConfig,
)
from .feature_extraction_encodec import EncodecFeatureExtractor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_encodec import (
ENCODEC_PRETRAINED_MODEL_ARCHIVE_LIST,
EncodecModel,
EncodecPreTrainedModel,
)
else:
import sys
lowerCAmelCase_ : List[str] = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 527
| 1
|
"""simple docstring"""
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_tf
if is_tf_available():
import tensorflow as tf
from tensorflow.python.eager import context
from tensorflow.python.framework import ops
from transformers import GradientAccumulator, create_optimizer
@require_tf
class __magic_name__ ( unittest.TestCase ):
def lowerCAmelCase ( self : Union[str, Any] , snake_case_ : Any , snake_case_ : Dict , snake_case_ : str ):
self.assertEqual(len(snake_case_ ) , len(snake_case_ ) )
for a, b in zip(snake_case_ , snake_case_ ):
self.assertAlmostEqual(snake_case_ , snake_case_ , delta=snake_case_ )
def lowerCAmelCase ( self : Optional[int] ):
__snake_case = GradientAccumulator()
accumulator([tf.constant([1.0, 2.0] )] )
accumulator([tf.constant([-2.0, 1.0] )] )
accumulator([tf.constant([-1.0, 2.0] )] )
with self.assertRaises(snake_case_ ):
accumulator([tf.constant([1.0, 1.0] ), tf.constant([2.0, 2.0] )] )
self.assertEqual(accumulator.step , 3 )
self.assertEqual(len(accumulator.gradients ) , 1 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [-2.0, 5.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist() , [0.0, 0.0] , tol=1e-2 )
def lowerCAmelCase ( self : Dict ):
__snake_case = None
ops.enable_eager_execution_internal()
__snake_case = tf.config.list_physical_devices("CPU" )
if len(snake_case_ ) == 1:
tf.config.set_logical_device_configuration(
physical_devices[0] , [tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()] )
__snake_case = tf.config.list_logical_devices(device_type="CPU" )
__snake_case = tf.distribute.MirroredStrategy(devices=devices[:2] )
with strategy.scope():
__snake_case = GradientAccumulator()
__snake_case = tf.Variable([4.0, 3.0] )
__snake_case , __snake_case = create_optimizer(5e-5 , 10 , 5 )
__snake_case = tf.Variable([0.0, 0.0] , trainable=snake_case_ )
def accumulate_on_replica(snake_case_ : Optional[Any] ):
accumulator([gradient] )
def apply_on_replica():
optimizer.apply_gradients(list(zip(accumulator.gradients , [variable] ) ) )
@tf.function
def accumulate(snake_case_ : Dict , snake_case_ : Any ):
with strategy.scope():
__snake_case = strategy.experimental_local_results(snake_case_ )
local_variables[0].assign(snake_case_ )
local_variables[1].assign(snake_case_ )
strategy.run(snake_case_ , args=(gradient_placeholder,) )
@tf.function
def apply_grad():
with strategy.scope():
strategy.run(snake_case_ )
def _check_local_values(snake_case_ : Tuple , snake_case_ : str ):
__snake_case = strategy.experimental_local_results(accumulator._gradients[0] )
self.assertListAlmostEqual(values[0].value() , snake_case_ , tol=1e-2 )
self.assertListAlmostEqual(values[1].value() , snake_case_ , tol=1e-2 )
accumulate([1.0, 2.0] , [-1.0, 1.0] )
accumulate([3.0, -1.0] , [-1.0, -1.0] )
accumulate([-2.0, 2.0] , [3.0, -2.0] )
self.assertEqual(accumulator.step , 3 )
_check_local_values([2.0, 3.0] , [1.0, -2.0] )
apply_grad()
self.assertListAlmostEqual(variable.value() , [4.0, 3.0] , tol=1e-2 )
accumulator.reset()
self.assertEqual(accumulator.step , 0 )
_check_local_values([0.0, 0.0] , [0.0, 0.0] )
| 614
|
"""simple docstring"""
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __magic_name__ :
_SCREAMING_SNAKE_CASE : float
_SCREAMING_SNAKE_CASE : TreeNode | None = None
_SCREAMING_SNAKE_CASE : TreeNode | None = None
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> bool:
"""simple docstring"""
def is_valid_tree(SCREAMING_SNAKE_CASE ) -> bool:
if node is None:
return True
if not isinstance(SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return False
try:
float(node.data )
except (TypeError, ValueError):
return False
return is_valid_tree(node.left ) and is_valid_tree(node.right )
if not is_valid_tree(SCREAMING_SNAKE_CASE ):
raise ValueError(
"Each node should be type of TreeNode and data should be float." )
def is_binary_search_tree_recursive_check(
SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> bool:
if node is None:
return True
return (
left_bound < node.data < right_bound
and is_binary_search_tree_recursive_check(node.left , SCREAMING_SNAKE_CASE , node.data )
and is_binary_search_tree_recursive_check(
node.right , node.data , SCREAMING_SNAKE_CASE )
)
return is_binary_search_tree_recursive_check(SCREAMING_SNAKE_CASE , -float("inf" ) , float("inf" ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 614
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__A = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__A = [
'''GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GraphormerForGraphClassification''',
'''GraphormerModel''',
'''GraphormerPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_graphormer import (
GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
GraphormerForGraphClassification,
GraphormerModel,
GraphormerPreTrainedModel,
)
else:
import sys
__A = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 593
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase = {
"""configuration_roberta""": ["""ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """RobertaConfig""", """RobertaOnnxConfig"""],
"""tokenization_roberta""": ["""RobertaTokenizer"""],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = ["""RobertaTokenizerFast"""]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
"""ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""RobertaForCausalLM""",
"""RobertaForMaskedLM""",
"""RobertaForMultipleChoice""",
"""RobertaForQuestionAnswering""",
"""RobertaForSequenceClassification""",
"""RobertaForTokenClassification""",
"""RobertaModel""",
"""RobertaPreTrainedModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
"""TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFRobertaForCausalLM""",
"""TFRobertaForMaskedLM""",
"""TFRobertaForMultipleChoice""",
"""TFRobertaForQuestionAnswering""",
"""TFRobertaForSequenceClassification""",
"""TFRobertaForTokenClassification""",
"""TFRobertaMainLayer""",
"""TFRobertaModel""",
"""TFRobertaPreTrainedModel""",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
_lowerCAmelCase = [
"""FlaxRobertaForCausalLM""",
"""FlaxRobertaForMaskedLM""",
"""FlaxRobertaForMultipleChoice""",
"""FlaxRobertaForQuestionAnswering""",
"""FlaxRobertaForSequenceClassification""",
"""FlaxRobertaForTokenClassification""",
"""FlaxRobertaModel""",
"""FlaxRobertaPreTrainedModel""",
]
if TYPE_CHECKING:
from .configuration_roberta import ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, RobertaConfig, RobertaOnnxConfig
from .tokenization_roberta import RobertaTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_roberta_fast import RobertaTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_roberta import (
ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
RobertaForCausalLM,
RobertaForMaskedLM,
RobertaForMultipleChoice,
RobertaForQuestionAnswering,
RobertaForSequenceClassification,
RobertaForTokenClassification,
RobertaModel,
RobertaPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_roberta import (
TF_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
TFRobertaForCausalLM,
TFRobertaForMaskedLM,
TFRobertaForMultipleChoice,
TFRobertaForQuestionAnswering,
TFRobertaForSequenceClassification,
TFRobertaForTokenClassification,
TFRobertaMainLayer,
TFRobertaModel,
TFRobertaPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_roberta import (
FlaxRobertaForCausalLM,
FlaxRobertaForMaskedLM,
FlaxRobertaForMultipleChoice,
FlaxRobertaForQuestionAnswering,
FlaxRobertaForSequenceClassification,
FlaxRobertaForTokenClassification,
FlaxRobertaModel,
FlaxRobertaPreTrainedModel,
)
else:
import sys
_lowerCAmelCase = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 259
| 0
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
A_ :Union[str, Any] = logging.get_logger(__name__)
class __A ( UpperCamelCase__ ):
"""simple docstring"""
UpperCamelCase__ : List[str] =["""pixel_values"""]
def __init__( self , lowerCamelCase__ = True , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = PILImageResampling.BILINEAR , lowerCamelCase__ = True , lowerCamelCase__ = 1 / 255 , lowerCamelCase__ = True , lowerCamelCase__ = None , lowerCamelCase__ = None , **lowerCamelCase__ , ):
"""simple docstring"""
super().__init__(**_a )
__UpperCamelCase : int =size if size is not None else {"""shortest_edge""": 384}
__UpperCamelCase : str =get_size_dict(_a , default_to_square=_a )
__UpperCamelCase : str =do_resize
__UpperCamelCase : Optional[Any] =size
# Default value set here for backwards compatibility where the value in config is None
__UpperCamelCase : List[str] =crop_pct if crop_pct is not None else 224 / 256
__UpperCamelCase : List[Any] =resample
__UpperCamelCase : Union[str, Any] =do_rescale
__UpperCamelCase : Optional[int] =rescale_factor
__UpperCamelCase : List[Any] =do_normalize
__UpperCamelCase : int =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
__UpperCamelCase : Union[str, Any] =image_std if image_std is not None else IMAGENET_STANDARD_STD
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = PILImageResampling.BICUBIC , lowerCamelCase__ = None , **lowerCamelCase__ , ):
"""simple docstring"""
__UpperCamelCase : Dict =get_size_dict(_a , default_to_square=_a )
if "shortest_edge" not in size:
raise ValueError(f'Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}' )
__UpperCamelCase : List[str] =size["""shortest_edge"""]
if shortest_edge < 384:
# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
__UpperCamelCase : Any =int(shortest_edge / crop_pct )
__UpperCamelCase : Union[str, Any] =get_resize_output_image_size(_a , size=_a , default_to_square=_a )
__UpperCamelCase : Any =resize(image=_a , size=_a , resample=_a , data_format=_a , **_a )
# then crop to (shortest_edge, shortest_edge)
return center_crop(image=_a , size=(shortest_edge, shortest_edge) , data_format=_a , **_a )
else:
# warping (no cropping) when evaluated at 384 or larger
return resize(
_a , size=(shortest_edge, shortest_edge) , resample=_a , data_format=_a , **_a )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , **lowerCamelCase__ , ):
"""simple docstring"""
return rescale(_a , scale=_a , data_format=_a , **_a )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ = None , **lowerCamelCase__ , ):
"""simple docstring"""
return normalize(_a , mean=_a , std=_a , data_format=_a , **_a )
def __lowercase ( self , lowerCamelCase__ , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = None , lowerCamelCase__ = ChannelDimension.FIRST , **lowerCamelCase__ , ):
"""simple docstring"""
__UpperCamelCase : Optional[int] =do_resize if do_resize is not None else self.do_resize
__UpperCamelCase : str =crop_pct if crop_pct is not None else self.crop_pct
__UpperCamelCase : Tuple =resample if resample is not None else self.resample
__UpperCamelCase : Union[str, Any] =do_rescale if do_rescale is not None else self.do_rescale
__UpperCamelCase : Any =rescale_factor if rescale_factor is not None else self.rescale_factor
__UpperCamelCase : Union[str, Any] =do_normalize if do_normalize is not None else self.do_normalize
__UpperCamelCase : Tuple =image_mean if image_mean is not None else self.image_mean
__UpperCamelCase : List[str] =image_std if image_std is not None else self.image_std
__UpperCamelCase : Any =size if size is not None else self.size
__UpperCamelCase : str =get_size_dict(_a , default_to_square=_a )
__UpperCamelCase : List[str] =make_list_of_images(_a )
if not valid_images(_a ):
raise ValueError(
'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
'torch.Tensor, tf.Tensor or jax.ndarray.' )
if do_resize and size is None or resample is None:
raise ValueError('Size and resample must be specified if do_resize is True.' )
if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
raise ValueError('crop_pct must be specified if size < 384.' )
if do_rescale and rescale_factor is None:
raise ValueError('Rescale factor must be specified if do_rescale is True.' )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError('Image mean and std must be specified if do_normalize is True.' )
# All transformations expect numpy arrays.
__UpperCamelCase : List[Any] =[to_numpy_array(_a ) for image in images]
if do_resize:
__UpperCamelCase : Dict =[self.resize(image=_a , size=_a , crop_pct=_a , resample=_a ) for image in images]
if do_rescale:
__UpperCamelCase : Optional[int] =[self.rescale(image=_a , scale=_a ) for image in images]
if do_normalize:
__UpperCamelCase : Optional[int] =[self.normalize(image=_a , mean=_a , std=_a ) for image in images]
__UpperCamelCase : Union[str, Any] =[to_channel_dimension_format(_a , _a ) for image in images]
__UpperCamelCase : Tuple ={"""pixel_values""": images}
return BatchFeature(data=_a , tensor_type=_a )
| 710
|
import unittest
from transformers.testing_utils import require_bsa
from transformers.utils import is_bsa_available
from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin
if is_bsa_available():
from transformers import MarkupLMFeatureExtractor
class __A ( unittest.TestCase ):
"""simple docstring"""
def __init__( self , lowerCamelCase__ ):
"""simple docstring"""
__UpperCamelCase : int =parent
def __lowercase ( self ):
"""simple docstring"""
return {}
def A ( ) -> Dict:
__UpperCamelCase : List[Any] ='<HTML>\n\n <HEAD>\n <TITLE>sample document</TITLE>\n </HEAD>\n\n <BODY BGCOLOR="FFFFFF">\n <HR>\n <a href="http://google.com">Goog</a>\n <H1>This is one header</H1>\n <H2>This is a another Header</H2>\n <P>Travel from\n <P>\n <B>SFO to JFK</B>\n <BR>\n <B><I>on May 2, 2015 at 2:00 pm. For details go to confirm.com </I></B>\n <HR>\n <div style="color:#0000FF">\n <h3>Traveler <b> name </b> is\n <p> John Doe </p>\n </div>'
__UpperCamelCase : Optional[int] ='\n <!DOCTYPE html>\n <html>\n <body>\n\n <h1>My First Heading</h1>\n <p>My first paragraph.</p>\n\n </body>\n </html>\n '
return [html_string_a, html_string_a]
@require_bsa
class __A ( a , unittest.TestCase ):
"""simple docstring"""
UpperCamelCase__ : Optional[int] =MarkupLMFeatureExtractor if is_bsa_available() else None
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Any =MarkupLMFeatureExtractionTester(self )
@property
def __lowercase ( self ):
"""simple docstring"""
return self.feature_extract_tester.prepare_feat_extract_dict()
def __lowercase ( self ):
"""simple docstring"""
__UpperCamelCase : Optional[Any] =self.feature_extraction_class()
# Test not batched input
__UpperCamelCase : Dict =get_html_strings()[0]
__UpperCamelCase : str =feature_extractor(lowerCamelCase__ )
# fmt: off
__UpperCamelCase : int =[['sample document', 'Goog', 'This is one header', 'This is a another Header', 'Travel from', 'SFO to JFK', 'on May 2, 2015 at 2:00 pm. For details go to confirm.com', 'Traveler', 'name', 'is', 'John Doe']]
__UpperCamelCase : Dict =[['/html/head/title', '/html/body/a', '/html/body/h1', '/html/body/h2', '/html/body/p', '/html/body/p/p/b[1]', '/html/body/p/p/b[2]/i', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/b', '/html/body/p/p/div/h3', '/html/body/p/p/div/h3/p']]
# fmt: on
self.assertEqual(encoding.nodes , lowerCamelCase__ )
self.assertEqual(encoding.xpaths , lowerCamelCase__ )
# Test batched
__UpperCamelCase : Optional[Any] =get_html_strings()
__UpperCamelCase : Tuple =feature_extractor(lowerCamelCase__ )
# fmt: off
__UpperCamelCase : str =expected_nodes + [['My First Heading', 'My first paragraph.']]
__UpperCamelCase : Tuple =expected_xpaths + [['/html/body/h1', '/html/body/p']]
self.assertEqual(len(encoding.nodes ) , 2 )
self.assertEqual(len(encoding.xpaths ) , 2 )
self.assertEqual(encoding.nodes , lowerCamelCase__ )
self.assertEqual(encoding.xpaths , lowerCamelCase__ )
| 154
| 0
|
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class snake_case_ :
'''simple docstring'''
def __init__( self : Tuple , _UpperCamelCase : Optional[int] , _UpperCamelCase : Union[str, Any]=1_3 , _UpperCamelCase : Dict=7 , _UpperCamelCase : List[str]=True , _UpperCamelCase : Any=True , _UpperCamelCase : Union[str, Any]=True , _UpperCamelCase : List[Any]=True , _UpperCamelCase : str=9_9 , _UpperCamelCase : str=3_2 , _UpperCamelCase : Union[str, Any]=2 , _UpperCamelCase : Optional[Any]=4 , _UpperCamelCase : Any=3_7 , _UpperCamelCase : Tuple="gelu" , _UpperCamelCase : Optional[Any]=0.1 , _UpperCamelCase : List[Any]=0.1 , _UpperCamelCase : Union[str, Any]=5_1_2 , _UpperCamelCase : Optional[int]=1_6 , _UpperCamelCase : Tuple=2 , _UpperCamelCase : Optional[Any]=0.02 , _UpperCamelCase : Union[str, Any]=3 , _UpperCamelCase : Optional[Any]=4 , _UpperCamelCase : List[str]=None , ) ->Tuple:
snake_case_ = parent
snake_case_ = 1_3
snake_case_ = 7
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = True
snake_case_ = 9_9
snake_case_ = 3_8_4
snake_case_ = 2
snake_case_ = 4
snake_case_ = 3_7
snake_case_ = '''gelu'''
snake_case_ = 0.1
snake_case_ = 0.1
snake_case_ = 5_1_2
snake_case_ = 1_6
snake_case_ = 2
snake_case_ = 0.02
snake_case_ = 3
snake_case_ = 4
snake_case_ = 1_2_8
snake_case_ = 2
snake_case_ = 9
snake_case_ = 1
snake_case_ = None
def snake_case__( self : Optional[Any] ) ->Optional[int]:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
snake_case_ = None
if self.use_input_mask:
snake_case_ = random_attention_mask([self.batch_size, self.seq_length] )
snake_case_ = None
if self.use_token_type_ids:
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
snake_case_ = None
snake_case_ = None
snake_case_ = None
if self.use_labels:
snake_case_ = ids_tensor([self.batch_size] , self.type_sequence_label_size )
snake_case_ = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
snake_case_ = ids_tensor([self.batch_size] , self.num_choices )
snake_case_ = ConvBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=_UpperCamelCase , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case__( self : Optional[int] , _UpperCamelCase : Any , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : List[Any] , _UpperCamelCase : Any , _UpperCamelCase : Dict , _UpperCamelCase : Optional[Any] , _UpperCamelCase : int ) ->Optional[int]:
snake_case_ = TFConvBertModel(config=_UpperCamelCase )
snake_case_ = {'''input_ids''': input_ids, '''attention_mask''': input_mask, '''token_type_ids''': token_type_ids}
snake_case_ = [input_ids, input_mask]
snake_case_ = model(_UpperCamelCase )
snake_case_ = model(_UpperCamelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case__( self : str , _UpperCamelCase : List[Any] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : Tuple , _UpperCamelCase : str , _UpperCamelCase : Optional[int] , _UpperCamelCase : Tuple , _UpperCamelCase : Union[str, Any] ) ->Optional[int]:
snake_case_ = TFConvBertForMaskedLM(config=_UpperCamelCase )
snake_case_ = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
snake_case_ = model(_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case__( self : Tuple , _UpperCamelCase : Any , _UpperCamelCase : Tuple , _UpperCamelCase : Union[str, Any] , _UpperCamelCase : int , _UpperCamelCase : str , _UpperCamelCase : int , _UpperCamelCase : List[Any] ) ->Dict:
snake_case_ = self.num_labels
snake_case_ = TFConvBertForSequenceClassification(config=_UpperCamelCase )
snake_case_ = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
snake_case_ = model(_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case__( self : List[Any] , _UpperCamelCase : Dict , _UpperCamelCase : Optional[int] , _UpperCamelCase : int , _UpperCamelCase : int , _UpperCamelCase : List[Any] , _UpperCamelCase : int , _UpperCamelCase : List[Any] ) ->str:
snake_case_ = self.num_choices
snake_case_ = TFConvBertForMultipleChoice(config=_UpperCamelCase )
snake_case_ = tf.tile(tf.expand_dims(_UpperCamelCase , 1 ) , (1, self.num_choices, 1) )
snake_case_ = tf.tile(tf.expand_dims(_UpperCamelCase , 1 ) , (1, self.num_choices, 1) )
snake_case_ = tf.tile(tf.expand_dims(_UpperCamelCase , 1 ) , (1, self.num_choices, 1) )
snake_case_ = {
'''input_ids''': multiple_choice_inputs_ids,
'''attention_mask''': multiple_choice_input_mask,
'''token_type_ids''': multiple_choice_token_type_ids,
}
snake_case_ = model(_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case__( self : Any , _UpperCamelCase : int , _UpperCamelCase : Optional[int] , _UpperCamelCase : Optional[int] , _UpperCamelCase : Optional[Any] , _UpperCamelCase : List[Any] , _UpperCamelCase : Dict , _UpperCamelCase : Optional[int] ) ->str:
snake_case_ = self.num_labels
snake_case_ = TFConvBertForTokenClassification(config=_UpperCamelCase )
snake_case_ = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
snake_case_ = model(_UpperCamelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case__( self : Optional[Any] , _UpperCamelCase : Tuple , _UpperCamelCase : Dict , _UpperCamelCase : Dict , _UpperCamelCase : List[Any] , _UpperCamelCase : str , _UpperCamelCase : Optional[int] , _UpperCamelCase : Optional[Any] ) ->Tuple:
snake_case_ = TFConvBertForQuestionAnswering(config=_UpperCamelCase )
snake_case_ = {
'''input_ids''': input_ids,
'''attention_mask''': input_mask,
'''token_type_ids''': token_type_ids,
}
snake_case_ = model(_UpperCamelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case__( self : List[str] ) ->List[Any]:
snake_case_ = self.prepare_config_and_inputs()
(
(
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
), (
snake_case_
),
) = config_and_inputs
snake_case_ = {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': input_mask}
return config, inputs_dict
@require_tf
class snake_case_ ( __A , __A , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE : int = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
SCREAMING_SNAKE_CASE : Dict = (
{
"feature-extraction": TFConvBertModel,
"fill-mask": TFConvBertForMaskedLM,
"question-answering": TFConvBertForQuestionAnswering,
"text-classification": TFConvBertForSequenceClassification,
"token-classification": TFConvBertForTokenClassification,
"zero-shot": TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
SCREAMING_SNAKE_CASE : Dict = False
SCREAMING_SNAKE_CASE : Union[str, Any] = False
SCREAMING_SNAKE_CASE : int = False
def snake_case__( self : str ) ->Union[str, Any]:
snake_case_ = TFConvBertModelTester(self )
snake_case_ = ConfigTester(self , config_class=_UpperCamelCase , hidden_size=3_7 )
def snake_case__( self : List[str] ) ->Optional[Any]:
self.config_tester.run_common_tests()
def snake_case__( self : str ) ->Optional[Any]:
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*_UpperCamelCase )
def snake_case__( self : Optional[Any] ) ->List[str]:
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*_UpperCamelCase )
def snake_case__( self : str ) ->Dict:
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*_UpperCamelCase )
def snake_case__( self : int ) ->str:
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*_UpperCamelCase )
def snake_case__( self : Optional[int] ) ->str:
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*_UpperCamelCase )
def snake_case__( self : str ) ->Union[str, Any]:
snake_case_ = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*_UpperCamelCase )
@slow
def snake_case__( self : str ) ->str:
snake_case_, snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = True
snake_case_ = True
if hasattr(_UpperCamelCase , '''use_cache''' ):
snake_case_ = True
snake_case_ = getattr(self.model_tester , '''encoder_seq_length''' , self.model_tester.seq_length )
snake_case_ = getattr(self.model_tester , '''key_length''' , _UpperCamelCase )
for model_class in self.all_model_classes:
snake_case_ = self._prepare_for_class(_UpperCamelCase , _UpperCamelCase )
snake_case_ = model_class(_UpperCamelCase )
snake_case_ = len(model(_UpperCamelCase ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(_UpperCamelCase , saved_model=_UpperCamelCase )
snake_case_ = os.path.join(_UpperCamelCase , '''saved_model''' , '''1''' )
snake_case_ = tf.keras.models.load_model(_UpperCamelCase )
snake_case_ = model(_UpperCamelCase )
if self.is_encoder_decoder:
snake_case_ = outputs['''encoder_hidden_states''']
snake_case_ = outputs['''encoder_attentions''']
else:
snake_case_ = outputs['''hidden_states''']
snake_case_ = outputs['''attentions''']
self.assertEqual(len(_UpperCamelCase ) , _UpperCamelCase )
snake_case_ = getattr(
self.model_tester , '''expected_num_hidden_layers''' , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(_UpperCamelCase ) , _UpperCamelCase )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(_UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def snake_case__( self : Union[str, Any] ) ->Tuple:
snake_case_ = TFConvBertModel.from_pretrained('''YituTech/conv-bert-base''' )
self.assertIsNotNone(_UpperCamelCase )
def snake_case__( self : List[str] ) ->Any:
snake_case_, snake_case_ = self.model_tester.prepare_config_and_inputs_for_common()
snake_case_ = True
snake_case_ = getattr(self.model_tester , '''decoder_seq_length''' , self.model_tester.seq_length )
snake_case_ = getattr(self.model_tester , '''encoder_seq_length''' , self.model_tester.seq_length )
snake_case_ = getattr(self.model_tester , '''key_length''' , _UpperCamelCase )
snake_case_ = getattr(self.model_tester , '''key_length''' , _UpperCamelCase )
def check_decoder_attentions_output(_UpperCamelCase : List[Any] ):
snake_case_ = len(_UpperCamelCase )
self.assertEqual(out_len % 2 , 0 )
snake_case_ = outputs.decoder_attentions
self.assertEqual(len(_UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(_UpperCamelCase : Dict ):
snake_case_ = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(_UpperCamelCase ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
snake_case_ = True
snake_case_ = False
snake_case_ = model_class(_UpperCamelCase )
snake_case_ = model(self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) )
snake_case_ = len(_UpperCamelCase )
self.assertEqual(config.output_hidden_states , _UpperCamelCase )
check_encoder_attentions_output(_UpperCamelCase )
if self.is_encoder_decoder:
snake_case_ = model_class(_UpperCamelCase )
snake_case_ = model(self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , _UpperCamelCase )
check_decoder_attentions_output(_UpperCamelCase )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
snake_case_ = True
snake_case_ = model_class(_UpperCamelCase )
snake_case_ = model(self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) )
self.assertEqual(config.output_hidden_states , _UpperCamelCase )
check_encoder_attentions_output(_UpperCamelCase )
# Check attention is always last and order is fine
snake_case_ = True
snake_case_ = True
snake_case_ = model_class(_UpperCamelCase )
snake_case_ = model(self._prepare_for_class(_UpperCamelCase , _UpperCamelCase ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(_UpperCamelCase ) )
self.assertEqual(model.config.output_hidden_states , _UpperCamelCase )
check_encoder_attentions_output(_UpperCamelCase )
@require_tf
class snake_case_ ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case__( self : Any ) ->int:
snake_case_ = TFConvBertModel.from_pretrained('''YituTech/conv-bert-base''' )
snake_case_ = tf.constant([[0, 1, 2, 3, 4, 5]] )
snake_case_ = model(_UpperCamelCase )[0]
snake_case_ = [1, 6, 7_6_8]
self.assertEqual(output.shape , _UpperCamelCase )
snake_case_ = tf.constant(
[
[
[-0.03475493, -0.4686034, -0.30638832],
[0.22637248, -0.26988646, -0.7423424],
[0.10324868, -0.45013508, -0.58280784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , _UpperCamelCase , atol=1e-4 )
| 39
|
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
get_image_size,
is_torch_available,
is_torch_tensor,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_torch_available():
import torch
if is_vision_available():
import PIL
__lowerCamelCase : List[str] = logging.get_logger(__name__)
def A__ ( _a : np.ndarray , _a : Union[int, Iterable[int]] , _a : bool , _a : int ):
'''simple docstring'''
def constraint_to_multiple_of(_a : List[str] , _a : int , _a : Tuple=0 , _a : Any=None ):
snake_case__ : Optional[int] =round(val / multiple ) * multiple
if max_val is not None and x > max_val:
snake_case__ : Any =math.floor(val / multiple ) * multiple
if x < min_val:
snake_case__ : Union[str, Any] =math.ceil(val / multiple ) * multiple
return x
snake_case__ : Optional[Any] =(output_size, output_size) if isinstance(_a , _a ) else output_size
snake_case__ , snake_case__ : Any =get_image_size(_a )
snake_case__ , snake_case__ : Optional[Any] =output_size
# determine new height and width
snake_case__ : int =output_height / input_height
snake_case__ : List[str] =output_width / input_width
if keep_aspect_ratio:
# scale as little as possible
if abs(1 - scale_width ) < abs(1 - scale_height ):
# fit width
snake_case__ : List[str] =scale_width
else:
# fit height
snake_case__ : List[Any] =scale_height
snake_case__ : Any =constraint_to_multiple_of(scale_height * input_height , multiple=_a )
snake_case__ : Dict =constraint_to_multiple_of(scale_width * input_width , multiple=_a )
return (new_height, new_width)
class _lowercase ( _A ):
_a : Tuple = ['pixel_values']
def __init__( self , a = True , a = None , a = PILImageResampling.BILINEAR , a = False , a = 1 , a = True , a = 1 / 2_5_5 , a = True , a = None , a = None , **a , ):
super().__init__(**a )
snake_case__ : Dict =size if size is not None else {"""height""": 3_8_4, """width""": 3_8_4}
snake_case__ : Union[str, Any] =get_size_dict(a )
snake_case__ : str =do_resize
snake_case__ : Union[str, Any] =size
snake_case__ : Any =keep_aspect_ratio
snake_case__ : List[Any] =ensure_multiple_of
snake_case__ : List[str] =resample
snake_case__ : Dict =do_rescale
snake_case__ : str =rescale_factor
snake_case__ : Any =do_normalize
snake_case__ : List[Any] =image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case__ : List[Any] =image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowercase__ ( self , a , a , a = False , a = 1 , a = PILImageResampling.BICUBIC , a = None , **a , ):
snake_case__ : Dict =get_size_dict(a )
if "height" not in size or "width" not in size:
raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}" )
snake_case__ : Union[str, Any] =get_resize_output_image_size(
a , output_size=(size["""height"""], size["""width"""]) , keep_aspect_ratio=a , multiple=a , )
return resize(a , size=a , resample=a , data_format=a , **a )
def lowercase__ ( self , a , a , a = None , **a , ):
return rescale(a , scale=a , data_format=a , **a )
def lowercase__ ( self , a , a , a , a = None , **a , ):
return normalize(a , mean=a , std=a , data_format=a , **a )
def lowercase__ ( self , a , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = None , a = ChannelDimension.FIRST , **a , ):
snake_case__ : Tuple =do_resize if do_resize is not None else self.do_resize
snake_case__ : Any =size if size is not None else self.size
snake_case__ : int =get_size_dict(a )
snake_case__ : Optional[int] =keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
snake_case__ : Tuple =ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
snake_case__ : Any =resample if resample is not None else self.resample
snake_case__ : List[Any] =do_rescale if do_rescale is not None else self.do_rescale
snake_case__ : Dict =rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case__ : str =do_normalize if do_normalize is not None else self.do_normalize
snake_case__ : Optional[Any] =image_mean if image_mean is not None else self.image_mean
snake_case__ : Optional[Any] =image_std if image_std is not None else self.image_std
snake_case__ : List[str] =make_list_of_images(a )
if not valid_images(a ):
raise ValueError(
"""Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
"""torch.Tensor, tf.Tensor or jax.ndarray.""" )
if do_resize and size is None or resample is None:
raise ValueError("""Size and resample must be specified if do_resize is True.""" )
if do_rescale and rescale_factor is None:
raise ValueError("""Rescale factor must be specified if do_rescale is True.""" )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("""Image mean and std must be specified if do_normalize is True.""" )
# All transformations expect numpy arrays.
snake_case__ : Optional[int] =[to_numpy_array(a ) for image in images]
if do_resize:
snake_case__ : int =[self.resize(image=a , size=a , resample=a ) for image in images]
if do_rescale:
snake_case__ : Optional[int] =[self.rescale(image=a , scale=a ) for image in images]
if do_normalize:
snake_case__ : str =[self.normalize(image=a , mean=a , std=a ) for image in images]
snake_case__ : List[Any] =[to_channel_dimension_format(a , a ) for image in images]
snake_case__ : Tuple ={"""pixel_values""": images}
return BatchFeature(data=a , tensor_type=a )
def lowercase__ ( self , a , a = None ):
snake_case__ : Dict =outputs.logits
# Resize logits and compute semantic segmentation maps
if target_sizes is not None:
if len(a ) != len(a ):
raise ValueError(
"""Make sure that you pass in as many target sizes as the batch dimension of the logits""" )
if is_torch_tensor(a ):
snake_case__ : Any =target_sizes.numpy()
snake_case__ : Union[str, Any] =[]
for idx in range(len(a ) ):
snake_case__ : int =torch.nn.functional.interpolate(
logits[idx].unsqueeze(dim=0 ) , size=target_sizes[idx] , mode="""bilinear""" , align_corners=a )
snake_case__ : Optional[Any] =resized_logits[0].argmax(dim=0 )
semantic_segmentation.append(a )
else:
snake_case__ : Union[str, Any] =logits.argmax(dim=1 )
snake_case__ : Any =[semantic_segmentation[i] for i in range(semantic_segmentation.shape[0] )]
return semantic_segmentation
| 385
| 0
|
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def __UpperCAmelCase( lowercase_ ):
return (data["data"], data["target"])
def __UpperCAmelCase( lowercase_ , lowercase_ ):
_lowerCamelCase : Optional[Any] = XGBClassifier()
classifier.fit(lowercase_ , lowercase_ )
return classifier
def __UpperCAmelCase( ):
_lowerCamelCase : Union[str, Any] = load_iris()
_lowerCamelCase, _lowerCamelCase : Optional[int] = data_handling(lowercase_ )
_lowerCamelCase, _lowerCamelCase, _lowerCamelCase, _lowerCamelCase : Dict = train_test_split(
lowercase_ , lowercase_ , test_size=0.2_5 )
_lowerCamelCase : List[str] = iris['''target_names''']
# Create an XGBoost Classifier from the training data
_lowerCamelCase : Union[str, Any] = xgboost(lowercase_ , lowercase_ )
# Display the confusion matrix of the classifier with both training and test sets
ConfusionMatrixDisplay.from_estimator(
lowercase_ , lowercase_ , lowercase_ , display_labels=lowercase_ , cmap='''Blues''' , normalize='''true''' , )
plt.title('''Normalized Confusion Matrix - IRIS Dataset''' )
plt.show()
if __name__ == "__main__":
import doctest
doctest.testmod(verbose=True)
main()
| 613
|
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_utils import shard
@slow
@require_flax
class __A ( unittest.TestCase ):
"""simple docstring"""
def __snake_case ( self):
"""simple docstring"""
super().tearDown()
gc.collect()
def __snake_case ( self):
"""simple docstring"""
_lowerCamelCase, _lowerCamelCase : Tuple = FlaxControlNetModel.from_pretrained(
'''lllyasviel/sd-controlnet-canny''' , from_pt=a__ , dtype=jnp.bfloataa)
_lowerCamelCase, _lowerCamelCase : Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , controlnet=a__ , from_pt=a__ , dtype=jnp.bfloataa)
_lowerCamelCase : Union[str, Any] = controlnet_params
_lowerCamelCase : str = '''bird'''
_lowerCamelCase : Union[str, Any] = jax.device_count()
_lowerCamelCase : Dict = pipe.prepare_text_inputs([prompts] * num_samples)
_lowerCamelCase : int = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png''')
_lowerCamelCase : Tuple = pipe.prepare_image_inputs([canny_image] * num_samples)
_lowerCamelCase : Union[str, Any] = jax.random.PRNGKey(0)
_lowerCamelCase : Tuple = jax.random.split(a__ , jax.device_count())
_lowerCamelCase : Dict = replicate(a__)
_lowerCamelCase : Tuple = shard(a__)
_lowerCamelCase : Union[str, Any] = shard(a__)
_lowerCamelCase : Optional[Any] = pipe(
prompt_ids=a__ , image=a__ , params=a__ , prng_seed=a__ , num_inference_steps=50 , jit=a__ , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
_lowerCamelCase : Optional[int] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
_lowerCamelCase : List[Any] = images[0, 253:256, 253:256, -1]
_lowerCamelCase : Union[str, Any] = jnp.asarray(jax.device_get(image_slice.flatten()))
_lowerCamelCase : Optional[int] = jnp.array(
[0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
print(F"""output_slice: {output_slice}""")
assert jnp.abs(output_slice - expected_slice).max() < 1e-2
def __snake_case ( self):
"""simple docstring"""
_lowerCamelCase, _lowerCamelCase : Any = FlaxControlNetModel.from_pretrained(
'''lllyasviel/sd-controlnet-openpose''' , from_pt=a__ , dtype=jnp.bfloataa)
_lowerCamelCase, _lowerCamelCase : Any = FlaxStableDiffusionControlNetPipeline.from_pretrained(
'''runwayml/stable-diffusion-v1-5''' , controlnet=a__ , from_pt=a__ , dtype=jnp.bfloataa)
_lowerCamelCase : Optional[Any] = controlnet_params
_lowerCamelCase : Optional[Any] = '''Chef in the kitchen'''
_lowerCamelCase : int = jax.device_count()
_lowerCamelCase : Dict = pipe.prepare_text_inputs([prompts] * num_samples)
_lowerCamelCase : List[Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png''')
_lowerCamelCase : str = pipe.prepare_image_inputs([pose_image] * num_samples)
_lowerCamelCase : Optional[Any] = jax.random.PRNGKey(0)
_lowerCamelCase : List[str] = jax.random.split(a__ , jax.device_count())
_lowerCamelCase : str = replicate(a__)
_lowerCamelCase : Union[str, Any] = shard(a__)
_lowerCamelCase : List[Any] = shard(a__)
_lowerCamelCase : Any = pipe(
prompt_ids=a__ , image=a__ , params=a__ , prng_seed=a__ , num_inference_steps=50 , jit=a__ , ).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
_lowerCamelCase : int = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
_lowerCamelCase : Dict = images[0, 253:256, 253:256, -1]
_lowerCamelCase : Optional[int] = jnp.asarray(jax.device_get(image_slice.flatten()))
_lowerCamelCase : Optional[int] = jnp.array(
[[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
print(F"""output_slice: {output_slice}""")
assert jnp.abs(output_slice - expected_slice).max() < 1e-2
| 613
| 1
|
'''simple docstring'''
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowercase_ ( __A : str ) -> None:
"""simple docstring"""
lowercase , lowercase : Any =analyze_text(__A )
lowercase : str =list(''' ''' + ascii_lowercase )
# what is our total sum of probabilities.
lowercase : str =sum(single_char_strings.values() )
# one length string
lowercase : 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:
lowercase : Dict =single_char_strings[ch]
lowercase : Dict =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
lowercase : int =sum(two_char_strings.values() )
lowercase : Tuple =0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
lowercase : str =cha + cha
if sequence in two_char_strings:
lowercase : Tuple =two_char_strings[sequence]
lowercase : Optional[Any] =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 lowercase_ ( __A : str ) -> tuple[dict, dict]:
"""simple docstring"""
lowercase : Union[str, Any] =Counter() # type: ignore
lowercase : Optional[int] =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 lowercase_ ( ) -> Optional[int]:
"""simple docstring"""
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()
| 94
|
from typing import TYPE_CHECKING
from ..utils import _LazyModule
_snake_case = {
"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
_snake_case = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 500
| 0
|
def lowercase ( a = 1000 ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ :Union[str, Any] = 2**power
SCREAMING_SNAKE_CASE_ :Optional[int] = str(a )
SCREAMING_SNAKE_CASE_ :Union[str, Any] = list(a )
SCREAMING_SNAKE_CASE_ :List[Any] = 0
for i in list_num:
sum_of_num += int(a )
return sum_of_num
if __name__ == "__main__":
SCREAMING_SNAKE_CASE__ = int(input("Enter the power of 2: ").strip())
print("2 ^ ", power, " = ", 2**power)
SCREAMING_SNAKE_CASE__ = solution(power)
print("Sum of the digits is: ", result)
| 720
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE__ = {
"configuration_albert": ["ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "AlbertConfig", "AlbertOnnxConfig"],
}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ["AlbertTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = ["AlbertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"AlbertForMaskedLM",
"AlbertForMultipleChoice",
"AlbertForPreTraining",
"AlbertForQuestionAnswering",
"AlbertForSequenceClassification",
"AlbertForTokenClassification",
"AlbertModel",
"AlbertPreTrainedModel",
"load_tf_weights_in_albert",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFAlbertForMaskedLM",
"TFAlbertForMultipleChoice",
"TFAlbertForPreTraining",
"TFAlbertForQuestionAnswering",
"TFAlbertForSequenceClassification",
"TFAlbertForTokenClassification",
"TFAlbertMainLayer",
"TFAlbertModel",
"TFAlbertPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE__ = [
"FlaxAlbertForMaskedLM",
"FlaxAlbertForMultipleChoice",
"FlaxAlbertForPreTraining",
"FlaxAlbertForQuestionAnswering",
"FlaxAlbertForSequenceClassification",
"FlaxAlbertForTokenClassification",
"FlaxAlbertModel",
"FlaxAlbertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_albert import ALBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, AlbertConfig, AlbertOnnxConfig
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert import AlbertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_albert_fast import AlbertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_albert import (
ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
AlbertForMaskedLM,
AlbertForMultipleChoice,
AlbertForPreTraining,
AlbertForQuestionAnswering,
AlbertForSequenceClassification,
AlbertForTokenClassification,
AlbertModel,
AlbertPreTrainedModel,
load_tf_weights_in_albert,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_albert import (
TF_ALBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFAlbertForMaskedLM,
TFAlbertForMultipleChoice,
TFAlbertForPreTraining,
TFAlbertForQuestionAnswering,
TFAlbertForSequenceClassification,
TFAlbertForTokenClassification,
TFAlbertMainLayer,
TFAlbertModel,
TFAlbertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
FlaxAlbertPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE__ = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 140
| 0
|
'''simple docstring'''
from math import pi
def UpperCamelCase__ ( __magic_name__ : int , __magic_name__ : int ) -> float:
'''simple docstring'''
return 2 * pi * radius * (angle / 3_60)
if __name__ == "__main__":
print(arc_length(90, 10))
| 38
|
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTokenizer,
BlipaConfig,
BlipaForConditionalGeneration,
BlipaProcessor,
BlipaVisionConfig,
BlipImageProcessor,
OPTConfig,
TaConfig,
)
from transformers.utils.constants import OPENAI_CLIP_MEAN, OPENAI_CLIP_STD
def lowerCAmelCase_ ():
"""simple docstring"""
UpperCAmelCase_: List[Any] = """https://storage.googleapis.com/sfr-vision-language-research/LAVIS/assets/merlion.png"""
UpperCAmelCase_: List[Any] = Image.open(requests.get(lowerCAmelCase__ , stream=lowerCAmelCase__ ).raw ).convert("""RGB""" )
return image
def lowerCAmelCase_ (lowerCAmelCase__: Any ):
"""simple docstring"""
UpperCAmelCase_: List[str] = []
# fmt: off
# vision encoder
rename_keys.append(("""visual_encoder.cls_token""", """vision_model.embeddings.class_embedding""") )
rename_keys.append(("""visual_encoder.pos_embed""", """vision_model.embeddings.position_embedding""") )
rename_keys.append(("""visual_encoder.patch_embed.proj.weight""", """vision_model.embeddings.patch_embedding.weight""") )
rename_keys.append(("""visual_encoder.patch_embed.proj.bias""", """vision_model.embeddings.patch_embedding.bias""") )
rename_keys.append(("""ln_vision.weight""", """vision_model.post_layernorm.weight""") )
rename_keys.append(("""ln_vision.bias""", """vision_model.post_layernorm.bias""") )
for i in range(config.vision_config.num_hidden_layers ):
rename_keys.append((F'visual_encoder.blocks.{i}.norm1.weight', F'vision_model.encoder.layers.{i}.layer_norm1.weight') )
rename_keys.append((F'visual_encoder.blocks.{i}.norm1.bias', F'vision_model.encoder.layers.{i}.layer_norm1.bias') )
rename_keys.append((F'visual_encoder.blocks.{i}.norm2.weight', F'vision_model.encoder.layers.{i}.layer_norm2.weight') )
rename_keys.append((F'visual_encoder.blocks.{i}.norm2.bias', F'vision_model.encoder.layers.{i}.layer_norm2.bias') )
rename_keys.append((F'visual_encoder.blocks.{i}.attn.qkv.weight', F'vision_model.encoder.layers.{i}.self_attn.qkv.weight') )
rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.weight', F'vision_model.encoder.layers.{i}.self_attn.projection.weight',) )
rename_keys.append((F'visual_encoder.blocks.{i}.attn.proj.bias', F'vision_model.encoder.layers.{i}.self_attn.projection.bias') )
rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.weight', F'vision_model.encoder.layers.{i}.mlp.fc1.weight') )
rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc1.bias', F'vision_model.encoder.layers.{i}.mlp.fc1.bias') )
rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.weight', F'vision_model.encoder.layers.{i}.mlp.fc2.weight') )
rename_keys.append((F'visual_encoder.blocks.{i}.mlp.fc2.bias', F'vision_model.encoder.layers.{i}.mlp.fc2.bias') )
# QFormer
rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.weight""", """qformer.layernorm.weight""") )
rename_keys.append(("""Qformer.bert.embeddings.LayerNorm.bias""", """qformer.layernorm.bias""") )
# fmt: on
return rename_keys
def lowerCAmelCase_ (lowerCAmelCase__: Optional[int] , lowerCAmelCase__: Dict , lowerCAmelCase__: str ):
"""simple docstring"""
UpperCAmelCase_: int = dct.pop(lowerCAmelCase__ )
UpperCAmelCase_: Optional[int] = val
def lowerCAmelCase_ (lowerCAmelCase__: List[str] , lowerCAmelCase__: str ):
"""simple docstring"""
for i in range(config.vision_config.num_hidden_layers ):
# read in original q and v biases
UpperCAmelCase_: Optional[Any] = state_dict.pop(F'visual_encoder.blocks.{i}.attn.q_bias' )
UpperCAmelCase_: List[str] = state_dict.pop(F'visual_encoder.blocks.{i}.attn.v_bias' )
# next, set bias in the state dict
UpperCAmelCase_: Dict = torch.cat((q_bias, torch.zeros_like(lowerCAmelCase__ , requires_grad=lowerCAmelCase__ ), v_bias) )
UpperCAmelCase_: Optional[Any] = qkv_bias
def lowerCAmelCase_ (lowerCAmelCase__: Optional[Any] , lowerCAmelCase__: Union[str, Any] ):
"""simple docstring"""
UpperCAmelCase_: List[str] = 3_6_4 if """coco""" in model_name else 2_2_4
UpperCAmelCase_: List[str] = BlipaVisionConfig(image_size=lowerCAmelCase__ ).to_dict()
# make sure the models have proper bos_token_id and eos_token_id set (important for generation)
# seems like flan-T5 models don't have bos_token_id properly set?
if "opt-2.7b" in model_name:
UpperCAmelCase_: List[Any] = OPTConfig.from_pretrained("""facebook/opt-2.7b""" , eos_token_id=lowerCAmelCase__ ).to_dict()
elif "opt-6.7b" in model_name:
UpperCAmelCase_: Union[str, Any] = OPTConfig.from_pretrained("""facebook/opt-6.7b""" , eos_token_id=lowerCAmelCase__ ).to_dict()
elif "t5-xl" in model_name:
UpperCAmelCase_: Dict = TaConfig.from_pretrained("""google/flan-t5-xl""" , dense_act_fn="""gelu""" , bos_token_id=1 ).to_dict()
elif "t5-xxl" in model_name:
UpperCAmelCase_: List[Any] = TaConfig.from_pretrained("""google/flan-t5-xxl""" , dense_act_fn="""gelu""" , bos_token_id=1 ).to_dict()
UpperCAmelCase_: Dict = BlipaConfig(vision_config=lowerCAmelCase__ , text_config=lowerCAmelCase__ )
return config, image_size
@torch.no_grad()
def lowerCAmelCase_ (lowerCAmelCase__: str , lowerCAmelCase__: Optional[Any]=None , lowerCAmelCase__: Any=False ):
"""simple docstring"""
UpperCAmelCase_: Any = (
AutoTokenizer.from_pretrained("""facebook/opt-2.7b""" )
if """opt""" in model_name
else AutoTokenizer.from_pretrained("""google/flan-t5-xl""" )
)
UpperCAmelCase_: Dict = tokenizer("""\n""" , add_special_tokens=lowerCAmelCase__ ).input_ids[0]
UpperCAmelCase_ , UpperCAmelCase_: Optional[Any] = get_blipa_config(lowerCAmelCase__ , eos_token_id=lowerCAmelCase__ )
UpperCAmelCase_: Tuple = BlipaForConditionalGeneration(lowerCAmelCase__ ).eval()
UpperCAmelCase_: Optional[Any] = {
"""blip2-opt-2.7b""": ("""blip2_opt""", """pretrain_opt2.7b"""),
"""blip2-opt-6.7b""": ("""blip2_opt""", """pretrain_opt6.7b"""),
"""blip2-opt-2.7b-coco""": ("""blip2_opt""", """caption_coco_opt2.7b"""),
"""blip2-opt-6.7b-coco""": ("""blip2_opt""", """caption_coco_opt6.7b"""),
"""blip2-flan-t5-xl""": ("""blip2_t5""", """pretrain_flant5xl"""),
"""blip2-flan-t5-xl-coco""": ("""blip2_t5""", """caption_coco_flant5xl"""),
"""blip2-flan-t5-xxl""": ("""blip2_t5""", """pretrain_flant5xxl"""),
}
UpperCAmelCase_ , UpperCAmelCase_: List[Any] = model_name_to_original[model_name]
# load original model
print("""Loading original model...""" )
UpperCAmelCase_: Any = """cuda""" if torch.cuda.is_available() else """cpu"""
UpperCAmelCase_ , UpperCAmelCase_ , UpperCAmelCase_: int = load_model_and_preprocess(
name=lowerCAmelCase__ , model_type=lowerCAmelCase__ , is_eval=lowerCAmelCase__ , device=lowerCAmelCase__ )
original_model.eval()
print("""Done!""" )
# update state dict keys
UpperCAmelCase_: List[Any] = original_model.state_dict()
UpperCAmelCase_: Union[str, Any] = create_rename_keys(lowerCAmelCase__ )
for src, dest in rename_keys:
rename_key(lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ )
# some keys can be renamed efficiently
for key, val in state_dict.copy().items():
UpperCAmelCase_: List[str] = state_dict.pop(lowerCAmelCase__ )
if key.startswith("""Qformer.bert""" ):
UpperCAmelCase_: Optional[Any] = key.replace("""Qformer.bert""" , """qformer""" )
if "attention.self" in key:
UpperCAmelCase_: int = key.replace("""self""" , """attention""" )
if "opt_proj" in key:
UpperCAmelCase_: Optional[Any] = key.replace("""opt_proj""" , """language_projection""" )
if "t5_proj" in key:
UpperCAmelCase_: Optional[int] = key.replace("""t5_proj""" , """language_projection""" )
if key.startswith("""opt""" ):
UpperCAmelCase_: List[Any] = key.replace("""opt""" , """language""" )
if key.startswith("""t5""" ):
UpperCAmelCase_: str = key.replace("""t5""" , """language""" )
UpperCAmelCase_: int = val
# read in qv biases
read_in_q_v_bias(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCAmelCase_ , UpperCAmelCase_: int = hf_model.load_state_dict(lowerCAmelCase__ , strict=lowerCAmelCase__ )
assert len(lowerCAmelCase__ ) == 0
assert unexpected_keys == ["qformer.embeddings.position_ids"]
UpperCAmelCase_: str = load_demo_image()
UpperCAmelCase_: str = vis_processors["""eval"""](lowerCAmelCase__ ).unsqueeze(0 ).to(lowerCAmelCase__ )
UpperCAmelCase_: Tuple = tokenizer(["""\n"""] , return_tensors="""pt""" ).input_ids.to(lowerCAmelCase__ )
# create processor
UpperCAmelCase_: List[str] = BlipImageProcessor(
size={"""height""": image_size, """width""": image_size} , image_mean=lowerCAmelCase__ , image_std=lowerCAmelCase__ )
UpperCAmelCase_: Dict = BlipaProcessor(image_processor=lowerCAmelCase__ , tokenizer=lowerCAmelCase__ )
UpperCAmelCase_: Union[str, Any] = processor(images=lowerCAmelCase__ , return_tensors="""pt""" ).pixel_values.to(lowerCAmelCase__ )
# make sure processor creates exact same pixel values
assert torch.allclose(lowerCAmelCase__ , lowerCAmelCase__ )
original_model.to(lowerCAmelCase__ )
hf_model.to(lowerCAmelCase__ )
with torch.no_grad():
if "opt" in model_name:
UpperCAmelCase_: Tuple = original_model({"""image""": original_pixel_values, """text_input""": [""""""]} ).logits
UpperCAmelCase_: Tuple = hf_model(lowerCAmelCase__ , lowerCAmelCase__ ).logits
else:
UpperCAmelCase_: List[str] = original_model(
{"""image""": original_pixel_values, """text_input""": ["""\n"""], """text_output""": ["""\n"""]} ).logits
UpperCAmelCase_: List[Any] = input_ids.masked_fill(input_ids == tokenizer.pad_token_id , -1_0_0 )
UpperCAmelCase_: List[Any] = hf_model(lowerCAmelCase__ , lowerCAmelCase__ , labels=lowerCAmelCase__ ).logits
assert original_logits.shape == logits.shape
print("""First values of original logits:""" , original_logits[0, :3, :3] )
print("""First values of HF logits:""" , logits[0, :3, :3] )
# assert values
if model_name == "blip2-flan-t5-xl":
UpperCAmelCase_: Tuple = torch.tensor(
[[-41.5850, -4.4440, -8.9922], [-47.4322, -5.9143, -1.7340]] , device=lowerCAmelCase__ )
assert torch.allclose(logits[0, :3, :3] , lowerCAmelCase__ , atol=1e-4 )
elif model_name == "blip2-flan-t5-xl-coco":
UpperCAmelCase_: int = torch.tensor(
[[-57.0109, -9.8967, -12.6280], [-68.6578, -12.7191, -10.5065]] , device=lowerCAmelCase__ )
else:
# cast to same type
UpperCAmelCase_: Union[str, Any] = logits.dtype
assert torch.allclose(original_logits.to(lowerCAmelCase__ ) , lowerCAmelCase__ , atol=1e-2 )
print("""Looks ok!""" )
print("""Generating a caption...""" )
UpperCAmelCase_: Tuple = """"""
UpperCAmelCase_: int = tokenizer(lowerCAmelCase__ , return_tensors="""pt""" ).input_ids.to(lowerCAmelCase__ )
UpperCAmelCase_: Tuple = original_model.generate({"""image""": original_pixel_values} )
UpperCAmelCase_: Tuple = hf_model.generate(
lowerCAmelCase__ , lowerCAmelCase__ , do_sample=lowerCAmelCase__ , num_beams=5 , max_length=3_0 , min_length=1 , top_p=0.9 , repetition_penalty=1.0 , length_penalty=1.0 , temperature=1 , )
print("""Original generation:""" , lowerCAmelCase__ )
UpperCAmelCase_: str = input_ids.shape[1]
UpperCAmelCase_: int = processor.batch_decode(outputs[:, prompt_length:] , skip_special_tokens=lowerCAmelCase__ )
UpperCAmelCase_: Dict = [text.strip() for text in output_text]
print("""HF generation:""" , lowerCAmelCase__ )
if pytorch_dump_folder_path is not None:
processor.save_pretrained(lowerCAmelCase__ )
hf_model.save_pretrained(lowerCAmelCase__ )
if push_to_hub:
processor.push_to_hub(F'nielsr/{model_name}' )
hf_model.push_to_hub(F'nielsr/{model_name}' )
if __name__ == "__main__":
a : Union[str, Any] = argparse.ArgumentParser()
a : int = [
'blip2-opt-2.7b',
'blip2-opt-6.7b',
'blip2-opt-2.7b-coco',
'blip2-opt-6.7b-coco',
'blip2-flan-t5-xl',
'blip2-flan-t5-xl-coco',
'blip2-flan-t5-xxl',
]
parser.add_argument(
'--model_name',
default='blip2-opt-2.7b',
choices=choices,
type=str,
help='Path to hf config.json of model to convert',
)
parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
parser.add_argument(
'--push_to_hub',
action='store_true',
help='Whether to push the model and processor to the hub after converting',
)
a : int = parser.parse_args()
convert_blipa_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 556
| 0
|
'''simple docstring'''
from typing import Callable, List, Optional, Union
import PIL
import torch
from transformers import (
CLIPImageProcessor,
CLIPSegForImageSegmentation,
CLIPSegProcessor,
CLIPTextModel,
CLIPTokenizer,
)
from diffusers import DiffusionPipeline
from diffusers.configuration_utils import FrozenDict
from diffusers.models import AutoencoderKL, UNetaDConditionModel
from diffusers.pipelines.stable_diffusion import StableDiffusionInpaintPipeline
from diffusers.pipelines.stable_diffusion.safety_checker import StableDiffusionSafetyChecker
from diffusers.schedulers import DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler
from diffusers.utils import deprecate, is_accelerate_available, logging
A__: int = logging.get_logger(__name__) # pylint: disable=invalid-name
class A__ ( lowercase_ ):
def __init__( self :Dict , SCREAMING_SNAKE_CASE :CLIPSegForImageSegmentation , SCREAMING_SNAKE_CASE :CLIPSegProcessor , SCREAMING_SNAKE_CASE :AutoencoderKL , SCREAMING_SNAKE_CASE :CLIPTextModel , SCREAMING_SNAKE_CASE :CLIPTokenizer , SCREAMING_SNAKE_CASE :UNetaDConditionModel , SCREAMING_SNAKE_CASE :Union[DDIMScheduler, PNDMScheduler, LMSDiscreteScheduler] , SCREAMING_SNAKE_CASE :StableDiffusionSafetyChecker , SCREAMING_SNAKE_CASE :CLIPImageProcessor , ) -> Tuple:
'''simple docstring'''
super().__init__()
if hasattr(scheduler.config , """steps_offset""" ) and scheduler.config.steps_offset != 1:
_a : str =(
f"The configuration file of this scheduler: {scheduler} is outdated. `steps_offset`"
f" should be set to 1 instead of {scheduler.config.steps_offset}. Please make sure "
"""to update the config accordingly as leaving `steps_offset` might led to incorrect results"""
""" in future versions. If you have downloaded this checkpoint from the Hugging Face Hub,"""
""" it would be very nice if you could open a Pull request for the `scheduler/scheduler_config.json`"""
""" file"""
)
deprecate("""steps_offset!=1""" , """1.0.0""" , UpperCamelCase__ , standard_warn=UpperCamelCase__ )
_a : Optional[Any] =dict(scheduler.config )
_a : Union[str, Any] =1
_a : Union[str, Any] =FrozenDict(UpperCamelCase__ )
if hasattr(scheduler.config , """skip_prk_steps""" ) and scheduler.config.skip_prk_steps is False:
_a : Dict =(
f"The configuration file of this scheduler: {scheduler} has not set the configuration"
""" `skip_prk_steps`. `skip_prk_steps` should be set to True in the configuration file. Please make"""
""" sure to update the config accordingly as not setting `skip_prk_steps` in the config might lead to"""
""" incorrect results in future versions. If you have downloaded this checkpoint from the Hugging Face"""
""" Hub, it would be very nice if you could open a Pull request for the"""
""" `scheduler/scheduler_config.json` file"""
)
deprecate("""skip_prk_steps not set""" , """1.0.0""" , UpperCamelCase__ , standard_warn=UpperCamelCase__ )
_a : List[Any] =dict(scheduler.config )
_a : List[str] =True
_a : List[Any] =FrozenDict(UpperCamelCase__ )
if safety_checker is None:
logger.warning(
f"You have disabled the safety checker for {self.__class__} by passing `safety_checker=None`. Ensure"
""" that you abide to the conditions of the Stable Diffusion license and do not expose unfiltered"""
""" results in services or applications open to the public. Both the diffusers team and Hugging Face"""
""" strongly recommend to keep the safety filter enabled in all public facing circumstances, disabling"""
""" it only for use-cases that involve analyzing network behavior or auditing its results. For more"""
""" information, please have a look at https://github.com/huggingface/diffusers/pull/254 .""" )
self.register_modules(
segmentation_model=UpperCamelCase__ , segmentation_processor=UpperCamelCase__ , vae=UpperCamelCase__ , text_encoder=UpperCamelCase__ , tokenizer=UpperCamelCase__ , unet=UpperCamelCase__ , scheduler=UpperCamelCase__ , safety_checker=UpperCamelCase__ , feature_extractor=UpperCamelCase__ , )
def __UpperCAmelCase ( self :List[str] , SCREAMING_SNAKE_CASE :Optional[Union[str, int]] = "auto" ) -> str:
'''simple docstring'''
if slice_size == "auto":
# half the attention head size is usually a good trade-off between
# speed and memory
_a : List[Any] =self.unet.config.attention_head_dim // 2
self.unet.set_attention_slice(UpperCamelCase__ )
def __UpperCAmelCase ( self :Tuple ) -> Dict:
'''simple docstring'''
self.enable_attention_slicing(UpperCamelCase__ )
def __UpperCAmelCase ( self :Optional[Any] ) -> Dict:
'''simple docstring'''
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
_a : List[str] =torch.device("""cuda""" )
for cpu_offloaded_model in [self.unet, self.text_encoder, self.vae, self.safety_checker]:
if cpu_offloaded_model is not None:
cpu_offload(UpperCamelCase__ , UpperCamelCase__ )
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def __UpperCAmelCase ( self :Any ) -> Any:
'''simple docstring'''
if self.device != torch.device("""meta""" ) or not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(UpperCamelCase__ , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
def __call__( self :Optional[Any] , SCREAMING_SNAKE_CASE :Union[str, List[str]] , SCREAMING_SNAKE_CASE :Union[torch.FloatTensor, PIL.Image.Image] , SCREAMING_SNAKE_CASE :str , SCREAMING_SNAKE_CASE :int = 5_1_2 , SCREAMING_SNAKE_CASE :int = 5_1_2 , SCREAMING_SNAKE_CASE :int = 5_0 , SCREAMING_SNAKE_CASE :float = 7.5 , SCREAMING_SNAKE_CASE :Optional[Union[str, List[str]]] = None , SCREAMING_SNAKE_CASE :Optional[int] = 1 , SCREAMING_SNAKE_CASE :float = 0.0 , SCREAMING_SNAKE_CASE :Optional[torch.Generator] = None , SCREAMING_SNAKE_CASE :Optional[torch.FloatTensor] = None , SCREAMING_SNAKE_CASE :Optional[str] = "pil" , SCREAMING_SNAKE_CASE :bool = True , SCREAMING_SNAKE_CASE :Optional[Callable[[int, int, torch.FloatTensor], None]] = None , SCREAMING_SNAKE_CASE :int = 1 , **SCREAMING_SNAKE_CASE :Optional[Any] , ) -> Dict:
'''simple docstring'''
_a : Any =self.segmentation_processor(
text=[text] , images=[image] , padding="""max_length""" , return_tensors="""pt""" ).to(self.device )
_a : str =self.segmentation_model(**UpperCamelCase__ )
_a : Any =torch.sigmoid(outputs.logits ).cpu().detach().unsqueeze(-1 ).numpy()
_a : List[str] =self.numpy_to_pil(UpperCamelCase__ )[0].resize(image.size )
# Run inpainting pipeline with the generated mask
_a : str =StableDiffusionInpaintPipeline(
vae=self.vae , text_encoder=self.text_encoder , tokenizer=self.tokenizer , unet=self.unet , scheduler=self.scheduler , safety_checker=self.safety_checker , feature_extractor=self.feature_extractor , )
return inpainting_pipeline(
prompt=UpperCamelCase__ , image=UpperCamelCase__ , mask_image=UpperCamelCase__ , height=UpperCamelCase__ , width=UpperCamelCase__ , num_inference_steps=UpperCamelCase__ , guidance_scale=UpperCamelCase__ , negative_prompt=UpperCamelCase__ , num_images_per_prompt=UpperCamelCase__ , eta=UpperCamelCase__ , generator=UpperCamelCase__ , latents=UpperCamelCase__ , output_type=UpperCamelCase__ , return_dict=UpperCamelCase__ , callback=UpperCamelCase__ , callback_steps=UpperCamelCase__ , )
| 711
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__: Optional[int] = {
'''configuration_megatron_bert''': ['''MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MegatronBertConfig'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A__: List[str] = [
'''MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''MegatronBertForCausalLM''',
'''MegatronBertForMaskedLM''',
'''MegatronBertForMultipleChoice''',
'''MegatronBertForNextSentencePrediction''',
'''MegatronBertForPreTraining''',
'''MegatronBertForQuestionAnswering''',
'''MegatronBertForSequenceClassification''',
'''MegatronBertForTokenClassification''',
'''MegatronBertModel''',
'''MegatronBertPreTrainedModel''',
]
if TYPE_CHECKING:
from .configuration_megatron_bert import MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, MegatronBertConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_megatron_bert import (
MEGATRON_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
MegatronBertForCausalLM,
MegatronBertForMaskedLM,
MegatronBertForMultipleChoice,
MegatronBertForNextSentencePrediction,
MegatronBertForPreTraining,
MegatronBertForQuestionAnswering,
MegatronBertForSequenceClassification,
MegatronBertForTokenClassification,
MegatronBertModel,
MegatronBertPreTrainedModel,
)
else:
import sys
A__: Any = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 506
| 0
|
'''simple docstring'''
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
lowerCAmelCase : Optional[int] = datasets.logging.get_logger(__name__)
lowerCAmelCase : List[str] = """\
@inproceedings{bleurt,
title={BLEURT: Learning Robust Metrics for Text Generation},
author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
booktitle={ACL},
year={2020},
url={https://arxiv.org/abs/2004.04696}
}
"""
lowerCAmelCase : List[Any] = """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).
See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""
lowerCAmelCase : str = """
BLEURT score.
Args:
`predictions` (list of str): prediction/candidate sentences
`references` (list of str): reference sentences
`checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.
Returns:
'scores': List of scores.
Examples:
>>> predictions = [\"hello there\", \"general kenobi\"]
>>> references = [\"hello there\", \"general kenobi\"]
>>> bleurt = datasets.load_metric(\"bleurt\")
>>> results = bleurt.compute(predictions=predictions, references=references)
>>> print([round(v, 2) for v in results[\"scores\"]])
[1.03, 1.04]
"""
lowerCAmelCase : Optional[Any] = {
"""bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
"""bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
"""bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
"""bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
"""bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
"""bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
"""BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
"""BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
"""BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
"""BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION , _KWARGS_DESCRIPTION )
class UpperCamelCase__ ( datasets.Metric ):
"""simple docstring"""
def a ( self ):
'''simple docstring'''
return datasets.MetricInfo(
description=_DESCRIPTION , citation=_CITATION , homepage='https://github.com/google-research/bleurt' , inputs_description=_KWARGS_DESCRIPTION , features=datasets.Features(
{
'predictions': datasets.Value('string' , id='sequence' ),
'references': datasets.Value('string' , id='sequence' ),
} ) , codebase_urls=['https://github.com/google-research/bleurt'] , reference_urls=['https://github.com/google-research/bleurt', 'https://arxiv.org/abs/2004.04696'] , )
def a ( self , snake_case__ ):
'''simple docstring'''
if self.config_name == "default":
logger.warning(
'Using default BLEURT-Base checkpoint for sequence maximum length 128. '
'You can use a bigger model for better results with e.g.: datasets.load_metric(\'bleurt\', \'bleurt-large-512\').' )
_lowerCAmelCase : Tuple = 'bleurt-base-128'
if self.config_name.lower() in CHECKPOINT_URLS:
_lowerCAmelCase : int = self.config_name.lower()
elif self.config_name.upper() in CHECKPOINT_URLS:
_lowerCAmelCase : str = self.config_name.upper()
else:
raise KeyError(
F'{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}' )
# download the model checkpoint specified by self.config_name and set up the scorer
_lowerCAmelCase : Optional[Any] = dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name] )
_lowerCAmelCase : str = score.BleurtScorer(os.path.join(snake_case__ , snake_case__ ) )
def a ( self , snake_case__ , snake_case__ ):
'''simple docstring'''
_lowerCAmelCase : Tuple = self.scorer.score(references=snake_case__ , candidates=snake_case__ )
return {"scores": scores}
| 444
|
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCamelCase__ ( metaclass=SCREAMING_SNAKE_CASE_ ):
"""simple docstring"""
__magic_name__ = ["torch", "torchsde"]
def __init__( self , *snake_case__ , **snake_case__ ):
'''simple docstring'''
requires_backends(self , ['torch', 'torchsde'] )
@classmethod
def a ( cls , *snake_case__ , **snake_case__ ):
'''simple docstring'''
requires_backends(cls , ['torch', 'torchsde'] )
@classmethod
def a ( cls , *snake_case__ , **snake_case__ ):
'''simple docstring'''
requires_backends(cls , ['torch', 'torchsde'] )
| 444
| 1
|
'''simple docstring'''
def a ( __a ) -> bool:
'''simple docstring'''
UpperCamelCase__ :set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
UpperCamelCase__ :set[int] = set()
return any(
node not in visited and depth_first_search(__a , __a , __a , __a )
for node in graph )
def a ( __a , __a , __a , __a ) -> bool:
'''simple docstring'''
visited.add(__a )
rec_stk.add(__a )
for node in graph[vertex]:
if node not in visited:
if depth_first_search(__a , __a , __a , __a ):
return True
elif node in rec_stk:
return True
# The node needs to be removed from recursion stack before function ends
rec_stk.remove(__a )
return False
if __name__ == "__main__":
from doctest import testmod
testmod()
| 718
|
'''simple docstring'''
import json
import sys
def a ( __a , __a ) -> str:
'''simple docstring'''
with open(__a , encoding='''utf-8''' ) as f:
UpperCamelCase__ :List[str] = json.load(__a )
UpperCamelCase__ :int = ['''<details>''', '''<summary>Show updated benchmarks!</summary>''', ''' ''']
for benchmark_name in sorted(__a ):
UpperCamelCase__ :Optional[Any] = results[benchmark_name]
UpperCamelCase__ :int = benchmark_name.split('''/''' )[-1]
output_md.append(f'''### Benchmark: {benchmark_file_name}''' )
UpperCamelCase__ :List[str] = '''| metric |'''
UpperCamelCase__ :str = '''|--------|'''
UpperCamelCase__ :Union[str, Any] = '''| new / old (diff) |'''
for metric_name in sorted(__a ):
UpperCamelCase__ :List[Any] = benchmark_res[metric_name]
UpperCamelCase__ :Optional[int] = metric_vals['''new''']
UpperCamelCase__ :Any = metric_vals.get('''old''' , __a )
UpperCamelCase__ :Optional[int] = metric_vals.get('''diff''' , __a )
UpperCamelCase__ :List[str] = f''' {new_val:f}''' if isinstance(__a , (int, float) ) else '''None'''
if old_val is not None:
val_str += f''' / {old_val:f}''' if isinstance(__a , (int, float) ) else "None"
if dif_val is not None:
val_str += f''' ({dif_val:f})''' if isinstance(__a , (int, float) ) else "None"
title += " " + metric_name + " |"
lines += "---|"
value += val_str + " |"
output_md += [title, lines, value, " "]
output_md.append('''</details>''' )
with open(__a , '''w''' , encoding='''utf-8''' ) as f:
f.writelines('''\n'''.join(__a ) )
if __name__ == "__main__":
__snake_case = sys.argv[1]
__snake_case = sys.argv[2]
format_json_to_md(input_json_file, output_md_file)
| 280
| 0
|
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def UpperCamelCase_( lowerCamelCase_ ) -> Dict:
_lowercase : List[Any] = SwinConfig()
_lowercase : List[str] = swin_name.split('_' )
_lowercase : Optional[int] = name_split[1]
_lowercase : List[Any] = int(name_split[4] )
_lowercase : Optional[int] = int(name_split[3][-1] )
if model_size == "tiny":
_lowercase : Tuple = 96
_lowercase : Tuple = (2, 2, 6, 2)
_lowercase : Any = (3, 6, 12, 24)
elif model_size == "small":
_lowercase : Union[str, Any] = 96
_lowercase : Union[str, Any] = (2, 2, 18, 2)
_lowercase : Dict = (3, 6, 12, 24)
elif model_size == "base":
_lowercase : Tuple = 128
_lowercase : Union[str, Any] = (2, 2, 18, 2)
_lowercase : str = (4, 8, 16, 32)
else:
_lowercase : Optional[int] = 192
_lowercase : Optional[Any] = (2, 2, 18, 2)
_lowercase : Optional[int] = (6, 12, 24, 48)
if "in22k" in swin_name:
_lowercase : int = 2_1841
else:
_lowercase : List[str] = 1000
_lowercase : Optional[Any] = 'huggingface/label-files'
_lowercase : int = 'imagenet-1k-id2label.json'
_lowercase : str = json.load(open(hf_hub_download(lowerCamelCase_ , lowerCamelCase_ , repo_type='dataset' ) , 'r' ) )
_lowercase : Optional[int] = {int(lowerCamelCase_ ): v for k, v in idalabel.items()}
_lowercase : List[str] = idalabel
_lowercase : str = {v: k for k, v in idalabel.items()}
_lowercase : Dict = img_size
_lowercase : List[Any] = num_classes
_lowercase : Optional[Any] = embed_dim
_lowercase : List[Any] = depths
_lowercase : Union[str, Any] = num_heads
_lowercase : Optional[Any] = window_size
return config
def UpperCamelCase_( lowerCamelCase_ ) -> Dict:
if "patch_embed.proj" in name:
_lowercase : List[str] = name.replace('patch_embed.proj' , 'embeddings.patch_embeddings.projection' )
if "patch_embed.norm" in name:
_lowercase : Any = name.replace('patch_embed.norm' , 'embeddings.norm' )
if "layers" in name:
_lowercase : Tuple = 'encoder.' + name
if "attn.proj" in name:
_lowercase : Dict = name.replace('attn.proj' , 'attention.output.dense' )
if "attn" in name:
_lowercase : str = name.replace('attn' , 'attention.self' )
if "norm1" in name:
_lowercase : List[Any] = name.replace('norm1' , 'layernorm_before' )
if "norm2" in name:
_lowercase : Any = name.replace('norm2' , 'layernorm_after' )
if "mlp.fc1" in name:
_lowercase : Dict = name.replace('mlp.fc1' , 'intermediate.dense' )
if "mlp.fc2" in name:
_lowercase : List[str] = name.replace('mlp.fc2' , 'output.dense' )
if name == "norm.weight":
_lowercase : Any = 'layernorm.weight'
if name == "norm.bias":
_lowercase : Union[str, Any] = 'layernorm.bias'
if "head" in name:
_lowercase : Tuple = name.replace('head' , 'classifier' )
else:
_lowercase : str = 'swin.' + name
return name
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> List[str]:
for key in orig_state_dict.copy().keys():
_lowercase : Any = orig_state_dict.pop(lowerCamelCase_ )
if "mask" in key:
continue
elif "qkv" in key:
_lowercase : Union[str, Any] = key.split('.' )
_lowercase : List[str] = int(key_split[1] )
_lowercase : Any = int(key_split[3] )
_lowercase : Any = model.swin.encoder.layers[layer_num].blocks[block_num].attention.self.all_head_size
if "weight" in key:
_lowercase : List[Any] = val[:dim, :]
_lowercase : List[Any] = val[
dim : dim * 2, :
]
_lowercase : Any = val[-dim:, :]
else:
_lowercase : Any = val[
:dim
]
_lowercase : Optional[Any] = val[
dim : dim * 2
]
_lowercase : Any = val[
-dim:
]
else:
_lowercase : List[str] = val
return orig_state_dict
def UpperCamelCase_( lowerCamelCase_ , lowerCamelCase_ ) -> int:
_lowercase : Union[str, Any] = timm.create_model(lowerCamelCase_ , pretrained=lowerCamelCase_ )
timm_model.eval()
_lowercase : Any = get_swin_config(lowerCamelCase_ )
_lowercase : str = SwinForImageClassification(lowerCamelCase_ )
model.eval()
_lowercase : List[Any] = convert_state_dict(timm_model.state_dict() , lowerCamelCase_ )
model.load_state_dict(lowerCamelCase_ )
_lowercase : Optional[Any] = 'http://images.cocodataset.org/val2017/000000039769.jpg'
_lowercase : List[str] = AutoImageProcessor.from_pretrained('microsoft/{}'.format(swin_name.replace('_' , '-' ) ) )
_lowercase : Dict = Image.open(requests.get(lowerCamelCase_ , stream=lowerCamelCase_ ).raw )
_lowercase : Tuple = image_processor(images=lowerCamelCase_ , return_tensors='pt' )
_lowercase : List[str] = timm_model(inputs['pixel_values'] )
_lowercase : Tuple = model(**lowerCamelCase_ ).logits
assert torch.allclose(lowerCamelCase_ , lowerCamelCase_ , atol=1e-3 )
print(F'''Saving model {swin_name} to {pytorch_dump_folder_path}''' )
model.save_pretrained(lowerCamelCase_ )
print(F'''Saving image processor to {pytorch_dump_folder_path}''' )
image_processor.save_pretrained(lowerCamelCase_ )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--swin_name",
default="swin_tiny_patch4_window7_224",
type=str,
help="Name of the Swin timm model you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
)
SCREAMING_SNAKE_CASE : Union[str, Any] = parser.parse_args()
convert_swin_checkpoint(args.swin_name, args.pytorch_dump_folder_path)
| 89
|
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import numpy as np
import pandas as pd
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
BartForSequenceClassification,
DataCollatorWithPadding,
EvalPrediction,
HfArgumentParser,
TapexTokenizer,
Trainer,
TrainingArguments,
default_data_collator,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.17.0.dev0")
require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
SCREAMING_SNAKE_CASE : Dict = logging.getLogger(__name__)
@dataclass
class _lowerCamelCase:
lowercase_ : Optional[str] = field(
default="""tab_fact""", metadata={"""help""": """The name of the dataset to use (via the datasets library)."""} )
lowercase_ : Optional[str] = field(
default="""tab_fact""", metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}, )
lowercase_ : int = field(
default=10_24, metadata={
"""help""": (
"""The maximum total input sequence length after tokenization. Sequences longer """
"""than this will be truncated, sequences shorter will be padded."""
