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from typing import Any
class _snake_case :
def __init__( self ,UpperCamelCase ) -> List[str]:
snake_case__ :Dict = data
snake_case__ :Any = None
def __repr__( self ) -> str:
return f'Node({self.data})'
class _snake_case :
def __init__( self ) -> Optional[Any]:
snake_case__ :str = None
def __iter__( self ) -> Any:
snake_case__ :Optional[Any] = self.head
while node:
yield node.data
snake_case__ :Dict = node.next
def __len__( self ) -> int:
return sum(1 for _ in self )
def __repr__( self ) -> str:
return "->".join([str(UpperCamelCase ) for item in self] )
def __getitem__( self ,UpperCamelCase ) -> Any:
if not 0 <= index < len(self ):
raise ValueError("list index out of range." )
for i, node in enumerate(self ):
if i == index:
return node
return None
def __setitem__( self ,UpperCamelCase ,UpperCamelCase ) -> None:
if not 0 <= index < len(self ):
raise ValueError("list index out of range." )
snake_case__ :Any = self.head
for _ in range(UpperCamelCase ):
snake_case__ :Any = current.next
snake_case__ :Optional[int] = data
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> None:
self.insert_nth(len(self ) ,UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> None:
self.insert_nth(0 ,UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> None:
if not 0 <= index <= len(self ):
raise IndexError("list index out of range" )
snake_case__ :Tuple = Node(UpperCamelCase )
if self.head is None:
snake_case__ :Dict = new_node
elif index == 0:
snake_case__ :Union[str, Any] = self.head # link new_node to head
snake_case__ :str = new_node
else:
snake_case__ :int = self.head
for _ in range(index - 1 ):
snake_case__ :Tuple = temp.next
snake_case__ :List[Any] = temp.next
snake_case__ :str = new_node
def lowerCAmelCase_ ( self ) -> None: # print every node data
print(self )
def lowerCAmelCase_ ( self ) -> Any:
return self.delete_nth(0 )
def lowerCAmelCase_ ( self ) -> Any: # delete from tail
return self.delete_nth(len(self ) - 1 )
def lowerCAmelCase_ ( self ,UpperCamelCase = 0 ) -> Any:
if not 0 <= index <= len(self ) - 1: # test if index is valid
raise IndexError("List index out of range." )
snake_case__ :Optional[Any] = self.head # default first node
if index == 0:
snake_case__ :Dict = self.head.next
else:
snake_case__ :Union[str, Any] = self.head
for _ in range(index - 1 ):
snake_case__ :Dict = temp.next
snake_case__ :Tuple = temp.next
snake_case__ :List[Any] = temp.next.next
return delete_node.data
def lowerCAmelCase_ ( self ) -> bool:
return self.head is None
def lowerCAmelCase_ ( self ) -> None:
snake_case__ :Dict = None
snake_case__ :Union[str, Any] = self.head
while current:
# Store the current node's next node.
snake_case__ :Any = current.next
# Make the current node's next point backwards
snake_case__ :Any = prev
# Make the previous node be the current node
snake_case__ :int = current
# Make the current node the next node (to progress iteration)
snake_case__ :Optional[int] = next_node
# Return prev in order to put the head at the end
snake_case__ :Tuple = prev
def lowercase_ ( ) -> None:
'''simple docstring'''
snake_case__ :List[Any] = LinkedList()
assert linked_list.is_empty() is True
assert str(__snake_case ) == ""
try:
linked_list.delete_head()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
try:
linked_list.delete_tail()
raise AssertionError # This should not happen.
except IndexError:
assert True # This should happen.
for i in range(10 ):
assert len(__snake_case ) == i
linked_list.insert_nth(__snake_case , i + 1 )
assert str(__snake_case ) == "->".join(str(__snake_case ) for i in range(1 , 11 ) )
linked_list.insert_head(0 )
linked_list.insert_tail(11 )
assert str(__snake_case ) == "->".join(str(__snake_case ) for i in range(0 , 12 ) )
assert linked_list.delete_head() == 0
assert linked_list.delete_nth(9 ) == 10
assert linked_list.delete_tail() == 11
assert len(__snake_case ) == 9
assert str(__snake_case ) == "->".join(str(__snake_case ) for i in range(1 , 10 ) )
assert all(linked_list[i] == i + 1 for i in range(0 , 9 ) ) is True
for i in range(0 , 9 ):
snake_case__ :int = -i
assert all(linked_list[i] == -i for i in range(0 , 9 ) ) is True
linked_list.reverse()
assert str(__snake_case ) == "->".join(str(__snake_case ) for i in range(-8 , 1 ) )
def lowercase_ ( ) -> None:
'''simple docstring'''
snake_case__ :str = [
-9,
1_00,
Node(77_34_51_12 ),
"dlrow olleH",
7,
55_55,
0,
-1_9_2.5_5_5_5_5,
"Hello, world!",
7_7.9,
Node(10 ),
None,
None,
1_2.2_0,
]
snake_case__ :Any = LinkedList()
for i in test_input:
linked_list.insert_tail(__snake_case )
# Check if it's empty or not
assert linked_list.is_empty() is False
assert (
str(__snake_case ) == "-9->100->Node(77345112)->dlrow olleH->7->5555->0->"
"-192.55555->Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the head
snake_case__ :Optional[int] = linked_list.delete_head()
assert result == -9
assert (
str(__snake_case ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None->12.2"
)
# Delete the tail
snake_case__ :Tuple = linked_list.delete_tail()
assert result == 1_2.2
assert (
str(__snake_case ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None->None"
)
# Delete a node in specific location in linked list
snake_case__ :List[str] = linked_list.delete_nth(10 )
assert result is None
assert (
str(__snake_case ) == "100->Node(77345112)->dlrow olleH->7->5555->0->-192.55555->"
"Hello, world!->77.9->Node(10)->None"
)
# Add a Node instance to its head
linked_list.insert_head(Node("Hello again, world!" ) )
assert (
str(__snake_case )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None"
)
# Add None to its tail
linked_list.insert_tail(__snake_case )
assert (
str(__snake_case )
== "Node(Hello again, world!)->100->Node(77345112)->dlrow olleH->"
"7->5555->0->-192.55555->Hello, world!->77.9->Node(10)->None->None"
)
# Reverse the linked list
linked_list.reverse()
assert (
str(__snake_case )
== "None->None->Node(10)->77.9->Hello, world!->-192.55555->0->5555->"
"7->dlrow olleH->Node(77345112)->100->Node(Hello again, world!)"
)
def lowercase_ ( ) -> List[str]:
'''simple docstring'''
from doctest import testmod
testmod()
snake_case__ :str = LinkedList()
linked_list.insert_head(input("Inserting 1st at head " ).strip() )
linked_list.insert_head(input("Inserting 2nd at head " ).strip() )
print("\nPrint list:" )
linked_list.print_list()
linked_list.insert_tail(input("\nInserting 1st at tail " ).strip() )
linked_list.insert_tail(input("Inserting 2nd at tail " ).strip() )
print("\nPrint list:" )
linked_list.print_list()
print("\nDelete head" )
linked_list.delete_head()
print("Delete tail" )
linked_list.delete_tail()
print("\nPrint list:" )
linked_list.print_list()
print("\nReverse linked list" )
linked_list.reverse()
print("\nPrint list:" )
linked_list.print_list()
print("\nString representation of linked list:" )
print(__snake_case )
print("\nReading/changing Node data using indexing:" )
print(F'Element at Position 1: {linked_list[1]}' )
snake_case__ :Union[str, Any] = input("Enter New Value: " ).strip()
print("New list:" )
print(__snake_case )
print(F'length of linked_list is : {len(__snake_case )}' )
if __name__ == "__main__":
main()
| 57
|
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> List[Any]:
# A mock response for an HTTP head request to emulate server down
snake_case__ :Tuple = mock.Mock()
snake_case__ :List[str] = 500
snake_case__ :Any = {}
snake_case__ :Union[str, Any] = HTTPError
snake_case__ :Tuple = {}
# Download this model to make sure it's in the cache.
snake_case__ :Any = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" ,return_value=UpperCamelCase ) as mock_head:
snake_case__ :Dict = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def lowerCAmelCase_ ( self ) -> Dict:
# A mock response for an HTTP head request to emulate server down
snake_case__ :Union[str, Any] = mock.Mock()
snake_case__ :int = 500
snake_case__ :Any = {}
snake_case__ :Dict = HTTPError
snake_case__ :List[Any] = {}
# Download this model to make sure it's in the cache.
snake_case__ :Optional[int] = GPTaTokenizerFast.from_pretrained("gpt2" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" ,return_value=UpperCamelCase ) as mock_head:
snake_case__ :Any = GPTaTokenizerFast.from_pretrained("gpt2" )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCAmelCase_ ( self ) -> int:
# This test is for deprecated behavior and can be removed in v5
try:
snake_case__ :Union[str, Any] = tempfile.mktemp()
with open(UpperCamelCase ,"wb" ) as f:
http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" ,UpperCamelCase )
snake_case__ :Tuple = AlbertTokenizer.from_pretrained(UpperCamelCase )
finally:
os.remove(UpperCamelCase )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile("tokenizer.json" ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open("tokenizer.json" ,"wb" ) as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json" ,UpperCamelCase )
snake_case__ :Dict = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1_000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("tokenizer.json" )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
# This test is for deprecated behavior and can be removed in v5
snake_case__ :Union[str, Any] = AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" )
@is_staging_test
class _snake_case ( unittest.TestCase ):
_A = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def lowerCAmelCase_ ( cls ) -> Optional[int]:
snake_case__ :List[str] = TOKEN
HfFolder.save_token(UpperCamelCase )
@classmethod
def lowerCAmelCase_ ( cls ) -> Union[str, Any]:
try:
delete_repo(token=cls._token ,repo_id="test-tokenizer" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="valid_org/test-tokenizer-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="test-dynamic-tokenizer" )
except HTTPError:
pass
def lowerCAmelCase_ ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :List[str] = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :str = BertTokenizer(UpperCamelCase )
tokenizer.push_to_hub("test-tokenizer" ,use_auth_token=self._token )
snake_case__ :Dict = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id="test-tokenizer" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(UpperCamelCase ,repo_id="test-tokenizer" ,push_to_hub=UpperCamelCase ,use_auth_token=self._token )
snake_case__ :List[str] = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def lowerCAmelCase_ ( self ) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :List[Any] = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Any = BertTokenizer(UpperCamelCase )
tokenizer.push_to_hub("valid_org/test-tokenizer-org" ,use_auth_token=self._token )
snake_case__ :Any = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id="valid_org/test-tokenizer-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
UpperCamelCase ,repo_id="valid_org/test-tokenizer-org" ,push_to_hub=UpperCamelCase ,use_auth_token=self._token )
snake_case__ :Union[str, Any] = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def lowerCAmelCase_ ( self ) -> Any:
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :str = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Optional[int] = CustomTokenizer(UpperCamelCase )
# No fast custom tokenizer
tokenizer.push_to_hub("test-dynamic-tokenizer" ,use_auth_token=self._token )
snake_case__ :Union[str, Any] = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizer" )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :int = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Tuple = BertTokenizerFast.from_pretrained(UpperCamelCase )
bert_tokenizer.save_pretrained(UpperCamelCase )
snake_case__ :List[Any] = CustomTokenizerFast.from_pretrained(UpperCamelCase )
tokenizer.push_to_hub("test-dynamic-tokenizer" ,use_auth_token=self._token )
snake_case__ :List[Any] = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizerFast" )
snake_case__ :List[str] = AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' ,use_fast=UpperCamelCase ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizer" )
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :int = Trie()
trie.add("Hello 友達" )
self.assertEqual(trie.data ,{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}} )
trie.add("Hello" )
trie.data
self.assertEqual(trie.data ,{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}} )
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :List[str] = Trie()
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) ,["[CLS] This is a extra_id_100"] )
trie.add("[CLS]" )
trie.add("extra_id_1" )
trie.add("extra_id_100" )
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) ,["[CLS]", " This is a ", "extra_id_100"] )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Optional[Any] = Trie()
trie.add("A" )
self.assertEqual(trie.split("ABC" ) ,["A", "BC"] )
self.assertEqual(trie.split("BCA" ) ,["BC", "A"] )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :Any = Trie()
trie.add("TOKEN]" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) ,["This is something ", "[SPECIAL_TOKEN]"] )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :List[Any] = Trie()
trie.add("A" )
trie.add("P" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) ,["This is something ", "[SPECIAL_TOKEN]"] )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :str = Trie()
trie.add("AB" )
trie.add("B" )
trie.add("C" )
self.assertEqual(trie.split("ABC" ) ,["AB", "C"] )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Dict = Trie()
trie.add("ABC" )
trie.add("B" )
trie.add("CD" )
self.assertEqual(trie.split("ABCD" ) ,["ABC", "D"] )
def lowerCAmelCase_ ( self ) -> int:
# Even if the offsets are wrong, we necessarily output correct string
# parts.
snake_case__ :Optional[int] = Trie()
snake_case__ :Union[str, Any] = trie.cut_text("ABC" ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(UpperCamelCase ,["AB", "C"] )
| 57
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCAmelCase : Tuple = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetrConfig",
"ConditionalDetrOnnxConfig",
]
}
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : Any = ["ConditionalDetrFeatureExtractor"]
__UpperCAmelCase : Union[str, Any] = ["ConditionalDetrImageProcessor"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : str = [
"CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST",
"ConditionalDetrForObjectDetection",
"ConditionalDetrForSegmentation",
"ConditionalDetrModel",
"ConditionalDetrPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP,
ConditionalDetrConfig,
ConditionalDetrOnnxConfig,
)
try:
if not is_vision_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .feature_extraction_conditional_detr import ConditionalDetrFeatureExtractor
from .image_processing_conditional_detr import ConditionalDetrImageProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_conditional_detr import (
CONDITIONAL_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrModel,
ConditionalDetrPreTrainedModel,
)
else:
import sys
__UpperCAmelCase : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
|
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 : Optional[Any] = 1_6
__UpperCAmelCase : Optional[int] = 3_2
def lowercase_ ( __snake_case : Accelerator , __snake_case : int = 16 , __snake_case : str = "bert-base-cased" ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :int = AutoTokenizer.from_pretrained(__snake_case )
snake_case__ :Optional[int] = load_dataset("glue" , "mrpc" )
def tokenize_function(__snake_case : Tuple ):
# max_length=None => use the model max length (it's actually the default)
snake_case__ :Any = 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
snake_case__ :List[Any] = 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
snake_case__ :Any = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(__snake_case : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__snake_case , padding="max_length" , max_length=1_28 , return_tensors="pt" )
return tokenizer.pad(__snake_case , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
snake_case__ :Any = DataLoader(
tokenized_datasets["train"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
snake_case__ :Tuple = DataLoader(
tokenized_datasets["validation"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
return train_dataloader, eval_dataloader
def lowercase_ ( __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
model.eval()
snake_case__ :Union[str, Any] = 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():
snake_case__ :List[Any] = model(**__snake_case )
snake_case__ :Any = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
snake_case__ , snake_case__ :Tuple = 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:
snake_case__ :List[str] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
snake_case__ :Optional[int] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__snake_case , references=__snake_case , )
snake_case__ :int = metric.compute()
return eval_metric["accuracy"]
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : Optional[Any] ) -> Any:
'''simple docstring'''
snake_case__ :Any = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ :Union[str, Any] = config["lr"]
snake_case__ :List[str] = int(config["num_epochs"] )
snake_case__ :Optional[Any] = int(config["seed"] )
snake_case__ :List[Any] = int(config["batch_size"] )
snake_case__ :List[Any] = args.model_name_or_path
set_seed(__snake_case )
snake_case__ , snake_case__ :List[Any] = 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)
snake_case__ :List[Any] = AutoModelForSequenceClassification.from_pretrained(__snake_case , return_dict=__snake_case )
# Instantiate optimizer
snake_case__ :int = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
snake_case__ :Tuple = optimizer_cls(params=model.parameters() , lr=__snake_case )
if accelerator.state.deepspeed_plugin is not None:
snake_case__ :List[str] = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
snake_case__ :Any = 1
snake_case__ :List[Any] = (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
):
snake_case__ :Optional[Any] = get_linear_schedule_with_warmup(
optimizer=__snake_case , num_warmup_steps=0 , num_training_steps=__snake_case , )
else:
snake_case__ :Any = 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.
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ :int = 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
snake_case__ :Dict = 0
# We also need to keep track of the stating epoch so files are named properly
snake_case__ :Union[str, Any] = 0
snake_case__ :List[str] = evaluate.load("glue" , "mrpc" )
snake_case__ :Optional[Any] = num_epochs
if args.partial_train_epoch is not None:
snake_case__ :List[Any] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
snake_case__ :Union[str, Any] = args.resume_from_checkpoint.split("epoch_" )[1]
snake_case__ :Dict = ""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
snake_case__ :str = int(__snake_case ) + 1
snake_case__ :List[Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
accelerator.print("resumed checkpoint performance:" , __snake_case )
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:
snake_case__ :Tuple = json.load(__snake_case )
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
snake_case__ :Optional[int] = {}
for epoch in range(__snake_case , __snake_case ):
model.train()
for step, batch in enumerate(__snake_case ):
snake_case__ :str = model(**__snake_case )
snake_case__ :List[str] = outputs.loss
snake_case__ :List[Any] = 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
snake_case__ :int = F'epoch_{epoch}'
snake_case__ :str = os.path.join(args.output_dir , __snake_case )
accelerator.save_state(__snake_case )
snake_case__ :Union[str, Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
snake_case__ :List[str] = accuracy
snake_case__ :List[str] = lr_scheduler.get_lr()[0]
snake_case__ :List[Any] = optimizer.param_groups[0]["lr"]
snake_case__ :Dict = epoch
snake_case__ :List[Any] = overall_step
accelerator.print(F'epoch {epoch}:' , __snake_case )
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(__snake_case , __snake_case )
def lowercase_ ( ) -> Any:
'''simple docstring'''
snake_case__ :List[Any] = 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(
"--resume_from_checkpoint" , type=__snake_case , default=__snake_case , help="If the training should continue from a checkpoint folder." , )
parser.add_argument(
"--partial_train_epoch" , type=__snake_case , default=__snake_case , help="If passed, the training will stop after this number of epochs." , )
parser.add_argument(
"--num_epochs" , type=__snake_case , default=2 , help="Number of train epochs." , )
snake_case__ :Any = parser.parse_args()
snake_case__ :int = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(__snake_case , __snake_case )
if __name__ == "__main__":
main()
| 57
| 1
|
import logging
import random
import ray
from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex
__UpperCAmelCase : Dict = logging.getLogger(__name__)
class _snake_case :
def __init__( self ) -> Optional[int]:
snake_case__ :List[Any] = False
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> List[str]:
if not self.initialized:
snake_case__ :Dict = RagRetriever(
UpperCamelCase ,question_encoder_tokenizer=UpperCamelCase ,generator_tokenizer=UpperCamelCase ,index=UpperCamelCase ,init_retrieval=UpperCamelCase ,)
snake_case__ :List[Any] = True
def lowerCAmelCase_ ( self ) -> int:
self.retriever.index.init_index()
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> List[str]:
snake_case__ , snake_case__ :List[Any] = self.retriever._main_retrieve(UpperCamelCase ,UpperCamelCase )
return doc_ids, retrieved_doc_embeds
class _snake_case ( _A ):
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ) -> Dict:
if index is not None and index.is_initialized() and len(UpperCamelCase ) > 0:
raise ValueError(
"When using Ray for distributed fine-tuning, "
"you'll need to provide the paths instead, "
"as the dataset and the index are loaded "
"separately. More info in examples/rag/use_own_knowledge_dataset.py " )
super().__init__(
UpperCamelCase ,question_encoder_tokenizer=UpperCamelCase ,generator_tokenizer=UpperCamelCase ,index=UpperCamelCase ,init_retrieval=UpperCamelCase ,)
snake_case__ :Union[str, Any] = retrieval_workers
if len(self.retrieval_workers ) > 0:
ray.get(
[
worker.create_rag_retriever.remote(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
for worker in self.retrieval_workers
] )
def lowerCAmelCase_ ( self ) -> List[Any]:
logger.info("initializing retrieval" )
if len(self.retrieval_workers ) > 0:
ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers] )
else:
# Non-distributed training. Load index into this same process.
self.index.init_index()
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> int:
if len(self.retrieval_workers ) > 0:
# Select a random retrieval actor.
snake_case__ :Optional[int] = self.retrieval_workers[random.randint(0 ,len(self.retrieval_workers ) - 1 )]
snake_case__ , snake_case__ :str = ray.get(random_worker.retrieve.remote(UpperCamelCase ,UpperCamelCase ) )
else:
snake_case__ , snake_case__ :List[Any] = self._main_retrieve(UpperCamelCase ,UpperCamelCase )
return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(UpperCamelCase )
@classmethod
def lowerCAmelCase_ ( cls ,UpperCamelCase ,UpperCamelCase=None ,**UpperCamelCase ) -> str:
return super(UpperCamelCase ,cls ).get_tokenizers(UpperCamelCase ,UpperCamelCase ,**UpperCamelCase )
@classmethod
def lowerCAmelCase_ ( cls ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ,**UpperCamelCase ) -> int:
snake_case__ :str = kwargs.pop("config" ,UpperCamelCase ) or RagConfig.from_pretrained(UpperCamelCase ,**UpperCamelCase )
snake_case__ :List[str] = RagTokenizer.from_pretrained(UpperCamelCase ,config=UpperCamelCase )
snake_case__ :Any = rag_tokenizer.question_encoder
snake_case__ :Any = rag_tokenizer.generator
if indexed_dataset is not None:
snake_case__ :List[str] = "custom"
snake_case__ :Tuple = CustomHFIndex(config.retrieval_vector_size ,UpperCamelCase )
else:
snake_case__ :Union[str, Any] = cls._build_index(UpperCamelCase )
return cls(
UpperCamelCase ,question_encoder_tokenizer=UpperCamelCase ,generator_tokenizer=UpperCamelCase ,retrieval_workers=UpperCamelCase ,index=UpperCamelCase ,)
| 57
|
from __future__ import annotations
class _snake_case :
def __init__( self ,UpperCamelCase ) -> None:
snake_case__ :Union[str, Any] = data
snake_case__ :Node | None = None
snake_case__ :Node | None = None
def lowercase_ ( __snake_case : Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowercase_ ( __snake_case : Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowercase_ ( __snake_case : Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowercase_ ( ) -> None: # Main function for testing.
'''simple docstring'''
snake_case__ :Dict = Node(1 )
snake_case__ :int = Node(2 )
snake_case__ :Optional[Any] = Node(3 )
snake_case__ :Tuple = Node(4 )
snake_case__ :str = Node(5 )
snake_case__ :Optional[Any] = Node(6 )
snake_case__ :List[Any] = Node(7 )
snake_case__ :List[str] = Node(8 )
snake_case__ :Tuple = Node(9 )
print(is_full_binary_tree(__snake_case ) )
print(depth_of_tree(__snake_case ) )
print("Tree is: " )
display(__snake_case )
if __name__ == "__main__":
main()
| 57
| 1
|
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObjectDetection,
ConditionalDetrForSegmentation,
ConditionalDetrImageProcessor,
)
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase : Dict = logging.get_logger(__name__)
# here we list all keys to be renamed (original name on the left, our name on the right)
__UpperCAmelCase : Optional[Any] = []
for i in range(6):
# 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}.cross_attn.out_proj.weight''',
F'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
)
)
rename_keys.append(
(
F'''transformer.decoder.layers.{i}.cross_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'''))
# q, k, v projections in self/cross-attention in decoder for conditional DETR
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', F'''decoder.layers.{i}.sa_qcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', F'''decoder.layers.{i}.sa_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', F'''decoder.layers.{i}.sa_qpos_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', F'''decoder.layers.{i}.sa_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.weight''', F'''decoder.layers.{i}.sa_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', F'''decoder.layers.{i}.ca_qcontent_proj.weight''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', F'''decoder.layers.{i}.ca_kcontent_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', F'''decoder.layers.{i}.ca_kpos_proj.weight''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.weight''', F'''decoder.layers.{i}.ca_v_proj.weight'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', F'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', F'''decoder.layers.{i}.sa_qcontent_proj.bias''')
)
rename_keys.append(
(F'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', F'''decoder.layers.{i}.sa_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', F'''decoder.layers.{i}.sa_qpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', F'''decoder.layers.{i}.sa_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.sa_v_proj.bias''', F'''decoder.layers.{i}.sa_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', F'''decoder.layers.{i}.ca_qcontent_proj.bias''')
)
# rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', F'''decoder.layers.{i}.ca_kcontent_proj.bias''')
)
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', F'''decoder.layers.{i}.ca_kpos_proj.bias'''))
rename_keys.append((F'''transformer.decoder.layers.{i}.ca_v_proj.bias''', F'''decoder.layers.{i}.ca_v_proj.bias'''))
rename_keys.append(
(F'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', F'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
)
# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
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"),
("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
]
)
def lowercase_ ( __snake_case : Tuple , __snake_case : List[Any] , __snake_case : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case__ :int = state_dict.pop(__snake_case )
snake_case__ :List[str] = val
def lowercase_ ( __snake_case : Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case__ :Optional[Any] = OrderedDict()
for key, value in state_dict.items():
if "backbone.0.body" in key:
snake_case__ :Tuple = key.replace("backbone.0.body" , "backbone.conv_encoder.model" )
snake_case__ :Any = value
else:
snake_case__ :Any = value
return new_state_dict
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : Optional[int]=False ) -> str:
'''simple docstring'''
snake_case__ :Union[str, Any] = ""
if is_panoptic:
snake_case__ :Dict = "conditional_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)
snake_case__ :List[Any] = state_dict.pop(F'{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight' )
snake_case__ :Tuple = 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
snake_case__ :Dict = in_proj_weight[:2_56, :]
snake_case__ :int = in_proj_bias[:2_56]
snake_case__ :str = in_proj_weight[2_56:5_12, :]
snake_case__ :Union[str, Any] = in_proj_bias[2_56:5_12]
snake_case__ :Union[str, Any] = in_proj_weight[-2_56:, :]
snake_case__ :Optional[int] = in_proj_bias[-2_56:]
def lowercase_ ( ) -> Optional[int]:
'''simple docstring'''
snake_case__ :str = "http://images.cocodataset.org/val2017/000000039769.jpg"
snake_case__ :Optional[int] = Image.open(requests.get(__snake_case , stream=__snake_case ).raw )
return im
@torch.no_grad()
def lowercase_ ( __snake_case : Any , __snake_case : Optional[int] ) -> Dict:
'''simple docstring'''
snake_case__ :Any = ConditionalDetrConfig()
# set backbone and dilation attributes
if "resnet101" in model_name:
snake_case__ :Dict = "resnet101"
if "dc5" in model_name:
snake_case__ :Any = True
snake_case__ :str = "panoptic" in model_name
if is_panoptic:
snake_case__ :Union[str, Any] = 2_50
else:
snake_case__ :Optional[int] = 91
snake_case__ :List[str] = "huggingface/label-files"
snake_case__ :int = "coco-detection-id2label.json"
snake_case__ :Union[str, Any] = json.load(open(hf_hub_download(__snake_case , __snake_case , repo_type="dataset" ) , "r" ) )
snake_case__ :Optional[int] = {int(__snake_case ): v for k, v in idalabel.items()}
snake_case__ :Optional[Any] = idalabel
snake_case__ :List[Any] = {v: k for k, v in idalabel.items()}
# load image processor
snake_case__ :Tuple = "coco_panoptic" if is_panoptic else "coco_detection"
snake_case__ :Dict = ConditionalDetrImageProcessor(format=__snake_case )
# prepare image
snake_case__ :List[str] = prepare_img()
snake_case__ :List[Any] = image_processor(images=__snake_case , return_tensors="pt" )
snake_case__ :Any = encoding["pixel_values"]
logger.info(F'Converting model {model_name}...' )
# load original model from torch hub
snake_case__ :Any = torch.hub.load("DeppMeng/ConditionalDETR" , __snake_case , pretrained=__snake_case ).eval()
snake_case__ :str = conditional_detr.state_dict()
# rename keys
for src, dest in rename_keys:
if is_panoptic:
snake_case__ :Optional[Any] = "conditional_detr." + src
rename_key(__snake_case , __snake_case , __snake_case )
snake_case__ :Optional[Any] = rename_backbone_keys(__snake_case )
# query, key and value matrices need special treatment
read_in_q_k_v(__snake_case , is_panoptic=__snake_case )
# important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
snake_case__ :Dict = "conditional_detr.model." if is_panoptic else "model."
for key in state_dict.copy().keys():
if is_panoptic:
if (
key.startswith("conditional_detr" )
and not key.startswith("class_labels_classifier" )
and not key.startswith("bbox_predictor" )
):
snake_case__ :int = state_dict.pop(__snake_case )
snake_case__ :int = val
elif "class_labels_classifier" in key or "bbox_predictor" in key:
snake_case__ :Any = state_dict.pop(__snake_case )
snake_case__ :List[Any] = val
elif key.startswith("bbox_attention" ) or key.startswith("mask_head" ):
continue
else:
snake_case__ :Any = state_dict.pop(__snake_case )
snake_case__ :List[Any] = val
else:
if not key.startswith("class_labels_classifier" ) and not key.startswith("bbox_predictor" ):
snake_case__ :Union[str, Any] = state_dict.pop(__snake_case )
snake_case__ :List[str] = val
# finally, create HuggingFace model and load state dict
snake_case__ :Tuple = ConditionalDetrForSegmentation(__snake_case ) if is_panoptic else ConditionalDetrForObjectDetection(__snake_case )
model.load_state_dict(__snake_case )
model.eval()
model.push_to_hub(repo_id=__snake_case , organization="DepuMeng" , commit_message="Add model" )
# verify our conversion
snake_case__ :List[str] = conditional_detr(__snake_case )
snake_case__ :Optional[Any] = model(__snake_case )
assert torch.allclose(outputs.logits , original_outputs["pred_logits"] , atol=1e-4 )
assert torch.allclose(outputs.pred_boxes , original_outputs["pred_boxes"] , atol=1e-4 )
if is_panoptic:
assert torch.allclose(outputs.pred_masks , original_outputs["pred_masks"] , atol=1e-4 )
# Save model and image processor
logger.info(F'Saving PyTorch model and image processor to {pytorch_dump_folder_path}...' )
Path(__snake_case ).mkdir(exist_ok=__snake_case )
model.save_pretrained(__snake_case )
image_processor.save_pretrained(__snake_case )
if __name__ == "__main__":
__UpperCAmelCase : List[Any] = argparse.ArgumentParser()
parser.add_argument(
"--model_name",
default="conditional_detr_resnet50",
type=str,
help="Name of the CONDITIONAL_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."
)
__UpperCAmelCase : Tuple = parser.parse_args()
convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)
| 57
|
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__UpperCAmelCase : List[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__UpperCAmelCase : int = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F'''{len(upper_files)} files contain uppercase characters:''')
print("\n".join(upper_files) + "\n")
__UpperCAmelCase : Any = [file for file in filepaths if " " in file]
if space_files:
print(F'''{len(space_files)} files contain space characters:''')
print("\n".join(space_files) + "\n")
__UpperCAmelCase : str = [file for file in filepaths if "-" in file]
if hyphen_files:
print(F'''{len(hyphen_files)} files contain hyphen characters:''')
print("\n".join(hyphen_files) + "\n")
__UpperCAmelCase : Dict = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F'''{len(nodir_files)} files are not in a directory:''')
print("\n".join(nodir_files) + "\n")
__UpperCAmelCase : int = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 57
| 1
|
import unittest
import numpy as np
from transformers import AlbertConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
import jax.numpy as jnp
from transformers.models.albert.modeling_flax_albert import (
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForPreTraining,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertModel,
)
class _snake_case ( unittest.TestCase ):
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=512 ,UpperCamelCase=16 ,UpperCamelCase=2 ,UpperCamelCase=0.02 ,UpperCamelCase=4 ,) -> Optional[int]:
snake_case__ :Optional[Any] = parent
snake_case__ :Tuple = batch_size
snake_case__ :Tuple = seq_length
snake_case__ :Optional[int] = is_training
snake_case__ :str = use_attention_mask
snake_case__ :Dict = use_token_type_ids
snake_case__ :int = use_labels
snake_case__ :Optional[Any] = vocab_size
snake_case__ :Dict = hidden_size
snake_case__ :List[str] = num_hidden_layers
snake_case__ :Tuple = num_attention_heads
snake_case__ :Optional[int] = intermediate_size
snake_case__ :Optional[int] = hidden_act
snake_case__ :Optional[int] = hidden_dropout_prob
snake_case__ :List[str] = attention_probs_dropout_prob
snake_case__ :Any = max_position_embeddings
snake_case__ :List[Any] = type_vocab_size
snake_case__ :List[Any] = type_sequence_label_size
snake_case__ :Optional[int] = initializer_range
snake_case__ :List[Any] = num_choices
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :Optional[Any] = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
snake_case__ :List[Any] = None
if self.use_attention_mask:
snake_case__ :Tuple = random_attention_mask([self.batch_size, self.seq_length] )
snake_case__ :List[str] = None
if self.use_token_type_ids:
snake_case__ :Optional[int] = ids_tensor([self.batch_size, self.seq_length] ,self.type_vocab_size )
snake_case__ :Tuple = AlbertConfig(
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=UpperCamelCase ,initializer_range=self.initializer_range ,)
return config, input_ids, token_type_ids, attention_mask
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ :Union[str, Any] = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ , snake_case__ :Any = config_and_inputs
snake_case__ :Optional[Any] = {"input_ids": input_ids, "token_type_ids": token_type_ids, "attention_mask": attention_mask}
return config, inputs_dict
@require_flax
class _snake_case ( _A , unittest.TestCase ):
_A = (
(
FlaxAlbertModel,
FlaxAlbertForPreTraining,
FlaxAlbertForMaskedLM,
FlaxAlbertForMultipleChoice,
FlaxAlbertForQuestionAnswering,
FlaxAlbertForSequenceClassification,
FlaxAlbertForTokenClassification,
FlaxAlbertForQuestionAnswering,
)
if is_flax_available()
else ()
)
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Union[str, Any] = FlaxAlbertModelTester(self )
@slow
def lowerCAmelCase_ ( self ) -> str:
for model_class_name in self.all_model_classes:
snake_case__ :Union[str, Any] = model_class_name.from_pretrained("albert-base-v2" )
snake_case__ :Any = model(np.ones((1, 1) ) )
self.assertIsNotNone(UpperCamelCase )
@require_flax
class _snake_case ( unittest.TestCase ):
@slow
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :Optional[int] = FlaxAlbertModel.from_pretrained("albert-base-v2" )
snake_case__ :Tuple = np.array([[0, 345, 232, 328, 740, 140, 1_695, 69, 6_078, 1_588, 2]] )
snake_case__ :List[Any] = np.array([[0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]] )
snake_case__ :Tuple = model(UpperCamelCase ,attention_mask=UpperCamelCase )[0]
snake_case__ :Optional[int] = (1, 11, 768)
self.assertEqual(output.shape ,UpperCamelCase )
snake_case__ :Tuple = np.array(
[[[-0.6513, 1.5035, -0.2766], [-0.6515, 1.5046, -0.2780], [-0.6512, 1.5049, -0.2784]]] )
self.assertTrue(jnp.allclose(output[:, 1:4, 1:4] ,UpperCamelCase ,atol=1E-4 ) )
| 57
|
def lowercase_ ( __snake_case : Tuple , __snake_case : Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case__ :Dict = ""
for i in table:
res += inp[i - 1]
return res
def lowercase_ ( __snake_case : List[str] ) -> int:
'''simple docstring'''
return data[1:] + data[0]
def lowercase_ ( __snake_case : int , __snake_case : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :Union[str, Any] = ""
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def lowercase_ ( __snake_case : Optional[int] , __snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :int = int("0b" + data[0] + data[-1] , 2 )
snake_case__ :Union[str, Any] = int("0b" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def lowercase_ ( __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : List[Any] , __snake_case : Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case__ :Tuple = message[:4]
snake_case__ :int = message[4:]
snake_case__ :int = apply_table(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = xor(__snake_case , __snake_case )
snake_case__ :Tuple = apply_sbox(__snake_case , temp[:4] ) # noqa: E741
snake_case__ :List[str] = apply_sbox(__snake_case , temp[4:] )
snake_case__ :int = "0" * (2 - len(__snake_case )) + l # noqa: E741
snake_case__ :int = "0" * (2 - len(__snake_case )) + r
snake_case__ :Optional[Any] = apply_table(l + r , __snake_case )
snake_case__ :Tuple = xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
__UpperCAmelCase : Dict = input("Enter 10 bit key: ")
__UpperCAmelCase : Tuple = input("Enter 8 bit message: ")
__UpperCAmelCase : Any = [6, 3, 7, 4, 8, 5, 1_0, 9]
__UpperCAmelCase : List[str] = [3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
__UpperCAmelCase : Tuple = [2, 4, 3, 1]
__UpperCAmelCase : List[Any] = [2, 6, 3, 1, 4, 8, 5, 7]
__UpperCAmelCase : Optional[Any] = [4, 1, 3, 5, 7, 2, 8, 6]
__UpperCAmelCase : Optional[int] = [4, 1, 2, 3, 2, 3, 4, 1]
__UpperCAmelCase : List[Any] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
__UpperCAmelCase : Union[str, Any] = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
__UpperCAmelCase : int = apply_table(key, paa_table)
__UpperCAmelCase : Dict = temp[:5]
__UpperCAmelCase : Optional[int] = temp[5:]
__UpperCAmelCase : Optional[int] = left_shift(left)
__UpperCAmelCase : Union[str, Any] = left_shift(right)
__UpperCAmelCase : int = apply_table(left + right, pa_table)
__UpperCAmelCase : Tuple = left_shift(left)
__UpperCAmelCase : Union[str, Any] = left_shift(right)
__UpperCAmelCase : Dict = left_shift(left)
__UpperCAmelCase : Optional[Any] = left_shift(right)
__UpperCAmelCase : Optional[int] = apply_table(left + right, pa_table)
# encryption
__UpperCAmelCase : Tuple = apply_table(message, IP)
__UpperCAmelCase : Tuple = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : List[Any] = temp[4:] + temp[:4]
__UpperCAmelCase : int = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : Union[str, Any] = apply_table(temp, IP_inv)
print("Cipher text is:", CT)
# decryption
__UpperCAmelCase : List[Any] = apply_table(CT, IP)
__UpperCAmelCase : List[Any] = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : int = temp[4:] + temp[:4]
__UpperCAmelCase : Union[str, Any] = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : Union[str, Any] = apply_table(temp, IP_inv)
print("Plain text after decypting is:", PT)
| 57
| 1
|
def lowercase_ ( __snake_case : int ) -> int:
'''simple docstring'''
if n == 1 or not isinstance(__snake_case , __snake_case ):
return 0
elif n == 2:
return 1
else:
snake_case__ :Any = [0, 1]
for i in range(2 , n + 1 ):
sequence.append(sequence[i - 1] + sequence[i - 2] )
return sequence[n]
def lowercase_ ( __snake_case : int ) -> int:
'''simple docstring'''
snake_case__ :List[str] = 0
snake_case__ :Dict = 2
while digits < n:
index += 1
snake_case__ :List[str] = len(str(fibonacci(__snake_case ) ) )
return index
def lowercase_ ( __snake_case : int = 10_00 ) -> int:
'''simple docstring'''
return fibonacci_digits_index(__snake_case )
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 57
|
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _snake_case ( _A , _A , _A ):
@register_to_config
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = False ,) -> int:
super().__init__()
snake_case__ :Union[str, Any] = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :int = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :Any = False
snake_case__ :List[Any] = nn.Dropout(p=UpperCamelCase )
snake_case__ :Tuple = TaConfig(
vocab_size=UpperCamelCase ,d_model=UpperCamelCase ,num_heads=UpperCamelCase ,d_kv=UpperCamelCase ,d_ff=UpperCamelCase ,dropout_rate=UpperCamelCase ,feed_forward_proj=UpperCamelCase ,is_decoder=UpperCamelCase ,is_encoder_decoder=UpperCamelCase ,)
snake_case__ :List[str] = nn.ModuleList()
for lyr_num in range(UpperCamelCase ):
snake_case__ :List[Any] = TaBlock(UpperCamelCase )
self.encoders.append(UpperCamelCase )
snake_case__ :Optional[Any] = TaLayerNorm(UpperCamelCase )
snake_case__ :Any = nn.Dropout(p=UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> int:
snake_case__ :str = self.token_embedder(UpperCamelCase )
snake_case__ :int = encoder_input_tokens.shape[1]
snake_case__ :List[Any] = torch.arange(UpperCamelCase ,device=encoder_input_tokens.device )
x += self.position_encoding(UpperCamelCase )
snake_case__ :Optional[int] = self.dropout_pre(UpperCamelCase )
# inverted the attention mask
snake_case__ :Optional[Any] = encoder_input_tokens.size()
snake_case__ :Dict = self.get_extended_attention_mask(UpperCamelCase ,UpperCamelCase )
for lyr in self.encoders:
snake_case__ :str = lyr(UpperCamelCase ,UpperCamelCase )[0]
snake_case__ :List[Any] = self.layer_norm(UpperCamelCase )
return self.dropout_post(UpperCamelCase ), encoder_inputs_mask
| 57
| 1
|
def lowercase_ ( ) -> Dict:
'''simple docstring'''
for n in range(1 , 1_00_00_00 ):
yield n * (n + 1) // 2
def lowercase_ ( __snake_case : List[Any] ) -> List[str]:
'''simple docstring'''
snake_case__ :Dict = 1
snake_case__ :Tuple = 2
while i * i <= n:
snake_case__ :str = 0
while n % i == 0:
n //= i
multiplicity += 1
divisors_count *= multiplicity + 1
i += 1
if n > 1:
divisors_count *= 2
return divisors_count
def lowercase_ ( ) -> Dict:
'''simple docstring'''
return next(i for i in triangle_number_generator() if count_divisors(__snake_case ) > 5_00 )
if __name__ == "__main__":
print(solution())
| 57
|
__UpperCAmelCase : int = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__UpperCAmelCase : List[str] = ["a", "b", "c", "d", "e"]
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : List[Any] , __snake_case : Tuple ) -> Optional[int]:
'''simple docstring'''
snake_case__ :List[Any] = start
# add current to visited
visited.append(__snake_case )
snake_case__ :List[str] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# if all neighbors visited add current to sort
sort.append(__snake_case )
# if all vertices haven't been visited select a new one to visit
if len(__snake_case ) != len(__snake_case ):
for vertice in vertices:
if vertice not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# return sort
return sort
if __name__ == "__main__":
__UpperCAmelCase : Tuple = topological_sort("a", [], [])
print(sort)
| 57
| 1
|
import math
def lowercase_ ( __snake_case : int ) -> bool:
'''simple docstring'''
return math.sqrt(__snake_case ) * math.sqrt(__snake_case ) == num
def lowercase_ ( __snake_case : int ) -> bool:
'''simple docstring'''
snake_case__ :str = 0
snake_case__ :str = n
while left <= right:
snake_case__ :Optional[int] = (left + right) // 2
if mid**2 == n:
return True
elif mid**2 > n:
snake_case__ :Union[str, Any] = mid - 1
else:
snake_case__ :Union[str, Any] = mid + 1
return False
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
|
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 _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCAmelCase_ ( self ) -> str:
snake_case__ , snake_case__ :Tuple = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-canny" ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ , snake_case__ :Any = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCamelCase ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ :List[str] = controlnet_params
snake_case__ :Union[str, Any] = "bird"
snake_case__ :Optional[int] = jax.device_count()
snake_case__ :Tuple = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case__ :Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" )
snake_case__ :str = pipe.prepare_image_inputs([canny_image] * num_samples )
snake_case__ :List[str] = jax.random.PRNGKey(0 )
snake_case__ :str = jax.random.split(UpperCamelCase ,jax.device_count() )
snake_case__ :int = replicate(UpperCamelCase )
snake_case__ :Any = shard(UpperCamelCase )
snake_case__ :Any = shard(UpperCamelCase )
snake_case__ :str = pipe(
prompt_ids=UpperCamelCase ,image=UpperCamelCase ,params=UpperCamelCase ,prng_seed=UpperCamelCase ,num_inference_steps=50 ,jit=UpperCamelCase ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
snake_case__ :List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case__ :Any = images[0, 253:256, 253:256, -1]
snake_case__ :Union[str, Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case__ :List[Any] = jnp.array(
[0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ , snake_case__ :List[str] = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-openpose" ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ , snake_case__ :Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCamelCase ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ :str = controlnet_params
snake_case__ :int = "Chef in the kitchen"
snake_case__ :List[Any] = jax.device_count()
snake_case__ :Dict = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case__ :Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" )
snake_case__ :Optional[int] = pipe.prepare_image_inputs([pose_image] * num_samples )
snake_case__ :List[str] = jax.random.PRNGKey(0 )
snake_case__ :Any = jax.random.split(UpperCamelCase ,jax.device_count() )
snake_case__ :Dict = replicate(UpperCamelCase )
snake_case__ :Tuple = shard(UpperCamelCase )
snake_case__ :Optional[int] = shard(UpperCamelCase )
snake_case__ :Optional[Any] = pipe(
prompt_ids=UpperCamelCase ,image=UpperCamelCase ,params=UpperCamelCase ,prng_seed=UpperCamelCase ,num_inference_steps=50 ,jit=UpperCamelCase ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
snake_case__ :int = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case__ :List[str] = images[0, 253:256, 253:256, -1]
snake_case__ :Tuple = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case__ :List[str] = jnp.array(
[[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 57
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|
import tensorflow as tf
from ...tf_utils import shape_list
class _snake_case ( tf.keras.layers.Layer ):
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=1 ,UpperCamelCase=False ,**UpperCamelCase ) -> Any:
super().__init__(**UpperCamelCase )
snake_case__ :List[Any] = vocab_size
snake_case__ :Any = d_embed
snake_case__ :Tuple = d_proj
snake_case__ :Any = cutoffs + [vocab_size]
snake_case__ :str = [0] + self.cutoffs
snake_case__ :str = div_val
snake_case__ :Dict = self.cutoffs[0]
snake_case__ :Tuple = len(self.cutoffs ) - 1
snake_case__ :int = self.shortlist_size + self.n_clusters
snake_case__ :List[str] = keep_order
snake_case__ :List[Any] = []
snake_case__ :Any = []
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> List[str]:
if self.n_clusters > 0:
snake_case__ :int = self.add_weight(
shape=(self.n_clusters, self.d_embed) ,initializer="zeros" ,trainable=UpperCamelCase ,name="cluster_weight" )
snake_case__ :str = self.add_weight(
shape=(self.n_clusters,) ,initializer="zeros" ,trainable=UpperCamelCase ,name="cluster_bias" )
if self.div_val == 1:
for i in range(len(self.cutoffs ) ):
if self.d_proj != self.d_embed:
snake_case__ :Optional[int] = self.add_weight(
shape=(self.d_embed, self.d_proj) ,initializer="zeros" ,trainable=UpperCamelCase ,name=f'out_projs_._{i}' ,)
self.out_projs.append(UpperCamelCase )
else:
self.out_projs.append(UpperCamelCase )
snake_case__ :str = self.add_weight(
shape=(self.vocab_size, self.d_embed) ,initializer="zeros" ,trainable=UpperCamelCase ,name=f'out_layers_._{i}_._weight' ,)
snake_case__ :Tuple = self.add_weight(
shape=(self.vocab_size,) ,initializer="zeros" ,trainable=UpperCamelCase ,name=f'out_layers_._{i}_._bias' ,)
self.out_layers.append((weight, bias) )
else:
for i in range(len(self.cutoffs ) ):
snake_case__ , snake_case__ :List[str] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
snake_case__ :Optional[Any] = self.d_embed // (self.div_val**i)
snake_case__ :List[str] = self.add_weight(
shape=(d_emb_i, self.d_proj) ,initializer="zeros" ,trainable=UpperCamelCase ,name=f'out_projs_._{i}' )
self.out_projs.append(UpperCamelCase )
snake_case__ :List[Any] = self.add_weight(
shape=(r_idx - l_idx, d_emb_i) ,initializer="zeros" ,trainable=UpperCamelCase ,name=f'out_layers_._{i}_._weight' ,)
snake_case__ :List[Any] = self.add_weight(
shape=(r_idx - l_idx,) ,initializer="zeros" ,trainable=UpperCamelCase ,name=f'out_layers_._{i}_._bias' ,)
self.out_layers.append((weight, bias) )
super().build(UpperCamelCase )
@staticmethod
def lowerCAmelCase_ ( UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ) -> str:
snake_case__ :Any = x
if proj is not None:
snake_case__ :List[Any] = tf.einsum("ibd,ed->ibe" ,UpperCamelCase ,UpperCamelCase )
return tf.einsum("ibd,nd->ibn" ,UpperCamelCase ,UpperCamelCase ) + b
@staticmethod
def lowerCAmelCase_ ( UpperCamelCase ,UpperCamelCase ) -> Optional[Any]:
snake_case__ :int = shape_list(UpperCamelCase )
snake_case__ :Union[str, Any] = tf.range(lp_size[0] ,dtype=target.dtype )
snake_case__ :Tuple = tf.stack([r, target] ,1 )
return tf.gather_nd(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=True ,UpperCamelCase=False ) -> str:
snake_case__ :int = 0
if self.n_clusters == 0:
snake_case__ :List[str] = self._logit(UpperCamelCase ,self.out_layers[0][0] ,self.out_layers[0][1] ,self.out_projs[0] )
if target is not None:
snake_case__ :Union[str, Any] = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=UpperCamelCase ,logits=UpperCamelCase )
snake_case__ :int = tf.nn.log_softmax(UpperCamelCase ,axis=-1 )
else:
snake_case__ :Optional[int] = shape_list(UpperCamelCase )
snake_case__ :str = []
snake_case__ :int = tf.zeros(hidden_sizes[:2] )
for i in range(len(self.cutoffs ) ):
snake_case__ , snake_case__ :List[Any] = self.cutoff_ends[i], self.cutoff_ends[i + 1]
if target is not None:
snake_case__ :Optional[int] = (target >= l_idx) & (target < r_idx)
snake_case__ :Any = tf.where(UpperCamelCase )
snake_case__ :List[Any] = tf.boolean_mask(UpperCamelCase ,UpperCamelCase ) - l_idx
if self.div_val == 1:
snake_case__ :Union[str, Any] = self.out_layers[0][0][l_idx:r_idx]
snake_case__ :int = self.out_layers[0][1][l_idx:r_idx]
else:
snake_case__ :int = self.out_layers[i][0]
snake_case__ :str = self.out_layers[i][1]
if i == 0:
snake_case__ :Optional[int] = tf.concat([cur_W, self.cluster_weight] ,0 )
snake_case__ :Union[str, Any] = tf.concat([cur_b, self.cluster_bias] ,0 )
snake_case__ :Optional[Any] = self._logit(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,self.out_projs[0] )
snake_case__ :Optional[Any] = tf.nn.log_softmax(UpperCamelCase )
out.append(head_logprob[..., : self.cutoffs[0]] )
if target is not None:
snake_case__ :int = tf.boolean_mask(UpperCamelCase ,UpperCamelCase )
snake_case__ :Optional[Any] = self._gather_logprob(UpperCamelCase ,UpperCamelCase )
else:
snake_case__ :Any = self._logit(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,self.out_projs[i] )
snake_case__ :Union[str, Any] = tf.nn.log_softmax(UpperCamelCase )
snake_case__ :str = self.cutoffs[0] + i - 1 # No probability for the head cluster
snake_case__ :Dict = head_logprob[..., cluster_prob_idx, None] + tail_logprob
out.append(UpperCamelCase )
if target is not None:
snake_case__ :Union[str, Any] = tf.boolean_mask(UpperCamelCase ,UpperCamelCase )
snake_case__ :Any = tf.boolean_mask(UpperCamelCase ,UpperCamelCase )
snake_case__ :str = self._gather_logprob(UpperCamelCase ,UpperCamelCase )
cur_logprob += cur_head_logprob[:, self.cutoff_ends[1] + i - 1]
if target is not None:
loss += tf.scatter_nd(UpperCamelCase ,-cur_logprob ,shape_list(UpperCamelCase ) )
snake_case__ :List[str] = tf.concat(UpperCamelCase ,axis=-1 )
if target is not None:
if return_mean:
snake_case__ :int = tf.reduce_mean(UpperCamelCase )
# Add the training-time loss value to the layer using `self.add_loss()`.
self.add_loss(UpperCamelCase )
# Log the loss as a metric (we could log arbitrary metrics,
# including different metrics for training and inference.
self.add_metric(UpperCamelCase ,name=self.name ,aggregation="mean" if return_mean else "" )
return out
| 57
|
def lowercase_ ( __snake_case : list ) -> list:
'''simple docstring'''
if any(not isinstance(__snake_case , __snake_case ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(__snake_case ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 57
| 1
|
from math import log
from scipy.constants import Boltzmann, physical_constants
__UpperCAmelCase : Union[str, Any] = 3_0_0 # TEMPERATURE (unit = K)
def lowercase_ ( __snake_case : float , __snake_case : float , __snake_case : float , ) -> float:
'''simple docstring'''
if donor_conc <= 0:
raise ValueError("Donor concentration should be positive" )
elif acceptor_conc <= 0:
raise ValueError("Acceptor concentration should be positive" )
elif intrinsic_conc <= 0:
raise ValueError("Intrinsic concentration should be positive" )
elif donor_conc <= intrinsic_conc:
raise ValueError(
"Donor concentration should be greater than intrinsic concentration" )
elif acceptor_conc <= intrinsic_conc:
raise ValueError(
"Acceptor concentration should be greater than intrinsic concentration" )
else:
return (
Boltzmann
* T
* log((donor_conc * acceptor_conc) / intrinsic_conc**2 )
/ physical_constants["electron volt"][0]
)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
|
from __future__ import annotations
def lowercase_ ( __snake_case : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("List is empty" )
return sum(__snake_case ) / len(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
| 1
|
class _snake_case :
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Dict:
snake_case__ :List[str] = name
snake_case__ :Any = value
snake_case__ :Optional[int] = weight
def __repr__( self ) -> Any:
return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
return self.value
def lowerCAmelCase_ ( self ) -> Optional[Any]:
return self.name
def lowerCAmelCase_ ( self ) -> Optional[Any]:
return self.weight
def lowerCAmelCase_ ( self ) -> Dict:
return self.value / self.weight
def lowercase_ ( __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : str ) -> str:
'''simple docstring'''
snake_case__ :List[str] = []
for i in range(len(__snake_case ) ):
menu.append(Things(name[i] , value[i] , weight[i] ) )
return menu
def lowercase_ ( __snake_case : List[str] , __snake_case : Tuple , __snake_case : Optional[int] ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :Union[str, Any] = sorted(__snake_case , key=__snake_case , reverse=__snake_case )
snake_case__ :Optional[int] = []
snake_case__ , snake_case__ :Tuple = 0.0, 0.0
for i in range(len(__snake_case ) ):
if (total_cost + items_copy[i].get_weight()) <= max_cost:
result.append(items_copy[i] )
total_cost += items_copy[i].get_weight()
total_value += items_copy[i].get_value()
return (result, total_value)
def lowercase_ ( ) -> Dict:
'''simple docstring'''
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
|
from __future__ import annotations
import math
def lowercase_ ( __snake_case : int , __snake_case : int , __snake_case : bool , __snake_case : list[int] , __snake_case : float ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if len(__snake_case ) == 0:
raise ValueError("Scores cannot be empty" )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
return min(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
def lowercase_ ( ) -> None:
'''simple docstring'''
snake_case__ :List[Any] = [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
snake_case__ :int = math.log(len(__snake_case ) , 2 )
print("Optimal value : " , end="" )
print(minimax(0 , 0 , __snake_case , __snake_case , __snake_case ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 57
| 1
|
from __future__ import annotations
def lowercase_ ( __snake_case : int | float | str , __snake_case : int | float | str ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
snake_case__ :Any = int(__snake_case )
snake_case__ :Union[str, Any] = int(__snake_case )
snake_case__ :list[str] = []
for temp in range(int(__snake_case ) ):
series.append(F'1 / {pow(temp + 1 , int(__snake_case ) )}' if series else "1" )
return series
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase : Union[str, Any] = int(input("Enter the last number (nth term) of the P-Series"))
__UpperCAmelCase : Optional[Any] = int(input("Enter the power for P-Series"))
print("Formula of P-Series => 1+1/2^p+1/3^p ..... 1/n^p")
print(p_series(nth_term, power))
| 57
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
def lowercase_ ( __snake_case : Any , __snake_case : Any ) -> Any:
'''simple docstring'''
snake_case__ :Optional[Any] = b.T
snake_case__ :Optional[Any] = np.sum(np.square(__snake_case ) , axis=1 )
snake_case__ :Tuple = np.sum(np.square(__snake_case ) , axis=0 )
snake_case__ :Union[str, Any] = np.matmul(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = aa[:, None] - 2 * ab + ba[None, :]
return d
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : int ) -> Any:
'''simple docstring'''
snake_case__ :Optional[Any] = x.reshape(-1 , 3 )
snake_case__ :List[str] = squared_euclidean_distance(__snake_case , __snake_case )
return np.argmin(__snake_case , axis=1 )
class _snake_case ( _A ):
_A = ['pixel_values']
def __init__( self ,UpperCamelCase = None ,UpperCamelCase = True ,UpperCamelCase = None ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = True ,UpperCamelCase = True ,**UpperCamelCase ,) -> None:
super().__init__(**UpperCamelCase )
snake_case__ :List[Any] = size if size is not None else {"height": 256, "width": 256}
snake_case__ :str = get_size_dict(UpperCamelCase )
snake_case__ :Dict = np.array(UpperCamelCase ) if clusters is not None else None
snake_case__ :str = do_resize
snake_case__ :List[str] = size
snake_case__ :List[Any] = resample
snake_case__ :Union[str, Any] = do_normalize
snake_case__ :int = do_color_quantize
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = None ,**UpperCamelCase ,) -> np.ndarray:
snake_case__ :List[str] = get_size_dict(UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}' )
return resize(
UpperCamelCase ,size=(size["height"], size["width"]) ,resample=UpperCamelCase ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,) -> np.ndarray:
snake_case__ :Tuple = rescale(image=UpperCamelCase ,scale=1 / 127.5 ,data_format=UpperCamelCase )
snake_case__ :List[Any] = image - 1
return image
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = ChannelDimension.FIRST ,**UpperCamelCase ,) -> PIL.Image.Image:
snake_case__ :Optional[int] = do_resize if do_resize is not None else self.do_resize
snake_case__ :int = size if size is not None else self.size
snake_case__ :Tuple = get_size_dict(UpperCamelCase )
snake_case__ :str = resample if resample is not None else self.resample
snake_case__ :Dict = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ :Tuple = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
snake_case__ :List[Any] = clusters if clusters is not None else self.clusters
snake_case__ :str = np.array(UpperCamelCase )
snake_case__ :int = make_list_of_images(UpperCamelCase )
if not valid_images(UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True." )