)
}, )
lowercase_ : bool = field(
default=_a, metadata={"""help""": """Overwrite the cached preprocessed datasets or not."""} )
lowercase_ : bool = field(
default=_a, 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."""
)
}, )
lowercase_ : Optional[int] = field(
default=_a, metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of training examples to this """
"""value if set."""
)
}, )
lowercase_ : Optional[int] = field(
default=_a, metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of evaluation examples to this """
"""value if set."""
)
}, )
lowercase_ : Optional[int] = field(
default=_a, metadata={
"""help""": (
"""For debugging purposes or quicker training, truncate the number of prediction examples to this """
"""value if set."""
)
}, )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """A csv or a json file containing the training data."""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """A csv or a json file containing the validation data."""} )
lowercase_ : Optional[str] = field(default=_a, metadata={"""help""": """A csv or a json file containing the test data."""} )
def UpperCamelCase ( self) -> Dict:
"""simple docstring"""
if self.dataset_name is not None:
pass
elif self.train_file is None or self.validation_file is None:
raise ValueError('Need either a GLUE task, a training/validation file or a dataset name.')
else:
_lowercase : int = self.train_file.split('.')[-1]
assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
_lowercase : Tuple = self.validation_file.split('.')[-1]
assert (
validation_extension == train_extension
), "`validation_file` should have the same extension (csv or json) as `train_file`."
@dataclass
class _lowerCamelCase:
lowercase_ : str = field(
default=_a, metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """Pretrained config name or path if not the same as model_name"""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """Pretrained tokenizer name or path if not the same as model_name"""} )
lowercase_ : Optional[str] = field(
default=_a, metadata={"""help""": """Where do you want to store the pretrained models downloaded from huggingface.co"""}, )
lowercase_ : bool = field(
default=_a, metadata={"""help""": """Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."""}, )
lowercase_ : str = field(
default="""main""", metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}, )
lowercase_ : bool = field(
default=_a, metadata={
"""help""": (
"""Will use the token generated when running `huggingface-cli login` (necessary to use this script """
"""with private models)."""
)
}, )
def UpperCamelCase_( ) -> Optional[int]:
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
_lowercase : Optional[int] = 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.
_lowercase , _lowercase , _lowercase : Tuple = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1] ) )
else:
_lowercase , _lowercase , _lowercase : Union[str, Any] = parser.parse_args_into_dataclasses()
# 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 )] , )
_lowercase : Union[str, Any] = training_args.get_process_log_level()
logger.setLevel(lowerCamelCase_ )
datasets.utils.logging.set_verbosity(lowerCamelCase_ )
transformers.utils.logging.set_verbosity(lowerCamelCase_ )
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Log on each process the small summary:
logger.warning(
F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
+ F'''distributed training: {bool(training_args.local_rank != -1 )}, 16-bits training: {training_args.fpaa}''' )
logger.info(F'''Training/evaluation parameters {training_args}''' )
# Detecting last checkpoint.
_lowercase : Optional[int] = None
if os.path.isdir(training_args.output_dir ) and training_args.do_train and not training_args.overwrite_output_dir:
_lowercase : 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 and training_args.resume_from_checkpoint is None:
logger.info(
F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.' )
# Set seed before initializing model.
set_seed(training_args.seed )
# Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
# or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
#
# For JSON files, this script will use the `question` column for the input question and `table` column for the corresponding table.
#
# If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
# single column. 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.
_lowercase : Dict = load_dataset(
data_args.dataset_name , data_args.dataset_config_name , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from your local files.
# CSV/JSON training and evaluation files are needed.
_lowercase : Optional[Any] = {'train': data_args.train_file, 'validation': data_args.validation_file}
# Get the test dataset: you can provide your own CSV/JSON test file (see below)
# when you use `do_predict` without specifying a GLUE benchmark task.
if training_args.do_predict:
if data_args.test_file is not None:
_lowercase : Tuple = data_args.train_file.split('.' )[-1]
_lowercase : int = data_args.test_file.split('.' )[-1]
assert (
test_extension == train_extension
), "`test_file` should have the same extension (csv or json) as `train_file`."
_lowercase : Any = data_args.test_file
else:
raise ValueError('Need either a GLUE task or a test file for `do_predict`.' )
for key in data_files.keys():
logger.info(F'''load a local file for {key}: {data_files[key]}''' )
if data_args.train_file.endswith('.csv' ):
# Loading a dataset from local csv files
_lowercase : str = load_dataset('csv' , data_files=lowerCamelCase_ , cache_dir=model_args.cache_dir )
else:
# Loading a dataset from local json files
_lowercase : Optional[int] = load_dataset('json' , data_files=lowerCamelCase_ , cache_dir=model_args.cache_dir )
# See more about loading any type of standard or custom dataset at
# https://huggingface.co/docs/datasets/loading_datasets.html.
# Labels
_lowercase : Optional[Any] = raw_datasets['train'].features['label'].names
_lowercase : Any = len(lowerCamelCase_ )
# Load pretrained model and tokenizer
#
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
# download model & vocab.
_lowercase : List[Any] = AutoConfig.from_pretrained(
model_args.config_name if model_args.config_name else model_args.model_name_or_path , num_labels=lowerCamelCase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# load tapex tokenizer
_lowercase : str = TapexTokenizer.from_pretrained(
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path , cache_dir=model_args.cache_dir , use_fast=model_args.use_fast_tokenizer , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , add_prefix_space=lowerCamelCase_ , )
_lowercase : Tuple = BartForSequenceClassification.from_pretrained(
model_args.model_name_or_path , from_tf=bool('.ckpt' in model_args.model_name_or_path ) , config=lowerCamelCase_ , cache_dir=model_args.cache_dir , revision=model_args.model_revision , use_auth_token=True if model_args.use_auth_token else None , )
# Padding strategy
if data_args.pad_to_max_length:
_lowercase : int = 'max_length'
else:
# We will pad later, dynamically at batch creation, to the max sequence length in each batch
_lowercase : str = False
# Some models have set the order of the labels to use, so let's make sure we do use it.
_lowercase : List[Any] = {'Refused': 0, 'Entailed': 1}
_lowercase : Union[str, Any] = {0: 'Refused', 1: 'Entailed'}
if data_args.max_seq_length > tokenizer.model_max_length:
logger.warning(
F'''The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the'''
F'''model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}.''' )
_lowercase : List[str] = min(data_args.max_seq_length , tokenizer.model_max_length )
def preprocess_tabfact_function(lowerCamelCase_ ):
# Tokenize the texts
def _convert_table_text_to_pandas(lowerCamelCase_ ):
_lowercase : int = [_table_row.split('#' ) for _table_row in _table_text.strip('\n' ).split('\n' )]
_lowercase : Any = pd.DataFrame.from_records(_table_content[1:] , columns=_table_content[0] )
return _table_pd
_lowercase : List[Any] = examples['statement']
_lowercase : Optional[Any] = list(map(_convert_table_text_to_pandas , examples['table_text'] ) )
_lowercase : Union[str, Any] = tokenizer(lowerCamelCase_ , lowerCamelCase_ , padding=lowerCamelCase_ , max_length=lowerCamelCase_ , truncation=lowerCamelCase_ )
_lowercase : Any = examples['label']
return result
with training_args.main_process_first(desc='dataset map pre-processing' ):
_lowercase : str = raw_datasets.map(
lowerCamelCase_ , batched=lowerCamelCase_ , load_from_cache_file=not data_args.overwrite_cache , desc='Running tokenizer on dataset' , )
if training_args.do_train:
if "train" not in raw_datasets:
raise ValueError('--do_train requires a train dataset' )
_lowercase : Any = raw_datasets['train']
if data_args.max_train_samples is not None:
_lowercase : str = train_dataset.select(range(data_args.max_train_samples ) )
if training_args.do_eval:
if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
raise ValueError('--do_eval requires a validation dataset' )
_lowercase : str = raw_datasets['validation']
if data_args.max_eval_samples is not None:
_lowercase : List[Any] = eval_dataset.select(range(data_args.max_eval_samples ) )
if training_args.do_predict or data_args.test_file is not None:
if "test" not in raw_datasets and "test_matched" not in raw_datasets:
raise ValueError('--do_predict requires a test dataset' )
_lowercase : Optional[int] = raw_datasets['test']
if data_args.max_predict_samples is not None:
_lowercase : List[str] = predict_dataset.select(range(data_args.max_predict_samples ) )
# Log a few random samples from the training set:
if training_args.do_train:
for index in random.sample(range(len(lowerCamelCase_ ) ) , 3 ):
logger.info(F'''Sample {index} of the training set: {train_dataset[index]}.''' )
# You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
# predictions and label_ids field) and has to return a dictionary string to float.
def compute_metrics(lowerCamelCase_ ):
_lowercase : Dict = p.predictions[0] if isinstance(p.predictions , lowerCamelCase_ ) else p.predictions
_lowercase : Tuple = np.argmax(lowerCamelCase_ , axis=1 )
return {"accuracy": (preds == p.label_ids).astype(np.floataa ).mean().item()}
# Data collator will default to DataCollatorWithPadding, so we change it if we already did the padding.
if data_args.pad_to_max_length:
_lowercase : Any = default_data_collator
elif training_args.fpaa:
_lowercase : str = DataCollatorWithPadding(lowerCamelCase_ , pad_to_multiple_of=8 )
else:
_lowercase : Optional[Any] = None
# Initialize our Trainer
_lowercase : List[str] = Trainer(
model=lowerCamelCase_ , args=lowerCamelCase_ , train_dataset=train_dataset if training_args.do_train else None , eval_dataset=eval_dataset if training_args.do_eval else None , compute_metrics=lowerCamelCase_ , tokenizer=lowerCamelCase_ , data_collator=lowerCamelCase_ , )
# Training
if training_args.do_train:
_lowercase : Optional[int] = None
if training_args.resume_from_checkpoint is not None:
_lowercase : List[Any] = training_args.resume_from_checkpoint
elif last_checkpoint is not None:
_lowercase : Optional[Any] = last_checkpoint
_lowercase : Optional[Any] = trainer.train(resume_from_checkpoint=lowerCamelCase_ )
_lowercase : List[Any] = train_result.metrics
_lowercase : Dict = (
data_args.max_train_samples if data_args.max_train_samples is not None else len(lowerCamelCase_ )
)
_lowercase : int = min(lowerCamelCase_ , len(lowerCamelCase_ ) )
trainer.save_model() # Saves the tokenizer too for easy upload
trainer.log_metrics('train' , lowerCamelCase_ )
trainer.save_metrics('train' , lowerCamelCase_ )
trainer.save_state()
# Evaluation
if training_args.do_eval:
logger.info('*** Evaluate ***' )
_lowercase : Tuple = trainer.evaluate(eval_dataset=lowerCamelCase_ )
_lowercase : Any = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(lowerCamelCase_ )
_lowercase : Optional[int] = min(lowerCamelCase_ , len(lowerCamelCase_ ) )
trainer.log_metrics('eval' , lowerCamelCase_ )
trainer.save_metrics('eval' , lowerCamelCase_ )
if training_args.do_predict:
logger.info('*** Predict ***' )
# Removing the `label` columns because it contains -1 and Trainer won't like that.
_lowercase : Any = predict_dataset.remove_columns('label' )
_lowercase : Optional[Any] = trainer.predict(lowerCamelCase_ , metric_key_prefix='predict' ).predictions
_lowercase : Union[str, Any] = np.argmax(lowerCamelCase_ , axis=1 )
_lowercase : Dict = os.path.join(training_args.output_dir , 'predict_results_tabfact.txt' )
if trainer.is_world_process_zero():
with open(lowerCamelCase_ , 'w' ) as writer:
logger.info('***** Predict Results *****' )
writer.write('index\tprediction\n' )
for index, item in enumerate(lowerCamelCase_ ):
_lowercase : List[str] = label_list[item]
writer.write(F'''{index}\t{item}\n''' )
_lowercase : str = {'finetuned_from': model_args.model_name_or_path, 'tasks': 'text-classification'}
if training_args.push_to_hub:
trainer.push_to_hub(**lowerCamelCase_ )
else:
trainer.create_model_card(**lowerCamelCase_ )
def UpperCamelCase_( lowerCamelCase_ ) -> Dict:
# For xla_spawn (TPUs)
main()
if __name__ == "__main__":
main()
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from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase__ =logging.get_logger(__name__)
UpperCamelCase__ ={
'weiweishi/roc-bert-base-zh': 'https://huggingface.co/weiweishi/roc-bert-base-zh/resolve/main/config.json',
}
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
__snake_case = 'roc_bert'
def __init__( self , __lowerCamelCase=3_0_5_2_2 , __lowerCamelCase=7_6_8 , __lowerCamelCase=1_2 , __lowerCamelCase=1_2 , __lowerCamelCase=3_0_7_2 , __lowerCamelCase="gelu" , __lowerCamelCase=0.1 , __lowerCamelCase=0.1 , __lowerCamelCase=5_1_2 , __lowerCamelCase=2 , __lowerCamelCase=0.02 , __lowerCamelCase=1E-12 , __lowerCamelCase=True , __lowerCamelCase=0 , __lowerCamelCase="absolute" , __lowerCamelCase=None , __lowerCamelCase=True , __lowerCamelCase=True , __lowerCamelCase=7_6_8 , __lowerCamelCase=9_1_0 , __lowerCamelCase=5_1_2 , __lowerCamelCase=2_4_8_5_8 , __lowerCamelCase=True , **__lowerCamelCase , ) -> int:
_SCREAMING_SNAKE_CASE : Optional[Any] = vocab_size
_SCREAMING_SNAKE_CASE : int = max_position_embeddings
_SCREAMING_SNAKE_CASE : List[str] = hidden_size
_SCREAMING_SNAKE_CASE : List[Any] = num_hidden_layers
_SCREAMING_SNAKE_CASE : Dict = num_attention_heads
_SCREAMING_SNAKE_CASE : int = intermediate_size
_SCREAMING_SNAKE_CASE : Optional[Any] = hidden_act
_SCREAMING_SNAKE_CASE : Union[str, Any] = hidden_dropout_prob
_SCREAMING_SNAKE_CASE : Tuple = attention_probs_dropout_prob
_SCREAMING_SNAKE_CASE : Optional[int] = initializer_range
_SCREAMING_SNAKE_CASE : List[Any] = type_vocab_size
_SCREAMING_SNAKE_CASE : Optional[Any] = layer_norm_eps
_SCREAMING_SNAKE_CASE : Tuple = use_cache
_SCREAMING_SNAKE_CASE : Union[str, Any] = enable_pronunciation
_SCREAMING_SNAKE_CASE : int = enable_shape
_SCREAMING_SNAKE_CASE : Union[str, Any] = pronunciation_embed_dim
_SCREAMING_SNAKE_CASE : Union[str, Any] = pronunciation_vocab_size
_SCREAMING_SNAKE_CASE : Optional[int] = shape_embed_dim
_SCREAMING_SNAKE_CASE : str = shape_vocab_size
_SCREAMING_SNAKE_CASE : Optional[Any] = concat_input
_SCREAMING_SNAKE_CASE : Union[str, Any] = position_embedding_type
_SCREAMING_SNAKE_CASE : str = classifier_dropout
super().__init__(pad_token_id=__lowerCamelCase , **__lowerCamelCase )
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|
def lowerCamelCase__ (__lowerCamelCase = 10**9 ):
_SCREAMING_SNAKE_CASE : List[str] = 1
_SCREAMING_SNAKE_CASE : Any = 2
_SCREAMING_SNAKE_CASE : List[Any] = 0
_SCREAMING_SNAKE_CASE : Dict = 0
_SCREAMING_SNAKE_CASE : Union[str, Any] = 0
while perimeter <= max_perimeter:
perimeters_sum += perimeter
prev_value += 2 * value
value += prev_value
_SCREAMING_SNAKE_CASE : Tuple = 2 * value + 2 if i % 2 == 0 else 2 * value - 2
i += 1
return perimeters_sum
if __name__ == "__main__":
print(f"{solution() = }")
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# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_ ( _SCREAMING_SNAKE_CASE ):
def __init__( self ,__snake_case ,__snake_case ):
"""simple docstring"""
super().__init__()
self.register_modules(unet=__snake_case ,scheduler=__snake_case )
@torch.no_grad()
def __call__( self ,__snake_case = 1 ,__snake_case = None ,__snake_case = 5_0 ,__snake_case = "pil" ,__snake_case = True ,**__snake_case ,):
"""simple docstring"""
A_ = torch.randn(
(batch_size, self.unet.config.in_channels, self.unet.config.sample_size, self.unet.config.sample_size) ,generator=__snake_case ,)
A_ = image.to(self.device )
# set step values
self.scheduler.set_timesteps(__snake_case )
for t in self.progress_bar(self.scheduler.timesteps ):
# 1. predict noise model_output
A_ = self.unet(__snake_case ,__snake_case ).sample
# 2. predict previous mean of image x_t-1 and add variance depending on eta
# eta corresponds to η in paper and should be between [0, 1]
# do x_t -> x_t-1
A_ = self.scheduler.step(__snake_case ,__snake_case ,__snake_case ).prev_sample
A_ = (image / 2 + 0.5).clamp(0 ,1 )
A_ = image.cpu().permute(0 ,2 ,3 ,1 ).numpy()
if output_type == "pil":
A_ = self.numpy_to_pil(__snake_case )
if not return_dict:
return (image,), "This is a local test"
return ImagePipelineOutput(images=__snake_case ), "This is a local test"
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|
from __future__ import annotations
def UpperCAmelCase_ ( _UpperCAmelCase :list[float] , _UpperCAmelCase :list[float] ) -> float:
'''simple docstring'''
A_ = sorted(numsa + numsa )
A_ , A_ = divmod(len(_UpperCAmelCase ) , 2 )
if mod == 1:
return all_numbers[div]
else:
return (all_numbers[div] + all_numbers[div - 1]) / 2
if __name__ == "__main__":
import doctest
doctest.testmod()
a__ : Optional[Any] = [float(x) for x in input('Enter the elements of first array: ').split()]
a__ : int = [float(x) for x in input('Enter the elements of second array: ').split()]
print(f'''The median of two arrays is: {median_of_two_arrays(array_a, array_a)}''')
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|
"""simple docstring"""
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
__lowerCAmelCase : Optional[Any] = logging.get_logger(__name__)
__lowerCAmelCase : Optional[int] = {'''vocab_file''': '''vocab.txt''', '''tokenizer_file''': '''tokenizer.json'''}
__lowerCAmelCase : 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'''
),
},
}
__lowerCAmelCase : Any = {
'''distilbert-base-uncased''': 512,
'''distilbert-base-uncased-distilled-squad''': 512,
'''distilbert-base-cased''': 512,
'''distilbert-base-cased-distilled-squad''': 512,
'''distilbert-base-german-cased''': 512,
'''distilbert-base-multilingual-cased''': 512,
}
__lowerCAmelCase : 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 SCREAMING_SNAKE_CASE ( lowerCAmelCase ):
'''simple docstring'''
snake_case__ : str = VOCAB_FILES_NAMES
snake_case__ : int = PRETRAINED_VOCAB_FILES_MAP
snake_case__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ : int = PRETRAINED_INIT_CONFIGURATION
snake_case__ : int = ['input_ids', 'attention_mask']
snake_case__ : Tuple = DistilBertTokenizer
def __init__( self :int , __magic_name__ :Union[str, Any]=None , __magic_name__ :Optional[Any]=None , __magic_name__ :int=True , __magic_name__ :Any="[UNK]" , __magic_name__ :List[Any]="[SEP]" , __magic_name__ :Optional[Any]="[PAD]" , __magic_name__ :List[Any]="[CLS]" , __magic_name__ :List[Any]="[MASK]" , __magic_name__ :List[Any]=True , __magic_name__ :Any=None , **__magic_name__ :Optional[int] , ) -> Dict:
'''simple docstring'''
super().__init__(
UpperCamelCase_ , tokenizer_file=UpperCamelCase_ , do_lower_case=UpperCamelCase_ , unk_token=UpperCamelCase_ , sep_token=UpperCamelCase_ , pad_token=UpperCamelCase_ , cls_token=UpperCamelCase_ , mask_token=UpperCamelCase_ , tokenize_chinese_chars=UpperCamelCase_ , strip_accents=UpperCamelCase_ , **UpperCamelCase_ , )
a__ = json.loads(self.backend_tokenizer.normalizer.__getstate__() )
if (
normalizer_state.get('''lowercase''' , UpperCamelCase_ ) != do_lower_case
or normalizer_state.get('''strip_accents''' , UpperCamelCase_ ) != strip_accents
or normalizer_state.get('''handle_chinese_chars''' , UpperCamelCase_ ) != tokenize_chinese_chars
):
a__ = getattr(UpperCamelCase_ , normalizer_state.pop('''type''' ) )
a__ = do_lower_case
a__ = strip_accents
a__ = tokenize_chinese_chars
a__ = normalizer_class(**UpperCamelCase_ )
a__ = do_lower_case
def _UpperCamelCase ( self :int , __magic_name__ :Any , __magic_name__ :List[str]=None ) -> Optional[int]:
'''simple docstring'''
a__ = [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 _UpperCamelCase ( self :Optional[Any] , __magic_name__ :List[int] , __magic_name__ :Optional[List[int]] = None ) -> List[int]:
'''simple docstring'''
a__ = [self.sep_token_id]
a__ = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def _UpperCamelCase ( self :Tuple , __magic_name__ :str , __magic_name__ :Optional[str] = None ) -> Tuple[str]:
'''simple docstring'''
a__ = self._tokenizer.model.save(UpperCamelCase_ , name=UpperCamelCase_ )
return tuple(UpperCamelCase_ )
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|
"""simple docstring"""
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def __snake_case ( UpperCamelCase ) -> float:
"""simple docstring"""
return np.dot(UpperCamelCase , UpperCamelCase )
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self :str , *,
__magic_name__ :float = np.inf , __magic_name__ :str = "linear" , __magic_name__ :float = 0.0 , ) -> None:
'''simple docstring'''
a__ = regularization
a__ = gamma
if kernel == "linear":
a__ = self.__linear
elif kernel == "rbf":
if self.gamma == 0:
raise ValueError('''rbf kernel requires gamma''' )
if not isinstance(self.gamma , (float, int) ):
raise ValueError('''gamma must be float or int''' )
if not self.gamma > 0:
raise ValueError('''gamma must be > 0''' )
a__ = self.__rbf
# in the future, there could be a default value like in sklearn
# sklear: def_gamma = 1/(n_features * X.var()) (wiki)
# previously it was 1/(n_features)
else:
a__ = F"Unknown kernel: {kernel}"
raise ValueError(__magic_name__ )
def _UpperCamelCase ( self :List[str] , __magic_name__ :ndarray , __magic_name__ :ndarray ) -> float:
'''simple docstring'''
return np.dot(__magic_name__ , __magic_name__ )
def _UpperCamelCase ( self :List[str] , __magic_name__ :ndarray , __magic_name__ :ndarray ) -> float:
'''simple docstring'''
return np.exp(-(self.gamma * norm_squared(vectora - vectora )) )
def _UpperCamelCase ( self :Optional[int] , __magic_name__ :list[ndarray] , __magic_name__ :ndarray ) -> None:
'''simple docstring'''
a__ = observations
a__ = classes
# using Wolfe's Dual to calculate w.
# Primal problem: minimize 1/2*norm_squared(w)
# constraint: yn(w . xn + b) >= 1
#
# With l a vector
# Dual problem: maximize sum_n(ln) -
# 1/2 * sum_n(sum_m(ln*lm*yn*ym*xn . xm))
# constraint: self.C >= ln >= 0
# and sum_n(ln*yn) = 0
# Then we get w using w = sum_n(ln*yn*xn)
# At the end we can get b ~= mean(yn - w . xn)
#
# Since we use kernels, we only need l_star to calculate b
# and to classify observations
((a__) , ) = np.shape(__magic_name__ )
def to_minimize(__magic_name__ :ndarray ) -> float:
a__ = 0
((a__) , ) = np.shape(__magic_name__ )
for i in range(__magic_name__ ):
for j in range(__magic_name__ ):
s += (
candidate[i]
* candidate[j]
* classes[i]
* classes[j]
* self.kernel(observations[i] , observations[j] )
)
return 1 / 2 * s - sum(__magic_name__ )
a__ = LinearConstraint(__magic_name__ , 0 , 0 )
a__ = Bounds(0 , self.regularization )
a__ = minimize(
__magic_name__ , np.ones(__magic_name__ ) , bounds=__magic_name__ , constraints=[ly_contraint] ).x
a__ = l_star
# calculating mean offset of separation plane to points
a__ = 0
for i in range(__magic_name__ ):
for j in range(__magic_name__ ):
s += classes[i] - classes[i] * self.optimum[i] * self.kernel(
observations[i] , observations[j] )
a__ = s / n
def _UpperCamelCase ( self :Dict , __magic_name__ :ndarray ) -> int:
'''simple docstring'''
a__ = sum(
self.optimum[n]
* self.classes[n]
* self.kernel(self.observations[n] , __magic_name__ )
for n in range(len(self.classes ) ) )
return 1 if s + self.offset >= 0 else -1
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
UpperCamelCase__ = logging.get_logger(__name__)
def _UpperCamelCase (a__ :Union[tf.Tensor, np.ndarray] ):
"""simple docstring"""
if isinstance(lowerCAmelCase__ , np.ndarray ):
return list(tensor.shape )
UpperCamelCase__ = tf.shape(lowerCAmelCase__ )
if tensor.shape == tf.TensorShape(lowerCAmelCase__ ):
return dynamic
UpperCamelCase__ = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(lowerCAmelCase__ )]
def _UpperCamelCase (a__ :tf.Tensor , a__ :Optional[int] = None , a__ :Optional[str] = None ):
"""simple docstring"""
return tf.nn.softmax(logits=logits + 1e-9 , axis=lowerCAmelCase__ , name=lowerCAmelCase__ )
def _UpperCamelCase (a__ :List[Any] , a__ :List[str] , a__ :Any , a__ :Union[str, Any]=1e-5 , a__ :str=-1 ):
"""simple docstring"""
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise NotImplementedError("""Only 1D weight and bias tensors are supported for now, with only a single axis.""" )
# Get mean and variance on the axis to be normalized
UpperCamelCase__ = tf.nn.moments(lowerCAmelCase__ , axes=[axis] , keepdims=lowerCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
UpperCamelCase__ = [1] * inputs.shape.rank
UpperCamelCase__ = shape_list(lowerCAmelCase__ )[axis]
UpperCamelCase__ = tf.reshape(lowerCAmelCase__ , lowerCAmelCase__ )
UpperCamelCase__ = tf.reshape(lowerCAmelCase__ , lowerCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
UpperCamelCase__ = tf.nn.batch_normalization(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , offset=lowerCAmelCase__ , scale=lowerCAmelCase__ , variance_epsilon=lowerCAmelCase__ , )
return outputs
def _UpperCamelCase (a__ :Optional[int] , a__ :Any=0 , a__ :List[str]=-1 ):
"""simple docstring"""
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
UpperCamelCase__ = tf.shape(lowerCAmelCase__ )
UpperCamelCase__ = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
UpperCamelCase__ = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(lowerCAmelCase__ , lowerCAmelCase__ )
def _UpperCamelCase (a__ :tf.Tensor ):
"""simple docstring"""
if not isinstance(lowerCAmelCase__ , tf.Tensor ):
UpperCamelCase__ = tf.convert_to_tensor(lowerCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
UpperCamelCase__ = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
UpperCamelCase__ = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
UpperCamelCase__ = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def _UpperCamelCase (a__ :tf.Tensor , a__ :int , a__ :str = "input_ids" ):
"""simple docstring"""
tf.debugging.assert_less(
lowerCAmelCase__ , tf.cast(lowerCAmelCase__ , dtype=tensor.dtype ) , message=(
f"""The maximum value of {tensor_name} ({tf.math.reduce_max(lowerCAmelCase__ )}) must be smaller than the embedding """
f"""layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."""
) , )
def _UpperCamelCase (a__ :Dict , a__ :str , a__ :Dict ):
"""simple docstring"""
UpperCamelCase__ = 6_4512
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
UpperCamelCase__ = [x for x in data if len(lowerCAmelCase__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"""The following attributes cannot be saved to HDF5 file because """
f"""they are larger than {HDF5_OBJECT_HEADER_LIMIT} """
f"""bytes: {bad_attributes}""" )
UpperCamelCase__ = np.asarray(lowerCAmelCase__ )
UpperCamelCase__ = 1
UpperCamelCase__ = np.array_split(lowerCAmelCase__ , lowerCAmelCase__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
UpperCamelCase__ = np.array_split(lowerCAmelCase__ , lowerCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(lowerCAmelCase__ ):
UpperCamelCase__ = chunk_data
else:
UpperCamelCase__ = data
def _UpperCamelCase (a__ :List[Any] , a__ :Tuple ):
"""simple docstring"""
if name in group.attrs:
UpperCamelCase__ = [n.decode("""utf8""" ) if hasattr(lowerCAmelCase__ , """decode""" ) else n for n in group.attrs[name]]
else:
UpperCamelCase__ = []
UpperCamelCase__ = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("""utf8""" ) if hasattr(lowerCAmelCase__ , """decode""" ) else n for n in group.attrs["""%s%d""" % (name, chunk_id)]] )
chunk_id += 1
return data
def _UpperCamelCase (a__ :Tuple ):
"""simple docstring"""
def _expand_single_ad_tensor(a__ :List[str] ):
if isinstance(lowerCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(lowerCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , lowerCAmelCase__ )
| 619
|
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import tensorflow as tf
from .utils import logging
__UpperCAmelCase = logging.get_logger(__name__)
def lowercase__ ( lowerCAmelCase__ : Union[tf.Tensor, np.ndarray] ) -> List[int]:
'''simple docstring'''
if isinstance(lowerCAmelCase__ , np.ndarray ):
return list(tensor.shape )
a__ : Optional[int] = tf.shape(lowerCAmelCase__ )
if tensor.shape == tf.TensorShape(lowerCAmelCase__ ):
return dynamic
a__ : Union[str, Any] = tensor.shape.as_list()
return [dynamic[i] if s is None else s for i, s in enumerate(lowerCAmelCase__ )]
def lowercase__ ( lowerCAmelCase__ : tf.Tensor , lowerCAmelCase__ : Optional[int] = None , lowerCAmelCase__ : Optional[str] = None ) -> tf.Tensor:
'''simple docstring'''
return tf.nn.softmax(logits=logits + 1E-9 , axis=lowerCAmelCase__ , name=lowerCAmelCase__ )
def lowercase__ ( lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : List[str] , lowerCAmelCase__ : Any , lowerCAmelCase__ : Union[str, Any]=1E-5 , lowerCAmelCase__ : str=-1 ) -> int:
'''simple docstring'''
# This is a very simplified functional layernorm, designed to duplicate
# the functionality of PyTorch nn.functional.layer_norm when this is needed to port
# models in Transformers.
if weight.shape.rank != 1 or bias.shape.rank != 1 or not isinstance(lowerCAmelCase__ , lowerCAmelCase__ ):
raise NotImplementedError("Only 1D weight and bias tensors are supported for now, with only a single axis." )
# Get mean and variance on the axis to be normalized
a__ , a__ : Dict = tf.nn.moments(lowerCAmelCase__ , axes=[axis] , keepdims=lowerCAmelCase__ )
if axis != -1:
# Reshape scale and weight to have the same rank as inputs, but with 1 dimensions
# on every dimension except axis
a__ : Dict = [1] * inputs.shape.rank
a__ : Dict = shape_list(lowerCAmelCase__ )[axis]
a__ : Any = tf.reshape(lowerCAmelCase__ , lowerCAmelCase__ )
a__ : List[Any] = tf.reshape(lowerCAmelCase__ , lowerCAmelCase__ )
# Compute layer normalization using the batch_normalization
# function.
a__ : List[str] = tf.nn.batch_normalization(
lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , offset=lowerCAmelCase__ , scale=lowerCAmelCase__ , variance_epsilon=lowerCAmelCase__ , )
return outputs
def lowercase__ ( lowerCAmelCase__ : Optional[int] , lowerCAmelCase__ : Any=0 , lowerCAmelCase__ : List[str]=-1 ) -> List[str]:
'''simple docstring'''
# Replicates the behavior of torch.flatten in TF
# If end_dim or start_dim is negative, count them from the end
if end_dim < 0:
end_dim += input.shape.rank
if start_dim < 0:
start_dim += input.shape.rank
if start_dim == end_dim:
return input
a__ : Optional[int] = tf.shape(lowerCAmelCase__ )
a__ : str = tf.math.reduce_prod(in_shape[start_dim : end_dim + 1] )
a__ : Union[str, Any] = tf.concat([in_shape[:start_dim], [flattened_dim], in_shape[end_dim + 1 :]] , axis=0 )
return tf.reshape(lowerCAmelCase__ , lowerCAmelCase__ )
def lowercase__ ( lowerCAmelCase__ : tf.Tensor ) -> tf.Tensor:
'''simple docstring'''
if not isinstance(lowerCAmelCase__ , tf.Tensor ):
a__ : Optional[Any] = tf.convert_to_tensor(lowerCAmelCase__ ) # Catches stray NumPy inputs
if encoder_attention_mask.shape.rank == 3:
a__ : Tuple = encoder_attention_mask[:, None, :, :]
if encoder_attention_mask.shape.rank == 2:
a__ : int = encoder_attention_mask[:, None, None, :]
# T5 has a mask that can compare sequence ids, we can simulate this here with this transposition
# Cf. https://github.com/tensorflow/mesh/blob/8d2465e9bc93129b913b5ccc6a59aa97abd96ec6/mesh_tensorflow
# /transformer/transformer_layers.py#L270
# encoder_extended_attention_mask = (encoder_extended_attention_mask ==
# encoder_extended_attention_mask.transpose(-1, -2))
a__ : int = (
tf.cast(1 , encoder_attention_mask.dtype ) - encoder_extended_attention_mask
) * encoder_extended_attention_mask.dtype.min
return encoder_extended_attention_mask
def lowercase__ ( lowerCAmelCase__ : tf.Tensor , lowerCAmelCase__ : int , lowerCAmelCase__ : str = "input_ids" ) -> None:
'''simple docstring'''
tf.debugging.assert_less(
lowerCAmelCase__ , tf.cast(lowerCAmelCase__ , dtype=tensor.dtype ) , message=(
F"The maximum value of {tensor_name} ({tf.math.reduce_max(lowerCAmelCase__ )}) must be smaller than the embedding "
F"layer's input dimension ({embed_dim}). The likely cause is some problem at tokenization time."
) , )
def lowercase__ ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : str , lowerCAmelCase__ : Dict ) -> Tuple:
'''simple docstring'''
a__ : Optional[Any] = 6_4_5_1_2
# Check that no item in `data` is larger than `HDF5_OBJECT_HEADER_LIMIT`
# because in that case even chunking the array would not make the saving
# possible.
a__ : Optional[Any] = [x for x in data if len(lowerCAmelCase__ ) > HDF5_OBJECT_HEADER_LIMIT]
# Expecting this to never be true.
if bad_attributes:
raise RuntimeError(
"The following attributes cannot be saved to HDF5 file because "
F"they are larger than {HDF5_OBJECT_HEADER_LIMIT} "
F"bytes: {bad_attributes}" )
a__ : List[str] = np.asarray(lowerCAmelCase__ )
a__ : List[str] = 1
a__ : str = np.array_split(lowerCAmelCase__ , lowerCAmelCase__ )
# This will never loop forever thanks to the test above.
while any(x.nbytes > HDF5_OBJECT_HEADER_LIMIT for x in chunked_data ):
num_chunks += 1
a__ : str = np.array_split(lowerCAmelCase__ , lowerCAmelCase__ )
if num_chunks > 1:
for chunk_id, chunk_data in enumerate(lowerCAmelCase__ ):
a__ : List[Any] = chunk_data
else:
a__ : List[str] = data
def lowercase__ ( lowerCAmelCase__ : List[Any] , lowerCAmelCase__ : Tuple ) -> Tuple:
'''simple docstring'''
if name in group.attrs:
a__ : str = [n.decode("utf8" ) if hasattr(lowerCAmelCase__ , "decode" ) else n for n in group.attrs[name]]
else:
a__ : str = []
a__ : Any = 0
while "%s%d" % (name, chunk_id) in group.attrs:
data.extend(
[n.decode("utf8" ) if hasattr(lowerCAmelCase__ , "decode" ) else n for n in group.attrs["%s%d" % (name, chunk_id)]] )
chunk_id += 1
return data
def lowercase__ ( lowerCAmelCase__ : Tuple ) -> List[Any]:
'''simple docstring'''
def _expand_single_ad_tensor(lowerCAmelCase__ : List[str] ):
if isinstance(lowerCAmelCase__ , tf.Tensor ) and t.shape.rank == 1:
return tf.expand_dims(lowerCAmelCase__ , axis=-1 )
return t
return tf.nest.map_structure(_expand_single_ad_tensor , lowerCAmelCase__ )
| 642
| 0
|
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class UpperCAmelCase ( nn.Module ):
lowercase = 42
lowercase = 42
lowercase = 0.0
lowercase = 1
lowercase = 1
lowercase = True
lowercase = False
lowercase = False
lowercase = False
lowercase = jnp.floataa
def lowerCamelCase_ ( self : List[str] ):
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = []
for i in range(self.num_layers ):
UpperCamelCase = self.in_channels if i == 0 else self.out_channels
UpperCamelCase = FlaxResnetBlockaD(
in_channels=__magic_name__ , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__magic_name__ )
UpperCamelCase = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__magic_name__ )
UpperCamelCase = resnets
UpperCamelCase = attentions
if self.add_downsample:
UpperCamelCase = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : Any , __magic_name__ : List[Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Union[str, Any] , __magic_name__ : Optional[int]=True ):
"""simple docstring"""
UpperCamelCase = ()
for resnet, attn in zip(self.resnets , self.attentions ):
UpperCamelCase = resnet(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
UpperCamelCase = attn(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
output_states += (hidden_states,)
if self.add_downsample:
UpperCamelCase = self.downsamplers_a(__magic_name__ )
output_states += (hidden_states,)
return hidden_states, output_states
class UpperCAmelCase ( nn.Module ):
lowercase = 42
lowercase = 42
lowercase = 0.0
lowercase = 1
lowercase = True
lowercase = jnp.floataa
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = []
for i in range(self.num_layers ):
UpperCamelCase = self.in_channels if i == 0 else self.out_channels
UpperCamelCase = FlaxResnetBlockaD(
in_channels=__magic_name__ , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__magic_name__ )
UpperCamelCase = resnets
if self.add_downsample:
UpperCamelCase = FlaxDownsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : List[str] , __magic_name__ : int , __magic_name__ : int , __magic_name__ : Any=True ):
"""simple docstring"""
UpperCamelCase = ()
for resnet in self.resnets:
UpperCamelCase = resnet(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
output_states += (hidden_states,)
if self.add_downsample:
UpperCamelCase = self.downsamplers_a(__magic_name__ )
output_states += (hidden_states,)
return hidden_states, output_states
class UpperCAmelCase ( nn.Module ):
lowercase = 42
lowercase = 42
lowercase = 42
lowercase = 0.0
lowercase = 1
lowercase = 1
lowercase = True
lowercase = False
lowercase = False
lowercase = False
lowercase = jnp.floataa
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = []
UpperCamelCase = []
for i in range(self.num_layers ):
UpperCamelCase = self.in_channels if (i == self.num_layers - 1) else self.out_channels
UpperCamelCase = self.prev_output_channel if i == 0 else self.out_channels
UpperCamelCase = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__magic_name__ )
UpperCamelCase = FlaxTransformeraDModel(
in_channels=self.out_channels , n_heads=self.num_attention_heads , d_head=self.out_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , only_cross_attention=self.only_cross_attention , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__magic_name__ )
UpperCamelCase = resnets
UpperCamelCase = attentions
if self.add_upsample:
UpperCamelCase = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : str , __magic_name__ : Any , __magic_name__ : Union[str, Any] , __magic_name__ : Optional[Any] , __magic_name__ : int , __magic_name__ : int=True ):
"""simple docstring"""
for resnet, attn in zip(self.resnets , self.attentions ):
# pop res hidden states
UpperCamelCase = res_hidden_states_tuple[-1]
UpperCamelCase = res_hidden_states_tuple[:-1]
UpperCamelCase = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
UpperCamelCase = resnet(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
UpperCamelCase = attn(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
if self.add_upsample:
UpperCamelCase = self.upsamplers_a(__magic_name__ )
return hidden_states
class UpperCAmelCase ( nn.Module ):
lowercase = 42
lowercase = 42
lowercase = 42
lowercase = 0.0
lowercase = 1
lowercase = True
lowercase = jnp.floataa
def lowerCamelCase_ ( self : List[Any] ):
"""simple docstring"""
UpperCamelCase = []
for i in range(self.num_layers ):
UpperCamelCase = self.in_channels if (i == self.num_layers - 1) else self.out_channels
UpperCamelCase = self.prev_output_channel if i == 0 else self.out_channels
UpperCamelCase = FlaxResnetBlockaD(
in_channels=resnet_in_channels + res_skip_channels , out_channels=self.out_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__magic_name__ )
UpperCamelCase = resnets
if self.add_upsample:
UpperCamelCase = FlaxUpsampleaD(self.out_channels , dtype=self.dtype )
def __call__( self : str , __magic_name__ : Dict , __magic_name__ : List[Any] , __magic_name__ : Tuple , __magic_name__ : str=True ):
"""simple docstring"""
for resnet in self.resnets:
# pop res hidden states
UpperCamelCase = res_hidden_states_tuple[-1]
UpperCamelCase = res_hidden_states_tuple[:-1]
UpperCamelCase = jnp.concatenate((hidden_states, res_hidden_states) , axis=-1 )
UpperCamelCase = resnet(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
if self.add_upsample:
UpperCamelCase = self.upsamplers_a(__magic_name__ )
return hidden_states
class UpperCAmelCase ( nn.Module ):
lowercase = 42
lowercase = 0.0
lowercase = 1
lowercase = 1
lowercase = False
lowercase = False
lowercase = jnp.floataa
def lowerCamelCase_ ( self : Any ):
"""simple docstring"""
UpperCamelCase = [
FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
]
UpperCamelCase = []
for _ in range(self.num_layers ):
UpperCamelCase = FlaxTransformeraDModel(
in_channels=self.in_channels , n_heads=self.num_attention_heads , d_head=self.in_channels // self.num_attention_heads , depth=1 , use_linear_projection=self.use_linear_projection , use_memory_efficient_attention=self.use_memory_efficient_attention , dtype=self.dtype , )
attentions.append(__magic_name__ )
UpperCamelCase = FlaxResnetBlockaD(
in_channels=self.in_channels , out_channels=self.in_channels , dropout_prob=self.dropout , dtype=self.dtype , )
resnets.append(__magic_name__ )
UpperCamelCase = resnets
UpperCamelCase = attentions
def __call__( self : Optional[int] , __magic_name__ : Optional[Any] , __magic_name__ : Dict , __magic_name__ : int , __magic_name__ : Any=True ):
"""simple docstring"""
UpperCamelCase = self.resnets[0](__magic_name__ , __magic_name__ )
for attn, resnet in zip(self.attentions , self.resnets[1:] ):
UpperCamelCase = attn(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
UpperCamelCase = resnet(__magic_name__ , __magic_name__ , deterministic=__magic_name__ )
return hidden_states
| 181
|
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__snake_case = logging.get_logger(__name__)
def _lowercase ( SCREAMING_SNAKE_CASE_ : Optional[int] ):
"""simple docstring"""
UpperCamelCase = DPTConfig()
if "large" in checkpoint_url:
UpperCamelCase = 1_024
UpperCamelCase = 4_096
UpperCamelCase = 24
UpperCamelCase = 16
UpperCamelCase = [5, 11, 17, 23]
UpperCamelCase = [256, 512, 1_024, 1_024]
UpperCamelCase = (1, 384, 384)
if "ade" in checkpoint_url:
UpperCamelCase = True
UpperCamelCase = 150
UpperCamelCase = """huggingface/label-files"""
UpperCamelCase = """ade20k-id2label.json"""
UpperCamelCase = json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , repo_type="""dataset""" ) ) , """r""" ) )
UpperCamelCase = {int(SCREAMING_SNAKE_CASE_ ): v for k, v in idalabel.items()}
UpperCamelCase = idalabel
UpperCamelCase = {v: k for k, v in idalabel.items()}
UpperCamelCase = [1, 150, 480, 480]
return config, expected_shape
def _lowercase ( SCREAMING_SNAKE_CASE_ : Union[str, Any] ):
"""simple docstring"""
UpperCamelCase = ["""pretrained.model.head.weight""", """pretrained.model.head.bias"""]
for k in ignore_keys:
state_dict.pop(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
def _lowercase ( SCREAMING_SNAKE_CASE_ : str ):
"""simple docstring"""
if (
"pretrained.model" in name
and "cls_token" not in name
and "pos_embed" not in name
and "patch_embed" not in name
):
UpperCamelCase = name.replace("""pretrained.model""" , """dpt.encoder""" )
if "pretrained.model" in name:
UpperCamelCase = name.replace("""pretrained.model""" , """dpt.embeddings""" )
if "patch_embed" in name:
UpperCamelCase = name.replace("""patch_embed""" , """patch_embeddings""" )
if "pos_embed" in name:
UpperCamelCase = name.replace("""pos_embed""" , """position_embeddings""" )
if "attn.proj" in name:
UpperCamelCase = name.replace("""attn.proj""" , """attention.output.dense""" )
if "proj" in name and "project" not in name:
UpperCamelCase = name.replace("""proj""" , """projection""" )
if "blocks" in name:
UpperCamelCase = name.replace("""blocks""" , """layer""" )
if "mlp.fc1" in name:
UpperCamelCase = name.replace("""mlp.fc1""" , """intermediate.dense""" )
if "mlp.fc2" in name:
UpperCamelCase = name.replace("""mlp.fc2""" , """output.dense""" )
if "norm1" in name:
UpperCamelCase = name.replace("""norm1""" , """layernorm_before""" )
if "norm2" in name:
UpperCamelCase = name.replace("""norm2""" , """layernorm_after""" )
if "scratch.output_conv" in name:
UpperCamelCase = name.replace("""scratch.output_conv""" , """head""" )
if "scratch" in name:
UpperCamelCase = name.replace("""scratch""" , """neck""" )
if "layer1_rn" in name:
UpperCamelCase = name.replace("""layer1_rn""" , """convs.0""" )
if "layer2_rn" in name:
UpperCamelCase = name.replace("""layer2_rn""" , """convs.1""" )
if "layer3_rn" in name:
UpperCamelCase = name.replace("""layer3_rn""" , """convs.2""" )
if "layer4_rn" in name:
UpperCamelCase = name.replace("""layer4_rn""" , """convs.3""" )
if "refinenet" in name:
UpperCamelCase = int(name[len("""neck.refinenet""" ) : len("""neck.refinenet""" ) + 1] )
# tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
UpperCamelCase = name.replace(f'refinenet{layer_idx}' , f'fusion_stage.layers.{abs(layer_idx-4 )}' )
if "out_conv" in name:
UpperCamelCase = name.replace("""out_conv""" , """projection""" )
if "resConfUnit1" in name:
UpperCamelCase = name.replace("""resConfUnit1""" , """residual_layer1""" )
if "resConfUnit2" in name:
UpperCamelCase = name.replace("""resConfUnit2""" , """residual_layer2""" )
if "conv1" in name:
UpperCamelCase = name.replace("""conv1""" , """convolution1""" )
if "conv2" in name:
UpperCamelCase = name.replace("""conv2""" , """convolution2""" )
# readout blocks
if "pretrained.act_postprocess1.0.project.0" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess1.0.project.0""" , """neck.reassemble_stage.readout_projects.0.0""" )
if "pretrained.act_postprocess2.0.project.0" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess2.0.project.0""" , """neck.reassemble_stage.readout_projects.1.0""" )
if "pretrained.act_postprocess3.0.project.0" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess3.0.project.0""" , """neck.reassemble_stage.readout_projects.2.0""" )
if "pretrained.act_postprocess4.0.project.0" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess4.0.project.0""" , """neck.reassemble_stage.readout_projects.3.0""" )
# resize blocks
if "pretrained.act_postprocess1.3" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess1.3""" , """neck.reassemble_stage.layers.0.projection""" )
if "pretrained.act_postprocess1.4" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess1.4""" , """neck.reassemble_stage.layers.0.resize""" )
if "pretrained.act_postprocess2.3" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess2.3""" , """neck.reassemble_stage.layers.1.projection""" )
if "pretrained.act_postprocess2.4" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess2.4""" , """neck.reassemble_stage.layers.1.resize""" )
if "pretrained.act_postprocess3.3" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess3.3""" , """neck.reassemble_stage.layers.2.projection""" )
if "pretrained.act_postprocess4.3" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess4.3""" , """neck.reassemble_stage.layers.3.projection""" )
if "pretrained.act_postprocess4.4" in name:
UpperCamelCase = name.replace("""pretrained.act_postprocess4.4""" , """neck.reassemble_stage.layers.3.resize""" )
if "pretrained" in name:
UpperCamelCase = name.replace("""pretrained""" , """dpt""" )
if "bn" in name:
UpperCamelCase = name.replace("""bn""" , """batch_norm""" )
if "head" in name:
UpperCamelCase = name.replace("""head""" , """head.head""" )
if "encoder.norm" in name:
UpperCamelCase = name.replace("""encoder.norm""" , """layernorm""" )
if "auxlayer" in name:
UpperCamelCase = name.replace("""auxlayer""" , """auxiliary_head.head""" )
return name
def _lowercase ( SCREAMING_SNAKE_CASE_ : str , SCREAMING_SNAKE_CASE_ : Tuple ):
"""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 = state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight' )
UpperCamelCase = state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias' )
# next, add query, keys and values (in that order) to the state dict
UpperCamelCase = in_proj_weight[: config.hidden_size, :]
UpperCamelCase = in_proj_bias[: config.hidden_size]
UpperCamelCase = in_proj_weight[
config.hidden_size : config.hidden_size * 2, :
]
UpperCamelCase = in_proj_bias[
config.hidden_size : config.hidden_size * 2
]
UpperCamelCase = in_proj_weight[
-config.hidden_size :, :
]
UpperCamelCase = in_proj_bias[-config.hidden_size :]
def _lowercase ( ):
"""simple docstring"""
UpperCamelCase = """http://images.cocodataset.org/val2017/000000039769.jpg"""
UpperCamelCase = Image.open(requests.get(SCREAMING_SNAKE_CASE_ , stream=SCREAMING_SNAKE_CASE_ ).raw )
return im
@torch.no_grad()
def _lowercase ( SCREAMING_SNAKE_CASE_ : List[Any] , SCREAMING_SNAKE_CASE_ : Dict , SCREAMING_SNAKE_CASE_ : Tuple , SCREAMING_SNAKE_CASE_ : List[str] ):
"""simple docstring"""
UpperCamelCase , UpperCamelCase = get_dpt_config(SCREAMING_SNAKE_CASE_ )
# load original state_dict from URL
UpperCamelCase = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ , map_location="""cpu""" )
# remove certain keys
remove_ignore_keys_(SCREAMING_SNAKE_CASE_ )
# rename keys
for key in state_dict.copy().keys():
UpperCamelCase = state_dict.pop(SCREAMING_SNAKE_CASE_ )
UpperCamelCase = val
# read in qkv matrices
read_in_q_k_v(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ )
# load HuggingFace model
UpperCamelCase = DPTForSemanticSegmentation(SCREAMING_SNAKE_CASE_ ) if """ade""" in checkpoint_url else DPTForDepthEstimation(SCREAMING_SNAKE_CASE_ )
model.load_state_dict(SCREAMING_SNAKE_CASE_ )
model.eval()
# Check outputs on an image
UpperCamelCase = 480 if """ade""" in checkpoint_url else 384
UpperCamelCase = DPTImageProcessor(size=SCREAMING_SNAKE_CASE_ )
UpperCamelCase = prepare_img()
UpperCamelCase = image_processor(SCREAMING_SNAKE_CASE_ , return_tensors="""pt""" )
# forward pass
UpperCamelCase = model(**SCREAMING_SNAKE_CASE_ ).logits if """ade""" in checkpoint_url else model(**SCREAMING_SNAKE_CASE_ ).predicted_depth
# Assert logits
UpperCamelCase = torch.tensor([[6.3199, 6.3629, 6.4148], [6.3850, 6.3615, 6.4166], [6.3519, 6.3176, 6.3575]] )
if "ade" in checkpoint_url:
UpperCamelCase = torch.tensor([[4.0480, 4.2420, 4.4360], [4.3124, 4.5693, 4.8261], [4.5768, 4.8965, 5.2163]] )
assert outputs.shape == torch.Size(SCREAMING_SNAKE_CASE_ )
assert (
torch.allclose(outputs[0, 0, :3, :3] , SCREAMING_SNAKE_CASE_ , atol=1e-4 )
if "ade" in checkpoint_url
else torch.allclose(outputs[0, :3, :3] , SCREAMING_SNAKE_CASE_ )
)
Path(SCREAMING_SNAKE_CASE_ ).mkdir(exist_ok=SCREAMING_SNAKE_CASE_ )
print(f'Saving model to {pytorch_dump_folder_path}' )
model.save_pretrained(SCREAMING_SNAKE_CASE_ )
print(f'Saving image processor to {pytorch_dump_folder_path}' )
image_processor.save_pretrained(SCREAMING_SNAKE_CASE_ )
if push_to_hub:
print("""Pushing model to hub...""" )
model.push_to_hub(
repo_path_or_name=Path(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , organization="""nielsr""" , commit_message="""Add model""" , use_temp_dir=SCREAMING_SNAKE_CASE_ , )
image_processor.push_to_hub(
repo_path_or_name=Path(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) , organization="""nielsr""" , commit_message="""Add image processor""" , use_temp_dir=SCREAMING_SNAKE_CASE_ , )
if __name__ == "__main__":
__snake_case = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--checkpoint_url",
default="https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt",
type=str,
help="URL of the original DPT checkpoint you'd like to convert.",
)
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the output PyTorch model directory.",
)
parser.add_argument(
"--push_to_hub",
action="store_true",
)
parser.add_argument(
"--model_name",
default="dpt-large",
type=str,
help="Name of the model, in case you're pushing to the hub.",
)
__snake_case = parser.parse_args()
convert_dpt_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 181
| 1
|
'''simple docstring'''
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaMaaaForConditionalGeneration, MaMaaaModel, MaMaaaTokenizer
from transformers.models.mam_aaa.modeling_mam_aaa import MaMaaaDecoder, MaMaaaEncoder
def snake_case__ ( _A: int , _A: List[str] , _A: Dict , _A: Union[str, Any]=None , _A: Tuple=None , _A: Dict=None , _A: List[Any]=None , _A: Optional[Any]=None , ) -> Any:
'''simple docstring'''
if attention_mask is None:
lowerCAmelCase = input_ids.ne(config.pad_token_id )
if decoder_attention_mask is None:
lowerCAmelCase = decoder_input_ids.ne(config.pad_token_id )
if head_mask is None:
lowerCAmelCase = torch.ones(config.encoder_layers , config.encoder_attention_heads , device=_A )
if decoder_head_mask is None:
lowerCAmelCase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_A )
if cross_attn_head_mask is None:
lowerCAmelCase = torch.ones(config.decoder_layers , config.decoder_attention_heads , device=_A )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
class a__:
'''simple docstring'''
def __init__( self , __lowerCAmelCase , __lowerCAmelCase=13 , __lowerCAmelCase=7 , __lowerCAmelCase=True , __lowerCAmelCase=False , __lowerCAmelCase=99 , __lowerCAmelCase=16 , __lowerCAmelCase=2 , __lowerCAmelCase=4 , __lowerCAmelCase=4 , __lowerCAmelCase="relu" , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.1 , __lowerCAmelCase=0.0 , __lowerCAmelCase=0.0 , __lowerCAmelCase=20 , __lowerCAmelCase=2 , __lowerCAmelCase=1 , __lowerCAmelCase=0 , ):
"""simple docstring"""
lowerCAmelCase = parent
lowerCAmelCase = batch_size
lowerCAmelCase = seq_length
lowerCAmelCase = is_training
lowerCAmelCase = use_labels
lowerCAmelCase = vocab_size
lowerCAmelCase = hidden_size
lowerCAmelCase = num_hidden_layers
lowerCAmelCase = num_attention_heads
lowerCAmelCase = intermediate_size
lowerCAmelCase = hidden_act
lowerCAmelCase = hidden_dropout_prob
lowerCAmelCase = attention_probs_dropout_prob
lowerCAmelCase = encoder_layerdrop
lowerCAmelCase = decoder_layerdrop
lowerCAmelCase = max_position_embeddings
lowerCAmelCase = eos_token_id
lowerCAmelCase = pad_token_id
lowerCAmelCase = bos_token_id
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
lowerCAmelCase = self.eos_token_id # Eos Token
lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size)
# we need to clamp the input ids here to avoid having pad token in between
# this is because for M2M100 the position_ids are prepared such that
# all pad tokens have pos id = 2 and rest are between 2..seq_length
# and the seq_length here is seq_length - num_pad_tokens
# but when using past, there is no way of knowing if the past input ids had
# pad tokens in them, which results in incorrect seq_lenth and which in turn results in
# position_ids being off by num_pad_tokens in past input
lowerCAmelCase = input_ids.clamp(self.pad_token_id + 1)
lowerCAmelCase = decoder_input_ids.clamp(self.pad_token_id + 1)
lowerCAmelCase = self.get_config()
lowerCAmelCase = prepare_mam_aaa_inputs_dict(__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase)
return config, inputs_dict
def a_ ( self):
"""simple docstring"""
return MaMaaaConfig(
vocab_size=self.vocab_size , d_model=self.hidden_size , encoder_layers=self.num_hidden_layers , decoder_layers=self.num_hidden_layers , encoder_attention_heads=self.num_attention_heads , decoder_attention_heads=self.num_attention_heads , encoder_ffn_dim=self.intermediate_size , decoder_ffn_dim=self.intermediate_size , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , encoder_layerdrop=self.encoder_layerdrop , decoder_layerdrop=self.decoder_layerdrop , max_position_embeddings=self.max_position_embeddings , eos_token_id=self.eos_token_id , bos_token_id=self.bos_token_id , pad_token_id=self.pad_token_id , )
def a_ ( self):
"""simple docstring"""
lowerCAmelCase , lowerCAmelCase = self.prepare_config_and_inputs()
return config, inputs_dict
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase):
"""simple docstring"""
lowerCAmelCase = MaMaaaModel(config=__lowerCAmelCase).get_decoder().to(__lowerCAmelCase).eval()
lowerCAmelCase = inputs_dict["""input_ids"""]
lowerCAmelCase = inputs_dict["""attention_mask"""]
lowerCAmelCase = inputs_dict["""head_mask"""]
# first forward pass
lowerCAmelCase = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , head_mask=__lowerCAmelCase , use_cache=__lowerCAmelCase)
lowerCAmelCase , lowerCAmelCase = outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
lowerCAmelCase = ids_tensor((self.batch_size, 3) , config.vocab_size)
lowerCAmelCase = ids_tensor((self.batch_size, 3) , 2)
# append to next input_ids and
lowerCAmelCase = torch.cat([input_ids, next_tokens] , dim=-1)
lowerCAmelCase = torch.cat([attention_mask, next_attn_mask] , dim=-1)
lowerCAmelCase = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase)["""last_hidden_state"""]
lowerCAmelCase = model(__lowerCAmelCase , attention_mask=__lowerCAmelCase , past_key_values=__lowerCAmelCase)[
"""last_hidden_state"""
]
# select random slice
lowerCAmelCase = ids_tensor((1,) , output_from_past.shape[-1]).item()
lowerCAmelCase = output_from_no_past[:, -3:, random_slice_idx].detach()
lowerCAmelCase = output_from_past[:, :, random_slice_idx].detach()
self.parent.assertTrue(output_from_past_slice.shape[1] == next_tokens.shape[1])
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(__lowerCAmelCase , __lowerCAmelCase , atol=1E-2))
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase):
"""simple docstring"""
lowerCAmelCase = MaMaaaModel(config=__lowerCAmelCase).to(__lowerCAmelCase).eval()
lowerCAmelCase = model(**__lowerCAmelCase)
lowerCAmelCase = outputs.encoder_last_hidden_state
lowerCAmelCase = outputs.last_hidden_state
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase = model.get_encoder()
encoder.save_pretrained(__lowerCAmelCase)
lowerCAmelCase = MaMaaaEncoder.from_pretrained(__lowerCAmelCase).to(__lowerCAmelCase)
lowerCAmelCase = encoder(inputs_dict["""input_ids"""] , attention_mask=inputs_dict["""attention_mask"""])[
0
]
self.parent.assertTrue((encoder_last_hidden_state_a - encoder_last_hidden_state).abs().max().item() < 1E-3)
with tempfile.TemporaryDirectory() as tmpdirname:
lowerCAmelCase = model.get_decoder()
decoder.save_pretrained(__lowerCAmelCase)
lowerCAmelCase = MaMaaaDecoder.from_pretrained(__lowerCAmelCase).to(__lowerCAmelCase)
lowerCAmelCase = decoder(
input_ids=inputs_dict["""decoder_input_ids"""] , attention_mask=inputs_dict["""decoder_attention_mask"""] , encoder_hidden_states=__lowerCAmelCase , encoder_attention_mask=inputs_dict["""attention_mask"""] , )[0]
self.parent.assertTrue((last_hidden_state_a - last_hidden_state).abs().max().item() < 1E-3)
@require_torch
class a__( lowerCAmelCase__ , lowerCAmelCase__ , lowerCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase_ : Tuple = (
(
MaMaaaModel,
MaMaaaForConditionalGeneration,
)
if is_torch_available()
else ()
)
UpperCAmelCase_ : str = (MaMaaaForConditionalGeneration,) if is_torch_available() else ()
UpperCAmelCase_ : Tuple = (
{
'''conversational''': MaMaaaForConditionalGeneration,
'''feature-extraction''': MaMaaaModel,
'''summarization''': MaMaaaForConditionalGeneration,
'''text2text-generation''': MaMaaaForConditionalGeneration,
'''translation''': MaMaaaForConditionalGeneration,
}
if is_torch_available()
else {}
)
UpperCAmelCase_ : str = True
UpperCAmelCase_ : int = True
UpperCAmelCase_ : Dict = False
UpperCAmelCase_ : List[Any] = False
def a_ ( self , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase):
"""simple docstring"""
if pipeline_test_casse_name == "TranslationPipelineTests":
# Get `ValueError: Translation requires a `src_lang` and a `tgt_lang` for this model`.
# `M2M100Config` was never used in pipeline tests: cannot create a simple tokenizer.
return True
return False
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = MaMaaaModelTester(self)
lowerCAmelCase = ConfigTester(self , config_class=__lowerCAmelCase)
def a_ ( self):
"""simple docstring"""
self.config_tester.run_common_tests()
def a_ ( self):
"""simple docstring"""
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
for model_class in self.all_model_classes:
lowerCAmelCase = model_class(__lowerCAmelCase)
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__lowerCAmelCase)
lowerCAmelCase , lowerCAmelCase = model_class.from_pretrained(__lowerCAmelCase , output_loading_info=__lowerCAmelCase)
self.assertEqual(info["""missing_keys"""] , [])
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_decoder_model_past_large_inputs(*__lowerCAmelCase)
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_encoder_decoder_model_standalone(*__lowerCAmelCase)
def a_ ( self):
"""simple docstring"""
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in (MaMaaaModel, MaMaaaForConditionalGeneration):
lowerCAmelCase = model_class(__lowerCAmelCase)
model.to(__lowerCAmelCase)
model.eval()
lowerCAmelCase = copy.deepcopy(self._prepare_for_class(__lowerCAmelCase , __lowerCAmelCase))
if not self.is_encoder_decoder:
lowerCAmelCase = inputs["""input_ids"""]
del inputs["input_ids"]
else:
lowerCAmelCase = inputs["""input_ids"""]
lowerCAmelCase = inputs.get("""decoder_input_ids""" , __lowerCAmelCase)
del inputs["input_ids"]
inputs.pop("""decoder_input_ids""" , __lowerCAmelCase)
lowerCAmelCase = model.get_input_embeddings()
if not self.is_encoder_decoder:
lowerCAmelCase = wte(__lowerCAmelCase)
else:
lowerCAmelCase = wte(__lowerCAmelCase)
lowerCAmelCase = wte(__lowerCAmelCase)
with torch.no_grad():
model(**__lowerCAmelCase)[0]
def a_ ( self):
"""simple docstring"""
lowerCAmelCase , lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
lowerCAmelCase = input_dict["""input_ids"""]
lowerCAmelCase = input_ids.ne(1).to(__lowerCAmelCase)
lowerCAmelCase = MaMaaaForConditionalGeneration(__lowerCAmelCase).eval().to(__lowerCAmelCase)
if torch_device == "cuda":
model.half()
model.generate(__lowerCAmelCase , attention_mask=__lowerCAmelCase)
model.generate(num_beams=4 , do_sample=__lowerCAmelCase , early_stopping=__lowerCAmelCase , num_return_sequences=3)
def snake_case__ ( _A: Dict ) -> List[str]:
'''simple docstring'''
return torch.tensor(_A , dtype=torch.long , device=_A )
__lowercase = 1e-4
@require_torch
@require_sentencepiece
@require_tokenizers
@slow
class a__( unittest.TestCase ):
'''simple docstring'''
@cached_property
def a_ ( self):
"""simple docstring"""
return MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M""")
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = MaMaaaModel.from_pretrained("""facebook/m2m100_418M""").to(__lowerCAmelCase)
lowerCAmelCase = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]])
lowerCAmelCase = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]])
lowerCAmelCase = prepare_mam_aaa_inputs_dict(model.config , __lowerCAmelCase , __lowerCAmelCase)
with torch.no_grad():
lowerCAmelCase = model(**__lowerCAmelCase)[0]
lowerCAmelCase = torch.Size((1, 11, 1024))
self.assertEqual(output.shape , __lowerCAmelCase)
# change to expected output here
lowerCAmelCase = torch.tensor(
[[-0.7780, -0.1676, 0.1038], [-6.7556, -1.3992, 0.0567], [-7.5383, -0.5920, -0.2779]] , device=__lowerCAmelCase)
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase))
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M""").to(__lowerCAmelCase)
# change to intended input
lowerCAmelCase = _long_tensor([[128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38, 2]])
lowerCAmelCase = _long_tensor([[2, 128028, 98, 12, 30527, 2732, 159, 7755, 61904, 39144, 38]])
lowerCAmelCase = prepare_mam_aaa_inputs_dict(model.config , __lowerCAmelCase , __lowerCAmelCase)
with torch.no_grad():
lowerCAmelCase = model(**__lowerCAmelCase)[0]
lowerCAmelCase = torch.Size((1, 11, model.config.vocab_size))
self.assertEqual(output.shape , __lowerCAmelCase)
# change to expected output here
lowerCAmelCase = torch.tensor(
[[-1.0448, -1.0411, 3.7992], [-3.2191, -3.2386, -1.3451], [-3.6210, -3.5993, 0.4925]] , device=__lowerCAmelCase)
self.assertTrue(torch.allclose(output[:, :3, :3] , __lowerCAmelCase , atol=__lowerCAmelCase))
def a_ ( self):
"""simple docstring"""
lowerCAmelCase = MaMaaaForConditionalGeneration.from_pretrained("""facebook/m2m100_418M""").to(__lowerCAmelCase)
lowerCAmelCase = MaMaaaTokenizer.from_pretrained("""facebook/m2m100_418M""" , src_lang="""fr""" , tgt_lang="""en""")
lowerCAmelCase = [
"""L'affaire NSA souligne l'absence totale de débat sur le renseignement""",
"""Selon moi, il y a deux niveaux de réponse de la part du gouvernement français.""",
"""Lorsque François Hollande téléphone à Barack Obama ou quand le ministre des affaires étrangères Laurent"""
""" Fabius convoque l'ambassadeur des Etats-Unis, ils réagissent à une vraie découverte, qui est celle de"""
""" l'ampleur de la surveillance américaine sur l'ensemble des communications en France.""",
]
# The below article tests that we don't add any hypotheses outside of the top n_beams
lowerCAmelCase = tokenizer(__lowerCAmelCase , padding=__lowerCAmelCase , return_tensors="""pt""")
lowerCAmelCase = model.generate(
input_ids=dct["""input_ids"""].to(__lowerCAmelCase) , attention_mask=dct["""attention_mask"""].to(__lowerCAmelCase) , num_beams=5 , forced_bos_token_id=tokenizer.get_lang_id("""en""") , )
lowerCAmelCase = [
"""The NSA case highlights the total absence of intelligence debate""",
"""I think there are two levels of response from the French government.""",
"""When François Hollande calls Barack Obama or when Foreign Minister Laurent Fabius calls the U.S."""
""" Ambassador, they respond to a real discovery, which is that of the scale of U.S. surveillance on all"""
""" communications in France.""",
]
lowerCAmelCase = tokenizer.batch_decode(
hypotheses_batch.tolist() , clean_up_tokenization_spaces=__lowerCAmelCase , skip_special_tokens=__lowerCAmelCase)
assert generated == expected_en
| 370
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__lowercase = {
'''configuration_chinese_clip''': [
'''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''ChineseCLIPConfig''',
'''ChineseCLIPOnnxConfig''',
'''ChineseCLIPTextConfig''',
'''ChineseCLIPVisionConfig''',
],
'''processing_chinese_clip''': ['''ChineseCLIPProcessor'''],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = ['''ChineseCLIPFeatureExtractor''']
__lowercase = ['''ChineseCLIPImageProcessor''']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowercase = [
'''CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ChineseCLIPModel''',
'''ChineseCLIPPreTrainedModel''',
'''ChineseCLIPTextModel''',
'''ChineseCLIPVisionModel''',
]
if TYPE_CHECKING:
from .configuration_chinese_clip import (
CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
ChineseCLIPConfig,
ChineseCLIPOnnxConfig,
ChineseCLIPTextConfig,
ChineseCLIPVisionConfig,
)
from .processing_chinese_clip import ChineseCLIPProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_chinese_clip import (
CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
ChineseCLIPModel,
ChineseCLIPPreTrainedModel,
ChineseCLIPTextModel,
ChineseCLIPVisionModel,
)
else:
import sys
__lowercase = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 370
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import floats_tensor, load_image, load_numpy, slow
from diffusers.utils.testing_utils import require_torch_gpu, torch_device
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
class lowerCamelCase_ ( UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
a__ : Any = ShapEImgaImgPipeline
a__ : str = ["""image"""]
a__ : Dict = ["""image"""]
a__ : Dict = [
"""num_images_per_prompt""",
"""num_inference_steps""",
"""generator""",
"""latents""",
"""guidance_scale""",
"""frame_size""",
"""output_type""",
"""return_dict""",
]
a__ : Tuple = False
@property
def UpperCamelCase__ ( self) -> Tuple:
return 32
@property
def UpperCamelCase__ ( self) -> str:
return 32
@property
def UpperCamelCase__ ( self) -> Dict:
return self.time_input_dim * 4
@property
def UpperCamelCase__ ( self) -> str:
return 8
@property
def UpperCamelCase__ ( self) -> List[Any]:
torch.manual_seed(0)
__UpperCamelCase :int = CLIPVisionConfig(
hidden_size=self.text_embedder_hidden_size , image_size=64 , projection_dim=self.text_embedder_hidden_size , intermediate_size=37 , num_attention_heads=4 , num_channels=3 , num_hidden_layers=5 , patch_size=1 , )
__UpperCamelCase :Union[str, Any] = CLIPVisionModel(__lowercase)
return model
@property
def UpperCamelCase__ ( self) -> Optional[Any]:
__UpperCamelCase :Optional[int] = CLIPImageProcessor(
crop_size=224 , do_center_crop=__lowercase , do_normalize=__lowercase , do_resize=__lowercase , image_mean=[0.48_14_54_66, 0.4_57_82_75, 0.40_82_10_73] , image_std=[0.26_86_29_54, 0.26_13_02_58, 0.27_57_77_11] , resample=3 , size=224 , )
return image_processor
@property
def UpperCamelCase__ ( self) -> Optional[int]:
torch.manual_seed(0)
__UpperCamelCase :Any = {
'''num_attention_heads''': 2,
'''attention_head_dim''': 16,
'''embedding_dim''': self.time_input_dim,
'''num_embeddings''': 32,
'''embedding_proj_dim''': self.text_embedder_hidden_size,
'''time_embed_dim''': self.time_embed_dim,
'''num_layers''': 1,
'''clip_embed_dim''': self.time_input_dim * 2,
'''additional_embeddings''': 0,
'''time_embed_act_fn''': '''gelu''',
'''norm_in_type''': '''layer''',
'''embedding_proj_norm_type''': '''layer''',
'''encoder_hid_proj_type''': None,
'''added_emb_type''': None,
}
__UpperCamelCase :List[str] = PriorTransformer(**__lowercase)
return model
@property
def UpperCamelCase__ ( self) -> Union[str, Any]:
torch.manual_seed(0)
__UpperCamelCase :Dict = {
'''param_shapes''': (
(self.renderer_dim, 93),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
(self.renderer_dim, 8),
),
'''d_latent''': self.time_input_dim,
'''d_hidden''': self.renderer_dim,
'''n_output''': 12,
'''background''': (
0.1,
0.1,
0.1,
),
}
__UpperCamelCase :str = ShapERenderer(**__lowercase)
return model
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :Optional[int] = self.dummy_prior
__UpperCamelCase :Optional[Any] = self.dummy_image_encoder
__UpperCamelCase :str = self.dummy_image_processor
__UpperCamelCase :List[str] = self.dummy_renderer
__UpperCamelCase :Tuple = HeunDiscreteScheduler(
beta_schedule='''exp''' , num_train_timesteps=1_024 , prediction_type='''sample''' , use_karras_sigmas=__lowercase , clip_sample=__lowercase , clip_sample_range=1.0 , )
__UpperCamelCase :int = {
'''prior''': prior,
'''image_encoder''': image_encoder,
'''image_processor''': image_processor,
'''renderer''': renderer,
'''scheduler''': scheduler,
}
return components
def UpperCamelCase__ ( self , __lowercase , __lowercase=0) -> Optional[Any]:
__UpperCamelCase :Any = floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowercase)).to(__lowercase)
if str(__lowercase).startswith('''mps'''):
__UpperCamelCase :str = torch.manual_seed(__lowercase)
else:
__UpperCamelCase :Optional[int] = torch.Generator(device=__lowercase).manual_seed(__lowercase)
__UpperCamelCase :Any = {
'''image''': input_image,
'''generator''': generator,
'''num_inference_steps''': 1,
'''frame_size''': 32,
'''output_type''': '''np''',
}
return inputs
def UpperCamelCase__ ( self) -> Dict:
__UpperCamelCase :Optional[Any] = '''cpu'''
__UpperCamelCase :Union[str, Any] = self.get_dummy_components()
__UpperCamelCase :Dict = self.pipeline_class(**__lowercase)
__UpperCamelCase :Any = pipe.to(__lowercase)
pipe.set_progress_bar_config(disable=__lowercase)
__UpperCamelCase :Dict = pipe(**self.get_dummy_inputs(__lowercase))
__UpperCamelCase :Tuple = output.images[0]
__UpperCamelCase :Optional[int] = image[0, -3:, -3:, -1]
assert image.shape == (20, 32, 32, 3)
__UpperCamelCase :List[Any] = np.array(
[
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
0.00_03_92_16,
])
assert np.abs(image_slice.flatten() - expected_slice).max() < 1E-2
def UpperCamelCase__ ( self) -> Dict:
# NOTE: Larger batch sizes cause this test to timeout, only test on smaller batches
self._test_inference_batch_consistent(batch_sizes=[1, 2])
def UpperCamelCase__ ( self) -> List[Any]:
__UpperCamelCase :Dict = torch_device == '''cpu'''
__UpperCamelCase :Union[str, Any] = True
self._test_inference_batch_single_identical(
batch_size=2 , test_max_difference=__lowercase , relax_max_difference=__lowercase , )
def UpperCamelCase__ ( self) -> Any:
__UpperCamelCase :int = self.get_dummy_components()
__UpperCamelCase :Tuple = self.pipeline_class(**__lowercase)
__UpperCamelCase :Union[str, Any] = pipe.to(__lowercase)
pipe.set_progress_bar_config(disable=__lowercase)
__UpperCamelCase :Dict = 1
__UpperCamelCase :Dict = 2
__UpperCamelCase :Optional[Any] = self.get_dummy_inputs(__lowercase)
for key in inputs.keys():
if key in self.batch_params:
__UpperCamelCase :Any = batch_size * [inputs[key]]
__UpperCamelCase :Optional[int] = pipe(**__lowercase , num_images_per_prompt=__lowercase)[0]
assert images.shape[0] == batch_size * num_images_per_prompt
@slow
@require_torch_gpu
class lowerCamelCase_ ( unittest.TestCase ):
'''simple docstring'''
def UpperCamelCase__ ( self) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def UpperCamelCase__ ( self) -> int:
__UpperCamelCase :Union[str, Any] = load_image(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main''' '''/shap_e/corgi.png''')
__UpperCamelCase :List[Any] = load_numpy(
'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'''
'''/shap_e/test_shap_e_img2img_out.npy''')
__UpperCamelCase :List[Any] = ShapEImgaImgPipeline.from_pretrained('''openai/shap-e-img2img''')
__UpperCamelCase :Tuple = pipe.to(__lowercase)
pipe.set_progress_bar_config(disable=__lowercase)
__UpperCamelCase :Union[str, Any] = torch.Generator(device=__lowercase).manual_seed(0)
__UpperCamelCase :Union[str, Any] = pipe(
__lowercase , generator=__lowercase , guidance_scale=3.0 , num_inference_steps=64 , frame_size=64 , output_type='''np''' , ).images[0]
assert images.shape == (20, 64, 64, 3)
assert_mean_pixel_difference(__lowercase , __lowercase)
| 705
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase = logging.get_logger(__name__)
__lowercase = {
'''microsoft/wavlm-base''': '''https://huggingface.co/microsoft/wavlm-base/resolve/main/config.json''',
# See all WavLM models at https://huggingface.co/models?filter=wavlm
}
class lowerCamelCase_ ( UpperCAmelCase_ ):
'''simple docstring'''
a__ : List[Any] = """wavlm"""
def __init__( self , __lowercase=32 , __lowercase=768 , __lowercase=12 , __lowercase=12 , __lowercase=3_072 , __lowercase="gelu" , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.0 , __lowercase=0.1 , __lowercase=0.1 , __lowercase=0.02 , __lowercase=1E-5 , __lowercase="group" , __lowercase="gelu" , __lowercase=(512, 512, 512, 512, 512, 512, 512) , __lowercase=(5, 2, 2, 2, 2, 2, 2) , __lowercase=(10, 3, 3, 3, 3, 2, 2) , __lowercase=False , __lowercase=128 , __lowercase=16 , __lowercase=320 , __lowercase=800 , __lowercase=False , __lowercase=True , __lowercase=0.05 , __lowercase=10 , __lowercase=2 , __lowercase=0.0 , __lowercase=10 , __lowercase=320 , __lowercase=2 , __lowercase=0.1 , __lowercase=100 , __lowercase=256 , __lowercase=256 , __lowercase=0.1 , __lowercase="mean" , __lowercase=False , __lowercase=False , __lowercase=256 , __lowercase=(512, 512, 512, 512, 1_500) , __lowercase=(5, 3, 3, 1, 1) , __lowercase=(1, 2, 3, 1, 1) , __lowercase=512 , __lowercase=80 , __lowercase=0 , __lowercase=1 , __lowercase=2 , __lowercase=False , __lowercase=3 , __lowercase=2 , __lowercase=3 , __lowercase=None , **__lowercase , ) -> Dict:
super().__init__(**__lowercase , pad_token_id=__lowercase , bos_token_id=__lowercase , eos_token_id=__lowercase)
__UpperCamelCase :Any = hidden_size
__UpperCamelCase :Tuple = feat_extract_norm
__UpperCamelCase :List[str] = feat_extract_activation
__UpperCamelCase :int = list(__lowercase)
__UpperCamelCase :List[Any] = list(__lowercase)
__UpperCamelCase :Union[str, Any] = list(__lowercase)
__UpperCamelCase :Optional[Any] = conv_bias
__UpperCamelCase :Tuple = num_buckets
__UpperCamelCase :Optional[int] = max_bucket_distance
__UpperCamelCase :Union[str, Any] = num_conv_pos_embeddings
__UpperCamelCase :Optional[Any] = num_conv_pos_embedding_groups
__UpperCamelCase :List[Any] = len(self.conv_dim)
__UpperCamelCase :Tuple = num_hidden_layers
__UpperCamelCase :str = intermediate_size
__UpperCamelCase :Union[str, Any] = hidden_act
__UpperCamelCase :Optional[int] = num_attention_heads
__UpperCamelCase :str = hidden_dropout
__UpperCamelCase :int = attention_dropout
__UpperCamelCase :Optional[int] = activation_dropout
__UpperCamelCase :str = feat_proj_dropout
__UpperCamelCase :List[Any] = final_dropout
__UpperCamelCase :int = layerdrop
__UpperCamelCase :List[Any] = layer_norm_eps
__UpperCamelCase :Optional[int] = initializer_range
__UpperCamelCase :Any = num_ctc_classes
__UpperCamelCase :Optional[int] = vocab_size
__UpperCamelCase :List[Any] = do_stable_layer_norm
__UpperCamelCase :str = use_weighted_layer_sum
__UpperCamelCase :Any = classifier_proj_size
if (
(len(self.conv_stride) != self.num_feat_extract_layers)
or (len(self.conv_kernel) != self.num_feat_extract_layers)
or (len(self.conv_dim) != self.num_feat_extract_layers)
):
raise ValueError(
'''Configuration for convolutional layers is incorrect. It is required that `len(config.conv_dim)` =='''
''' `len(config.conv_stride)` == `len(config.conv_kernel)`, but is `len(config.conv_dim) ='''
f""" {len(self.conv_dim)}`, `len(config.conv_stride) = {len(self.conv_stride)}`,"""
f""" `len(config.conv_kernel) = {len(self.conv_kernel)}`.""")
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
__UpperCamelCase :Union[str, Any] = apply_spec_augment
__UpperCamelCase :Optional[Any] = mask_time_prob
__UpperCamelCase :Union[str, Any] = mask_time_length
__UpperCamelCase :Optional[int] = mask_time_min_masks
__UpperCamelCase :str = mask_feature_prob
__UpperCamelCase :Tuple = mask_feature_length
# parameters for pretraining with codevector quantized representations
__UpperCamelCase :Optional[Any] = num_codevectors_per_group
__UpperCamelCase :List[Any] = num_codevector_groups
__UpperCamelCase :str = contrastive_logits_temperature
__UpperCamelCase :Tuple = num_negatives
__UpperCamelCase :Any = codevector_dim
__UpperCamelCase :Union[str, Any] = proj_codevector_dim
__UpperCamelCase :Tuple = diversity_loss_weight
# ctc loss
__UpperCamelCase :int = ctc_loss_reduction
__UpperCamelCase :Any = ctc_zero_infinity
# adapter
__UpperCamelCase :List[Any] = add_adapter
__UpperCamelCase :Dict = adapter_kernel_size
__UpperCamelCase :Any = adapter_stride
__UpperCamelCase :Optional[int] = num_adapter_layers
__UpperCamelCase :Union[str, Any] = output_hidden_size or hidden_size
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
__UpperCamelCase :int = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
__UpperCamelCase :Optional[Any] = list(__lowercase)
__UpperCamelCase :Optional[Any] = list(__lowercase)
__UpperCamelCase :List[str] = list(__lowercase)
__UpperCamelCase :List[Any] = xvector_output_dim
@property
def UpperCamelCase__ ( self) -> Any:
return functools.reduce(operator.mul , self.conv_stride , 1)
| 452
| 0
|
import math
import unittest
from transformers import BioGptConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
BioGptForCausalLM,
BioGptForSequenceClassification,
BioGptForTokenClassification,
BioGptModel,
BioGptTokenizer,
)
from transformers.models.biogpt.modeling_biogpt import BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST
class __lowerCAmelCase :
def __init__( self , lowerCAmelCase , lowerCAmelCase=13 , lowerCAmelCase=7 , lowerCAmelCase=True , lowerCAmelCase=True , lowerCAmelCase=False , lowerCAmelCase=True , lowerCAmelCase=99 , lowerCAmelCase=32 , lowerCAmelCase=5 , lowerCAmelCase=4 , lowerCAmelCase=37 , lowerCAmelCase="gelu" , lowerCAmelCase=0.1 , lowerCAmelCase=0.1 , lowerCAmelCase=512 , lowerCAmelCase=16 , lowerCAmelCase=2 , lowerCAmelCase=0.02 , lowerCAmelCase=3 , lowerCAmelCase=4 , lowerCAmelCase=None , ) -> str:
'''simple docstring'''
_lowercase =parent
_lowercase =batch_size
_lowercase =seq_length
_lowercase =is_training
_lowercase =use_input_mask
_lowercase =use_token_type_ids
_lowercase =use_labels
_lowercase =vocab_size
_lowercase =hidden_size
_lowercase =num_hidden_layers
_lowercase =num_attention_heads
_lowercase =intermediate_size
_lowercase =hidden_act
_lowercase =hidden_dropout_prob
_lowercase =attention_probs_dropout_prob
_lowercase =max_position_embeddings
_lowercase =type_vocab_size
_lowercase =type_sequence_label_size
_lowercase =initializer_range
_lowercase =num_labels
_lowercase =num_choices
_lowercase =scope
def A__ ( self ) -> List[str]:
'''simple docstring'''
_lowercase =ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowercase =None
if self.use_input_mask:
_lowercase =random_attention_mask([self.batch_size, self.seq_length] )
_lowercase =None
if self.use_token_type_ids:
_lowercase =ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
_lowercase =None
_lowercase =None
_lowercase =None
if self.use_labels:
_lowercase =ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowercase =ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowercase =ids_tensor([self.batch_size] , self.num_choices )
_lowercase =self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def A__ ( self ) -> Any:
'''simple docstring'''
return BioGptConfig(
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 , )
def A__ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> Union[str, Any]:
'''simple docstring'''
_lowercase =BioGptModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
_lowercase =model(lowerCAmelCase , attention_mask=lowerCAmelCase )
_lowercase =model(lowerCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def A__ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , ) -> List[Any]:
'''simple docstring'''
_lowercase =BioGptForCausalLM(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
_lowercase =model(lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase , labels=lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def A__ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , *lowerCAmelCase ) -> Tuple:
'''simple docstring'''
_lowercase =BioGptModel(config=lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
# create attention mask
_lowercase =torch.ones(input_ids.shape , dtype=torch.long , device=lowerCAmelCase )
_lowercase =self.seq_length // 2
_lowercase =0
# first forward pass
_lowercase , _lowercase =model(lowerCAmelCase , attention_mask=lowerCAmelCase ).to_tuple()
# create hypothetical next token and extent to next_input_ids
_lowercase =ids_tensor((self.batch_size, 1) , config.vocab_size )
# change a random masked slice from input_ids
_lowercase =ids_tensor((1,) , lowerCAmelCase ).item() + 1
_lowercase =ids_tensor((self.batch_size, 1) , config.vocab_size ).squeeze(-1 )
_lowercase =random_other_next_tokens
# append to next input_ids and attn_mask
_lowercase =torch.cat([input_ids, next_tokens] , dim=-1 )
_lowercase =torch.cat(
[attn_mask, torch.ones((attn_mask.shape[0], 1) , dtype=torch.long , device=lowerCAmelCase )] , dim=1 , )
# get two different outputs
_lowercase =model(lowerCAmelCase , attention_mask=lowerCAmelCase )['last_hidden_state']
_lowercase =model(lowerCAmelCase , past_key_values=lowerCAmelCase , attention_mask=lowerCAmelCase )['last_hidden_state']
# select random slice
_lowercase =ids_tensor((1,) , output_from_past.shape[-1] ).item()
_lowercase =output_from_no_past[:, -1, random_slice_idx].detach()
_lowercase =output_from_past[:, 0, random_slice_idx].detach()
# test that outputs are equal for slice
self.parent.assertTrue(torch.allclose(lowerCAmelCase , lowerCAmelCase , atol=1e-3 ) )
def A__ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , *lowerCAmelCase ) -> List[Any]:
'''simple docstring'''
_lowercase =BioGptModel(config=lowerCAmelCase ).to(lowerCAmelCase ).eval()
_lowercase =torch.ones(input_ids.shape , dtype=torch.long , device=lowerCAmelCase )
# first forward pass
_lowercase =model(lowerCAmelCase , attention_mask=lowerCAmelCase , use_cache=lowerCAmelCase )
_lowercase , _lowercase =outputs.to_tuple()
# create hypothetical multiple next token and extent to next_input_ids
_lowercase =ids_tensor((self.batch_size, 3) , config.vocab_size )
_lowercase =ids_tensor((self.batch_size, 3) , 2 )
# append to next input_ids and
_lowercase =torch.cat([input_ids, next_tokens] , dim=-1 )
_lowercase =torch.cat([attention_mask, next_attn_mask] , dim=-1 )
_lowercase =model(lowerCAmelCase , attention_mask=lowerCAmelCase )['last_hidden_state']
_lowercase =model(lowerCAmelCase , attention_mask=lowerCAmelCase , past_key_values=lowerCAmelCase )[
'last_hidden_state'
]
# select random slice
_lowercase =ids_tensor((1,) , output_from_past.shape[-1] ).item()
_lowercase =output_from_no_past[:, -3:, random_slice_idx].detach()
_lowercase =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 A__ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , *lowerCAmelCase , lowerCAmelCase=False ) -> int:
'''simple docstring'''
_lowercase =BioGptForCausalLM(lowerCAmelCase )
model.to(lowerCAmelCase )
if gradient_checkpointing:
model.gradient_checkpointing_enable()
_lowercase =model(lowerCAmelCase , labels=lowerCAmelCase )
self.parent.assertEqual(result.loss.shape , () )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
result.loss.backward()
def A__ ( self , lowerCAmelCase , *lowerCAmelCase ) -> Optional[int]:
'''simple docstring'''
_lowercase =BioGptModel(lowerCAmelCase )
_lowercase =model.config.initializer_range / math.sqrt(2 * model.config.num_hidden_layers )
for key in model.state_dict().keys():
if "c_proj" in key and "weight" in key:
self.parent.assertLessEqual(abs(torch.std(model.state_dict()[key] ) - model_std ) , 0.001 )
self.parent.assertLessEqual(abs(torch.mean(model.state_dict()[key] ) - 0.0 ) , 0.01 )
def A__ ( self , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , *lowerCAmelCase ) -> int:
'''simple docstring'''
_lowercase =self.num_labels
_lowercase =BioGptForTokenClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
_lowercase =model(lowerCAmelCase , attention_mask=lowerCAmelCase , token_type_ids=lowerCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def A__ ( self ) -> Dict:
'''simple docstring'''
_lowercase =self.prepare_config_and_inputs()
(
(
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) , (
_lowercase
) ,
) =config_and_inputs
_lowercase ={'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , unittest.TestCase ):
_a = (
(BioGptModel, BioGptForCausalLM, BioGptForSequenceClassification, BioGptForTokenClassification)
if is_torch_available()
else ()
)
_a = (BioGptForCausalLM,) if is_torch_available() else ()
_a = (
{
"""feature-extraction""": BioGptModel,
"""text-classification""": BioGptForSequenceClassification,
"""text-generation""": BioGptForCausalLM,
"""token-classification""": BioGptForTokenClassification,
"""zero-shot""": BioGptForSequenceClassification,
}
if is_torch_available()
else {}
)
_a = False
def A__ ( self ) -> str:
'''simple docstring'''
_lowercase =BioGptModelTester(self )
_lowercase =ConfigTester(self , config_class=lowerCAmelCase , hidden_size=37 )
def A__ ( self ) -> int:
'''simple docstring'''
self.config_tester.run_common_tests()
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*lowerCAmelCase )
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
_lowercase =self.model_tester.prepare_config_and_inputs()
for type in ["absolute", "relative_key", "relative_key_query"]:
_lowercase =type
self.model_tester.create_and_check_model(*lowerCAmelCase )
def A__ ( self ) -> List[str]:
'''simple docstring'''
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_attention_mask_past(*lowerCAmelCase )
def A__ ( self ) -> List[Any]:
'''simple docstring'''
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_forward_and_backwards(*lowerCAmelCase , gradient_checkpointing=lowerCAmelCase )
def A__ ( self ) -> Union[str, Any]:
'''simple docstring'''
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_model_past_large_inputs(*lowerCAmelCase )
def A__ ( self ) -> Optional[int]:
'''simple docstring'''
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_weight_initialization(*lowerCAmelCase )
def A__ ( self ) -> Dict:
'''simple docstring'''
_lowercase =self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_biogpt_for_token_classification(*lowerCAmelCase )
@slow
def A__ ( self ) -> List[Any]:
'''simple docstring'''
_lowercase =BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(lowerCAmelCase )
_lowercase =BioGptTokenizer.from_pretrained('microsoft/biogpt' )
_lowercase ='left'
# Define PAD Token = EOS Token = 50256
_lowercase =tokenizer.eos_token
_lowercase =model.config.eos_token_id
# use different length sentences to test batching
_lowercase =[
'Hello, my dog is a little',
'Today, I',
]
_lowercase =tokenizer(lowerCAmelCase , return_tensors='pt' , padding=lowerCAmelCase )
_lowercase =inputs['input_ids'].to(lowerCAmelCase )
_lowercase =model.generate(
input_ids=lowerCAmelCase , attention_mask=inputs['attention_mask'].to(lowerCAmelCase ) , )
_lowercase =tokenizer(sentences[0] , return_tensors='pt' ).input_ids.to(lowerCAmelCase )
_lowercase =model.generate(input_ids=lowerCAmelCase )
_lowercase =inputs_non_padded.shape[-1] - inputs['attention_mask'][-1].long().sum().cpu().item()
_lowercase =tokenizer(sentences[1] , return_tensors='pt' ).input_ids.to(lowerCAmelCase )
_lowercase =model.generate(input_ids=lowerCAmelCase , max_length=model.config.max_length - num_paddings )
_lowercase =tokenizer.batch_decode(lowerCAmelCase , skip_special_tokens=lowerCAmelCase )
_lowercase =tokenizer.decode(output_non_padded[0] , skip_special_tokens=lowerCAmelCase )
_lowercase =tokenizer.decode(output_padded[0] , skip_special_tokens=lowerCAmelCase )
_lowercase =[
'Hello, my dog is a little bit bigger than a little bit.',
'Today, I have a good idea of how to use the information',
]
self.assertListEqual(lowerCAmelCase , lowerCAmelCase )
self.assertListEqual(lowerCAmelCase , [non_padded_sentence, padded_sentence] )
@slow
def A__ ( self ) -> List[str]:
'''simple docstring'''
for model_name in BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowercase =BioGptModel.from_pretrained(lowerCAmelCase )
self.assertIsNotNone(lowerCAmelCase )
def A__ ( self ) -> Dict:
'''simple docstring'''
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
_lowercase =3
_lowercase =input_dict['input_ids']
_lowercase =input_ids.ne(1 ).to(lowerCAmelCase )
_lowercase =ids_tensor([self.model_tester.batch_size] , self.model_tester.type_sequence_label_size )
_lowercase =BioGptForSequenceClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
_lowercase =model(lowerCAmelCase , attention_mask=lowerCAmelCase , labels=lowerCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
def A__ ( self ) -> int:
'''simple docstring'''
_lowercase , _lowercase =self.model_tester.prepare_config_and_inputs_for_common()
_lowercase =3
_lowercase ='multi_label_classification'
_lowercase =input_dict['input_ids']
_lowercase =input_ids.ne(1 ).to(lowerCAmelCase )
_lowercase =ids_tensor(
[self.model_tester.batch_size, config.num_labels] , self.model_tester.type_sequence_label_size ).to(torch.float )
_lowercase =BioGptForSequenceClassification(lowerCAmelCase )
model.to(lowerCAmelCase )
model.eval()
_lowercase =model(lowerCAmelCase , attention_mask=lowerCAmelCase , labels=lowerCAmelCase )
self.assertEqual(result.logits.shape , (self.model_tester.batch_size, self.model_tester.num_labels) )
@require_torch
class __lowerCAmelCase ( unittest.TestCase ):
@slow
def A__ ( self ) -> Dict:
'''simple docstring'''
_lowercase =BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
_lowercase =torch.tensor([[2, 4_805, 9, 656, 21]] )
_lowercase =model(lowerCAmelCase )[0]
_lowercase =42_384
_lowercase =torch.Size((1, 5, vocab_size) )
self.assertEqual(output.shape , lowerCAmelCase )
_lowercase =torch.tensor(
[[[-9.5236, -9.8918, 10.4557], [-11.0469, -9.6423, 8.1022], [-8.8664, -7.8826, 5.5325]]] )
self.assertTrue(torch.allclose(output[:, :3, :3] , lowerCAmelCase , atol=1e-4 ) )
@slow
def A__ ( self ) -> Optional[Any]:
'''simple docstring'''
_lowercase =BioGptTokenizer.from_pretrained('microsoft/biogpt' )
_lowercase =BioGptForCausalLM.from_pretrained('microsoft/biogpt' )
model.to(lowerCAmelCase )
torch.manual_seed(0 )
_lowercase =tokenizer('COVID-19 is' , return_tensors='pt' ).to(lowerCAmelCase )
_lowercase =model.generate(
**lowerCAmelCase , min_length=100 , max_length=1_024 , num_beams=5 , early_stopping=lowerCAmelCase , )
_lowercase =tokenizer.decode(output_ids[0] , skip_special_tokens=lowerCAmelCase )
_lowercase =(
'COVID-19 is a global pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the'
' causative agent of coronavirus disease 2019 (COVID-19), which has spread to more than 200 countries and'
' territories, including the United States (US), Canada, Australia, New Zealand, the United Kingdom (UK),'
' and the United States of America (USA), as of March 11, 2020, with more than 800,000 confirmed cases and'
' more than 800,000 deaths.'
)
self.assertEqual(lowerCAmelCase , lowerCAmelCase )
| 291
|
# Copyright 2021 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 packaging import version
from .. import __version__
from .constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD, IMAGENET_STANDARD_MEAN, IMAGENET_STANDARD_STD
from .doc import (
add_code_sample_docstrings,
add_end_docstrings,
add_start_docstrings,
add_start_docstrings_to_model_forward,
copy_func,
replace_return_docstrings,
)
from .generic import (
ContextManagers,
ExplicitEnum,
ModelOutput,
PaddingStrategy,
TensorType,
add_model_info_to_auto_map,
cached_property,
can_return_loss,
expand_dims,
find_labels,
flatten_dict,
infer_framework,
is_jax_tensor,
is_numpy_array,
is_tensor,
is_tf_symbolic_tensor,
is_tf_tensor,
is_torch_device,
is_torch_dtype,
is_torch_tensor,
reshape,
squeeze,
strtobool,
tensor_size,
to_numpy,
to_py_obj,
transpose,
working_or_temp_dir,
)
from .hub import (
CLOUDFRONT_DISTRIB_PREFIX,
DISABLE_TELEMETRY,
HF_MODULES_CACHE,
HUGGINGFACE_CO_PREFIX,
HUGGINGFACE_CO_RESOLVE_ENDPOINT,
PYTORCH_PRETRAINED_BERT_CACHE,
PYTORCH_TRANSFORMERS_CACHE,
S3_BUCKET_PREFIX,
TRANSFORMERS_CACHE,
TRANSFORMERS_DYNAMIC_MODULE_NAME,
EntryNotFoundError,
PushToHubMixin,
RepositoryNotFoundError,
RevisionNotFoundError,
cached_file,
default_cache_path,
define_sagemaker_information,
download_url,
extract_commit_hash,
get_cached_models,
get_file_from_repo,
get_full_repo_name,
has_file,
http_user_agent,
is_offline_mode,
is_remote_url,
move_cache,
send_example_telemetry,
try_to_load_from_cache,
)
from .import_utils import (
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
TORCH_FX_REQUIRED_VERSION,
USE_JAX,
USE_TF,
USE_TORCH,
DummyObject,
OptionalDependencyNotAvailable,
_LazyModule,
ccl_version,
direct_transformers_import,
get_torch_version,
is_accelerate_available,
is_apex_available,
is_bitsandbytes_available,
is_bsa_available,
is_coloredlogs_available,
is_cython_available,
is_datasets_available,
is_decord_available,
is_detectrona_available,
is_faiss_available,
is_flax_available,
is_ftfy_available,
is_in_notebook,
is_ipex_available,
is_jieba_available,
is_jumanpp_available,
is_kenlm_available,
is_keras_nlp_available,
is_librosa_available,
is_natten_available,
is_ninja_available,
is_onnx_available,
is_openai_available,
is_optimum_available,
is_pandas_available,
is_peft_available,
is_phonemizer_available,
is_protobuf_available,
is_psutil_available,
is_pyanvml_available,
is_pyctcdecode_available,
is_pytesseract_available,
is_pytest_available,
is_pytorch_quantization_available,
is_rjieba_available,
is_sacremoses_available,
is_safetensors_available,
is_sagemaker_dp_enabled,
is_sagemaker_mp_enabled,
is_scipy_available,
is_sentencepiece_available,
is_seqio_available,
is_sklearn_available,
is_soundfile_availble,
is_spacy_available,
is_speech_available,
is_sudachi_available,
is_tensorflow_probability_available,
is_tensorflow_text_available,
is_tfaonnx_available,
is_tf_available,
is_timm_available,
is_tokenizers_available,
is_torch_available,
is_torch_bfaa_available,
is_torch_bfaa_cpu_available,
is_torch_bfaa_gpu_available,
is_torch_compile_available,
is_torch_cuda_available,
is_torch_fx_available,
is_torch_fx_proxy,
is_torch_mps_available,
is_torch_neuroncore_available,
is_torch_tensorrt_fx_available,
is_torch_tfaa_available,
is_torch_tpu_available,
is_torchaudio_available,
is_torchdistx_available,
is_torchdynamo_available,
is_torchvision_available,
is_training_run_on_sagemaker,
is_vision_available,
requires_backends,
torch_only_method,
)
lowercase_ = 'pytorch_model.bin'
lowercase_ = 'pytorch_model.bin.index.json'
lowercase_ = 'adapter_config.json'
lowercase_ = 'adapter_model.bin'
lowercase_ = 'adapter_model.safetensors'
lowercase_ = 'tf_model.h5'
lowercase_ = 'tf_model.h5.index.json'
lowercase_ = 'model.ckpt'
lowercase_ = 'flax_model.msgpack'
lowercase_ = 'flax_model.msgpack.index.json'
lowercase_ = 'model.safetensors'
lowercase_ = 'model.safetensors.index.json'
lowercase_ = 'config.json'
lowercase_ = 'preprocessor_config.json'
lowercase_ = FEATURE_EXTRACTOR_NAME
lowercase_ = 'generation_config.json'
lowercase_ = 'modelcard.json'
lowercase_ = '▁'
lowercase_ = SENTENCEPIECE_UNDERLINE # Kept for backward compatibility
lowercase_ = [
[[0, 1, 0, 1], [1, 0, 0, 1]]
] * 2 # Needs to have 0s and 1s only since XLM uses it for langs too.
lowercase_ = [[7, 6, 0, 0, 1], [1, 2, 3, 0, 0], [0, 0, 0, 4, 5]]
lowercase_ = [[1, 1, 1, 1, 1], [1, 1, 1, 0, 0], [0, 0, 0, 1, 1]]
def a ( A__ : List[str] ) -> str:
"""simple docstring"""
if version.parse(A__ ) < version.parse(A__ ):
if "dev" in min_version:
_lowercase =(
'This example requires a source install from HuggingFace Transformers (see '
'`https://huggingface.co/docs/transformers/installation#install-from-source`),'
)
else:
_lowercase =F'''This example requires a minimum version of {min_version},'''
error_message += F''' but the version found is {__version__}.\n'''
raise ImportError(
error_message
+ 'Check out https://github.com/huggingface/transformers/tree/main/examples#important-note for the examples corresponding to other '
'versions of HuggingFace Transformers.' )
| 291
| 1
|
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()
lowerCAmelCase_ = logging.get_logger()
@dataclass
class _A :
_UpperCamelCase : nn.Module
_UpperCamelCase : List[nn.Module] = field(default_factory=_lowerCamelCase )
_UpperCamelCase : list = field(default_factory=_lowerCamelCase )
def __a ( self : Any , _A : Union[str, Any] , _A : Tensor , _A : Tensor ) -> str:
"""simple docstring"""
lowercase : Optional[Any] = len(list(m.modules() ) ) == 1 or isinstance(_A , nn.Convad ) or isinstance(_A , nn.BatchNormad )
if has_not_submodules:
self.traced.append(_A )
def __call__( self : str , _A : Tensor ) -> Dict:
"""simple docstring"""
for m in self.module.modules():
self.handles.append(m.register_forward_hook(self._forward_hook ) )
self.module(_A )
[x.remove() for x in self.handles]
return self
@property
def __a ( self : str ) -> Dict:
"""simple docstring"""
return list(filter(lambda _A : len(list(x.state_dict().keys() ) ) > 0 , self.traced ) )
@dataclass
class _A :
_UpperCamelCase : nn.Module
_UpperCamelCase : nn.Module
_UpperCamelCase : int = 1
_UpperCamelCase : List = field(default_factory=_lowerCamelCase )
_UpperCamelCase : List = field(default_factory=_lowerCamelCase )
_UpperCamelCase : bool = True
def __call__( self : Optional[Any] , _A : Tensor ) -> int:
"""simple docstring"""
lowercase : str = Tracker(self.dest )(_A ).parametrized
lowercase : int = Tracker(self.src )(_A ).parametrized
lowercase : Union[str, Any] = list(filter(lambda _A : type(_A ) not in self.src_skip , _A ) )
lowercase : Optional[int] = list(filter(lambda _A : type(_A ) not in self.dest_skip , _A ) )
if len(_A ) != len(_A ) and self.raise_if_mismatch:
raise Exception(
f"""Numbers of operations are different. Source module has {len(_A )} operations while"""
f""" destination module has {len(_A )}.""" )
for dest_m, src_m in zip(_A , _A ):
dest_m.load_state_dict(src_m.state_dict() )
if self.verbose == 1:
print(f"""Transfered from={src_m} to={dest_m}""" )
class _A ( nn.Module ):
def __init__( self : Union[str, Any] , _A : nn.Module ) -> str:
"""simple docstring"""
super().__init__()
lowercase : 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}"""
lowercase : List[Any] = len(_A ) + 1
feature_blocks.append((f"""res{block_index}""", v) )
lowercase : Tuple = nn.ModuleDict(_A )
def __a ( self : Union[str, Any] , _A : Tensor ) -> Any:
"""simple docstring"""
return get_trunk_forward_outputs(
_A , out_feat_keys=_A , feature_blocks=self._feature_blocks , )
class _A ( _lowerCamelCase ):
def __a ( self : Optional[int] , _A : str ) -> str:
"""simple docstring"""
lowercase : int = x.split('''-''' )
return x_split[0] + x_split[1] + "_" + "".join(x_split[2:] )
def __getitem__( self : int , _A : str ) -> Callable[[], Tuple[nn.Module, Dict]]:
"""simple docstring"""
if x not in self:
lowercase : Tuple = self.convert_name_to_timm(_A )
lowercase : int = partial(lambda: (timm.create_model(_A , pretrained=_A ).eval(), None) )
else:
lowercase : Tuple = super().__getitem__(_A )
return val
class _A ( _lowerCamelCase ):
def __getitem__( self : Union[str, Any] , _A : str ) -> Callable[[], nn.Module]:
"""simple docstring"""
if "seer" in x and "in1k" not in x:
lowercase : Union[str, Any] = RegNetModel
else:
lowercase : Optional[Any] = RegNetForImageClassification
return val
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ ) -> Optional[Any]:
'''simple docstring'''
for from_key, to_key in keys:
lowercase : int = from_state_dict[from_key].clone()
print(F"""Copied key={from_key} to={to_key}""" )
return to_state_dict
def snake_case( __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ , __magic_name__ = True , ) -> Any:
'''simple docstring'''
print(F"""Converting {name}...""" )
with torch.no_grad():
lowercase , lowercase : Union[str, Any] = from_model_func()
lowercase : Dict = our_model_func(__magic_name__ ).eval()
lowercase : List[Any] = ModuleTransfer(src=__magic_name__ , dest=__magic_name__ , raise_if_mismatch=__magic_name__ )
lowercase : Union[str, Any] = torch.randn((1, 3, 2_24, 2_24) )
module_transfer(__magic_name__ )
if from_state_dict is not None:
lowercase : int = []
# for seer - in1k finetuned we have to manually copy the head
if "seer" in name and "in1k" in name:
lowercase : List[Any] = [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
lowercase : Optional[int] = manually_copy_vissl_head(__magic_name__ , our_model.state_dict() , __magic_name__ )
our_model.load_state_dict(__magic_name__ )
lowercase : Optional[Any] = our_model(__magic_name__ , output_hidden_states=__magic_name__ )
lowercase : int = (
our_outputs.logits if isinstance(__magic_name__ , __magic_name__ ) else our_outputs.last_hidden_state
)
lowercase : Tuple = from_model(__magic_name__ )
lowercase : int = from_output[-1] if type(__magic_name__ ) 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:
lowercase : Union[str, Any] = our_outputs.hidden_states[-1]
assert torch.allclose(__magic_name__ , __magic_name__ ), "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=__magic_name__ , )
lowercase : int = 2_24 if '''seer''' not in name else 3_84
# we can use the convnext one
lowercase : int = AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k''' , size=__magic_name__ )
image_processor.push_to_hub(
repo_path_or_name=save_directory / name , commit_message='''Add image processor''' , use_temp_dir=__magic_name__ , )
print(F"""Pushed {name}""" )
def snake_case( __magic_name__ , __magic_name__ = None , __magic_name__ = True ) -> int:
'''simple docstring'''
lowercase : Optional[Any] = '''imagenet-1k-id2label.json'''
lowercase : Optional[Any] = 10_00
lowercase : Optional[int] = (1, num_labels)
lowercase : Dict = '''huggingface/label-files'''
lowercase : Optional[Any] = num_labels
lowercase : Union[str, Any] = json.load(open(cached_download(hf_hub_url(__magic_name__ , __magic_name__ , repo_type='''dataset''' ) ) , '''r''' ) )
lowercase : Optional[int] = {int(__magic_name__ ): v for k, v in idalabel.items()}
lowercase : Optional[Any] = idalabel
lowercase : Dict = {v: k for k, v in idalabel.items()}
lowercase : List[str] = partial(__magic_name__ , num_labels=__magic_name__ , idalabel=__magic_name__ , labelaid=__magic_name__ )
lowercase : Any = {
'''regnet-x-002''': ImageNetPreTrainedConfig(
depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 , layer_type='''x''' ),
'''regnet-x-004''': ImageNetPreTrainedConfig(
depths=[1, 2, 7, 12] , hidden_sizes=[32, 64, 1_60, 3_84] , groups_width=16 , layer_type='''x''' ),
'''regnet-x-006''': ImageNetPreTrainedConfig(
depths=[1, 3, 5, 7] , hidden_sizes=[48, 96, 2_40, 5_28] , groups_width=24 , layer_type='''x''' ),
'''regnet-x-008''': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 5] , hidden_sizes=[64, 1_28, 2_88, 6_72] , groups_width=16 , layer_type='''x''' ),
'''regnet-x-016''': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 2] , hidden_sizes=[72, 1_68, 4_08, 9_12] , groups_width=24 , layer_type='''x''' ),
'''regnet-x-032''': ImageNetPreTrainedConfig(
depths=[2, 6, 15, 2] , hidden_sizes=[96, 1_92, 4_32, 10_08] , groups_width=48 , layer_type='''x''' ),
'''regnet-x-040''': ImageNetPreTrainedConfig(
depths=[2, 5, 14, 2] , hidden_sizes=[80, 2_40, 5_60, 13_60] , groups_width=40 , layer_type='''x''' ),
'''regnet-x-064''': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 3_92, 7_84, 16_24] , groups_width=56 , layer_type='''x''' ),
'''regnet-x-080''': ImageNetPreTrainedConfig(
depths=[2, 5, 15, 1] , hidden_sizes=[80, 2_40, 7_20, 19_20] , groups_width=1_20 , layer_type='''x''' ),
'''regnet-x-120''': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 , layer_type='''x''' ),
'''regnet-x-160''': ImageNetPreTrainedConfig(
depths=[2, 6, 13, 1] , hidden_sizes=[2_56, 5_12, 8_96, 20_48] , groups_width=1_28 , layer_type='''x''' ),
'''regnet-x-320''': ImageNetPreTrainedConfig(
depths=[2, 7, 13, 1] , hidden_sizes=[3_36, 6_72, 13_44, 25_20] , groups_width=1_68 , layer_type='''x''' ),
# y variant
'''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7] , hidden_sizes=[24, 56, 1_52, 3_68] , groups_width=8 ),
'''regnet-y-004''': ImageNetPreTrainedConfig(
depths=[1, 3, 6, 6] , hidden_sizes=[48, 1_04, 2_08, 4_40] , groups_width=8 ),
'''regnet-y-006''': ImageNetPreTrainedConfig(
depths=[1, 3, 7, 4] , hidden_sizes=[48, 1_12, 2_56, 6_08] , groups_width=16 ),
'''regnet-y-008''': ImageNetPreTrainedConfig(
depths=[1, 3, 8, 2] , hidden_sizes=[64, 1_28, 3_20, 7_68] , groups_width=16 ),
'''regnet-y-016''': ImageNetPreTrainedConfig(
depths=[2, 6, 17, 2] , hidden_sizes=[48, 1_20, 3_36, 8_88] , groups_width=24 ),
'''regnet-y-032''': ImageNetPreTrainedConfig(
depths=[2, 5, 13, 1] , hidden_sizes=[72, 2_16, 5_76, 15_12] , groups_width=24 ),
'''regnet-y-040''': ImageNetPreTrainedConfig(
depths=[2, 6, 12, 2] , hidden_sizes=[1_28, 1_92, 5_12, 10_88] , groups_width=64 ),
'''regnet-y-064''': ImageNetPreTrainedConfig(
depths=[2, 7, 14, 2] , hidden_sizes=[1_44, 2_88, 5_76, 12_96] , groups_width=72 ),
'''regnet-y-080''': ImageNetPreTrainedConfig(
depths=[2, 4, 10, 1] , hidden_sizes=[1_68, 4_48, 8_96, 20_16] , groups_width=56 ),
'''regnet-y-120''': ImageNetPreTrainedConfig(
depths=[2, 5, 11, 1] , hidden_sizes=[2_24, 4_48, 8_96, 22_40] , groups_width=1_12 ),
'''regnet-y-160''': ImageNetPreTrainedConfig(
depths=[2, 4, 11, 1] , hidden_sizes=[2_24, 4_48, 12_32, 30_24] , groups_width=1_12 ),
'''regnet-y-320''': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
# models created by SEER -> https://arxiv.org/abs/2202.08360
'''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'''regnet-y-1280-seer''': RegNetConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'''regnet-y-2560-seer''': RegNetConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
# finetuned on imagenet
'''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[2_32, 6_96, 13_92, 37_12] , groups_width=2_32 ),
'''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 5, 12, 1] , hidden_sizes=[3_28, 9_84, 19_68, 49_20] , groups_width=3_28 ),
'''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[5_28, 10_56, 29_04, 73_92] , groups_width=2_64 ),
'''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
depths=[3, 7, 16, 1] , hidden_sizes=[6_40, 16_96, 25_44, 50_88] , groups_width=6_40 ),
'''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
depths=[2, 7, 17, 1] , hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80] , groups_width=10_10 ),
}
lowercase : List[str] = NameToOurModelFuncMap()
lowercase : str = NameToFromModelFuncMap()
# add seer weights logic
def load_using_classy_vision(__magic_name__ , __magic_name__ ) -> Tuple[nn.Module, Dict]:
lowercase : Union[str, Any] = torch.hub.load_state_dict_from_url(__magic_name__ , model_dir=str(__magic_name__ ) , map_location='''cpu''' )
lowercase : Any = model_func()
# check if we have a head, if yes add it
lowercase : Union[str, Any] = files['''classy_state_dict''']['''base_model''']['''model''']
lowercase : Union[str, Any] = model_state_dict['''trunk''']
model.load_state_dict(__magic_name__ )
return model.eval(), model_state_dict["heads"]
# pretrained
lowercase : str = partial(
__magic_name__ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
lowercase : List[Any] = partial(
__magic_name__ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
lowercase : List[Any] = partial(
__magic_name__ , '''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() ) , )
lowercase : Any = partial(
__magic_name__ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch''' , lambda: FakeRegNetVisslWrapper(
RegNet(RegNetParams(depth=27 , group_width=10_10 , w_a=17_44 , w_a=6_2_0.8_3 , w_m=2.5_2 ) ) ) , )
# IN1K finetuned
lowercase : Any = partial(
__magic_name__ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
lowercase : Any = partial(
__magic_name__ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaagf() ) , )
lowercase : Dict = partial(
__magic_name__ , '''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch''' , lambda: FakeRegNetVisslWrapper(RegNetYaaagf() ) , )
lowercase : int = partial(
__magic_name__ , '''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=10_10 , w_a=17_44 , w_a=6_2_0.8_3 , w_m=2.5_2 ) ) ) , )
if model_name:
convert_weight_and_push(
__magic_name__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , names_to_config[model_name] , __magic_name__ , __magic_name__ , )
else:
for model_name, config in names_to_config.items():
convert_weight_and_push(
__magic_name__ , names_to_from_model_map[model_name] , names_to_ours_model_map[model_name] , __magic_name__ , __magic_name__ , __magic_name__ , )
return config, expected_shape
if __name__ == "__main__":
lowerCAmelCase_ = 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.',
)
lowerCAmelCase_ = parser.parse_args()
lowerCAmelCase_ = 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)
| 596
|
from pathlib import Path
import fire
from tqdm import tqdm
def snake_case( __magic_name__="ro" , __magic_name__="en" , __magic_name__="wmt16" , __magic_name__=None ) -> None:
'''simple docstring'''
try:
import datasets
except (ModuleNotFoundError, ImportError):
raise ImportError('''run pip install datasets''' )
lowercase : Optional[Any] = F"""{src_lang}-{tgt_lang}"""
print(F"""Converting {dataset}-{pair}""" )
lowercase : str = datasets.load_dataset(__magic_name__ , __magic_name__ )
if save_dir is None:
lowercase : Dict = F"""{dataset}-{pair}"""
lowercase : int = Path(__magic_name__ )
save_dir.mkdir(exist_ok=__magic_name__ )
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
lowercase : int = '''val''' if split == '''validation''' else split
lowercase : Any = save_dir.joinpath(F"""{fn}.source""" )
lowercase : Optional[Any] = save_dir.joinpath(F"""{fn}.target""" )
lowercase : Union[str, Any] = src_path.open('''w+''' )
lowercase : Union[str, Any] = 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] ):
lowercase : Optional[int] = 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)
| 596
| 1
|
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import DistilBertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
)
class _SCREAMING_SNAKE_CASE ( __UpperCAmelCase ):
'''simple docstring'''
def __init__( self : Optional[int] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Optional[int]=13 , UpperCAmelCase_ : Union[str, Any]=7 , UpperCAmelCase_ : Optional[int]=True , UpperCAmelCase_ : List[str]=True , UpperCAmelCase_ : Union[str, Any]=False , UpperCAmelCase_ : Optional[Any]=True , UpperCAmelCase_ : Optional[int]=99 , UpperCAmelCase_ : List[Any]=32 , UpperCAmelCase_ : Optional[Any]=5 , UpperCAmelCase_ : Optional[int]=4 , UpperCAmelCase_ : Optional[int]=37 , UpperCAmelCase_ : Optional[Any]="gelu" , UpperCAmelCase_ : Union[str, Any]=0.1 , UpperCAmelCase_ : Optional[Any]=0.1 , UpperCAmelCase_ : List[str]=512 , UpperCAmelCase_ : Optional[Any]=16 , UpperCAmelCase_ : List[Any]=2 , UpperCAmelCase_ : Dict=0.02 , UpperCAmelCase_ : List[str]=3 , UpperCAmelCase_ : Optional[int]=4 , UpperCAmelCase_ : Optional[Any]=None , ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = parent
_lowerCAmelCase = batch_size
_lowerCAmelCase = seq_length
_lowerCAmelCase = is_training
_lowerCAmelCase = use_input_mask
_lowerCAmelCase = use_token_type_ids
_lowerCAmelCase = use_labels
_lowerCAmelCase = vocab_size
_lowerCAmelCase = hidden_size
_lowerCAmelCase = num_hidden_layers
_lowerCAmelCase = num_attention_heads
_lowerCAmelCase = intermediate_size
_lowerCAmelCase = hidden_act
_lowerCAmelCase = hidden_dropout_prob
_lowerCAmelCase = attention_probs_dropout_prob
_lowerCAmelCase = max_position_embeddings
_lowerCAmelCase = type_vocab_size
_lowerCAmelCase = type_sequence_label_size
_lowerCAmelCase = initializer_range
_lowerCAmelCase = num_labels
_lowerCAmelCase = num_choices
_lowerCAmelCase = scope
def __lowerCamelCase ( self : str ) -> List[Any]:
"""simple docstring"""
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_lowerCAmelCase = None
if self.use_input_mask:
_lowerCAmelCase = random_attention_mask([self.batch_size, self.seq_length] )
_lowerCAmelCase = None
_lowerCAmelCase = None
_lowerCAmelCase = None
if self.use_labels:
_lowerCAmelCase = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_lowerCAmelCase = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_lowerCAmelCase = ids_tensor([self.batch_size] , self.num_choices )
_lowerCAmelCase = self.get_config()
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def __lowerCamelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
return DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
def __lowerCamelCase ( self : Optional[Any] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Tuple , UpperCAmelCase_ : str , UpperCAmelCase_ : List[str] ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = DistilBertModel(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
_lowerCAmelCase = model(UpperCamelCase_ , UpperCamelCase_ )
_lowerCAmelCase = model(UpperCamelCase_ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def __lowerCamelCase ( self : List[str] , UpperCAmelCase_ : int , UpperCAmelCase_ : Dict , UpperCAmelCase_ : Any , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : List[Any] ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = DistilBertForMaskedLM(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
_lowerCAmelCase = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def __lowerCamelCase ( self : str , UpperCAmelCase_ : Optional[Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : Dict , UpperCAmelCase_ : int , UpperCAmelCase_ : Optional[int] ) -> Tuple:
"""simple docstring"""
_lowerCAmelCase = DistilBertForQuestionAnswering(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
_lowerCAmelCase = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , start_positions=UpperCamelCase_ , end_positions=UpperCamelCase_ )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def __lowerCamelCase ( self : Union[str, Any] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : int , UpperCAmelCase_ : Tuple ) -> Tuple:
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = DistilBertForSequenceClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
_lowerCAmelCase = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def __lowerCamelCase ( self : Dict , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : Union[str, Any] , UpperCAmelCase_ : str , UpperCAmelCase_ : List[Any] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[Any] ) -> Dict:
"""simple docstring"""
_lowerCAmelCase = self.num_labels
_lowerCAmelCase = DistilBertForTokenClassification(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
_lowerCAmelCase = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def __lowerCamelCase ( self : int , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[str] , UpperCAmelCase_ : Any , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : Optional[int] , UpperCAmelCase_ : List[Any] ) -> int:
"""simple docstring"""
_lowerCAmelCase = self.num_choices
_lowerCAmelCase = DistilBertForMultipleChoice(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
_lowerCAmelCase = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
_lowerCAmelCase = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , labels=UpperCamelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def __lowerCamelCase ( self : Optional[int] ) -> str:
"""simple docstring"""
_lowerCAmelCase = self.prepare_config_and_inputs()
((_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase) , (_lowerCAmelCase)) = config_and_inputs
_lowerCAmelCase = {'input_ids': input_ids, 'attention_mask': input_mask}
return config, inputs_dict
@require_torch
class _SCREAMING_SNAKE_CASE ( __UpperCAmelCase , __UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_: Union[str, Any] = (
(
DistilBertModel,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
)
if is_torch_available()
else None
)
SCREAMING_SNAKE_CASE_: Optional[int] = (
{
"feature-extraction": DistilBertModel,
"fill-mask": DistilBertForMaskedLM,
"question-answering": DistilBertForQuestionAnswering,
"text-classification": DistilBertForSequenceClassification,
"token-classification": DistilBertForTokenClassification,
"zero-shot": DistilBertForSequenceClassification,
}
if is_torch_available()
else {}
)
SCREAMING_SNAKE_CASE_: str = True
SCREAMING_SNAKE_CASE_: Union[str, Any] = True
SCREAMING_SNAKE_CASE_: List[Any] = True
SCREAMING_SNAKE_CASE_: int = True
def __lowerCamelCase ( self : Any ) -> Any:
"""simple docstring"""
_lowerCAmelCase = DistilBertModelTester(self )
_lowerCAmelCase = ConfigTester(self , config_class=UpperCamelCase_ , dim=37 )
def __lowerCamelCase ( self : Union[str, Any] ) -> Dict:
"""simple docstring"""
self.config_tester.run_common_tests()
def __lowerCamelCase ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*UpperCamelCase_ )
def __lowerCamelCase ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*UpperCamelCase_ )
def __lowerCamelCase ( self : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*UpperCamelCase_ )
def __lowerCamelCase ( self : int ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*UpperCamelCase_ )
def __lowerCamelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*UpperCamelCase_ )
def __lowerCamelCase ( self : Optional[int] ) -> Any:
"""simple docstring"""
_lowerCAmelCase = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*UpperCamelCase_ )
@slow
def __lowerCamelCase ( self : Dict ) -> Optional[int]:
"""simple docstring"""
for model_name in DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
_lowerCAmelCase = DistilBertModel.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
@slow
@require_torch_gpu
def __lowerCamelCase ( self : Any ) -> Dict:
"""simple docstring"""
_lowerCAmelCase , _lowerCAmelCase = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
# BertForMultipleChoice behaves incorrectly in JIT environments.
if model_class == DistilBertForMultipleChoice:
return
_lowerCAmelCase = True
_lowerCAmelCase = model_class(config=UpperCamelCase_ )
_lowerCAmelCase = self._prepare_for_class(UpperCamelCase_ , UpperCamelCase_ )
_lowerCAmelCase = torch.jit.trace(
UpperCamelCase_ , (inputs_dict['input_ids'].to('cpu' ), inputs_dict['attention_mask'].to('cpu' )) )
with tempfile.TemporaryDirectory() as tmp:
torch.jit.save(UpperCamelCase_ , os.path.join(UpperCamelCase_ , 'traced_model.pt' ) )
_lowerCAmelCase = torch.jit.load(os.path.join(UpperCamelCase_ , 'traced_model.pt' ) , map_location=UpperCamelCase_ )
loaded(inputs_dict['input_ids'].to(UpperCamelCase_ ) , inputs_dict['attention_mask'].to(UpperCamelCase_ ) )
@require_torch
class _SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@slow
def __lowerCamelCase ( self : Dict ) -> List[Any]:
"""simple docstring"""
_lowerCAmelCase = DistilBertModel.from_pretrained('distilbert-base-uncased' )
_lowerCAmelCase = torch.tensor([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
_lowerCAmelCase = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
_lowerCAmelCase = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ )[0]
_lowerCAmelCase = torch.Size((1, 11, 768) )
self.assertEqual(output.shape , UpperCamelCase_ )
_lowerCAmelCase = torch.tensor(
[[[-0.1639, 0.3299, 0.1648], [-0.1746, 0.3289, 0.1710], [-0.1884, 0.3357, 0.1810]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , UpperCamelCase_ , atol=1E-4 ) )
| 580
|
"""simple docstring"""
__lowerCamelCase = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__lowerCamelCase = ["a", "b", "c", "d", "e"]
def lowercase ( __UpperCamelCase , __UpperCamelCase , __UpperCamelCase ) -> int:
__magic_name__ = start
# add current to visited
visited.append(__UpperCamelCase )
__magic_name__ = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
__magic_name__ = topological_sort(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# if all neighbors visited add current to sort
sort.append(__UpperCamelCase )
# if all vertices haven't been visited select a new one to visit
if len(__UpperCamelCase ) != len(__UpperCamelCase ):
for vertice in vertices:
if vertice not in visited:
__magic_name__ = topological_sort(__UpperCamelCase , __UpperCamelCase , __UpperCamelCase )
# return sort
return sort
if __name__ == "__main__":
__lowerCamelCase = topological_sort("a", [], [])
print(sort)
| 490
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
"google/canine-s": "https://huggingface.co/google/canine-s/resolve/main/config.json",
# See all CANINE models at https://huggingface.co/models?filter=canine
}
class lowerCamelCase ( A_ ):
UpperCAmelCase__ : Dict = "canine"
def __init__(self : List[Any] , _A : Union[str, Any]=7_6_8 , _A : Any=1_2 , _A : List[Any]=1_2 , _A : List[Any]=3_0_7_2 , _A : Dict="gelu" , _A : Optional[Any]=0.1 , _A : Tuple=0.1 , _A : str=1_6_3_8_4 , _A : Union[str, Any]=1_6 , _A : Any=0.02 , _A : List[str]=1E-12 , _A : Union[str, Any]=0 , _A : Dict=0Xe0_00 , _A : List[Any]=0Xe0_01 , _A : int=4 , _A : str=4 , _A : Optional[int]=8 , _A : Optional[Any]=1_6_3_8_4 , _A : Optional[Any]=1_2_8 , **_A : Union[str, Any] , ) -> Union[str, Any]:
super().__init__(pad_token_id=_A , bos_token_id=_A , eos_token_id=_A , **_A )
snake_case = max_position_embeddings
snake_case = hidden_size
snake_case = num_hidden_layers
snake_case = num_attention_heads
snake_case = intermediate_size
snake_case = hidden_act
snake_case = hidden_dropout_prob
snake_case = attention_probs_dropout_prob
snake_case = initializer_range
snake_case = type_vocab_size
snake_case = layer_norm_eps
# Character config:
snake_case = downsampling_rate
snake_case = upsampling_kernel_size
snake_case = num_hash_functions
snake_case = num_hash_buckets
snake_case = local_transformer_stride
| 706
|
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_A = "▁"
_A = {"vocab_file": "spiece.model"}
_A = {
"vocab_file": {"google/pegasus-xsum": "https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model"}
}
_A = {
"google/pegasus-xsum": 5_12,
}
_A = logging.get_logger(__name__)
class lowerCamelCase ( A_ ):
UpperCAmelCase__ : Tuple = VOCAB_FILES_NAMES
UpperCAmelCase__ : Tuple = VOCAB_FILES_NAMES
UpperCAmelCase__ : List[str] = PRETRAINED_VOCAB_FILES_MAP
UpperCAmelCase__ : Optional[int] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
UpperCAmelCase__ : Optional[Any] = ["input_ids", "attention_mask"]
def __init__(self : Optional[Any] , _A : Any , _A : List[Any]="<pad>" , _A : int="</s>" , _A : Dict="<unk>" , _A : str="<mask_2>" , _A : Optional[int]="<mask_1>" , _A : Optional[Any]=None , _A : Tuple=1_0_3 , _A : Optional[Dict[str, Any]] = None , **_A : List[str] , ) -> None:
snake_case = offset
if additional_special_tokens is not None:
if not isinstance(_A , _A ):
raise TypeError(
f'additional_special_tokens should be of type {type(_A )}, but is'
f' {type(_A )}' )
snake_case = (
([mask_token_sent] + additional_special_tokens)
if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
else additional_special_tokens
)
# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
additional_special_tokens_extended += [
f'<unk_{i}>' for i in range(len(_A ) , self.offset - 1 )
]
if len(set(_A ) ) != len(_A ):
raise ValueError(
"Please make sure that the provided additional_special_tokens do not contain an incorrectly"
f' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.' )
snake_case = additional_special_tokens_extended
else:
snake_case = [mask_token_sent] if mask_token_sent is not None else []
additional_special_tokens += [f'<unk_{i}>' for i in range(2 , self.offset )]
snake_case = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=_A , unk_token=_A , mask_token=_A , pad_token=_A , mask_token_sent=_A , offset=_A , additional_special_tokens=_A , sp_model_kwargs=self.sp_model_kwargs , **_A , )
snake_case = mask_token_sent
snake_case = vocab_file
snake_case = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(_A )
# add special tokens to encoder dict
snake_case = {
0: self.pad_token,
1: self.eos_token,
}
if self.mask_token_sent is not None:
self.encoder.update(
{
2: self.mask_token_sent,
3: self.mask_token,
} )
if self.offset > 0:
# entries 2-104 are only used for pretraining and called <mask_1>, <mask_2>, unk_2, ...unk_102
# mask_token_sent is already added to list -> so start at 1
self.encoder.update({i + 3: additional_special_tokens[i] for i in range(1 , self.offset - 1 )} )
snake_case = {v: k for k, v in self.encoder.items()}
@property
def UpperCAmelCase(self : str ) -> int:
return len(self.sp_model ) + self.offset
def UpperCAmelCase(self : List[str] ) -> Dict[str, int]:
snake_case = {self.convert_ids_to_tokens(_A ): i for i in range(self.vocab_size )}
vocab.update(self.added_tokens_encoder )
return vocab
def __getstate__(self : Any ) -> List[Any]:
snake_case = self.__dict__.copy()
snake_case = None
return state
def __setstate__(self : str , _A : Union[str, Any] ) -> Tuple:
snake_case = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
snake_case = {}
snake_case = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def UpperCAmelCase(self : List[Any] , _A : str ) -> List[str]:
return self.sp_model.encode(_A , out_type=_A )
def UpperCAmelCase(self : List[str] , _A : str ) -> int:
if token in self.decoder:
return self.decoder[token]
elif token in self.added_tokens_decoder:
return self.added_tokens_decoder[token]
snake_case = self.sp_model.piece_to_id(_A )
return sp_id + self.offset
def UpperCAmelCase(self : Union[str, Any] , _A : int ) -> str:
if index in self.encoder:
return self.encoder[index]
elif index in self.added_tokens_encoder:
return self.added_tokens_encoder[index]
else:
snake_case = self.sp_model.IdToPiece(index - self.offset )
return token
def UpperCAmelCase(self : List[Any] , _A : Tuple ) -> Tuple:
snake_case = []
snake_case = ""
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
out_string += self.sp_model.decode(_A ) + token
snake_case = []
else:
current_sub_tokens.append(_A )
out_string += self.sp_model.decode(_A )
return out_string.strip()
def UpperCAmelCase(self : List[Any] , _A : Tuple=False ) -> Tuple:
return 1
def UpperCAmelCase(self : Tuple , _A : Optional[int] ) -> Tuple:
snake_case = set(self.all_special_ids ) # call it once instead of inside list comp
all_special_ids.remove(self.unk_token_id ) # <unk> is only sometimes special
return [1 if x in all_special_ids else 0 for x in seq]
def UpperCAmelCase(self : str , _A : List , _A : Optional[List] = None , _A : bool = False ) -> List[int]:
if already_has_special_tokens:
return self._special_token_mask(_A )
elif token_ids_a is None:
return self._special_token_mask(_A ) + [1]
else:
return self._special_token_mask(token_ids_a + token_ids_a ) + [1]
def UpperCAmelCase(self : int , _A : Dict , _A : List[Any]=None ) -> List[int]:
if token_ids_a is None:
return token_ids_a + [self.eos_token_id]
# We don't expect to process pairs, but leave the pair logic for API consistency
return token_ids_a + token_ids_a + [self.eos_token_id]
def UpperCAmelCase(self : Optional[Any] , _A : str , _A : Optional[str] = None ) -> Tuple[str]:
if not os.path.isdir(_A ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
snake_case = os.path.join(
_A , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_A ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , _A )
elif not os.path.isfile(self.vocab_file ):
with open(_A , "wb" ) as fi:
snake_case = self.sp_model.serialized_model_proto()
fi.write(_A )
return (out_vocab_file,)
| 294
| 0
|
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is_vision_available():
from PIL import Image
else:
class __lowerCAmelCase :
"""simple docstring"""
@staticmethod
def lowerCAmelCase__ ( *_lowerCAmelCase : Dict , **_lowerCAmelCase : Dict ) -> int:
"""simple docstring"""
pass
@is_pipeline_test
@require_torch
@require_vision
class __lowerCAmelCase ( unittest.TestCase ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def lowerCAmelCase__ ( self : str , _lowerCAmelCase : Dict , _lowerCAmelCase : Dict , _lowerCAmelCase : Optional[Any] ) -> List[Any]:
"""simple docstring"""
snake_case_ = pipeline("visual-question-answering" , model="hf-internal-testing/tiny-vilt-random-vqa" )
snake_case_ = [
{
"image": Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" ),
"question": "How many cats are there?",
},
{
"image": "./tests/fixtures/tests_samples/COCO/000000039769.png",
"question": "How many cats are there?",
},
]
return vqa_pipeline, examples
def lowerCAmelCase__ ( self : List[Any] , _lowerCAmelCase : int , _lowerCAmelCase : Optional[Any] ) -> List[str]:
"""simple docstring"""
snake_case_ = vqa_pipeline(_lowerCAmelCase , top_k=1 )
self.assertEqual(
_lowerCAmelCase , [
[{"score": ANY(_lowerCAmelCase ), "answer": ANY(_lowerCAmelCase )}],
[{"score": ANY(_lowerCAmelCase ), "answer": ANY(_lowerCAmelCase )}],
] , )
@require_torch
def lowerCAmelCase__ ( self : Optional[Any] ) -> Optional[Any]:
"""simple docstring"""
snake_case_ = pipeline("visual-question-answering" , model="hf-internal-testing/tiny-vilt-random-vqa" )
snake_case_ = "./tests/fixtures/tests_samples/COCO/000000039769.png"
snake_case_ = "How many cats are there?"
snake_case_ = vqa_pipeline(image=_lowerCAmelCase , question="How many cats are there?" , top_k=2 )
self.assertEqual(
_lowerCAmelCase , [{"score": ANY(_lowerCAmelCase ), "answer": ANY(_lowerCAmelCase )}, {"score": ANY(_lowerCAmelCase ), "answer": ANY(_lowerCAmelCase )}] )
snake_case_ = vqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
_lowerCAmelCase , [{"score": ANY(_lowerCAmelCase ), "answer": ANY(_lowerCAmelCase )}, {"score": ANY(_lowerCAmelCase ), "answer": ANY(_lowerCAmelCase )}] )
@slow
@require_torch
def lowerCAmelCase__ ( self : Dict ) -> Dict:
"""simple docstring"""
snake_case_ = pipeline("visual-question-answering" , model="dandelin/vilt-b32-finetuned-vqa" )
snake_case_ = "./tests/fixtures/tests_samples/COCO/000000039769.png"
snake_case_ = "How many cats are there?"
snake_case_ = vqa_pipeline(image=_lowerCAmelCase , question=_lowerCAmelCase , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [{"score": 0.8_799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
snake_case_ = vqa_pipeline({"image": image, "question": question} , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [{"score": 0.8_799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
snake_case_ = vqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] , top_k=2 )
self.assertEqual(
nested_simplify(_lowerCAmelCase , decimals=4 ) , [[{"score": 0.8_799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2 , )
@require_tf
@unittest.skip("Visual question answering not implemented in TF" )
def lowerCAmelCase__ ( self : Any ) -> Optional[Any]:
"""simple docstring"""
pass
| 283
|
from __future__ import annotations
def _lowerCAmelCase ( lowerCAmelCase_ :int , lowerCAmelCase_ :int )->list[str]:
'''simple docstring'''
if partitions <= 0:
raise ValueError("partitions must be a positive number!" )
if partitions > number_of_bytes:
raise ValueError("partitions can not > number_of_bytes!" )
snake_case_ = number_of_bytes // partitions
snake_case_ = []
for i in range(lowerCAmelCase_ ):
snake_case_ = i * bytes_per_partition + 1
snake_case_ = (
number_of_bytes if i == partitions - 1 else (i + 1) * bytes_per_partition
)
allocation_list.append(F'''{start_bytes}-{end_bytes}''' )
return allocation_list
if __name__ == "__main__":
import doctest
doctest.testmod()
| 283
| 1
|
'''simple docstring'''
from math import factorial
def lowerCAmelCase ( UpperCAmelCase, UpperCAmelCase, UpperCAmelCase ) ->float:
"""simple docstring"""
if successes > trials:
raise ValueError('''successes must be lower or equal to trials''' )
if trials < 0 or successes < 0:
raise ValueError('''the function is defined for non-negative integers''' )
if not isinstance(UpperCamelCase__, UpperCamelCase__ ) or not isinstance(UpperCamelCase__, UpperCamelCase__ ):
raise ValueError('''the function is defined for non-negative integers''' )
if not 0 < prob < 1:
raise ValueError('''prob has to be in range of 1 - 0''' )
__magic_name__ : str = (prob**successes) * ((1 - prob) ** (trials - successes))
# Calculate the binomial coefficient: n! / k!(n-k)!
__magic_name__ : str = float(factorial(UpperCamelCase__ ) )
coefficient /= factorial(UpperCamelCase__ ) * factorial(trials - successes )
return probability * coefficient
if __name__ == "__main__":
from doctest import testmod
testmod()
print('''Probability of 2 successes out of 4 trails''')
print('''with probability of 0.75 is:''', end=''' ''')
print(binomial_distribution(2, 4, 0.7_5))
| 703
|
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squeeze,
transpose,
)
if is_flax_available():
import jax.numpy as jnp
if is_tf_available():
import tensorflow as tf
if is_torch_available():
import torch
class A__ ( unittest.TestCase ):
def lowercase ( self ) -> Dict:
"""simple docstring"""
__magic_name__ : List[Any] = {
'''task_specific_params''': {
'''summarization''': {'''length_penalty''': 1.0, '''max_length''': 128, '''min_length''': 12, '''num_beams''': 4},
'''summarization_cnn''': {'''length_penalty''': 2.0, '''max_length''': 142, '''min_length''': 56, '''num_beams''': 4},
'''summarization_xsum''': {'''length_penalty''': 1.0, '''max_length''': 62, '''min_length''': 11, '''num_beams''': 6},
}
}
__magic_name__ : int = {
'''task_specific_params.summarization.length_penalty''': 1.0,
'''task_specific_params.summarization.max_length''': 128,
'''task_specific_params.summarization.min_length''': 12,
'''task_specific_params.summarization.num_beams''': 4,
'''task_specific_params.summarization_cnn.length_penalty''': 2.0,
'''task_specific_params.summarization_cnn.max_length''': 142,
'''task_specific_params.summarization_cnn.min_length''': 56,
'''task_specific_params.summarization_cnn.num_beams''': 4,
'''task_specific_params.summarization_xsum.length_penalty''': 1.0,
'''task_specific_params.summarization_xsum.max_length''': 62,
'''task_specific_params.summarization_xsum.min_length''': 11,
'''task_specific_params.summarization_xsum.num_beams''': 6,
}
self.assertEqual(flatten_dict(lowerCamelCase ) , lowerCamelCase )
def lowercase ( self ) -> Tuple:
"""simple docstring"""
__magic_name__ : Optional[Any] = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(transpose(lowerCamelCase ) , x.transpose() ) )
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(transpose(lowerCamelCase , axes=(1, 2, 0) ) , x.transpose((1, 2, 0) ) ) )
@require_torch
def lowercase ( self ) -> Optional[int]:
"""simple docstring"""
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 )
__magic_name__ : List[str] = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase ) , transpose(lowerCamelCase ).numpy() ) )
__magic_name__ : int = np.random.randn(3 , 4 , 5 )
__magic_name__ : Union[str, Any] = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase , axes=(1, 2, 0) ) , transpose(lowerCamelCase , axes=(1, 2, 0) ).numpy() ) )
@require_tf
def lowercase ( self ) -> Tuple:
"""simple docstring"""
__magic_name__ : Dict = np.random.randn(3 , 4 )
__magic_name__ : Any = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase ) , transpose(lowerCamelCase ).numpy() ) )
__magic_name__ : str = np.random.randn(3 , 4 , 5 )
__magic_name__ : Optional[int] = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase , axes=(1, 2, 0) ) , transpose(lowerCamelCase , axes=(1, 2, 0) ).numpy() ) )
@require_flax
def lowercase ( self ) -> int:
"""simple docstring"""
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 )
__magic_name__ : Optional[Any] = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase ) , np.asarray(transpose(lowerCamelCase ) ) ) )
__magic_name__ : int = np.random.randn(3 , 4 , 5 )
__magic_name__ : Tuple = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(transpose(lowerCamelCase , axes=(1, 2, 0) ) , np.asarray(transpose(lowerCamelCase , axes=(1, 2, 0) ) ) ) )
def lowercase ( self ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ : Dict = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (4, 3) ) , np.reshape(lowerCamelCase , (4, 3) ) ) )
__magic_name__ : Optional[int] = np.random.randn(3 , 4 , 5 )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (12, 5) ) , np.reshape(lowerCamelCase , (12, 5) ) ) )
@require_torch
def lowercase ( self ) -> int:
"""simple docstring"""
__magic_name__ : Tuple = np.random.randn(3 , 4 )
__magic_name__ : List[Any] = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (4, 3) ) , reshape(lowerCamelCase , (4, 3) ).numpy() ) )
__magic_name__ : List[str] = np.random.randn(3 , 4 , 5 )
__magic_name__ : Tuple = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (12, 5) ) , reshape(lowerCamelCase , (12, 5) ).numpy() ) )
@require_tf
def lowercase ( self ) -> Union[str, Any]:
"""simple docstring"""
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 )
__magic_name__ : List[str] = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (4, 3) ) , reshape(lowerCamelCase , (4, 3) ).numpy() ) )
__magic_name__ : Optional[Any] = np.random.randn(3 , 4 , 5 )
__magic_name__ : str = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (12, 5) ) , reshape(lowerCamelCase , (12, 5) ).numpy() ) )
@require_flax
def lowercase ( self ) -> Tuple:
"""simple docstring"""
__magic_name__ : Dict = np.random.randn(3 , 4 )
__magic_name__ : Optional[Any] = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (4, 3) ) , np.asarray(reshape(lowerCamelCase , (4, 3) ) ) ) )
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 , 5 )
__magic_name__ : List[Any] = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(reshape(lowerCamelCase , (12, 5) ) , np.asarray(reshape(lowerCamelCase , (12, 5) ) ) ) )
def lowercase ( self ) -> Dict:
"""simple docstring"""
__magic_name__ : Optional[Any] = np.random.randn(1 , 3 , 4 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase ) , np.squeeze(lowerCamelCase ) ) )
__magic_name__ : int = np.random.randn(1 , 4 , 1 , 5 )
self.assertTrue(np.allclose(squeeze(lowerCamelCase , axis=2 ) , np.squeeze(lowerCamelCase , axis=2 ) ) )
@require_torch
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
__magic_name__ : Any = np.random.randn(1 , 3 , 4 )
__magic_name__ : List[str] = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase ) , squeeze(lowerCamelCase ).numpy() ) )
__magic_name__ : Union[str, Any] = np.random.randn(1 , 4 , 1 , 5 )
__magic_name__ : Tuple = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase , axis=2 ) , squeeze(lowerCamelCase , axis=2 ).numpy() ) )
@require_tf
def lowercase ( self ) -> str:
"""simple docstring"""
__magic_name__ : Optional[int] = np.random.randn(1 , 3 , 4 )
__magic_name__ : Any = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase ) , squeeze(lowerCamelCase ).numpy() ) )
__magic_name__ : int = np.random.randn(1 , 4 , 1 , 5 )
__magic_name__ : str = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase , axis=2 ) , squeeze(lowerCamelCase , axis=2 ).numpy() ) )
@require_flax
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
__magic_name__ : str = np.random.randn(1 , 3 , 4 )
__magic_name__ : List[str] = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase ) , np.asarray(squeeze(lowerCamelCase ) ) ) )
__magic_name__ : Optional[int] = np.random.randn(1 , 4 , 1 , 5 )
__magic_name__ : Optional[int] = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(squeeze(lowerCamelCase , axis=2 ) , np.asarray(squeeze(lowerCamelCase , axis=2 ) ) ) )
def lowercase ( self ) -> Optional[int]:
"""simple docstring"""
__magic_name__ : Tuple = np.random.randn(3 , 4 )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase , axis=1 ) , np.expand_dims(lowerCamelCase , axis=1 ) ) )
@require_torch
def lowercase ( self ) -> List[Any]:
"""simple docstring"""
__magic_name__ : Union[str, Any] = np.random.randn(3 , 4 )
__magic_name__ : str = torch.tensor(lowerCamelCase )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase , axis=1 ) , expand_dims(lowerCamelCase , axis=1 ).numpy() ) )
@require_tf
def lowercase ( self ) -> Any:
"""simple docstring"""
__magic_name__ : List[str] = np.random.randn(3 , 4 )
__magic_name__ : Union[str, Any] = tf.constant(lowerCamelCase )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase , axis=1 ) , expand_dims(lowerCamelCase , axis=1 ).numpy() ) )
@require_flax
def lowercase ( self ) -> Optional[Any]:
"""simple docstring"""
__magic_name__ : List[Any] = np.random.randn(3 , 4 )
__magic_name__ : int = jnp.array(lowerCamelCase )
self.assertTrue(np.allclose(expand_dims(lowerCamelCase , axis=1 ) , np.asarray(expand_dims(lowerCamelCase , axis=1 ) ) ) )
| 336
| 0
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
'''asapp/sew-d-tiny-100k''': '''https://huggingface.co/asapp/sew-d-tiny-100k/resolve/main/config.json''',
# See all SEW-D models at https://huggingface.co/models?filter=sew-d
}
class A_ ( __lowerCamelCase ):
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = """sew-d"""
def __init__( self , snake_case=32 , snake_case=768 , snake_case=12 , snake_case=12 , snake_case=3072 , snake_case=2 , snake_case=512 , snake_case=256 , snake_case=True , snake_case=True , snake_case=("p2c", "c2p") , snake_case="layer_norm" , snake_case="gelu_python" , snake_case=0.1 , snake_case=0.1 , snake_case=0.1 , snake_case=0.0 , snake_case=0.1 , snake_case=0.02 , snake_case=1E-7 , snake_case=1E-5 , snake_case="group" , snake_case="gelu" , snake_case=(64, 128, 128, 128, 128, 256, 256, 256, 256, 512, 512, 512, 512) , snake_case=(5, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1) , snake_case=(10, 3, 1, 3, 1, 3, 1, 3, 1, 2, 1, 2, 1) , snake_case=False , snake_case=128 , snake_case=16 , snake_case=True , snake_case=0.05 , snake_case=10 , snake_case=2 , snake_case=0.0 , snake_case=10 , snake_case=0 , snake_case="mean" , snake_case=False , snake_case=False , snake_case=256 , snake_case=0 , snake_case=1 , snake_case=2 , **snake_case , ):
super().__init__(**snake_case , pad_token_id=snake_case , bos_token_id=snake_case , eos_token_id=snake_case )
lowercase = hidden_size
lowercase = feat_extract_norm
lowercase = feat_extract_activation
lowercase = list(snake_case )
lowercase = list(snake_case )
lowercase = list(snake_case )
lowercase = conv_bias
lowercase = num_conv_pos_embeddings
lowercase = num_conv_pos_embedding_groups
lowercase = len(self.conv_dim )
lowercase = num_hidden_layers
lowercase = intermediate_size
lowercase = squeeze_factor
lowercase = max_position_embeddings
lowercase = position_buckets
lowercase = share_att_key
lowercase = relative_attention
lowercase = norm_rel_ebd
lowercase = list(snake_case )
lowercase = hidden_act
lowercase = num_attention_heads
lowercase = hidden_dropout
lowercase = attention_dropout
lowercase = activation_dropout
lowercase = feat_proj_dropout
lowercase = final_dropout
lowercase = layer_norm_eps
lowercase = feature_layer_norm_eps
lowercase = initializer_range
lowercase = vocab_size
if (
(len(self.conv_stride ) != self.num_feat_extract_layers)
or (len(self.conv_kernel ) != self.num_feat_extract_layers)
or (len(self.conv_dim ) != self.num_feat_extract_layers)
):
raise ValueError(
'Configuration for convolutional layers is incorrect.'