# All transformations expect numpy arrays.
snake_case__ :Union[str, Any] = [to_numpy_array(UpperCamelCase ) for image in images]
if do_resize:
snake_case__ :int = [self.resize(image=UpperCamelCase ,size=UpperCamelCase ,resample=UpperCamelCase ) for image in images]
if do_normalize:
snake_case__ :Any = [self.normalize(image=UpperCamelCase ) for image in images]
if do_color_quantize:
snake_case__ :Optional[Any] = [to_channel_dimension_format(UpperCamelCase ,ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
snake_case__ :Union[str, Any] = np.array(UpperCamelCase )
snake_case__ :Optional[int] = color_quantize(UpperCamelCase ,UpperCamelCase ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
snake_case__ :List[Any] = images.shape[0]
snake_case__ :str = images.reshape(UpperCamelCase ,-1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
snake_case__ :Any = list(UpperCamelCase )
else:
snake_case__ :List[str] = [to_channel_dimension_format(UpperCamelCase ,UpperCamelCase ) for image in images]
snake_case__ :List[str] = {"input_ids": images}
return BatchFeature(data=UpperCamelCase ,tensor_type=UpperCamelCase )
| 57
| 1
|
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase : Dict = logging.get_logger(__name__)
__UpperCAmelCase : List[str] = {
"s-JoL/Open-Llama-V1": "https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json",
}
class _snake_case ( _A ):
_A = 'open-llama'
def __init__( self ,UpperCamelCase=100_000 ,UpperCamelCase=4_096 ,UpperCamelCase=11_008 ,UpperCamelCase=32 ,UpperCamelCase=32 ,UpperCamelCase="silu" ,UpperCamelCase=2_048 ,UpperCamelCase=0.02 ,UpperCamelCase=1E-6 ,UpperCamelCase=True ,UpperCamelCase=0 ,UpperCamelCase=1 ,UpperCamelCase=2 ,UpperCamelCase=False ,UpperCamelCase=True ,UpperCamelCase=0.1 ,UpperCamelCase=0.1 ,UpperCamelCase=True ,UpperCamelCase=True ,UpperCamelCase=None ,**UpperCamelCase ,) -> str:
snake_case__ :str = vocab_size
snake_case__ :Union[str, Any] = max_position_embeddings
snake_case__ :Union[str, Any] = hidden_size
snake_case__ :str = intermediate_size
snake_case__ :List[Any] = num_hidden_layers
snake_case__ :Tuple = num_attention_heads
snake_case__ :List[str] = hidden_act
snake_case__ :str = initializer_range
snake_case__ :Optional[Any] = rms_norm_eps
snake_case__ :Union[str, Any] = use_cache
snake_case__ :Optional[Any] = kwargs.pop(
"use_memorry_efficient_attention" ,UpperCamelCase )
snake_case__ :Optional[int] = hidden_dropout_prob
snake_case__ :str = attention_dropout_prob
snake_case__ :List[Any] = use_stable_embedding
snake_case__ :Optional[int] = shared_input_output_embedding
snake_case__ :Tuple = rope_scaling
self._rope_scaling_validation()
super().__init__(
pad_token_id=UpperCamelCase ,bos_token_id=UpperCamelCase ,eos_token_id=UpperCamelCase ,tie_word_embeddings=UpperCamelCase ,**UpperCamelCase ,)
def lowerCAmelCase_ ( self ) -> Dict:
if self.rope_scaling is None:
return
if not isinstance(self.rope_scaling ,UpperCamelCase ) or len(self.rope_scaling ) != 2:
raise ValueError(
"`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
f'got {self.rope_scaling}' )
snake_case__ :int = self.rope_scaling.get("type" ,UpperCamelCase )
snake_case__ :List[Any] = self.rope_scaling.get("factor" ,UpperCamelCase )
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
raise ValueError(
f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}' )
if rope_scaling_factor is None or not isinstance(UpperCamelCase ,UpperCamelCase ) or rope_scaling_factor <= 1.0:
raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}' )
| 57
|
import pytest
__UpperCAmelCase : int = "__dummy_dataset1__"
__UpperCAmelCase : int = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n def _info(self):\n features = datasets.Features(\n {\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n \"ner_tags\": datasets.Sequence(\n datasets.features.ClassLabel(\n names=[\n \"O\",\n \"B-PER\",\n \"I-PER\",\n \"B-ORG\",\n \"I-ORG\",\n \"B-LOC\",\n \"I-LOC\",\n ]\n )\n ),\n \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n }\n )\n return datasets.DatasetInfo(features=features)\n\n def _split_generators(self, dl_manager):\n dl_path = dl_manager.download(URLS)\n return [\n datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n ]\n\n def _generate_examples(self, filepath):\n with open(filepath, \"r\", encoding=\"utf-8\") as f:\n for i, line in enumerate(f):\n yield i, json.loads(line)\n"
@pytest.fixture
def lowercase_ ( ) -> Optional[Any]:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def lowercase_ ( ) -> Optional[int]:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def lowercase_ ( __snake_case : Optional[int] , __snake_case : List[Any] , __snake_case : Any ) -> Dict:
'''simple docstring'''
snake_case__ :Optional[Any] = dataset_loading_script_name
snake_case__ :Optional[Any] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=__snake_case )
snake_case__ :List[Any] = script_dir / F'{script_name}.py'
with open(__snake_case , "w" ) as f:
f.write(__snake_case )
return str(__snake_case )
| 57
| 1
|
from collections.abc import Sequence
from queue import Queue
class _snake_case :
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ,UpperCamelCase=None ) -> Tuple:
snake_case__ :Any = start
snake_case__ :int = end
snake_case__ :str = val
snake_case__ :Union[str, Any] = (start + end) // 2
snake_case__ :List[Any] = left
snake_case__ :int = right
def __repr__( self ) -> int:
return f'SegmentTreeNode(start={self.start}, end={self.end}, val={self.val})'
class _snake_case :
def __init__( self ,UpperCamelCase ,UpperCamelCase ) -> Optional[int]:
snake_case__ :List[Any] = collection
snake_case__ :str = function
if self.collection:
snake_case__ :Any = self._build_tree(0 ,len(UpperCamelCase ) - 1 )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> List[str]:
self._update_tree(self.root ,UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> Optional[int]:
return self._query_range(self.root ,UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> int:
if start == end:
return SegmentTreeNode(UpperCamelCase ,UpperCamelCase ,self.collection[start] )
snake_case__ :List[Any] = (start + end) // 2
snake_case__ :Dict = self._build_tree(UpperCamelCase ,UpperCamelCase )
snake_case__ :str = self._build_tree(mid + 1 ,UpperCamelCase )
return SegmentTreeNode(UpperCamelCase ,UpperCamelCase ,self.fn(left.val ,right.val ) ,UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Union[str, Any]:
if node.start == i and node.end == i:
snake_case__ :Optional[Any] = val
return
if i <= node.mid:
self._update_tree(node.left ,UpperCamelCase ,UpperCamelCase )
else:
self._update_tree(node.right ,UpperCamelCase ,UpperCamelCase )
snake_case__ :Tuple = self.fn(node.left.val ,node.right.val )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Union[str, Any]:
if node.start == i and node.end == j:
return node.val
if i <= node.mid:
if j <= node.mid:
# range in left child tree
return self._query_range(node.left ,UpperCamelCase ,UpperCamelCase )
else:
# range in left child tree and right child tree
return self.fn(
self._query_range(node.left ,UpperCamelCase ,node.mid ) ,self._query_range(node.right ,node.mid + 1 ,UpperCamelCase ) ,)
else:
# range in right child tree
return self._query_range(node.right ,UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
if self.root is not None:
snake_case__ :Optional[int] = Queue()
queue.put(self.root )
while not queue.empty():
snake_case__ :Tuple = queue.get()
yield node
if node.left is not None:
queue.put(node.left )
if node.right is not None:
queue.put(node.right )
if __name__ == "__main__":
import operator
for fn in [operator.add, max, min]:
print("*" * 5_0)
__UpperCAmelCase : Dict = SegmentTree([2, 1, 5, 3, 4], fn)
for node in arr.traverse():
print(node)
print()
arr.update(1, 5)
for node in arr.traverse():
print(node)
print()
print(arr.query_range(3, 4)) # 7
print(arr.query_range(2, 2)) # 5
print(arr.query_range(1, 3)) # 13
print()
| 57
|
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 57
| 1
|
import argparse
import torch
from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( __snake_case : Tuple , __snake_case : Dict , __snake_case : Any ) -> List[str]:
'''simple docstring'''
snake_case__ :Union[str, Any] = MobileBertConfig.from_json_file(__snake_case )
print(F'Building PyTorch model from configuration: {config}' )
snake_case__ :Tuple = MobileBertForPreTraining(__snake_case )
# Load weights from tf checkpoint
snake_case__ :Union[str, Any] = load_tf_weights_in_mobilebert(__snake_case , __snake_case , __snake_case )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
torch.save(model.state_dict() , __snake_case )
if __name__ == "__main__":
__UpperCAmelCase : List[str] = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--mobilebert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained MobileBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__UpperCAmelCase : Dict = parser.parse_args()
convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)
| 57
|
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__UpperCAmelCase : Dict = True
except ImportError:
__UpperCAmelCase : List[Any] = False
__UpperCAmelCase : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowercase_ ( __snake_case : Namespace ) -> Dict:
'''simple docstring'''
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class _snake_case ( _A ):
@staticmethod
def lowerCAmelCase_ ( UpperCamelCase ) -> Any:
snake_case__ :Dict = parser.add_parser("add-new-model" )
add_new_model_parser.add_argument("--testing" ,action="store_true" ,help="If in testing mode." )
add_new_model_parser.add_argument("--testing_file" ,type=UpperCamelCase ,help="Configuration file on which to run." )
add_new_model_parser.add_argument(
"--path" ,type=UpperCamelCase ,help="Path to cookiecutter. Should only be used for testing purposes." )
add_new_model_parser.set_defaults(func=UpperCamelCase )
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ,*UpperCamelCase ) -> Any:
snake_case__ :Union[str, Any] = testing
snake_case__ :Union[str, Any] = testing_file
snake_case__ :List[str] = path
def lowerCAmelCase_ ( self ) -> List[Any]:
warnings.warn(
"The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
"It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
"checks, you should use `transformers-cli add-new-model-like` instead." )
if not _has_cookiecutter:
raise ImportError(
"Model creation dependencies are required to use the `add_new_model` command. Install them by running "
"the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n" )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
snake_case__ :Tuple = [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
if len(UpperCamelCase ) > 0:
raise ValueError(
"Several directories starting with `cookiecutter-template-` in current working directory. "
"Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
"change your working directory." )
snake_case__ :str = (
Path(UpperCamelCase ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
snake_case__ :Tuple = path_to_transformer_root / "templates" / "adding_a_new_model"
# Execute cookiecutter
if not self._testing:
cookiecutter(str(UpperCamelCase ) )
else:
with open(self._testing_file ,"r" ) as configuration_file:
snake_case__ :str = json.load(UpperCamelCase )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) ,no_input=UpperCamelCase ,extra_context=UpperCamelCase ,)
snake_case__ :List[Any] = [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]
# Retrieve configuration
with open(directory + "/configuration.json" ,"r" ) as configuration_file:
snake_case__ :Dict = json.load(UpperCamelCase )
snake_case__ :Optional[Any] = configuration["lowercase_modelname"]
snake_case__ :List[Any] = configuration["generate_tensorflow_pytorch_and_flax"]
os.remove(f'{directory}/configuration.json' )
snake_case__ :Any = "PyTorch" in generate_tensorflow_pytorch_and_flax
snake_case__ :Any = "TensorFlow" in generate_tensorflow_pytorch_and_flax
snake_case__ :Any = "Flax" in generate_tensorflow_pytorch_and_flax
snake_case__ :Dict = f'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
os.makedirs(UpperCamelCase ,exist_ok=UpperCamelCase )
os.makedirs(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}' ,exist_ok=UpperCamelCase )
# Tests require submodules as they have parent imports
with open(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py' ,"w" ):
pass
shutil.move(
f'{directory}/__init__.py' ,f'{model_dir}/__init__.py' ,)
shutil.move(
f'{directory}/configuration_{lowercase_model_name}.py' ,f'{model_dir}/configuration_{lowercase_model_name}.py' ,)
def remove_copy_lines(UpperCamelCase ):
with open(UpperCamelCase ,"r" ) as f:
snake_case__ :List[str] = f.readlines()
with open(UpperCamelCase ,"w" ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(UpperCamelCase )
if output_pytorch:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_{lowercase_model_name}.py' ,f'{model_dir}/modeling_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_{lowercase_model_name}.py' )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_tf_{lowercase_model_name}.py' ,f'{model_dir}/modeling_tf_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_tf_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_tf_{lowercase_model_name}.py' )
if output_flax:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_flax_{lowercase_model_name}.py' ,f'{model_dir}/modeling_flax_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_flax_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/{lowercase_model_name}.md' ,f'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md' ,)
shutil.move(
f'{directory}/tokenization_{lowercase_model_name}.py' ,f'{model_dir}/tokenization_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/tokenization_fast_{lowercase_model_name}.py' ,f'{model_dir}/tokenization_{lowercase_model_name}_fast.py' ,)
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ):
# Create temp file
snake_case__ , snake_case__ :Optional[Any] = mkstemp()
snake_case__ :Optional[Any] = False
with fdopen(UpperCamelCase ,"w" ) as new_file:
with open(UpperCamelCase ) as old_file:
for line in old_file:
new_file.write(UpperCamelCase )
if line_to_copy_below in line:
snake_case__ :Optional[Any] = True
for line_to_copy in lines_to_copy:
new_file.write(UpperCamelCase )
if not line_found:
raise ValueError(f'Line {line_to_copy_below} was not found in file.' )
# Copy the file permissions from the old file to the new file
copymode(UpperCamelCase ,UpperCamelCase )
# Remove original file
remove(UpperCamelCase )
# Move new file
move(UpperCamelCase ,UpperCamelCase )
def skip_units(UpperCamelCase ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(UpperCamelCase ):
with open(UpperCamelCase ) as datafile:
snake_case__ :int = []
snake_case__ :Optional[int] = False
snake_case__ :List[str] = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
snake_case__ :Optional[Any] = line.split("\"" )[1]
snake_case__ :Tuple = skip_units(UpperCamelCase )
elif "# Below: " in line and "##" not in line:
snake_case__ :Optional[Any] = line.split("\"" )[1]
snake_case__ :List[str] = skip_units(UpperCamelCase )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
snake_case__ :Tuple = []
elif "# Replace with" in line and "##" not in line:
snake_case__ :Optional[Any] = []
elif "##" not in line:
lines_to_copy.append(UpperCamelCase )
remove(UpperCamelCase )
replace_in_files(f'{directory}/to_replace_{lowercase_model_name}.py' )
os.rmdir(UpperCamelCase )
| 57
| 1
|
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _snake_case ( _A ):
_A = ''
_A = (
None # protocol passed in prefix to the url. ex: "gzip", for gzip://file.txt::http://foo.bar/file.txt.gz
)
_A = None # compression type in fsspec. ex: "gzip"
_A = None # extension of the filename to strip. ex: "".gz" to get file.txt from file.txt.gz
def __init__( self ,UpperCamelCase = "" ,UpperCamelCase = None ,UpperCamelCase = None ,**UpperCamelCase ) -> Union[str, Any]:
super().__init__(self ,**UpperCamelCase )
# always open as "rb" since fsspec can then use the TextIOWrapper to make it work for "r" mode
snake_case__ :str = fsspec.open(
UpperCamelCase ,mode="rb" ,protocol=UpperCamelCase ,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__ :Any = os.path.basename(self.file.path.split("::" )[0] )
snake_case__ :List[Any] = (
self.compressed_name[: self.compressed_name.rindex("." )]
if "." in self.compressed_name
else self.compressed_name
)
snake_case__ :Optional[int] = None
@classmethod
def lowerCAmelCase_ ( cls ,UpperCamelCase ) -> Union[str, Any]:
# compressed file paths are always relative to the archive root
return super()._strip_protocol(UpperCamelCase ).lstrip("/" )
def lowerCAmelCase_ ( self ) -> List[Any]:
if self.dir_cache is None:
snake_case__ :Any = {**self.file.fs.info(self.file.path ), "name": self.uncompressed_name}
snake_case__ :List[str] = {f["name"]: f}
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Optional[int]:
return self.file.open().read()
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = "rb" ,UpperCamelCase=None ,UpperCamelCase=True ,UpperCamelCase=None ,**UpperCamelCase ,) -> Union[str, Any]:
snake_case__ :List[str] = self._strip_protocol(UpperCamelCase )
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 _snake_case ( _A ):
_A = 'bz2'
_A = 'bz2'
_A = '.bz2'
class _snake_case ( _A ):
_A = 'gzip'
_A = 'gzip'
_A = '.gz'
class _snake_case ( _A ):
_A = 'lz4'
_A = 'lz4'
_A = '.lz4'
class _snake_case ( _A ):
_A = 'xz'
_A = 'xz'
_A = '.xz'
class _snake_case ( _A ):
_A = 'zstd'
_A = 'zstd'
_A = '.zst'
def __init__( self ,UpperCamelCase ,UpperCamelCase = "rb" ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = DEFAULT_BLOCK_SIZE ,**UpperCamelCase ,) -> Union[str, Any]:
super().__init__(
fo=UpperCamelCase ,mode=UpperCamelCase ,target_protocol=UpperCamelCase ,target_options=UpperCamelCase ,block_size=UpperCamelCase ,**UpperCamelCase ,)
# 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__ :Optional[Any] = self.file.__enter__
class _snake_case :
def __init__( self ,UpperCamelCase ) -> Optional[int]:
snake_case__ :Dict = file_
def __enter__( self ) -> str:
self._file.__enter__()
return self
def __exit__( self ,*UpperCamelCase ,**UpperCamelCase ) -> Dict:
self._file.__exit__(*UpperCamelCase ,**UpperCamelCase )
def __iter__( self ) -> List[str]:
return iter(self._file )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
return next(self._file )
def __getattr__( self ,UpperCamelCase ) -> Union[str, Any]:
return getattr(self._file ,UpperCamelCase )
def fixed_enter(*UpperCamelCase ,**UpperCamelCase ):
return WrappedFile(_enter(*UpperCamelCase ,**UpperCamelCase ) )
snake_case__ :List[str] = fixed_enter
| 57
|
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : List[Any] = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__UpperCAmelCase : List[Any] = {
"vocab_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
},
"merges_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
},
}
__UpperCAmelCase : str = {"allegro/herbert-base-cased": 5_1_4}
__UpperCAmelCase : List[str] = {}
class _snake_case ( _A ):
_A = VOCAB_FILES_NAMES
_A = PRETRAINED_VOCAB_FILES_MAP
_A = PRETRAINED_INIT_CONFIGURATION
_A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A = HerbertTokenizer
def __init__( self ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase="<s>" ,UpperCamelCase="<unk>" ,UpperCamelCase="<pad>" ,UpperCamelCase="<mask>" ,UpperCamelCase="</s>" ,**UpperCamelCase ,) -> Dict:
super().__init__(
UpperCamelCase ,UpperCamelCase ,tokenizer_file=UpperCamelCase ,cls_token=UpperCamelCase ,unk_token=UpperCamelCase ,pad_token=UpperCamelCase ,mask_token=UpperCamelCase ,sep_token=UpperCamelCase ,**UpperCamelCase ,)
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> List[int]:
snake_case__ :Optional[int] = [self.cls_token_id]
snake_case__ :Any = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase ,token_ids_a=UpperCamelCase ,already_has_special_tokens=UpperCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(UpperCamelCase )) + [1]
return [1] + ([0] * len(UpperCamelCase )) + [1] + ([0] * len(UpperCamelCase )) + [1]
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> List[int]:
snake_case__ :Any = [self.sep_token_id]
snake_case__ :Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> Tuple[str]:
snake_case__ :List[str] = self._tokenizer.model.save(UpperCamelCase ,name=UpperCamelCase )
return tuple(UpperCamelCase )
| 57
| 1
|
__UpperCAmelCase : int = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__UpperCAmelCase : List[str] = ["a", "b", "c", "d", "e"]
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : List[Any] , __snake_case : Tuple ) -> Optional[int]:
'''simple docstring'''
snake_case__ :List[Any] = start
# add current to visited
visited.append(__snake_case )
snake_case__ :List[str] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# if all neighbors visited add current to sort
sort.append(__snake_case )
# if all vertices haven't been visited select a new one to visit
if len(__snake_case ) != len(__snake_case ):
for vertice in vertices:
if vertice not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# return sort
return sort
if __name__ == "__main__":
__UpperCAmelCase : Tuple = topological_sort("a", [], [])
print(sort)
| 57
|
def lowercase_ ( __snake_case : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
snake_case__ :List[str] = 4
snake_case__ :Optional[int] = (1 << p) - 1
for _ in range(p - 2 ):
snake_case__ :List[Any] = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(1_1))
| 57
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|
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
__UpperCAmelCase : Any = re.compile(R"\b(a|an|the)\b", re.UNICODE)
__UpperCAmelCase : Optional[Any] = None
def lowercase_ ( ) -> Any:
'''simple docstring'''
snake_case__ :int = argparse.ArgumentParser("Official evaluation script for SQuAD version 2.0." )
parser.add_argument("data_file" , metavar="data.json" , help="Input data JSON file." )
parser.add_argument("pred_file" , metavar="pred.json" , help="Model predictions." )
parser.add_argument(
"--out-file" , "-o" , metavar="eval.json" , help="Write accuracy metrics to file (default is stdout)." )
parser.add_argument(
"--na-prob-file" , "-n" , metavar="na_prob.json" , help="Model estimates of probability of no answer." )
parser.add_argument(
"--na-prob-thresh" , "-t" , type=__snake_case , default=1.0 , help="Predict \"\" if no-answer probability exceeds this (default = 1.0)." , )
parser.add_argument(
"--out-image-dir" , "-p" , metavar="out_images" , default=__snake_case , help="Save precision-recall curves to directory." )
parser.add_argument("--verbose" , "-v" , action="store_true" )
if len(sys.argv ) == 1:
parser.print_help()
sys.exit(1 )
return parser.parse_args()
def lowercase_ ( __snake_case : int ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :Optional[int] = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case__ :Optional[Any] = bool(qa["answers"]["text"] )
return qid_to_has_ans
def lowercase_ ( __snake_case : str ) -> List[Any]:
'''simple docstring'''
def remove_articles(__snake_case : List[Any] ):
return ARTICLES_REGEX.sub(" " , __snake_case )
def white_space_fix(__snake_case : int ):
return " ".join(text.split() )
def remove_punc(__snake_case : List[str] ):
snake_case__ :Union[str, Any] = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__snake_case : List[str] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__snake_case ) ) ) )
def lowercase_ ( __snake_case : Optional[int] ) -> Optional[int]:
'''simple docstring'''
if not s:
return []
return normalize_answer(__snake_case ).split()
def lowercase_ ( __snake_case : str , __snake_case : Optional[Any] ) -> List[Any]:
'''simple docstring'''
return int(normalize_answer(__snake_case ) == normalize_answer(__snake_case ) )
def lowercase_ ( __snake_case : str , __snake_case : int ) -> Dict:
'''simple docstring'''
snake_case__ :int = get_tokens(__snake_case )
snake_case__ :int = get_tokens(__snake_case )
snake_case__ :int = collections.Counter(__snake_case ) & collections.Counter(__snake_case )
snake_case__ :Optional[int] = sum(common.values() )
if len(__snake_case ) == 0 or len(__snake_case ) == 0:
# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
return int(gold_toks == pred_toks )
if num_same == 0:
return 0
snake_case__ :Union[str, Any] = 1.0 * num_same / len(__snake_case )
snake_case__ :List[Any] = 1.0 * num_same / len(__snake_case )
snake_case__ :List[Any] = (2 * precision * recall) / (precision + recall)
return fa
def lowercase_ ( __snake_case : Dict , __snake_case : Tuple ) -> Optional[int]:
'''simple docstring'''
snake_case__ :Optional[int] = {}
snake_case__ :int = {}
for article in dataset:
for p in article["paragraphs"]:
for qa in p["qas"]:
snake_case__ :str = qa["id"]
snake_case__ :Optional[int] = [t for t in qa["answers"]["text"] if normalize_answer(__snake_case )]
if not gold_answers:
# For unanswerable questions, only correct answer is empty string
snake_case__ :Optional[int] = [""]
if qid not in preds:
print(F'Missing prediction for {qid}' )
continue
snake_case__ :Optional[Any] = preds[qid]
# Take max over all gold answers
snake_case__ :Dict = max(compute_exact(__snake_case , __snake_case ) for a in gold_answers )
snake_case__ :int = max(compute_fa(__snake_case , __snake_case ) for a in gold_answers )
return exact_scores, fa_scores
def lowercase_ ( __snake_case : Optional[int] , __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :Optional[Any] = {}
for qid, s in scores.items():
snake_case__ :Any = na_probs[qid] > na_prob_thresh
if pred_na:
snake_case__ :Optional[Any] = float(not qid_to_has_ans[qid] )
else:
snake_case__ :Any = s
return new_scores
def lowercase_ ( __snake_case : Optional[int] , __snake_case : int , __snake_case : Union[str, Any]=None ) -> Optional[Any]:
'''simple docstring'''
if not qid_list:
snake_case__ :str = len(__snake_case )
return collections.OrderedDict(
[
("exact", 1_0_0.0 * sum(exact_scores.values() ) / total),
("f1", 1_0_0.0 * sum(fa_scores.values() ) / total),
("total", total),
] )
else:
snake_case__ :Tuple = len(__snake_case )
return collections.OrderedDict(
[
("exact", 1_0_0.0 * sum(exact_scores[k] for k in qid_list ) / total),
("f1", 1_0_0.0 * sum(fa_scores[k] for k in qid_list ) / total),
("total", total),
] )
def lowercase_ ( __snake_case : List[Any] , __snake_case : Dict , __snake_case : Dict ) -> Optional[Any]:
'''simple docstring'''
for k in new_eval:
snake_case__ :Tuple = new_eval[k]
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : List[str] , __snake_case : Union[str, Any] , __snake_case : str ) -> int:
'''simple docstring'''
plt.step(__snake_case , __snake_case , color="b" , alpha=0.2 , where="post" )
plt.fill_between(__snake_case , __snake_case , step="post" , alpha=0.2 , color="b" )
plt.xlabel("Recall" )
plt.ylabel("Precision" )
plt.xlim([0.0, 1.0_5] )
plt.ylim([0.0, 1.0_5] )
plt.title(__snake_case )
plt.savefig(__snake_case )
plt.clf()
def lowercase_ ( __snake_case : Tuple , __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : int , __snake_case : Optional[Any]=None , __snake_case : Any=None ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :Tuple = sorted(__snake_case , key=lambda __snake_case : na_probs[k] )
snake_case__ :Tuple = 0.0
snake_case__ :List[Any] = 1.0
snake_case__ :Optional[Any] = 0.0
snake_case__ :Optional[Any] = [1.0]
snake_case__ :str = [0.0]
snake_case__ :int = 0.0
for i, qid in enumerate(__snake_case ):
if qid_to_has_ans[qid]:
true_pos += scores[qid]
snake_case__ :int = true_pos / float(i + 1 )
snake_case__ :int = true_pos / float(__snake_case )
if i == len(__snake_case ) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
# i.e., if we can put a threshold after this point
avg_prec += cur_p * (cur_r - recalls[-1])
precisions.append(__snake_case )
recalls.append(__snake_case )
if out_image:
plot_pr_curve(__snake_case , __snake_case , __snake_case , __snake_case )
return {"ap": 1_0_0.0 * avg_prec}
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : int , __snake_case : str , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Tuple ) -> Optional[Any]:
'''simple docstring'''
if out_image_dir and not os.path.exists(__snake_case ):
os.makedirs(__snake_case )
snake_case__ :List[Any] = sum(1 for v in qid_to_has_ans.values() if v )
if num_true_pos == 0:
return
snake_case__ :Dict = make_precision_recall_eval(
__snake_case , __snake_case , __snake_case , __snake_case , out_image=os.path.join(__snake_case , "pr_exact.png" ) , title="Precision-Recall curve for Exact Match score" , )
snake_case__ :int = make_precision_recall_eval(
__snake_case , __snake_case , __snake_case , __snake_case , out_image=os.path.join(__snake_case , "pr_f1.png" ) , title="Precision-Recall curve for F1 score" , )
snake_case__ :Tuple = {k: float(__snake_case ) for k, v in qid_to_has_ans.items()}
snake_case__ :Optional[int] = make_precision_recall_eval(
__snake_case , __snake_case , __snake_case , __snake_case , out_image=os.path.join(__snake_case , "pr_oracle.png" ) , title="Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)" , )
merge_eval(__snake_case , __snake_case , "pr_exact" )
merge_eval(__snake_case , __snake_case , "pr_f1" )
merge_eval(__snake_case , __snake_case , "pr_oracle" )
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Union[str, Any] , __snake_case : Any ) -> Optional[int]:
'''simple docstring'''
if not qid_list:
return
snake_case__ :Optional[Any] = [na_probs[k] for k in qid_list]
snake_case__ :Optional[Any] = np.ones_like(__snake_case ) / float(len(__snake_case ) )
plt.hist(__snake_case , weights=__snake_case , bins=20 , range=(0.0, 1.0) )
plt.xlabel("Model probability of no-answer" )
plt.ylabel("Proportion of dataset" )
plt.title(F'Histogram of no-answer probability: {name}' )
plt.savefig(os.path.join(__snake_case , F'na_prob_hist_{name}.png' ) )
plt.clf()
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : Optional[int] , __snake_case : List[Any] , __snake_case : List[Any] ) -> List[str]:
'''simple docstring'''
snake_case__ :int = sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k] )
snake_case__ :str = num_no_ans
snake_case__ :Any = cur_score
snake_case__ :int = 0.0
snake_case__ :Any = sorted(__snake_case , key=lambda __snake_case : na_probs[k] )
for i, qid in enumerate(__snake_case ):
if qid not in scores:
continue
if qid_to_has_ans[qid]:
snake_case__ :Any = scores[qid]
else:
if preds[qid]:
snake_case__ :Optional[int] = -1
else:
snake_case__ :List[str] = 0
cur_score += diff
if cur_score > best_score:
snake_case__ :List[str] = cur_score
snake_case__ :List[Any] = na_probs[qid]
return 1_0_0.0 * best_score / len(__snake_case ), best_thresh
def lowercase_ ( __snake_case : Optional[int] , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : List[str] ) -> Optional[int]:
'''simple docstring'''
snake_case__ , snake_case__ :Tuple = find_best_thresh(__snake_case , __snake_case , __snake_case , __snake_case )
snake_case__ , snake_case__ :Dict = find_best_thresh(__snake_case , __snake_case , __snake_case , __snake_case )
snake_case__ :Any = best_exact
snake_case__ :Tuple = exact_thresh
snake_case__ :Any = best_fa
snake_case__ :str = fa_thresh
def lowercase_ ( ) -> Tuple:
'''simple docstring'''
with open(OPTS.data_file ) as f:
snake_case__ :Any = json.load(__snake_case )
snake_case__ :Any = dataset_json["data"]
with open(OPTS.pred_file ) as f:
snake_case__ :List[str] = json.load(__snake_case )
if OPTS.na_prob_file:
with open(OPTS.na_prob_file ) as f:
snake_case__ :str = json.load(__snake_case )
else:
snake_case__ :Optional[Any] = {k: 0.0 for k in preds}
snake_case__ :List[str] = make_qid_to_has_ans(__snake_case ) # maps qid to True/False
snake_case__ :Tuple = [k for k, v in qid_to_has_ans.items() if v]
snake_case__ :List[str] = [k for k, v in qid_to_has_ans.items() if not v]
snake_case__ , snake_case__ :Any = get_raw_scores(__snake_case , __snake_case )
snake_case__ :List[str] = apply_no_ans_threshold(__snake_case , __snake_case , __snake_case , OPTS.na_prob_thresh )
snake_case__ :List[str] = apply_no_ans_threshold(__snake_case , __snake_case , __snake_case , OPTS.na_prob_thresh )
snake_case__ :List[Any] = make_eval_dict(__snake_case , __snake_case )
if has_ans_qids:
snake_case__ :Optional[Any] = make_eval_dict(__snake_case , __snake_case , qid_list=__snake_case )
merge_eval(__snake_case , __snake_case , "HasAns" )
if no_ans_qids:
snake_case__ :Any = make_eval_dict(__snake_case , __snake_case , qid_list=__snake_case )
merge_eval(__snake_case , __snake_case , "NoAns" )
if OPTS.na_prob_file:
find_all_best_thresh(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
if OPTS.na_prob_file and OPTS.out_image_dir:
run_precision_recall_analysis(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , OPTS.out_image_dir )
histogram_na_prob(__snake_case , __snake_case , OPTS.out_image_dir , "hasAns" )
histogram_na_prob(__snake_case , __snake_case , OPTS.out_image_dir , "noAns" )
if OPTS.out_file:
with open(OPTS.out_file , "w" ) as f:
json.dump(__snake_case , __snake_case )
else:
print(json.dumps(__snake_case , indent=2 ) )
if __name__ == "__main__":
__UpperCAmelCase : List[Any] = parse_args()
if OPTS.out_image_dir:
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
main()
| 57
|
from typing import Any
def lowercase_ ( __snake_case : list , __snake_case : list , __snake_case : dict , __snake_case : dict , __snake_case : dict , ) -> list:
'''simple docstring'''
_validation(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
# Creates data structures and fill initial step
snake_case__ :dict = {}
snake_case__ :dict = {}
for state in states_space:
snake_case__ :List[Any] = observations_space[0]
snake_case__ :str = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
snake_case__ :str = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(__snake_case ) ):
snake_case__ :Any = observations_space[o]
snake_case__ :Tuple = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
snake_case__ :Tuple = ""
snake_case__ :Union[str, Any] = -1
for k_state in states_space:
snake_case__ :int = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
snake_case__ :str = probability
snake_case__ :Tuple = k_state
# Update probabilities and pointers dicts
snake_case__ :List[str] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
snake_case__ :List[str] = arg_max
# The final observation
snake_case__ :str = observations_space[len(__snake_case ) - 1]
# argmax for given final observation
snake_case__ :Optional[int] = ""
snake_case__ :List[str] = -1
for k_state in states_space:
snake_case__ :List[str] = probabilities[(k_state, final_observation)]
if probability > max_probability:
snake_case__ :List[str] = probability
snake_case__ :int = k_state
snake_case__ :Any = arg_max
# Process pointers backwards
snake_case__ :int = last_state
snake_case__ :List[str] = []
for o in range(len(__snake_case ) - 1 , -1 , -1 ):
result.append(__snake_case )
snake_case__ :List[str] = pointers[previous, observations_space[o]]
result.reverse()
return result
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
_validate_not_empty(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
_validate_lists(__snake_case , __snake_case )
_validate_dicts(
__snake_case , __snake_case , __snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("There's an empty parameter" )
def lowercase_ ( __snake_case : Any , __snake_case : Any ) -> None:
'''simple docstring'''
_validate_list(__snake_case , "observations_space" )
_validate_list(__snake_case , "states_space" )
def lowercase_ ( __snake_case : Any , __snake_case : str ) -> None:
'''simple docstring'''
if not isinstance(_object , __snake_case ):
snake_case__ :Optional[int] = F'{var_name} must be a list'
raise ValueError(__snake_case )
else:
for x in _object:
if not isinstance(__snake_case , __snake_case ):
snake_case__ :Any = F'{var_name} must be a list of strings'
raise ValueError(__snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
_validate_dict(__snake_case , "initial_probabilities" , __snake_case )
_validate_nested_dict(__snake_case , "transition_probabilities" )
_validate_nested_dict(__snake_case , "emission_probabilities" )
def lowercase_ ( __snake_case : Any , __snake_case : str ) -> None:
'''simple docstring'''
_validate_dict(_object , __snake_case , __snake_case )
for x in _object.values():
_validate_dict(__snake_case , __snake_case , __snake_case , __snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : str , __snake_case : type , __snake_case : bool = False ) -> None:
'''simple docstring'''
if not isinstance(_object , __snake_case ):
snake_case__ :str = F'{var_name} must be a dict'
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object ):
snake_case__ :List[Any] = F'{var_name} all keys must be strings'
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object.values() ):
snake_case__ :Optional[int] = "nested dictionary " if nested else ""
snake_case__ :int = F'{var_name} {nested_text}all values must be {value_type.__name__}'
raise ValueError(__snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 57
| 1
|
def lowercase_ ( __snake_case : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
snake_case__ :List[str] = 4
snake_case__ :Optional[int] = (1 << p) - 1
for _ in range(p - 2 ):
snake_case__ :List[Any] = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(1_1))
| 57
|
def lowercase_ ( __snake_case : str ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__snake_case ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 57
| 1
|
import importlib
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Union
import torch
from ..utils import BaseOutput
__UpperCAmelCase : Union[str, Any] = "scheduler_config.json"
class _snake_case ( _A ):
_A = 1
_A = 2
_A = 3
_A = 4
_A = 5
_A = 6
_A = 7
_A = 8
_A = 9
_A = 10
_A = 11
_A = 12
_A = 13
_A = 14
@dataclass
class _snake_case ( _A ):
_A = 42
class _snake_case :
_A = SCHEDULER_CONFIG_NAME
_A = []
_A = True
@classmethod
def lowerCAmelCase_ ( cls ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase=False ,**UpperCamelCase ,) -> Tuple:
snake_case__ , snake_case__ , snake_case__ :str = cls.load_config(
pretrained_model_name_or_path=UpperCamelCase ,subfolder=UpperCamelCase ,return_unused_kwargs=UpperCamelCase ,return_commit_hash=UpperCamelCase ,**UpperCamelCase ,)
return cls.from_config(UpperCamelCase ,return_unused_kwargs=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = False ,**UpperCamelCase ) -> Union[str, Any]:
self.save_config(save_directory=UpperCamelCase ,push_to_hub=UpperCamelCase ,**UpperCamelCase )
@property
def lowerCAmelCase_ ( self ) -> Optional[Any]:
return self._get_compatibles()
@classmethod
def lowerCAmelCase_ ( cls ) -> str:
snake_case__ :Dict = list(set([cls.__name__] + cls._compatibles ) )
snake_case__ :Optional[int] = importlib.import_module(__name__.split("." )[0] )
snake_case__ :int = [
getattr(UpperCamelCase ,UpperCamelCase ) for c in compatible_classes_str if hasattr(UpperCamelCase ,UpperCamelCase )
]
return compatible_classes
| 57
|
def lowercase_ ( __snake_case : int = 10_00 ) -> int:
'''simple docstring'''
snake_case__ :int = 3
snake_case__ :int = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 57
| 1
|
def lowercase_ ( __snake_case : str ) -> list:
'''simple docstring'''
if n_term == "":
return []
snake_case__ :list = []
for temp in range(int(__snake_case ) ):
series.append(F'1/{temp + 1}' if series else "1" )
return series
if __name__ == "__main__":
__UpperCAmelCase : int = input("Enter the last number (nth term) of the Harmonic Series")
print("Formula of Harmonic Series => 1+1/2+1/3 ..... 1/n")
print(harmonic_series(nth_term))
| 57
|
import os
import sys
import unittest
__UpperCAmelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase : Tuple = os.path.join(git_repo_path, "src", "diffusers")
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Tuple = find_backend(" if not is_torch_available():" )
self.assertEqual(UpperCamelCase ,"torch" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
snake_case__ :Tuple = find_backend(" if not (is_torch_available() and is_transformers_available()):" )
self.assertEqual(UpperCamelCase ,"torch_and_transformers" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
snake_case__ :str = find_backend(
" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):" )
self.assertEqual(UpperCamelCase ,"torch_and_transformers_and_onnx" )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :int = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch" ,UpperCamelCase )
self.assertIn("torch_and_transformers" ,UpperCamelCase )
self.assertIn("flax_and_transformers" ,UpperCamelCase )
self.assertIn("torch_and_transformers_and_onnx" ,UpperCamelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn("UNet2DModel" ,objects["torch"] )
self.assertIn("FlaxUNet2DConditionModel" ,objects["flax"] )
self.assertIn("StableDiffusionPipeline" ,objects["torch_and_transformers"] )
self.assertIn("FlaxStableDiffusionPipeline" ,objects["flax_and_transformers"] )
self.assertIn("LMSDiscreteScheduler" ,objects["torch_and_scipy"] )
self.assertIn("OnnxStableDiffusionPipeline" ,objects["torch_and_transformers_and_onnx"] )
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Union[str, Any] = create_dummy_object("CONSTANT" ,"'torch'" )
self.assertEqual(UpperCamelCase ,"\nCONSTANT = None\n" )
snake_case__ :Optional[Any] = create_dummy_object("function" ,"'torch'" )
self.assertEqual(
UpperCamelCase ,"\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n" )
snake_case__ :str = "\nclass FakeClass(metaclass=DummyObject):\n _backends = 'torch'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, 'torch')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n"
snake_case__ :List[str] = create_dummy_object("FakeClass" ,"'torch'" )
self.assertEqual(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :Tuple = "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n"
snake_case__ :int = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]} )
self.assertEqual(dummy_files["torch"] ,UpperCamelCase )
| 57
| 1
|
import os
import sys
import unittest
__UpperCAmelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase : Tuple = os.path.join(git_repo_path, "src", "diffusers")
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Tuple = find_backend(" if not is_torch_available():" )
self.assertEqual(UpperCamelCase ,"torch" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
snake_case__ :Tuple = find_backend(" if not (is_torch_available() and is_transformers_available()):" )
self.assertEqual(UpperCamelCase ,"torch_and_transformers" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
snake_case__ :str = find_backend(
" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):" )
self.assertEqual(UpperCamelCase ,"torch_and_transformers_and_onnx" )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :int = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch" ,UpperCamelCase )
self.assertIn("torch_and_transformers" ,UpperCamelCase )
self.assertIn("flax_and_transformers" ,UpperCamelCase )
self.assertIn("torch_and_transformers_and_onnx" ,UpperCamelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn("UNet2DModel" ,objects["torch"] )
self.assertIn("FlaxUNet2DConditionModel" ,objects["flax"] )
self.assertIn("StableDiffusionPipeline" ,objects["torch_and_transformers"] )
self.assertIn("FlaxStableDiffusionPipeline" ,objects["flax_and_transformers"] )
self.assertIn("LMSDiscreteScheduler" ,objects["torch_and_scipy"] )
self.assertIn("OnnxStableDiffusionPipeline" ,objects["torch_and_transformers_and_onnx"] )
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Union[str, Any] = create_dummy_object("CONSTANT" ,"'torch'" )
self.assertEqual(UpperCamelCase ,"\nCONSTANT = None\n" )
snake_case__ :Optional[Any] = create_dummy_object("function" ,"'torch'" )
self.assertEqual(
UpperCamelCase ,"\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n" )
snake_case__ :str = "\nclass FakeClass(metaclass=DummyObject):\n _backends = 'torch'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, 'torch')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n"
snake_case__ :List[str] = create_dummy_object("FakeClass" ,"'torch'" )
self.assertEqual(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :Tuple = "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n"
snake_case__ :int = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]} )
self.assertEqual(dummy_files["torch"] ,UpperCamelCase )
| 57
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase : Tuple = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : List[Any] = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
__UpperCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
| 1
|
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _snake_case ( _A ):
_A = (DDPMScheduler,)
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> List[Any]:
snake_case__ :Any = {
"num_train_timesteps": 1_000,
"beta_start": 0.0001,
"beta_end": 0.02,
"beta_schedule": "linear",
"variance_type": "fixed_small",
"clip_sample": True,
}
config.update(**UpperCamelCase )
return config
def lowerCAmelCase_ ( self ) -> Dict:
for timesteps in [1, 5, 100, 1_000]:
self.check_over_configs(num_train_timesteps=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
for beta_start, beta_end in zip([0.0001, 0.001, 0.01, 0.1] ,[0.002, 0.02, 0.2, 2] ):
self.check_over_configs(beta_start=UpperCamelCase ,beta_end=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
for schedule in ["linear", "squaredcos_cap_v2"]:
self.check_over_configs(beta_schedule=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[Any]:
for variance in ["fixed_small", "fixed_large", "other"]:
self.check_over_configs(variance_type=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Dict:
for clip_sample in [True, False]:
self.check_over_configs(clip_sample=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[Any]:
self.check_over_configs(thresholding=UpperCamelCase )
for threshold in [0.5, 1.0, 2.0]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(
thresholding=UpperCamelCase ,prediction_type=UpperCamelCase ,sample_max_value=UpperCamelCase ,)
def lowerCAmelCase_ ( self ) -> Optional[Any]:
for prediction_type in ["epsilon", "sample", "v_prediction"]:
self.check_over_configs(prediction_type=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[str]:
for t in [0, 500, 999]:
self.check_over_forward(time_step=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[str]:
snake_case__ :Optional[int] = self.scheduler_classes[0]
snake_case__ :Tuple = self.get_scheduler_config()
snake_case__ :Union[str, Any] = scheduler_class(**UpperCamelCase )
assert torch.sum(torch.abs(scheduler._get_variance(0 ) - 0.0 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(487 ) - 0.00979 ) ) < 1E-5
assert torch.sum(torch.abs(scheduler._get_variance(999 ) - 0.02 ) ) < 1E-5
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :str = self.scheduler_classes[0]
snake_case__ :Optional[Any] = self.get_scheduler_config()
snake_case__ :str = scheduler_class(**UpperCamelCase )
snake_case__ :Any = len(UpperCamelCase )
snake_case__ :Union[str, Any] = self.dummy_model()
snake_case__ :Optional[Any] = self.dummy_sample_deter
snake_case__ :int = torch.manual_seed(0 )
for t in reversed(range(UpperCamelCase ) ):
# 1. predict noise residual
snake_case__ :Optional[int] = model(UpperCamelCase ,UpperCamelCase )
# 2. predict previous mean of sample x_t-1
snake_case__ :Union[str, Any] = scheduler.step(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,generator=UpperCamelCase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
snake_case__ :Union[str, Any] = pred_prev_sample
snake_case__ :int = torch.sum(torch.abs(UpperCamelCase ) )
snake_case__ :List[Any] = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_sum.item() - 258.9606 ) < 1E-2
assert abs(result_mean.item() - 0.3372 ) < 1E-3
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :List[Any] = self.scheduler_classes[0]
snake_case__ :Tuple = self.get_scheduler_config(prediction_type="v_prediction" )
snake_case__ :int = scheduler_class(**UpperCamelCase )
snake_case__ :List[Any] = len(UpperCamelCase )
snake_case__ :int = self.dummy_model()
snake_case__ :Any = self.dummy_sample_deter
snake_case__ :List[str] = torch.manual_seed(0 )
for t in reversed(range(UpperCamelCase ) ):
# 1. predict noise residual
snake_case__ :Optional[Any] = model(UpperCamelCase ,UpperCamelCase )
# 2. predict previous mean of sample x_t-1
snake_case__ :str = scheduler.step(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,generator=UpperCamelCase ).prev_sample
# if t > 0:
# noise = self.dummy_sample_deter
# variance = scheduler.get_variance(t) ** (0.5) * noise
#
# sample = pred_prev_sample + variance
snake_case__ :List[Any] = pred_prev_sample
snake_case__ :str = torch.sum(torch.abs(UpperCamelCase ) )
snake_case__ :int = torch.mean(torch.abs(UpperCamelCase ) )
assert abs(result_sum.item() - 202.0296 ) < 1E-2
assert abs(result_mean.item() - 0.2631 ) < 1E-3
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :str = self.scheduler_classes[0]
snake_case__ :Dict = self.get_scheduler_config()
snake_case__ :Any = scheduler_class(**UpperCamelCase )
snake_case__ :Tuple = [100, 87, 50, 1, 0]
scheduler.set_timesteps(timesteps=UpperCamelCase )
snake_case__ :List[Any] = scheduler.timesteps
for i, timestep in enumerate(UpperCamelCase ):
if i == len(UpperCamelCase ) - 1:
snake_case__ :Any = -1
else:
snake_case__ :Optional[Any] = timesteps[i + 1]
snake_case__ :str = scheduler.previous_timestep(UpperCamelCase )
snake_case__ :Any = prev_t.item()
self.assertEqual(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ :Dict = self.scheduler_classes[0]
snake_case__ :List[str] = self.get_scheduler_config()
snake_case__ :int = scheduler_class(**UpperCamelCase )
snake_case__ :Optional[int] = [100, 87, 50, 51, 0]
with self.assertRaises(UpperCamelCase ,msg="`custom_timesteps` must be in descending order." ):
scheduler.set_timesteps(timesteps=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :Optional[Any] = self.scheduler_classes[0]
snake_case__ :str = self.get_scheduler_config()
snake_case__ :List[Any] = scheduler_class(**UpperCamelCase )
snake_case__ :Tuple = [100, 87, 50, 1, 0]
snake_case__ :Optional[Any] = len(UpperCamelCase )
with self.assertRaises(UpperCamelCase ,msg="Can only pass one of `num_inference_steps` or `custom_timesteps`." ):
scheduler.set_timesteps(num_inference_steps=UpperCamelCase ,timesteps=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :Union[str, Any] = self.scheduler_classes[0]
snake_case__ :Any = self.get_scheduler_config()
snake_case__ :List[str] = scheduler_class(**UpperCamelCase )
snake_case__ :List[str] = [scheduler.config.num_train_timesteps]
with self.assertRaises(
UpperCamelCase ,msg="`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}" ,):
scheduler.set_timesteps(timesteps=UpperCamelCase )
| 57
|
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> List[Any]:
# A mock response for an HTTP head request to emulate server down
snake_case__ :Tuple = mock.Mock()
snake_case__ :List[str] = 500
snake_case__ :Any = {}
snake_case__ :Union[str, Any] = HTTPError
snake_case__ :Tuple = {}
# Download this model to make sure it's in the cache.
snake_case__ :Any = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" ,return_value=UpperCamelCase ) as mock_head:
snake_case__ :Dict = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def lowerCAmelCase_ ( self ) -> Dict:
# A mock response for an HTTP head request to emulate server down
snake_case__ :Union[str, Any] = mock.Mock()
snake_case__ :int = 500
snake_case__ :Any = {}
snake_case__ :Dict = HTTPError
snake_case__ :List[Any] = {}
# Download this model to make sure it's in the cache.
snake_case__ :Optional[int] = GPTaTokenizerFast.from_pretrained("gpt2" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" ,return_value=UpperCamelCase ) as mock_head:
snake_case__ :Any = GPTaTokenizerFast.from_pretrained("gpt2" )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCAmelCase_ ( self ) -> int:
# This test is for deprecated behavior and can be removed in v5
try:
snake_case__ :Union[str, Any] = tempfile.mktemp()
with open(UpperCamelCase ,"wb" ) as f:
http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" ,UpperCamelCase )
snake_case__ :Tuple = AlbertTokenizer.from_pretrained(UpperCamelCase )
finally:
os.remove(UpperCamelCase )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile("tokenizer.json" ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open("tokenizer.json" ,"wb" ) as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json" ,UpperCamelCase )
snake_case__ :Dict = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1_000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("tokenizer.json" )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
# This test is for deprecated behavior and can be removed in v5
snake_case__ :Union[str, Any] = AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" )
@is_staging_test
class _snake_case ( unittest.TestCase ):
_A = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def lowerCAmelCase_ ( cls ) -> Optional[int]:
snake_case__ :List[str] = TOKEN
HfFolder.save_token(UpperCamelCase )
@classmethod
def lowerCAmelCase_ ( cls ) -> Union[str, Any]:
try:
delete_repo(token=cls._token ,repo_id="test-tokenizer" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="valid_org/test-tokenizer-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="test-dynamic-tokenizer" )
except HTTPError:
pass
def lowerCAmelCase_ ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :List[str] = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :str = BertTokenizer(UpperCamelCase )
tokenizer.push_to_hub("test-tokenizer" ,use_auth_token=self._token )
snake_case__ :Dict = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id="test-tokenizer" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(UpperCamelCase ,repo_id="test-tokenizer" ,push_to_hub=UpperCamelCase ,use_auth_token=self._token )
snake_case__ :List[str] = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def lowerCAmelCase_ ( self ) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :List[Any] = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Any = BertTokenizer(UpperCamelCase )
tokenizer.push_to_hub("valid_org/test-tokenizer-org" ,use_auth_token=self._token )
snake_case__ :Any = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id="valid_org/test-tokenizer-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
UpperCamelCase ,repo_id="valid_org/test-tokenizer-org" ,push_to_hub=UpperCamelCase ,use_auth_token=self._token )
snake_case__ :Union[str, Any] = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def lowerCAmelCase_ ( self ) -> Any:
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :str = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Optional[int] = CustomTokenizer(UpperCamelCase )
# No fast custom tokenizer
tokenizer.push_to_hub("test-dynamic-tokenizer" ,use_auth_token=self._token )
snake_case__ :Union[str, Any] = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizer" )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :int = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Tuple = BertTokenizerFast.from_pretrained(UpperCamelCase )
bert_tokenizer.save_pretrained(UpperCamelCase )
snake_case__ :List[Any] = CustomTokenizerFast.from_pretrained(UpperCamelCase )
tokenizer.push_to_hub("test-dynamic-tokenizer" ,use_auth_token=self._token )
snake_case__ :List[Any] = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizerFast" )
snake_case__ :List[str] = AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' ,use_fast=UpperCamelCase ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizer" )
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :int = Trie()
trie.add("Hello 友達" )
self.assertEqual(trie.data ,{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}} )
trie.add("Hello" )
trie.data
self.assertEqual(trie.data ,{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}} )
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :List[str] = Trie()
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) ,["[CLS] This is a extra_id_100"] )
trie.add("[CLS]" )
trie.add("extra_id_1" )
trie.add("extra_id_100" )
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) ,["[CLS]", " This is a ", "extra_id_100"] )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Optional[Any] = Trie()
trie.add("A" )
self.assertEqual(trie.split("ABC" ) ,["A", "BC"] )
self.assertEqual(trie.split("BCA" ) ,["BC", "A"] )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :Any = Trie()
trie.add("TOKEN]" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) ,["This is something ", "[SPECIAL_TOKEN]"] )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :List[Any] = Trie()
trie.add("A" )
trie.add("P" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) ,["This is something ", "[SPECIAL_TOKEN]"] )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :str = Trie()
trie.add("AB" )
trie.add("B" )
trie.add("C" )
self.assertEqual(trie.split("ABC" ) ,["AB", "C"] )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Dict = Trie()
trie.add("ABC" )
trie.add("B" )
trie.add("CD" )
self.assertEqual(trie.split("ABCD" ) ,["ABC", "D"] )
def lowerCAmelCase_ ( self ) -> int:
# Even if the offsets are wrong, we necessarily output correct string
# parts.
snake_case__ :Optional[int] = Trie()
snake_case__ :Union[str, Any] = trie.cut_text("ABC" ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(UpperCamelCase ,["AB", "C"] )
| 57
| 1
|
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCAmelCase : Any = {
"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 _snake_case ( _A ):
_A = 'ibert'
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="absolute" ,UpperCamelCase=False ,UpperCamelCase="none" ,**UpperCamelCase ,) -> Any:
super().__init__(pad_token_id=UpperCamelCase ,bos_token_id=UpperCamelCase ,eos_token_id=UpperCamelCase ,**UpperCamelCase )
snake_case__ :Dict = vocab_size
snake_case__ :List[str] = hidden_size
snake_case__ :Optional[Any] = num_hidden_layers
snake_case__ :List[Any] = num_attention_heads
snake_case__ :str = hidden_act
snake_case__ :Any = intermediate_size
snake_case__ :List[str] = hidden_dropout_prob
snake_case__ :List[Any] = attention_probs_dropout_prob
snake_case__ :Dict = max_position_embeddings
snake_case__ :Optional[Any] = type_vocab_size
snake_case__ :List[str] = initializer_range
snake_case__ :Any = layer_norm_eps
snake_case__ :Any = position_embedding_type
snake_case__ :List[Any] = quant_mode
snake_case__ :List[Any] = force_dequant
class _snake_case ( _A ):
@property
def lowerCAmelCase_ ( self ) -> Mapping[str, Mapping[int, str]]:
if self.task == "multiple-choice":
snake_case__ :Tuple = {0: "batch", 1: "choice", 2: "sequence"}
else:
snake_case__ :Any = {0: "batch", 1: "sequence"}
return OrderedDict(
[
("input_ids", dynamic_axis),
("attention_mask", dynamic_axis),
] )
| 57
|
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 : Optional[Any] = 1_6
__UpperCAmelCase : Optional[int] = 3_2
def lowercase_ ( __snake_case : Accelerator , __snake_case : int = 16 , __snake_case : str = "bert-base-cased" ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :int = AutoTokenizer.from_pretrained(__snake_case )
snake_case__ :Optional[int] = load_dataset("glue" , "mrpc" )
def tokenize_function(__snake_case : Tuple ):
# max_length=None => use the model max length (it's actually the default)
snake_case__ :Any = 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
snake_case__ :List[Any] = 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
snake_case__ :Any = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(__snake_case : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__snake_case , padding="max_length" , max_length=1_28 , return_tensors="pt" )
return tokenizer.pad(__snake_case , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
snake_case__ :Any = DataLoader(
tokenized_datasets["train"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
snake_case__ :Tuple = DataLoader(
tokenized_datasets["validation"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
return train_dataloader, eval_dataloader
def lowercase_ ( __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
model.eval()
snake_case__ :Union[str, Any] = 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():
snake_case__ :List[Any] = model(**__snake_case )
snake_case__ :Any = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
snake_case__ , snake_case__ :Tuple = 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:
snake_case__ :List[str] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
snake_case__ :Optional[int] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__snake_case , references=__snake_case , )
snake_case__ :int = metric.compute()
return eval_metric["accuracy"]
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : Optional[Any] ) -> Any:
'''simple docstring'''
snake_case__ :Any = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ :Union[str, Any] = config["lr"]
snake_case__ :List[str] = int(config["num_epochs"] )
snake_case__ :Optional[Any] = int(config["seed"] )
snake_case__ :List[Any] = int(config["batch_size"] )
snake_case__ :List[Any] = args.model_name_or_path
set_seed(__snake_case )
snake_case__ , snake_case__ :List[Any] = 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)
snake_case__ :List[Any] = AutoModelForSequenceClassification.from_pretrained(__snake_case , return_dict=__snake_case )
# Instantiate optimizer
snake_case__ :int = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
snake_case__ :Tuple = optimizer_cls(params=model.parameters() , lr=__snake_case )
if accelerator.state.deepspeed_plugin is not None:
snake_case__ :List[str] = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
snake_case__ :Any = 1
snake_case__ :List[Any] = (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
):
snake_case__ :Optional[Any] = get_linear_schedule_with_warmup(
optimizer=__snake_case , num_warmup_steps=0 , num_training_steps=__snake_case , )
else:
snake_case__ :Any = 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.
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ :int = 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
snake_case__ :Dict = 0
# We also need to keep track of the stating epoch so files are named properly
snake_case__ :Union[str, Any] = 0
snake_case__ :List[str] = evaluate.load("glue" , "mrpc" )
snake_case__ :Optional[Any] = num_epochs
if args.partial_train_epoch is not None:
snake_case__ :List[Any] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
snake_case__ :Union[str, Any] = args.resume_from_checkpoint.split("epoch_" )[1]
snake_case__ :Dict = ""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
snake_case__ :str = int(__snake_case ) + 1
snake_case__ :List[Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
accelerator.print("resumed checkpoint performance:" , __snake_case )
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:
snake_case__ :Tuple = json.load(__snake_case )
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
snake_case__ :Optional[int] = {}
for epoch in range(__snake_case , __snake_case ):
model.train()
for step, batch in enumerate(__snake_case ):
snake_case__ :str = model(**__snake_case )
snake_case__ :List[str] = outputs.loss
snake_case__ :List[Any] = 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
snake_case__ :int = F'epoch_{epoch}'
snake_case__ :str = os.path.join(args.output_dir , __snake_case )
accelerator.save_state(__snake_case )
snake_case__ :Union[str, Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
snake_case__ :List[str] = accuracy
snake_case__ :List[str] = lr_scheduler.get_lr()[0]
snake_case__ :List[Any] = optimizer.param_groups[0]["lr"]
snake_case__ :Dict = epoch
snake_case__ :List[Any] = overall_step
accelerator.print(F'epoch {epoch}:' , __snake_case )
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(__snake_case , __snake_case )
def lowercase_ ( ) -> Any:
'''simple docstring'''
snake_case__ :List[Any] = 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(
"--resume_from_checkpoint" , type=__snake_case , default=__snake_case , help="If the training should continue from a checkpoint folder." , )
parser.add_argument(
"--partial_train_epoch" , type=__snake_case , default=__snake_case , help="If passed, the training will stop after this number of epochs." , )
parser.add_argument(
"--num_epochs" , type=__snake_case , default=2 , help="Number of train epochs." , )
snake_case__ :Any = parser.parse_args()
snake_case__ :int = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(__snake_case , __snake_case )
if __name__ == "__main__":
main()
| 57
| 1
|
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, BertTokenizer, BlipImageProcessor, BlipProcessor, PreTrainedTokenizerFast
@require_vision
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :str = tempfile.mkdtemp()
snake_case__ :Dict = BlipImageProcessor()
snake_case__ :Optional[Any] = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-BertModel" )
snake_case__ :Optional[Any] = BlipProcessor(UpperCamelCase ,UpperCamelCase )
processor.save_pretrained(self.tmpdirname )
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> Tuple:
return AutoProcessor.from_pretrained(self.tmpdirname ,**UpperCamelCase ).tokenizer
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> Optional[int]:
return AutoProcessor.from_pretrained(self.tmpdirname ,**UpperCamelCase ).image_processor
def lowerCAmelCase_ ( self ) -> List[Any]:
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :Optional[int] = [np.random.randint(255 ,size=(3, 30, 400) ,dtype=np.uinta )]
snake_case__ :Union[str, Any] = [Image.fromarray(np.moveaxis(UpperCamelCase ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def lowerCAmelCase_ ( self ) -> List[str]:
snake_case__ :Optional[Any] = BlipProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
snake_case__ :str = self.get_tokenizer(bos_token="(BOS)" ,eos_token="(EOS)" )
snake_case__ :str = self.get_image_processor(do_normalize=UpperCamelCase ,padding_value=1.0 )
snake_case__ :List[Any] = BlipProcessor.from_pretrained(
self.tmpdirname ,bos_token="(BOS)" ,eos_token="(EOS)" ,do_normalize=UpperCamelCase ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,UpperCamelCase )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :Union[str, Any] = self.get_image_processor()
snake_case__ :Optional[int] = self.get_tokenizer()
snake_case__ :List[str] = BlipProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
snake_case__ :int = self.prepare_image_inputs()
snake_case__ :Any = image_processor(UpperCamelCase ,return_tensors="np" )
snake_case__ :str = processor(images=UpperCamelCase ,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 lowerCAmelCase_ ( self ) -> str:
snake_case__ :Union[str, Any] = self.get_image_processor()
snake_case__ :List[str] = self.get_tokenizer()
snake_case__ :str = BlipProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
snake_case__ :int = "lower newer"
snake_case__ :Dict = processor(text=UpperCamelCase )
snake_case__ :Dict = tokenizer(UpperCamelCase ,return_token_type_ids=UpperCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Optional[int] = self.get_image_processor()
snake_case__ :Optional[Any] = self.get_tokenizer()
snake_case__ :str = BlipProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
snake_case__ :Optional[Any] = "lower newer"
snake_case__ :List[Any] = self.prepare_image_inputs()
snake_case__ :str = processor(text=UpperCamelCase ,images=UpperCamelCase )
self.assertListEqual(list(inputs.keys() ) ,["pixel_values", "input_ids", "attention_mask"] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase ):
processor()
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :Optional[int] = self.get_image_processor()
snake_case__ :Dict = self.get_tokenizer()
snake_case__ :Optional[int] = BlipProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
snake_case__ :Optional[int] = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case__ :str = processor.batch_decode(UpperCamelCase )
snake_case__ :Optional[Any] = tokenizer.batch_decode(UpperCamelCase )
self.assertListEqual(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :Union[str, Any] = self.get_image_processor()
snake_case__ :str = self.get_tokenizer()
snake_case__ :int = BlipProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
snake_case__ :Optional[Any] = "lower newer"
snake_case__ :List[str] = self.prepare_image_inputs()
snake_case__ :Any = processor(text=UpperCamelCase ,images=UpperCamelCase )
# For now the processor supports only ['pixel_values', 'input_ids', 'attention_mask']
self.assertListEqual(list(inputs.keys() ) ,["pixel_values", "input_ids", "attention_mask"] )
| 57
|
from __future__ import annotations
class _snake_case :
def __init__( self ,UpperCamelCase ) -> None:
snake_case__ :Union[str, Any] = data
snake_case__ :Node | None = None
snake_case__ :Node | None = None
def lowercase_ ( __snake_case : Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowercase_ ( __snake_case : Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowercase_ ( __snake_case : Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowercase_ ( ) -> None: # Main function for testing.
'''simple docstring'''
snake_case__ :Dict = Node(1 )
snake_case__ :int = Node(2 )
snake_case__ :Optional[Any] = Node(3 )
snake_case__ :Tuple = Node(4 )
snake_case__ :str = Node(5 )
snake_case__ :Optional[Any] = Node(6 )
snake_case__ :List[Any] = Node(7 )
snake_case__ :List[str] = Node(8 )
snake_case__ :Tuple = Node(9 )
print(is_full_binary_tree(__snake_case ) )
print(depth_of_tree(__snake_case ) )
print("Tree is: " )
display(__snake_case )
if __name__ == "__main__":
main()
| 57
| 1
|
def lowercase_ ( __snake_case : int ) -> list[int]:
'''simple docstring'''
if num <= 0:
raise ValueError("Input must be a positive integer" )
snake_case__ :int = [True] * (num + 1)
snake_case__ :Dict = 2
while p * p <= num:
if primes[p]:
for i in range(p * p , num + 1 , __snake_case ):
snake_case__ :Optional[int] = False
p += 1
return [prime for prime in range(2 , num + 1 ) if primes[prime]]
if __name__ == "__main__":
import doctest
doctest.testmod()
__UpperCAmelCase : Optional[int] = int(input("Enter a positive integer: ").strip())
print(prime_sieve_eratosthenes(user_num))
| 57
|
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__UpperCAmelCase : List[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__UpperCAmelCase : int = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F'''{len(upper_files)} files contain uppercase characters:''')
print("\n".join(upper_files) + "\n")
__UpperCAmelCase : Any = [file for file in filepaths if " " in file]
if space_files:
print(F'''{len(space_files)} files contain space characters:''')
print("\n".join(space_files) + "\n")
__UpperCAmelCase : str = [file for file in filepaths if "-" in file]
if hyphen_files:
print(F'''{len(hyphen_files)} files contain hyphen characters:''')
print("\n".join(hyphen_files) + "\n")
__UpperCAmelCase : Dict = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F'''{len(nodir_files)} files are not in a directory:''')
print("\n".join(nodir_files) + "\n")
__UpperCAmelCase : int = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 57
| 1
|
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
__UpperCAmelCase : Union[str, Any] = [
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of the"
" final seconds on board Flight 9525. The Germanwings co-pilot says he had a \"previous episode of severe"
" depression\" German airline confirms it knew of Andreas Lubitz's depression years before he took control.",
"The Palestinian Authority officially becomes the 123rd member of the International Criminal Court. The formal"
" accession was marked with a ceremony at The Hague, in the Netherlands. The Palestinians signed the ICC's"
" founding Rome Statute in January. Israel and the United States opposed the Palestinians' efforts to join the"
" body.",
"Amnesty International releases its annual report on the death penalty. The report catalogs the use of"
" state-sanctioned killing as a punitive measure across the globe. At least 607 people were executed around the"
" world in 2014, compared to 778 in 2013. The U.S. remains one of the worst offenders for imposing capital"
" punishment.",
]
__UpperCAmelCase : Dict = [
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports ."
" Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz"
" had informed his Lufthansa training school of an episode of severe depression, airline says .",
"Membership gives the ICC jurisdiction over alleged crimes committed in Palestinian territories since last June ."
" Israel and the United States opposed the move, which could open the door to war crimes investigations against"
" Israelis .",
"Amnesty's annual death penalty report catalogs encouraging signs, but setbacks in numbers of those sentenced to"
" death . Organization claims that governments around the world are using the threat of terrorism to advance"
" executions . The number of executions worldwide has gone down by almost 22% compared with 2013, but death"
" sentences up by 28% .",
]
def lowercase_ ( ) -> str:
'''simple docstring'''
snake_case__ :List[str] = calculate_rouge(__snake_case , __snake_case , bootstrap_aggregation=__snake_case , rouge_keys=["rouge2", "rougeL"] )
assert isinstance(__snake_case , __snake_case )
snake_case__ :Optional[int] = calculate_rouge(__snake_case , __snake_case , bootstrap_aggregation=__snake_case , rouge_keys=["rouge2"] )
assert (
pd.DataFrame(no_aggregation["rouge2"] ).fmeasure.mean()
== pd.DataFrame(no_aggregation_just_ra["rouge2"] ).fmeasure.mean()
)
def lowercase_ ( ) -> Any:
'''simple docstring'''
snake_case__ :List[Any] = "rougeLsum"
snake_case__ :Optional[Any] = calculate_rouge(__snake_case , __snake_case , newline_sep=__snake_case , rouge_keys=[k] )[k]
snake_case__ :Optional[Any] = calculate_rouge(__snake_case , __snake_case , newline_sep=__snake_case , rouge_keys=[k] )[k]
assert score > score_no_sep
def lowercase_ ( ) -> str:
'''simple docstring'''
snake_case__ :str = ["rouge1", "rouge2", "rougeL"]
snake_case__ :Optional[Any] = calculate_rouge(__snake_case , __snake_case , newline_sep=__snake_case , rouge_keys=__snake_case )
snake_case__ :Union[str, Any] = calculate_rouge(__snake_case , __snake_case , newline_sep=__snake_case , rouge_keys=__snake_case )
assert score_sep == score_no_sep
def lowercase_ ( ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :List[str] = [
"Her older sister, Margot Frank, died in 1945, a month earlier than previously thought.",
"Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports .",
]
snake_case__ :Optional[Any] = [
"Margot Frank, died in 1945, a month earlier than previously thought.",
"Prosecutor: \"No videos were used in the crash investigation\" German papers say they saw a cell phone video of"
" the final seconds on board Flight 9525.",
]
assert calculate_rouge(__snake_case , __snake_case , newline_sep=__snake_case ) == calculate_rouge(__snake_case , __snake_case , newline_sep=__snake_case )
def lowercase_ ( ) -> List[Any]:
'''simple docstring'''
snake_case__ :Optional[Any] = [
"\" \"a person who has such a video needs to immediately give it to the investigators,\" prosecutor says .<n> \"it is a very disturbing scene,\" editor-in-chief of bild online tells \"erin burnett: outfront\" "
]
snake_case__ :Optional[int] = [
" Marseille prosecutor says \"so far no videos were used in the crash investigation\" despite media reports . Journalists at Bild and Paris Match are \"very confident\" the video clip is real, an editor says . Andreas Lubitz had informed his Lufthansa training school of an episode of severe depression, airline says ."
]
snake_case__ :Any = calculate_rouge(__snake_case , __snake_case , rouge_keys=["rougeLsum"] , newline_sep=__snake_case )["rougeLsum"]
snake_case__ :Union[str, Any] = calculate_rouge(__snake_case , __snake_case , rouge_keys=["rougeLsum"] )["rougeLsum"]
assert new_score > prev_score
def lowercase_ ( ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :int = Path("examples/seq2seq/test_data/wmt_en_ro" )
snake_case__ :Any = calculate_rouge_path(data_dir.joinpath("test.source" ) , data_dir.joinpath("test.target" ) )
assert isinstance(__snake_case , __snake_case )
snake_case__ :List[str] = calculate_rouge_path(
data_dir.joinpath("test.source" ) , data_dir.joinpath("test.target" ) , bootstrap_aggregation=__snake_case )
assert isinstance(__snake_case , __snake_case )
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|
def lowercase_ ( __snake_case : Tuple , __snake_case : Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case__ :Dict = ""
for i in table:
res += inp[i - 1]
return res
def lowercase_ ( __snake_case : List[str] ) -> int:
'''simple docstring'''
return data[1:] + data[0]
def lowercase_ ( __snake_case : int , __snake_case : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :Union[str, Any] = ""
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def lowercase_ ( __snake_case : Optional[int] , __snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :int = int("0b" + data[0] + data[-1] , 2 )
snake_case__ :Union[str, Any] = int("0b" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def lowercase_ ( __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : List[Any] , __snake_case : Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case__ :Tuple = message[:4]
snake_case__ :int = message[4:]
snake_case__ :int = apply_table(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = xor(__snake_case , __snake_case )
snake_case__ :Tuple = apply_sbox(__snake_case , temp[:4] ) # noqa: E741
snake_case__ :List[str] = apply_sbox(__snake_case , temp[4:] )
snake_case__ :int = "0" * (2 - len(__snake_case )) + l # noqa: E741
snake_case__ :int = "0" * (2 - len(__snake_case )) + r
snake_case__ :Optional[Any] = apply_table(l + r , __snake_case )
snake_case__ :Tuple = xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
__UpperCAmelCase : Dict = input("Enter 10 bit key: ")
__UpperCAmelCase : Tuple = input("Enter 8 bit message: ")
__UpperCAmelCase : Any = [6, 3, 7, 4, 8, 5, 1_0, 9]
__UpperCAmelCase : List[str] = [3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
__UpperCAmelCase : Tuple = [2, 4, 3, 1]
__UpperCAmelCase : List[Any] = [2, 6, 3, 1, 4, 8, 5, 7]
__UpperCAmelCase : Optional[Any] = [4, 1, 3, 5, 7, 2, 8, 6]
__UpperCAmelCase : Optional[int] = [4, 1, 2, 3, 2, 3, 4, 1]
__UpperCAmelCase : List[Any] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
__UpperCAmelCase : Union[str, Any] = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
__UpperCAmelCase : int = apply_table(key, paa_table)
__UpperCAmelCase : Dict = temp[:5]
__UpperCAmelCase : Optional[int] = temp[5:]
__UpperCAmelCase : Optional[int] = left_shift(left)
__UpperCAmelCase : Union[str, Any] = left_shift(right)
__UpperCAmelCase : int = apply_table(left + right, pa_table)
__UpperCAmelCase : Tuple = left_shift(left)
__UpperCAmelCase : Union[str, Any] = left_shift(right)
__UpperCAmelCase : Dict = left_shift(left)
__UpperCAmelCase : Optional[Any] = left_shift(right)
__UpperCAmelCase : Optional[int] = apply_table(left + right, pa_table)
# encryption
__UpperCAmelCase : Tuple = apply_table(message, IP)
__UpperCAmelCase : Tuple = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : List[Any] = temp[4:] + temp[:4]
__UpperCAmelCase : int = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : Union[str, Any] = apply_table(temp, IP_inv)
print("Cipher text is:", CT)
# decryption
__UpperCAmelCase : List[Any] = apply_table(CT, IP)
__UpperCAmelCase : List[Any] = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : int = temp[4:] + temp[:4]
__UpperCAmelCase : Union[str, Any] = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : Union[str, Any] = apply_table(temp, IP_inv)
print("Plain text after decypting is:", PT)
| 57
| 1
|
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
RobertaSeriesConfig,
RobertaSeriesModelWithTransformation,
)
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
enable_full_determinism()
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
@property
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :Optional[Any] = 1
snake_case__ :Dict = 3
snake_case__ :int = (32, 32)
snake_case__ :Optional[int] = floats_tensor((batch_size, num_channels) + sizes ,rng=random.Random(0 ) ).to(UpperCamelCase )
return image
@property
def lowerCAmelCase_ ( self ) -> Any:
torch.manual_seed(0 )
snake_case__ :Union[str, Any] = UNetaDConditionModel(
block_out_channels=(32, 64) ,layers_per_block=2 ,sample_size=32 ,in_channels=4 ,out_channels=4 ,down_block_types=("DownBlock2D", "CrossAttnDownBlock2D") ,up_block_types=("CrossAttnUpBlock2D", "UpBlock2D") ,cross_attention_dim=32 ,)
return model
@property
def lowerCAmelCase_ ( self ) -> Optional[Any]:
torch.manual_seed(0 )
snake_case__ :List[str] = AutoencoderKL(
block_out_channels=[32, 64] ,in_channels=3 ,out_channels=3 ,down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"] ,up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"] ,latent_channels=4 ,)
return model
@property
def lowerCAmelCase_ ( self ) -> Optional[Any]:
torch.manual_seed(0 )
snake_case__ :Optional[Any] = RobertaSeriesConfig(
hidden_size=32 ,project_dim=32 ,intermediate_size=37 ,layer_norm_eps=1E-05 ,num_attention_heads=4 ,num_hidden_layers=5 ,pad_token_id=1 ,vocab_size=5_006 ,)
return RobertaSeriesModelWithTransformation(UpperCamelCase )
@property
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
def extract(*UpperCamelCase ,**UpperCamelCase ):
class _snake_case :
def __init__( self ) -> int:
snake_case__ :Dict = torch.ones([0] )
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> str:
self.pixel_values.to(UpperCamelCase )
return self
return Out()
return extract
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Dict = "cpu" # ensure determinism for the device-dependent torch.Generator
snake_case__ :Optional[Any] = self.dummy_cond_unet
snake_case__ :str = PNDMScheduler(skip_prk_steps=UpperCamelCase )
snake_case__ :Optional[int] = self.dummy_vae
snake_case__ :Dict = self.dummy_text_encoder
snake_case__ :Optional[Any] = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
snake_case__ :Optional[Any] = 77
snake_case__ :Dict = self.dummy_image.to(UpperCamelCase )
snake_case__ :List[str] = init_image / 2 + 0.5
# make sure here that pndm scheduler skips prk
snake_case__ :List[str] = AltDiffusionImgaImgPipeline(
unet=UpperCamelCase ,scheduler=UpperCamelCase ,vae=UpperCamelCase ,text_encoder=UpperCamelCase ,tokenizer=UpperCamelCase ,safety_checker=UpperCamelCase ,feature_extractor=self.dummy_extractor ,)
snake_case__ :Tuple = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,do_normalize=UpperCamelCase )
snake_case__ :Dict = alt_pipe.to(UpperCamelCase )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase )
snake_case__ :Union[str, Any] = "A painting of a squirrel eating a burger"
snake_case__ :int = torch.Generator(device=UpperCamelCase ).manual_seed(0 )
snake_case__ :int = alt_pipe(
[prompt] ,generator=UpperCamelCase ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="np" ,image=UpperCamelCase ,)
snake_case__ :str = output.images
snake_case__ :Optional[int] = torch.Generator(device=UpperCamelCase ).manual_seed(0 )
snake_case__ :List[str] = alt_pipe(
[prompt] ,generator=UpperCamelCase ,guidance_scale=6.0 ,num_inference_steps=2 ,output_type="np" ,image=UpperCamelCase ,return_dict=UpperCamelCase ,)[0]
snake_case__ :Any = image[0, -3:, -3:, -1]
snake_case__ :Any = image_from_tuple[0, -3:, -3:, -1]
assert image.shape == (1, 32, 32, 3)
snake_case__ :str = np.array([0.4427, 0.3731, 0.4249, 0.4941, 0.4546, 0.4148, 0.4193, 0.4666, 0.4499] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 5E-3
assert np.abs(image_from_tuple_slice.flatten() - expected_slice ).max() < 5E-3
@unittest.skipIf(torch_device != "cuda" ,"This test requires a GPU" )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Optional[int] = self.dummy_cond_unet
snake_case__ :Optional[int] = PNDMScheduler(skip_prk_steps=UpperCamelCase )
snake_case__ :Optional[int] = self.dummy_vae
snake_case__ :Tuple = self.dummy_text_encoder
snake_case__ :Tuple = XLMRobertaTokenizer.from_pretrained("hf-internal-testing/tiny-xlm-roberta" )
snake_case__ :Union[str, Any] = 77
snake_case__ :Dict = self.dummy_image.to(UpperCamelCase )
# put models in fp16
snake_case__ :Optional[Any] = unet.half()
snake_case__ :Dict = vae.half()
snake_case__ :Optional[Any] = bert.half()
# make sure here that pndm scheduler skips prk
snake_case__ :Union[str, Any] = AltDiffusionImgaImgPipeline(
unet=UpperCamelCase ,scheduler=UpperCamelCase ,vae=UpperCamelCase ,text_encoder=UpperCamelCase ,tokenizer=UpperCamelCase ,safety_checker=UpperCamelCase ,feature_extractor=self.dummy_extractor ,)
snake_case__ :Optional[Any] = VaeImageProcessor(vae_scale_factor=alt_pipe.vae_scale_factor ,do_normalize=UpperCamelCase )
snake_case__ :Dict = alt_pipe.to(UpperCamelCase )
alt_pipe.set_progress_bar_config(disable=UpperCamelCase )
snake_case__ :Optional[Any] = "A painting of a squirrel eating a burger"
snake_case__ :int = torch.manual_seed(0 )
snake_case__ :Tuple = alt_pipe(
[prompt] ,generator=UpperCamelCase ,num_inference_steps=2 ,output_type="np" ,image=UpperCamelCase ,).images
assert image.shape == (1, 32, 32, 3)
@unittest.skipIf(torch_device != "cuda" ,"This test requires a GPU" )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :List[Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
# resize to resolution that is divisible by 8 but not 16 or 32
snake_case__ :List[str] = init_image.resize((760, 504) )
snake_case__ :Any = "BAAI/AltDiffusion"
snake_case__ :Union[str, Any] = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCamelCase ,safety_checker=UpperCamelCase ,)
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing()
snake_case__ :List[Any] = "A fantasy landscape, trending on artstation"
snake_case__ :str = torch.manual_seed(0 )
snake_case__ :List[str] = pipe(
prompt=UpperCamelCase ,image=UpperCamelCase ,strength=0.75 ,guidance_scale=7.5 ,generator=UpperCamelCase ,output_type="np" ,)
snake_case__ :Optional[int] = output.images[0]
snake_case__ :str = image[255:258, 383:386, -1]
assert image.shape == (504, 760, 3)
snake_case__ :str = np.array([0.9358, 0.9397, 0.9599, 0.9901, 1.0000, 1.0000, 0.9882, 1.0000, 1.0000] )
assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2
@slow
@require_torch_gpu
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Tuple:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :Tuple = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
"/img2img/sketch-mountains-input.jpg" )
snake_case__ :Dict = init_image.resize((768, 512) )
snake_case__ :Tuple = load_numpy(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/img2img/fantasy_landscape_alt.npy" )
snake_case__ :Optional[int] = "BAAI/AltDiffusion"
snake_case__ :List[Any] = AltDiffusionImgaImgPipeline.from_pretrained(
UpperCamelCase ,safety_checker=UpperCamelCase ,)
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
pipe.enable_attention_slicing()
snake_case__ :Any = "A fantasy landscape, trending on artstation"
snake_case__ :List[Any] = torch.manual_seed(0 )
snake_case__ :str = pipe(
prompt=UpperCamelCase ,image=UpperCamelCase ,strength=0.75 ,guidance_scale=7.5 ,generator=UpperCamelCase ,output_type="np" ,)
snake_case__ :Optional[Any] = output.images[0]
assert image.shape == (512, 768, 3)
# img2img is flaky across GPUs even in fp32, so using MAE here
assert np.abs(expected_image - image ).max() < 1E-2
| 57
|
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _snake_case ( _A , _A , _A ):
@register_to_config
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = False ,) -> int:
super().__init__()
snake_case__ :Union[str, Any] = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :int = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :Any = False
snake_case__ :List[Any] = nn.Dropout(p=UpperCamelCase )
snake_case__ :Tuple = TaConfig(
vocab_size=UpperCamelCase ,d_model=UpperCamelCase ,num_heads=UpperCamelCase ,d_kv=UpperCamelCase ,d_ff=UpperCamelCase ,dropout_rate=UpperCamelCase ,feed_forward_proj=UpperCamelCase ,is_decoder=UpperCamelCase ,is_encoder_decoder=UpperCamelCase ,)
snake_case__ :List[str] = nn.ModuleList()
for lyr_num in range(UpperCamelCase ):
snake_case__ :List[Any] = TaBlock(UpperCamelCase )
self.encoders.append(UpperCamelCase )
snake_case__ :Optional[Any] = TaLayerNorm(UpperCamelCase )
snake_case__ :Any = nn.Dropout(p=UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> int:
snake_case__ :str = self.token_embedder(UpperCamelCase )
snake_case__ :int = encoder_input_tokens.shape[1]
snake_case__ :List[Any] = torch.arange(UpperCamelCase ,device=encoder_input_tokens.device )
x += self.position_encoding(UpperCamelCase )
snake_case__ :Optional[int] = self.dropout_pre(UpperCamelCase )
# inverted the attention mask
snake_case__ :Optional[Any] = encoder_input_tokens.size()
snake_case__ :Dict = self.get_extended_attention_mask(UpperCamelCase ,UpperCamelCase )
for lyr in self.encoders:
snake_case__ :str = lyr(UpperCamelCase ,UpperCamelCase )[0]
snake_case__ :List[Any] = self.layer_norm(UpperCamelCase )
return self.dropout_post(UpperCamelCase ), encoder_inputs_mask
| 57
| 1
|
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( __snake_case : int , __snake_case : str , __snake_case : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
snake_case__ :Tuple = RemBertConfig.from_json_file(__snake_case )
print("Building PyTorch model from configuration: {}".format(str(__snake_case ) ) )
snake_case__ :Optional[int] = RemBertModel(__snake_case )
# Load weights from tf checkpoint
load_tf_weights_in_rembert(__snake_case , __snake_case , __snake_case )
# Save pytorch-model
print("Save PyTorch model to {}".format(__snake_case ) )
torch.save(model.state_dict() , __snake_case )
if __name__ == "__main__":
__UpperCAmelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
"--tf_checkpoint_path", default=None, type=str, required=True, help="Path to the TensorFlow checkpoint path."
)
parser.add_argument(
"--rembert_config_file",
default=None,
type=str,
required=True,
help=(
"The config json file corresponding to the pre-trained RemBERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
__UpperCAmelCase : Dict = parser.parse_args()
convert_rembert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.rembert_config_file, args.pytorch_dump_path)
| 57
|
__UpperCAmelCase : int = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__UpperCAmelCase : List[str] = ["a", "b", "c", "d", "e"]
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : List[Any] , __snake_case : Tuple ) -> Optional[int]:
'''simple docstring'''
snake_case__ :List[Any] = start
# add current to visited
visited.append(__snake_case )
snake_case__ :List[str] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# if all neighbors visited add current to sort
sort.append(__snake_case )
# if all vertices haven't been visited select a new one to visit
if len(__snake_case ) != len(__snake_case ):
for vertice in vertices:
if vertice not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# return sort
return sort
if __name__ == "__main__":
__UpperCAmelCase : Tuple = topological_sort("a", [], [])
print(sort)
| 57
| 1
|
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 57
|
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 _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCAmelCase_ ( self ) -> str:
snake_case__ , snake_case__ :Tuple = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-canny" ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ , snake_case__ :Any = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCamelCase ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ :List[str] = controlnet_params
snake_case__ :Union[str, Any] = "bird"
snake_case__ :Optional[int] = jax.device_count()
snake_case__ :Tuple = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case__ :Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" )
snake_case__ :str = pipe.prepare_image_inputs([canny_image] * num_samples )
snake_case__ :List[str] = jax.random.PRNGKey(0 )
snake_case__ :str = jax.random.split(UpperCamelCase ,jax.device_count() )
snake_case__ :int = replicate(UpperCamelCase )
snake_case__ :Any = shard(UpperCamelCase )
snake_case__ :Any = shard(UpperCamelCase )
snake_case__ :str = pipe(
prompt_ids=UpperCamelCase ,image=UpperCamelCase ,params=UpperCamelCase ,prng_seed=UpperCamelCase ,num_inference_steps=50 ,jit=UpperCamelCase ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
snake_case__ :List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case__ :Any = images[0, 253:256, 253:256, -1]
snake_case__ :Union[str, Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case__ :List[Any] = jnp.array(
[0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ , snake_case__ :List[str] = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-openpose" ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ , snake_case__ :Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCamelCase ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ :str = controlnet_params
snake_case__ :int = "Chef in the kitchen"
snake_case__ :List[Any] = jax.device_count()
snake_case__ :Dict = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case__ :Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" )
snake_case__ :Optional[int] = pipe.prepare_image_inputs([pose_image] * num_samples )
snake_case__ :List[str] = jax.random.PRNGKey(0 )
snake_case__ :Any = jax.random.split(UpperCamelCase ,jax.device_count() )
snake_case__ :Dict = replicate(UpperCamelCase )
snake_case__ :Tuple = shard(UpperCamelCase )
snake_case__ :Optional[int] = shard(UpperCamelCase )
snake_case__ :Optional[Any] = pipe(
prompt_ids=UpperCamelCase ,image=UpperCamelCase ,params=UpperCamelCase ,prng_seed=UpperCamelCase ,num_inference_steps=50 ,jit=UpperCamelCase ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
snake_case__ :int = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case__ :List[str] = images[0, 253:256, 253:256, -1]
snake_case__ :Tuple = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case__ :List[str] = jnp.array(
[[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 57
| 1
|
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
__UpperCAmelCase : int = get_tests_dir("fixtures/test_sentencepiece_no_bos.model")
@require_sentencepiece
@require_tokenizers
class _snake_case ( _A , unittest.TestCase ):
_A = PegasusTokenizer
_A = PegasusTokenizerFast
_A = True
_A = True
def lowerCAmelCase_ ( self ) -> Tuple:
super().setUp()
# We have a SentencePiece fixture for testing
snake_case__ :List[Any] = PegasusTokenizer(UpperCamelCase )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowerCAmelCase_ ( self ) -> int:
return PegasusTokenizer.from_pretrained("google/pegasus-large" )
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> PegasusTokenizer:
return PegasusTokenizer.from_pretrained(self.tmpdirname ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Optional[Any]:
return ("This is a test", "This is a test")
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :int = "</s>"
snake_case__ :str = 1
self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase ) ,UpperCamelCase )
self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase ) ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[str]:
snake_case__ :Tuple = list(self.get_tokenizer().get_vocab().keys() )
self.assertEqual(vocab_keys[0] ,"<pad>" )
self.assertEqual(vocab_keys[1] ,"</s>" )
self.assertEqual(vocab_keys[-1] ,"v" )
self.assertEqual(len(UpperCamelCase ) ,1_103 )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
self.assertEqual(self.get_tokenizer().vocab_size ,1_103 )
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :List[Any] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
snake_case__ :Dict = self.tokenizer_class.from_pretrained(self.tmpdirname )
snake_case__ :int = (
"Let's see which <unk> is the better <unk_token_11> one <mask_1> It seems like this <mask_2> was important"
" </s> <pad> <pad> <pad>"
)
snake_case__ :List[str] = rust_tokenizer([raw_input_str] ,return_tensors=UpperCamelCase ,add_special_tokens=UpperCamelCase ).input_ids[0]
snake_case__ :Tuple = py_tokenizer([raw_input_str] ,return_tensors=UpperCamelCase ,add_special_tokens=UpperCamelCase ).input_ids[0]
self.assertListEqual(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :Optional[int] = self._large_tokenizer
# <mask_1> masks whole sentence while <mask_2> masks single word
snake_case__ :Optional[int] = "<mask_1> To ensure a <mask_2> flow of bank resolutions."
snake_case__ :Optional[Any] = [2, 413, 615, 114, 3, 1_971, 113, 1_679, 10_710, 107, 1]
snake_case__ :Optional[Any] = tokenizer([raw_input_str] ,return_tensors=UpperCamelCase ).input_ids[0]
self.assertListEqual(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Optional[int] = self._large_tokenizer
# The tracebacks for the following asserts are **better** without messages or self.assertEqual
assert tokenizer.vocab_size == 96_103
assert tokenizer.pad_token_id == 0
assert tokenizer.eos_token_id == 1
assert tokenizer.offset == 103
assert tokenizer.unk_token_id == tokenizer.offset + 2 == 105
assert tokenizer.unk_token == "<unk>"
assert tokenizer.model_max_length == 1_024
snake_case__ :List[Any] = "To ensure a smooth flow of bank resolutions."
snake_case__ :int = [413, 615, 114, 2_291, 1_971, 113, 1_679, 10_710, 107, 1]
snake_case__ :Optional[Any] = tokenizer([raw_input_str] ,return_tensors=UpperCamelCase ).input_ids[0]
self.assertListEqual(UpperCamelCase ,UpperCamelCase )
assert tokenizer.convert_ids_to_tokens([0, 1, 2, 3] ) == ["<pad>", "</s>", "<mask_1>", "<mask_2>"]
@require_torch
def lowerCAmelCase_ ( self ) -> List[str]:
snake_case__ :List[str] = ["This is going to be way too long." * 150, "short example"]
snake_case__ :Dict = ["not super long but more than 5 tokens", "tiny"]
snake_case__ :Tuple = self._large_tokenizer(UpperCamelCase ,padding=UpperCamelCase ,truncation=UpperCamelCase ,return_tensors="pt" )
snake_case__ :Any = self._large_tokenizer(
text_target=UpperCamelCase ,max_length=5 ,padding=UpperCamelCase ,truncation=UpperCamelCase ,return_tensors="pt" )
assert batch.input_ids.shape == (2, 1_024)
assert batch.attention_mask.shape == (2, 1_024)
assert targets["input_ids"].shape == (2, 5)
assert len(UpperCamelCase ) == 2 # input_ids, attention_mask.
@slow
def lowerCAmelCase_ ( self ) -> str:
# fmt: off
snake_case__ :Union[str, Any] = {"input_ids": [[38_979, 143, 18_485, 606, 130, 26_669, 87_686, 121, 54_189, 1_129, 111, 26_669, 87_686, 121, 9_114, 14_787, 121, 13_249, 158, 592, 956, 121, 14_621, 31_576, 143, 62_613, 108, 9_688, 930, 43_430, 11_562, 62_613, 304, 108, 11_443, 897, 108, 9_314, 17_415, 63_399, 108, 11_443, 7_614, 18_316, 118, 4_284, 7_148, 12_430, 143, 1_400, 25_703, 158, 111, 4_284, 7_148, 11_772, 143, 21_297, 1_064, 158, 122, 204, 3_506, 1_754, 1_133, 14_787, 1_581, 115, 33_224, 4_482, 111, 1_355, 110, 29_173, 317, 50_833, 108, 20_147, 94_665, 111, 77_198, 107, 1], [110, 62_613, 117, 638, 112, 1_133, 121, 20_098, 1_355, 79_050, 13_872, 135, 1_596, 53_541, 1_352, 141, 13_039, 5_542, 124, 302, 518, 111, 268, 2_956, 115, 149, 4_427, 107, 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], [139, 1_235, 2_799, 18_289, 17_780, 204, 109, 9_474, 1_296, 107, 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]], "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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]} # noqa: E501
# fmt: on
self.tokenizer_integration_test_util(
expected_encoding=UpperCamelCase ,model_name="google/bigbird-pegasus-large-arxiv" ,revision="ba85d0851d708441f91440d509690f1ab6353415" ,)
@require_sentencepiece
@require_tokenizers
class _snake_case ( _A , unittest.TestCase ):
_A = PegasusTokenizer
_A = PegasusTokenizerFast
_A = True
_A = True
def lowerCAmelCase_ ( self ) -> List[Any]:
super().setUp()
# We have a SentencePiece fixture for testing
snake_case__ :Any = PegasusTokenizer(UpperCamelCase ,offset=0 ,mask_token_sent=UpperCamelCase ,mask_token="[MASK]" )
tokenizer.save_pretrained(self.tmpdirname )
@cached_property
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
return PegasusTokenizer.from_pretrained("google/bigbird-pegasus-large-arxiv" )
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> PegasusTokenizer:
return PegasusTokenizer.from_pretrained(self.tmpdirname ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> str:
return ("This is a test", "This is a test")
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Optional[Any] = self.rust_tokenizer_class.from_pretrained(self.tmpdirname )
snake_case__ :int = self.tokenizer_class.from_pretrained(self.tmpdirname )
snake_case__ :Optional[int] = (
"Let's see which <unk> is the better <unk_token> one [MASK] It seems like this [MASK] was important </s>"
" <pad> <pad> <pad>"
)
snake_case__ :str = rust_tokenizer([raw_input_str] ,return_tensors=UpperCamelCase ,add_special_tokens=UpperCamelCase ).input_ids[0]
snake_case__ :int = py_tokenizer([raw_input_str] ,return_tensors=UpperCamelCase ,add_special_tokens=UpperCamelCase ).input_ids[0]
self.assertListEqual(UpperCamelCase ,UpperCamelCase )
@require_torch
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :List[str] = ["This is going to be way too long." * 1_000, "short example"]
snake_case__ :List[str] = ["not super long but more than 5 tokens", "tiny"]
snake_case__ :Dict = self._large_tokenizer(UpperCamelCase ,padding=UpperCamelCase ,truncation=UpperCamelCase ,return_tensors="pt" )
snake_case__ :Dict = self._large_tokenizer(
text_target=UpperCamelCase ,max_length=5 ,padding=UpperCamelCase ,truncation=UpperCamelCase ,return_tensors="pt" )
assert batch.input_ids.shape == (2, 4_096)
assert batch.attention_mask.shape == (2, 4_096)
assert targets["input_ids"].shape == (2, 5)
assert len(UpperCamelCase ) == 2 # input_ids, attention_mask.
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :Optional[int] = (
"This is an example string that is used to test the original TF implementation against the HF"
" implementation"
)
snake_case__ :Union[str, Any] = self._large_tokenizer(UpperCamelCase ).input_ids
self.assertListEqual(
UpperCamelCase ,[182, 117, 142, 587, 4_211, 120, 117, 263, 112, 804, 109, 856, 25_016, 3_137, 464, 109, 26_955, 3_137, 1] ,)
| 57
|
def lowercase_ ( __snake_case : list ) -> list:
'''simple docstring'''
if any(not isinstance(__snake_case , __snake_case ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(__snake_case ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 57
| 1
|
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME, VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
# We do this to be able to load python 2 datasets pickles
# See e.g. https://stackoverflow.com/questions/2121874/python-pickling-after-changing-a-modules-directory/2121918#2121918
__UpperCAmelCase : Tuple = data_utils.TransfoXLTokenizer
__UpperCAmelCase : Union[str, Any] = data_utils.TransfoXLCorpus
__UpperCAmelCase : Optional[Any] = data_utils
__UpperCAmelCase : Optional[Any] = data_utils
def lowercase_ ( __snake_case : List[str] , __snake_case : Any , __snake_case : Optional[Any] , __snake_case : List[Any] ) -> Union[str, Any]:
'''simple docstring'''
if transfo_xl_dataset_file:
# Convert a pre-processed corpus (see original TensorFlow repo)
with open(__snake_case , "rb" ) as fp:
snake_case__ :Optional[int] = pickle.load(__snake_case , encoding="latin1" )
# Save vocabulary and dataset cache as Dictionaries (should be better than pickles for the long-term)
snake_case__ :Tuple = pytorch_dump_folder_path + "/" + VOCAB_FILES_NAMES["pretrained_vocab_file"]
print(F'Save vocabulary to {pytorch_vocab_dump_path}' )
snake_case__ :Union[str, Any] = corpus.vocab.__dict__
torch.save(__snake_case , __snake_case )
snake_case__ :str = corpus.__dict__
corpus_dict_no_vocab.pop("vocab" , __snake_case )
snake_case__ :str = pytorch_dump_folder_path + "/" + CORPUS_NAME
print(F'Save dataset to {pytorch_dataset_dump_path}' )
torch.save(__snake_case , __snake_case )
if tf_checkpoint_path:
# Convert a pre-trained TensorFlow model
snake_case__ :Tuple = os.path.abspath(__snake_case )
snake_case__ :int = os.path.abspath(__snake_case )
print(F'Converting Transformer XL checkpoint from {tf_path} with config at {config_path}.' )
# Initialise PyTorch model
if transfo_xl_config_file == "":
snake_case__ :Dict = TransfoXLConfig()
else:
snake_case__ :List[Any] = TransfoXLConfig.from_json_file(__snake_case )
print(F'Building PyTorch model from configuration: {config}' )
snake_case__ :str = TransfoXLLMHeadModel(__snake_case )
snake_case__ :Any = load_tf_weights_in_transfo_xl(__snake_case , __snake_case , __snake_case )
# Save pytorch-model
snake_case__ :Dict = os.path.join(__snake_case , __snake_case )
snake_case__ :int = os.path.join(__snake_case , __snake_case )
print(F'Save PyTorch model to {os.path.abspath(__snake_case )}' )
torch.save(model.state_dict() , __snake_case )
print(F'Save configuration file to {os.path.abspath(__snake_case )}' )
with open(__snake_case , "w" , encoding="utf-8" ) as f:
f.write(config.to_json_string() )
if __name__ == "__main__":
__UpperCAmelCase : Dict = argparse.ArgumentParser()
parser.add_argument(
"--pytorch_dump_folder_path",
default=None,
type=str,
required=True,
help="Path to the folder to store the PyTorch model or dataset/vocab.",
)
parser.add_argument(
"--tf_checkpoint_path",
default="",
type=str,
help="An optional path to a TensorFlow checkpoint path to be converted.",
)
parser.add_argument(
"--transfo_xl_config_file",
default="",
type=str,
help=(
"An optional config json file corresponding to the pre-trained BERT model. \n"
"This specifies the model architecture."
),
)
parser.add_argument(
"--transfo_xl_dataset_file",
default="",
type=str,
help="An optional dataset file to be converted in a vocabulary.",
)
__UpperCAmelCase : Tuple = parser.parse_args()
convert_transfo_xl_checkpoint_to_pytorch(
args.tf_checkpoint_path,
args.transfo_xl_config_file,
args.pytorch_dump_folder_path,
args.transfo_xl_dataset_file,
)
| 57
|
from __future__ import annotations
def lowercase_ ( __snake_case : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("List is empty" )
return sum(__snake_case ) / len(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
| 1
|
import argparse
import os
import re
import packaging.version
__UpperCAmelCase : Dict = "examples/"
__UpperCAmelCase : int = {
"examples": (re.compile(R"^check_min_version\(\"[^\"]+\"\)\s*$", re.MULTILINE), "check_min_version(\"VERSION\")\n"),
"init": (re.compile(R"^__version__\s+=\s+\"([^\"]+)\"\s*$", re.MULTILINE), "__version__ = \"VERSION\"\n"),
"setup": (re.compile(R"^(\s*)version\s*=\s*\"[^\"]+\",", re.MULTILINE), R"\1version=\"VERSION\","),
"doc": (re.compile(R"^(\s*)release\s*=\s*\"[^\"]+\"$", re.MULTILINE), "release = \"VERSION\"\n"),
}
__UpperCAmelCase : Any = {
"init": "src/diffusers/__init__.py",
"setup": "setup.py",
}
__UpperCAmelCase : Dict = "README.md"
def lowercase_ ( __snake_case : Any , __snake_case : Union[str, Any] , __snake_case : Tuple ) -> Any:
'''simple docstring'''
with open(__snake_case , "r" , encoding="utf-8" , newline="\n" ) as f:
snake_case__ :Optional[int] = f.read()
snake_case__ , snake_case__ :Optional[Any] = REPLACE_PATTERNS[pattern]
snake_case__ :Dict = replace.replace("VERSION" , __snake_case )
snake_case__ :Union[str, Any] = re_pattern.sub(__snake_case , __snake_case )
with open(__snake_case , "w" , encoding="utf-8" , newline="\n" ) as f:
f.write(__snake_case )
def lowercase_ ( __snake_case : Any ) -> int:
'''simple docstring'''
for folder, directories, fnames in os.walk(__snake_case ):
# Removing some of the folders with non-actively maintained examples from the walk
if "research_projects" in directories:
directories.remove("research_projects" )
if "legacy" in directories:
directories.remove("legacy" )
for fname in fnames:
if fname.endswith(".py" ):
update_version_in_file(os.path.join(__snake_case , __snake_case ) , __snake_case , pattern="examples" )
def lowercase_ ( __snake_case : Optional[int] , __snake_case : str=False ) -> int:
'''simple docstring'''
for pattern, fname in REPLACE_FILES.items():
update_version_in_file(__snake_case , __snake_case , __snake_case )
if not patch:
update_version_in_examples(__snake_case )
def lowercase_ ( ) -> str:
'''simple docstring'''
snake_case__ :int = "🤗 Transformers currently provides the following architectures"
snake_case__ :List[Any] = "1. Want to contribute a new model?"
with open(__snake_case , "r" , encoding="utf-8" , newline="\n" ) as f:
snake_case__ :str = f.readlines()
# Find the start of the list.
snake_case__ :Dict = 0
while not lines[start_index].startswith(_start_prompt ):
start_index += 1
start_index += 1
snake_case__ :List[str] = start_index
# Update the lines in the model list.
while not lines[index].startswith(_end_prompt ):
if lines[index].startswith("1." ):
snake_case__ :Union[str, Any] = lines[index].replace(
"https://huggingface.co/docs/diffusers/main/model_doc" , "https://huggingface.co/docs/diffusers/model_doc" , )
index += 1
with open(__snake_case , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(__snake_case )
def lowercase_ ( ) -> Dict:
'''simple docstring'''
with open(REPLACE_FILES["init"] , "r" ) as f:
snake_case__ :Optional[Any] = f.read()
snake_case__ :int = REPLACE_PATTERNS["init"][0].search(__snake_case ).groups()[0]
return packaging.version.parse(__snake_case )
def lowercase_ ( __snake_case : List[str]=False ) -> Tuple:
'''simple docstring'''
snake_case__ :Dict = get_version()
if patch and default_version.is_devrelease:
raise ValueError("Can't create a patch version from the dev branch, checkout a released version!" )
if default_version.is_devrelease:
snake_case__ :List[str] = default_version.base_version
elif patch:
snake_case__ :Optional[Any] = F'{default_version.major}.{default_version.minor}.{default_version.micro + 1}'
else:
snake_case__ :List[str] = F'{default_version.major}.{default_version.minor + 1}.0'
# Now let's ask nicely if that's the right one.
snake_case__ :List[str] = input(F'Which version are you releasing? [{default_version}]' )
if len(__snake_case ) == 0:
snake_case__ :Optional[Any] = default_version
print(F'Updating version to {version}.' )
global_version_update(__snake_case , patch=__snake_case )
def lowercase_ ( ) -> str:
'''simple docstring'''
snake_case__ :Dict = get_version()
snake_case__ :Optional[Any] = F'{current_version.major}.{current_version.minor + 1}.0.dev0'
snake_case__ :int = current_version.base_version
# Check with the user we got that right.
snake_case__ :Union[str, Any] = input(F'Which version are we developing now? [{dev_version}]' )
if len(__snake_case ) == 0:
snake_case__ :Tuple = dev_version
print(F'Updating version to {version}.' )
global_version_update(__snake_case )
# print("Cleaning main README, don't forget to run `make fix-copies`.")
# clean_main_ref_in_model_list()
if __name__ == "__main__":
__UpperCAmelCase : str = argparse.ArgumentParser()
parser.add_argument("--post_release", action="store_true", help="Whether this is pre or post release.")
parser.add_argument("--patch", action="store_true", help="Whether or not this is a patch release.")
__UpperCAmelCase : Union[str, Any] = parser.parse_args()
if not args.post_release:
pre_release_work(patch=args.patch)
elif args.patch:
print("Nothing to do after a patch :-)")
else:
post_release_work()
| 57
|
from __future__ import annotations
import math
def lowercase_ ( __snake_case : int , __snake_case : int , __snake_case : bool , __snake_case : list[int] , __snake_case : float ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if len(__snake_case ) == 0:
raise ValueError("Scores cannot be empty" )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
return min(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
def lowercase_ ( ) -> None:
'''simple docstring'''
snake_case__ :List[Any] = [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
snake_case__ :int = math.log(len(__snake_case ) , 2 )
print("Optimal value : " , end="" )
print(minimax(0 , 0 , __snake_case , __snake_case , __snake_case ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 57
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase : int = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
"XCLIPVisionConfig",
],
"processing_x_clip": ["XCLIPProcessor"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : List[str] = [
"XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST",
"XCLIPModel",
"XCLIPPreTrainedModel",
"XCLIPTextModel",
"XCLIPVisionModel",
]
if TYPE_CHECKING:
from .configuration_x_clip import (
XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
XCLIPConfig,
XCLIPTextConfig,
XCLIPVisionConfig,
)
from .processing_x_clip import XCLIPProcessor
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_x_clip import (
XCLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
XCLIPModel,
XCLIPPreTrainedModel,
XCLIPTextModel,
XCLIPVisionModel,
)
else:
import sys
__UpperCAmelCase : Optional[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
def lowercase_ ( __snake_case : Any , __snake_case : Any ) -> Any:
'''simple docstring'''
snake_case__ :Optional[Any] = b.T
snake_case__ :Optional[Any] = np.sum(np.square(__snake_case ) , axis=1 )
snake_case__ :Tuple = np.sum(np.square(__snake_case ) , axis=0 )
snake_case__ :Union[str, Any] = np.matmul(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = aa[:, None] - 2 * ab + ba[None, :]
return d
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : int ) -> Any:
'''simple docstring'''
snake_case__ :Optional[Any] = x.reshape(-1 , 3 )
snake_case__ :List[str] = squared_euclidean_distance(__snake_case , __snake_case )
return np.argmin(__snake_case , axis=1 )
class _snake_case ( _A ):
_A = ['pixel_values']
def __init__( self ,UpperCamelCase = None ,UpperCamelCase = True ,UpperCamelCase = None ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = True ,UpperCamelCase = True ,**UpperCamelCase ,) -> None:
super().__init__(**UpperCamelCase )
snake_case__ :List[Any] = size if size is not None else {"height": 256, "width": 256}
snake_case__ :str = get_size_dict(UpperCamelCase )
snake_case__ :Dict = np.array(UpperCamelCase ) if clusters is not None else None
snake_case__ :str = do_resize
snake_case__ :List[str] = size
snake_case__ :List[Any] = resample
snake_case__ :Union[str, Any] = do_normalize
snake_case__ :int = do_color_quantize
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = None ,**UpperCamelCase ,) -> np.ndarray:
snake_case__ :List[str] = get_size_dict(UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}' )
return resize(
UpperCamelCase ,size=(size["height"], size["width"]) ,resample=UpperCamelCase ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,) -> np.ndarray:
snake_case__ :Tuple = rescale(image=UpperCamelCase ,scale=1 / 127.5 ,data_format=UpperCamelCase )
snake_case__ :List[Any] = image - 1
return image
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = ChannelDimension.FIRST ,**UpperCamelCase ,) -> PIL.Image.Image:
snake_case__ :Optional[int] = do_resize if do_resize is not None else self.do_resize
snake_case__ :int = size if size is not None else self.size
snake_case__ :Tuple = get_size_dict(UpperCamelCase )
snake_case__ :str = resample if resample is not None else self.resample
snake_case__ :Dict = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ :Tuple = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
snake_case__ :List[Any] = clusters if clusters is not None else self.clusters
snake_case__ :str = np.array(UpperCamelCase )
snake_case__ :int = make_list_of_images(UpperCamelCase )
if not valid_images(UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True." )
# All transformations expect numpy arrays.
snake_case__ :Union[str, Any] = [to_numpy_array(UpperCamelCase ) for image in images]
if do_resize:
snake_case__ :int = [self.resize(image=UpperCamelCase ,size=UpperCamelCase ,resample=UpperCamelCase ) for image in images]
if do_normalize:
snake_case__ :Any = [self.normalize(image=UpperCamelCase ) for image in images]
if do_color_quantize:
snake_case__ :Optional[Any] = [to_channel_dimension_format(UpperCamelCase ,ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
snake_case__ :Union[str, Any] = np.array(UpperCamelCase )
snake_case__ :Optional[int] = color_quantize(UpperCamelCase ,UpperCamelCase ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
snake_case__ :List[Any] = images.shape[0]
snake_case__ :str = images.reshape(UpperCamelCase ,-1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
snake_case__ :Any = list(UpperCamelCase )
else:
snake_case__ :List[str] = [to_channel_dimension_format(UpperCamelCase ,UpperCamelCase ) for image in images]
snake_case__ :List[str] = {"input_ids": images}
return BatchFeature(data=UpperCamelCase ,tensor_type=UpperCamelCase )
| 57
| 1
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_CLIP_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
class _snake_case ( _A ):
_A = ['pixel_values']
def __init__( self ,UpperCamelCase = True ,UpperCamelCase = None ,UpperCamelCase = PILImageResampling.BICUBIC ,UpperCamelCase = True ,UpperCamelCase = 1 / 255 ,UpperCamelCase = True ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = True ,**UpperCamelCase ,) -> None:
super().__init__(**UpperCamelCase )
snake_case__ :Optional[Any] = size if size is not None else {"height": 384, "width": 384}
snake_case__ :Tuple = get_size_dict(UpperCamelCase ,default_to_square=UpperCamelCase )
snake_case__ :int = do_resize
snake_case__ :Optional[int] = size
snake_case__ :Optional[int] = resample
snake_case__ :List[str] = do_rescale
snake_case__ :Tuple = rescale_factor
snake_case__ :Optional[Any] = do_normalize
snake_case__ :Optional[int] = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
snake_case__ :List[str] = image_std if image_std is not None else OPENAI_CLIP_STD
snake_case__ :Tuple = do_convert_rgb
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = PILImageResampling.BICUBIC ,UpperCamelCase = None ,**UpperCamelCase ,) -> np.ndarray:
snake_case__ :Optional[Any] = get_size_dict(UpperCamelCase ,default_to_square=UpperCamelCase )
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__ :str = (size["height"], size["width"])
return resize(UpperCamelCase ,size=UpperCamelCase ,resample=UpperCamelCase ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = None ,**UpperCamelCase ,) -> str:
return rescale(UpperCamelCase ,scale=UpperCamelCase ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = None ,**UpperCamelCase ,) -> np.ndarray:
return normalize(UpperCamelCase ,mean=UpperCamelCase ,std=UpperCamelCase ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = ChannelDimension.FIRST ,**UpperCamelCase ,) -> PIL.Image.Image:
snake_case__ :str = do_resize if do_resize is not None else self.do_resize
snake_case__ :List[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__ :Union[str, Any] = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case__ :Any = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ :int = image_mean if image_mean is not None else self.image_mean
snake_case__ :Tuple = image_std if image_std is not None else self.image_std
snake_case__ :str = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb
snake_case__ :Optional[Any] = size if size is not None else self.size
snake_case__ :Union[str, Any] = get_size_dict(UpperCamelCase ,default_to_square=UpperCamelCase )
snake_case__ :Optional[Any] = make_list_of_images(UpperCamelCase )
if not valid_images(UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# PIL RGBA images are converted to RGB
if do_convert_rgb:
snake_case__ :List[Any] = [convert_to_rgb(UpperCamelCase ) for image in images]
# All transformations expect numpy arrays.
snake_case__ :str = [to_numpy_array(UpperCamelCase ) for image in images]
if do_resize:
snake_case__ :List[Any] = [self.resize(image=UpperCamelCase ,size=UpperCamelCase ,resample=UpperCamelCase ) for image in images]
if do_rescale:
snake_case__ :int = [self.rescale(image=UpperCamelCase ,scale=UpperCamelCase ) for image in images]
if do_normalize:
snake_case__ :Optional[int] = [self.normalize(image=UpperCamelCase ,mean=UpperCamelCase ,std=UpperCamelCase ) for image in images]
snake_case__ :str = [to_channel_dimension_format(UpperCamelCase ,UpperCamelCase ) for image in images]
snake_case__ :List[Any] = BatchFeature(data={"pixel_values": images} ,tensor_type=UpperCamelCase )
return encoded_outputs
| 57
|
import pytest
__UpperCAmelCase : int = "__dummy_dataset1__"
__UpperCAmelCase : int = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n def _info(self):\n features = datasets.Features(\n {\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n \"ner_tags\": datasets.Sequence(\n datasets.features.ClassLabel(\n names=[\n \"O\",\n \"B-PER\",\n \"I-PER\",\n \"B-ORG\",\n \"I-ORG\",\n \"B-LOC\",\n \"I-LOC\",\n ]\n )\n ),\n \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n }\n )\n return datasets.DatasetInfo(features=features)\n\n def _split_generators(self, dl_manager):\n dl_path = dl_manager.download(URLS)\n return [\n datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n ]\n\n def _generate_examples(self, filepath):\n with open(filepath, \"r\", encoding=\"utf-8\") as f:\n for i, line in enumerate(f):\n yield i, json.loads(line)\n"
@pytest.fixture
def lowercase_ ( ) -> Optional[Any]:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def lowercase_ ( ) -> Optional[int]:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def lowercase_ ( __snake_case : Optional[int] , __snake_case : List[Any] , __snake_case : Any ) -> Dict:
'''simple docstring'''
snake_case__ :Optional[Any] = dataset_loading_script_name
snake_case__ :Optional[Any] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=__snake_case )
snake_case__ :List[Any] = script_dir / F'{script_name}.py'
with open(__snake_case , "w" ) as f:
f.write(__snake_case )
return str(__snake_case )
| 57
| 1
|
import argparse
import pytorch_lightning as pl
import torch
from torch import nn
from transformers import LongformerForQuestionAnswering, LongformerModel
class _snake_case ( pl.LightningModule ):
def __init__( self ,UpperCamelCase ) -> Dict:
super().__init__()
snake_case__ :Union[str, Any] = model
snake_case__ :List[str] = 2
snake_case__ :Optional[int] = nn.Linear(self.model.config.hidden_size ,self.num_labels )
def lowerCAmelCase_ ( self ) -> Dict:
pass
def lowercase_ ( __snake_case : str , __snake_case : str , __snake_case : str ) -> List[Any]:
'''simple docstring'''
snake_case__ :Tuple = LongformerModel.from_pretrained(__snake_case )
snake_case__ :Optional[Any] = LightningModel(__snake_case )
snake_case__ :List[str] = torch.load(__snake_case , map_location=torch.device("cpu" ) )
lightning_model.load_state_dict(ckpt["state_dict"] )
# init longformer question answering model
snake_case__ :Tuple = LongformerForQuestionAnswering.from_pretrained(__snake_case )
# 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(__snake_case )
print(F'Conversion successful. Model saved under {pytorch_dump_folder_path}' )
if __name__ == "__main__":
__UpperCAmelCase : Tuple = 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."
)
__UpperCAmelCase : Union[str, Any] = parser.parse_args()
convert_longformer_qa_checkpoint_to_pytorch(
args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
)
| 57
|
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 57
| 1
|
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _snake_case ( _A , _A , _A ):
@register_to_config
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = False ,) -> int:
super().__init__()
snake_case__ :Union[str, Any] = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :int = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :Any = False
snake_case__ :List[Any] = nn.Dropout(p=UpperCamelCase )
snake_case__ :Tuple = TaConfig(
vocab_size=UpperCamelCase ,d_model=UpperCamelCase ,num_heads=UpperCamelCase ,d_kv=UpperCamelCase ,d_ff=UpperCamelCase ,dropout_rate=UpperCamelCase ,feed_forward_proj=UpperCamelCase ,is_decoder=UpperCamelCase ,is_encoder_decoder=UpperCamelCase ,)
snake_case__ :List[str] = nn.ModuleList()
for lyr_num in range(UpperCamelCase ):
snake_case__ :List[Any] = TaBlock(UpperCamelCase )
self.encoders.append(UpperCamelCase )
snake_case__ :Optional[Any] = TaLayerNorm(UpperCamelCase )
snake_case__ :Any = nn.Dropout(p=UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> int:
snake_case__ :str = self.token_embedder(UpperCamelCase )
snake_case__ :int = encoder_input_tokens.shape[1]
snake_case__ :List[Any] = torch.arange(UpperCamelCase ,device=encoder_input_tokens.device )
x += self.position_encoding(UpperCamelCase )
snake_case__ :Optional[int] = self.dropout_pre(UpperCamelCase )
# inverted the attention mask
snake_case__ :Optional[Any] = encoder_input_tokens.size()
snake_case__ :Dict = self.get_extended_attention_mask(UpperCamelCase ,UpperCamelCase )
for lyr in self.encoders:
snake_case__ :str = lyr(UpperCamelCase ,UpperCamelCase )[0]
snake_case__ :List[Any] = self.layer_norm(UpperCamelCase )
return self.dropout_post(UpperCamelCase ), encoder_inputs_mask
| 57
|
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__UpperCAmelCase : Dict = True
except ImportError:
__UpperCAmelCase : List[Any] = False
__UpperCAmelCase : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowercase_ ( __snake_case : Namespace ) -> Dict:
'''simple docstring'''
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class _snake_case ( _A ):
@staticmethod
def lowerCAmelCase_ ( UpperCamelCase ) -> Any:
snake_case__ :Dict = parser.add_parser("add-new-model" )
add_new_model_parser.add_argument("--testing" ,action="store_true" ,help="If in testing mode." )
add_new_model_parser.add_argument("--testing_file" ,type=UpperCamelCase ,help="Configuration file on which to run." )
add_new_model_parser.add_argument(
"--path" ,type=UpperCamelCase ,help="Path to cookiecutter. Should only be used for testing purposes." )
add_new_model_parser.set_defaults(func=UpperCamelCase )
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ,*UpperCamelCase ) -> Any:
snake_case__ :Union[str, Any] = testing
snake_case__ :Union[str, Any] = testing_file
snake_case__ :List[str] = path
def lowerCAmelCase_ ( self ) -> List[Any]:
warnings.warn(
"The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
"It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
"checks, you should use `transformers-cli add-new-model-like` instead." )
if not _has_cookiecutter:
raise ImportError(
"Model creation dependencies are required to use the `add_new_model` command. Install them by running "
"the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n" )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
snake_case__ :Tuple = [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
if len(UpperCamelCase ) > 0:
raise ValueError(
"Several directories starting with `cookiecutter-template-` in current working directory. "
"Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
"change your working directory." )
snake_case__ :str = (
Path(UpperCamelCase ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
snake_case__ :Tuple = path_to_transformer_root / "templates" / "adding_a_new_model"
# Execute cookiecutter
if not self._testing:
cookiecutter(str(UpperCamelCase ) )
else:
with open(self._testing_file ,"r" ) as configuration_file:
snake_case__ :str = json.load(UpperCamelCase )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) ,no_input=UpperCamelCase ,extra_context=UpperCamelCase ,)
snake_case__ :List[Any] = [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]
# Retrieve configuration
with open(directory + "/configuration.json" ,"r" ) as configuration_file:
snake_case__ :Dict = json.load(UpperCamelCase )
snake_case__ :Optional[Any] = configuration["lowercase_modelname"]
snake_case__ :List[Any] = configuration["generate_tensorflow_pytorch_and_flax"]
os.remove(f'{directory}/configuration.json' )
snake_case__ :Any = "PyTorch" in generate_tensorflow_pytorch_and_flax
snake_case__ :Any = "TensorFlow" in generate_tensorflow_pytorch_and_flax
snake_case__ :Any = "Flax" in generate_tensorflow_pytorch_and_flax
snake_case__ :Dict = f'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
os.makedirs(UpperCamelCase ,exist_ok=UpperCamelCase )
os.makedirs(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}' ,exist_ok=UpperCamelCase )
# Tests require submodules as they have parent imports
with open(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py' ,"w" ):
pass
shutil.move(
f'{directory}/__init__.py' ,f'{model_dir}/__init__.py' ,)
shutil.move(
f'{directory}/configuration_{lowercase_model_name}.py' ,f'{model_dir}/configuration_{lowercase_model_name}.py' ,)
def remove_copy_lines(UpperCamelCase ):
with open(UpperCamelCase ,"r" ) as f:
snake_case__ :List[str] = f.readlines()
with open(UpperCamelCase ,"w" ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(UpperCamelCase )
if output_pytorch:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_{lowercase_model_name}.py' ,f'{model_dir}/modeling_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_{lowercase_model_name}.py' )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_tf_{lowercase_model_name}.py' ,f'{model_dir}/modeling_tf_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_tf_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_tf_{lowercase_model_name}.py' )
if output_flax:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_flax_{lowercase_model_name}.py' ,f'{model_dir}/modeling_flax_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_flax_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/{lowercase_model_name}.md' ,f'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md' ,)
shutil.move(
f'{directory}/tokenization_{lowercase_model_name}.py' ,f'{model_dir}/tokenization_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/tokenization_fast_{lowercase_model_name}.py' ,f'{model_dir}/tokenization_{lowercase_model_name}_fast.py' ,)
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ):
# Create temp file
snake_case__ , snake_case__ :Optional[Any] = mkstemp()
snake_case__ :Optional[Any] = False
with fdopen(UpperCamelCase ,"w" ) as new_file:
with open(UpperCamelCase ) as old_file:
for line in old_file:
new_file.write(UpperCamelCase )
if line_to_copy_below in line:
snake_case__ :Optional[Any] = True
for line_to_copy in lines_to_copy:
new_file.write(UpperCamelCase )
if not line_found:
raise ValueError(f'Line {line_to_copy_below} was not found in file.' )
# Copy the file permissions from the old file to the new file
copymode(UpperCamelCase ,UpperCamelCase )
# Remove original file
remove(UpperCamelCase )
# Move new file
move(UpperCamelCase ,UpperCamelCase )
def skip_units(UpperCamelCase ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(UpperCamelCase ):
with open(UpperCamelCase ) as datafile:
snake_case__ :int = []
snake_case__ :Optional[int] = False
snake_case__ :List[str] = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
snake_case__ :Optional[Any] = line.split("\"" )[1]
snake_case__ :Tuple = skip_units(UpperCamelCase )
elif "# Below: " in line and "##" not in line:
snake_case__ :Optional[Any] = line.split("\"" )[1]
snake_case__ :List[str] = skip_units(UpperCamelCase )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
snake_case__ :Tuple = []
elif "# Replace with" in line and "##" not in line:
snake_case__ :Optional[Any] = []
elif "##" not in line:
lines_to_copy.append(UpperCamelCase )
remove(UpperCamelCase )
replace_in_files(f'{directory}/to_replace_{lowercase_model_name}.py' )
os.rmdir(UpperCamelCase )
| 57
| 1
|
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_tf_available():
import tensorflow as tf
from transformers import TFAutoModelForSeqaSeqLM, TFPegasusForConditionalGeneration, TFPegasusModel
@require_tf
class _snake_case :
_A = PegasusConfig
_A = {}
_A = 'gelu'
def __init__( self ,UpperCamelCase ,UpperCamelCase=13 ,UpperCamelCase=7 ,UpperCamelCase=True ,UpperCamelCase=False ,UpperCamelCase=99 ,UpperCamelCase=32 ,UpperCamelCase=2 ,UpperCamelCase=4 ,UpperCamelCase=37 ,UpperCamelCase=0.1 ,UpperCamelCase=0.1 ,UpperCamelCase=40 ,UpperCamelCase=2 ,UpperCamelCase=1 ,UpperCamelCase=0 ,) -> int:
snake_case__ :Tuple = parent
snake_case__ :Any = batch_size
snake_case__ :Optional[Any] = seq_length
snake_case__ :Tuple = is_training
snake_case__ :Union[str, Any] = use_labels
snake_case__ :int = vocab_size
snake_case__ :Dict = hidden_size
snake_case__ :List[Any] = num_hidden_layers
snake_case__ :Any = num_attention_heads
snake_case__ :str = intermediate_size
snake_case__ :int = hidden_dropout_prob
snake_case__ :Optional[Any] = attention_probs_dropout_prob
snake_case__ :List[str] = max_position_embeddings
snake_case__ :Dict = eos_token_id
snake_case__ :Optional[int] = pad_token_id
snake_case__ :Any = bos_token_id
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :int = ids_tensor([self.batch_size, self.seq_length - 1] ,self.vocab_size )
snake_case__ :Any = tf.expand_dims(tf.constant([self.eos_token_id] * self.batch_size ) ,1 )
snake_case__ :List[Any] = tf.concat([input_ids, eos_tensor] ,axis=1 )
snake_case__ :Tuple = ids_tensor([self.batch_size, self.seq_length] ,self.vocab_size )
snake_case__ :str = self.config_cls(
vocab_size=self.vocab_size ,d_model=self.hidden_size ,encoder_layers=self.num_hidden_layers ,decoder_layers=self.num_hidden_layers ,encoder_attention_heads=self.num_attention_heads ,decoder_attention_heads=self.num_attention_heads ,encoder_ffn_dim=self.intermediate_size ,decoder_ffn_dim=self.intermediate_size ,dropout=self.hidden_dropout_prob ,attention_dropout=self.attention_probs_dropout_prob ,max_position_embeddings=self.max_position_embeddings ,eos_token_ids=[2] ,bos_token_id=self.bos_token_id ,pad_token_id=self.pad_token_id ,decoder_start_token_id=self.pad_token_id ,**self.config_updates ,)
snake_case__ :Optional[Any] = prepare_pegasus_inputs_dict(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
return config, inputs_dict
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> Optional[int]:
snake_case__ :List[str] = TFPegasusModel(config=UpperCamelCase ).get_decoder()
snake_case__ :Optional[Any] = inputs_dict["input_ids"]
snake_case__ :str = input_ids[:1, :]
snake_case__ :Tuple = inputs_dict["attention_mask"][:1, :]
snake_case__ :Any = inputs_dict["head_mask"]
snake_case__ :Any = 1
# first forward pass
snake_case__ :Any = model(UpperCamelCase ,attention_mask=UpperCamelCase ,head_mask=UpperCamelCase ,use_cache=UpperCamelCase )
snake_case__ , snake_case__ :Optional[int] = outputs.to_tuple()
# create hypothetical next token and extent to next_input_ids
snake_case__ :Optional[int] = ids_tensor((self.batch_size, 3) ,config.vocab_size )
snake_case__ :Optional[Any] = tf.cast(ids_tensor((self.batch_size, 3) ,2 ) ,tf.inta )
# append to next input_ids and
snake_case__ :Optional[Any] = tf.concat([input_ids, next_tokens] ,axis=-1 )
snake_case__ :List[Any] = tf.concat([attention_mask, next_attn_mask] ,axis=-1 )
snake_case__ :Optional[int] = model(UpperCamelCase ,attention_mask=UpperCamelCase )[0]
snake_case__ :str = model(UpperCamelCase ,attention_mask=UpperCamelCase ,past_key_values=UpperCamelCase )[0]
self.parent.assertEqual(next_tokens.shape[1] ,output_from_past.shape[1] )
# select random slice
snake_case__ :Optional[Any] = int(ids_tensor((1,) ,output_from_past.shape[-1] ) )
snake_case__ :str = output_from_no_past[:, -3:, random_slice_idx]
snake_case__ :str = output_from_past[:, :, random_slice_idx]
# test that outputs are equal for slice
tf.debugging.assert_near(UpperCamelCase ,UpperCamelCase ,rtol=1E-3 )
def lowercase_ ( __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : Any , __snake_case : Dict=None , __snake_case : Union[str, Any]=None , __snake_case : Union[str, Any]=None , __snake_case : Optional[int]=None , __snake_case : Optional[int]=None , ) -> Tuple:
'''simple docstring'''
if attention_mask is None:
snake_case__ :List[Any] = tf.cast(tf.math.not_equal(__snake_case , config.pad_token_id ) , tf.inta )
if decoder_attention_mask is None:
snake_case__ :Dict = tf.concat(
[
tf.ones(decoder_input_ids[:, :1].shape , dtype=tf.inta ),
tf.cast(tf.math.not_equal(decoder_input_ids[:, 1:] , config.pad_token_id ) , tf.inta ),
] , axis=-1 , )
if head_mask is None:
snake_case__ :Optional[Any] = tf.ones((config.encoder_layers, config.encoder_attention_heads) )
if decoder_head_mask is None:
snake_case__ :str = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
if cross_attn_head_mask is None:
snake_case__ :Optional[int] = tf.ones((config.decoder_layers, config.decoder_attention_heads) )
return {
"input_ids": input_ids,
"decoder_input_ids": decoder_input_ids,
"attention_mask": attention_mask,
"decoder_attention_mask": decoder_attention_mask,
"head_mask": head_mask,
"decoder_head_mask": decoder_head_mask,
"cross_attn_head_mask": cross_attn_head_mask,
}
@require_tf
class _snake_case ( _A , _A , unittest.TestCase ):
_A = (TFPegasusForConditionalGeneration, TFPegasusModel) if is_tf_available() else ()
_A = (TFPegasusForConditionalGeneration,) if is_tf_available() else ()
_A = (
{
'conversational': TFPegasusForConditionalGeneration,
'feature-extraction': TFPegasusModel,
'summarization': TFPegasusForConditionalGeneration,
'text2text-generation': TFPegasusForConditionalGeneration,
'translation': TFPegasusForConditionalGeneration,
}
if is_tf_available()
else {}
)
_A = True
_A = False
_A = False
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :Dict = TFPegasusModelTester(self )
snake_case__ :Any = ConfigTester(self ,config_class=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Any:
self.config_tester.run_common_tests()
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
self.model_tester.check_decoder_model_past_large_inputs(*UpperCamelCase )
@require_sentencepiece
@require_tokenizers
@require_tf
class _snake_case ( unittest.TestCase ):
_A = [
' PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow.',
' The London trio are up for best UK act and best album, as well as getting two nominations in the best song category."We got told like this morning \'Oh I think you\'re nominated\'", said Dappy."And I was like \'Oh yeah, which one?\' And now we\'ve got nominated for four awards. I mean, wow!"Bandmate Fazer added: "We thought it\'s best of us to come down and mingle with everyone and say hello to the cameras. And now we find we\'ve got four nominations."The band have two shots at the best song prize, getting the nod for their Tynchy Stryder collaboration Number One, and single Strong Again.Their album Uncle B will also go up against records by the likes of Beyonce and Kanye West.N-Dubz picked up the best newcomer Mobo in 2007, but female member Tulisa said they wouldn\'t be too disappointed if they didn\'t win this time around."At the end of the day we\'re grateful to be where we are in our careers."If it don\'t happen then it don\'t happen - live to fight another day and keep on making albums and hits for the fans."Dappy also revealed they could be performing live several times on the night.The group will be doing Number One and also a possible rendition of the War Child single, I Got Soul.The charity song is a re-working of The Killers\' All These Things That I\'ve Done and is set to feature artists like Chipmunk, Ironik and Pixie Lott.This year\'s Mobos will be held outside of London for the first time, in Glasgow on 30 September.N-Dubz said they were looking forward to performing for their Scottish fans and boasted about their recent shows north of the border."We just done Edinburgh the other day," said Dappy."We smashed up an N-Dubz show over there. We done Aberdeen about three or four months ago - we smashed up that show over there! Everywhere we go we smash it up!" ',
]
_A = [
'California\'s largest electricity provider has cut power to hundreds of thousands of customers in an effort to'
' reduce the risk of wildfires.',
'N-Dubz have revealed they\'re "grateful" to have been nominated for four Mobo Awards.',
] # differs slightly from pytorch, likely due to numerical differences in linear layers
_A = 'google/pegasus-xsum'
@cached_property
def lowerCAmelCase_ ( self ) -> Tuple:
return AutoTokenizer.from_pretrained(self.model_name )
@cached_property
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :Optional[int] = TFAutoModelForSeqaSeqLM.from_pretrained(self.model_name )
return model
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> Any:
snake_case__ :Optional[Any] = self.translate_src_text(**UpperCamelCase )
assert self.expected_text == generated_words
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> str:
snake_case__ :Dict = self.tokenizer(self.src_text ,**UpperCamelCase ,padding=UpperCamelCase ,return_tensors="tf" )
snake_case__ :str = self.model.generate(
model_inputs.input_ids ,attention_mask=model_inputs.attention_mask ,num_beams=2 ,use_cache=UpperCamelCase ,)
snake_case__ :int = self.tokenizer.batch_decode(generated_ids.numpy() ,skip_special_tokens=UpperCamelCase )
return generated_words
@slow
def lowerCAmelCase_ ( self ) -> Any:
self._assert_generated_batch_equal_expected()
| 57
|
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : List[Any] = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__UpperCAmelCase : List[Any] = {
"vocab_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
},
"merges_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
},
}
__UpperCAmelCase : str = {"allegro/herbert-base-cased": 5_1_4}
__UpperCAmelCase : List[str] = {}
class _snake_case ( _A ):
_A = VOCAB_FILES_NAMES
_A = PRETRAINED_VOCAB_FILES_MAP
_A = PRETRAINED_INIT_CONFIGURATION
_A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A = HerbertTokenizer
def __init__( self ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase="<s>" ,UpperCamelCase="<unk>" ,UpperCamelCase="<pad>" ,UpperCamelCase="<mask>" ,UpperCamelCase="</s>" ,**UpperCamelCase ,) -> Dict:
super().__init__(
UpperCamelCase ,UpperCamelCase ,tokenizer_file=UpperCamelCase ,cls_token=UpperCamelCase ,unk_token=UpperCamelCase ,pad_token=UpperCamelCase ,mask_token=UpperCamelCase ,sep_token=UpperCamelCase ,**UpperCamelCase ,)
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> List[int]:
snake_case__ :Optional[int] = [self.cls_token_id]
snake_case__ :Any = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase ,token_ids_a=UpperCamelCase ,already_has_special_tokens=UpperCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(UpperCamelCase )) + [1]
return [1] + ([0] * len(UpperCamelCase )) + [1] + ([0] * len(UpperCamelCase )) + [1]
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> List[int]:
snake_case__ :Any = [self.sep_token_id]
snake_case__ :Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> Tuple[str]:
snake_case__ :List[str] = self._tokenizer.model.save(UpperCamelCase ,name=UpperCamelCase )
return tuple(UpperCamelCase )
| 57
| 1
|
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__UpperCAmelCase : List[str] = TypeVar("T")
class _snake_case ( Generic[T] ):
def __init__( self ,UpperCamelCase ,UpperCamelCase ) -> None:
snake_case__ :Any | T = None
snake_case__ :int = len(UpperCamelCase )
snake_case__ :list[T] = [any_type for _ in range(self.N )] + arr
snake_case__ :Optional[Any] = fnc
self.build()
def lowerCAmelCase_ ( self ) -> None:
for p in range(self.N - 1 ,0 ,-1 ):
snake_case__ :Optional[Any] = self.fn(self.st[p * 2] ,self.st[p * 2 + 1] )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> None:
p += self.N
snake_case__ :Optional[Any] = v
while p > 1:
snake_case__ :List[str] = p // 2
snake_case__ :List[Any] = self.fn(self.st[p * 2] ,self.st[p * 2 + 1] )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> T | None: # noqa: E741
snake_case__ , snake_case__ :List[str] = l + self.N, r + self.N
snake_case__ :T | None = None
while l <= r:
if l % 2 == 1:
snake_case__ :List[str] = self.st[l] if res is None else self.fn(UpperCamelCase ,self.st[l] )
if r % 2 == 0:
snake_case__ :Union[str, Any] = self.st[r] if res is None else self.fn(UpperCamelCase ,self.st[r] )
snake_case__ , snake_case__ :List[str] = (l + 1) // 2, (r - 1) // 2
return res
if __name__ == "__main__":
from functools import reduce
__UpperCAmelCase : Optional[Any] = [1, 1_0, -2, 9, -3, 8, 4, -7, 5, 6, 1_1, -1_2]
__UpperCAmelCase : Dict = {
0: 7,
1: 2,
2: 6,
3: -1_4,
4: 5,
5: 4,
6: 7,
7: -1_0,
8: 9,
9: 1_0,
1_0: 1_2,
1_1: 1,
}
__UpperCAmelCase : Optional[int] = SegmentTree(test_array, min)
__UpperCAmelCase : Dict = SegmentTree(test_array, max)
__UpperCAmelCase : Dict = SegmentTree(test_array, lambda a, b: a + b)
def lowercase_ ( ) -> None:
'''simple docstring'''
for i in range(len(__snake_case ) ):
for j in range(__snake_case , len(__snake_case ) ):
snake_case__ :Optional[Any] = reduce(__snake_case , test_array[i : j + 1] )
snake_case__ :str = reduce(__snake_case , test_array[i : j + 1] )
snake_case__ :List[str] = reduce(lambda __snake_case , __snake_case : a + b , test_array[i : j + 1] )
assert min_range == min_segment_tree.query(__snake_case , __snake_case )
assert max_range == max_segment_tree.query(__snake_case , __snake_case )
assert sum_range == sum_segment_tree.query(__snake_case , __snake_case )
test_all_segments()
for index, value in test_updates.items():
__UpperCAmelCase : Optional[Any] = value
min_segment_tree.update(index, value)
max_segment_tree.update(index, value)
sum_segment_tree.update(index, value)
test_all_segments()
| 57
|
def lowercase_ ( __snake_case : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
snake_case__ :List[str] = 4
snake_case__ :Optional[int] = (1 << p) - 1
for _ in range(p - 2 ):
snake_case__ :List[Any] = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(1_1))
| 57
| 1
|
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 _snake_case :
@staticmethod
def lowerCAmelCase_ ( *UpperCamelCase ,**UpperCamelCase ) -> List[str]:
pass
@is_pipeline_test
@require_torch
@require_vision
class _snake_case ( unittest.TestCase ):
_A = MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Optional[Any]:
snake_case__ :str = pipeline("visual-question-answering" ,model="hf-internal-testing/tiny-vilt-random-vqa" )
snake_case__ :str = [
{
"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 ,UpperCamelCase ,UpperCamelCase ) -> List[str]:
snake_case__ :Dict = vqa_pipeline(UpperCamelCase ,top_k=1 )
self.assertEqual(
UpperCamelCase ,[
[{"score": ANY(UpperCamelCase ), "answer": ANY(UpperCamelCase )}],
[{"score": ANY(UpperCamelCase ), "answer": ANY(UpperCamelCase )}],
] ,)
@require_torch
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :List[Any] = pipeline("visual-question-answering" ,model="hf-internal-testing/tiny-vilt-random-vqa" )
snake_case__ :Optional[int] = "./tests/fixtures/tests_samples/COCO/000000039769.png"
snake_case__ :Optional[int] = "How many cats are there?"
snake_case__ :str = vqa_pipeline(image=UpperCamelCase ,question="How many cats are there?" ,top_k=2 )
self.assertEqual(
UpperCamelCase ,[{"score": ANY(UpperCamelCase ), "answer": ANY(UpperCamelCase )}, {"score": ANY(UpperCamelCase ), "answer": ANY(UpperCamelCase )}] )
snake_case__ :Optional[Any] = vqa_pipeline({"image": image, "question": question} ,top_k=2 )
self.assertEqual(
UpperCamelCase ,[{"score": ANY(UpperCamelCase ), "answer": ANY(UpperCamelCase )}, {"score": ANY(UpperCamelCase ), "answer": ANY(UpperCamelCase )}] )
@slow
@require_torch
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :List[str] = pipeline("visual-question-answering" ,model="dandelin/vilt-b32-finetuned-vqa" )
snake_case__ :Tuple = "./tests/fixtures/tests_samples/COCO/000000039769.png"
snake_case__ :Union[str, Any] = "How many cats are there?"