'It is required that `len(config.conv_dim)` == `len(config.conv_stride)` == `len(config.conv_kernel)`,'
F'''but is `len(config.conv_dim) = {len(self.conv_dim )}`, `len(config.conv_stride)'''
F'''= {len(self.conv_stride )}`, `len(config.conv_kernel) = {len(self.conv_kernel )}`.''' )
# fine-tuning config parameters for SpecAugment: https://arxiv.org/abs/1904.08779
lowercase = apply_spec_augment
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
# sequence classification
lowercase = use_weighted_layer_sum
lowercase = classifier_proj_size
@property
def SCREAMING_SNAKE_CASE__ ( self ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 84
|
from __future__ import annotations
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE ):
lowercase = str(__SCREAMING_SNAKE_CASE )
return n == n[::-1]
def UpperCAmelCase_ ( __SCREAMING_SNAKE_CASE = 100_0000 ):
lowercase = 0
for i in range(1 , __SCREAMING_SNAKE_CASE ):
if is_palindrome(__SCREAMING_SNAKE_CASE ) and is_palindrome(bin(__SCREAMING_SNAKE_CASE ).split('b' )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 84
| 1
|
"""simple docstring"""
from __future__ import annotations
import pandas as pd
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = [0] * no_of_processes
UpperCAmelCase = [0] * no_of_processes
# Copy the burst time into remaining_time[]
for i in range(lowerCAmelCase ):
UpperCAmelCase = burst_time[i]
UpperCAmelCase = 0
UpperCAmelCase = 0
UpperCAmelCase = 999999999
UpperCAmelCase = 0
UpperCAmelCase = False
# Process until all processes are completed
while complete != no_of_processes:
for j in range(lowerCAmelCase ):
if arrival_time[j] <= increment_time and remaining_time[j] > 0:
if remaining_time[j] < minm:
UpperCAmelCase = remaining_time[j]
UpperCAmelCase = j
UpperCAmelCase = True
if not check:
increment_time += 1
continue
remaining_time[short] -= 1
UpperCAmelCase = remaining_time[short]
if minm == 0:
UpperCAmelCase = 999999999
if remaining_time[short] == 0:
complete += 1
UpperCAmelCase = False
# Find finish time of current process
UpperCAmelCase = increment_time + 1
# Calculate waiting time
UpperCAmelCase = finish_time - arrival_time[short]
UpperCAmelCase = finar - burst_time[short]
if waiting_time[short] < 0:
UpperCAmelCase = 0
# Increment time
increment_time += 1
return waiting_time
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = [0] * no_of_processes
for i in range(lowerCAmelCase ):
UpperCAmelCase = burst_time[i] + waiting_time[i]
return turn_around_time
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = 0
UpperCAmelCase = 0
for i in range(lowerCAmelCase ):
UpperCAmelCase = total_waiting_time + waiting_time[i]
UpperCAmelCase = total_turn_around_time + turn_around_time[i]
print(F'''Average waiting time = {total_waiting_time / no_of_processes:.5f}''' )
print("""Average turn around time =""" , total_turn_around_time / no_of_processes )
if __name__ == "__main__":
print('''Enter how many process you want to analyze''')
lowerCAmelCase_ : Optional[Any] = int(input())
lowerCAmelCase_ : List[Any] = [0] * no_of_processes
lowerCAmelCase_ : Optional[Any] = [0] * no_of_processes
lowerCAmelCase_ : int = list(range(1, no_of_processes + 1))
for i in range(no_of_processes):
print('''Enter the arrival time and burst time for process:--''' + str(i + 1))
lowerCAmelCase_ , lowerCAmelCase_ : Optional[Any] = map(int, input().split())
lowerCAmelCase_ : str = calculate_waitingtime(arrival_time, burst_time, no_of_processes)
lowerCAmelCase_ : str = burst_time
lowerCAmelCase_ : List[Any] = no_of_processes
lowerCAmelCase_ : int = waiting_time
lowerCAmelCase_ : int = calculate_turnaroundtime(bt, n, wt)
calculate_average_times(waiting_time, turn_around_time, no_of_processes)
lowerCAmelCase_ : List[Any] = pd.DataFrame(
list(zip(processes, burst_time, arrival_time, waiting_time, turn_around_time)),
columns=[
'''Process''',
'''BurstTime''',
'''ArrivalTime''',
'''WaitingTime''',
'''TurnAroundTime''',
],
)
# Printing the dataFrame
pd.set_option('''display.max_rows''', fcfs.shape[0] + 1)
print(fcfs)
| 378
|
"""simple docstring"""
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = 0
UpperCAmelCase = len(lowerCAmelCase )
for i in range(n - 1 ):
for j in range(i + 1 , lowerCAmelCase ):
if arr[i] > arr[j]:
num_inversions += 1
return num_inversions
def _lowerCAmelCase ( lowerCAmelCase ):
'''simple docstring'''
if len(lowerCAmelCase ) <= 1:
return arr, 0
UpperCAmelCase = len(lowerCAmelCase ) // 2
UpperCAmelCase = arr[0:mid]
UpperCAmelCase = arr[mid:]
UpperCAmelCase , UpperCAmelCase = count_inversions_recursive(lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase = count_inversions_recursive(lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase = _count_cross_inversions(lowerCAmelCase , lowerCAmelCase )
UpperCAmelCase = inversion_p + inversions_q + cross_inversions
return c, num_inversions
def _lowerCAmelCase ( lowerCAmelCase , lowerCAmelCase ):
'''simple docstring'''
UpperCAmelCase = []
UpperCAmelCase = UpperCAmelCase = UpperCAmelCase = 0
while i < len(lowerCAmelCase ) and j < len(lowerCAmelCase ):
if p[i] > q[j]:
# if P[1] > Q[j], then P[k] > Q[k] for all i < k <= len(P)
# These are all inversions. The claim emerges from the
# property that P is sorted.
num_inversion += len(lowerCAmelCase ) - i
r.append(q[j] )
j += 1
else:
r.append(p[i] )
i += 1
if i < len(lowerCAmelCase ):
r.extend(p[i:] )
else:
r.extend(q[j:] )
return r, num_inversion
def _lowerCAmelCase ( ):
'''simple docstring'''
UpperCAmelCase = [10, 2, 1, 5, 5, 2, 11]
# this arr has 8 inversions:
# (10, 2), (10, 1), (10, 5), (10, 5), (10, 2), (2, 1), (5, 2), (5, 2)
UpperCAmelCase = count_inversions_bf(lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase = count_inversions_recursive(lowerCAmelCase )
assert num_inversions_bf == num_inversions_recursive == 8
print("""number of inversions = """ , lowerCAmelCase )
# testing an array with zero inversion (a sorted arr_1)
arr_a.sort()
UpperCAmelCase = count_inversions_bf(lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase = count_inversions_recursive(lowerCAmelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ , lowerCAmelCase )
# an empty list should also have zero inversions
UpperCAmelCase = []
UpperCAmelCase = count_inversions_bf(lowerCAmelCase )
UpperCAmelCase , UpperCAmelCase = count_inversions_recursive(lowerCAmelCase )
assert num_inversions_bf == num_inversions_recursive == 0
print("""number of inversions = """ , lowerCAmelCase )
if __name__ == "__main__":
main()
| 378
| 1
|
from PIL import Image
def _snake_case ( lowerCAmelCase : Image ):
"""simple docstring"""
SCREAMING_SNAKE_CASE_ : List[Any] = image.size
SCREAMING_SNAKE_CASE_ : Optional[Any] = 0
SCREAMING_SNAKE_CASE_ : Union[str, Any] = image.load()
for i in range(_lowerCAmelCase ):
for j in range(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : List[Any] = pixels[j, i]
mean += pixel
mean //= width * height
for j in range(_lowerCAmelCase ):
for i in range(_lowerCAmelCase ):
SCREAMING_SNAKE_CASE_ : Optional[int] = 2_5_5 if pixels[i, j] > mean else 0
return image
if __name__ == "__main__":
__lowerCamelCase : List[str] = mean_threshold(Image.open('''path_to_image''').convert('''L'''))
image.save('''output_image_path''')
| 216
|
"""simple docstring"""
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _UpperCAmelCase ( datasets.BeamBasedBuilder):
def __snake_case ( self ) -> Any:
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({"""content""": datasets.Value("""string""" )} ) , supervised_keys=_A , )
def __snake_case ( self , _A , _A ) -> Union[str, Any]:
'''simple docstring'''
return [datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""examples""": get_test_dummy_examples()} )]
def __snake_case ( self , _A , _A ) -> Optional[Any]:
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_A )
class _UpperCAmelCase ( datasets.BeamBasedBuilder):
def __snake_case ( self ) -> int:
'''simple docstring'''
return datasets.DatasetInfo(
features=datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) , supervised_keys=_A , )
def __snake_case ( self , _A , _A ) -> Dict:
'''simple docstring'''
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN , gen_kwargs={"""examples""": get_test_nested_examples()} )
]
def __snake_case ( self , _A , _A ) -> Any:
'''simple docstring'''
import apache_beam as beam
return pipeline | "Load Examples" >> beam.Create(_A )
def UpperCamelCase ( ) -> Any:
return [(i, {"content": content}) for i, content in enumerate(["""foo""", """bar""", """foobar"""] )]
def UpperCamelCase ( ) -> List[Any]:
return [(i, {"a": {"b": [content]}}) for i, content in enumerate(["""foo""", """bar""", """foobar"""] )]
class _UpperCAmelCase ( __a):
@require_beam
def __snake_case ( self ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase : str = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_UpperCAmelCase : Optional[Any] = DummyBeamDataset(cache_dir=_A , beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_A , builder.name , """default""" , """0.0.0""" , f'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"""content""": datasets.Value("""string""" )} ) )
_UpperCAmelCase : str = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , _A )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , _A )
self.assertDictEqual(dset["""train"""][0] , get_test_dummy_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] , get_test_dummy_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_A , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
@require_beam
def __snake_case ( self ) -> int:
'''simple docstring'''
import apache_beam as beam
_UpperCAmelCase : Optional[Any] = beam.io.parquetio.WriteToParquet
_UpperCAmelCase : Any = len(get_test_dummy_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_UpperCAmelCase : Optional[Any] = DummyBeamDataset(cache_dir=_A , beam_runner="""DirectRunner""" )
with patch("""apache_beam.io.parquetio.WriteToParquet""" ) as write_parquet_mock:
_UpperCAmelCase : int = partial(_A , num_shards=2 )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(
_A , builder.name , """default""" , """0.0.0""" , f'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertTrue(
os.path.exists(
os.path.join(
_A , builder.name , """default""" , """0.0.0""" , f'''{builder.name}-train-00000-of-00002.arrow''' ) ) )
self.assertDictEqual(builder.info.features , datasets.Features({"""content""": datasets.Value("""string""" )} ) )
_UpperCAmelCase : Optional[int] = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , _A )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , _A )
# Order is not preserved when sharding, so we just check that all the elements are there
self.assertListEqual(sorted(dset["""train"""]["""content"""] ) , sorted(["""foo""", """bar""", """foobar"""] ) )
self.assertTrue(
os.path.exists(os.path.join(_A , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
@require_beam
def __snake_case ( self ) -> List[str]:
'''simple docstring'''
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_UpperCAmelCase : List[Any] = DummyBeamDataset(cache_dir=_A )
self.assertRaises(datasets.builder.MissingBeamOptions , builder.download_and_prepare )
@require_beam
def __snake_case ( self ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase : List[Any] = len(get_test_nested_examples() )
with tempfile.TemporaryDirectory() as tmp_cache_dir:
_UpperCAmelCase : str = NestedBeamDataset(cache_dir=_A , beam_runner="""DirectRunner""" )
builder.download_and_prepare()
self.assertTrue(
os.path.exists(
os.path.join(_A , builder.name , """default""" , """0.0.0""" , f'''{builder.name}-train.arrow''' ) ) )
self.assertDictEqual(
builder.info.features , datasets.Features({"""a""": datasets.Sequence({"""b""": datasets.Value("""string""" )} )} ) )
_UpperCAmelCase : Tuple = builder.as_dataset()
self.assertEqual(dset["""train"""].num_rows , _A )
self.assertEqual(dset["""train"""].info.splits["""train"""].num_examples , _A )
self.assertDictEqual(dset["""train"""][0] , get_test_nested_examples()[0][1] )
self.assertDictEqual(
dset["""train"""][expected_num_examples - 1] , get_test_nested_examples()[expected_num_examples - 1][1] )
self.assertTrue(
os.path.exists(os.path.join(_A , builder.name , """default""" , """0.0.0""" , """dataset_info.json""" ) ) )
del dset
| 238
| 0
|
'''simple docstring'''
from manim import *
class SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
def snake_case__ ( self : Any ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase : Dict = Rectangle(height=0.5 , width=0.5 )
_UpperCamelCase : Tuple = Rectangle(height=0.4_6 , width=0.4_6 ).set_stroke(width=0 )
_UpperCamelCase : str = [mem.copy() for i in range(6 )]
_UpperCamelCase : int = [mem.copy() for i in range(6 )]
_UpperCamelCase : Union[str, Any] = VGroup(*lowercase__ ).arrange(lowercase__ , buff=0 )
_UpperCamelCase : Tuple = VGroup(*lowercase__ ).arrange(lowercase__ , buff=0 )
_UpperCamelCase : Optional[Any] = VGroup(lowercase__ , lowercase__ ).arrange(lowercase__ , buff=0 )
_UpperCamelCase : List[str] = Text("CPU" , font_size=24 )
_UpperCamelCase : Union[str, Any] = Group(lowercase__ , lowercase__ ).arrange(lowercase__ , buff=0.5 , aligned_edge=lowercase__ )
cpu.move_to([-2.5, -0.5, 0] )
self.add(lowercase__ )
_UpperCamelCase : str = [mem.copy() for i in range(4 )]
_UpperCamelCase : Dict = VGroup(*lowercase__ ).arrange(lowercase__ , buff=0 )
_UpperCamelCase : List[str] = Text("GPU" , font_size=24 )
_UpperCamelCase : str = Group(lowercase__ , lowercase__ ).arrange(lowercase__ , buff=0.5 , aligned_edge=lowercase__ )
gpu.move_to([-1, -1, 0] )
self.add(lowercase__ )
_UpperCamelCase : Optional[Any] = [mem.copy() for i in range(6 )]
_UpperCamelCase : List[Any] = VGroup(*lowercase__ ).arrange(lowercase__ , buff=0 )
_UpperCamelCase : str = Text("Model" , font_size=24 )
_UpperCamelCase : Any = Group(lowercase__ , lowercase__ ).arrange(lowercase__ , buff=0.5 , aligned_edge=lowercase__ )
model.move_to([3, -1.0, 0] )
self.add(lowercase__ )
_UpperCamelCase : int = []
for i, rect in enumerate(lowercase__ ):
rect.set_stroke(lowercase__ )
# target = fill.copy().set_fill(YELLOW, opacity=0.7)
# target.move_to(rect)
# self.add(target)
_UpperCamelCase : Dict = Rectangle(height=0.4_6 / 4 , width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(lowercase__ , opacity=0.7 )
if i == 0:
cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT ) , buff=0.0_2 , direction=lowercase__ )
cpu_target.set_x(cpu_target.get_x() + 0.1 )
elif i == 3:
cpu_target.next_to(cpu_targs[0] , direction=lowercase__ , buff=0.0 )
else:
cpu_target.next_to(cpu_targs[i - 1] , direction=lowercase__ , buff=0.0 )
self.add(lowercase__ )
cpu_targs.append(lowercase__ )
_UpperCamelCase : List[str] = [mem.copy() for i in range(6 )]
_UpperCamelCase : Union[str, Any] = VGroup(*lowercase__ ).arrange(lowercase__ , buff=0 )
_UpperCamelCase : str = Text("Loaded Checkpoint" , font_size=24 )
_UpperCamelCase : int = Group(lowercase__ , lowercase__ ).arrange(lowercase__ , aligned_edge=lowercase__ , buff=0.4 )
checkpoint.move_to([3, 0.5, 0] )
_UpperCamelCase : Union[str, Any] = Square(side_length=2.2 )
key.move_to([-5, 2, 0] )
_UpperCamelCase : str = MarkupText(
f'''<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model''' , font_size=18 , )
key_text.move_to([-5, 2.4, 0] )
self.add(lowercase__ , lowercase__ )
_UpperCamelCase : Union[str, Any] = MarkupText(
f'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' , font_size=18 , )
blue_text.next_to(lowercase__ , DOWN * 2.4 , aligned_edge=key_text.get_left() )
_UpperCamelCase : Optional[Any] = MarkupText(
f'''Next, a <i><span fgcolor="{BLUE}">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor="{BLUE}">single shard</span>.''' , font_size=24 , )
step_a.move_to([2, 2, 0] )
self.play(Write(lowercase__ ) , Write(lowercase__ ) )
self.play(Write(lowercase__ , run_time=1 ) , Create(lowercase__ , run_time=1 ) )
_UpperCamelCase : Union[str, Any] = []
_UpperCamelCase : Tuple = []
for i, rect in enumerate(lowercase__ ):
_UpperCamelCase : Dict = fill.copy().set_fill(lowercase__ , opacity=0.7 )
target.move_to(lowercase__ )
first_animations.append(GrowFromCenter(lowercase__ , run_time=1 ) )
_UpperCamelCase : Optional[Any] = target.copy()
cpu_target.generate_target()
if i < 5:
cpu_target.target.move_to(cpu_left_col_base[i + 1] )
else:
cpu_target.target.move_to(cpu_right_col_base[i - 5] )
second_animations.append(MoveToTarget(lowercase__ , run_time=1.5 ) )
self.play(*lowercase__ )
self.play(*lowercase__ )
self.wait()
| 204
|
'''simple docstring'''
from typing import List, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ : Optional[int] = logging.get_logger(__name__)
lowerCAmelCase_ : Tuple = {
"""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 ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
UpperCAmelCase__ = '''time_series_transformer'''
UpperCAmelCase__ = {
'''hidden_size''': '''d_model''',
'''num_attention_heads''': '''encoder_attention_heads''',
'''num_hidden_layers''': '''encoder_layers''',
}
def __init__( self : int , lowercase__ : Optional[int] = None , lowercase__ : Optional[int] = None , lowercase__ : str = "student_t" , lowercase__ : str = "nll" , lowercase__ : int = 1 , lowercase__ : List[int] = [1, 2, 3, 4, 5, 6, 7] , lowercase__ : Optional[Union[str, bool]] = "mean" , lowercase__ : int = 0 , lowercase__ : int = 0 , lowercase__ : int = 0 , lowercase__ : int = 0 , lowercase__ : Optional[List[int]] = None , lowercase__ : Optional[List[int]] = None , lowercase__ : int = 32 , lowercase__ : int = 32 , lowercase__ : int = 2 , lowercase__ : int = 2 , lowercase__ : int = 2 , lowercase__ : int = 2 , lowercase__ : bool = True , lowercase__ : str = "gelu" , lowercase__ : int = 64 , lowercase__ : float = 0.1 , lowercase__ : float = 0.1 , lowercase__ : float = 0.1 , lowercase__ : float = 0.1 , lowercase__ : float = 0.1 , lowercase__ : int = 100 , lowercase__ : float = 0.0_2 , lowercase__ : Optional[int]=True , **lowercase__ : Optional[Any] , ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase : int = prediction_length
_UpperCamelCase : Optional[Any] = context_length or prediction_length
_UpperCamelCase : List[str] = distribution_output
_UpperCamelCase : Optional[Any] = loss
_UpperCamelCase : Tuple = input_size
_UpperCamelCase : Optional[int] = num_time_features
_UpperCamelCase : Union[str, Any] = lags_sequence
_UpperCamelCase : int = scaling
_UpperCamelCase : Dict = num_dynamic_real_features
_UpperCamelCase : str = num_static_real_features
_UpperCamelCase : Any = num_static_categorical_features
if cardinality and num_static_categorical_features > 0:
if len(lowercase__ ) != num_static_categorical_features:
raise ValueError(
"The cardinality should be a list of the same length as `num_static_categorical_features`" )
_UpperCamelCase : Dict = cardinality
else:
_UpperCamelCase : str = [0]
if embedding_dimension and num_static_categorical_features > 0:
if len(lowercase__ ) != num_static_categorical_features:
raise ValueError(
"The embedding dimension should be a list of the same length as `num_static_categorical_features`" )
_UpperCamelCase : int = embedding_dimension
else:
_UpperCamelCase : Optional[Any] = [min(50 , (cat + 1) // 2 ) for cat in self.cardinality]
_UpperCamelCase : Union[str, Any] = num_parallel_samples
# Transformer architecture configuration
_UpperCamelCase : int = input_size * len(lowercase__ ) + self._number_of_features
_UpperCamelCase : Optional[Any] = d_model
_UpperCamelCase : str = encoder_attention_heads
_UpperCamelCase : List[Any] = decoder_attention_heads
_UpperCamelCase : str = encoder_ffn_dim
_UpperCamelCase : List[str] = decoder_ffn_dim
_UpperCamelCase : Union[str, Any] = encoder_layers
_UpperCamelCase : Optional[int] = decoder_layers
_UpperCamelCase : int = dropout
_UpperCamelCase : Optional[int] = attention_dropout
_UpperCamelCase : int = activation_dropout
_UpperCamelCase : List[Any] = encoder_layerdrop
_UpperCamelCase : int = decoder_layerdrop
_UpperCamelCase : List[Any] = activation_function
_UpperCamelCase : Any = init_std
_UpperCamelCase : Optional[Any] = use_cache
super().__init__(is_encoder_decoder=lowercase__ , **lowercase__ )
@property
def snake_case__ ( self : Union[str, Any] ) ->int:
'''simple docstring'''
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
)
| 204
| 1
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'hustvl/yolos-small': 'https://huggingface.co/hustvl/yolos-small/resolve/main/config.json',
# See all YOLOS models at https://huggingface.co/models?filter=yolos
}
class a ( snake_case_ ):
"""simple docstring"""
__lowerCAmelCase = """yolos"""
def __init__( self , snake_case_=768 , snake_case_=12 , snake_case_=12 , snake_case_=3072 , snake_case_="gelu" , snake_case_=0.0 , snake_case_=0.0 , snake_case_=0.0_2 , snake_case_=1e-1_2 , snake_case_=[512, 864] , snake_case_=16 , snake_case_=3 , snake_case_=True , snake_case_=100 , snake_case_=True , snake_case_=False , snake_case_=1 , snake_case_=5 , snake_case_=2 , snake_case_=5 , snake_case_=2 , snake_case_=0.1 , **snake_case_ , ):
'''simple docstring'''
super().__init__(**A_ )
__UpperCAmelCase: List[str] = hidden_size
__UpperCAmelCase: Tuple = num_hidden_layers
__UpperCAmelCase: List[Any] = num_attention_heads
__UpperCAmelCase: Optional[int] = intermediate_size
__UpperCAmelCase: List[Any] = hidden_act
__UpperCAmelCase: str = hidden_dropout_prob
__UpperCAmelCase: List[Any] = attention_probs_dropout_prob
__UpperCAmelCase: int = initializer_range
__UpperCAmelCase: Optional[Any] = layer_norm_eps
__UpperCAmelCase: int = image_size
__UpperCAmelCase: str = patch_size
__UpperCAmelCase: str = num_channels
__UpperCAmelCase: List[str] = qkv_bias
__UpperCAmelCase: Dict = num_detection_tokens
__UpperCAmelCase: List[str] = use_mid_position_embeddings
__UpperCAmelCase: Optional[Any] = auxiliary_loss
# Hungarian matcher
__UpperCAmelCase: str = class_cost
__UpperCAmelCase: List[str] = bbox_cost
__UpperCAmelCase: Tuple = giou_cost
# Loss coefficients
__UpperCAmelCase: Any = bbox_loss_coefficient
__UpperCAmelCase: List[Any] = giou_loss_coefficient
__UpperCAmelCase: Any = eos_coefficient
class a ( snake_case_ ):
"""simple docstring"""
__lowerCAmelCase = version.parse("""1.11""" )
@property
def lowercase_ ( self ):
'''simple docstring'''
return OrderedDict(
[
("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
] )
@property
def lowercase_ ( self ):
'''simple docstring'''
return 1e-4
@property
def lowercase_ ( self ):
'''simple docstring'''
return 12
| 523
|
'''simple docstring'''
lowerCAmelCase : Optional[Any] = {
'A': ['B', 'C', 'E'],
'B': ['A', 'D', 'E'],
'C': ['A', 'F', 'G'],
'D': ['B'],
'E': ['A', 'B', 'D'],
'F': ['C'],
'G': ['C'],
}
def A_( A : dict , A : str , A : Optional[Any]):
UpperCamelCase = set()
# keep track of all the paths to be checked
UpperCamelCase = [[start]]
# return path if start is goal
if start == goal:
return [start]
# keeps looping until all possible paths have been checked
while queue:
# pop the first path from the queue
UpperCamelCase = queue.pop(0)
# get the last node from the path
UpperCamelCase = path[-1]
if node not in explored:
UpperCamelCase = graph[node]
# go through all neighbour nodes, construct a new path and
# push it into the queue
for neighbour in neighbours:
UpperCamelCase = list(A)
new_path.append(A)
queue.append(A)
# return path if neighbour is goal
if neighbour == goal:
return new_path
# mark node as explored
explored.add(A)
# in case there's no path between the 2 nodes
return []
def A_( A : dict , A : str , A : Tuple):
if not graph or start not in graph or target not in graph:
return -1
if start == target:
return 0
UpperCamelCase = [start]
UpperCamelCase = set(A)
# Keep tab on distances from `start` node.
UpperCamelCase = {start: 0, target: -1}
while queue:
UpperCamelCase = queue.pop(0)
if node == target:
UpperCamelCase = (
dist[node] if dist[target] == -1 else min(dist[target] , dist[node])
)
for adjacent in graph[node]:
if adjacent not in visited:
visited.add(A)
queue.append(A)
UpperCamelCase = dist[node] + 1
return dist[target]
if __name__ == "__main__":
print(bfs_shortest_path(demo_graph, 'G', 'D')) # returns ['G', 'C', 'A', 'B', 'D']
print(bfs_shortest_path_distance(demo_graph, 'G', 'D')) # returns 4
| 3
| 0
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__lowerCamelCase : str = {
"""configuration_groupvit""": [
"""GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""GroupViTConfig""",
"""GroupViTOnnxConfig""",
"""GroupViTTextConfig""",
"""GroupViTVisionConfig""",
],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : Dict = [
"""GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""GroupViTModel""",
"""GroupViTPreTrainedModel""",
"""GroupViTTextModel""",
"""GroupViTVisionModel""",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__lowerCamelCase : str = [
"""TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST""",
"""TFGroupViTModel""",
"""TFGroupViTPreTrainedModel""",
"""TFGroupViTTextModel""",
"""TFGroupViTVisionModel""",
]
if TYPE_CHECKING:
from .configuration_groupvit import (
GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP,
GroupViTConfig,
GroupViTOnnxConfig,
GroupViTTextConfig,
GroupViTVisionConfig,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_groupvit import (
GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GroupViTModel,
GroupViTPreTrainedModel,
GroupViTTextModel,
GroupViTVisionModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_groupvit import (
TF_GROUPVIT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFGroupViTModel,
TFGroupViTPreTrainedModel,
TFGroupViTTextModel,
TFGroupViTVisionModel,
)
else:
import sys
__lowerCamelCase : int = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 712
|
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import KandinskyPipeline, KandinskyPriorPipeline
else:
from .pipeline_kandinsky import KandinskyPipeline
from .pipeline_kandinsky_imgaimg import KandinskyImgaImgPipeline
from .pipeline_kandinsky_inpaint import KandinskyInpaintPipeline
from .pipeline_kandinsky_prior import KandinskyPriorPipeline, KandinskyPriorPipelineOutput
from .text_encoder import MultilingualCLIP
| 25
| 0
|
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
class a_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case_( self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def snake_case_( self ) -> Any:
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = UNetaDModel(
sample_size=(32, 64) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=("""AttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """AttnUpBlock2D""") , )
return model
@property
def snake_case_( self ) -> List[str]:
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = UNetaDConditionModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=("""CrossAttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """CrossAttnUpBlock2D""") , cross_attention_dim=10 , )
return model
@property
def snake_case_( self ) -> str:
torch.manual_seed(0 )
_SCREAMING_SNAKE_CASE = AutoencoderKL(
sample_size=(128, 64) , in_channels=1 , out_channels=1 , latent_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=("""DownEncoderBlock2D""", """DownEncoderBlock2D""") , up_block_types=("""UpDecoderBlock2D""", """UpDecoderBlock2D""") , )
_SCREAMING_SNAKE_CASE = UNetaDModel(
sample_size=(64, 32) , in_channels=1 , out_channels=1 , layers_per_block=2 , block_out_channels=(128, 128) , down_block_types=("""AttnDownBlock2D""", """DownBlock2D""") , up_block_types=("""UpBlock2D""", """AttnUpBlock2D""") , )
return vqvae, unet
@slow
def snake_case_( self ) -> str:
_SCREAMING_SNAKE_CASE = """cpu""" # ensure determinism for the device-dependent torch.Generator
_SCREAMING_SNAKE_CASE = Mel(
x_res=self.dummy_unet.config.sample_size[1] , y_res=self.dummy_unet.config.sample_size[0] , )
_SCREAMING_SNAKE_CASE = DDPMScheduler()
_SCREAMING_SNAKE_CASE = AudioDiffusionPipeline(vqvae=A , unet=self.dummy_unet , mel=A , scheduler=A )
_SCREAMING_SNAKE_CASE = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
_SCREAMING_SNAKE_CASE = torch.Generator(device=A ).manual_seed(42 )
_SCREAMING_SNAKE_CASE = pipe(generator=A , steps=4 )
_SCREAMING_SNAKE_CASE = output.audios[0]
_SCREAMING_SNAKE_CASE = output.images[0]
_SCREAMING_SNAKE_CASE = torch.Generator(device=A ).manual_seed(42 )
_SCREAMING_SNAKE_CASE = pipe(generator=A , steps=4 , return_dict=A )
_SCREAMING_SNAKE_CASE = output[0][0]
assert audio.shape == (1, (self.dummy_unet.config.sample_size[1] - 1) * mel.hop_length)
assert (
image.height == self.dummy_unet.config.sample_size[0]
and image.width == self.dummy_unet.config.sample_size[1]
)
_SCREAMING_SNAKE_CASE = np.frombuffer(image.tobytes() , dtype="""uint8""" )[:10]
_SCREAMING_SNAKE_CASE = np.frombuffer(image_from_tuple.tobytes() , dtype="""uint8""" )[:10]
_SCREAMING_SNAKE_CASE = np.array([69, 255, 255, 255, 0, 0, 77, 181, 12, 127] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() == 0
_SCREAMING_SNAKE_CASE = Mel(
x_res=self.dummy_vqvae_and_unet[0].config.sample_size[1] , y_res=self.dummy_vqvae_and_unet[0].config.sample_size[0] , )
_SCREAMING_SNAKE_CASE = DDIMScheduler()
_SCREAMING_SNAKE_CASE = self.dummy_vqvae_and_unet
_SCREAMING_SNAKE_CASE = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=dummy_vqvae_and_unet[1] , mel=A , scheduler=A )
_SCREAMING_SNAKE_CASE = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
np.random.seed(0 )
_SCREAMING_SNAKE_CASE = np.random.uniform(-1 , 1 , ((dummy_vqvae_and_unet[0].config.sample_size[1] - 1) * mel.hop_length,) )
_SCREAMING_SNAKE_CASE = torch.Generator(device=A ).manual_seed(42 )
_SCREAMING_SNAKE_CASE = pipe(raw_audio=A , generator=A , start_step=5 , steps=10 )
_SCREAMING_SNAKE_CASE = output.images[0]
assert (
image.height == self.dummy_vqvae_and_unet[0].config.sample_size[0]
and image.width == self.dummy_vqvae_and_unet[0].config.sample_size[1]
)
_SCREAMING_SNAKE_CASE = np.frombuffer(image.tobytes() , dtype="""uint8""" )[:10]
_SCREAMING_SNAKE_CASE = np.array([120, 117, 110, 109, 138, 167, 138, 148, 132, 121] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
_SCREAMING_SNAKE_CASE = self.dummy_unet_condition
_SCREAMING_SNAKE_CASE = AudioDiffusionPipeline(
vqvae=self.dummy_vqvae_and_unet[0] , unet=A , mel=A , scheduler=A )
_SCREAMING_SNAKE_CASE = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
np.random.seed(0 )
_SCREAMING_SNAKE_CASE = torch.rand((1, 1, 10) )
_SCREAMING_SNAKE_CASE = pipe(generator=A , encoding=A )
_SCREAMING_SNAKE_CASE = output.images[0]
_SCREAMING_SNAKE_CASE = np.frombuffer(image.tobytes() , dtype="""uint8""" )[:10]
_SCREAMING_SNAKE_CASE = np.array([107, 103, 120, 127, 142, 122, 113, 122, 97, 111] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
@slow
@require_torch_gpu
class a_ ( unittest.TestCase ):
'''simple docstring'''
def snake_case_( self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case_( self ) -> str:
_SCREAMING_SNAKE_CASE = torch_device
_SCREAMING_SNAKE_CASE = DiffusionPipeline.from_pretrained("""teticio/audio-diffusion-ddim-256""" )
_SCREAMING_SNAKE_CASE = pipe.to(A )
pipe.set_progress_bar_config(disable=A )
_SCREAMING_SNAKE_CASE = torch.Generator(device=A ).manual_seed(42 )
_SCREAMING_SNAKE_CASE = pipe(generator=A )
_SCREAMING_SNAKE_CASE = output.audios[0]
_SCREAMING_SNAKE_CASE = output.images[0]
assert audio.shape == (1, (pipe.unet.config.sample_size[1] - 1) * pipe.mel.hop_length)
assert image.height == pipe.unet.config.sample_size[0] and image.width == pipe.unet.config.sample_size[1]
_SCREAMING_SNAKE_CASE = np.frombuffer(image.tobytes() , dtype="""uint8""" )[:10]
_SCREAMING_SNAKE_CASE = np.array([151, 167, 154, 144, 122, 134, 121, 105, 70, 26] )
assert np.abs(image_slice.flatten() - expected_slice ).max() == 0
| 314
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_deit import DeiTImageProcessor
lowercase_ = logging.get_logger(__name__)
class a_ ( snake_case_ ):
'''simple docstring'''
def __init__( self , *A , **A ) -> None:
warnings.warn(
"""The class DeiTFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"""
""" use DeiTImageProcessor instead.""" , A , )
super().__init__(*A , **A )
| 314
| 1
|
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class __SCREAMING_SNAKE_CASE ( lowerCamelCase__ , unittest.TestCase ):
UpperCAmelCase = DiTPipeline
UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
UpperCAmelCase = PipelineTesterMixin.required_optional_params - {
'''latents''',
'''num_images_per_prompt''',
'''callback''',
'''callback_steps''',
}
UpperCAmelCase = CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
UpperCAmelCase = False
def a_ ( self ) -> Any:
torch.manual_seed(0 )
_a = TransformeraDModel(
sample_size=16 , num_layers=2 , patch_size=4 , attention_head_dim=8 , num_attention_heads=2 , in_channels=4 , out_channels=8 , attention_bias=_lowerCAmelCase , activation_fn="gelu-approximate" , num_embeds_ada_norm=1_000 , norm_type="ada_norm_zero" , norm_elementwise_affine=_lowerCAmelCase , )
_a = AutoencoderKL()
_a = DDIMScheduler()
_a = {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
return components
def a_ ( self , __UpperCamelCase , __UpperCamelCase=0 ) -> Union[str, Any]:
if str(_lowerCAmelCase ).startswith("mps" ):
_a = torch.manual_seed(_lowerCAmelCase )
else:
_a = torch.Generator(device=_lowerCAmelCase ).manual_seed(_lowerCAmelCase )
_a = {
"class_labels": [1],
"generator": generator,
"num_inference_steps": 2,
"output_type": "numpy",
}
return inputs
def a_ ( self ) -> Optional[Any]:
_a = "cpu"
_a = self.get_dummy_components()
_a = self.pipeline_class(**_lowerCAmelCase )
pipe.to(_lowerCAmelCase )
pipe.set_progress_bar_config(disable=_lowerCAmelCase )
_a = self.get_dummy_inputs(_lowerCAmelCase )
_a = pipe(**_lowerCAmelCase ).images
_a = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 16, 16, 3) )
_a = np.array([0.29_46, 0.66_01, 0.43_29, 0.32_96, 0.41_44, 0.53_19, 0.72_73, 0.50_13, 0.44_57] )
_a = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(_lowerCAmelCase , 1e-3 )
def a_ ( self ) -> Tuple:
self._test_inference_batch_single_identical(relax_max_difference=_lowerCAmelCase , expected_max_diff=1e-3 )
@unittest.skipIf(
torch_device != "cuda" or not is_xformers_available() , reason="XFormers attention is only available with CUDA and `xformers` installed" , )
def a_ ( self ) -> str:
self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3 )
@require_torch_gpu
@slow
class __SCREAMING_SNAKE_CASE ( unittest.TestCase ):
def a_ ( self ) -> List[Any]:
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def a_ ( self ) -> Any:
_a = torch.manual_seed(0 )
_a = DiTPipeline.from_pretrained("facebook/DiT-XL-2-256" )
pipe.to("cuda" )
_a = ["vase", "umbrella", "white shark", "white wolf"]
_a = pipe.get_label_ids(_lowerCAmelCase )
_a = pipe(_lowerCAmelCase , generator=_lowerCAmelCase , num_inference_steps=40 , output_type="np" ).images
for word, image in zip(_lowerCAmelCase , _lowerCAmelCase ):
_a = load_numpy(
f"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy" )
assert np.abs((expected_image - image).max() ) < 1e-2
def a_ ( self ) -> Union[str, Any]:
_a = DiTPipeline.from_pretrained("facebook/DiT-XL-2-512" )
_a = DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
pipe.to("cuda" )
_a = ["vase", "umbrella"]
_a = pipe.get_label_ids(_lowerCAmelCase )
_a = torch.manual_seed(0 )
_a = pipe(_lowerCAmelCase , generator=_lowerCAmelCase , num_inference_steps=25 , output_type="np" ).images
for word, image in zip(_lowerCAmelCase , _lowerCAmelCase ):
_a = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
f"/dit/{word}_512.npy" )
assert np.abs((expected_image - image).max() ) < 1e-1
| 709
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json",
"YituTech/conv-bert-medium-small": (
"https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json"
),
"YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json",
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class __SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
UpperCAmelCase = '''convbert'''
def __init__( self , __UpperCamelCase=30_522 , __UpperCamelCase=768 , __UpperCamelCase=12 , __UpperCamelCase=12 , __UpperCamelCase=3_072 , __UpperCamelCase="gelu" , __UpperCamelCase=0.1 , __UpperCamelCase=0.1 , __UpperCamelCase=512 , __UpperCamelCase=2 , __UpperCamelCase=0.02 , __UpperCamelCase=1e-12 , __UpperCamelCase=1 , __UpperCamelCase=0 , __UpperCamelCase=2 , __UpperCamelCase=768 , __UpperCamelCase=2 , __UpperCamelCase=9 , __UpperCamelCase=1 , __UpperCamelCase=None , **__UpperCamelCase , ) -> Optional[int]:
super().__init__(
pad_token_id=__UpperCamelCase , bos_token_id=__UpperCamelCase , eos_token_id=__UpperCamelCase , **__UpperCamelCase , )
_a = vocab_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = intermediate_size
_a = hidden_act
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = initializer_range
_a = layer_norm_eps
_a = embedding_size
_a = head_ratio
_a = conv_kernel_size
_a = num_groups
_a = classifier_dropout
class __SCREAMING_SNAKE_CASE ( lowerCamelCase__ ):
@property
def a_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
_a = {0: "batch", 1: "choice", 2: "sequence"}
else:
_a = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 276
| 0
|
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers.models.distilbert.modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertModel,
)
class SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__( self : str , lowercase__ : Any , ) ->Optional[int]:
'''simple docstring'''
_UpperCamelCase : List[str] = parent
_UpperCamelCase : List[str] = 13
_UpperCamelCase : Dict = 7
_UpperCamelCase : int = True
_UpperCamelCase : Union[str, Any] = True
_UpperCamelCase : Any = False
_UpperCamelCase : str = True
_UpperCamelCase : Dict = 99
_UpperCamelCase : str = 32
_UpperCamelCase : Optional[int] = 2
_UpperCamelCase : List[Any] = 4
_UpperCamelCase : List[Any] = 37
_UpperCamelCase : Any = "gelu"
_UpperCamelCase : Dict = 0.1
_UpperCamelCase : Any = 0.1
_UpperCamelCase : Optional[Any] = 512
_UpperCamelCase : Optional[Any] = 16
_UpperCamelCase : Optional[int] = 2
_UpperCamelCase : str = 0.0_2
_UpperCamelCase : Tuple = 3
_UpperCamelCase : Optional[Any] = 4
_UpperCamelCase : List[str] = None
def snake_case__ ( self : str ) ->List[str]:
'''simple docstring'''
_UpperCamelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
_UpperCamelCase : Union[str, Any] = None
if self.use_input_mask:
_UpperCamelCase : Any = random_attention_mask([self.batch_size, self.seq_length] )
_UpperCamelCase : Any = None
_UpperCamelCase : int = None
_UpperCamelCase : Dict = None
if self.use_labels:
_UpperCamelCase : int = ids_tensor([self.batch_size] , self.type_sequence_label_size )
_UpperCamelCase : Optional[int] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
_UpperCamelCase : List[str] = ids_tensor([self.batch_size] , self.num_choices )
_UpperCamelCase : Union[str, Any] = DistilBertConfig(
vocab_size=self.vocab_size , dim=self.hidden_size , n_layers=self.num_hidden_layers , n_heads=self.num_attention_heads , hidden_dim=self.intermediate_size , hidden_act=self.hidden_act , dropout=self.hidden_dropout_prob , attention_dropout=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , initializer_range=self.initializer_range , )
return config, input_ids, input_mask, sequence_labels, token_labels, choice_labels
def snake_case__ ( self : str , lowercase__ : Dict , lowercase__ : Tuple , lowercase__ : Union[str, Any] , lowercase__ : Dict , lowercase__ : Union[str, Any] , lowercase__ : Dict ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase : str = TFDistilBertModel(config=__UpperCAmelCase )
_UpperCamelCase : Optional[Any] = {"input_ids": input_ids, "attention_mask": input_mask}
_UpperCamelCase : List[Any] = model(__UpperCAmelCase )
_UpperCamelCase : int = [input_ids, input_mask]
_UpperCamelCase : Tuple = model(__UpperCAmelCase )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def snake_case__ ( self : Optional[Any] , lowercase__ : int , lowercase__ : Optional[Any] , lowercase__ : List[Any] , lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : List[str] ) ->Any:
'''simple docstring'''
_UpperCamelCase : Optional[Any] = TFDistilBertForMaskedLM(config=__UpperCAmelCase )
_UpperCamelCase : int = {"input_ids": input_ids, "attention_mask": input_mask}
_UpperCamelCase : Optional[Any] = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def snake_case__ ( self : str , lowercase__ : Optional[Any] , lowercase__ : Union[str, Any] , lowercase__ : str , lowercase__ : int , lowercase__ : str , lowercase__ : Optional[int] ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase : List[str] = TFDistilBertForQuestionAnswering(config=__UpperCAmelCase )
_UpperCamelCase : List[str] = {
"input_ids": input_ids,
"attention_mask": input_mask,
}
_UpperCamelCase : Optional[Any] = model(__UpperCAmelCase )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def snake_case__ ( self : List[str] , lowercase__ : Union[str, Any] , lowercase__ : Union[str, Any] , lowercase__ : Optional[int] , lowercase__ : List[str] , lowercase__ : Optional[int] , lowercase__ : Optional[Any] ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase : Union[str, Any] = self.num_labels
_UpperCamelCase : List[Any] = TFDistilBertForSequenceClassification(__UpperCAmelCase )
_UpperCamelCase : Optional[int] = {"input_ids": input_ids, "attention_mask": input_mask}
_UpperCamelCase : Tuple = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def snake_case__ ( self : List[str] , lowercase__ : Union[str, Any] , lowercase__ : str , lowercase__ : str , lowercase__ : Optional[Any] , lowercase__ : List[Any] , lowercase__ : int ) ->List[Any]:
'''simple docstring'''
_UpperCamelCase : Any = self.num_choices
_UpperCamelCase : int = TFDistilBertForMultipleChoice(__UpperCAmelCase )
_UpperCamelCase : Tuple = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
_UpperCamelCase : int = tf.tile(tf.expand_dims(__UpperCAmelCase , 1 ) , (1, self.num_choices, 1) )
_UpperCamelCase : Any = {
"input_ids": multiple_choice_inputs_ids,
"attention_mask": multiple_choice_input_mask,
}
_UpperCamelCase : Any = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def snake_case__ ( self : Tuple , lowercase__ : int , lowercase__ : Any , lowercase__ : Optional[int] , lowercase__ : List[Any] , lowercase__ : Tuple , lowercase__ : Optional[Any] ) ->List[str]:
'''simple docstring'''
_UpperCamelCase : Tuple = self.num_labels
_UpperCamelCase : Dict = TFDistilBertForTokenClassification(__UpperCAmelCase )
_UpperCamelCase : Any = {"input_ids": input_ids, "attention_mask": input_mask}
_UpperCamelCase : Dict = model(__UpperCAmelCase )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def snake_case__ ( self : List[str] ) ->Union[str, Any]:
'''simple docstring'''
_UpperCamelCase : List[str] = self.prepare_config_and_inputs()
((_UpperCamelCase) , (_UpperCamelCase) , (_UpperCamelCase) , (_UpperCamelCase) , (_UpperCamelCase) , (_UpperCamelCase)) : Tuple = config_and_inputs
_UpperCamelCase : Dict = {"input_ids": input_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_tf
class SCREAMING_SNAKE_CASE ( UpperCAmelCase_ , UpperCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
UpperCAmelCase__ = (
(
TFDistilBertModel,
TFDistilBertForMaskedLM,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertForMultipleChoice,
)
if is_tf_available()
else None
)
UpperCAmelCase__ = (
{
'feature-extraction': TFDistilBertModel,
'fill-mask': TFDistilBertForMaskedLM,
'question-answering': TFDistilBertForQuestionAnswering,
'text-classification': TFDistilBertForSequenceClassification,
'token-classification': TFDistilBertForTokenClassification,
'zero-shot': TFDistilBertForSequenceClassification,
}
if is_tf_available()
else {}
)
UpperCAmelCase__ = False
UpperCAmelCase__ = False
def snake_case__ ( self : Any ) ->Optional[Any]:
'''simple docstring'''
_UpperCamelCase : Dict = TFDistilBertModelTester(self )
_UpperCamelCase : List[Any] = ConfigTester(self , config_class=__UpperCAmelCase , dim=37 )
def snake_case__ ( self : Union[str, Any] ) ->Dict:
'''simple docstring'''
self.config_tester.run_common_tests()
def snake_case__ ( self : Dict ) ->Tuple:
'''simple docstring'''
_UpperCamelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_model(*__UpperCAmelCase )
def snake_case__ ( self : Tuple ) ->Dict:
'''simple docstring'''
_UpperCamelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_masked_lm(*__UpperCAmelCase )
def snake_case__ ( self : Dict ) ->List[Any]:
'''simple docstring'''
_UpperCamelCase : Dict = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_question_answering(*__UpperCAmelCase )
def snake_case__ ( self : Optional[int] ) ->List[str]:
'''simple docstring'''
_UpperCamelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_sequence_classification(*__UpperCAmelCase )
def snake_case__ ( self : Union[str, Any] ) ->List[Any]:
'''simple docstring'''
_UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_multiple_choice(*__UpperCAmelCase )
def snake_case__ ( self : Dict ) ->int:
'''simple docstring'''
_UpperCamelCase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_distilbert_for_token_classification(*__UpperCAmelCase )
@slow
def snake_case__ ( self : List[str] ) ->Dict:
'''simple docstring'''
for model_name in list(TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST[:1] ):
_UpperCamelCase : str = TFDistilBertModel.from_pretrained(__UpperCAmelCase )
self.assertIsNotNone(__UpperCAmelCase )
@require_tf
class SCREAMING_SNAKE_CASE ( unittest.TestCase ):
'''simple docstring'''
@slow
def snake_case__ ( self : Union[str, Any] ) ->Union[str, Any]:
'''simple docstring'''
_UpperCamelCase : Optional[Any] = TFDistilBertModel.from_pretrained("distilbert-base-uncased" )
_UpperCamelCase : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
_UpperCamelCase : Any = model(__UpperCAmelCase )[0]
_UpperCamelCase : List[str] = [1, 6, 768]
self.assertEqual(output.shape , __UpperCAmelCase )
_UpperCamelCase : Tuple = tf.constant(
[
[
[0.1_9_2_6_1_8_8_5, -0.1_3_7_3_2_9_5_5, 0.4_1_1_9_7_9_9],
[0.2_2_1_5_0_1_5_6, -0.0_7_4_2_2_6_6_1, 0.3_9_0_3_7_2_0_4],
[0.2_2_7_5_6_0_1_8, -0.0_8_9_6_4_1_4, 0.3_7_0_1_4_6_7],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __UpperCAmelCase , atol=1e-4 )
| 435
|
import warnings
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A ( UpperCAmelCase_ ):
__UpperCAmelCase : Any = ['image_processor', 'tokenizer']
__UpperCAmelCase : List[str] = 'FlavaImageProcessor'
__UpperCAmelCase : Dict = ('BertTokenizer', 'BertTokenizerFast')
def __init__(self : int , __UpperCAmelCase : Dict=None , __UpperCAmelCase : Union[str, Any]=None , **__UpperCAmelCase : Dict ) -> List[Any]:
"""simple docstring"""
UpperCAmelCase__ = 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 , )
UpperCAmelCase__ = kwargs.pop("feature_extractor" )
UpperCAmelCase__ = image_processor if image_processor is not None else feature_extractor
if image_processor is None:
raise ValueError("You need to specify an `image_processor`." )
if tokenizer is None:
raise ValueError("You need to specify a `tokenizer`." )
super().__init__(__UpperCAmelCase , __UpperCAmelCase )
UpperCAmelCase__ = self.image_processor
def __call__(self : Optional[int] , __UpperCAmelCase : Optional[ImageInput] = None , __UpperCAmelCase : Optional[Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]]] = None , __UpperCAmelCase : bool = True , __UpperCAmelCase : Union[bool, str, PaddingStrategy] = False , __UpperCAmelCase : Union[bool, str, TruncationStrategy] = False , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : int = 0 , __UpperCAmelCase : Optional[int] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : Optional[bool] = None , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = False , __UpperCAmelCase : bool = True , __UpperCAmelCase : Optional[Union[str, TensorType]] = None , **__UpperCAmelCase : Any , ) -> Union[str, Any]:
"""simple docstring"""
if text is None and images is None:
raise ValueError("You have to specify either text or images. Both cannot be none." )
if text is not None:
UpperCAmelCase__ = self.tokenizer(
text=__UpperCAmelCase , add_special_tokens=__UpperCAmelCase , padding=__UpperCAmelCase , truncation=__UpperCAmelCase , max_length=__UpperCAmelCase , stride=__UpperCAmelCase , pad_to_multiple_of=__UpperCAmelCase , return_token_type_ids=__UpperCAmelCase , return_attention_mask=__UpperCAmelCase , return_overflowing_tokens=__UpperCAmelCase , return_special_tokens_mask=__UpperCAmelCase , return_offsets_mapping=__UpperCAmelCase , return_length=__UpperCAmelCase , verbose=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase , )
if images is not None:
UpperCAmelCase__ = self.image_processor(
__UpperCAmelCase , return_image_mask=__UpperCAmelCase , return_codebook_pixels=__UpperCAmelCase , return_tensors=__UpperCAmelCase , **__UpperCAmelCase , )
if text is not None and images is not None:
encoding.update(__UpperCAmelCase )
return encoding
elif text is not None:
return encoding
else:
return BatchEncoding(data=dict(**__UpperCAmelCase ) , tensor_type=__UpperCAmelCase )
def lowercase_ (self : Optional[Any] , *__UpperCAmelCase : str , **__UpperCAmelCase : Tuple ) -> int:
"""simple docstring"""
return self.tokenizer.batch_decode(*__UpperCAmelCase , **__UpperCAmelCase )
def lowercase_ (self : int , *__UpperCAmelCase : List[str] , **__UpperCAmelCase : Union[str, Any] ) -> Any:
"""simple docstring"""
return self.tokenizer.decode(*__UpperCAmelCase , **__UpperCAmelCase )
@property
def lowercase_ (self : Tuple ) -> Dict:
"""simple docstring"""
UpperCAmelCase__ = self.tokenizer.model_input_names
UpperCAmelCase__ = self.image_processor.model_input_names
return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )
@property
def lowercase_ (self : Any ) -> Optional[int]:
"""simple docstring"""
warnings.warn(
"`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead." , __UpperCAmelCase , )
return self.image_processor_class
@property
def lowercase_ (self : str ) -> Tuple:
"""simple docstring"""
warnings.warn(
"`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead." , __UpperCAmelCase , )
return self.image_processor
| 486
| 0
|
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffusionInstructPixaPixPipeline,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
from ..pipeline_params import (
IMAGE_TO_IMAGE_IMAGE_PARAMS,
TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
enable_full_determinism()
class lowerCamelCase_( _a, _a, _a, unittest.TestCase ):
'''simple docstring'''
lowercase__ : Tuple = StableDiffusionInstructPixaPixPipeline
lowercase__ : Optional[Any] = TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
lowercase__ : Any = TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
lowercase__ : Dict = IMAGE_TO_IMAGE_IMAGE_PARAMS
lowercase__ : List[str] = IMAGE_TO_IMAGE_IMAGE_PARAMS
def snake_case__ ( self ):
torch.manual_seed(0 )
_lowerCamelCase = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=2 , sample_size=3_2 , in_channels=8 , out_channels=4 , down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''') , up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''') , cross_attention_dim=3_2 , )
_lowerCamelCase = PNDMScheduler(skip_prk_steps=snake_case_ )
torch.manual_seed(0 )
_lowerCamelCase = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''] , up_block_types=['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''] , latent_channels=4 , )
torch.manual_seed(0 )
_lowerCamelCase = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , )
_lowerCamelCase = CLIPTextModel(snake_case_ )
_lowerCamelCase = CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip''' )
_lowerCamelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
'''vae''': vae,
'''text_encoder''': text_encoder,
'''tokenizer''': tokenizer,
'''safety_checker''': None,
'''feature_extractor''': None,
}
return components
def snake_case__ ( self , lowerCamelCase__ , lowerCamelCase__=0 ):
_lowerCamelCase = floats_tensor((1, 3, 3_2, 3_2) , rng=random.Random(snake_case_ ) ).to(snake_case_ )
_lowerCamelCase = image.cpu().permute(0 , 2 , 3 , 1 )[0]
_lowerCamelCase = Image.fromarray(np.uinta(snake_case_ ) ).convert('''RGB''' )
if str(snake_case_ ).startswith('''mps''' ):
_lowerCamelCase = torch.manual_seed(snake_case_ )
else:
_lowerCamelCase = torch.Generator(device=snake_case_ ).manual_seed(snake_case_ )
_lowerCamelCase = {
'''prompt''': '''A painting of a squirrel eating a burger''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 2,
'''guidance_scale''': 6.0,
'''image_guidance_scale''': 1,
'''output_type''': '''numpy''',
}
return inputs
def snake_case__ ( self ):
_lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase = self.get_dummy_components()
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline(**snake_case_ )
_lowerCamelCase = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_lowerCamelCase = self.get_dummy_inputs(snake_case_ )
_lowerCamelCase = sd_pipe(**snake_case_ ).images
_lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
_lowerCamelCase = np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case__ ( self ):
_lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase = self.get_dummy_components()
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline(**snake_case_ )
_lowerCamelCase = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_lowerCamelCase = self.get_dummy_inputs(snake_case_ )
_lowerCamelCase = '''french fries'''
_lowerCamelCase = sd_pipe(**snake_case_ , negative_prompt=snake_case_ )
_lowerCamelCase = output.images
_lowerCamelCase = image[0, -3:, -3:, -1]
assert image.shape == (1, 3_2, 3_2, 3)
_lowerCamelCase = np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case__ ( self ):
_lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase = self.get_dummy_components()
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline(**snake_case_ )
_lowerCamelCase = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_lowerCamelCase = self.get_dummy_inputs(snake_case_ )
_lowerCamelCase = [inputs['''prompt''']] * 2
_lowerCamelCase = np.array(inputs['''image'''] ).astype(np.floataa ) / 2_5_5.0
_lowerCamelCase = torch.from_numpy(snake_case_ ).unsqueeze(0 ).to(snake_case_ )
_lowerCamelCase = image / 2 + 0.5
_lowerCamelCase = image.permute(0 , 3 , 1 , 2 )
_lowerCamelCase = image.repeat(2 , 1 , 1 , 1 )
_lowerCamelCase = sd_pipe(**snake_case_ ).images
_lowerCamelCase = image[-1, -3:, -3:, -1]
assert image.shape == (2, 3_2, 3_2, 3)
_lowerCamelCase = np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case__ ( self ):
_lowerCamelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
_lowerCamelCase = self.get_dummy_components()
_lowerCamelCase = EulerAncestralDiscreteScheduler(
beta_start=0.0_0_0_8_5 , beta_end=0.0_1_2 , beta_schedule='''scaled_linear''' )
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline(**snake_case_ )
_lowerCamelCase = sd_pipe.to(snake_case_ )
sd_pipe.set_progress_bar_config(disable=snake_case_ )
_lowerCamelCase = self.get_dummy_inputs(snake_case_ )
_lowerCamelCase = sd_pipe(**snake_case_ ).images
_lowerCamelCase = image[0, -3:, -3:, -1]
_lowerCamelCase = [round(snake_case_ , 4 ) for x in image_slice.flatten().tolist()]
print(''','''.join([str(snake_case_ ) for x in slice] ) )
assert image.shape == (1, 3_2, 3_2, 3)
_lowerCamelCase = np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-3
def snake_case__ ( self ):
super().test_inference_batch_single_identical(expected_max_diff=3e-3 )
def snake_case__ ( self ):
_lowerCamelCase = self.get_dummy_components()
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline(**snake_case_ )
_lowerCamelCase = VaeImageProcessor(do_resize=snake_case_ , do_normalize=snake_case_ )
_lowerCamelCase = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
_lowerCamelCase = pipe(**self.get_dummy_inputs_by_type(snake_case_ , input_image_type='''pt''' ) )[0]
_lowerCamelCase = components['''vae''']
_lowerCamelCase = self.get_dummy_inputs_by_type(snake_case_ , input_image_type='''pt''' )
for image_param in self.image_latents_params:
if image_param in inputs.keys():
_lowerCamelCase = vae.encode(inputs[image_param] ).latent_dist.mode()
_lowerCamelCase = pipe(**snake_case_ )[0]
_lowerCamelCase = np.abs(out - out_latents_inputs ).max()
self.assertLess(snake_case_ , 1e-4 , '''passing latents as image input generate different result from passing image''' )
@slow
@require_torch_gpu
class lowerCamelCase_( unittest.TestCase ):
'''simple docstring'''
def snake_case__ ( self ):
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def snake_case__ ( self , lowerCamelCase__=0 ):
_lowerCamelCase = torch.manual_seed(snake_case_ )
_lowerCamelCase = load_image(
'''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg''' )
_lowerCamelCase = {
'''prompt''': '''turn him into a cyborg''',
'''image''': image,
'''generator''': generator,
'''num_inference_steps''': 3,
'''guidance_scale''': 7.5,
'''image_guidance_scale''': 1.0,
'''output_type''': '''numpy''',
}
return inputs
def snake_case__ ( self ):
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=snake_case_ )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
_lowerCamelCase = self.get_inputs()
_lowerCamelCase = pipe(**snake_case_ ).images
_lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase = np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case__ ( self ):
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=snake_case_ )
_lowerCamelCase = LMSDiscreteScheduler.from_config(pipe.scheduler.config )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
_lowerCamelCase = self.get_inputs()
_lowerCamelCase = pipe(**snake_case_ ).images
_lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase = np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case__ ( self ):
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=snake_case_ )
_lowerCamelCase = DDIMScheduler.from_config(pipe.scheduler.config )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
_lowerCamelCase = self.get_inputs()
_lowerCamelCase = pipe(**snake_case_ ).images
_lowerCamelCase = image[0, -3:, -3:, -1].flatten()
assert image.shape == (1, 5_1_2, 5_1_2, 3)
_lowerCamelCase = np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3] )
assert np.abs(expected_slice - image_slice ).max() < 1e-3
def snake_case__ ( self ):
_lowerCamelCase = 0
def callback_fn(lowerCamelCase__ , lowerCamelCase__ , lowerCamelCase__ ) -> None:
_lowerCamelCase = True
nonlocal number_of_steps
number_of_steps += 1
if step == 1:
_lowerCamelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
_lowerCamelCase = latents[0, -3:, -3:, -1]
_lowerCamelCase = np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
elif step == 2:
_lowerCamelCase = latents.detach().cpu().numpy()
assert latents.shape == (1, 4, 6_4, 6_4)
_lowerCamelCase = latents[0, -3:, -3:, -1]
_lowerCamelCase = np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5] )
assert np.abs(latents_slice.flatten() - expected_slice ).max() < 5e-2
_lowerCamelCase = False
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=snake_case_ , torch_dtype=torch.floataa )
_lowerCamelCase = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
_lowerCamelCase = self.get_inputs()
pipe(**snake_case_ , callback=snake_case_ , callback_steps=1 )
assert callback_fn.has_been_called
assert number_of_steps == 3
def snake_case__ ( self ):
torch.cuda.empty_cache()
torch.cuda.reset_max_memory_allocated()
torch.cuda.reset_peak_memory_stats()
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
'''timbrooks/instruct-pix2pix''' , safety_checker=snake_case_ , torch_dtype=torch.floataa )
_lowerCamelCase = pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing(1 )
pipe.enable_sequential_cpu_offload()
_lowerCamelCase = self.get_inputs()
_lowerCamelCase = pipe(**snake_case_ )
_lowerCamelCase = torch.cuda.max_memory_allocated()
# make sure that less than 2.2 GB is allocated
assert mem_bytes < 2.2 * 1_0**9
def snake_case__ ( self ):
_lowerCamelCase = self.get_inputs()
# resize to resolution that is divisible by 8 but not 16 or 32
_lowerCamelCase = inputs['''image'''].resize((5_0_4, 5_0_4) )
_lowerCamelCase = '''timbrooks/instruct-pix2pix'''
_lowerCamelCase = StableDiffusionInstructPixaPixPipeline.from_pretrained(
snake_case_ , safety_checker=snake_case_ , )
pipe.to(snake_case_ )
pipe.set_progress_bar_config(disable=snake_case_ )
pipe.enable_attention_slicing()
_lowerCamelCase = pipe(**snake_case_ )
_lowerCamelCase = output.images[0]
_lowerCamelCase = image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]
assert image.shape == (5_0_4, 5_0_4, 3)
_lowerCamelCase = np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5e-3
| 720
|
"""simple docstring"""
import numpy as np
def lowerCAmelCase_( lowercase_ : np.ndarray , lowercase_ : np.ndarray , lowercase_ : float = 1e-12 , lowercase_ : int = 1_00 , ) -> tuple[float, np.ndarray]:
assert np.shape(lowercase_ )[0] == np.shape(lowercase_ )[1]
# Ensure proper dimensionality.
assert np.shape(lowercase_ )[0] == np.shape(lowercase_ )[0]
# Ensure inputs are either both complex or both real
assert np.iscomplexobj(lowercase_ ) == np.iscomplexobj(lowercase_ )
_lowerCamelCase = np.iscomplexobj(lowercase_ )
if is_complex:
# Ensure complex input_matrix is Hermitian
assert np.array_equal(lowercase_ , input_matrix.conj().T )
# Set convergence to False. Will define convergence when we exceed max_iterations
# or when we have small changes from one iteration to next.
_lowerCamelCase = False
_lowerCamelCase = 0
_lowerCamelCase = 0
_lowerCamelCase = 1e12
while not convergence:
# Multiple matrix by the vector.
_lowerCamelCase = np.dot(lowercase_ , lowercase_ )
# Normalize the resulting output vector.
_lowerCamelCase = w / np.linalg.norm(lowercase_ )
# Find rayleigh quotient
# (faster than usual b/c we know vector is normalized already)
_lowerCamelCase = vector.conj().T if is_complex else vector.T
_lowerCamelCase = np.dot(lowercase_ , np.dot(lowercase_ , lowercase_ ) )
# Check convergence.
_lowerCamelCase = np.abs(lambda_ - lambda_previous ) / lambda_
iterations += 1
if error <= error_tol or iterations >= max_iterations:
_lowerCamelCase = True
_lowerCamelCase = lambda_
if is_complex:
_lowerCamelCase = np.real(lambda_ )
return lambda_, vector
def lowerCAmelCase_( ) -> None:
_lowerCamelCase = np.array([[41, 4, 20], [4, 26, 30], [20, 30, 50]] )
_lowerCamelCase = np.array([41, 4, 20] )
_lowerCamelCase = real_input_matrix.astype(np.complexaaa )
_lowerCamelCase = np.triu(1j * complex_input_matrix , 1 )
complex_input_matrix += imag_matrix
complex_input_matrix += -1 * imag_matrix.T
_lowerCamelCase = np.array([41, 4, 20] ).astype(np.complexaaa )
for problem_type in ["real", "complex"]:
if problem_type == "real":
_lowerCamelCase = real_input_matrix
_lowerCamelCase = real_vector
elif problem_type == "complex":
_lowerCamelCase = complex_input_matrix
_lowerCamelCase = complex_vector
# Our implementation.
_lowerCamelCase , _lowerCamelCase = power_iteration(lowercase_ , lowercase_ )
# Numpy implementation.
# Get eigenvalues and eigenvectors using built-in numpy
# eigh (eigh used for symmetric or hermetian matrices).
_lowerCamelCase , _lowerCamelCase = np.linalg.eigh(lowercase_ )
# Last eigenvalue is the maximum one.
_lowerCamelCase = eigen_values[-1]
# Last column in this matrix is eigenvector corresponding to largest eigenvalue.
_lowerCamelCase = eigen_vectors[:, -1]
# Check our implementation and numpy gives close answers.
assert np.abs(eigen_value - eigen_value_max ) <= 1e-6
# Take absolute values element wise of each eigenvector.
# as they are only unique to a minus sign.
assert np.linalg.norm(np.abs(lowercase_ ) - np.abs(lowercase_ ) ) <= 1e-6
if __name__ == "__main__":
import doctest
doctest.testmod()
test_power_iteration()
| 623
| 0
|
'''simple docstring'''
from collections.abc import Sequence
def lowerCAmelCase_ ( snake_case_ : Sequence[int] | None = None ) -> int:
'''simple docstring'''
if nums is None or not nums:
raise ValueError("Input sequence should not be empty" )
UpperCAmelCase_ = nums[0]
for i in range(1 , len(snake_case_ ) ):
UpperCAmelCase_ = nums[i]
UpperCAmelCase_ = max(snake_case_ , ans + num , snake_case_ )
return ans
if __name__ == "__main__":
import doctest
doctest.testmod()
# Try on a sample input from the user
SCREAMING_SNAKE_CASE_: Any =int(input('Enter number of elements : ').strip())
SCREAMING_SNAKE_CASE_: Union[str, Any] =list(map(int, input('\nEnter the numbers : ').strip().split()))[:n]
print(max_subsequence_sum(array))
| 78
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ = {
'''configuration_git''': ['''GIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GitConfig''', '''GitVisionConfig'''],
'''processing_git''': ['''GitProcessor'''],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
UpperCamelCase__ = [
'''GIT_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''GitForCausalLM''',
'''GitModel''',
'''GitPreTrainedModel''',
'''GitVisionModel''',
]
if TYPE_CHECKING:
from .configuration_git import GIT_PRETRAINED_CONFIG_ARCHIVE_MAP, GitConfig, GitVisionConfig
from .processing_git import GitProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_git import (
GIT_PRETRAINED_MODEL_ARCHIVE_LIST,
GitForCausalLM,
GitModel,
GitPreTrainedModel,
GitVisionModel,
)
else:
import sys
UpperCamelCase__ = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 227
| 0
|
'''simple docstring'''
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_available():
from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
from ..tf_utils import stable_softmax
UpperCamelCase : Dict = logging.get_logger(__name__)
@add_end_docstrings(a )
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,**_lowerCAmelCase ):
super().__init__(**_lowerCAmelCase )
requires_backends(self ,"""vision""" )
self.check_model_type(
TF_MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING
if self.framework == """tf"""
else MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING )
def __call__( self ,_lowerCAmelCase ,**_lowerCAmelCase ):
return super().__call__(_lowerCAmelCase ,**_lowerCAmelCase )
def UpperCamelCase_ ( self ,**_lowerCAmelCase ):
lowerCamelCase__ = {}
if "candidate_labels" in kwargs:
lowerCamelCase__ = kwargs["""candidate_labels"""]
if "hypothesis_template" in kwargs:
lowerCamelCase__ = kwargs["""hypothesis_template"""]
return preprocess_params, {}, {}
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase=None ,_lowerCAmelCase="This is a photo of {}." ):
lowerCamelCase__ = load_image(_lowerCAmelCase )
lowerCamelCase__ = self.image_processor(images=[image] ,return_tensors=self.framework )
lowerCamelCase__ = candidate_labels
lowerCamelCase__ = [hypothesis_template.format(_lowerCAmelCase ) for x in candidate_labels]
lowerCamelCase__ = self.tokenizer(_lowerCAmelCase ,return_tensors=self.framework ,padding=_lowerCAmelCase )
lowerCamelCase__ = [text_inputs]
return inputs
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = model_inputs.pop("""candidate_labels""" )
lowerCamelCase__ = model_inputs.pop("""text_inputs""" )
if isinstance(text_inputs[0] ,_lowerCAmelCase ):
lowerCamelCase__ = text_inputs[0]
else:
# Batching case.
lowerCamelCase__ = text_inputs[0][0]
lowerCamelCase__ = self.model(**_lowerCAmelCase ,**_lowerCAmelCase )
lowerCamelCase__ = {
"""candidate_labels""": candidate_labels,
"""logits""": outputs.logits_per_image,
}
return model_outputs
def UpperCamelCase_ ( self ,_lowerCAmelCase ):
lowerCamelCase__ = model_outputs.pop("""candidate_labels""" )
lowerCamelCase__ = model_outputs["""logits"""][0]
if self.framework == "pt":
lowerCamelCase__ = logits.softmax(dim=-1 ).squeeze(-1 )
lowerCamelCase__ = probs.tolist()
if not isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = [scores]
elif self.framework == "tf":
lowerCamelCase__ = stable_softmax(_lowerCAmelCase ,axis=-1 )
lowerCamelCase__ = probs.numpy().tolist()
else:
raise ValueError(F'''Unsupported framework: {self.framework}''' )
lowerCamelCase__ = [
{"""score""": score, """label""": candidate_label}
for score, candidate_label in sorted(zip(_lowerCAmelCase ,_lowerCAmelCase ) ,key=lambda _lowerCAmelCase : -x[0] )
]
return result
| 9
|
'''simple docstring'''
import json
from typing import Iterator, List, Union
from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing
class UpperCamelCase__ (a ):
'''simple docstring'''
def __init__( self ,_lowerCAmelCase = "▁" ,_lowerCAmelCase = True ,_lowerCAmelCase = "<unk>" ,_lowerCAmelCase = "</s>" ,_lowerCAmelCase = "<pad>" ,):
lowerCamelCase__ = {
"""pad""": {"""id""": 0, """token""": pad_token},
"""eos""": {"""id""": 1, """token""": eos_token},
"""unk""": {"""id""": 2, """token""": unk_token},
}
lowerCamelCase__ = [None] * len(self.special_tokens )
for token_dict in self.special_tokens.values():
lowerCamelCase__ = token_dict["""token"""]
lowerCamelCase__ = Tokenizer(Unigram() )
lowerCamelCase__ = normalizers.Sequence(
[
normalizers.Nmt(),
normalizers.NFKC(),
normalizers.Replace(Regex(""" {2,}""" ) ,""" """ ),
normalizers.Lowercase(),
] )
lowerCamelCase__ = pre_tokenizers.Sequence(
[
pre_tokenizers.Metaspace(replacement=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase ),
pre_tokenizers.Digits(individual_digits=_lowerCAmelCase ),
pre_tokenizers.Punctuation(),
] )
lowerCamelCase__ = decoders.Metaspace(replacement=_lowerCAmelCase ,add_prefix_space=_lowerCAmelCase )
lowerCamelCase__ = TemplateProcessing(
single=F'''$A {self.special_tokens["eos"]["token"]}''' ,special_tokens=[(self.special_tokens["""eos"""]["""token"""], self.special_tokens["""eos"""]["""id"""])] ,)
lowerCamelCase__ = {
"""model""": """SentencePieceUnigram""",
"""replacement""": replacement,
"""add_prefix_space""": add_prefix_space,
}
super().__init__(_lowerCAmelCase ,_lowerCAmelCase )
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = 80_00 ,_lowerCAmelCase = True ,):
lowerCamelCase__ = trainers.UnigramTrainer(
vocab_size=_lowerCAmelCase ,special_tokens=self.special_tokens_list ,show_progress=_lowerCAmelCase ,)
if isinstance(_lowerCAmelCase ,_lowerCAmelCase ):
lowerCamelCase__ = [files]
self._tokenizer.train(_lowerCAmelCase ,trainer=_lowerCAmelCase )
self.add_unk_id()
def UpperCamelCase_ ( self ,_lowerCAmelCase ,_lowerCAmelCase = 80_00 ,_lowerCAmelCase = True ,):
lowerCamelCase__ = trainers.UnigramTrainer(
vocab_size=_lowerCAmelCase ,special_tokens=self.special_tokens_list ,show_progress=_lowerCAmelCase ,)
self._tokenizer.train_from_iterator(_lowerCAmelCase ,trainer=_lowerCAmelCase )
self.add_unk_id()
def UpperCamelCase_ ( self ):
lowerCamelCase__ = json.loads(self._tokenizer.to_str() )
lowerCamelCase__ = self.special_tokens["""unk"""]["""id"""]
lowerCamelCase__ = Tokenizer.from_str(json.dumps(_lowerCAmelCase ) )
| 9
| 1
|
"""simple docstring"""
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def lowerCamelCase_( _lowerCamelCase ) -> Any:
'''simple docstring'''
_lowerCamelCase : Any = [
"encoder.version",
"decoder.version",
"model.encoder.version",
"model.decoder.version",
"decoder.output_projection.weight",
"_float_tensor",
"encoder.embed_positions._float_tensor",
"decoder.embed_positions._float_tensor",
]
for k in ignore_keys:
state_dict.pop(_lowerCamelCase , _lowerCamelCase )
def lowerCamelCase_( _lowerCamelCase ) -> str:
'''simple docstring'''
_lowerCamelCase, _lowerCamelCase : List[str] = emb.weight.shape
_lowerCamelCase : Dict = nn.Linear(_lowerCamelCase , _lowerCamelCase , bias=_lowerCamelCase )
_lowerCamelCase : Any = emb.weight.data
return lin_layer
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase=None ) -> Any:
'''simple docstring'''
_lowerCamelCase : Union[str, Any] = {}
for old_key in state_dict.keys():
_lowerCamelCase : List[str] = old_key
if "moe_layer.experts." in key:
if expert_idx is not None:
_lowerCamelCase : Any = key.replace("moe_layer.experts.0" , F"""ffn.experts.expert_{expert_idx}""" )
else:
_lowerCamelCase : Optional[Any] = key.replace("moe_layer.experts." , "ffn.experts.expert_" )
if "gate" in key:
_lowerCamelCase : int = key.replace(".moe_layer.gate.wg" , ".ffn.router.classifier" )
if "fc2" and "experts" not in key:
_lowerCamelCase : Tuple = key.replace(".fc2." , ".ffn.fc2." )
if "fc1" and "experts" not in key:
_lowerCamelCase : Union[str, Any] = key.replace(".fc1." , ".ffn.fc1." )
if ".encoder_attn." in key:
_lowerCamelCase : Union[str, Any] = key.replace(".encoder_attn." , ".cross_attention." )
if "encoder_attn_layer_norm" in key:
_lowerCamelCase : int = key.replace("encoder_attn_layer_norm" , "cross_attention_layer_norm" )
if "final_layer_norm" in key:
_lowerCamelCase : Tuple = key.replace("final_layer_norm" , "ff_layer_norm" )
_lowerCamelCase : List[str] = state_dict[old_key]
return new_dict
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase , _lowerCamelCase = WEIGHTS_NAME ) -> List[str]:
'''simple docstring'''
_lowerCamelCase : List[str] = []
_lowerCamelCase : List[Any] = 0
os.makedirs(_lowerCamelCase , exist_ok=_lowerCamelCase )
for expert in range(_lowerCamelCase ):
_lowerCamelCase : Dict = switch_checkpoint_path + F"""-rank-{expert}.pt"""
if os.path.isfile(_lowerCamelCase ):
_lowerCamelCase : List[str] = torch.load(_lowerCamelCase )["model"]
remove_ignore_keys_(_lowerCamelCase )
_lowerCamelCase : Any = rename_fairseq_keys(_lowerCamelCase , _lowerCamelCase )
_lowerCamelCase : Tuple = os.path.join(
_lowerCamelCase , weights_name.replace(".bin" , F"""-{len(_lowerCamelCase )+1:05d}-of-???.bin""" ) )
torch.save(_lowerCamelCase , _lowerCamelCase )
sharded_state_dicts.append(expert_state.keys() )
total_size += sum([value.numel() for key, value in expert_state.items()] ) * dtype_byte_size(
expert_state[list(_lowerCamelCase )[0]].dtype )
# Add the last block
_lowerCamelCase : List[str] = os.path.join(_lowerCamelCase , weights_name.replace(".bin" , F"""-{len(_lowerCamelCase )+1:05d}-of-???.bin""" ) )
_lowerCamelCase : List[str] = torch.load(switch_checkpoint_path + "-shared.pt" )["model"]
remove_ignore_keys_(_lowerCamelCase )
_lowerCamelCase : int = rename_fairseq_keys(_lowerCamelCase , _lowerCamelCase )
_lowerCamelCase : List[Any] = shared_weights["decoder.embed_tokens.weight"]
sharded_state_dicts.append(shared_weights.keys() )
# If we only have the shared weights (dummy model/experts saved on the same file)
if len(_lowerCamelCase ) == 1:
_lowerCamelCase : Union[str, Any] = os.path.join(_lowerCamelCase , _lowerCamelCase )
torch.save(_lowerCamelCase , _lowerCamelCase )
return {weights_name: sharded_state_dicts[0]}, None
else:
torch.save(_lowerCamelCase , _lowerCamelCase )
# Otherwise, let's build the index
_lowerCamelCase : Optional[int] = {}
for idx, shard in enumerate(_lowerCamelCase ):
_lowerCamelCase : int = weights_name.replace(".bin" , F"""-{idx+1:05d}-of-{len(_lowerCamelCase ):05d}.bin""" )
_lowerCamelCase : str = os.path.join(_lowerCamelCase , weights_name.replace(".bin" , F"""-{idx+1:05d}-of-???.bin""" ) )
os.rename(_lowerCamelCase , os.path.join(_lowerCamelCase , _lowerCamelCase ) )
for key in shard:
_lowerCamelCase : Optional[Any] = shard_file
# Add the metadata
_lowerCamelCase : Union[str, Any] = {"total_size": total_size}
_lowerCamelCase : Optional[Any] = {"metadata": metadata, "weight_map": weight_map}
with open(os.path.join(_lowerCamelCase , _lowerCamelCase ) , "w" , encoding="utf-8" ) as f:
_lowerCamelCase : Union[str, Any] = json.dumps(_lowerCamelCase , indent=2 , sort_keys=_lowerCamelCase ) + "\n"
f.write(_lowerCamelCase )
return metadata, index
if __name__ == "__main__":
_lowerCAmelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'''--nllb_moe_checkpoint_path''',
default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000''',
type=str,
required=False,
help='''Path to a directory containing a folder per layer. Follows the original Google format.''',
)
parser.add_argument('''--dtype''', default='''float32''', type=str, required=False, help='''dtype of the saved model''')
parser.add_argument(
'''--pytorch_dump_folder_path''',
default='''/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b''',
type=str,
required=False,
help='''Path to the output pytorch model.''',
)
_lowerCAmelCase : List[str] = parser.parse_args()
_lowerCAmelCase , _lowerCAmelCase : Any = shard_on_the_fly(
args.nllb_moe_checkpoint_path,
args.pytorch_dump_folder_path,
128,
args.dtype,
)
_lowerCAmelCase : List[Any] = NllbMoeConfig.from_pretrained(
'''facebook/nllb-200-3.3B''', encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
)
config.save_pretrained(args.pytorch_dump_folder_path)
_lowerCAmelCase : Union[str, Any] = NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
print('''Done''')
model.save_pretrained(args.pytorch_dump_folder_path)
| 46
|
"""simple docstring"""
_UpperCamelCase = {
"""meter""": """m""",
"""kilometer""": """km""",
"""megametre""": """Mm""",
"""gigametre""": """Gm""",
"""terametre""": """Tm""",
"""petametre""": """Pm""",
"""exametre""": """Em""",
"""zettametre""": """Zm""",
"""yottametre""": """Ym""",
}
# Exponent of the factor(meter)
_UpperCamelCase = {
"""m""": 0,
"""km""": 3,
"""Mm""": 6,
"""Gm""": 9,
"""Tm""": 12,
"""Pm""": 15,
"""Em""": 18,
"""Zm""": 21,
"""Ym""": 24,
}
def _a ( _snake_case , _snake_case , _snake_case ):
"""simple docstring"""
UpperCAmelCase = from_type.lower().strip("""s""" )
UpperCAmelCase = to_type.lower().strip("""s""" )
UpperCAmelCase = UNIT_SYMBOL.get(_snake_case , _snake_case )
UpperCAmelCase = UNIT_SYMBOL.get(_snake_case , _snake_case )
if from_sanitized not in METRIC_CONVERSION:
UpperCAmelCase = (
F'''Invalid \'from_type\' value: {from_type!r}.\n'''
F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
)
raise ValueError(_snake_case )
if to_sanitized not in METRIC_CONVERSION:
UpperCAmelCase = (
F'''Invalid \'to_type\' value: {to_type!r}.\n'''
F'''Conversion abbreviations are: {', '.join(_snake_case )}'''
)
raise ValueError(_snake_case )
UpperCAmelCase = METRIC_CONVERSION[from_sanitized]
UpperCAmelCase = METRIC_CONVERSION[to_sanitized]
UpperCAmelCase = 1
if from_exponent > to_exponent:
UpperCAmelCase = from_exponent - to_exponent
else:
UpperCAmelCase = -(to_exponent - from_exponent)
return value * pow(10 , _snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 341
| 0
|
'''simple docstring'''
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYPE_CHECKING:
import torch
class UpperCamelCase ( TensorFormatter[Mapping, "torch.Tensor", Mapping] ):
"""simple docstring"""
def __init__( self : Optional[int] , UpperCAmelCase_ : Tuple=None , **UpperCAmelCase_ : List[Any]):
"""simple docstring"""
super().__init__(features=UpperCAmelCase_)
a : str = torch_tensor_kwargs
import torch # noqa import torch at initialization
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] , UpperCAmelCase_ : Union[str, Any]):
"""simple docstring"""
import torch
if isinstance(UpperCAmelCase_ , UpperCAmelCase_) and column:
if all(
isinstance(UpperCAmelCase_ , torch.Tensor) and x.shape == column[0].shape and x.dtype == column[0].dtype
for x in column):
return torch.stack(UpperCAmelCase_)
return column
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , UpperCAmelCase_ : Optional[Any]):
"""simple docstring"""
import torch
if isinstance(UpperCAmelCase_ , (str, bytes, type(UpperCAmelCase_))):
return value
elif isinstance(UpperCAmelCase_ , (np.character, np.ndarray)) and np.issubdtype(value.dtype , np.character):
return value.tolist()
a : int = {}
if isinstance(UpperCAmelCase_ , (np.number, np.ndarray)) and np.issubdtype(value.dtype , np.integer):
a : List[Any] = {'dtype': torch.intaa}
elif isinstance(UpperCAmelCase_ , (np.number, np.ndarray)) and np.issubdtype(value.dtype , np.floating):
a : Dict = {'dtype': torch.floataa}
elif config.PIL_AVAILABLE and "PIL" in sys.modules:
import PIL.Image
if isinstance(UpperCAmelCase_ , PIL.Image.Image):
a : int = np.asarray(UpperCAmelCase_)
return torch.tensor(UpperCAmelCase_ , **{**default_dtype, **self.torch_tensor_kwargs})
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , UpperCAmelCase_ : List[str]):
"""simple docstring"""
import torch
# support for torch, tf, jax etc.
if hasattr(UpperCAmelCase_ , '__array__') and not isinstance(UpperCAmelCase_ , torch.Tensor):
a : Dict = data_struct.__array__()
# support for nested types like struct of list of struct
if isinstance(UpperCAmelCase_ , np.ndarray):
if data_struct.dtype == object: # torch tensors cannot be instantied from an array of objects
return self._consolidate([self.recursive_tensorize(UpperCAmelCase_) for substruct in data_struct])
elif isinstance(UpperCAmelCase_ , (list, tuple)):
return self._consolidate([self.recursive_tensorize(UpperCAmelCase_) for substruct in data_struct])
return self._tensorize(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : int , UpperCAmelCase_ : dict):
"""simple docstring"""
return map_nested(self._recursive_tensorize , UpperCAmelCase_ , map_list=UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : List[Any] , UpperCAmelCase_ : pa.Table):
"""simple docstring"""
a : Any = self.numpy_arrow_extractor().extract_row(UpperCAmelCase_)
a : Any = self.python_features_decoder.decode_row(UpperCAmelCase_)
return self.recursive_tensorize(UpperCAmelCase_)
def SCREAMING_SNAKE_CASE_ ( self : int , UpperCAmelCase_ : pa.Table):
"""simple docstring"""
a : List[Any] = self.numpy_arrow_extractor().extract_column(UpperCAmelCase_)
a : Any = self.python_features_decoder.decode_column(UpperCAmelCase_ , pa_table.column_names[0])
a : Union[str, Any] = self.recursive_tensorize(UpperCAmelCase_)
a : List[str] = self._consolidate(UpperCAmelCase_)
return column
def SCREAMING_SNAKE_CASE_ ( self : Tuple , UpperCAmelCase_ : pa.Table):
"""simple docstring"""
a : List[str] = self.numpy_arrow_extractor().extract_batch(UpperCAmelCase_)
a : Dict = self.python_features_decoder.decode_batch(UpperCAmelCase_)
a : Tuple = self.recursive_tensorize(UpperCAmelCase_)
for column_name in batch:
a : Any = self._consolidate(batch[column_name])
return batch
| 708
|
'''simple docstring'''
import os
def SCREAMING_SNAKE_CASE__ ( ) -> List[Any]:
"""simple docstring"""
a : List[str] = os.path.join(os.path.dirname(snake_case ) , 'num.txt' )
with open(snake_case ) as file_hand:
return str(sum(int(snake_case ) for line in file_hand ) )[:10]
if __name__ == "__main__":
print(solution())
| 610
| 0
|
'''simple docstring'''
from typing import Dict, List, Optional
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
lowerCamelCase :Optional[int] = logging.get_logger(__name__)