snake_case__ :List[Any] = vqa_pipeline(image=UpperCamelCase ,question=UpperCamelCase ,top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase ,decimals=4 ) ,[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
snake_case__ :Optional[Any] = vqa_pipeline({"image": image, "question": question} ,top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase ,decimals=4 ) ,[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}] )
snake_case__ :Tuple = vqa_pipeline(
[{"image": image, "question": question}, {"image": image, "question": question}] ,top_k=2 )
self.assertEqual(
nested_simplify(UpperCamelCase ,decimals=4 ) ,[[{"score": 0.8799, "answer": "2"}, {"score": 0.296, "answer": "1"}]] * 2 ,)
@require_tf
@unittest.skip("Visual question answering not implemented in TF" )
def lowerCAmelCase_ ( self ) -> List[Any]:
pass
| 57
|
from typing import Any
def lowercase_ ( __snake_case : list , __snake_case : list , __snake_case : dict , __snake_case : dict , __snake_case : dict , ) -> list:
'''simple docstring'''
_validation(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
# Creates data structures and fill initial step
snake_case__ :dict = {}
snake_case__ :dict = {}
for state in states_space:
snake_case__ :List[Any] = observations_space[0]
snake_case__ :str = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
snake_case__ :str = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(__snake_case ) ):
snake_case__ :Any = observations_space[o]
snake_case__ :Tuple = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
snake_case__ :Tuple = ""
snake_case__ :Union[str, Any] = -1
for k_state in states_space:
snake_case__ :int = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
snake_case__ :str = probability
snake_case__ :Tuple = k_state
# Update probabilities and pointers dicts
snake_case__ :List[str] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
snake_case__ :List[str] = arg_max
# The final observation
snake_case__ :str = observations_space[len(__snake_case ) - 1]
# argmax for given final observation
snake_case__ :Optional[int] = ""
snake_case__ :List[str] = -1
for k_state in states_space:
snake_case__ :List[str] = probabilities[(k_state, final_observation)]
if probability > max_probability:
snake_case__ :List[str] = probability
snake_case__ :int = k_state
snake_case__ :Any = arg_max
# Process pointers backwards
snake_case__ :int = last_state
snake_case__ :List[str] = []
for o in range(len(__snake_case ) - 1 , -1 , -1 ):
result.append(__snake_case )
snake_case__ :List[str] = pointers[previous, observations_space[o]]
result.reverse()
return result
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
_validate_not_empty(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
_validate_lists(__snake_case , __snake_case )
_validate_dicts(
__snake_case , __snake_case , __snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("There's an empty parameter" )
def lowercase_ ( __snake_case : Any , __snake_case : Any ) -> None:
'''simple docstring'''
_validate_list(__snake_case , "observations_space" )
_validate_list(__snake_case , "states_space" )
def lowercase_ ( __snake_case : Any , __snake_case : str ) -> None:
'''simple docstring'''
if not isinstance(_object , __snake_case ):
snake_case__ :Optional[int] = F'{var_name} must be a list'
raise ValueError(__snake_case )
else:
for x in _object:
if not isinstance(__snake_case , __snake_case ):
snake_case__ :Any = F'{var_name} must be a list of strings'
raise ValueError(__snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
_validate_dict(__snake_case , "initial_probabilities" , __snake_case )
_validate_nested_dict(__snake_case , "transition_probabilities" )
_validate_nested_dict(__snake_case , "emission_probabilities" )
def lowercase_ ( __snake_case : Any , __snake_case : str ) -> None:
'''simple docstring'''
_validate_dict(_object , __snake_case , __snake_case )
for x in _object.values():
_validate_dict(__snake_case , __snake_case , __snake_case , __snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : str , __snake_case : type , __snake_case : bool = False ) -> None:
'''simple docstring'''
if not isinstance(_object , __snake_case ):
snake_case__ :str = F'{var_name} must be a dict'
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object ):
snake_case__ :List[Any] = F'{var_name} all keys must be strings'
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object.values() ):
snake_case__ :Optional[int] = "nested dictionary " if nested else ""
snake_case__ :int = F'{var_name} {nested_text}all values must be {value_type.__name__}'
raise ValueError(__snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 57
| 1
|
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# You may need to add --master_addr $MASTER_ADDR --master_port $MASTER_PORT if using a custom addr:port
#
# You can also use the rdzv API: --rdzv_endpoint $MASTER_ADDR:$MASTER_PORT --rdzv_backend c10d
#
# use torch.distributed.launch instead of torch.distributed.run for torch < 1.9
#
# If you get a hanging in `barrier` calls you have some network issues, you may try to debug this with:
#
# NCCL_DEBUG=INFO python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 torch-distributed-gpu-test.py
#
# which should tell you what's going on behind the scenes.
#
#
# This script can be run via `srun` in the SLURM environment as well. Here is a SLURM script that
# runs on 2 nodes of 4 gpus per node:
#
# #SBATCH --job-name=test-nodes # name
# #SBATCH --nodes=2 # nodes
# #SBATCH --ntasks-per-node=1 # crucial - only 1 task per dist per node!
# #SBATCH --cpus-per-task=10 # number of cores per tasks
# #SBATCH --gres=gpu:4 # number of gpus
# #SBATCH --time 0:05:00 # maximum execution time (HH:MM:SS)
# #SBATCH --output=%x-%j.out # output file name
#
# GPUS_PER_NODE=4
# MASTER_ADDR=$(scontrol show hostnames $SLURM_JOB_NODELIST | head -n 1)
# MASTER_PORT=6000
#
# srun --jobid $SLURM_JOBID bash -c 'python -m torch.distributed.run \
# --nproc_per_node $GPUS_PER_NODE --nnodes $SLURM_NNODES --node_rank $SLURM_PROCID \
# --master_addr $MASTER_ADDR --master_port $MASTER_PORT \
# torch-distributed-gpu-test.py'
#
import fcntl
import os
import socket
import torch
import torch.distributed as dist
def lowercase_ ( *__snake_case : Tuple ) -> str:
'''simple docstring'''
with open(__snake_case , "r" ) as fh:
fcntl.flock(__snake_case , fcntl.LOCK_EX )
try:
print(*__snake_case )
finally:
fcntl.flock(__snake_case , fcntl.LOCK_UN )
__UpperCAmelCase : Optional[int] = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(local_rank)
__UpperCAmelCase : Union[str, Any] = torch.device("cuda", local_rank)
__UpperCAmelCase : str = socket.gethostname()
__UpperCAmelCase : Optional[int] = F'''[{hostname}-{local_rank}]'''
try:
# test distributed
dist.init_process_group("nccl")
dist.all_reduce(torch.ones(1).to(device), op=dist.ReduceOp.SUM)
dist.barrier()
# test cuda is available and can allocate memory
torch.cuda.is_available()
torch.ones(1).cuda(local_rank)
# global rank
__UpperCAmelCase : str = dist.get_rank()
__UpperCAmelCase : Optional[Any] = dist.get_world_size()
printflock(F'''{gpu} is OK (global rank: {rank}/{world_size})''')
dist.barrier()
if rank == 0:
printflock(F'''pt={torch.__version__}, cuda={torch.version.cuda}, nccl={torch.cuda.nccl.version()}''')
except Exception:
printflock(F'''{gpu} is broken''')
raise
| 57
|
def lowercase_ ( __snake_case : str ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__snake_case ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 57
| 1
|
import math
from collections.abc import Iterator
from itertools import takewhile
def lowercase_ ( __snake_case : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number are in format of 6k +/- 1
for i in range(5 , int(math.sqrt(__snake_case ) + 1 ) , 6 ):
if number % i == 0 or number % (i + 2) == 0:
return False
return True
def lowercase_ ( ) -> Iterator[int]:
'''simple docstring'''
snake_case__ :str = 2
while True:
if is_prime(__snake_case ):
yield num
num += 1
def lowercase_ ( __snake_case : int = 2_00_00_00 ) -> int:
'''simple docstring'''
return sum(takewhile(lambda __snake_case : x < n , prime_generator() ) )
if __name__ == "__main__":
print(F'''{solution() = }''')
| 57
|
def lowercase_ ( __snake_case : int = 10_00 ) -> int:
'''simple docstring'''
snake_case__ :int = 3
snake_case__ :int = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 57
| 1
|
import argparse
import json
import os
from collections import OrderedDict
import torch
from transformers import LukeConfig, LukeForMaskedLM, MLukeTokenizer, XLMRobertaTokenizer
from transformers.tokenization_utils_base import AddedToken
@torch.no_grad()
def lowercase_ ( __snake_case : str , __snake_case : Optional[Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : str ) -> List[Any]:
'''simple docstring'''
with open(__snake_case ) as metadata_file:
snake_case__ :str = json.load(__snake_case )
snake_case__ :Optional[Any] = LukeConfig(use_entity_aware_attention=__snake_case , **metadata["model_config"] )
# Load in the weights from the checkpoint_path
snake_case__ :Any = torch.load(__snake_case , map_location="cpu" )["module"]
# Load the entity vocab file
snake_case__ :Optional[int] = load_original_entity_vocab(__snake_case )
# add an entry for [MASK2]
snake_case__ :Any = max(entity_vocab.values() ) + 1
config.entity_vocab_size += 1
snake_case__ :Dict = XLMRobertaTokenizer.from_pretrained(metadata["model_config"]["bert_model_name"] )
# Add special tokens to the token vocabulary for downstream tasks
snake_case__ :List[Any] = AddedToken("<ent>" , lstrip=__snake_case , rstrip=__snake_case )
snake_case__ :List[Any] = AddedToken("<ent2>" , lstrip=__snake_case , rstrip=__snake_case )
tokenizer.add_special_tokens({"additional_special_tokens": [entity_token_a, entity_token_a]} )
config.vocab_size += 2
print(F'Saving tokenizer to {pytorch_dump_folder_path}' )
tokenizer.save_pretrained(__snake_case )
with open(os.path.join(__snake_case , "tokenizer_config.json" ) , "r" ) as f:
snake_case__ :str = json.load(__snake_case )
snake_case__ :Optional[Any] = "MLukeTokenizer"
with open(os.path.join(__snake_case , "tokenizer_config.json" ) , "w" ) as f:
json.dump(__snake_case , __snake_case )
with open(os.path.join(__snake_case , MLukeTokenizer.vocab_files_names["entity_vocab_file"] ) , "w" ) as f:
json.dump(__snake_case , __snake_case )
snake_case__ :List[Any] = MLukeTokenizer.from_pretrained(__snake_case )
# Initialize the embeddings of the special tokens
snake_case__ :List[str] = tokenizer.convert_tokens_to_ids(["@"] )[0]
snake_case__ :Optional[Any] = tokenizer.convert_tokens_to_ids(["#"] )[0]
snake_case__ :Tuple = state_dict["embeddings.word_embeddings.weight"]
snake_case__ :Dict = word_emb[ent_init_index].unsqueeze(0 )
snake_case__ :Union[str, Any] = word_emb[enta_init_index].unsqueeze(0 )
snake_case__ :Any = torch.cat([word_emb, ent_emb, enta_emb] )
# add special tokens for 'entity_predictions.bias'
for bias_name in ["lm_head.decoder.bias", "lm_head.bias"]:
snake_case__ :Dict = state_dict[bias_name]
snake_case__ :Optional[int] = decoder_bias[ent_init_index].unsqueeze(0 )
snake_case__ :str = decoder_bias[enta_init_index].unsqueeze(0 )
snake_case__ :Dict = torch.cat([decoder_bias, ent_decoder_bias, enta_decoder_bias] )
# Initialize the query layers of the entity-aware self-attention mechanism
for layer_index in range(config.num_hidden_layers ):
for matrix_name in ["query.weight", "query.bias"]:
snake_case__ :int = F'encoder.layer.{layer_index}.attention.self.'
snake_case__ :Dict = state_dict[prefix + matrix_name]
snake_case__ :Optional[Any] = state_dict[prefix + matrix_name]
snake_case__ :Tuple = state_dict[prefix + matrix_name]
# Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
snake_case__ :List[str] = state_dict["entity_embeddings.entity_embeddings.weight"]
snake_case__ :Dict = entity_emb[entity_vocab["[MASK]"]].unsqueeze(0 )
snake_case__ :List[Any] = torch.cat([entity_emb, entity_mask_emb] )
# add [MASK2] for 'entity_predictions.bias'
snake_case__ :Union[str, Any] = state_dict["entity_predictions.bias"]
snake_case__ :Tuple = entity_prediction_bias[entity_vocab["[MASK]"]].unsqueeze(0 )
snake_case__ :Union[str, Any] = torch.cat([entity_prediction_bias, entity_mask_bias] )
snake_case__ :List[Any] = LukeForMaskedLM(config=__snake_case ).eval()
state_dict.pop("entity_predictions.decoder.weight" )
state_dict.pop("lm_head.decoder.weight" )
state_dict.pop("lm_head.decoder.bias" )
snake_case__ :str = OrderedDict()
for key, value in state_dict.items():
if not (key.startswith("lm_head" ) or key.startswith("entity_predictions" )):
snake_case__ :Tuple = state_dict[key]
else:
snake_case__ :Optional[int] = state_dict[key]
snake_case__ , snake_case__ :str = model.load_state_dict(__snake_case , strict=__snake_case )
if set(__snake_case ) != {"luke.embeddings.position_ids"}:
raise ValueError(F'Unexpected unexpected_keys: {unexpected_keys}' )
if set(__snake_case ) != {
"lm_head.decoder.weight",
"lm_head.decoder.bias",
"entity_predictions.decoder.weight",
}:
raise ValueError(F'Unexpected missing_keys: {missing_keys}' )
model.tie_weights()
assert (model.luke.embeddings.word_embeddings.weight == model.lm_head.decoder.weight).all()
assert (model.luke.entity_embeddings.entity_embeddings.weight == model.entity_predictions.decoder.weight).all()
# Check outputs
snake_case__ :int = MLukeTokenizer.from_pretrained(__snake_case , task="entity_classification" )
snake_case__ :Any = "ISO 639-3 uses the code fas for the dialects spoken across Iran and アフガニスタン (Afghanistan)."
snake_case__ :str = (0, 9)
snake_case__ :Optional[Any] = tokenizer(__snake_case , entity_spans=[span] , return_tensors="pt" )
snake_case__ :List[Any] = model(**__snake_case )
# Verify word hidden states
if model_size == "large":
raise NotImplementedError
else: # base
snake_case__ :int = torch.Size((1, 33, 7_68) )
snake_case__ :List[Any] = torch.tensor([[0.0_8_9_2, 0.0_5_9_6, -0.2_8_1_9], [0.0_1_3_4, 0.1_1_9_9, 0.0_5_7_3], [-0.0_1_6_9, 0.0_9_2_7, 0.0_6_4_4]] )
if not (outputs.last_hidden_state.shape == expected_shape):
raise ValueError(
F'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}' )
if not torch.allclose(outputs.last_hidden_state[0, :3, :3] , __snake_case , atol=1e-4 ):
raise ValueError
# Verify entity hidden states
if model_size == "large":
raise NotImplementedError
else: # base
snake_case__ :int = torch.Size((1, 1, 7_68) )
snake_case__ :Optional[int] = torch.tensor([[-0.1_4_8_2, 0.0_6_0_9, 0.0_3_2_2]] )
if not (outputs.entity_last_hidden_state.shape == expected_shape):
raise ValueError(
F'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
F' {expected_shape}' )
if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3] , __snake_case , atol=1e-4 ):
raise ValueError
# Verify masked word/entity prediction
snake_case__ :Union[str, Any] = MLukeTokenizer.from_pretrained(__snake_case )
snake_case__ :Optional[int] = "Tokyo is the capital of <mask>."
snake_case__ :List[Any] = (24, 30)
snake_case__ :Any = tokenizer(__snake_case , entity_spans=[span] , return_tensors="pt" )
snake_case__ :Optional[Any] = model(**__snake_case )
snake_case__ :int = encoding["input_ids"][0].tolist()
snake_case__ :Optional[int] = input_ids.index(tokenizer.convert_tokens_to_ids("<mask>" ) )
snake_case__ :Tuple = outputs.logits[0][mask_position_id].argmax(dim=-1 )
assert "Japan" == tokenizer.decode(__snake_case )
snake_case__ :Optional[Any] = outputs.entity_logits[0][0].argmax().item()
snake_case__ :List[Any] = [
entity for entity, entity_id in tokenizer.entity_vocab.items() if entity_id == predicted_entity_id
]
assert [e for e in multilingual_predicted_entities if e.startswith("en:" )][0] == "en:Japan"
# Finally, save our PyTorch model and tokenizer
print("Saving PyTorch model to {}".format(__snake_case ) )
model.save_pretrained(__snake_case )
def lowercase_ ( __snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :List[Any] = ["[MASK]", "[PAD]", "[UNK]"]
snake_case__ :List[Any] = [json.loads(__snake_case ) for line in open(__snake_case )]
snake_case__ :Tuple = {}
for entry in data:
snake_case__ :str = entry["id"]
for entity_name, language in entry["entities"]:
if entity_name in SPECIAL_TOKENS:
snake_case__ :List[str] = entity_id
break
snake_case__ :Optional[Any] = F'{language}:{entity_name}'
snake_case__ :Dict = entity_id
return new_mapping
if __name__ == "__main__":
__UpperCAmelCase : Union[str, Any] = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
parser.add_argument(
"--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
)
parser.add_argument(
"--entity_vocab_path",
default=None,
type=str,
help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
)
parser.add_argument(
"--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
)
__UpperCAmelCase : Tuple = parser.parse_args()
convert_luke_checkpoint(
args.checkpoint_path,
args.metadata_path,
args.entity_vocab_path,
args.pytorch_dump_folder_path,
args.model_size,
)
| 57
|
import os
import sys
import unittest
__UpperCAmelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase : Tuple = os.path.join(git_repo_path, "src", "diffusers")
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Tuple = find_backend(" if not is_torch_available():" )
self.assertEqual(UpperCamelCase ,"torch" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
snake_case__ :Tuple = find_backend(" if not (is_torch_available() and is_transformers_available()):" )
self.assertEqual(UpperCamelCase ,"torch_and_transformers" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
snake_case__ :str = find_backend(
" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):" )
self.assertEqual(UpperCamelCase ,"torch_and_transformers_and_onnx" )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :int = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch" ,UpperCamelCase )
self.assertIn("torch_and_transformers" ,UpperCamelCase )
self.assertIn("flax_and_transformers" ,UpperCamelCase )
self.assertIn("torch_and_transformers_and_onnx" ,UpperCamelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn("UNet2DModel" ,objects["torch"] )
self.assertIn("FlaxUNet2DConditionModel" ,objects["flax"] )
self.assertIn("StableDiffusionPipeline" ,objects["torch_and_transformers"] )
self.assertIn("FlaxStableDiffusionPipeline" ,objects["flax_and_transformers"] )
self.assertIn("LMSDiscreteScheduler" ,objects["torch_and_scipy"] )
self.assertIn("OnnxStableDiffusionPipeline" ,objects["torch_and_transformers_and_onnx"] )
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Union[str, Any] = create_dummy_object("CONSTANT" ,"'torch'" )
self.assertEqual(UpperCamelCase ,"\nCONSTANT = None\n" )
snake_case__ :Optional[Any] = create_dummy_object("function" ,"'torch'" )
self.assertEqual(
UpperCamelCase ,"\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n" )
snake_case__ :str = "\nclass FakeClass(metaclass=DummyObject):\n _backends = 'torch'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, 'torch')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n"
snake_case__ :List[str] = create_dummy_object("FakeClass" ,"'torch'" )
self.assertEqual(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :Tuple = "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n"
snake_case__ :int = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]} )
self.assertEqual(dummy_files["torch"] ,UpperCamelCase )
| 57
| 1
|
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import BaseOutput, is_torch_available, is_transformers_available
@dataclass
class _snake_case ( _A ):
_A = 42
_A = 42
if is_transformers_available() and is_torch_available():
from .pipeline_semantic_stable_diffusion import SemanticStableDiffusionPipeline
| 57
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase : Tuple = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : List[Any] = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
__UpperCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
| 1
|
import math
class _snake_case :
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> int:
snake_case__ :List[Any] = 0.0
snake_case__ :Tuple = 0.0
for i in range(len(UpperCamelCase ) ):
da += math.pow((sample[i] - weights[0][i]) ,2 )
da += math.pow((sample[i] - weights[1][i]) ,2 )
return 0 if da > da else 1
return 0
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> list[list[int | float]]:
for i in range(len(UpperCamelCase ) ):
weights[j][i] += alpha * (sample[i] - weights[j][i])
return weights
def lowercase_ ( ) -> None:
'''simple docstring'''
snake_case__ :List[Any] = [[1, 1, 0, 0], [0, 0, 0, 1], [1, 0, 0, 0], [0, 0, 1, 1]]
# weight initialization ( n, C )
snake_case__ :Tuple = [[0.2, 0.6, 0.5, 0.9], [0.8, 0.4, 0.7, 0.3]]
# training
snake_case__ :Optional[int] = SelfOrganizingMap()
snake_case__ :Any = 3
snake_case__ :Dict = 0.5
for _ in range(__snake_case ):
for j in range(len(__snake_case ) ):
# training sample
snake_case__ :List[Any] = training_samples[j]
# Compute the winning vector
snake_case__ :Tuple = self_organizing_map.get_winner(__snake_case , __snake_case )
# Update the winning vector
snake_case__ :Union[str, Any] = self_organizing_map.update(__snake_case , __snake_case , __snake_case , __snake_case )
# classify test sample
snake_case__ :Optional[int] = [0, 0, 0, 1]
snake_case__ :int = self_organizing_map.get_winner(__snake_case , __snake_case )
# results
print(F'Clusters that the test sample belongs to : {winner}' )
print(F'Weights that have been trained : {weights}' )
# running the main() function
if __name__ == "__main__":
main()
| 57
|
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> List[Any]:
# A mock response for an HTTP head request to emulate server down
snake_case__ :Tuple = mock.Mock()
snake_case__ :List[str] = 500
snake_case__ :Any = {}
snake_case__ :Union[str, Any] = HTTPError
snake_case__ :Tuple = {}
# Download this model to make sure it's in the cache.
snake_case__ :Any = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" ,return_value=UpperCamelCase ) as mock_head:
snake_case__ :Dict = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def lowerCAmelCase_ ( self ) -> Dict:
# A mock response for an HTTP head request to emulate server down
snake_case__ :Union[str, Any] = mock.Mock()
snake_case__ :int = 500
snake_case__ :Any = {}
snake_case__ :Dict = HTTPError
snake_case__ :List[Any] = {}
# Download this model to make sure it's in the cache.
snake_case__ :Optional[int] = GPTaTokenizerFast.from_pretrained("gpt2" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" ,return_value=UpperCamelCase ) as mock_head:
snake_case__ :Any = GPTaTokenizerFast.from_pretrained("gpt2" )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCAmelCase_ ( self ) -> int:
# This test is for deprecated behavior and can be removed in v5
try:
snake_case__ :Union[str, Any] = tempfile.mktemp()
with open(UpperCamelCase ,"wb" ) as f:
http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" ,UpperCamelCase )
snake_case__ :Tuple = AlbertTokenizer.from_pretrained(UpperCamelCase )
finally:
os.remove(UpperCamelCase )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile("tokenizer.json" ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open("tokenizer.json" ,"wb" ) as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json" ,UpperCamelCase )
snake_case__ :Dict = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1_000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("tokenizer.json" )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
# This test is for deprecated behavior and can be removed in v5
snake_case__ :Union[str, Any] = AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" )
@is_staging_test
class _snake_case ( unittest.TestCase ):
_A = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def lowerCAmelCase_ ( cls ) -> Optional[int]:
snake_case__ :List[str] = TOKEN
HfFolder.save_token(UpperCamelCase )
@classmethod
def lowerCAmelCase_ ( cls ) -> Union[str, Any]:
try:
delete_repo(token=cls._token ,repo_id="test-tokenizer" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="valid_org/test-tokenizer-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="test-dynamic-tokenizer" )
except HTTPError:
pass
def lowerCAmelCase_ ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :List[str] = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :str = BertTokenizer(UpperCamelCase )
tokenizer.push_to_hub("test-tokenizer" ,use_auth_token=self._token )
snake_case__ :Dict = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id="test-tokenizer" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(UpperCamelCase ,repo_id="test-tokenizer" ,push_to_hub=UpperCamelCase ,use_auth_token=self._token )
snake_case__ :List[str] = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def lowerCAmelCase_ ( self ) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :List[Any] = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Any = BertTokenizer(UpperCamelCase )
tokenizer.push_to_hub("valid_org/test-tokenizer-org" ,use_auth_token=self._token )
snake_case__ :Any = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id="valid_org/test-tokenizer-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
UpperCamelCase ,repo_id="valid_org/test-tokenizer-org" ,push_to_hub=UpperCamelCase ,use_auth_token=self._token )
snake_case__ :Union[str, Any] = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def lowerCAmelCase_ ( self ) -> Any:
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :str = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Optional[int] = CustomTokenizer(UpperCamelCase )
# No fast custom tokenizer
tokenizer.push_to_hub("test-dynamic-tokenizer" ,use_auth_token=self._token )
snake_case__ :Union[str, Any] = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizer" )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :int = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Tuple = BertTokenizerFast.from_pretrained(UpperCamelCase )
bert_tokenizer.save_pretrained(UpperCamelCase )
snake_case__ :List[Any] = CustomTokenizerFast.from_pretrained(UpperCamelCase )
tokenizer.push_to_hub("test-dynamic-tokenizer" ,use_auth_token=self._token )
snake_case__ :List[Any] = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizerFast" )
snake_case__ :List[str] = AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' ,use_fast=UpperCamelCase ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizer" )
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :int = Trie()
trie.add("Hello 友達" )
self.assertEqual(trie.data ,{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}} )
trie.add("Hello" )
trie.data
self.assertEqual(trie.data ,{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}} )
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :List[str] = Trie()
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) ,["[CLS] This is a extra_id_100"] )
trie.add("[CLS]" )
trie.add("extra_id_1" )
trie.add("extra_id_100" )
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) ,["[CLS]", " This is a ", "extra_id_100"] )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Optional[Any] = Trie()
trie.add("A" )
self.assertEqual(trie.split("ABC" ) ,["A", "BC"] )
self.assertEqual(trie.split("BCA" ) ,["BC", "A"] )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :Any = Trie()
trie.add("TOKEN]" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) ,["This is something ", "[SPECIAL_TOKEN]"] )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :List[Any] = Trie()
trie.add("A" )
trie.add("P" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) ,["This is something ", "[SPECIAL_TOKEN]"] )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :str = Trie()
trie.add("AB" )
trie.add("B" )
trie.add("C" )
self.assertEqual(trie.split("ABC" ) ,["AB", "C"] )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Dict = Trie()
trie.add("ABC" )
trie.add("B" )
trie.add("CD" )
self.assertEqual(trie.split("ABCD" ) ,["ABC", "D"] )
def lowerCAmelCase_ ( self ) -> int:
# Even if the offsets are wrong, we necessarily output correct string
# parts.
snake_case__ :Optional[int] = Trie()
snake_case__ :Union[str, Any] = trie.cut_text("ABC" ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(UpperCamelCase ,["AB", "C"] )
| 57
| 1
|
def lowercase_ ( __snake_case : list ) -> list:
'''simple docstring'''
if len(__snake_case ) < 2:
return collection
def circle_sort_util(__snake_case : list , __snake_case : int , __snake_case : int ) -> bool:
snake_case__ :List[Any] = False
if low == high:
return swapped
snake_case__ :List[Any] = low
snake_case__ :List[Any] = high
while left < right:
if collection[left] > collection[right]:
snake_case__ , snake_case__ :Any = (
collection[right],
collection[left],
)
snake_case__ :Tuple = True
left += 1
right -= 1
if left == right and collection[left] > collection[right + 1]:
snake_case__ , snake_case__ :Optional[Any] = (
collection[right + 1],
collection[left],
)
snake_case__ :Union[str, Any] = True
snake_case__ :Optional[int] = low + int((high - low) / 2 )
snake_case__ :int = circle_sort_util(__snake_case , __snake_case , __snake_case )
snake_case__ :int = circle_sort_util(__snake_case , mid + 1 , __snake_case )
return swapped or left_swap or right_swap
snake_case__ :Tuple = True
while is_not_sorted is True:
snake_case__ :Any = circle_sort_util(__snake_case , 0 , len(__snake_case ) - 1 )
return collection
if __name__ == "__main__":
__UpperCAmelCase : Dict = input("Enter numbers separated by a comma:\n").strip()
__UpperCAmelCase : Dict = [int(item) for item in user_input.split(",")]
print(circle_sort(unsorted))
| 57
|
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 : Optional[Any] = 1_6
__UpperCAmelCase : Optional[int] = 3_2
def lowercase_ ( __snake_case : Accelerator , __snake_case : int = 16 , __snake_case : str = "bert-base-cased" ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :int = AutoTokenizer.from_pretrained(__snake_case )
snake_case__ :Optional[int] = load_dataset("glue" , "mrpc" )
def tokenize_function(__snake_case : Tuple ):
# max_length=None => use the model max length (it's actually the default)
snake_case__ :Any = 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
snake_case__ :List[Any] = 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
snake_case__ :Any = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(__snake_case : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__snake_case , padding="max_length" , max_length=1_28 , return_tensors="pt" )
return tokenizer.pad(__snake_case , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
snake_case__ :Any = DataLoader(
tokenized_datasets["train"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
snake_case__ :Tuple = DataLoader(
tokenized_datasets["validation"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
return train_dataloader, eval_dataloader
def lowercase_ ( __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
model.eval()
snake_case__ :Union[str, Any] = 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():
snake_case__ :List[Any] = model(**__snake_case )
snake_case__ :Any = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
snake_case__ , snake_case__ :Tuple = 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:
snake_case__ :List[str] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
snake_case__ :Optional[int] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__snake_case , references=__snake_case , )
snake_case__ :int = metric.compute()
return eval_metric["accuracy"]
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : Optional[Any] ) -> Any:
'''simple docstring'''
snake_case__ :Any = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ :Union[str, Any] = config["lr"]
snake_case__ :List[str] = int(config["num_epochs"] )
snake_case__ :Optional[Any] = int(config["seed"] )
snake_case__ :List[Any] = int(config["batch_size"] )
snake_case__ :List[Any] = args.model_name_or_path
set_seed(__snake_case )
snake_case__ , snake_case__ :List[Any] = 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)
snake_case__ :List[Any] = AutoModelForSequenceClassification.from_pretrained(__snake_case , return_dict=__snake_case )
# Instantiate optimizer
snake_case__ :int = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
snake_case__ :Tuple = optimizer_cls(params=model.parameters() , lr=__snake_case )
if accelerator.state.deepspeed_plugin is not None:
snake_case__ :List[str] = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
snake_case__ :Any = 1
snake_case__ :List[Any] = (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
):
snake_case__ :Optional[Any] = get_linear_schedule_with_warmup(
optimizer=__snake_case , num_warmup_steps=0 , num_training_steps=__snake_case , )
else:
snake_case__ :Any = 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.
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ :int = 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
snake_case__ :Dict = 0
# We also need to keep track of the stating epoch so files are named properly
snake_case__ :Union[str, Any] = 0
snake_case__ :List[str] = evaluate.load("glue" , "mrpc" )
snake_case__ :Optional[Any] = num_epochs
if args.partial_train_epoch is not None:
snake_case__ :List[Any] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
snake_case__ :Union[str, Any] = args.resume_from_checkpoint.split("epoch_" )[1]
snake_case__ :Dict = ""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
snake_case__ :str = int(__snake_case ) + 1
snake_case__ :List[Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
accelerator.print("resumed checkpoint performance:" , __snake_case )
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:
snake_case__ :Tuple = json.load(__snake_case )
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
snake_case__ :Optional[int] = {}
for epoch in range(__snake_case , __snake_case ):
model.train()
for step, batch in enumerate(__snake_case ):
snake_case__ :str = model(**__snake_case )
snake_case__ :List[str] = outputs.loss
snake_case__ :List[Any] = 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
snake_case__ :int = F'epoch_{epoch}'
snake_case__ :str = os.path.join(args.output_dir , __snake_case )
accelerator.save_state(__snake_case )
snake_case__ :Union[str, Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
snake_case__ :List[str] = accuracy
snake_case__ :List[str] = lr_scheduler.get_lr()[0]
snake_case__ :List[Any] = optimizer.param_groups[0]["lr"]
snake_case__ :Dict = epoch
snake_case__ :List[Any] = overall_step
accelerator.print(F'epoch {epoch}:' , __snake_case )
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(__snake_case , __snake_case )
def lowercase_ ( ) -> Any:
'''simple docstring'''
snake_case__ :List[Any] = 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(
"--resume_from_checkpoint" , type=__snake_case , default=__snake_case , help="If the training should continue from a checkpoint folder." , )
parser.add_argument(
"--partial_train_epoch" , type=__snake_case , default=__snake_case , help="If passed, the training will stop after this number of epochs." , )
parser.add_argument(
"--num_epochs" , type=__snake_case , default=2 , help="Number of train epochs." , )
snake_case__ :Any = parser.parse_args()
snake_case__ :int = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(__snake_case , __snake_case )
if __name__ == "__main__":
main()
| 57
| 1
|
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _snake_case ( _A , unittest.TestCase ):
_A = CTRLTokenizer
_A = False
_A = False
def lowerCAmelCase_ ( self ) -> int:
super().setUp()
# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
snake_case__ :Tuple = ["adapt", "re@@", "a@@", "apt", "c@@", "t", "<unk>"]
snake_case__ :Dict = dict(zip(UpperCamelCase ,range(len(UpperCamelCase ) ) ) )
snake_case__ :Dict = ["#version: 0.2", "a p", "ap t</w>", "r e", "a d", "ad apt</w>", ""]
snake_case__ :Tuple = {"unk_token": "<unk>"}
snake_case__ :Optional[int] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
snake_case__ :Any = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(UpperCamelCase ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCamelCase ) )
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> Tuple:
kwargs.update(self.special_tokens_map )
return CTRLTokenizer.from_pretrained(self.tmpdirname ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> int:
snake_case__ :str = "adapt react readapt apt"
snake_case__ :Union[str, Any] = "adapt react readapt apt"
return input_text, output_text
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Dict = CTRLTokenizer(self.vocab_file ,self.merges_file ,**self.special_tokens_map )
snake_case__ :Dict = "adapt react readapt apt"
snake_case__ :Tuple = "adapt re@@ a@@ c@@ t re@@ adapt apt".split()
snake_case__ :str = tokenizer.tokenize(UpperCamelCase )
self.assertListEqual(UpperCamelCase ,UpperCamelCase )
snake_case__ :Optional[Any] = tokens + [tokenizer.unk_token]
snake_case__ :Tuple = [0, 1, 2, 4, 5, 1, 0, 3, 6]
self.assertListEqual(tokenizer.convert_tokens_to_ids(UpperCamelCase ) ,UpperCamelCase )
| 57
|
from __future__ import annotations
class _snake_case :
def __init__( self ,UpperCamelCase ) -> None:
snake_case__ :Union[str, Any] = data
snake_case__ :Node | None = None
snake_case__ :Node | None = None
def lowercase_ ( __snake_case : Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowercase_ ( __snake_case : Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowercase_ ( __snake_case : Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowercase_ ( ) -> None: # Main function for testing.
'''simple docstring'''
snake_case__ :Dict = Node(1 )
snake_case__ :int = Node(2 )
snake_case__ :Optional[Any] = Node(3 )
snake_case__ :Tuple = Node(4 )
snake_case__ :str = Node(5 )
snake_case__ :Optional[Any] = Node(6 )
snake_case__ :List[Any] = Node(7 )
snake_case__ :List[str] = Node(8 )
snake_case__ :Tuple = Node(9 )
print(is_full_binary_tree(__snake_case ) )
print(depth_of_tree(__snake_case ) )
print("Tree is: " )
display(__snake_case )
if __name__ == "__main__":
main()
| 57
| 1
|
import inspect
import unittest
import warnings
from math import ceil, floor
from transformers import LevitConfig
from transformers.file_utils import cached_property, 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, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin
if is_torch_available():
import torch
from transformers import (
MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
MODEL_MAPPING,
LevitForImageClassification,
LevitForImageClassificationWithTeacher,
LevitModel,
)
from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import LevitImageProcessor
class _snake_case ( _A ):
def lowerCAmelCase_ ( self ) -> List[str]:
snake_case__ :Optional[Any] = self.config_class(**self.inputs_dict )
self.parent.assertTrue(hasattr(UpperCamelCase ,"hidden_sizes" ) )
self.parent.assertTrue(hasattr(UpperCamelCase ,"num_attention_heads" ) )
class _snake_case :
def __init__( self ,UpperCamelCase ,UpperCamelCase=13 ,UpperCamelCase=64 ,UpperCamelCase=3 ,UpperCamelCase=3 ,UpperCamelCase=2 ,UpperCamelCase=1 ,UpperCamelCase=16 ,UpperCamelCase=[128, 256, 384] ,UpperCamelCase=[4, 6, 8] ,UpperCamelCase=[2, 3, 4] ,UpperCamelCase=[16, 16, 16] ,UpperCamelCase=0 ,UpperCamelCase=[2, 2, 2] ,UpperCamelCase=[2, 2, 2] ,UpperCamelCase=0.02 ,UpperCamelCase=True ,UpperCamelCase=True ,UpperCamelCase=2 ,) -> List[Any]:
snake_case__ :Union[str, Any] = parent
snake_case__ :str = batch_size
snake_case__ :Optional[Any] = image_size
snake_case__ :str = num_channels
snake_case__ :Tuple = kernel_size
snake_case__ :str = stride
snake_case__ :Optional[int] = padding
snake_case__ :List[Any] = hidden_sizes
snake_case__ :List[str] = num_attention_heads
snake_case__ :Any = depths
snake_case__ :Union[str, Any] = key_dim
snake_case__ :List[Any] = drop_path_rate
snake_case__ :List[Any] = patch_size
snake_case__ :List[str] = attention_ratio
snake_case__ :List[Any] = mlp_ratio
snake_case__ :Optional[Any] = initializer_range
snake_case__ :Union[str, Any] = [
["Subsample", key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
["Subsample", key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
]
snake_case__ :List[Any] = is_training
snake_case__ :Any = use_labels
snake_case__ :str = num_labels
snake_case__ :Union[str, Any] = initializer_range
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ :Optional[Any] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case__ :Any = None
if self.use_labels:
snake_case__ :Union[str, Any] = ids_tensor([self.batch_size] ,self.num_labels )
snake_case__ :str = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase_ ( self ) -> List[Any]:
return LevitConfig(
image_size=self.image_size ,num_channels=self.num_channels ,kernel_size=self.kernel_size ,stride=self.stride ,padding=self.padding ,patch_size=self.patch_size ,hidden_sizes=self.hidden_sizes ,num_attention_heads=self.num_attention_heads ,depths=self.depths ,key_dim=self.key_dim ,drop_path_rate=self.drop_path_rate ,mlp_ratio=self.mlp_ratio ,attention_ratio=self.attention_ratio ,initializer_range=self.initializer_range ,down_ops=self.down_ops ,)
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> int:
snake_case__ :Dict = LevitModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
snake_case__ :int = model(UpperCamelCase )
snake_case__ :Tuple = (self.image_size, self.image_size)
snake_case__ , snake_case__ :int = image_size[0], image_size[1]
for _ in range(4 ):
snake_case__ :int = floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
snake_case__ :Optional[int] = floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
self.parent.assertEqual(
result.last_hidden_state.shape ,(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1]) ,)
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Dict:
snake_case__ :List[str] = self.num_labels
snake_case__ :Any = LevitForImageClassification(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
snake_case__ :str = model(UpperCamelCase ,labels=UpperCamelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.num_labels) )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Dict = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ :List[str] = config_and_inputs
snake_case__ :int = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _snake_case ( _A , _A , unittest.TestCase ):
_A = (
(LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
if is_torch_available()
else ()
)
_A = (
{
'feature-extraction': LevitModel,
'image-classification': (LevitForImageClassification, LevitForImageClassificationWithTeacher),
}
if is_torch_available()
else {}
)
_A = False
_A = False
_A = False
_A = False
_A = False
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :List[str] = LevitModelTester(self )
snake_case__ :Tuple = ConfigTester(self ,config_class=UpperCamelCase ,has_text_modality=UpperCamelCase ,hidden_size=37 )
def lowerCAmelCase_ ( self ) -> Any:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase_ ( self ) -> Dict:
return
@unittest.skip(reason="Levit does not use inputs_embeds" )
def lowerCAmelCase_ ( self ) -> Any:
pass
@unittest.skip(reason="Levit does not support input and output embeddings" )
def lowerCAmelCase_ ( self ) -> Dict:
pass
@unittest.skip(reason="Levit does not output attentions" )
def lowerCAmelCase_ ( self ) -> int:
pass
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ , snake_case__ :Optional[Any] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ :Tuple = model_class(UpperCamelCase )
snake_case__ :Tuple = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ :Union[str, Any] = [*signature.parameters.keys()]
snake_case__ :Optional[int] = ["pixel_values"]
self.assertListEqual(arg_names[:1] ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Optional[int]:
def check_hidden_states_output(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ):
snake_case__ :Union[str, Any] = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case__ :int = model(**self._prepare_for_class(UpperCamelCase ,UpperCamelCase ) )
snake_case__ :Tuple = outputs.hidden_states
snake_case__ :str = len(self.model_tester.depths ) + 1
self.assertEqual(len(UpperCamelCase ) ,UpperCamelCase )
snake_case__ :Optional[Any] = (self.model_tester.image_size, self.model_tester.image_size)
snake_case__ , snake_case__ :List[str] = image_size[0], image_size[1]
for _ in range(4 ):
snake_case__ :str = floor(
(
(height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
snake_case__ :List[str] = floor(
(
(width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
/ self.model_tester.stride
)
+ 1 )
# verify the first hidden states (first block)
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[
height * width,
self.model_tester.hidden_sizes[0],
] ,)
snake_case__ , snake_case__ :Dict = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes:
snake_case__ :Dict = True
check_hidden_states_output(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case__ :Dict = True
check_hidden_states_output(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
pass
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=False ) -> str:
snake_case__ :Tuple = super()._prepare_for_class(UpperCamelCase ,UpperCamelCase ,return_labels=UpperCamelCase )
if return_labels:
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
del inputs_dict["labels"]
return inputs_dict
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Union[str, Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :int = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[str]:
if not self.model_tester.is_training:
return
snake_case__ , snake_case__ :Union[str, Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ :int = True
for model_class in self.all_model_classes:
# LevitForImageClassificationWithTeacher supports inference-only
if (
model_class in get_values(UpperCamelCase )
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
snake_case__ :Optional[int] = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.train()
snake_case__ :Dict = self._prepare_for_class(UpperCamelCase ,UpperCamelCase ,return_labels=UpperCamelCase )
snake_case__ :List[Any] = model(**UpperCamelCase ).loss
loss.backward()
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ , snake_case__ :Optional[int] = self.model_tester.prepare_config_and_inputs_for_common()
if not self.model_tester.is_training:
return
snake_case__ :int = False
snake_case__ :Any = True
for model_class in self.all_model_classes:
if model_class in get_values(UpperCamelCase ) or not model_class.supports_gradient_checkpointing:
continue
# LevitForImageClassificationWithTeacher supports inference-only
if model_class.__name__ == "LevitForImageClassificationWithTeacher":
continue
snake_case__ :str = model_class(UpperCamelCase )
model.gradient_checkpointing_enable()
model.to(UpperCamelCase )
model.train()
snake_case__ :Any = self._prepare_for_class(UpperCamelCase ,UpperCamelCase ,return_labels=UpperCamelCase )
snake_case__ :List[Any] = model(**UpperCamelCase ).loss
loss.backward()
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ , snake_case__ :Dict = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ :Dict = [
{"title": "multi_label_classification", "num_labels": 2, "dtype": torch.float},
{"title": "single_label_classification", "num_labels": 1, "dtype": torch.long},
{"title": "regression", "num_labels": 1, "dtype": torch.float},
]
for model_class in self.all_model_classes:
if (
model_class
not in [
*get_values(UpperCamelCase ),
]
or model_class.__name__ == "LevitForImageClassificationWithTeacher"
):
continue
for problem_type in problem_types:
with self.subTest(msg=f'Testing {model_class} with {problem_type["title"]}' ):
snake_case__ :int = problem_type["title"]
snake_case__ :Union[str, Any] = problem_type["num_labels"]
snake_case__ :Any = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.train()
snake_case__ :Optional[int] = self._prepare_for_class(UpperCamelCase ,UpperCamelCase ,return_labels=UpperCamelCase )
if problem_type["num_labels"] > 1:
snake_case__ :Optional[int] = inputs["labels"].unsqueeze(1 ).repeat(1 ,problem_type["num_labels"] )
snake_case__ :Dict = inputs["labels"].to(problem_type["dtype"] )
# This tests that we do not trigger the warning form PyTorch "Using a target size that is different
# to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
# they have the same size." which is a symptom something in wrong for the regression problem.
# See https://github.com/huggingface/transformers/issues/11780
with warnings.catch_warnings(record=UpperCamelCase ) as warning_list:
snake_case__ :Optional[int] = model(**UpperCamelCase ).loss
for w in warning_list:
if "Using a target size that is different to the input size" in str(w.message ):
raise ValueError(
f'Something is going wrong in the regression problem: intercepted {w.message}' )
loss.backward()
@slow
def lowerCAmelCase_ ( self ) -> Optional[int]:
for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case__ :List[Any] = LevitModel.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
def lowercase_ ( ) -> Dict:
'''simple docstring'''
snake_case__ :Union[str, Any] = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
return image
@require_torch
@require_vision
class _snake_case ( unittest.TestCase ):
@cached_property
def lowerCAmelCase_ ( self ) -> Tuple:
return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )
@slow
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Dict = LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
UpperCamelCase )
snake_case__ :Union[str, Any] = self.default_image_processor
snake_case__ :Optional[int] = prepare_img()
snake_case__ :int = image_processor(images=UpperCamelCase ,return_tensors="pt" ).to(UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case__ :Optional[int] = model(**UpperCamelCase )
# verify the logits
snake_case__ :Union[str, Any] = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape ,UpperCamelCase )
snake_case__ :str = torch.tensor([1.0448, -0.3745, -1.8317] ).to(UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,UpperCamelCase ,atol=1E-4 ) )
| 57
|
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__UpperCAmelCase : List[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__UpperCAmelCase : int = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F'''{len(upper_files)} files contain uppercase characters:''')
print("\n".join(upper_files) + "\n")
__UpperCAmelCase : Any = [file for file in filepaths if " " in file]
if space_files:
print(F'''{len(space_files)} files contain space characters:''')
print("\n".join(space_files) + "\n")
__UpperCAmelCase : str = [file for file in filepaths if "-" in file]
if hyphen_files:
print(F'''{len(hyphen_files)} files contain hyphen characters:''')
print("\n".join(hyphen_files) + "\n")
__UpperCAmelCase : Dict = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F'''{len(nodir_files)} files are not in a directory:''')
print("\n".join(nodir_files) + "\n")
__UpperCAmelCase : int = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 57
| 1
|
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _snake_case ( _A ):
@staticmethod
@abstractmethod
def lowerCAmelCase_ ( UpperCamelCase ) -> Optional[int]:
raise NotImplementedError()
@abstractmethod
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
raise NotImplementedError()
| 57
|
def lowercase_ ( __snake_case : Tuple , __snake_case : Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case__ :Dict = ""
for i in table:
res += inp[i - 1]
return res
def lowercase_ ( __snake_case : List[str] ) -> int:
'''simple docstring'''
return data[1:] + data[0]
def lowercase_ ( __snake_case : int , __snake_case : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :Union[str, Any] = ""
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def lowercase_ ( __snake_case : Optional[int] , __snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :int = int("0b" + data[0] + data[-1] , 2 )
snake_case__ :Union[str, Any] = int("0b" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def lowercase_ ( __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : List[Any] , __snake_case : Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case__ :Tuple = message[:4]
snake_case__ :int = message[4:]
snake_case__ :int = apply_table(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = xor(__snake_case , __snake_case )
snake_case__ :Tuple = apply_sbox(__snake_case , temp[:4] ) # noqa: E741
snake_case__ :List[str] = apply_sbox(__snake_case , temp[4:] )
snake_case__ :int = "0" * (2 - len(__snake_case )) + l # noqa: E741
snake_case__ :int = "0" * (2 - len(__snake_case )) + r
snake_case__ :Optional[Any] = apply_table(l + r , __snake_case )
snake_case__ :Tuple = xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
__UpperCAmelCase : Dict = input("Enter 10 bit key: ")
__UpperCAmelCase : Tuple = input("Enter 8 bit message: ")
__UpperCAmelCase : Any = [6, 3, 7, 4, 8, 5, 1_0, 9]
__UpperCAmelCase : List[str] = [3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
__UpperCAmelCase : Tuple = [2, 4, 3, 1]
__UpperCAmelCase : List[Any] = [2, 6, 3, 1, 4, 8, 5, 7]
__UpperCAmelCase : Optional[Any] = [4, 1, 3, 5, 7, 2, 8, 6]
__UpperCAmelCase : Optional[int] = [4, 1, 2, 3, 2, 3, 4, 1]
__UpperCAmelCase : List[Any] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
__UpperCAmelCase : Union[str, Any] = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
__UpperCAmelCase : int = apply_table(key, paa_table)
__UpperCAmelCase : Dict = temp[:5]
__UpperCAmelCase : Optional[int] = temp[5:]
__UpperCAmelCase : Optional[int] = left_shift(left)
__UpperCAmelCase : Union[str, Any] = left_shift(right)
__UpperCAmelCase : int = apply_table(left + right, pa_table)
__UpperCAmelCase : Tuple = left_shift(left)
__UpperCAmelCase : Union[str, Any] = left_shift(right)
__UpperCAmelCase : Dict = left_shift(left)
__UpperCAmelCase : Optional[Any] = left_shift(right)
__UpperCAmelCase : Optional[int] = apply_table(left + right, pa_table)
# encryption
__UpperCAmelCase : Tuple = apply_table(message, IP)
__UpperCAmelCase : Tuple = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : List[Any] = temp[4:] + temp[:4]
__UpperCAmelCase : int = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : Union[str, Any] = apply_table(temp, IP_inv)
print("Cipher text is:", CT)
# decryption
__UpperCAmelCase : List[Any] = apply_table(CT, IP)
__UpperCAmelCase : List[Any] = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : int = temp[4:] + temp[:4]
__UpperCAmelCase : Union[str, Any] = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : Union[str, Any] = apply_table(temp, IP_inv)
print("Plain text after decypting is:", PT)
| 57
| 1
|
# Copyright 2022 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import os
import platform
import numpy as np
import psutil
import torch
from accelerate import __version__ as version
from accelerate.commands.config import default_config_file, load_config_from_file
from ..utils import is_npu_available, is_xpu_available
def lowercase_ ( __snake_case : Optional[Any]=None ) -> str:
'''simple docstring'''
if subparsers is not None:
snake_case__ :Optional[Any] = subparsers.add_parser("env" )
else:
snake_case__ :List[str] = argparse.ArgumentParser("Accelerate env command" )
parser.add_argument(
"--config_file" , default=__snake_case , help="The config file to use for the default values in the launching script." )
if subparsers is not None:
parser.set_defaults(func=__snake_case )
return parser
def lowercase_ ( __snake_case : int ) -> int:
'''simple docstring'''
snake_case__ :Dict = torch.__version__
snake_case__ :int = torch.cuda.is_available()
snake_case__ :str = is_xpu_available()
snake_case__ :List[str] = is_npu_available()
snake_case__ :List[str] = "Not found"
# Get the default from the config file.
if args.config_file is not None or os.path.isfile(__snake_case ):
snake_case__ :List[Any] = load_config_from_file(args.config_file ).to_dict()
snake_case__ :Union[str, Any] = {
"`Accelerate` version": version,
"Platform": platform.platform(),
"Python version": platform.python_version(),
"Numpy version": np.__version__,
"PyTorch version (GPU?)": F'{pt_version} ({pt_cuda_available})',
"PyTorch XPU available": str(__snake_case ),
"PyTorch NPU available": str(__snake_case ),
"System RAM": F'{psutil.virtual_memory().total / 10_24 ** 3:.2f} GB',
}
if pt_cuda_available:
snake_case__ :Dict = torch.cuda.get_device_name()
print("\nCopy-and-paste the text below in your GitHub issue\n" )
print("\n".join([F'- {prop}: {val}' for prop, val in info.items()] ) )
print("- `Accelerate` default config:" if args.config_file is None else "- `Accelerate` config passed:" )
snake_case__ :List[str] = (
"\n".join([F'\t- {prop}: {val}' for prop, val in accelerate_config.items()] )
if isinstance(__snake_case , __snake_case )
else F'\t{accelerate_config}'
)
print(__snake_case )
snake_case__ :str = accelerate_config
return info
def lowercase_ ( ) -> int:
'''simple docstring'''
snake_case__ :Union[str, Any] = env_command_parser()
snake_case__ :Union[str, Any] = parser.parse_args()
env_command(__snake_case )
return 0
if __name__ == "__main__":
raise SystemExit(main())
| 57
|
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _snake_case ( _A , _A , _A ):
@register_to_config
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = False ,) -> int:
super().__init__()
snake_case__ :Union[str, Any] = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :int = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :Any = False
snake_case__ :List[Any] = nn.Dropout(p=UpperCamelCase )
snake_case__ :Tuple = TaConfig(
vocab_size=UpperCamelCase ,d_model=UpperCamelCase ,num_heads=UpperCamelCase ,d_kv=UpperCamelCase ,d_ff=UpperCamelCase ,dropout_rate=UpperCamelCase ,feed_forward_proj=UpperCamelCase ,is_decoder=UpperCamelCase ,is_encoder_decoder=UpperCamelCase ,)
snake_case__ :List[str] = nn.ModuleList()
for lyr_num in range(UpperCamelCase ):
snake_case__ :List[Any] = TaBlock(UpperCamelCase )
self.encoders.append(UpperCamelCase )
snake_case__ :Optional[Any] = TaLayerNorm(UpperCamelCase )
snake_case__ :Any = nn.Dropout(p=UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> int:
snake_case__ :str = self.token_embedder(UpperCamelCase )
snake_case__ :int = encoder_input_tokens.shape[1]
snake_case__ :List[Any] = torch.arange(UpperCamelCase ,device=encoder_input_tokens.device )
x += self.position_encoding(UpperCamelCase )
snake_case__ :Optional[int] = self.dropout_pre(UpperCamelCase )
# inverted the attention mask
snake_case__ :Optional[Any] = encoder_input_tokens.size()
snake_case__ :Dict = self.get_extended_attention_mask(UpperCamelCase ,UpperCamelCase )
for lyr in self.encoders:
snake_case__ :str = lyr(UpperCamelCase ,UpperCamelCase )[0]
snake_case__ :List[Any] = self.layer_norm(UpperCamelCase )
return self.dropout_post(UpperCamelCase ), encoder_inputs_mask
| 57
| 1
|
import argparse
import json
from typing import List
from ltp import LTP
from transformers import BertTokenizer
def lowercase_ ( __snake_case : Optional[int] ) -> Optional[int]:
'''simple docstring'''
if (
(cp >= 0X4_e00 and cp <= 0X9_fff)
or (cp >= 0X3_400 and cp <= 0X4_dbf) #
or (cp >= 0X20_000 and cp <= 0X2a_6df) #
or (cp >= 0X2a_700 and cp <= 0X2b_73f) #
or (cp >= 0X2b_740 and cp <= 0X2b_81f) #
or (cp >= 0X2b_820 and cp <= 0X2c_eaf) #
or (cp >= 0Xf_900 and cp <= 0Xf_aff)
or (cp >= 0X2f_800 and cp <= 0X2f_a1f) #
): #
return True
return False
def lowercase_ ( __snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
for char in word:
snake_case__ :Optional[Any] = ord(__snake_case )
if not _is_chinese_char(__snake_case ):
return 0
return 1
def lowercase_ ( __snake_case : List[str] ) -> Any:
'''simple docstring'''
snake_case__ :List[Any] = set()
for token in tokens:
snake_case__ :Optional[Any] = len(__snake_case ) > 1 and is_chinese(__snake_case )
if chinese_word:
word_set.add(__snake_case )
snake_case__ :List[str] = list(__snake_case )
return word_list
def lowercase_ ( __snake_case : List[str] , __snake_case : set() ) -> Dict:
'''simple docstring'''
if not chinese_word_set:
return bert_tokens
snake_case__ :Dict = max([len(__snake_case ) for w in chinese_word_set] )
snake_case__ :int = bert_tokens
snake_case__ , snake_case__ :Optional[Any] = 0, len(__snake_case )
while start < end:
snake_case__ :int = True
if is_chinese(bert_word[start] ):
snake_case__ :Any = min(end - start , __snake_case )
for i in range(__snake_case , 1 , -1 ):
snake_case__ :List[Any] = "".join(bert_word[start : start + i] )
if whole_word in chinese_word_set:
for j in range(start + 1 , start + i ):
snake_case__ :int = "##" + bert_word[j]
snake_case__ :Optional[int] = start + i
snake_case__ :Tuple = False
break
if single_word:
start += 1
return bert_word
def lowercase_ ( __snake_case : List[str] , __snake_case : LTP , __snake_case : BertTokenizer ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :List[str] = []
for i in range(0 , len(__snake_case ) , 1_00 ):
snake_case__ :Dict = ltp_tokenizer.seg(lines[i : i + 1_00] )[0]
snake_case__ :Dict = [get_chinese_word(__snake_case ) for r in res]
ltp_res.extend(__snake_case )
assert len(__snake_case ) == len(__snake_case )
snake_case__ :List[Any] = []
for i in range(0 , len(__snake_case ) , 1_00 ):
snake_case__ :List[str] = bert_tokenizer(lines[i : i + 1_00] , add_special_tokens=__snake_case , truncation=__snake_case , max_length=5_12 )
bert_res.extend(res["input_ids"] )
assert len(__snake_case ) == len(__snake_case )
snake_case__ :Optional[Any] = []
for input_ids, chinese_word in zip(__snake_case , __snake_case ):
snake_case__ :str = []
for id in input_ids:
snake_case__ :List[Any] = bert_tokenizer._convert_id_to_token(__snake_case )
input_tokens.append(__snake_case )
snake_case__ :Union[str, Any] = add_sub_symbol(__snake_case , __snake_case )
snake_case__ :Optional[int] = []
# We only save pos of chinese subwords start with ##, which mean is part of a whole word.
for i, token in enumerate(__snake_case ):
if token[:2] == "##":
snake_case__ :Dict = token[2:]
# save chinese tokens' pos
if len(__snake_case ) == 1 and _is_chinese_char(ord(__snake_case ) ):
ref_id.append(__snake_case )
ref_ids.append(__snake_case )
assert len(__snake_case ) == len(__snake_case )
return ref_ids
def lowercase_ ( __snake_case : List[Any] ) -> List[str]:
'''simple docstring'''
with open(args.file_name , "r" , encoding="utf-8" ) as f:
snake_case__ :Dict = f.readlines()
snake_case__ :Dict = [line.strip() for line in data if len(__snake_case ) > 0 and not line.isspace()] # avoid delimiter like '\u2029'
snake_case__ :str = LTP(args.ltp ) # faster in GPU device
snake_case__ :List[str] = BertTokenizer.from_pretrained(args.bert )
snake_case__ :List[Any] = prepare_ref(__snake_case , __snake_case , __snake_case )
with open(args.save_path , "w" , encoding="utf-8" ) as f:
snake_case__ :int = [json.dumps(__snake_case ) + "\n" for ref in ref_ids]
f.writelines(__snake_case )
if __name__ == "__main__":
__UpperCAmelCase : Optional[int] = argparse.ArgumentParser(description="prepare_chinese_ref")
parser.add_argument(
"--file_name",
type=str,
default="./resources/chinese-demo.txt",
help="file need process, same as training data in lm",
)
parser.add_argument(
"--ltp", type=str, default="./resources/ltp", help="resources for LTP tokenizer, usually a path"
)
parser.add_argument("--bert", type=str, default="./resources/robert", help="resources for Bert tokenizer")
parser.add_argument("--save_path", type=str, default="./resources/ref.txt", help="path to save res")
__UpperCAmelCase : List[Any] = parser.parse_args()
main(args)
| 57
|
__UpperCAmelCase : int = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__UpperCAmelCase : List[str] = ["a", "b", "c", "d", "e"]
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : List[Any] , __snake_case : Tuple ) -> Optional[int]:
'''simple docstring'''
snake_case__ :List[Any] = start
# add current to visited
visited.append(__snake_case )
snake_case__ :List[str] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# if all neighbors visited add current to sort
sort.append(__snake_case )
# if all vertices haven't been visited select a new one to visit
if len(__snake_case ) != len(__snake_case ):
for vertice in vertices:
if vertice not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# return sort
return sort
if __name__ == "__main__":
__UpperCAmelCase : Tuple = topological_sort("a", [], [])
print(sort)
| 57
| 1
|
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__UpperCAmelCase : List[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__UpperCAmelCase : int = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F'''{len(upper_files)} files contain uppercase characters:''')
print("\n".join(upper_files) + "\n")
__UpperCAmelCase : Any = [file for file in filepaths if " " in file]
if space_files:
print(F'''{len(space_files)} files contain space characters:''')
print("\n".join(space_files) + "\n")
__UpperCAmelCase : str = [file for file in filepaths if "-" in file]
if hyphen_files:
print(F'''{len(hyphen_files)} files contain hyphen characters:''')
print("\n".join(hyphen_files) + "\n")
__UpperCAmelCase : Dict = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F'''{len(nodir_files)} files are not in a directory:''')
print("\n".join(nodir_files) + "\n")
__UpperCAmelCase : int = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 57
|
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 _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCAmelCase_ ( self ) -> str:
snake_case__ , snake_case__ :Tuple = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-canny" ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ , snake_case__ :Any = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCamelCase ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ :List[str] = controlnet_params
snake_case__ :Union[str, Any] = "bird"
snake_case__ :Optional[int] = jax.device_count()
snake_case__ :Tuple = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case__ :Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" )
snake_case__ :str = pipe.prepare_image_inputs([canny_image] * num_samples )
snake_case__ :List[str] = jax.random.PRNGKey(0 )
snake_case__ :str = jax.random.split(UpperCamelCase ,jax.device_count() )
snake_case__ :int = replicate(UpperCamelCase )
snake_case__ :Any = shard(UpperCamelCase )
snake_case__ :Any = shard(UpperCamelCase )
snake_case__ :str = pipe(
prompt_ids=UpperCamelCase ,image=UpperCamelCase ,params=UpperCamelCase ,prng_seed=UpperCamelCase ,num_inference_steps=50 ,jit=UpperCamelCase ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
snake_case__ :List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case__ :Any = images[0, 253:256, 253:256, -1]
snake_case__ :Union[str, Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case__ :List[Any] = jnp.array(
[0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ , snake_case__ :List[str] = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-openpose" ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ , snake_case__ :Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCamelCase ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ :str = controlnet_params
snake_case__ :int = "Chef in the kitchen"
snake_case__ :List[Any] = jax.device_count()
snake_case__ :Dict = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case__ :Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" )
snake_case__ :Optional[int] = pipe.prepare_image_inputs([pose_image] * num_samples )
snake_case__ :List[str] = jax.random.PRNGKey(0 )
snake_case__ :Any = jax.random.split(UpperCamelCase ,jax.device_count() )
snake_case__ :Dict = replicate(UpperCamelCase )
snake_case__ :Tuple = shard(UpperCamelCase )
snake_case__ :Optional[int] = shard(UpperCamelCase )
snake_case__ :Optional[Any] = pipe(
prompt_ids=UpperCamelCase ,image=UpperCamelCase ,params=UpperCamelCase ,prng_seed=UpperCamelCase ,num_inference_steps=50 ,jit=UpperCamelCase ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
snake_case__ :int = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case__ :List[str] = images[0, 253:256, 253:256, -1]
snake_case__ :Tuple = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case__ :List[str] = jnp.array(
[[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 57
| 1
|
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaFeatureExtractor,
WavaVecaModel,
logging,
)
logging.set_verbosity_info()
__UpperCAmelCase : Any = logging.get_logger(__name__)
__UpperCAmelCase : Optional[Any] = {
"post_extract_proj": "feature_projection.projection",
"encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
"self_attn.k_proj": "encoder.layers.*.attention.k_proj",
"self_attn.v_proj": "encoder.layers.*.attention.v_proj",
"self_attn.q_proj": "encoder.layers.*.attention.q_proj",
"self_attn.out_proj": "encoder.layers.*.attention.out_proj",
"self_attn_layer_norm": "encoder.layers.*.layer_norm",
"fc1": "encoder.layers.*.feed_forward.intermediate_dense",
"fc2": "encoder.layers.*.feed_forward.output_dense",
"final_layer_norm": "encoder.layers.*.final_layer_norm",
"encoder.layer_norm": "encoder.layer_norm",
"w2v_model.layer_norm": "feature_projection.layer_norm",
"quantizer.weight_proj": "quantizer.weight_proj",
"quantizer.vars": "quantizer.codevectors",
"project_q": "project_q",
"final_proj": "project_hid",
"w2v_encoder.proj": "lm_head",
"mask_emb": "masked_spec_embed",
}
__UpperCAmelCase : List[Any] = [
"lm_head",
"quantizer.weight_proj",
"quantizer.codevectors",
"project_q",
"project_hid",
]
def lowercase_ ( __snake_case : Optional[int] , __snake_case : Union[str, Any] , __snake_case : Optional[int] , __snake_case : int , __snake_case : Union[str, Any] ) -> str:
'''simple docstring'''
for attribute in key.split("." ):
snake_case__ :Union[str, Any] = getattr(__snake_case , __snake_case )
if weight_type is not None:
snake_case__ :Optional[Any] = getattr(__snake_case , __snake_case ).shape
else:
snake_case__ :str = hf_pointer.shape
assert hf_shape == value.shape, (
F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
F' {value.shape} for {full_name}'
)
if weight_type == "weight":
snake_case__ :Optional[int] = value
elif weight_type == "weight_g":
snake_case__ :Union[str, Any] = value
elif weight_type == "weight_v":
snake_case__ :Any = value
elif weight_type == "bias":
snake_case__ :int = value
else:
snake_case__ :Optional[int] = value
logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.' )
def lowercase_ ( __snake_case : Any , __snake_case : Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case__ :Any = []
snake_case__ :Any = fairseq_model.state_dict()
snake_case__ :Tuple = hf_model.feature_extractor
# if encoder has different dim to decoder -> use proj_weight
snake_case__ :str = None
for name, value in fairseq_dict.items():
snake_case__ :str = False
if "conv_layers" in name:
load_conv_layer(
__snake_case , __snake_case , __snake_case , __snake_case , hf_model.config.feat_extract_norm == "group" , )
snake_case__ :int = True
elif name.split("." )[0] == "proj":
snake_case__ :Tuple = fairseq_model.proj
snake_case__ :Optional[Any] = True
else:
for key, mapped_key in MAPPING.items():
if key in name or key.split("w2v_model." )[-1] == name.split("." )[0]:
snake_case__ :Optional[int] = True
if "*" in mapped_key:
snake_case__ :Tuple = name.split(__snake_case )[0].split("." )[-2]
snake_case__ :Tuple = mapped_key.replace("*" , __snake_case )
if "weight_g" in name:
snake_case__ :Dict = "weight_g"
elif "weight_v" in name:
snake_case__ :Union[str, Any] = "weight_v"
elif "bias" in name:
snake_case__ :Optional[int] = "bias"
elif "weight" in name:
snake_case__ :List[Any] = "weight"
else:
snake_case__ :Any = None
set_recursively(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
continue
if not is_used:
unused_weights.append(__snake_case )
logger.warning(F'Unused weights: {unused_weights}' )
return proj_weight
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : List[Any] , __snake_case : List[str] , __snake_case : Dict ) -> List[str]:
'''simple docstring'''
snake_case__ :Tuple = full_name.split("conv_layers." )[-1]
snake_case__ :Optional[int] = name.split("." )
snake_case__ :Tuple = int(items[0] )
snake_case__ :List[str] = int(items[1] )
if type_id == 0:
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
)
snake_case__ :Tuple = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
)
snake_case__ :List[Any] = value
logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.' )
elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
if "bias" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
" found."
)
snake_case__ :Any = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
elif "weight" in name:
assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
F'{full_name} has size {value.shape}, but'
F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
)
snake_case__ :Dict = value
logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.' )
else:
unused_weights.append(__snake_case )
def lowercase_ ( __snake_case : int ) -> Dict:
'''simple docstring'''
snake_case__ , snake_case__ :Optional[int] = emb.weight.shape
snake_case__ :Union[str, Any] = nn.Linear(__snake_case , __snake_case , bias=__snake_case )
snake_case__ :str = emb.weight.data
return lin_layer
def lowercase_ ( __snake_case : Any ) -> Optional[Any]:
'''simple docstring'''
with open(__snake_case , "r" , encoding="utf-8" ) as f:
snake_case__ :Union[str, Any] = f.readlines()
snake_case__ :Dict = [line.split(" " )[0] for line in lines]
snake_case__ :Union[str, Any] = len(__snake_case )
snake_case__ :int = {
"<s>": 0,
"<pad>": 1,
"</s>": 2,
"<unk>": 3,
}
vocab_dict.update(dict(zip(__snake_case , range(4 , num_words + 4 ) ) ) )
return vocab_dict
@torch.no_grad()
def lowercase_ ( __snake_case : Dict , __snake_case : Tuple , __snake_case : List[str] , __snake_case : Any , __snake_case : Tuple , __snake_case : int , __snake_case : List[str] , ) -> str:
'''simple docstring'''
snake_case__ :List[str] = WavaVecaConfig.from_pretrained(__snake_case )
snake_case__ :Optional[int] = SpeechaTextaConfig.from_pretrained(
__snake_case , vocab_size=__snake_case , decoder_layers=__snake_case , do_stable_layer_norm=__snake_case )
snake_case__ :Dict = WavaVecaFeatureExtractor(
feature_size=1 , sampling_rate=1_60_00 , padding_value=0 , do_normalize=__snake_case , return_attention_mask=__snake_case , )
snake_case__ , snake_case__ , snake_case__ :Any = fairseq.checkpoint_utils.load_model_ensemble_and_task(
[checkpoint_path] , arg_overrides={"data": "/".join(dict_path.split("/" )[:-1] )} )
snake_case__ :Optional[int] = model[0].eval()
# set weights for wav2vec2 encoder
snake_case__ :str = WavaVecaModel(__snake_case )
snake_case__ :Optional[int] = recursively_load_weights_wavaveca(model.encoder , __snake_case )
snake_case__ :Union[str, Any] = SpeechaTextaForCausalLM(__snake_case )
snake_case__ , snake_case__ :Optional[int] = hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict() , strict=__snake_case )
# set output linear layer
unexpected_keys.remove("embed_out" )
snake_case__ :int = nn.Parameter(model.decoder.embed_out.detach() )
# layer norm is init to identity matrix so leaving it is fine
logger.warning(F'The following keys are missing when loading the decoder weights: {missing_keys}' )
logger.warning(F'The following keys are unexpected when loading the decoder weights: {unexpected_keys}' )
snake_case__ :List[Any] = SpeechEncoderDecoderModel(encoder=__snake_case , decoder=__snake_case )
snake_case__ :List[Any] = False
# add projection layer
snake_case__ :int = nn.Parameter(projection_layer.weight )
snake_case__ :Optional[int] = nn.Parameter(projection_layer.bias )
snake_case__ :Union[str, Any] = create_vocab_dict(__snake_case )
with open(os.path.join(__snake_case , "vocab.json" ) , "w" ) as fp:
json.dump(__snake_case , __snake_case )
snake_case__ :Tuple = SpeechaTextaTokenizer(os.path.join(__snake_case , "vocab.json" ) )
tokenizer.save_pretrained(__snake_case )
snake_case__ :Dict = hf_wavavec.config.to_dict()
snake_case__ :int = tokenizer.pad_token_id
snake_case__ :Union[str, Any] = tokenizer.bos_token_id
snake_case__ :Tuple = tokenizer.eos_token_id
snake_case__ :Optional[Any] = "speech_to_text_2"
snake_case__ :Optional[int] = "wav2vec2"
snake_case__ :Union[str, Any] = SpeechEncoderDecoderConfig.from_dict(__snake_case )
hf_wavavec.save_pretrained(__snake_case )
feature_extractor.save_pretrained(__snake_case )
if __name__ == "__main__":
__UpperCAmelCase : Union[str, Any] = 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(
"--encoder_config_path",
default="facebook/wav2vec2-large-lv60",
type=str,
help="Path to hf encoder wav2vec2 checkpoint config",
)
parser.add_argument(
"--decoder_config_path",
default="facebook/s2t-small-mustc-en-fr-st",
type=str,
help="Path to hf decoder s2t checkpoint config",
)
parser.add_argument("--vocab_size", default=1_0_2_2_4, type=int, help="Vocab size of decoder")
parser.add_argument("--num_decoder_layers", default=7, type=int, help="Number of decoder layers")
__UpperCAmelCase : Union[str, Any] = parser.parse_args()
convert_wavaveca_checkpoint(
args.checkpoint_path,
args.pytorch_dump_folder_path,
args.dict_path,
encoder_config_path=args.encoder_config_path,
decoder_config_path=args.decoder_config_path,
vocab_size=args.vocab_size,
num_decoder_layers=args.num_decoder_layers,
)
| 57
|
def lowercase_ ( __snake_case : list ) -> list:
'''simple docstring'''
if any(not isinstance(__snake_case , __snake_case ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(__snake_case ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 57
| 1
|
__UpperCAmelCase : Any = "0.18.2"
from .configuration_utils import ConfigMixin
from .utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_inflect_available,
is_invisible_watermark_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_librosa_available,
is_note_seq_available,
is_onnx_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
is_transformers_available,
is_transformers_version,
is_unidecode_available,
logging,
)
try:
if not is_onnx_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_onnx_objects import * # noqa F403
else:
from .pipelines import OnnxRuntimeModel
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_pt_objects import * # noqa F403
else:
from .models import (
AutoencoderKL,
ControlNetModel,
ModelMixin,
PriorTransformer,
TaFilmDecoder,
TransformeraDModel,
UNetaDModel,
UNetaDConditionModel,
UNetaDModel,
UNetaDConditionModel,
VQModel,
)
from .optimization import (
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
get_scheduler,
)
from .pipelines import (
AudioPipelineOutput,
ConsistencyModelPipeline,
DanceDiffusionPipeline,
DDIMPipeline,
DDPMPipeline,
DiffusionPipeline,
DiTPipeline,
ImagePipelineOutput,
KarrasVePipeline,
LDMPipeline,
LDMSuperResolutionPipeline,
PNDMPipeline,
RePaintPipeline,
ScoreSdeVePipeline,
)
from .schedulers import (
CMStochasticIterativeScheduler,
DDIMInverseScheduler,
DDIMParallelScheduler,
DDIMScheduler,
DDPMParallelScheduler,
DDPMScheduler,
DEISMultistepScheduler,
DPMSolverMultistepInverseScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
HeunDiscreteScheduler,
IPNDMScheduler,
KarrasVeScheduler,
KDPMaAncestralDiscreteScheduler,
KDPMaDiscreteScheduler,
PNDMScheduler,
RePaintScheduler,
SchedulerMixin,
ScoreSdeVeScheduler,
UnCLIPScheduler,
UniPCMultistepScheduler,
VQDiffusionScheduler,
)
from .training_utils import EMAModel
try:
if not (is_torch_available() and is_scipy_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_scipy_objects import * # noqa F403
else:
from .schedulers import LMSDiscreteScheduler
try:
if not (is_torch_available() and is_torchsde_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_torchsde_objects import * # noqa F403
else:
from .schedulers import DPMSolverSDEScheduler
try:
if not (is_torch_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
AltDiffusionImgaImgPipeline,
AltDiffusionPipeline,
AudioLDMPipeline,
CycleDiffusionPipeline,
IFImgaImgPipeline,
IFImgaImgSuperResolutionPipeline,
IFInpaintingPipeline,
IFInpaintingSuperResolutionPipeline,
IFPipeline,
IFSuperResolutionPipeline,
ImageTextPipelineOutput,
KandinskyImgaImgPipeline,
KandinskyInpaintPipeline,
KandinskyPipeline,
KandinskyPriorPipeline,
KandinskyVaaControlnetImgaImgPipeline,
KandinskyVaaControlnetPipeline,
KandinskyVaaImgaImgPipeline,
KandinskyVaaInpaintPipeline,
KandinskyVaaPipeline,
KandinskyVaaPriorEmbaEmbPipeline,
KandinskyVaaPriorPipeline,
LDMTextToImagePipeline,
PaintByExamplePipeline,
SemanticStableDiffusionPipeline,
ShapEImgaImgPipeline,
ShapEPipeline,
StableDiffusionAttendAndExcitePipeline,
StableDiffusionControlNetImgaImgPipeline,
StableDiffusionControlNetInpaintPipeline,
StableDiffusionControlNetPipeline,
StableDiffusionDepthaImgPipeline,
StableDiffusionDiffEditPipeline,
StableDiffusionImageVariationPipeline,
StableDiffusionImgaImgPipeline,
StableDiffusionInpaintPipeline,
StableDiffusionInpaintPipelineLegacy,
StableDiffusionInstructPixaPixPipeline,
StableDiffusionLatentUpscalePipeline,
StableDiffusionLDMaDPipeline,
StableDiffusionModelEditingPipeline,
StableDiffusionPanoramaPipeline,
StableDiffusionParadigmsPipeline,
StableDiffusionPipeline,
StableDiffusionPipelineSafe,
StableDiffusionPixaPixZeroPipeline,
StableDiffusionSAGPipeline,
StableDiffusionUpscalePipeline,
StableUnCLIPImgaImgPipeline,
StableUnCLIPPipeline,
TextToVideoSDPipeline,
TextToVideoZeroPipeline,
UnCLIPImageVariationPipeline,
UnCLIPPipeline,
UniDiffuserModel,
UniDiffuserPipeline,
UniDiffuserTextDecoder,
VersatileDiffusionDualGuidedPipeline,
VersatileDiffusionImageVariationPipeline,
VersatileDiffusionPipeline,
VersatileDiffusionTextToImagePipeline,
VideoToVideoSDPipeline,
VQDiffusionPipeline,
)
try:
if not (is_torch_available() and is_transformers_available() and is_invisible_watermark_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_invisible_watermark_objects import * # noqa F403
else:
from .pipelines import StableDiffusionXLImgaImgPipeline, StableDiffusionXLPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_k_diffusion_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_k_diffusion_objects import * # noqa F403
else:
from .pipelines import StableDiffusionKDiffusionPipeline
try:
if not (is_torch_available() and is_transformers_available() and is_onnx_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_transformers_and_onnx_objects import * # noqa F403
else:
from .pipelines import (
OnnxStableDiffusionImgaImgPipeline,
OnnxStableDiffusionInpaintPipeline,
OnnxStableDiffusionInpaintPipelineLegacy,
OnnxStableDiffusionPipeline,
OnnxStableDiffusionUpscalePipeline,
StableDiffusionOnnxPipeline,
)
try:
if not (is_torch_available() and is_librosa_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_torch_and_librosa_objects import * # noqa F403
else:
from .pipelines import AudioDiffusionPipeline, Mel
try:
if not (is_transformers_available() and is_torch_available() and is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_transformers_and_torch_and_note_seq_objects import * # noqa F403
else:
from .pipelines import SpectrogramDiffusionPipeline
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_objects import * # noqa F403
else:
from .models.controlnet_flax import FlaxControlNetModel
from .models.modeling_flax_utils import FlaxModelMixin
from .models.unet_ad_condition_flax import FlaxUNetaDConditionModel
from .models.vae_flax import FlaxAutoencoderKL
from .pipelines import FlaxDiffusionPipeline
from .schedulers import (
FlaxDDIMScheduler,
FlaxDDPMScheduler,
FlaxDPMSolverMultistepScheduler,
FlaxKarrasVeScheduler,
FlaxLMSDiscreteScheduler,
FlaxPNDMScheduler,
FlaxSchedulerMixin,
FlaxScoreSdeVeScheduler,
)
try:
if not (is_flax_available() and is_transformers_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_flax_and_transformers_objects import * # noqa F403
else:
from .pipelines import (
FlaxStableDiffusionControlNetPipeline,
FlaxStableDiffusionImgaImgPipeline,
FlaxStableDiffusionInpaintPipeline,
FlaxStableDiffusionPipeline,
)
try:
if not (is_note_seq_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from .utils.dummy_note_seq_objects import * # noqa F403
else:
from .pipelines import MidiProcessor
| 57
|
from __future__ import annotations
def lowercase_ ( __snake_case : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("List is empty" )
return sum(__snake_case ) / len(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
| 1
|
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import IMAGE_PROCESSOR_NAME, is_vision_available
if is_vision_available():
from PIL import Image
from transformers import CLIPImageProcessor, CLIPProcessor
@require_vision
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ :Tuple = tempfile.mkdtemp()
# fmt: off
snake_case__ :List[Any] = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
# fmt: on
snake_case__ :Optional[Any] = dict(zip(UpperCamelCase ,range(len(UpperCamelCase ) ) ) )
snake_case__ :Any = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
snake_case__ :Optional[int] = {"unk_token": "<unk>"}
snake_case__ :List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["vocab_file"] )
snake_case__ :List[str] = os.path.join(self.tmpdirname ,VOCAB_FILES_NAMES["merges_file"] )
with open(self.vocab_file ,"w" ,encoding="utf-8" ) as fp:
fp.write(json.dumps(UpperCamelCase ) + "\n" )
with open(self.merges_file ,"w" ,encoding="utf-8" ) as fp:
fp.write("\n".join(UpperCamelCase ) )
snake_case__ :Optional[Any] = {
"do_resize": True,
"size": 20,
"do_center_crop": True,
"crop_size": 18,
"do_normalize": True,
"image_mean": [0.48145466, 0.4578275, 0.40821073],
"image_std": [0.26862954, 0.26130258, 0.27577711],
}
snake_case__ :int = os.path.join(self.tmpdirname ,UpperCamelCase )
with open(self.image_processor_file ,"w" ,encoding="utf-8" ) as fp:
json.dump(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> Optional[Any]:
return CLIPTokenizer.from_pretrained(self.tmpdirname ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> List[Any]:
return CLIPTokenizerFast.from_pretrained(self.tmpdirname ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> List[str]:
return CLIPImageProcessor.from_pretrained(self.tmpdirname ,**UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Dict:
shutil.rmtree(self.tmpdirname )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Optional[int] = [np.random.randint(255 ,size=(3, 30, 400) ,dtype=np.uinta )]
snake_case__ :Optional[int] = [Image.fromarray(np.moveaxis(UpperCamelCase ,0 ,-1 ) ) for x in image_inputs]
return image_inputs
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :str = self.get_tokenizer()
snake_case__ :Union[str, Any] = self.get_rust_tokenizer()
snake_case__ :Union[str, Any] = self.get_image_processor()
snake_case__ :Any = CLIPProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
processor_slow.save_pretrained(self.tmpdirname )
snake_case__ :Optional[int] = CLIPProcessor.from_pretrained(self.tmpdirname ,use_fast=UpperCamelCase )
snake_case__ :Optional[int] = CLIPProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
processor_fast.save_pretrained(self.tmpdirname )
snake_case__ :str = CLIPProcessor.from_pretrained(self.tmpdirname )
self.assertEqual(processor_slow.tokenizer.get_vocab() ,tokenizer_slow.get_vocab() )
self.assertEqual(processor_fast.tokenizer.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertEqual(tokenizer_slow.get_vocab() ,tokenizer_fast.get_vocab() )
self.assertIsInstance(processor_slow.tokenizer ,UpperCamelCase )
self.assertIsInstance(processor_fast.tokenizer ,UpperCamelCase )
self.assertEqual(processor_slow.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertEqual(processor_fast.image_processor.to_json_string() ,image_processor.to_json_string() )
self.assertIsInstance(processor_slow.image_processor ,UpperCamelCase )
self.assertIsInstance(processor_fast.image_processor ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Optional[int] = CLIPProcessor(tokenizer=self.get_tokenizer() ,image_processor=self.get_image_processor() )
processor.save_pretrained(self.tmpdirname )
snake_case__ :Dict = self.get_tokenizer(bos_token="(BOS)" ,eos_token="(EOS)" )
snake_case__ :Optional[Any] = self.get_image_processor(do_normalize=UpperCamelCase ,padding_value=1.0 )
snake_case__ :Any = CLIPProcessor.from_pretrained(
self.tmpdirname ,bos_token="(BOS)" ,eos_token="(EOS)" ,do_normalize=UpperCamelCase ,padding_value=1.0 )
self.assertEqual(processor.tokenizer.get_vocab() ,tokenizer_add_kwargs.get_vocab() )
self.assertIsInstance(processor.tokenizer ,UpperCamelCase )
self.assertEqual(processor.image_processor.to_json_string() ,image_processor_add_kwargs.to_json_string() )
self.assertIsInstance(processor.image_processor ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :Any = self.get_image_processor()
snake_case__ :Dict = self.get_tokenizer()
snake_case__ :List[str] = CLIPProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
snake_case__ :List[Any] = self.prepare_image_inputs()
snake_case__ :Optional[Any] = image_processor(UpperCamelCase ,return_tensors="np" )
snake_case__ :Dict = processor(images=UpperCamelCase ,return_tensors="np" )
for key in input_image_proc.keys():
self.assertAlmostEqual(input_image_proc[key].sum() ,input_processor[key].sum() ,delta=1E-2 )
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :Tuple = self.get_image_processor()
snake_case__ :int = self.get_tokenizer()
snake_case__ :Dict = CLIPProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
snake_case__ :Optional[int] = "lower newer"
snake_case__ :List[str] = processor(text=UpperCamelCase )
snake_case__ :Optional[Any] = tokenizer(UpperCamelCase )
for key in encoded_tok.keys():
self.assertListEqual(encoded_tok[key] ,encoded_processor[key] )
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ :Tuple = self.get_image_processor()
snake_case__ :Optional[Any] = self.get_tokenizer()
snake_case__ :List[Any] = CLIPProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
snake_case__ :str = "lower newer"
snake_case__ :Dict = self.prepare_image_inputs()
snake_case__ :int = processor(text=UpperCamelCase ,images=UpperCamelCase )
self.assertListEqual(list(inputs.keys() ) ,["input_ids", "attention_mask", "pixel_values"] )
# test if it raises when no input is passed
with pytest.raises(UpperCamelCase ):
processor()
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :int = self.get_image_processor()
snake_case__ :Optional[Any] = self.get_tokenizer()
snake_case__ :Any = CLIPProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
snake_case__ :int = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
snake_case__ :Any = processor.batch_decode(UpperCamelCase )
snake_case__ :Dict = tokenizer.batch_decode(UpperCamelCase )
self.assertListEqual(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :Any = self.get_image_processor()
snake_case__ :Any = self.get_tokenizer()
snake_case__ :Dict = CLIPProcessor(tokenizer=UpperCamelCase ,image_processor=UpperCamelCase )
snake_case__ :Dict = "lower newer"
snake_case__ :Any = self.prepare_image_inputs()
snake_case__ :str = processor(text=UpperCamelCase ,images=UpperCamelCase )
self.assertListEqual(list(inputs.keys() ) ,processor.model_input_names )
| 57
|
from __future__ import annotations
import math
def lowercase_ ( __snake_case : int , __snake_case : int , __snake_case : bool , __snake_case : list[int] , __snake_case : float ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if len(__snake_case ) == 0:
raise ValueError("Scores cannot be empty" )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
return min(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
def lowercase_ ( ) -> None:
'''simple docstring'''
snake_case__ :List[Any] = [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
snake_case__ :int = math.log(len(__snake_case ) , 2 )
print("Optimal value : " , end="" )
print(minimax(0 , 0 , __snake_case , __snake_case , __snake_case ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 57
| 1
|
import numpy as np
def lowercase_ ( __snake_case : np.array ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
def lowercase_ ( __snake_case : Any , __snake_case : Any ) -> Any:
'''simple docstring'''
snake_case__ :Optional[Any] = b.T
snake_case__ :Optional[Any] = np.sum(np.square(__snake_case ) , axis=1 )
snake_case__ :Tuple = np.sum(np.square(__snake_case ) , axis=0 )
snake_case__ :Union[str, Any] = np.matmul(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = aa[:, None] - 2 * ab + ba[None, :]
return d
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : int ) -> Any:
'''simple docstring'''
snake_case__ :Optional[Any] = x.reshape(-1 , 3 )
snake_case__ :List[str] = squared_euclidean_distance(__snake_case , __snake_case )
return np.argmin(__snake_case , axis=1 )
class _snake_case ( _A ):
_A = ['pixel_values']
def __init__( self ,UpperCamelCase = None ,UpperCamelCase = True ,UpperCamelCase = None ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = True ,UpperCamelCase = True ,**UpperCamelCase ,) -> None:
super().__init__(**UpperCamelCase )
snake_case__ :List[Any] = size if size is not None else {"height": 256, "width": 256}
snake_case__ :str = get_size_dict(UpperCamelCase )
snake_case__ :Dict = np.array(UpperCamelCase ) if clusters is not None else None
snake_case__ :str = do_resize
snake_case__ :List[str] = size
snake_case__ :List[Any] = resample
snake_case__ :Union[str, Any] = do_normalize
snake_case__ :int = do_color_quantize
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = None ,**UpperCamelCase ,) -> np.ndarray:
snake_case__ :List[str] = get_size_dict(UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}' )
return resize(
UpperCamelCase ,size=(size["height"], size["width"]) ,resample=UpperCamelCase ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,) -> np.ndarray:
snake_case__ :Tuple = rescale(image=UpperCamelCase ,scale=1 / 127.5 ,data_format=UpperCamelCase )
snake_case__ :List[Any] = image - 1
return image
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = ChannelDimension.FIRST ,**UpperCamelCase ,) -> PIL.Image.Image:
snake_case__ :Optional[int] = do_resize if do_resize is not None else self.do_resize
snake_case__ :int = size if size is not None else self.size
snake_case__ :Tuple = get_size_dict(UpperCamelCase )
snake_case__ :str = resample if resample is not None else self.resample
snake_case__ :Dict = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ :Tuple = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
snake_case__ :List[Any] = clusters if clusters is not None else self.clusters
snake_case__ :str = np.array(UpperCamelCase )
snake_case__ :int = make_list_of_images(UpperCamelCase )
if not valid_images(UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True." )
# All transformations expect numpy arrays.
snake_case__ :Union[str, Any] = [to_numpy_array(UpperCamelCase ) for image in images]
if do_resize:
snake_case__ :int = [self.resize(image=UpperCamelCase ,size=UpperCamelCase ,resample=UpperCamelCase ) for image in images]
if do_normalize:
snake_case__ :Any = [self.normalize(image=UpperCamelCase ) for image in images]
if do_color_quantize:
snake_case__ :Optional[Any] = [to_channel_dimension_format(UpperCamelCase ,ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
snake_case__ :Union[str, Any] = np.array(UpperCamelCase )
snake_case__ :Optional[int] = color_quantize(UpperCamelCase ,UpperCamelCase ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
snake_case__ :List[Any] = images.shape[0]
snake_case__ :str = images.reshape(UpperCamelCase ,-1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
snake_case__ :Any = list(UpperCamelCase )
else:
snake_case__ :List[str] = [to_channel_dimension_format(UpperCamelCase ,UpperCamelCase ) for image in images]
snake_case__ :List[str] = {"input_ids": images}
return BatchFeature(data=UpperCamelCase ,tensor_type=UpperCamelCase )
| 57
| 1
|
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transformers import BartTokenizer, RagTokenizer, TaTokenizer
def lowercase_ ( __snake_case : int , __snake_case : Tuple , __snake_case : Optional[Any] , __snake_case : List[str] , __snake_case : List[Any]=True , __snake_case : Optional[int]="pt" ) -> Any:
'''simple docstring'''
snake_case__ :List[Any] = {"add_prefix_space": True} if isinstance(__snake_case , __snake_case ) and not line.startswith(" " ) else {}
snake_case__ :Tuple = padding_side
return tokenizer(
[line] , max_length=__snake_case , padding="max_length" if pad_to_max_length else None , truncation=__snake_case , return_tensors=__snake_case , add_special_tokens=__snake_case , **__snake_case , )
def lowercase_ ( __snake_case : List[Any] , __snake_case : Optional[Any] , __snake_case : Optional[int]=None , ) -> Any:
'''simple docstring'''
snake_case__ :Dict = input_ids.ne(__snake_case ).any(dim=0 )
if attention_mask is None:
return input_ids[:, keep_column_mask]
else:
return (input_ids[:, keep_column_mask], attention_mask[:, keep_column_mask])
class _snake_case ( _A ):
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase="train" ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase="" ,) -> str:
super().__init__()
snake_case__ :Dict = Path(UpperCamelCase ).joinpath(type_path + ".source" )
snake_case__ :Union[str, Any] = Path(UpperCamelCase ).joinpath(type_path + ".target" )
snake_case__ :int = self.get_char_lens(self.src_file )
snake_case__ :str = max_source_length
snake_case__ :int = max_target_length
assert min(self.src_lens ) > 0, f'found empty line in {self.src_file}'
snake_case__ :Union[str, Any] = tokenizer
snake_case__ :Optional[Any] = prefix
if n_obs is not None:
snake_case__ :Tuple = self.src_lens[:n_obs]
snake_case__ :Dict = src_lang
snake_case__ :str = tgt_lang
def __len__( self ) -> int:
return len(self.src_lens )
def __getitem__( self ,UpperCamelCase ) -> Dict[str, torch.Tensor]:
snake_case__ :int = index + 1 # linecache starts at 1
snake_case__ :Tuple = self.prefix + linecache.getline(str(self.src_file ) ,UpperCamelCase ).rstrip("\n" )
snake_case__ :Any = linecache.getline(str(self.tgt_file ) ,UpperCamelCase ).rstrip("\n" )
assert source_line, f'empty source line for index {index}'
assert tgt_line, f'empty tgt line for index {index}'
# Need to add eos token manually for T5
if isinstance(self.tokenizer ,UpperCamelCase ):
source_line += self.tokenizer.eos_token
tgt_line += self.tokenizer.eos_token
# Pad source and target to the right
snake_case__ :Dict = (
self.tokenizer.question_encoder if isinstance(self.tokenizer ,UpperCamelCase ) else self.tokenizer
)
snake_case__ :Optional[Any] = self.tokenizer.generator if isinstance(self.tokenizer ,UpperCamelCase ) else self.tokenizer
snake_case__ :List[Any] = encode_line(UpperCamelCase ,UpperCamelCase ,self.max_source_length ,"right" )
snake_case__ :str = encode_line(UpperCamelCase ,UpperCamelCase ,self.max_target_length ,"right" )
snake_case__ :List[str] = source_inputs["input_ids"].squeeze()
snake_case__ :Optional[Any] = target_inputs["input_ids"].squeeze()
snake_case__ :List[str] = source_inputs["attention_mask"].squeeze()
return {
"input_ids": source_ids,
"attention_mask": src_mask,
"decoder_input_ids": target_ids,
}
@staticmethod
def lowerCAmelCase_ ( UpperCamelCase ) -> int:
return [len(UpperCamelCase ) for x in Path(UpperCamelCase ).open().readlines()]
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Dict[str, torch.Tensor]:
snake_case__ :Union[str, Any] = torch.stack([x["input_ids"] for x in batch] )
snake_case__ :Optional[Any] = torch.stack([x["attention_mask"] for x in batch] )
snake_case__ :Tuple = torch.stack([x["decoder_input_ids"] for x in batch] )
snake_case__ :Dict = (
self.tokenizer.generator.pad_token_id
if isinstance(self.tokenizer ,UpperCamelCase )
else self.tokenizer.pad_token_id
)
snake_case__ :Union[str, Any] = (
self.tokenizer.question_encoder.pad_token_id
if isinstance(self.tokenizer ,UpperCamelCase )
else self.tokenizer.pad_token_id
)
snake_case__ :Optional[Any] = trim_batch(UpperCamelCase ,UpperCamelCase )
snake_case__ , snake_case__ :Optional[Any] = trim_batch(UpperCamelCase ,UpperCamelCase ,attention_mask=UpperCamelCase )
snake_case__ :int = {
"input_ids": source_ids,
"attention_mask": source_mask,
"decoder_input_ids": y,
}
return batch
__UpperCAmelCase : Union[str, Any] = getLogger(__name__)
def lowercase_ ( __snake_case : List[List] ) -> Dict:
'''simple docstring'''
return list(itertools.chain.from_iterable(__snake_case ) )
def lowercase_ ( __snake_case : str ) -> None:
'''simple docstring'''
snake_case__ :Optional[Any] = get_git_info()
save_json(__snake_case , os.path.join(__snake_case , "git_log.json" ) )
def lowercase_ ( __snake_case : int , __snake_case : Tuple , __snake_case : Tuple=4 , **__snake_case : Optional[Any] ) -> Tuple:
'''simple docstring'''
with open(__snake_case , "w" ) as f:
json.dump(__snake_case , __snake_case , indent=__snake_case , **__snake_case )
def lowercase_ ( __snake_case : Dict ) -> Any:
'''simple docstring'''
with open(__snake_case ) as f:
return json.load(__snake_case )
def lowercase_ ( ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :Tuple = git.Repo(search_parent_directories=__snake_case )
snake_case__ :Any = {
"repo_id": str(__snake_case ),
"repo_sha": str(repo.head.object.hexsha ),
"repo_branch": str(repo.active_branch ),
"hostname": str(socket.gethostname() ),
}
return repo_infos
def lowercase_ ( __snake_case : Callable , __snake_case : Iterable ) -> List:
'''simple docstring'''
return list(map(__snake_case , __snake_case ) )
def lowercase_ ( __snake_case : Optional[int] , __snake_case : List[Any] ) -> Tuple:
'''simple docstring'''
with open(__snake_case , "wb" ) as f:
return pickle.dump(__snake_case , __snake_case )
def lowercase_ ( __snake_case : str ) -> List[Any]:
'''simple docstring'''
def remove_articles(__snake_case : Tuple ):
return re.sub(R"\b(a|an|the)\b" , " " , __snake_case )
def white_space_fix(__snake_case : str ):
return " ".join(text.split() )
def remove_punc(__snake_case : Optional[Any] ):
snake_case__ :str = set(string.punctuation )
return "".join(ch for ch in text if ch not in exclude )
def lower(__snake_case : Optional[int] ):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(__snake_case ) ) ) )
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : Optional[int] ) -> int:
'''simple docstring'''
snake_case__ :Union[str, Any] = normalize_answer(__snake_case ).split()
snake_case__ :Any = normalize_answer(__snake_case ).split()
snake_case__ :Optional[Any] = Counter(__snake_case ) & Counter(__snake_case )
snake_case__ :Union[str, Any] = sum(common.values() )
if num_same == 0:
return 0
snake_case__ :Tuple = 1.0 * num_same / len(__snake_case )
snake_case__ :Any = 1.0 * num_same / len(__snake_case )
snake_case__ :Any = (2 * precision * recall) / (precision + recall)
return fa
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : Optional[Any] ) -> Tuple:
'''simple docstring'''
return normalize_answer(__snake_case ) == normalize_answer(__snake_case )
def lowercase_ ( __snake_case : List[str] , __snake_case : List[str] ) -> Dict:
'''simple docstring'''
assert len(__snake_case ) == len(__snake_case )
snake_case__ :Union[str, Any] = 0
for hypo, pred in zip(__snake_case , __snake_case ):
em += exact_match_score(__snake_case , __snake_case )
if len(__snake_case ) > 0:
em /= len(__snake_case )
return {"em": em}
def lowercase_ ( __snake_case : int ) -> Dict:
'''simple docstring'''
return model_prefix.startswith("rag" )
def lowercase_ ( __snake_case : Dict , __snake_case : Optional[int] , __snake_case : Dict ) -> List[Any]:
'''simple docstring'''
snake_case__ :Optional[int] = {p: p for p in extra_params}
# T5 models don't have `dropout` param, they have `dropout_rate` instead
snake_case__ :Tuple = "dropout_rate"
for p in extra_params:
if getattr(__snake_case , __snake_case , __snake_case ):
if not hasattr(__snake_case , __snake_case ) and not hasattr(__snake_case , equivalent_param[p] ):
logger.info("config doesn't have a `{}` attribute".format(__snake_case ) )
delattr(__snake_case , __snake_case )
continue
snake_case__ :int = p if hasattr(__snake_case , __snake_case ) else equivalent_param[p]
setattr(__snake_case , __snake_case , getattr(__snake_case , __snake_case ) )
delattr(__snake_case , __snake_case )
return hparams, config
| 57
|
import pytest
__UpperCAmelCase : int = "__dummy_dataset1__"
__UpperCAmelCase : int = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n def _info(self):\n features = datasets.Features(\n {\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n \"ner_tags\": datasets.Sequence(\n datasets.features.ClassLabel(\n names=[\n \"O\",\n \"B-PER\",\n \"I-PER\",\n \"B-ORG\",\n \"I-ORG\",\n \"B-LOC\",\n \"I-LOC\",\n ]\n )\n ),\n \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n }\n )\n return datasets.DatasetInfo(features=features)\n\n def _split_generators(self, dl_manager):\n dl_path = dl_manager.download(URLS)\n return [\n datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n ]\n\n def _generate_examples(self, filepath):\n with open(filepath, \"r\", encoding=\"utf-8\") as f:\n for i, line in enumerate(f):\n yield i, json.loads(line)\n"
@pytest.fixture
def lowercase_ ( ) -> Optional[Any]:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def lowercase_ ( ) -> Optional[int]:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def lowercase_ ( __snake_case : Optional[int] , __snake_case : List[Any] , __snake_case : Any ) -> Dict:
'''simple docstring'''
snake_case__ :Optional[Any] = dataset_loading_script_name
snake_case__ :Optional[Any] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=__snake_case )
snake_case__ :List[Any] = script_dir / F'{script_name}.py'
with open(__snake_case , "w" ) as f:
f.write(__snake_case )
return str(__snake_case )
| 57
| 1
|
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
__UpperCAmelCase : Optional[int] = "src/transformers"
__UpperCAmelCase : Tuple = "docs/source/en/tasks"
def lowercase_ ( __snake_case : str , __snake_case : Union[str, Any] , __snake_case : int ) -> str:
'''simple docstring'''
with open(__snake_case , "r" , encoding="utf-8" , newline="\n" ) as f:
snake_case__ :int = f.readlines()
# Find the start prompt.
snake_case__ :Optional[Any] = 0
while not lines[start_index].startswith(__snake_case ):
start_index += 1
start_index += 1
snake_case__ :Optional[int] = start_index
while not lines[end_index].startswith(__snake_case ):
end_index += 1
end_index -= 1
while len(lines[start_index] ) <= 1:
start_index += 1
while len(lines[end_index] ) <= 1:
end_index -= 1
end_index += 1
return "".join(lines[start_index:end_index] ), start_index, end_index, lines
# This is to make sure the transformers module imported is the one in the repo.
__UpperCAmelCase : List[str] = direct_transformers_import(TRANSFORMERS_PATH)
__UpperCAmelCase : Dict = {
"asr.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CTC_MAPPING_NAMES,
"audio_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES,
"language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
"image_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES,
"masked_language_modeling.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MASKED_LM_MAPPING_NAMES,
"multiple_choice.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES,
"object_detection.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES,
"question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES,
"semantic_segmentation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING_NAMES,
"sequence_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES,
"summarization.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"token_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES,
"translation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
"video_classification.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES,
"document_question_answering.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES,
"monocular_depth_estimation.md": transformers_module.models.auto.modeling_auto.MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES,
}
# This list contains model types used in some task guides that are not in `CONFIG_MAPPING_NAMES` (therefore not in any
# `MODEL_MAPPING_NAMES` or any `MODEL_FOR_XXX_MAPPING_NAMES`).
__UpperCAmelCase : List[Any] = {
"summarization.md": ("nllb",),
"translation.md": ("nllb",),
}
def lowercase_ ( __snake_case : Tuple ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :Union[str, Any] = TASK_GUIDE_TO_MODELS[task_guide]
snake_case__ :Union[str, Any] = SPECIAL_TASK_GUIDE_TO_MODEL_TYPES.get(__snake_case , set() )
snake_case__ :str = {
code: name
for code, name in transformers_module.MODEL_NAMES_MAPPING.items()
if (code in model_maping_names or code in special_model_types)
}
return ", ".join([F'[{name}](../model_doc/{code})' for code, name in model_names.items()] ) + "\n"
def lowercase_ ( __snake_case : int , __snake_case : List[Any]=False ) -> Dict:
'''simple docstring'''
snake_case__ , snake_case__ , snake_case__ , snake_case__ :Dict = _find_text_in_file(
filename=os.path.join(__snake_case , __snake_case ) , start_prompt="<!--This tip is automatically generated by `make fix-copies`, do not fill manually!-->" , end_prompt="<!--End of the generated tip-->" , )
snake_case__ :Union[str, Any] = get_model_list_for_task(__snake_case )
if current_list != new_list:
if overwrite:
with open(os.path.join(__snake_case , __snake_case ) , "w" , encoding="utf-8" , newline="\n" ) as f:
f.writelines(lines[:start_index] + [new_list] + lines[end_index:] )
else:
raise ValueError(
F'The list of models that can be used in the {task_guide} guide needs an update. Run `make fix-copies`'
" to fix this." )
if __name__ == "__main__":
__UpperCAmelCase : Dict = argparse.ArgumentParser()
parser.add_argument("--fix_and_overwrite", action="store_true", help="Whether to fix inconsistencies.")
__UpperCAmelCase : Tuple = parser.parse_args()
for task_guide in TASK_GUIDE_TO_MODELS.keys():
check_model_list_for_task(task_guide, args.fix_and_overwrite)
| 57
|
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 57
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase : Tuple = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : List[Any] = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
__UpperCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
|
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__UpperCAmelCase : Dict = True
except ImportError:
__UpperCAmelCase : List[Any] = False
__UpperCAmelCase : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowercase_ ( __snake_case : Namespace ) -> Dict:
'''simple docstring'''
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class _snake_case ( _A ):
@staticmethod
def lowerCAmelCase_ ( UpperCamelCase ) -> Any:
snake_case__ :Dict = parser.add_parser("add-new-model" )
add_new_model_parser.add_argument("--testing" ,action="store_true" ,help="If in testing mode." )
add_new_model_parser.add_argument("--testing_file" ,type=UpperCamelCase ,help="Configuration file on which to run." )
add_new_model_parser.add_argument(
"--path" ,type=UpperCamelCase ,help="Path to cookiecutter. Should only be used for testing purposes." )
add_new_model_parser.set_defaults(func=UpperCamelCase )
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ,*UpperCamelCase ) -> Any:
snake_case__ :Union[str, Any] = testing
snake_case__ :Union[str, Any] = testing_file
snake_case__ :List[str] = path
def lowerCAmelCase_ ( self ) -> List[Any]:
warnings.warn(
"The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
"It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
"checks, you should use `transformers-cli add-new-model-like` instead." )
if not _has_cookiecutter:
raise ImportError(
"Model creation dependencies are required to use the `add_new_model` command. Install them by running "
"the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n" )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
snake_case__ :Tuple = [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
if len(UpperCamelCase ) > 0:
raise ValueError(
"Several directories starting with `cookiecutter-template-` in current working directory. "
"Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
"change your working directory." )
snake_case__ :str = (
Path(UpperCamelCase ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
snake_case__ :Tuple = path_to_transformer_root / "templates" / "adding_a_new_model"
# Execute cookiecutter
if not self._testing:
cookiecutter(str(UpperCamelCase ) )
else:
with open(self._testing_file ,"r" ) as configuration_file:
snake_case__ :str = json.load(UpperCamelCase )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) ,no_input=UpperCamelCase ,extra_context=UpperCamelCase ,)
snake_case__ :List[Any] = [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]
# Retrieve configuration
with open(directory + "/configuration.json" ,"r" ) as configuration_file:
snake_case__ :Dict = json.load(UpperCamelCase )
snake_case__ :Optional[Any] = configuration["lowercase_modelname"]
snake_case__ :List[Any] = configuration["generate_tensorflow_pytorch_and_flax"]
os.remove(f'{directory}/configuration.json' )
snake_case__ :Any = "PyTorch" in generate_tensorflow_pytorch_and_flax
snake_case__ :Any = "TensorFlow" in generate_tensorflow_pytorch_and_flax
snake_case__ :Any = "Flax" in generate_tensorflow_pytorch_and_flax
snake_case__ :Dict = f'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
os.makedirs(UpperCamelCase ,exist_ok=UpperCamelCase )
os.makedirs(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}' ,exist_ok=UpperCamelCase )
# Tests require submodules as they have parent imports
with open(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py' ,"w" ):
pass
shutil.move(
f'{directory}/__init__.py' ,f'{model_dir}/__init__.py' ,)
shutil.move(
f'{directory}/configuration_{lowercase_model_name}.py' ,f'{model_dir}/configuration_{lowercase_model_name}.py' ,)
def remove_copy_lines(UpperCamelCase ):
with open(UpperCamelCase ,"r" ) as f:
snake_case__ :List[str] = f.readlines()
with open(UpperCamelCase ,"w" ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(UpperCamelCase )
if output_pytorch:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_{lowercase_model_name}.py' ,f'{model_dir}/modeling_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_{lowercase_model_name}.py' )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_tf_{lowercase_model_name}.py' ,f'{model_dir}/modeling_tf_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_tf_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_tf_{lowercase_model_name}.py' )
if output_flax:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_flax_{lowercase_model_name}.py' ,f'{model_dir}/modeling_flax_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_flax_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/{lowercase_model_name}.md' ,f'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md' ,)
shutil.move(
f'{directory}/tokenization_{lowercase_model_name}.py' ,f'{model_dir}/tokenization_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/tokenization_fast_{lowercase_model_name}.py' ,f'{model_dir}/tokenization_{lowercase_model_name}_fast.py' ,)
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ):
# Create temp file
snake_case__ , snake_case__ :Optional[Any] = mkstemp()
snake_case__ :Optional[Any] = False
with fdopen(UpperCamelCase ,"w" ) as new_file:
with open(UpperCamelCase ) as old_file:
for line in old_file:
new_file.write(UpperCamelCase )
if line_to_copy_below in line:
snake_case__ :Optional[Any] = True
for line_to_copy in lines_to_copy:
new_file.write(UpperCamelCase )
if not line_found:
raise ValueError(f'Line {line_to_copy_below} was not found in file.' )
# Copy the file permissions from the old file to the new file
copymode(UpperCamelCase ,UpperCamelCase )
# Remove original file
remove(UpperCamelCase )
# Move new file
move(UpperCamelCase ,UpperCamelCase )
def skip_units(UpperCamelCase ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(UpperCamelCase ):
with open(UpperCamelCase ) as datafile:
snake_case__ :int = []
snake_case__ :Optional[int] = False
snake_case__ :List[str] = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
snake_case__ :Optional[Any] = line.split("\"" )[1]
snake_case__ :Tuple = skip_units(UpperCamelCase )
elif "# Below: " in line and "##" not in line:
snake_case__ :Optional[Any] = line.split("\"" )[1]
snake_case__ :List[str] = skip_units(UpperCamelCase )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
snake_case__ :Tuple = []
elif "# Replace with" in line and "##" not in line:
snake_case__ :Optional[Any] = []
elif "##" not in line:
lines_to_copy.append(UpperCamelCase )
remove(UpperCamelCase )
replace_in_files(f'{directory}/to_replace_{lowercase_model_name}.py' )
os.rmdir(UpperCamelCase )
| 57
| 1
|
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric
from .utils import for_all_test_methods, local, slow
# mark all tests as integration
__UpperCAmelCase : Any = pytest.mark.integration
__UpperCAmelCase : List[str] = {"comet"}
__UpperCAmelCase : List[str] = importlib.util.find_spec("fairseq") is not None
__UpperCAmelCase : Optional[int] = {"code_eval"}
__UpperCAmelCase : Optional[Any] = os.name == "nt"
__UpperCAmelCase : Tuple = {"bertscore", "frugalscore", "perplexity"}
__UpperCAmelCase : str = importlib.util.find_spec("transformers") is not None
def lowercase_ ( __snake_case : str ) -> str:
'''simple docstring'''
@wraps(__snake_case )
def wrapper(self : Union[str, Any] , __snake_case : Dict ):
if not _has_fairseq and metric_name in REQUIRE_FAIRSEQ:
self.skipTest("\"test requires Fairseq\"" )
else:
test_case(self , __snake_case )
return wrapper
def lowercase_ ( __snake_case : str ) -> Any:
'''simple docstring'''
@wraps(__snake_case )
def wrapper(self : Any , __snake_case : Any ):
if not _has_transformers and metric_name in REQUIRE_TRANSFORMERS:
self.skipTest("\"test requires transformers\"" )
else:
test_case(self , __snake_case )
return wrapper
def lowercase_ ( __snake_case : Optional[int] ) -> List[Any]:
'''simple docstring'''
@wraps(__snake_case )
def wrapper(self : Optional[int] , __snake_case : Tuple ):
if _on_windows and metric_name in UNSUPPORTED_ON_WINDOWS:
self.skipTest("\"test not supported on Windows\"" )
else:
test_case(self , __snake_case )
return wrapper
def lowercase_ ( ) -> Dict:
'''simple docstring'''
snake_case__ :List[str] = [metric_dir.split(os.sep )[-2] for metric_dir in glob.glob("./metrics/*/" )]
return [{"testcase_name": x, "metric_name": x} for x in metrics if x != "gleu"] # gleu is unfinished
@parameterized.named_parameters(get_local_metric_names() )
@for_all_test_methods(
_A , _A , _A )
@local
class _snake_case ( parameterized.TestCase ):
_A = {}
_A = None
@pytest.mark.filterwarnings("ignore:metric_module_factory is deprecated:FutureWarning" )
@pytest.mark.filterwarnings("ignore:load_metric is deprecated:FutureWarning" )
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Optional[Any]:
snake_case__ :List[Any] = "[...]"
snake_case__ :List[Any] = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" ,UpperCamelCase ) ).module_path )
snake_case__ :Union[str, Any] = datasets.load.import_main_class(metric_module.__name__ ,dataset=UpperCamelCase )
# check parameters
snake_case__ :Optional[int] = inspect.signature(metric._compute ).parameters
self.assertTrue(all(p.kind != p.VAR_KEYWORD for p in parameters.values() ) ) # no **kwargs
# run doctest
with self.patch_intensive_calls(UpperCamelCase ,metric_module.__name__ ):
with self.use_local_metrics():
try:
snake_case__ :Tuple = doctest.testmod(UpperCamelCase ,verbose=UpperCamelCase ,raise_on_error=UpperCamelCase )
except doctest.UnexpectedException as e:
raise e.exc_info[1] # raise the exception that doctest caught
self.assertEqual(results.failed ,0 )
self.assertGreater(results.attempted ,1 )
@slow
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Dict:
snake_case__ :List[Any] = "[...]"
snake_case__ :Optional[Any] = importlib.import_module(
datasets.load.metric_module_factory(os.path.join("metrics" ,UpperCamelCase ) ).module_path )
# run doctest
with self.use_local_metrics():
snake_case__ :str = doctest.testmod(UpperCamelCase ,verbose=UpperCamelCase ,raise_on_error=UpperCamelCase )
self.assertEqual(results.failed ,0 )
self.assertGreater(results.attempted ,1 )
@contextmanager
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> Optional[int]:
if metric_name in self.INTENSIVE_CALLS_PATCHER:
with self.INTENSIVE_CALLS_PATCHER[metric_name](UpperCamelCase ):
yield
else:
yield
@contextmanager
def lowerCAmelCase_ ( self ) -> Dict:
def load_local_metric(UpperCamelCase ,*UpperCamelCase ,**UpperCamelCase ):
return load_metric(os.path.join("metrics" ,UpperCamelCase ) ,*UpperCamelCase ,**UpperCamelCase )
with patch("datasets.load_metric" ) as mock_load_metric:
snake_case__ :Any = load_local_metric
yield
@classmethod
def lowerCAmelCase_ ( cls ,UpperCamelCase ) -> Any:
def wrapper(UpperCamelCase ):
snake_case__ :Optional[Any] = contextmanager(UpperCamelCase )
snake_case__ :Optional[Any] = patcher
return patcher
return wrapper
@LocalMetricTest.register_intensive_calls_patcher("bleurt" )
def lowercase_ ( __snake_case : List[Any] ) -> int:
'''simple docstring'''
import tensorflow.compat.va as tf
from bleurt.score import Predictor
tf.flags.DEFINE_string("sv" , "" , "" ) # handle pytest cli flags
class _snake_case ( _A ):
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> List[str]:
assert len(input_dict["input_ids"] ) == 2
return np.array([1.03, 1.04] )
# mock predict_fn which is supposed to do a forward pass with a bleurt model
with patch("bleurt.score._create_predictor" ) as mock_create_predictor:
snake_case__ :List[Any] = MockedPredictor()
yield
@LocalMetricTest.register_intensive_calls_patcher("bertscore" )
def lowercase_ ( __snake_case : Dict ) -> Dict:
'''simple docstring'''
import torch
def bert_cos_score_idf(__snake_case : List[Any] , __snake_case : Tuple , *__snake_case : Optional[int] , **__snake_case : List[Any] ):
return torch.tensor([[1.0, 1.0, 1.0]] * len(__snake_case ) )
# mock get_model which is supposed to do download a bert model
# mock bert_cos_score_idf which is supposed to do a forward pass with a bert model
with patch("bert_score.scorer.get_model" ), patch(
"bert_score.scorer.bert_cos_score_idf" ) as mock_bert_cos_score_idf:
snake_case__ :Any = bert_cos_score_idf
yield
@LocalMetricTest.register_intensive_calls_patcher("comet" )
def lowercase_ ( __snake_case : Union[str, Any] ) -> Optional[int]:
'''simple docstring'''
def load_from_checkpoint(__snake_case : List[Any] ):
class _snake_case :
def lowerCAmelCase_ ( self ,UpperCamelCase ,*UpperCamelCase ,**UpperCamelCase ) -> Dict:
assert len(UpperCamelCase ) == 2
snake_case__ :int = [0.19, 0.92]
return scores, sum(UpperCamelCase ) / len(UpperCamelCase )
return Model()
# mock load_from_checkpoint which is supposed to do download a bert model
# mock load_from_checkpoint which is supposed to do download a bert model
with patch("comet.download_model" ) as mock_download_model:
snake_case__ :Union[str, Any] = None
with patch("comet.load_from_checkpoint" ) as mock_load_from_checkpoint:
snake_case__ :Union[str, Any] = load_from_checkpoint
yield
def lowercase_ ( ) -> List[str]:
'''simple docstring'''
snake_case__ :Optional[Any] = load_metric(os.path.join("metrics" , "seqeval" ) )
snake_case__ :List[str] = "ERROR"
snake_case__ :Dict = F'Scheme should be one of [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU], got {wrong_scheme}'
with pytest.raises(__snake_case , match=re.escape(__snake_case ) ):
metric.compute(predictions=[] , references=[] , scheme=__snake_case )
| 57
|
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : List[Any] = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__UpperCAmelCase : List[Any] = {
"vocab_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
},
"merges_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
},
}
__UpperCAmelCase : str = {"allegro/herbert-base-cased": 5_1_4}
__UpperCAmelCase : List[str] = {}
class _snake_case ( _A ):
_A = VOCAB_FILES_NAMES
_A = PRETRAINED_VOCAB_FILES_MAP
_A = PRETRAINED_INIT_CONFIGURATION
_A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A = HerbertTokenizer
def __init__( self ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase="<s>" ,UpperCamelCase="<unk>" ,UpperCamelCase="<pad>" ,UpperCamelCase="<mask>" ,UpperCamelCase="</s>" ,**UpperCamelCase ,) -> Dict:
super().__init__(
UpperCamelCase ,UpperCamelCase ,tokenizer_file=UpperCamelCase ,cls_token=UpperCamelCase ,unk_token=UpperCamelCase ,pad_token=UpperCamelCase ,mask_token=UpperCamelCase ,sep_token=UpperCamelCase ,**UpperCamelCase ,)
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> List[int]:
snake_case__ :Optional[int] = [self.cls_token_id]
snake_case__ :Any = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase ,token_ids_a=UpperCamelCase ,already_has_special_tokens=UpperCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(UpperCamelCase )) + [1]
return [1] + ([0] * len(UpperCamelCase )) + [1] + ([0] * len(UpperCamelCase )) + [1]
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> List[int]:
snake_case__ :Any = [self.sep_token_id]
snake_case__ :Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> Tuple[str]:
snake_case__ :List[str] = self._tokenizer.model.save(UpperCamelCase ,name=UpperCamelCase )
return tuple(UpperCamelCase )
| 57
| 1
|
from statistics import mean
import numpy as np
def lowercase_ ( __snake_case : list , __snake_case : list , __snake_case : list , __snake_case : int ) -> list:
'''simple docstring'''
snake_case__ :str = 0
# Number of processes finished
snake_case__ :List[Any] = 0
# Displays the finished process.
# If it is 0, the performance is completed if it is 1, before the performance.
snake_case__ :str = [0] * no_of_process
# List to include calculation results
snake_case__ :Any = [0] * no_of_process
# Sort by arrival time.
snake_case__ :Tuple = [burst_time[i] for i in np.argsort(__snake_case )]
snake_case__ :Optional[Any] = [process_name[i] for i in np.argsort(__snake_case )]
arrival_time.sort()
while no_of_process > finished_process_count:
snake_case__ :str = 0
while finished_process[i] == 1:
i += 1
if current_time < arrival_time[i]:
snake_case__ :Optional[int] = arrival_time[i]
snake_case__ :Dict = 0
# Index showing the location of the process being performed
snake_case__ :Optional[int] = 0
# Saves the current response ratio.
snake_case__ :List[str] = 0
for i in range(0 , __snake_case ):
if finished_process[i] == 0 and arrival_time[i] <= current_time:
snake_case__ :int = (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
i
]
if response_ratio < temp:
snake_case__ :Dict = temp
snake_case__ :List[Any] = i
# Calculate the turn around time
snake_case__ :List[Any] = current_time + burst_time[loc] - arrival_time[loc]
current_time += burst_time[loc]
# Indicates that the process has been performed.
snake_case__ :List[Any] = 1
# Increase finished_process_count by 1
finished_process_count += 1
return turn_around_time
def lowercase_ ( __snake_case : list , __snake_case : list , __snake_case : list , __snake_case : int ) -> list:
'''simple docstring'''
snake_case__ :Any = [0] * no_of_process
for i in range(0 , __snake_case ):
snake_case__ :Optional[Any] = turn_around_time[i] - burst_time[i]
return waiting_time
if __name__ == "__main__":
__UpperCAmelCase : Optional[Any] = 5
__UpperCAmelCase : Any = ["A", "B", "C", "D", "E"]
__UpperCAmelCase : str = [1, 2, 3, 4, 5]
__UpperCAmelCase : Any = [1, 2, 3, 4, 5]
__UpperCAmelCase : str = calculate_turn_around_time(
process_name, arrival_time, burst_time, no_of_process
)
__UpperCAmelCase : Tuple = calculate_waiting_time(
process_name, turn_around_time, burst_time, no_of_process
)
print("Process name \tArrival time \tBurst time \tTurn around time \tWaiting time")
for i in range(0, no_of_process):
print(
F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
)
print(F'''average waiting time : {mean(waiting_time):.5f}''')
print(F'''average turn around time : {mean(turn_around_time):.5f}''')
| 57
|
def lowercase_ ( __snake_case : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
snake_case__ :List[str] = 4
snake_case__ :Optional[int] = (1 << p) - 1
for _ in range(p - 2 ):
snake_case__ :List[Any] = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(1_1))
| 57
| 1
|
from abc import ABC, abstractmethod
from typing import List, Optional
class _snake_case ( _A ):
def __init__( self ) -> str:
# test for the above condition
self.test()
def lowerCAmelCase_ ( self ) -> List[str]:
snake_case__ :List[str] = 0
snake_case__ :str = False
while not completed:
if counter == 1:
self.reset()
snake_case__ :Optional[int] = self.advance()
if not self.does_advance(UpperCamelCase ):
raise Exception(
"Custom Constraint is not defined correctly. self.does_advance(self.advance()) must be true." )
snake_case__ , snake_case__ , snake_case__ :Union[str, Any] = self.update(UpperCamelCase )
counter += 1
if counter > 10_000:
raise Exception("update() does not fulfill the constraint." )
if self.remaining() != 0:
raise Exception("Custom Constraint is not defined correctly." )
@abstractmethod
def lowerCAmelCase_ ( self ) -> Dict:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Optional[int]:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Union[str, Any]:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCAmelCase_ ( self ) -> Tuple:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCAmelCase_ ( self ) -> str:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
@abstractmethod
def lowerCAmelCase_ ( self ,UpperCamelCase=False ) -> Any:
raise NotImplementedError(
f'{self.__class__} is an abstract class. Only classes inheriting this class can be called.' )
class _snake_case ( _A ):
def __init__( self ,UpperCamelCase ) -> str:
super(UpperCamelCase ,self ).__init__()
if not isinstance(UpperCamelCase ,UpperCamelCase ) or len(UpperCamelCase ) == 0:
raise ValueError(f'`token_ids` has to be a non-empty list, but is {token_ids}.' )
if any((not isinstance(UpperCamelCase ,UpperCamelCase ) or token_id < 0) for token_id in token_ids ):
raise ValueError(f'Each list in `token_ids` has to be a list of positive integers, but is {token_ids}.' )
snake_case__ :List[Any] = token_ids
snake_case__ :List[str] = len(self.token_ids )
snake_case__ :Optional[Any] = -1 # the index of the currently fulfilled step
snake_case__ :Dict = False
def lowerCAmelCase_ ( self ) -> List[str]:
if self.completed:
return None
return self.token_ids[self.fulfilled_idx + 1]
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Optional[int]:
if not isinstance(UpperCamelCase ,UpperCamelCase ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(UpperCamelCase )}' )
if self.completed:
return False
return token_id == self.token_ids[self.fulfilled_idx + 1]
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> List[str]:
if not isinstance(UpperCamelCase ,UpperCamelCase ):
raise ValueError(f'`token_id` has to be an `int`, but is {token_id} of type {type(UpperCamelCase )}' )
snake_case__ :Tuple = False
snake_case__ :str = False
snake_case__ :Tuple = False
if self.does_advance(UpperCamelCase ):
self.fulfilled_idx += 1
snake_case__ :int = True
if self.fulfilled_idx == (self.seqlen - 1):
snake_case__ :List[Any] = True
snake_case__ :Tuple = completed
else:
# failed to make progress.
snake_case__ :List[Any] = True
self.reset()
return stepped, completed, reset
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Union[str, Any] = False
snake_case__ :Union[str, Any] = 0
def lowerCAmelCase_ ( self ) -> List[Any]:
return self.seqlen - (self.fulfilled_idx + 1)
def lowerCAmelCase_ ( self ,UpperCamelCase=False ) -> Optional[Any]:
snake_case__ :Tuple = PhrasalConstraint(self.token_ids )
if stateful:
snake_case__ :str = self.seqlen
snake_case__ :Optional[Any] = self.fulfilled_idx
snake_case__ :List[str] = self.completed
return new_constraint
class _snake_case :
def __init__( self ,UpperCamelCase ,UpperCamelCase=True ) -> Tuple:
snake_case__ :Optional[int] = max([len(UpperCamelCase ) for one in nested_token_ids] )
snake_case__ :Union[str, Any] = {}
for token_ids in nested_token_ids:
snake_case__ :Tuple = root
for tidx, token_id in enumerate(UpperCamelCase ):
if token_id not in level:
snake_case__ :Tuple = {}
snake_case__ :List[str] = level[token_id]
if no_subsets and self.has_subsets(UpperCamelCase ,UpperCamelCase ):
raise ValueError(
"Each list in `nested_token_ids` can't be a complete subset of another list, but is"
f' {nested_token_ids}.' )
snake_case__ :List[Any] = root
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Union[str, Any]:
snake_case__ :str = self.trie
for current_token in current_seq:
snake_case__ :Any = start[current_token]
snake_case__ :Union[str, Any] = list(start.keys() )
return next_tokens
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> List[Any]:
snake_case__ :str = self.next_tokens(UpperCamelCase )
return len(UpperCamelCase ) == 0
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> str:
snake_case__ :int = list(root.values() )
if len(UpperCamelCase ) == 0:
return 1
else:
return sum([self.count_leaves(UpperCamelCase ) for nn in next_nodes] )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> Dict:
snake_case__ :Any = self.count_leaves(UpperCamelCase )
return len(UpperCamelCase ) != leaf_count
class _snake_case ( _A ):
def __init__( self ,UpperCamelCase ) -> int:
super(UpperCamelCase ,self ).__init__()
if not isinstance(UpperCamelCase ,UpperCamelCase ) or len(UpperCamelCase ) == 0:
raise ValueError(f'`nested_token_ids` has to be a non-empty list, but is {nested_token_ids}.' )
if any(not isinstance(UpperCamelCase ,UpperCamelCase ) for token_ids in nested_token_ids ):
raise ValueError(f'`nested_token_ids` has to be a list of lists, but is {nested_token_ids}.' )
if any(
any((not isinstance(UpperCamelCase ,UpperCamelCase ) or token_id < 0) for token_id in token_ids )
for token_ids in nested_token_ids ):
raise ValueError(
f'Each list in `nested_token_ids` has to be a list of positive integers, but is {nested_token_ids}.' )
snake_case__ :Dict = DisjunctiveTrie(UpperCamelCase )
snake_case__ :List[Any] = nested_token_ids
snake_case__ :Tuple = self.trie.max_height
snake_case__ :Tuple = []
snake_case__ :str = False
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :List[Any] = self.trie.next_tokens(self.current_seq )
if len(UpperCamelCase ) == 0:
return None
else:
return token_list
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> int:
if not isinstance(UpperCamelCase ,UpperCamelCase ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCamelCase )}' )
snake_case__ :Optional[Any] = self.trie.next_tokens(self.current_seq )
return token_id in next_tokens
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> str:
if not isinstance(UpperCamelCase ,UpperCamelCase ):
raise ValueError(f'`token_id` is supposed to be type `int`, but is {token_id} of type {type(UpperCamelCase )}' )
snake_case__ :Optional[int] = False
snake_case__ :Tuple = False
snake_case__ :Dict = False
if self.does_advance(UpperCamelCase ):
self.current_seq.append(UpperCamelCase )
snake_case__ :Optional[Any] = True
else:
snake_case__ :Optional[int] = True
self.reset()
snake_case__ :Dict = self.trie.reached_leaf(self.current_seq )
snake_case__ :Any = completed
return stepped, completed, reset
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :Optional[Any] = False
snake_case__ :Optional[int] = []
def lowerCAmelCase_ ( self ) -> List[str]:
if self.completed:
# since this can be completed without reaching max height
return 0
else:
return self.seqlen - len(self.current_seq )
def lowerCAmelCase_ ( self ,UpperCamelCase=False ) -> Optional[Any]:
snake_case__ :Tuple = DisjunctiveConstraint(self.token_ids )
if stateful:
snake_case__ :str = self.seqlen
snake_case__ :Tuple = self.current_seq
snake_case__ :Optional[Any] = self.completed
return new_constraint
class _snake_case :
def __init__( self ,UpperCamelCase ) -> Union[str, Any]:
snake_case__ :Dict = constraints
# max # of steps required to fulfill a given constraint
snake_case__ :Any = max([c.seqlen for c in constraints] )
snake_case__ :Dict = len(UpperCamelCase )
snake_case__ :int = False
self.init_state()
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :Optional[int] = []
snake_case__ :Tuple = None
snake_case__ :Any = [constraint.copy(stateful=UpperCamelCase ) for constraint in self.constraints]
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :List[str] = 0
if self.inprogress_constraint:
# extra points for having a constraint mid-fulfilled
add += self.max_seqlen - self.inprogress_constraint.remaining()
return (len(self.complete_constraints ) * self.max_seqlen) + add
def lowerCAmelCase_ ( self ) -> List[str]:
snake_case__ :Any = []
if self.inprogress_constraint is None:
for constraint in self.pending_constraints: # "pending" == "unfulfilled yet"
snake_case__ :int = constraint.advance()
if isinstance(UpperCamelCase ,UpperCamelCase ):
token_list.append(UpperCamelCase )
elif isinstance(UpperCamelCase ,UpperCamelCase ):
token_list.extend(UpperCamelCase )
else:
snake_case__ :List[Any] = self.inprogress_constraint.advance()
if isinstance(UpperCamelCase ,UpperCamelCase ):
token_list.append(UpperCamelCase )
elif isinstance(UpperCamelCase ,UpperCamelCase ):
token_list.extend(UpperCamelCase )
if len(UpperCamelCase ) == 0:
return None
else:
return token_list
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> List[str]:
self.init_state()
if token_ids is not None:
for token in token_ids:
# completes or steps **one** constraint
snake_case__ , snake_case__ :Optional[Any] = self.add(UpperCamelCase )
# the entire list of constraints are fulfilled
if self.completed:
break
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Any:
if not isinstance(UpperCamelCase ,UpperCamelCase ):
raise ValueError(f'`token_id` should be an `int`, but is `{token_id}`.' )
snake_case__ , snake_case__ :Optional[Any] = False, False
if self.completed:
snake_case__ :List[Any] = True
snake_case__ :str = False
return complete, stepped
if self.inprogress_constraint is not None:
# In the middle of fulfilling a constraint. If the `token_id` *does* makes an incremental progress to current
# job, simply update the state
snake_case__ , snake_case__ , snake_case__ :Dict = self.inprogress_constraint.update(UpperCamelCase )
if reset:
# 1. If the next token breaks the progress, then we must restart.
# e.g. constraint = "I love pies" and sequence so far is "I love" but `token_id` == "books".
# But that doesn't mean we self.init_state(), since we only reset the state for this particular
# constraint, not the full list of constraints.
self.pending_constraints.append(self.inprogress_constraint.copy(stateful=UpperCamelCase ) )
snake_case__ :Any = None
if complete:
# 2. If the next token completes the constraint, move it to completed list, set
# inprogress to None. If there are no pending constraints either, then this full list of constraints
# is complete.
self.complete_constraints.append(self.inprogress_constraint )
snake_case__ :str = None
if len(self.pending_constraints ) == 0:
# we're done!
snake_case__ :int = True
else:
# Not in the middle of fulfilling a constraint. So does this `token_id` helps us step towards any of our list
# of constraints?
for cidx, pending_constraint in enumerate(self.pending_constraints ):
if pending_constraint.does_advance(UpperCamelCase ):
snake_case__ , snake_case__ , snake_case__ :Union[str, Any] = pending_constraint.update(UpperCamelCase )
if not stepped:
raise Exception(
"`constraint.update(token_id)` is not yielding incremental progress, "
"even though `constraint.does_advance(token_id)` is true." )
if complete:
self.complete_constraints.append(UpperCamelCase )
snake_case__ :Union[str, Any] = None
if not complete and stepped:
snake_case__ :Union[str, Any] = pending_constraint
if complete or stepped:
# If we made any progress at all, then it's at least not a "pending constraint".
snake_case__ :List[str] = (
self.pending_constraints[:cidx] + self.pending_constraints[cidx + 1 :]
)
if len(self.pending_constraints ) == 0 and self.inprogress_constraint is None:
# If there's no longer any pending after this and no inprogress either, then we must be
# complete.
snake_case__ :int = True
break # prevent accidentally stepping through multiple constraints with just one token.