lowerCamelCase :str = {
'''nielsr/canine-s''': 2_0_4_8,
}
# Unicode defines 1,114,112 total “codepoints”
lowerCamelCase :int = 1_1_1_4_1_1_2
# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
lowerCamelCase :Optional[Any] = 0
lowerCamelCase :List[Any] = 0xE000
lowerCamelCase :Tuple = 0xE001
lowerCamelCase :Optional[Any] = 0xE002
lowerCamelCase :List[str] = 0xE003
lowerCamelCase :Dict = 0xE004
# Maps special codepoints to human-readable names.
lowerCamelCase :Dict[int, str] = {
# Special symbols are represented using codepoints values that are valid,
# but designated as "Private Use", meaning that they will never be assigned
# characters by the Unicode Consortium, and are thus safe for use here.
#
# NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
# excluded and should fail with a hard error.
CLS: "[CLS]",
SEP: "[SEP]",
BOS: "[BOS]",
MASK: "[MASK]",
PAD: "[PAD]",
RESERVED: "[RESERVED]",
}
# Maps special codepoint human-readable names to their codepoint values.
lowerCamelCase :Dict[str, int] = {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}
class _lowerCAmelCase ( __UpperCAmelCase ):
__SCREAMING_SNAKE_CASE : Optional[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__(self , lowercase=chr(lowercase ) , lowercase=chr(lowercase ) , lowercase=chr(lowercase ) , lowercase=chr(lowercase ) , lowercase=chr(lowercase ) , lowercase=chr(lowercase ) , lowercase=False , lowercase=2048 , **lowercase , ):
A_ : str = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else bos_token
A_ : int = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else eos_token
A_ : Dict = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else sep_token
A_ : Optional[int] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else cls_token
A_ : Union[str, Any] = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else pad_token
# Mask token behave like a normal word, i.e. include the space before it
A_ : Dict = AddedToken(lowercase , lstrip=lowercase , rstrip=lowercase ) if isinstance(lowercase , lowercase ) else mask_token
super().__init__(
bos_token=lowercase , eos_token=lowercase , sep_token=lowercase , cls_token=lowercase , pad_token=lowercase , mask_token=lowercase , add_prefix_space=lowercase , model_max_length=lowercase , **lowercase , )
# Creates a mapping for looking up the IDs of special symbols.
A_ : Dict[str, int] = {}
for codepoint, name in SPECIAL_CODEPOINTS.items():
A_ : Tuple = codepoint
# Creates a mapping for looking up the string forms of special symbol IDs.
A_ : Dict[int, str] = {
codepoint: name for name, codepoint in self._special_codepoints.items()
}
A_ : str = UNICODE_VOCAB_SIZE
A_ : Tuple = len(self._special_codepoints )
@property
def _a (self ):
return self._unicode_vocab_size
def _a (self , lowercase ):
return list(lowercase )
def _a (self , lowercase ):
try:
return ord(lowercase )
except TypeError:
raise ValueError(F'invalid token: \'{token}\'' )
def _a (self , lowercase ):
try:
if index in SPECIAL_CODEPOINTS:
return SPECIAL_CODEPOINTS[index]
return chr(lowercase )
except TypeError:
raise ValueError(F'invalid id: {index}' )
def _a (self , lowercase ):
return "".join(lowercase )
def _a (self , lowercase , lowercase = None ):
A_ : Any = [self.sep_token_id]
A_ : List[Any] = [self.cls_token_id]
A_ : List[Any] = cls + token_ids_a + sep
if token_ids_a is not None:
result += token_ids_a + sep
return result
def _a (self , lowercase , lowercase = None , lowercase = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=lowercase , token_ids_a=lowercase , already_has_special_tokens=lowercase )
A_ : str = [1] + ([0] * len(lowercase )) + [1]
if token_ids_a is not None:
result += ([0] * len(lowercase )) + [1]
return result
def _a (self , lowercase , lowercase = None ):
A_ : str = [self.sep_token_id]
A_ : Optional[int] = [self.cls_token_id]
A_ : Any = len(cls + token_ids_a + sep ) * [0]
if token_ids_a is not None:
result += len(token_ids_a + sep ) * [1]
return result
def _a (self , lowercase , lowercase = None ):
return ()
| 667
|
'''simple docstring'''
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import (
AutoProcessor,
BertTokenizerFast,
BlipImageProcessor,
GPTaTokenizer,
InstructBlipProcessor,
PreTrainedTokenizerFast,
)
@require_vision
class _lowerCAmelCase ( unittest.TestCase ):
def _a (self ):
A_ : Union[str, Any] = tempfile.mkdtemp()
A_ : List[Any] = BlipImageProcessor()
A_ : Optional[int] = GPTaTokenizer.from_pretrained("""hf-internal-testing/tiny-random-GPT2Model""" )
A_ : Any = BertTokenizerFast.from_pretrained("""hf-internal-testing/tiny-random-bert""" )
A_ : Dict = InstructBlipProcessor(lowercase , lowercase , lowercase )
processor.save_pretrained(self.tmpdirname )
def _a (self , **lowercase ):
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase ).tokenizer
def _a (self , **lowercase ):
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase ).image_processor
def _a (self , **lowercase ):
return AutoProcessor.from_pretrained(self.tmpdirname , **lowercase ).qformer_tokenizer
def _a (self ):
shutil.rmtree(self.tmpdirname )
def _a (self ):
A_ : List[str] = [np.random.randint(255 , size=(3, 30, 400) , dtype=np.uinta )]
A_ : Optional[Any] = [Image.fromarray(np.moveaxis(lowercase , 0 , -1 ) ) for x in image_inputs]
return image_inputs
def _a (self ):
A_ : str = InstructBlipProcessor(
tokenizer=self.get_tokenizer() , image_processor=self.get_image_processor() , qformer_tokenizer=self.get_qformer_tokenizer() , )
processor.save_pretrained(self.tmpdirname )
A_ : Tuple = self.get_tokenizer(bos_token="""(BOS)""" , eos_token="""(EOS)""" )
A_ : Optional[Any] = self.get_image_processor(do_normalize=lowercase , padding_value=1.0 )
A_ : str = InstructBlipProcessor.from_pretrained(
self.tmpdirname , bos_token="""(BOS)""" , eos_token="""(EOS)""" , do_normalize=lowercase , padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer , lowercase )
self.assertEqual(processor.image_processor.to_json_string() , image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor , lowercase )
self.assertIsInstance(processor.qformer_tokenizer , lowercase )
def _a (self ):
A_ : Any = self.get_image_processor()
A_ : Union[str, Any] = self.get_tokenizer()
A_ : List[str] = self.get_qformer_tokenizer()
A_ : int = InstructBlipProcessor(
tokenizer=lowercase , image_processor=lowercase , qformer_tokenizer=lowercase )
A_ : List[Any] = self.prepare_image_inputs()
A_ : Union[str, Any] = image_processor(lowercase , return_tensors="""np""" )
A_ : Dict = processor(images=lowercase , return_tensors="""np""" )
for key in input_feat_extract.keys():
self.assertAlmostEqual(input_feat_extract[key].sum() , input_processor[key].sum() , delta=1E-2 )
def _a (self ):
A_ : List[Any] = self.get_image_processor()
A_ : Optional[Any] = self.get_tokenizer()
A_ : Any = self.get_qformer_tokenizer()
A_ : List[str] = InstructBlipProcessor(
tokenizer=lowercase , image_processor=lowercase , qformer_tokenizer=lowercase )
A_ : str = """lower newer"""
A_ : List[Any] = processor(text=lowercase )
A_ : Optional[int] = tokenizer(lowercase , return_token_type_ids=lowercase )
A_ : List[Any] = qformer_tokenizer(lowercase , return_token_type_ids=lowercase )
for key in encoded_tokens.keys():
self.assertListEqual(encoded_tokens[key] , encoded_processor[key] )
for key in encoded_tokens_qformer.keys():
self.assertListEqual(encoded_tokens_qformer[key] , encoded_processor["""qformer_""" + key] )
def _a (self ):
A_ : int = self.get_image_processor()
A_ : Union[str, Any] = self.get_tokenizer()
A_ : Union[str, Any] = self.get_qformer_tokenizer()
A_ : Any = InstructBlipProcessor(
tokenizer=lowercase , image_processor=lowercase , qformer_tokenizer=lowercase )
A_ : Optional[int] = """lower newer"""
A_ : Optional[int] = self.prepare_image_inputs()
A_ : Tuple = processor(text=lowercase , images=lowercase )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
# test if it raises when no input is passed
with pytest.raises(lowercase ):
processor()
def _a (self ):
A_ : Dict = self.get_image_processor()
A_ : str = self.get_tokenizer()
A_ : Optional[int] = self.get_qformer_tokenizer()
A_ : int = InstructBlipProcessor(
tokenizer=lowercase , image_processor=lowercase , qformer_tokenizer=lowercase )
A_ : Tuple = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
A_ : Optional[int] = processor.batch_decode(lowercase )
A_ : Dict = tokenizer.batch_decode(lowercase )
self.assertListEqual(lowercase , lowercase )
def _a (self ):
A_ : Any = self.get_image_processor()
A_ : Dict = self.get_tokenizer()
A_ : Union[str, Any] = self.get_qformer_tokenizer()
A_ : Optional[int] = InstructBlipProcessor(
tokenizer=lowercase , image_processor=lowercase , qformer_tokenizer=lowercase )
A_ : List[Any] = """lower newer"""
A_ : Optional[Any] = self.prepare_image_inputs()
A_ : Any = processor(text=lowercase , images=lowercase )
self.assertListEqual(
list(inputs.keys() ) , ["""input_ids""", """attention_mask""", """qformer_input_ids""", """qformer_attention_mask""", """pixel_values"""] , )
| 667
| 1
|
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _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 (
MODEL_MAPPING,
BeitForImageClassification,
BeitForMaskedImageModeling,
BeitForSemanticSegmentation,
BeitModel,
)
from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
import PIL
from PIL import Image
from transformers import BeitImageProcessor
class __UpperCAmelCase:
"""simple docstring"""
def __init__( self , snake_case__ , snake_case__=100 , snake_case__=13 , snake_case__=30 , snake_case__=2 , snake_case__=3 , snake_case__=True , snake_case__=True , snake_case__=32 , snake_case__=4 , snake_case__=4 , snake_case__=37 , snake_case__="gelu" , snake_case__=0.1 , snake_case__=0.1 , snake_case__=10 , snake_case__=0.02 , snake_case__=3 , snake_case__=None , snake_case__=[0, 1, 2, 3] , ):
'''simple docstring'''
lowercase__ : Any= parent
lowercase__ : Tuple= 100
lowercase__ : Optional[int]= batch_size
lowercase__ : Optional[int]= image_size
lowercase__ : List[Any]= patch_size
lowercase__ : Union[str, Any]= num_channels
lowercase__ : Tuple= is_training
lowercase__ : Union[str, Any]= use_labels
lowercase__ : Any= hidden_size
lowercase__ : Optional[int]= num_hidden_layers
lowercase__ : List[str]= num_attention_heads
lowercase__ : int= intermediate_size
lowercase__ : Optional[int]= hidden_act
lowercase__ : Dict= hidden_dropout_prob
lowercase__ : Optional[Any]= attention_probs_dropout_prob
lowercase__ : Optional[Any]= type_sequence_label_size
lowercase__ : str= initializer_range
lowercase__ : Union[str, Any]= scope
lowercase__ : Union[str, Any]= out_indices
lowercase__ : List[str]= num_labels
# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
lowercase__ : Tuple= (image_size // patch_size) ** 2
lowercase__ : Any= num_patches + 1
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
lowercase__ : int= None
lowercase__ : Union[str, Any]= None
if self.use_labels:
lowercase__ : Tuple= ids_tensor([self.batch_size] , self.type_sequence_label_size )
lowercase__ : str= ids_tensor([self.batch_size, self.image_size, self.image_size] , self.num_labels )
lowercase__ : str= self.get_config()
return config, pixel_values, labels, pixel_labels
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return BeitConfig(
vocab_size=self.vocab_size , image_size=self.image_size , patch_size=self.patch_size , num_channels=self.num_channels , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , is_decoder=snake_case__ , initializer_range=self.initializer_range , out_indices=self.out_indices , )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
lowercase__ : Optional[int]= BeitModel(config=snake_case__ )
model.to(snake_case__ )
model.eval()
lowercase__ : List[str]= model(snake_case__ )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
lowercase__ : int= BeitForMaskedImageModeling(config=snake_case__ )
model.to(snake_case__ )
model.eval()
lowercase__ : Dict= model(snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length - 1, self.vocab_size) )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
lowercase__ : Optional[Any]= self.type_sequence_label_size
lowercase__ : int= BeitForImageClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
lowercase__ : List[Any]= model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
# test greyscale images
lowercase__ : Optional[Any]= 1
lowercase__ : int= BeitForImageClassification(snake_case__ )
model.to(snake_case__ )
model.eval()
lowercase__ : Dict= floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
lowercase__ : List[Any]= model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.type_sequence_label_size) )
def UpperCAmelCase_ ( self , snake_case__ , snake_case__ , snake_case__ , snake_case__ ):
'''simple docstring'''
lowercase__ : Dict= self.num_labels
lowercase__ : str= BeitForSemanticSegmentation(snake_case__ )
model.to(snake_case__ )
model.eval()
lowercase__ : List[str]= model(snake_case__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
lowercase__ : Union[str, Any]= model(snake_case__ , labels=snake_case__ )
self.parent.assertEqual(
result.logits.shape , (self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= self.prepare_config_and_inputs()
lowercase__ : str= config_and_inputs
lowercase__ : Optional[int]= {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class __UpperCAmelCase( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ , unittest.TestCase ):
"""simple docstring"""
__lowerCamelCase = (
(BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
if is_torch_available()
else ()
)
__lowerCamelCase = (
{
"feature-extraction": BeitModel,
"image-classification": BeitForImageClassification,
"image-segmentation": BeitForSemanticSegmentation,
}
if is_torch_available()
else {}
)
__lowerCamelCase = False
__lowerCamelCase = False
__lowerCamelCase = False
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Any= BeitModelTester(self )
lowercase__ : List[str]= ConfigTester(self , config_class=snake_case__ , has_text_modality=snake_case__ , hidden_size=37 )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
self.config_tester.run_common_tests()
@unittest.skip(reason="BEiT does not use inputs_embeds" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
@require_torch_multi_gpu
@unittest.skip(reason="BEiT has some layers using `add_module` which doesn't work well with `nn.DataParallel`" )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
pass
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Optional[int]= model_class(snake_case__ )
self.assertIsInstance(model.get_input_embeddings() , (nn.Module) )
lowercase__ : str= model.get_output_embeddings()
self.assertTrue(x is None or isinstance(snake_case__ , nn.Linear ) )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : str= self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
lowercase__ : Optional[Any]= model_class(snake_case__ )
lowercase__ : List[Any]= inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
lowercase__ : List[str]= [*signature.parameters.keys()]
lowercase__ : Optional[int]= ["pixel_values"]
self.assertListEqual(arg_names[:1] , snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Union[str, Any]= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Any= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Optional[Any]= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : str= self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_semantic_segmentation(*snake_case__ )
def UpperCAmelCase_ ( self ):
'''simple docstring'''
if not self.model_tester.is_training:
return
lowercase__ : Any= self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Tuple= True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if model_class in [*get_values(snake_case__ ), BeitForMaskedImageModeling]:
continue
lowercase__ : str= model_class(snake_case__ )
model.to(snake_case__ )
model.train()
lowercase__ : Tuple= self._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
lowercase__ : Union[str, Any]= model(**snake_case__ ).loss
loss.backward()
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[Any]= self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
lowercase__ : Optional[Any]= False
lowercase__ : Any= True
for model_class in self.all_model_classes:
# we don't test BeitForMaskedImageModeling
if (
model_class in [*get_values(snake_case__ ), BeitForMaskedImageModeling]
or not model_class.supports_gradient_checkpointing
):
continue
lowercase__ : int= model_class(snake_case__ )
model.gradient_checkpointing_enable()
model.to(snake_case__ )
model.train()
lowercase__ : Union[str, Any]= self._prepare_for_class(snake_case__ , snake_case__ , return_labels=snake_case__ )
lowercase__ : Tuple= model(**snake_case__ ).loss
loss.backward()
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : int= self.model_tester.prepare_config_and_inputs_for_common()
lowercase__ : Tuple= _config_zero_init(snake_case__ )
for model_class in self.all_model_classes:
lowercase__ : List[Any]= model_class(config=snake_case__ )
for name, param in model.named_parameters():
# we skip lambda parameters as these require special initial values
# determined by config.layer_scale_init_value
if "lambda" in name:
continue
if param.requires_grad:
self.assertIn(
((param.data.mean() * 1e9).round() / 1e9).item() , [0.0, 1.0] , msg=F'''Parameter {name} of model {model_class} seems not properly initialized''' , )
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
lowercase__ : Tuple= BeitModel.from_pretrained(snake_case__ )
self.assertIsNotNone(snake_case__ )
def lowercase__() ->Union[str, Any]:
"""simple docstring"""
lowercase__ : Dict= Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class __UpperCAmelCase( unittest.TestCase ):
"""simple docstring"""
@cached_property
def UpperCAmelCase_ ( self ):
'''simple docstring'''
return BeitImageProcessor.from_pretrained("microsoft/beit-base-patch16-224" ) if is_vision_available() else None
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[str]= BeitForMaskedImageModeling.from_pretrained("microsoft/beit-base-patch16-224-pt22k" ).to(snake_case__ )
lowercase__ : Optional[Any]= self.default_image_processor
lowercase__ : Dict= prepare_img()
lowercase__ : Optional[Any]= image_processor(images=snake_case__ , return_tensors="pt" ).pixel_values.to(snake_case__ )
# prepare bool_masked_pos
lowercase__ : str= torch.ones((1, 196) , dtype=torch.bool ).to(snake_case__ )
# forward pass
with torch.no_grad():
lowercase__ : int= model(pixel_values=snake_case__ , bool_masked_pos=snake_case__ )
lowercase__ : Any= outputs.logits
# verify the logits
lowercase__ : str= torch.Size((1, 196, 8192) )
self.assertEqual(logits.shape , snake_case__ )
lowercase__ : List[Any]= torch.tensor(
[[-3.24_37, 0.50_72, -13.91_74], [-3.24_56, 0.49_48, -13.94_01], [-3.20_33, 0.51_21, -13.85_50]] ).to(snake_case__ )
self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3] , snake_case__ , atol=1e-2 ) )
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[Any]= BeitForImageClassification.from_pretrained("microsoft/beit-base-patch16-224" ).to(snake_case__ )
lowercase__ : Tuple= self.default_image_processor
lowercase__ : int= prepare_img()
lowercase__ : Tuple= image_processor(images=snake_case__ , return_tensors="pt" ).to(snake_case__ )
# forward pass
with torch.no_grad():
lowercase__ : Union[str, Any]= model(**snake_case__ )
lowercase__ : Any= outputs.logits
# verify the logits
lowercase__ : Union[str, Any]= torch.Size((1, 1000) )
self.assertEqual(logits.shape , snake_case__ )
lowercase__ : int= torch.tensor([-1.23_85, -1.09_87, -1.01_08] ).to(snake_case__ )
self.assertTrue(torch.allclose(logits[0, :3] , snake_case__ , atol=1e-4 ) )
lowercase__ : str= 281
self.assertEqual(logits.argmax(-1 ).item() , snake_case__ )
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : List[Any]= BeitForImageClassification.from_pretrained("microsoft/beit-large-patch16-224-pt22k-ft22k" ).to(
snake_case__ )
lowercase__ : List[str]= self.default_image_processor
lowercase__ : str= prepare_img()
lowercase__ : Optional[int]= image_processor(images=snake_case__ , return_tensors="pt" ).to(snake_case__ )
# forward pass
with torch.no_grad():
lowercase__ : int= model(**snake_case__ )
lowercase__ : Any= outputs.logits
# verify the logits
lowercase__ : List[str]= torch.Size((1, 21841) )
self.assertEqual(logits.shape , snake_case__ )
lowercase__ : Tuple= torch.tensor([1.68_81, -0.27_87, 0.59_01] ).to(snake_case__ )
self.assertTrue(torch.allclose(logits[0, :3] , snake_case__ , atol=1e-4 ) )
lowercase__ : Tuple= 2396
self.assertEqual(logits.argmax(-1 ).item() , snake_case__ )
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Any= BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
lowercase__ : Optional[int]= model.to(snake_case__ )
lowercase__ : Tuple= BeitImageProcessor(do_resize=snake_case__ , size=640 , do_center_crop=snake_case__ )
lowercase__ : str= load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
lowercase__ : List[Any]= Image.open(ds[0]["file"] )
lowercase__ : int= image_processor(images=snake_case__ , return_tensors="pt" ).to(snake_case__ )
# forward pass
with torch.no_grad():
lowercase__ : Tuple= model(**snake_case__ )
lowercase__ : List[str]= outputs.logits
# verify the logits
lowercase__ : Dict= torch.Size((1, 150, 160, 160) )
self.assertEqual(logits.shape , snake_case__ )
lowercase__ : Tuple= version.parse(PIL.__version__ ) < version.parse("9.0.0" )
if is_pillow_less_than_a:
lowercase__ : List[Any]= torch.tensor(
[
[[-4.92_25, -2.39_54, -3.05_22], [-2.88_22, -1.00_46, -1.75_61], [-2.95_49, -1.32_28, -2.13_47]],
[[-5.81_68, -3.41_29, -4.07_78], [-3.86_51, -2.22_14, -3.02_77], [-3.83_56, -2.46_43, -3.35_35]],
[[-0.00_78, 3.99_52, 4.07_54], [2.98_56, 4.69_44, 5.00_35], [3.24_13, 4.78_13, 4.99_69]],
] , device=snake_case__ , )
else:
lowercase__ : Optional[Any]= torch.tensor(
[
[[-4.89_60, -2.36_88, -3.03_55], [-2.84_78, -0.98_36, -1.74_18], [-2.94_49, -1.33_32, -2.14_56]],
[[-5.80_81, -3.41_24, -4.10_06], [-3.85_61, -2.20_81, -3.03_23], [-3.83_65, -2.46_01, -3.36_69]],
[[-0.03_09, 3.98_68, 4.05_40], [2.96_40, 4.68_77, 4.99_76], [3.20_81, 4.76_90, 4.99_42]],
] , device=snake_case__ , )
self.assertTrue(torch.allclose(logits[0, :3, :3, :3] , snake_case__ , atol=1e-4 ) )
@slow
def UpperCAmelCase_ ( self ):
'''simple docstring'''
lowercase__ : Dict= BeitForSemanticSegmentation.from_pretrained("microsoft/beit-base-finetuned-ade-640-640" )
lowercase__ : int= model.to(snake_case__ )
lowercase__ : Union[str, Any]= BeitImageProcessor(do_resize=snake_case__ , size=640 , do_center_crop=snake_case__ )
lowercase__ : int= load_dataset("hf-internal-testing/fixtures_ade20k" , split="test" )
lowercase__ : str= Image.open(ds[0]["file"] )
lowercase__ : Optional[int]= image_processor(images=snake_case__ , return_tensors="pt" ).to(snake_case__ )
# forward pass
with torch.no_grad():
lowercase__ : Optional[Any]= model(**snake_case__ )
lowercase__ : Optional[int]= outputs.logits.detach().cpu()
lowercase__ : List[str]= image_processor.post_process_semantic_segmentation(outputs=snake_case__ , target_sizes=[(500, 300)] )
lowercase__ : List[str]= torch.Size((500, 300) )
self.assertEqual(segmentation[0].shape , snake_case__ )
lowercase__ : str= image_processor.post_process_semantic_segmentation(outputs=snake_case__ )
lowercase__ : str= torch.Size((160, 160) )
self.assertEqual(segmentation[0].shape , snake_case__ )
| 714
|
"""simple docstring"""
def lowercase__(A ) ->list[int]:
"""simple docstring"""
lowercase__ : List[str]= len(A )
for i in range(A ):
for j in range(i + 1 , A ):
if numbers[j] < numbers[i]:
lowercase__, lowercase__ : List[str]= numbers[j], numbers[i]
return numbers
if __name__ == "__main__":
a : Dict = input("""Enter numbers separated by a comma:\n""").strip()
a : List[str] = [int(item) for item in user_input.split(""",""")]
print(exchange_sort(unsorted))
| 85
| 0
|
from maths.prime_factors import prime_factors
def snake_case__ ( __SCREAMING_SNAKE_CASE ) -> List[Any]:
if not isinstance(UpperCamelCase__ , UpperCamelCase__ ):
UpperCAmelCase_ = f'''Input value of [number={number}] must be an integer'''
raise TypeError(UpperCamelCase__ )
if number < 1:
raise ValueError("Input must be a positive integer" )
return -1 if len(prime_factors(UpperCamelCase__ ) ) % 2 else 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 579
|
"""simple docstring"""
import numpy as np
import pandas as pd
from sklearn.preprocessing import Normalizer
from sklearn.svm import SVR
from statsmodels.tsa.statespace.sarimax import SARIMAX
def lowerCamelCase_ (UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list ):
_UpperCAmelCase : List[Any] = np.array([[1, item, train_mtch[i]] for i, item in enumerate(UpperCamelCase__ )] )
_UpperCAmelCase : Tuple = np.array(UpperCamelCase__ )
_UpperCAmelCase : Any = np.dot(np.dot(np.linalg.inv(np.dot(x.transpose() , UpperCamelCase__ ) ) , x.transpose() ) , UpperCamelCase__ )
return abs(beta[0] + test_dt[0] * beta[1] + test_mtch[0] + beta[2] )
def lowerCamelCase_ (UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list ):
_UpperCAmelCase : Tuple = (1, 2, 1)
_UpperCAmelCase : Tuple = (1, 1, 0, 7)
_UpperCAmelCase : Tuple = SARIMAX(
UpperCamelCase__ , exog=UpperCamelCase__ , order=UpperCamelCase__ , seasonal_order=UpperCamelCase__ )
_UpperCAmelCase : Any = model.fit(disp=UpperCamelCase__ , maxiter=600 , method='''nm''' )
_UpperCAmelCase : int = model_fit.predict(1 , len(UpperCamelCase__ ) , exog=[test_match] )
return result[0]
def lowerCamelCase_ (UpperCamelCase__ : list , UpperCamelCase__ : list , UpperCamelCase__ : list ):
_UpperCAmelCase : str = SVR(kernel='''rbf''' , C=1 , gamma=0.1 , epsilon=0.1 )
regressor.fit(UpperCamelCase__ , UpperCamelCase__ )
_UpperCAmelCase : Dict = regressor.predict(UpperCamelCase__ )
return y_pred[0]
def lowerCamelCase_ (UpperCamelCase__ : list ):
train_user.sort()
_UpperCAmelCase : Union[str, Any] = np.percentile(UpperCamelCase__ , 25 )
_UpperCAmelCase : Optional[int] = np.percentile(UpperCamelCase__ , 75 )
_UpperCAmelCase : Dict = qa - qa
_UpperCAmelCase : List[str] = qa - (iqr * 0.1)
return low_lim
def lowerCamelCase_ (UpperCamelCase__ : list , UpperCamelCase__ : float ):
_UpperCAmelCase : Dict = 0
_UpperCAmelCase : Dict = 0
for i in list_vote:
if i > actual_result:
_UpperCAmelCase : str = not_safe + 1
else:
if abs(abs(UpperCamelCase__ ) - abs(UpperCamelCase__ ) ) <= 0.1:
safe += 1
else:
not_safe += 1
return safe > not_safe
if __name__ == "__main__":
# data_input_df = pd.read_csv("ex_data.csv", header=None)
_lowerCAmelCase :Any = [[18_231, 0.0, 1], [22_621, 1.0, 2], [15_675, 0.0, 3], [23_583, 1.0, 4]]
_lowerCAmelCase :str = pd.DataFrame(
data_input, columns=['total_user', 'total_even', 'days']
)
_lowerCAmelCase :Dict = Normalizer().fit_transform(data_input_df.values)
# split data
_lowerCAmelCase :Optional[Any] = normalize_df[:, 2].tolist()
_lowerCAmelCase :Optional[Any] = normalize_df[:, 0].tolist()
_lowerCAmelCase :Tuple = normalize_df[:, 1].tolist()
# for svr (input variable = total date and total match)
_lowerCAmelCase :Union[str, Any] = normalize_df[:, [1, 2]].tolist()
_lowerCAmelCase :str = x[: len(x) - 1]
_lowerCAmelCase :Dict = x[len(x) - 1 :]
# for linear regression & sarimax
_lowerCAmelCase :Dict = total_date[: len(total_date) - 1]
_lowerCAmelCase :List[Any] = total_user[: len(total_user) - 1]
_lowerCAmelCase :Dict = total_match[: len(total_match) - 1]
_lowerCAmelCase :Optional[Any] = total_date[len(total_date) - 1 :]
_lowerCAmelCase :List[str] = total_user[len(total_user) - 1 :]
_lowerCAmelCase :str = total_match[len(total_match) - 1 :]
# voting system with forecasting
_lowerCAmelCase :Union[str, Any] = [
linear_regression_prediction(
trn_date, trn_user, trn_match, tst_date, tst_match
),
sarimax_predictor(trn_user, trn_match, tst_match),
support_vector_regressor(x_train, x_test, trn_user),
]
# check the safety of today's data
_lowerCAmelCase :Any = '' if data_safety_checker(res_vote, tst_user) else 'not '
print('Today\'s data is {not_str}safe.')
| 506
| 0
|
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 lowerCAmelCase_ ( UpperCAmelCase__ , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase: Dict = MgpstrTokenizer
_lowerCamelCase: Dict = False
_lowerCamelCase: Optional[int] = {}
_lowerCamelCase: Optional[int] = False
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Union[str, Any]:
super().setUp()
# fmt: off
A = ["""[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
A = dict(zip(A_ ,range(len(A_ ) ) ) )
A = 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(A_ ) + '\n' )
def _SCREAMING_SNAKE_CASE ( self : Any ,**A_ : int ) -> Optional[int]:
return MgpstrTokenizer.from_pretrained(self.tmpdirname ,**A_ )
def _SCREAMING_SNAKE_CASE ( self : str ,A_ : List[str] ) -> Optional[int]:
A = """tester"""
A = """tester"""
return input_text, output_text
@unittest.skip('MGP-STR always lower cases letters.' )
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> str:
pass
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> List[str]:
A = self.get_tokenizers(do_lower_case=A_ )
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
A = """[SPECIAL_TOKEN]"""
tokenizer.add_special_tokens({'cls_token': special_token} )
A = tokenizer.encode([special_token] ,add_special_tokens=A_ )
self.assertEqual(len(A_ ) ,1 )
A = tokenizer.decode(A_ ,skip_special_tokens=A_ )
self.assertTrue(special_token not in decoded )
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[Any]:
A = self.get_tokenizers()
for tokenizer in tokenizers:
with self.subTest(F'{tokenizer.__class__.__name__}' ):
A = self.get_input_output_texts(A_ )
A = tokenizer.tokenize(A_ )
A = tokenizer.convert_tokens_to_ids(A_ )
A = tokenizer.encode(A_ ,add_special_tokens=A_ )
self.assertListEqual(A_ ,A_ )
A = tokenizer.convert_ids_to_tokens(A_ )
self.assertNotEqual(len(A_ ) ,0 )
A = tokenizer.decode(A_ )
self.assertIsInstance(A_ ,A_ )
self.assertEqual(text_a.replace(' ' ,'' ) ,A_ )
@unittest.skip('MGP-STR tokenizer only handles one sequence.' )
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> Union[str, Any]:
pass
@unittest.skip('inputs cannot be pretokenized in MgpstrTokenizer' )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> List[str]:
pass
| 713
|
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel
if is_vision_available():
from transformers import MaskaFormerImageProcessor
if is_vision_available():
from PIL import Image
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self : Optional[Any] ,A_ : Optional[Any] ,A_ : Optional[int]=2 ,A_ : Any=True ,A_ : List[str]=False ,A_ : Tuple=10 ,A_ : List[Any]=3 ,A_ : Any=32 * 8 ,A_ : Dict=32 * 8 ,A_ : List[Any]=4 ,A_ : Tuple=64 ,) -> List[str]:
A = parent
A = batch_size
A = is_training
A = use_auxiliary_loss
A = num_queries
A = num_channels
A = min_size
A = max_size
A = num_labels
A = hidden_dim
A = hidden_dim
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> List[str]:
A = floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size] ).to(
A_ )
A = torch.ones([self.batch_size, self.min_size, self.max_size] ,device=A_ )
A = (
torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size] ,device=A_ ) > 0.5
).float()
A = (torch.rand((self.batch_size, self.num_labels) ,device=A_ ) > 0.5).long()
A = self.get_config()
return config, pixel_values, pixel_mask, mask_labels, class_labels
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Optional[int]:
A = MaskaFormerConfig(
hidden_size=self.hidden_dim ,)
A = self.num_queries
A = self.num_labels
A = [1, 1, 1, 1]
A = self.num_channels
A = 64
A = 128
A = self.hidden_dim
A = self.hidden_dim
A = self.hidden_dim
return config
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Any:
A , A , A , A , A = self.prepare_config_and_inputs()
A = {'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
return config, inputs_dict
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ,A_ : Union[str, Any] ,A_ : Optional[int] ) -> Union[str, Any]:
A = output.encoder_hidden_states
A = output.pixel_decoder_hidden_states
A = output.transformer_decoder_hidden_states
self.parent.assertTrue(len(A_ ) ,len(config.backbone_config.depths ) )
self.parent.assertTrue(len(A_ ) ,len(config.backbone_config.depths ) )
self.parent.assertTrue(len(A_ ) ,config.decoder_layers )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ,A_ : List[Any] ,A_ : Dict ,A_ : List[str] ,A_ : Union[str, Any]=False ) -> str:
with torch.no_grad():
A = MaskaFormerModel(config=A_ )
model.to(A_ )
model.eval()
A = model(pixel_values=A_ ,pixel_mask=A_ )
A = model(A_ ,output_hidden_states=A_ )
self.parent.assertEqual(
output.transformer_decoder_last_hidden_state.shape ,(self.batch_size, self.num_queries, self.hidden_dim) ,)
# let's ensure the other two hidden state exists
self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(output.encoder_last_hidden_state is not None )
if output_hidden_states:
self.check_output_hidden_state(A_ ,A_ )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ,A_ : List[Any] ,A_ : Any ,A_ : Dict ,A_ : Any ,A_ : Dict ) -> Optional[Any]:
A = MaskaFormerForUniversalSegmentation(config=A_ )
model.to(A_ )
model.eval()
def comm_check_on_output(A_ : str ):
# let's still check that all the required stuff is there
self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None )
self.parent.assertTrue(result.encoder_last_hidden_state is not None )
# okay, now we need to check the logits shape
# due to the encoder compression, masks have a //4 spatial size
self.parent.assertEqual(
result.masks_queries_logits.shape ,(self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4) ,)
# + 1 for null class
self.parent.assertEqual(
result.class_queries_logits.shape ,(self.batch_size, self.num_queries, self.num_labels + 1) )
with torch.no_grad():
A = model(pixel_values=A_ ,pixel_mask=A_ )
A = model(A_ )
comm_check_on_output(A_ )
A = model(
pixel_values=A_ ,pixel_mask=A_ ,mask_labels=A_ ,class_labels=A_ )
comm_check_on_output(A_ )
self.parent.assertTrue(result.loss is not None )
self.parent.assertEqual(result.loss.shape ,torch.Size([1] ) )
@require_torch
class lowerCAmelCase_ ( _lowercase , _lowercase , unittest.TestCase ):
'''simple docstring'''
_lowerCamelCase: Union[str, Any] = (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
_lowerCamelCase: Optional[Any] = {'''feature-extraction''': MaskaFormerModel} if is_torch_available() else {}
_lowerCamelCase: int = False
_lowerCamelCase: Dict = False
_lowerCamelCase: List[str] = False
_lowerCamelCase: int = False
def _SCREAMING_SNAKE_CASE ( self : int ) -> Dict:
A = MaskaFormerModelTester(self )
A = ConfigTester(self ,config_class=A_ ,has_text_modality=A_ )
def _SCREAMING_SNAKE_CASE ( self : int ) -> Optional[Any]:
self.config_tester.run_common_tests()
def _SCREAMING_SNAKE_CASE ( self : Any ) -> Dict:
A , A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(A_ ,**A_ ,output_hidden_states=A_ )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> str:
A = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*A_ )
@unittest.skip(reason='Mask2Former does not use inputs_embeds' )
def _SCREAMING_SNAKE_CASE ( self : List[Any] ) -> Tuple:
pass
@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method' )
def _SCREAMING_SNAKE_CASE ( self : Union[str, Any] ) -> Union[str, Any]:
pass
@unittest.skip(reason='Mask2Former is not a generative model' )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Dict:
pass
@unittest.skip(reason='Mask2Former does not use token embeddings' )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Union[str, Any]:
pass
@require_torch_multi_gpu
@unittest.skip(
reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`' )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> str:
pass
@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> Any:
pass
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Tuple:
A , A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A = model_class(A_ )
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] ,A_ )
@slow
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Any:
for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
A = MaskaFormerModel.from_pretrained(A_ )
self.assertIsNotNone(A_ )
def _SCREAMING_SNAKE_CASE ( self : Any ) -> List[str]:
A = (self.model_tester.min_size,) * 2
A = {
'pixel_values': torch.randn((2, 3, *size) ,device=A_ ),
'mask_labels': torch.randn((2, 10, *size) ,device=A_ ),
'class_labels': torch.zeros(2 ,10 ,device=A_ ).long(),
}
A = self.model_tester.get_config()
A = MaskaFormerForUniversalSegmentation(A_ ).to(A_ )
A = model(**A_ )
self.assertTrue(outputs.loss is not None )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> Optional[Any]:
A , A = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.create_and_check_maskaformer_model(A_ ,**A_ ,output_hidden_states=A_ )
def _SCREAMING_SNAKE_CASE ( self : Tuple ) -> List[str]:
A , A = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
A = model_class(A_ ).to(A_ )
A = model(**A_ ,output_attentions=A_ )
self.assertTrue(outputs.attentions is not None )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Tuple:
if not self.model_tester.is_training:
return
A = self.all_model_classes[1]
A , A , A , A , A = self.model_tester.prepare_config_and_inputs()
A = model_class(A_ )
model.to(A_ )
model.train()
A = model(A_ ,mask_labels=A_ ,class_labels=A_ ).loss
loss.backward()
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Tuple:
A = self.all_model_classes[1]
A , A , A , A , A = self.model_tester.prepare_config_and_inputs()
A = True
A = True
A = model_class(A_ ).to(A_ )
model.train()
A = model(A_ ,mask_labels=A_ ,class_labels=A_ )
A = outputs.encoder_hidden_states[0]
encoder_hidden_states.retain_grad()
A = outputs.pixel_decoder_hidden_states[0]
pixel_decoder_hidden_states.retain_grad()
A = outputs.transformer_decoder_hidden_states[0]
transformer_decoder_hidden_states.retain_grad()
A = outputs.attentions[0]
attentions.retain_grad()
outputs.loss.backward(retain_graph=A_ )
self.assertIsNotNone(encoder_hidden_states.grad )
self.assertIsNotNone(pixel_decoder_hidden_states.grad )
self.assertIsNotNone(transformer_decoder_hidden_states.grad )
self.assertIsNotNone(attentions.grad )
_lowercase = 1e-4
def _snake_case ( ):
A = Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png' )
return image
@require_vision
@slow
class lowerCAmelCase_ ( unittest.TestCase ):
'''simple docstring'''
@cached_property
def _SCREAMING_SNAKE_CASE ( self : Dict ) -> int:
return "facebook/mask2former-swin-small-coco-instance"
@cached_property
def _SCREAMING_SNAKE_CASE ( self : int ) -> List[str]:
return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints ) if is_vision_available() else None
def _SCREAMING_SNAKE_CASE ( self : Optional[int] ) -> Optional[int]:
A = MaskaFormerModel.from_pretrained(self.model_checkpoints ).to(A_ )
A = self.default_image_processor
A = prepare_img()
A = image_processor(A_ ,return_tensors='pt' ).to(A_ )
A = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(A_ ,(1, 3, 384, 384) )
with torch.no_grad():
A = model(**A_ )
A = torch.tensor(
[[-0.27_90, -1.07_17, -1.16_68], [-0.51_28, -0.31_28, -0.49_87], [-0.58_32, 0.19_71, -0.01_97]] ).to(A_ )
self.assertTrue(
torch.allclose(
outputs.encoder_last_hidden_state[0, 0, :3, :3] ,A_ ,atol=A_ ) )
A = torch.tensor(
[[0.89_73, 1.18_47, 1.17_76], [1.19_34, 1.50_40, 1.51_28], [1.11_53, 1.44_86, 1.49_51]] ).to(A_ )
self.assertTrue(
torch.allclose(
outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3] ,A_ ,atol=A_ ) )
A = torch.tensor(
[[2.11_52, 1.70_00, -0.86_03], [1.58_08, 1.80_04, -0.93_53], [1.60_43, 1.74_95, -0.59_99]] ).to(A_ )
self.assertTrue(
torch.allclose(
outputs.transformer_decoder_last_hidden_state[0, :3, :3] ,A_ ,atol=A_ ) )
def _SCREAMING_SNAKE_CASE ( self : Optional[Any] ) -> Union[str, Any]:
A = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(A_ ).eval()
A = self.default_image_processor
A = prepare_img()
A = image_processor(A_ ,return_tensors='pt' ).to(A_ )
A = inputs['pixel_values'].shape
# check size is divisible by 32
self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0 )
# check size
self.assertEqual(A_ ,(1, 3, 384, 384) )
with torch.no_grad():
A = model(**A_ )
# masks_queries_logits
A = outputs.masks_queries_logits
self.assertEqual(
masks_queries_logits.shape ,(1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4) )
A = [
[-8.78_39, -9.00_56, -8.81_21],
[-7.41_04, -7.03_13, -6.54_01],
[-6.61_05, -6.34_27, -6.46_75],
]
A = torch.tensor(A_ ).to(A_ )
self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3] ,A_ ,atol=A_ ) )
# class_queries_logits
A = outputs.class_queries_logits
self.assertEqual(class_queries_logits.shape ,(1, model.config.num_queries, model.config.num_labels + 1) )
A = torch.tensor(
[
[1.83_24, -8.08_35, -4.19_22],
[0.84_50, -9.00_50, -3.60_53],
[0.30_45, -7.72_93, -3.02_75],
] ).to(A_ )
self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3] ,A_ ,atol=A_ ) )
def _SCREAMING_SNAKE_CASE ( self : List[str] ) -> int:
A = MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints ).to(A_ ).eval()
A = self.default_image_processor
A = image_processor(
[np.zeros((3, 800, 1333) ), np.zeros((3, 800, 1333) )] ,segmentation_maps=[np.zeros((384, 384) ).astype(np.floataa ), np.zeros((384, 384) ).astype(np.floataa )] ,return_tensors='pt' ,)
A = inputs['pixel_values'].to(A_ )
A = [el.to(A_ ) for el in inputs['mask_labels']]
A = [el.to(A_ ) for el in inputs['class_labels']]
with torch.no_grad():
A = model(**A_ )
self.assertTrue(outputs.loss is not None )
| 22
| 0
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
# See all MEGATRON_BERT models at https://huggingface.co/models?filter=bert
}
class SCREAMING_SNAKE_CASE ( _lowerCamelCase ):
__lowerCamelCase : List[Any] ='''megatron-bert'''
def __init__( self : Optional[Any] , __lowercase : str=29056 , __lowercase : Any=1024 , __lowercase : Optional[Any]=24 , __lowercase : Optional[Any]=16 , __lowercase : Dict=4096 , __lowercase : Union[str, Any]="gelu" , __lowercase : List[str]=0.1 , __lowercase : Any=0.1 , __lowercase : str=512 , __lowercase : List[Any]=2 , __lowercase : Optional[int]=0.02 , __lowercase : Tuple=1E-12 , __lowercase : List[str]=0 , __lowercase : List[str]="absolute" , __lowercase : int=True , **__lowercase : Dict , ):
'''simple docstring'''
super().__init__(pad_token_id=_SCREAMING_SNAKE_CASE , **_SCREAMING_SNAKE_CASE )
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = type_vocab_size
__a = initializer_range
__a = layer_norm_eps
__a = position_embedding_type
__a = use_cache
| 225
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : str = {'configuration_unispeech': ['UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP', 'UniSpeechConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
A_ : Optional[int] = [
'UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST',
'UniSpeechForCTC',
'UniSpeechForPreTraining',
'UniSpeechForSequenceClassification',
'UniSpeechModel',
'UniSpeechPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_unispeech import UNISPEECH_PRETRAINED_CONFIG_ARCHIVE_MAP, UniSpeechConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_unispeech import (
UNISPEECH_PRETRAINED_MODEL_ARCHIVE_LIST,
UniSpeechForCTC,
UniSpeechForPreTraining,
UniSpeechForSequenceClassification,
UniSpeechModel,
UniSpeechPreTrainedModel,
)
else:
import sys
A_ : int = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
| 265
| 0
|
from random import shuffle
import tensorflow as tf
from numpy import array
def UpperCamelCase__( UpperCamelCase__ : int , UpperCamelCase__ : str )->Optional[Any]:
A__ = int(UpperCamelCase__ )
assert noofclusters < len(UpperCamelCase__ )
# Find out the dimensionality
A__ = len(vectors[0] )
# Will help select random centroids from among the available vectors
A__ = list(range(len(UpperCamelCase__ ) ) )
shuffle(UpperCamelCase__ )
# GRAPH OF COMPUTATION
# We initialize a new graph and set it as the default during each run
# of this algorithm. This ensures that as this function is called
# multiple times, the default graph doesn't keep getting crowded with
# unused ops and Variables from previous function calls.
A__ = tf.Graph()
with graph.as_default():
# SESSION OF COMPUTATION
A__ = tf.Session()
##CONSTRUCTING THE ELEMENTS OF COMPUTATION
##First lets ensure we have a Variable vector for each centroid,
##initialized to one of the vectors from the available data points
A__ = [
tf.Variable(vectors[vector_indices[i]] ) for i in range(UpperCamelCase__ )
]
##These nodes will assign the centroid Variables the appropriate
##values
A__ = tf.placeholder('''float64''' , [dim] )
A__ = []
for centroid in centroids:
cent_assigns.append(tf.assign(UpperCamelCase__ , UpperCamelCase__ ) )
##Variables for cluster assignments of individual vectors(initialized
##to 0 at first)
A__ = [tf.Variable(0 ) for i in range(len(UpperCamelCase__ ) )]
##These nodes will assign an assignment Variable the appropriate
##value
A__ = tf.placeholder('''int32''' )
A__ = []
for assignment in assignments:
cluster_assigns.append(tf.assign(UpperCamelCase__ , UpperCamelCase__ ) )
##Now lets construct the node that will compute the mean
# The placeholder for the input
A__ = tf.placeholder('''float''' , [None, dim] )
# The Node/op takes the input and computes a mean along the 0th
# dimension, i.e. the list of input vectors
A__ = tf.reduce_mean(UpperCamelCase__ , 0 )
##Node for computing Euclidean distances
# Placeholders for input
A__ = tf.placeholder('''float''' , [dim] )
A__ = tf.placeholder('''float''' , [dim] )
A__ = tf.sqrt(tf.reduce_sum(tf.pow(tf.sub(UpperCamelCase__ , UpperCamelCase__ ) , 2 ) ) )
##This node will figure out which cluster to assign a vector to,
##based on Euclidean distances of the vector from the centroids.
# Placeholder for input
A__ = tf.placeholder('''float''' , [noofclusters] )
A__ = tf.argmin(UpperCamelCase__ , 0 )
##INITIALIZING STATE VARIABLES
##This will help initialization of all Variables defined with respect
##to the graph. The Variable-initializer should be defined after
##all the Variables have been constructed, so that each of them
##will be included in the initialization.
A__ = tf.initialize_all_variables()
# Initialize all variables
sess.run(UpperCamelCase__ )
##CLUSTERING ITERATIONS
# Now perform the Expectation-Maximization steps of K-Means clustering
# iterations. To keep things simple, we will only do a set number of
# iterations, instead of using a Stopping Criterion.
A__ = 1_00
for _ in range(UpperCamelCase__ ):
##EXPECTATION STEP
##Based on the centroid locations till last iteration, compute
##the _expected_ centroid assignments.
# Iterate over each vector
for vector_n in range(len(UpperCamelCase__ ) ):
A__ = vectors[vector_n]
# Compute Euclidean distance between this vector and each
# centroid. Remember that this list cannot be named
#'centroid_distances', since that is the input to the
# cluster assignment node.
A__ = [
sess.run(UpperCamelCase__ , feed_dict={va: vect, va: sess.run(UpperCamelCase__ )} )
for centroid in centroids
]
# Now use the cluster assignment node, with the distances
# as the input
A__ = sess.run(
UpperCamelCase__ , feed_dict={centroid_distances: distances} )
# Now assign the value to the appropriate state variable
sess.run(
cluster_assigns[vector_n] , feed_dict={assignment_value: assignment} )
##MAXIMIZATION STEP
# Based on the expected state computed from the Expectation Step,
# compute the locations of the centroids so as to maximize the
# overall objective of minimizing within-cluster Sum-of-Squares
for cluster_n in range(UpperCamelCase__ ):
# Collect all the vectors assigned to this cluster
A__ = [
vectors[i]
for i in range(len(UpperCamelCase__ ) )
if sess.run(assignments[i] ) == cluster_n
]
# Compute new centroid location
A__ = sess.run(
UpperCamelCase__ , feed_dict={mean_input: array(UpperCamelCase__ )} )
# Assign value to appropriate variable
sess.run(
cent_assigns[cluster_n] , feed_dict={centroid_value: new_location} )
# Return centroids and assignments
A__ = sess.run(UpperCamelCase__ )
A__ = sess.run(UpperCamelCase__ )
return centroids, assignments
| 714
|
def UpperCamelCase__( UpperCamelCase__ : Optional[int] , UpperCamelCase__ : List[Any] )->List[str]:
A__ = [1]
for i in range(2 , UpperCamelCase__ ):
factorials.append(factorials[-1] * i )
assert 0 <= k < factorials[-1] * n, "k out of bounds"
A__ = []
A__ = list(range(UpperCamelCase__ ) )
# Find permutation
while factorials:
A__ = factorials.pop()
A__ , A__ = divmod(UpperCamelCase__ , UpperCamelCase__ )
permutation.append(elements[number] )
elements.remove(elements[number] )
permutation.append(elements[0] )
return permutation
if __name__ == "__main__":
import doctest
doctest.testmod()
| 212
| 0
|
from __future__ import annotations
def __UpperCamelCase ( A ): # This function is recursive
UpperCamelCase__ = len(A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
return array
# Else
UpperCamelCase__ = array[0]
UpperCamelCase__ = False
UpperCamelCase__ = 1
UpperCamelCase__ = []
while not is_found and i < array_length:
if array[i] < pivot:
UpperCamelCase__ = True
UpperCamelCase__ = [element for element in array[i:] if element >= array[i]]
UpperCamelCase__ = longest_subsequence(A )
if len(A ) > len(A ):
UpperCamelCase__ = temp_array
else:
i += 1
UpperCamelCase__ = [element for element in array[1:] if element >= pivot]
UpperCamelCase__ = [pivot, *longest_subsequence(A )]
if len(A ) > len(A ):
return temp_array
else:
return longest_subseq
if __name__ == "__main__":
import doctest
doctest.testmod()
| 415
|
import time
import warnings
from abc import ABC
from copy import deepcopy
from typing import Optional
import torch
from ..utils import add_start_docstrings, logging
__magic_name__ =logging.get_logger(__name__)
__magic_name__ =r'''
Args:
input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
Indices of input sequence tokens in the vocabulary.
Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
[`PreTrainedTokenizer.__call__`] for details.
[What are input IDs?](../glossary#input-ids)
scores (`torch.FloatTensor` of shape `(batch_size, config.vocab_size)`):
Prediction scores of a language modeling head. These can be scores for each vocabulary token before SoftMax
or scores for each vocabulary token after SoftMax.
kwargs (`Dict[str, Any]`, *optional*):
Additional stopping criteria specific kwargs.
Return:
`bool`. `False` indicates we should continue, `True` indicates we should stop.
'''
class _A ( __UpperCamelCase ):
@add_start_docstrings(SCREAMING_SNAKE_CASE_ )
def __call__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> bool:
'''simple docstring'''
raise NotImplementedError('''StoppingCriteria needs to be subclassed''' )
class _A ( __UpperCamelCase ):
def __init__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ = max_length
UpperCamelCase__ = max_position_embeddings
@add_start_docstrings(SCREAMING_SNAKE_CASE_ )
def __call__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> bool:
'''simple docstring'''
UpperCamelCase__ = input_ids.shape[-1]
UpperCamelCase__ = cur_len >= self.max_length
if self.max_position_embeddings is not None and not is_done and cur_len >= self.max_position_embeddings:
logger.warning_once(
'''This is a friendly reminder - the current text generation call will exceed the model\'s predefined '''
F"maximum length ({self.max_position_embeddings}). Depending on the model, you may observe "
'''exceptions, performance degradation, or nothing at all.''' )
return is_done
class _A ( __UpperCamelCase ):
def __init__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> str:
'''simple docstring'''
warnings.warn(
'''The class `MaxNewTokensCriteria` is deprecated. '''
F"Please use `MaxLengthCriteria(max_length={start_length + max_new_tokens})` "
'''with `max_length = start_length + max_new_tokens` instead.''' , SCREAMING_SNAKE_CASE_ , )
UpperCamelCase__ = start_length
UpperCamelCase__ = max_new_tokens
UpperCamelCase__ = start_length + max_new_tokens
@add_start_docstrings(SCREAMING_SNAKE_CASE_ )
def __call__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> bool:
'''simple docstring'''
return input_ids.shape[-1] >= self.max_length
class _A ( __UpperCamelCase ):
def __init__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = None ) -> Optional[int]:
'''simple docstring'''
UpperCamelCase__ = max_time
UpperCamelCase__ = time.time() if initial_timestamp is None else initial_timestamp
@add_start_docstrings(SCREAMING_SNAKE_CASE_ )
def __call__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> bool:
'''simple docstring'''
return time.time() - self.initial_timestamp > self.max_time
class _A ( __UpperCamelCase ):
@add_start_docstrings(SCREAMING_SNAKE_CASE_ )
def __call__(self , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_CASE_ ) -> bool:
'''simple docstring'''
return any(criteria(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) for criteria in self )
@property
def _a (self ) -> Optional[int]:
'''simple docstring'''
for stopping_criterium in self:
if isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return stopping_criterium.max_length
elif isinstance(SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return stopping_criterium.max_length
return None
def __UpperCamelCase ( A , A ):
UpperCamelCase__ = stopping_criteria.max_length
UpperCamelCase__ = deepcopy(A )
if stopping_max_length is not None and stopping_max_length != max_length:
warnings.warn('''You set different `max_length` for stopping criteria and `max_length` parameter''' , A )
elif stopping_max_length is None:
new_stopping_criteria.append(MaxLengthCriteria(max_length=A ) )
return new_stopping_criteria
| 415
| 1
|
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import AutoTokenizer, BarkProcessor
from transformers.testing_utils import require_torch, slow
@require_torch
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : Optional[int] ) -> Tuple:
SCREAMING_SNAKE_CASE_ = "ylacombe/bark-small"
SCREAMING_SNAKE_CASE_ = tempfile.mkdtemp()
SCREAMING_SNAKE_CASE_ = "en_speaker_1"
SCREAMING_SNAKE_CASE_ = "This is a test string"
SCREAMING_SNAKE_CASE_ = "speaker_embeddings_path.json"
SCREAMING_SNAKE_CASE_ = "speaker_embeddings"
def __A ( self : List[str] , **__magic_name__ : Dict ) -> Tuple:
return AutoTokenizer.from_pretrained(self.checkpoint , **__magic_name__ )
def __A ( self : Tuple ) -> Optional[int]:
shutil.rmtree(self.tmpdirname )
def __A ( self : Optional[int] ) -> str:
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BarkProcessor(tokenizer=__magic_name__ )
processor.save_pretrained(self.tmpdirname )
SCREAMING_SNAKE_CASE_ = BarkProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer.get_vocab() )
@slow
def __A ( self : int ) -> Union[str, Any]:
SCREAMING_SNAKE_CASE_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
processor.save_pretrained(
self.tmpdirname , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , speaker_embeddings_directory=self.speaker_embeddings_directory , )
SCREAMING_SNAKE_CASE_ = self.get_tokenizer(bos_token="(BOS)" , eos_token="(EOS)" )
SCREAMING_SNAKE_CASE_ = BarkProcessor.from_pretrained(
self.tmpdirname , self.speaker_embeddings_dict_path , bos_token="(BOS)" , eos_token="(EOS)" , )
self.assertEqual(processor.tokenizer.get_vocab() , tokenizer_add_kwargs.get_vocab() )
def __A ( self : Optional[Any] ) -> Any:
SCREAMING_SNAKE_CASE_ = BarkProcessor.from_pretrained(
pretrained_processor_name_or_path=self.checkpoint , speaker_embeddings_dict_path=self.speaker_embeddings_dict_path , )
SCREAMING_SNAKE_CASE_ = 35
SCREAMING_SNAKE_CASE_ = 2
SCREAMING_SNAKE_CASE_ = 8
SCREAMING_SNAKE_CASE_ = {
"semantic_prompt": np.ones(__magic_name__ ),
"coarse_prompt": np.ones((nb_codebooks_coarse, seq_len) ),
"fine_prompt": np.ones((nb_codebooks_total, seq_len) ),
}
# test providing already loaded voice_preset
SCREAMING_SNAKE_CASE_ = processor(text=self.input_string , voice_preset=__magic_name__ )
SCREAMING_SNAKE_CASE_ = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__magic_name__ , np.array([] ) ).tolist() )
# test loading voice preset from npz file
SCREAMING_SNAKE_CASE_ = os.path.join(self.tmpdirname , "file.npz" )
np.savez(__magic_name__ , **__magic_name__ )
SCREAMING_SNAKE_CASE_ = processor(text=self.input_string , voice_preset=__magic_name__ )
SCREAMING_SNAKE_CASE_ = inputs["history_prompt"]
for key in voice_preset:
self.assertListEqual(voice_preset[key].tolist() , processed_voice_preset.get(__magic_name__ , np.array([] ) ).tolist() )
# test loading voice preset from the hub
SCREAMING_SNAKE_CASE_ = processor(text=self.input_string , voice_preset=self.voice_preset )
def __A ( self : Tuple ) -> str:
SCREAMING_SNAKE_CASE_ = self.get_tokenizer()
SCREAMING_SNAKE_CASE_ = BarkProcessor(tokenizer=__magic_name__ )
SCREAMING_SNAKE_CASE_ = processor(text=self.input_string )
SCREAMING_SNAKE_CASE_ = tokenizer(
self.input_string , padding="max_length" , max_length=256 , add_special_tokens=__magic_name__ , return_attention_mask=__magic_name__ , return_token_type_ids=__magic_name__ , )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] , encoded_processor[key].squeeze().tolist() )
| 707
|
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device
A : List[str] = False
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
pass
@nightly
@require_torch_gpu
class lowerCamelCase (unittest.TestCase ):
"""simple docstring"""
def __A ( self : Union[str, Any] ) -> List[Any]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def __A ( self : str ) -> Any:
SCREAMING_SNAKE_CASE_ = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "A painting of a squirrel eating a burger "
SCREAMING_SNAKE_CASE_ = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE_ = pipe(
prompt=__magic_name__ , generator=__magic_name__ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(__magic_name__ )
SCREAMING_SNAKE_CASE_ = VersatileDiffusionTextToImagePipeline.from_pretrained(__magic_name__ )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
SCREAMING_SNAKE_CASE_ = generator.manual_seed(0 )
SCREAMING_SNAKE_CASE_ = pipe(
prompt=__magic_name__ , generator=__magic_name__ , guidance_scale=7.5 , num_inference_steps=2 , output_type="numpy" ).images
assert np.abs(image - new_image ).sum() < 1e-5, "Models don't have the same forward pass"
def __A ( self : int ) -> Optional[int]:
SCREAMING_SNAKE_CASE_ = VersatileDiffusionTextToImagePipeline.from_pretrained(
"shi-labs/versatile-diffusion" , torch_dtype=torch.floataa )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
SCREAMING_SNAKE_CASE_ = "A painting of a squirrel eating a burger "
SCREAMING_SNAKE_CASE_ = torch.manual_seed(0 )
SCREAMING_SNAKE_CASE_ = pipe(
prompt=__magic_name__ , generator=__magic_name__ , guidance_scale=7.5 , num_inference_steps=50 , output_type="numpy" ).images
SCREAMING_SNAKE_CASE_ = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
SCREAMING_SNAKE_CASE_ = np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-2
| 356
| 0
|
"""simple docstring"""
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification
logging.set_verbosity_info()
__snake_case : int = logging.get_logger(__name__)
__snake_case : Optional[int] = {
'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',
'adapter_layer': 'encoder.layers.*.adapter_layer',
'w2v_model.layer_norm': 'feature_projection.layer_norm',
'quantizer.weight_proj': 'quantizer.weight_proj',
'quantizer.vars': 'quantizer.codevectors',
'project_q': 'project_q',
'final_proj': 'project_hid',
'w2v_encoder.proj': 'lm_head',
'mask_emb': 'masked_spec_embed',
'pooling_layer.linear': 'projector',
'pooling_layer.projection': 'classifier',
}
__snake_case : Optional[int] = [
'lm_head',
'quantizer.weight_proj',
'quantizer.codevectors',
'project_q',
'project_hid',
'projector',
'classifier',
]
def _lowercase ( __snake_case ) -> Optional[Any]:
__lowerCAmelCase : List[str] = {}
with open(_lowercase ,"r" ) as file:
for line_number, line in enumerate(_lowercase ):
__lowerCAmelCase : Tuple = line.strip()
if line:
__lowerCAmelCase : List[str] = line.split()
__lowerCAmelCase : Tuple = line_number
__lowerCAmelCase : List[Any] = words[0]
__lowerCAmelCase : str = value
return result
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> List[str]:
for attribute in key.split("." ):
__lowerCAmelCase : Dict = getattr(_lowercase ,_lowercase )
__lowerCAmelCase : Tuple = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_lowercase ):
__lowerCAmelCase : Dict = PARAM_MAPPING[full_name.split("." )[-1]]
__lowerCAmelCase : Union[str, Any] = "param"
if weight_type is not None and weight_type != "param":
__lowerCAmelCase : Tuple = getattr(_lowercase ,_lowercase ).shape
elif weight_type is not None and weight_type == "param":
__lowerCAmelCase : Dict = hf_pointer
for attribute in hf_param_name.split("." ):
__lowerCAmelCase : str = getattr(_lowercase ,_lowercase )
__lowerCAmelCase : int = shape_pointer.shape
# let's reduce dimension
__lowerCAmelCase : List[str] = value[0]
else:
__lowerCAmelCase : Optional[Any] = hf_pointer.shape
if hf_shape != value.shape:
raise ValueError(
F"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
F""" {value.shape} for {full_name}""" )
if weight_type == "weight":
__lowerCAmelCase : List[Any] = value
elif weight_type == "weight_g":
__lowerCAmelCase : int = value
elif weight_type == "weight_v":
__lowerCAmelCase : List[str] = value
elif weight_type == "bias":
__lowerCAmelCase : Optional[int] = value
elif weight_type == "param":
for attribute in hf_param_name.split("." ):
__lowerCAmelCase : Dict = getattr(_lowercase ,_lowercase )
__lowerCAmelCase : Union[str, Any] = value
else:
__lowerCAmelCase : int = value
logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}.""" )
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> List[Any]:
__lowerCAmelCase : Optional[Any] = None
for param_key in PARAM_MAPPING.keys():
if full_name.endswith(_lowercase ):
__lowerCAmelCase : Optional[Any] = PARAM_MAPPING[full_name.split("." )[-1]]
__lowerCAmelCase : Union[str, Any] = "param"
if weight_type is not None and weight_type != "param":
__lowerCAmelCase : str = ".".join([key, weight_type] )
elif weight_type is not None and weight_type == "param":
__lowerCAmelCase : Any = ".".join([key, hf_param_name] )
else:
__lowerCAmelCase : Optional[int] = key
__lowerCAmelCase : List[str] = value if "lm_head" in full_key else value[0]
__snake_case : Optional[int] = {
'W_a': 'linear_1.weight',
'W_b': 'linear_2.weight',
'b_a': 'linear_1.bias',
'b_b': 'linear_2.bias',
'ln_W': 'norm.weight',
'ln_b': 'norm.bias',
}
def _lowercase ( __snake_case ,__snake_case ,__snake_case=None ,__snake_case=None ) -> List[Any]:
__lowerCAmelCase : int = False
for key, mapped_key in MAPPING.items():
__lowerCAmelCase : Tuple = "wav2vec2." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
__lowerCAmelCase : Optional[Any] = True
if "*" in mapped_key:
__lowerCAmelCase : List[str] = name.split(_lowercase )[0].split("." )[-2]
__lowerCAmelCase : str = mapped_key.replace("*" ,_lowercase )
if "weight_g" in name:
__lowerCAmelCase : Union[str, Any] = "weight_g"
elif "weight_v" in name:
__lowerCAmelCase : List[str] = "weight_v"
elif "bias" in name:
__lowerCAmelCase : Optional[Any] = "bias"
elif "weight" in name:
# TODO: don't match quantizer.weight_proj
__lowerCAmelCase : Tuple = "weight"
else:
__lowerCAmelCase : Union[str, Any] = None
if hf_dict is not None:
rename_dict(_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase )
else:
set_recursively(_lowercase ,_lowercase ,_lowercase ,_lowercase ,_lowercase )
return is_used
return is_used
def _lowercase ( __snake_case ,__snake_case ,__snake_case ) -> Dict:
__lowerCAmelCase : List[Any] = []
__lowerCAmelCase : Any = fairseq_model.state_dict()
__lowerCAmelCase : List[Any] = hf_model.wavaveca.feature_extractor
for name, value in fairseq_dict.items():
__lowerCAmelCase : int = False
if "conv_layers" in name:
load_conv_layer(
_lowercase ,_lowercase ,_lowercase ,_lowercase ,hf_model.config.feat_extract_norm == "group" ,)
__lowerCAmelCase : List[str] = True
else:
__lowerCAmelCase : Optional[Any] = load_wavaveca_layer(_lowercase ,_lowercase ,_lowercase )
if not is_used:
unused_weights.append(_lowercase )
logger.warning(F"""Unused weights: {unused_weights}""" )
def _lowercase ( __snake_case ,__snake_case ,__snake_case ,__snake_case ,__snake_case ) -> Any:
__lowerCAmelCase : Any = full_name.split("conv_layers." )[-1]
__lowerCAmelCase : Dict = name.split("." )
__lowerCAmelCase : Any = int(items[0] )
__lowerCAmelCase : Dict = int(items[1] )
if type_id == 0:
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.""" )
__lowerCAmelCase : int = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.""" )
__lowerCAmelCase : Optional[Any] = value
logger.info(F"""Feat extract conv layer {layer_id} was initialized from {full_name}.""" )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.""" )
__lowerCAmelCase : List[str] = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
elif "weight" in name:
if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
raise ValueError(
F"""{full_name} has size {value.shape}, but"""
F""" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.""" )
__lowerCAmelCase : Dict = value
logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
else:
unused_weights.append(_lowercase )
@torch.no_grad()
def _lowercase ( __snake_case ,__snake_case ,__snake_case=None ,__snake_case=None ,__snake_case=True ,__snake_case=False ) -> Dict:
if config_path is not None:
__lowerCAmelCase : Union[str, Any] = WavaVecaConfig.from_pretrained(_lowercase )
else:
__lowerCAmelCase : str = WavaVecaConfig()
if is_seq_class:
__lowerCAmelCase : Union[str, Any] = read_txt_into_dict(_lowercase )
__lowerCAmelCase : Optional[Any] = idalabel
__lowerCAmelCase : str = WavaVecaForSequenceClassification(_lowercase )
__lowerCAmelCase : Optional[Any] = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=16_000 ,padding_value=0 ,do_normalize=_lowercase ,return_attention_mask=_lowercase ,)
feature_extractor.save_pretrained(_lowercase )
elif is_finetuned:
if dict_path:
__lowerCAmelCase : Union[str, Any] = Dictionary.load(_lowercase )
# important change bos & pad token id since CTC symbol is <pad> and
# not <s> as in fairseq
__lowerCAmelCase : Tuple = target_dict.pad_index
__lowerCAmelCase : Dict = target_dict.bos_index
__lowerCAmelCase : int = target_dict.eos_index
__lowerCAmelCase : str = len(target_dict.symbols )
__lowerCAmelCase : Any = os.path.join(_lowercase ,"vocab.json" )
if not os.path.isdir(_lowercase ):
logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_lowercase ) )
return
os.makedirs(_lowercase ,exist_ok=_lowercase )
__lowerCAmelCase : Dict = target_dict.indices
# fairseq has the <pad> and <s> switched
__lowerCAmelCase : Union[str, Any] = 0
__lowerCAmelCase : Any = 1
with open(_lowercase ,"w" ,encoding="utf-8" ) as vocab_handle:
json.dump(_lowercase ,_lowercase )
__lowerCAmelCase : List[Any] = WavaVecaCTCTokenizer(
_lowercase ,unk_token=target_dict.unk_word ,pad_token=target_dict.pad_word ,bos_token=target_dict.bos_word ,eos_token=target_dict.eos_word ,word_delimiter_token="|" ,do_lower_case=_lowercase ,)
__lowerCAmelCase : Optional[Any] = True if config.feat_extract_norm == "layer" else False
__lowerCAmelCase : Union[str, Any] = WavaVecaFeatureExtractor(
feature_size=1 ,sampling_rate=16_000 ,padding_value=0 ,do_normalize=_lowercase ,return_attention_mask=_lowercase ,)
__lowerCAmelCase : Optional[int] = WavaVecaProcessor(feature_extractor=_lowercase ,tokenizer=_lowercase )
processor.save_pretrained(_lowercase )
__lowerCAmelCase : List[Any] = WavaVecaForCTC(_lowercase )
else:
__lowerCAmelCase : str = WavaVecaForPreTraining(_lowercase )
if is_finetuned or is_seq_class:
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : List[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] ,arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
else:
__lowerCAmelCase : str = argparse.Namespace(task="audio_pretraining" )
__lowerCAmelCase : Dict = fairseq.tasks.setup_task(_lowercase )
__lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase : List[Any] = fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path] ,task=_lowercase )
__lowerCAmelCase : Any = model[0].eval()
recursively_load_weights(_lowercase ,_lowercase ,not is_finetuned )
hf_wavavec.save_pretrained(_lowercase )
if __name__ == "__main__":
__snake_case : int = 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'
)
parser.add_argument(
'--is_seq_class',
action='store_true',
help='Whether the model to convert is a fine-tuned sequence classification model or not',
)
__snake_case : int = parser.parse_args()
__snake_case : str = not args.not_finetuned and not args.is_seq_class
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.config_path,
args.dict_path,
is_finetuned,
args.is_seq_class,
)
| 293
|
import pytest
import datasets
# Import fixture modules as plugins
__snake_case = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def _A ( _lowercase , _lowercase ) -> Tuple:
"""simple docstring"""
for item in items:
if any(marker in item.keywords for marker in ['integration', 'unit'] ):
continue
item.add_marker(pytest.mark.unit )
def _A ( _lowercase ) -> str:
"""simple docstring"""
config.addinivalue_line('markers' , 'torchaudio_latest: mark test to run with torchaudio>=0.12' )
@pytest.fixture(autouse=_lowercase )
def _A ( _lowercase , _lowercase ) -> Any:
"""simple docstring"""
__UpperCamelCase = tmp_path_factory.getbasetemp() / 'cache'
__UpperCamelCase = test_hf_cache_home / 'datasets'
__UpperCamelCase = test_hf_cache_home / 'metrics'
__UpperCamelCase = test_hf_cache_home / 'modules'
monkeypatch.setattr('datasets.config.HF_DATASETS_CACHE' , str(_lowercase ) )
monkeypatch.setattr('datasets.config.HF_METRICS_CACHE' , str(_lowercase ) )
monkeypatch.setattr('datasets.config.HF_MODULES_CACHE' , str(_lowercase ) )
__UpperCamelCase = test_hf_datasets_cache / 'downloads'
monkeypatch.setattr('datasets.config.DOWNLOADED_DATASETS_PATH' , str(_lowercase ) )
__UpperCamelCase = test_hf_datasets_cache / 'downloads' / 'extracted'
monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH' , str(_lowercase ) )
@pytest.fixture(autouse=_lowercase , scope='session' )
def _A ( ) -> Dict:
"""simple docstring"""
datasets.disable_progress_bar()
@pytest.fixture(autouse=_lowercase )
def _A ( _lowercase ) -> Tuple:
"""simple docstring"""
monkeypatch.setattr('datasets.config.HF_UPDATE_DOWNLOAD_COUNTS' , _lowercase )
@pytest.fixture
def _A ( _lowercase ) -> Any:
"""simple docstring"""
monkeypatch.setattr('sqlalchemy.util.deprecations.SILENCE_UBER_WARNING' , _lowercase )
| 1
| 0
|
"""simple docstring"""
import math
import numpy as np
import qiskit
from qiskit import Aer, ClassicalRegister, QuantumCircuit, QuantumRegister, execute
def snake_case__ ( _SCREAMING_SNAKE_CASE = 3 ) ->qiskit.result.counts.Counts:
if isinstance(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
raise TypeError("""number of qubits must be a integer.""" )
if number_of_qubits <= 0:
raise ValueError("""number of qubits must be > 0.""" )
if math.floor(_SCREAMING_SNAKE_CASE ) != number_of_qubits:
raise ValueError("""number of qubits must be exact integer.""" )
if number_of_qubits > 1_0:
raise ValueError("""number of qubits too large to simulate(>10).""" )
UpperCAmelCase__ = QuantumRegister(_SCREAMING_SNAKE_CASE , """qr""" )
UpperCAmelCase__ = ClassicalRegister(_SCREAMING_SNAKE_CASE , """cr""" )
UpperCAmelCase__ = QuantumCircuit(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
UpperCAmelCase__ = number_of_qubits
for i in range(_SCREAMING_SNAKE_CASE ):
quantum_circuit.h(number_of_qubits - i - 1 )
counter -= 1
for j in range(_SCREAMING_SNAKE_CASE ):
quantum_circuit.cp(np.pi / 2 ** (counter - j) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
for k in range(number_of_qubits // 2 ):
quantum_circuit.swap(_SCREAMING_SNAKE_CASE , number_of_qubits - k - 1 )
# measure all the qubits
quantum_circuit.measure(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# simulate with 10000 shots
UpperCAmelCase__ = Aer.get_backend("""qasm_simulator""" )
UpperCAmelCase__ = execute(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , shots=1_0_0_0_0 )
return job.result().get_counts(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
print(
F'''Total count for quantum fourier transform state is: \
{quantum_fourier_transform(3)}'''
)
| 713
|
"""simple docstring"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
is_accelerate_available,
is_accelerate_version,
logging,
randn_tensor,
replace_example_docstring,
)
a : str = logging.get_logger(__name__) # pylint: disable=invalid-name
a : Any = '''
Examples:
```py
>>> import torch
>>> import numpy as np
>>> from diffusers import KandinskyV22PriorPipeline, KandinskyV22ControlnetPipeline
>>> from transformers import pipeline
>>> from diffusers.utils import load_image
>>> def make_hint(image, depth_estimator):
... image = depth_estimator(image)["depth"]
... image = np.array(image)
... image = image[:, :, None]
... image = np.concatenate([image, image, image], axis=2)
... detected_map = torch.from_numpy(image).float() / 255.0
... hint = detected_map.permute(2, 0, 1)
... return hint
>>> depth_estimator = pipeline("depth-estimation")
>>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16
... )
>>> pipe_prior = pipe_prior.to("cuda")
>>> pipe = KandinskyV22ControlnetPipeline.from_pretrained(
... "kandinsky-community/kandinsky-2-2-controlnet-depth", torch_dtype=torch.float16
... )
>>> pipe = pipe.to("cuda")
>>> img = load_image(
... "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
... "/kandinsky/cat.png"
... ).resize((768, 768))
>>> hint = make_hint(img, depth_estimator).unsqueeze(0).half().to("cuda")
>>> prompt = "A robot, 4k photo"
>>> negative_prior_prompt = "lowres, text, error, cropped, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, out of frame, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck, username, watermark, signature"
>>> generator = torch.Generator(device="cuda").manual_seed(43)
>>> image_emb, zero_image_emb = pipe_prior(
... prompt=prompt, negative_prompt=negative_prior_prompt, generator=generator
... ).to_tuple()
>>> images = pipe(
... image_embeds=image_emb,
... negative_image_embeds=zero_image_emb,
... hint=hint,
... num_inference_steps=50,
... generator=generator,
... height=768,
... width=768,
... ).images
>>> images[0].save("robot_cat.png")
```
'''
def snake_case__ ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=8 ) ->str:
UpperCAmelCase__ = height // scale_factor**2
if height % scale_factor**2 != 0:
new_height += 1
UpperCAmelCase__ = width // scale_factor**2
if width % scale_factor**2 != 0:
new_width += 1
return new_height * scale_factor, new_width * scale_factor
class _UpperCamelCase ( __UpperCamelCase ):
'''simple docstring'''
def __init__( self , __lowercase , __lowercase , __lowercase , ):
super().__init__()
self.register_modules(
unet=__lowercase , scheduler=__lowercase , movq=__lowercase , )
UpperCAmelCase__ = 2 ** (len(self.movq.config.block_out_channels ) - 1)
def A__ ( self , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ):
if latents is None:
UpperCAmelCase__ = randn_tensor(__lowercase , generator=__lowercase , device=__lowercase , dtype=__lowercase )
else:
if latents.shape != shape:
raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {shape}''' )
UpperCAmelCase__ = latents.to(__lowercase )
UpperCAmelCase__ = latents * scheduler.init_noise_sigma
return latents
def A__ ( self , __lowercase=0 ):
if is_accelerate_available():
from accelerate import cpu_offload
else:
raise ImportError("""Please install accelerate via `pip install accelerate`""" )
UpperCAmelCase__ = torch.device(F'''cuda:{gpu_id}''' )
UpperCAmelCase__ = [
self.unet,
self.movq,
]
for cpu_offloaded_model in models:
if cpu_offloaded_model is not None:
cpu_offload(__lowercase , __lowercase )
def A__ ( self , __lowercase=0 ):
if is_accelerate_available() and is_accelerate_version(""">=""" , """0.17.0.dev0""" ):
from accelerate import cpu_offload_with_hook
else:
raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.""" )
UpperCAmelCase__ = torch.device(F'''cuda:{gpu_id}''' )
if self.device.type != "cpu":
self.to("""cpu""" , silence_dtype_warnings=__lowercase )
torch.cuda.empty_cache() # otherwise we don't see the memory savings (but they probably exist)
UpperCAmelCase__ = None
for cpu_offloaded_model in [self.unet, self.movq]:
UpperCAmelCase__ , UpperCAmelCase__ = cpu_offload_with_hook(__lowercase , __lowercase , prev_module_hook=__lowercase )
# We'll offload the last model manually.
UpperCAmelCase__ = hook
@property
# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
def A__ ( self ):
if not hasattr(self.unet , """_hf_hook""" ):
return self.device
for module in self.unet.modules():
if (
hasattr(__lowercase , """_hf_hook""" )
and hasattr(module._hf_hook , """execution_device""" )
and module._hf_hook.execution_device is not None
):
return torch.device(module._hf_hook.execution_device )
return self.device
@torch.no_grad()
@replace_example_docstring(__lowercase )
def __call__( self , __lowercase , __lowercase , __lowercase , __lowercase = 512 , __lowercase = 512 , __lowercase = 100 , __lowercase = 4.0 , __lowercase = 1 , __lowercase = None , __lowercase = None , __lowercase = "pil" , __lowercase = True , ):
UpperCAmelCase__ = self._execution_device
UpperCAmelCase__ = guidance_scale > 1.0
if isinstance(__lowercase , __lowercase ):
UpperCAmelCase__ = torch.cat(__lowercase , dim=0 )
if isinstance(__lowercase , __lowercase ):
UpperCAmelCase__ = torch.cat(__lowercase , dim=0 )
if isinstance(__lowercase , __lowercase ):
UpperCAmelCase__ = torch.cat(__lowercase , dim=0 )
UpperCAmelCase__ = image_embeds.shape[0] * num_images_per_prompt
if do_classifier_free_guidance:
UpperCAmelCase__ = image_embeds.repeat_interleave(__lowercase , dim=0 )
UpperCAmelCase__ = negative_image_embeds.repeat_interleave(__lowercase , dim=0 )
UpperCAmelCase__ = hint.repeat_interleave(__lowercase , dim=0 )
UpperCAmelCase__ = torch.cat([negative_image_embeds, image_embeds] , dim=0 ).to(dtype=self.unet.dtype , device=__lowercase )
UpperCAmelCase__ = torch.cat([hint, hint] , dim=0 ).to(dtype=self.unet.dtype , device=__lowercase )
self.scheduler.set_timesteps(__lowercase , device=__lowercase )
UpperCAmelCase__ = self.scheduler.timesteps
UpperCAmelCase__ = self.movq.config.latent_channels
UpperCAmelCase__ , UpperCAmelCase__ = downscale_height_and_width(__lowercase , __lowercase , self.movq_scale_factor )
# create initial latent
UpperCAmelCase__ = self.prepare_latents(
(batch_size, num_channels_latents, height, width) , image_embeds.dtype , __lowercase , __lowercase , __lowercase , self.scheduler , )
for i, t in enumerate(self.progress_bar(__lowercase ) ):
# expand the latents if we are doing classifier free guidance
UpperCAmelCase__ = torch.cat([latents] * 2 ) if do_classifier_free_guidance else latents
UpperCAmelCase__ = {"""image_embeds""": image_embeds, """hint""": hint}
UpperCAmelCase__ = self.unet(
sample=__lowercase , timestep=__lowercase , encoder_hidden_states=__lowercase , added_cond_kwargs=__lowercase , return_dict=__lowercase , )[0]
if do_classifier_free_guidance:
UpperCAmelCase__ , UpperCAmelCase__ = noise_pred.split(latents.shape[1] , dim=1 )
UpperCAmelCase__ , UpperCAmelCase__ = noise_pred.chunk(2 )
UpperCAmelCase__ , UpperCAmelCase__ = variance_pred.chunk(2 )
UpperCAmelCase__ = noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
UpperCAmelCase__ = torch.cat([noise_pred, variance_pred_text] , dim=1 )
if not (
hasattr(self.scheduler.config , """variance_type""" )
and self.scheduler.config.variance_type in ["learned", "learned_range"]
):
UpperCAmelCase__ , UpperCAmelCase__ = noise_pred.split(latents.shape[1] , dim=1 )
# compute the previous noisy sample x_t -> x_t-1
UpperCAmelCase__ = self.scheduler.step(
__lowercase , __lowercase , __lowercase , generator=__lowercase , )[0]
# post-processing
UpperCAmelCase__ = self.movq.decode(__lowercase , force_not_quantize=__lowercase )["""sample"""]
if output_type not in ["pt", "np", "pil"]:
raise ValueError(F'''Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}''' )
if output_type in ["np", "pil"]:
UpperCAmelCase__ = image * 0.5 + 0.5
UpperCAmelCase__ = image.clamp(0 , 1 )
UpperCAmelCase__ = image.cpu().permute(0 , 2 , 3 , 1 ).float().numpy()
if output_type == "pil":
UpperCAmelCase__ = self.numpy_to_pil(__lowercase )
if not return_dict:
return (image,)
return ImagePipelineOutput(images=__lowercase )
| 422
| 0
|
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""facebook/wav2vec2-base-960h""": """https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.json""",
# See all Wav2Vec2 models at https://huggingface.co/models?filter=wav2vec2
}
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Any = """wav2vec2"""
def __init__( self , __magic_name__=3_2 , __magic_name__=7_6_8 , __magic_name__=1_2 , __magic_name__=1_2 , __magic_name__=3_0_7_2 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=0.0 , __magic_name__=0.0 , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=0.02 , __magic_name__=1e-5 , __magic_name__="group" , __magic_name__="gelu" , __magic_name__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2, 5_1_2) , __magic_name__=(5, 2, 2, 2, 2, 2, 2) , __magic_name__=(1_0, 3, 3, 3, 3, 2, 2) , __magic_name__=False , __magic_name__=1_2_8 , __magic_name__=1_6 , __magic_name__=False , __magic_name__=True , __magic_name__=0.05 , __magic_name__=1_0 , __magic_name__=2 , __magic_name__=0.0 , __magic_name__=1_0 , __magic_name__=0 , __magic_name__=3_2_0 , __magic_name__=2 , __magic_name__=0.1 , __magic_name__=1_0_0 , __magic_name__=2_5_6 , __magic_name__=2_5_6 , __magic_name__=0.1 , __magic_name__="sum" , __magic_name__=False , __magic_name__=False , __magic_name__=2_5_6 , __magic_name__=(5_1_2, 5_1_2, 5_1_2, 5_1_2, 1_5_0_0) , __magic_name__=(5, 3, 3, 1, 1) , __magic_name__=(1, 2, 3, 1, 1) , __magic_name__=5_1_2 , __magic_name__=0 , __magic_name__=1 , __magic_name__=2 , __magic_name__=False , __magic_name__=3 , __magic_name__=2 , __magic_name__=3 , __magic_name__=None , __magic_name__=None , **__magic_name__ , ):
super().__init__(**__magic_name__ , pad_token_id=__magic_name__ , bos_token_id=__magic_name__ , eos_token_id=__magic_name__ )
lowerCamelCase : Union[str, Any] = hidden_size
lowerCamelCase : int = feat_extract_norm
lowerCamelCase : Optional[Any] = feat_extract_activation
lowerCamelCase : List[str] = list(__magic_name__ )
lowerCamelCase : Optional[int] = list(__magic_name__ )
lowerCamelCase : Union[str, Any] = list(__magic_name__ )
lowerCamelCase : Optional[Any] = conv_bias
lowerCamelCase : Optional[int] = num_conv_pos_embeddings
lowerCamelCase : Tuple = num_conv_pos_embedding_groups
lowerCamelCase : str = len(self.conv_dim )
lowerCamelCase : List[str] = num_hidden_layers
lowerCamelCase : List[Any] = intermediate_size
lowerCamelCase : Union[str, Any] = hidden_act
lowerCamelCase : Optional[int] = num_attention_heads
lowerCamelCase : str = hidden_dropout
lowerCamelCase : Tuple = attention_dropout
lowerCamelCase : int = activation_dropout
lowerCamelCase : Optional[Any] = feat_proj_dropout
lowerCamelCase : Optional[Any] = final_dropout
lowerCamelCase : Optional[int] = layerdrop
lowerCamelCase : List[str] = layer_norm_eps
lowerCamelCase : Optional[Any] = initializer_range
lowerCamelCase : List[str] = vocab_size
lowerCamelCase : List[Any] = do_stable_layer_norm
lowerCamelCase : List[str] = 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
lowerCamelCase : Dict = apply_spec_augment
lowerCamelCase : Optional[int] = mask_time_prob
lowerCamelCase : Dict = mask_time_length
lowerCamelCase : Optional[int] = mask_time_min_masks
lowerCamelCase : List[str] = mask_feature_prob
lowerCamelCase : int = mask_feature_length
lowerCamelCase : Dict = mask_feature_min_masks
# parameters for pretraining with codevector quantized representations
lowerCamelCase : int = num_codevectors_per_group
lowerCamelCase : int = num_codevector_groups
lowerCamelCase : int = contrastive_logits_temperature
lowerCamelCase : List[str] = feat_quantizer_dropout
lowerCamelCase : int = num_negatives
lowerCamelCase : Dict = codevector_dim
lowerCamelCase : Optional[Any] = proj_codevector_dim
lowerCamelCase : Optional[Any] = diversity_loss_weight
# ctc loss
lowerCamelCase : int = ctc_loss_reduction
lowerCamelCase : Optional[int] = ctc_zero_infinity
# adapter
lowerCamelCase : Any = add_adapter
lowerCamelCase : str = adapter_kernel_size
lowerCamelCase : Any = adapter_stride
lowerCamelCase : Union[str, Any] = num_adapter_layers
lowerCamelCase : Optional[int] = output_hidden_size or hidden_size
lowerCamelCase : Optional[int] = adapter_attn_dim
# SequenceClassification-specific parameter. Feel free to ignore for other classes.
lowerCamelCase : Union[str, Any] = classifier_proj_size
# XVector-specific parameters. Feel free to ignore for other classes.
lowerCamelCase : int = list(__magic_name__ )
lowerCamelCase : Any = list(__magic_name__ )
lowerCamelCase : Tuple = list(__magic_name__ )
lowerCamelCase : int = xvector_output_dim
@property
def UpperCamelCase__ ( self ):
return functools.reduce(operator.mul , self.conv_stride , 1 )
| 681
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase =logging.get_logger(__name__)
_lowerCamelCase ={
"""edbeeching/decision-transformer-gym-hopper-medium""": (
"""https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"""
),
# See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class A__ ( __SCREAMING_SNAKE_CASE):
_UpperCAmelCase : Optional[int] = """decision_transformer"""
_UpperCAmelCase : str = ["""past_key_values"""]
_UpperCAmelCase : Any = {
"""max_position_embeddings""": """n_positions""",
"""num_attention_heads""": """n_head""",
"""num_hidden_layers""": """n_layer""",
}
def __init__( self , __magic_name__=1_7 , __magic_name__=4 , __magic_name__=1_2_8 , __magic_name__=4_0_9_6 , __magic_name__=True , __magic_name__=1 , __magic_name__=1_0_2_4 , __magic_name__=3 , __magic_name__=1 , __magic_name__=None , __magic_name__="relu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=1e-5 , __magic_name__=0.02 , __magic_name__=True , __magic_name__=True , __magic_name__=5_0_2_5_6 , __magic_name__=5_0_2_5_6 , __magic_name__=False , __magic_name__=False , **__magic_name__ , ):
lowerCamelCase : Optional[int] = state_dim
lowerCamelCase : int = act_dim
lowerCamelCase : int = hidden_size
lowerCamelCase : Union[str, Any] = max_ep_len
lowerCamelCase : Optional[int] = action_tanh
lowerCamelCase : Any = vocab_size
lowerCamelCase : List[str] = n_positions
lowerCamelCase : List[Any] = n_layer
lowerCamelCase : Dict = n_head
lowerCamelCase : Optional[Any] = n_inner
lowerCamelCase : Tuple = activation_function
lowerCamelCase : Tuple = resid_pdrop
lowerCamelCase : str = embd_pdrop
lowerCamelCase : Dict = attn_pdrop
lowerCamelCase : Tuple = layer_norm_epsilon
lowerCamelCase : Tuple = initializer_range
lowerCamelCase : Tuple = scale_attn_weights
lowerCamelCase : str = use_cache
lowerCamelCase : List[Any] = scale_attn_by_inverse_layer_idx
lowerCamelCase : List[str] = reorder_and_upcast_attn
lowerCamelCase : Optional[Any] = bos_token_id
lowerCamelCase : str = eos_token_id
super().__init__(bos_token_id=__magic_name__ , eos_token_id=__magic_name__ , **__magic_name__ )
| 681
| 1
|
'''simple docstring'''
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase=7 ):
__magic_name__ : List[Any] =None
if token is not None:
__magic_name__ : Tuple ={"""Accept""": """application/vnd.github+json""", """Authorization""": F"Bearer {token}"}
# The id of a workflow (not of a workflow run)
__magic_name__ : int ="""636036"""
__magic_name__ : Optional[int] =F"https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += F"?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"
__magic_name__ : List[Any] =requests.get(__A , headers=__A ).json()
return result["workflow_runs"]
def lowerCAmelCase_ ( lowerCamelCase ):
__magic_name__ : Union[str, Any] =get_daily_ci_runs(__A )
__magic_name__ : Optional[Any] =None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
__magic_name__ : str =workflow_run["""id"""]
break
return workflow_run_id
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : str =get_last_daily_ci_runs(__A )
if workflow_run_id is not None:
__magic_name__ : Tuple =get_artifacts_links(worflow_run_id=__A , token=__A )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
__magic_name__ : Optional[Any] =artifacts_links[artifact_name]
download_artifact(
artifact_name=__A , artifact_url=__A , output_dir=__A , token=__A )
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
get_last_daily_ci_artifacts(__A , __A , __A )
__magic_name__ : List[str] ={}
for artifact_name in artifact_names:
__magic_name__ : str =os.path.join(__A , F"{artifact_name}.zip" )
if os.path.isfile(__A ):
__magic_name__ : Tuple ={}
with zipfile.ZipFile(__A ) as z:
for filename in z.namelist():
if not os.path.isdir(__A ):
# read the file
with z.open(__A ) as f:
__magic_name__ : Any =f.read().decode("""UTF-8""" )
return results
| 715
|
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
UpperCAmelCase_ : Dict = 637_8137.0
UpperCAmelCase_ : List[Any] = 635_6752.31_4245
UpperCAmelCase_ : List[str] = 6378137
def lowerCAmelCase_ ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
__magic_name__ : str =(AXIS_A - AXIS_B) / AXIS_A
# Parametric latitudes
# https://en.wikipedia.org/wiki/Latitude#Parametric_(or_reduced)_latitude
__magic_name__ : str =atan((1 - flattening) * tan(radians(lowerCamelCase ) ) )
__magic_name__ : List[Any] =atan((1 - flattening) * tan(radians(lowerCamelCase ) ) )
# Compute central angle between two points
# using haversine theta. sigma = haversine_distance / equatorial radius
__magic_name__ : List[Any] =haversine_distance(lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ) / EQUATORIAL_RADIUS
# Intermediate P and Q values
__magic_name__ : Tuple =(b_lata + b_lata) / 2
__magic_name__ : int =(b_lata - b_lata) / 2
# Intermediate X value
# X = (sigma - sin(sigma)) * sin^2Pcos^2Q / cos^2(sigma/2)
__magic_name__ : Optional[int] =(sin(lowerCamelCase ) ** 2) * (cos(lowerCamelCase ) ** 2)
__magic_name__ : Any =cos(sigma / 2 ) ** 2
__magic_name__ : List[Any] =(sigma - sin(lowerCamelCase )) * (x_numerator / x_demonimator)
# Intermediate Y value
# Y = (sigma + sin(sigma)) * cos^2Psin^2Q / sin^2(sigma/2)
__magic_name__ : Any =(cos(lowerCamelCase ) ** 2) * (sin(lowerCamelCase ) ** 2)
__magic_name__ : Optional[Any] =sin(sigma / 2 ) ** 2
__magic_name__ : str =(sigma + sin(lowerCamelCase )) * (y_numerator / y_denominator)
return EQUATORIAL_RADIUS * (sigma - ((flattening / 2) * (x_value + y_value)))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 367
| 0
|
import importlib.util
import os
import platform
from argparse import ArgumentParser
import huggingface_hub
from .. import __version__ as version
from ..utils import (
is_accelerate_available,
is_flax_available,
is_safetensors_available,
is_tf_available,
is_torch_available,
)
from . import BaseTransformersCLICommand
def __snake_case ( lowerCAmelCase_ ) -> Union[str, Any]:
return EnvironmentCommand()
def __snake_case ( lowerCAmelCase_ ) -> Tuple:
return EnvironmentCommand(args.accelerate_config_file )
class __snake_case ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
@staticmethod
def lowercase_ ( A_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = parser.add_parser('''env''' )
download_parser.set_defaults(func=A_ )
download_parser.add_argument(
'''--accelerate-config_file''' , default=A_ , help='''The accelerate config file to use for the default values in the launching script.''' , )
download_parser.set_defaults(func=A_ )
def __init__( self , A_ , *A_ ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = accelerate_config_file
def lowercase_ ( self ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ = '''not installed'''
if is_safetensors_available():
import safetensors
SCREAMING_SNAKE_CASE__ = safetensors.__version__
elif importlib.util.find_spec('''safetensors''' ) is not None:
import safetensors
SCREAMING_SNAKE_CASE__ = f'''{safetensors.__version__} but is ignored because of PyTorch version too old.'''
SCREAMING_SNAKE_CASE__ = '''not installed'''
SCREAMING_SNAKE_CASE__ = SCREAMING_SNAKE_CASE__ = '''not found'''
if is_accelerate_available():
import accelerate
from accelerate.commands.config import default_config_file, load_config_from_file
SCREAMING_SNAKE_CASE__ = accelerate.__version__
# Get the default from the config file.
if self._accelerate_config_file is not None or os.path.isfile(A_ ):
SCREAMING_SNAKE_CASE__ = load_config_from_file(self._accelerate_config_file ).to_dict()
SCREAMING_SNAKE_CASE__ = (
'''\n'''.join([f'''\t- {prop}: {val}''' for prop, val in accelerate_config.items()] )
if isinstance(A_ , A_ )
else f'''\t{accelerate_config}'''
)
SCREAMING_SNAKE_CASE__ = '''not installed'''
SCREAMING_SNAKE_CASE__ = '''NA'''
if is_torch_available():
import torch
SCREAMING_SNAKE_CASE__ = torch.__version__
SCREAMING_SNAKE_CASE__ = torch.cuda.is_available()
SCREAMING_SNAKE_CASE__ = '''not installed'''
SCREAMING_SNAKE_CASE__ = '''NA'''
if is_tf_available():
import tensorflow as tf
SCREAMING_SNAKE_CASE__ = tf.__version__
try:
# deprecated in v2.1
SCREAMING_SNAKE_CASE__ = tf.test.is_gpu_available()
except AttributeError:
# returns list of devices, convert to bool
SCREAMING_SNAKE_CASE__ = bool(tf.config.list_physical_devices('''GPU''' ) )
SCREAMING_SNAKE_CASE__ = '''not installed'''
SCREAMING_SNAKE_CASE__ = '''not installed'''
SCREAMING_SNAKE_CASE__ = '''not installed'''
SCREAMING_SNAKE_CASE__ = '''NA'''
if is_flax_available():
import flax
import jax
import jaxlib
SCREAMING_SNAKE_CASE__ = flax.__version__
SCREAMING_SNAKE_CASE__ = jax.__version__
SCREAMING_SNAKE_CASE__ = jaxlib.__version__
SCREAMING_SNAKE_CASE__ = jax.lib.xla_bridge.get_backend().platform
SCREAMING_SNAKE_CASE__ = {
'''`transformers` version''': version,
'''Platform''': platform.platform(),
'''Python version''': platform.python_version(),
'''Huggingface_hub version''': huggingface_hub.__version__,
'''Safetensors version''': f'''{safetensors_version}''',
'''Accelerate version''': f'''{accelerate_version}''',
'''Accelerate config''': f'''{accelerate_config_str}''',
'''PyTorch version (GPU?)''': f'''{pt_version} ({pt_cuda_available})''',
'''Tensorflow version (GPU?)''': f'''{tf_version} ({tf_cuda_available})''',
'''Flax version (CPU?/GPU?/TPU?)''': f'''{flax_version} ({jax_backend})''',
'''Jax version''': f'''{jax_version}''',
'''JaxLib version''': f'''{jaxlib_version}''',
'''Using GPU in script?''': '''<fill in>''',
'''Using distributed or parallel set-up in script?''': '''<fill in>''',
}
print('''\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n''' )
print(self.format_dict(A_ ) )
return info
@staticmethod
def lowercase_ ( A_ ):
'''simple docstring'''
return "\n".join([f'''- {prop}: {val}''' for prop, val in d.items()] ) + "\n"
| 100
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a = {
'''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 = _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
| 7
| 0
|
"""simple docstring"""
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
blenderbot,
blenderbot_small,
blip,
blip_a,
bloom,
bridgetower,
byta,
camembert,
canine,
chinese_clip,
clap,
clip,
clipseg,
codegen,
conditional_detr,
convbert,
convnext,
convnextva,
cpm,
cpmant,
ctrl,
cvt,
dataavec,
deberta,
deberta_va,
decision_transformer,
deformable_detr,
deit,
deprecated,
deta,
detr,
dialogpt,
dinat,
distilbert,
dit,
donut,
dpr,
dpt,
efficientformer,
efficientnet,
electra,
encodec,
encoder_decoder,
ernie,
ernie_m,
esm,
falcon,
flaubert,
flava,
fnet,
focalnet,
fsmt,
funnel,
git,
glpn,
gpta,
gpt_bigcode,
gpt_neo,
gpt_neox,
gpt_neox_japanese,
gpt_swa,
gptj,
gptsan_japanese,
graphormer,
groupvit,
herbert,
hubert,
ibert,
imagegpt,
informer,
instructblip,
jukebox,
layoutlm,
layoutlmva,
layoutlmva,
layoutxlm,
led,
levit,
lilt,
llama,
longformer,
longta,
luke,
lxmert,
mam_aaa,
marian,
markuplm,
maskaformer,
maskformer,
mbart,
mbartaa,
mega,
megatron_bert,
megatron_gpta,
mgp_str,
mluke,
mobilebert,
mobilenet_va,
mobilenet_va,
mobilevit,
mobilevitva,
mpnet,
mra,
mta,
musicgen,
mvp,
nat,
nezha,
nllb,
nllb_moe,
nystromformer,
oneformer,
open_llama,
openai,
opt,
owlvit,
pegasus,
pegasus_x,
perceiver,
phobert,
pixastruct,
plbart,
poolformer,
prophetnet,
qdqbert,
rag,
realm,
reformer,
regnet,
rembert,
resnet,
roberta,
roberta_prelayernorm,
roc_bert,
roformer,
rwkv,
sam,
segformer,
sew,
sew_d,
speech_encoder_decoder,
speech_to_text,
speech_to_text_a,
speechta,
splinter,
squeezebert,
swiftformer,
swin,
swinasr,
swinva,
switch_transformers,
ta,
table_transformer,
tapas,
time_series_transformer,
timesformer,
timm_backbone,
transfo_xl,
trocr,
tvlt,
umta,
unispeech,
unispeech_sat,
upernet,
videomae,
vilt,
vision_encoder_decoder,
vision_text_dual_encoder,
visual_bert,
vit,
vit_hybrid,
vit_mae,
vit_msn,
vivit,
wavaveca,
wavaveca_conformer,
wavaveca_phoneme,
wavaveca_with_lm,
wavlm,
whisper,
x_clip,
xglm,
xlm,
xlm_prophetnet,
xlm_roberta,
xlm_roberta_xl,
xlnet,
xmod,
yolos,
yoso,
)
| 712
|
"""simple docstring"""
import random
from .binary_exp_mod import bin_exp_mod
def UpperCamelCase_ ( SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=1_0_0_0 ):
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
SCREAMING_SNAKE_CASE = n - 1
SCREAMING_SNAKE_CASE = 0
while d % 2 == 0:
d /= 2
exp += 1
# n - 1=d*(2**exp)
SCREAMING_SNAKE_CASE = 0
while count < prec:
SCREAMING_SNAKE_CASE = random.randint(2, n - 1 )
SCREAMING_SNAKE_CASE = bin_exp_mod(SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ )
if b != 1:
SCREAMING_SNAKE_CASE = True
for _ in range(SCREAMING_SNAKE_CASE_ ):
if b == n - 1:
SCREAMING_SNAKE_CASE = False
break
SCREAMING_SNAKE_CASE = b * b
b %= n
if flag:
return False
count += 1
return True
if __name__ == "__main__":
snake_case = abs(int(input('Enter bound : ').strip()))
print('Here\'s the list of primes:')
print(', '.join(str(i) for i in range(n + 1) if is_prime_big(i)))
| 406
| 0
|
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 _A ( UpperCamelCase ):
"""simple docstring"""
def __init__( self : int , *__SCREAMING_SNAKE_CASE : List[str] , **__SCREAMING_SNAKE_CASE : Any ) -> List[Any]:
super().__init__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_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 _a ( self : str , **__SCREAMING_SNAKE_CASE : str ) -> str:
__UpperCAmelCase ={}
if "threshold" in kwargs:
__UpperCAmelCase =kwargs["""threshold"""]
return {}, {}, postprocess_kwargs
def __call__( self : Union[str, Any] , *__SCREAMING_SNAKE_CASE : Dict , **__SCREAMING_SNAKE_CASE : Union[str, Any] ) -> Union[Predictions, List[Prediction]]:
return super().__call__(*__SCREAMING_SNAKE_CASE , **__SCREAMING_SNAKE_CASE )
def _a ( self : List[str] , __SCREAMING_SNAKE_CASE : Optional[int] ) -> str:
__UpperCAmelCase =load_image(__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =torch.IntTensor([[image.height, image.width]] )
__UpperCAmelCase =self.image_processor(images=[image] , return_tensors="""pt""" )
if self.tokenizer is not None:
__UpperCAmelCase =self.tokenizer(text=inputs["""words"""] , boxes=inputs["""boxes"""] , return_tensors="""pt""" )
__UpperCAmelCase =target_size
return inputs
def _a ( self : Tuple , __SCREAMING_SNAKE_CASE : Dict ) -> Optional[int]:
__UpperCAmelCase =model_inputs.pop("""target_size""" )
__UpperCAmelCase =self.model(**__SCREAMING_SNAKE_CASE )
__UpperCAmelCase =outputs.__class__({"""target_size""": target_size, **outputs} )
if self.tokenizer is not None:
__UpperCAmelCase =model_inputs["""bbox"""]
return model_outputs
def _a ( self : Union[str, Any] , __SCREAMING_SNAKE_CASE : List[str] , __SCREAMING_SNAKE_CASE : Any=0.9 ) -> str:
__UpperCAmelCase =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.
__UpperCAmelCase , __UpperCAmelCase =target_size[0].tolist()
def unnormalize(__SCREAMING_SNAKE_CASE : Union[str, Any] ):
return self._get_bounding_box(
torch.Tensor(
[
(width * bbox[0] / 1000),
(height * bbox[1] / 1000),
(width * bbox[2] / 1000),
(height * bbox[3] / 1000),
] ) )
__UpperCAmelCase , __UpperCAmelCase =model_outputs["""logits"""].squeeze(0 ).softmax(dim=-1 ).max(dim=-1 )
__UpperCAmelCase =[self.model.config.idalabel[prediction] for prediction in classes.tolist()]
__UpperCAmelCase =[unnormalize(__SCREAMING_SNAKE_CASE ) for bbox in model_outputs["""bbox"""].squeeze(0 )]
__UpperCAmelCase =["""score""", """label""", """box"""]
__UpperCAmelCase =[dict(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) ) for vals in zip(scores.tolist() , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) if vals[0] > threshold]
else:
# This is a regular ForObjectDetectionModel
__UpperCAmelCase =self.image_processor.post_process_object_detection(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE )
__UpperCAmelCase =raw_annotations[0]
__UpperCAmelCase =raw_annotation["""scores"""]
__UpperCAmelCase =raw_annotation["""labels"""]
__UpperCAmelCase =raw_annotation["""boxes"""]
__UpperCAmelCase =scores.tolist()
__UpperCAmelCase =[self.model.config.idalabel[label.item()] for label in labels]
__UpperCAmelCase =[self._get_bounding_box(__SCREAMING_SNAKE_CASE ) for box in boxes]
# {"scores": [...], ...} --> [{"score":x, ...}, ...]
__UpperCAmelCase =["""score""", """label""", """box"""]
__UpperCAmelCase =[
dict(zip(__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE ) )
for vals in zip(raw_annotation["""scores"""] , raw_annotation["""labels"""] , raw_annotation["""boxes"""] )
]
return annotation
def _a ( self : Any , __SCREAMING_SNAKE_CASE : "torch.Tensor" ) -> Dict[str, int]:
if self.framework != "pt":
raise ValueError("""The ObjectDetectionPipeline is only available in PyTorch.""" )
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase =box.int().tolist()
__UpperCAmelCase ={
"""xmin""": xmin,
"""ymin""": ymin,
"""xmax""": xmax,
"""ymax""": ymax,
}
return bbox
| 68
|
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_params import UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS, UNCONDITIONAL_AUDIO_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin
enable_full_determinism()
class A_ ( _a , unittest.TestCase ):
'''simple docstring'''
a__ = DanceDiffusionPipeline
a__ = UNCONDITIONAL_AUDIO_GENERATION_PARAMS
a__ = PipelineTesterMixin.required_optional_params - {
"callback",
"latents",
"callback_steps",
"output_type",
"num_images_per_prompt",
}
a__ = UNCONDITIONAL_AUDIO_GENERATION_BATCH_PARAMS
a__ = False
a__ = False
def lowerCAmelCase_ (self ) -> Dict:
torch.manual_seed(0 )
__UpperCAmelCase = UNetaDModel(
block_out_channels=(32, 32, 64) , extra_in_channels=16 , sample_size=512 , sample_rate=16_000 , in_channels=2 , out_channels=2 , flip_sin_to_cos=lowercase__ , use_timestep_embedding=lowercase__ , time_embedding_type='''fourier''' , mid_block_type='''UNetMidBlock1D''' , down_block_types=('''DownBlock1DNoSkip''', '''DownBlock1D''', '''AttnDownBlock1D''') , up_block_types=('''AttnUpBlock1D''', '''UpBlock1D''', '''UpBlock1DNoSkip''') , )
__UpperCAmelCase = IPNDMScheduler()
__UpperCAmelCase = {
'''unet''': unet,
'''scheduler''': scheduler,
}
return components
def lowerCAmelCase_ (self , lowercase__ , lowercase__=0 ) -> Dict:
if str(lowercase__ ).startswith('''mps''' ):
__UpperCAmelCase = torch.manual_seed(lowercase__ )
else:
__UpperCAmelCase = torch.Generator(device=lowercase__ ).manual_seed(lowercase__ )
__UpperCAmelCase = {
'''batch_size''': 1,
'''generator''': generator,
'''num_inference_steps''': 4,
}
return inputs
def lowerCAmelCase_ (self ) -> int:
__UpperCAmelCase = '''cpu''' # ensure determinism for the device-dependent torch.Generator
__UpperCAmelCase = self.get_dummy_components()
__UpperCAmelCase = DanceDiffusionPipeline(**lowercase__ )
__UpperCAmelCase = pipe.to(lowercase__ )
pipe.set_progress_bar_config(disable=lowercase__ )
__UpperCAmelCase = self.get_dummy_inputs(lowercase__ )
__UpperCAmelCase = pipe(**lowercase__ )
__UpperCAmelCase = output.audios
__UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, components["unet"].sample_size)
__UpperCAmelCase = np.array([-0.7265, 1.0000, -0.8388, 0.1175, 0.9498, -1.0000] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
@skip_mps
def lowerCAmelCase_ (self ) -> Union[str, Any]:
return super().test_save_load_local()
@skip_mps
def lowerCAmelCase_ (self ) -> List[str]:
return super().test_dict_tuple_outputs_equivalent(expected_max_difference=3E-3 )
@skip_mps
def lowerCAmelCase_ (self ) -> Optional[int]:
return super().test_save_load_optional_components()
@skip_mps
def lowerCAmelCase_ (self ) -> Any:
return super().test_attention_slicing_forward_pass()
def lowerCAmelCase_ (self ) -> Tuple:
super().test_inference_batch_single_identical(expected_max_diff=3E-3 )
@slow
@require_torch_gpu
class A_ ( unittest.TestCase ):
'''simple docstring'''
def lowerCAmelCase_ (self ) -> Dict:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ (self ) -> Tuple:
__UpperCAmelCase = torch_device
__UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' )
__UpperCAmelCase = pipe.to(lowercase__ )
pipe.set_progress_bar_config(disable=lowercase__ )
__UpperCAmelCase = torch.manual_seed(0 )
__UpperCAmelCase = pipe(generator=lowercase__ , num_inference_steps=100 , audio_length_in_s=4.096 )
__UpperCAmelCase = output.audios
__UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
__UpperCAmelCase = np.array([-0.0192, -0.0231, -0.0318, -0.0059, 0.0002, -0.0020] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
def lowerCAmelCase_ (self ) -> Optional[Any]:
__UpperCAmelCase = torch_device
__UpperCAmelCase = DanceDiffusionPipeline.from_pretrained('''harmonai/maestro-150k''' , torch_dtype=torch.floataa )
__UpperCAmelCase = pipe.to(lowercase__ )
pipe.set_progress_bar_config(disable=lowercase__ )
__UpperCAmelCase = torch.manual_seed(0 )
__UpperCAmelCase = pipe(generator=lowercase__ , num_inference_steps=100 , audio_length_in_s=4.096 )
__UpperCAmelCase = output.audios
__UpperCAmelCase = audio[0, -3:, -3:]
assert audio.shape == (1, 2, pipe.unet.sample_size)
__UpperCAmelCase = np.array([-0.0367, -0.0488, -0.0771, -0.0525, -0.0444, -0.0341] )
assert np.abs(audio_slice.flatten() - expected_slice ).max() < 1E-2
| 303
| 0
|
'''simple docstring'''
from __future__ import annotations
from cmath import sqrt
def _SCREAMING_SNAKE_CASE ( lowercase : Dict , lowercase : Optional[Any] , lowercase : Optional[Any] ):
'''simple docstring'''
if a == 0:
raise ValueError('Coefficient \'a\' must not be zero.' )
lowerCamelCase_ = b * b - 4 * a * c
lowerCamelCase_ = (-b + sqrt(_lowerCamelCase )) / (2 * a)
lowerCamelCase_ = (-b - sqrt(_lowerCamelCase )) / (2 * a)
return (
root_a.real if not root_a.imag else root_a,
root_a.real if not root_a.imag else root_a,
)
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowerCamelCase_ = quadratic_roots(a=5 , b=6 , c=1 )
print(f"""The solutions are: {solutiona} and {solutiona}""" )
if __name__ == "__main__":
main()
| 717
|
import argparse
import json
import logging
import os
import sys
from unittest.mock import patch
from transformers.testing_utils import TestCasePlus, get_gpu_count, slow
lowerCamelCase : List[Any] = [
os.path.join(os.path.dirname(__file__), dirname)
for dirname in [
"text-classification",
"language-modeling",
"summarization",
"token-classification",
"question-answering",
]
]
sys.path.extend(SRC_DIRS)
if SRC_DIRS is not None:
import run_clm_flax
import run_flax_glue
import run_flax_ner
import run_mlm_flax
import run_qa
import run_summarization_flax
import run_ta_mlm_flax
logging.basicConfig(level=logging.DEBUG)
lowerCamelCase : Tuple = logging.getLogger()
def _SCREAMING_SNAKE_CASE ( ):
'''simple docstring'''
lowerCamelCase_ = argparse.ArgumentParser()
parser.add_argument('-f' )
lowerCamelCase_ = parser.parse_args()
return args.f
def _SCREAMING_SNAKE_CASE ( lowercase : Optional[Any] , lowercase : Dict="eval" ):
'''simple docstring'''
lowerCamelCase_ = os.path.join(lowercase , f"""{split}_results.json""" )
if os.path.exists(lowercase ):
with open(lowercase , 'r' ) as f:
return json.load(lowercase )
raise ValueError(f"""can't find {path}""" )
lowerCamelCase : str = logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
class A( UpperCamelCase ):
'''simple docstring'''
def a__ ( self : Union[str, Any] ) -> List[str]:
"""simple docstring"""
lowerCamelCase_ = self.get_auto_remove_tmp_dir()
lowerCamelCase_ = f"""
run_glue.py
--model_name_or_path distilbert-base-uncased
--output_dir {tmp_dir}
--train_file ./tests/fixtures/tests_samples/MRPC/train.csv
--validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--learning_rate=1e-4
--eval_steps=2
--warmup_steps=2
--seed=42
--max_seq_length=128
""".split()
with patch.object(A_ , 'argv' , A_ ):
run_flax_glue.main()
lowerCamelCase_ = get_results(A_ )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
@slow
def a__ ( self : List[str] ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = self.get_auto_remove_tmp_dir()
lowerCamelCase_ = f"""
run_clm_flax.py
--model_name_or_path distilgpt2
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--block_size 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A_ , 'argv' , A_ ):
run_clm_flax.main()
lowerCamelCase_ = get_results(A_ )
self.assertLess(result['eval_perplexity'] , 100 )
@slow
def a__ ( self : str ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ = self.get_auto_remove_tmp_dir()
lowerCamelCase_ = f"""
run_summarization.py
--model_name_or_path t5-small
--train_file tests/fixtures/tests_samples/xsum/sample.json
--validation_file tests/fixtures/tests_samples/xsum/sample.json
--test_file tests/fixtures/tests_samples/xsum/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=8
--do_train
--do_eval
--do_predict
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
--predict_with_generate
""".split()
with patch.object(A_ , 'argv' , A_ ):
run_summarization_flax.main()
lowerCamelCase_ = get_results(A_ , split='test' )
self.assertGreaterEqual(result['test_rouge1'] , 10 )
self.assertGreaterEqual(result['test_rouge2'] , 2 )
self.assertGreaterEqual(result['test_rougeL'] , 7 )
self.assertGreaterEqual(result['test_rougeLsum'] , 7 )
@slow
def a__ ( self : Optional[int] ) -> str:
"""simple docstring"""
lowerCamelCase_ = self.get_auto_remove_tmp_dir()
lowerCamelCase_ = f"""
run_mlm.py
--model_name_or_path distilroberta-base
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--output_dir {tmp_dir}
--overwrite_output_dir
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--logging_steps 2 --eval_steps 2
--do_train
--do_eval
--num_train_epochs=1
""".split()
with patch.object(A_ , 'argv' , A_ ):
run_mlm_flax.main()
lowerCamelCase_ = get_results(A_ )
self.assertLess(result['eval_perplexity'] , 42 )
@slow
def a__ ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
lowerCamelCase_ = self.get_auto_remove_tmp_dir()
lowerCamelCase_ = f"""
run_t5_mlm_flax.py
--model_name_or_path t5-small
--train_file ./tests/fixtures/sample_text.txt
--validation_file ./tests/fixtures/sample_text.txt
--do_train
--do_eval
--max_seq_length 128
--per_device_train_batch_size 4
--per_device_eval_batch_size 4
--num_train_epochs 2
--logging_steps 2 --eval_steps 2
--output_dir {tmp_dir}
--overwrite_output_dir
""".split()
with patch.object(A_ , 'argv' , A_ ):
run_ta_mlm_flax.main()
lowerCamelCase_ = get_results(A_ )
self.assertGreaterEqual(result['eval_accuracy'] , 0.42 )
@slow
def a__ ( self : int ) -> Tuple:
"""simple docstring"""
lowerCamelCase_ = 7 if get_gpu_count() > 1 else 2
lowerCamelCase_ = self.get_auto_remove_tmp_dir()
lowerCamelCase_ = f"""
run_flax_ner.py
--model_name_or_path bert-base-uncased
--train_file tests/fixtures/tests_samples/conll/sample.json
--validation_file tests/fixtures/tests_samples/conll/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--do_train
--do_eval
--warmup_steps=2
--learning_rate=2e-4
--logging_steps 2 --eval_steps 2
--per_device_train_batch_size=2
--per_device_eval_batch_size=2
--num_train_epochs={epochs}
--seed 7
""".split()
with patch.object(A_ , 'argv' , A_ ):
run_flax_ner.main()
lowerCamelCase_ = get_results(A_ )
self.assertGreaterEqual(result['eval_accuracy'] , 0.75 )
self.assertGreaterEqual(result['eval_f1'] , 0.3 )
@slow
def a__ ( self : str ) -> int:
"""simple docstring"""
lowerCamelCase_ = self.get_auto_remove_tmp_dir()
lowerCamelCase_ = f"""
run_qa.py
--model_name_or_path bert-base-uncased
--version_2_with_negative
--train_file tests/fixtures/tests_samples/SQUAD/sample.json
--validation_file tests/fixtures/tests_samples/SQUAD/sample.json
--output_dir {tmp_dir}
--overwrite_output_dir
--num_train_epochs=3
--warmup_steps=2
--do_train
--do_eval
--logging_steps 2 --eval_steps 2
--learning_rate=2e-4
--per_device_train_batch_size=2
--per_device_eval_batch_size=1
""".split()
with patch.object(A_ , 'argv' , A_ ):
run_qa.main()
lowerCamelCase_ = get_results(A_ )
self.assertGreaterEqual(result['eval_f1'] , 30 )
self.assertGreaterEqual(result['eval_exact'] , 30 )
| 651
| 0
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_flava import FlavaImageProcessor
_UpperCAmelCase : Any = logging.get_logger(__name__)
class lowercase_ ( _UpperCamelCase ):
"""simple docstring"""
def __init__( self : Any, *UpperCamelCase__ : str, **UpperCamelCase__ : Optional[int] ) -> None:
warnings.warn(
'The class FlavaFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please'
' use FlavaImageProcessor instead.', UpperCamelCase__, )
super().__init__(*UpperCamelCase__, **UpperCamelCase__ )
| 107
|
'''simple docstring'''
from __future__ import annotations
def _SCREAMING_SNAKE_CASE ( __snake_case : int | str ):
_A = str(__snake_case )
return n == n[::-1]
def _SCREAMING_SNAKE_CASE ( __snake_case : int = 1_0_0_0_0_0_0 ):
_A = 0
for i in range(1 , __snake_case ):
if is_palindrome(__snake_case ) and is_palindrome(bin(__snake_case ).split('b' )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 107
| 1
|
from __future__ import annotations
_lowercase : Tuple =1.6_0_2_1E-1_9 # units = C
def A__ ( lowercase: float, lowercase: float, lowercase: float, ) -> tuple[str, float]:
if (conductivity, electron_conc, mobility).count(0 ) != 1:
raise ValueError('You cannot supply more or less than 2 values' )
elif conductivity < 0:
raise ValueError('Conductivity cannot be negative' )
elif electron_conc < 0:
raise ValueError('Electron concentration cannot be negative' )
elif mobility < 0:
raise ValueError('mobility cannot be negative' )
elif conductivity == 0:
return (
"conductivity",
mobility * electron_conc * ELECTRON_CHARGE,
)
elif electron_conc == 0:
return (
"electron_conc",
conductivity / (mobility * ELECTRON_CHARGE),
)
else:
return (
"mobility",
conductivity / (electron_conc * ELECTRON_CHARGE),
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 661
|
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_lowercase : Optional[int] =get_tests_dir('''fixtures/test_sentencepiece.model''')
@require_sentencepiece
@require_tokenizers
class SCREAMING_SNAKE_CASE_ ( lowerCAmelCase_ , unittest.TestCase ):
'''simple docstring'''
lowercase : List[str] = XLMRobertaTokenizer
lowercase : Dict = XLMRobertaTokenizerFast
lowercase : str = True
lowercase : Tuple = True
def SCREAMING_SNAKE_CASE_ ( self : int ) -> Optional[Any]:
super().setUp()
# We have a SentencePiece fixture for testing
A : List[str] =XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
tokenizer.save_pretrained(self.tmpdirname )
def SCREAMING_SNAKE_CASE_ ( self : int ) -> List[Any]:
A : List[str] ='<pad>'
A : int =1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(SCREAMING_SNAKE_CASE__ ) , SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Any:
A : List[str] =list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] , '<s>' )
self.assertEqual(vocab_keys[1] , '<pad>' )
self.assertEqual(vocab_keys[-1] , '<mask>' )
self.assertEqual(len(SCREAMING_SNAKE_CASE__ ) , 10_02 )
def SCREAMING_SNAKE_CASE_ ( self : Tuple ) -> Dict:
self.assertEqual(self.get_tokenizer().vocab_size , 10_02 )
def SCREAMING_SNAKE_CASE_ ( self : List[str] ) -> str:
A : Union[str, Any] =XLMRobertaTokenizer(SCREAMING_SNAKE_CASE__ , keep_accents=SCREAMING_SNAKE_CASE__ )
A : str =tokenizer.tokenize('This is a test' )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , ['▁This', '▁is', '▁a', '▁t', 'est'] )
self.assertListEqual(
tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ ) , [value + tokenizer.fairseq_offset for value in [2_85, 46, 10, 1_70, 3_82]] , )
A : Any =tokenizer.tokenize('I was born in 92000, and this is falsé.' )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'9',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'é',
'.',
] , )
A : Tuple =tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
value + tokenizer.fairseq_offset
for value in [8, 21, 84, 55, 24, 19, 7, 2, 6_02, 3_47, 3_47, 3_47, 3, 12, 66, 46, 72, 80, 6, 2, 4]
# ^ unk: 2 + 1 = 3 unk: 2 + 1 = 3 ^
] , )
A : Union[str, Any] =tokenizer.convert_ids_to_tokens(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(
SCREAMING_SNAKE_CASE__ , [
SPIECE_UNDERLINE + 'I',
SPIECE_UNDERLINE + 'was',
SPIECE_UNDERLINE + 'b',
'or',
'n',
SPIECE_UNDERLINE + 'in',
SPIECE_UNDERLINE + '',
'<unk>',
'2',
'0',
'0',
'0',
',',
SPIECE_UNDERLINE + 'and',
SPIECE_UNDERLINE + 'this',
SPIECE_UNDERLINE + 'is',
SPIECE_UNDERLINE + 'f',
'al',
's',
'<unk>',
'.',
] , )
def SCREAMING_SNAKE_CASE_ ( self : Any ) -> Optional[int]:
if not self.test_slow_tokenizer:
# as we don't have a slow version, we can't compare the outputs between slow and fast versions
return
A : Any =(self.rust_tokenizer_class, 'hf-internal-testing/tiny-xlm-roberta', {})
for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
with self.subTest(f'{tokenizer.__class__.__name__} ({pretrained_name})' ):
A : List[Any] =self.rust_tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
A : Dict =self.tokenizer_class.from_pretrained(SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
A : str =tempfile.mkdtemp()
A : Optional[int] =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__ )
A : Optional[Any] =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__ )
# Checks it save with the same files + the tokenizer.json file for the fast one
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
A : List[str] =tuple(f for f in tokenizer_r_files if 'tokenizer.json' not in f )
self.assertSequenceEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Checks everything loads correctly in the same way
A : Tuple =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__ )
A : Dict =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
# self.assertEqual(getattr(tokenizer_rp, key), getattr(tokenizer_pp, key))
# self.assertEqual(getattr(tokenizer_rp, key + "_id"), getattr(tokenizer_pp, key + "_id"))
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
# Save tokenizer rust, legacy_format=True
A : Optional[int] =tempfile.mkdtemp()
A : Optional[int] =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__ , legacy_format=SCREAMING_SNAKE_CASE__ )
A : List[Any] =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__ )
# Checks it save with the same files
self.assertSequenceEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
# Checks everything loads correctly in the same way
A : Tuple =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__ )
A : Tuple =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
# Save tokenizer rust, legacy_format=False
A : List[Any] =tempfile.mkdtemp()
A : Optional[int] =tokenizer_r.save_pretrained(SCREAMING_SNAKE_CASE__ , legacy_format=SCREAMING_SNAKE_CASE__ )
A : str =tokenizer_p.save_pretrained(SCREAMING_SNAKE_CASE__ )
# Checks it saved the tokenizer.json file
self.assertTrue(any('tokenizer.json' in f for f in tokenizer_r_files ) )
# Checks everything loads correctly in the same way
A : List[Any] =tokenizer_r.from_pretrained(SCREAMING_SNAKE_CASE__ )
A : List[Any] =tokenizer_p.from_pretrained(SCREAMING_SNAKE_CASE__ )
# Check special tokens are set accordingly on Rust and Python
for key in tokenizer_pp.special_tokens_map:
self.assertTrue(hasattr(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ) )
shutil.rmtree(SCREAMING_SNAKE_CASE__ )
@cached_property
def SCREAMING_SNAKE_CASE_ ( self : str ) -> Optional[int]:
return XLMRobertaTokenizer.from_pretrained('xlm-roberta-base' )
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> Any:
with tempfile.NamedTemporaryFile() as f:
shutil.copyfile(SCREAMING_SNAKE_CASE__ , f.name )
A : Optional[Any] =XLMRobertaTokenizer(f.name , keep_accents=SCREAMING_SNAKE_CASE__ )
A : int =pickle.dumps(SCREAMING_SNAKE_CASE__ )
pickle.loads(SCREAMING_SNAKE_CASE__ )
def SCREAMING_SNAKE_CASE_ ( self : Optional[Any] ) -> Union[str, Any]:
if not self.test_rust_tokenizer:
return
A : Union[str, Any] =self.get_tokenizer()
A : int =self.get_rust_tokenizer()
A : List[str] ='I was born in 92000, and this is falsé.'
A : Union[str, Any] =tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
A : Optional[int] =rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
A : Any =tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
A : Tuple =rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ , add_special_tokens=SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
A : Optional[Any] =self.get_rust_tokenizer()
A : int =tokenizer.encode(SCREAMING_SNAKE_CASE__ )
A : Dict =rust_tokenizer.encode(SCREAMING_SNAKE_CASE__ )
self.assertListEqual(SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Dict ) -> List[str]:
A : Any ='Hello World!'
A : Optional[Any] =[0, 3_53_78, 66_61, 38, 2]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(SCREAMING_SNAKE_CASE__ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__ ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self : List[Any] ) -> str:
A : Any =(
'This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'
' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'
)
A : int =[
0,
32_93,
83,
10,
45_52,
49_89,
79_86,
6_78,
10,
59_15,
1_11,
17_94_59,
12_48_50,
4,
60_44,
2_37,
12,
6,
5,
6,
4,
67_80,
7_05,
15,
13_88,
44,
3_78,
1_01_14,
7_11,
1_52,
20,
6,
5,
2_23_76,
6_42,
12_21,
1_51_90,
3_41_53,
4_50,
56_08,
9_59,
11_19,
5_77_02,
1_36,
1_86,
47,
10_98,
2_93_67,
47,
# 4426, # What fairseq tokenizes from "<unk>": "_<"
# 3678, # What fairseq tokenizes from "<unk>": "unk"
# 2740, # What fairseq tokenizes from "<unk>": ">"
3, # What we tokenize from "<unk>": "<unk>"
6, # Residue from the tokenization: an extra sentencepiece underline
4,
60_44,
2_37,
62_84,
5_09_01,
5_28,
31,
90,
34,
9_27,
2,
]
# xlmr = torch.hub.load('pytorch/fairseq', 'xlmr.base') # xlmr.large has same tokenizer
# xlmr.eval()
# xlmr.encode(symbols)
self.assertListEqual(SCREAMING_SNAKE_CASE__ , self.big_tokenizer.encode(SCREAMING_SNAKE_CASE__ ) )
@slow
def SCREAMING_SNAKE_CASE_ ( self : Any ) -> Any:
# fmt: off
A : List[Any] ={'input_ids': [[0, 1_10_62, 8_27_72, 7, 15, 8_27_72, 5_38, 5_15_29, 2_37, 1_71_98, 12_90, 2_06, 9, 21_51_75, 13_14, 1_36, 1_71_98, 12_90, 2_06, 9, 5_63_59, 42, 12_20_09, 9, 1_64_66, 16, 8_73_44, 45_37, 9, 47_17, 7_83_81, 6, 15_99_58, 7, 15, 2_44_80, 6_18, 4, 5_27, 2_26_93, 54_28, 4, 27_77, 2_44_80, 98_74, 4, 4_35_23, 5_94, 4, 8_03, 1_83_92, 3_31_89, 18, 4, 4_35_23, 2_44_47, 1_23_99, 1_00, 2_49_55, 8_36_58, 96_26, 14_40_57, 15, 8_39, 2_23_35, 16, 1_36, 2_49_55, 8_36_58, 8_34_79, 15, 3_91_02, 7_24, 16, 6_78, 6_45, 27_89, 13_28, 45_89, 42, 12_20_09, 11_57_74, 23, 8_05, 13_28, 4_68_76, 7, 1_36, 5_38_94, 19_40, 4_22_27, 4_11_59, 1_77_21, 8_23, 4_25, 4, 2_75_12, 9_87_22, 2_06, 1_36, 55_31, 49_70, 9_19, 1_73_36, 5, 2], [0, 2_00_80, 6_18, 83, 8_27_75, 47, 4_79, 9, 15_17, 73, 5_38_94, 3_33, 8_05_81, 11_01_17, 1_88_11, 52_56, 12_95, 51, 15_25_26, 2_97, 79_86, 3_90, 12_44_16, 5_38, 3_54_31, 2_14, 98, 1_50_44, 2_57_37, 1_36, 71_08, 4_37_01, 23, 7_56, 13_53_55, 7, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [0, 5_81, 6_37_73, 11_94_55, 6, 14_77_97, 8_82_03, 7, 6_45, 70, 21, 32_85, 1_02_69, 5, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=SCREAMING_SNAKE_CASE__ , model_name='xlm-roberta-base' , revision='d9d8a8ea5eb94b1c6654ae9249df7793cd2933d3' , )
| 661
| 1
|
'''simple docstring'''
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class SCREAMING_SNAKE_CASE__ ( _UpperCamelCase ):
def __init__( self : int , a_ : NestedDataStructureLike[PathLike] , a_ : Optional[NamedSplit] = None , a_ : Optional[Features] = None , a_ : str = None , a_ : bool = False , a_ : bool = False , a_ : Optional[int] = None , **a_ : List[str] , ):
"""simple docstring"""
super().__init__(
a_ , split=a_ , features=a_ , cache_dir=a_ , keep_in_memory=a_ , streaming=a_ , num_proc=a_ , **a_ , )
__snake_case = path_or_paths if isinstance(a_ , a_ ) else {self.split: path_or_paths}
__snake_case = Text(
cache_dir=a_ , data_files=a_ , features=a_ , **a_ , )
def A ( self : str ):
"""simple docstring"""
if self.streaming:
__snake_case = self.builder.as_streaming_dataset(split=self.split )
# Build regular (map-style) dataset
else:
__snake_case = None
__snake_case = None
__snake_case = None
__snake_case = None
self.builder.download_and_prepare(
download_config=a_ , download_mode=a_ , verification_mode=a_ , base_path=a_ , num_proc=self.num_proc , )
__snake_case = self.builder.as_dataset(
split=self.split , verification_mode=a_ , in_memory=self.keep_in_memory )
return dataset
| 69
|
'''simple docstring'''
import argparse
from collections import OrderedDict
from pathlib import Path
import torch
from transformers import (
VisualBertConfig,
VisualBertForMultipleChoice,
VisualBertForPreTraining,
VisualBertForQuestionAnswering,
VisualBertForVisualReasoning,
)
from transformers.utils import logging
logging.set_verbosity_info()
__lowerCAmelCase = logging.get_logger(__name__)
__lowerCAmelCase = [
("bert.bert", "visual_bert"),
("bert.cls", "cls"),
("bert.classifier", "cls"),
("token_type_embeddings_visual", "visual_token_type_embeddings"),
("position_embeddings_visual", "visual_position_embeddings"),
("projection", "visual_projection"),
]
__lowerCAmelCase = [
"nlvr2_coco_pre_trained.th",
"nlvr2_fine_tuned.th",
"nlvr2_pre_trained.th",
"vcr_coco_pre_train.th",
"vcr_fine_tune.th",
"vcr_pre_train.th",
"vqa_coco_pre_trained.th",
"vqa_fine_tuned.th",
"vqa_pre_trained.th",
]
def __UpperCamelCase ( lowercase_ : int ):
"""simple docstring"""
a_ = torch.load(lowercase_ , map_location='cpu' )
return sd
def __UpperCamelCase ( lowercase_ : List[str] , lowercase_ : Tuple , lowercase_ : Union[str, Any]=rename_keys_prefix ):
"""simple docstring"""
a_ = OrderedDict()
a_ = torch.arange(config.max_position_embeddings ).expand((1, -1) )
# detector_d = OrderedDict()
for key in d:
if "detector" in key:
# detector_d[key.replace('detector.','')] = d[key]
continue
a_ = key
for name_pair in rename_keys_prefix:
a_ = new_key.replace(name_pair[0] , name_pair[1] )
a_ = d[key]
if key == "bert.cls.predictions.decoder.weight":
# Old bert code didn't have `decoder.bias`, but was added separately
a_ = new_d['cls.predictions.bias']
return new_d
@torch.no_grad()
def __UpperCamelCase ( lowercase_ : Union[str, Any] , lowercase_ : Union[str, Any] ):
"""simple docstring"""
assert (
checkpoint_path.split('/' )[-1] in ACCEPTABLE_CHECKPOINTS
), F'The checkpoint provided must be in {ACCEPTABLE_CHECKPOINTS}.'
# Get Config
if "pre" in checkpoint_path:
a_ = 'pretraining'
if "vcr" in checkpoint_path:
a_ = {'visual_embedding_dim': 512}
elif "vqa_advanced" in checkpoint_path:
a_ = {'visual_embedding_dim': 2_048}
elif "vqa" in checkpoint_path:
a_ = {'visual_embedding_dim': 2_048}
elif "nlvr" in checkpoint_path:
a_ = {'visual_embedding_dim': 1_024}
else:
raise NotImplementedError(F'No implementation found for `{checkpoint_path}`.' )
else:
if "vcr" in checkpoint_path:
a_ = {'visual_embedding_dim': 512}
a_ = 'multichoice'
elif "vqa_advanced" in checkpoint_path:
a_ = {'visual_embedding_dim': 2_048}
a_ = 'vqa_advanced'
elif "vqa" in checkpoint_path:
a_ = {'visual_embedding_dim': 2_048, 'num_labels': 3_129}
a_ = 'vqa'
elif "nlvr" in checkpoint_path:
a_ = {
'visual_embedding_dim': 1_024,
'num_labels': 2,
}
a_ = 'nlvr'
a_ = VisualBertConfig(**lowercase_ )
# Load State Dict
a_ = load_state_dict(lowercase_ )
a_ = get_new_dict(lowercase_ , lowercase_ )
if model_type == "pretraining":
a_ = VisualBertForPreTraining(lowercase_ )
elif model_type == "vqa":
a_ = VisualBertForQuestionAnswering(lowercase_ )
elif model_type == "nlvr":
a_ = VisualBertForVisualReasoning(lowercase_ )
elif model_type == "multichoice":
a_ = VisualBertForMultipleChoice(lowercase_ )
model.load_state_dict(lowercase_ )
# Save Checkpoints
Path(lowercase_ ).mkdir(exist_ok=lowercase_ )
model.save_pretrained(lowercase_ )
if __name__ == "__main__":
__lowerCAmelCase = argparse.ArgumentParser()
# Required parameters
parser.add_argument("orig_checkpoint_path", type=str, help="A path to .th on local filesystem.")
parser.add_argument("pytorch_dump_folder_path", type=str, help="Path to the output PyTorch model.")
__lowerCAmelCase = parser.parse_args()
convert_visual_bert_checkpoint(args.orig_checkpoint_path, args.pytorch_dump_folder_path)
| 536
| 0
|
'''simple docstring'''
from importlib import import_module
from .logging import get_logger
__UpperCAmelCase :int = get_logger(__name__)
class a :
"""simple docstring"""
def __init__( self : Optional[int] , snake_case : Tuple , snake_case : Tuple=None ) -> Union[str, Any]:
__UpperCAmelCase : List[Any] = attrs or []
if module is not None:
for key in module.__dict__:
if key in attrs or not key.startswith('''__''' ):
setattr(self , snake_case , getattr(snake_case , snake_case ) )
__UpperCAmelCase : Optional[Any] = module._original_module if isinstance(snake_case , _PatchedModuleObj ) else module
class a :
"""simple docstring"""
SCREAMING_SNAKE_CASE : Union[str, Any] = []
def __init__( self : Tuple , snake_case : Optional[int] , snake_case : str , snake_case : Tuple , snake_case : int=None ) -> List[str]:
__UpperCAmelCase : int = obj
__UpperCAmelCase : int = target
__UpperCAmelCase : List[str] = new
__UpperCAmelCase : Optional[int] = target.split('''.''' )[0]
__UpperCAmelCase : Any = {}
__UpperCAmelCase : Any = attrs or []
def __enter__( self : str ) -> Optional[int]:
*__UpperCAmelCase , __UpperCAmelCase : Optional[int] = self.target.split('''.''' )
# Patch modules:
# it's used to patch attributes of submodules like "os.path.join";
# in this case we need to patch "os" and "os.path"
for i in range(len(snake_case ) ):
try:
__UpperCAmelCase : List[Any] = import_module('''.'''.join(submodules[: i + 1] ) )
except ModuleNotFoundError:
continue
# We iterate over all the globals in self.obj in case we find "os" or "os.path"
for attr in self.obj.__dir__():
__UpperCAmelCase : List[Any] = getattr(self.obj , snake_case )
# We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
# This allows to patch renamed modules like "from os import path as ospath".
if obj_attr is submodule or (
(isinstance(snake_case , _PatchedModuleObj ) and obj_attr._original_module is submodule)
):
__UpperCAmelCase : Any = obj_attr
# patch at top level
setattr(self.obj , snake_case , _PatchedModuleObj(snake_case , attrs=self.attrs ) )
__UpperCAmelCase : int = getattr(self.obj , snake_case )
# construct lower levels patches
for key in submodules[i + 1 :]:
setattr(snake_case , snake_case , _PatchedModuleObj(getattr(snake_case , snake_case , snake_case ) , attrs=self.attrs ) )
__UpperCAmelCase : int = getattr(snake_case , snake_case )
# finally set the target attribute
setattr(snake_case , snake_case , self.new )
# Patch attribute itself:
# it's used for builtins like "open",
# and also to patch "os.path.join" we may also need to patch "join"
# itself if it was imported as "from os.path import join".
if submodules: # if it's an attribute of a submodule like "os.path.join"
try:
__UpperCAmelCase : Dict = getattr(import_module('''.'''.join(snake_case ) ) , snake_case )
except (AttributeError, ModuleNotFoundError):
return
# We iterate over all the globals in self.obj in case we find "os.path.join"
for attr in self.obj.__dir__():
# We don't check for the name of the global, but rather if its value *is* "os.path.join".
# This allows to patch renamed attributes like "from os.path import join as pjoin".
if getattr(self.obj , snake_case ) is attr_value:
__UpperCAmelCase : int = getattr(self.obj , snake_case )
setattr(self.obj , snake_case , self.new )
elif target_attr in globals()["__builtins__"]: # if it'a s builtin like "open"
__UpperCAmelCase : int = globals()['''__builtins__'''][target_attr]
setattr(self.obj , snake_case , self.new )
else:
raise RuntimeError(f'Tried to patch attribute {target_attr} instead of a submodule.' )
def __exit__( self : str , *snake_case : Union[str, Any] ) -> List[str]:
for attr in list(self.original ):
setattr(self.obj , snake_case , self.original.pop(snake_case ) )
def lowerCamelCase__ ( self : Optional[Any] ) -> str:
self.__enter__()
self._active_patches.append(self )
def lowerCamelCase__ ( self : str ) -> Tuple:
try:
self._active_patches.remove(self )
except ValueError:
# If the patch hasn't been started this will fail
return None
return self.__exit__()
| 266
|
'''simple docstring'''
from __future__ import annotations
from fractions import Fraction
from math import gcd, sqrt
def _a ( _lowercase : int ):
'''simple docstring'''
__UpperCAmelCase : int = int(number**0.5 )
return number == sq * sq
def _a ( _lowercase : int , _lowercase : int , _lowercase : int , _lowercase : int , _lowercase : int , _lowercase : int ):
'''simple docstring'''
__UpperCAmelCase : int = x_num * y_den * z_den + y_num * x_den * z_den + z_num * x_den * y_den
__UpperCAmelCase : int = x_den * y_den * z_den
__UpperCAmelCase : int = gcd(_lowercase , _lowercase )
top //= hcf
bottom //= hcf
return top, bottom
def _a ( _lowercase : int = 35 ):
'''simple docstring'''
__UpperCAmelCase : set = set()
__UpperCAmelCase : int
__UpperCAmelCase : Fraction = Fraction(0 )
__UpperCAmelCase : tuple[int, int]
for x_num in range(1 , order + 1 ):
for x_den in range(x_num + 1 , order + 1 ):
for y_num in range(1 , order + 1 ):
for y_den in range(y_num + 1 , order + 1 ):
# n=1
__UpperCAmelCase : Optional[int] = x_num * y_den + x_den * y_num
__UpperCAmelCase : Dict = x_den * y_den
__UpperCAmelCase : List[Any] = gcd(_lowercase , _lowercase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__UpperCAmelCase : Dict = add_three(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
unique_s.add(_lowercase )
# n=2
__UpperCAmelCase : Optional[int] = (
x_num * x_num * y_den * y_den + x_den * x_den * y_num * y_num
)
__UpperCAmelCase : Any = x_den * x_den * y_den * y_den
if is_sq(_lowercase ) and is_sq(_lowercase ):
__UpperCAmelCase : List[Any] = int(sqrt(_lowercase ) )
__UpperCAmelCase : Tuple = int(sqrt(_lowercase ) )
__UpperCAmelCase : Union[str, Any] = gcd(_lowercase , _lowercase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__UpperCAmelCase : Union[str, Any] = add_three(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
unique_s.add(_lowercase )
# n=-1
__UpperCAmelCase : Union[str, Any] = x_num * y_num
__UpperCAmelCase : List[Any] = x_den * y_num + x_num * y_den
__UpperCAmelCase : Any = gcd(_lowercase , _lowercase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__UpperCAmelCase : Optional[Any] = add_three(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
unique_s.add(_lowercase )
# n=2
__UpperCAmelCase : Optional[Any] = x_num * x_num * y_num * y_num
__UpperCAmelCase : Dict = (
x_den * x_den * y_num * y_num + x_num * x_num * y_den * y_den
)
if is_sq(_lowercase ) and is_sq(_lowercase ):
__UpperCAmelCase : Any = int(sqrt(_lowercase ) )
__UpperCAmelCase : List[Any] = int(sqrt(_lowercase ) )
__UpperCAmelCase : Any = gcd(_lowercase , _lowercase )
z_num //= hcf
z_den //= hcf
if 0 < z_num < z_den <= order:
__UpperCAmelCase : int = add_three(
_lowercase , _lowercase , _lowercase , _lowercase , _lowercase , _lowercase )
unique_s.add(_lowercase )
for num, den in unique_s:
total += Fraction(_lowercase , _lowercase )
return total.denominator + total.numerator
if __name__ == "__main__":
print(f"""{solution() = }""")
| 266
| 1
|
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
__magic_name__ : Any = {"""processing_wav2vec2_with_lm""": ["""Wav2Vec2ProcessorWithLM"""]}
if TYPE_CHECKING:
from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
import sys
__magic_name__ : Any = _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
| 102
|
"""simple docstring"""
import argparse
a = '''docs/source/_static/js/custom.js'''
def _snake_case ( _snake_case : Dict ) -> Any:
'''simple docstring'''
with open(_snake_case , encoding='utf-8' , newline='\n' ) as f:
_A = f.readlines()
_A = 0
# First let's put the right version
while not lines[index].startswith('const stableVersion =' ):
index += 1
_A = F'''const stableVersion = "v{version}"\n'''
# Then update the dictionary
while not lines[index].startswith('const versionMapping = {' ):
index += 1
# We go until the end
while not lines[index].startswith('}' ):
index += 1
# We add the new version at the end
lines[index - 1] += F''' "v{version}": "v{version}",\n'''
with open(_snake_case , 'w' , encoding='utf-8' , newline='\n' ) as f:
f.writelines(_snake_case )
if __name__ == "__main__":
a = argparse.ArgumentParser()
parser.add_argument('''--version''', help='''Release version.''')