return complete, stepped
def lowerCAmelCase_ ( self ,UpperCamelCase=True ) -> Optional[int]:
snake_case__ :List[str] = ConstraintListState(self.constraints ) # we actually never though self.constraints objects
# throughout this process. So it's at initialization state.
if stateful:
snake_case__ :Optional[Any] = [
constraint.copy(stateful=UpperCamelCase ) for constraint in self.complete_constraints
]
if self.inprogress_constraint is not None:
snake_case__ :List[Any] = self.inprogress_constraint.copy(stateful=UpperCamelCase )
snake_case__ :str = [constraint.copy() for constraint in self.pending_constraints]
return new_state
| 57
|
from typing import Any
def lowercase_ ( __snake_case : list , __snake_case : list , __snake_case : dict , __snake_case : dict , __snake_case : dict , ) -> list:
'''simple docstring'''
_validation(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
# Creates data structures and fill initial step
snake_case__ :dict = {}
snake_case__ :dict = {}
for state in states_space:
snake_case__ :List[Any] = observations_space[0]
snake_case__ :str = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
snake_case__ :str = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(__snake_case ) ):
snake_case__ :Any = observations_space[o]
snake_case__ :Tuple = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
snake_case__ :Tuple = ""
snake_case__ :Union[str, Any] = -1
for k_state in states_space:
snake_case__ :int = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
snake_case__ :str = probability
snake_case__ :Tuple = k_state
# Update probabilities and pointers dicts
snake_case__ :List[str] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
snake_case__ :List[str] = arg_max
# The final observation
snake_case__ :str = observations_space[len(__snake_case ) - 1]
# argmax for given final observation
snake_case__ :Optional[int] = ""
snake_case__ :List[str] = -1
for k_state in states_space:
snake_case__ :List[str] = probabilities[(k_state, final_observation)]
if probability > max_probability:
snake_case__ :List[str] = probability
snake_case__ :int = k_state
snake_case__ :Any = arg_max
# Process pointers backwards
snake_case__ :int = last_state
snake_case__ :List[str] = []
for o in range(len(__snake_case ) - 1 , -1 , -1 ):
result.append(__snake_case )
snake_case__ :List[str] = pointers[previous, observations_space[o]]
result.reverse()
return result
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
_validate_not_empty(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
_validate_lists(__snake_case , __snake_case )
_validate_dicts(
__snake_case , __snake_case , __snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("There's an empty parameter" )
def lowercase_ ( __snake_case : Any , __snake_case : Any ) -> None:
'''simple docstring'''
_validate_list(__snake_case , "observations_space" )
_validate_list(__snake_case , "states_space" )
def lowercase_ ( __snake_case : Any , __snake_case : str ) -> None:
'''simple docstring'''
if not isinstance(_object , __snake_case ):
snake_case__ :Optional[int] = F'{var_name} must be a list'
raise ValueError(__snake_case )
else:
for x in _object:
if not isinstance(__snake_case , __snake_case ):
snake_case__ :Any = F'{var_name} must be a list of strings'
raise ValueError(__snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
_validate_dict(__snake_case , "initial_probabilities" , __snake_case )
_validate_nested_dict(__snake_case , "transition_probabilities" )
_validate_nested_dict(__snake_case , "emission_probabilities" )
def lowercase_ ( __snake_case : Any , __snake_case : str ) -> None:
'''simple docstring'''
_validate_dict(_object , __snake_case , __snake_case )
for x in _object.values():
_validate_dict(__snake_case , __snake_case , __snake_case , __snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : str , __snake_case : type , __snake_case : bool = False ) -> None:
'''simple docstring'''
if not isinstance(_object , __snake_case ):
snake_case__ :str = F'{var_name} must be a dict'
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object ):
snake_case__ :List[Any] = F'{var_name} all keys must be strings'
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object.values() ):
snake_case__ :Optional[int] = "nested dictionary " if nested else ""
snake_case__ :int = F'{var_name} {nested_text}all values must be {value_type.__name__}'
raise ValueError(__snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
| 57
| 1
|
from __future__ import annotations
__UpperCAmelCase : Any = []
def lowercase_ ( __snake_case : list[list[int]] , __snake_case : int , __snake_case : int ) -> bool:
'''simple docstring'''
for i in range(len(__snake_case ) ):
if board[row][i] == 1:
return False
for i in range(len(__snake_case ) ):
if board[i][column] == 1:
return False
for i, j in zip(range(__snake_case , -1 , -1 ) , range(__snake_case , -1 , -1 ) ):
if board[i][j] == 1:
return False
for i, j in zip(range(__snake_case , -1 , -1 ) , range(__snake_case , len(__snake_case ) ) ):
if board[i][j] == 1:
return False
return True
def lowercase_ ( __snake_case : list[list[int]] , __snake_case : int ) -> bool:
'''simple docstring'''
if row >= len(__snake_case ):
solution.append(__snake_case )
printboard(__snake_case )
print()
return True
for i in range(len(__snake_case ) ):
if is_safe(__snake_case , __snake_case , __snake_case ):
snake_case__ :Union[str, Any] = 1
solve(__snake_case , row + 1 )
snake_case__ :int = 0
return False
def lowercase_ ( __snake_case : list[list[int]] ) -> None:
'''simple docstring'''
for i in range(len(__snake_case ) ):
for j in range(len(__snake_case ) ):
if board[i][j] == 1:
print("Q" , end=" " )
else:
print("." , end=" " )
print()
# n=int(input("The no. of queens"))
__UpperCAmelCase : Tuple = 8
__UpperCAmelCase : Optional[Any] = [[0 for i in range(n)] for j in range(n)]
solve(board, 0)
print("The total no. of solutions are :", len(solution))
| 57
|
def lowercase_ ( __snake_case : str ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__snake_case ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
| 57
| 1
|
def lowercase_ ( __snake_case : str , __snake_case : int ) -> str:
'''simple docstring'''
snake_case__ :list[list[str]] = [[] for _ in range(__snake_case )]
snake_case__ :List[str] = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative" )
if key == 1 or len(__snake_case ) <= key:
return input_string
for position, character in enumerate(__snake_case ):
snake_case__ :str = position % (lowest * 2) # puts it in bounds
snake_case__ :str = min(__snake_case , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append(__snake_case )
snake_case__ :Union[str, Any] = ["".join(__snake_case ) for row in temp_grid]
snake_case__ :Optional[int] = "".join(__snake_case )
return output_string
def lowercase_ ( __snake_case : str , __snake_case : int ) -> str:
'''simple docstring'''
snake_case__ :Union[str, Any] = []
snake_case__ :Tuple = key - 1
if key <= 0:
raise ValueError("Height of grid can't be 0 or negative" )
if key == 1:
return input_string
snake_case__ :list[list[str]] = [[] for _ in range(__snake_case )] # generates template
for position in range(len(__snake_case ) ):
snake_case__ :Tuple = position % (lowest * 2) # puts it in bounds
snake_case__ :Union[str, Any] = min(__snake_case , lowest * 2 - num ) # creates zigzag pattern
temp_grid[num].append("*" )
snake_case__ :str = 0
for row in temp_grid: # fills in the characters
snake_case__ :Dict = input_string[counter : counter + len(__snake_case )]
grid.append(list(__snake_case ) )
counter += len(__snake_case )
snake_case__ :Union[str, Any] = "" # reads as zigzag
for position in range(len(__snake_case ) ):
snake_case__ :Any = position % (lowest * 2) # puts it in bounds
snake_case__ :int = min(__snake_case , lowest * 2 - num ) # creates zigzag pattern
output_string += grid[num][0]
grid[num].pop(0 )
return output_string
def lowercase_ ( __snake_case : str ) -> dict[int, str]:
'''simple docstring'''
snake_case__ :Tuple = {}
for key_guess in range(1 , len(__snake_case ) ): # tries every key
snake_case__ :Dict = decrypt(__snake_case , __snake_case )
return results
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
|
def lowercase_ ( __snake_case : int = 10_00 ) -> int:
'''simple docstring'''
snake_case__ :int = 3
snake_case__ :int = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 57
| 1
|
import json
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import torch
if is_vision_available():
from transformers import OneFormerImageProcessor
from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput
if is_vision_available():
from PIL import Image
def lowercase_ ( __snake_case : Optional[int] , __snake_case : Optional[Any]="shi-labs/oneformer_demo" ) -> List[str]:
'''simple docstring'''
with open(hf_hub_download(__snake_case , __snake_case , repo_type="dataset" ) , "r" ) as f:
snake_case__ :Any = json.load(__snake_case )
snake_case__ :Dict = {}
snake_case__ :int = []
snake_case__ :Optional[int] = []
for key, info in class_info.items():
snake_case__ :Optional[int] = info["name"]
class_names.append(info["name"] )
if info["isthing"]:
thing_ids.append(int(__snake_case ) )
snake_case__ :Any = thing_ids
snake_case__ :Dict = class_names
return metadata
class _snake_case ( unittest.TestCase ):
def __init__( self ,UpperCamelCase ,UpperCamelCase=7 ,UpperCamelCase=3 ,UpperCamelCase=30 ,UpperCamelCase=400 ,UpperCamelCase=None ,UpperCamelCase=True ,UpperCamelCase=True ,UpperCamelCase=[0.5, 0.5, 0.5] ,UpperCamelCase=[0.5, 0.5, 0.5] ,UpperCamelCase=10 ,UpperCamelCase=False ,UpperCamelCase=255 ,UpperCamelCase="shi-labs/oneformer_demo" ,UpperCamelCase="ade20k_panoptic.json" ,UpperCamelCase=10 ,) -> Tuple:
snake_case__ :Any = parent
snake_case__ :Optional[int] = batch_size
snake_case__ :Any = num_channels
snake_case__ :Dict = min_resolution
snake_case__ :Any = max_resolution
snake_case__ :Optional[Any] = do_resize
snake_case__ :Dict = {"shortest_edge": 32, "longest_edge": 1_333} if size is None else size
snake_case__ :List[str] = do_normalize
snake_case__ :Optional[Any] = image_mean
snake_case__ :List[Any] = image_std
snake_case__ :List[Any] = class_info_file
snake_case__ :Optional[Any] = prepare_metadata(UpperCamelCase ,UpperCamelCase )
snake_case__ :str = num_text
snake_case__ :List[str] = repo_path
# for the post_process_functions
snake_case__ :List[str] = 2
snake_case__ :str = 10
snake_case__ :Optional[int] = 10
snake_case__ :Tuple = 3
snake_case__ :List[Any] = 4
snake_case__ :Any = num_labels
snake_case__ :List[Any] = do_reduce_labels
snake_case__ :Union[str, Any] = ignore_index
def lowerCAmelCase_ ( self ) -> List[Any]:
return {
"do_resize": self.do_resize,
"size": self.size,
"do_normalize": self.do_normalize,
"image_mean": self.image_mean,
"image_std": self.image_std,
"num_labels": self.num_labels,
"do_reduce_labels": self.do_reduce_labels,
"ignore_index": self.ignore_index,
"class_info_file": self.class_info_file,
"metadata": self.metadata,
"num_text": self.num_text,
}
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase=False ) -> List[str]:
if not batched:
snake_case__ :int = image_inputs[0]
if isinstance(UpperCamelCase ,Image.Image ):
snake_case__ , snake_case__ :Any = image.size
else:
snake_case__ , snake_case__ :List[str] = image.shape[1], image.shape[2]
if w < h:
snake_case__ :List[str] = int(self.size["shortest_edge"] * h / w )
snake_case__ :Optional[int] = self.size["shortest_edge"]
elif w > h:
snake_case__ :Dict = self.size["shortest_edge"]
snake_case__ :str = int(self.size["shortest_edge"] * w / h )
else:
snake_case__ :Optional[Any] = self.size["shortest_edge"]
snake_case__ :Dict = self.size["shortest_edge"]
else:
snake_case__ :Optional[int] = []
for image in image_inputs:
snake_case__ , snake_case__ :List[Any] = self.get_expected_values([image] )
expected_values.append((expected_height, expected_width) )
snake_case__ :Tuple = max(UpperCamelCase ,key=lambda UpperCamelCase : item[0] )[0]
snake_case__ :Tuple = max(UpperCamelCase ,key=lambda UpperCamelCase : item[1] )[1]
return expected_height, expected_width
def lowerCAmelCase_ ( self ) -> str:
return OneFormerForUniversalSegmentationOutput(
# +1 for null class
class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1) ) ,masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width) ) ,)
@require_torch
@require_vision
class _snake_case ( _A , unittest.TestCase ):
_A = OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
# only for test_image_processing_common.test_image_proc_to_json_string
_A = image_processing_class
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Union[str, Any] = OneFormerImageProcessorTester(self )
@property
def lowerCAmelCase_ ( self ) -> Tuple:
return self.image_processing_tester.prepare_image_processor_dict()
def lowerCAmelCase_ ( self ) -> List[str]:
snake_case__ :str = self.image_processing_class(**self.image_processor_dict )
self.assertTrue(hasattr(UpperCamelCase ,"image_mean" ) )
self.assertTrue(hasattr(UpperCamelCase ,"image_std" ) )
self.assertTrue(hasattr(UpperCamelCase ,"do_normalize" ) )
self.assertTrue(hasattr(UpperCamelCase ,"do_resize" ) )
self.assertTrue(hasattr(UpperCamelCase ,"size" ) )
self.assertTrue(hasattr(UpperCamelCase ,"ignore_index" ) )
self.assertTrue(hasattr(UpperCamelCase ,"class_info_file" ) )
self.assertTrue(hasattr(UpperCamelCase ,"num_text" ) )
self.assertTrue(hasattr(UpperCamelCase ,"repo_path" ) )
self.assertTrue(hasattr(UpperCamelCase ,"metadata" ) )
self.assertTrue(hasattr(UpperCamelCase ,"do_reduce_labels" ) )
def lowerCAmelCase_ ( self ) -> str:
pass
def lowerCAmelCase_ ( self ) -> Optional[int]:
# Initialize image_processor
snake_case__ :Optional[Any] = self.image_processing_class(**self.image_processor_dict )
# create random PIL images
snake_case__ :List[str] = prepare_image_inputs(self.image_processing_tester ,equal_resolution=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase ,Image.Image )
# Test not batched input
snake_case__ :Any = image_processor(image_inputs[0] ,["semantic"] ,return_tensors="pt" ).pixel_values
snake_case__ , snake_case__ :Optional[int] = self.image_processing_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape ,(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,)
# Test batched
snake_case__ , snake_case__ :Optional[Any] = self.image_processing_tester.get_expected_values(UpperCamelCase ,batched=UpperCamelCase )
snake_case__ :Optional[int] = image_processor(
UpperCamelCase ,["semantic"] * len(UpperCamelCase ) ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) ,)
def lowerCAmelCase_ ( self ) -> List[str]:
# Initialize image_processor
snake_case__ :Any = self.image_processing_class(**self.image_processor_dict )
# create random numpy tensors
snake_case__ :Union[str, Any] = prepare_image_inputs(self.image_processing_tester ,equal_resolution=UpperCamelCase ,numpify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase ,np.ndarray )
# Test not batched input
snake_case__ :str = image_processor(image_inputs[0] ,["semantic"] ,return_tensors="pt" ).pixel_values
snake_case__ , snake_case__ :str = self.image_processing_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape ,(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,)
# Test batched
snake_case__ , snake_case__ :int = self.image_processing_tester.get_expected_values(UpperCamelCase ,batched=UpperCamelCase )
snake_case__ :Tuple = image_processor(
UpperCamelCase ,["semantic"] * len(UpperCamelCase ) ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) ,)
def lowerCAmelCase_ ( self ) -> Tuple:
# Initialize image_processor
snake_case__ :Optional[int] = self.image_processing_class(**self.image_processor_dict )
# create random PyTorch tensors
snake_case__ :Tuple = prepare_image_inputs(self.image_processing_tester ,equal_resolution=UpperCamelCase ,torchify=UpperCamelCase )
for image in image_inputs:
self.assertIsInstance(UpperCamelCase ,torch.Tensor )
# Test not batched input
snake_case__ :Optional[Any] = image_processor(image_inputs[0] ,["semantic"] ,return_tensors="pt" ).pixel_values
snake_case__ , snake_case__ :List[Any] = self.image_processing_tester.get_expected_values(UpperCamelCase )
self.assertEqual(
encoded_images.shape ,(1, self.image_processing_tester.num_channels, expected_height, expected_width) ,)
# Test batched
snake_case__ , snake_case__ :Any = self.image_processing_tester.get_expected_values(UpperCamelCase ,batched=UpperCamelCase )
snake_case__ :List[Any] = image_processor(
UpperCamelCase ,["semantic"] * len(UpperCamelCase ) ,return_tensors="pt" ).pixel_values
self.assertEqual(
encoded_images.shape ,(
self.image_processing_tester.batch_size,
self.image_processing_tester.num_channels,
expected_height,
expected_width,
) ,)
def lowerCAmelCase_ ( self ,UpperCamelCase=False ,UpperCamelCase=False ,UpperCamelCase="np" ) -> List[Any]:
snake_case__ :Any = self.image_processing_class(**self.image_processor_dict )
# prepare image and target
snake_case__ :Dict = self.image_processing_tester.num_labels
snake_case__ :Tuple = None
snake_case__ :Optional[Any] = None
snake_case__ :Dict = prepare_image_inputs(self.image_processing_tester ,equal_resolution=UpperCamelCase )
if with_segmentation_maps:
snake_case__ :str = num_labels
if is_instance_map:
snake_case__ :List[str] = list(range(UpperCamelCase ) ) * 2
snake_case__ :Tuple = dict(enumerate(UpperCamelCase ) )
snake_case__ :int = [
np.random.randint(0 ,high * 2 ,(img.size[1], img.size[0]) ).astype(np.uinta ) for img in image_inputs
]
if segmentation_type == "pil":
snake_case__ :int = [Image.fromarray(UpperCamelCase ) for annotation in annotations]
snake_case__ :List[str] = image_processor(
UpperCamelCase ,["semantic"] * len(UpperCamelCase ) ,UpperCamelCase ,return_tensors="pt" ,instance_id_to_semantic_id=UpperCamelCase ,pad_and_return_pixel_mask=UpperCamelCase ,)
return inputs
def lowerCAmelCase_ ( self ) -> int:
pass
def lowerCAmelCase_ ( self ) -> int:
def common(UpperCamelCase=False ,UpperCamelCase=None ):
snake_case__ :Any = self.comm_get_image_processor_inputs(
with_segmentation_maps=UpperCamelCase ,is_instance_map=UpperCamelCase ,segmentation_type=UpperCamelCase )
snake_case__ :Optional[Any] = inputs["mask_labels"]
snake_case__ :Any = inputs["class_labels"]
snake_case__ :List[Any] = inputs["pixel_values"]
snake_case__ :List[str] = inputs["text_inputs"]
# check the batch_size
for mask_label, class_label, text_input in zip(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ):
self.assertEqual(mask_label.shape[0] ,class_label.shape[0] )
# this ensure padding has happened
self.assertEqual(mask_label.shape[1:] ,pixel_values.shape[2:] )
self.assertEqual(len(UpperCamelCase ) ,self.image_processing_tester.num_text )
common()
common(is_instance_map=UpperCamelCase )
common(is_instance_map=UpperCamelCase ,segmentation_type="pil" )
common(is_instance_map=UpperCamelCase ,segmentation_type="pil" )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :Optional[Any] = np.zeros((20, 50) )
snake_case__ :Optional[Any] = 1
snake_case__ :Optional[Any] = 1
snake_case__ :Any = 1
snake_case__ :Dict = binary_mask_to_rle(UpperCamelCase )
self.assertEqual(len(UpperCamelCase ) ,4 )
self.assertEqual(rle[0] ,21 )
self.assertEqual(rle[1] ,45 )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :List[str] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes ,max_seq_length=77 ,task_seq_length=77 ,class_info_file="ade20k_panoptic.json" ,num_text=self.image_processing_tester.num_text ,repo_path="shi-labs/oneformer_demo" ,)
snake_case__ :Optional[int] = self.image_processing_tester.get_fake_oneformer_outputs()
snake_case__ :List[Any] = fature_extractor.post_process_semantic_segmentation(UpperCamelCase )
self.assertEqual(len(UpperCamelCase ) ,self.image_processing_tester.batch_size )
self.assertEqual(
segmentation[0].shape ,(
self.image_processing_tester.height,
self.image_processing_tester.width,
) ,)
snake_case__ :List[Any] = [(1, 4) for i in range(self.image_processing_tester.batch_size )]
snake_case__ :Optional[Any] = fature_extractor.post_process_semantic_segmentation(UpperCamelCase ,target_sizes=UpperCamelCase )
self.assertEqual(segmentation[0].shape ,target_sizes[0] )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :List[str] = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes ,max_seq_length=77 ,task_seq_length=77 ,class_info_file="ade20k_panoptic.json" ,num_text=self.image_processing_tester.num_text ,repo_path="shi-labs/oneformer_demo" ,)
snake_case__ :List[str] = self.image_processing_tester.get_fake_oneformer_outputs()
snake_case__ :Dict = image_processor.post_process_instance_segmentation(UpperCamelCase ,threshold=0 )
self.assertTrue(len(UpperCamelCase ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("segmentation" in el )
self.assertTrue("segments_info" in el )
self.assertEqual(type(el["segments_info"] ) ,UpperCamelCase )
self.assertEqual(
el["segmentation"].shape ,(self.image_processing_tester.height, self.image_processing_tester.width) )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :str = self.image_processing_class(
num_labels=self.image_processing_tester.num_classes ,max_seq_length=77 ,task_seq_length=77 ,class_info_file="ade20k_panoptic.json" ,num_text=self.image_processing_tester.num_text ,repo_path="shi-labs/oneformer_demo" ,)
snake_case__ :Tuple = self.image_processing_tester.get_fake_oneformer_outputs()
snake_case__ :List[Any] = image_processor.post_process_panoptic_segmentation(UpperCamelCase ,threshold=0 )
self.assertTrue(len(UpperCamelCase ) == self.image_processing_tester.batch_size )
for el in segmentation:
self.assertTrue("segmentation" in el )
self.assertTrue("segments_info" in el )
self.assertEqual(type(el["segments_info"] ) ,UpperCamelCase )
self.assertEqual(
el["segmentation"].shape ,(self.image_processing_tester.height, self.image_processing_tester.width) )
| 57
|
import os
import sys
import unittest
__UpperCAmelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase : Tuple = os.path.join(git_repo_path, "src", "diffusers")
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Tuple = find_backend(" if not is_torch_available():" )
self.assertEqual(UpperCamelCase ,"torch" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
snake_case__ :Tuple = find_backend(" if not (is_torch_available() and is_transformers_available()):" )
self.assertEqual(UpperCamelCase ,"torch_and_transformers" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
snake_case__ :str = find_backend(
" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):" )
self.assertEqual(UpperCamelCase ,"torch_and_transformers_and_onnx" )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :int = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch" ,UpperCamelCase )
self.assertIn("torch_and_transformers" ,UpperCamelCase )
self.assertIn("flax_and_transformers" ,UpperCamelCase )
self.assertIn("torch_and_transformers_and_onnx" ,UpperCamelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn("UNet2DModel" ,objects["torch"] )
self.assertIn("FlaxUNet2DConditionModel" ,objects["flax"] )
self.assertIn("StableDiffusionPipeline" ,objects["torch_and_transformers"] )
self.assertIn("FlaxStableDiffusionPipeline" ,objects["flax_and_transformers"] )
self.assertIn("LMSDiscreteScheduler" ,objects["torch_and_scipy"] )
self.assertIn("OnnxStableDiffusionPipeline" ,objects["torch_and_transformers_and_onnx"] )
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Union[str, Any] = create_dummy_object("CONSTANT" ,"'torch'" )
self.assertEqual(UpperCamelCase ,"\nCONSTANT = None\n" )
snake_case__ :Optional[Any] = create_dummy_object("function" ,"'torch'" )
self.assertEqual(
UpperCamelCase ,"\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n" )
snake_case__ :str = "\nclass FakeClass(metaclass=DummyObject):\n _backends = 'torch'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, 'torch')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n"
snake_case__ :List[str] = create_dummy_object("FakeClass" ,"'torch'" )
self.assertEqual(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :Tuple = "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n"
snake_case__ :int = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]} )
self.assertEqual(dummy_files["torch"] ,UpperCamelCase )
| 57
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase : Optional[int] = {
"configuration_upernet": ["UperNetConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : str = [
"UperNetForSemanticSegmentation",
"UperNetPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_upernet import UperNetConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_upernet import UperNetForSemanticSegmentation, UperNetPreTrainedModel
else:
import sys
__UpperCAmelCase : List[str] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase : Tuple = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : List[Any] = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
__UpperCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__UpperCAmelCase : List[Any] = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : Any = [
"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
__UpperCAmelCase : Optional[int] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
|
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> List[Any]:
# A mock response for an HTTP head request to emulate server down
snake_case__ :Tuple = mock.Mock()
snake_case__ :List[str] = 500
snake_case__ :Any = {}
snake_case__ :Union[str, Any] = HTTPError
snake_case__ :Tuple = {}
# Download this model to make sure it's in the cache.
snake_case__ :Any = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" ,return_value=UpperCamelCase ) as mock_head:
snake_case__ :Dict = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def lowerCAmelCase_ ( self ) -> Dict:
# A mock response for an HTTP head request to emulate server down
snake_case__ :Union[str, Any] = mock.Mock()
snake_case__ :int = 500
snake_case__ :Any = {}
snake_case__ :Dict = HTTPError
snake_case__ :List[Any] = {}
# Download this model to make sure it's in the cache.
snake_case__ :Optional[int] = GPTaTokenizerFast.from_pretrained("gpt2" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" ,return_value=UpperCamelCase ) as mock_head:
snake_case__ :Any = GPTaTokenizerFast.from_pretrained("gpt2" )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCAmelCase_ ( self ) -> int:
# This test is for deprecated behavior and can be removed in v5
try:
snake_case__ :Union[str, Any] = tempfile.mktemp()
with open(UpperCamelCase ,"wb" ) as f:
http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" ,UpperCamelCase )
snake_case__ :Tuple = AlbertTokenizer.from_pretrained(UpperCamelCase )
finally:
os.remove(UpperCamelCase )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile("tokenizer.json" ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open("tokenizer.json" ,"wb" ) as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json" ,UpperCamelCase )
snake_case__ :Dict = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1_000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("tokenizer.json" )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
# This test is for deprecated behavior and can be removed in v5
snake_case__ :Union[str, Any] = AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" )
@is_staging_test
class _snake_case ( unittest.TestCase ):
_A = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def lowerCAmelCase_ ( cls ) -> Optional[int]:
snake_case__ :List[str] = TOKEN
HfFolder.save_token(UpperCamelCase )
@classmethod
def lowerCAmelCase_ ( cls ) -> Union[str, Any]:
try:
delete_repo(token=cls._token ,repo_id="test-tokenizer" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="valid_org/test-tokenizer-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="test-dynamic-tokenizer" )
except HTTPError:
pass
def lowerCAmelCase_ ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :List[str] = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :str = BertTokenizer(UpperCamelCase )
tokenizer.push_to_hub("test-tokenizer" ,use_auth_token=self._token )
snake_case__ :Dict = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id="test-tokenizer" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(UpperCamelCase ,repo_id="test-tokenizer" ,push_to_hub=UpperCamelCase ,use_auth_token=self._token )
snake_case__ :List[str] = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def lowerCAmelCase_ ( self ) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :List[Any] = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Any = BertTokenizer(UpperCamelCase )
tokenizer.push_to_hub("valid_org/test-tokenizer-org" ,use_auth_token=self._token )
snake_case__ :Any = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id="valid_org/test-tokenizer-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
UpperCamelCase ,repo_id="valid_org/test-tokenizer-org" ,push_to_hub=UpperCamelCase ,use_auth_token=self._token )
snake_case__ :Union[str, Any] = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def lowerCAmelCase_ ( self ) -> Any:
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :str = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Optional[int] = CustomTokenizer(UpperCamelCase )
# No fast custom tokenizer
tokenizer.push_to_hub("test-dynamic-tokenizer" ,use_auth_token=self._token )
snake_case__ :Union[str, Any] = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizer" )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :int = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Tuple = BertTokenizerFast.from_pretrained(UpperCamelCase )
bert_tokenizer.save_pretrained(UpperCamelCase )
snake_case__ :List[Any] = CustomTokenizerFast.from_pretrained(UpperCamelCase )
tokenizer.push_to_hub("test-dynamic-tokenizer" ,use_auth_token=self._token )
snake_case__ :List[Any] = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizerFast" )
snake_case__ :List[str] = AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' ,use_fast=UpperCamelCase ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizer" )
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :int = Trie()
trie.add("Hello 友達" )
self.assertEqual(trie.data ,{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}} )
trie.add("Hello" )
trie.data
self.assertEqual(trie.data ,{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}} )
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :List[str] = Trie()
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) ,["[CLS] This is a extra_id_100"] )
trie.add("[CLS]" )
trie.add("extra_id_1" )
trie.add("extra_id_100" )
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) ,["[CLS]", " This is a ", "extra_id_100"] )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Optional[Any] = Trie()
trie.add("A" )
self.assertEqual(trie.split("ABC" ) ,["A", "BC"] )
self.assertEqual(trie.split("BCA" ) ,["BC", "A"] )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :Any = Trie()
trie.add("TOKEN]" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) ,["This is something ", "[SPECIAL_TOKEN]"] )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :List[Any] = Trie()
trie.add("A" )
trie.add("P" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) ,["This is something ", "[SPECIAL_TOKEN]"] )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :str = Trie()
trie.add("AB" )
trie.add("B" )
trie.add("C" )
self.assertEqual(trie.split("ABC" ) ,["AB", "C"] )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Dict = Trie()
trie.add("ABC" )
trie.add("B" )
trie.add("CD" )
self.assertEqual(trie.split("ABCD" ) ,["ABC", "D"] )
def lowerCAmelCase_ ( self ) -> int:
# Even if the offsets are wrong, we necessarily output correct string
# parts.
snake_case__ :Optional[int] = Trie()
snake_case__ :Union[str, Any] = trie.cut_text("ABC" ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(UpperCamelCase ,["AB", "C"] )
| 57
| 1
|
from __future__ import annotations
class _snake_case :
def __init__( self ,UpperCamelCase ) -> None:
snake_case__ :Optional[Any] = order
# a_{0} ... a_{k}
snake_case__ :Any = [1.0] + [0.0] * order
# b_{0} ... b_{k}
snake_case__ :List[Any] = [1.0] + [0.0] * order
# x[n-1] ... x[n-k]
snake_case__ :int = [0.0] * self.order
# y[n-1] ... y[n-k]
snake_case__ :Any = [0.0] * self.order
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> None:
if len(UpperCamelCase ) < self.order:
snake_case__ :Tuple = [1.0, *a_coeffs]
if len(UpperCamelCase ) != self.order + 1:
snake_case__ :Any = (
f'Expected a_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(UpperCamelCase )}'
)
raise ValueError(UpperCamelCase )
if len(UpperCamelCase ) != self.order + 1:
snake_case__ :Union[str, Any] = (
f'Expected b_coeffs to have {self.order + 1} elements '
f'for {self.order}-order filter, got {len(UpperCamelCase )}'
)
raise ValueError(UpperCamelCase )
snake_case__ :List[str] = a_coeffs
snake_case__ :List[str] = b_coeffs
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> float:
snake_case__ :Tuple = 0.0
# Start at index 1 and do index 0 at the end.
for i in range(1 ,self.order + 1 ):
result += (
self.b_coeffs[i] * self.input_history[i - 1]
- self.a_coeffs[i] * self.output_history[i - 1]
)
snake_case__ :str = (result + self.b_coeffs[0] * sample) / self.a_coeffs[0]
snake_case__ :str = self.input_history[:-1]
snake_case__ :List[str] = self.output_history[:-1]
snake_case__ :Any = sample
snake_case__ :str = result
return result
| 57
|
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 : Optional[Any] = 1_6
__UpperCAmelCase : Optional[int] = 3_2
def lowercase_ ( __snake_case : Accelerator , __snake_case : int = 16 , __snake_case : str = "bert-base-cased" ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :int = AutoTokenizer.from_pretrained(__snake_case )
snake_case__ :Optional[int] = load_dataset("glue" , "mrpc" )
def tokenize_function(__snake_case : Tuple ):
# max_length=None => use the model max length (it's actually the default)
snake_case__ :Any = 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
snake_case__ :List[Any] = 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
snake_case__ :Any = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(__snake_case : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__snake_case , padding="max_length" , max_length=1_28 , return_tensors="pt" )
return tokenizer.pad(__snake_case , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
snake_case__ :Any = DataLoader(
tokenized_datasets["train"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
snake_case__ :Tuple = DataLoader(
tokenized_datasets["validation"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
return train_dataloader, eval_dataloader
def lowercase_ ( __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
model.eval()
snake_case__ :Union[str, Any] = 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():
snake_case__ :List[Any] = model(**__snake_case )
snake_case__ :Any = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
snake_case__ , snake_case__ :Tuple = 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:
snake_case__ :List[str] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
snake_case__ :Optional[int] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__snake_case , references=__snake_case , )
snake_case__ :int = metric.compute()
return eval_metric["accuracy"]
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : Optional[Any] ) -> Any:
'''simple docstring'''
snake_case__ :Any = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ :Union[str, Any] = config["lr"]
snake_case__ :List[str] = int(config["num_epochs"] )
snake_case__ :Optional[Any] = int(config["seed"] )
snake_case__ :List[Any] = int(config["batch_size"] )
snake_case__ :List[Any] = args.model_name_or_path
set_seed(__snake_case )
snake_case__ , snake_case__ :List[Any] = 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)
snake_case__ :List[Any] = AutoModelForSequenceClassification.from_pretrained(__snake_case , return_dict=__snake_case )
# Instantiate optimizer
snake_case__ :int = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
snake_case__ :Tuple = optimizer_cls(params=model.parameters() , lr=__snake_case )
if accelerator.state.deepspeed_plugin is not None:
snake_case__ :List[str] = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
snake_case__ :Any = 1
snake_case__ :List[Any] = (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
):
snake_case__ :Optional[Any] = get_linear_schedule_with_warmup(
optimizer=__snake_case , num_warmup_steps=0 , num_training_steps=__snake_case , )
else:
snake_case__ :Any = 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.
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ :int = 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
snake_case__ :Dict = 0
# We also need to keep track of the stating epoch so files are named properly
snake_case__ :Union[str, Any] = 0
snake_case__ :List[str] = evaluate.load("glue" , "mrpc" )
snake_case__ :Optional[Any] = num_epochs
if args.partial_train_epoch is not None:
snake_case__ :List[Any] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
snake_case__ :Union[str, Any] = args.resume_from_checkpoint.split("epoch_" )[1]
snake_case__ :Dict = ""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
snake_case__ :str = int(__snake_case ) + 1
snake_case__ :List[Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
accelerator.print("resumed checkpoint performance:" , __snake_case )
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:
snake_case__ :Tuple = json.load(__snake_case )
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
snake_case__ :Optional[int] = {}
for epoch in range(__snake_case , __snake_case ):
model.train()
for step, batch in enumerate(__snake_case ):
snake_case__ :str = model(**__snake_case )
snake_case__ :List[str] = outputs.loss
snake_case__ :List[Any] = 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
snake_case__ :int = F'epoch_{epoch}'
snake_case__ :str = os.path.join(args.output_dir , __snake_case )
accelerator.save_state(__snake_case )
snake_case__ :Union[str, Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
snake_case__ :List[str] = accuracy
snake_case__ :List[str] = lr_scheduler.get_lr()[0]
snake_case__ :List[Any] = optimizer.param_groups[0]["lr"]
snake_case__ :Dict = epoch
snake_case__ :List[Any] = overall_step
accelerator.print(F'epoch {epoch}:' , __snake_case )
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(__snake_case , __snake_case )
def lowercase_ ( ) -> Any:
'''simple docstring'''
snake_case__ :List[Any] = 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(
"--resume_from_checkpoint" , type=__snake_case , default=__snake_case , help="If the training should continue from a checkpoint folder." , )
parser.add_argument(
"--partial_train_epoch" , type=__snake_case , default=__snake_case , help="If passed, the training will stop after this number of epochs." , )
parser.add_argument(
"--num_epochs" , type=__snake_case , default=2 , help="Number of train epochs." , )
snake_case__ :Any = parser.parse_args()
snake_case__ :int = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(__snake_case , __snake_case )
if __name__ == "__main__":
main()
| 57
| 1
|
import unittest
import numpy as np
def lowercase_ ( __snake_case : np.ndarray , __snake_case : np.ndarray , __snake_case : np.ndarray , __snake_case : np.ndarray | None = None , ) -> np.ndarray:
'''simple docstring'''
snake_case__ :Any = np.shape(__snake_case )
snake_case__ :Dict = np.shape(__snake_case )
snake_case__ :Tuple = np.shape(__snake_case )
if shape_a[0] != shape_b[0]:
snake_case__ :int = (
"Expected the same number of rows for A and B. "
F'Instead found A of size {shape_a} and B of size {shape_b}'
)
raise ValueError(__snake_case )
if shape_b[1] != shape_c[1]:
snake_case__ :Dict = (
"Expected the same number of columns for B and C. "
F'Instead found B of size {shape_b} and C of size {shape_c}'
)
raise ValueError(__snake_case )
snake_case__ :List[Any] = pseudo_inv
if a_inv is None:
try:
snake_case__ :Dict = np.linalg.inv(__snake_case )
except np.linalg.LinAlgError:
raise ValueError(
"Input matrix A is not invertible. Cannot compute Schur complement." )
return mat_c - mat_b.T @ a_inv @ mat_b
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> None:
snake_case__ :Any = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
snake_case__ :Optional[int] = np.array([[0, 3], [3, 0], [2, 3]] )
snake_case__ :List[str] = np.array([[2, 1], [6, 3]] )
snake_case__ :List[str] = schur_complement(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
snake_case__ :Any = np.block([[a, b], [b.T, c]] )
snake_case__ :List[str] = np.linalg.det(UpperCamelCase )
snake_case__ :Dict = np.linalg.det(UpperCamelCase )
snake_case__ :Any = np.linalg.det(UpperCamelCase )
self.assertAlmostEqual(UpperCamelCase ,det_a * det_s )
def lowerCAmelCase_ ( self ) -> None:
snake_case__ :Tuple = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
snake_case__ :List[str] = np.array([[0, 3], [3, 0], [2, 3]] )
snake_case__ :Union[str, Any] = np.array([[2, 1], [6, 3]] )
with self.assertRaises(UpperCamelCase ):
schur_complement(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> None:
snake_case__ :Any = np.array([[1, 2, 1], [2, 1, 2], [3, 2, 4]] )
snake_case__ :str = np.array([[0, 3], [3, 0], [2, 3]] )
snake_case__ :Dict = np.array([[2, 1, 3], [6, 3, 5]] )
with self.assertRaises(UpperCamelCase ):
schur_complement(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
if __name__ == "__main__":
import doctest
doctest.testmod()
unittest.main()
| 57
|
from __future__ import annotations
class _snake_case :
def __init__( self ,UpperCamelCase ) -> None:
snake_case__ :Union[str, Any] = data
snake_case__ :Node | None = None
snake_case__ :Node | None = None
def lowercase_ ( __snake_case : Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowercase_ ( __snake_case : Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowercase_ ( __snake_case : Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowercase_ ( ) -> None: # Main function for testing.
'''simple docstring'''
snake_case__ :Dict = Node(1 )
snake_case__ :int = Node(2 )
snake_case__ :Optional[Any] = Node(3 )
snake_case__ :Tuple = Node(4 )
snake_case__ :str = Node(5 )
snake_case__ :Optional[Any] = Node(6 )
snake_case__ :List[Any] = Node(7 )
snake_case__ :List[str] = Node(8 )
snake_case__ :Tuple = Node(9 )
print(is_full_binary_tree(__snake_case ) )
print(depth_of_tree(__snake_case ) )
print("Tree is: " )
display(__snake_case )
if __name__ == "__main__":
main()
| 57
| 1
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase : int = {
"configuration_lilt": ["LILT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LiltConfig"],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : Tuple = [
"LILT_PRETRAINED_MODEL_ARCHIVE_LIST",
"LiltForQuestionAnswering",
"LiltForSequenceClassification",
"LiltForTokenClassification",
"LiltModel",
"LiltPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_lilt import LILT_PRETRAINED_CONFIG_ARCHIVE_MAP, LiltConfig
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_lilt import (
LILT_PRETRAINED_MODEL_ARCHIVE_LIST,
LiltForQuestionAnswering,
LiltForSequenceClassification,
LiltForTokenClassification,
LiltModel,
LiltPreTrainedModel,
)
else:
import sys
__UpperCAmelCase : List[Any] = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
|
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__UpperCAmelCase : List[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__UpperCAmelCase : int = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F'''{len(upper_files)} files contain uppercase characters:''')
print("\n".join(upper_files) + "\n")
__UpperCAmelCase : Any = [file for file in filepaths if " " in file]
if space_files:
print(F'''{len(space_files)} files contain space characters:''')
print("\n".join(space_files) + "\n")
__UpperCAmelCase : str = [file for file in filepaths if "-" in file]
if hyphen_files:
print(F'''{len(hyphen_files)} files contain hyphen characters:''')
print("\n".join(hyphen_files) + "\n")
__UpperCAmelCase : Dict = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F'''{len(nodir_files)} files are not in a directory:''')
print("\n".join(nodir_files) + "\n")
__UpperCAmelCase : int = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 57
| 1
|
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_camembert import CamembertTokenizer
else:
__UpperCAmelCase : Any = None
__UpperCAmelCase : int = logging.get_logger(__name__)
__UpperCAmelCase : List[str] = {"vocab_file": "sentencepiece.bpe.model", "tokenizer_file": "tokenizer.json"}
__UpperCAmelCase : str = {
"vocab_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/sentencepiece.bpe.model",
},
"tokenizer_file": {
"camembert-base": "https://huggingface.co/camembert-base/resolve/main/tokenizer.json",
},
}
__UpperCAmelCase : Optional[int] = {
"camembert-base": 5_1_2,
}
__UpperCAmelCase : int = "▁"
class _snake_case ( _A ):
_A = VOCAB_FILES_NAMES
_A = PRETRAINED_VOCAB_FILES_MAP
_A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A = ['input_ids', 'attention_mask']
_A = CamembertTokenizer
def __init__( self ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase="<s>" ,UpperCamelCase="</s>" ,UpperCamelCase="</s>" ,UpperCamelCase="<s>" ,UpperCamelCase="<unk>" ,UpperCamelCase="<pad>" ,UpperCamelCase="<mask>" ,UpperCamelCase=["<s>NOTUSED", "</s>NOTUSED"] ,**UpperCamelCase ,) -> int:
# Mask token behave like a normal word, i.e. include the space before it
snake_case__ :List[str] = AddedToken(UpperCamelCase ,lstrip=UpperCamelCase ,rstrip=UpperCamelCase ) if isinstance(UpperCamelCase ,UpperCamelCase ) else mask_token
super().__init__(
UpperCamelCase ,tokenizer_file=UpperCamelCase ,bos_token=UpperCamelCase ,eos_token=UpperCamelCase ,sep_token=UpperCamelCase ,cls_token=UpperCamelCase ,unk_token=UpperCamelCase ,pad_token=UpperCamelCase ,mask_token=UpperCamelCase ,additional_special_tokens=UpperCamelCase ,**UpperCamelCase ,)
snake_case__ :int = vocab_file
snake_case__ :Dict = False if not self.vocab_file else True
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> List[int]:
if token_ids_a is None:
return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
snake_case__ :Optional[int] = [self.cls_token_id]
snake_case__ :Tuple = [self.sep_token_id]
return cls + token_ids_a + sep + sep + token_ids_a + sep
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> List[int]:
snake_case__ :str = [self.sep_token_id]
snake_case__ :List[Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep + sep + token_ids_a + sep ) * [0]
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> Tuple[str]:
if not self.can_save_slow_tokenizer:
raise ValueError(
"Your fast tokenizer does not have the necessary information to save the vocabulary for a slow "
"tokenizer." )
if not os.path.isdir(UpperCamelCase ):
logger.error(f'Vocabulary path ({save_directory}) should be a directory' )
return
snake_case__ :Dict = os.path.join(
UpperCamelCase ,(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"] )
if os.path.abspath(self.vocab_file ) != os.path.abspath(UpperCamelCase ):
copyfile(self.vocab_file ,UpperCamelCase )
return (out_vocab_file,)
| 57
|
def lowercase_ ( __snake_case : Tuple , __snake_case : Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case__ :Dict = ""
for i in table:
res += inp[i - 1]
return res
def lowercase_ ( __snake_case : List[str] ) -> int:
'''simple docstring'''
return data[1:] + data[0]
def lowercase_ ( __snake_case : int , __snake_case : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :Union[str, Any] = ""
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def lowercase_ ( __snake_case : Optional[int] , __snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :int = int("0b" + data[0] + data[-1] , 2 )
snake_case__ :Union[str, Any] = int("0b" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def lowercase_ ( __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : List[Any] , __snake_case : Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case__ :Tuple = message[:4]
snake_case__ :int = message[4:]
snake_case__ :int = apply_table(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = xor(__snake_case , __snake_case )
snake_case__ :Tuple = apply_sbox(__snake_case , temp[:4] ) # noqa: E741
snake_case__ :List[str] = apply_sbox(__snake_case , temp[4:] )
snake_case__ :int = "0" * (2 - len(__snake_case )) + l # noqa: E741
snake_case__ :int = "0" * (2 - len(__snake_case )) + r
snake_case__ :Optional[Any] = apply_table(l + r , __snake_case )
snake_case__ :Tuple = xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
__UpperCAmelCase : Dict = input("Enter 10 bit key: ")
__UpperCAmelCase : Tuple = input("Enter 8 bit message: ")
__UpperCAmelCase : Any = [6, 3, 7, 4, 8, 5, 1_0, 9]
__UpperCAmelCase : List[str] = [3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
__UpperCAmelCase : Tuple = [2, 4, 3, 1]
__UpperCAmelCase : List[Any] = [2, 6, 3, 1, 4, 8, 5, 7]
__UpperCAmelCase : Optional[Any] = [4, 1, 3, 5, 7, 2, 8, 6]
__UpperCAmelCase : Optional[int] = [4, 1, 2, 3, 2, 3, 4, 1]
__UpperCAmelCase : List[Any] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
__UpperCAmelCase : Union[str, Any] = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
__UpperCAmelCase : int = apply_table(key, paa_table)
__UpperCAmelCase : Dict = temp[:5]
__UpperCAmelCase : Optional[int] = temp[5:]
__UpperCAmelCase : Optional[int] = left_shift(left)
__UpperCAmelCase : Union[str, Any] = left_shift(right)
__UpperCAmelCase : int = apply_table(left + right, pa_table)
__UpperCAmelCase : Tuple = left_shift(left)
__UpperCAmelCase : Union[str, Any] = left_shift(right)
__UpperCAmelCase : Dict = left_shift(left)
__UpperCAmelCase : Optional[Any] = left_shift(right)
__UpperCAmelCase : Optional[int] = apply_table(left + right, pa_table)
# encryption
__UpperCAmelCase : Tuple = apply_table(message, IP)
__UpperCAmelCase : Tuple = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : List[Any] = temp[4:] + temp[:4]
__UpperCAmelCase : int = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : Union[str, Any] = apply_table(temp, IP_inv)
print("Cipher text is:", CT)
# decryption
__UpperCAmelCase : List[Any] = apply_table(CT, IP)
__UpperCAmelCase : List[Any] = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : int = temp[4:] + temp[:4]
__UpperCAmelCase : Union[str, Any] = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : Union[str, Any] = apply_table(temp, IP_inv)
print("Plain text after decypting is:", PT)
| 57
| 1
|
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class _snake_case ( unittest.TestCase ):
_A = inspect.getfile(accelerate.test_utils )
_A = os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ['scripts', 'test_cli.py'] )
_A = ['accelerate', 'launch']
_A = Path.home() / '.cache/huggingface/accelerate'
_A = 'default_config.yaml'
_A = config_folder / config_file
_A = config_folder / '_default_config.yaml'
_A = Path('tests/test_configs' )
@classmethod
def lowerCAmelCase_ ( cls ) -> List[str]:
if cls.config_path.is_file():
cls.config_path.rename(cls.changed_path )
@classmethod
def lowerCAmelCase_ ( cls ) -> List[str]:
if cls.changed_path.is_file():
cls.changed_path.rename(cls.config_path )
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Tuple = self.base_cmd
if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
cmd += ["--multi_gpu"]
execute_subprocess_async(cmd + [self.test_file_path] ,env=os.environ.copy() )
def lowerCAmelCase_ ( self ) -> int:
for config in sorted(self.test_config_path.glob("**/*.yaml" ) ):
with self.subTest(config_file=UpperCamelCase ):
execute_subprocess_async(
self.base_cmd + ["--config_file", str(UpperCamelCase ), self.test_file_path] ,env=os.environ.copy() )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
execute_subprocess_async(["accelerate", "test"] ,env=os.environ.copy() )
class _snake_case ( unittest.TestCase ):
_A = 'test-tpu'
_A = 'us-central1-a'
_A = 'ls'
_A = ['accelerate', 'tpu-config']
_A = 'cd /usr/share'
_A = 'tests/test_samples/test_command_file.sh'
_A = 'Running gcloud compute tpus tpu-vm ssh'
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :Dict = run_command(
self.cmd
+ ["--command", self.command, "--tpu_zone", self.tpu_zone, "--tpu_name", self.tpu_name, "--debug"] ,return_stdout=UpperCamelCase ,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' ,UpperCamelCase ,)
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :Tuple = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/0_12_0.yaml",
"--command",
self.command,
"--tpu_zone",
self.tpu_zone,
"--tpu_name",
self.tpu_name,
"--debug",
] ,return_stdout=UpperCamelCase ,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' ,UpperCamelCase ,)
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :Dict = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--debug"] ,return_stdout=UpperCamelCase )
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' ,UpperCamelCase ,)
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :Dict = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--command", self.command, "--debug"] ,return_stdout=UpperCamelCase ,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all' ,UpperCamelCase ,)
def lowerCAmelCase_ ( self ) -> List[str]:
snake_case__ :Tuple = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/latest.yaml",
"--command",
self.command,
"--command",
"echo \"Hello World\"",
"--debug",
] ,return_stdout=UpperCamelCase ,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo "Hello World" --worker all' ,UpperCamelCase ,)
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :int = run_command(
self.cmd
+ ["--config_file", "tests/test_configs/latest.yaml", "--command_file", self.command_file, "--debug"] ,return_stdout=UpperCamelCase ,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' ,UpperCamelCase ,)
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :Optional[Any] = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/0_12_0.yaml",
"--command_file",
self.command_file,
"--tpu_zone",
self.tpu_zone,
"--tpu_name",
self.tpu_name,
"--debug",
] ,return_stdout=UpperCamelCase ,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo "hello world"; echo "this is a second command" --worker all' ,UpperCamelCase ,)
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :Optional[Any] = run_command(
self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--install_accelerate", "--debug"] ,return_stdout=UpperCamelCase ,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo "hello world"; echo "this is a second command" --worker all' ,UpperCamelCase ,)
def lowerCAmelCase_ ( self ) -> List[str]:
snake_case__ :List[Any] = run_command(
self.cmd
+ [
"--config_file",
"tests/test_configs/latest.yaml",
"--install_accelerate",
"--accelerate_version",
"12.0.0",
"--debug",
] ,return_stdout=UpperCamelCase ,)
self.assertIn(
f'{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo "hello world"; echo "this is a second command" --worker all' ,UpperCamelCase ,)
| 57
|
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _snake_case ( _A , _A , _A ):
@register_to_config
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = False ,) -> int:
super().__init__()
snake_case__ :Union[str, Any] = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :int = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :Any = False
snake_case__ :List[Any] = nn.Dropout(p=UpperCamelCase )
snake_case__ :Tuple = TaConfig(
vocab_size=UpperCamelCase ,d_model=UpperCamelCase ,num_heads=UpperCamelCase ,d_kv=UpperCamelCase ,d_ff=UpperCamelCase ,dropout_rate=UpperCamelCase ,feed_forward_proj=UpperCamelCase ,is_decoder=UpperCamelCase ,is_encoder_decoder=UpperCamelCase ,)
snake_case__ :List[str] = nn.ModuleList()
for lyr_num in range(UpperCamelCase ):
snake_case__ :List[Any] = TaBlock(UpperCamelCase )
self.encoders.append(UpperCamelCase )
snake_case__ :Optional[Any] = TaLayerNorm(UpperCamelCase )
snake_case__ :Any = nn.Dropout(p=UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> int:
snake_case__ :str = self.token_embedder(UpperCamelCase )
snake_case__ :int = encoder_input_tokens.shape[1]
snake_case__ :List[Any] = torch.arange(UpperCamelCase ,device=encoder_input_tokens.device )
x += self.position_encoding(UpperCamelCase )
snake_case__ :Optional[int] = self.dropout_pre(UpperCamelCase )
# inverted the attention mask
snake_case__ :Optional[Any] = encoder_input_tokens.size()
snake_case__ :Dict = self.get_extended_attention_mask(UpperCamelCase ,UpperCamelCase )
for lyr in self.encoders:
snake_case__ :str = lyr(UpperCamelCase ,UpperCamelCase )[0]
snake_case__ :List[Any] = self.layer_norm(UpperCamelCase )
return self.dropout_post(UpperCamelCase ), encoder_inputs_mask
| 57
| 1
|
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCAmelCase : List[Any] = {
"configuration_distilbert": [
"DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"DistilBertConfig",
"DistilBertOnnxConfig",
],
"tokenization_distilbert": ["DistilBertTokenizer"],
}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : Dict = ["DistilBertTokenizerFast"]
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : str = [
"DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"DistilBertForMaskedLM",
"DistilBertForMultipleChoice",
"DistilBertForQuestionAnswering",
"DistilBertForSequenceClassification",
"DistilBertForTokenClassification",
"DistilBertModel",
"DistilBertPreTrainedModel",
]
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : Dict = [
"TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
"TFDistilBertForMaskedLM",
"TFDistilBertForMultipleChoice",
"TFDistilBertForQuestionAnswering",
"TFDistilBertForSequenceClassification",
"TFDistilBertForTokenClassification",
"TFDistilBertMainLayer",
"TFDistilBertModel",
"TFDistilBertPreTrainedModel",
]
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : Union[str, Any] = [
"FlaxDistilBertForMaskedLM",
"FlaxDistilBertForMultipleChoice",
"FlaxDistilBertForQuestionAnswering",
"FlaxDistilBertForSequenceClassification",
"FlaxDistilBertForTokenClassification",
"FlaxDistilBertModel",
"FlaxDistilBertPreTrainedModel",
]
if TYPE_CHECKING:
from .configuration_distilbert import (
DISTILBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
DistilBertConfig,
DistilBertOnnxConfig,
)
from .tokenization_distilbert import DistilBertTokenizer
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_distilbert_fast import DistilBertTokenizerFast
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_distilbert import (
DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
DistilBertForMaskedLM,
DistilBertForMultipleChoice,
DistilBertForQuestionAnswering,
DistilBertForSequenceClassification,
DistilBertForTokenClassification,
DistilBertModel,
DistilBertPreTrainedModel,
)
try:
if not is_tf_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_tf_distilbert import (
TF_DISTILBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
TFDistilBertForMaskedLM,
TFDistilBertForMultipleChoice,
TFDistilBertForQuestionAnswering,
TFDistilBertForSequenceClassification,
TFDistilBertForTokenClassification,
TFDistilBertMainLayer,
TFDistilBertModel,
TFDistilBertPreTrainedModel,
)
try:
if not is_flax_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .modeling_flax_distilbert import (
FlaxDistilBertForMaskedLM,
FlaxDistilBertForMultipleChoice,
FlaxDistilBertForQuestionAnswering,
FlaxDistilBertForSequenceClassification,
FlaxDistilBertForTokenClassification,
FlaxDistilBertModel,
FlaxDistilBertPreTrainedModel,
)
else:
import sys
__UpperCAmelCase : int = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
|
__UpperCAmelCase : int = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__UpperCAmelCase : List[str] = ["a", "b", "c", "d", "e"]
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : List[Any] , __snake_case : Tuple ) -> Optional[int]:
'''simple docstring'''
snake_case__ :List[Any] = start
# add current to visited
visited.append(__snake_case )
snake_case__ :List[str] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# if all neighbors visited add current to sort
sort.append(__snake_case )
# if all vertices haven't been visited select a new one to visit
if len(__snake_case ) != len(__snake_case ):
for vertice in vertices:
if vertice not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# return sort
return sort
if __name__ == "__main__":
__UpperCAmelCase : Tuple = topological_sort("a", [], [])
print(sort)
| 57
| 1
|
def lowercase_ ( __snake_case : int ) -> list:
'''simple docstring'''
snake_case__ :Optional[Any] = int(__snake_case )
if n_element < 1:
snake_case__ :str = ValueError("a should be a positive number" )
raise my_error
snake_case__ :List[str] = [1]
snake_case__ , snake_case__ , snake_case__ :List[str] = (0, 0, 0)
snake_case__ :Tuple = 1
while index < n_element:
while hamming_list[i] * 2 <= hamming_list[-1]:
i += 1
while hamming_list[j] * 3 <= hamming_list[-1]:
j += 1
while hamming_list[k] * 5 <= hamming_list[-1]:
k += 1
hamming_list.append(
min(hamming_list[i] * 2 , hamming_list[j] * 3 , hamming_list[k] * 5 ) )
index += 1
return hamming_list
if __name__ == "__main__":
__UpperCAmelCase : List[Any] = input("Enter the last number (nth term) of the Hamming Number Series: ")
print("Formula of Hamming Number Series => 2^i * 3^j * 5^k")
__UpperCAmelCase : Optional[Any] = hamming(int(n))
print("-----------------------------------------------------")
print(F'''The list with nth numbers is: {hamming_numbers}''')
print("-----------------------------------------------------")
| 57
|
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 _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCAmelCase_ ( self ) -> str:
snake_case__ , snake_case__ :Tuple = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-canny" ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ , snake_case__ :Any = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCamelCase ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ :List[str] = controlnet_params
snake_case__ :Union[str, Any] = "bird"
snake_case__ :Optional[int] = jax.device_count()
snake_case__ :Tuple = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case__ :Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" )
snake_case__ :str = pipe.prepare_image_inputs([canny_image] * num_samples )
snake_case__ :List[str] = jax.random.PRNGKey(0 )
snake_case__ :str = jax.random.split(UpperCamelCase ,jax.device_count() )
snake_case__ :int = replicate(UpperCamelCase )
snake_case__ :Any = shard(UpperCamelCase )
snake_case__ :Any = shard(UpperCamelCase )
snake_case__ :str = pipe(
prompt_ids=UpperCamelCase ,image=UpperCamelCase ,params=UpperCamelCase ,prng_seed=UpperCamelCase ,num_inference_steps=50 ,jit=UpperCamelCase ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
snake_case__ :List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case__ :Any = images[0, 253:256, 253:256, -1]
snake_case__ :Union[str, Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case__ :List[Any] = jnp.array(
[0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ , snake_case__ :List[str] = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-openpose" ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ , snake_case__ :Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCamelCase ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ :str = controlnet_params
snake_case__ :int = "Chef in the kitchen"
snake_case__ :List[Any] = jax.device_count()
snake_case__ :Dict = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case__ :Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" )
snake_case__ :Optional[int] = pipe.prepare_image_inputs([pose_image] * num_samples )
snake_case__ :List[str] = jax.random.PRNGKey(0 )
snake_case__ :Any = jax.random.split(UpperCamelCase ,jax.device_count() )
snake_case__ :Dict = replicate(UpperCamelCase )
snake_case__ :Tuple = shard(UpperCamelCase )
snake_case__ :Optional[int] = shard(UpperCamelCase )
snake_case__ :Optional[Any] = pipe(
prompt_ids=UpperCamelCase ,image=UpperCamelCase ,params=UpperCamelCase ,prng_seed=UpperCamelCase ,num_inference_steps=50 ,jit=UpperCamelCase ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
snake_case__ :int = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case__ :List[str] = images[0, 253:256, 253:256, -1]
snake_case__ :Tuple = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case__ :List[str] = jnp.array(
[[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 57
| 1
|
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
__UpperCAmelCase : Tuple = False
class _snake_case ( unittest.TestCase ):
pass
@nightly
@require_torch_gpu
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Optional[int]:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
torch.cuda.empty_cache()
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :Optional[Any] = VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
# remove text_unet
pipe.remove_unused_weights()
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
snake_case__ :int = "A painting of a squirrel eating a burger "
snake_case__ :int = torch.manual_seed(0 )
snake_case__ :List[str] = pipe(
prompt=UpperCamelCase ,generator=UpperCamelCase ,guidance_scale=7.5 ,num_inference_steps=2 ,output_type="numpy" ).images
with tempfile.TemporaryDirectory() as tmpdirname:
pipe.save_pretrained(UpperCamelCase )
snake_case__ :Dict = VersatileDiffusionTextToImagePipeline.from_pretrained(UpperCamelCase )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
snake_case__ :List[str] = generator.manual_seed(0 )
snake_case__ :int = pipe(
prompt=UpperCamelCase ,generator=UpperCamelCase ,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 lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :Dict = VersatileDiffusionTextToImagePipeline.from_pretrained(
"shi-labs/versatile-diffusion" ,torch_dtype=torch.floataa )
pipe.to(UpperCamelCase )
pipe.set_progress_bar_config(disable=UpperCamelCase )
snake_case__ :Optional[Any] = "A painting of a squirrel eating a burger "
snake_case__ :Optional[Any] = torch.manual_seed(0 )
snake_case__ :Any = pipe(
prompt=UpperCamelCase ,generator=UpperCamelCase ,guidance_scale=7.5 ,num_inference_steps=50 ,output_type="numpy" ).images
snake_case__ :Any = image[0, 253:256, 253:256, -1]
assert image.shape == (1, 512, 512, 3)
snake_case__ :int = 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
| 57
|
def lowercase_ ( __snake_case : list ) -> list:
'''simple docstring'''
if any(not isinstance(__snake_case , __snake_case ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(__snake_case ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 57
| 1
|
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( __snake_case : Tuple , __snake_case : str , __snake_case : List[Any] ) -> Optional[Any]:
'''simple docstring'''
return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : Union[str, Any] , __snake_case : Dict , __snake_case : List[str]="attention" ) -> Tuple:
'''simple docstring'''
snake_case__ :List[str] = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/key/kernel'][:, i, :, :] )
snake_case__ :int = k_tmp.reshape(k_tmp.shape[0] , k_tmp.shape[1] * k_tmp.shape[2] )
snake_case__ :Dict = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/out/kernel'][:, i, :, :] )
snake_case__ :Tuple = o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] , o_tmp.shape[2] )
snake_case__ :Optional[Any] = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/query/kernel'][:, i, :, :] )
snake_case__ :Union[str, Any] = q_tmp.reshape(q_tmp.shape[0] , q_tmp.shape[1] * q_tmp.shape[2] )
snake_case__ :List[str] = np.ascontiguousarray(params[F'{prefix}/{prefix}/{layer_name}/value/kernel'][:, i, :, :] )
snake_case__ :str = v_tmp.reshape(v_tmp.shape[0] , v_tmp.shape[1] * v_tmp.shape[2] )
return k, o, q, v
def lowercase_ ( __snake_case : Dict , __snake_case : Union[str, Any] , __snake_case : str , __snake_case : int=False ) -> List[Any]:
'''simple docstring'''
if split_mlp_wi:
snake_case__ :str = params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
snake_case__ :str = params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
snake_case__ :int = (wi_a, wi_a)
else:
snake_case__ :Optional[int] = params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]
snake_case__ :Optional[int] = params[F'{prefix}/{prefix}/mlp/wo/kernel'][:, i, :]
return wi, wo
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : Optional[Any] , __snake_case : Tuple , __snake_case : Any ) -> Optional[int]:
'''simple docstring'''
return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]
def lowercase_ ( __snake_case : dict , *, __snake_case : int , __snake_case : bool , __snake_case : bool = False ) -> Dict:
'''simple docstring'''
snake_case__ :Tuple = traverse_util.flatten_dict(variables["target"] )
snake_case__ :Dict = {"/".join(__snake_case ): v for k, v in old.items()}
# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
snake_case__ :Optional[Any] = "encoder/encoder/mlp/wi_0/kernel" in old
print("Split MLP:" , __snake_case )
snake_case__ :Union[str, Any] = collections.OrderedDict()
# Shared embeddings.
snake_case__ :List[str] = old["token_embedder/embedding"]
# Encoder.
for i in range(__snake_case ):
# Block i, layer 0 (Self Attention).
snake_case__ :Tuple = tax_layer_norm_lookup(__snake_case , __snake_case , "encoder" , "pre_attention_layer_norm" )
snake_case__ , snake_case__ , snake_case__ , snake_case__ :List[str] = tax_attention_lookup(__snake_case , __snake_case , "encoder" , "attention" )
snake_case__ :str = layer_norm
snake_case__ :Optional[int] = k.T
snake_case__ :Tuple = o.T
snake_case__ :Optional[int] = q.T
snake_case__ :List[str] = v.T
# Block i, layer 1 (MLP).
snake_case__ :Any = tax_layer_norm_lookup(__snake_case , __snake_case , "encoder" , "pre_mlp_layer_norm" )
snake_case__ , snake_case__ :Union[str, Any] = tax_mlp_lookup(__snake_case , __snake_case , "encoder" , __snake_case )
snake_case__ :List[Any] = layer_norm
if split_mlp_wi:
snake_case__ :int = wi[0].T
snake_case__ :Optional[Any] = wi[1].T
else:
snake_case__ :List[str] = wi.T
snake_case__ :Dict = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
snake_case__ :str = tax_relpos_bias_lookup(
__snake_case , __snake_case , "encoder" ).T
snake_case__ :int = old["encoder/encoder_norm/scale"]
if not scalable_attention:
snake_case__ :List[Any] = tax_relpos_bias_lookup(
__snake_case , 0 , "encoder" ).T
snake_case__ :Union[str, Any] = tax_relpos_bias_lookup(
__snake_case , 0 , "decoder" ).T
if not is_encoder_only:
# Decoder.
for i in range(__snake_case ):
# Block i, layer 0 (Self Attention).
snake_case__ :Dict = tax_layer_norm_lookup(__snake_case , __snake_case , "decoder" , "pre_self_attention_layer_norm" )
snake_case__ , snake_case__ , snake_case__ , snake_case__ :List[Any] = tax_attention_lookup(__snake_case , __snake_case , "decoder" , "self_attention" )
snake_case__ :List[Any] = layer_norm
snake_case__ :Tuple = k.T
snake_case__ :Optional[int] = o.T
snake_case__ :Tuple = q.T
snake_case__ :List[str] = v.T
# Block i, layer 1 (Cross Attention).
snake_case__ :List[Any] = tax_layer_norm_lookup(__snake_case , __snake_case , "decoder" , "pre_cross_attention_layer_norm" )
snake_case__ , snake_case__ , snake_case__ , snake_case__ :int = tax_attention_lookup(__snake_case , __snake_case , "decoder" , "encoder_decoder_attention" )
snake_case__ :List[Any] = layer_norm
snake_case__ :Optional[Any] = k.T
snake_case__ :List[Any] = o.T
snake_case__ :str = q.T
snake_case__ :Optional[Any] = v.T
# Block i, layer 2 (MLP).
snake_case__ :Optional[int] = tax_layer_norm_lookup(__snake_case , __snake_case , "decoder" , "pre_mlp_layer_norm" )
snake_case__ , snake_case__ :Dict = tax_mlp_lookup(__snake_case , __snake_case , "decoder" , __snake_case )
snake_case__ :int = layer_norm
if split_mlp_wi:
snake_case__ :Optional[Any] = wi[0].T
snake_case__ :List[str] = wi[1].T
else:
snake_case__ :Optional[int] = wi.T
snake_case__ :Optional[int] = wo.T
if scalable_attention:
# convert the rel_embedding of each layer
snake_case__ :Optional[Any] = tax_relpos_bias_lookup(__snake_case , __snake_case , "decoder" ).T
snake_case__ :Dict = old["decoder/decoder_norm/scale"]
# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
if "decoder/logits_dense/kernel" in old:
snake_case__ :Optional[Any] = old["decoder/logits_dense/kernel"].T
return new
def lowercase_ ( __snake_case : str , __snake_case : bool ) -> Any:
'''simple docstring'''
snake_case__ :Optional[Any] = collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )
# Add what is missing.
if "encoder.embed_tokens.weight" not in state_dict:
snake_case__ :Tuple = state_dict["shared.weight"]
if not is_encoder_only:
if "decoder.embed_tokens.weight" not in state_dict:
snake_case__ :Optional[Any] = state_dict["shared.weight"]
if "lm_head.weight" not in state_dict: # For old 1.0 models.
print("Using shared word embeddings as lm_head." )
snake_case__ :Optional[int] = state_dict["shared.weight"]
return state_dict
def lowercase_ ( __snake_case : str , __snake_case : Any , __snake_case : Union[str, Any] , __snake_case : List[str] , __snake_case : Any ) -> Optional[int]:
'''simple docstring'''
snake_case__ :List[str] = checkpoints.load_tax_checkpoint(__snake_case )
snake_case__ :List[Any] = convert_tax_to_pytorch(
__snake_case , num_layers=config.num_layers , is_encoder_only=__snake_case , scalable_attention=__snake_case )
snake_case__ :Optional[Any] = make_state_dict(__snake_case , __snake_case )
model.load_state_dict(__snake_case , strict=__snake_case )
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : Tuple , __snake_case : Union[str, Any] , __snake_case : bool = False , __snake_case : bool = False , ) -> List[Any]:
'''simple docstring'''
snake_case__ :Dict = MTaConfig.from_json_file(__snake_case )
print(F'Building PyTorch model from configuration: {config}' )
# Non-v1.1 checkpoints could also use T5Model, but this works for all.
# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
if is_encoder_only:
snake_case__ :Any = UMTaEncoderModel(__snake_case )
else:
snake_case__ :List[Any] = UMTaForConditionalGeneration(__snake_case )
# Load weights from tf checkpoint
load_tax_weights_in_ta(__snake_case , __snake_case , __snake_case , __snake_case , __snake_case )
# Save pytorch-model
print(F'Save PyTorch model to {pytorch_dump_path}' )
model.save_pretrained(__snake_case )
# Verify that we can load the checkpoint.
model.from_pretrained(__snake_case )
print("Done" )
if __name__ == "__main__":
__UpperCAmelCase : Dict = argparse.ArgumentParser(description="Converts a native T5X checkpoint into a PyTorch checkpoint.")
# Required parameters
parser.add_argument(
"--t5x_checkpoint_path", default=None, type=str, required=True, help="Path to the T5X checkpoint."
)
parser.add_argument(
"--config_file",
default=None,
type=str,
required=True,
help="The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
)
parser.add_argument(
"--is_encoder_only", action="store_true", help="Check if the model is encoder-decoder model", default=False
)
parser.add_argument(
"--scalable_attention",
action="store_true",
help="Whether the model uses scaled attention (umt5 model)",
default=False,
)
__UpperCAmelCase : str = parser.parse_args()
convert_tax_checkpoint_to_pytorch(
args.tax_checkpoint_path,
args.config_file,
args.pytorch_dump_path,
args.is_encoder_only,
args.scalable_attention,
)
| 57
|
from __future__ import annotations
def lowercase_ ( __snake_case : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("List is empty" )
return sum(__snake_case ) / len(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
| 1
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,
)
from ...image_utils import (
IMAGENET_STANDARD_MEAN,
IMAGENET_STANDARD_STD,
ChannelDimension,
ImageInput,
PILImageResampling,
is_valid_image,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__UpperCAmelCase : Tuple = logging.get_logger(__name__)
def lowercase_ ( __snake_case : Dict ) -> List[List[ImageInput]]:
'''simple docstring'''
if isinstance(__snake_case , (list, tuple) ) and isinstance(videos[0] , (list, tuple) ) and is_valid_image(videos[0][0] ):
return videos
elif isinstance(__snake_case , (list, tuple) ) and is_valid_image(videos[0] ):
return [videos]
elif is_valid_image(__snake_case ):
return [[videos]]
raise ValueError(F'Could not make batched video from {videos}' )
class _snake_case ( _A ):
_A = ['pixel_values']
def __init__( self ,UpperCamelCase = True ,UpperCamelCase = None ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = True ,UpperCamelCase = None ,UpperCamelCase = True ,UpperCamelCase = 1 / 255 ,UpperCamelCase = True ,UpperCamelCase = None ,UpperCamelCase = None ,**UpperCamelCase ,) -> None:
super().__init__(**UpperCamelCase )
snake_case__ :Union[str, Any] = size if size is not None else {"shortest_edge": 224}
snake_case__ :Any = get_size_dict(UpperCamelCase ,default_to_square=UpperCamelCase )
snake_case__ :Tuple = crop_size if crop_size is not None else {"height": 224, "width": 224}
snake_case__ :Optional[Any] = get_size_dict(UpperCamelCase ,param_name="crop_size" )
snake_case__ :Any = do_resize
snake_case__ :str = size
snake_case__ :Optional[Any] = do_center_crop
snake_case__ :Union[str, Any] = crop_size
snake_case__ :int = resample
snake_case__ :List[str] = do_rescale
snake_case__ :Optional[Any] = rescale_factor
snake_case__ :str = do_normalize
snake_case__ :str = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
snake_case__ :Dict = image_std if image_std is not None else IMAGENET_STANDARD_STD
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = None ,**UpperCamelCase ,) -> np.ndarray:
snake_case__ :Any = get_size_dict(UpperCamelCase ,default_to_square=UpperCamelCase )
if "shortest_edge" in size:
snake_case__ :Any = get_resize_output_image_size(UpperCamelCase ,size["shortest_edge"] ,default_to_square=UpperCamelCase )
elif "height" in size and "width" in size:
snake_case__ :int = (size["height"], size["width"])
else:
raise ValueError(f'Size must have \'height\' and \'width\' or \'shortest_edge\' as keys. Got {size.keys()}' )
return resize(UpperCamelCase ,size=UpperCamelCase ,resample=UpperCamelCase ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = None ,**UpperCamelCase ,) -> np.ndarray:
snake_case__ :Optional[Any] = get_size_dict(UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'Size must have \'height\' and \'width\' as keys. Got {size.keys()}' )
return center_crop(UpperCamelCase ,size=(size["height"], size["width"]) ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = None ,**UpperCamelCase ,) -> Tuple:
return rescale(UpperCamelCase ,scale=UpperCamelCase ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = None ,**UpperCamelCase ,) -> np.ndarray:
return normalize(UpperCamelCase ,mean=UpperCamelCase ,std=UpperCamelCase ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = ChannelDimension.FIRST ,) -> np.ndarray:
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_center_crop and crop_size is None:
raise ValueError("Crop size must be specified if do_center_crop is True." )
if do_rescale and rescale_factor is None:
raise ValueError("Rescale factor must be specified if do_rescale is True." )
if do_normalize and (image_mean is None or image_std is None):
raise ValueError("Image mean and std must be specified if do_normalize is True." )
# All transformations expect numpy arrays.
snake_case__ :Any = to_numpy_array(UpperCamelCase )
if do_resize:
snake_case__ :str = self.resize(image=UpperCamelCase ,size=UpperCamelCase ,resample=UpperCamelCase )
if do_center_crop:
snake_case__ :Optional[int] = self.center_crop(UpperCamelCase ,size=UpperCamelCase )
if do_rescale:
snake_case__ :int = self.rescale(image=UpperCamelCase ,scale=UpperCamelCase )
if do_normalize:
snake_case__ :Any = self.normalize(image=UpperCamelCase ,mean=UpperCamelCase ,std=UpperCamelCase )
snake_case__ :Optional[Any] = to_channel_dimension_format(UpperCamelCase ,UpperCamelCase )
return image
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = ChannelDimension.FIRST ,**UpperCamelCase ,) -> PIL.Image.Image:
snake_case__ :Any = do_resize if do_resize is not None else self.do_resize
snake_case__ :Dict = resample if resample is not None else self.resample
snake_case__ :Dict = do_center_crop if do_center_crop is not None else self.do_center_crop
snake_case__ :List[Any] = do_rescale if do_rescale is not None else self.do_rescale
snake_case__ :Optional[int] = rescale_factor if rescale_factor is not None else self.rescale_factor
snake_case__ :Union[str, Any] = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ :Dict = image_mean if image_mean is not None else self.image_mean
snake_case__ :Optional[int] = image_std if image_std is not None else self.image_std
snake_case__ :str = size if size is not None else self.size
snake_case__ :Union[str, Any] = get_size_dict(UpperCamelCase ,default_to_square=UpperCamelCase )
snake_case__ :Dict = crop_size if crop_size is not None else self.crop_size
snake_case__ :Tuple = get_size_dict(UpperCamelCase ,param_name="crop_size" )
if not valid_images(UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
snake_case__ :int = make_batched(UpperCamelCase )
snake_case__ :int = [
[
self._preprocess_image(
image=UpperCamelCase ,do_resize=UpperCamelCase ,size=UpperCamelCase ,resample=UpperCamelCase ,do_center_crop=UpperCamelCase ,crop_size=UpperCamelCase ,do_rescale=UpperCamelCase ,rescale_factor=UpperCamelCase ,do_normalize=UpperCamelCase ,image_mean=UpperCamelCase ,image_std=UpperCamelCase ,data_format=UpperCamelCase ,)
for img in video
]
for video in videos
]
snake_case__ :List[str] = {"pixel_values": videos}
return BatchFeature(data=UpperCamelCase ,tensor_type=UpperCamelCase )
| 57
|
from __future__ import annotations
import math
def lowercase_ ( __snake_case : int , __snake_case : int , __snake_case : bool , __snake_case : list[int] , __snake_case : float ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if len(__snake_case ) == 0:
raise ValueError("Scores cannot be empty" )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
return min(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
def lowercase_ ( ) -> None:
'''simple docstring'''
snake_case__ :List[Any] = [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
snake_case__ :int = math.log(len(__snake_case ) , 2 )
print("Optimal value : " , end="" )
print(minimax(0 , 0 , __snake_case , __snake_case , __snake_case ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 57
| 1
|
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_backbone_common import BackboneTesterMixin
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 (
FocalNetBackbone,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetModel,
)
from transformers.models.focalnet.modeling_focalnet import FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST
if is_vision_available():
from PIL import Image
from transformers import AutoImageProcessor
class _snake_case :
def __init__( self ,UpperCamelCase ,UpperCamelCase=13 ,UpperCamelCase=32 ,UpperCamelCase=2 ,UpperCamelCase=3 ,UpperCamelCase=16 ,UpperCamelCase=[32, 64, 128] ,UpperCamelCase=[1, 2, 1] ,UpperCamelCase=[2, 2, 4] ,UpperCamelCase=2 ,UpperCamelCase=2.0 ,UpperCamelCase=True ,UpperCamelCase=0.0 ,UpperCamelCase=0.0 ,UpperCamelCase=0.1 ,UpperCamelCase="gelu" ,UpperCamelCase=False ,UpperCamelCase=True ,UpperCamelCase=0.02 ,UpperCamelCase=1E-5 ,UpperCamelCase=True ,UpperCamelCase=None ,UpperCamelCase=True ,UpperCamelCase=10 ,UpperCamelCase=8 ,UpperCamelCase=["stage1", "stage2"] ,UpperCamelCase=[1, 2] ,) -> Optional[Any]:
snake_case__ :int = parent
snake_case__ :Tuple = batch_size
snake_case__ :int = image_size
snake_case__ :Any = patch_size
snake_case__ :Any = num_channels
snake_case__ :Union[str, Any] = embed_dim
snake_case__ :Any = hidden_sizes
snake_case__ :Dict = depths
snake_case__ :int = num_heads
snake_case__ :int = window_size
snake_case__ :Optional[int] = mlp_ratio
snake_case__ :List[str] = qkv_bias
snake_case__ :Optional[Any] = hidden_dropout_prob
snake_case__ :Optional[int] = attention_probs_dropout_prob
snake_case__ :Optional[Any] = drop_path_rate
snake_case__ :Optional[int] = hidden_act
snake_case__ :str = use_absolute_embeddings
snake_case__ :Any = patch_norm
snake_case__ :int = layer_norm_eps
snake_case__ :str = initializer_range
snake_case__ :Tuple = is_training
snake_case__ :Any = scope
snake_case__ :Any = use_labels
snake_case__ :List[Any] = type_sequence_label_size
snake_case__ :List[str] = encoder_stride
snake_case__ :str = out_features
snake_case__ :List[str] = out_indices
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :Optional[int] = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )
snake_case__ :Dict = None
if self.use_labels:
snake_case__ :Optional[int] = ids_tensor([self.batch_size] ,self.type_sequence_label_size )
snake_case__ :Any = self.get_config()
return config, pixel_values, labels
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
return FocalNetConfig(
image_size=self.image_size ,patch_size=self.patch_size ,num_channels=self.num_channels ,embed_dim=self.embed_dim ,hidden_sizes=self.hidden_sizes ,depths=self.depths ,num_heads=self.num_heads ,window_size=self.window_size ,mlp_ratio=self.mlp_ratio ,qkv_bias=self.qkv_bias ,hidden_dropout_prob=self.hidden_dropout_prob ,attention_probs_dropout_prob=self.attention_probs_dropout_prob ,drop_path_rate=self.drop_path_rate ,hidden_act=self.hidden_act ,use_absolute_embeddings=self.use_absolute_embeddings ,path_norm=self.patch_norm ,layer_norm_eps=self.layer_norm_eps ,initializer_range=self.initializer_range ,encoder_stride=self.encoder_stride ,out_features=self.out_features ,out_indices=self.out_indices ,)
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> List[Any]:
snake_case__ :List[Any] = FocalNetModel(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
snake_case__ :Any = model(UpperCamelCase )
snake_case__ :Optional[int] = ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths ) - 1))
snake_case__ :Optional[Any] = int(config.embed_dim * 2 ** (len(config.depths ) - 1) )
self.parent.assertEqual(result.last_hidden_state.shape ,(self.batch_size, expected_seq_len, expected_dim) )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> List[str]:
snake_case__ :List[Any] = FocalNetBackbone(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
snake_case__ :List[str] = model(UpperCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) ,len(config.out_features ) )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.image_size, 8, 8] )
# verify channels
self.parent.assertEqual(len(model.channels ) ,len(config.out_features ) )
self.parent.assertListEqual(model.channels ,config.hidden_sizes[:-1] )
# verify backbone works with out_features=None
snake_case__ :Optional[int] = None
snake_case__ :Union[str, Any] = FocalNetBackbone(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
snake_case__ :Tuple = model(UpperCamelCase )
# verify feature maps
self.parent.assertEqual(len(result.feature_maps ) ,1 )
self.parent.assertListEqual(list(result.feature_maps[0].shape ) ,[self.batch_size, self.image_size * 2, 4, 4] )
# verify channels
self.parent.assertEqual(len(model.channels ) ,1 )
self.parent.assertListEqual(model.channels ,[config.hidden_sizes[-1]] )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> int:
snake_case__ :Any = FocalNetForMaskedImageModeling(config=UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
snake_case__ :str = model(UpperCamelCase )
self.parent.assertEqual(
result.reconstruction.shape ,(self.batch_size, self.num_channels, self.image_size, self.image_size) )
# test greyscale images
snake_case__ :Union[str, Any] = 1
snake_case__ :str = FocalNetForMaskedImageModeling(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
snake_case__ :str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case__ :Optional[int] = model(UpperCamelCase )
self.parent.assertEqual(result.reconstruction.shape ,(self.batch_size, 1, self.image_size, self.image_size) )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Union[str, Any]:
snake_case__ :List[Any] = self.type_sequence_label_size
snake_case__ :int = FocalNetForImageClassification(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
snake_case__ :str = model(UpperCamelCase ,labels=UpperCamelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
# test greyscale images
snake_case__ :List[str] = 1
snake_case__ :Union[str, Any] = FocalNetForImageClassification(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
snake_case__ :str = floats_tensor([self.batch_size, 1, self.image_size, self.image_size] )
snake_case__ :Union[str, Any] = model(UpperCamelCase )
self.parent.assertEqual(result.logits.shape ,(self.batch_size, self.type_sequence_label_size) )
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :List[str] = self.prepare_config_and_inputs()
snake_case__ , snake_case__ , snake_case__ :str = config_and_inputs
snake_case__ :Any = {"pixel_values": pixel_values}
return config, inputs_dict
@require_torch
class _snake_case ( _A , _A , unittest.TestCase ):
_A = (
(
FocalNetModel,
FocalNetForImageClassification,
FocalNetForMaskedImageModeling,
FocalNetBackbone,
)
if is_torch_available()
else ()
)
_A = (
{'feature-extraction': FocalNetModel, 'image-classification': FocalNetForImageClassification}
if is_torch_available()
else {}
)
_A = False
_A = False
_A = False
_A = False
_A = False
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Any = FocalNetModelTester(self )
snake_case__ :Any = ConfigTester(self ,config_class=UpperCamelCase ,embed_dim=37 ,has_text_modality=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Tuple:
self.create_and_test_config_common_properties()
self.config_tester.create_and_test_config_to_json_string()
self.config_tester.create_and_test_config_to_json_file()
self.config_tester.create_and_test_config_from_and_save_pretrained()
self.config_tester.create_and_test_config_with_num_labels()
self.config_tester.check_config_can_be_init_without_params()
self.config_tester.check_config_arguments_init()
def lowerCAmelCase_ ( self ) -> str:
return
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :str = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_model(*UpperCamelCase )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_backbone(*UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :Tuple = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_masked_image_modeling(*UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[str]:
snake_case__ :Optional[Any] = self.model_tester.prepare_config_and_inputs()
self.model_tester.create_and_check_for_image_classification(*UpperCamelCase )
@unittest.skip(reason="FocalNet does not use inputs_embeds" )
def lowerCAmelCase_ ( self ) -> Dict:
pass
@unittest.skip(reason="FocalNet does not use feedforward chunking" )
def lowerCAmelCase_ ( self ) -> List[Any]:
pass
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ , snake_case__ :int = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
snake_case__ :List[Any] = model_class(UpperCamelCase )
self.assertIsInstance(model.get_input_embeddings() ,(nn.Module) )
snake_case__ :Tuple = model.get_output_embeddings()
self.assertTrue(x is None or isinstance(UpperCamelCase ,nn.Linear ) )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ , snake_case__ :List[str] = self.model_tester.prepare_config_and_inputs_for_common()
for model_class in self.all_model_classes[:-1]:
snake_case__ :Union[str, Any] = model_class(UpperCamelCase )
snake_case__ :int = inspect.signature(model.forward )
# signature.parameters is an OrderedDict => so arg_names order is deterministic
snake_case__ :Tuple = [*signature.parameters.keys()]
snake_case__ :List[str] = ["pixel_values"]
self.assertListEqual(arg_names[:1] ,UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> Optional[int]:
snake_case__ :List[str] = model_class(UpperCamelCase )
model.to(UpperCamelCase )
model.eval()
with torch.no_grad():
snake_case__ :Any = model(**self._prepare_for_class(UpperCamelCase ,UpperCamelCase ) )
snake_case__ :Optional[int] = outputs.hidden_states
snake_case__ :Union[str, Any] = getattr(
self.model_tester ,"expected_num_hidden_layers" ,len(self.model_tester.depths ) + 1 )
self.assertEqual(len(UpperCamelCase ) ,UpperCamelCase )
# FocalNet has a different seq_length
snake_case__ :List[str] = (
config.patch_size
if isinstance(config.patch_size ,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case__ :Dict = (image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
self.assertListEqual(
list(hidden_states[0].shape[-2:] ) ,[num_patches, self.model_tester.embed_dim] ,)
snake_case__ :List[str] = outputs.reshaped_hidden_states
self.assertEqual(len(UpperCamelCase ) ,UpperCamelCase )
snake_case__ , snake_case__ , snake_case__ , snake_case__ :Dict = reshaped_hidden_states[0].shape
snake_case__ :Any = (
reshaped_hidden_states[0].view(UpperCamelCase ,UpperCamelCase ,height * width ).permute(0 ,2 ,1 )
)
self.assertListEqual(
list(reshaped_hidden_states.shape[-2:] ) ,[num_patches, self.model_tester.embed_dim] ,)
def lowerCAmelCase_ ( self ) -> str:
snake_case__ , snake_case__ :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ :Optional[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size ,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
for model_class in self.all_model_classes[:-1]:
snake_case__ :List[Any] = True
self.check_hidden_states_output(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case__ :Any = True
self.check_hidden_states_output(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ , snake_case__ :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ :List[Any] = 3
snake_case__ :List[Any] = (
self.model_tester.image_size
if isinstance(self.model_tester.image_size ,collections.abc.Iterable )
else (self.model_tester.image_size, self.model_tester.image_size)
)
snake_case__ :Optional[int] = (
config.patch_size
if isinstance(config.patch_size ,collections.abc.Iterable )
else (config.patch_size, config.patch_size)
)
snake_case__ :Tuple = image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
snake_case__ :Optional[int] = image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])
for model_class in self.all_model_classes[:-1]:
snake_case__ :Tuple = True
self.check_hidden_states_output(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,(padded_height, padded_width) )
# check that output_hidden_states also work using config
del inputs_dict["output_hidden_states"]
snake_case__ :int = True
self.check_hidden_states_output(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,(padded_height, padded_width) )
@slow
def lowerCAmelCase_ ( self ) -> str:
for model_name in FOCALNET_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
snake_case__ :Tuple = FocalNetModel.from_pretrained(UpperCamelCase )
self.assertIsNotNone(UpperCamelCase )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ , snake_case__ :List[Any] = self.model_tester.prepare_config_and_inputs_for_common()
snake_case__ :int = _config_zero_init(UpperCamelCase )
for model_class in self.all_model_classes:
snake_case__ :Dict = model_class(config=UpperCamelCase )
for name, param in model.named_parameters():
if "embeddings" not in name and param.requires_grad:
self.assertIn(
((param.data.mean() * 1E9).round() / 1E9).item() ,[0.0, 1.0] ,msg=f'Parameter {name} of model {model_class} seems not properly initialized' ,)
@require_vision
@require_torch
class _snake_case ( unittest.TestCase ):
@cached_property
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
# TODO update organization
return AutoImageProcessor.from_pretrained("microsoft/focalnet-tiny" ) if is_vision_available() else None
@slow
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ :str = FocalNetForImageClassification.from_pretrained("microsoft/focalnet-tiny" ).to(UpperCamelCase )
snake_case__ :Optional[int] = self.default_image_processor
snake_case__ :Tuple = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png" )
snake_case__ :int = image_processor(images=UpperCamelCase ,return_tensors="pt" ).to(UpperCamelCase )
# forward pass
with torch.no_grad():
snake_case__ :Union[str, Any] = model(**UpperCamelCase )
# verify the logits
snake_case__ :Tuple = torch.Size((1, 1_000) )
self.assertEqual(outputs.logits.shape ,UpperCamelCase )
snake_case__ :Union[str, Any] = torch.tensor([0.2166, -0.4368, 0.2191] ).to(UpperCamelCase )
self.assertTrue(torch.allclose(outputs.logits[0, :3] ,UpperCamelCase ,atol=1E-4 ) )
self.assertTrue(outputs.logits.argmax(dim=-1 ).item() ,281 )
@require_torch
class _snake_case ( _A , unittest.TestCase ):
_A = (FocalNetBackbone,) if is_torch_available() else ()
_A = FocalNetConfig
_A = False
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ :Any = FocalNetModelTester(self )
| 57
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
def lowercase_ ( __snake_case : Any , __snake_case : Any ) -> Any:
'''simple docstring'''
snake_case__ :Optional[Any] = b.T
snake_case__ :Optional[Any] = np.sum(np.square(__snake_case ) , axis=1 )
snake_case__ :Tuple = np.sum(np.square(__snake_case ) , axis=0 )
snake_case__ :Union[str, Any] = np.matmul(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = aa[:, None] - 2 * ab + ba[None, :]
return d
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : int ) -> Any:
'''simple docstring'''
snake_case__ :Optional[Any] = x.reshape(-1 , 3 )
snake_case__ :List[str] = squared_euclidean_distance(__snake_case , __snake_case )
return np.argmin(__snake_case , axis=1 )
class _snake_case ( _A ):
_A = ['pixel_values']
def __init__( self ,UpperCamelCase = None ,UpperCamelCase = True ,UpperCamelCase = None ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = True ,UpperCamelCase = True ,**UpperCamelCase ,) -> None:
super().__init__(**UpperCamelCase )
snake_case__ :List[Any] = size if size is not None else {"height": 256, "width": 256}
snake_case__ :str = get_size_dict(UpperCamelCase )
snake_case__ :Dict = np.array(UpperCamelCase ) if clusters is not None else None
snake_case__ :str = do_resize
snake_case__ :List[str] = size
snake_case__ :List[Any] = resample
snake_case__ :Union[str, Any] = do_normalize
snake_case__ :int = do_color_quantize
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = None ,**UpperCamelCase ,) -> np.ndarray:
snake_case__ :List[str] = get_size_dict(UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}' )
return resize(
UpperCamelCase ,size=(size["height"], size["width"]) ,resample=UpperCamelCase ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,) -> np.ndarray:
snake_case__ :Tuple = rescale(image=UpperCamelCase ,scale=1 / 127.5 ,data_format=UpperCamelCase )
snake_case__ :List[Any] = image - 1
return image
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = ChannelDimension.FIRST ,**UpperCamelCase ,) -> PIL.Image.Image:
snake_case__ :Optional[int] = do_resize if do_resize is not None else self.do_resize
snake_case__ :int = size if size is not None else self.size
snake_case__ :Tuple = get_size_dict(UpperCamelCase )
snake_case__ :str = resample if resample is not None else self.resample
snake_case__ :Dict = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ :Tuple = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
snake_case__ :List[Any] = clusters if clusters is not None else self.clusters
snake_case__ :str = np.array(UpperCamelCase )
snake_case__ :int = make_list_of_images(UpperCamelCase )
if not valid_images(UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True." )
# All transformations expect numpy arrays.
snake_case__ :Union[str, Any] = [to_numpy_array(UpperCamelCase ) for image in images]
if do_resize:
snake_case__ :int = [self.resize(image=UpperCamelCase ,size=UpperCamelCase ,resample=UpperCamelCase ) for image in images]
if do_normalize:
snake_case__ :Any = [self.normalize(image=UpperCamelCase ) for image in images]
if do_color_quantize:
snake_case__ :Optional[Any] = [to_channel_dimension_format(UpperCamelCase ,ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
snake_case__ :Union[str, Any] = np.array(UpperCamelCase )
snake_case__ :Optional[int] = color_quantize(UpperCamelCase ,UpperCamelCase ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
snake_case__ :List[Any] = images.shape[0]
snake_case__ :str = images.reshape(UpperCamelCase ,-1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
snake_case__ :Any = list(UpperCamelCase )
else:
snake_case__ :List[str] = [to_channel_dimension_format(UpperCamelCase ,UpperCamelCase ) for image in images]
snake_case__ :List[str] = {"input_ids": images}
return BatchFeature(data=UpperCamelCase ,tensor_type=UpperCamelCase )
| 57
| 1
|
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
__UpperCAmelCase : Dict = models.Sequential()
# Step 1 - Convolution
# Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
# (3,3) is the kernel size (filter matrix)
classifier.add(
layers.ConvaD(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation="relu")
)
# Step 2 - Pooling
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Adding a second convolutional layer
classifier.add(layers.ConvaD(3_2, (3, 3), activation="relu"))
classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))
# Step 3 - Flattening
classifier.add(layers.Flatten())
# Step 4 - Full connection
classifier.add(layers.Dense(units=1_2_8, activation="relu"))
classifier.add(layers.Dense(units=1, activation="sigmoid"))
# Compiling the CNN
classifier.compile(
optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
)
# Part 2 - Fitting the CNN to the images
# Load Trained model weights
# from keras.models import load_model
# regressor=load_model('cnn.h5')
__UpperCAmelCase : str = tf.keras.preprocessing.image.ImageDataGenerator(
rescale=1.0 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
)
__UpperCAmelCase : Tuple = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)
__UpperCAmelCase : List[Any] = train_datagen.flow_from_directory(
"dataset/training_set", target_size=(6_4, 6_4), batch_size=3_2, class_mode="binary"
)
__UpperCAmelCase : Tuple = test_datagen.flow_from_directory(
"dataset/test_set", target_size=(6_4, 6_4), batch_size=3_2, class_mode="binary"
)
classifier.fit_generator(
training_set, steps_per_epoch=5, epochs=3_0, validation_data=test_set
)
classifier.save("cnn.h5")
# Part 3 - Making new predictions
__UpperCAmelCase : Tuple = tf.keras.preprocessing.image.load_img(
"dataset/single_prediction/image.png", target_size=(6_4, 6_4)
)
__UpperCAmelCase : Optional[int] = tf.keras.preprocessing.image.img_to_array(test_image)
__UpperCAmelCase : Tuple = np.expand_dims(test_image, axis=0)
__UpperCAmelCase : Optional[Any] = classifier.predict(test_image)
# training_set.class_indices
if result[0][0] == 0:
__UpperCAmelCase : Dict = "Normal"
if result[0][0] == 1:
__UpperCAmelCase : Tuple = "Abnormality detected"
| 57
|
import pytest
__UpperCAmelCase : int = "__dummy_dataset1__"
__UpperCAmelCase : int = "\nimport json\nimport os\n\nimport datasets\n\n\nREPO_URL = \"https://huggingface.co/datasets/albertvillanova/tests-raw-jsonl/resolve/main/\"\nURLS = {\"train\": REPO_URL + \"wikiann-bn-train.jsonl\", \"validation\": REPO_URL + \"wikiann-bn-validation.jsonl\"}\n\n\nclass __DummyDataset1__(datasets.GeneratorBasedBuilder):\n\n def _info(self):\n features = datasets.Features(\n {\n \"tokens\": datasets.Sequence(datasets.Value(\"string\")),\n \"ner_tags\": datasets.Sequence(\n datasets.features.ClassLabel(\n names=[\n \"O\",\n \"B-PER\",\n \"I-PER\",\n \"B-ORG\",\n \"I-ORG\",\n \"B-LOC\",\n \"I-LOC\",\n ]\n )\n ),\n \"langs\": datasets.Sequence(datasets.Value(\"string\")),\n \"spans\": datasets.Sequence(datasets.Value(\"string\")),\n }\n )\n return datasets.DatasetInfo(features=features)\n\n def _split_generators(self, dl_manager):\n dl_path = dl_manager.download(URLS)\n return [\n datasets.SplitGenerator(datasets.Split.TRAIN, gen_kwargs={\"filepath\": dl_path[\"train\"]}),\n datasets.SplitGenerator(datasets.Split.VALIDATION, gen_kwargs={\"filepath\": dl_path[\"validation\"]}),\n ]\n\n def _generate_examples(self, filepath):\n with open(filepath, \"r\", encoding=\"utf-8\") as f:\n for i, line in enumerate(f):\n yield i, json.loads(line)\n"
@pytest.fixture
def lowercase_ ( ) -> Optional[Any]:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_NAME
@pytest.fixture
def lowercase_ ( ) -> Optional[int]:
'''simple docstring'''
return DATASET_LOADING_SCRIPT_CODE
@pytest.fixture
def lowercase_ ( __snake_case : Optional[int] , __snake_case : List[Any] , __snake_case : Any ) -> Dict:
'''simple docstring'''
snake_case__ :Optional[Any] = dataset_loading_script_name
snake_case__ :Optional[Any] = tmp_path / "datasets" / script_name
script_dir.mkdir(parents=__snake_case )
snake_case__ :List[Any] = script_dir / F'{script_name}.py'
with open(__snake_case , "w" ) as f:
f.write(__snake_case )
return str(__snake_case )
| 57
| 1
|
import random
import sys
import numpy as np
from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
__UpperCAmelCase : Optional[Any] = "Usage of script: script_name <size_of_canvas:int>"
__UpperCAmelCase : List[Any] = [0] * 1_0_0 + [1] * 1_0
random.shuffle(choice)
def lowercase_ ( __snake_case : int ) -> list[list[bool]]:
'''simple docstring'''
snake_case__ :Union[str, Any] = [[False for i in range(__snake_case )] for j in range(__snake_case )]
return canvas
def lowercase_ ( __snake_case : list[list[bool]] ) -> None:
'''simple docstring'''
for i, row in enumerate(__snake_case ):
for j, _ in enumerate(__snake_case ):
snake_case__ :List[Any] = bool(random.getrandbits(1 ) )
def lowercase_ ( __snake_case : list[list[bool]] ) -> list[list[bool]]:
'''simple docstring'''
snake_case__ :Optional[int] = np.array(__snake_case )
snake_case__ :Optional[int] = np.array(create_canvas(current_canvas.shape[0] ) )
for r, row in enumerate(__snake_case ):
for c, pt in enumerate(__snake_case ):
snake_case__ :List[Any] = __judge_point(
__snake_case , current_canvas[r - 1 : r + 2, c - 1 : c + 2] )
snake_case__ :Optional[int] = next_gen_canvas
del next_gen_canvas # cleaning memory as we move on.
snake_case__ :list[list[bool]] = current_canvas.tolist()
return return_canvas
def lowercase_ ( __snake_case : bool , __snake_case : list[list[bool]] ) -> bool:
'''simple docstring'''
snake_case__ :str = 0
snake_case__ :int = 0
# finding dead or alive neighbours count.
for i in neighbours:
for status in i:
if status:
alive += 1
else:
dead += 1
# handling duplicate entry for focus pt.
if pt:
alive -= 1
else:
dead -= 1
# running the rules of game here.
snake_case__ :List[str] = pt
if pt:
if alive < 2:
snake_case__ :str = False
elif alive == 2 or alive == 3:
snake_case__ :Optional[Any] = True
elif alive > 3:
snake_case__ :int = False
else:
if alive == 3:
snake_case__ :List[Any] = True
return state
if __name__ == "__main__":
if len(sys.argv) != 2:
raise Exception(usage_doc)
__UpperCAmelCase : Any = int(sys.argv[1])
# main working structure of this module.
__UpperCAmelCase : Union[str, Any] = create_canvas(canvas_size)
seed(c)
__UpperCAmelCase , __UpperCAmelCase : int = plt.subplots()
fig.show()
__UpperCAmelCase : str = ListedColormap(["w", "k"])
try:
while True:
__UpperCAmelCase : Dict = run(c)
ax.matshow(c, cmap=cmap)
fig.canvas.draw()
ax.cla()
except KeyboardInterrupt:
# do nothing.
pass
| 57
|
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy_torch_and_transformers_objects import * # noqa F403
else:
from .multicontrolnet import MultiControlNetModel
from .pipeline_controlnet import StableDiffusionControlNetPipeline
from .pipeline_controlnet_imgaimg import StableDiffusionControlNetImgaImgPipeline
from .pipeline_controlnet_inpaint import StableDiffusionControlNetInpaintPipeline
if is_transformers_available() and is_flax_available():
from .pipeline_flax_controlnet import FlaxStableDiffusionControlNetPipeline
| 57
| 1
|
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def lowercase_ ( __snake_case : List[Any] ) -> List[str]:
'''simple docstring'''
snake_case__ :Optional[Any] = args.pruning_method
snake_case__ :str = args.threshold
snake_case__ :List[str] = args.model_name_or_path.rstrip("/" )
snake_case__ :List[Any] = args.target_model_path
print(F'Load fine-pruned model from {model_name_or_path}' )
snake_case__ :str = torch.load(os.path.join(__snake_case , "pytorch_model.bin" ) )
snake_case__ :List[Any] = {}
for name, tensor in model.items():
if "embeddings" in name or "LayerNorm" in name or "pooler" in name:
snake_case__ :Dict = tensor
print(F'Copied layer {name}' )
elif "classifier" in name or "qa_output" in name:
snake_case__ :List[Any] = tensor
print(F'Copied layer {name}' )
elif "bias" in name:
snake_case__ :Union[str, Any] = tensor
print(F'Copied layer {name}' )
else:
if pruning_method == "magnitude":
snake_case__ :Dict = MagnitudeBinarizer.apply(inputs=__snake_case , threshold=__snake_case )
snake_case__ :Union[str, Any] = tensor * mask
print(F'Pruned layer {name}' )
elif pruning_method == "topK":
if "mask_scores" in name:
continue
snake_case__ :Dict = name[:-6]
snake_case__ :Optional[int] = model[F'{prefix_}mask_scores']
snake_case__ :int = TopKBinarizer.apply(__snake_case , __snake_case )
snake_case__ :Tuple = tensor * mask
print(F'Pruned layer {name}' )
elif pruning_method == "sigmoied_threshold":
if "mask_scores" in name:
continue
snake_case__ :int = name[:-6]
snake_case__ :Optional[Any] = model[F'{prefix_}mask_scores']
snake_case__ :int = ThresholdBinarizer.apply(__snake_case , __snake_case , __snake_case )
snake_case__ :Tuple = tensor * mask
print(F'Pruned layer {name}' )
elif pruning_method == "l0":
if "mask_scores" in name:
continue
snake_case__ :Dict = name[:-6]
snake_case__ :int = model[F'{prefix_}mask_scores']
snake_case__ , snake_case__ :Any = -0.1, 1.1
snake_case__ :List[Any] = torch.sigmoid(__snake_case )
snake_case__ :Optional[Any] = s * (r - l) + l
snake_case__ :List[Any] = s_bar.clamp(min=0.0 , max=1.0 )
snake_case__ :int = tensor * mask
print(F'Pruned layer {name}' )
else:
raise ValueError("Unknown pruning method" )
if target_model_path is None:
snake_case__ :Optional[int] = os.path.join(
os.path.dirname(__snake_case ) , F'bertarized_{os.path.basename(__snake_case )}' )
if not os.path.isdir(__snake_case ):
shutil.copytree(__snake_case , __snake_case )
print(F'\nCreated folder {target_model_path}' )
torch.save(__snake_case , os.path.join(__snake_case , "pytorch_model.bin" ) )
print("\nPruned model saved! See you later!" )
if __name__ == "__main__":
__UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"--pruning_method",
choices=["l0", "magnitude", "topK", "sigmoied_threshold"],
type=str,
required=True,
help=(
"Pruning Method (l0 = L0 regularization, magnitude = Magnitude pruning, topK = Movement pruning,"
" sigmoied_threshold = Soft movement pruning)"
),
)
parser.add_argument(
"--threshold",
type=float,
required=False,
help=(
"For `magnitude` and `topK`, it is the level of remaining weights (in %) in the fine-pruned model."
"For `sigmoied_threshold`, it is the threshold \tau against which the (sigmoied) scores are compared."
"Not needed for `l0`"
),
)
parser.add_argument(
"--model_name_or_path",
type=str,
required=True,
help="Folder containing the model that was previously fine-pruned",
)
parser.add_argument(
"--target_model_path",
default=None,
type=str,
required=False,
help="Folder containing the model that was previously fine-pruned",
)
__UpperCAmelCase : Dict = parser.parse_args()
main(args)
| 57
|
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
__UpperCAmelCase : Dict = True
except ImportError:
__UpperCAmelCase : List[Any] = False
__UpperCAmelCase : Dict = logging.get_logger(__name__) # pylint: disable=invalid-name
def lowercase_ ( __snake_case : Namespace ) -> Dict:
'''simple docstring'''
return AddNewModelCommand(args.testing , args.testing_file , path=args.path )
class _snake_case ( _A ):
@staticmethod
def lowerCAmelCase_ ( UpperCamelCase ) -> Any:
snake_case__ :Dict = parser.add_parser("add-new-model" )
add_new_model_parser.add_argument("--testing" ,action="store_true" ,help="If in testing mode." )
add_new_model_parser.add_argument("--testing_file" ,type=UpperCamelCase ,help="Configuration file on which to run." )
add_new_model_parser.add_argument(
"--path" ,type=UpperCamelCase ,help="Path to cookiecutter. Should only be used for testing purposes." )
add_new_model_parser.set_defaults(func=UpperCamelCase )
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ,*UpperCamelCase ) -> Any:
snake_case__ :Union[str, Any] = testing
snake_case__ :Union[str, Any] = testing_file
snake_case__ :List[str] = path
def lowerCAmelCase_ ( self ) -> List[Any]:
warnings.warn(
"The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
"It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
"checks, you should use `transformers-cli add-new-model-like` instead." )
if not _has_cookiecutter:
raise ImportError(
"Model creation dependencies are required to use the `add_new_model` command. Install them by running "
"the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n" )
# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
snake_case__ :Tuple = [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
if len(UpperCamelCase ) > 0:
raise ValueError(
"Several directories starting with `cookiecutter-template-` in current working directory. "
"Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
"change your working directory." )
snake_case__ :str = (
Path(UpperCamelCase ).parent.parent.parent.parent if self._path is None else Path(self._path ).parent.parent
)
snake_case__ :Tuple = path_to_transformer_root / "templates" / "adding_a_new_model"
# Execute cookiecutter
if not self._testing:
cookiecutter(str(UpperCamelCase ) )
else:
with open(self._testing_file ,"r" ) as configuration_file:
snake_case__ :str = json.load(UpperCamelCase )
cookiecutter(
str(path_to_cookiecutter if self._path is None else self._path ) ,no_input=UpperCamelCase ,extra_context=UpperCamelCase ,)
snake_case__ :List[Any] = [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]
# Retrieve configuration
with open(directory + "/configuration.json" ,"r" ) as configuration_file:
snake_case__ :Dict = json.load(UpperCamelCase )
snake_case__ :Optional[Any] = configuration["lowercase_modelname"]
snake_case__ :List[Any] = configuration["generate_tensorflow_pytorch_and_flax"]
os.remove(f'{directory}/configuration.json' )
snake_case__ :Any = "PyTorch" in generate_tensorflow_pytorch_and_flax
snake_case__ :Any = "TensorFlow" in generate_tensorflow_pytorch_and_flax
snake_case__ :Any = "Flax" in generate_tensorflow_pytorch_and_flax
snake_case__ :Dict = f'{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'
os.makedirs(UpperCamelCase ,exist_ok=UpperCamelCase )
os.makedirs(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}' ,exist_ok=UpperCamelCase )
# Tests require submodules as they have parent imports
with open(f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py' ,"w" ):
pass
shutil.move(
f'{directory}/__init__.py' ,f'{model_dir}/__init__.py' ,)
shutil.move(
f'{directory}/configuration_{lowercase_model_name}.py' ,f'{model_dir}/configuration_{lowercase_model_name}.py' ,)
def remove_copy_lines(UpperCamelCase ):
with open(UpperCamelCase ,"r" ) as f:
snake_case__ :List[str] = f.readlines()
with open(UpperCamelCase ,"w" ) as f:
for line in lines:
if "# Copied from transformers." not in line:
f.write(UpperCamelCase )
if output_pytorch:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_{lowercase_model_name}.py' ,f'{model_dir}/modeling_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_{lowercase_model_name}.py' )
if output_tensorflow:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_tf_{lowercase_model_name}.py' ,f'{model_dir}/modeling_tf_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_tf_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_tf_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_tf_{lowercase_model_name}.py' )
if output_flax:
if not self._testing:
remove_copy_lines(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/modeling_flax_{lowercase_model_name}.py' ,f'{model_dir}/modeling_flax_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/test_modeling_flax_{lowercase_model_name}.py' ,f'{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py' ,)
else:
os.remove(f'{directory}/modeling_flax_{lowercase_model_name}.py' )
os.remove(f'{directory}/test_modeling_flax_{lowercase_model_name}.py' )
shutil.move(
f'{directory}/{lowercase_model_name}.md' ,f'{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md' ,)
shutil.move(
f'{directory}/tokenization_{lowercase_model_name}.py' ,f'{model_dir}/tokenization_{lowercase_model_name}.py' ,)
shutil.move(
f'{directory}/tokenization_fast_{lowercase_model_name}.py' ,f'{model_dir}/tokenization_{lowercase_model_name}_fast.py' ,)
from os import fdopen, remove
from shutil import copymode, move
from tempfile import mkstemp
def replace(UpperCamelCase ,UpperCamelCase ,UpperCamelCase ):
# Create temp file
snake_case__ , snake_case__ :Optional[Any] = mkstemp()
snake_case__ :Optional[Any] = False
with fdopen(UpperCamelCase ,"w" ) as new_file:
with open(UpperCamelCase ) as old_file:
for line in old_file:
new_file.write(UpperCamelCase )
if line_to_copy_below in line:
snake_case__ :Optional[Any] = True
for line_to_copy in lines_to_copy:
new_file.write(UpperCamelCase )
if not line_found:
raise ValueError(f'Line {line_to_copy_below} was not found in file.' )
# Copy the file permissions from the old file to the new file
copymode(UpperCamelCase ,UpperCamelCase )
# Remove original file
remove(UpperCamelCase )
# Move new file
move(UpperCamelCase ,UpperCamelCase )
def skip_units(UpperCamelCase ):
return (
("generating PyTorch" in line and not output_pytorch)
or ("generating TensorFlow" in line and not output_tensorflow)
or ("generating Flax" in line and not output_flax)
)
def replace_in_files(UpperCamelCase ):
with open(UpperCamelCase ) as datafile:
snake_case__ :int = []
snake_case__ :Optional[int] = False
snake_case__ :List[str] = False
for line in datafile:
if "# To replace in: " in line and "##" not in line:
snake_case__ :Optional[Any] = line.split("\"" )[1]
snake_case__ :Tuple = skip_units(UpperCamelCase )
elif "# Below: " in line and "##" not in line:
snake_case__ :Optional[Any] = line.split("\"" )[1]
snake_case__ :List[str] = skip_units(UpperCamelCase )
elif "# End." in line and "##" not in line:
if not skip_file and not skip_snippet:
replace(UpperCamelCase ,UpperCamelCase ,UpperCamelCase )
snake_case__ :Tuple = []
elif "# Replace with" in line and "##" not in line:
snake_case__ :Optional[Any] = []
elif "##" not in line:
lines_to_copy.append(UpperCamelCase )
remove(UpperCamelCase )
replace_in_files(f'{directory}/to_replace_{lowercase_model_name}.py' )
os.rmdir(UpperCamelCase )
| 57
| 1
|
from math import pi, sqrt
def lowercase_ ( __snake_case : float ) -> float:
'''simple docstring'''
if num <= 0:
raise ValueError("math domain error" )
if num > 1_7_1.5:
raise OverflowError("math range error" )
elif num - int(__snake_case ) not in (0, 0.5):
raise NotImplementedError("num must be an integer or a half-integer" )
elif num == 0.5:
return sqrt(__snake_case )
else:
return 1.0 if num == 1 else (num - 1) * gamma(num - 1 )
def lowercase_ ( ) -> None:
'''simple docstring'''
assert gamma(0.5 ) == sqrt(__snake_case )
assert gamma(1 ) == 1.0
assert gamma(2 ) == 1.0
if __name__ == "__main__":
from doctest import testmod
testmod()
__UpperCAmelCase : str = 1.0
while num:
__UpperCAmelCase : List[str] = float(input("Gamma of: "))
print(F'''gamma({num}) = {gamma(num)}''')
print("\nEnter 0 to exit...")
| 57
|
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
__UpperCAmelCase : str = logging.get_logger(__name__)
__UpperCAmelCase : List[Any] = {"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}
__UpperCAmelCase : List[Any] = {
"vocab_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/vocab.json"
},
"merges_file": {
"allegro/herbert-base-cased": "https://huggingface.co/allegro/herbert-base-cased/resolve/main/merges.txt"
},
}
__UpperCAmelCase : str = {"allegro/herbert-base-cased": 5_1_4}
__UpperCAmelCase : List[str] = {}
class _snake_case ( _A ):
_A = VOCAB_FILES_NAMES
_A = PRETRAINED_VOCAB_FILES_MAP
_A = PRETRAINED_INIT_CONFIGURATION
_A = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
_A = HerbertTokenizer
def __init__( self ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase=None ,UpperCamelCase="<s>" ,UpperCamelCase="<unk>" ,UpperCamelCase="<pad>" ,UpperCamelCase="<mask>" ,UpperCamelCase="</s>" ,**UpperCamelCase ,) -> Dict:
super().__init__(
UpperCamelCase ,UpperCamelCase ,tokenizer_file=UpperCamelCase ,cls_token=UpperCamelCase ,unk_token=UpperCamelCase ,pad_token=UpperCamelCase ,mask_token=UpperCamelCase ,sep_token=UpperCamelCase ,**UpperCamelCase ,)
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> List[int]:
snake_case__ :Optional[int] = [self.cls_token_id]
snake_case__ :Any = [self.sep_token_id]
if token_ids_a is None:
return cls + token_ids_a + sep
return cls + token_ids_a + sep + token_ids_a + sep
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = False ) -> List[int]:
if already_has_special_tokens:
return super().get_special_tokens_mask(
token_ids_a=UpperCamelCase ,token_ids_a=UpperCamelCase ,already_has_special_tokens=UpperCamelCase )
if token_ids_a is None:
return [1] + ([0] * len(UpperCamelCase )) + [1]
return [1] + ([0] * len(UpperCamelCase )) + [1] + ([0] * len(UpperCamelCase )) + [1]
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> List[int]:
snake_case__ :Any = [self.sep_token_id]
snake_case__ :Union[str, Any] = [self.cls_token_id]
if token_ids_a is None:
return len(cls + token_ids_a + sep ) * [0]
return len(cls + token_ids_a + sep ) * [0] + len(token_ids_a + sep ) * [1]
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ) -> Tuple[str]:
snake_case__ :List[str] = self._tokenizer.model.save(UpperCamelCase ,name=UpperCamelCase )
return tuple(UpperCamelCase )
| 57
| 1
|
import itertools
from dataclasses import dataclass
from typing import Optional
import pandas as pd
import pyarrow as pa
import datasets
from datasets.table import table_cast
@dataclass
class _snake_case ( datasets.BuilderConfig ):
_A = None
class _snake_case ( datasets.ArrowBasedBuilder ):
_A = PandasConfig
def lowerCAmelCase_ ( self ) -> str:
return datasets.DatasetInfo(features=self.config.features )
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> List[str]:
if not self.config.data_files:
raise ValueError(f'At least one data file must be specified, but got data_files={self.config.data_files}' )
snake_case__ :Any = dl_manager.download_and_extract(self.config.data_files )
if isinstance(UpperCamelCase ,(str, list, tuple) ):
snake_case__ :List[Any] = data_files
if isinstance(UpperCamelCase ,UpperCamelCase ):
snake_case__ :Any = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
snake_case__ :int = [dl_manager.iter_files(UpperCamelCase ) for file in files]
return [datasets.SplitGenerator(name=datasets.Split.TRAIN ,gen_kwargs={"files": files} )]
snake_case__ :Tuple = []
for split_name, files in data_files.items():
if isinstance(UpperCamelCase ,UpperCamelCase ):
snake_case__ :List[Any] = [files]
# Use `dl_manager.iter_files` to skip hidden files in an extracted archive
snake_case__ :Optional[Any] = [dl_manager.iter_files(UpperCamelCase ) for file in files]
splits.append(datasets.SplitGenerator(name=UpperCamelCase ,gen_kwargs={"files": files} ) )
return splits
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> pa.Table:
if self.config.features is not None:
# more expensive cast to support nested features with keys in a different order
# allows str <-> int/float or str to Audio for example
snake_case__ :Any = table_cast(UpperCamelCase ,self.config.features.arrow_schema )
return pa_table
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Optional[Any]:
for i, file in enumerate(itertools.chain.from_iterable(UpperCamelCase ) ):
with open(UpperCamelCase ,"rb" ) as f:
snake_case__ :Optional[int] = pa.Table.from_pandas(pd.read_pickle(UpperCamelCase ) )
yield i, self._cast_table(UpperCamelCase )
| 57
|
def lowercase_ ( __snake_case : int ) -> bool:
'''simple docstring'''
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
snake_case__ :List[str] = 4
snake_case__ :Optional[int] = (1 << p) - 1
for _ in range(p - 2 ):
snake_case__ :List[Any] = ((s * s) - 2) % m
return s == 0
if __name__ == "__main__":
print(lucas_lehmer_test(7))
print(lucas_lehmer_test(1_1))
| 57
| 1
|
from typing import Any
def lowercase_ ( __snake_case : list , __snake_case : list , __snake_case : dict , __snake_case : dict , __snake_case : dict , ) -> list:
'''simple docstring'''
_validation(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
# Creates data structures and fill initial step
snake_case__ :dict = {}
snake_case__ :dict = {}
for state in states_space:
snake_case__ :List[Any] = observations_space[0]
snake_case__ :str = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
snake_case__ :str = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(__snake_case ) ):
snake_case__ :Any = observations_space[o]
snake_case__ :Tuple = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
snake_case__ :Tuple = ""
snake_case__ :Union[str, Any] = -1
for k_state in states_space:
snake_case__ :int = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
snake_case__ :str = probability
snake_case__ :Tuple = k_state
# Update probabilities and pointers dicts
snake_case__ :List[str] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
snake_case__ :List[str] = arg_max
# The final observation
snake_case__ :str = observations_space[len(__snake_case ) - 1]
# argmax for given final observation
snake_case__ :Optional[int] = ""
snake_case__ :List[str] = -1
for k_state in states_space:
snake_case__ :List[str] = probabilities[(k_state, final_observation)]
if probability > max_probability:
snake_case__ :List[str] = probability
snake_case__ :int = k_state
snake_case__ :Any = arg_max
# Process pointers backwards
snake_case__ :int = last_state
snake_case__ :List[str] = []
for o in range(len(__snake_case ) - 1 , -1 , -1 ):
result.append(__snake_case )
snake_case__ :List[str] = pointers[previous, observations_space[o]]
result.reverse()
return result
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
_validate_not_empty(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
_validate_lists(__snake_case , __snake_case )
_validate_dicts(
__snake_case , __snake_case , __snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("There's an empty parameter" )
def lowercase_ ( __snake_case : Any , __snake_case : Any ) -> None:
'''simple docstring'''
_validate_list(__snake_case , "observations_space" )
_validate_list(__snake_case , "states_space" )
def lowercase_ ( __snake_case : Any , __snake_case : str ) -> None:
'''simple docstring'''
if not isinstance(_object , __snake_case ):
snake_case__ :Optional[int] = F'{var_name} must be a list'
raise ValueError(__snake_case )
else:
for x in _object:
if not isinstance(__snake_case , __snake_case ):
snake_case__ :Any = F'{var_name} must be a list of strings'
raise ValueError(__snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
_validate_dict(__snake_case , "initial_probabilities" , __snake_case )
_validate_nested_dict(__snake_case , "transition_probabilities" )
_validate_nested_dict(__snake_case , "emission_probabilities" )
def lowercase_ ( __snake_case : Any , __snake_case : str ) -> None:
'''simple docstring'''
_validate_dict(_object , __snake_case , __snake_case )
for x in _object.values():
_validate_dict(__snake_case , __snake_case , __snake_case , __snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : str , __snake_case : type , __snake_case : bool = False ) -> None:
'''simple docstring'''
if not isinstance(_object , __snake_case ):
snake_case__ :str = F'{var_name} must be a dict'
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object ):
snake_case__ :List[Any] = F'{var_name} all keys must be strings'
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object.values() ):
snake_case__ :Optional[int] = "nested dictionary " if nested else ""
snake_case__ :int = F'{var_name} {nested_text}all values must be {value_type.__name__}'
raise ValueError(__snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
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|
from typing import Any
def lowercase_ ( __snake_case : list , __snake_case : list , __snake_case : dict , __snake_case : dict , __snake_case : dict , ) -> list:
'''simple docstring'''
_validation(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
# Creates data structures and fill initial step
snake_case__ :dict = {}
snake_case__ :dict = {}
for state in states_space:
snake_case__ :List[Any] = observations_space[0]
snake_case__ :str = (
initial_probabilities[state] * emission_probabilities[state][observation]
)
snake_case__ :str = None
# Fills the data structure with the probabilities of
# different transitions and pointers to previous states
for o in range(1 , len(__snake_case ) ):
snake_case__ :Any = observations_space[o]
snake_case__ :Tuple = observations_space[o - 1]
for state in states_space:
# Calculates the argmax for probability function
snake_case__ :Tuple = ""
snake_case__ :Union[str, Any] = -1
for k_state in states_space:
snake_case__ :int = (
probabilities[(k_state, prior_observation)]
* transition_probabilities[k_state][state]
* emission_probabilities[state][observation]
)
if probability > max_probability:
snake_case__ :str = probability
snake_case__ :Tuple = k_state
# Update probabilities and pointers dicts
snake_case__ :List[str] = (
probabilities[(arg_max, prior_observation)]
* transition_probabilities[arg_max][state]
* emission_probabilities[state][observation]
)
snake_case__ :List[str] = arg_max
# The final observation
snake_case__ :str = observations_space[len(__snake_case ) - 1]
# argmax for given final observation
snake_case__ :Optional[int] = ""
snake_case__ :List[str] = -1
for k_state in states_space:
snake_case__ :List[str] = probabilities[(k_state, final_observation)]
if probability > max_probability:
snake_case__ :List[str] = probability
snake_case__ :int = k_state
snake_case__ :Any = arg_max
# Process pointers backwards
snake_case__ :int = last_state
snake_case__ :List[str] = []
for o in range(len(__snake_case ) - 1 , -1 , -1 ):
result.append(__snake_case )
snake_case__ :List[str] = pointers[previous, observations_space[o]]
result.reverse()
return result
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
_validate_not_empty(
__snake_case , __snake_case , __snake_case , __snake_case , __snake_case , )
_validate_lists(__snake_case , __snake_case )
_validate_dicts(
__snake_case , __snake_case , __snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
if not all(
[
observations_space,
states_space,
initial_probabilities,
transition_probabilities,
emission_probabilities,
] ):
raise ValueError("There's an empty parameter" )
def lowercase_ ( __snake_case : Any , __snake_case : Any ) -> None:
'''simple docstring'''
_validate_list(__snake_case , "observations_space" )
_validate_list(__snake_case , "states_space" )
def lowercase_ ( __snake_case : Any , __snake_case : str ) -> None:
'''simple docstring'''
if not isinstance(_object , __snake_case ):
snake_case__ :Optional[int] = F'{var_name} must be a list'
raise ValueError(__snake_case )
else:
for x in _object:
if not isinstance(__snake_case , __snake_case ):
snake_case__ :Any = F'{var_name} must be a list of strings'
raise ValueError(__snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : Any , __snake_case : Any , ) -> None:
'''simple docstring'''
_validate_dict(__snake_case , "initial_probabilities" , __snake_case )
_validate_nested_dict(__snake_case , "transition_probabilities" )
_validate_nested_dict(__snake_case , "emission_probabilities" )
def lowercase_ ( __snake_case : Any , __snake_case : str ) -> None:
'''simple docstring'''
_validate_dict(_object , __snake_case , __snake_case )
for x in _object.values():
_validate_dict(__snake_case , __snake_case , __snake_case , __snake_case )
def lowercase_ ( __snake_case : Any , __snake_case : str , __snake_case : type , __snake_case : bool = False ) -> None:
'''simple docstring'''
if not isinstance(_object , __snake_case ):
snake_case__ :str = F'{var_name} must be a dict'
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object ):
snake_case__ :List[Any] = F'{var_name} all keys must be strings'
raise ValueError(__snake_case )
if not all(isinstance(__snake_case , __snake_case ) for x in _object.values() ):
snake_case__ :Optional[int] = "nested dictionary " if nested else ""
snake_case__ :int = F'{var_name} {nested_text}all values must be {value_type.__name__}'
raise ValueError(__snake_case )
if __name__ == "__main__":
from doctest import testmod
testmod()
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| 1
|
from __future__ import annotations
from math import pi
def lowercase_ ( __snake_case : float , __snake_case : float , __snake_case : float ) -> dict[str, float]:
'''simple docstring'''
if (inductance, frequency, reactance).count(0 ) != 1:
raise ValueError("One and only one argument must be 0" )
if inductance < 0:
raise ValueError("Inductance cannot be negative" )
if frequency < 0:
raise ValueError("Frequency cannot be negative" )
if reactance < 0:
raise ValueError("Inductive reactance cannot be negative" )
if inductance == 0:
return {"inductance": reactance / (2 * pi * frequency)}
elif frequency == 0:
return {"frequency": reactance / (2 * pi * inductance)}
elif reactance == 0:
return {"reactance": 2 * pi * frequency * inductance}
else:
raise ValueError("Exactly one argument must be 0" )
if __name__ == "__main__":
import doctest
doctest.testmod()
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|
def lowercase_ ( __snake_case : str ) -> list:
'''simple docstring'''
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(__snake_case ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__("doctest").testmod()
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| 1
|
import glob
import os
import random
from string import ascii_lowercase, digits
import cva
__UpperCAmelCase : int = ""
__UpperCAmelCase : Optional[int] = ""
__UpperCAmelCase : str = ""
__UpperCAmelCase : Optional[int] = 1 # (0 is vertical, 1 is horizontal)
def lowercase_ ( ) -> None:
'''simple docstring'''
snake_case__ , snake_case__ :Dict = get_dataset(__snake_case , __snake_case )
print("Processing..." )
snake_case__ , snake_case__ , snake_case__ :Tuple = update_image_and_anno(__snake_case , __snake_case , __snake_case )
for index, image in enumerate(__snake_case ):
# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
snake_case__ :str = random_chars(32 )
snake_case__ :Tuple = paths[index].split(os.sep )[-1].rsplit("." , 1 )[0]
snake_case__ :List[str] = F'{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}'
cva.imwrite(F'/{file_root}.jpg' , __snake_case , [cva.IMWRITE_JPEG_QUALITY, 85] )
print(F'Success {index+1}/{len(__snake_case )} with {file_name}' )
snake_case__ :Optional[int] = []
for anno in new_annos[index]:
snake_case__ :Dict = F'{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}'
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 lowercase_ ( __snake_case : str , __snake_case : str ) -> tuple[list, list]:
'''simple docstring'''
snake_case__ :Optional[int] = []
snake_case__ :Dict = []
for label_file in glob.glob(os.path.join(__snake_case , "*.txt" ) ):
snake_case__ :List[str] = label_file.split(os.sep )[-1].rsplit("." , 1 )[0]
with open(__snake_case ) as in_file:
snake_case__ :str = in_file.readlines()
snake_case__ :Dict = os.path.join(__snake_case , F'{label_name}.jpg' )
snake_case__ :Optional[Any] = []
for obj_list in obj_lists:
snake_case__ :Union[str, Any] = obj_list.rstrip("\n" ).split(" " )
boxes.append(
[
int(obj[0] ),
float(obj[1] ),
float(obj[2] ),
float(obj[3] ),
float(obj[4] ),
] )
if not boxes:
continue
img_paths.append(__snake_case )
labels.append(__snake_case )
return img_paths, labels
def lowercase_ ( __snake_case : list , __snake_case : list , __snake_case : int = 1 ) -> tuple[list, list, list]:
'''simple docstring'''
snake_case__ :int = []
snake_case__ :List[Any] = []
snake_case__ :Optional[int] = []
for idx in range(len(__snake_case ) ):
snake_case__ :Any = []
snake_case__ :Any = img_list[idx]
path_list.append(__snake_case )
snake_case__ :Dict = anno_list[idx]
snake_case__ :Tuple = cva.imread(__snake_case )
if flip_type == 1:
snake_case__ :Dict = cva.flip(__snake_case , __snake_case )
for bbox in img_annos:
snake_case__ :int = 1 - bbox[1]
new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]] )
elif flip_type == 0:
snake_case__ :Union[str, Any] = cva.flip(__snake_case , __snake_case )
for bbox in img_annos:
snake_case__ :str = 1 - bbox[2]
new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]] )
new_annos_lists.append(__snake_case )
new_imgs_list.append(__snake_case )
return new_imgs_list, new_annos_lists, path_list
def lowercase_ ( __snake_case : int = 32 ) -> str:
'''simple docstring'''
assert number_char > 1, "The number of character should greater than 1"
snake_case__ :Dict = ascii_lowercase + digits
return "".join(random.choice(__snake_case ) for _ in range(__snake_case ) )
if __name__ == "__main__":
main()
print("DONE ✅")
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|
def lowercase_ ( __snake_case : int = 10_00 ) -> int:
'''simple docstring'''
snake_case__ :int = 3
snake_case__ :int = 0
while a < n:
if a % 3 == 0 or a % 5 == 0:
result += a
elif a % 15 == 0:
result -= a
a += 1
return result
if __name__ == "__main__":
print(F'''{solution() = }''')
| 57
| 1
|
from __future__ import annotations
def lowercase_ ( __snake_case : dict , __snake_case : str ) -> set[str]:
'''simple docstring'''
snake_case__ , snake_case__ :Tuple = set(__snake_case ), [start]
while stack:
snake_case__ :Dict = stack.pop()
explored.add(__snake_case )
# Differences from BFS:
# 1) pop last element instead of first one
# 2) add adjacent elements to stack without exploring them
for adj in reversed(graph[v] ):
if adj not in explored:
stack.append(__snake_case )
return explored
__UpperCAmelCase : Optional[int] = {
"A": ["B", "C", "D"],
"B": ["A", "D", "E"],
"C": ["A", "F"],
"D": ["B", "D"],
"E": ["B", "F"],
"F": ["C", "E", "G"],
"G": ["F"],
}
if __name__ == "__main__":
import doctest
doctest.testmod()
print(depth_first_search(G, "A"))
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|
import os
import sys
import unittest
__UpperCAmelCase : str = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_dummies # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init # noqa: E402
# Align TRANSFORMERS_PATH in check_dummies with the current path
__UpperCAmelCase : Tuple = os.path.join(git_repo_path, "src", "diffusers")
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Tuple = find_backend(" if not is_torch_available():" )
self.assertEqual(UpperCamelCase ,"torch" )
# backend_with_underscore = find_backend(" if not is_tensorflow_text_available():")
# self.assertEqual(backend_with_underscore, "tensorflow_text")
snake_case__ :Tuple = find_backend(" if not (is_torch_available() and is_transformers_available()):" )
self.assertEqual(UpperCamelCase ,"torch_and_transformers" )
# double_backend_with_underscore = find_backend(
# " if not (is_sentencepiece_available() and is_tensorflow_text_available()):"
# )
# self.assertEqual(double_backend_with_underscore, "sentencepiece_and_tensorflow_text")
snake_case__ :str = find_backend(
" if not (is_torch_available() and is_transformers_available() and is_onnx_available()):" )
self.assertEqual(UpperCamelCase ,"torch_and_transformers_and_onnx" )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :int = read_init()
# We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
self.assertIn("torch" ,UpperCamelCase )
self.assertIn("torch_and_transformers" ,UpperCamelCase )
self.assertIn("flax_and_transformers" ,UpperCamelCase )
self.assertIn("torch_and_transformers_and_onnx" ,UpperCamelCase )
# Likewise, we can't assert on the exact content of a key
self.assertIn("UNet2DModel" ,objects["torch"] )
self.assertIn("FlaxUNet2DConditionModel" ,objects["flax"] )
self.assertIn("StableDiffusionPipeline" ,objects["torch_and_transformers"] )
self.assertIn("FlaxStableDiffusionPipeline" ,objects["flax_and_transformers"] )
self.assertIn("LMSDiscreteScheduler" ,objects["torch_and_scipy"] )
self.assertIn("OnnxStableDiffusionPipeline" ,objects["torch_and_transformers_and_onnx"] )
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Union[str, Any] = create_dummy_object("CONSTANT" ,"'torch'" )
self.assertEqual(UpperCamelCase ,"\nCONSTANT = None\n" )
snake_case__ :Optional[Any] = create_dummy_object("function" ,"'torch'" )
self.assertEqual(
UpperCamelCase ,"\ndef function(*args, **kwargs):\n requires_backends(function, 'torch')\n" )
snake_case__ :str = "\nclass FakeClass(metaclass=DummyObject):\n _backends = 'torch'\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, 'torch')\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, 'torch')\n"
snake_case__ :List[str] = create_dummy_object("FakeClass" ,"'torch'" )
self.assertEqual(UpperCamelCase ,UpperCamelCase )
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :Tuple = "# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n requires_backends(function, [\"torch\"])\n\n\nclass FakeClass(metaclass=DummyObject):\n _backends = [\"torch\"]\n\n def __init__(self, *args, **kwargs):\n requires_backends(self, [\"torch\"])\n\n @classmethod\n def from_config(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n\n @classmethod\n def from_pretrained(cls, *args, **kwargs):\n requires_backends(cls, [\"torch\"])\n"
snake_case__ :int = create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]} )
self.assertEqual(dummy_files["torch"] ,UpperCamelCase )
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| 1
|
def lowercase_ ( __snake_case : int ) -> str:
'''simple docstring'''
if isinstance(__snake_case , __snake_case ):
raise TypeError("'float' object cannot be interpreted as an integer" )
if isinstance(__snake_case , __snake_case ):
raise TypeError("'str' object cannot be interpreted as an integer" )
if num == 0:
return "0b0"
snake_case__ :Optional[int] = False
if num < 0:
snake_case__ :Optional[int] = True
snake_case__ :List[str] = -num
snake_case__ :list[int] = []
while num > 0:
binary.insert(0 , num % 2 )
num >>= 1
if negative:
return "-0b" + "".join(str(__snake_case ) for e in binary )
return "0b" + "".join(str(__snake_case ) for e in binary )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCAmelCase : Tuple = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
__UpperCAmelCase : List[Any] = ["BartphoTokenizer"]
if TYPE_CHECKING:
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
pass
else:
from .tokenization_bartpho import BartphoTokenizer
else:
import sys
__UpperCAmelCase : Tuple = _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
| 57
| 1
|
import argparse
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCAmelCase : int = logging.get_logger(__name__)
def lowercase_ ( __snake_case : Optional[Any] ) -> List[str]:
'''simple docstring'''
snake_case__ :str = OrderedDict()
for key, value in state_dict.items():
if key.startswith("module.encoder" ):
snake_case__ :List[str] = key.replace("module.encoder" , "glpn.encoder" )
if key.startswith("module.decoder" ):
snake_case__ :Dict = key.replace("module.decoder" , "decoder.stages" )
if "patch_embed" in key:
# replace for example patch_embed1 by patch_embeddings.0
snake_case__ :List[str] = key[key.find("patch_embed" ) + len("patch_embed" )]
snake_case__ :Tuple = key.replace(F'patch_embed{idx}' , F'patch_embeddings.{int(__snake_case )-1}' )
if "norm" in key:
snake_case__ :Any = key.replace("norm" , "layer_norm" )
if "glpn.encoder.layer_norm" in key:
# replace for example layer_norm1 by layer_norm.0
snake_case__ :Optional[Any] = key[key.find("glpn.encoder.layer_norm" ) + len("glpn.encoder.layer_norm" )]
snake_case__ :Tuple = key.replace(F'layer_norm{idx}' , F'layer_norm.{int(__snake_case )-1}' )
if "layer_norm1" in key:
snake_case__ :Optional[Any] = key.replace("layer_norm1" , "layer_norm_1" )
if "layer_norm2" in key:
snake_case__ :str = key.replace("layer_norm2" , "layer_norm_2" )
if "block" in key:
# replace for example block1 by block.0
snake_case__ :Any = key[key.find("block" ) + len("block" )]
snake_case__ :int = key.replace(F'block{idx}' , F'block.{int(__snake_case )-1}' )
if "attn.q" in key:
snake_case__ :Any = key.replace("attn.q" , "attention.self.query" )
if "attn.proj" in key:
snake_case__ :Optional[Any] = key.replace("attn.proj" , "attention.output.dense" )
if "attn" in key:
snake_case__ :Optional[int] = key.replace("attn" , "attention.self" )
if "fc1" in key:
snake_case__ :int = key.replace("fc1" , "dense1" )
if "fc2" in key:
snake_case__ :Any = key.replace("fc2" , "dense2" )
if "linear_pred" in key:
snake_case__ :int = key.replace("linear_pred" , "classifier" )
if "linear_fuse" in key:
snake_case__ :str = key.replace("linear_fuse.conv" , "linear_fuse" )
snake_case__ :Any = key.replace("linear_fuse.bn" , "batch_norm" )
if "linear_c" in key:
# replace for example linear_c4 by linear_c.3
snake_case__ :Optional[Any] = key[key.find("linear_c" ) + len("linear_c" )]
snake_case__ :List[Any] = key.replace(F'linear_c{idx}' , F'linear_c.{int(__snake_case )-1}' )
if "bot_conv" in key:
snake_case__ :Optional[Any] = key.replace("bot_conv" , "0.convolution" )
if "skip_conv1" in key:
snake_case__ :List[Any] = key.replace("skip_conv1" , "1.convolution" )
if "skip_conv2" in key:
snake_case__ :int = key.replace("skip_conv2" , "2.convolution" )
if "fusion1" in key:
snake_case__ :List[str] = key.replace("fusion1" , "1.fusion" )
if "fusion2" in key:
snake_case__ :Optional[int] = key.replace("fusion2" , "2.fusion" )
if "fusion3" in key:
snake_case__ :Union[str, Any] = key.replace("fusion3" , "3.fusion" )
if "fusion" in key and "conv" in key:
snake_case__ :Union[str, Any] = key.replace("conv" , "convolutional_layer" )
if key.startswith("module.last_layer_depth" ):
snake_case__ :int = key.replace("module.last_layer_depth" , "head.head" )
snake_case__ :Tuple = value
return new_state_dict
def lowercase_ ( __snake_case : List[str] , __snake_case : Union[str, Any] ) -> Optional[Any]:
'''simple docstring'''
for i in range(config.num_encoder_blocks ):
for j in range(config.depths[i] ):
# read in weights + bias of keys and values (which is a single matrix in the original implementation)
snake_case__ :Dict = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.weight' )
snake_case__ :List[Any] = state_dict.pop(F'glpn.encoder.block.{i}.{j}.attention.self.kv.bias' )
# next, add keys and values (in that order) to the state dict
snake_case__ :Union[str, Any] = kv_weight[
: config.hidden_sizes[i], :
]
snake_case__ :Optional[int] = kv_bias[: config.hidden_sizes[i]]
snake_case__ :List[str] = kv_weight[
config.hidden_sizes[i] :, :
]
snake_case__ :List[Any] = kv_bias[config.hidden_sizes[i] :]
def lowercase_ ( ) -> Tuple:
'''simple docstring'''
snake_case__ :Optional[int] = "http://images.cocodataset.org/val2017/000000039769.jpg"
snake_case__ :Any = Image.open(requests.get(__snake_case , stream=__snake_case ).raw )
return image
@torch.no_grad()
def lowercase_ ( __snake_case : List[Any] , __snake_case : List[str] , __snake_case : Dict=False , __snake_case : Tuple=None ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :Union[str, Any] = GLPNConfig(hidden_sizes=[64, 1_28, 3_20, 5_12] , decoder_hidden_size=64 , depths=[3, 8, 27, 3] )
# load image processor (only resize + rescale)
snake_case__ :List[Any] = GLPNImageProcessor()
# prepare image
snake_case__ :Dict = prepare_img()
snake_case__ :Dict = image_processor(images=__snake_case , return_tensors="pt" ).pixel_values
logger.info("Converting model..." )
# load original state dict
snake_case__ :Optional[Any] = torch.load(__snake_case , map_location=torch.device("cpu" ) )
# rename keys
snake_case__ :List[str] = rename_keys(__snake_case )
# key and value matrices need special treatment
read_in_k_v(__snake_case , __snake_case )
# create HuggingFace model and load state dict
snake_case__ :List[str] = GLPNForDepthEstimation(__snake_case )
model.load_state_dict(__snake_case )
model.eval()
# forward pass
snake_case__ :List[str] = model(__snake_case )
snake_case__ :List[str] = outputs.predicted_depth
# verify output
if model_name is not None:
if "nyu" in model_name:
snake_case__ :Union[str, Any] = torch.tensor(
[[4.4_1_4_7, 4.0_8_7_3, 4.0_6_7_3], [3.7_8_9_0, 3.2_8_8_1, 3.1_5_2_5], [3.7_6_7_4, 3.5_4_2_3, 3.4_9_1_3]] )
elif "kitti" in model_name:
snake_case__ :Optional[int] = torch.tensor(
[[3.4_2_9_1, 2.7_8_6_5, 2.5_1_5_1], [3.2_8_4_1, 2.7_0_2_1, 2.3_5_0_2], [3.1_1_4_7, 2.4_6_2_5, 2.2_4_8_1]] )
else:
raise ValueError(F'Unknown model name: {model_name}' )
snake_case__ :str = torch.Size([1, 4_80, 6_40] )
assert predicted_depth.shape == expected_shape
assert torch.allclose(predicted_depth[0, :3, :3] , __snake_case , atol=1e-4 )
print("Looks ok!" )
# finally, push to hub if required
if push_to_hub:
logger.info("Pushing model and image processor to the hub..." )
model.push_to_hub(
repo_path_or_name=Path(__snake_case , __snake_case ) , organization="nielsr" , commit_message="Add model" , use_temp_dir=__snake_case , )
image_processor.push_to_hub(
repo_path_or_name=Path(__snake_case , __snake_case ) , organization="nielsr" , commit_message="Add image processor" , use_temp_dir=__snake_case , )
if __name__ == "__main__":
__UpperCAmelCase : Optional[Any] = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path",
default=None,
type=str,
help="Path to the original PyTorch checkpoint (.pth file).",
)
parser.add_argument(
"--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
)
parser.add_argument(
"--push_to_hub", action="store_true", help="Whether to upload the model to the HuggingFace hub."