a = parser.parse_args()
update_custom_js(args.version)
| 7
| 0
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'kssteven/ibert-roberta-base': 'https://huggingface.co/kssteven/ibert-roberta-base/resolve/main/config.json',
'kssteven/ibert-roberta-large': 'https://huggingface.co/kssteven/ibert-roberta-large/resolve/main/config.json',
'kssteven/ibert-roberta-large-mnli': (
'https://huggingface.co/kssteven/ibert-roberta-large-mnli/resolve/main/config.json'
),
}
class lowerCAmelCase_ ( snake_case__ ):
"""simple docstring"""
a_ :Any ="""ibert"""
def __init__( self : Optional[Any] , SCREAMING_SNAKE_CASE__ : List[str]=3_0_5_2_2 , SCREAMING_SNAKE_CASE__ : Optional[int]=7_6_8 , SCREAMING_SNAKE_CASE__ : int=1_2 , SCREAMING_SNAKE_CASE__ : int=1_2 , SCREAMING_SNAKE_CASE__ : Tuple=3_0_7_2 , SCREAMING_SNAKE_CASE__ : int="gelu" , SCREAMING_SNAKE_CASE__ : List[str]=0.1 , SCREAMING_SNAKE_CASE__ : Any=0.1 , SCREAMING_SNAKE_CASE__ : List[str]=5_1_2 , SCREAMING_SNAKE_CASE__ : Tuple=2 , SCREAMING_SNAKE_CASE__ : Dict=0.0_2 , SCREAMING_SNAKE_CASE__ : List[str]=1E-12 , SCREAMING_SNAKE_CASE__ : int=1 , SCREAMING_SNAKE_CASE__ : str=0 , SCREAMING_SNAKE_CASE__ : Optional[int]=2 , SCREAMING_SNAKE_CASE__ : Any="absolute" , SCREAMING_SNAKE_CASE__ : Any=False , SCREAMING_SNAKE_CASE__ : List[Any]="none" , **SCREAMING_SNAKE_CASE__ : List[str] , ):
'''simple docstring'''
super().__init__(pad_token_id=SCREAMING_SNAKE_CASE__ , bos_token_id=SCREAMING_SNAKE_CASE__ , eos_token_id=SCREAMING_SNAKE_CASE__ , **SCREAMING_SNAKE_CASE__ )
__a = vocab_size
__a = hidden_size
__a = num_hidden_layers
__a = num_attention_heads
__a = hidden_act
__a = intermediate_size
__a = hidden_dropout_prob
__a = attention_probs_dropout_prob
__a = max_position_embeddings
__a = type_vocab_size
__a = initializer_range
__a = layer_norm_eps
__a = position_embedding_type
__a = quant_mode
__a = force_dequant
class lowerCAmelCase_ ( snake_case__ ):
"""simple docstring"""
@property
def __a ( self : Optional[int] ):
'''simple docstring'''
if self.task == "multiple-choice":
__a = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
__a = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 201
|
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE_ = {
'configuration_bridgetower': [
'BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP',
'BridgeTowerConfig',
'BridgeTowerTextConfig',
'BridgeTowerVisionConfig',
],
'processing_bridgetower': ['BridgeTowerProcessor'],
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = ['BridgeTowerImageProcessor']
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
SCREAMING_SNAKE_CASE_ = [
'BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST',
'BridgeTowerForContrastiveLearning',
'BridgeTowerForImageAndTextRetrieval',
'BridgeTowerForMaskedLM',
'BridgeTowerModel',
'BridgeTowerPreTrainedModel',
]
if TYPE_CHECKING:
from .configuration_bridgetower import (
BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP,
BridgeTowerConfig,
BridgeTowerTextConfig,
BridgeTowerVisionConfig,
)
from .processing_bridgetower import BridgeTowerProcessor
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .image_processing_bridgetower import BridgeTowerImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_bridgetower import (
BRIDGETOWER_PRETRAINED_MODEL_ARCHIVE_LIST,
BridgeTowerForContrastiveLearning,
BridgeTowerForImageAndTextRetrieval,
BridgeTowerForMaskedLM,
BridgeTowerModel,
BridgeTowerPreTrainedModel,
)
else:
import sys
SCREAMING_SNAKE_CASE_ = _LazyModule(__name__, globals()['__file__'], _import_structure)
| 201
| 1
|
'''simple docstring'''
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
DebertaVaForMaskedLM,
DebertaVaForMultipleChoice,
DebertaVaForQuestionAnswering,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaModel,
)
from transformers.models.deberta_va.modeling_deberta_va import DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST
class __A (__magic_name__ ):
def __init__( self , UpperCamelCase_ , UpperCamelCase_=13 , UpperCamelCase_=7 , UpperCamelCase_=True , UpperCamelCase_=True , UpperCamelCase_=True , UpperCamelCase_=True , UpperCamelCase_=99 , UpperCamelCase_=32 , UpperCamelCase_=5 , UpperCamelCase_=4 , UpperCamelCase_=37 , UpperCamelCase_="gelu" , UpperCamelCase_=0.1 , UpperCamelCase_=0.1 , UpperCamelCase_=5_12 , UpperCamelCase_=16 , UpperCamelCase_=2 , UpperCamelCase_=0.0_2 , UpperCamelCase_=False , UpperCamelCase_=True , UpperCamelCase_="None" , UpperCamelCase_=3 , UpperCamelCase_=4 , UpperCamelCase_=None , ):
__UpperCAmelCase : Tuple = parent
__UpperCAmelCase : Dict = batch_size
__UpperCAmelCase : Union[str, Any] = seq_length
__UpperCAmelCase : Optional[Any] = is_training
__UpperCAmelCase : Union[str, Any] = use_input_mask
__UpperCAmelCase : Union[str, Any] = use_token_type_ids
__UpperCAmelCase : Optional[Any] = use_labels
__UpperCAmelCase : List[Any] = vocab_size
__UpperCAmelCase : Optional[Any] = hidden_size
__UpperCAmelCase : Union[str, Any] = num_hidden_layers
__UpperCAmelCase : Any = num_attention_heads
__UpperCAmelCase : Union[str, Any] = intermediate_size
__UpperCAmelCase : Any = hidden_act
__UpperCAmelCase : str = hidden_dropout_prob
__UpperCAmelCase : Any = attention_probs_dropout_prob
__UpperCAmelCase : Any = max_position_embeddings
__UpperCAmelCase : str = type_vocab_size
__UpperCAmelCase : Any = type_sequence_label_size
__UpperCAmelCase : Optional[int] = initializer_range
__UpperCAmelCase : Tuple = num_labels
__UpperCAmelCase : str = num_choices
__UpperCAmelCase : int = relative_attention
__UpperCAmelCase : Optional[Any] = position_biased_input
__UpperCAmelCase : Optional[Any] = pos_att_type
__UpperCAmelCase : Optional[int] = scope
def _snake_case ( self ):
__UpperCAmelCase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__UpperCAmelCase : List[Any] = None
if self.use_input_mask:
__UpperCAmelCase : str = ids_tensor([self.batch_size, self.seq_length] , vocab_size=2 )
__UpperCAmelCase : List[Any] = None
if self.use_token_type_ids:
__UpperCAmelCase : Any = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__UpperCAmelCase : Any = None
__UpperCAmelCase : str = None
__UpperCAmelCase : int = None
if self.use_labels:
__UpperCAmelCase : Optional[int] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__UpperCAmelCase : Union[str, Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__UpperCAmelCase : str = ids_tensor([self.batch_size] , self.num_choices )
__UpperCAmelCase : Optional[int] = self.get_config()
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def _snake_case ( self ):
return DebertaVaConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , relative_attention=self.relative_attention , position_biased_input=self.position_biased_input , pos_att_type=self.pos_att_type , )
def _snake_case ( self , UpperCamelCase_ ):
self.parent.assertListEqual(list(result.loss.size() ) , [] )
def _snake_case ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
__UpperCAmelCase : Tuple = DebertaVaModel(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__UpperCAmelCase : List[Any] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ )[0]
__UpperCAmelCase : List[str] = model(UpperCamelCase_ , token_type_ids=UpperCamelCase_ )[0]
__UpperCAmelCase : List[str] = model(UpperCamelCase_ )[0]
self.parent.assertListEqual(list(sequence_output.size() ) , [self.batch_size, self.seq_length, self.hidden_size] )
def _snake_case ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
__UpperCAmelCase : Tuple = DebertaVaForMaskedLM(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__UpperCAmelCase : Optional[Any] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def _snake_case ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
__UpperCAmelCase : Dict = self.num_labels
__UpperCAmelCase : Dict = DebertaVaForSequenceClassification(UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__UpperCAmelCase : int = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertListEqual(list(result.logits.size() ) , [self.batch_size, self.num_labels] )
self.check_loss_output(UpperCamelCase_ )
def _snake_case ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
__UpperCAmelCase : Any = self.num_labels
__UpperCAmelCase : List[Any] = DebertaVaForTokenClassification(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__UpperCAmelCase : int = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_ )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def _snake_case ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
__UpperCAmelCase : Any = DebertaVaForQuestionAnswering(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__UpperCAmelCase : List[Any] = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , start_positions=UpperCamelCase_ , end_positions=UpperCamelCase_ , )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def _snake_case ( self , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ ):
__UpperCAmelCase : Optional[int] = DebertaVaForMultipleChoice(config=UpperCamelCase_ )
model.to(UpperCamelCase_ )
model.eval()
__UpperCAmelCase : str = input_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase : Optional[Any] = token_type_ids.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase : Union[str, Any] = input_mask.unsqueeze(1 ).expand(-1 , self.num_choices , -1 ).contiguous()
__UpperCAmelCase : Optional[Any] = model(
UpperCamelCase_ , attention_mask=UpperCamelCase_ , token_type_ids=UpperCamelCase_ , labels=UpperCamelCase_ , )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def _snake_case ( self ):
__UpperCAmelCase : int = self.prepare_config_and_inputs()
(
(
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) , (
__UpperCAmelCase
) ,
) : int = config_and_inputs
__UpperCAmelCase : Tuple = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": input_mask}
return config, inputs_dict
@require_torch
class __A (__magic_name__ , __magic_name__ , unittest.TestCase ):
snake_case :Tuple = (
(
DebertaVaModel,
DebertaVaForMaskedLM,
DebertaVaForSequenceClassification,
DebertaVaForTokenClassification,
DebertaVaForQuestionAnswering,
DebertaVaForMultipleChoice,
)
if is_torch_available()
else ()
)
snake_case :Optional[int] = (
{
"feature-extraction": DebertaVaModel,
"fill-mask": DebertaVaForMaskedLM,
"question-answering": DebertaVaForQuestionAnswering,
"text-classification": DebertaVaForSequenceClassification,
"token-classification": DebertaVaForTokenClassification,
"zero-shot": DebertaVaForSequenceClassification,
}
if is_torch_available()
else {}
)
snake_case :Dict = True
snake_case :Any = False
snake_case :Optional[int] = False
snake_case :str = False
snake_case :List[str] = False
def _snake_case ( self ):
__UpperCAmelCase : Optional[int] = DebertaVaModelTester(self )
__UpperCAmelCase : Dict = ConfigTester(self , config_class=UpperCamelCase_ , hidden_size=37 )
def _snake_case ( self ):
self.config_tester.run_common_tests()
def _snake_case ( self ):
__UpperCAmelCase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_model(*UpperCamelCase_ )
def _snake_case ( self ):
__UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_sequence_classification(*UpperCamelCase_ )
def _snake_case ( self ):
__UpperCAmelCase : Any = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_masked_lm(*UpperCamelCase_ )
def _snake_case ( self ):
__UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_question_answering(*UpperCamelCase_ )
def _snake_case ( self ):
__UpperCAmelCase : str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_token_classification(*UpperCamelCase_ )
def _snake_case ( self ):
__UpperCAmelCase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_deberta_for_multiple_choice(*UpperCamelCase_ )
@slow
def _snake_case ( self ):
for model_name in DEBERTA_V2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
__UpperCAmelCase : List[Any] = DebertaVaModel.from_pretrained(UpperCamelCase_ )
self.assertIsNotNone(UpperCamelCase_ )
@require_torch
@require_sentencepiece
@require_tokenizers
class __A (unittest.TestCase ):
@unittest.skip(reason="Model not available yet" )
def _snake_case ( self ):
pass
@slow
def _snake_case ( self ):
__UpperCAmelCase : Optional[Any] = DebertaVaModel.from_pretrained("microsoft/deberta-v2-xlarge" )
__UpperCAmelCase : Tuple = torch.tensor([[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]] )
__UpperCAmelCase : Optional[int] = torch.tensor([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
with torch.no_grad():
__UpperCAmelCase : List[str] = model(UpperCamelCase_ , attention_mask=UpperCamelCase_ )[0]
# compare the actual values for a slice.
__UpperCAmelCase : int = torch.tensor(
[[[0.2_3_5_6, 0.1_9_4_8, 0.0_3_6_9], [-0.1_0_6_3, 0.3_5_8_6, -0.5_1_5_2], [-0.6_3_9_9, -0.0_2_5_9, -0.2_5_2_5]]] )
self.assertTrue(torch.allclose(output[:, 1:4, 1:4] , UpperCamelCase_ , atol=1E-4 ) , f"""{output[:, 1:4, 1:4]}""" )
| 168
|
'''simple docstring'''
import os
import sys
import unittest
_a : 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 get_test_info # noqa: E402
from get_test_info import ( # noqa: E402
get_model_to_test_mapping,
get_model_to_tester_mapping,
get_test_to_tester_mapping,
)
_a : int = os.path.join("tests", "models", "bert", "test_modeling_bert.py")
_a : int = os.path.join("tests", "models", "blip", "test_modeling_blip.py")
class __A (unittest.TestCase ):
def _snake_case ( self ):
__UpperCAmelCase : Any = get_test_to_tester_mapping(UpperCamelCase_ )
__UpperCAmelCase : Dict = get_test_to_tester_mapping(UpperCamelCase_ )
__UpperCAmelCase : List[Any] = {"BertModelTest": "BertModelTester"}
__UpperCAmelCase : Optional[Any] = {
"BlipModelTest": "BlipModelTester",
"BlipTextImageModelTest": "BlipTextImageModelsModelTester",
"BlipTextModelTest": "BlipTextModelTester",
"BlipTextRetrievalModelTest": "BlipTextRetrievalModelTester",
"BlipVQAModelTest": "BlipVQAModelTester",
"BlipVisionModelTest": "BlipVisionModelTester",
}
self.assertEqual(get_test_info.to_json(UpperCamelCase_ ) , UpperCamelCase_ )
self.assertEqual(get_test_info.to_json(UpperCamelCase_ ) , UpperCamelCase_ )
def _snake_case ( self ):
__UpperCAmelCase : Tuple = get_model_to_test_mapping(UpperCamelCase_ )
__UpperCAmelCase : Tuple = get_model_to_test_mapping(UpperCamelCase_ )
__UpperCAmelCase : Any = {
"BertForMaskedLM": ["BertModelTest"],
"BertForMultipleChoice": ["BertModelTest"],
"BertForNextSentencePrediction": ["BertModelTest"],
"BertForPreTraining": ["BertModelTest"],
"BertForQuestionAnswering": ["BertModelTest"],
"BertForSequenceClassification": ["BertModelTest"],
"BertForTokenClassification": ["BertModelTest"],
"BertLMHeadModel": ["BertModelTest"],
"BertModel": ["BertModelTest"],
}
__UpperCAmelCase : int = {
"BlipForConditionalGeneration": ["BlipTextImageModelTest"],
"BlipForImageTextRetrieval": ["BlipTextRetrievalModelTest"],
"BlipForQuestionAnswering": ["BlipVQAModelTest"],
"BlipModel": ["BlipModelTest"],
"BlipTextModel": ["BlipTextModelTest"],
"BlipVisionModel": ["BlipVisionModelTest"],
}
self.assertEqual(get_test_info.to_json(UpperCamelCase_ ) , UpperCamelCase_ )
self.assertEqual(get_test_info.to_json(UpperCamelCase_ ) , UpperCamelCase_ )
def _snake_case ( self ):
__UpperCAmelCase : List[Any] = get_model_to_tester_mapping(UpperCamelCase_ )
__UpperCAmelCase : int = get_model_to_tester_mapping(UpperCamelCase_ )
__UpperCAmelCase : Optional[int] = {
"BertForMaskedLM": ["BertModelTester"],
"BertForMultipleChoice": ["BertModelTester"],
"BertForNextSentencePrediction": ["BertModelTester"],
"BertForPreTraining": ["BertModelTester"],
"BertForQuestionAnswering": ["BertModelTester"],
"BertForSequenceClassification": ["BertModelTester"],
"BertForTokenClassification": ["BertModelTester"],
"BertLMHeadModel": ["BertModelTester"],
"BertModel": ["BertModelTester"],
}
__UpperCAmelCase : Union[str, Any] = {
"BlipForConditionalGeneration": ["BlipTextImageModelsModelTester"],
"BlipForImageTextRetrieval": ["BlipTextRetrievalModelTester"],
"BlipForQuestionAnswering": ["BlipVQAModelTester"],
"BlipModel": ["BlipModelTester"],
"BlipTextModel": ["BlipTextModelTester"],
"BlipVisionModel": ["BlipVisionModelTester"],
}
self.assertEqual(get_test_info.to_json(UpperCamelCase_ ) , UpperCamelCase_ )
self.assertEqual(get_test_info.to_json(UpperCamelCase_ ) , UpperCamelCase_ )
| 168
| 1
|
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A : str = logging.get_logger(__name__)
A : Union[str, Any] = {
"vocab_file": "vocab.txt",
"merges_file": "bpe.codes",
}
A : Dict = {
"vocab_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/vocab.txt",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/vocab.txt",
},
"merges_file": {
"vinai/phobert-base": "https://huggingface.co/vinai/phobert-base/resolve/main/bpe.codes",
"vinai/phobert-large": "https://huggingface.co/vinai/phobert-large/resolve/main/bpe.codes",
},
}
A : int = {
"vinai/phobert-base": 256,
"vinai/phobert-large": 256,
}
def lowercase_ ( _A : str ):
lowerCamelCase__ : List[str] = set()
lowerCamelCase__ : Optional[Any] = word[0]
for char in word[1:]:
pairs.add((prev_char, char) )
lowerCamelCase__ : List[Any] = char
lowerCamelCase__ : str = set(_lowerCAmelCase )
return pairs
class _lowercase ( __UpperCAmelCase):
"""simple docstring"""
A__ = VOCAB_FILES_NAMES
A__ = PRETRAINED_VOCAB_FILES_MAP
A__ = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
def __init__( self : Tuple , __lowerCamelCase : str , __lowerCamelCase : Optional[int] , __lowerCamelCase : Optional[Any]="<s>" , __lowerCamelCase : Any="</s>" , __lowerCamelCase : str="</s>" , __lowerCamelCase : int="<s>" , __lowerCamelCase : int="<unk>" , __lowerCamelCase : Optional[Any]="<pad>" , __lowerCamelCase : List[str]="<mask>" , **__lowerCamelCase : int , ):
'''simple docstring'''
super().__init__(
bos_token=_lowerCamelCase , eos_token=_lowerCamelCase , unk_token=_lowerCamelCase , sep_token=_lowerCamelCase , cls_token=_lowerCamelCase , pad_token=_lowerCamelCase , mask_token=_lowerCamelCase , **_lowerCamelCase , )
lowerCamelCase__ : List[Any] = vocab_file
lowerCamelCase__ : List[str] = merges_file
lowerCamelCase__ : int = {}
lowerCamelCase__ : Optional[int] = 0
lowerCamelCase__ : List[str] = 1
lowerCamelCase__ : Any = 2
lowerCamelCase__ : str = 3
self.add_from_file(_lowerCamelCase )
lowerCamelCase__ : Tuple = {v: k for k, v in self.encoder.items()}
with open(_lowerCamelCase , encoding="utf-8" ) as merges_handle:
lowerCamelCase__ : Union[str, Any] = merges_handle.read().split("\n" )[:-1]
lowerCamelCase__ : Dict = [tuple(merge.split()[:-1] ) for merge in merges]
lowerCamelCase__ : List[str] = dict(zip(_lowerCamelCase , range(len(_lowerCamelCase ) ) ) )
lowerCamelCase__ : List[str] = {}
def lowerCAmelCase ( self : List[str] , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
lowerCamelCase__ : Any = [self.cls_token_id]
lowerCamelCase__ : List[Any] = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCAmelCase ( self : Union[str, Any] , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None , __lowerCamelCase : bool = 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 None:
return [1] + ([0] * len(_lowerCamelCase )) + [1]
return [1] + ([0] * len(_lowerCamelCase )) + [1, 1] + ([0] * len(_lowerCamelCase )) + [1]
def lowerCAmelCase ( self : List[Any] , __lowerCamelCase : List[int] , __lowerCamelCase : Optional[List[int]] = None ):
'''simple docstring'''
lowerCamelCase__ : Tuple = [self.sep_token_id]
lowerCamelCase__ : str = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
@property
def lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
return len(self.encoder )
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
return dict(self.encoder , **self.added_tokens_encoder )
def lowerCAmelCase ( self : int , __lowerCamelCase : Any ):
'''simple docstring'''
if token in self.cache:
return self.cache[token]
lowerCamelCase__ : int = tuple(_lowerCamelCase )
lowerCamelCase__ : List[Any] = tuple(list(word[:-1] ) + [word[-1] + "</w>"] )
lowerCamelCase__ : List[Any] = get_pairs(_lowerCamelCase )
if not pairs:
return token
while True:
lowerCamelCase__ : Union[str, Any] = min(_lowerCamelCase , key=lambda __lowerCamelCase : self.bpe_ranks.get(_lowerCamelCase , float("inf" ) ) )
if bigram not in self.bpe_ranks:
break
lowerCamelCase__ , lowerCamelCase__ : Union[str, Any] = bigram
lowerCamelCase__ : int = []
lowerCamelCase__ : List[str] = 0
while i < len(_lowerCamelCase ):
try:
lowerCamelCase__ : Tuple = word.index(_lowerCamelCase , _lowerCamelCase )
except ValueError:
new_word.extend(word[i:] )
break
else:
new_word.extend(word[i:j] )
lowerCamelCase__ : str = j
if word[i] == first and i < len(_lowerCamelCase ) - 1 and word[i + 1] == second:
new_word.append(first + second )
i += 2
else:
new_word.append(word[i] )
i += 1
lowerCamelCase__ : Tuple = tuple(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = new_word
if len(_lowerCamelCase ) == 1:
break
else:
lowerCamelCase__ : List[Any] = get_pairs(_lowerCamelCase )
lowerCamelCase__ : Dict = "@@ ".join(_lowerCamelCase )
lowerCamelCase__ : Optional[int] = word[:-4]
lowerCamelCase__ : Dict = word
return word
def lowerCAmelCase ( self : Union[str, Any] , __lowerCamelCase : str ):
'''simple docstring'''
lowerCamelCase__ : int = []
lowerCamelCase__ : Optional[Any] = re.findall(R"\S+\n?" , _lowerCamelCase )
for token in words:
split_tokens.extend(list(self.bpe(_lowerCamelCase ).split(" " ) ) )
return split_tokens
def lowerCAmelCase ( self : List[Any] , __lowerCamelCase : Tuple ):
'''simple docstring'''
return self.encoder.get(_lowerCamelCase , self.encoder.get(self.unk_token ) )
def lowerCAmelCase ( self : Optional[int] , __lowerCamelCase : Optional[int] ):
'''simple docstring'''
return self.decoder.get(_lowerCamelCase , self.unk_token )
def lowerCAmelCase ( self : Any , __lowerCamelCase : List[Any] ):
'''simple docstring'''
lowerCamelCase__ : int = " ".join(_lowerCamelCase ).replace("@@ " , "" ).strip()
return out_string
def lowerCAmelCase ( self : int , __lowerCamelCase : str , __lowerCamelCase : Optional[str] = None ):
'''simple docstring'''
if not os.path.isdir(_lowerCamelCase ):
logger.error(f"Vocabulary path ({save_directory}) should be a directory" )
return
lowerCamelCase__ : Optional[int] = os.path.join(
_lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
lowerCamelCase__ : List[str] = os.path.join(
_lowerCamelCase , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(_lowerCamelCase ):
copyfile(self.vocab_file , _lowerCamelCase )
if os.path.abspath(self.merges_file ) != os.path.abspath(_lowerCamelCase ):
copyfile(self.merges_file , _lowerCamelCase )
return out_vocab_file, out_merge_file
def lowerCAmelCase ( self : str , __lowerCamelCase : Union[str, Any] ):
'''simple docstring'''
if isinstance(_lowerCamelCase , _lowerCamelCase ):
try:
with open(_lowerCamelCase , "r" , encoding="utf-8" ) as fd:
self.add_from_file(_lowerCamelCase )
except FileNotFoundError as fnfe:
raise fnfe
except UnicodeError:
raise Exception(f"Incorrect encoding detected in {f}, please rebuild the dataset" )
return
lowerCamelCase__ : str = f.readlines()
for lineTmp in lines:
lowerCamelCase__ : Any = lineTmp.strip()
lowerCamelCase__ : List[Any] = line.rfind(" " )
if idx == -1:
raise ValueError("Incorrect dictionary format, expected \'<token> <cnt>\'" )
lowerCamelCase__ : Optional[Any] = line[:idx]
lowerCamelCase__ : Any = len(self.encoder )
| 700
|
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
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 ( lowercase__ , unittest.TestCase):
"""simple docstring"""
A__ = KandinskyVaaImgaImgPipeline
A__ = ["image_embeds", "negative_image_embeds", "image"]
A__ = [
"image_embeds",
"negative_image_embeds",
"image",
]
A__ = [
"generator",
"height",
"width",
"strength",
"guidance_scale",
"num_inference_steps",
"return_dict",
"guidance_scale",
"num_images_per_prompt",
"output_type",
"return_dict",
]
A__ = False
@property
def lowerCAmelCase ( self : Dict ):
'''simple docstring'''
return 32
@property
def lowerCAmelCase ( self : Tuple ):
'''simple docstring'''
return 32
@property
def lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
return self.time_input_dim
@property
def lowerCAmelCase ( self : List[str] ):
'''simple docstring'''
return self.time_input_dim * 4
@property
def lowerCAmelCase ( self : Optional[Any] ):
'''simple docstring'''
return 100
@property
def lowerCAmelCase ( self : Union[str, Any] ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : Optional[Any] = {
"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__ : Tuple = UNetaDConditionModel(**__lowerCamelCase )
return model
@property
def lowerCAmelCase ( self : int ):
'''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 lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
torch.manual_seed(0 )
lowerCamelCase__ : int = VQModel(**self.dummy_movq_kwargs )
return model
def lowerCAmelCase ( self : Optional[int] ):
'''simple docstring'''
lowerCamelCase__ : List[str] = self.dummy_unet
lowerCamelCase__ : Optional[Any] = self.dummy_movq
lowerCamelCase__ : Optional[int] = {
"num_train_timesteps": 1000,
"beta_schedule": "linear",
"beta_start": 0.0_0_0_8_5,
"beta_end": 0.0_1_2,
"clip_sample": False,
"set_alpha_to_one": False,
"steps_offset": 0,
"prediction_type": "epsilon",
"thresholding": False,
}
lowerCamelCase__ : List[Any] = DDIMScheduler(**__lowerCamelCase )
lowerCamelCase__ : Tuple = {
"unet": unet,
"scheduler": scheduler,
"movq": movq,
}
return components
def lowerCAmelCase ( self : List[str] , __lowerCamelCase : str , __lowerCamelCase : int=0 ):
'''simple docstring'''
lowerCamelCase__ : int = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
lowerCamelCase__ : Union[str, Any] = floats_tensor((1, self.text_embedder_hidden_size) , rng=random.Random(seed + 1 ) ).to(
__lowerCamelCase )
# create init_image
lowerCamelCase__ : Union[str, Any] = floats_tensor((1, 3, 64, 64) , rng=random.Random(__lowerCamelCase ) ).to(__lowerCamelCase )
lowerCamelCase__ : List[Any] = image.cpu().permute(0 , 2 , 3 , 1 )[0]
lowerCamelCase__ : Optional[int] = Image.fromarray(np.uinta(__lowerCamelCase ) ).convert("RGB" ).resize((256, 256) )
if str(__lowerCamelCase ).startswith("mps" ):
lowerCamelCase__ : Optional[int] = torch.manual_seed(__lowerCamelCase )
else:
lowerCamelCase__ : List[str] = torch.Generator(device=__lowerCamelCase ).manual_seed(__lowerCamelCase )
lowerCamelCase__ : Tuple = {
"image": init_image,
"image_embeds": image_embeds,
"negative_image_embeds": negative_image_embeds,
"generator": generator,
"height": 64,
"width": 64,
"num_inference_steps": 10,
"guidance_scale": 7.0,
"strength": 0.2,
"output_type": "np",
}
return inputs
def lowerCAmelCase ( self : int ):
'''simple docstring'''
lowerCamelCase__ : Dict = "cpu"
lowerCamelCase__ : str = self.get_dummy_components()
lowerCamelCase__ : Optional[int] = self.pipeline_class(**__lowerCamelCase )
lowerCamelCase__ : List[str] = pipe.to(__lowerCamelCase )
pipe.set_progress_bar_config(disable=__lowerCamelCase )
lowerCamelCase__ : Optional[Any] = pipe(**self.get_dummy_inputs(__lowerCamelCase ) )
lowerCamelCase__ : List[str] = output.images
lowerCamelCase__ : Optional[Any] = pipe(
**self.get_dummy_inputs(__lowerCamelCase ) , return_dict=__lowerCamelCase , )[0]
lowerCamelCase__ : int = image[0, -3:, -3:, -1]
lowerCamelCase__ : int = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 64, 64, 3)
lowerCamelCase__ : str = np.array(
[0.6_1_9_9_7_7_8, 0.6_3_9_8_4_4_0_6, 0.4_6_1_4_5_7_8_5, 0.6_2_9_4_4_9_8_4, 0.5_6_2_2_2_1_5, 0.4_7_3_0_6_1_3_2, 0.4_7_4_4_1_4_5_6, 0.4_6_0_7_6_0_6, 0.4_8_7_1_9_2_6_3] )
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 lowerCAmelCase ( self : Dict ):
'''simple docstring'''
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase ( self : List[Any] ):
'''simple docstring'''
lowerCamelCase__ : List[str] = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/kandinskyv22/kandinskyv22_img2img_frog.npy" )
lowerCamelCase__ : Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main" "/kandinsky/cat.png" )
lowerCamelCase__ : Any = "A red cartoon frog, 4k"
lowerCamelCase__ : str = KandinskyVaaPriorPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-prior" , torch_dtype=torch.floataa )
pipe_prior.to(__lowerCamelCase )
lowerCamelCase__ : Tuple = KandinskyVaaImgaImgPipeline.from_pretrained(
"kandinsky-community/kandinsky-2-2-decoder" , torch_dtype=torch.floataa )
lowerCamelCase__ : str = pipeline.to(__lowerCamelCase )
pipeline.set_progress_bar_config(disable=__lowerCamelCase )
lowerCamelCase__ : Tuple = torch.Generator(device="cpu" ).manual_seed(0 )
lowerCamelCase__ , lowerCamelCase__ : List[str] = pipe_prior(
__lowerCamelCase , generator=__lowerCamelCase , num_inference_steps=5 , negative_prompt="" , ).to_tuple()
lowerCamelCase__ : Optional[Any] = pipeline(
image=__lowerCamelCase , image_embeds=__lowerCamelCase , negative_image_embeds=__lowerCamelCase , generator=__lowerCamelCase , num_inference_steps=100 , height=768 , width=768 , strength=0.2 , output_type="np" , )
lowerCamelCase__ : Union[str, Any] = output.images[0]
assert image.shape == (768, 768, 3)
assert_mean_pixel_difference(__lowerCamelCase , __lowerCamelCase )
| 5
| 0
|
from __future__ import annotations
def lowerCAmelCase_ (lowerCAmelCase__: int | str ):
"""simple docstring"""
UpperCAmelCase_: Optional[int] = str(lowerCAmelCase__ )
return n == n[::-1]
def lowerCAmelCase_ (lowerCAmelCase__: int = 1_0_0_0_0_0_0 ):
"""simple docstring"""
UpperCAmelCase_: int = 0
for i in range(1 , lowerCAmelCase__ ):
if is_palindrome(lowerCAmelCase__ ) and is_palindrome(bin(lowerCAmelCase__ ).split("""b""" )[1] ):
total += i
return total
if __name__ == "__main__":
print(solution(int(str(input().strip()))))
| 556
|
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFFunnelBaseModel,
TFFunnelForMaskedLM,
TFFunnelForMultipleChoice,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForSequenceClassification,
TFFunnelForTokenClassification,
TFFunnelModel,
)
class _a :
def __init__(self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_=13, SCREAMING_SNAKE_CASE_=7, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=True, SCREAMING_SNAKE_CASE_=99, SCREAMING_SNAKE_CASE_=[1, 1, 2], SCREAMING_SNAKE_CASE_=1, SCREAMING_SNAKE_CASE_=32, SCREAMING_SNAKE_CASE_=4, SCREAMING_SNAKE_CASE_=8, SCREAMING_SNAKE_CASE_=37, SCREAMING_SNAKE_CASE_="gelu_new", SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.1, SCREAMING_SNAKE_CASE_=0.0, SCREAMING_SNAKE_CASE_=512, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=0.0_2, SCREAMING_SNAKE_CASE_=3, SCREAMING_SNAKE_CASE_=4, SCREAMING_SNAKE_CASE_=None, SCREAMING_SNAKE_CASE_=False, ) -> Tuple:
UpperCAmelCase_: Any = parent
UpperCAmelCase_: Optional[Any] = batch_size
UpperCAmelCase_: Dict = seq_length
UpperCAmelCase_: Union[str, Any] = is_training
UpperCAmelCase_: Optional[Any] = use_input_mask
UpperCAmelCase_: Optional[Any] = use_token_type_ids
UpperCAmelCase_: int = use_labels
UpperCAmelCase_: List[str] = vocab_size
UpperCAmelCase_: Optional[int] = block_sizes
UpperCAmelCase_: Tuple = num_decoder_layers
UpperCAmelCase_: List[Any] = d_model
UpperCAmelCase_: Dict = n_head
UpperCAmelCase_: Optional[Any] = d_head
UpperCAmelCase_: Optional[Any] = d_inner
UpperCAmelCase_: str = hidden_act
UpperCAmelCase_: str = hidden_dropout
UpperCAmelCase_: Union[str, Any] = attention_dropout
UpperCAmelCase_: Dict = activation_dropout
UpperCAmelCase_: str = max_position_embeddings
UpperCAmelCase_: Dict = type_vocab_size
UpperCAmelCase_: str = 2
UpperCAmelCase_: Dict = num_labels
UpperCAmelCase_: Optional[int] = num_choices
UpperCAmelCase_: Optional[int] = scope
UpperCAmelCase_: List[Any] = initializer_std
# Used in the tests to check the size of the first attention layer
UpperCAmelCase_: Tuple = n_head
# Used in the tests to check the size of the first hidden state
UpperCAmelCase_: Union[str, Any] = self.d_model
# Used in the tests to check the number of output hidden states/attentions
UpperCAmelCase_: str = sum(self.block_sizes ) + (0 if base else self.num_decoder_layers)
# FunnelModel adds two hidden layers: input embeddings and the sum of the upsampled encoder hidden state with
# the last hidden state of the first block (which is the first hidden state of the decoder).
if not base:
UpperCAmelCase_: Dict = self.num_hidden_layers + 2
def __snake_case (self ) -> Union[str, Any]:
UpperCAmelCase_: Dict = ids_tensor([self.batch_size, self.seq_length], self.vocab_size )
UpperCAmelCase_: Dict = None
if self.use_input_mask:
UpperCAmelCase_: List[str] = random_attention_mask([self.batch_size, self.seq_length] )
UpperCAmelCase_: Union[str, Any] = None
if self.use_token_type_ids:
UpperCAmelCase_: List[Any] = ids_tensor([self.batch_size, self.seq_length], self.type_vocab_size )
UpperCAmelCase_: Any = None
UpperCAmelCase_: str = None
UpperCAmelCase_: Any = None
if self.use_labels:
UpperCAmelCase_: Dict = ids_tensor([self.batch_size], self.type_sequence_label_size )
UpperCAmelCase_: Optional[Any] = ids_tensor([self.batch_size, self.seq_length], self.num_labels )
UpperCAmelCase_: str = ids_tensor([self.batch_size], self.num_choices )
UpperCAmelCase_: Dict = FunnelConfig(
vocab_size=self.vocab_size, block_sizes=self.block_sizes, num_decoder_layers=self.num_decoder_layers, d_model=self.d_model, n_head=self.n_head, d_head=self.d_head, d_inner=self.d_inner, hidden_act=self.hidden_act, hidden_dropout=self.hidden_dropout, attention_dropout=self.attention_dropout, activation_dropout=self.activation_dropout, max_position_embeddings=self.max_position_embeddings, type_vocab_size=self.type_vocab_size, initializer_std=self.initializer_std, )
return (
config,
input_ids,
token_type_ids,
input_mask,
sequence_labels,
token_labels,
choice_labels,
)
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, ) -> int:
UpperCAmelCase_: str = TFFunnelModel(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: List[Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase_: Tuple = model(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: int = [input_ids, input_mask]
UpperCAmelCase_: Dict = model(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Union[str, Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
UpperCAmelCase_: Union[str, Any] = False
UpperCAmelCase_: int = TFFunnelModel(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: List[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
UpperCAmelCase_: Optional[Any] = False
UpperCAmelCase_: str = TFFunnelModel(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: List[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.d_model) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, ) -> Any:
UpperCAmelCase_: Dict = TFFunnelBaseModel(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Tuple = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase_: Tuple = model(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Optional[int] = [input_ids, input_mask]
UpperCAmelCase_: List[str] = model(SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: int = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model) )
UpperCAmelCase_: List[str] = False
UpperCAmelCase_: str = TFFunnelBaseModel(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Any = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 3, self.d_model) )
UpperCAmelCase_: List[Any] = False
UpperCAmelCase_: List[Any] = TFFunnelBaseModel(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: List[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, 2, self.d_model) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, ) -> Dict:
UpperCAmelCase_: List[Any] = TFFunnelForPreTraining(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Union[str, Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase_: List[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, ) -> str:
UpperCAmelCase_: Union[str, Any] = TFFunnelForMaskedLM(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: List[str] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase_: List[Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.vocab_size) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, ) -> int:
UpperCAmelCase_: Tuple = self.num_labels
UpperCAmelCase_: Optional[int] = TFFunnelForSequenceClassification(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Union[str, Any] = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase_: Dict = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_labels) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, ) -> int:
UpperCAmelCase_: Tuple = self.num_choices
UpperCAmelCase_: List[str] = TFFunnelForMultipleChoice(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: int = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_, 1 ), (1, self.num_choices, 1) )
UpperCAmelCase_: Any = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_, 1 ), (1, self.num_choices, 1) )
UpperCAmelCase_: int = tf.tile(tf.expand_dims(SCREAMING_SNAKE_CASE_, 1 ), (1, self.num_choices, 1) )
UpperCAmelCase_: Tuple = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
UpperCAmelCase_: Tuple = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.num_choices) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, ) -> int:
UpperCAmelCase_: List[Any] = self.num_labels
UpperCAmelCase_: Dict = TFFunnelForTokenClassification(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Tuple = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase_: Union[str, Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.logits.shape, (self.batch_size, self.seq_length, self.num_labels) )
def __snake_case (self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_, ) -> str:
UpperCAmelCase_: Any = TFFunnelForQuestionAnswering(config=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Dict = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
UpperCAmelCase_: Union[str, Any] = model(SCREAMING_SNAKE_CASE_ )
self.parent.assertEqual(result.start_logits.shape, (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape, (self.batch_size, self.seq_length) )
def __snake_case (self ) -> int:
UpperCAmelCase_: Optional[Any] = self.prepare_config_and_inputs()
(
(
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) , (
UpperCAmelCase_
) ,
): Tuple = config_and_inputs
UpperCAmelCase_: Tuple = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class _a ( _lowerCAmelCase , _lowerCAmelCase , unittest.TestCase ):
A = (
(
TFFunnelModel,
TFFunnelForMaskedLM,
TFFunnelForPreTraining,
TFFunnelForQuestionAnswering,
TFFunnelForTokenClassification,
)
if is_tf_available()
else ()
)
A = (
{
'''feature-extraction''': (TFFunnelBaseModel, TFFunnelModel),
'''fill-mask''': TFFunnelForMaskedLM,
'''question-answering''': TFFunnelForQuestionAnswering,
'''text-classification''': TFFunnelForSequenceClassification,
'''token-classification''': TFFunnelForTokenClassification,
'''zero-shot''': TFFunnelForSequenceClassification,
}
if is_tf_available()
else {}
)
A = False
A = False
def __snake_case (self ) -> Tuple:
UpperCAmelCase_: Union[str, Any] = TFFunnelModelTester(self )
UpperCAmelCase_: List[str] = ConfigTester(self, config_class=SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Any:
self.config_tester.run_common_tests()
def __snake_case (self ) -> int:
UpperCAmelCase_: List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> int:
UpperCAmelCase_: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Optional[int]:
UpperCAmelCase_: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Optional[Any]:
UpperCAmelCase_: Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> List[Any]:
UpperCAmelCase_: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*SCREAMING_SNAKE_CASE_ )
@require_tf
class _a ( _lowerCAmelCase , unittest.TestCase ):
A = (
(TFFunnelBaseModel, TFFunnelForMultipleChoice, TFFunnelForSequenceClassification) if is_tf_available() else ()
)
A = False
A = False
def __snake_case (self ) -> Dict:
UpperCAmelCase_: List[Any] = TFFunnelModelTester(self, base=SCREAMING_SNAKE_CASE_ )
UpperCAmelCase_: Dict = ConfigTester(self, config_class=SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Union[str, Any]:
self.config_tester.run_common_tests()
def __snake_case (self ) -> Union[str, Any]:
UpperCAmelCase_: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_base_model(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Union[str, Any]:
UpperCAmelCase_: Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*SCREAMING_SNAKE_CASE_ )
def __snake_case (self ) -> Tuple:
UpperCAmelCase_: Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*SCREAMING_SNAKE_CASE_ )
| 556
| 1
|
def A_ ( lowercase_ ) ->Union[str, Any]:
"""simple docstring"""
assert column_title.isupper()
SCREAMING_SNAKE_CASE = 0
SCREAMING_SNAKE_CASE = len(_lowerCAmelCase ) - 1
SCREAMING_SNAKE_CASE = 0
while index >= 0:
SCREAMING_SNAKE_CASE = (ord(column_title[index] ) - 6_4) * pow(2_6 , _lowerCAmelCase )
answer += value
power += 1
index -= 1
return answer
if __name__ == "__main__":
from doctest import testmod
testmod()
| 711
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["NllbTokenizer"]
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase = ["NllbTokenizerFast"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb import NllbTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_nllb_fast import NllbTokenizerFast
else:
import sys
__UpperCAmelCase = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 259
| 0
|
'''simple docstring'''
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=lowercase )
class lowercase__ ( lowercase ):
# `task` is not a ClassVar since we want it to be part of the `asdict` output for JSON serialization
lowercase__ = field(default="""text-classification""" , metadata={"""include_in_asdict_even_if_is_default""": True} )
lowercase__ = Features({"""text""": Value("""string""" )} )
lowercase__ = Features({"""labels""": ClassLabel} )
lowercase__ = "text"
lowercase__ = "labels"
def UpperCamelCase_ ( self : List[str] ,lowerCamelCase__ : List[str] ):
'''simple docstring'''
if self.label_column not in features:
raise ValueError(F'Column {self.label_column} is not present in features.' )
if not isinstance(features[self.label_column] ,lowerCamelCase__ ):
raise ValueError(F'Column {self.label_column} is not a ClassLabel.' )
_UpperCamelCase : Tuple = copy.deepcopy(self )
_UpperCamelCase : Tuple = self.label_schema.copy()
_UpperCamelCase : List[str] = features[self.label_column]
_UpperCamelCase : Any = label_schema
return task_template
@property
def UpperCamelCase_ ( self : Union[str, Any] ):
'''simple docstring'''
return {
self.text_column: "text",
self.label_column: "labels",
}
| 195
|
'''simple docstring'''
def A__ ( UpperCAmelCase_ , UpperCAmelCase_ ):
return int((input_a, input_a).count(0 ) != 0 )
def A__ ( ):
assert nand_gate(0 , 0 ) == 1
assert nand_gate(0 , 1 ) == 1
assert nand_gate(1 , 0 ) == 1
assert nand_gate(1 , 1 ) == 0
if __name__ == "__main__":
print(nand_gate(0, 0))
print(nand_gate(0, 1))
print(nand_gate(1, 0))
print(nand_gate(1, 1))
| 195
| 1
|
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _A ( pl.LightningModule ):
def __init__( self , _SCREAMING_SNAKE_CASE ):
super().__init__()
_UpperCAmelCase = model
_UpperCAmelCase = 2
_UpperCAmelCase = nn.Linear(self.model.config.hidden_size , self.num_labels )
def UpperCAmelCase ( self ):
pass
def _SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case ) -> Tuple:
_UpperCAmelCase = LongformerModel.from_pretrained(__UpperCamelCase )
_UpperCAmelCase = LightningModel(__UpperCamelCase )
_UpperCAmelCase = torch.load(__UpperCamelCase , map_location=torch.device("""cpu""" ) )
lightning_model.load_state_dict(ckpt["""state_dict"""] )
# init longformer question answering model
_UpperCAmelCase = LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase )
# transfer weights
longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict() )
longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict() )
longformer_for_qa.eval()
# save model
longformer_for_qa.save_pretrained(__UpperCamelCase )
print(f"Conversion successful. Model saved under {pytorch_dump_folder_path}" )
if __name__ == "__main__":
a = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--longformer_model",
default=None,
type=str,
required=True,
help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
)
parser.add_argument(
"--longformer_question_answering_ckpt_path",
default=None,
type=str,
required=True,
help="Path the official PyTorch Lightning Checkpoint.",
)
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_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 711
|
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_down_block, get_up_block
@dataclass
class _A ( __lowercase ):
__a = 42
class _A ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=("DownEncoderBlock2D",) , _SCREAMING_SNAKE_CASE=(64,) , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE="silu" , _SCREAMING_SNAKE_CASE=True , ):
super().__init__()
_UpperCAmelCase = layers_per_block
_UpperCAmelCase = torch.nn.Convad(
_SCREAMING_SNAKE_CASE , block_out_channels[0] , kernel_size=3 , stride=1 , padding=1 , )
_UpperCAmelCase = None
_UpperCAmelCase = nn.ModuleList([] )
# down
_UpperCAmelCase = block_out_channels[0]
for i, down_block_type in enumerate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = output_channel
_UpperCAmelCase = block_out_channels[i]
_UpperCAmelCase = i == len(_SCREAMING_SNAKE_CASE ) - 1
_UpperCAmelCase = get_down_block(
_SCREAMING_SNAKE_CASE , num_layers=self.layers_per_block , in_channels=_SCREAMING_SNAKE_CASE , out_channels=_SCREAMING_SNAKE_CASE , add_downsample=not is_final_block , resnet_eps=1e-6 , downsample_padding=0 , resnet_act_fn=_SCREAMING_SNAKE_CASE , resnet_groups=_SCREAMING_SNAKE_CASE , attention_head_dim=_SCREAMING_SNAKE_CASE , temb_channels=_SCREAMING_SNAKE_CASE , )
self.down_blocks.append(_SCREAMING_SNAKE_CASE )
# mid
_UpperCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=_SCREAMING_SNAKE_CASE , output_scale_factor=1 , resnet_time_scale_shift="""default""" , attention_head_dim=block_out_channels[-1] , resnet_groups=_SCREAMING_SNAKE_CASE , temb_channels=_SCREAMING_SNAKE_CASE , )
# out
_UpperCAmelCase = nn.GroupNorm(num_channels=block_out_channels[-1] , num_groups=_SCREAMING_SNAKE_CASE , eps=1e-6 )
_UpperCAmelCase = nn.SiLU()
_UpperCAmelCase = 2 * out_channels if double_z else out_channels
_UpperCAmelCase = nn.Convad(block_out_channels[-1] , _SCREAMING_SNAKE_CASE , 3 , padding=1 )
_UpperCAmelCase = False
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = x
_UpperCAmelCase = self.conv_in(_SCREAMING_SNAKE_CASE )
if self.training and self.gradient_checkpointing:
def create_custom_forward(_SCREAMING_SNAKE_CASE ):
def custom_forward(*_SCREAMING_SNAKE_CASE ):
return module(*_SCREAMING_SNAKE_CASE )
return custom_forward
# down
if is_torch_version(""">=""" , """1.11.0""" ):
for down_block in self.down_blocks:
_UpperCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , use_reentrant=_SCREAMING_SNAKE_CASE )
# middle
_UpperCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , _SCREAMING_SNAKE_CASE , use_reentrant=_SCREAMING_SNAKE_CASE )
else:
for down_block in self.down_blocks:
_UpperCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE )
# middle
_UpperCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(self.mid_block ) , _SCREAMING_SNAKE_CASE )
else:
# down
for down_block in self.down_blocks:
_UpperCAmelCase = down_block(_SCREAMING_SNAKE_CASE )
# middle
_UpperCAmelCase = self.mid_block(_SCREAMING_SNAKE_CASE )
# post-process
_UpperCAmelCase = self.conv_norm_out(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.conv_act(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.conv_out(_SCREAMING_SNAKE_CASE )
return sample
class _A ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=3 , _SCREAMING_SNAKE_CASE=("UpDecoderBlock2D",) , _SCREAMING_SNAKE_CASE=(64,) , _SCREAMING_SNAKE_CASE=2 , _SCREAMING_SNAKE_CASE=32 , _SCREAMING_SNAKE_CASE="silu" , _SCREAMING_SNAKE_CASE="group" , ):
super().__init__()
_UpperCAmelCase = layers_per_block
_UpperCAmelCase = nn.Convad(
_SCREAMING_SNAKE_CASE , block_out_channels[-1] , kernel_size=3 , stride=1 , padding=1 , )
_UpperCAmelCase = None
_UpperCAmelCase = nn.ModuleList([] )
_UpperCAmelCase = in_channels if norm_type == """spatial""" else None
# mid
_UpperCAmelCase = UNetMidBlockaD(
in_channels=block_out_channels[-1] , resnet_eps=1e-6 , resnet_act_fn=_SCREAMING_SNAKE_CASE , output_scale_factor=1 , resnet_time_scale_shift="""default""" if norm_type == """group""" else norm_type , attention_head_dim=block_out_channels[-1] , resnet_groups=_SCREAMING_SNAKE_CASE , temb_channels=_SCREAMING_SNAKE_CASE , )
# up
_UpperCAmelCase = list(reversed(_SCREAMING_SNAKE_CASE ) )
_UpperCAmelCase = reversed_block_out_channels[0]
for i, up_block_type in enumerate(_SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = output_channel
_UpperCAmelCase = reversed_block_out_channels[i]
_UpperCAmelCase = i == len(_SCREAMING_SNAKE_CASE ) - 1
_UpperCAmelCase = get_up_block(
_SCREAMING_SNAKE_CASE , num_layers=self.layers_per_block + 1 , in_channels=_SCREAMING_SNAKE_CASE , out_channels=_SCREAMING_SNAKE_CASE , prev_output_channel=_SCREAMING_SNAKE_CASE , add_upsample=not is_final_block , resnet_eps=1e-6 , resnet_act_fn=_SCREAMING_SNAKE_CASE , resnet_groups=_SCREAMING_SNAKE_CASE , attention_head_dim=_SCREAMING_SNAKE_CASE , temb_channels=_SCREAMING_SNAKE_CASE , resnet_time_scale_shift=_SCREAMING_SNAKE_CASE , )
self.up_blocks.append(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = output_channel
# out
if norm_type == "spatial":
_UpperCAmelCase = SpatialNorm(block_out_channels[0] , _SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = nn.GroupNorm(num_channels=block_out_channels[0] , num_groups=_SCREAMING_SNAKE_CASE , eps=1e-6 )
_UpperCAmelCase = nn.SiLU()
_UpperCAmelCase = nn.Convad(block_out_channels[0] , _SCREAMING_SNAKE_CASE , 3 , padding=1 )
_UpperCAmelCase = False
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None ):
_UpperCAmelCase = z
_UpperCAmelCase = self.conv_in(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = next(iter(self.up_blocks.parameters() ) ).dtype
if self.training and self.gradient_checkpointing:
def create_custom_forward(_SCREAMING_SNAKE_CASE ):
def custom_forward(*_SCREAMING_SNAKE_CASE ):
return module(*_SCREAMING_SNAKE_CASE )
return custom_forward
if is_torch_version(""">=""" , """1.11.0""" ):
# middle
_UpperCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , use_reentrant=_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = sample.to(_SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
_UpperCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , use_reentrant=_SCREAMING_SNAKE_CASE )
else:
# middle
_UpperCAmelCase = torch.utils.checkpoint.checkpoint(
create_custom_forward(self.mid_block ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = sample.to(_SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
_UpperCAmelCase = torch.utils.checkpoint.checkpoint(create_custom_forward(_SCREAMING_SNAKE_CASE ) , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
else:
# middle
_UpperCAmelCase = self.mid_block(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = sample.to(_SCREAMING_SNAKE_CASE )
# up
for up_block in self.up_blocks:
_UpperCAmelCase = up_block(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
# post-process
if latent_embeds is None:
_UpperCAmelCase = self.conv_norm_out(_SCREAMING_SNAKE_CASE )
else:
_UpperCAmelCase = self.conv_norm_out(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.conv_act(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = self.conv_out(_SCREAMING_SNAKE_CASE )
return sample
class _A ( nn.Module ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="random" , _SCREAMING_SNAKE_CASE=False , _SCREAMING_SNAKE_CASE=True ):
super().__init__()
_UpperCAmelCase = n_e
_UpperCAmelCase = vq_embed_dim
_UpperCAmelCase = beta
_UpperCAmelCase = legacy
_UpperCAmelCase = nn.Embedding(self.n_e , self.vq_embed_dim )
self.embedding.weight.data.uniform_(-1.0 / self.n_e , 1.0 / self.n_e )
_UpperCAmelCase = remap
if self.remap is not None:
self.register_buffer("""used""" , torch.tensor(np.load(self.remap ) ) )
_UpperCAmelCase = self.used.shape[0]
_UpperCAmelCase = unknown_index # "random" or "extra" or integer
if self.unknown_index == "extra":
_UpperCAmelCase = self.re_embed
_UpperCAmelCase = self.re_embed + 1
print(
F"Remapping {self.n_e} indices to {self.re_embed} indices. "
F"Using {self.unknown_index} for unknown indices." )
else:
_UpperCAmelCase = n_e
_UpperCAmelCase = sane_index_shape
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = inds.shape
assert len(_SCREAMING_SNAKE_CASE ) > 1
_UpperCAmelCase = inds.reshape(ishape[0] , -1 )
_UpperCAmelCase = self.used.to(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = (inds[:, :, None] == used[None, None, ...]).long()
_UpperCAmelCase = match.argmax(-1 )
_UpperCAmelCase = match.sum(2 ) < 1
if self.unknown_index == "random":
_UpperCAmelCase = torch.randint(0 , self.re_embed , size=new[unknown].shape ).to(device=new.device )
else:
_UpperCAmelCase = self.unknown_index
return new.reshape(_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = inds.shape
assert len(_SCREAMING_SNAKE_CASE ) > 1
_UpperCAmelCase = inds.reshape(ishape[0] , -1 )
_UpperCAmelCase = self.used.to(_SCREAMING_SNAKE_CASE )
if self.re_embed > self.used.shape[0]: # extra token
_UpperCAmelCase = 0 # simply set to zero
_UpperCAmelCase = torch.gather(used[None, :][inds.shape[0] * [0], :] , 1 , _SCREAMING_SNAKE_CASE )
return back.reshape(_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE ):
# reshape z -> (batch, height, width, channel) and flatten
_UpperCAmelCase = z.permute(0 , 2 , 3 , 1 ).contiguous()
_UpperCAmelCase = z.view(-1 , self.vq_embed_dim )
# distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z
_UpperCAmelCase = torch.argmin(torch.cdist(_SCREAMING_SNAKE_CASE , self.embedding.weight ) , dim=1 )
_UpperCAmelCase = self.embedding(_SCREAMING_SNAKE_CASE ).view(z.shape )
_UpperCAmelCase = None
_UpperCAmelCase = None
# compute loss for embedding
if not self.legacy:
_UpperCAmelCase = self.beta * torch.mean((z_q.detach() - z) ** 2 ) + torch.mean((z_q - z.detach()) ** 2 )
else:
_UpperCAmelCase = torch.mean((z_q.detach() - z) ** 2 ) + self.beta * torch.mean((z_q - z.detach()) ** 2 )
# preserve gradients
_UpperCAmelCase = z + (z_q - z).detach()
# reshape back to match original input shape
_UpperCAmelCase = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
if self.remap is not None:
_UpperCAmelCase = min_encoding_indices.reshape(z.shape[0] , -1 ) # add batch axis
_UpperCAmelCase = self.remap_to_used(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = min_encoding_indices.reshape(-1 , 1 ) # flatten
if self.sane_index_shape:
_UpperCAmelCase = min_encoding_indices.reshape(z_q.shape[0] , z_q.shape[2] , z_q.shape[3] )
return z_q, loss, (perplexity, min_encodings, min_encoding_indices)
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ):
# shape specifying (batch, height, width, channel)
if self.remap is not None:
_UpperCAmelCase = indices.reshape(shape[0] , -1 ) # add batch axis
_UpperCAmelCase = self.unmap_to_all(_SCREAMING_SNAKE_CASE )
_UpperCAmelCase = indices.reshape(-1 ) # flatten again
# get quantized latent vectors
_UpperCAmelCase = self.embedding(_SCREAMING_SNAKE_CASE )
if shape is not None:
_UpperCAmelCase = z_q.view(_SCREAMING_SNAKE_CASE )
# reshape back to match original input shape
_UpperCAmelCase = z_q.permute(0 , 3 , 1 , 2 ).contiguous()
return z_q
class _A ( __lowercase ):
def __init__( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=False ):
_UpperCAmelCase = parameters
_UpperCAmelCase , _UpperCAmelCase = torch.chunk(_SCREAMING_SNAKE_CASE , 2 , dim=1 )
_UpperCAmelCase = torch.clamp(self.logvar , -30.0 , 20.0 )
_UpperCAmelCase = deterministic
_UpperCAmelCase = torch.exp(0.5 * self.logvar )
_UpperCAmelCase = torch.exp(self.logvar )
if self.deterministic:
_UpperCAmelCase = _UpperCAmelCase = torch.zeros_like(
self.mean , device=self.parameters.device , dtype=self.parameters.dtype )
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE = None ):
# make sure sample is on the same device as the parameters and has same dtype
_UpperCAmelCase = randn_tensor(
self.mean.shape , generator=_SCREAMING_SNAKE_CASE , device=self.parameters.device , dtype=self.parameters.dtype )
_UpperCAmelCase = self.mean + self.std * sample
return x
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE=None ):
if self.deterministic:
return torch.Tensor([0.0] )
else:
if other is None:
return 0.5 * torch.sum(torch.pow(self.mean , 2 ) + self.var - 1.0 - self.logvar , dim=[1, 2, 3] )
else:
return 0.5 * torch.sum(
torch.pow(self.mean - other.mean , 2 ) / other.var
+ self.var / other.var
- 1.0
- self.logvar
+ other.logvar , dim=[1, 2, 3] , )
def UpperCAmelCase ( self , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=[1, 2, 3] ):
if self.deterministic:
return torch.Tensor([0.0] )
_UpperCAmelCase = np.log(2.0 * np.pi )
return 0.5 * torch.sum(logtwopi + self.logvar + torch.pow(sample - self.mean , 2 ) / self.var , dim=_SCREAMING_SNAKE_CASE )
def UpperCAmelCase ( self ):
return self.mean
| 175
| 0
|
"""simple docstring"""
def lowercase_ ( _lowercase : int , _lowercase : Tuple ):
'''simple docstring'''
if b == 0:
return 1
if (b % 2) == 0:
return actual_power(A__ , int(b / 2 ) ) * actual_power(A__ , int(b / 2 ) )
else:
return a * actual_power(A__ , int(b / 2 ) ) * actual_power(A__ , int(b / 2 ) )
def lowercase_ ( _lowercase : int , _lowercase : Any ):
'''simple docstring'''
if b < 0:
return 1 / actual_power(A__ , A__ )
return actual_power(A__ , A__ )
if __name__ == "__main__":
print(power(-2, -3))
| 595
|
"""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 DetrConfig, DetrForObjectDetection, DetrForSegmentation, DetrImageProcessor, ResNetConfig
from transformers.utils import logging
logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
def A__ ( A__ ) -> List[str]:
'''simple docstring'''
if "resnet-50" in model_name:
_UpperCAmelCase = ResNetConfig.from_pretrained("microsoft/resnet-50" )
elif "resnet-101" in model_name:
_UpperCAmelCase = ResNetConfig.from_pretrained("microsoft/resnet-101" )
else:
raise ValueError("Model name should include either resnet50 or resnet101" )
_UpperCAmelCase = DetrConfig(use_timm_backbone=A__ , backbone_config=A__ )
# set label attributes
_UpperCAmelCase = "panoptic" in model_name
if is_panoptic:
_UpperCAmelCase = 250
else:
_UpperCAmelCase = 91
_UpperCAmelCase = "huggingface/label-files"
_UpperCAmelCase = "coco-detection-id2label.json"
_UpperCAmelCase = json.load(open(hf_hub_download(A__ , A__ , repo_type="dataset" ) , "r" ) )
_UpperCAmelCase = {int(A__ ): v for k, v in idalabel.items()}
_UpperCAmelCase = idalabel
_UpperCAmelCase = {v: k for k, v in idalabel.items()}
return config, is_panoptic
def A__ ( A__ ) -> Dict:
'''simple docstring'''
_UpperCAmelCase = []
# stem
# fmt: off
rename_keys.append(("backbone.0.body.conv1.weight", "backbone.conv_encoder.model.embedder.embedder.convolution.weight") )
rename_keys.append(("backbone.0.body.bn1.weight", "backbone.conv_encoder.model.embedder.embedder.normalization.weight") )
rename_keys.append(("backbone.0.body.bn1.bias", "backbone.conv_encoder.model.embedder.embedder.normalization.bias") )
rename_keys.append(("backbone.0.body.bn1.running_mean", "backbone.conv_encoder.model.embedder.embedder.normalization.running_mean") )
rename_keys.append(("backbone.0.body.bn1.running_var", "backbone.conv_encoder.model.embedder.embedder.normalization.running_var") )
# stages
for stage_idx in range(len(config.backbone_config.depths ) ):
for layer_idx in range(config.backbone_config.depths[stage_idx] ):
# shortcut
if layer_idx == 0:
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.0.weight""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.convolution.weight""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.weight""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.weight""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.bias""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.bias""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_mean""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_mean""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.downsample.1.running_var""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.shortcut.normalization.running_var""",
) )
# 3 convs
for i in range(3 ):
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.conv{i+1}.weight""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.convolution.weight""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.weight""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.weight""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.bias""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.bias""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_mean""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_mean""",
) )
rename_keys.append(
(
F"""backbone.0.body.layer{stage_idx + 1}.{layer_idx}.bn{i+1}.running_var""",
F"""backbone.conv_encoder.model.encoder.stages.{stage_idx}.layers.{layer_idx}.layer.{i}.normalization.running_var""",
) )
# fmt: on
for i in range(config.encoder_layers ):
# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
rename_keys.append(
(
F"""transformer.encoder.layers.{i}.self_attn.out_proj.weight""",
F"""encoder.layers.{i}.self_attn.out_proj.weight""",
) )
rename_keys.append(
(F"""transformer.encoder.layers.{i}.self_attn.out_proj.bias""", F"""encoder.layers.{i}.self_attn.out_proj.bias""") )
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.weight""", F"""encoder.layers.{i}.fc1.weight""") )
rename_keys.append((F"""transformer.encoder.layers.{i}.linear1.bias""", F"""encoder.layers.{i}.fc1.bias""") )
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.weight""", F"""encoder.layers.{i}.fc2.weight""") )
rename_keys.append((F"""transformer.encoder.layers.{i}.linear2.bias""", F"""encoder.layers.{i}.fc2.bias""") )
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.weight""", F"""encoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm1.bias""", F"""encoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append(
(F"""transformer.encoder.layers.{i}.norm2.weight""", F"""encoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((F"""transformer.encoder.layers.{i}.norm2.bias""", F"""encoder.layers.{i}.final_layer_norm.bias""") )
# decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.self_attn.out_proj.weight""",
F"""decoder.layers.{i}.self_attn.out_proj.weight""",
) )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.self_attn.out_proj.bias""", F"""decoder.layers.{i}.self_attn.out_proj.bias""") )
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.weight""",
F"""decoder.layers.{i}.encoder_attn.out_proj.weight""",
) )
rename_keys.append(
(
F"""transformer.decoder.layers.{i}.multihead_attn.out_proj.bias""",
F"""decoder.layers.{i}.encoder_attn.out_proj.bias""",
) )
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.weight""", F"""decoder.layers.{i}.fc1.weight""") )
rename_keys.append((F"""transformer.decoder.layers.{i}.linear1.bias""", F"""decoder.layers.{i}.fc1.bias""") )
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.weight""", F"""decoder.layers.{i}.fc2.weight""") )
rename_keys.append((F"""transformer.decoder.layers.{i}.linear2.bias""", F"""decoder.layers.{i}.fc2.bias""") )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.weight""", F"""decoder.layers.{i}.self_attn_layer_norm.weight""") )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm1.bias""", F"""decoder.layers.{i}.self_attn_layer_norm.bias""") )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.weight""", F"""decoder.layers.{i}.encoder_attn_layer_norm.weight""") )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm2.bias""", F"""decoder.layers.{i}.encoder_attn_layer_norm.bias""") )
rename_keys.append(
(F"""transformer.decoder.layers.{i}.norm3.weight""", F"""decoder.layers.{i}.final_layer_norm.weight""") )
rename_keys.append((F"""transformer.decoder.layers.{i}.norm3.bias""", F"""decoder.layers.{i}.final_layer_norm.bias""") )
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
rename_keys.extend(
[
("input_proj.weight", "input_projection.weight"),
("input_proj.bias", "input_projection.bias"),
("query_embed.weight", "query_position_embeddings.weight"),
("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
("class_embed.weight", "class_labels_classifier.weight"),
("class_embed.bias", "class_labels_classifier.bias"),
("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
] )
return rename_keys
def A__ ( A__ , A__ , A__ ) -> Union[str, Any]:
'''simple docstring'''
_UpperCAmelCase = state_dict.pop(A__ )
_UpperCAmelCase = val
def A__ ( A__ , A__=False ) -> Optional[Any]:
'''simple docstring'''
_UpperCAmelCase = ""
if is_panoptic:
_UpperCAmelCase = "detr."
# first: transformer encoder
for i in range(6 ):
# read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
_UpperCAmelCase = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight""" )
_UpperCAmelCase = state_dict.pop(F"""{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase = in_proj_weight[:256, :]
_UpperCAmelCase = in_proj_bias[:256]
_UpperCAmelCase = in_proj_weight[256:512, :]
_UpperCAmelCase = in_proj_bias[256:512]
_UpperCAmelCase = in_proj_weight[-256:, :]
_UpperCAmelCase = in_proj_bias[-256:]
# next: transformer decoder (which is a bit more complex because it also includes cross-attention)
for i in range(6 ):
# read in weights + bias of input projection layer of self-attention
_UpperCAmelCase = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_weight""" )
_UpperCAmelCase = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.self_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) to the state dict
_UpperCAmelCase = in_proj_weight[:256, :]
_UpperCAmelCase = in_proj_bias[:256]
_UpperCAmelCase = in_proj_weight[256:512, :]
_UpperCAmelCase = in_proj_bias[256:512]
_UpperCAmelCase = in_proj_weight[-256:, :]
_UpperCAmelCase = in_proj_bias[-256:]
# read in weights + bias of input projection layer of cross-attention
_UpperCAmelCase = state_dict.pop(
F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_weight""" )
_UpperCAmelCase = state_dict.pop(F"""{prefix}transformer.decoder.layers.{i}.multihead_attn.in_proj_bias""" )
# next, add query, keys and values (in that order) of cross-attention to the state dict
_UpperCAmelCase = in_proj_weight_cross_attn[:256, :]
_UpperCAmelCase = in_proj_bias_cross_attn[:256]
_UpperCAmelCase = in_proj_weight_cross_attn[256:512, :]
_UpperCAmelCase = in_proj_bias_cross_attn[256:512]
_UpperCAmelCase = in_proj_weight_cross_attn[-256:, :]
_UpperCAmelCase = in_proj_bias_cross_attn[-256:]
def A__ ( ) -> List[str]:
'''simple docstring'''
_UpperCAmelCase = "http://images.cocodataset.org/val2017/000000039769.jpg"
_UpperCAmelCase = Image.open(requests.get(A__ , stream=A__ ).raw )
return im
@torch.no_grad()
def A__ ( A__ , A__=None , A__=False ) -> Optional[int]:
'''simple docstring'''
_UpperCAmelCase , _UpperCAmelCase = get_detr_config(A__ )
# load original model from torch hub
_UpperCAmelCase = {
"detr-resnet-50": "detr_resnet50",
"detr-resnet-101": "detr_resnet101",
}
logger.info(F"""Converting model {model_name}...""" )
_UpperCAmelCase = torch.hub.load("facebookresearch/detr" , model_name_to_original_name[model_name] , pretrained=A__ ).eval()
_UpperCAmelCase = detr.state_dict()
# rename keys
for src, dest in create_rename_keys(A__ ):
if is_panoptic:
_UpperCAmelCase = "detr." + src
rename_key(A__ , A__ , A__ )
# query, key and value matrices need special treatment
read_in_q_k_v(A__ , is_panoptic=A__ )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
_UpperCAmelCase = "detr.model." if is_panoptic else "model."
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("detr" )
and not key.startswith("class_labels_classifier" )
and not key.startswith("bbox_predictor" )
):
_UpperCAmelCase = state_dict.pop(A__ )
_UpperCAmelCase = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
_UpperCAmelCase = state_dict.pop(A__ )
_UpperCAmelCase = val
elif key.startswith("bbox_attention" ) or key.startswith("mask_head" ):
continue
else:
_UpperCAmelCase = state_dict.pop(A__ )
_UpperCAmelCase = val
else:
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
_UpperCAmelCase = state_dict.pop(A__ )
_UpperCAmelCase = val
# finally, create HuggingFace model and load state dict
_UpperCAmelCase = DetrForSegmentation(A__ ) if is_panoptic else DetrForObjectDetection(A__ )
model.load_state_dict(A__ )
model.eval()
# verify our conversion on an image
_UpperCAmelCase = "coco_panoptic" if is_panoptic else "coco_detection"
_UpperCAmelCase = DetrImageProcessor(format=A__ )
_UpperCAmelCase = processor(images=prepare_img() , return_tensors="pt" )
_UpperCAmelCase = encoding["pixel_values"]
_UpperCAmelCase = detr(A__ )
_UpperCAmelCase = model(A__ )
assert torch.allclose(outputs.logits , original_outputs["pred_logits"] , atol=1E-3 )
assert torch.allclose(outputs.pred_boxes , original_outputs["pred_boxes"] , atol=1E-3 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["pred_masks"] , atol=1E-4 )
print("Looks ok!" )
if pytorch_dump_folder_path is not None:
# Save model and image processor
logger.info(F"""Saving PyTorch model and image processor to {pytorch_dump_folder_path}...""" )
Path(A__ ).mkdir(exist_ok=A__ )
model.save_pretrained(A__ )
processor.save_pretrained(A__ )
if push_to_hub:
# Upload model and image processor to the hub
logger.info("Uploading PyTorch model and image processor to the hub..." )
model.push_to_hub(F"""nielsr/{model_name}""" )
processor.push_to_hub(F"""nielsr/{model_name}""" )
if __name__ == "__main__":
SCREAMING_SNAKE_CASE_ = argparse.ArgumentParser()
parser.add_argument(
'''--model_name''',
default='''detr-resnet-50''',
type=str,
choices=['''detr-resnet-50''', '''detr-resnet-101'''],
help='''Name of the DETR model you\'d like to convert.''',
)
parser.add_argument(
'''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the folder to output PyTorch model.'''
)
parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Whether to push the model to the hub or not.''')
SCREAMING_SNAKE_CASE_ = parser.parse_args()
convert_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)
| 426
| 0
|
'''simple docstring'''
from __future__ import annotations
def __lowerCamelCase ( __lowerCAmelCase : list[int] ) -> int:
snake_case = len(__lowerCAmelCase ) // 2
# choose the middle 3 elements
snake_case = lst[m - 1 : m + 2]
# if middle element is peak
if three[1] > three[0] and three[1] > three[2]:
return three[1]
# if increasing, recurse on right
elif three[0] < three[2]:
if len(lst[:m] ) == 2:
m -= 1
return peak(lst[m:] )
# decreasing
else:
if len(lst[:m] ) == 2:
m += 1
return peak(lst[:m] )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 517
|
'''simple docstring'''
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class _lowerCAmelCase ( ctypes.Structure ):
"""simple docstring"""
snake_case_ = [("size", ctypes.c_int), ("visible", ctypes.c_byte)]
def __lowerCamelCase ( ) -> Optional[int]:
if os.name == "nt":
snake_case = CursorInfo()
snake_case = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(__lowerCAmelCase , ctypes.byref(__lowerCAmelCase ) )
snake_case = False
ctypes.windll.kernelaa.SetConsoleCursorInfo(__lowerCAmelCase , ctypes.byref(__lowerCAmelCase ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25l""" )
sys.stdout.flush()
def __lowerCamelCase ( ) -> Tuple:
if os.name == "nt":
snake_case = CursorInfo()
snake_case = ctypes.windll.kernelaa.GetStdHandle(-11 )
ctypes.windll.kernelaa.GetConsoleCursorInfo(__lowerCAmelCase , ctypes.byref(__lowerCAmelCase ) )
snake_case = True
ctypes.windll.kernelaa.SetConsoleCursorInfo(__lowerCAmelCase , ctypes.byref(__lowerCAmelCase ) )
elif os.name == "posix":
sys.stdout.write("""\033[?25h""" )
sys.stdout.flush()
@contextmanager
def __lowerCamelCase ( ) -> Optional[Any]:
try:
hide_cursor()
yield
finally:
show_cursor()
| 517
| 1
|
from __future__ import annotations
def _a ( UpperCamelCase_ : list , UpperCamelCase_ : int , UpperCamelCase_ : int , UpperCamelCase_ : int ) -> list:
"""simple docstring"""
lowerCAmelCase__ = []
lowerCAmelCase__ , lowerCAmelCase__ = input_list[low:mid], input_list[mid : high + 1]
while left and right:
result.append((left if left[0] <= right[0] else right).pop(0 ) )
lowerCAmelCase__ = result + left + right
return input_list
def _a ( UpperCamelCase_ : list ) -> list:
"""simple docstring"""
if len(UpperCamelCase_ ) <= 1:
return input_list
lowerCAmelCase__ = list(UpperCamelCase_ )
# iteration for two-way merging
lowerCAmelCase__ = 2
while p <= len(UpperCamelCase_ ):
# getting low, high and middle value for merge-sort of single list
for i in range(0 , len(UpperCamelCase_ ) , UpperCamelCase_ ):
lowerCAmelCase__ = i
lowerCAmelCase__ = i + p - 1
lowerCAmelCase__ = (low + high + 1) // 2
lowerCAmelCase__ = merge(UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ , UpperCamelCase_ )
# final merge of last two parts
if p * 2 >= len(UpperCamelCase_ ):
lowerCAmelCase__ = i
lowerCAmelCase__ = merge(UpperCamelCase_ , 0 , UpperCamelCase_ , len(UpperCamelCase_ ) - 1 )
break
p *= 2
return input_list
if __name__ == "__main__":
a_ = input('''Enter numbers separated by a comma:\n''').strip()
if user_input == "":
a_ = []
else:
a_ = [int(item.strip()) for item in user_input.split(''',''')]
print(iter_merge_sort(unsorted))
| 339
|
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_ = {
'''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
'''YituTech/conv-bert-medium-small''': (
'''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
),
'''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
# See all ConvBERT models at https://huggingface.co/models?filter=convbert
}
class lowercase__ ( _UpperCAmelCase ):
a_ ="""convbert"""
def __init__( self , __UpperCAmelCase=30522 , __UpperCAmelCase=768 , __UpperCAmelCase=12 , __UpperCAmelCase=12 , __UpperCAmelCase=3072 , __UpperCAmelCase="gelu" , __UpperCAmelCase=0.1 , __UpperCAmelCase=0.1 , __UpperCAmelCase=512 , __UpperCAmelCase=2 , __UpperCAmelCase=0.02 , __UpperCAmelCase=1E-1_2 , __UpperCAmelCase=1 , __UpperCAmelCase=0 , __UpperCAmelCase=2 , __UpperCAmelCase=768 , __UpperCAmelCase=2 , __UpperCAmelCase=9 , __UpperCAmelCase=1 , __UpperCAmelCase=None , **__UpperCAmelCase , )-> Any:
'''simple docstring'''
super().__init__(
pad_token_id=__UpperCAmelCase , bos_token_id=__UpperCAmelCase , eos_token_id=__UpperCAmelCase , **__UpperCAmelCase , )
lowerCAmelCase__ = vocab_size
lowerCAmelCase__ = hidden_size
lowerCAmelCase__ = num_hidden_layers
lowerCAmelCase__ = num_attention_heads
lowerCAmelCase__ = intermediate_size
lowerCAmelCase__ = hidden_act
lowerCAmelCase__ = hidden_dropout_prob
lowerCAmelCase__ = attention_probs_dropout_prob
lowerCAmelCase__ = max_position_embeddings
lowerCAmelCase__ = type_vocab_size
lowerCAmelCase__ = initializer_range
lowerCAmelCase__ = layer_norm_eps
lowerCAmelCase__ = embedding_size
lowerCAmelCase__ = head_ratio
lowerCAmelCase__ = conv_kernel_size
lowerCAmelCase__ = num_groups
lowerCAmelCase__ = classifier_dropout
class lowercase__ ( _UpperCAmelCase ):
@property
def UpperCAmelCase ( self )-> Mapping[str, Mapping[int, str]]:
'''simple docstring'''
if self.task == "multiple-choice":
lowerCAmelCase__ = {0: "batch", 1: "choice", 2: "sequence"}
else:
lowerCAmelCase__ = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 339
| 1
|
'''simple docstring'''
def A__ ( __lowerCAmelCase : int ):
if not isinstance(__lowerCAmelCase , __lowerCAmelCase ) or number < 0:
raise ValueError("""Input must be a non-negative integer""" )
lowerCamelCase__ = 0
while number:
# This way we arrive at next set bit (next 1) instead of looping
# through each bit and checking for 1s hence the
# loop won't run 32 times it will only run the number of `1` times
number &= number - 1
count += 1
return count
if __name__ == "__main__":
import doctest
doctest.testmod()
| 9
|
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def A__ ( __lowerCAmelCase : Union[str, Any] ):
lowerCamelCase__ = [
"""encoder.version""",
"""decoder.version""",
"""model.encoder.version""",
"""model.decoder.version""",
"""decoder.output_projection.weight""",
"""_float_tensor""",
"""encoder.embed_positions._float_tensor""",
"""decoder.embed_positions._float_tensor""",
]
for k in ignore_keys:
state_dict.pop(__lowerCAmelCase , __lowerCAmelCase )
def A__ ( __lowerCAmelCase : Tuple ):
lowerCamelCase__ , lowerCamelCase__ = emb.weight.shape
lowerCamelCase__ = nn.Linear(__lowerCAmelCase , __lowerCAmelCase , bias=__lowerCAmelCase )
lowerCamelCase__ = emb.weight.data
return lin_layer
def A__ ( __lowerCAmelCase : Dict ):
lowerCamelCase__ = torch.load(__lowerCAmelCase , map_location="""cpu""" )
lowerCamelCase__ = mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
lowerCamelCase__ = mam_aaa["""model"""]
remove_ignore_keys_(__lowerCAmelCase )
lowerCamelCase__ = state_dict["""encoder.embed_tokens.weight"""].shape[0]
lowerCamelCase__ = MaMaaaConfig(
vocab_size=__lowerCAmelCase , max_position_embeddings=1024 , encoder_layers=args.encoder_layers , decoder_layers=args.decoder_layers , encoder_attention_heads=args.encoder_attention_heads , decoder_attention_heads=args.decoder_attention_heads , encoder_ffn_dim=args.encoder_ffn_embed_dim , decoder_ffn_dim=args.decoder_ffn_embed_dim , d_model=args.encoder_embed_dim , encoder_layerdrop=args.encoder_layerdrop , decoder_layerdrop=args.decoder_layerdrop , dropout=args.dropout , attention_dropout=args.attention_dropout , activation_dropout=args.activation_dropout , activation_function="""relu""" , )
lowerCamelCase__ = state_dict["""decoder.embed_tokens.weight"""]
lowerCamelCase__ = MaMaaaForConditionalGeneration(__lowerCAmelCase )
model.model.load_state_dict(__lowerCAmelCase , strict=__lowerCAmelCase )
lowerCamelCase__ = make_linear_from_emb(model.model.shared )
return model
if __name__ == "__main__":
UpperCamelCase : Optional[Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
UpperCamelCase : Tuple = parser.parse_args()
UpperCamelCase : List[Any] = convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
model.save_pretrained(args.pytorch_dump_folder_path)
| 9
| 1
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_lowercase = logging.get_logger(__name__)
_lowercase = {
'''xlm-roberta-base''': '''https://huggingface.co/xlm-roberta-base/resolve/main/config.json''',
'''xlm-roberta-large''': '''https://huggingface.co/xlm-roberta-large/resolve/main/config.json''',
'''xlm-roberta-large-finetuned-conll02-dutch''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-dutch/resolve/main/config.json'''
),
'''xlm-roberta-large-finetuned-conll02-spanish''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll02-spanish/resolve/main/config.json'''
),
'''xlm-roberta-large-finetuned-conll03-english''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-english/resolve/main/config.json'''
),
'''xlm-roberta-large-finetuned-conll03-german''': (
'''https://huggingface.co/xlm-roberta-large-finetuned-conll03-german/resolve/main/config.json'''
),
}
class __A ( __a ):
UpperCamelCase :int = '''xlm-roberta'''
def __init__(self , __magic_name__=30522 , __magic_name__=768 , __magic_name__=12 , __magic_name__=12 , __magic_name__=3072 , __magic_name__="gelu" , __magic_name__=0.1 , __magic_name__=0.1 , __magic_name__=512 , __magic_name__=2 , __magic_name__=0.02 , __magic_name__=1E-12 , __magic_name__=1 , __magic_name__=0 , __magic_name__=2 , __magic_name__="absolute" , __magic_name__=True , __magic_name__=None , **__magic_name__ , ):
super().__init__(pad_token_id=a_ , bos_token_id=a_ , eos_token_id=a_ , **a_ )
lowerCamelCase__ : str = vocab_size
lowerCamelCase__ : int = hidden_size
lowerCamelCase__ : Union[str, Any] = num_hidden_layers
lowerCamelCase__ : str = num_attention_heads
lowerCamelCase__ : int = hidden_act
lowerCamelCase__ : Union[str, Any] = intermediate_size
lowerCamelCase__ : int = hidden_dropout_prob
lowerCamelCase__ : Dict = attention_probs_dropout_prob
lowerCamelCase__ : Tuple = max_position_embeddings
lowerCamelCase__ : Any = type_vocab_size
lowerCamelCase__ : Tuple = initializer_range
lowerCamelCase__ : Optional[Any] = layer_norm_eps
lowerCamelCase__ : Any = position_embedding_type
lowerCamelCase__ : Tuple = use_cache
lowerCamelCase__ : Optional[int] = classifier_dropout
class __A ( __a ):
@property
def _snake_case (self ):
if self.task == "multiple-choice":
lowerCamelCase__ : Optional[int] = {0: """batch""", 1: """choice""", 2: """sequence"""}
else:
lowerCamelCase__ : List[str] = {0: """batch""", 1: """sequence"""}
return OrderedDict(
[
("""input_ids""", dynamic_axis),
("""attention_mask""", dynamic_axis),
] )
| 157
|
"""simple docstring"""
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
import numpy as np
# Parrameters
A_ = (7_20, 12_80) # Height, Width
A_ = (0.4, 0.6) # if height or width lower than this scale, drop it.