)
parser.add_argument(
"--model_name",
default="glpn-kitti",
type=str,
help="Name of the model in case you're pushing to the hub.",
)
__UpperCAmelCase : str = parser.parse_args()
convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)
| 57
|
import os
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from huggingface_hub.file_download import http_get
from requests.exceptions import HTTPError
from transformers import (
AlbertTokenizer,
AutoTokenizer,
BertTokenizer,
BertTokenizerFast,
GPTaTokenizerFast,
is_tokenizers_available,
)
from transformers.testing_utils import TOKEN, USER, is_staging_test, require_tokenizers
from transformers.tokenization_utils import Trie
sys.path.append(str(Path(__file__).parent.parent / "utils"))
from test_module.custom_tokenization import CustomTokenizer # noqa E402
if is_tokenizers_available():
from test_module.custom_tokenization_fast import CustomTokenizerFast
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> List[Any]:
# A mock response for an HTTP head request to emulate server down
snake_case__ :Tuple = mock.Mock()
snake_case__ :List[str] = 500
snake_case__ :Any = {}
snake_case__ :Union[str, Any] = HTTPError
snake_case__ :Tuple = {}
# Download this model to make sure it's in the cache.
snake_case__ :Any = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" ,return_value=UpperCamelCase ) as mock_head:
snake_case__ :Dict = BertTokenizer.from_pretrained("hf-internal-testing/tiny-random-bert" )
# This check we did call the fake head request
mock_head.assert_called()
@require_tokenizers
def lowerCAmelCase_ ( self ) -> Dict:
# A mock response for an HTTP head request to emulate server down
snake_case__ :Union[str, Any] = mock.Mock()
snake_case__ :int = 500
snake_case__ :Any = {}
snake_case__ :Dict = HTTPError
snake_case__ :List[Any] = {}
# Download this model to make sure it's in the cache.
snake_case__ :Optional[int] = GPTaTokenizerFast.from_pretrained("gpt2" )
# Under the mock environment we get a 500 error when trying to reach the tokenizer.
with mock.patch("requests.Session.request" ,return_value=UpperCamelCase ) as mock_head:
snake_case__ :Any = GPTaTokenizerFast.from_pretrained("gpt2" )
# This check we did call the fake head request
mock_head.assert_called()
def lowerCAmelCase_ ( self ) -> int:
# This test is for deprecated behavior and can be removed in v5
try:
snake_case__ :Union[str, Any] = tempfile.mktemp()
with open(UpperCamelCase ,"wb" ) as f:
http_get("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" ,UpperCamelCase )
snake_case__ :Tuple = AlbertTokenizer.from_pretrained(UpperCamelCase )
finally:
os.remove(UpperCamelCase )
# Supporting this legacy load introduced a weird bug where the tokenizer would load local files if they are in
# the current folder and have the right name.
if os.path.isfile("tokenizer.json" ):
# We skip the test if the user has a `tokenizer.json` in this folder to avoid deleting it.
return
try:
with open("tokenizer.json" ,"wb" ) as f:
http_get("https://huggingface.co/hf-internal-testing/tiny-random-bert/blob/main/tokenizer.json" ,UpperCamelCase )
snake_case__ :Dict = AutoTokenizer.from_pretrained("hf-internal-testing/tiny-random-gpt2" )
# The tiny random BERT has a vocab size of 1024, tiny gpt2 as a vocab size of 1000
self.assertEqual(tokenizer.vocab_size ,1_000 )
# Tokenizer should depend on the remote checkpoint, not the local tokenizer.json file.
finally:
os.remove("tokenizer.json" )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
# This test is for deprecated behavior and can be removed in v5
snake_case__ :Union[str, Any] = AlbertTokenizer.from_pretrained("https://huggingface.co/albert-base-v1/resolve/main/spiece.model" )
@is_staging_test
class _snake_case ( unittest.TestCase ):
_A = ['[UNK]', '[CLS]', '[SEP]', '[PAD]', '[MASK]', 'bla', 'blou']
@classmethod
def lowerCAmelCase_ ( cls ) -> Optional[int]:
snake_case__ :List[str] = TOKEN
HfFolder.save_token(UpperCamelCase )
@classmethod
def lowerCAmelCase_ ( cls ) -> Union[str, Any]:
try:
delete_repo(token=cls._token ,repo_id="test-tokenizer" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="valid_org/test-tokenizer-org" )
except HTTPError:
pass
try:
delete_repo(token=cls._token ,repo_id="test-dynamic-tokenizer" )
except HTTPError:
pass
def lowerCAmelCase_ ( self ) -> Optional[Any]:
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :List[str] = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :str = BertTokenizer(UpperCamelCase )
tokenizer.push_to_hub("test-tokenizer" ,use_auth_token=self._token )
snake_case__ :Dict = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id="test-tokenizer" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(UpperCamelCase ,repo_id="test-tokenizer" ,push_to_hub=UpperCamelCase ,use_auth_token=self._token )
snake_case__ :List[str] = BertTokenizer.from_pretrained(f'{USER}/test-tokenizer' )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
def lowerCAmelCase_ ( self ) -> Optional[int]:
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :List[Any] = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Any = BertTokenizer(UpperCamelCase )
tokenizer.push_to_hub("valid_org/test-tokenizer-org" ,use_auth_token=self._token )
snake_case__ :Any = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
# Reset repo
delete_repo(token=self._token ,repo_id="valid_org/test-tokenizer-org" )
# Push to hub via save_pretrained
with tempfile.TemporaryDirectory() as tmp_dir:
tokenizer.save_pretrained(
UpperCamelCase ,repo_id="valid_org/test-tokenizer-org" ,push_to_hub=UpperCamelCase ,use_auth_token=self._token )
snake_case__ :Union[str, Any] = BertTokenizer.from_pretrained("valid_org/test-tokenizer-org" )
self.assertDictEqual(new_tokenizer.vocab ,tokenizer.vocab )
@require_tokenizers
def lowerCAmelCase_ ( self ) -> Any:
CustomTokenizer.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :str = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Optional[int] = CustomTokenizer(UpperCamelCase )
# No fast custom tokenizer
tokenizer.push_to_hub("test-dynamic-tokenizer" ,use_auth_token=self._token )
snake_case__ :Union[str, Any] = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the CustomTokenizer class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizer" )
# Fast and slow custom tokenizer
CustomTokenizerFast.register_for_auto_class()
with tempfile.TemporaryDirectory() as tmp_dir:
snake_case__ :int = os.path.join(UpperCamelCase ,"vocab.txt" )
with open(UpperCamelCase ,"w" ,encoding="utf-8" ) as vocab_writer:
vocab_writer.write("".join([x + "\n" for x in self.vocab_tokens] ) )
snake_case__ :Tuple = BertTokenizerFast.from_pretrained(UpperCamelCase )
bert_tokenizer.save_pretrained(UpperCamelCase )
snake_case__ :List[Any] = CustomTokenizerFast.from_pretrained(UpperCamelCase )
tokenizer.push_to_hub("test-dynamic-tokenizer" ,use_auth_token=self._token )
snake_case__ :List[Any] = AutoTokenizer.from_pretrained(f'{USER}/test-dynamic-tokenizer' ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizerFast" )
snake_case__ :List[str] = AutoTokenizer.from_pretrained(
f'{USER}/test-dynamic-tokenizer' ,use_fast=UpperCamelCase ,trust_remote_code=UpperCamelCase )
# Can't make an isinstance check because the new_model.config is from the FakeConfig class of a dynamic module
self.assertEqual(tokenizer.__class__.__name__ ,"CustomTokenizer" )
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> List[Any]:
snake_case__ :int = Trie()
trie.add("Hello 友達" )
self.assertEqual(trie.data ,{"H": {"e": {"l": {"l": {"o": {" ": {"友": {"達": {"": 1}}}}}}}}} )
trie.add("Hello" )
trie.data
self.assertEqual(trie.data ,{"H": {"e": {"l": {"l": {"o": {"": 1, " ": {"友": {"達": {"": 1}}}}}}}}} )
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :List[str] = Trie()
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) ,["[CLS] This is a extra_id_100"] )
trie.add("[CLS]" )
trie.add("extra_id_1" )
trie.add("extra_id_100" )
self.assertEqual(trie.split("[CLS] This is a extra_id_100" ) ,["[CLS]", " This is a ", "extra_id_100"] )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Optional[Any] = Trie()
trie.add("A" )
self.assertEqual(trie.split("ABC" ) ,["A", "BC"] )
self.assertEqual(trie.split("BCA" ) ,["BC", "A"] )
def lowerCAmelCase_ ( self ) -> Dict:
snake_case__ :Any = Trie()
trie.add("TOKEN]" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) ,["This is something ", "[SPECIAL_TOKEN]"] )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :List[Any] = Trie()
trie.add("A" )
trie.add("P" )
trie.add("[SPECIAL_TOKEN]" )
self.assertEqual(trie.split("This is something [SPECIAL_TOKEN]" ) ,["This is something ", "[SPECIAL_TOKEN]"] )
def lowerCAmelCase_ ( self ) -> Tuple:
snake_case__ :str = Trie()
trie.add("AB" )
trie.add("B" )
trie.add("C" )
self.assertEqual(trie.split("ABC" ) ,["AB", "C"] )
def lowerCAmelCase_ ( self ) -> Union[str, Any]:
snake_case__ :Dict = Trie()
trie.add("ABC" )
trie.add("B" )
trie.add("CD" )
self.assertEqual(trie.split("ABCD" ) ,["ABC", "D"] )
def lowerCAmelCase_ ( self ) -> int:
# Even if the offsets are wrong, we necessarily output correct string
# parts.
snake_case__ :Optional[int] = Trie()
snake_case__ :Union[str, Any] = trie.cut_text("ABC" ,[0, 0, 2, 1, 2, 3] )
self.assertEqual(UpperCamelCase ,["AB", "C"] )
| 57
| 1
|
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 : Optional[int] = 1_6
__UpperCAmelCase : List[Any] = 3_2
def lowercase_ ( __snake_case : Accelerator , __snake_case : int = 16 , __snake_case : str = "bert-base-cased" ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :List[str] = AutoTokenizer.from_pretrained(__snake_case )
snake_case__ :str = load_dataset("glue" , "mrpc" )
def tokenize_function(__snake_case : Tuple ):
# max_length=None => use the model max length (it's actually the default)
snake_case__ :Optional[Any] = 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
snake_case__ :int = 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
snake_case__ :Optional[int] = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(__snake_case : List[str] ):
# 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_28 , return_tensors="pt" )
return tokenizer.pad(__snake_case , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
snake_case__ :str = DataLoader(
tokenized_datasets["train"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
snake_case__ :List[Any] = DataLoader(
tokenized_datasets["validation"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
return train_dataloader, eval_dataloader
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : Any ) -> int:
'''simple docstring'''
snake_case__ :int = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ :int = config["lr"]
snake_case__ :int = int(config["num_epochs"] )
snake_case__ :List[Any] = int(config["seed"] )
snake_case__ :List[str] = int(config["batch_size"] )
snake_case__ :List[Any] = args.model_name_or_path
set_seed(__snake_case )
snake_case__ , snake_case__ :Dict = 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)
snake_case__ :Union[str, Any] = AutoModelForSequenceClassification.from_pretrained(__snake_case , return_dict=__snake_case )
# Instantiate optimizer
snake_case__ :List[str] = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
snake_case__ :List[Any] = optimizer_cls(params=model.parameters() , lr=__snake_case )
if accelerator.state.deepspeed_plugin is not None:
snake_case__ :Tuple = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
snake_case__ :Optional[Any] = 1
snake_case__ :Optional[Any] = (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
):
snake_case__ :Dict = get_linear_schedule_with_warmup(
optimizer=__snake_case , num_warmup_steps=0 , num_training_steps=__snake_case , )
else:
snake_case__ :List[Any] = 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.
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ :List[str] = 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
snake_case__ :List[str] = 0
# We also need to keep track of the stating epoch so files are named properly
snake_case__ :Optional[int] = 0
# Now we train the model
snake_case__ :List[Any] = evaluate.load("glue" , "mrpc" )
snake_case__ :Tuple = 0
snake_case__ :List[str] = {}
for epoch in range(__snake_case , __snake_case ):
model.train()
for step, batch in enumerate(__snake_case ):
snake_case__ :int = model(**__snake_case )
snake_case__ :List[Any] = outputs.loss
snake_case__ :Tuple = 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()
snake_case__ :Optional[int] = 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():
snake_case__ :Optional[int] = model(**__snake_case )
snake_case__ :Optional[int] = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
snake_case__ , snake_case__ :Any = 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:
snake_case__ :Optional[Any] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
snake_case__ :int = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__snake_case , references=__snake_case , )
snake_case__ :int = metric.compute()
# Use accelerator.print to print only on the main process.
accelerator.print(F'epoch {epoch}:' , __snake_case )
snake_case__ :List[Any] = eval_metric["accuracy"]
if best_performance < eval_metric["accuracy"]:
snake_case__ :List[str] = 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 lowercase_ ( ) -> List[str]:
'''simple docstring'''
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=__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." , )
snake_case__ :int = parser.parse_args()
snake_case__ :Optional[int] = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(__snake_case , __snake_case )
if __name__ == "__main__":
main()
| 57
|
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 : Optional[Any] = 1_6
__UpperCAmelCase : Optional[int] = 3_2
def lowercase_ ( __snake_case : Accelerator , __snake_case : int = 16 , __snake_case : str = "bert-base-cased" ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :int = AutoTokenizer.from_pretrained(__snake_case )
snake_case__ :Optional[int] = load_dataset("glue" , "mrpc" )
def tokenize_function(__snake_case : Tuple ):
# max_length=None => use the model max length (it's actually the default)
snake_case__ :Any = 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
snake_case__ :List[Any] = 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
snake_case__ :Any = tokenized_datasets.rename_column("label" , "labels" )
def collate_fn(__snake_case : Dict ):
# On TPU it's best to pad everything to the same length or training will be very slow.
if accelerator.distributed_type == DistributedType.TPU:
return tokenizer.pad(__snake_case , padding="max_length" , max_length=1_28 , return_tensors="pt" )
return tokenizer.pad(__snake_case , padding="longest" , return_tensors="pt" )
# Instantiate dataloaders.
snake_case__ :Any = DataLoader(
tokenized_datasets["train"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
snake_case__ :Tuple = DataLoader(
tokenized_datasets["validation"] , shuffle=__snake_case , collate_fn=__snake_case , batch_size=__snake_case )
return train_dataloader, eval_dataloader
def lowercase_ ( __snake_case : List[Any] , __snake_case : Union[str, Any] , __snake_case : int , __snake_case : Optional[int] ) -> Tuple:
'''simple docstring'''
model.eval()
snake_case__ :Union[str, Any] = 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():
snake_case__ :List[Any] = model(**__snake_case )
snake_case__ :Any = outputs.logits.argmax(dim=-1 )
# It is slightly faster to call this once, than multiple times
snake_case__ , snake_case__ :Tuple = 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:
snake_case__ :List[str] = predictions[: len(eval_dataloader.dataset ) - samples_seen]
snake_case__ :Optional[int] = references[: len(eval_dataloader.dataset ) - samples_seen]
else:
samples_seen += references.shape[0]
metric.add_batch(
predictions=__snake_case , references=__snake_case , )
snake_case__ :int = metric.compute()
return eval_metric["accuracy"]
def lowercase_ ( __snake_case : Union[str, Any] , __snake_case : Optional[Any] ) -> Any:
'''simple docstring'''
snake_case__ :Any = Accelerator()
# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
snake_case__ :Union[str, Any] = config["lr"]
snake_case__ :List[str] = int(config["num_epochs"] )
snake_case__ :Optional[Any] = int(config["seed"] )
snake_case__ :List[Any] = int(config["batch_size"] )
snake_case__ :List[Any] = args.model_name_or_path
set_seed(__snake_case )
snake_case__ , snake_case__ :List[Any] = 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)
snake_case__ :List[Any] = AutoModelForSequenceClassification.from_pretrained(__snake_case , return_dict=__snake_case )
# Instantiate optimizer
snake_case__ :int = (
AdamW
if accelerator.state.deepspeed_plugin is None
or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
else DummyOptim
)
snake_case__ :Tuple = optimizer_cls(params=model.parameters() , lr=__snake_case )
if accelerator.state.deepspeed_plugin is not None:
snake_case__ :List[str] = accelerator.state.deepspeed_plugin.deepspeed_config[
"gradient_accumulation_steps"
]
else:
snake_case__ :Any = 1
snake_case__ :List[Any] = (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
):
snake_case__ :Optional[Any] = get_linear_schedule_with_warmup(
optimizer=__snake_case , num_warmup_steps=0 , num_training_steps=__snake_case , )
else:
snake_case__ :Any = 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.
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ :int = 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
snake_case__ :Dict = 0
# We also need to keep track of the stating epoch so files are named properly
snake_case__ :Union[str, Any] = 0
snake_case__ :List[str] = evaluate.load("glue" , "mrpc" )
snake_case__ :Optional[Any] = num_epochs
if args.partial_train_epoch is not None:
snake_case__ :List[Any] = args.partial_train_epoch
if args.resume_from_checkpoint:
accelerator.load_state(args.resume_from_checkpoint )
snake_case__ :Union[str, Any] = args.resume_from_checkpoint.split("epoch_" )[1]
snake_case__ :Dict = ""
for char in epoch_string:
if char.isdigit():
state_epoch_num += char
else:
break
snake_case__ :str = int(__snake_case ) + 1
snake_case__ :List[Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
accelerator.print("resumed checkpoint performance:" , __snake_case )
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:
snake_case__ :Tuple = json.load(__snake_case )
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
snake_case__ :Optional[int] = {}
for epoch in range(__snake_case , __snake_case ):
model.train()
for step, batch in enumerate(__snake_case ):
snake_case__ :str = model(**__snake_case )
snake_case__ :List[str] = outputs.loss
snake_case__ :List[Any] = 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
snake_case__ :int = F'epoch_{epoch}'
snake_case__ :str = os.path.join(args.output_dir , __snake_case )
accelerator.save_state(__snake_case )
snake_case__ :Union[str, Any] = evaluation_loop(__snake_case , __snake_case , __snake_case , __snake_case )
snake_case__ :List[str] = accuracy
snake_case__ :List[str] = lr_scheduler.get_lr()[0]
snake_case__ :List[Any] = optimizer.param_groups[0]["lr"]
snake_case__ :Dict = epoch
snake_case__ :List[Any] = overall_step
accelerator.print(F'epoch {epoch}:' , __snake_case )
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(__snake_case , __snake_case )
def lowercase_ ( ) -> Any:
'''simple docstring'''
snake_case__ :List[Any] = 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(
"--resume_from_checkpoint" , type=__snake_case , default=__snake_case , help="If the training should continue from a checkpoint folder." , )
parser.add_argument(
"--partial_train_epoch" , type=__snake_case , default=__snake_case , help="If passed, the training will stop after this number of epochs." , )
parser.add_argument(
"--num_epochs" , type=__snake_case , default=2 , help="Number of train epochs." , )
snake_case__ :Any = parser.parse_args()
snake_case__ :int = {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 42, "batch_size": 16}
training_function(__snake_case , __snake_case )
if __name__ == "__main__":
main()
| 57
| 1
|
import warnings
from ...utils import logging
from .image_processing_clip import CLIPImageProcessor
__UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
class _snake_case ( _A ):
def __init__( self ,*UpperCamelCase ,**UpperCamelCase ) -> None:
warnings.warn(
"The class CLIPFeatureExtractor is deprecated and will be removed in version 5 of Transformers. Please"
" use CLIPImageProcessor instead." ,UpperCamelCase ,)
super().__init__(*UpperCamelCase ,**UpperCamelCase )
| 57
|
from __future__ import annotations
class _snake_case :
def __init__( self ,UpperCamelCase ) -> None:
snake_case__ :Union[str, Any] = data
snake_case__ :Node | None = None
snake_case__ :Node | None = None
def lowercase_ ( __snake_case : Node | None ) -> None: # In Order traversal of the tree
'''simple docstring'''
if tree:
display(tree.left )
print(tree.data )
display(tree.right )
def lowercase_ ( __snake_case : Node | None ) -> int:
'''simple docstring'''
return 1 + max(depth_of_tree(tree.left ) , depth_of_tree(tree.right ) ) if tree else 0
def lowercase_ ( __snake_case : Node ) -> bool:
'''simple docstring'''
if not tree:
return True
if tree.left and tree.right:
return is_full_binary_tree(tree.left ) and is_full_binary_tree(tree.right )
else:
return not tree.left and not tree.right
def lowercase_ ( ) -> None: # Main function for testing.
'''simple docstring'''
snake_case__ :Dict = Node(1 )
snake_case__ :int = Node(2 )
snake_case__ :Optional[Any] = Node(3 )
snake_case__ :Tuple = Node(4 )
snake_case__ :str = Node(5 )
snake_case__ :Optional[Any] = Node(6 )
snake_case__ :List[Any] = Node(7 )
snake_case__ :List[str] = Node(8 )
snake_case__ :Tuple = Node(9 )
print(is_full_binary_tree(__snake_case ) )
print(depth_of_tree(__snake_case ) )
print("Tree is: " )
display(__snake_case )
if __name__ == "__main__":
main()
| 57
| 1
|
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def lowercase_ ( __snake_case : str ) -> None:
'''simple docstring'''
snake_case__ , snake_case__ :List[Any] = analyze_text(__snake_case )
snake_case__ :List[str] = list(" " + ascii_lowercase )
# what is our total sum of probabilities.
snake_case__ :int = sum(single_char_strings.values() )
# one length string
snake_case__ :Optional[int] = 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:
snake_case__ :Union[str, Any] = single_char_strings[ch]
snake_case__ :Dict = my_str / all_sum
my_fir_sum += prob * math.loga(__snake_case ) # entropy formula.
# print entropy
print(F'{round(-1 * my_fir_sum ):.1f}' )
# two len string
snake_case__ :int = sum(two_char_strings.values() )
snake_case__ :List[Any] = 0
# for each alpha (two in size) calculate entropy.
for cha in my_alphas:
for cha in my_alphas:
snake_case__ :str = cha + cha
if sequence in two_char_strings:
snake_case__ :Dict = two_char_strings[sequence]
snake_case__ :Any = int(__snake_case ) / all_sum
my_sec_sum += prob * math.loga(__snake_case )
# 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_ ( __snake_case : str ) -> tuple[dict, dict]:
'''simple docstring'''
snake_case__ :List[str] = Counter() # type: ignore
snake_case__ :List[str] = 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(__snake_case ) - 1 ):
single_char_strings[text[i]] += 1
two_char_strings[text[i : i + 2]] += 1
return single_char_strings, two_char_strings
def lowercase_ ( ) -> str:
'''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()
| 57
|
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
__UpperCAmelCase : List[Any] = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
__UpperCAmelCase : int = [file for file in filepaths if file != file.lower()]
if upper_files:
print(F'''{len(upper_files)} files contain uppercase characters:''')
print("\n".join(upper_files) + "\n")
__UpperCAmelCase : Any = [file for file in filepaths if " " in file]
if space_files:
print(F'''{len(space_files)} files contain space characters:''')
print("\n".join(space_files) + "\n")
__UpperCAmelCase : str = [file for file in filepaths if "-" in file]
if hyphen_files:
print(F'''{len(hyphen_files)} files contain hyphen characters:''')
print("\n".join(hyphen_files) + "\n")
__UpperCAmelCase : Dict = [file for file in filepaths if os.sep not in file]
if nodir_files:
print(F'''{len(nodir_files)} files are not in a directory:''')
print("\n".join(nodir_files) + "\n")
__UpperCAmelCase : int = len(upper_files + space_files + hyphen_files + nodir_files)
if bad_files:
import sys
sys.exit(bad_files)
| 57
| 1
|
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
logging.set_verbosity_info()
def lowercase_ ( __snake_case : str , __snake_case : str , __snake_case : str ) -> Union[str, Any]:
'''simple docstring'''
def get_masked_lm_array(__snake_case : str ):
snake_case__ :Tuple = F'masked_lm/{name}/.ATTRIBUTES/VARIABLE_VALUE'
snake_case__ :Tuple = tf.train.load_variable(__snake_case , __snake_case )
if "kernel" in name:
snake_case__ :List[str] = array.transpose()
return torch.from_numpy(__snake_case )
def get_encoder_array(__snake_case : str ):
snake_case__ :Optional[Any] = F'encoder/{name}/.ATTRIBUTES/VARIABLE_VALUE'
snake_case__ :Union[str, Any] = tf.train.load_variable(__snake_case , __snake_case )
if "kernel" in name:
snake_case__ :Optional[int] = array.transpose()
return torch.from_numpy(__snake_case )
def get_encoder_layer_array(__snake_case : int , __snake_case : str ):
snake_case__ :Dict = F'encoder/_transformer_layers/{layer_index}/{name}/.ATTRIBUTES/VARIABLE_VALUE'
snake_case__ :Optional[int] = tf.train.load_variable(__snake_case , __snake_case )
if "kernel" in name:
snake_case__ :Any = array.transpose()
return torch.from_numpy(__snake_case )
def get_encoder_attention_layer_array(__snake_case : int , __snake_case : str , __snake_case : Dict ):
snake_case__ :List[Any] = F'encoder/_transformer_layers/{layer_index}/_attention_layer/{name}/.ATTRIBUTES/VARIABLE_VALUE'
snake_case__ :Optional[Any] = tf.train.load_variable(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = array.reshape(__snake_case )
if "kernel" in name:
snake_case__ :Optional[Any] = array.transpose()
return torch.from_numpy(__snake_case )
print(F'Loading model based on config from {config_path}...' )
snake_case__ :Optional[Any] = BertConfig.from_json_file(__snake_case )
snake_case__ :Any = BertForMaskedLM(__snake_case )
# Layers
for layer_index in range(0 , config.num_hidden_layers ):
snake_case__ :BertLayer = model.bert.encoder.layer[layer_index]
# Self-attention
snake_case__ :BertSelfAttention = layer.attention.self
snake_case__ :Union[str, Any] = get_encoder_attention_layer_array(
__snake_case , "_query_dense/kernel" , self_attn.query.weight.data.shape )
snake_case__ :Any = get_encoder_attention_layer_array(
__snake_case , "_query_dense/bias" , self_attn.query.bias.data.shape )
snake_case__ :Optional[Any] = get_encoder_attention_layer_array(
__snake_case , "_key_dense/kernel" , self_attn.key.weight.data.shape )
snake_case__ :Tuple = get_encoder_attention_layer_array(
__snake_case , "_key_dense/bias" , self_attn.key.bias.data.shape )
snake_case__ :Any = get_encoder_attention_layer_array(
__snake_case , "_value_dense/kernel" , self_attn.value.weight.data.shape )
snake_case__ :Optional[Any] = get_encoder_attention_layer_array(
__snake_case , "_value_dense/bias" , self_attn.value.bias.data.shape )
# Self-attention Output
snake_case__ :BertSelfOutput = layer.attention.output
snake_case__ :Optional[Any] = get_encoder_attention_layer_array(
__snake_case , "_output_dense/kernel" , self_output.dense.weight.data.shape )
snake_case__ :Tuple = get_encoder_attention_layer_array(
__snake_case , "_output_dense/bias" , self_output.dense.bias.data.shape )
snake_case__ :Any = get_encoder_layer_array(__snake_case , "_attention_layer_norm/gamma" )
snake_case__ :Dict = get_encoder_layer_array(__snake_case , "_attention_layer_norm/beta" )
# Intermediate
snake_case__ :BertIntermediate = layer.intermediate
snake_case__ :Optional[int] = get_encoder_layer_array(__snake_case , "_intermediate_dense/kernel" )
snake_case__ :Union[str, Any] = get_encoder_layer_array(__snake_case , "_intermediate_dense/bias" )
# Output
snake_case__ :BertOutput = layer.output
snake_case__ :Any = get_encoder_layer_array(__snake_case , "_output_dense/kernel" )
snake_case__ :Any = get_encoder_layer_array(__snake_case , "_output_dense/bias" )
snake_case__ :Any = get_encoder_layer_array(__snake_case , "_output_layer_norm/gamma" )
snake_case__ :Any = get_encoder_layer_array(__snake_case , "_output_layer_norm/beta" )
# Embeddings
snake_case__ :Dict = get_encoder_array("_position_embedding_layer/embeddings" )
snake_case__ :Any = get_encoder_array("_type_embedding_layer/embeddings" )
snake_case__ :Dict = get_encoder_array("_embedding_norm_layer/gamma" )
snake_case__ :List[str] = get_encoder_array("_embedding_norm_layer/beta" )
# LM Head
snake_case__ :Any = model.cls.predictions.transform
snake_case__ :List[str] = get_masked_lm_array("dense/kernel" )
snake_case__ :Optional[int] = get_masked_lm_array("dense/bias" )
snake_case__ :List[Any] = get_masked_lm_array("layer_norm/gamma" )
snake_case__ :Optional[Any] = get_masked_lm_array("layer_norm/beta" )
snake_case__ :Optional[Any] = get_masked_lm_array("embedding_table" )
# Pooling
snake_case__ :Union[str, Any] = BertPooler(config=__snake_case )
snake_case__ :BertPooler = get_encoder_array("_pooler_layer/kernel" )
snake_case__ :BertPooler = get_encoder_array("_pooler_layer/bias" )
# Export final model
model.save_pretrained(__snake_case )
# Integration test - should load without any errors ;)
snake_case__ :str = BertForMaskedLM.from_pretrained(__snake_case )
print(new_model.eval() )
print("Model conversion was done sucessfully!" )
if __name__ == "__main__":
__UpperCAmelCase : Tuple = argparse.ArgumentParser()
parser.add_argument(
"--tf_checkpoint_path", type=str, required=True, help="Path to the TensorFlow Token Dropping checkpoint path."
)
parser.add_argument(
"--bert_config_file",
type=str,
required=True,
help="The config json file corresponding to the BERT model. This specifies the model architecture.",
)
parser.add_argument(
"--pytorch_dump_path",
type=str,
required=True,
help="Path to the output PyTorch model.",
)
__UpperCAmelCase : List[str] = parser.parse_args()
convert_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)
| 57
|
def lowercase_ ( __snake_case : Tuple , __snake_case : Optional[int] ) -> List[Any]:
'''simple docstring'''
snake_case__ :Dict = ""
for i in table:
res += inp[i - 1]
return res
def lowercase_ ( __snake_case : List[str] ) -> int:
'''simple docstring'''
return data[1:] + data[0]
def lowercase_ ( __snake_case : int , __snake_case : Union[str, Any] ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :Union[str, Any] = ""
for i in range(len(__snake_case ) ):
if a[i] == b[i]:
res += "0"
else:
res += "1"
return res
def lowercase_ ( __snake_case : Optional[int] , __snake_case : Dict ) -> Union[str, Any]:
'''simple docstring'''
snake_case__ :int = int("0b" + data[0] + data[-1] , 2 )
snake_case__ :Union[str, Any] = int("0b" + data[1:3] , 2 )
return bin(s[row][col] )[2:]
def lowercase_ ( __snake_case : Dict , __snake_case : Optional[Any] , __snake_case : Dict , __snake_case : List[Any] , __snake_case : Optional[int] ) -> List[str]:
'''simple docstring'''
snake_case__ :Tuple = message[:4]
snake_case__ :int = message[4:]
snake_case__ :int = apply_table(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = xor(__snake_case , __snake_case )
snake_case__ :Tuple = apply_sbox(__snake_case , temp[:4] ) # noqa: E741
snake_case__ :List[str] = apply_sbox(__snake_case , temp[4:] )
snake_case__ :int = "0" * (2 - len(__snake_case )) + l # noqa: E741
snake_case__ :int = "0" * (2 - len(__snake_case )) + r
snake_case__ :Optional[Any] = apply_table(l + r , __snake_case )
snake_case__ :Tuple = xor(__snake_case , __snake_case )
return temp + right
if __name__ == "__main__":
__UpperCAmelCase : Dict = input("Enter 10 bit key: ")
__UpperCAmelCase : Tuple = input("Enter 8 bit message: ")
__UpperCAmelCase : Any = [6, 3, 7, 4, 8, 5, 1_0, 9]
__UpperCAmelCase : List[str] = [3, 5, 2, 7, 4, 1_0, 1, 9, 8, 6]
__UpperCAmelCase : Tuple = [2, 4, 3, 1]
__UpperCAmelCase : List[Any] = [2, 6, 3, 1, 4, 8, 5, 7]
__UpperCAmelCase : Optional[Any] = [4, 1, 3, 5, 7, 2, 8, 6]
__UpperCAmelCase : Optional[int] = [4, 1, 2, 3, 2, 3, 4, 1]
__UpperCAmelCase : List[Any] = [[1, 0, 3, 2], [3, 2, 1, 0], [0, 2, 1, 3], [3, 1, 3, 2]]
__UpperCAmelCase : Union[str, Any] = [[0, 1, 2, 3], [2, 0, 1, 3], [3, 0, 1, 0], [2, 1, 0, 3]]
# key generation
__UpperCAmelCase : int = apply_table(key, paa_table)
__UpperCAmelCase : Dict = temp[:5]
__UpperCAmelCase : Optional[int] = temp[5:]
__UpperCAmelCase : Optional[int] = left_shift(left)
__UpperCAmelCase : Union[str, Any] = left_shift(right)
__UpperCAmelCase : int = apply_table(left + right, pa_table)
__UpperCAmelCase : Tuple = left_shift(left)
__UpperCAmelCase : Union[str, Any] = left_shift(right)
__UpperCAmelCase : Dict = left_shift(left)
__UpperCAmelCase : Optional[Any] = left_shift(right)
__UpperCAmelCase : Optional[int] = apply_table(left + right, pa_table)
# encryption
__UpperCAmelCase : Tuple = apply_table(message, IP)
__UpperCAmelCase : Tuple = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : List[Any] = temp[4:] + temp[:4]
__UpperCAmelCase : int = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : Union[str, Any] = apply_table(temp, IP_inv)
print("Cipher text is:", CT)
# decryption
__UpperCAmelCase : List[Any] = apply_table(CT, IP)
__UpperCAmelCase : List[Any] = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : int = temp[4:] + temp[:4]
__UpperCAmelCase : Union[str, Any] = function(expansion, sa, sa, keya, temp)
__UpperCAmelCase : Union[str, Any] = apply_table(temp, IP_inv)
print("Plain text after decypting is:", PT)
| 57
| 1
|
def lowercase_ ( __snake_case : int = 10_00 ) -> int:
'''simple docstring'''
snake_case__ :Union[str, Any] = 2**power
snake_case__ :List[str] = 0
while n:
snake_case__ , snake_case__ :Dict = r + n % 10, n // 10
return r
if __name__ == "__main__":
print(solution(int(str(input()).strip())))
| 57
|
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _snake_case ( _A , _A , _A ):
@register_to_config
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = False ,) -> int:
super().__init__()
snake_case__ :Union[str, Any] = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :int = nn.Embedding(UpperCamelCase ,UpperCamelCase )
snake_case__ :Any = False
snake_case__ :List[Any] = nn.Dropout(p=UpperCamelCase )
snake_case__ :Tuple = TaConfig(
vocab_size=UpperCamelCase ,d_model=UpperCamelCase ,num_heads=UpperCamelCase ,d_kv=UpperCamelCase ,d_ff=UpperCamelCase ,dropout_rate=UpperCamelCase ,feed_forward_proj=UpperCamelCase ,is_decoder=UpperCamelCase ,is_encoder_decoder=UpperCamelCase ,)
snake_case__ :List[str] = nn.ModuleList()
for lyr_num in range(UpperCamelCase ):
snake_case__ :List[Any] = TaBlock(UpperCamelCase )
self.encoders.append(UpperCamelCase )
snake_case__ :Optional[Any] = TaLayerNorm(UpperCamelCase )
snake_case__ :Any = nn.Dropout(p=UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ) -> int:
snake_case__ :str = self.token_embedder(UpperCamelCase )
snake_case__ :int = encoder_input_tokens.shape[1]
snake_case__ :List[Any] = torch.arange(UpperCamelCase ,device=encoder_input_tokens.device )
x += self.position_encoding(UpperCamelCase )
snake_case__ :Optional[int] = self.dropout_pre(UpperCamelCase )
# inverted the attention mask
snake_case__ :Optional[Any] = encoder_input_tokens.size()
snake_case__ :Dict = self.get_extended_attention_mask(UpperCamelCase ,UpperCamelCase )
for lyr in self.encoders:
snake_case__ :str = lyr(UpperCamelCase ,UpperCamelCase )[0]
snake_case__ :List[Any] = self.layer_norm(UpperCamelCase )
return self.dropout_post(UpperCamelCase ), encoder_inputs_mask
| 57
| 1
|
import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import patch_environment
@require_huggingface_suite
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> Any:
snake_case__ :Any = inspect.getfile(accelerate.test_utils )
snake_case__ :List[Any] = os.path.sep.join(
mod_file.split(os.path.sep )[:-1] + ["scripts", "external_deps", "test_metrics.py"] )
from accelerate.test_utils.scripts.external_deps import test_metrics # noqa: F401
snake_case__ :List[Any] = test_metrics
@require_cpu
def lowerCAmelCase_ ( self ) -> int:
debug_launcher(self.test_metrics.main ,num_processes=1 )
@require_cpu
def lowerCAmelCase_ ( self ) -> List[Any]:
debug_launcher(self.test_metrics.main )
@require_single_gpu
def lowerCAmelCase_ ( self ) -> Tuple:
self.test_metrics.main()
@require_multi_gpu
def lowerCAmelCase_ ( self ) -> Tuple:
print(f'Found {torch.cuda.device_count()} devices.' )
snake_case__ :Optional[int] = ["torchrun", f'--nproc_per_node={torch.cuda.device_count()}', self.test_file_path]
with patch_environment(omp_num_threads=1 ):
execute_subprocess_async(UpperCamelCase ,env=os.environ.copy() )
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|
__UpperCAmelCase : int = {"a": ["c", "b"], "b": ["d", "e"], "c": [], "d": [], "e": []}
__UpperCAmelCase : List[str] = ["a", "b", "c", "d", "e"]
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : List[Any] , __snake_case : Tuple ) -> Optional[int]:
'''simple docstring'''
snake_case__ :List[Any] = start
# add current to visited
visited.append(__snake_case )
snake_case__ :List[str] = edges[current]
for neighbor in neighbors:
# if neighbor not in visited, visit
if neighbor not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# if all neighbors visited add current to sort
sort.append(__snake_case )
# if all vertices haven't been visited select a new one to visit
if len(__snake_case ) != len(__snake_case ):
for vertice in vertices:
if vertice not in visited:
snake_case__ :Any = topological_sort(__snake_case , __snake_case , __snake_case )
# return sort
return sort
if __name__ == "__main__":
__UpperCAmelCase : Tuple = topological_sort("a", [], [])
print(sort)
| 57
| 1
|
def lowercase_ ( ) -> List[str]:
'''simple docstring'''
snake_case__ :Optional[int] = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
snake_case__ :Dict = 6
snake_case__ :Tuple = 1
snake_case__ :Optional[int] = 19_01
snake_case__ :List[str] = 0
while year < 20_01:
day += 7
if (year % 4 == 0 and year % 1_00 != 0) or (year % 4_00 == 0):
if day > days_per_month[month - 1] and month != 2:
month += 1
snake_case__ :Tuple = day - days_per_month[month - 2]
elif day > 29 and month == 2:
month += 1
snake_case__ :List[str] = day - 29
else:
if day > days_per_month[month - 1]:
month += 1
snake_case__ :List[Any] = day - days_per_month[month - 2]
if month > 12:
year += 1
snake_case__ :str = 1
if year < 20_01 and day == 1:
sundays += 1
return sundays
if __name__ == "__main__":
print(solution())
| 57
|
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 _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ) -> int:
# clean up the VRAM after each test
super().tearDown()
gc.collect()
def lowerCAmelCase_ ( self ) -> str:
snake_case__ , snake_case__ :Tuple = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-canny" ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ , snake_case__ :Any = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCamelCase ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ :List[str] = controlnet_params
snake_case__ :Union[str, Any] = "bird"
snake_case__ :Optional[int] = jax.device_count()
snake_case__ :Tuple = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case__ :Union[str, Any] = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png" )
snake_case__ :str = pipe.prepare_image_inputs([canny_image] * num_samples )
snake_case__ :List[str] = jax.random.PRNGKey(0 )
snake_case__ :str = jax.random.split(UpperCamelCase ,jax.device_count() )
snake_case__ :int = replicate(UpperCamelCase )
snake_case__ :Any = shard(UpperCamelCase )
snake_case__ :Any = shard(UpperCamelCase )
snake_case__ :str = pipe(
prompt_ids=UpperCamelCase ,image=UpperCamelCase ,params=UpperCamelCase ,prng_seed=UpperCamelCase ,num_inference_steps=50 ,jit=UpperCamelCase ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
snake_case__ :List[str] = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case__ :Any = images[0, 253:256, 253:256, -1]
snake_case__ :Union[str, Any] = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case__ :List[Any] = jnp.array(
[0.167969, 0.116699, 0.081543, 0.154297, 0.132812, 0.108887, 0.169922, 0.169922, 0.205078] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
def lowerCAmelCase_ ( self ) -> Optional[int]:
snake_case__ , snake_case__ :List[str] = FlaxControlNetModel.from_pretrained(
"lllyasviel/sd-controlnet-openpose" ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ , snake_case__ :Optional[Any] = FlaxStableDiffusionControlNetPipeline.from_pretrained(
"runwayml/stable-diffusion-v1-5" ,controlnet=UpperCamelCase ,from_pt=UpperCamelCase ,dtype=jnp.bfloataa )
snake_case__ :str = controlnet_params
snake_case__ :int = "Chef in the kitchen"
snake_case__ :List[Any] = jax.device_count()
snake_case__ :Dict = pipe.prepare_text_inputs([prompts] * num_samples )
snake_case__ :Any = load_image(
"https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png" )
snake_case__ :Optional[int] = pipe.prepare_image_inputs([pose_image] * num_samples )
snake_case__ :List[str] = jax.random.PRNGKey(0 )
snake_case__ :Any = jax.random.split(UpperCamelCase ,jax.device_count() )
snake_case__ :Dict = replicate(UpperCamelCase )
snake_case__ :Tuple = shard(UpperCamelCase )
snake_case__ :Optional[int] = shard(UpperCamelCase )
snake_case__ :Optional[Any] = pipe(
prompt_ids=UpperCamelCase ,image=UpperCamelCase ,params=UpperCamelCase ,prng_seed=UpperCamelCase ,num_inference_steps=50 ,jit=UpperCamelCase ,).images
assert images.shape == (jax.device_count(), 1, 768, 512, 3)
snake_case__ :int = images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:] )
snake_case__ :List[str] = images[0, 253:256, 253:256, -1]
snake_case__ :Tuple = jnp.asarray(jax.device_get(image_slice.flatten() ) )
snake_case__ :List[str] = jnp.array(
[[0.271484, 0.261719, 0.275391, 0.277344, 0.279297, 0.291016, 0.294922, 0.302734, 0.302734]] )
print(f'output_slice: {output_slice}' )
assert jnp.abs(output_slice - expected_slice ).max() < 1E-2
| 57
| 1
|
def lowercase_ ( __snake_case : int = 10_00 ) -> int:
'''simple docstring'''
snake_case__ :Union[str, Any] = 2**power
snake_case__ :Any = str(__snake_case )
snake_case__ :Tuple = list(__snake_case )
snake_case__ :str = 0
for i in list_num:
sum_of_num += int(__snake_case )
return sum_of_num
if __name__ == "__main__":
__UpperCAmelCase : Any = int(input("Enter the power of 2: ").strip())
print("2 ^ ", power, " = ", 2**power)
__UpperCAmelCase : Tuple = solution(power)
print("Sum of the digits is: ", result)
| 57
|
def lowercase_ ( __snake_case : list ) -> list:
'''simple docstring'''
if any(not isinstance(__snake_case , __snake_case ) or x < 0 for x in sequence ):
raise TypeError("Sequence must be list of non-negative integers" )
for _ in range(len(__snake_case ) ):
for i, (rod_upper, rod_lower) in enumerate(zip(__snake_case , sequence[1:] ) ):
if rod_upper > rod_lower:
sequence[i] -= rod_upper - rod_lower
sequence[i + 1] += rod_upper - rod_lower
return sequence
if __name__ == "__main__":
assert bead_sort([5, 4, 3, 2, 1]) == [1, 2, 3, 4, 5]
assert bead_sort([7, 9, 4, 3, 5]) == [3, 4, 5, 7, 9]
| 57
| 1
|
from __future__ import annotations
import time
import numpy as np
__UpperCAmelCase : Dict = [8, 5, 9, 7]
__UpperCAmelCase : List[Any] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
__UpperCAmelCase : Any = [
[3, 2, 1, 4],
[0, 2, 5, 2],
[5, 1, 0, 5],
[1, 5, 3, 0],
[3, 0, 3, 3],
]
class _snake_case :
def __init__( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,) -> None:
snake_case__ :Optional[int] = claim_vector
snake_case__ :Any = allocated_resources_table
snake_case__ :Dict = maximum_claim_table
def lowerCAmelCase_ ( self ) -> list[int]:
return [
sum(p_item[i] for p_item in self.__allocated_resources_table )
for i in range(len(self.__allocated_resources_table[0] ) )
]
def lowerCAmelCase_ ( self ) -> list[int]:
return np.array(self.__claim_vector ) - np.array(
self.__processes_resource_summation() )
def lowerCAmelCase_ ( self ) -> list[list[int]]:
return [
list(np.array(self.__maximum_claim_table[i] ) - np.array(UpperCamelCase ) )
for i, allocated_resource in enumerate(self.__allocated_resources_table )
]
def lowerCAmelCase_ ( self ) -> dict[int, list[int]]:
return {self.__need().index(UpperCamelCase ): i for i in self.__need()}
def lowerCAmelCase_ ( self ,**UpperCamelCase ) -> None:
snake_case__ :Optional[Any] = self.__need()
snake_case__ :Any = self.__allocated_resources_table
snake_case__ :Optional[int] = self.__available_resources()
snake_case__ :Dict = self.__need_index_manager()
for kw, val in kwargs.items():
if kw and val is True:
self.__pretty_data()
print("_" * 50 + "\n" )
while need_list:
snake_case__ :int = False
for each_need in need_list:
snake_case__ :int = True
for index, need in enumerate(UpperCamelCase ):
if need > available_resources[index]:
snake_case__ :Tuple = False
break
if execution:
snake_case__ :List[Any] = True
# get the original index of the process from ind_ctrl db
for original_need_index, need_clone in need_index_manager.items():
if each_need == need_clone:
snake_case__ :Any = original_need_index
print(f'Process {process_number + 1} is executing.' )
# remove the process run from stack
need_list.remove(UpperCamelCase )
# update available/freed resources stack
snake_case__ :List[str] = np.array(UpperCamelCase ) + np.array(
alloc_resources_table[process_number] )
print(
"Updated available resource stack for processes: "
+ " ".join([str(UpperCamelCase ) for x in available_resources] ) )
break
if safe:
print("The process is in a safe state.\n" )
else:
print("System in unsafe state. Aborting...\n" )
break
def lowerCAmelCase_ ( self ) -> List[Any]:
print(" " * 9 + "Allocated Resource Table" )
for item in self.__allocated_resources_table:
print(
f'P{self.__allocated_resources_table.index(UpperCamelCase ) + 1}'
+ " ".join(f'{it:>8}' for it in item )
+ "\n" )
print(" " * 9 + "System Resource Table" )
for item in self.__maximum_claim_table:
print(
f'P{self.__maximum_claim_table.index(UpperCamelCase ) + 1}'
+ " ".join(f'{it:>8}' for it in item )
+ "\n" )
print(
"Current Usage by Active Processes: "
+ " ".join(str(UpperCamelCase ) for x in self.__claim_vector ) )
print(
"Initial Available Resources: "
+ " ".join(str(UpperCamelCase ) for x in self.__available_resources() ) )
time.sleep(1 )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
|
from __future__ import annotations
def lowercase_ ( __snake_case : list ) -> float:
'''simple docstring'''
if not nums:
raise ValueError("List is empty" )
return sum(__snake_case ) / len(__snake_case )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 57
| 1
|
import os
import tempfile
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from torch import nn
from transformers import (
Adafactor,
AdamW,
get_constant_schedule,
get_constant_schedule_with_warmup,
get_cosine_schedule_with_warmup,
get_cosine_with_hard_restarts_schedule_with_warmup,
get_inverse_sqrt_schedule,
get_linear_schedule_with_warmup,
get_polynomial_decay_schedule_with_warmup,
)
def lowercase_ ( __snake_case : Optional[int] , __snake_case : List[str]=10 ) -> Optional[Any]:
'''simple docstring'''
snake_case__ :Any = []
for _ in range(__snake_case ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
return lrs
def lowercase_ ( __snake_case : str , __snake_case : Union[str, Any]=10 ) -> str:
'''simple docstring'''
snake_case__ :List[Any] = []
for step in range(__snake_case ):
lrs.append(scheduler.get_lr()[0] )
scheduler.step()
if step == num_steps // 2:
with tempfile.TemporaryDirectory() as tmpdirname:
snake_case__ :Union[str, Any] = os.path.join(__snake_case , "schedule.bin" )
torch.save(scheduler.state_dict() , __snake_case )
snake_case__ :List[Any] = torch.load(__snake_case )
scheduler.load_state_dict(__snake_case )
return lrs
@require_torch
class _snake_case ( unittest.TestCase ):
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ) -> List[str]:
self.assertEqual(len(UpperCamelCase ) ,len(UpperCamelCase ) )
for a, b in zip(UpperCamelCase ,UpperCamelCase ):
self.assertAlmostEqual(UpperCamelCase ,UpperCamelCase ,delta=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> str:
snake_case__ :Union[str, Any] = torch.tensor([0.1, -0.2, -0.1] ,requires_grad=UpperCamelCase )
snake_case__ :List[Any] = torch.tensor([0.4, 0.2, -0.5] )
snake_case__ :int = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
snake_case__ :List[str] = AdamW(params=[w] ,lr=2E-1 ,weight_decay=0.0 )
for _ in range(100 ):
snake_case__ :str = criterion(UpperCamelCase ,UpperCamelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() ,[0.4, 0.2, -0.5] ,tol=1E-2 )
def lowerCAmelCase_ ( self ) -> int:
snake_case__ :int = torch.tensor([0.1, -0.2, -0.1] ,requires_grad=UpperCamelCase )
snake_case__ :Optional[Any] = torch.tensor([0.4, 0.2, -0.5] )
snake_case__ :Optional[int] = nn.MSELoss()
# No warmup, constant schedule, no gradient clipping
snake_case__ :Any = Adafactor(
params=[w] ,lr=1E-2 ,eps=(1E-30, 1E-3) ,clip_threshold=1.0 ,decay_rate=-0.8 ,betaa=UpperCamelCase ,weight_decay=0.0 ,relative_step=UpperCamelCase ,scale_parameter=UpperCamelCase ,warmup_init=UpperCamelCase ,)
for _ in range(1_000 ):
snake_case__ :Any = criterion(UpperCamelCase ,UpperCamelCase )
loss.backward()
optimizer.step()
w.grad.detach_() # No zero_grad() function on simple tensors. we do it ourselves.
w.grad.zero_()
self.assertListAlmostEqual(w.tolist() ,[0.4, 0.2, -0.5] ,tol=1E-2 )
@require_torch
class _snake_case ( unittest.TestCase ):
_A = nn.Linear(50 , 50 ) if is_torch_available() else None
_A = AdamW(m.parameters() , lr=1_0.0 ) if is_torch_available() else None
_A = 10
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase=None ) -> int:
self.assertEqual(len(UpperCamelCase ) ,len(UpperCamelCase ) )
for a, b in zip(UpperCamelCase ,UpperCamelCase ):
self.assertAlmostEqual(UpperCamelCase ,UpperCamelCase ,delta=UpperCamelCase ,msg=UpperCamelCase )
def lowerCAmelCase_ ( self ) -> Optional[Any]:
snake_case__ :Dict = {"num_warmup_steps": 2, "num_training_steps": 10}
# schedulers doct format
# function: (sched_args_dict, expected_learning_rates)
snake_case__ :Dict = {
get_constant_schedule: ({}, [10.0] * self.num_steps),
get_constant_schedule_with_warmup: (
{"num_warmup_steps": 4},
[0.0, 2.5, 5.0, 7.5, 10.0, 10.0, 10.0, 10.0, 10.0, 10.0],
),
get_linear_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 8.75, 7.5, 6.25, 5.0, 3.75, 2.5, 1.25],
),
get_cosine_schedule_with_warmup: (
{**common_kwargs},
[0.0, 5.0, 10.0, 9.61, 8.53, 6.91, 5.0, 3.08, 1.46, 0.38],
),
get_cosine_with_hard_restarts_schedule_with_warmup: (
{**common_kwargs, "num_cycles": 2},
[0.0, 5.0, 10.0, 8.53, 5.0, 1.46, 10.0, 8.53, 5.0, 1.46],
),
get_polynomial_decay_schedule_with_warmup: (
{**common_kwargs, "power": 2.0, "lr_end": 1E-7},
[0.0, 5.0, 10.0, 7.656, 5.625, 3.906, 2.5, 1.406, 0.625, 0.156],
),
get_inverse_sqrt_schedule: (
{"num_warmup_steps": 2},
[0.0, 5.0, 10.0, 8.165, 7.071, 6.325, 5.774, 5.345, 5.0, 4.714],
),
}
for scheduler_func, data in scheds.items():
snake_case__ , snake_case__ :List[Any] = data
snake_case__ :List[str] = scheduler_func(self.optimizer ,**UpperCamelCase )
self.assertEqual(len([scheduler.get_lr()[0]] ) ,1 )
snake_case__ :int = unwrap_schedule(UpperCamelCase ,self.num_steps )
self.assertListAlmostEqual(
UpperCamelCase ,UpperCamelCase ,tol=1E-2 ,msg=f'failed for {scheduler_func} in normal scheduler' ,)
snake_case__ :str = scheduler_func(self.optimizer ,**UpperCamelCase )
if scheduler_func.__name__ != "get_constant_schedule":
LambdaScheduleWrapper.wrap_scheduler(UpperCamelCase ) # wrap to test picklability of the schedule
snake_case__ :Dict = unwrap_and_save_reload_schedule(UpperCamelCase ,self.num_steps )
self.assertListEqual(UpperCamelCase ,UpperCamelCase ,msg=f'failed for {scheduler_func} in save and reload' )
class _snake_case :
def __init__( self ,UpperCamelCase ) -> List[str]:
snake_case__ :Any = fn
def __call__( self ,*UpperCamelCase ,**UpperCamelCase ) -> Dict:
return self.fn(*UpperCamelCase ,**UpperCamelCase )
@classmethod
def lowerCAmelCase_ ( self ,UpperCamelCase ) -> Any:
snake_case__ :Dict = list(map(self ,scheduler.lr_lambdas ) )
| 57
|
from __future__ import annotations
import math
def lowercase_ ( __snake_case : int , __snake_case : int , __snake_case : bool , __snake_case : list[int] , __snake_case : float ) -> int:
'''simple docstring'''
if depth < 0:
raise ValueError("Depth cannot be less than 0" )
if len(__snake_case ) == 0:
raise ValueError("Scores cannot be empty" )
if depth == height:
return scores[node_index]
if is_max:
return max(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
return min(
minimax(depth + 1 , node_index * 2 , __snake_case , __snake_case , __snake_case ) , minimax(depth + 1 , node_index * 2 + 1 , __snake_case , __snake_case , __snake_case ) , )
def lowercase_ ( ) -> None:
'''simple docstring'''
snake_case__ :List[Any] = [90, 23, 6, 33, 21, 65, 1_23, 3_44_23]
snake_case__ :int = math.log(len(__snake_case ) , 2 )
print("Optimal value : " , end="" )
print(minimax(0 , 0 , __snake_case , __snake_case , __snake_case ) )
if __name__ == "__main__":
import doctest
doctest.testmod()
main()
| 57
| 1
|
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def lowercase_ ( __snake_case : List[str] ) -> str:
'''simple docstring'''
snake_case__ :str = {}
snake_case__ :Optional[int] = job["started_at"]
snake_case__ :int = job["completed_at"]
snake_case__ :Optional[Any] = date_parser.parse(SCREAMING_SNAKE_CASE_ )
snake_case__ :List[str] = date_parser.parse(SCREAMING_SNAKE_CASE_ )
snake_case__ :str = round((end_datetime - start_datetime).total_seconds() / 6_0.0 )
snake_case__ :List[str] = start
snake_case__ :List[Any] = end
snake_case__ :str = duration_in_min
return job_info
def lowercase_ ( __snake_case : Any , __snake_case : List[Any]=None ) -> str:
'''simple docstring'''
snake_case__ :List[Any] = None
if token is not None:
snake_case__ :Any = {"Accept": "application/vnd.github+json", "Authorization": F'Bearer {token}'}
snake_case__ :List[Any] = F'https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'
snake_case__ :str = requests.get(SCREAMING_SNAKE_CASE_ , headers=SCREAMING_SNAKE_CASE_ ).json()
snake_case__ :str = {}
try:
job_time.update({job["name"]: extract_time_from_single_job(SCREAMING_SNAKE_CASE_ ) for job in result["jobs"]} )
snake_case__ :Optional[Any] = math.ceil((result["total_count"] - 1_00) / 1_00 )
for i in range(SCREAMING_SNAKE_CASE_ ):
snake_case__ :List[str] = requests.get(url + F'&page={i + 2}' , headers=SCREAMING_SNAKE_CASE_ ).json()
job_time.update({job["name"]: extract_time_from_single_job(SCREAMING_SNAKE_CASE_ ) for job in result["jobs"]} )
return job_time
except Exception:
print(F'Unknown error, could not fetch links:\n{traceback.format_exc()}' )
return {}
if __name__ == "__main__":
__UpperCAmelCase : int = argparse.ArgumentParser()
# Required parameters
parser.add_argument("--workflow_run_id", type=str, required=True, help="A GitHub Actions workflow run id.")
__UpperCAmelCase : int = parser.parse_args()
__UpperCAmelCase : Optional[int] = get_job_time(args.workflow_run_id)
__UpperCAmelCase : Optional[Any] = dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))
for k, v in job_time.items():
print(F'''{k}: {v['duration']}''')
| 700
|
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
PILImageResampling,
make_list_of_images,
to_numpy_array,
valid_images,
)
from ...utils import TensorType, is_vision_available, logging
if is_vision_available():
import PIL
__UpperCAmelCase : Optional[Any] = logging.get_logger(__name__)
def lowercase_ ( __snake_case : Any , __snake_case : Any ) -> Any:
'''simple docstring'''
snake_case__ :Optional[Any] = b.T
snake_case__ :Optional[Any] = np.sum(np.square(__snake_case ) , axis=1 )
snake_case__ :Tuple = np.sum(np.square(__snake_case ) , axis=0 )
snake_case__ :Union[str, Any] = np.matmul(__snake_case , __snake_case )
snake_case__ :Union[str, Any] = aa[:, None] - 2 * ab + ba[None, :]
return d
def lowercase_ ( __snake_case : Optional[Any] , __snake_case : int ) -> Any:
'''simple docstring'''
snake_case__ :Optional[Any] = x.reshape(-1 , 3 )
snake_case__ :List[str] = squared_euclidean_distance(__snake_case , __snake_case )
return np.argmin(__snake_case , axis=1 )
class _snake_case ( _A ):
_A = ['pixel_values']
def __init__( self ,UpperCamelCase = None ,UpperCamelCase = True ,UpperCamelCase = None ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = True ,UpperCamelCase = True ,**UpperCamelCase ,) -> None:
super().__init__(**UpperCamelCase )
snake_case__ :List[Any] = size if size is not None else {"height": 256, "width": 256}
snake_case__ :str = get_size_dict(UpperCamelCase )
snake_case__ :Dict = np.array(UpperCamelCase ) if clusters is not None else None
snake_case__ :str = do_resize
snake_case__ :List[str] = size
snake_case__ :List[Any] = resample
snake_case__ :Union[str, Any] = do_normalize
snake_case__ :int = do_color_quantize
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase ,UpperCamelCase = PILImageResampling.BILINEAR ,UpperCamelCase = None ,**UpperCamelCase ,) -> np.ndarray:
snake_case__ :List[str] = get_size_dict(UpperCamelCase )
if "height" not in size or "width" not in size:
raise ValueError(f'Size dictionary must contain both height and width keys. Got {size.keys()}' )
return resize(
UpperCamelCase ,size=(size["height"], size["width"]) ,resample=UpperCamelCase ,data_format=UpperCamelCase ,**UpperCamelCase )
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,) -> np.ndarray:
snake_case__ :Tuple = rescale(image=UpperCamelCase ,scale=1 / 127.5 ,data_format=UpperCamelCase )
snake_case__ :List[Any] = image - 1
return image
def lowerCAmelCase_ ( self ,UpperCamelCase ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = None ,UpperCamelCase = ChannelDimension.FIRST ,**UpperCamelCase ,) -> PIL.Image.Image:
snake_case__ :Optional[int] = do_resize if do_resize is not None else self.do_resize
snake_case__ :int = size if size is not None else self.size
snake_case__ :Tuple = get_size_dict(UpperCamelCase )
snake_case__ :str = resample if resample is not None else self.resample
snake_case__ :Dict = do_normalize if do_normalize is not None else self.do_normalize
snake_case__ :Tuple = do_color_quantize if do_color_quantize is not None else self.do_color_quantize
snake_case__ :List[Any] = clusters if clusters is not None else self.clusters
snake_case__ :str = np.array(UpperCamelCase )
snake_case__ :int = make_list_of_images(UpperCamelCase )
if not valid_images(UpperCamelCase ):
raise ValueError(
"Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
"torch.Tensor, tf.Tensor or jax.ndarray." )
if do_resize and size is None or resample is None:
raise ValueError("Size and resample must be specified if do_resize is True." )
if do_color_quantize and clusters is None:
raise ValueError("Clusters must be specified if do_color_quantize is True." )
# All transformations expect numpy arrays.
snake_case__ :Union[str, Any] = [to_numpy_array(UpperCamelCase ) for image in images]
if do_resize:
snake_case__ :int = [self.resize(image=UpperCamelCase ,size=UpperCamelCase ,resample=UpperCamelCase ) for image in images]
if do_normalize:
snake_case__ :Any = [self.normalize(image=UpperCamelCase ) for image in images]
if do_color_quantize:
snake_case__ :Optional[Any] = [to_channel_dimension_format(UpperCamelCase ,ChannelDimension.LAST ) for image in images]
# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
snake_case__ :Union[str, Any] = np.array(UpperCamelCase )
snake_case__ :Optional[int] = color_quantize(UpperCamelCase ,UpperCamelCase ).reshape(images.shape[:-1] )
# flatten to (batch_size, height*width)
snake_case__ :List[Any] = images.shape[0]
snake_case__ :str = images.reshape(UpperCamelCase ,-1 )
# We need to convert back to a list of images to keep consistent behaviour across processors.
snake_case__ :Any = list(UpperCamelCase )
else:
snake_case__ :List[str] = [to_channel_dimension_format(UpperCamelCase ,UpperCamelCase ) for image in images]
snake_case__ :List[str] = {"input_ids": images}
return BatchFeature(data=UpperCamelCase ,tensor_type=UpperCamelCase )
| 57
| 0
|
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