A_ = 1 / 1_00
A_ = ''''''
A_ = ''''''
A_ = ''''''
A_ = 2_50
def UpperCAmelCase__ ():
"""simple docstring"""
_snake_case , _snake_case : Any = get_dataset(snake_case__ , snake_case__ )
for index in range(snake_case__ ):
_snake_case : List[Any] = random.sample(range(len(snake_case__ ) ) , 4 )
_snake_case , _snake_case , _snake_case : Tuple = update_image_and_anno(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , filter_scale=snake_case__ , )
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
_snake_case : List[Any] = random_chars(32 )
_snake_case : List[Any] = path.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
_snake_case : Union[str, Any] = F"{OUTPUT_DIR}/{file_name}_MOSAIC_{letter_code}"
cva.imwrite(F"{file_root}.jpg" , snake_case__ , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F"Succeeded {index+1}/{NUMBER_IMAGES} with {file_name}" )
_snake_case : Optional[Any] = []
for anno in new_annos:
_snake_case : List[str] = anno[3] - anno[1]
_snake_case : Any = anno[4] - anno[2]
_snake_case : Any = anno[1] + width / 2
_snake_case : List[Any] = anno[2] + height / 2
_snake_case : Any = F"{anno[0]} {x_center} {y_center} {width} {height}"
annos_list.append(snake_case__ )
with open(F"{file_root}.txt" , """w""" ) as outfile:
outfile.write("""\n""".join(line for line in annos_list ) )
def UpperCAmelCase__ (snake_case__ : str , snake_case__ : str ):
"""simple docstring"""
_snake_case : str = []
_snake_case : Optional[int] = []
for label_file in glob.glob(os.path.join(snake_case__ , """*.txt""" ) ):
_snake_case : Optional[Any] = label_file.split(os.sep )[-1].rsplit(""".""" , 1 )[0]
with open(snake_case__ ) as in_file:
_snake_case : Union[str, Any] = in_file.readlines()
_snake_case : Optional[Any] = os.path.join(snake_case__ , F"{label_name}.jpg" )
_snake_case : Tuple = []
for obj_list in obj_lists:
_snake_case : Optional[int] = obj_list.rstrip("""\n""" ).split(""" """ )
_snake_case : Union[str, Any] = float(obj[1] ) - float(obj[3] ) / 2
_snake_case : Tuple = float(obj[2] ) - float(obj[4] ) / 2
_snake_case : List[str] = float(obj[1] ) + float(obj[3] ) / 2
_snake_case : Union[str, Any] = float(obj[2] ) + float(obj[4] ) / 2
boxes.append([int(obj[0] ), xmin, ymin, xmax, ymax] )
if not boxes:
continue
img_paths.append(snake_case__ )
labels.append(snake_case__ )
return img_paths, labels
def UpperCAmelCase__ (snake_case__ : list , snake_case__ : list , snake_case__ : list[int] , snake_case__ : tuple[int, int] , snake_case__ : tuple[float, float] , snake_case__ : float = 0.0 , ):
"""simple docstring"""
_snake_case : str = np.zeros([output_size[0], output_size[1], 3] , dtype=np.uinta )
_snake_case : Any = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_snake_case : List[Any] = scale_range[0] + random.random() * (scale_range[1] - scale_range[0])
_snake_case : List[str] = int(scale_x * output_size[1] )
_snake_case : Any = int(scale_y * output_size[0] )
_snake_case : Optional[Any] = []
_snake_case : List[str] = []
for i, index in enumerate(snake_case__ ):
_snake_case : str = all_img_list[index]
path_list.append(snake_case__ )
_snake_case : Any = all_annos[index]
_snake_case : Tuple = cva.imread(snake_case__ )
if i == 0: # top-left
_snake_case : Tuple = cva.resize(snake_case__ , (divid_point_x, divid_point_y) )
_snake_case : int = img
for bbox in img_annos:
_snake_case : str = bbox[1] * scale_x
_snake_case : Optional[int] = bbox[2] * scale_y
_snake_case : Dict = bbox[3] * scale_x
_snake_case : Any = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 1: # top-right
_snake_case : Dict = cva.resize(snake_case__ , (output_size[1] - divid_point_x, divid_point_y) )
_snake_case : int = img
for bbox in img_annos:
_snake_case : Any = scale_x + bbox[1] * (1 - scale_x)
_snake_case : Union[str, Any] = bbox[2] * scale_y
_snake_case : List[str] = scale_x + bbox[3] * (1 - scale_x)
_snake_case : Tuple = bbox[4] * scale_y
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
elif i == 2: # bottom-left
_snake_case : Union[str, Any] = cva.resize(snake_case__ , (divid_point_x, output_size[0] - divid_point_y) )
_snake_case : Optional[Any] = img
for bbox in img_annos:
_snake_case : int = bbox[1] * scale_x
_snake_case : Union[str, Any] = scale_y + bbox[2] * (1 - scale_y)
_snake_case : Optional[int] = bbox[3] * scale_x
_snake_case : Any = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
else: # bottom-right
_snake_case : int = cva.resize(
snake_case__ , (output_size[1] - divid_point_x, output_size[0] - divid_point_y) )
_snake_case : List[Any] = img
for bbox in img_annos:
_snake_case : str = scale_x + bbox[1] * (1 - scale_x)
_snake_case : Any = scale_y + bbox[2] * (1 - scale_y)
_snake_case : Optional[Any] = scale_x + bbox[3] * (1 - scale_x)
_snake_case : Union[str, Any] = scale_y + bbox[4] * (1 - scale_y)
new_anno.append([bbox[0], xmin, ymin, xmax, ymax] )
# Remove bounding box small than scale of filter
if filter_scale > 0:
_snake_case : Optional[int] = [
anno
for anno in new_anno
if filter_scale < (anno[3] - anno[1]) and filter_scale < (anno[4] - anno[2])
]
return output_img, new_anno, path_list[0]
def UpperCAmelCase__ (snake_case__ : int ):
"""simple docstring"""
assert number_char > 1, "The number of character should greater than 1"
_snake_case : int = ascii_lowercase + digits
return "".join(random.choice(snake_case__ ) for _ in range(snake_case__ ) )
if __name__ == "__main__":
main()
print('''DONE ✅''')
| 609
| 0
|
'''simple docstring'''
import argparse
import os
from pathlib import Path
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import PegasusConfig, PegasusForConditionalGeneration, PegasusTokenizer
from transformers.models.pegasus.configuration_pegasus import DEFAULTS, task_specific_params
lowerCAmelCase_ : str = [
# replace left string with right string to get the relevant state_dict key (identical state dict to bart)
['memory_attention', 'encoder_attn'],
['attention', 'attn'],
['/', '.'],
['.LayerNorm.gamma', '_layer_norm.weight'],
['.LayerNorm.beta', '_layer_norm.bias'],
['r.layer_', 'r.layers.'],
['output_proj', 'out_proj'],
['ffn.dense_1.', 'fc2.'],
['ffn.dense.', 'fc1.'],
['ffn_layer_norm', 'final_layer_norm'],
['kernel', 'weight'],
['encoder_layer_norm.', 'encoder.layer_norm.'],
['decoder_layer_norm.', 'decoder.layer_norm.'],
['embeddings.weights', 'shared.weight'],
]
def _lowerCamelCase ( lowercase : Tuple ) -> Optional[int]:
for pegasus_name, hf_name in PATTERNS:
_a = k.replace(lowercase , lowercase )
return k
def _lowerCamelCase ( lowercase : dict , lowercase : dict ) -> PegasusForConditionalGeneration:
_a = DEFAULTS.copy()
cfg_kwargs.update(lowercase )
_a = PegasusConfig(**lowercase )
_a = PegasusForConditionalGeneration(lowercase )
_a = torch_model.model.state_dict()
_a = {}
for k, v in tf_weights.items():
_a = rename_state_dict_key(lowercase )
if new_k not in sd:
raise ValueError(F'could not find new key {new_k} in state dict. (converted from {k})' )
if "dense" in k or "proj" in new_k:
_a = v.T
_a = torch.tensor(lowercase , dtype=sd[new_k].dtype )
assert v.shape == sd[new_k].shape, F'{new_k}, {k}, {v.shape}, {sd[new_k].shape}'
# make sure embedding.padding_idx is respected
_a = torch.zeros_like(mapping["shared.weight"][cfg.pad_token_id + 1] )
_a = mapping["shared.weight"]
_a = mapping["shared.weight"]
_a = {k: torch.zeros_like(lowercase ) for k, v in sd.items() if k.endswith("bias" ) and k not in mapping}
mapping.update(**lowercase )
_a , _a = torch_model.model.load_state_dict(lowercase , strict=lowercase )
_a = [
k for k in missing if k not in ["encoder.embed_positions.weight", "decoder.embed_positions.weight"]
]
assert unexpected_missing == [], F'no matches found for the following torch keys {unexpected_missing}'
assert extra == [], F'no matches found for the following tf keys {extra}'
return torch_model
def _lowerCamelCase ( lowercase : Union[str, Any]="./ckpt/aeslc/model.ckpt-32000" ) -> Dict:
_a = tf.train.list_variables(lowercase )
_a = {}
_a = ["Adafactor", "global_step"]
for name, shape in tqdm(lowercase , desc="converting tf checkpoint to dict" ):
_a = any(pat in name for pat in ignore_name )
if skip_key:
continue
_a = tf.train.load_variable(lowercase , lowercase )
_a = array
return tf_weights
def _lowerCamelCase ( lowercase : str , lowercase : str ) -> Union[str, Any]:
# save tokenizer first
_a = Path(lowercase ).parent.name
_a = task_specific_params[F'summarization_{dataset}']["max_position_embeddings"]
_a = PegasusTokenizer.from_pretrained("sshleifer/pegasus" , model_max_length=lowercase )
assert tok.model_max_length == desired_max_model_length
tok.save_pretrained(lowercase )
# convert model
_a = get_tf_weights_as_numpy(lowercase )
_a = task_specific_params[F'summarization_{dataset}']
if dataset == "large":
_a = task_specific_params
_a = convert_pegasus(lowercase , lowercase )
torch_model.save_pretrained(lowercase )
_a = torch_model.state_dict()
sd.pop("model.decoder.embed_positions.weight" )
sd.pop("model.encoder.embed_positions.weight" )
torch.save(lowercase , Path(lowercase ) / "pytorch_model.bin" )
if __name__ == "__main__":
lowerCAmelCase_ : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument('tf_ckpt_path', type=str, help='passed to tf.train.list_variables')
parser.add_argument('save_dir', default=None, type=str, help='Path to the output PyTorch model.')
lowerCAmelCase_ : Optional[int] = parser.parse_args()
if args.save_dir is None:
lowerCAmelCase_ : List[Any] = Path(args.tf_ckpt_path).parent.name
lowerCAmelCase_ : Optional[Any] = os.path.join('pegasus', dataset)
convert_pegasus_ckpt_to_pytorch(args.tf_ckpt_path, args.save_dir)
| 521
|
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Dict = logging.get_logger(__name__)
lowerCAmelCase_ : Any = {
'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 __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
__a ='roformer'
def __init__( self : Optional[Any] , __a : Dict=5_00_00 , __a : Any=None , __a : Tuple=7_68 , __a : Optional[Any]=12 , __a : Optional[Any]=12 , __a : List[Any]=30_72 , __a : Dict="gelu" , __a : Tuple=0.1 , __a : List[str]=0.1 , __a : int=15_36 , __a : Tuple=2 , __a : List[str]=0.02 , __a : Dict=1e-1_2 , __a : Optional[Any]=0 , __a : Any=False , __a : Tuple=True , **__a : str , ):
super().__init__(pad_token_id=__a , **__a )
_a = vocab_size
_a = hidden_size if embedding_size is None else embedding_size
_a = hidden_size
_a = num_hidden_layers
_a = num_attention_heads
_a = hidden_act
_a = intermediate_size
_a = hidden_dropout_prob
_a = attention_probs_dropout_prob
_a = max_position_embeddings
_a = type_vocab_size
_a = initializer_range
_a = layer_norm_eps
_a = rotary_value
_a = use_cache
class __SCREAMING_SNAKE_CASE (lowerCamelCase_ ):
"""simple docstring"""
@property
def UpperCamelCase__ ( self : Any ):
if self.task == "multiple-choice":
_a = {0: "batch", 1: "choice", 2: "sequence"}
else:
_a = {0: "batch", 1: "sequence"}
_a = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
("token_type_ids", dynamic_axis),
] )
| 521
| 1
|
"""simple docstring"""
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def lowercase ( lowerCAmelCase__ : Any , lowerCAmelCase__ : List[str]=7 ) -> Union[str, Any]:
__a = None
if token is not None:
__a = {'''Accept''': '''application/vnd.github+json''', '''Authorization''': f'''Bearer {token}'''}
# The id of a workflow (not of a workflow run)
__a = '''636036'''
__a = f'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
url += f'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''
__a = requests.get(__UpperCAmelCase , headers=__UpperCAmelCase ).json()
return result["workflow_runs"]
def lowercase ( lowerCAmelCase__ : Optional[Any] ) -> Any:
__a = get_daily_ci_runs(__UpperCAmelCase )
__a = None
for workflow_run in workflow_runs:
if workflow_run["status"] == "completed":
__a = workflow_run['''id''']
break
return workflow_run_id
def lowercase ( lowerCAmelCase__ : Optional[Any] , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : Dict ) -> Optional[int]:
__a = get_last_daily_ci_runs(__UpperCAmelCase )
if workflow_run_id is not None:
__a = get_artifacts_links(worflow_run_id=__UpperCAmelCase , token=__UpperCAmelCase )
for artifact_name in artifact_names:
if artifact_name in artifacts_links:
__a = artifacts_links[artifact_name]
download_artifact(
artifact_name=__UpperCAmelCase , artifact_url=__UpperCAmelCase , output_dir=__UpperCAmelCase , token=__UpperCAmelCase )
def lowercase ( lowerCAmelCase__ : Dict , lowerCAmelCase__ : Union[str, Any] , lowerCAmelCase__ : List[str] ) -> Tuple:
get_last_daily_ci_artifacts(__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase )
__a = {}
for artifact_name in artifact_names:
__a = os.path.join(__UpperCAmelCase , f'''{artifact_name}.zip''' )
if os.path.isfile(__UpperCAmelCase ):
__a = {}
with zipfile.ZipFile(__UpperCAmelCase ) as z:
for filename in z.namelist():
if not os.path.isdir(__UpperCAmelCase ):
# read the file
with z.open(__UpperCAmelCase ) as f:
__a = f.read().decode('''UTF-8''' )
return results
| 695
|
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if import IFWatermarker
from diffusers.utils.testing_utils import torch_device
from ..test_pipelines_common import to_np
class lowercase__ :
'''simple docstring'''
def UpperCamelCase__ ( self ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase__ : List[str] = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
UpperCamelCase__ : Tuple = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
UpperCamelCase__ : Optional[Any] = UNetaDConditionModel(
sample_size=32, layers_per_block=1, block_out_channels=[32, 64], down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
], mid_block_type='''UNetMidBlock2DSimpleCrossAttn''', up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''], in_channels=3, out_channels=6, cross_attention_dim=32, encoder_hid_dim=32, attention_head_dim=8, addition_embed_type='''text''', addition_embed_type_num_heads=2, cross_attention_norm='''group_norm''', resnet_time_scale_shift='''scale_shift''', act_fn='''gelu''', )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
UpperCamelCase__ : Dict = DDPMScheduler(
num_train_timesteps=1000, beta_schedule='''squaredcos_cap_v2''', beta_start=0.0001, beta_end=0.02, thresholding=__magic_name__, dynamic_thresholding_ratio=0.95, sample_max_value=1.0, prediction_type='''epsilon''', variance_type='''learned_range''', )
torch.manual_seed(0 )
UpperCamelCase__ : Optional[int] = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCamelCase__ ( self ) -> List[str]:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase__ : Dict = TaEncoderModel.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
UpperCamelCase__ : int = AutoTokenizer.from_pretrained('''hf-internal-testing/tiny-random-t5''' )
torch.manual_seed(0 )
UpperCamelCase__ : List[Any] = UNetaDConditionModel(
sample_size=32, layers_per_block=[1, 2], block_out_channels=[32, 64], down_block_types=[
'''ResnetDownsampleBlock2D''',
'''SimpleCrossAttnDownBlock2D''',
], mid_block_type='''UNetMidBlock2DSimpleCrossAttn''', up_block_types=['''SimpleCrossAttnUpBlock2D''', '''ResnetUpsampleBlock2D'''], in_channels=6, out_channels=6, cross_attention_dim=32, encoder_hid_dim=32, attention_head_dim=8, addition_embed_type='''text''', addition_embed_type_num_heads=2, cross_attention_norm='''group_norm''', resnet_time_scale_shift='''scale_shift''', act_fn='''gelu''', class_embed_type='''timestep''', mid_block_scale_factor=1.414, time_embedding_act_fn='''gelu''', time_embedding_dim=32, )
unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
torch.manual_seed(0 )
UpperCamelCase__ : Optional[int] = DDPMScheduler(
num_train_timesteps=1000, beta_schedule='''squaredcos_cap_v2''', beta_start=0.0001, beta_end=0.02, thresholding=__magic_name__, dynamic_thresholding_ratio=0.95, sample_max_value=1.0, prediction_type='''epsilon''', variance_type='''learned_range''', )
torch.manual_seed(0 )
UpperCamelCase__ : Optional[Any] = DDPMScheduler(
num_train_timesteps=1000, beta_schedule='''squaredcos_cap_v2''', beta_start=0.0001, beta_end=0.02, )
torch.manual_seed(0 )
UpperCamelCase__ : str = IFWatermarker()
return {
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"unet": unet,
"scheduler": scheduler,
"image_noising_scheduler": image_noising_scheduler,
"watermarker": watermarker,
"safety_checker": None,
"feature_extractor": None,
}
def UpperCamelCase__ ( self ) -> Dict:
"""simple docstring"""
UpperCamelCase__ : str = self.get_dummy_components()
UpperCamelCase__ : List[str] = self.pipeline_class(**__magic_name__ )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
UpperCamelCase__ : Any = self.get_dummy_inputs(__magic_name__ )
UpperCamelCase__ : Tuple = inputs['''prompt''']
UpperCamelCase__ : Optional[Any] = inputs['''generator''']
UpperCamelCase__ : Union[str, Any] = inputs['''num_inference_steps''']
UpperCamelCase__ : Dict = inputs['''output_type''']
if "image" in inputs:
UpperCamelCase__ : Optional[int] = inputs['''image''']
else:
UpperCamelCase__ : Any = None
if "mask_image" in inputs:
UpperCamelCase__ : List[str] = inputs['''mask_image''']
else:
UpperCamelCase__ : Union[str, Any] = None
if "original_image" in inputs:
UpperCamelCase__ : List[Any] = inputs['''original_image''']
else:
UpperCamelCase__ : Tuple = None
UpperCamelCase__ ,UpperCamelCase__ : List[Any] = pipe.encode_prompt(__magic_name__ )
# inputs with prompt converted to embeddings
UpperCamelCase__ : Any = {
'''prompt_embeds''': prompt_embeds,
'''negative_prompt_embeds''': negative_prompt_embeds,
'''generator''': generator,
'''num_inference_steps''': num_inference_steps,
'''output_type''': output_type,
}
if image is not None:
UpperCamelCase__ : int = image
if mask_image is not None:
UpperCamelCase__ : List[Any] = mask_image
if original_image is not None:
UpperCamelCase__ : Optional[Any] = original_image
# set all optional components to None
for optional_component in pipe._optional_components:
setattr(__magic_name__, __magic_name__, __magic_name__ )
UpperCamelCase__ : Union[str, Any] = pipe(**__magic_name__ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__magic_name__ )
UpperCamelCase__ : Any = self.pipeline_class.from_pretrained(__magic_name__ )
pipe_loaded.to(__magic_name__ )
pipe_loaded.set_progress_bar_config(disable=__magic_name__ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
for optional_component in pipe._optional_components:
self.assertTrue(
getattr(__magic_name__, __magic_name__ ) is None, f"`{optional_component}` did not stay set to None after loading.", )
UpperCamelCase__ : List[str] = self.get_dummy_inputs(__magic_name__ )
UpperCamelCase__ : int = inputs['''generator''']
UpperCamelCase__ : Union[str, Any] = inputs['''num_inference_steps''']
UpperCamelCase__ : Union[str, Any] = inputs['''output_type''']
# inputs with prompt converted to embeddings
UpperCamelCase__ : Optional[Any] = {
'''prompt_embeds''': prompt_embeds,
'''negative_prompt_embeds''': negative_prompt_embeds,
'''generator''': generator,
'''num_inference_steps''': num_inference_steps,
'''output_type''': output_type,
}
if image is not None:
UpperCamelCase__ : List[Any] = image
if mask_image is not None:
UpperCamelCase__ : List[str] = mask_image
if original_image is not None:
UpperCamelCase__ : str = original_image
UpperCamelCase__ : str = pipe_loaded(**__magic_name__ )[0]
UpperCamelCase__ : Optional[int] = np.abs(to_np(__magic_name__ ) - to_np(__magic_name__ ) ).max()
self.assertLess(__magic_name__, 1E-4 )
def UpperCamelCase__ ( self ) -> Any:
"""simple docstring"""
UpperCamelCase__ : List[str] = self.get_dummy_components()
UpperCamelCase__ : Optional[Any] = self.pipeline_class(**__magic_name__ )
pipe.to(__magic_name__ )
pipe.set_progress_bar_config(disable=__magic_name__ )
UpperCamelCase__ : Optional[int] = self.get_dummy_inputs(__magic_name__ )
UpperCamelCase__ : Dict = pipe(**__magic_name__ )[0]
with tempfile.TemporaryDirectory() as tmpdir:
pipe.save_pretrained(__magic_name__ )
UpperCamelCase__ : Optional[Any] = self.pipeline_class.from_pretrained(__magic_name__ )
pipe_loaded.to(__magic_name__ )
pipe_loaded.set_progress_bar_config(disable=__magic_name__ )
pipe_loaded.unet.set_attn_processor(AttnAddedKVProcessor() ) # For reproducibility tests
UpperCamelCase__ : str = self.get_dummy_inputs(__magic_name__ )
UpperCamelCase__ : Optional[Any] = pipe_loaded(**__magic_name__ )[0]
UpperCamelCase__ : str = np.abs(to_np(__magic_name__ ) - to_np(__magic_name__ ) ).max()
self.assertLess(__magic_name__, 1E-4 )
| 253
| 0
|
'''simple docstring'''
def __A ( _SCREAMING_SNAKE_CASE : Any ):
"""simple docstring"""
return [
{
0: [1, 2],
1: [0, 2],
2: [0, 1, 3, 5],
3: [2, 4],
4: [3],
5: [2, 6, 8],
6: [5, 7],
7: [6, 8],
8: [5, 7],
},
{
0: [6],
1: [9],
2: [4, 5],
3: [4],
4: [2, 3],
5: [2],
6: [0, 7],
7: [6],
8: [],
9: [1],
},
{
0: [4],
1: [6],
2: [],
3: [5, 6, 7],
4: [0, 6],
5: [3, 8, 9],
6: [1, 3, 4, 7],
7: [3, 6, 8, 9],
8: [5, 7],
9: [5, 7],
},
{
0: [1, 3],
1: [0, 2, 4],
2: [1, 3, 4],
3: [0, 2, 4],
4: [1, 2, 3],
},
][index]
def __A ( _SCREAMING_SNAKE_CASE : dict[int, list[int]] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : Optional[int] = 0
__SCREAMING_SNAKE_CASE : Optional[Any] = len(_SCREAMING_SNAKE_CASE ) # No of vertices in graph
__SCREAMING_SNAKE_CASE : List[Any] = [0] * n
__SCREAMING_SNAKE_CASE : List[Any] = [False] * n
def dfs(_SCREAMING_SNAKE_CASE : int , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CASE : str ):
__SCREAMING_SNAKE_CASE : int = True
__SCREAMING_SNAKE_CASE : Union[str, Any] = id_
id_ += 1
for to in graph[at]:
if to == parent:
pass
elif not visited[to]:
dfs(_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE , id_ )
__SCREAMING_SNAKE_CASE : Any = min(low[at] , low[to] )
if id_ <= low[to]:
bridges.append((at, to) if at < to else (to, at) )
else:
# This edge is a back edge and cannot be a bridge
__SCREAMING_SNAKE_CASE : List[str] = min(low[at] , low[to] )
__SCREAMING_SNAKE_CASE : list[tuple[int, int]] = []
for i in range(_SCREAMING_SNAKE_CASE ):
if not visited[i]:
dfs(_SCREAMING_SNAKE_CASE , -1 , _SCREAMING_SNAKE_CASE , id_ )
return bridges
if __name__ == "__main__":
import doctest
doctest.testmod()
| 564
|
'''simple docstring'''
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {'''vocab_file''': '''spiece.model'''}
lowercase = {
'''vocab_file''': {
'''t5-small''': '''https://huggingface.co/t5-small/resolve/main/spiece.model''',
'''t5-base''': '''https://huggingface.co/t5-base/resolve/main/spiece.model''',
'''t5-large''': '''https://huggingface.co/t5-large/resolve/main/spiece.model''',
'''t5-3b''': '''https://huggingface.co/t5-3b/resolve/main/spiece.model''',
'''t5-11b''': '''https://huggingface.co/t5-11b/resolve/main/spiece.model''',
}
}
# TODO(PVP) - this should be removed in Transformers v5
lowercase = {
'''t5-small''': 512,
'''t5-base''': 512,
'''t5-large''': 512,
'''t5-3b''': 512,
'''t5-11b''': 512,
}
lowercase = '''▁'''
class __lowerCamelCase ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
snake_case__ : str = VOCAB_FILES_NAMES
snake_case__ : List[Any] = PRETRAINED_VOCAB_FILES_MAP
snake_case__ : List[Any] = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
snake_case__ : Any = ['''input_ids''', '''attention_mask''']
def __init__( self , a__ , a__="</s>" , a__="<unk>" , a__="<pad>" , a__=100 , a__=None , a__ = None , a__=True , **a__ , ):
# Add extra_ids to the special token list
if extra_ids > 0 and additional_special_tokens is None:
__SCREAMING_SNAKE_CASE : Optional[Any] = [f'<extra_id_{i}>' for i in range(a__ )]
elif extra_ids > 0 and additional_special_tokens is not None:
# Check that we have the right number of extra_id special tokens
__SCREAMING_SNAKE_CASE : Union[str, Any] = len(set(filter(lambda a__ : bool("extra_id" in str(a__ ) ) , a__ ) ) )
if extra_tokens != extra_ids:
raise ValueError(
f'Both extra_ids ({extra_ids}) and additional_special_tokens ({additional_special_tokens}) are'
" provided to T5Tokenizer. In this case the additional_special_tokens must include the extra_ids"
" tokens" )
if legacy:
logger.warning_once(
f'You are using the legacy behaviour of the {self.__class__}. This means that tokens that come after special tokens will not be properly handled. We recommend you to'
" read the related pull request available at https://github.com/huggingface/transformers/pull/24565" )
__SCREAMING_SNAKE_CASE : int = legacy
__SCREAMING_SNAKE_CASE : str = {} if sp_model_kwargs is None else sp_model_kwargs
super().__init__(
eos_token=a__ , unk_token=a__ , pad_token=a__ , extra_ids=a__ , additional_special_tokens=a__ , sp_model_kwargs=self.sp_model_kwargs , legacy=a__ , **a__ , )
__SCREAMING_SNAKE_CASE : Dict = vocab_file
__SCREAMING_SNAKE_CASE : Union[str, Any] = extra_ids
__SCREAMING_SNAKE_CASE : Optional[int] = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(a__ )
@staticmethod
def a_ ( a__ , a__ , a__ ):
if pretrained_model_name_or_path in TaTokenizer.max_model_input_sizes:
__SCREAMING_SNAKE_CASE : Optional[int] = TaTokenizer.max_model_input_sizes[pretrained_model_name_or_path]
if init_max_model_length is not None and init_max_model_length != max_model_length:
return init_max_model_length
elif init_max_model_length is None:
warnings.warn(
"This tokenizer was incorrectly instantiated with a model max length of"
f' {deprecated_max_model_length} which will be corrected in Transformers v5.\nFor now, this'
" behavior is kept to avoid breaking backwards compatibility when padding/encoding with"
" `truncation is True`.\n- Be aware that you SHOULD NOT rely on"
f' {pretrained_model_name_or_path} automatically truncating your input to'
f' {deprecated_max_model_length} when padding/encoding.\n- If you want to encode/pad to sequences'
f' longer than {deprecated_max_model_length} you can either instantiate this tokenizer with'
" `model_max_length` or pass `max_length` when encoding/padding.\n- To avoid this warning, please"
" instantiate this tokenizer with `model_max_length` set to your preferred value." , a__ , )
return max_model_length
@property
def a_ ( self ):
return self.sp_model.get_piece_size() + self._extra_ids
def a_ ( self ):
__SCREAMING_SNAKE_CASE : str = {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__ , a__ = None , a__ = False ):
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=a__ , token_ids_a=a__ , already_has_special_tokens=a__ )
# normal case: some special tokens
if token_ids_a is None:
return ([0] * len(a__ )) + [1]
return ([0] * len(a__ )) + [1] + ([0] * len(a__ )) + [1]
def a_ ( self ):
return list(
set(filter(lambda a__ : bool(re.search(R"<extra_id_\d+>" , a__ ) ) is not None , self.additional_special_tokens ) ) )
def a_ ( self ):
return [self._convert_token_to_id(a__ ) for token in self.get_sentinel_tokens()]
def a_ ( self , a__ ):
if len(a__ ) > 0 and token_ids[-1] == self.eos_token_id:
warnings.warn(
f'This sequence already has {self.eos_token}. In future versions this behavior may lead to duplicated'
" eos tokens being added." )
return token_ids
else:
return token_ids + [self.eos_token_id]
def a_ ( self , a__ , a__ = None ):
__SCREAMING_SNAKE_CASE : Union[str, Any] = [self.eos_token_id]
if token_ids_a is None:
return len(token_ids_a + eos ) * [0]
return len(token_ids_a + eos + token_ids_a + eos ) * [0]
def a_ ( self , a__ , a__ = None ):
__SCREAMING_SNAKE_CASE : List[str] = self._add_eos_if_not_present(a__ )
if token_ids_a is None:
return token_ids_a
else:
__SCREAMING_SNAKE_CASE : Any = self._add_eos_if_not_present(a__ )
return token_ids_a + token_ids_a
def __getstate__( self ):
__SCREAMING_SNAKE_CASE : Any = self.__dict__.copy()
__SCREAMING_SNAKE_CASE : List[str] = None
return state
def __setstate__( self , a__ ):
__SCREAMING_SNAKE_CASE : List[str] = d
# for backward compatibility
if not hasattr(self , "sp_model_kwargs" ):
__SCREAMING_SNAKE_CASE : List[Any] = {}
__SCREAMING_SNAKE_CASE : str = spm.SentencePieceProcessor(**self.sp_model_kwargs )
self.sp_model.Load(self.vocab_file )
def a_ ( self , a__ , **a__ ):
# Replace the SPIECE_UNDERLINE with a space to make sure SPIECE_UNDERLINE is only used at
# the beginning of the text
if not self.legacy:
__SCREAMING_SNAKE_CASE : str = SPIECE_UNDERLINE + text.replace(a__ , " " )
return super().tokenize(a__ , **a__ )
def a_ ( self , a__ , **a__ ):
if not self.legacy:
__SCREAMING_SNAKE_CASE : Union[str, Any] = text.startswith(a__ )
if is_first:
__SCREAMING_SNAKE_CASE : str = text[1:]
__SCREAMING_SNAKE_CASE : List[str] = self.sp_model.encode(a__ , out_type=a__ )
if not self.legacy and not is_first and not text.startswith(" " ) and tokens[0].startswith(a__ ):
__SCREAMING_SNAKE_CASE : Optional[int] = ([tokens[0][1:]] if len(tokens[0] ) > 1 else []) + tokens[1:]
return tokens
def a_ ( self , a__ ):
if token.startswith("<extra_id_" ):
__SCREAMING_SNAKE_CASE : Any = re.match(R"<extra_id_(\d+)>" , a__ )
__SCREAMING_SNAKE_CASE : str = int(match.group(1 ) )
return self.vocab_size - num - 1
return self.sp_model.piece_to_id(a__ )
def a_ ( self , a__ ):
if index < self.sp_model.get_piece_size():
__SCREAMING_SNAKE_CASE : Any = self.sp_model.IdToPiece(a__ )
else:
__SCREAMING_SNAKE_CASE : Tuple = f'<extra_id_{self.vocab_size - 1 - index}>'
return token
def a_ ( self , a__ ):
__SCREAMING_SNAKE_CASE : Tuple = []
__SCREAMING_SNAKE_CASE : Union[str, Any] = ""
__SCREAMING_SNAKE_CASE : int = False
for token in tokens:
# make sure that special tokens are not decoded using sentencepiece model
if token in self.all_special_tokens:
if not prev_is_special:
out_string += " "
out_string += self.sp_model.decode(a__ ) + token
__SCREAMING_SNAKE_CASE : Union[str, Any] = True
__SCREAMING_SNAKE_CASE : Any = []
else:
current_sub_tokens.append(a__ )
__SCREAMING_SNAKE_CASE : Dict = False
out_string += self.sp_model.decode(a__ )
return out_string.strip()
def a_ ( self , a__ , a__ = None ):
if not os.path.isdir(a__ ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
__SCREAMING_SNAKE_CASE : Optional[int] = os.path.join(
a__ , (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(a__ ) and os.path.isfile(self.vocab_file ):
copyfile(self.vocab_file , a__ )
elif not os.path.isfile(self.vocab_file ):
with open(a__ , "wb" ) as fi:
__SCREAMING_SNAKE_CASE : Dict = self.sp_model.serialized_model_proto()
fi.write(a__ )
return (out_vocab_file,)
| 564
| 1
|
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy, skip_mps, slow
from diffusers.utils.testing_utils import require_torch_gpu
from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin
A : Optional[int] = False
@skip_mps
class lowerCAmelCase ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , unittest.TestCase ):
'''simple docstring'''
A = StableDiffusionAttendAndExcitePipeline
A = False
A = TEXT_TO_IMAGE_PARAMS
A = TEXT_TO_IMAGE_BATCH_PARAMS.union({'token_indices'} )
A = TEXT_TO_IMAGE_IMAGE_PARAMS
A = TEXT_TO_IMAGE_IMAGE_PARAMS
@classmethod
def lowerCamelCase__ ( cls :Optional[int] ) -> Tuple:
"""simple docstring"""
super().setUpClass()
torch.use_deterministic_algorithms(lowercase__ )
@classmethod
def lowerCamelCase__ ( cls :Dict ) -> List[str]:
"""simple docstring"""
super().tearDownClass()
torch.use_deterministic_algorithms(lowercase__ )
def lowerCamelCase__ ( self :Optional[int] ) -> int:
"""simple docstring"""
torch.manual_seed(0 )
UpperCamelCase__ = UNetaDConditionModel(
block_out_channels=(3_2, 6_4) , layers_per_block=1 , sample_size=3_2 , in_channels=4 , out_channels=4 , down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") , up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") , cross_attention_dim=3_2 , attention_head_dim=(2, 4) , use_linear_projection=lowercase__ , )
UpperCamelCase__ = DDIMScheduler(
beta_start=0.00_085 , beta_end=0.012 , beta_schedule="scaled_linear" , clip_sample=lowercase__ , set_alpha_to_one=lowercase__ , )
torch.manual_seed(0 )
UpperCamelCase__ = AutoencoderKL(
block_out_channels=[3_2, 6_4] , in_channels=3 , out_channels=3 , down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] , up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] , latent_channels=4 , sample_size=1_2_8 , )
torch.manual_seed(0 )
UpperCamelCase__ = CLIPTextConfig(
bos_token_id=0 , eos_token_id=2 , hidden_size=3_2 , intermediate_size=3_7 , layer_norm_eps=1e-05 , num_attention_heads=4 , num_hidden_layers=5 , pad_token_id=1 , vocab_size=1_0_0_0 , hidden_act="gelu" , projection_dim=5_1_2 , )
UpperCamelCase__ = CLIPTextModel(lowercase__ )
UpperCamelCase__ = CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip" )
UpperCamelCase__ = {
"unet": unet,
"scheduler": scheduler,
"vae": vae,
"text_encoder": text_encoder,
"tokenizer": tokenizer,
"safety_checker": None,
"feature_extractor": None,
}
return components
def lowerCamelCase__ ( self :int , lowerCamelCase_ :Optional[int] , lowerCamelCase_ :Union[str, Any]=0 ) -> Tuple:
"""simple docstring"""
if str(lowercase__ ).startswith("mps" ):
UpperCamelCase__ = torch.manual_seed(lowercase__ )
else:
UpperCamelCase__ = torch.Generator(device=lowercase__ ).manual_seed(lowercase__ )
UpperCamelCase__ = {
"prompt": "a cat and a frog",
"token_indices": [2, 5],
"generator": generator,
"num_inference_steps": 1,
"guidance_scale": 6.0,
"output_type": "numpy",
"max_iter_to_alter": 2,
"thresholds": {0: 0.7},
}
return inputs
def lowerCamelCase__ ( self :int ) -> List[Any]:
"""simple docstring"""
UpperCamelCase__ = "cpu"
UpperCamelCase__ = self.get_dummy_components()
UpperCamelCase__ = self.pipeline_class(**lowercase__ )
pipe.to(lowercase__ )
pipe.set_progress_bar_config(disable=lowercase__ )
UpperCamelCase__ = self.get_dummy_inputs(lowercase__ )
UpperCamelCase__ = pipe(**lowercase__ ).images
UpperCamelCase__ = image[0, -3:, -3:, -1]
self.assertEqual(image.shape , (1, 6_4, 6_4, 3) )
UpperCamelCase__ = np.array(
[0.63_905_364, 0.62_897_307, 0.48_599_017, 0.5_133_624, 0.5_550_048, 0.45_769_516, 0.50_326_973, 0.5_023_139, 0.45_384_496] )
UpperCamelCase__ = np.abs(image_slice.flatten() - expected_slice ).max()
self.assertLessEqual(lowercase__ , 1e-3 )
def lowerCamelCase__ ( self :List[str] ) -> Optional[int]:
"""simple docstring"""
super().test_cpu_offload_forward_pass(expected_max_diff=5e-4 )
def lowerCamelCase__ ( self :Optional[Any] ) -> Tuple:
"""simple docstring"""
self._test_inference_batch_consistent(batch_sizes=[1, 2] )
def lowerCamelCase__ ( self :Dict ) -> Union[str, Any]:
"""simple docstring"""
self._test_inference_batch_single_identical(batch_size=2 , expected_max_diff=7e-4 )
def lowerCamelCase__ ( self :str ) -> Any:
"""simple docstring"""
super().test_dict_tuple_outputs_equivalent(expected_max_difference=3e-3 )
def lowerCamelCase__ ( self :List[str] ) -> List[Any]:
"""simple docstring"""
super().test_pt_np_pil_outputs_equivalent(expected_max_diff=5e-4 )
def lowerCamelCase__ ( self :Any ) -> Optional[int]:
"""simple docstring"""
super().test_save_load_local(expected_max_difference=5e-4 )
def lowerCamelCase__ ( self :Optional[int] ) -> int:
"""simple docstring"""
super().test_save_load_optional_components(expected_max_difference=4e-4 )
@require_torch_gpu
@slow
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@classmethod
def lowerCamelCase__ ( cls :Optional[Any] ) -> Tuple:
"""simple docstring"""
super().setUpClass()
torch.use_deterministic_algorithms(lowercase__ )
@classmethod
def lowerCamelCase__ ( cls :List[Any] ) -> Any:
"""simple docstring"""
super().tearDownClass()
torch.use_deterministic_algorithms(lowercase__ )
def lowerCamelCase__ ( self :Optional[int] ) -> Optional[int]:
"""simple docstring"""
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCamelCase__ ( self :Tuple ) -> Tuple:
"""simple docstring"""
UpperCamelCase__ = torch.manual_seed(5_1 )
UpperCamelCase__ = StableDiffusionAttendAndExcitePipeline.from_pretrained(
"CompVis/stable-diffusion-v1-4" , safety_checker=lowercase__ , torch_dtype=torch.floataa )
pipe.to("cuda" )
UpperCamelCase__ = "a painting of an elephant with glasses"
UpperCamelCase__ = [5, 7]
UpperCamelCase__ = pipe(
prompt=lowercase__ , token_indices=lowercase__ , guidance_scale=7.5 , generator=lowercase__ , num_inference_steps=5 , max_iter_to_alter=5 , output_type="numpy" , ).images[0]
UpperCamelCase__ = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/attend-and-excite/elephant_glasses.npy" )
assert np.abs((expected_image - image).max() ) < 5e-1
| 516
|
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_chinese_clip import ChineseCLIPImageProcessor
snake_case_ = logging.get_logger(__name__)
class SCREAMING_SNAKE_CASE__ ( _UpperCAmelCase ):
def __init__( self , *lowercase__ , **lowercase__ ):
"""simple docstring"""
warnings.warn(
"The class ChineseCLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
" Please use ChineseCLIPImageProcessor instead." , lowercase__ , )
super().__init__(*lowercase__ , **lowercase__ )
| 421
| 0
|
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def _a ( _SCREAMING_SNAKE_CASE = "AAPL" ) -> str:
snake_case_ = f"""https://in.finance.yahoo.com/quote/{symbol}?s={symbol}"""
snake_case_ = BeautifulSoup(requests.get(_SCREAMING_SNAKE_CASE ).text , """html.parser""" )
snake_case_ = """My(6px) Pos(r) smartphone_Mt(6px)"""
return soup.find("""div""" , class_=class_ ).find("""span""" ).text
if __name__ == "__main__":
for symbol in "AAPL AMZN IBM GOOG MSFT ORCL".split():
print(f"""Current {symbol:<4} stock price is {stock_price(symbol):>8}""")
| 2
|
"""simple docstring"""
import argparse
from pathlib import Path
from typing import Dict, OrderedDict, Tuple
import torch
from audiocraft.models import MusicGen
from transformers import (
AutoFeatureExtractor,
AutoTokenizer,
EncodecModel,
MusicgenDecoderConfig,
MusicgenForConditionalGeneration,
MusicgenProcessor,
TaEncoderModel,
)
from transformers.models.musicgen.modeling_musicgen import MusicgenForCausalLM
from transformers.utils import logging
logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
__SCREAMING_SNAKE_CASE : int = ['model.decoder.embed_positions.weights']
def _a ( _SCREAMING_SNAKE_CASE ) -> str:
if "emb" in name:
snake_case_ = name.replace("""emb""" , """model.decoder.embed_tokens""" )
if "transformer" in name:
snake_case_ = name.replace("""transformer""" , """model.decoder""" )
if "cross_attention" in name:
snake_case_ = name.replace("""cross_attention""" , """encoder_attn""" )
if "linear1" in name:
snake_case_ = name.replace("""linear1""" , """fc1""" )
if "linear2" in name:
snake_case_ = name.replace("""linear2""" , """fc2""" )
if "norm1" in name:
snake_case_ = name.replace("""norm1""" , """self_attn_layer_norm""" )
if "norm_cross" in name:
snake_case_ = name.replace("""norm_cross""" , """encoder_attn_layer_norm""" )
if "norm2" in name:
snake_case_ = name.replace("""norm2""" , """final_layer_norm""" )
if "out_norm" in name:
snake_case_ = name.replace("""out_norm""" , """model.decoder.layer_norm""" )
if "linears" in name:
snake_case_ = name.replace("""linears""" , """lm_heads""" )
if "condition_provider.conditioners.description.output_proj" in name:
snake_case_ = name.replace("""condition_provider.conditioners.description.output_proj""" , """enc_to_dec_proj""" )
return name
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE ) -> Tuple[Dict, Dict]:
snake_case_ = list(state_dict.keys() )
snake_case_ = {}
for key in keys:
snake_case_ = state_dict.pop(_SCREAMING_SNAKE_CASE )
snake_case_ = rename_keys(_SCREAMING_SNAKE_CASE )
if "in_proj_weight" in key:
# split fused qkv proj
snake_case_ = val[:hidden_size, :]
snake_case_ = val[hidden_size : 2 * hidden_size, :]
snake_case_ = val[-hidden_size:, :]
elif "enc_to_dec_proj" in key:
snake_case_ = val
else:
snake_case_ = val
return state_dict, enc_dec_proj_state_dict
def _a ( _SCREAMING_SNAKE_CASE ) -> MusicgenDecoderConfig:
if checkpoint == "small":
# default config values
snake_case_ = 1_024
snake_case_ = 24
snake_case_ = 16
elif checkpoint == "medium":
snake_case_ = 1_536
snake_case_ = 48
snake_case_ = 24
elif checkpoint == "large":
snake_case_ = 2_048
snake_case_ = 48
snake_case_ = 32
else:
raise ValueError(f"""Checkpoint should be one of `['small', 'medium', 'large']`, got {checkpoint}.""" )
snake_case_ = MusicgenDecoderConfig(
hidden_size=_SCREAMING_SNAKE_CASE , ffn_dim=hidden_size * 4 , num_hidden_layers=_SCREAMING_SNAKE_CASE , num_attention_heads=_SCREAMING_SNAKE_CASE , )
return config
@torch.no_grad()
def _a ( _SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE=None , _SCREAMING_SNAKE_CASE="cpu" ) -> Tuple:
snake_case_ = MusicGen.get_pretrained(_SCREAMING_SNAKE_CASE , device=_SCREAMING_SNAKE_CASE )
snake_case_ = decoder_config_from_checkpoint(_SCREAMING_SNAKE_CASE )
snake_case_ = fairseq_model.lm.state_dict()
snake_case_ , snake_case_ = rename_state_dict(
_SCREAMING_SNAKE_CASE , hidden_size=decoder_config.hidden_size )
snake_case_ = TaEncoderModel.from_pretrained("""t5-base""" )
snake_case_ = EncodecModel.from_pretrained("""facebook/encodec_32khz""" )
snake_case_ = MusicgenForCausalLM(_SCREAMING_SNAKE_CASE ).eval()
# load all decoder weights - expect that we'll be missing embeddings and enc-dec projection
snake_case_ , snake_case_ = decoder.load_state_dict(_SCREAMING_SNAKE_CASE , strict=_SCREAMING_SNAKE_CASE )
for key in missing_keys.copy():
if key.startswith(("""text_encoder""", """audio_encoder""") ) or key in EXPECTED_MISSING_KEYS:
missing_keys.remove(_SCREAMING_SNAKE_CASE )
if len(_SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(f"""Missing key(s) in state_dict: {missing_keys}""" )
if len(_SCREAMING_SNAKE_CASE ) > 0:
raise ValueError(f"""Unexpected key(s) in state_dict: {unexpected_keys}""" )
# init the composite model
snake_case_ = MusicgenForConditionalGeneration(text_encoder=_SCREAMING_SNAKE_CASE , audio_encoder=_SCREAMING_SNAKE_CASE , decoder=_SCREAMING_SNAKE_CASE )
# load the pre-trained enc-dec projection (from the decoder state dict)
model.enc_to_dec_proj.load_state_dict(_SCREAMING_SNAKE_CASE )
# check we can do a forward pass
snake_case_ = torch.arange(0 , 8 , dtype=torch.long ).reshape(2 , -1 )
snake_case_ = input_ids.reshape(2 * 4 , -1 )
with torch.no_grad():
snake_case_ = model(input_ids=_SCREAMING_SNAKE_CASE , decoder_input_ids=_SCREAMING_SNAKE_CASE ).logits
if logits.shape != (8, 1, 2_048):
raise ValueError("""Incorrect shape for logits""" )
# now construct the processor
snake_case_ = AutoTokenizer.from_pretrained("""t5-base""" )
snake_case_ = AutoFeatureExtractor.from_pretrained("""facebook/encodec_32khz""" , padding_side="""left""" )
snake_case_ = MusicgenProcessor(feature_extractor=_SCREAMING_SNAKE_CASE , tokenizer=_SCREAMING_SNAKE_CASE )
# set the appropriate bos/pad token ids
snake_case_ = 2_048
snake_case_ = 2_048
# set other default generation config params
snake_case_ = int(30 * audio_encoder.config.frame_rate )
snake_case_ = True
snake_case_ = 3.0
if pytorch_dump_folder is not None:
Path(_SCREAMING_SNAKE_CASE ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE )
logger.info(f"""Saving model {checkpoint} to {pytorch_dump_folder}""" )
model.save_pretrained(_SCREAMING_SNAKE_CASE )
processor.save_pretrained(_SCREAMING_SNAKE_CASE )
if repo_id:
logger.info(f"""Pushing model {checkpoint} to {repo_id}""" )
model.push_to_hub(_SCREAMING_SNAKE_CASE )
processor.push_to_hub(_SCREAMING_SNAKE_CASE )
if __name__ == "__main__":
__SCREAMING_SNAKE_CASE : Tuple = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--checkpoint',
default='small',
type=str,
help='Checkpoint size of the MusicGen model you\'d like to convert. Can be one of: `[\'small\', \'medium\', \'large\']`.',
)
parser.add_argument(
'--pytorch_dump_folder',
required=True,
default=None,
type=str,
help='Path to the output PyTorch model directory.',
)
parser.add_argument(
'--push_to_hub', default=None, type=str, help='Where to upload the converted model on the 🤗 hub.'
)
parser.add_argument(
'--device', default='cpu', type=str, help='Torch device to run the conversion, either cpu or cuda.'
)
__SCREAMING_SNAKE_CASE : int = parser.parse_args()
convert_musicgen_checkpoint(args.checkpoint, args.pytorch_dump_folder, args.push_to_hub)
| 2
| 1
|
a__: Tuple = 256
# Modulus to hash a string
a__: Dict = 1_000_003
def UpperCamelCase__( UpperCamelCase__ : str , UpperCamelCase__ : List[str] )->Optional[Any]:
A__ = len(__lowercase )
A__ = len(__lowercase )
if p_len > t_len:
return False
A__ = 0
A__ = 0
A__ = 1
# Calculating the hash of pattern and substring of text
for i in range(__lowercase ):
A__ = (ord(pattern[i] ) + p_hash * alphabet_size) % modulus
A__ = (ord(text[i] ) + text_hash * alphabet_size) % modulus
if i == p_len - 1:
continue
A__ = (modulus_power * alphabet_size) % modulus
for i in range(0 , t_len - p_len + 1 ):
if text_hash == p_hash and text[i : i + p_len] == pattern:
return True
if i == t_len - p_len:
continue
# Calculate the https://en.wikipedia.org/wiki/Rolling_hash
A__ = (
(text_hash - ord(text[i] ) * modulus_power) * alphabet_size
+ ord(text[i + p_len] )
) % modulus
return False
def UpperCamelCase__( )->Tuple:
A__ = '''abc1abc12'''
A__ = '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
A__ = '''alskfjaldsk23adsfabcabc'''
assert rabin_karp(__lowercase , __lowercase ) and not rabin_karp(__lowercase , __lowercase )
# Test 2)
A__ = '''ABABX'''
A__ = '''ABABZABABYABABX'''
assert rabin_karp(__lowercase , __lowercase )
# Test 3)
A__ = '''AAAB'''
A__ = '''ABAAAAAB'''
assert rabin_karp(__lowercase , __lowercase )
# Test 4)
A__ = '''abcdabcy'''
A__ = '''abcxabcdabxabcdabcdabcy'''
assert rabin_karp(__lowercase , __lowercase )
# Test 5)
A__ = '''Lü'''
A__ = '''Lüsai'''
assert rabin_karp(__lowercase , __lowercase )
A__ = '''Lue'''
assert not rabin_karp(__lowercase , __lowercase )
print('''Success.''' )
if __name__ == "__main__":
test_rabin_karp()
| 190
|
"""simple docstring"""
def _A ( __lowercase , __lowercase ):
"""simple docstring"""
while second != 0:
lowerCamelCase__ = first & second
first ^= second
lowerCamelCase__ = c << 1
return first
if __name__ == "__main__":
import doctest
doctest.testmod()
__magic_name__ = int(input("""Enter the first number: """).strip())
__magic_name__ = int(input("""Enter the second number: """).strip())
print(F'{add(first, second) = }')
| 129
| 0
|
import dataclasses
import re
import string
from typing import Any, Dict, Iterator, List, Mapping, Optional, Sequence, Tuple
import numpy as np
from . import residue_constants
lowerCamelCase : List[str] = Mapping[str, np.ndarray]
lowerCamelCase : Any = Mapping[str, Any] # Is a nested dict.
lowerCamelCase : List[Any] = 0.01
@dataclasses.dataclass(frozen=lowercase__ )
class lowerCAmelCase :
'''simple docstring'''
_A : int = 42 # [num_res, num_atom_type, 3]
# Amino-acid type for each residue represented as an integer between 0 and
# 20, where 20 is 'X'.
_A : Dict = 42 # [num_res]
# Binary float mask to indicate presence of a particular atom. 1.0 if an atom
# is present and 0.0 if not. This should be used for loss masking.
_A : int = 42 # [num_res, num_atom_type]
# Residue index as used in PDB. It is not necessarily continuous or 0-indexed.
_A : str = 42 # [num_res]
# B-factors, or temperature factors, of each residue (in sq. angstroms units),
# representing the displacement of the residue from its ground truth mean
# value.
_A : Optional[Any] = 42 # [num_res, num_atom_type]
# Chain indices for multi-chain predictions
_A : List[Any] = None
# Optional remark about the protein. Included as a comment in output PDB
# files
_A : List[str] = None
# Templates used to generate this protein (prediction-only)
_A : Optional[int] = None
# Chain corresponding to each parent
_A : Dict = None
def snake_case_ ( lowerCAmelCase_ : str ):
__lowercase : List[str] = r"(\[[A-Z]+\]\n)"
__lowercase : List[str] = [tag.strip() for tag in re.split(_lowerCamelCase , _lowerCamelCase ) if len(_lowerCamelCase ) > 0]
__lowercase : Iterator[Tuple[str, List[str]]] = zip(tags[0::2] , [l.split("""\n""" ) for l in tags[1::2]] )
__lowercase : List[str] = ["N", "CA", "C"]
__lowercase : Optional[int] = None
__lowercase : Union[str, Any] = None
__lowercase : List[str] = None
for g in groups:
if "[PRIMARY]" == g[0]:
__lowercase : List[str] = g[1][0].strip()
for i in range(len(_lowerCamelCase ) ):
if seq[i] not in residue_constants.restypes:
__lowercase : Dict = "X" # FIXME: strings are immutable
__lowercase : Tuple = np.array(
[residue_constants.restype_order.get(_lowerCamelCase , residue_constants.restype_num ) for res_symbol in seq] )
elif "[TERTIARY]" == g[0]:
__lowercase : List[List[float]] = []
for axis in range(3 ):
tertiary.append(list(map(_lowerCamelCase , g[1][axis].split() ) ) )
__lowercase : List[Any] = np.array(_lowerCamelCase )
__lowercase : str = np.zeros((len(tertiary[0] ) // 3, residue_constants.atom_type_num, 3) ).astype(np.floataa )
for i, atom in enumerate(_lowerCamelCase ):
__lowercase : Tuple = np.transpose(tertiary_np[:, i::3] )
atom_positions *= PICO_TO_ANGSTROM
elif "[MASK]" == g[0]:
__lowercase : str = np.array(list(map({"""-""": 0, """+""": 1}.get , g[1][0].strip() ) ) )
__lowercase : Optional[int] = np.zeros(
(
len(_lowerCamelCase ),
residue_constants.atom_type_num,
) ).astype(np.floataa )
for i, atom in enumerate(_lowerCamelCase ):
__lowercase : int = 1
atom_mask *= mask[..., None]
assert aatype is not None
return Protein(
atom_positions=_lowerCamelCase , atom_mask=_lowerCamelCase , aatype=_lowerCamelCase , residue_index=np.arange(len(_lowerCamelCase ) ) , b_factors=_lowerCamelCase , )
def snake_case_ ( lowerCAmelCase_ : Protein , lowerCAmelCase_ : int = 0 ):
__lowercase : List[str] = []
__lowercase : int = prot.remark
if remark is not None:
pdb_headers.append(F"REMARK {remark}" )
__lowercase : str = prot.parents
__lowercase : int = prot.parents_chain_index
if parents is not None and parents_chain_index is not None:
__lowercase : Union[str, Any] = [p for i, p in zip(_lowerCamelCase , _lowerCamelCase ) if i == chain_id]
if parents is None or len(_lowerCamelCase ) == 0:
__lowercase : List[str] = ["N/A"]
pdb_headers.append(F"PARENT {' '.join(_lowerCamelCase )}" )
return pdb_headers
def snake_case_ ( lowerCAmelCase_ : Protein , lowerCAmelCase_ : str ):
__lowercase : List[str] = []
__lowercase : List[str] = pdb_str.split("""\n""" )
__lowercase : Optional[Any] = prot.remark
if remark is not None:
out_pdb_lines.append(F"REMARK {remark}" )
__lowercase : List[List[str]]
if prot.parents is not None and len(prot.parents ) > 0:
__lowercase : Any = []
if prot.parents_chain_index is not None:
__lowercase : Dict[str, List[str]] = {}
for p, i in zip(prot.parents , prot.parents_chain_index ):
parent_dict.setdefault(str(_lowerCamelCase ) , [] )
parent_dict[str(_lowerCamelCase )].append(_lowerCamelCase )
__lowercase : List[str] = max([int(_lowerCamelCase ) for chain_idx in parent_dict] )
for i in range(max_idx + 1 ):
__lowercase : Tuple = parent_dict.get(str(_lowerCamelCase ) , ["""N/A"""] )
parents_per_chain.append(_lowerCamelCase )
else:
parents_per_chain.append(list(prot.parents ) )
else:
__lowercase : List[Any] = [["N/A"]]
def make_parent_line(lowerCAmelCase_ : Sequence[str] ) -> str:
return F"PARENT {' '.join(_lowerCamelCase )}"
out_pdb_lines.append(make_parent_line(parents_per_chain[0] ) )
__lowercase : List[Any] = 0
for i, l in enumerate(_lowerCamelCase ):
if "PARENT" not in l and "REMARK" not in l:
out_pdb_lines.append(_lowerCamelCase )
if "TER" in l and "END" not in lines[i + 1]:
chain_counter += 1
if not chain_counter >= len(_lowerCamelCase ):
__lowercase : Union[str, Any] = parents_per_chain[chain_counter]
else:
__lowercase : Dict = ["N/A"]
out_pdb_lines.append(make_parent_line(_lowerCamelCase ) )
return "\n".join(_lowerCamelCase )
def snake_case_ ( lowerCAmelCase_ : Protein ):
__lowercase : Union[str, Any] = residue_constants.restypes + ["X"]
def res_atoa(lowerCAmelCase_ : int ) -> str:
return residue_constants.restype_atoa.get(restypes[r] , """UNK""" )
__lowercase : str = residue_constants.atom_types
__lowercase : List[str] = []
__lowercase : Dict = prot.atom_mask
__lowercase : Tuple = prot.aatype
__lowercase : str = prot.atom_positions
__lowercase : str = prot.residue_index.astype(np.intaa )
__lowercase : Tuple = prot.b_factors
__lowercase : Dict = prot.chain_index
if np.any(aatype > residue_constants.restype_num ):
raise ValueError("""Invalid aatypes.""" )
__lowercase : int = get_pdb_headers(_lowerCamelCase )
if len(_lowerCamelCase ) > 0:
pdb_lines.extend(_lowerCamelCase )
__lowercase : str = aatype.shape[0]
__lowercase : Union[str, Any] = 1
__lowercase : Union[str, Any] = 0
__lowercase : Union[str, Any] = string.ascii_uppercase
__lowercase : Dict = None
# Add all atom sites.
for i in range(_lowerCamelCase ):
__lowercase : List[str] = res_atoa(aatype[i] )
for atom_name, pos, mask, b_factor in zip(_lowerCamelCase , atom_positions[i] , atom_mask[i] , b_factors[i] ):
if mask < 0.5:
continue
__lowercase : Optional[Any] = "ATOM"
__lowercase : List[Any] = atom_name if len(_lowerCamelCase ) == 4 else F" {atom_name}"
__lowercase : Tuple = ""
__lowercase : Tuple = ""
__lowercase : Optional[Any] = 1.00
__lowercase : Optional[Any] = atom_name[0] # Protein supports only C, N, O, S, this works.
__lowercase : List[str] = ""
__lowercase : Any = "A"
if chain_index is not None:
__lowercase : int = chain_tags[chain_index[i]]
# PDB is a columnar format, every space matters here!
__lowercase : Optional[int] = (
F"{record_type:<6}{atom_index:>5} {name:<4}{alt_loc:>1}"
F"{res_name_a:>3} {chain_tag:>1}"
F"{residue_index[i]:>4}{insertion_code:>1} "
F"{pos[0]:>8.3f}{pos[1]:>8.3f}{pos[2]:>8.3f}"
F"{occupancy:>6.2f}{b_factor:>6.2f} "
F"{element:>2}{charge:>2}"
)
pdb_lines.append(_lowerCamelCase )
atom_index += 1
__lowercase : Optional[int] = i == n - 1
if chain_index is not None:
if i != n - 1 and chain_index[i + 1] != prev_chain_index:
__lowercase : Optional[Any] = True
__lowercase : Any = chain_index[i + 1]
if should_terminate:
# Close the chain.
__lowercase : Tuple = "TER"
__lowercase : List[Any] = (
F"{chain_end:<6}{atom_index:>5} {res_atoa(aatype[i] ):>3} {chain_tag:>1}{residue_index[i]:>4}"
)
pdb_lines.append(_lowerCamelCase )
atom_index += 1
if i != n - 1:
# "prev" is a misnomer here. This happens at the beginning of
# each new chain.
pdb_lines.extend(get_pdb_headers(_lowerCamelCase , _lowerCamelCase ) )
pdb_lines.append("""END""" )
pdb_lines.append("""""" )
return "\n".join(_lowerCamelCase )
def snake_case_ ( lowerCAmelCase_ : Protein ):
return residue_constants.STANDARD_ATOM_MASK[prot.aatype]
def snake_case_ ( lowerCAmelCase_ : FeatureDict , lowerCAmelCase_ : ModelOutput , lowerCAmelCase_ : Optional[np.ndarray] = None , lowerCAmelCase_ : Optional[np.ndarray] = None , lowerCAmelCase_ : Optional[str] = None , lowerCAmelCase_ : Optional[Sequence[str]] = None , lowerCAmelCase_ : Optional[Sequence[int]] = None , ):
return Protein(
aatype=features["""aatype"""] , atom_positions=result["""final_atom_positions"""] , atom_mask=result["""final_atom_mask"""] , residue_index=features["""residue_index"""] + 1 , b_factors=b_factors if b_factors is not None else np.zeros_like(result["""final_atom_mask"""] ) , chain_index=_lowerCamelCase , remark=_lowerCamelCase , parents=_lowerCamelCase , parents_chain_index=_lowerCamelCase , )
| 704
|
from __future__ import annotations
import os
import tempfile
import unittest
from transformers import ConvBertConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import (
TFConvBertForMaskedLM,
TFConvBertForMultipleChoice,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertModel,
)
class lowerCAmelCase :
'''simple docstring'''
def __init__( self : Union[str, Any] , __a : Dict , __a : Union[str, Any]=13 , __a : Dict=7 , __a : Dict=True , __a : Dict=True , __a : Any=True , __a : List[str]=True , __a : int=99 , __a : Optional[int]=32 , __a : str=2 , __a : int=4 , __a : List[str]=37 , __a : Union[str, Any]="gelu" , __a : Union[str, Any]=0.1 , __a : Union[str, Any]=0.1 , __a : List[Any]=512 , __a : int=16 , __a : Union[str, Any]=2 , __a : Union[str, Any]=0.02 , __a : List[str]=3 , __a : Dict=4 , __a : Optional[Any]=None , ) -> Optional[Any]:
"""simple docstring"""
__lowercase : Any = parent
__lowercase : Tuple = 13
__lowercase : Dict = 7
__lowercase : List[Any] = True
__lowercase : Tuple = True
__lowercase : List[str] = True
__lowercase : Any = True
__lowercase : Optional[int] = 99
__lowercase : str = 384
__lowercase : Optional[Any] = 2
__lowercase : Dict = 4
__lowercase : str = 37
__lowercase : Optional[int] = """gelu"""
__lowercase : int = 0.1
__lowercase : Union[str, Any] = 0.1
__lowercase : Tuple = 512
__lowercase : Tuple = 16
__lowercase : Optional[int] = 2
__lowercase : Optional[Any] = 0.02
__lowercase : Dict = 3
__lowercase : Union[str, Any] = 4
__lowercase : Tuple = 128
__lowercase : Optional[Any] = 2
__lowercase : int = 9
__lowercase : List[Any] = 1
__lowercase : Union[str, Any] = None
def lowerCAmelCase ( self : str ) -> List[Any]:
"""simple docstring"""
__lowercase : Any = ids_tensor([self.batch_size, self.seq_length] , self.vocab_size )
__lowercase : Optional[Any] = None
if self.use_input_mask:
__lowercase : Dict = random_attention_mask([self.batch_size, self.seq_length] )
__lowercase : Dict = None
if self.use_token_type_ids:
__lowercase : Dict = ids_tensor([self.batch_size, self.seq_length] , self.type_vocab_size )
__lowercase : Optional[Any] = None
__lowercase : str = None
__lowercase : Tuple = None
if self.use_labels:
__lowercase : List[str] = ids_tensor([self.batch_size] , self.type_sequence_label_size )
__lowercase : List[Any] = ids_tensor([self.batch_size, self.seq_length] , self.num_labels )
__lowercase : str = ids_tensor([self.batch_size] , self.num_choices )
__lowercase : Optional[int] = ConvBertConfig(
vocab_size=self.vocab_size , hidden_size=self.hidden_size , num_hidden_layers=self.num_hidden_layers , num_attention_heads=self.num_attention_heads , intermediate_size=self.intermediate_size , hidden_act=self.hidden_act , hidden_dropout_prob=self.hidden_dropout_prob , attention_probs_dropout_prob=self.attention_probs_dropout_prob , max_position_embeddings=self.max_position_embeddings , type_vocab_size=self.type_vocab_size , initializer_range=self.initializer_range , return_dict=__a , )
return config, input_ids, token_type_ids, input_mask, sequence_labels, token_labels, choice_labels
def lowerCAmelCase ( self : Dict , __a : List[Any] , __a : List[str] , __a : Union[str, Any] , __a : str , __a : Union[str, Any] , __a : Tuple , __a : Tuple ) -> Dict:
"""simple docstring"""
__lowercase : Dict = TFConvBertModel(config=__a )
__lowercase : Tuple = {"""input_ids""": input_ids, """attention_mask""": input_mask, """token_type_ids""": token_type_ids}
__lowercase : Any = [input_ids, input_mask]
__lowercase : Dict = model(__a )
__lowercase : str = model(__a )
self.parent.assertEqual(result.last_hidden_state.shape , (self.batch_size, self.seq_length, self.hidden_size) )
def lowerCAmelCase ( self : Tuple , __a : Union[str, Any] , __a : Any , __a : Tuple , __a : Union[str, Any] , __a : str , __a : Dict , __a : str ) -> Dict:
"""simple docstring"""
__lowercase : Optional[int] = TFConvBertForMaskedLM(config=__a )
__lowercase : List[Any] = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
__lowercase : Any = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.vocab_size) )
def lowerCAmelCase ( self : Optional[int] , __a : int , __a : Any , __a : Optional[int] , __a : int , __a : int , __a : List[Any] , __a : Optional[int] ) -> List[Any]:
"""simple docstring"""
__lowercase : str = self.num_labels
__lowercase : List[Any] = TFConvBertForSequenceClassification(config=__a )
__lowercase : int = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
__lowercase : List[str] = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_labels) )
def lowerCAmelCase ( self : Optional[int] , __a : Any , __a : Optional[Any] , __a : int , __a : Optional[int] , __a : Tuple , __a : int , __a : int ) -> Dict:
"""simple docstring"""
__lowercase : Tuple = self.num_choices
__lowercase : Dict = TFConvBertForMultipleChoice(config=__a )
__lowercase : List[str] = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) )
__lowercase : int = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) )
__lowercase : str = tf.tile(tf.expand_dims(__a , 1 ) , (1, self.num_choices, 1) )
__lowercase : str = {
"""input_ids""": multiple_choice_inputs_ids,
"""attention_mask""": multiple_choice_input_mask,
"""token_type_ids""": multiple_choice_token_type_ids,
}
__lowercase : Dict = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.num_choices) )
def lowerCAmelCase ( self : List[str] , __a : str , __a : List[str] , __a : List[str] , __a : List[str] , __a : Any , __a : Tuple , __a : Union[str, Any] ) -> Optional[int]:
"""simple docstring"""
__lowercase : Tuple = self.num_labels
__lowercase : Tuple = TFConvBertForTokenClassification(config=__a )
__lowercase : Dict = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
__lowercase : str = model(__a )
self.parent.assertEqual(result.logits.shape , (self.batch_size, self.seq_length, self.num_labels) )
def lowerCAmelCase ( self : List[Any] , __a : Optional[int] , __a : List[str] , __a : Optional[Any] , __a : int , __a : Tuple , __a : Any , __a : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__lowercase : Any = TFConvBertForQuestionAnswering(config=__a )
__lowercase : str = {
"""input_ids""": input_ids,
"""attention_mask""": input_mask,
"""token_type_ids""": token_type_ids,
}
__lowercase : List[Any] = model(__a )
self.parent.assertEqual(result.start_logits.shape , (self.batch_size, self.seq_length) )
self.parent.assertEqual(result.end_logits.shape , (self.batch_size, self.seq_length) )
def lowerCAmelCase ( self : Tuple ) -> Tuple:
"""simple docstring"""
__lowercase : Tuple = self.prepare_config_and_inputs()
(
(
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) , (
__lowercase
) ,
) : int = config_and_inputs
__lowercase : Union[str, Any] = {"""input_ids""": input_ids, """token_type_ids""": token_type_ids, """attention_mask""": input_mask}
return config, inputs_dict
@require_tf
class lowerCAmelCase ( __a , __a , unittest.TestCase ):
'''simple docstring'''
_A : Dict = (
(
TFConvBertModel,
TFConvBertForMaskedLM,
TFConvBertForQuestionAnswering,
TFConvBertForSequenceClassification,
TFConvBertForTokenClassification,
TFConvBertForMultipleChoice,
)
if is_tf_available()
else ()
)
_A : str = (
{
'''feature-extraction''': TFConvBertModel,
'''fill-mask''': TFConvBertForMaskedLM,
'''question-answering''': TFConvBertForQuestionAnswering,
'''text-classification''': TFConvBertForSequenceClassification,
'''token-classification''': TFConvBertForTokenClassification,
'''zero-shot''': TFConvBertForSequenceClassification,
}
if is_tf_available()
else {}
)
_A : Union[str, Any] = False
_A : List[str] = False
_A : Dict = False
def lowerCAmelCase ( self : List[str] ) -> int:
"""simple docstring"""
__lowercase : int = TFConvBertModelTester(self )
__lowercase : Tuple = ConfigTester(self , config_class=__a , hidden_size=37 )
def lowerCAmelCase ( self : Optional[int] ) -> Optional[Any]:
"""simple docstring"""
self.config_tester.run_common_tests()
def lowerCAmelCase ( self : Optional[Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase : Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*__a )
def lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase : Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_lm(*__a )
def lowerCAmelCase ( self : Union[str, Any] ) -> Union[str, Any]:
"""simple docstring"""
__lowercase : int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_multiple_choice(*__a )
def lowerCAmelCase ( self : str ) -> Any:
"""simple docstring"""
__lowercase : List[str] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_question_answering(*__a )
def lowerCAmelCase ( self : str ) -> str:
"""simple docstring"""
__lowercase : List[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_sequence_classification(*__a )
def lowerCAmelCase ( self : str ) -> Any:
"""simple docstring"""
__lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_token_classification(*__a )
@slow
def lowerCAmelCase ( self : str ) -> Any:
"""simple docstring"""
__lowercase , __lowercase : Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase : Union[str, Any] = True
__lowercase : List[Any] = True
if hasattr(__a , """use_cache""" ):
__lowercase : Optional[Any] = True
__lowercase : List[str] = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
__lowercase : int = getattr(self.model_tester , """key_length""" , __a )
for model_class in self.all_model_classes:
__lowercase : Optional[Any] = self._prepare_for_class(__a , __a )
__lowercase : Tuple = model_class(__a )
__lowercase : Tuple = len(model(__a ) )
with tempfile.TemporaryDirectory() as tmpdirname:
model.save_pretrained(__a , saved_model=__a )
__lowercase : List[Any] = os.path.join(__a , """saved_model""" , """1""" )
__lowercase : str = tf.keras.models.load_model(__a )
__lowercase : Optional[int] = model(__a )
if self.is_encoder_decoder:
__lowercase : Union[str, Any] = outputs["""encoder_hidden_states"""]
__lowercase : Union[str, Any] = outputs["""encoder_attentions"""]
else:
__lowercase : Union[str, Any] = outputs["""hidden_states"""]
__lowercase : List[str] = outputs["""attentions"""]
self.assertEqual(len(__a ) , __a )
__lowercase : List[Any] = getattr(
self.model_tester , """expected_num_hidden_layers""" , self.model_tester.num_hidden_layers + 1 )
self.assertEqual(len(__a ) , __a )
self.assertListEqual(
list(output_hidden_states[0].shape[-2:] ) , [self.model_tester.seq_length, self.model_tester.hidden_size] , )
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(output_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
@slow
def lowerCAmelCase ( self : Union[str, Any] ) -> List[Any]:
"""simple docstring"""
__lowercase : str = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
self.assertIsNotNone(__a )
def lowerCAmelCase ( self : Any ) -> List[Any]:
"""simple docstring"""
__lowercase , __lowercase : int = self.model_tester.prepare_config_and_inputs_for_common()
__lowercase : List[str] = True
__lowercase : List[Any] = getattr(self.model_tester , """decoder_seq_length""" , self.model_tester.seq_length )
__lowercase : Optional[int] = getattr(self.model_tester , """encoder_seq_length""" , self.model_tester.seq_length )
__lowercase : List[str] = getattr(self.model_tester , """key_length""" , __a )
__lowercase : List[Any] = getattr(self.model_tester , """key_length""" , __a )
def check_decoder_attentions_output(__a : List[str] ):
__lowercase : Union[str, Any] = len(__a )
self.assertEqual(out_len % 2 , 0 )
__lowercase : Any = outputs.decoder_attentions
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(decoder_attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, decoder_seq_length, decoder_key_length] , )
def check_encoder_attentions_output(__a : str ):
__lowercase : str = [
t.numpy() for t in (outputs.encoder_attentions if config.is_encoder_decoder else outputs.attentions)
]
self.assertEqual(len(__a ) , self.model_tester.num_hidden_layers )
self.assertListEqual(
list(attentions[0].shape[-3:] ) , [self.model_tester.num_attention_heads / 2, encoder_seq_length, encoder_key_length] , )
for model_class in self.all_model_classes:
__lowercase : int = True
__lowercase : Any = False
__lowercase : List[Any] = model_class(__a )
__lowercase : Tuple = model(self._prepare_for_class(__a , __a ) )
__lowercase : Dict = len(__a )
self.assertEqual(config.output_hidden_states , __a )
check_encoder_attentions_output(__a )
if self.is_encoder_decoder:
__lowercase : Any = model_class(__a )
__lowercase : List[str] = model(self._prepare_for_class(__a , __a ) )
self.assertEqual(config.output_hidden_states , __a )
check_decoder_attentions_output(__a )
# Check that output attentions can also be changed via the config
del inputs_dict["output_attentions"]
__lowercase : Dict = True
__lowercase : Optional[Any] = model_class(__a )
__lowercase : Optional[int] = model(self._prepare_for_class(__a , __a ) )
self.assertEqual(config.output_hidden_states , __a )
check_encoder_attentions_output(__a )
# Check attention is always last and order is fine
__lowercase : List[str] = True
__lowercase : List[Any] = True
__lowercase : Any = model_class(__a )
__lowercase : Optional[int] = model(self._prepare_for_class(__a , __a ) )
self.assertEqual(out_len + (2 if self.is_encoder_decoder else 1) , len(__a ) )
self.assertEqual(model.config.output_hidden_states , __a )
check_encoder_attentions_output(__a )
@require_tf
class lowerCAmelCase ( unittest.TestCase ):
'''simple docstring'''
@slow
def lowerCAmelCase ( self : Dict ) -> Union[str, Any]:
"""simple docstring"""
__lowercase : List[str] = TFConvBertModel.from_pretrained("""YituTech/conv-bert-base""" )
__lowercase : str = tf.constant([[0, 1, 2, 3, 4, 5]] )
__lowercase : Tuple = model(__a )[0]
__lowercase : Any = [1, 6, 768]
self.assertEqual(output.shape , __a )
__lowercase : Optional[Any] = tf.constant(
[
[
[-0.03475493, -0.4686034, -0.30638832],
[0.22637248, -0.26988646, -0.7423424],
[0.10324868, -0.45013508, -0.58280784],
]
] )
tf.debugging.assert_near(output[:, :3, :3] , __a , atol=1E-4 )
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